Maize Improvement: Current Advances in Yield, Quality, and Stress Tolerance under Changing Climatic Scenarios 3031216393, 9783031216398

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Shabir Hussain Wani Zahoor Ahmad Dar Gyanendra Pratap Singh   Editors

Maize Improvement Current Advances in Yield, Quality, and Stress Tolerance under Changing Climatic Scenarios

Maize Improvement

Shabir Hussain Wani Zahoor Ahmad Dar  •  Gyanendra Pratap Singh Editors

Maize Improvement Current Advances in Yield, Quality, and Stress Tolerance under Changing Climatic Scenarios

Editors Shabir Hussain Wani Mountain Research Center for Field Crops, Khudwani Sher-e-Kashmir University of Agricultural Sciences and Technology Srinagar, Jammu and Kashmir, India

Zahoor Ahmad Dar Dryland Agriculture Research Station Sher-e-Kashmir University of Agricultural Sciences and Technology Srinagar, Jammu and Kashmir, India

Gyanendra Pratap Singh ICAR-National Bureau of Plant Genetic Resources New Delhi, India

ISBN 978-3-031-21639-8    ISBN 978-3-031-21640-4 (eBook) https://doi.org/10.1007/978-3-031-21640-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

 Genome Diversity in Maize ����������������������������������������������������������������������������    1 Deepu Pandita, S. Parthasarathy, D. Dhivyapriya, R. Premkumar, Anu Pandita, and Shabir Hussain Wani Advancement in QTL Mapping to Develop Resistance Against European Corn Borer (ECB) in Maize��������������������������������������������   25 Asifa Shahzadi, Samra Farooq, Ali Razzaq, Fozia Saleem, Gelyn D. Sapin, Shabir Hussain Wani, and Vincent Pamugas Reyes  Dissection of QTLs for Biotic Stress Resistance in Maize����������������������������   41 Rajkumar U. Zunjare, K. T. Ravikiran, Firoz Hossain, Vignesh Muthusamy, Rahul D. Gajghate, Jayant S. Bhat, Mukesh Choudhary, and Nivedita Shettigar Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize��������������������������������������������������������������������������������������������������   83 Baljeet Singh, Shabir Hussain Wani, Sarvjeet Kukreja, Vijay Kumar, and Umesh Goutam  Genomic Selection in Maize Breeding������������������������������������������������������������   99 Vishal Singh and Amita Kaundal  Transcriptional Factor: A Molecular Switch to Adapt Abiotic Stress Mechanisms in Maize��������������������������������������������������������������������������������������  109 Muhammad Qudrat Ullah Farooqi, Sanathanee Sachchithananthan, Muhammad Afzal, and Zahra Zahra Physiological and Biochemical Responses in Maize under Drought Stress��������������������������������������������������������������������������������������������������  117 Suphia Rafique

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 Current Biotechnological Approaches in Maize Improvement ������������������  137 Moutoshi Chakraborty, Saurab Kishore Munshi, Ashraful Haque, Md. Abul Kalam Azad, Tofazzal Islam, Mobashwer Alam, and Muhammad J. A. Shiddiky  Advances in Genome Editing for Maize Improvement��������������������������������  181 Samra Farooq, Asifa Shahzadi, Ali Razzaq, Fozia Saleem, Shabir Hussain Wani, and Karansher Sandhu Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize (Zea mays L.) ������������������������������������������������������������������  195 Seema Sheoran, Manisha Saini, Vinita Ramtekey, Mamta Gupta, Mohd Kyum, and Pardeep Kumar Genetic Improvement of Specialty Corn for Nutritional Quality Traits����������������������������������������������������������������������������������������������������  235 Firoz Hossain, Rajkumar U. Zunjare, Vignesh Muthusamy, Ashwani Kumar, Jayanthi Madhavan, Gopinath Ikkurti, Ashvinkumar Katral, Zahirul A. Talukder, Rashmi Chhabra, Gulab Chand, Vinay Bhatt, Irum Gul, Subhra J. Mishra, Hriipulou Duo, Suman Dutta, Nisrita Gain, Priyanka Chauhan, Shalma Maman, Shashidhar B. Reddappa, and Ravindra Kumar Kasana Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate Resilience ������������������������������������������������������������  259 P. S. Basavaraj, Jagadish Rane, M. D. Prathibha, K. M. Boraiah, and Mahesh Kumar  Maize Improvement Using Recent Omics Approaches��������������������������������  289 Gopal W. Narkhede and K. N. S. Usha Kiranmayee Fungal Pathogen-Induced Modulation of Structural and Functional Proteins in Zea mays L.����������������������������������������������������������������  303 Ankit Singh, Shalini Sharma, Gourav Choudhir, and Sushil Kumar Role of Plant Growth-Promoting Rhizobacteria Mitigating Drought Stress in Maize����������������������������������������������������������������������������������  323 Shifa Shaffique, Muhammad Imran, Shabir Hussain Wani, Anjali Pande, Waqas Rahim, Muhamad Aaqil khan, Sang-Mo Kang, and In-Jung Lee

Genome Diversity in Maize Deepu Pandita , S. Parthasarathy, D. Dhivyapriya, R. Premkumar, Anu Pandita, and Shabir Hussain Wani

1 Introduction The sciences such as genetics, omics, biotechnology, and breeding have evolved as four interrelated and compatible fields for an extensive and delicate investigation of crop species and their accurate and efficient development. Even as genetic factors and plant breeding have made tremendous contributions toward many new approaches and techniques for the comprehension of plant gene sequences and the advancement of a massive range of crop cultivars with desired characteristics, genomics might have featured the chemical nature of genotypes, transcription factors, and genome sequences and also offered resources for plant breeding. The chronology of crop genome analysis will indeed illustrate the decoding of such complete genomes of the prototype life form Arabidopsis thaliana in the year 2000. This led to the establishment of an exciting new era in the study of plant genomes, followed by the deciphering of the genome of the agricultural and reference crop rice in 2002. Since that time, the proportion of plant species’ genomes that have been sequenced has increased dramatically due to the development of genomic strategies that are D. Pandita (*) Government Department of School Education, Jammu, Jammu and Kashmir, India S. Parthasarathy · D. Dhivyapriya Amrita School of Agricultural Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, India R. Premkumar Department of Genetics and Plant Breeding, Tamil Nadu Agricultural University, Coimbatore, India A. Pandita Vatsalya Clinic, Krishna Nagar, New Delhi, India S. H. Wani Mountain Research Centre for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_1

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both more affordable and more trustworthy as well as, most importantly, the advancement of the platform, which includes both domestic and global initiatives that involve affiliates from agencies and organizations. The genomic fingerprints of hundreds of various plant species have recently been found due to advancements in molecular biology strategic breakthroughs. By the end of the year 2020, a total of 1031 genomes representing 788 unique species of plants will have been sequenced and disclosed, and this number is expected to continue to increase at an exponential rate (Sun et al. 2021). There is a wide variety of additional markers that are based on hybridization, markers that are based on PCR, and based on assisted sources of genetic linkage mapping and localization of desired genes that control phenotypic characteristics and even genomic clusters (QTL (quantitative trait locus)-to-QTN (quantitative trait nucleotide) mapping), including polygenic traits in a large number of experimental and field crops (Nadeem et al. 2018). During this period, various novel mapping populations beyond F2 were deployed, and several computer programs were developed for map creation, functional genomics, and polygenic clusters or QTLs (Osman et al. 2013). Studies on evolutionary and phylogenetic relationships as well as research on genetic diversity and fingerprinting use molecular markers. Map-­ based cloning was another application of available molecular markers. Markers closely connected to the genomes were utilized in the process of crop development using a technique known as marker-assisted selection. Throughout the last decade and a half of the twentieth century, the approaches of molecular genetic mapping and molecular breeding were responsible for a significant impact that was felt worldwide. And yet, methods retained “implicit” ways for explication and usage of genomic sequences since most of the chromosomes remained undiscovered, and the whole biochemical description had still to be unraveled. Genome sequencing has enabled a complete mapping approach for crop plants and their wild relatives. Consequently, our understanding of the whole scope of genetic sequences and the breadth of their coverage has been continuously expanding in the modern period. However, the material is spread across published studies and literature reviews in journals and website domains of the consortium and repositories. Maize agronomic variables have occurred for thousands of years. It has been nearly 30 years since mapping QTLs for agronomic variables, including productivity, was first described in maize by integrated technologies including omic approaches and computational tools that range from traditional breeding to molecular cloning, mainly positional cloning and transposon tagging. Since this groundbreaking and revolutionary study, the pace of growth in maize genomes and their breeding uses has been astonishing. Recent advances in the worldwide characterization of the diversity, structure, evolution, and functions of the maize genome can be developed to increase the adequacy of choosing appropriate parents for various crossing programs to produce incorporation that results in elite inbreds, gene/QTL cloning, and research. Appropriate methodologies, genetic pools, and tools must be established in order to achieve a level of precision in desired quantitative characteristics. Both forward and backward genetics can be used to find, connect, and cause mutations in plants. The genomics of maize offers crop breeders a powerful toolset for customizing inbreds and

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high-­yielding varieties better prepared to tackle the difficulties raised by climate catastrophe and global warming while assuring ecologically sustainable economic exploitation of one of the most vital crops. Maize is now one of the world’s most valuable crops, contributing billions of dollars annually to agricultural production. The remarkable adaptability and output of maize as a cereal, fodder, and pasture crop have resulted in its extensive production at the cost of other crops (Altieri et al. 2015). In American agriculture, maize has become a leading supplier of food and fuel, increasing its nonfood applications. Maize has been at the forefront of the genetic transformation debate, being among the first crops with commercially available transgenic forms (Gupta et al. 2021). In addition to its substantial achievements in agriculture and the economy, maize is considered the first model crop for which a genomic map was created in 1935 (Xu et al. 2009). In maize, fundamental genetic features like cytological crossovers and recombination, inheritance and genomics, transposable elements, imprinting, nucleolar organizers, telomeres, epigenetic modifications, epigenetic gene control, and physical linkages of genes were uncovered for the first time. These essential genetic features were revealed to be features of every eukaryotic genome. Due to the convenience of its evaluation, specifically the easiness of crossbreeding and phenotypic plasticity, it is widely used. These significant genetic efforts are still in progress, leading the way in describing the genomes of angiosperms, which are inherently unstable and change over time. The release of a draft sequencing of the maize reference genome B73 inbred using BAC-by-BAC Sanger method in 2009 is a large transposon-rich genome of ten chromosomes with a 2.3-gigabase size like the human genome. It revealed the organization and gene content of the first genome (32,000 genes) of a crop of average size and is the most complicated genome decoded to date (Schnable et  al. 2009). Periodically, it has been reorganized, and the existing draft reference genome is version 3 with complete particulars of genome assembly, gene expression, contiguous loci, and relationship to other model species (http://www.ncbi.nlm.nih.gov/genome/?term=maize; http://www.maizegdb.org), further four more gene assemblies released in last few years (Yang et al. 2019b). The most recent complete-genome duplication event in maize occurred between 5 and 12 million years ago, just following the divide between maize and sorghum. A recent study found that almost 60% of the total protein-coding genes in the B73v4 genome had a syntenic ortholog in the genome of sorghum (Brohammer et al. 2018).

2 Landraces of Maize The word “maize” originated from the Taino (an ancient Arawakan language) verb mahiz, which the pre-Columbian Americans used. According to historical excavations, Mexico’s historic Aztec, Mayan, and Olmec cultures relied on maize as their staple food and most cherished grain (Tanumihardjo et al. 2020). The miracle of maize’s conception in science is a source of genetic evolutionary discussions (Choudhary et al. 2021). Because it is an outcrossing plant by nature, maize has an

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inheritance pattern that is more similar to that of other outcrossing organisms, like humans, than that of self-pollinating crops (Nannas and Dawe 2015). Conversely, a wild progenitor teosinte (a grass species) is acknowledged as one of its evolutionary predecessors. Remarkable is the fact that farmers facilitated maize genesis. Ancient Mesoamerican growers recognized that this evolutionary variation of teosinte mimicked food and preserved seeds from their best cobs for the following harvest. Over millennia of natural selection based on the diverse desires of farmers and under the impact of different climates and geographies, maize emerged into a species with a great deal of heterogeneity. Since that Mexico is the center of maize origin and domestication, the phenotypic diversity of the crop maize (Zea mays) across Mexico’s geography from tropical rainforests to arid semideserts is noteworthy (Caldu-Primo et al. 2017). Landraces are a concept used to characterize this pattern. Previous research has determined that external circumstances, particularly altitude (0–2700 masl), offer a better explanation than landrace for the genetic variance identified in Mexican maize. Agronomists continue to employ landraces extensively because they view them as dependable and selective categories that can be used to classify collections (Caldu-Primo et al. 2017). This substantial height difference is associated with a wide variety of atmospheric moisture gradients. These gradients are connected with the elevational responsiveness of maize cultivars. Variability in the context of agronomy indicates high genotype-by-environment correlations, which makes it challenging to grow landraces at heights other than those at which they were initially discovered. Gene flow between populations of domesticated maize and populations of its ancestors in the wild is an underlying factor that impacts altitudinal diversification (teosintes Zea mays ssp. mexicana and Zea mays ssp. parviglumis). The two-teosinte subspecies have an allopatric distribution pattern, with Zea mays ssp. mexicana expanding in the mountains and Zea mays ssp. parviglumis (Balsas teosinte) appearing on the plains (Sánchez González et  al. 2018). Landraces are dynamic, historically significant populations with a particular personality. They are frequently racially distinct and cultivated regionally, linked by a system of seed selections for producers, fieldwork conditions, and traditional knowledge. There are now 59 maize landraces grown in Mexico, notably Wellhausen’s germplasms, even though more may remain unidentified (Arteaga et al. 2016). There is a substantial amount of gene flow between the two species, resulting in the alteration of the genomic content of highland maize populations by teosinte mexicana in primary focus areas. Incredibly diverse phenotypes of maize have existed since its domestication approximately 9000 years ago (Schnable et al. 2009). In addition, they determined that the corn varieties grown in the Mexican mountains were the earliest extant varieties and dispersed throughout America through two main thoroughfares. Hundreds of maize landraces attest to the phenological diversity of this maize plant. Thus, the Mexican landraces are essential for studying the development of the maize genome and could serve as a hereditary repository for any other tailoring maize to new environmental and disease challenges. It is essential to have a genetic, regional, and cultural understanding of these landraces to keep them, maintain them, and make better use of them. Landraces are the result of indigenous shortlisting,

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which was done to satisfy the performance and variation needs of indigenous foods and behaviors, such as religious beliefs concerning the color and shape of the cob. Mexican landraces are endemic and embedded in their tradition. Since recorded history began, around 60 indigenous communities and mestizo farmers in Mexico have been involved in this particular occupation. For instance, many different landrace characteristics are linked to specific components of Mexican cuisine (Sweeney et al. 2013). Since both rural producers and urban residents consume these components, their preferences and decisions regarding landraces and characteristics are influenced by these components. The landraces of maize have been significantly improved by the enormous economic and scientific developments that have taken place during the past 15 years. Meanwhile, in 1909, while collecting conventional maize in the USA, a field employee observed a strange maize cob that was initially thought to be a distinct species. The species called Z. ramosa from the Latin ramosus indicated many branches because of its unusual ear form. Due to the change of a particular gene, later designated ramosa1, the crop has been more heavily forked than typical, resulting in looser, irregular rows of kernels and a tassel that was significantly bushier than the heads of regular maize plants. More than 10,000 maize landraces, typically constituting a generation of genetically diverse individuals, are maintained in gene banks following international standards worldwide. In some countries, like Mexico, even small farmers cultivate these (Mayer et al. 2020). In particular, to landraces, the gene banks house hundreds of maize wild relative accessions such as Tripsacum species and teosinte and ancestors. These resources constitute the genetic makeup of maize and may contain numerous unidentified genes, alleles, and haplogroups that seem to have been exploited for crop improvement (Huang et al. 2021). After Christopher Columbus explored in the New World and introduced maize to European nations, the crop eventually made its way to other parts of the world. At this point, its production is more significant than wheat and rice combined. Maize has overtaken other cereals as the most productive crop, and as a result, it plays a considerable part in feeding human beings, either explicitly or implicitly. A debate regarding who discovered maize at the commencement of the previous century has been going on for quite some time. The Northeast Indian states are considered the foundation of maize diversity. It offers a unique assortment of landraces that farmers have maintained and utilized for various reasons. Among these is the very adaptable Murli makai (Sikkim Primitive), which is found in Sikkim, Uttarakhand, and Himachal Pradesh. Numerous studies have demonstrated phenotypic and genotypic differences for kernel patterns in Northeastern Indian maize germplasm, such as Kaali makai with a dark purplish-black kernel, Putali makai with a transposon-induced pericarp variant allele, Paheli makai with a golden flint kernel type, Rathi makai with a dark red kernel, Seti makai with a white kernel type, Kuchungtakmar with multicolor kernals, and other lines like Gadbade makai, Kukharey makai, Kuchungdari, and Chaptey maka (Singode and Prasanna 2010). The Punjab Agricultural University in Ludhiana, India, has developed a composite variety dubbed “Parbhat” that is resistant to various diseases, provides high productivity, and retains performance

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stability. Using the renowned OPV Suwan-1 of Thailand, landraces from the Indian states of Jammu and Kashmir and Uttarakhand were used in a breeding experiment (India). The study led to the formation of improved, hilly region-adapted cultivars (Gadag et al. 2021). Him-129, Him-128, and other “Vivek” hybrids are among the most outstanding hybrids with early maturity and resistance to diseases produced by this technique. Him-129 possesses traits that confer remarkable resistance to leaf blight. One of the most popular baby corn cultivars in Uttarakhand, India, is the hybrid VL Baby Corn, which contains the prodigious Sikkim Primitive.

3 Genome of Maize Multifold genome duplication has occurred in the maize nucleotide, like the one roughly 70 million years ago (mya) (Paterson et  al. 2004), and a second whole-­ genome duplication incident around 5–12 mya (Swigoňová et al. 2004). This distinguishes maize from its related plant, Sorghum bicolor (Paterson et al. 2009), which further experienced genome duplication at some point in its evolutionary history. The maize genome comprises ten chromosomes, each of which has its distinct structure and has been through various stages of chromatin modification at different points in its development (Muller et al. 2019). The maize genomic region has dramatically increased, reaching 2.3 gigabases in size, due to an increase in long terminal repeat (LTR) retrotransposons over the past three million years or more (Schnable et  al. 2009), as an evolutionary development. Maize diploid genome consists of noncoding repeats interspaced by regions of distinct DNA or DNA with low-copy numbers carrying mono genes or short gene clusters. Transposable elements (TEs), ribosomal DNA (rDNA), and high-copy short tandem repeats are some of the repetitive elements in maize. These elements are primarily found at centromeres, heterochromatic knobs, and telomeres, and they considerably contribute to the species’ diverse selection (Wicker et al. 2009). There is a large amount of genetic and phenotypic variation in maize. The genome of maize is quite extensive, with the total number of protein-coding genes ranging between 42,000 and 56,000. In terms of size, gene content, organizational structure, and the amount of repeat content, the genome of the maize plant is more active than those of closely related animal genomes (Bruggmann et al. 2006). There is a possibility that the intricate genome mechanism of maize can be interpreted as an evolutionary process that is constructing a more basic form into an essential and unique model organism. A wide variety of factors may have caused this phenomenon, including whole-genome duplications or polyploidization, recombination and gene conversion events, translocation of genes or transposon gene segments, single-­ base mutations, segmental duplications, DNA transposition and retro-transposition, expansion and contraction of simple sequence repeats, and recombination between different types of genes (Schmutz et al. 2010). Maize contains approximately 68% of orthologous crops, which are collinear with rice GW2 and sorghum due to numerous instances of gene duplication (Li et al. 2010). On the other hand, 99% of these

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genes are found to be orthologous between the two species, indicating that gene loss occurred before cereal species divergence. Several studies have demonstrated that different species of grass each have their unique local organization of orthologous areas. In contrast to preserving homologous chromosomes, the maize genome has experienced reassortment of homologous portions acquired from both progenitor genomes. After initially being established cytologically across nonhomologous chromosomes, these regions were subsequently demonstrated genetically using linkage analysis mapping of non-tandem gene duplication. A comprehensive genetic study of maize’s homologous areas was conducted using DNA markers and comparative mapping with the crop’s close relatives (Messing et al. 2004). The complexity of maize is caused by structural variants (SVs), including the chromosomal number and structural changes. Also contributing to this complexity are deletions, insertions, inversions, and translocations (Gabur et  al. 2019). The B73 reference sequence represents only a minor portion of the maize pangenome (Haberer et al. 2020). Numerous sequence-level variants exist in maize, including single nucleotide polymorphisms (SNPs), small insertions/deletions, presence/absence variation (PAV), and copy number variation (CNV) (Dolatabadian et  al. 2017). Moreover, epigenetic mechanisms are responsible for generating variety through variations in transcript quantity. The types of flint corn and dent corn, for instance, demonstrate variation within the same species. When maize is crossed, the heterochromatic sections of the genome, also known as Knob regions, influence the local recombination. They belong to the satellite tandem repeats in flint corn and are characterized as such. The tandemly repeated sequence units that comprise maize knobs are 180 bp in length and closely linked to 202 bp. The syntenic order and consolidated gene annotations only reveal moderate pan-genomic variations. Furthermore, the maize genome’s dynamic nature is demonstrated by examining heterochromatic knobs and orthologous long terminal repeat retrotransposons (Haberer et al. 2019). Transposable elements are DNA sequences that are mobile and “selfish,” meaning that they are only concerned with their survival and can migrate from their initial location to other locations in the genome. Regardless of whether the transposition intermediary is RNA or DNA, maize transposons are divided into two classes: Class I (retrotransposons) and Class II (DNA transposons) (Eichten et al. 2012). Reverse transcription, which involves the duplication of retrotransposons followed by the insertion of each new copy at a different site in the genome, results in the gain of about one element. In contrast, the vast majority of Class II transposable components undergo a process requiring cutting and insertion. Certain plant DNA transposons are believed to belong to a family capable of homologous recombination translocation and employing a DNA replication method known as a rolling circle (Ramakrishnan et al. 2022). Class I and Class II transposons may be independent. If they contain all the components necessary for translocation or are nonautonomous, their translocation is contingent on the corresponding autonomous element. Possessing all the elements required for transposition constitutes autonomy (Sabot and Schulman 2006). Initially, LINEs (long interspersed nuclear elements) and SINEs (short

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interspersed nuclear elements) are uncommon in maize, accounting for less than 1% of its genome (Baucom et al. 2009). Barbara McClintock was the first to characterize the activator (Ac) and dissociation (Ds) elements; these are the transposons with the highest level of characterization. Transposition requires the presence of an independent Ac element, whereas Ds elements are not independent and must be trans-activated by an Ac element (Halpern 2016). Genes identified in maize chloroplasts and mitochondria have been thoroughly described, emphasizing cytoplasmic male sterility. McClintock first referred to these entities as regulating elements because they participate in the actions of genes as well as the structure of the genome (Klein and O’Neill 2018). These movable genetic segments in genomes serve an essential purpose in epigenetics and gene control, and they also provide hosts with benefits that facilitate adaptive evolution (Catlin and Josephs 2022). Most TEs are made up of repeating components, which may be interspersed with either active or passive sequences. The research conducted by Bourque et al. (2018) condensed the characteristics of TEs into ten primary points. TEs can take a variety of shapes and forms, and their mechanisms of transposition are also diverse. Even though they are movable genetic segments within genomes, they are not dispersed randomly and are responsible for mutations in plant species. Gene expression can be caused or repressed by transposable elements, which contribute to genomic rearrangements. This impacts both the germline and the soma. TEs are also activated in response to stressful environmental situations, synthesizing genes and noncoding RNAs. This, in turn, modifies regulatory networks, which are necessary for transcriptional and posttranscriptional processes. Experiments conducted in the past on maize demonstrated conclusively that TEs could cause substantial changes to the chromosomal structures they modify. For example, the Ac elements found in maize are capable of causing deletions, inversions, translocations, and other types of rearrangements (Sharma et al. 2021). This suggests that Ac elements have the potential to influence the order of chromosomal rearrangements in plants, which can result in changes to gene expression as well as reproductive isolation. The study of maize’s pan-genome and core-genome dates back to the early genetic and cytogenetic examinations (Hirsch et al. 2014). However, this field of research has received greater emphasis in recent decades, partly due to the advent of NGS (next-generation sequencing) and omic tools. When compared to the number of genes present in the genome of each individual (*40,000 genes), it is anticipated that there are at least 1.5 times as many genes in the genome, which is over 60,000 genes. In addition, there is a high level of diversity outside of the gene domain (Bennetzen et al. 2004). This variance has been related to complex traits, and it is regarded to be the largest source of the frequently found elevated amounts of heterosis in maize lines due to this observation. Genomic research has centered mainly on maize as a model plant due to the abundance of genetic and genomic materials available and the wide variety of germplasms (Schnable et al. 2009). Compared to other organisms that grow from a single cell, maize has a reasonably long life cycle. Pollination of maize occurs approximately 60  days after planting, and the seeds require an additional 30–45 days to mature after pollination. James Birchler’s team

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developed fast-flowering “mini-maize” that can go from seeds to corn in 60 days and is available from the Maize Genetics Stock Center to promote faster generation (Nannas and Dawe 2015). The latest information about the maize genome, called B73-Ab10, shows that chromosomes can be put together without the help of humans by using an optical map-based process along with sequencing methods from both PacBio and Nanopore. This may be the first time that any chromosome from the massive, diverse genome has been sequenced, and it demonstrates immense promise by combining modern sequencing technology with assembly techniques (Liu et al. 2020). Over the past two decades, significant advances in sequencing technology have resulted in the abundance of over a thousand genomes for bryophytes to angiosperms. Additionally, tremendous progress has been made in fabricating high-quality chromosome-scale genomic assemblies. This is especially true for most maize genotypes, which typically possess giant genomes and complicated genomic characteristics. As a result of the exponential growth in reference genomes and pan-genome studies, our experience of the hereditable causes of genetic diversity has increased, and we currently maintain unprecedented access to a spectrum of genes and variants linked to specific traits and environmental adaptation. This has caused a paradigm change in the biology of maize. Early, generating a high-quality maize reference genome without gaps is tricky, and heterozygosity is the most significant obstacle to overcome in this endeavor in maize. It is now possible to assemble the maize genome without any gaps, indicating that it will soon be possible to sequence the complete genomes of other species, from telomere to telomere (Sun et al. 2021). Before nearly a decade had elapsed, the technological limitations caused the inferiority of hundreds of genomes. In addition, 371 out of 613 of the previously stated draught assemblies were assembled employing NGS systems alone and no supplementary fastening technologies. Draft sequences are inadequate for answering scientific questions on the speciation of biological species, the recent development of biological species, and precisely associating nucleotide changes with phenotypes (Feuillet et al. 2011). Even though numerous fault-correction processes have been applied to the reference genomes of model plant species, main crops, and their wild relatives, almost none of these genomes are even close to being gapless. The vast majority of these genomes are incomplete and fragmented in the regions that include a high concentration of transposable elements (TEs), tandem arrays, and ribosomal gene clusters. However, most of these genomes have complete and accurate information for their low-copy genic regions (Quezada-Martinez et  al. 2021). The majority of these genomes have densely packed portions with fragmented and inaccurate repeating arrays, transposable elements (TEs), and ribosomal gene clusters. On the other hand, the low-copy genic regions of most of these genomes are complete and accurate (Lloyd et al. 2019). Recently, a three-dimensional resolution of the previously identified two-dimensional genome has been achieved through mapping open chromatin and detecting chromatin linkages using ChiA-PET (chromatin interaction analysis by paired-end tag sequencing technology) mapped genome-wide chromatin interactions and revealing their connections to gene expression regulation and Hi-C analysis of the chromatin interaction outlines (Peng et  al. 2019). Recent

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genomic research demonstrates that the genomes of numerous organisms, including cultivated plants, are transcribed more often than previously assumed. Chromatin-­ enriched noncoding RNAs (cheRNAs) including lncRNAs, snoRNAs, and tRNAs play a wider regulatory role at multiple gene expression levels in both human and plant genomes especially rice (Zhang et al. 2022). The genome also produces these noncoding RNAs in addition to the protein-coding mRNAs. It contributes to the transcriptional regulation during plant somatic cell reproduction. Interacting RNA and DNA interacting complexes ligated and sequenced, or RADICL-seq, is a technique for mapping the RNA-chromatin interactions that occur all through the genomic sequence in the nucleus and will be used for maize genome studies. RADICL-seq is a proximal ligation-based technology that, compared to previous methods, reduces the bias for embryonic transcription while increasing genome coverage and mapping accuracy. RADICL-seq demonstrates distinct patterns of genomic occupancy for several types of transcripts, as well as cell type-specific RNA-chromatin interaction, and highlights the role that transcription plays in forming chromatin structure (Bonetti et al. 2020). Zhang et al. (2022) used benchmarking universal single-copy orthologs (BUSCO), to reveal the level of completeness of the elite maize line HuangZaoSi (HZS) de novo assembly was on par with that of the B73 high-quality reference genome. The BUSCO (v3.0.2, embryophyta_odb9) study determined that the HZS genome had comparative genetic coverage with the proteomes of PacBio-assembled maize genomes. It was based on the fact that the HZS genome has 40,893 protein-coding genes. This data, when taken together, pointed to the assembly and annotation of the HZS genome having a high level of quality. The Maize Genetic and Genomics Database (MaizeGDB) can be accessed online at http://www.maizegdb.org. This database is utilized by maize breeders. It includes a huge volume of peer-reviewed literatures and tools on maize, as well as genomic maps, data on mutations and genotypes, and instant access to order genetic stock samples. In particular, it features a genome explorer which has been rigorously curated and annotated, as well as several tools for gene expression studies and Ac/ Ds and Mu insertional mutations. Deciphering physical maps, mutant traits, and quantitative data can be significantly aided by comparative mapping. Maize is one of the model cereal crops for which advanced toolkits for genotyping and mapping are available. Gramene (http://www.gramene.org) is a portal designed exclusively for genome-wide analyses (Monaco et al. 2014). Through the MaizeGDB database, contact to the Maize Genetics Cooperation Stock Center can be achieved. This center is accessed at http://maizecoop.cropsci. uiuc.edu/ and contains a compendium of both conventional and modern genetic information. The Stock Center provides access to the vast proportion of the chromosomal variants and mutations addressed in the literatures. Obtaining seeds to initiate scientific studies is not only simple but also inexpensive. The International Maize and Wheat Improvement Center (CIMMYT) http://www.cimmyt.org/ and the Germplasm Resources Information Network (GRIN) http://www.ars-­grin.gov provide access to germplasm supplies for maize breeding and diversification. Some of these sources are unavailable at the Stock Center, including inbred lines and

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landraces. There is additional information about maize resources available online. The Huazhong Agricultural University’s National Key Laboratory of Crop Genetic Improvement developed ZEAMAP, a comprehensive source containing multiple reference genome assemblies, genetic maps, genetic mapping loci, annotations, high-quality genetic variants, phenotype, haplotype, open chromatin regions, chromatin accessibility, chromatin interactions, histone modification and DNA methylation, transcriptomes, metagenomics, metabolic engineering, population structures, populational DNA methylation signals, insertion and deletions (InDels), single nucleotide polymorphisms (SNPs), and structure variations (SVs) and within maize accessions. Multi-omic datasets can be dynamically visualized; integrated with Blast, Synteny, CRISPR, and GWAS (genome-wide association studies); and crossreferenced using this user-friendly system and user account management database, ZEAMAP (http://www.zeamap.com/) (Gui et al. 2020).

4 Intraspecific and Intergenic Diversity of Maize 4.1 Interspecific Crosses While the tremendous variability found within maize was associated with much domestication, it is also coherent with solitary domestication and subsequent expansion. This is because maize is a plant with a long history of being cultivated. Genomic investigations, which would include exhaustive collections of maize and teosinte, can discern between these two hypotheses. Due to endosperm collapse approximately 21 days after pollination, hybridization among both Z. mays (2n = 20) and Z. perennis (2n = 40) may only produce 0.1–1% fertile embryo. The reason for this is that the two species are genetically distinct from one another. Nevertheless, hybrids with a genetic makeup of 2n = 30 have been produced using embryo rescue (Molina et al. 2013). In addition, maize and annual teosinte has a high level of sexual compatibility, and it is well established that the two can generate fertile hybrid offspring (Blake 2015). It has been suggested that all teosintes, except for the tetraploid variant of Z. perennis, are capable of producing viable hybrids with maize. Conversely, maize teosinte hybrids are not very fit and do not affect gene introgression in subsequent generations (Chaudhary et  al. 2014). In addition, maize and annual teosinte has a high level of sexual compatibility, and it is well recognized that the two species can generate fertile hybrid offspring (Stewart et al. 2003). It has been reported that all teosintes, except for the tetraploid variety of Z. perennis, can be crossed with maize to produce fertile hybrids. On the other hand, maize teosinte hybrids are not very fit and do not affect gene introgression in following generations (Yan et  al. 2020). They developed intergeneric crossings between Z. mays (2n = 4x = 40, genome: MMMM), Z. perennis (2n = 4x = 40, genome: PPPP), and Tripsacum dactyloides (2n = 4x = 72, genome: TTTT), resulted in the development of a trispecific hybrid known as MTP, referred to as tripsazea. A unique progeny

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(tripsazea) with 2n = 74 chromosomes was produced as a result of a hybrid between maize Tripsacum (2n = 4x = 56, genome: MMTT) and Z. perennis, which resulted in the production of 37 progenies with varying numbers of chromosomes (37–74). Tripsazea is a perennial plant that displays the phenotypic features inherited from its parent, which served as its progenitor. Early on, the chance of different teosintes producing natural hybrids differs: Z. luxurians hybridize with maize only occasionally, whereas Zea mays sp. mexicana has several couplings with maize species (Blake 2015). The analysis of molecular data reveals that there is gene flow between teosintes and maize and that there is also modest introgression of teosintes and maize into each other’s genomes, albeit in both directions. These findings are consistent with the hypothesis that maize and teosintes evolved from a common ancestor. The procedure used was biased fractionation, which consisted of the preferential removal of genes from one of the homologs. The ability of various teosintes to create natural hybrids varies; for instance, Z. luxurians cross with maize sporadically, but Z. mays sp. mexicana produces high-yielding varieties frequently. The examination of molecular data reveals that there is gene flow between maize and teosintes and that there is little introgression of maize and teosintes into each other’s genomes but in both directions (Lal et al. 2020).

4.2 Intergeneric Crosses Tripsacum species (T. dactyloides, T. lanceolatum, T. floridanum, and T. pilosum) were deliberately bred with maize to develop high-yielding varieties. However, these hybrids are extremely sterile and unsustainable. Due to chromosome number differences and the absence of chromosome pairing, sterility is common in such broad crossovers (Savidan and Berthaud 1994). In average, the maize Tripsacum hybrids have 28 chromosomes, 10 from maize and 18 from Tripsacum, with sterile pollen, and have reduced fertility (Abdoul-Raouf et al. 2017). Limited research has been conducted on the Asiatic Maydeae (Chionachne, Coix, Polytoca, Sclerachne, and Trilobachne), and there have been no reports on the ability of these genes to hybridize with Z. mays. Genomic research utilizing isozyme analysis demonstrates that the Asiatic groups are notably dissimilar from teosintes and maize (Moreno-­ Letelier et al. 2020). Despite having the same number of chromosomes (2n = 20), maize and Coix sp. chromosomal investigations found substantial structural differences between these genomes (Swigoňová et al. 2004). The similarity in the number of chromosomes suggests that crossover between maize and Asian species may be possible. Research has been conducted on Chionachne, Coix, and Trilobachne in an effort to uncover disease-resistant traits that may have applications in the production of maize (Sahoo et al. 2021). With high fertilization and embryo development frequencies, maize rapidly is a pollen donor and crosses to develop double haploids of hexaploid wheat (2n = 42) (Triticum aestivum). During the earliest phases of meiosis, maize chromosomes are removed from the genome, resulting in haploid seeds. There seems to be little evidence suggesting that maize and hexaploid wheat could

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naturally produce viable hybrids. There have been unconfirmed reports of hybridization between maize and sugar cane (Saccharum sp.). The only cultivated species is Z. mays subsp. mays; all other species and subspecies are teosintes or wild grasses. In addition to the standard complement of A chromosomes, maize plants may include one or more B chromosomes, which do not mate with A chromosomes during meiosis (Pace et al. 2013). In maize, multiple fertilization is uncommon and embryo development happens without the formation of an endosperm. Numerous embryos can grow. Unless the seed is grown in vitro, it will abort due to the lack of nutrients usually supplied by the endosperm. During early embryogenesis, maize chromosomes are eliminated, resulting in the formation of haploid wheat. During cell division, maize chromosomal kinetochores cannot connect to the metaphase plate’s spindle microtubules (Laurie et al. 1990). All types of Z. mays ssp. mays cross-pollinate freely and produce healthy hybrids. Since maize is predominantly outcrossing, there is a high likelihood of intraspecific hybridization between neighboring plants if blooming times coincide and other conditions are appropriate. Volunteers are improbable agents of gene transfer in maize since maize cobs cannot shed seeds on their own (Hanna et  al. 2004). The comparatively large mass and size of the pollen grains favor pollen implantation within around 60 m of the mother plant, with little or no cross-pollination beyond 300 m (Halsey et al. 2005). They examined the spatial and temporal aspects of pollen-mediated genetic drift in maize. Under their experimental parameters, they discovered that 200 m were sufficient to limit outcrossing to less than 0.1%. In estimating the likelihood of cross-pollination in maize, pollen competition is also essential. Because of the production of millions of pollens, “far” pollen will be heavily outnumbered by “local” pollen even if it travels excellent distances; by reducing pollen competition, restricting pollen production, including through cytoplasmic male sterility or detasseling, may paradoxically increase the likelihood of long-distance cross-pollination (Chandler and Dunwell 2008). The separation distances among genetically modified (GM) and non-GM maize cultivars have also been investigated regarding gene flow along with complete analysis of the variables in gene flow among genetically modified and nonmodified maize. It is crucial to regulate gene flow among GM and non-GM corn by surrounding GM crops with non-GM corn to reduce GM pollen concentration (Ricci et  al. 2016). Various incompatibilities exist between these subspecies of Z. mays and are considered to have evolved to prevent spontaneous hybridization. The pollen-pistil incompatibility produced by six or more alleles of the gametophyte-1 (ga1) genes has been extensively studied. This method involves the inhibition or delay of ga1 pollen tubes in ga1-s/- silks, resulting in a prezygotic impediment to crossing (Chen et al. 2022). Historically, teosintes were categorized within the genera Euchlaena. It is known that maize and all teosintes except Z. perennis are monogamous and yield fertile crosses in Guatemala and Mexico, where their distribution is predicted. Z. mays ssp. mays hybridize spontaneously with teosinte mexicana, and parviglumis teosinte show the highest gene flow prevalence (Wang et al. 2008). The hybrids of Z. mays ssp. mays and Z. mays ssp. mexicana exhibit significant statistical heterosis when

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compared to the wild teosinte but not when related to the cultivated ancestor. Gene flow is uncommon despite the ease of crossbreeding, and all species remain able to cohabit as genetically distinct individuals. Rarely gene flow has occurred in both directions (reciprocal introgression), but numerous factors are likely to support gene flow from teosinte to maize over gene flow from maize to teosinte (Guadagnuolo et al. 2006). In some populations of Z. mays teosintes, when teosinte is the female parent and maize is the male parent, there is an indication of a restriction on crossability. This restriction has been linked to the Tcb1 gene or genetic cluster (Lu et al. 2014). The Tcb1 gene is situated on chromosome 4 within the teosinte incompatibility complex and provides resistance to crossover before fertilization. It is tenuously linked to the Ga1 gene. It has been hypothesized that intragenic gene flow between the genera Zea and Tripsacum, both of which are members of the subfamily Panicoideae and tribe Maydeae, led to the introduction of maize at some time in its evolution. As a direct result of this, numerous attempts have been made to outcross Z. mays with Tripsacum spp. On the other hand, each of these experiments has been conducted in circumstances that have been tightly controlled, and there is no indication that crossing occurs spontaneously. Perennial herbaceous grasses of the genus Tripsacum can be found from the Northern USA down to Paraguay in South America. The wheat chromosomal base number is x = 18 while that of maize is x = 10. Consequently, cytogenetic relationships in hybridization have garnered considerable interest. The closest relative of Zea is the genus Tripsacum, which has an essential chromosomal number of 2n = 18. These two genera can inbreed and generate progeny. In tropical climates, both T. dactyloides and T. andersonii are grown as forage crops. T. dactyloides, often known as eastern gamagrass, was reported. The latest research in Australia focuses on the forage value of Tripsacum plants and their hybrids (maize × T. dactyloides hybrid) (Abdoul-Raouf et al. 2017). In early hybridizations (using Tripsacum as the source of the fertile pollen), adequate fertilization could only be obtained by shortening the distance that the Tripsacum stamen had to traverse by shortening the maize styles. It appears that the process of gene transmission from T. dactyloides, T. floridanum, T. lanceolatum, and T. pilosum to Zea mays is complicated. Furthermore, it has been found that at least 54 different chromatin pairs can be obtained by varying the Tripsacum parent used in the cross and the events that take place in the early backcross generations. The hybrids are either infertile or partially capable of female reproduction after multiple backcrosses to maize, removing the Tripsacum chromosomes. It has been commercially exploited in maize/Tripsacum cultivars to introgress diplosporous apomictic regeneration into a maize context; this would probably allow the formation of immortalized commercial lines of apomictic maize. Polyploids in the genus Tripsacum demonstrate diplosporous apomictic reproduction. Maize/Tripsacum crosses have also acquired traits such as high silage biomass. These hybrids also have more excellent disease resistance (Chaudhary et al. 2014). The Maydeae tribe also includes five Asian taxa: Coix, Chionachne, Polytoca, Sclerachne, and Trilobachne. It has been reported that only Z. mays and Coix lachryma-­jobi are hybrids. In this instance, around 6% of cross-pollination produced a hybrid seed, even when Coix was used as the female parent. Multiple Coix

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species are permitted for importation into Australia. Coix gasteenii is a threatened species in the vicinity of the Cape York Peninsula bioregion (Prasanna et al. 2006). Maize has also been bred with a relative of the subfamily Panicoideae. A single intergeneric hybrid was developed between Saccharum officinarum with 2n = 80 and Z. mays with two new B chromosomes employing maize as the pollen parent. The individual, having cells’ possessed chromosomal frequencies varying from 52 to 58, persisted for 30 years with clonal replication and was stimulated with gibberellic acid to unfold its flower (Dalton 2013). Possible intergeneric hybrids of maize have only evolved with members of the Pooideae subfamily, the largest subfamily of the Poaceae. Information indicates that Panicoideae chromosomes are damaged immediately after fertilization when Panicoideae are crossed with Pooideae. In regulated crosses of Avena sativa (oat) hexaploid (2n  =  42) with maize, approximately 31% of the embryos may contain between one and four maize chromosomes in addition to a total provision of oat chromosomes; such seeds are referred to as partial hybrids. Oat/maize hybrids serve as essential resources for maize genome sequencing. The hybrid crops are only suitable for cultivation after embryo rescue (Idziak-Helmcke et al. 2020), with barley (2n = 14) and rye (2n = 14) serving as the female parents and Z. mays serving as the pollen donor. Crossings were conducted on in vivo grown flowers without any attempt to save embryos. No embryos occurred in the H. vulgare × Z. mays cross; however, other S. cereale × Z. mays crossings generated globular seed embryos that degraded after 6–10 days, perhaps due to insufficient or nonexistent endosperm development. The growth of seed embryos from barley florets pollinated using maize has been achieved after embryo rescue. There was no cytogenetic indication of total maize chromosomal preservation in most plants generated from the sterile haploid embryos (2n  =  7) (Khokhar et al. 2019).

5 Maize Genome Evolution and Diversity Crop domestication was essential to developing Charles Darwin’s natural selection theory. It motivates contemporary evolutionists to examine a variety of developmental biology-related challenges. Numerous studies have been conducted on the ancestry and evolutionary history of maize. Archaeological investigations and evolutionary analysis indicate that maize likely evolved in Southern Mexico through a single domestication process. Teosinte, a lowland grass, is the plant from which maize is said to have originated. Comparing the population genetics of maize and teosinte disclosed recent variety in multiple regions of the genome, a moderate bottleneck that reduced genetic variation during crossbreeding, and post-domestication genetic drift from teosinte into maize that improved the plant’s ability to process adaptation against adverse environmental conditions (Yang et al. 2019a). Maize is one of the most intensively explored species of plants in genetic history. In addition to its agricultural and commercial significance as a crop for food, feed, and fuel, maize has exceptional biological properties as a paradigm for genetic diversity and

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genome assembly (Lawrence et  al. 2008). These traits make maize an excellent genetic research subject. The maize gene pool, comprised of domesticated and wild relatives, possesses a significant amount of inherent variety. The preponderance of the maize genome consists of non-genic and repeated areas, which are split by islands of unique or low-copy DNA that contain single genes or tiny gene clusters (Kaur and Gaikwad 2019). This is analogous to the composition of other large plant species with extensive genomes. The repeating elements, including transposable elements (TEs), ribosomal DNA (rDNA), and high-copy short tandem repeats, are often located around telomeres, centromeres, and heterochromatin knobs. They contribute significantly to the population’s wide variety of diversity (Thakur et al. 2021). Farmers in Mexico cultivated maize during the first period by selecting quality seeds to grow. They noticed that not all crops were identical. Particular yields were higher than others, and some sources may have been more delicious or smoother to mill. Then, growers gathered the specific desirable attributes and seeded them for the subsequent season. This process is referred to as natural selection or assortment. During evolution, corn cobs became larger and acquired longer lines of kernels, eventually acquiring the appearance of contemporary maize. In terms of crop enhancement, genetic diversity is of the utmost importance. Landraces are abundant sources of variation; yet, there seem to be currently no practical solutions for the targeted application of quantitative traits found in landraces (Mayer et  al. 2020). The plant and flower structures of teosinte and maize are quite distinct. Typically, teosinte-producing plants have numerous long stems, each of which bears several small heads and tassels along with its height. In comparison, maize plants usually have one or two shorter, single-ear-bearing components. A maize plant typically produces only two ears, each of which has several hundred grains, whereas a teosinte plant may produce hundreds of ears, each containing only ten grains. Variability in growth habit and ear head length among these species results from their distinct reproductive methods (Sahoo et al. 2021). Teosinte has a flexible regular development, enabling it to spread copiously in ideal circumstances and generate hundreds of ear heads or to remain small and poorly branched in unfavorable conditions and produce only a few. This versatility enables the teosinte to survive in a wide range of conditions. Regardless of the climate in which maize is grown, its more rigid development pattern results in producing one or two enormous ear heads. This modification promotes the gathering of grains by human cultivators. Including a transposable element called Hopscotch into the gene promoter of a teosinte-specific gene TEOSINTE BRANCHED1 and other transcription factors, CYCLOIDEA or PROLIFERATING CELL NUCLEAR ANTIGEN FACTOR (TCP) family impacted the development of maize by altering gene expression. A single point mutation in the teosinte glume architecture (tga1), coding a squamosa-promoter binding protein (SBP) transcription factor, caused the hard fruit case around teosinte naked kernels to disintegrate, revealing the soft grain (Bruno 2019). This was a further significant step in the domestication of maize. The transition of the maize genome and the formation of intraspecific genome diversification are both influenced by several effective mechanisms, including

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whole-­genome and segmental duplications; DNA transposition and retrotransposition; target and translocation of genes or gene segments by retro-transposons, recombination, and gene conversion events; and single point mutations and development of simple sequence repeats (SSRs). The utility of maize’s wild relatives, such as the perennial teosinte (Zea diploperennis) and the gama grass (Tripsacum dactyloides), in the process of developing genetically enhanced maize with resistance to Striga africana, abundant in African countries (Mammadov et al. 2018). Several similarities exist between gama grass and the wild ancestor of domesticated maize. These characteristics include the occurrence of separate males and females in the inflorescence of the plant, the enormous length of the plant, the presence of more prominent white midveins in the foliage, linear flower spikes with sessile (without a stalk) flowers, and warm season photosynthesis physiology (Smalley and Blake 2003). In contrast, cultivated maize is an annual rather than a perennial plant, and the shape of its “cobs” prevents it from fertilizing itself without human involvement (McCann 2005). Due to its genetic diversity, maize is a crop amenable to genetic modification. Many advantageous alleles contribute to yield improvement, stress tolerance, resistance to disease, and nutritional property improvement in worldwide maize germplasm (Wang et al. 2016). These alleles are located inside the maize genome. On the other hand, these advantageous genes are typically scattered among a significant number of germplasm or landraces. As a result of the development of next-­generation sequencing (NGS) technologies, the genome-wide association study, often known as GWAS, has gained widespread recognition as a method for determining genotype-­ phenotype relationships in different species (Xiao et al. 2017). Over the past decade, the enormous progress made in maize has positioned it to become a desirable crop for GWAS. In 2008, however, a maize linkage study on a genome-wide scale was published for the first time. The study analyzed 8590 loci in 553 elite maize inbreds to find the genes that affect kernel fatty acid concentration. This association mapping was initially utilized on plants at the start of the twenty-first century for candidate gene association studies in maize (Beló et al. 2008). This study was undertaken to investigate whether certain traits are inherited or otherwise. A genome-wide association study was performed on maize seedlings, and 83 genetic differences were identified. These variants have been mapped to 42 potential genes. The strongest GWAS signal indicates a link between the characteristics and natural variability in the ZmVPP1 gene, which codes for a vacuolar-type H+ pyrophosphatase. This is the most significant correlation identified. The ability to stimulate transcription of the ZmVPP1 gene in response to drought is provided by a 366-bp insertion, including three MYB cis regions in the promoter of drought-tolerant genotypes. Higher photosynthetic activity and root development likely contributed to the enhanced drought tolerance in transgenic maize with enhanced ZmVPP1 expression (Wang et al. 2016). Using omic data, specific genetic designs, and appropriate analytical methods will facilitate the transition from the GWAS era to the omic-­ wide association study era, comprehensively integrating all layers of data to identify numerous biological factors underlying phenotypic variation. This will enable the finding of phenotypic variants that were previously unknown. This objective will be

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achieved by specifying various additional physical variables that contribute to phenotypic variation. In the earliest days of cytogenetics, significant line-specific alterations in the distribution of heterochromatin, also known as C banding and heterochromatic knobs, were discovered through research (Figueroa et al. 2012). Additionally, it was found that some maize and teosintes contain supernumerary chromosomes, also known as B chromosomes (Birchler and Yang 2021). It has been demonstrated that these cytogenetic differences have a substantial positive association with these DNA content variations. Extremely variable quantities of naturally occurring genetic variation exist in the maize genome; the levels of this diversity might vary depending on the lines utilized for comparison. When comparing two inbred lines of maize, approximately one nucleotide alteration occurs in every hundred bases. It is the typical rate of a single nucleotide mutation. This average polymorphism frequency is tenfold more significant than what has been observed among humans. It is also higher than the level observed between humans and chimpanzees. Compared to the rates of polymorphism reported in mammalian populations, this amount of intraspecies variety is high, which is both an unusual and remarkable observations (Batley et al. 2003). Even after a significant increase in the amount of DNA present in each nucleus of maize, there does not appear to be any apparent change in the plant’s phenotype. LTR retrotransposons have produced the most recent and significant contributions to the size of the maize genome (SanMiguel et al. 1998). It has been demonstrated that the amount and distribution of these retrotransposons vary significantly between haplotypes. It has been found that the quantity of copy numbers present in tandem repeats at rDNA loci, centromere knobs, and centromeres themselves can vary. Recently, researchers have turned their attention from areas that do not contain copy number variation (CNV) to regions that do contain presence-absence variation (PAV). Copy number variations (CNVs) and polymorphic allele variants (PAVs) play a significant role in the heterosis and diversity of the maize genome. This is becoming increasingly evident due to advancements in genome-wide hybridization technologies and increasing knowledge of the sequences of numerous maize lines made possible by next-generation technologies. In addition to sequence variation, maize genomes also display high levels of structural variation (Lyra et al. 2019). More recently, research into heterosis has focused on determining the function of single-parent expression (SPE) genes (Baldauf et al. 2018). A condition is known as single-parent expression, or SPE is one in which expression can only be found in one of the hybrid’s two parental lines. According to the findings of these investigations, heterosis may be caused by the fact that more genes are conveyed in hybrids compared to either of the hybrid’s parents due to SPE gene expression complementation. SPE patterns can emerge either as a result of transcription variations (on/off) between the two constant parental alleles (non-PAV SPE) or as a direct result of the same gene in one of the parental inbred lines. Both of these scenarios are possible for SPE patterns (PAV SPE) (Li et al. 2021). In maize, similar cereals, and ornamental grasses, researchers have successfully identified the ramosa1 gene pathway controlling SPM-SM conversion and

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established that it controls the orientation of floral stalks and their overall length (Du et al. 2022). In maize, ramosa inflorescences are classical mutants with branch meristem-­spikelet pair meristem deficits. In tassel and ear inflorescences, mutants with similar abnormalities, such as ramosa 1, ra1, ra2, ra3, and rel2 (ramose enhancer locus2), develop many long branches. According to the study, when early farmers were domesticating maize from its wild progenitor, teosinte, they selected crops with variations of the ramosa1 gene that confined growth to the ear. This resulted in the narrow rows of seeds and compact ears characteristic of modern maize on the cob (Calfee et al. 2021).

6 Concluding Remarks Genomic selection is currently an extension of traditional selection methods. In conventional selection methods, many genotypes are first evaluated at the DNA level, and then a selected fraction is assessed at the more expensive agronomic level. This process aims to identify a limited number of superior experimental variety candidates ultimately. It is anticipated that more targeted approaches will develop due to a more comprehensive understanding of which genes and related allele combinations would result in the ideal genotype for a specific environment. This understanding will enable the construction of such strategies. Traditional recombinant technology (which involves crossing well-selected founder genotypes) or editing multiple genes may theoretically be used to produce these designer genotypes. Alternatively, these designer genotypes could be made via the direct manipulation of many genes. However, obstacles still need to be overcome to obtain a more comprehensive understanding of trait variation. These obstacles include the traditional difficulties associated with the minor genetic effects of QTL and the difficulty in making predictions when complex G × G and G × E interactions are present. Despite this, the breeder’s toolbox is gradually supplemented with more contemporary technologies. It is no longer a question of whether a tool or strategy is available; instead, the question is which among the ever-increasing number of possibilities ought to be selected, given the limited financial resources, to optimize both genetic gain and economic return. The regulatory framework surrounding genome editing will significantly impact the extent to which it can be made available to plant breeders as a potent tool. Even though the restrictions governing its usage are lax in certain nations, they may be more stringent in others, limiting its application. PacBio has indeed recently modified its methods to ensure high-accurate long-read sequencing (HiFi), using the circular consensus sequencing (CCS) method. This method has a base-level resolution that is higher than 99% and was developed to solve the problem of error-prone sequencing. This method can be utilized for research on maize genomics. There have been employed effectively for sequencing of many plant species and displayed both high feature and robustness. The need to use a wider variety of genetic resources may be prompted by climate change. In the long run, this may help narrow the gap between actual and prospective yields and raise the bar for potential outcomes.

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References Abdoul-Raouf SM, Ju Q, Jianyu M, Zhizhai L (2017) Utilization of wild relatives for maize (Zea mays L.) improvement. Afr J Plant Sci 11(5):105–113 Altieri MA, Nicholls CI, Henao A, Lana MA (2015) Agroecology and the design of climate change-resilient farming systems. Agron Sustain Dev 35(3):869–890 Arteaga MC, Moreno-Letelier A, Mastretta-Yanes A, Vázquez-Lobo A, Breña-Ochoa A, Moreno-­ Estrada A, Eguiarte LE, Piñero D (2016) Genomic variation in recently collected maize landraces from Mexico. Gen Data 7:38–45 Baldauf JA, Marcon C, Lithio A, Vedder L, Altrogge L, Piepho HP, Schoof H, Nettleton D, Hochholdinger F (2018) Single-parent expression is a general mechanism driving extensive complementation of non-syntenic genes in maize hybrids. Curr Biol 28:431–437.e4 Batley J, Barker G, O’Sullivan H, Edwards KJ, Edwards D (2003) Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data. Plant Physiol 132(1):84–91 Baucom RS, Estill JC, Chaparro C, Upshaw N, Jogi A, Deragon JM, Westerman RP, SanMiguel PJ, Bennetzen JL (2009) Exceptional diversity, non-random distribution, and rapid evolution of retroelements in the B73 maize genome. PLoS gen 5(11):e1000732 Beló A, Zheng P, Luck S, Shen B, Meyer DJ, Li B, Tingey S, Rafalski A (2008) Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol Gen Gen 279(1):1–10 Bennetzen JL, Coleman C, Liu R, Ma J, Ramakrishna W (2004) Consistent over-estimation of gene number in complex plant genomes. Curr Opin Plant Biol 7(6):732–736 Birchler JA, Yang H (2021) The supernumerary B chromosome of maize: drive and genomic conflict. Open Biol 11(11):210197 Blake M (2015) Maize for the gods: unearthing the 9,000-year history of corn. Univ of California Press Bonetti A, Agostini F, Suzuki AM, Hashimoto K, Pascarella G, Gimenez J, Roos L, Nash AJ, Ghilotti M, Cameron CJ, Valentine M (2020) RADICL-seq identifies general and cell type– specific principles of genome-wide RNA-chromatin interactions. Nat Commun 11(1):1–4 Bourque G, Burns KH, Gehring M, Gorbunova V, Seluanov A, Hammell M, Imbeault M, Izsvák Z, Levin HL, Macfarlan TS, Mager DL, Feschotte C (2018) Ten things you should know about transposable elements. Genome Biol 19(1):199 Brohammer AB, Kono TJ, Springer NM, McGaugh SE, Hirsch CN (2018) The limited role of differential fractionation in genome content variation and function in maize (Zea mays L.) inbred lines. Plant J 93:131–141 Bruggmann R, Bharti AK, Gundlach H, Lai J, Young S, Pontaroli AC, Wei F, Haberer G, Fuks G, Du C, Raymond C (2006) Uneven chromosome contraction and expansion in the maize genome. Genome Res 16(10):1241–1251 Bruno MC (2019) Indigenous American agricultural contributions to modern global food systems. In: Oxford research encyclopedia of environmental science, p 25 Caldu-Primo JL, Mastretta-Yanes A, Wegier A, Piñero D (2017) Finding a needle in a haystack: distinguishing Mexican maize landraces using a small number of SNPs. Front Genet 8:45 Calfee E, Gates D, Lorant A, Perkins MT, Coop G, Ross-Ibarra J (2021) Selective sorting of ancestral introgression in maize and teosinte along an elevational cline. PLoS Genet 17(10):e1009810 Catlin NS, Josephs EB (2022) The important contribution of transposable elements to phenotypic variation and evolution. Curr Opin Plant Biol 65:102140 Chandler S, Dunwell JM (2008) Gene flow, risk assessment and the environmental release of transgenic plants. Crit Rev Plant Sci 27(1):25–49 Chaudhary HK, Kaila V, Rather SA (2014) Maize. In: Alien gene transfer in crop plants, vol 2. Springer, New York, pp 27–50 Chen Z, Zhang Z, Zhang H, Li K, Cai D, Zhao L, Liu J, Chen H (2022) A pair of non-mendelian genes at the Ga2 locus confer unilateral cross-incompatibility in maize. Nat Commun 13(1):1

Genome Diversity in Maize

21

Choudhary M, Grover K, Singh M (2021) Maize significance in Indian food situation to mitigate malnutrition. Cereal Chem 98(2):212–221 Dalton SJ (2013) Biotechnology of miscanthus. In: Biotechnology of neglected and underutilized crops. Springer, Dordrecht, pp 243–294 Dolatabadian A, Patel DA, Edwards D, Batley J (2017) Copy number variation and disease resistance in plants. Theor Appl Genet 130(12):2479–2490 Du Y, Wu B, Xing Y, Zhang Z (2022) Conservation and divergence: regulatory networks underlying reproductive branching in rice and maize. J Adv Res 41:179–190 Eichten SR, Ellis NA, Makarevitch I, Yeh CT, Gent JI, Guo L, McGinnis KM, Zhang X, Schnable PS, Vaughn MW, Dawe RK (2012) Spreading of heterochromatin is limited to specific families of maize retrotransposons. PLoS Genet 8(12):e1003127 Feuillet C, Leach JE, Rogers J, Schnable PS, Eversole K (2011) Crop genome sequencing: lessons and rationales. Trends Plant Sci 16(2):77–88 Figueroa DM, Amarillo IE, Bass HW (2012) Cytogenetic mapping in plants. In: Plant cytogenetics. Springer, New York, pp 79–119 Gabur I, Chawla HS, Snowdon RJ, Parkin IA (2019) Connecting genome structural variation with complex traits in crop plants. Theor Appl Genet 132(3):733–750 Gadag RN, Bhat JS, Mukri G, Gogoi R, Suby SB, Das AK, Yadav S, Yadava P, Nithyashree ML, Naidu GK, Yadav SK (2021) Resistance to biotic stress: theory and applications in maize breeding. In: Genomic designing for biotic stress resistant cereal crops. Springer, Cham, pp 129–175 Guadagnuolo R, Clegg J, Ellstrand NC (2006) Relative fitness of transgenic vs. non-transgenic maize× teosinte hybrids: a field evaluation. Ecol Appl 16(5):1967–1974 Gui S, Yang L, Li J, Luo J, Xu X, Yuan J, Chen L, Li W, Yang X, Wu S, Li S (2020) ZEAMAP, a comprehensive database adapted to the maize multi-omics era. iScience 23(6):101241 Gupta S, Kumar A, Patel R, Kumar V (2021) Genetically modified crop regulations: scope and opportunity using the CRISPR-Cas9 genome editing approach. Mol Biol Rep 48(5):4851–4863 Haberer G, Bauer E, Kamal N, Gundlach H, Fischer I, Seidel MA, Spannagl M, Marcon C, Ruban A, Urbany C, Nemri A (2019) European maize genomes unveil pan-genomic dynamics of repeats and genes. bioRxiv 1:766444 Haberer G, Kamal N, Bauer E, Gundlach H, Fischer I, Seidel MA, Spannagl M, Marcon C, Ruban A, Urbany C, Nemri A (2020) European maize genomes highlight intraspecies variation in repeat and gene content. Nat Genet 52(9):950–957 Halpern ME (2016) Barbara McClintock on defining the unstable genome. Genetics 204(1):3–4 Halsey ME, Remund KM, Davis CA, Qualls M, Eppard PJ, Berberich SA (2005) Isolation of maize from pollen-mediated gene flow by time and distance. Crop Sci 45(6):2172–2185 Hanna WW, Baltensperger DD, Seetharam A (2004) Pearl millet and other millets. Warm-season (C4) grasses 45:537–560 Hirsch CN, Foerster JM, Johnson JM, Sekhon RS, Muttoni G, Vaillancourt B, Peñagaricano F, Lindquist E, Pedraza MA, Barry K, de Leon N (2014) Insights into the maize pan-genome and pan-transcriptome. Plant Cell 26(1):121–135 Huang Y, Huang W, Meng Z, Braz GT, Li Y, Wang K, Wang H, Lai J, Jiang J, Dong Z, Jin W (2021) Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation. Genome Biol 22(1):1–28 Idziak-Helmcke D, Warzecha T, Sowa M, Warchoł M, Dziurka K, Czyczyło-Mysza I, Skrzypek E (2020) 3-D nucleus architecture in oat× maize addition lines. Int J Mol Sci 21(12):4280 Kaur P, Gaikwad K (2019) Principles and implications of various genome enrichment approaches for targeted sequencing of plant genomes. In: Plant biotechnology: progress in genomic era. Springer, Singapore, pp 43–75 Khokhar MI, Razaq A, Iqbal J, Anwar MJ, Iqbal MZ, ur Rehman S (2019) Choice of maize genotype affects wheat haploid seed and success of embryo rescue. RADS J Biol Res Appl Sci 10(1):1–5 Klein SJ, O’Neill RJ (2018) Transposable elements: genome innovation, chromosome diversity, and centromere conflict. Chromosom Res 26(1):5–23

22

D. Pandita et al.

Lal M, Bhardwaj E, Chahar N, Dangwal M, Das S (2020) (Trans) gene flow: mechanisms, biosafety concerns and mitigation for containment. In: Reproductive ecology of flowering plants: patterns and processes. Springer, Singapore, pp 335–394 Laurie DA, O’Donoughue LS, Bennett MD (1990) Wheat x maize and other wide sexual hybrids: their potential for genetic manipulation and crop improvement. In: Gene manipulation in plant improvement II. Springer, Boston, pp 95–126 Lawrence CJ, Harper LC, Schaeffer ML, Sen TZ, Seigfried TE, Campbell DA (2008) MaizeGDB: the maize model organism database for basic, translational, and applied research. Int J Plant Genomics 2008:496957 Li Q, Li L, Yang X, Warburton ML, Bai G, Dai J, Li J, Yan J (2010) Relationship, evolutionary fate and function of two maize co-orthologs of rice GW2associated with kernel size and weight. BMC Plant Biol 10(1):1–5 Li Z, Zhou P, Della Coletta R, Zhang T, Brohammer AB, H O’Connor C, Vaillancourt B, Lipzen A, Daum C, Barry K, de Leon N (2021) Single-parent expression drives dynamic gene expression complementation in maize hybrids. Plant J 105(1):93–107 Liu J, Seetharam AS, Chougule K, Ou S, Swentowsky KW, Gent JI, Llaca V, Woodhouse MR, Manchanda N, Presting GG, Kudrna DA (2020) Gapless assembly of maize chromosomes using long-read technologies. Genome Biol 21(1):1–7 Lloyd JP, Bowman MJ, Azodi CB, Sowers RP, Moghe GD, Childs KL, Shiu SH (2019) Evolutionary characteristics of intergenic transcribed regions indicate rare novel genes and widespread noisy transcription in the Poaceae. Sci Rep 9(1):1–4 Lu Y, Kermicle JL, Evans M (2014) Genetic and cellular analysis of cross-incompatibility in Zea mays. Plant Reprod 27(1):19–29 Lyra DH, Galli G, Alves FC, Granato ÍS, Vidotti MS, Bandeira e Sousa M, Morosini JS, Crossa J, Fritsche-Neto R (2019) Modeling copy number variation in the genomic prediction of maize hybrids. Theor Appl Genet 132(1):273–288 Mammadov J, Buyyarapu R, Guttikonda SK, Parliament K, Abdurakhmonov IY, Kumpatla SP (2018) Wild relatives of maize, rice, cotton, and soybean: treasure troves for tolerance to biotic and abiotic stresses. Front Plant Sci 9:886 Mayer M, Hölker AC, González-Segovia E, Bauer E, Presterl T, Ouzunova M, Melchinger AE, Schön CC (2020) Discovery of beneficial haplotypes for complex traits in maize landraces. Nat Commun 11(1):4954 McCann JC (2005) Maize and grace: Africa’s encounter with a New World crop, 1500–2000. Harvard University Press Messing J, Bharti AK, Karlowski WM, Gundlach H, Kim HR, Yu Y, Wei F, Fuks G, Soderlund CA, Mayer KF, Wing RA (2004) Sequence composition and genome organization of maize. Proc Natl Acad Sci U S A 101(40):14349–14354 Molina MD, López CG, Staltari S, Chorzempa SE, Moreno Ferrero V (2013) Cryptic homoeology analysis in species and hybrids of genus Zea. Biol Plant 57(3):449–456 Monaco MK, Stein J, Naithani S, Wei S, Dharmawardhana P, Kumari S, Amarasinghe V, Youens-­ Clark K, Thomason J, Preece J, Pasternak S (2014) Gramene 2013: comparative plant genomics resources. Nucleic Acids Res 42(D1):D1193–1199 Moreno-Letelier A, Aguirre-Liguori JA, Piñero D, Vázquez-Lobo A, Eguiarte LE (2020) The relevance of gene flow with wild relatives in understanding the domestication process. R Soc Open Sci 7(4):191545 Muller H, Gil J Jr, Drinnenberg IA (2019) The impact of centromeres on spatial genome architecture. Trends Genet 35(8):565–578 Nadeem MA, Nawaz MA, Shahid MQ, Doğan Y, Comertpay G, Yıldız M, Hatipoğlu R, Ahmad F, Alsaleh A, Labhane N, Özkan H (2018) DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol Biotechnol Equip 32(2):261–285 Nannas NJ, Dawe RK (2015) Genetic and genomic toolbox of Zea mays. Gene 199(3):655–669

Genome Diversity in Maize

23

Osman KA, Tang B, Wang Y, Chen J, Yu F, Li L, Han X, Zhang Z, Yan J, Zheng Y, Yue B (2013) Dynamic QTL analysis and candidate gene mapping for waterlogging tolerance at maize seedling stage. PLoS One 8(11):e79305 Pace CD, Ricciardi L, Kumar A, Pavan S, Lotti C, Dixit S, Emani C (2013) Identification of traits, genes, and crops of the future. In: Genomics and breeding for climate-resilient crops. Springer, Berlin/Heidelberg, pp 27–177 Paterson AH, Bowers JE, Chapman B (2004) Ancient polyploidization predating divergence of the cereals, and its consequences for comparative genomics. Proc Natl Acad Sci U S A 101(26):9903–9908 Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, Haberer G, Hellsten U, Mitros T, Poliakov A, Schmutz J (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457(7229):551–556 Peng Y, Xiong D, Zhao L, Ouyang W, Wang S, Sun J, Zhang Q, Guan P, Xie L, Li W, Li G (2019) Chromatin interaction maps reveal genetic regulation for quantitative traits in maize. Nat Commun 10(1):2632 Prasanna BM, Singode A, Garg A, Kumar R (2006) Differentiation of the American and Oriental Maydeae accessions using the maize simple sequence repeat (SSR) markers. J Plant Biochem Biotechnol 15(2):85–90 Quezada-Martinez D, Addo Nyarko CP, Schiessl SV, Mason AS (2021) Using wild relatives and related species to build climate resilience in Brassica crops. Theor Appl Genet 134(6):1711–1728 Ramakrishnan M, Satish L, Sharma A, Kurungara Vinod K, Emamverdian A, Zhou M, Wei Q (2022) Transposable elements in plants: recent advancements, tools and prospects. Plant Mol Biol Rep 12:1–8 Ricci B, Messéan A, Lelièvre A, Coleno FC, Angevin F (2016) Improving the management of coexistence between GM and non-GM maize with a spatially explicit model of cross-­pollination. Eur J Agron 77:90–100 Sabot F, Schulman AH (2006) Parasitism and the retrotransposon life cycle in plants: a hitchhiker’s guide to the genome. Heredity 97(6):381–388 Sahoo S, Adhikari S, Joshi A, Singh NK (2021) Use of wild progenitor teosinte in maize (Zea mays subsp. mays) improvement: present status and future prospects. Trop Plant Biol 14(2):156–179 Sánchez González JD, Ruiz Corral JA, García GM, Ojeda GR, Larios LD, Holland JB, Medrano RM, García Romero GE (2018) Ecogeography of teosinte. PLoS One 13(2):e0192676 SanMiguel P, Gaut BS, Tikhonov A, Nakajima Y, Bennetzen JL (1998) The paleontology of intergene retrotransposons of maize. Nat Genet 20(1):43–45 Savidan Y, Berthaud J (1994) Maize× Tripsacum hybridization and the potential for apomixis transfer for maize improvement. In: Maize. Springer, Berlin/Heidelberg, pp 69–83 Schmutz J, Cannon SB, Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL, Song Q, Thelen JJ, Cheng J, Xu D (2010) Genome sequence of the palaeopolyploid soybean. Nature 463(7278):178–183 Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA, Minx P (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326(5956):1112–1115 Sharma SP, Zuo T, Peterson T (2021) Transposon-induced inversions activate gene expression in the maize pericarp. Genetics 218(2):iyab062 Singode A, Prasanna BM (2010) Analysis of genetic diversity in the North Eastern Himalayan maize landraces using microsatellite markers. J Plant Biochem Biotechnol 19(1):33–41 Smalley J, Blake M (2003) Sweet beginnings: stalk sugar and the domestication of maize. Curr Anthropol 4(5):675–703 Stewart CN, Halfhill MD, Warwick SI (2003) Transgene introgression from genetically modified crops to their wild relatives. Nat Rev Genet 4(10):806–817 Sun Y, Shang L, Zhu QH, Fan L, Guo L (2021) Twenty years of plant genome sequencing: achievements and challenges. Trends Plant Sci 27(4):391–401 Sweeney S, Steigerwald DG, Davenport F, Eakin H (2013) Mexican maize production: evolving organizational and spatial structures since 1980. App Geogr 39:78–92

24

D. Pandita et al.

Swigoňová Z, Lai J, Ma J, Ramakrishna W, Llaca V, Bennetzen JL, Messing J (2004) Close split of sorghum and maize genome progenitors. Genome Res 14(10a):1916–1923 Tanumihardjo SA, McCulley L, Roh R, Lopez-Ridaura S, Palacios-Rojas N, Gunaratna NS (2020) Maize agro-food systems to ensure food and nutrition security in reference to the sustainable development goals. Glob Food Sec 25:100327 Thakur J, Packiaraj J, Henikoff S (2021) Sequence, chromatin and evolution of satellite DNA. Int J Mol Sci 22(9):4309 Wang L, Yang A, He C, Qu M, Zhang J (2008) Creation of new maize germplasm using alien introgression from Zea mays ssp. mexicana. Euphytica 164(3):789–801 Wang X, Wang H, Liu S, Ferjani A, Li J, Yan J, Yang X, Qin F (2016) Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat Genet 48(10):1233–1241 Wicker T, Taudien S, Houben A, Keller B, Graner A, Platzer M, Stein N (2009) A whole-genome snapshot of 454 sequences exposes the composition of the barley genome and provides evidence for parallel evolution of genome size in wheat and barley. Plant J 59(5):712–722 Xiao Y, Liu H, Wu L, Warburton M, Yan J (2017) Genome-wide association studies in maize: praise and stargaze. Mol Plant 10(3):359–374 Xu Y, Skinner DJ, Wu H, Palacios-Rojas N, Araus JL, Yan J, Gao S, Warburton ML, Crouch JH (2009) Advances in maize genomics and their value for enhancing genetic gains from breeding. Int J Plant Genomics 957602:1–30 Yan X, Cheng M, Li Y, Wu Z, Li Y, Li X, He R, Yang C, Zhao Y, Li H, Wen X (2020) Tripsazea, a novel trihybrid of Zea mays, Tripsacum dactyloides, and Zea perennis. G3: Genes, Genomes, Genetics 10(2):839–848 Yang CJ, Samayoa LF, Bradbury PJ, Olukolu BA, Xue W, York AM, Tuholski MR, Wang W, Daskalska LL, Neumeyer MA, Sanchez-Gonzalez JD (2019a) The genetic architecture of teosinte catalyzed and constrained maize domestication. Proc Natl Acad Sci U S A 116(12):5643–5652 Yang N, Liu J, Gao Q, Gui S, Chen L, Yang L, Huang J, Deng T, Luo J, He L, Wang Y (2019b) Genome assembly of a tropical maize inbred line provides insights into structural variation and crop improvement. Nat Genet 51(6):1052–1059 Zhang YC, Zhou YF, Cheng Y, Huang JH, Lian JP, Yang L, He RR, Lei MQ, Liu YW, Yuan C, Zhao WL (2022) Genome-wide analysis and functional annotation of chromatin-enriched noncoding RNAs in rice during somatic cell regeneration. Genome Biol 23(1):1–27

Advancement in QTL Mapping to Develop Resistance Against European Corn Borer (ECB) in Maize Asifa Shahzadi, Samra Farooq, Ali Razzaq, Fozia Saleem, Gelyn D. Sapin, Shabir Hussain Wani , and Vincent Pamugas Reyes

1 Introduction Maize (Zea mays L.) is a major crop with great productivity globally. It is a versatile crop that can grow in a wide range of agro-climatic zones such as tropical, subtropical, and temperate regions (Joshi et al. 2005). Maize, as a vital part of the world’s food supply, contributes to feeding an ever-increasing human population and also provides a nutritive source in livestock consumption. In addition, it is increasingly being used to produce bioethanol, a sustainable energy source and a viable substitute for fossil fuels (Vaughan et al. 2018). Global climate change is the most serious concern for agricultural production. Developing and underdeveloped countries both depend on agriculture for economical balance, but the advent of climate change has greatly impacted the productivity of crops. Climate change often results to disruption of ecosystems by changing the Earth’s temperature which brings extreme weather disturbances such as drought, Asifa Shahzadi, Samra Farooq and Ali Razzaq contributed equally with all other contributors. A. Shahzadi · S. Farooq · A. Razzaq · F. Saleem Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture Faisalabad, Faisalabad, Pakistan G. D. Sapin Institute of Weed Science, Entomology and Plant Pathology, College of Agriculture and Food Science, University of the Philippines Los Baños, College, Laguna, Philippines S. H. Wani Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, J&K, India V. P. Reyes (*) Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_2

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heat, and rainfall events. The fluctuation in temperature increases the chance of more insect/pest attacks and makes crops more vulnerable to these environmental stresses (Raza et  al. 2019). Due to less financial and technological advances in developing countries, their agriculture sector is more prone to the impacts of climate change. Biotic factors are living components that reduce the agricultural production to a greater extent and directly affect the food supply. Diseases, due to insect pest, are the major biotic factors that contribute to reduced maize yields. Although more than 3000 million kilograms of pesticides have been applied yearly, insect pests still cause damage to nearly 14% of all the potential crop yields including maize (Mwololo et al. 2021). Maize is increasingly threatened by unprecedented environmental changes. It was reported that maize production has been reduced by 3.8% globally during 1980–2008 due to changing climate trends (Lobell et al. 2011). Furthermore, maize is also vulnerable to disease and insect and pest attack which cause severe damages to maize growth and yield (Miedaner and Juroszek 2021). The European corn borer (ECB), Ostrinia nubilalis (Hübner), is one of the most damaging insect pests in maize and causes great loss to maize grain yield, especially in Europe and the United States (Martel et al. 2003). The ECB is mainly found in Europe and was unintentionally transferred to the United States in 1917. It is estimated that about $1 billion in  economic losses per year are caused by ECB (Hutchison et  al. 2010). Therefore, a need to develop resistant maize varieties against ECB is necessary to maintain maize production globally. Molecular breeding approaches provide an efficient way for genetic improvement of maize. In recent years, significant progress has been achieved in genotyping and mapping the quantitative trait loci (QTL) that can be used to develop resistant cultivars. In this chapter, we discuss the mode of action and damages caused by ECB. We highlighted the recent advances in QTL mapping technique for the identification of genomic regions associated to ECB resistance in maize.

2 European Corn Borer (ECB), Ostrinia nubilalis (Hübner 1796) The ECB (Ostrinia nubilalis Hübner) is polyphagous that belongs to the family Crambidae. Mutuura and Munroe (1970) recognize 20 species under the genus Ostrinia. The species under this genus are classified into three groups based on the number of uncus lobes in male genitalia, namely, the simple (group I), bifid uncus species group (group II), and trilobed uncus species group (group III) (Frolov et al. 2007; Ishikawa et  al. 1999; Mutuura and Munroe 1970). Meanwhile, group III includes ten species and is further divided based on the male mid-tibia morphology (i.e., small or simple, medium, massive) (Frolov et al. 2007). According to Ishikawa et al. (1999), the mtDNA analysis supports the grouping of species in groups II and

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III.  The ECB belongs to a trilobed group with small mid-tibiae. In the trilobed group, only two species are feeding on the cultivated maize, namely, ECB and Asian corn borer (ACB), Ostrinia furnacalis (Guenée) (Kim et  al. 1999; Ishikawa et al. 1999). The ECB is indigenous to Europe and widely spread in several countries in Africa, Asia, and Northern America (CABI 2021).This became invasive in North America in the early 1990s due to multiple accidental introductions (reviewed in Coates et al. 2018). ECB has been recorded in 224 host plant species (Ponsard et al. 2004), but it feeds mainly on mugwort (Artemisia vulgaris L) and maize (Martel et al. 2003). Using allozyme markers, the ECB population feeding on mugwort has differed from the ECB population feeding on maize (Martel et al. 2003). The ECB has undergone four developmental stages, namely, egg, larva, pupa, and adult. The ECB adults laid egg masses on the underside of maize leaves during the late whorl stage before anthesis. The eggs will hatch after 10–14 days. The first and second instar larvae feed within the whorls of leaf tissues before attacking the flowers and feeding on the developing anthers. After the first and second larval stages, the larvae feed on pollens that collect in leaf axils and attack the ears and shanks before burrowing into the plant’s stem (Hoffmann and Schmutterer 1983). During winter, ECBs remain to hibernate as full-grown larvae, and once the ambient temperatures exceed 50 °F, the development of ECB resumes (Rice and Hodgson 2017). The number of generations per year is mostly determined by variations in insect developmental stages after hibernation under different climatic conditions to break diapause (Dopman et al. 2010). The larval feeding on leaves and burrowing into the tassels, ears, and stalks allows the secondary disease infections to grow (Franeta et al. 2019) resulting in reducing the quality and quantity of maize. Fungal infections that grow on injured plant’s parts are producing mycotoxins (aflatoxins produced by the genus fungi Aspergillus and Penicillium, zearalenone (ZEA), fumonisins (FUMs), and moniliformin (MON) produced by the genus fungi Fusarium) which are particularly toxic to animals and human health (Franeta et al. 2019). Sometimes FUM contaminations occur without symptoms even when the fungal ear rot (FER) severity is high because many physiochemical, environmental, and agronomic conditions affect its production (Galić et al. 2019b). ECB appears in all maize-grown areas, and according to Sobek and Munkvold (1999), it is the carrying agent of the Fusarium verticillioides and F. proliferatum, which caused FUMs in maize grain. Moreover, it attacks other crops along with maize. The strains of ECB adult females blend pheromone differently by the ratios of (E)-11- and (Z)-11-tetradecenyl acetate (E11- and Z11-14:OAc) (Coates et  al. 2019). The E- and Z-strain females produce blends of 99:1 and 3:97 (E)-11- and (Z)-11-tetradencyl acetate (11-14,OAc), respectively (Coates et al. 2019; Kochansky et al. 1975). As maize production rapidly increased in Central Europe, the Z-strain of ECB developed into an important maize pest and mostly depends on maize as the host plant. However, the polyphagous E-strain larvae attack numerous grown plant species and enter their large stems easily (Magg 2004). In Central Europe, ECB is univoltine that completes one generation in 1 year while in temperate areas ECB is

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multivoltine that occurs in two or more generations depending on climate conditions. In temperate regions, ECB plays a significant role by causing the contamination in maize grain and introducing the Fusarium verticillioides that caused FUMs, which degrades the quality of maize by contaminating it with mycotoxins (Blandino et al. 2015). In the early stages of growth, ECB larvae feed on small leaves, and before flowering, they begin tunneling through ears and shoots. After flowering, ECB caused damage to stalk’s vascular tissue in current European weather conditions and loss the crop yield (Foiada et al. 2015). In North Italy, there are generally two generations of ECB, larvae of the first generation attack during the vegetative stage while the second generation attack the reproductive stage. Furthermore, second-­generation larvae have a significant role in producing mycotoxins in maize kernels and also inducing the spread of Fusarium infection (Scarpino et al. 2015). ECB is considered a major vector for transmitting fungal infection because of the nature of their larval feeding. ECB-infected maize ears are injured by FUM at a 40% greater rate than normal plants (Shiri et  al. 2021). Fusarium spp. fungi cause diseases that harm maize production throughout the world. The fumonisin mycotoxins produced by Fusarium ear rot (FER) decrease grain yield and negatively affect the health of human and animals (Galić et al. 2019b). Maize sensitivity to ECB attack varies during different growth stages which results in yield loss. Mostly, ECB control measures are not applied properly due to the fact that they will neither affect nor destroy the crop entirely.

3 Damages of ECB in Maize ECB is the most common maize insect pest with significant economic damage to grain yield across the world (Franeta et al. 2018). Its damage is variable and significant to maize production. Usually, ECB larvae damage the crop and reduced the yield. They cause serious physical damage by tunneling into stalks and ears which results in dropped ears or lodged plants (Scarpino et al. 2015). Stalk cavities, damaged kernels, and ear rot are the major damages caused by ECB that reduced the stability of plants, resulted in yield loss, and reduced the quality of maize (Fachgebiet and Genetik 2004). Due to feeding and tunneling behaviors, ECB larvae can cause up to 30% yield loss in regions where the ECB population is higher (Archer et al. 2000). A lot of factors influence the severity of ECB infestations including soil water regime (Çakir 2004), nitrogen fertilization, and relative air humidity. ECB reproduces three to four times and causes damage in all parts of the plant except roots. Around 95% of damages were identified in untreated fields during harvest and 30% yield loss was experienced (Bağdatlı 2019). The damages caused by ECB have intensified due to abandoning numerous farmlands and severely damaged maize stalks. ECB incidence, damage, and severity are variables depending on season and employed pest management. During the favorable season of insect growth and development, grain damage may be greater (Scarpino et  al. 2015),  and two

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generations of ECB in one maize planting season. Thus, the severity of damage in both generations is variable (Buendgen et al. 1990; Lemic et al. 2019). Furthermore, sensitivity to secondary mold infections occurs due to ECB damage in plants such as Ustilago maydis or Fusarium spp. (Munkvold et al. 1999; Blandino et al. 2015). Due to its feeding activities, the ECB is a significant vector of Fusarium sp. that caused FER development in Europe, and it also enhances the rate of FUM contamination and thereby reduces the maize grain yield. These secondary infections lead to extra yield loss and have an impact on grain quality (Munkvold et al. 1999). The fumonisin infection severity enhances damage significantly in maize caused by ECB (Galić et al. 2019a, b). Tunneling date, vertical distribution of stalk cavities, and climatic variables must be addressed when predicting yield losses. Identifying hosts that are resistant to ECB could be done by comparing the tunnel length of dissected stalks, damage rating before harvesting, and yield with control plots. A better knowledge of pest’s biological processes such as larval development, distribution of larvae within plants, its impact on yield loss, and evaluation of economic threshold are needed information to design an effective ECB control program.

4 Conventional and Transgenic Methods to Control ECB ECB populations have exceeded the economic barrier in several maize-growing areas, forcing farmers to implement ECB control measures. The tunneling behavior of ECB larvae inside the maize stalk could be difficult in controlling the larvae. To fight this pest, several approaches have been employed such as crop rotation, development of resistant and tolerant hybrids, forecasting, monitoring of insect pests, and chemical measures (Vuković et al. 2015). To reduce overwintering of the ECB population in the field, cultural methods are being employed such as plowing, grazing, and stalk shredding or burning stalks. An efficient program to control ECB should be started in the early season by scouting. This will determine the ECB population and scouting should be done once a week. Insecticides with broad-spectrum activity are efficient and effective in killing adult ECB (Bzowska-Bakalarz et al. 2020). The timing of insecticide applications with the ECB life cycle is very critical. Contact insecticide would be difficult to target larvae that had penetrated the stalk, shank, and ear tip. Granular insecticides can be used to control the first generation of the early instar larvae. These insecticides stay in whorl longer than liquid insecticides. Controlling early larval instars is easier than late instar larvae due to their feeding sites, which feed on plant surfaces (Myers et al. 1993; Dafoe et al. 2013). Chemical control of ECB in maize fields is frequently required, and a variety of insecticides are available to date. The effectiveness of control treatments for pests varies greatly and significantly. Good results can be achieved if the treatments are applied correctly and timely in the crop fields (Vukovi et al. 2014). The insecticide should be applied 7–10 days later upon the appearance of the ECB moth. Effective chemical control can be evaluated by quantifying the percentage of maize stalks

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with holes. This means that only 15% of the plants should have holes in the stalk. Corn borers are generally attracted to early and late planting, and thus, a high infestation rate could be anticipated. Hence, insecticides are often required to control economically devastating infestations (Jiménez-Galindo et al. 2019). In addition to chemical control, the discovery of resistant varieties and identification of maize genotypes that are showing resistance to ECB from the collection of gene banks and breeding materials could be a great help in controlling the ECB and significant yield loss due to ECB. Grain toughness is a physical attribute that has been linked to grain storage pest resistance through establishing non-preference conditions (García-Lara et al. 2004). There are three mechanisms of host plant resistance, namely, antixenosis, tolerance, and antibiosis. A condition in which a  plant is more resistant to phytophagous insects than susceptible plants is referred to as antixenosis. These host plants have combination of characteristics that prevent insects from using them as food, shelter, and oviposition site because either stalk or leaf structural morphology or phenotypic traits are not favorable for the insect pest (i.e., presence of trichomes, leaf toughness). Stover digestibility of maize is also reduced by the compounds that prevent insect attack, which is particularly relevant to silage maize (Shiri et  al. 2021). Consumption of resistant plants has an antibiosis impact on insect development if the effects on insects are increased in larval mortality and reduced feeding activity. Phenolic and dehydrodiferulic acid are secondary metabolic compounds and act as natural defense molecules against ECB and other insect pests (Miedaner and Juroszek 2021). Tolerance or resistance refers to the plant’s ability to grow, reproduce, withstand the attack of insects, repair injury to a certain extent, and reduce the damages  to crop yield without affecting the economic loss by the grain yield or quality (Mitchell et al. 2016). In the vegetative stages of growth, 90% of 400 maize hybrids in the market have exhibited some level of resistance. In addition to resistance, new variants of maize are resistant to ECB damage to a greater extent (Lemić et al. 2019). Biological pest control based on Trichogramma brassica was purposefully introduced to control the ECBs, and it quickly became a popular technique. It is reported to be used in an area of about 150,000 hectares every year. Trichogramma-based biological pest control fits perfectly into increasingly popular approach of integrated pest management (IPM). However, it has some drawbacks such as the application procedure being too long, fuel-consuming, and causing compaction of soil (Bzowska-Bakalarz et al. 2020). Farmers have the option to purchase corn seeds that are genetically modified (GM) or transgenic corn, which contains insecticidal protein derived from Bt (Bacillus thuringiensis). Bt corn has been used since 1996 and has caused significant mortality in ECB larvae. The Bt gene is incorporated into the maize via horizontal gene transfer (Pilcher and Rice 1998). Adopting GM maize is the most widely used strategy against ECB which controls 99% of ECB larvae (Franeta et al. 2019). GM crops can substantially enhance farmers’ ability to manage major insect pests, which is both environmentally friendly and effective unless the target pest quickly overcomes the Bt toxins. Transgenic hybrids could decrease the level of Fusarium

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infection in maize kernels due to the link between Fusarium infection and ECB feeding (Munkvold et  al. 1999). However, this strategy has some disadvantages such as the following: it is more costly to create such hybrids than naturally grown crops, and pests may develop resistance to the toxins if farmers do not follow the high-dose plus strategy and the recommended protocol in planting and managing Bt corn. Genetically engineered maize has been accepted in the United States. However, this also has encountered criticisms in other parts of the world because of concerns about food security and ethical issues. A model was developed based on regression analysis and the measured results were not satisfactory. For grain damage, general combining abilities could be used for resistance in current selection programs. A significant tolerance has been observed in Portugal, Spanish, and Greek populations against stalk damage and ear caused by O. nubilalis and S. nonagrioides (Malvar and Butro 2004). Sufficient sources of tolerance exist that might be beneficial in breeding programs. Cultivation of hybrids that show resistance toward ECB is needed to develop. Stalk damage caused by ECB can be reduced with higher stalk toughness that increased resistance toward ECB (Papst et al. 2005). For improving ECB resistance in maize, marker-­ assisted selection (MAS) has been employed as a powerful strategy. The number of stalk tunnels, as well as the length of the tunnels, was used to evaluate resistance. And for monogenic traits, cheaper MAS can be used.

5 Advances in QTL Mapping for ECB Resistance in Maize ECB has been observed to be resistant to several control strategies. Therefore, researchers are moving toward different approaches such as the use of quantitative trait loci (QTL) mapping to identify loci that are associated with resistance to this disease. A QTL is an genomic region that is associated to a certain phenotypic traits. Linkage analysis and its association are used frequently for QTL mapping. To find the genes controlling quantitative traits, several approaches such as linkage analysis and association mapping are conducted (Samantara et al. 2021; Kitony et al. 2021). The major objectives of QTL mapping in plants are to enhance our understanding of quantitative trait loci and genomic structure, as well as to find markers that can be used for marker-assisted breeding (MAB). In major crop species, such as maize, molecular markers have been employed for mapping of resistance genes (Mwololo et al. 2021). Within QTL, resistance is transferred in both generations of ECB1 and ECB2 with additive gene activity. It is easy to impart resistance to ECB1 to produce maize hybrids (Abel et al. 2000). Imparting resistance through introgression of QTL via marker-assisted selection (MAS) is an alternative to laborious conventional phenotypic methods, thus, reducing the phenotyping drawbacks such as cost and time consumption. Type of resistance is a major factor for specific and appropriate resistance breeding approach. The type of resistance can be qualitative or quantitative. For

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qualitative resistance, one or few R genes are employed. As for quantitative resistance, QTL with major gene additive effects are employed. The presence of a virulent pathogen in gene of interest could minimize the efficiency of a qualitative resource of R genes. Therefore, it is necessary to identify genes or QTL that are more durable and effective against various pathogen species. ECB resistance is clearly a quantitative trait. The studies showed that conventional phenotypic selection strategy might be sufficient for improvement of ECB resistance but is time and labor intensive. QTL for ECB stalk tunneling were detected on same or adjacent bins that indicated the almost common resistance mechanisms of both maize borer species (Ordas et al. 2010). Resistance achieved through QTL is predominantly in small to medium effect. Fewer studies are available for ECB resistance in the early maturing European corn germplasm. Recently, artificial infestation has been used for resistance against ECB and found genotypic and phenotypic variation for significant traits in a large number of European borer groups. QTL have been identified in tropical and temperate varieties for maize ECB. MAS could be competitive with PCR-based marker systems for improving ECB resistance. Bohn et  al. (2000) mapped and characterized QTL and then evaluated MAS strategy against resistance of ECB in maize and found comparison of QTL for agronomic traits in European dent corn. They measured relative grain yield (RGY), tunnel lengths (TL), and stalk damage ratings (SDR), in conducted experiment. Estimation of genotypic variance was high. They found six QTL for TL, five QTL for SDR, and not even a single QTL for RGY (Bohn et al. 2000). QTL for ECB resistance remained consistent in cross of D06 and D408 population (Table 1). Cardinal et  al. (2001) identified a QTL for tunnel length, anthesis, and plant height influenced by ECB. For QTL analysis, maize cross populations were grown, and genotypic and phenotypic data were examined. As a result, a QTL for plant tunneling was identified in this cross population, which was found at the same cross of two distinct populations. A significant and moderate genetic relationship has been found between plant height and tunneling (Cardinal et al. 2001; Krakowsky et al. 2002). Jampatong et al. (2002) determined the effect of QTL for resistance against leaf-feeding damage caused by first-generation ECB and stalk tunneling caused by second-generation ECB.  Krakowsky et  al. (2004) used 191 recombinant inbred lines for the evaluation of stalk tunneling including plant height and anthesis. The objectives of the study were to map QTL for ECB stalk tunneling resistance and determine the genetic relationship between these characteristics. By providing alleles for resistance to TL in chromosomes 1, 5, and 6, the subtropical line of CML103 may improve the tolerance of the B73 line without lowering grain production. QTL have also been localized for stalk breakage and TL caused by ECB with many additive gene actions in the bin 6.05 (Papst et al. 2004; Krakowsky et al. 2004; Orsini et al. 2012). Although MAS appears to be a viable technique for ECB resistance (Jampatong et al. 2002; Flint-Garcia et al. 2003), other investigations have shown that it does not produce relative efficiency. To increase the efficacy of MAS for resistance to ECB in maize cultivars, the correlation between per se and testcross (TC) must be known. In temperate and tropical maize germplasm, there is relatively

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Table 1  Identified QTL and their location on chromosome and genetic or phenotypic variance for resistance traits in maize cross breeds Variance (genetic or Location on Markers chromosome Trait QTL phenotypic) Crossbreeds 1 QTL on chr 5 50% genetic D06 (resistant) × D408 93 Stalk 1, QTL variance (susceptible) RFLP damage 2 QTL on chr and ratings 5, 2 SSR (SDR) 1 QTL on chr 6, and 1 QTL on chr 8 6 1 QTL on Tunnel QTL chr 1 length 1 QTL on (TL) chr 3 2 QTL on chr 5 1 QTL on chr 9 1 QTL on chr 10 RFLP _5 QTL on Tunneling 9 59% genetic B73 × B52 SSR chrs 2, 3, 5, 7, QTL variation for and 9. tunneling _1 QTL on chr 8 in combined analysis. _Evidence for more than 1 QTL on chrs 2, 3, 5, and 9 Plant 10 2 QTL on 1, height QTL 1 QTL on7 and 1 QTL on 9 Anthesis 8 1, 3 and 9 QTL De811 × B73 88 1, 3, 4, 5, Tunneling 7 42% RFLP and 8 QTL phenotypic variance

Reference Bohn et al. (2000)

Cardinal et al. (2001)

Krakowsky et al. (2002) (continued)

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Table 1 (continued)

Trait Leaf feeding damage (ECB1)

Variance (genetic or QTL phenotypic) 9 58% QTL phenotypic variance

Crossbreeds B73Ht (susceptible) × Mo47 (resistant)

Markers 97 RFLP 1 SSR

Location on chromosome 3 QTL on chr 1, 1 QTL on chr 2, 2 QTL on chr 4, and 1 QTL on each chrs 5, 6, and 8 2 QTL on each chrs 5 and 6 1 QTL on each chrs 2, 8, and 9 QTL found on chrs 1 and 2 for largest resistance effect

Reference Jampatong et al. (2002)

Stalk tunneling (ECB2)

7 57.5% QTL phenotypic variance

Stalk damage rating (SDR)

6 27.4% QTL genotypic variance

D06 (resistant) × D408 93 RFLP (susceptible). Again and cross with D171 2 SSR

Stalk tunneling Anthesis Plant height Leaf damage ECB tunneling

10 42% QTL phenotypic variation

B73 (susceptible) × De811 (resistant)

113 RFLP and 33 SSR

3 QTL 13 QTL

B73 × B52

161 RFLP 1 SSR

7, 9, 5, and 8

KW4773 (resistant) × WBB53 (susceptible)

164 SNP 88 SSR

Resistance for Orsini et al. (2012) ECB1 on chr 5 For ECB2 on chrs 2 and 6

Stalk breakage Leaf feeding Plant height

3 1 2

One-half QTL variance explained by tunneling 36% genetic variance 25% genetic variance 20% genetic variance

Papst et al. (2004)

Krakowsky et al. (2004)

Cardinal and Lee (2005)

(continued)

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Table 1 (continued) Variance (genetic or QTL phenotypic) 8 ˂30% phenotypic variance

Crossbreeds 3 inbred line

Tunnel length (TL)

10

Pop1: 85 DH lines

Anthesis (ANT) Number of tunnels (NT)

9

Pop2: 243 DH lines

4

Pop3: 262 DH lines

Trait Stalk damage ratings (SDR)

Location on Markers chromosome 4790 SDR QTL on SNP chrs 3 and 5 colocalized with NT and TL QTL SDR QTL on chr 2 colocalized with QTL of TL. For trait TL, other QTL did not colocalize with SDR

Reference Foiada et al. (2015)

little evidence on the relationship between hybrid performance and line per se for ECB resistance (Papst 2004). Papst (2004) characterized QTL for checking their consistency for TC performance and per se and to identify the agronomic and forage quality traits. Maize hybrid lines were created from the cross of resistant and susceptible maize lines. The maize hybrid lines were then crossed with D171 line, and testcross performance was evaluated for resistance against ECB and agronomic traits. Consistency of mapped QTL was poor for both TC performance and per se due to the variable genetics of the traits. These findings suggested that MAS can only be used for ECB resistance, if characterized QTL per se expressed in combination of hybrids. Even though QTL consistency was low, simulated experiments indicated that the most identified QTL would be used for MAS even though likely to be false positive. They concluded that MAS would not be a competent method for improvement of stalk damage ratings. However, novel molecular approaches are needed that would provide the opportunity to use identified QTL data set for ECB resistance. Cardinal and Lee (2005) conducted a study to access relationship of stalk tunneling caused by ECB and cell wall components from lines of B73 and B52. A number of QTL were identified located at variable positions for cell wall components and other traits. The QTL have been located on chromosomes 1 and 5 for tunnel length caused by European corn borer (Papst et al. 2004; Cardinal and Lee 2005). Orsini et al. (2012) studied numerous QTL for resistance against ECB in maize and found first-generation QTL on chromosome 5 while QTL for second-generation on chromosomes 2 and 6. The authors also identified QTL for stalk breakage, leaf feeding, and plant height with their phenotypic variance. The phenotypic variance for first-­ generation ECB accounted for 11.1% on chromosome 5 and 6.4% and 9% phenotypic variance on chromosomes 2 and 6, respectively, for second-generation ECB (Orsini et al. 2012). Resistance of first- and second-generation ECBs showed a little

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association. Foiada et al. (2015) studied the whole genome selection for ECB resistance to stalk damage in maize instead of selection based on individual QTL. A total of three biparental populations were phenotyped with natural and artificial infestation and genotyped with SNP markers. The stalk damage ratings (SDR), testcrosses (TS), tunnel length (TL), and lines per se were evaluated for resistance. They concluded that when progressing toward a QTL-based strategy for ECB stalk damage resistance, the performance of marker-assisted selection approach can be improved. However, researchers have been working for 10 years to detect a greater number of putative QTL by using molecular markers, but it is still unanswered whether they can be utilized for MAS to enhance ECB resistance in maize. To resolve this issue, the creation of an introgression library in conjunction with high-density markers was developed (Kolkman et  al. 2020). In addition, utilization of  high-throughput genotyping  platforms  can improve the accuracy of QTL mapping  (Farooqi et  al. 2022; Reyes et al. 2022). The identification of these QTL and genes as well as introgressing them into widely adapted varieties could result to lines that are resistant to ECB.

6 Summary The evolutionary arms race between insect pests and plants is a complex process. Over time, plants have evolved sophisticated defense strategies against herbivores. However, in major crops were genetic diversity has eroded, vulnerability to pests have been observed. Similarly, genetic uniformity in plant population often leads to susceptibility in arrays of biotic stresses. In maize, ECB is a major issue as it decreases the crop productivity. Although conventional approaches such as application of pesticide is available, ecological threats such as residue runoff is a major concern. To address this, various steps have been conducted such as transgenic experiments. However, this approach isn’t widely accepted due to some concerns. One of the alternatives is the utilization of DNA markers to identify genomic regions associated to ECB resistance. Over the years, different QTL associated to ECB resistance were identified. However, limited studies were carried out to evaluate its effect in large-scale breeding programs. Future direction in ECB researches should be directed in the actual application of these QTL in field set-up. In addition, development of new genetic resources will beneficial to further accelerate the improvement of ECB resistance in maize. 

References Abel CA, Berhow MA, Wilson RL, Binder BF, Hibbard BE (2000) Evaluation of conventional resistance to European corn borer (Lepidoptera: Crambidae) and western corn rootworm (Coleoptera: Chrysomelidae) in experimental maize lines developed from a backcross breeding program. J Econ Entomol 93(6):1814–1821. https://doi.org/10.1603/0022-­0493-­93.6.1814

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Archer TL, Schuster G, Patrick C, Cronholm G, Bynum ED et al (2000) Whorl and stalk damage by European and Southwestern corn borers to four events of Bacillus thuringiensis transgenic maize. Crop Prot 19(3):181–190. https://doi.org/10.1016/S0261-­2194(99)00092-­7 Bağdatlı MC (2019) Effects of different irrigation levels on European corn borer (Ostrinia nubilalis (Hübner)) populations. PLoS One 14(3):e0212595 Blandino M, Scarpino V, Vanara F, Sulyok M, Krska R et al (2015) Role of the European corn borer (Ostrinia nubilalis) on contamination of maize with 13 Fusarium mycotoxins. Food Addit Contam Part A Chem Anal Control Expo Risk Assess 32(4):533–543. https://doi.org/10.108 0/19440049.2014.966158 Bohn M, Schulz B, Kreps R, Klein D, Melchinger AE (2000) QTL mapping for resistance against the European corn borer (Ostrinia nubilalis H.) in early maturing European dent germplasm. Theor Appl Genet 101(5–6):907–917. https://doi.org/10.1007/s001220051561 Buendgen MR, Coors JG, Grombacher AW, Russell WA (1990) European corn borer resistance and cell wall composition of three maize populations. Crop Sci 30(3):505–510. https://doi. org/10.2135/cropsci1990.0011183x003000030005x Bzowska-Bakalarz M, Bulak P, Bereś PK, Czarnigowska A, Czarnigowski J et al (2020) Using gyroplane for application of Trichogramma spp. against the European corn borer in maize. Pest Manag Sci 76(6):2243–2250 CABI (2021) Invasive species compendium. CAB International, Wallingford, UK. https://www. cabi.org/isc Çakir R (2004) Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crop Res 89(1):1–16. https://doi.org/10.1016/j.fcr.2004.01.005 Cardinal AJ, Lee M (2005) Genetic relationships between resistance to stalk-tunneling by the European corn borer and cell-wall components in maize population B73xB52. Theor Appl Genet 111(1):1–7. https://doi.org/10.1007/s00122-­004-­1831-­5 Cardinal AJ, Lee M, Sharopova N, Woodman-clikeman WL, Long MJ et al (2001) Tunneling by the European corn borer in maize. Crop Sci 41(3134):835–845 Coates BS, Dopman EB, Wanner KW, Sappington TW (2018) Genomic mechanisms of sympatric ecological and sexual divergence in a model agricultural pest, the European corn borer. Curr Opin Insect Sci 26:50–56 Coates BS, Kozak GM, Seok Kim K, Sun J, Wang Y, Fleischer SJ, Dopman EB, Sappington TW (2019) Influence of host plant, geography and pheromone strain on genomic differentiation in sympatric populations of Ostrinia nubilalis. Mol Ecol 28(19):4439–4452. https://doi. org/10.1111/mec.15234 Dafoe NJ, Thomas JD, Shirk PD, Legaspi ME, Vaughan MM et al (2013) European corn borer (Ostrinia nubilalis) induced responses enhance susceptibility in maize. PLoS One 8(9):e73394 Dopman EB, Robbins PS, Seaman A (2010) Components of reproductive isolation between North American pheromone strains of the European corn borer. Evol Int J Org Evol 64(4):881–902 Fachgebiet H, Genetik A (2004) Resistance of maize (Zea mays L.) against the European corn borer (Ostrinia nubilalis Hb.) and its association with mycotoxins produced by Fusarium spp. Farooqi MQU, Nawaz G, Wani SH, Choudhary JR, Rana M, Sah RP, ... & Siddique, KH (2022) Recent developments in multi-omics and breeding strategies for abiotic stress tolerance in maize (Zea mays L.). Front Plant Sci 13 Flint-Garcia SA, Darrah LL, McMullen MD, Hibbard BE (2003) Phenotypic versus marker-­ assisted selection for stalk strength and second-generation European corn borer resistance in maize. Theor Appl Genet 107(7):1331–1336. https://doi.org/10.1007/s00122-­003-­1387-­9 Foiada F, Westermeier P, Kessel B, Ouzunova M, Wimmer V et al (2015) Improving resistance to the European corn borer: a comprehensive study in elite maize using QTL mapping and genome-wide prediction. Theor Appl Genet 128(5):875–891 Franeta F, Mirčić D, Todorović D, Milovac Ž, Granica N et al (2018) Effects of different insecticides on the antioxidative defense system of the European Corn Borer (Ostrinia nubilalis Hübner) (Lepidoptera: Crambidae) larvae. Arch Biol Sci 70(4):765–773. https://doi. org/10.2298/ABS180701042F

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A. Shahzadi et al.

Franeta F, Mikić S, Milovac Ž, Mitrović B, Inđić D et al (2019) Maize defence mechanisms against the European corn borer, Ostrinia nubilalis Hübner (Lepidoptera: Crambidae). Int J Pest Manag 65(1):23–32 Frolov AN, Bourguet D, Ponsard S (2007) Reconsidering the taxonomy of several Ostrinia species in the light of reproductive isolation: a tale for Ernst Mayr. Biol J Linn Soc 91:49–72. https:// doi.org/10.1111/j.1095-­8312.2007.00779.x Galić V, Šimić D, Franić M, Brkić A (2019a) Analysis of Fusarium ear rot and fumonisin contamination in testcrosses of a maize biparental population. Crop Breed Appl Biotechnol 19:40–46 Galić V, Šimić D, Franić M, Brkić A, Jambrović A et al (2019b) Analysis of Fusarium ear rot and fumonisin contamination in testcrosses of a maize biparental population. Crop Breed Appl Biotechnol 19(1):40–46 García-Lara S, Bergvinson DJ, Burt AJ, Ramputh AI, Díaz-Pontones DM et al (2004) The role of pericarp cell wall components in maize weevil resistance. Crop Sci 44(5):1546–1552 Hoffmann GM, Schmutterer H (1983) Parasitäre Krankheiten und Schädlinge an landwirtschaftlichen Kulturpflanzen Hutchison WD, Burkness EC, Mitchell PD, Moon RD, Leslie TW, Fleischer SJ et al (2010) Areawide suppression of European corn borer with Bt maize reaps savings to non-Bt maize growers. Science 330(6001):222–225. https://doi.org/10.1126/science.1190242 Ishikawa Y, Takanashi T, Kim CG, Hoshizaki S, Tatsuki S, Huang Y (1999) Ostrinia spp. in Japan: their host plants and sex pheromones. Entomol Exp Appl 91:237–244. https://doi. org/10.1046/j.1570-­7458.1999.00489.x Jampatong C, Mcmullen MD, Barry BD, Darrah LL, Byrne PF et al (2002) Resistance derived from the maize inbred Mo47. Crop Sci 42:584–593 Jiménez-Galindo JC, Malvar RA, Butrón A, Santiago R, Samayoa LF et al (2019) Mapping of resistance to corn borers in a MAGIC population of maize. BMC Plant Biol 19(1):1–17 Joshi PK, Singh NP, Singh NN, Gerpacio RV, Pingali PL (2005) Maize in India: production systems, constraints, and research priorities Kim C, Hoshizaki S, Huang Y, Tatsuki S, Ishikawa Y (1999) Usefulness of mitochondrial COII gene sequences in examining phylogenetic relationships in the Asian corn borer, Ostrinia furnacalis, and allied species (Lepidoptera: Pyralidae). Appl Entomol Zool 34:405–412 Kitony JK, Sunohara H, Tasaki M, Mori J-I, Shimazu A, Reyes VP, Yasui H, Yamagata Y, Yoshimura A, Yamasaki M, Nishiuchi S, Doi K (2021) Development of an aus-derived nested association mapping (aus-NAM) population in rice. Plan Theory 10(6):1255 Kochansky J, Cardé RT, Liebherr J, Roelofs WL (1975) Sex pheromone of the European corn borer, Ostrinia nubilalis (Lepidoptera: Pyralidae), in New  York. J Chem Ecol 1:225–231. https://doi.org/10.1007/BF00987871 Kolkman JM, Strable J, Harline K, Kroon DE, Wiesner-Hanks T et  al (2020) Maize introgression library provides evidence for the involvement of liguleless1 in resistance to northern leaf blight. G3 Genes, Genomes, Genet 10(10):3611–3612. https://doi.org/10.1534/g3.120.401500 Krakowsky MD, Brinkman MJ, Woodman-Clikeman WL, Lee M (2002) Genetic components of resistance to stalk tunneling by the European corn borer in maize. Crop Sci 42(4):1309–1315. https://doi.org/10.2135/cropsci2002.1309 Krakowsky MD, Lee M, Woodman-Clikeman WL, Long MJ, Sharopova N (2004) QTL mapping of resistance to stalk tunneling by the European corn borer in RILs of maize population B73 X De811. Crop Sci 44(1):274–282. https://doi.org/10.2135/cropsci2004.2740 Lemic D, Mandić J, Čačija M, Drmić Z (2019) European corn borer and its parasites overwintering abundance and damages on different corn FAO maturity groups Pojava kukuruznog moljca i njegovih parazita nakon prezimljenja i štete na hibridima različitih FAO grupa. J Cent Eur Agric 20(1):447–460 Lemić D, Mandić J, Čačija M, Drmić Z, Mrganić M et  al (2019) European corn borer and its parasites overwintering abundance and damages on different corn FAO maturity groups. J Cent Eur Agric 20(1):447–460

Advancement in QTL Mapping to Develop Resistance Against European Corn Borer…

39

Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620 Magg T (2004) Resistance of maize (Zea mays L.) against the European corn borer (Ostrinia nubilalis Hb.) and its association with mycotoxins produced by Fusarium spp. Malvar RA, Butro A (2004) Evaluation of the European Union maize landrace core collection for resistance to Sesamia nonagrioides (Lepidoptera : Noctuidae) and Ostrinia nubilalis (Lepidoptera : Crambidae). J Econ Entomol 97(1984):628–634 Martel C, Réjasse A, Rousset F, Bethenod MT, Bourguet D (2003) Host-plant-associated genetic differentiation in Northern French populations of the European corn borer. Heredity 90(2):141–149 Miedaner T, Juroszek P (2021) Global warming and increasing maize cultivation demand comprehensive efforts in disease and insect resistance breeding in north-western Europe. Plant Pathol 70(5):1032–1046. https://doi.org/10.1111/ppa.13365 Mitchell C, Brennan RM, Graham J, Karley AJ (2016) Plant defense against herbivorous pests: exploiting resistance and tolerance traits for sustainable crop protection. Front Plant Sci 7:1132 Munkvold GP, Hellmich RL, Rice LG (1999) Comparison of fumonisin concentrations in kernels of transgenic Bt maize hybrids and nontransgenic hybrids. Plant Dis 83(2):130–138. https:// doi.org/10.1094/PDIS.1999.83.2.130 Mutuura A, Munroe E (1970) Taxonomy and distribution of the European corn borer and allied species: genus Ostrinia (Lepidoptera: Pyralidae). Mem Ent Soc Canada 71:1–112 Mwololo JK, Mugo S, Otim M, Munyiri SW, Okori P (2021) Quantitative trait loci mapping in maize for resistance to larger grain borer. Maydica 63(3):9 Myers SW, Ballweg M, Wedberg JL (1993) Assessing the impact of European corn borer on corn grown for silage Ordas B, Malvar RA, Santiago R, Butron A (2010) QTL mapping for Mediterranean corn borer resistance in European flint germplasm using recombinant inbred lines. BMC Genomics 11(1). https://doi.org/10.1186/1471-­2164-­11-­174 Orsini E, Krchov LM, Uphaus J, Melchinger AE (2012) Mapping of QTL for resistance to first and second generation of European corn borer using an integrated SNP and SSR linkage map. Euphytica 183(2):197–206. https://doi.org/10.1007/s10681-­011-­0441-­y Papst O (2004) QTL mapping for European corn borer resistance (Ostrinia nubilalis Hb.), agronomic and forage quality traits of testcross progenies in early-maturing European maize (Zea mays L.) germplasm. Theor Appl Genet:1545–1554. https://doi.org/10.1007/s00122-­003-­1579-­3 Papst C, Bohn M, Utz HF, Melchinger AE, Klein D et al (2004) Resistance mechanisms against European corn borer (Ostrinia nubilalis Hb.) in early-maturing dent maize germplasm. Hybridmais, Züchtung und Verwertung (= Maize Breed. Util. 54 Papst C, Utz HF, Melchinger AE, Eder J, Magg T, Klein D, Bohn M (2005) Mycotoxins produced by Fusarium spp. in Isogenic Bt vs. non-Bt Maize hybrids under European corn borer pressure. Agron J 97(1):219–224. https://doi.org/10.2134/agronj2005.0219a Pilcher CD, Rice ME (1998) Management of European corn borer (Lepidoptera: Crambidae) and corn rootworms (Coleoptera: Chrysomelidae) with transgenic corn: a survey of farmer perceptions. Am Entomol 44(1):36–44 Ponsard S, Bethenod MT, Bontemps A, Pélozuelo L, Souqual MC, Bourguet D (2004) Carbon stable isotopes: a tool for studying the mating, oviposition, and spatial distribution of races of European corn borer, Ostrinia nubilalis, among host plants in the field. Can J Zool 82:1177–1185. https://doi.org/10.1139/Z04-­075 Raza A, Razzaq A, Mehmood SS, Zou X, Zhang X, Lv Y, Xu J (2019) Impact of climate change on crops adaptation and strategies to tackle its outcome: a review. Plan Theory 8(2):34 Reyes VP, Kitony JK, Nishiuchi S, Makihara D, & Doi K (2022).Utilization of genotyping-bysequencing (GBS) for rice pre-breeding and improvement: a review. Life 12(11):1752 Rice ME, Hodgson EW (2017) Ecology and management of European corn borer in Iowa field corn. Iowa State Univ Extension Outreach, pp 1–12

40

A. Shahzadi et al.

Samantara K, Reyes VP, Agrawal N, Mohapatra SR, Jena KK (2021) Advances and trends on the utilization of multi-parent advanced generation intercross (MAGIC) for crop improvement. Euphytica 217(10):189 Scarpino V, Reyneri A, Vanara F, Scopel C, Causin R et al (2015) Relationship between European corn borer injury, Fusarium proliferatum and F. subglutinans infection and moniliformin contamination in maize. F.  Crop Res 183(March 2021):69–78. https://doi.org/10.1016/j. fcr.2015.07.014 Shiri M, Ebrahimi L, Badali A (2021) Evaluation of yield and some resistance-related traits of three maize hybrids against European corn borer Ostrinia nubilalis (Hubner). Cereal Res Commun:1–8 Sobek EA, Munkvold GP (1999) European corn borer (Lepidoptera: Pyralidae) larvae as vectors of Fusarium moniliforme, causing kernel rot and symptomless infection of maize kernels. J Econ Entomol 92:503–509. https://doi.org/10.1093/jee/92.3.503 Vaughan MM, Block A, Christensen SA, Allen LH, Schmelz EA (2018) The effects of climate change associated abiotic stresses on maize phytochemical defenses. Phytochem Rev 17(1):37–49 Vukovi S, Ini D, Marinkovi B, Crnobarac J, Grahovac M (2014) Suzbijanje kukuruznog plamenca i kukuruzne sovice primenom insekticida iz aviona. Biljn Lek 44(1):58–64 Vuković S, Indić D, Grahovac M, Franeta F (2015) Protection of sweet corn from Ostrinia nubilalis Hbn. and Helicoverpa armigera Hbn. Commun Agric Appl Biol Sci 80(2):161–167

Dissection of QTLs for Biotic Stress Resistance in Maize Rajkumar U. Zunjare, K. T. Ravikiran, Firoz Hossain, Vignesh Muthusamy, Rahul D. Gajghate, Jayant S. Bhat, Mukesh Choudhary, and Nivedita Shettigar

1 Introduction Maize (Zea mays L.) is an important annual cereal crop of family Poaceae, having wider adaptability under varied environmental conditions, and popularly used for human food, animal feed and industrial usage (Shiferaw et al. 2011). It is originated in the highlands of Mexico (~8700  years ago) and known as ‘queen of cereals’, owing to its highest genetic yield potential (Piperno et al. 2009). Maize is grown on ~193.25 million hectare (mha) with a production of 1116.19 million tonnes (mt) with a productivity of 5.78 tha−1 (USDA 2020). The area under maize spans from the latitude 58° N to the latitude 40° S, and it is harvested every month of the year in one region or the other. Akin to any crop species, maize production is severely plagued by several abiotic and biotic stresses (Gong et  al. 2014). Biotic stress includes stresses caused by virus, bacteria, fungi and nematodes, parasites and insect pests. Crops are regularly exposed to biotic stresses, which cause changes in metabolism and damages at various levels resulting in faltered productivity (Gimenez et  al. 2018). The diseases are major culprits of biotic stress-induced

R. U. Zunjare (*) · F. Hossain · V. Muthusamy · J. S. Bhat ICAR-Indian Agricultural Research Institute, New Delhi, India K. T. Ravikiran ICAR-Central Soil Salinity Research Institute, Regional Research Station, Lucknow, India R. D. Gajghate ICAR-Indian Grassland and Fodder Research Institute, Jhansi, India M. Choudhary ICAR-Indian Institute of Maize Research, Ludhiana, India N. Shettigar ICAR-Research Complex for NEH Region, Umiam, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_3

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damages in maize followed by insect pests and parasitic weeds mainly Striga hermonthica (Keno et al. 2018). The outbreaks of diseases in maize are one of the well-documented crop epidemics in the world’s history. For instance, the outbreak of southern corn leaf blight (SCLB), caused by Bipolaris maydis race T, was the first reported disease outbreak in maize during 1970–1971. This was mainly due to the indiscriminate use of T-type male sterile cytoplasm (cms  – Texas) which occupied 85% of corn fields in the United States (Ullstrup 1972). With around 50–100% yield reduction, the economic loss due to this disease reached as high as one billion dollars. An outbreak due to northern corn leaf blight (NCLB) was observed in North China due to the extensive cultivation of a high-yielding, but susceptible, variety ‘Xian Yu335’ (Pu 2013). Similarly, a severe epidemic of maize lethal necrosis (MLN), a viral disease, was documented in Kenya in September 2011, which disseminated to other African countries. The yield loss due to this disease was estimated up to 126,000 metric tons which translated into $52 million in Kenya in 2012 (Mahuku et al. 2015; Terefe and Gudero 2019). Tar spot complex (TSC) caused by three fungal pathogens is another major disease in Latin American countries and has the reported potential of inciting up to 51% yield loss in maize. TSC first reported in 1904 in Mexico later spread to other countries (Mottaleb et al. 2019). Recently, FAW (fall armyworm) is causing devastation of maize crop across the countries (Prasanna et al. 2022). In India, it is first reported from Bangalore Rural and Chikkaballapur districts during May–June 2018 (Ganiger et al. 2018) and South Karnataka during the first fortnight of July 2018 (ICAR-NBAIR pest alert, 2018), the pest infected 40–70% of the crop, quickly spreading to the rest of the country (Tippannavar et  al. 2019). Thus, disease and pests pose a serious threat to sustainable production of maize across the globe. Breeding for stress-tolerant genotypes is an important and economically viable strategy to combat various biotic stresses. This can significantly reduce the dependence on chemical control for the management of diseases and pests and enhance the export value of the produce while ensuring the consumer safety. The information on the genetic control of the target trait is an important prerequisite of any breeding programme (Zunjare et al. 2015a; Muthusamy et al. 2016), which guides the breeders in choosing the most adequate breeding strategy. With the advent of molecular markers and statistical models, mapping of quantitative trait loci (QTLs) or genomic regions encoding a particular trait has picked up pace. The principle of ‘linkage mapping’ has been widely employed for mapping QTLs in crop plants through developing biparental segregating population for trait of interest (Collard et al. 2005). However, association mapping or linkage disequilibrium (LD) mapping is based on statistical associations of genetic markers with phenotypes in natural populations (Huang and Han 2013). With the rapid advancements in genomics, decreasing genotyping cost and available genome sequences, genome-wide LD mapping has become popular and powerful approach to dissect genetic architecture of complex traits (Huang and Han 2013). Both mapping techniques have their own pros and cons; for instance, the power of QTL detection is higher in case of biparental mapping study while resolution of QTL is higher for LD-based mapping. Several studies have been reported for identification of QTLs and the underpinning

Dissection of QTLs for Biotic Stress Resistance in Maize

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candidate genes for various biotic stresses in maize. An attempt was made to review these reports in a systematic manner. The chapter was organized into different sections starting with a brief description of important pests and diseases of maize followed by defence mechanisms of biotic stress resistance, QTL mapping studies and attempts to improve maize genotypes using QTLs or genes.

2 Biotic Stresses: Types, Major Symptoms and Losses Caused The most prevalent maize diseases are leaf blight, ear rot, maize rough dwarf disease, sugarcane mosaic disease, maize streak virus disease, maize dwarf mosaic disease, maize lethal necrosis virus disease and high plain disease, while the most prevalent insect pests are borers, fall armyworm, shoot fly and the storage weevils (Table 1). The detail of above biotic stresses are briefly described here under.

2.1 Major Diseases Fungal Diseases 1. Turcicum leaf blight: Turcicum leaf blight (TLB) or northern corn leaf blight (NCLB) is the most important foliar disease in northern hills, northeastern hills and peninsular part of India. The fungus Setosphaeria turcica (anamorph Exserohilum turcicum) is the causal pathogen with characteristic symptom of ‘cigar-­shaped’ tan or greyish lesions on leaf surface (Chen et al. 2016a). It is prevalent in majority of maize-cultivating countries of the world having cool climate (temperatures 20–25 °C), 90–100% relative humidity and low luminosity (Wu et al. 2014). Under favourable condition of infection with no treatment, it has potential to cause yield loss up to 70%. 2. Gibberella ear rot (GER): It is caused by Fusarium graminearum fungus which also causes rot diseases in the stalks and roots of maize. The main symptoms include reddish grain formation at the ear tip initially. GER reduces the yield and quality of kernels and triggers the accumulation of mycotoxins (vomitoxin and zearalenone) which cause serious health problems in humans and animals (Brauner et al. 2017). 3. Diplodia ear rot (DER): It is caused by pathogen Stenocarpella maydis and main symptoms are bleached husks, whitish fungal growth on grains and rotted ears. Early infections of DER lead to complete ear rotting, while late infections may result in partial rotting of ears. It results into loss of nutritive value of kernels and deposition of fungus causing mycotoxins (Baer et al. 2021). 4. Aspergillus ear rot (AER): It is caused by fungus called Aspergillus that produces aflatoxin which is carcinogen and can potentially be dangerous to ­livestock

Fusarium ear rot Fusarium verticillioides

5.

Viral diseases 1. Maize rough dwarf disease

Aspergillus ear rot

4.

Maize rough dwarf virus (MRDV), Mal de Rio Cuarto virus (MRCV), rice black-­ streaked dwarf virus (RBSDV) and southern rice black-streaked dwarf virus (SRBSDV)

Aspergillus flavus

Stenocarpella maydis

Diplodia ear rot

3.

Fusarium graminearum

Gibberella ear rot

Setosphaeria turcica

Scientific name

2.

S. no. Name of pest Fungal diseases 1. Turcicum leaf blight Wu et al. (2014)

Woloshuk and Wise (2010) and Galiano-­ Carneiro et al. (2021b) Baer et al. (2021)

Yield reduction up to 70% Mycotoxin contamination Up to 80% reduction in yield Aflatoxin contamination

‘Cigar-shaped’ tan or greyish lesions on the leaf Pinkish fungal growth on the tip of cob Tan spots on husks or bleached husks Olive-green mould on ears

Northern China, Europe and South America

Dwarfing, malformed tassel and dark green leaves

100% yield loss in severely affected areas

Zhang et al. (2021)

Woloshuk and Wise (2011), Mitchell et al. (2016) and Womack et al. (2020) Ding et al. (2008) and Wen et al. (2021)

Reference(s)

Damage caused

Typical symptoms

Yield reduction up to China, United States Tan or brown kernels with white or pink fungal 50% coupled with and Southern mycotoxin growth Europe contamination

Northern and northeastern hills and peninsular India Most of the maize-growing countries South America, Africa, United States and Canada Most of the maize-growing countries

Major country prevalence (major area

Table 1  List of the important biotic stresses prevalent in maize

44 R. U. Zunjare et al.

Maize streak virus disease

Maize dwarf mosaic disease

Maize lethal necrosis virus disease

High plains disease

3.

4.

5.

6.

Insects 1. Spotted stem borer

Sugarcane mosaic disease

2.

Chilo partellus

Wheat mosaic virus (WMoV)

Maize chlorotic mottle virus (MCMV)

Maize dwarf mosaic virus (MDMV)

Maize streak virus (MSV)

Asia and in most countries of East and Southern sub-Saharan Africa

‘Dead heart’ symptom

Shades of green on a background of paler green to yellow chlorotic areas Yellowish and light Primarily Africa, some parts of South green streaks on leaves Asia and the United States Stippled (small, Most of the discoloured specks) maize-growing mottle or mosaic of light countries and dark green Long yellow stripes on Primarily Africa, some parts of Asia, leaves North America and South America Stunted yellowish plants Prevalent in North America, some parts with one or more of Europe and South yellowish or reddish-­ America purple bands

Sugarcane mosaic virus (SCMV) Almost all maize-growing areas

Redinbaugh and Stewart (2018)

Up to 100% yield losses

Yield loss up to 26–80%

(continued)

Ong’amo et al. (2016)

Jensen et al. (1996)

Kannan et al. (2018)

Up to 70% yield loss

–

Shepherd et al. (2010)

100% yield loss in severely affected areas

Yield reduction can be up to 50%

Dissection of QTLs for Biotic Stress Resistance in Maize 45

Shoot fly

Weevils

5.

6.

4.

Mediterranean corn borer Fall army worm

3.

S. no. Name of pest 2. Pink borer

Table 1 (continued)

Typical symptoms Younger plants show dead hearts while mature plants are prone to lodging Sesamia nonagrioides Lefebvre Southern European ‘Dead heart’ and stem countries and Africa breakages Papery windows to Spodoptera frugiperda Almost all oblong holes on leaves, maize-growing complete defoliation. countries Damage to tassel and corn ears Atherigona spp. Northwestern plain ‘Dead heart’ symptom zone of India Moisture on seed leading Almost all Maize weevil (Sitophilus to seed sprouting during zeamais), Rice weevil (Sitophilus maize-producing storage, fungal growth  countries oryzae), Granary weevil (Sitophilus granaries)

Scientific name Sesamia inference

Major country prevalence (major area Peninsular India

Reference(s) Baladhiya et al. (2018)

About 28–45% grain yield loss 12–20% grain loss is quite common and may reach up to 80%

Zunjare (2012) and Nwosu (2018)

Jindal (2013)

Yield loss of up to Zanakis et al. (2009) 80% Nearly complete yield Ganiger et al. (2018), loss Tippannavar et al. (2019)

Damage caused Yield loss up to 26–79%

46 R. U. Zunjare et al.

Dissection of QTLs for Biotic Stress Resistance in Maize

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(Woloshuk and Wise 2011). Aflatoxin contamination of maize is a major problem in the southern parts of the United States (Mitchell et al. 2016). Symptoms of AER include green to yellowish fungal growth on and between grains near the ear tip mostly (Woloshuk and Wise 2011). 5. Fusarium ear rot (FER): This maize disease is prevalent worldwide affecting grain yield and quality that is caused by fungus Fusarium verticillioides (Ding et al. 2008). Disease incidence is usually 10–20% in China, but in favourable conditions, it can reach up to 50% (Wen et al. 2021). The disease also poses a serious health hazard due to the accumulation of mycotoxin called fumonisin. Typical symptoms include scattered individual kernels with whitish pink to lavender Fusarium growth. Fungus-affected grains may have a ‘starburst’ pattern of white streaks. Viral Diseases 1. Maize rough dwarf virus disease (MRDV): It is a damaging viral disease with symptoms like internode shortening, malformed tassels and significant delays in vegetative growth (Wang et  al. 2019). It is primarily caused by three viruses, namely, maize rough dwarf virus (MRDV), Mal de Rio Cuarto virus (MRCV) and rice black-streaked dwarf virus (RBSDV). MRDV and MRCV are prevalent in Europe and in South America, respectively, while RBSDV is prevalent in China. The small brown plant hopper Laodelphax striatellus is the carrier of RBSDV virus (Wang et al. 2003). 2. Sugarcane mosaic virus disease (SCMV): It is one of the serious pathogens causing severe yield losses in both sugarcane and maize. SCMV was first detected in sugarcane in 1919 and in maize in 1963, both in the United States (Signoret 2008). High incidence of SCMV was also reported in maize in China and Argentina (Perera et al. 2009; Xu et al. 2008). The mosaic pattern with contrasting shades of green to yellow chlorotic areas typically appears on SCMV incidence. The complete plant may become stunted on early infection of SCMV. The disease is spread by several aphid species. 3. Maize streak virus disease (MSV): It is endemic to sub-Saharan Africa, and yield losses due to this virus are reportedly as high as 100% with an annual economic loss of US$120  M and US$480  M (Martin and Shepherd 2009). Hence, it is considered as the biggest threat to the food security of sub-Saharan Africa (Shepherd et al. 2010). Among several strains of MSV, MSV-A causes economically tangible disease (Martin et al. 2001). The virus is mainly transmitted by leafhopper Cicadulina spp. frequently found in the late sown maize fields or with susceptible varieties (Muimba-Kankolongo 2018). The virus mainly damages the plants younger than six weeks old. The top and bottom surfaces of leaves have yellowish and light green streaks on younger plants while mature plants show whitish, yellow and light green streaks running parallel to the leaf veins. The infected plants can be severely stunted if the crop is attacked during the 4–5 leaf stages. Abnormal bunching of flowers and shoots and reddish pigmentation may also be observed in later stages.

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4. Maize dwarf mosaic virus disease (MDMV): This disease is caused by MDMV belonging to the genus Potyvirus and is the most common disease of monocotyledonous plants (Kannan et al. 2018). The yield loss due to MDMV disease can be up to 70% resulting from disturbed photosynthesis and increased respiration (Mikel et al. 1981). The stunted, bunchy infected plants with short internodes have a stippled mottle or mosaic of light and dark green on the youngest leaves. The more yellowing will appear as plants mature and temperatures rise. The virus is also transmitted by various aphid species (over 20). 5. Maize lethal necrosis (MLN) disease: This is due to maize chlorotic mottle virus (MCMV) which also belongs to Potyviridae. The disease is transmitted by maize thrips (Frankliniella williamsi). As mentioned earlier, this virus caused an outbreak in southern rift valley of Kenya during 2011 and later spread to other regions of Africa (Redinbaugh and Stewart 2018). MLN disease causes long yellow stripes on leaves which are wider than those caused by maize streak virus disease. As the disease progresses, the maize leaves turn yellow and dry out from the edges towards the midrib. MLN disease can also cause dwarfing and premature aging of the plants, and ultimately the plants die. Late infection prevents tassel formation and produces poorly filled cobs. 6. High plains disease (HPD): This disease is caused by wheat mosaic virus (WMoV) and noted first time in the 1990s on maize in Idaho and later in Utah in 1994 (Jensen et al. 1996). This is one of the extremely difficult diseases to control since it is reported to be seed borne (Jensen et  al. 1996). The symptoms include weakened root systems, stunted growth and yellowing of the leaves, sometimes with yellow streaks and flecks. Reddish-purple discolourations or wide yellow bands are often seen on mature leaves. The bands turn tan or pale brown as the tissue dies. HPD in sweet corn is transmitted by insect called wheat curl mite (Aceria tosichella).

2.2 Major Insect Pests 1. Stem borer (SB): SB (Chilo partellus) is a major insect pest and infests maize during the kharif season all over India causing a yield loss of about 26–80% in different agro-climatic regions. Typical symptoms involve a ‘dead heart’ due to the withering of central shoot. The larvae later mine and feed on internal tissues. When cut open, tunnel can be observed inside the stem wherever larvae have traversed. This is visible externally as bored holes on the stem near the nodes (Ong’amo et  al. 2016). The younger larvae crawl and feed on tender folded leaves causing typical ‘shot hole’ symptom. 2. Pink borer (PB): PB (Sesamia inferens) affects maize crop with yield loss in the range of 25.7–78.9%. PB larvae feed inside the leaf sheath in groups on the epidermal layer, preferably on first three leaf sheaths. The larvae later enter the plant at the base by making a hole and damage the inner portion of the stem. The

Dissection of QTLs for Biotic Stress Resistance in Maize

3.

4.

5.

6.

49

younger plants show dead hearts while the old plants become weak and are prone to lodging due to heavy winds (Baladhiya et al. 2018). Mediterranean corn borer (MCB): MCB (Sesamia nonagrioides Lefebvre) or corn stalk borer is the most damaging pest of maize in Southern European countries. They can incite a yield loss of up to 80% (Zanakis et  al. 2009). In the European Mediterranean area, MCB coexists with ECB, and this duo has the potential of causing a higher damage to the plants (Velasco et al. 2007). Larvae feed inside the stems led to ‘dead heart’ symptoms as terminal leaves die. Fall armyworm (FAW): FAW (Spodoptera frugiperda) is one of the most destructive pests of maize across the world. As referred before, this pest was under high alert during 2018 with outbreak starting from Karnataka state to other parts of India. Initially, the symptoms appear as elongated papery windows across the leaves caused by first and second instar FAW larvae which feed by scrapping on leaf surface. Once the larva enters third instar, its feeding causes ragged-edged round to oblong holes on leaves. The fifth instar starts feeding voraciously, consuming larger areas of leaves, while the sixth instar larva extensively defoliates the leaves. In reproductive stage of the maize crop, tassel and corn ears are the vulnerable parts. Tassel damage is most common, which does not lead to economic damage, but boring into corn ears directly affects the yield. Sweet corn ear is more prone to FAW damage, which render the ears unmarketable (Ganiger et al. 2018; Tippannavar et al. 2019). Shoot fly (SF): SF (Atherigona spp.) is a serious pest in spring maize crop sown in February–March of the year in northwestern plain zone of India and reported to cause up to 60% plant loss. The SF affects the maize plants at the seedling stage where maggots feed on young growing plants resulting in drying of the seedlings or ‘dead heart’ (Jindal 2013). Weevils: Post harvest grain loss due to weevil insects belonging to genus Sitophilus is a major biotic stress concern in maize. Sitophilus zeamais (maize weevil) is found in Latin America, Europe and Africa; and Sitophilus granaries (granary weevil) is prevalent in temperate climate, while Sitophilus oryzae (rice weevil) is supposedly originated in Indian sub-continent (Zunjare 2012). Larvae of weevils feed within the grain kernels and adults emerge making holes on the grain. Hence, eggs, larvae and pupae are not visible on the grain. Only adults can be found wandering over the surface of grain (Hossain et al. 2007; Zunjare et al. 2014, 2016). Nearly, 12–20% grain loss is quite common and may reach up to 80% under favourable conditions of infestation (Zunjare et al. 2015a, b, c).

In addition to above insect pests, there are other minor pests of maize such as cob borer (Helicoverpa armigera Hubner), tobacco caterpillar (Spodoptera litura Fabricius), flower chafer beetle (Chiloloba acuta Wiedemann), Oxycetonia versicolor (Fabricius), armyworm (Mythimna separata Walker), cut worm (Agrotis ipsilon Rott.), grasshopper (Hieroglyphus nigrorepletus Bol.), aphid (Rhopalosiphum maidis Fitch), leafhoppers (Pyrilla perpusilla Walker) and Angoumois grain moth (Sitotroga cerealella Olivier).

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3 Defence Mechanisms in Maize Against Pathogens and Insects Plants, being sessile in nature, have to either maintain harmony with these insect pests and diseases or fight against them for their survival since they cannot run away from the predators like animals. Maize plants recognize several kinds of elicitors released by insects. Oral secretion and ovipositional fluids of the herbivorous insects are the principal sources of these elicitors. For instance, oral secretions from Mythimna separata caterpillar contain >10 different kinds of fatty acid – amino acid conjugates, the most abundant being hydroxylated FAC volicitin (Qi et al. 2016). M. separata secretions trigger the enhanced production of jasmonic acid and its derivative JA-Ile (JA-isoleucine conjugate). In addition, maize plants can also detect other insect-derived elicitors, such as caeliferin and inceptin (Schmelz et al. 2011). Maize land races release some volatile compounds in response to oviposition by stem borer Chilo partellus, which attracts both egg and larval parasitosis (Tamiru et al. 2011).

3.1 Phytoalexins After the recognition of elicitors, the plants put forth various defence mechanisms – physical and chemical to contain the damage being caused by the insect. Secondary metabolites constitute major class of chemical defence elicited in the plants, popularly referred to as ‘phytoalexins’ (Smith 1996). There are two types of secondary metabolites in plants  – volatile and nonvolatile. The main objective of releasing volatile secondary metabolites is to attract natural enemies of invading pest, which is termed as indirect defence response. For instance, maize plants release indole, a volatile aromatic compound for protection against S. littoralis as direct defence (Veyrat et al. 2016). Another compound, methyl salicylate (MeSA), also acts as a strong deterrent against leafhoppers (Cicadulina storeyi) in maize (Oluwafemi et al. 2011). Both of these compounds can prime the plants against the attack of respective pests. The production of a terpene compound, (E)-β-caryophyllene, confers resistance to Diabrotica virgifera by attracting an entomopathogenic nematode Heterorhabditis megidis (Rasmann et al. 2005). This seems to be effective against stem borer as well (C. partellus) by attracting its natural enemy, an egg parasitoid C. sesamiae (Tamiru et al. 2011). The attack of S. littoralis was reported to incite the localized production of 1,3-benzoxazin-4-ones, phospholipids, N-hydroxycinnamoyl tyramines, azelaic acid and tryptophan in maize seedlings (Marti et  al. 2013). Benzoxazinoids (BXs) are a group of well-characterized compounds which play a significant role in maize defence against herbivorous insects (Handrick et al. 2016). BXs are demonstrated to be toxic to European corn borer (O. nubilalis) and Asian corn borer (O. furnacalis). For instance, DIMBOA (2,4-dihydroxy-7-­methoxy-1,4benzoxazin-3-one) is popularly reported to be effective against these corn borers;

Dissection of QTLs for Biotic Stress Resistance in Maize

51

however, the underlying mechanism still needs to be elucidated (Glauser et  al. 2011). Lectins are glycoproteins which function in defending the plants against a range of pests. These are jasmonate inducible and were reported in maize apart from other monocots such as rice, barley, wheat and rye (Jiang et al. 2006). In addition, oxylipins such as 9-oxylipin and 10-oxo-11-phytoenoic acid are strongly induced in maize silks upon infection by corn earworm (Helicoverpa zea) (Christensen et al. 2014).

3.2 Phytoanticipins These are constitutively produced defence-related compounds which are inactive in native state but are activated and recruited once there is an insect attack. Thus, these compounds are produced in anticipation of an insect attack, hence termed ‘phytoanticipins’. For example, maize silks constitutively produce maysin which is a C-glycosyl flavone. However, the quinones which are derived from it are toxic to corn earworm (Waiss Jr et  al. 1979). Sometimes, the dormant compound and its activator are localized in different organelles within the cell. Upon tissue disruption by the invading insect, they are brought together which leads to the production of active compound. Benzoxazinoids are stored in vacuoles as glucosides. Due to the tissue disruption, these glucosides are hydrolysed by plastid localized glucosidases leading to the production of toxic aglycones (Frey et al. 2009). Several of the above mechanisms are also active against pathogens in maize. Phytoalexins such as kauralexins and zealexins are also induced against pathogens (Schmelz et  al. 2011; Huffaker et  al. 2011), and benzoxazinoids are produced mainly against fungal pathogens (Ahmad et al. 2011). In addition, maize utilizes certain physical barriers to ward off or curtail the spread of the pathogen. Increased accumulation of suberin offers barrier against Fusarium graminearum (Santiago et al. 2007) and Aspergillus flavus (Spangler 2008). Deposition of callose in the cell wall and local hypersensitivity reaction are the other physical barriers deployed by maize (Morris et al. 1998). Ribosome-inactivating proteins and PR (pathogenesis-­ related) proteins are also synthesized in response to pathogen attack (Moeller and Tiffin 2005). Thus, the mechanisms of maize tolerance to pathogens are poorly understood relative to insect pests and need further study.

4 QTL Analysis for Biotic Stresses Host plant resistance in maize for the biotic stress is of two categories: (i) qualitative resistance based on single gene resistance (R genes) and (ii) quantitative resistance based on multi-gene resistance. Most of the genetic resistance exploited by maize breeders display quantitative inheritance. This may be because maize is relatively more genetically diverse than other cereals owing to its outcrossing (Buckler et al.

52

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2001). Maize breeders, therefore, can access more diversity within adapted germplasm and identify major and minor QTLs to achieve effective levels of biotic stress resistance (Balint-Kurti and Johal 2009). Many of the QTLs governing biotic stress resistance in maize are minor with small additive effects which work in tandem. For instance, both NCLB and SCLB of maize show quantitative inheritance with the preponderance of both additive and non-additive gene actions (Ranganatha et  al. 2017; Jakhar et al. 2021). Hence, reciprocal recurrent selection (RRS) is the suitable method for improving resistance to these diseases. We present here the details of QTLs/genomic regions identified so far for various diseases and insect pests based on analysis of both biparental mapping populations and association mapping panels (Table 2).

4.1  Turcicum Leaf Blight/Northern Corn Leaf Blight Initially, Welz and Geiger (2000) re-analysed the data from previous authors (before 2000) and reported 23 QTLs, many being major QTLs with R2 values as high as 25.6%. Nearly, 29 QTLs were identified using a large nested association mapping (NAM) population (5000 inbred lines) using 1.6 million SNPs, and most of the underlying candidate genes included protein kinases with plausible role in plant defence response (Poland et al. 2011). Van Inghelandt et al. (2012) reported four SNPs which showed significant association with NCLB resistance. The NCLB resistance corrected for flowering time each with phenotypic variability explained (PVE) of 0.36–14.29% in a set of 4149 maize inbred lines representing Europe and North America. Two stable QTLs were identified of which qNCLB5.04 explained about 19% and 20% of phenotypic variation in the experimental years 2012 and 2013, respectively, and it was associated with both NCLB score and lesion width and hence proposed as valuable candidate for marker-assisted selection (MAS) (Chen et al. 2016a). Tian et al. (2018) reported six QTLs in P178 × G41-based backcross inbred lines (BILs) with PVE range of 4–23%, including two stable QTLs on chromosomes 2 and 8. Another major QTL, qNCLB7.02 with a PVE of 10.11–5.29% was reported by Wang et al. (2018). Three QTLs were identified for NCLB resistance, among which qNCLB-8-2 contributed maximum PVE of 16.34% followed 10.24%  by qNCBL-5 (Ranganatha et  al. 2021). Recently, GWAS (genome-wide association study) using three association panels revealed nearly 22 SNPs and 17 significant haplotypes which co-localized with previously reported genes and QTLs for NCLB resistance in maize (Rashid et al. 2020). In an intercontinental trial with a set of 742 F1-derived doubled haploid (DH) lines along with test crosses, a total of 17 QTLs were identified accounting for a variance of 3.57–30.98%, of which two QTLs, q4 and q5, were found stable across locations and seasons evaluated in Brazil and Europe (Galiano-Carneiro et al. 2021a).

S. no. Biotic stress Disease resistance 1 Turcicum leaf blight or northern corn leaf blight

3315 SNPs 88,268 SNPs 768 SNPs 15k SNP chip

1487 genotypes

5000 genotypes F2:3 families

207 RILs

569 F2:3 families 314 F11 lines 344 F2:3 742 DH lines

Ki14 × B73

GWAS

NAM CM212 × CM 338

K22 × By815

PH234 × PHBP3 and PH234 × PH84K Qi319 × Ye478

CML153 × SKV50

T1 × A1, T1 × A2, T1 × A10, T2 × A3, T2 × A4, T2 × A5 and T5 × A11

56,110 SNPs (MaizeSNP50 BeadChip)

1.6 million SSR

(continued)

Ranganatha et al. (2021) Galiano-­Carneiro et al. (2021a)

Wang et al. (2018)

Hegde et al. (2018)

Chen et al. (2016a)

5 QTLs

5 QTLs

Poland et al. (2011) Singh and Srivastava (2015)

Zwonitzer et al. (2010) Van Inghelandt et al. (2012)

Reference(s)

29 QTLs 2 QTLs

4 SNPs

7 QTLs

QTLs identified

CIM using IciMapping 11 QTLs software 4.0 WinQTL Cartographer v 3 QTLs 2.5 RQTL 17 QTLs

Single marker analysis and multiple forward regression SAS QTL cartographer 2.5 using composite interval mapping CIM in Windows QTL Cartographer 2.5 and MCIM in QTL Network 2.0 software –

MAPMAKER/EXP 3.0

109 RILs

Parents 765 SNPs and 106 SSRs 8244 SNPs

Number and types of markers Software and method Mapping population used

Table 2  List of QTLs identified for different biotic stresses in maize

Dissection of QTLs for Biotic Stress Resistance in Maize 53

2

Gibberella ear rot

S. no. Biotic stress

Table 2 (continued)

298 inbred lines IBM Syn10 DH population F2:3 families

DH lines

B73 × Mo17-doubled haploid

T3 × A6, T3 × A7, T3 × A8, T3 × A12, T4 × A4 and T4 × A5

Cheng351 × ZW18, Dan598 × ZW18 and JiV203 × ZW18

298 F5 RILs

LP4637 × L4674

CG62 × CO387

GWAS

Parents BB (crossbred from B73 × B97) and BC (crossbred from B73 × CML322)

6618 recombination bin markers GenoBaits Maize10K chip containing 10 K SNP markers 15 k SNP chip

768 SNPs

CIM on mppR version 1.2.1

MT-CIM, WinQTL Cartographer V2.5 CIM in QTL cartographer (version 2.5) CIM in QTL cartographer (version 2.5)

Number and types of markers Software and method Mapping population used ICIM method in the 170 RILs of BB, 163 1100 SNPs QTL loci mapping RILs of BC and 3 software (version 3.2) parental populations (B73, B97 and CML322) as controls CAAM, DTMA and 64,344 SNPs for EMMAX model using CAAM, 69,254 SVS version 8.6.0 IMAS association for DTMA and panels 69,286 for IMAS panels 144 RILs 162 SSR markers Composite interval mapping (CIM) – QTL Cartographer version 2.0 Reference(s) Xia et al. (2020)

4 QTLs

Galiano-Carneiro et al. (2021b)

Wen et al. (2020)

Yuan et al. (2020)

10 QTLs

17 QTLs

Giomi et al. (2016)

11 QTLs (silk inoculation) 18 QTLs (kernel inoculation) 4 QTLs

Ali et al. (2005)

22 SNPs and Rashid et al. (2020) 17 significant haplotypes

QTLs identified 12 QTLs

54 R. U. Zunjare et al.

Diplodia ear rot

Fusarium ear rot

3

4

CIM in QTL cartographer (version 2.5_009) CIM using mppR package in R

246 SSR markers CIM and MCIM methods, Wincartographer 2.0 software BT-1 × N6 250 RILs 207 SSRs Mixed model using QTLNetwork-2.0 GWAS 279 inbreds 47,445 SNPs TASSEL GWAS 1687l diverse inbred 200,978 SNPs MLM in GAPIT lines MLM in TASSEL V3 56,110 SNP Tropical maize inbred lines 940 lines markers (GWAS) IciMapping v3.2 POP1, 201 lines; CML495 × LA POSTASEQ. C7 F64-2-6-2- POP2, 277 families; 1250 SNPs POP3, 268 families; (biparental QTL 2-B-B-B, mapping) and POP4, 272 CML492 × LPSMT, families CML495 × LPSMT and CML449 × LPSMT LP4637 × L4674 298 F5 RILs 768 SNPs MT-CIM, WinQTL Cartographer V2.5 BT-1 × N6 174 F9:10 207 SSRs (QTL IciMapping v4.0 MLM in TASSEL V5 217 inbred lines mapping) GBS

15K SNP markers

DH lines (210, 232, 90 and 151, respectively)

03MZF-CK83 × 10MZF L4LW 07MZF-VDF6 × 10MZFL4LW 04MZFKQF0 × 03MZF-CK83 07MZF-SCH9 × 10MZFL4LW 87-1 × Zong 3

187 RILs, F7:8 population

56 SSR primers

123, F2:3 families

WL118-10 × CZL- 8

Li et al. (2011) Zila et al. (2013) Zila et al. (2014) Chen et al. (2016b)

Giomi et al. (2016) Ju et al. (2017)

4 QTLs 3 MTAs Inconclusive 45 SNPs, 15 haplotypes and 15 QTLs

4 QTLs 8 QTLs 57 MTAs 43 genes

(continued)

Ding et al. (2008)

Baer et al. (2021)

Tembo et al. (2014)

6 QTLs

11 QTLs

3 QTLs

Dissection of QTLs for Biotic Stress Resistance in Maize 55

S. no. Biotic stress

Table 2 (continued)

F2:3 families

Cheng351 × ZW18, Dan598 × ZW18 and JiV203 × ZW18

GWAS

MAGIC (672 RILs) with eight founder parents (A509, EP17, EP43, EP53, EP125, F473, EP86 and PB130) 874 inbred lines

GWAS

GWAS

GWAS

GE440 × Sd7 and NC300 × Gm1002 GWAS GWAS

Parents CO441 × CO354

GLM and MLM

FarmCPU

GLM -3034 MTAs MLM – 19 MTAs 20 QTLs

39 MTAs under 17 QTLs 13 QTLs

110 QTLs

TASSEL version 5.2.37

MLM in Tassel V5.2.25

14 MTAs 12 MTAs

7 QTLs

QTLs identified 15 QTLs

Map manager QTX version 0.22 TASSEL version 5.2 rrBLUP in R

Software and method MAPQTL 6.0 software

CIM in QTL GenoBaits Maize10K chip cartographer (version containing ten K 2.5) SNP markers

955,690 SNPs

1, 0000, 000 SNPs

Number and types of markers Mapping population used F2:3 population of 369 SSR 188 progenies markers, 13,292 SNPs and 963 INDEL F2:3 population of 7 SSRs and 1 280progenies STS marker 183 genotypes 267,525 242 maize inbred 23,153 DArT-seq lines markers 7386 SNPs NAM B73 × CML333, B73 × CML52, B73 × CML69 and B73 × NC358 256 inbred lines 990,000 SNP markers

Wen et al. (2020)

Liu et al. (2021)

Butrón et al. (2019)

Samayoa et al. (2019)

Morales et al. (2019)

Abdel-Rahman et al. (2016) Coan et al. (2018) de Jong et al. (2018)

Reference(s) Maschietto et al. (2017)

56 R. U. Zunjare et al.

Aspergillus ear rot

Maize rough dwarf virus disease

5

6

50 F9 lines Extreme genotypes (F2 population) 241 F8 RIL and 242 F7 RILs 199 RILs

GWAS S221 × K36

Zheng58 × D863F and Zheng58 × ZS301 80,044 × 80,007

102 RILs 89 F8 RILs

CO441 × CO354

90,110 × Ye478 X178 × B73

F2:3 families

Mp705 × Mp719

227 F2:3 lines

241 F2:3 families

Mp715 × Va35

Mo17 × BLS14

210 F2:3 mapping population 250 F2:3 lines

B73 × Mp715

IciMapping v3.0 CIM in MapQTL version 5.0 TASSEL 2.0 BSA-Seq

181 SSR markers ICIM method in IciMapping 4.0 3k SNP makers JoinMap (version 2.5)

512 SSRs 514 SNPs, and 72 SSR markers 56,110 SNPs 709,670 SNPs

CIM in Win-QTL Cartographer v 2.5 1200 SNP and CIM in QTL 29 SSR markers cartographer (version 2.5) 1247 SNP and CIM in QTL 29 SSR markers cartographer (version 2.5) 369 SSR markers MAPQTL 6.0 software and 13,292 SNPs and 963 INDELs 180 SSRs PLABQTL

562 SSR

Zhang et al. (2021)

1 QTL

(continued)

Wang et al. (2019)

Tao et al. (2013a) Li et al. (2018)

Di Renzo et al. (2004) Luan et al. (2012) Shi et al. (2012)

Mashietto et al. (2017)

10 QTLs

1 QTL 1 QTL

5 QTLs 2 QTLs

2 QTLs

32 QTLs

Womack et al. (2020)

Smith et al. (2019)

8 QTL

7 QTLs

Dhakal et al. (2016)

12 QTL

Dissection of QTLs for Biotic Stress Resistance in Maize 57

8

Grey leaf spot

S. no. Biotic stress 7 Sugarcane mosaic virus disease

Table 2 (continued)

30 extreme individuals (BC1F1) 3 DH and 2 F3 populations

10 MTAs

337,110 SNPs

TASSEL ver5.2

Benson et al. (2015) He et al. (2018)

Liu et al. (2016a, b)

Kibe et al. (2020)

1 major QTL Sun et al. (2021)

16 QTLs 5 QTLs

7 QTLs

4 QTLs

7 QTLs

22 QTLs

– WinQTL cartographer 2.5 BAS-Seq

CIM in QTL cartographer IciMapping v3.1

MAPMAKER/EXP 3.0

De Souza et al. (2008) Balint-Kurti et al. (2008a, b) Zwonitzer et al. (2010) Zhang et al. (2012)

3 QTLs 5 QTLs

Duble et al. (2000)

Xu et al. (1999)

2 genes

2 QTLs

Reference(s) Xia et al. (1999)

QTLs identified 5 QTL

1047–2699 SNPs IciMapping version 4.1

–

765 SNPs and 106 SSRs 161 F2:3 183 polymorphic SSRs 167 polymorphic 204 F2:3 SSRs 25 NAM populations – 233 F2:3 families 124 SSRs

109 RILs

CML 550 × CML494 CML550 × CML504 CML550 × CML511 CZL0618 × LaPostaSeqC7F71-1-2-1-1B CZL074 × LaPostaSeqC7F103-1-2-1-1B GWAS 410 lines

WGR × DZ01

NAM 08-641 × 446

YML32 × Ye478

Y32 × Q11

Ki14 × B73

Mo17 × B73

L520 × L19

F6 × FAP0259A

FAP1360A × F7

Parents D32 × D145

Number and types of markers Software and method Mapping population used 87 RFLP and 7 CIM in PLABQTL 219 F3 or SSR software immortalized F2 families MapMaker/Exp (version 75 BC4:5 RFLP (2), SSR 3.0b) (24) and AFLP (8) markers 121 F3 2 SSRS from Xia CIM in e PLABQTL et al. (1999) 19 SSRs CIM in MapMaker/EXP 150 F2 individuals 3.0 302 RILs – CIM in MapQTL5

58 R. U. Zunjare et al.

9

Southern leaf blight

158 RILs

133 RILs 158 RILs 142 and 186 lines, respectively 193 and 144, respectively 109 RILs 5000 RILs 25 NAM families 5000 RILs 207 RILs

Mo17 × B73

NC300 × B104

B73 × Mo17

H99 × B73 B73 × B52 B73rhm1 × NC250A NC250A × B73

Ki14 × B73

NAM NAM NAM LM5 × CM140

158 RFLP and 14 SSRs 220 RFLPs, 32 SSRs and 78 SNPs 765 SNPs and 106 SSRs 1106 SNPs 7386 SNPs 7386 80 polymorphic SSRs

–

234 markers (RFLP, isozyme and SSR) 113 SSRs

Tassel 3.0 QTL cartographer v2.5

–

MAPMAKER/EXP 3.0

32 QTLs 33 QTLs 48 QTLs 4 QTLs

9 QTLs

9 QTLs

JoinMap® 3.0

QTL cartographer v2.5

4 stable QTLs 3 QTLs

7 QTLs

8 QTLs

QTL cartographer v2.5

QTL cartographer v2.5

PLABQTL

(continued)

Zwonitzer et al. (2010) Kump et al. (2011) Bian et al. (2014) Li et al. (2018) Kaur et al. (2019)

Balint-Kurti et al. (2006) Balint-Kurti et al. (2007) Balint-Kurti et al. (2008a, b) Zwonitzer et al. (2009)

Carson et al. (2004)

Dissection of QTLs for Biotic Stress Resistance in Maize 59

Maize weevil

European corn borer

2

3

Insect resistance 1 Mediterranean corn borer (MCB)

S. no. Biotic stress

Table 2 (continued)

F2 population 226 F3 families 183 RILs 244 F2:3 147 F2:3 204 F2:3

B73 × B52

D06 × D408

B73 × B52

B73Ht × Mo47

De811 × B73

D06 × D408

170 SSRs

93 RFLP and 2 SSR

582 RFLPs +1 SSR 103 RFLP

CIM

CIM using QTL cartographer CIM using PLABQTL

87 RFLP makers IM using Mapmaker/ QTL 93 RFLP +2 CIM using PLABQTL SSRs 65 SSRs CIM using PLABQTL

CIM

CIM

RILs

146 RILs

EP42 × A637

151 SSRs

243 RILs

B73 × Mo17

163 F2 progeny

178 RILs

EP42 × EP39

50k SNPs

CIM in PlabMQTL,

CIM in PlabMQTL,

163 F2 progeny

302 inbreds

GWAS

CML290 × Muneng-8128 C0 HC1-18 -2-1-1 CML290 × Muneng-8128 C0 HC1-18 -2-1-1 P84 × Kilima

121 inbred lines

B73 × CML103

285 SNP markers 1478 SNPs MLM using Tassel 4.1.26 226 SSR markers CIM in PLABQTL software 587 markers CIM in PLABQTL software 130 polymorphic CIM in PlabMQTL SSRs software 151 SSRs CIM

171 RILs

A637 × A509

Parents

Number and types of markers Software and method Mapping population used

6 QTLs

7 QTLs

16 QTLs

9 QTLs

11 QTLs

8 QTLs

15 QTLs

17 QTLs

23 QTLs

6QTLs

3 QTLs

3 QTLs

25 SNPs

8 QTL

5 QTLs

QTLs identified

Cardinal et al. (2001) Jampatong et al. (2002) Krakowsky et al. (2002) Papst et al. (2004)

Bohn et al. (2000)

Samayoa et al. (2014) García-Lara et al. (2009) García-Lara et al. (2010) Castro-Álvarez et al. (2015) Schön et al. (1993)

Ordas et al. (2009)

Jiménez-Galindo et al. (2017) Samayoa et al. (2015a) Samayoa et al. (2015b) Ordas et al. (2010)

Reference(s)

60 R. U. Zunjare et al.

Southwestern corn borer

Fall army warm

4

5 427 F2 230 F2:3

Mp704 × Mo17

230 F2:3

Mp704 × Mo17

A619 × Mp708

427 F2

A619 × Mp708

230 F2:3

230 F2:3

Mp704 × Mo17

Mp704 × Mo17

215 F2:3 475 F2:3 472 F2 individuals

CML131 × CML67 Ki3 × CML139 Ki3 × CML139

224 SSRs and SNPs

91 SSRs

224 SSRs and SNPs 73 SSRs

91 SSRs

73 SSRs

108 and 122 RFLPs 282 RFLPs

24 QTLs

7 QTLs

5 QTLs

29 QTLs

4 QTLs

10 QTLs

7 QTLs

CIM MIM using QTL Cartographer MIM using QTL Cartographer CIM and MIM using QTL Cartographer 2.5 MIM using QTL Cartographer MIM using QTL Cartographer CIM and MIM using QTL Cartographer 2.5

14 QTLs

CIM

Womack et al. (2018)

Brooks et al. (2007)

Womack et al. (2018) Brooks et al. (2005)

Brooks et al. (2007)

Khairallah et al. (1998) Brooks et al. (2005)

Groh et al. (1998) Dissection of QTLs for Biotic Stress Resistance in Maize 61

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4.2  Gibberella Ear Rot (GER) One major QTL (28.9% PVE) was identified for GER on chromosome 3 (Ali et al. 2005). Around 17 QTLs were identified for  GER in three mapping populations derived using three different donors (Cheng351, Dan598 and JiV203), of which qRger7.1, from the resistant parent, Cheng351, was a major QTL with 20.16–41.84% PVE (Wen et  al. 2020). One stable QTL, q1, was also  identified for GER with 10.17–21.84% PVE by Galiano-Carneiro et al. (2021b).

4.3  Diplodia Ear Rot (DER) Tembo et al. (2014) identified the QTL ‘Sm_4,1’ for resistance to S. maydis, causing DER on chromosome 4. Also, they have identified a QTL with pleiotropic effect on chromosome 1 which is  22 cM from umc1269 marker. In another study, about eleven QTLs were identified for DER using multi-parent mapping populations and four different models of which one QTL on chromosome 5 showed maximum PVE of 7.18% (Baer et al. 2021).

4.4  Fusarium Ear Rot (FER) FER is considered a complex trait controlled by many minor QTLs, with moderate heritability and high environmental influence posing significant hurdles for breeding-­FER-resistant varieties. Genomic selection is the viable option for improving resistance against this disease. The first report on QTLs for resistance to FER was given by Perez-Brito et al. (2001) who found nine and seven QTLs in two F2 populations with a PVE of 30–44%. In a subsequent study, two separate populations were utilized for mapping FER resistance and resistance to fumonisins. Seven QTLs were found with cumulative PVE of 47% for FER resistance and nine QTLs explained 67% of the variation for mean fumonisin concentration (Robertson-Hoyt et al. 2006). Two stable QTLs on chromosome 3 were reported by Ding et al. (2008) coupled with a major QTL with 13–22% PVE on the same chromosome. Four QTLs were identified on chromosomes 3, 4, 5 and 6, of which the one on chromosome 4 was a major QTL with PVE of 10.2% (Li et  al. 2011). Another major QTL was reported on chromosome 4 (with PVE of 17.95%), which was later validated in near isogenic line (NIL) background (Chen et al. 2012). Seven QTLs and the associated markers were reported for FER resistance in terms of grain yield per main ear and test weight (Abdel-Rahman et  al. 2016). Combined GWAS (using 818 tropical maize inbred lines) and QTL analysis (using four biparental mapping populations) revealed eight co-localized QTLs on chromosomes 2, 3, 4, 5, 9 and 10, with 38 putative candidate genes related to disease resistance (Chen et al. 2016b). In addition,

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for 8 QTLs, 43 candidate genes (in vicinity of 57 SNPs) were mapped for F. verticillioides resistance. Among these genes, GRMZM2G0081223, AC213654.3_FG004 and GRMZM2G099255 were common across both mapping strategies and can be considered as valid candidate genes for FER resistance (Ju et al. 2017). This study has also validated the markers reported by previous authors. In another study, 15 and 17 QTLs were identified for FER resistance and fumonisin B1 (FB1) mycotoxin, respectively, and 24 candidate genes were validated by coupling with transcriptomic data (Maschietto et  al. 2017). In contrast, a SNP-based GWAS study identified several minor QTLs governing FER resistance. Through mixed linear model (MLM), 19 marker trait associations (MTAs) were identified with an average PVE of 1.60% (Liu et al. 2021). Around 20 QTLs were identified for FER resistance among which qRfer1, qRfer10, and qRfer17 were found to be major QTLs explaining phenotypic variation as high as 26.58 to 43.36%, 11.76 to 18.02%, and 12.02 to 32 21.81%, respectively (Wen et al., 2021). A combined QTL analysis for both FER and GER resistance unveiled four QTLs reflecting that LP4637 (donor) provides dual tolerance to both the ear rot genus (Giomi et al. 2016). Thirty-nine MTAs were identified for resistance to fumonisin accumulation in maize kernels separately which assembled into 17 QTLs (Samayoa et al. 2019). A multi-parent advanced generation intercross (MAGIC) population derived from eight founder parents was analysed for resistance to FER, and 13 putative QTLs were identified with minor affects. Nevertheless, there are distinct regions, 210–220 Mb on chromosome 3 and 166–173 Mb on chromosome 7 which harbour QTLs for FER and fumonisin content resistance in maize (Butron et al. 2019). NBS-LRR receptors and transcription factors involved in redox reaction and peroxidase activity seem to play a pivotal role in FER resistance (de Jong et al. 2018). This was discovered through DArT Seq-based GWAS study using 242 maize inbred lines. Two MTAs were found in the genes governing programmed cell death when 267 inbred lines were analysed using 47,445 SNPs (Zila et al. 2013). No disease resistance-related genes were identified in a GWAS study by Zila et al. (2014) despite a large panel of 1687 inbred lines iterating the complex nature of FER tolerance. A nested association mapping (NAM) population-based mapping (four populations with common parent) unveiled 110 QTLs for FER and fumonisin resistance (Morales et al. 2019). Four defence-­ related genes, a gibberellin 2-oxidase4, a glucosyltransferase, a Ras-related protein RHN1 and a phosphoribosylanthranilate transferase (PAT), are found to be putative candidate genes for FER in 183 field maize and popcorn inbred lines (Coan et al. 2018).

4.5 Stalk Rot Two QTLs, qRfg1 (major) and qRfg2 (minor), were mapped from the resistant inbred 1145 (Yang et al. 2010; Zhang et al. 2012a). The ZmCCT gene was found to be the causal gene at qRfg1 (Wang et al. 2017). The resistance in maize plants is governed by insertion or deletion activities of CACTA-like transposon in the

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promoter of ZmCCT which causes histone modification and DNA methylation. The transposon insertion silences the gene and renders the plants susceptible to stalk rot. ZmAuxRP1 is the candidate gene of qRfg2. Upon infection, the expression of this gene is downregulated resulting in the arrest of root growth, albeit increasing the resistance to stalk rot (Ye et al. 2019). ZmAuxRP1 is reported to enhance the resistance of FER as well. Resistance of Anthracnose stalk rot (ASR) is governed by Rcg1, a major QTL which explained ~50% variation (Jung et  al. 1994), and the underpinning candidate gene is an NB-LRR governing resistance gene (Frey et al. 2011). Diplodia stalk rot seems to be under the control of both additive and non-­ additive gene actions, and hence, RRS is the best breeding method for tolerance improvement (Carson and Hooker 1981; Badu-Apraku et al. 1987).

4.6 Maize Rough Dwarf Disease (MRDD) Two QTLs were reported for disease susceptibility index (DSI) in Mo17 × BLS14derived F2:3 population. These two QTLs jointly governed 36.2% of PVE (Di Renzo et al. 2004). A major QTL, qMrdd8, was identified on chromosome 8 for MRDD resistance with a PVE of 24.6–37.3% across the environments (Shi et al. 2012). This was later fine-mapped to a region of 347 kb and coupled with RNA-Seq two candidate genes CG1 and CG2 that were identified (Liu et al. 2016a). A recessive major QTL, qMrdd1, was found on chromosome 8 using GWAS which was subsequently validated and fine-mapped to a 1.2 Mb region (Tao et al. 2013a). The underlying candidate gene was later found to be Rab GDP dissociation inhibitor alpha (RabGDIα) which is responsible for host susceptibility (Liu et al. 2020). Another major QTL qMRD8 was identified on chromosome 8 with a PVE of 12.0–28.9% in addition to other QTLs, qMRD2, qMRD6, qMRD7 and qMRD10 (Luan et al. 2012). A single dominant locus was identified on chromosome 8 using SLAF-Seq-based bulked segregant analysis (BSA), and two SSR markers 6F29R29 and 6F34R34 were reported to be linked to this QTL. This region harbours around 32 candidate genes with defence-related functions (Li et al. 2018). Recently, a partially dominant resistance QTL, qMrdd2, for MRDD (with 20.4% PVE) was identified on chromosome 2  in recombinant inbred line (RIL) population derived from 80,007 (resistant) × 80,044 (sensitive) (Zhang et al. 2021).

4.7 Sugarcane Mosaic Virus (SCMV) Disease Initial report on QTLs for resistance to SCMV came from Xia et al. (1999) who identified five QTLs located on chromosomes 1, 3, 5, 6 and 10, with two stable and prominent QTLs on chromosomes 3 and 6. Later, these QTLs were validated in a different mapping population (F3 from F6 × FAP0259A) and named as Scm1 (chromosome 6) and Scm2 (chromosome 3), the resistant alleles of Scm1 matched with those reported by Xia et  al. (1999), whereas the alleles of Scm2 were different.

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However, later by saturating these two regions with a greater number of microsatellite markers, Yuan et al. (2003) demonstrated that Scm1 locus houses two QTLs. Similarly, two QTLs, Scm1 and Scm2, were reported for SMV resistance on chromosomes 6 and 3, respectively (Xu et al. 1999). Three QTLs were identified for SMV disease, two QTLs (Scm2a, PVE of 13.34%, and Scm2b, PVE of 41.85%) clustered together on chromosome 3, whereas third QTL was found on chromosome 6, Scm1 with a PVE of 7.66% (De Souza et al. 2008). Thus, regions on chromosomes 3 and 6 seem to harbour genes for SCMV disease resistance in maize which was further confirmed by a meta-QTL analysis (Lü et al. 2008).

4.8 Grey Leaf Spot (GLS) Resistance to GLS is under the control of multitude of genes with cumulative additive effects and a significant G × E interaction (Lyimo et al. 2011). An advanced generation intercross RIL population derived from the cross Mo17 × B73 was utilized to identify five QTLs, and drawing parallels with previously reported QTLs, two hotspots were identified (Balint-Kurti et  al. 2008a). Two stable and major QTLs, qRgls1 and qRgls2 on chromosomes 8 and 5, were consistently detected across locations. Furthermore, qRgls1 was fine-mapped to an interval of 1.4  Mb (Zhang et al. 2012). QTLs identified for flowering time and GLS resistance were found to overlap reflecting the relation between these two traits in YML32 × Ye478-­ based F2:3 population. A major QTL, qRgls.yaas-8-llqFt.yaas-8 with PVE of ~18% and 16.2%, was identified for GLS disease score and flowering time, respectively (Liu et  al. 2016b). Three major QTLs, qGLS1.04, qGLS2.09 and qGLS4.05 with PVE of >10%, were reported using a NAM population. Later, qGLS1.04 was fine-­ mapped to two intervals of 6.5  Mb and 5.2  Mb, and the underpinning candidate gene was also identified, that is, putative flavin monooxygenase gene (Benson et al. 2015). Four stable QTLs qRgls.CH-4, qRgls.CH-1, qRgls.CH-2 and qRgls.CH-6 were in F2:3 population (08-641 and 446 as parents), of which qRgls.CH-6 was novel (He et  al. 2018). Through linkage mapping, 22 QTLs were identified with qGLS7–105 on chromosome 7 being the major QTL (PVE of 28.2%), and 14 QTLs were found through GWAS with PVE of 6–8% individually (Kibe et al. 2020). A major QTL, qRgls1.06, explaining 55% of the total variance was identified through BSA-Seq for resistance to GLS which was later fine-mapped to 2.38  Mb region (Sun et al. 2021).

4.9 Southern Corn Leaf Blight (SCLB) Three major QTLs on chromosomes 1, 2 and 3 were identified for SLLB resistance in a RIL population derived from Mo17 × B73 (Carson et al. 2004). Similarly, two major QTLs accounting for 80% of phenotypic variance were identified on chromosomes 3 and 9 using a RIL population of the cross NC300 × B104 (Balint-Kurti

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et al. 2006). Later, same group identified four stable QTLs in a different mapping population, B73  ×  Mo17, two on chromosome 3 (within same interval) and one each on chromosomes 1 and 8 (Balint-Kurti et al. 2007). The QTL found on chromosome 3 almost matched in its location with the one identified previously by Carson et al. (2004). This QTL was further fine-mapped to a 0.5 cM interval using two sets of populations, NIL F2:3 one derived from the initial cross Mo17 × B73 and a RIL F2:3 from two RILs of B73 × Mo17, and the candidate genes were elucidated (Kump et al. 2010). Three QTLs were identified using two more mapping populations developed from H99 × B73 and B73 × B52. Furthermore, a cursory comparison with previously reported QTLs revealed two QTL hotspots on chromosomes 3 and 6 (Balint-Kurti et al. 2008b). Nine QTLs each were identified in two sets of mapping populations – B73 rhm1 × NC250A and NC250A × B73 – one developed specifically for SCLB and Ki14  ×  B73 developed for exploring multiple disease resistance (Zwonitzer et al. 2009, 2010). Similarly, in NAM populations, ~35 QTLs with small additive effects were identified for SCLB resistance along with several underlying defence-related genes such as LRR receptor kinase, AP2 transcription factors, etc. (Kump et al. 2011; Bian et al. 2014). In another study, a large NAM population (5000 recombinant inbred lines from 25 parents with B73 as a common parent) was used to map 48 QTLs for SCLB resistance. Further, among the candidate genes identified, three genes encoding AN1-like zinc finger domain containing protein, LRR protein and BCL-2-associated athanogene 3 protein were separately validated (Li et  al. 2018). Four putative QTLs on chromosomes 3, 8 and 9 were found to confer resistance to SLB with a cumulative PVE of 54% (Kaur et al. 2019). In a QTL analysis for multiple disease resistance, a single RIL population derived from Ki14 × B73, nine, eight and six QTLs were identified for SCLB, GLS and NCLB resistance, respectively, with many QTLs overlapping across diseases (Zwonitzer et al. 2010). Combined QTL mapping for three SCLB, NCLB and GLS using four donors (NC304, NC344, Ki3, NC262) and two recurrent parents (Oh7B, H100) revealed QTLs overlapping for two or more diseases – two QTLs for SCLB and NCLB, seven QTLs for SCLB and GLS and two QTLs for NCLB and GLS and six for all the three diseases (Lopez-Zuniga et al. 2019). Around 44 QTLs identified for resistance against NCLB, SCLB and GLS in previous studies were validated using 12 F2:3 populations, of which 16 QTLs were confirmed and can serve as valuable candidates for MAS (Martins et al. 2019).

5 QTLs for Insect Resistance 5.1 Mediterranean Corn Borer (MCB) The resistance to MCB is assessed through length of stem tunnels made by MCB larvae. Grain yield under infestations also seems to be equally important trait for screening for MCB tolerance. In an initial report, three QTLs were reported, one for kernel damage and two for stalk tunnelling. Two of these QTLs co-localized with

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previously reported QTLs for European corn borer (Ordas et al. 2009). In a similar study, three more QTLs were identified for MCB resistance, and underlying five candidate genes involved in cell wall biosynthesis were proposed. Thus, cell wall characteristics primarily decide feeding behaviour of borers (Ordas et al. 2010). Six QTLs were found for various MCB-resistant traits such as tunnel length (1), stalk lodging (1) and ear resistance (4), with PVE of 19.8%, 11.5% and 25–63%, respectively. QTLs governing yield under infestation seem to control MCB tolerance (Samayoa et al. 2014). However, in another study by the same group, no genetic correlation was obtained between tunnel length and grain yield reflecting that this association is background specific. Samayoa et al. (2015a) identified eight QTLs for MCB resistance, most of them being major QTLs including one QTL that can reduce tunnel length up to 8 cm (Samayoa et al. 2015a). A GWAS study using a panel of 302 inbreds revealed 25 MTAs for MCB resistance with PVE of 6–9%. The genes with or close to these SNPs are mostly defence related (Samayoa et al. 2015b). Five QTLs were reported for MCB resistance in terms of tunnel length (3), kernel resistance (1) and stalk damage (1), of which one QTL (in the bin 10.02–10.03) displayed major effect (PVE – 13% for stalk damage and 10% for tunnel length) (Jimenez-Galindo et  al. 2017). Interestingly allelic variants of markers linked to these QTLs provided yield advantage as in previous study.

5.2 European Corn Borer (ECB) Like MCB, resistance to ECB is a quantitative trait with tunnel length being the suitable proxy for mapping studies. Genomic regions responsible for resistance from donor inbred line B52 were found on chromosomes 1, 2, 3 and 4 in a first study (Onukogu et  al. 1978). Later, eight QTLs were reported for tunnel length which showed cumulative PVE of 38%, among which two were major QTLs with PVE >13% (Schon et al. 1993). Likewise, six QTLs for tunnel length and five for silk damage rating were reported with a total genotype variance of 50%, and only one QTL was common between these two traits (Bohn et al. 2000). Using RILs derived from the same cross (B73 × B52) as that of Schon et al. (1993), nine QTLs were identified for ECB resistance with a cumulative PV of 59%. However, only one QTL coincided with the previous study (Cardinal et  al. 2001). Jampatong et  al. (2002) reported nine QTLs (on chromosomes 1, 2, 4, 5, 6 and 8) for first-generation ECB and seven (on chromosomes 2, 5, 6, 8 and 9) for second-generation ECB.  However, majority of QTLs were inconsistent across environments. Using De811 as a donor, seven QTLs were detected for ECB resistance. In comparison with the population derived from B73 × B52, only one QTL could match reflecting different genes contributing resistance to ECB (Krakowsky et al. 2002). Two sets of populations were utilized in another study – F2:3 and a test cross population. Four and eight QTLs were found for stalk damage rating (SDR) and tunnel length using F2:3 of the original cross, while six QTLs were identified using test cross progenies. However, of these, only three QTLs for SDR matched between two populations (Papst et al. 2004).

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5.3 Southwestern Corn Borer (SWCB) Resistance to SWCB is a polygenic trait with primarily additive gene action and is assessed phenotypically by observing leaf damage area. Using two mapping populations, CML131 × CML67 and Ki3 × CML139, nine QTLs (chromosomes 1, 5, 7, 8 and 9) were identified in former and five QTLs (chromosomes 1, 6, 8 and 9) in latter population, and no common QTLs were observed between two populations (Groh et al. 1998). In the same year, seven QTLs were reported using a F2 population derived from the cross, Ki3 × CML139, which accounted for a cumulative PV of 30% (Khairallah et al. 1998). Brooks et al. (2005) reported around eight QTLs and two interactions for SWCB resistance using a population generated from the cross of Mp704 and Mo17. Furthermore, mir family of genes and glossy15 locus located on chromosomes 6 and 9, respectively, were proposed as candidate genes for SWCB resistance. In continuation of this work, addition of 151 more SSR markers resulted into 29 QTLs (through CIM) explaining up to 29% of PVE (Womack et al. 2018). Four more QTLs were unveiled for SWCB resistance using Mp708 as a donor parent. The QTLs found on chromosomes 1, 5, 7 and 9 corroborated with those identified using Mp704 in previous study (Brooks et al. 2007).

5.4 Fall Armyworm (FAW) The resistance to FAW seems to show close association with resistance to SWCB facilitating simultaneous improvement of both these traits. As a testament to this, QTL studies have coupled analysis of both insect pests, and many of the QTLs coincided for these two pests. Brooks et al. (2005) found seven QTLs governing resistance to FAW, of which two QTLs coincided with those identified for SWCB. Similarly, in 2007, the same group reported seven QTLs using a different donor parent. Of these, the QTLs found on chromosomes 1, 5, 7 and 9 overlapped with the QTLs identified for SWCB resistance (Brooks et al. 2007). By enriching the linkage map of Brooks et  al. (2005) with additional SSR and SNP markers, Womack et al. (2018) reported 24 and 36 QTLs for FAW resistance through CIM and MIM, respectively. Thus, many of the genomic regions governing different herbivorous insect pests coincide, indicating few genetic entities confer broad-spectrum resistance against a variety of herbivorous pests in maize and in turn suggesting the apparent possibility of simultaneous improvement of resistance to these pests. This was further attested by a GWAS study using 341 maize genotypes where single candidate gene was responsible for multiple QTNs for pest resistance (Badji et al. 2020). Additionally, a meta-QTL analysis of these QTLs revealed that majority of the regions governing resistance to herbivorous insects in maize harbour QTLs for cell wall constituents such as members of hydroxycinnamate group as well as fibre components indicating their vital role in resistance (Badji et al. 2018).

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5.5 Maize Weevil (MW)  There are limited reports on identifying the genomic regions governing MW resistance. In a first report by Garcia-Lara et al. (2009), five QTLs were found for grain damage, six QTLs for grain weight loss, seven for MW susceptibility index and three for number of adult progenies explaining 28%, 22%, 27% and 10% of PVE, respectively. Almost half of these QTLs showed significant G × E interaction. Later, using the same population, Garcia-Lara et al. (2010) reported 17 QTLs for 11 biochemical parameters with PVE ranging from 25% to 47%. Similarly, 15 QTLs were identified for MW resistance in a RIL population derived from P84 × Kilima with a PVE of 14–51%. Individually, six QTLs were found for grain weight loss, four QTLs for flour production and five for adult progeny (Castro-Álvarez et  al. 2015). Badji et al. (2020) recently conducted GWAS using 341 tropical maize lines to study the genetic control of resistance to multiple insect pests like MW, SB and FAW. They identified revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits across the maize genome. Sixteen QTNs were closely associated with multiple traits and six were associated with resistance to both FAW and MW, discovering the pleiotropic genetic control.

6 Qualitative Resistance: R Genes Qualitative resistance is generally correlated with hypersensitive response at infection site and is also exploited in maize (Steffenson 1992). Examples of Ht genes for resistance to NCLB (Welz and Geiger 2000) and the Rp genes for resistance to common rust (Ramakrishna et al. 2002) have been used in maize breeding. Hm1 was the first reported disease resistance gene conferring resistance to maize leaf blight and ear mould caused by Cochliobolus carbonum (race 1). This gene encodes an NADPH-dependent HC-toxin reductase which neutralizes HC-toxin responsible for the disease (Johal and Briggs 1992). The locus rp1 on chromosome 10 harbours around 14 race-specific resistance genes for common rust in maize caused by P. sorghi (Hulbert 1997). Among these genes, Rp1-D was characterized and is a NB-LRR resistance gene (Collins et al. 1999). However, races virulent to this gene emerged later. At least 18 race-specific genes have been identified for resistance to southern rust in maize, which is more notorious than common rust (Zhu et al. 2021). ZmWAK is the underpinning gene for the major QTL, qHSR1, governing resistance to head smut caused by Sphacelotheca reiliana on chromosome 2 (Zuo et al. 2015). ZmWAK codes for a wall-associated receptor-like protein kinase which primarily functions to restrict the spread of the soil-borne pathogen from root to aboveground parts. A closely related gene, ZmWAK-RLK1, was found to be a candidate gene in Htn1 locus mapped for NCLB resistance (Hurni et al. 2015). ZmREM6.3 is a candidate gene of another NCLB resistance QTL qNLB1.02B73 (Jamann et  al. 2016), whereas ZmREM1.3 overexpression in maize plants gave resistance to southern rust

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(Wang et al. 2019). Another NB-LRR gene, Rcg1, which governs resistance to ASR was characterized using transposon tagging (Frey et al. 2011). ZmCCoAOMT2 was proposed as candidate gene for GLS resistance which encodes caffeoyl-CoA O-methyltransferase involved in lignin production (Yang et al. 2017). This gene was also found to be responsible for resistance to SCLB.  In addition, rhm1, another locus conferring SCLB resistance, contains only one causal gene encoding lysine histidine transporter 1 (Zhao et al. 2012). ZmFBL41 was identified as gene responsible for quantitative resistance against banded leaf and sheath blight in maize. A mutant with two amino acid substitutions in ZmFBL41 prevented its interaction with ZmCAD thwarting the degradation of the latter and resulting in lignin accumulation and resistance (Li et al. 2019). As mentioned before, ZmCCT and ZmAuxRP1 are the causal genes of qRfg1, a major QTL, and qRfg2, a minor QTL governing resistance to stalk rot, respectively. The expression of ZmCCT is regulated by CACTA-like transposon. The insertion of transposon silences the gene with minimum or no response to pathogen attack (Wang et  al. 2017). ZmAuxRP1 enhances the synthesis of auxin indole-3-acetic acid (IAA) while repressing benzoxazinoid defence compounds (BXs). It also provides resistance to FER (Ye et al. 2019). Mutants (knockdown) of LOX3 (lipoxygenase) gene displayed reduced ear rot symptoms with decreased conidia formation of Fusarium verticillioides and the subsequent production of mycotoxin fumonisin B1 (Gao et al. 2007, 2009). Another gene, ZmLOX12, however, supressed the disease production (Christensen et al. 2015). Similarly, h-type thioredoxin encoding gene ZmTrxh is the causal gene of Scmv1, a major locus for SCMV resistance mapped on chromosome 6 (Tao et al. 2013b). ZmABP1 is the causal gene for Scmv2, another SCMV resistance locus which encodes an auxin-binding protein acting during the later stages of viral infection (Leng et  al. 2017). ZmGDIα gene is associated with a major QTL, qMrdd1, for MRDD resistance, which encodes a Rab GDP dissociation inhibitor alpha (RabGDIα) (Liu et al. 2020). The recessive allele of the gene ZmGDIα-hel generated by the insertion of helitron transposon into intron 10 renders an alternative splice variant which reduces disease severity by ~30%. Pythium stalk rot in maize is caused by Pythium aphanidermatum and Pythium inflatum. Two dominant genes, RpiQI319-1 and RpiQI319-2, reportedly confer resistance to Pythium stalk rot (Song et al. 2015). Thus, several candidate genes underpinning resistance to biotic stresses, particularly diseases, in maize have been elucidated and characterized, and many more need to be discovered and characterized in the future.

7 Utilization of QTLs Identified in MAS Programmes MAS provides valuable alternative for conventional breeding for accelerated development of improved crop varieties with host plant resistance. MAS significantly reduces the time through efficient selection, irrespective of stage. Marker-assisted

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backcross breeding (MABB) can be utilized to correct a specific defect of otherwise popular variety using linked or gene-based marker (Zunjare et al. 2018; Xu et al. 2020). This is particularly relevant in maize biotic stress breeding because of extensive QTL and GWAS reports available in literature. Further many of these QTLs were fine-mapped and validated making them suitable inputs for MAS. However, only few introgression studies exist for biotic stress resistance genes in maize compared to rice and wheat. Zhao et al. (2012) introgressed head smut resistance QTL, qHSR1 (ZmWAK gene), through MABC into ten diverse inbred lines that exhibited improved head smut resistance and yield (Zuo et  al. 2015). The introgression of ZmWAK into a Chinese maize line Tongsipingtou led to the development of head smut-resistant variety, Jidan558. Marker-assisted gene pyramiding of two genes, Scmv1 and Scmv2, into the background of maize line, F7, resulted into a completely tolerant line (nearly isogenic line) against sugarcane mosaic virus (Xing et al. 2006). Yang et al. (2017) also developed a multiple disease-resistant line (against SCLB and GLS) by introgression of qMdr9.02. Three putative QTLs for southwestern corn borer were simultaneously mapped and transferred from CML67 into recurrent parent CML204 (Willcox et al. 2002). A major QTL for MRDD, qMrdd8, was introgressed from the donor X178 into the background of seven recipient parents, Huangzao4, Chang7-2, Ye478, Zheng58, Zhonghuang68, B73 and Ji846 using four foreground markers (Xu et al. 2020). MLND resistance QTLs were transferred from KS23-6 into nine locally adapted inbreds using KSAP assays (Awata et al. 2021). Except these, to the best of our knowledge, there were no attempts to introgress genes/QTLs for biotic stress resistance in maize despite the availability of many well-characterized and cloned genes.

8 Conclusion Diseases and pests are the important stresses encountered by maize crop which often result in complete yield loss. Available literature suggests report of numerous QTLs for various biotic stresses. Many of them have been fine-mapped, and the causal candidate genes have been identified and characterized. Methodical deployment of these genes and QTLs into the susceptible inbreds is highly essential to evolve biotic stress-resilient maize hybrids. Relative to diseases, mapping studies for insect pests received lesser attention, though they are more devastating than diseases. Lack of easy-to-adopt screening protocols might be the reason for this. However, the silver lining here is that resistance to herbivorous insects seems to be controlled by few genomic hot spots, thus facilitating multiple insect resistance by the transfer of a few QTLs. With the advent of high-throughput genotyping techniques, MAS should be exploited to a maximum extent to fast track the development of biotic stress-tolerant maize varieties.

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References Abdel-Rahman MM, Bayoumi SR, Barakat MN (2016) Identification of molecular markers linked to Fusarium ear rot genes in maize plants Zea mays L. Biotechnol Biotechnol Equip 30(4):692–699 Ahmad S, Veyrat N, Gordon-Weeks R, Zhang Y, Martin J, Smart L, Glauser G, Erb M, Flors V, Frey M, Ton J (2011) Benzoxazinoid metabolites regulate innate immunity against aphids and fungi in maize. Plant Physiol 157:317–327 Ali ML, Taylor JH, Jie L, Sun G, William M, Kasha KJ, Reid LM, Pauls KP (2005) Molecular mapping of QTLs for resistance to Gibberella ear rot, in corn, caused by Fusarium graminearum. Genome 48(3):521–533 Awata LAO, Ifie BE, Danquah E, Jumbo MB, Suresh LM, Gowda M, Marchelo-Dragga PW, Olsen MS, Shorinola O, Yao NK, Boddupalli PM, Tongoona PB (2021) Introgression of maize lethal necrosis resistance quantitative trait loci into susceptible maize populations and validation of the resistance under field conditions in Naivasha, Kenya. Front Plant Sci 12:699 Badji A, Otim M, Machida L, Odong T, Kwemoi DB, Okii D, Agbahoungba S, Mwila N, Kumi F, Ibanda A, Mugo S, Kyamanywa S, Rubaihayo P (2018) Maize combined insect resistance genomic regions and their co-localization with cell wall constituents revealed by tissue-specific QTL meta-analyses. Front Plant Sci 9:895 Badji A, Kwemoi DB, Machida L, Okii D, Mwila N, Agbahoungba S, Kumi F, Ibanda A, Bararyenya A, Solemanegy M, Odong T (2020) Genetic basis of maize resistance to multiple insect pests: integrated genome-wide comparative mapping and candidate gene prioritization. Genes 11(6):689 Badu-Apraku B, Gracen VE, Bergstrom GC (1987) A major gene for resistance to anthracnose stalk rot in maize. Phytopathology 77:957–959 Baer O, Laude T, Reano C, Gregorio G, Diaz M, Pabro L, Tamba L, Baltazar N, Fabreag M, Pocsedio A, Kumar A (2021) Diplodia ear rot resistance QTL identified in maize (Zea mays L.) using multi-parent double-haploid population mapping. SABRAO J Breed Genet 53(1) Baladhiya HC, Sisodiya DB, Pathan NP (2018) A review on pink stem borer, Sesamia inferens Walker: a threat to cereals. J Entomol Zool Stud 6(3):1235–1239 Balint-Kurti PJ, Johal GS (2009) Maize disease resistance. In: Handbook of maize: its biology. Springer, New York, pp 229–250 Balint-Kurti PJ, Krakowsky MD, Jines MP, Robertson LA, Molnár TL, Goodman MM, Holland JB (2006) Identification of quantitative trait loci for resistance to southern leaf blight and days to anthesis in a maize recombinant inbred line population. Phytopathology 96(10):1067–1071 Balint-Kurti PJ, Zwonitzer JC, Wisser RJ, Carson ML, Oropeza-Rosas MA, Holland JB, Szalma SJ (2007) Precise mapping of quantitative trait loci for resistance to southern leaf blight, caused by Cochliobolus heterostrophus race O, and flowering time using advanced intercross maize lines. Genetics 176(1):645–657 Balint-Kurti PJ, Wisser R, Zwonitzer JC (2008a) Use of an advanced intercross line population for precise mapping of quantitative trait loci for gray leaf spot resistance in maize. Crop Sci 48(5):1696–1704 Balint-Kurti PJ, Zwonitzer JC, Pe ME, Pea G, Lee M, Cardinal AJ (2008b) Identification of quantitative trait loci for resistance to southern leaf blight and days to anthesis in two maize recombinant inbred line populations. Phytopathology 98:315–320 Benson JM, Poland JA, Benson BM, Stromberg EL, Nelson RJ (2015) Resistance to gray leaf spot of maize: genetic architecture and mechanisms elucidated through nested association mapping and near-isogenic line analysis. PLoS Genet 11(3):e1005045 Bian Y, Yang Q, Balint-Kurti PJ, Wisser RJ, Holland JB (2014) Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population. BMC Genomics 15(1):1–15 Bohn M, Schulz B, Kreps R, Klein D, Melchinger AE (2000) QTL mapping for resistance against the European corn borer (Ostrinia nubilalis H.) in early maturing European dent germplasm. Theor Appl Genet 101(5):907–917

Dissection of QTLs for Biotic Stress Resistance in Maize

73

Brauner PC, Melchinger AE, Schrag TA, Utz HF, Schipprack W, Kessel B, Ouzunova M, Miedaner T (2017) Low validation rate of quantitative trait loci for Gibberella ear rot resistance in European maize. Theor Appl Genet 130:175–186 Brooks TD, Willcox MC, Williams WP, Buckley PM (2005) Quantitative trait loci conferring resistance to fall armyworm and southwestern corn borer leaf feeding damage. Crop Sci 45(6):2430–2434 Brooks TD, Bushman BS, Williams WP, McMullen MD, Buckley PM (2007) Genetic basis of resistance to fall armyworm (Lepidoptera: Noctuidae) and southwestern corn borer (Lepidoptera: Crambidae) leaf-feeding damage in maize. J Econ Entomol 100(4):1470–1475 Buckler ES, Thornsberry JM, Kresovich S (2001) Molecular diversity, structure and domestication of grasses. Genet Res 77:213–218 Butron A, Santiago R, Cao A, Samayoa LF, Malvar RA (2019) QTLs for resistance to fusarium ear rot in a multiparent advanced generation intercross (MAGIC) maize population. Plant Dis 103(5):897–904 Cardinal AJ, Lee M, Sharopova N, Woodman-Clikeman WL, Long MJ (2001) Genetic mapping and analysis of quantitative trait loci for resistance to stalk tunneling by the European corn borer in maize. Crop Sci 41(3):835–845 Carson ML, Hooker AL (1981) Inheritance of resistance to stalk rot of corn caused by Colletotrichum graminicola. In: Carson ML (ed). The American Phytopathological Society, Phytopathology, vol 71, p 1190 Carson ML, Stuber CW, Senior ML (2004) Identification and mapping of quantitative trait loci conditioning resistance to southern leaf blight of maize caused by Cochliobolus heterostrophus race O. Phytopathology 94(8):862–867 Castro-Álvarez FF, William M, Bergvinson DJ, García-Lara S (2015) Genetic mapping of QTL for maize weevil resistance in a RIL population of tropical maize. Theor Appl Genet 128(3):411–419 Chen J, Ding J, Li H, Li Z, Sun X, Li J, Wang R, Dai X, Dong H, Song W, Chen W (2012) Detection and verification of quantitative trait loci for resistance to Fusarium ear rot in maize. Mol Breed 30:1649–1656 Chen G, Wang X, Long S, Jaqueth J, Li B, Yan J, Ding J (2016a) Mapping of QTL conferring resistance to northern corn leaf blight using high-density SNPs in maize. Mol Breed 36:4. https:// doi.org/10.1007/s11032-­015-­0421-­3 Chen J, Shrestha R, Ding J, Zheng H, Mu C, Wu J, Mahuku G (2016b) Genome-wide association study and QTL mapping reveal genomic loci associated with Fusarium ear rot resistance in tropical maize germplasm. G3: Genes, Genomes, Genetics 6(12):3803–3815 Christensen SA, Nemchenko A, Park YS, Borrego E, Huang PC, Schmelz EA, Kunze S, Feussner I, Yalpani N, Meeley R, Kolomiets MV (2014) The novel monocot-specific 9-lipoxygenase ZmLOX12 is required to mount an effective jasmonate-mediated defense against Fusarium verticillioides in maize. Mol Plant-Microbe Interact 27(11):1263–1276 Christensen SA, Huffaker A, Kaplan F, Sims J, Ziemann S, Doehlemann G, Ji L, Schmitz RJ, Kolomiets MV, Alborn HT, Mori N, Jander G, Ni X, Sartor RC, Byers S, Abdo Z, Schmelz EA (2015) Maize death acids, 9-lipoxygenase-derived cyclopente(a)nones, display activity as cytotoxic phytoalexins and transcriptional mediators. Proc Natl Acad Sci U S A 112(36):11407–11412 Coan MMD, Senhorinho HJC, Pinto RJB, Scapim CA, Tessmann DJ, Williams WP, Warburton ML (2018) Genome-wide association study of resistance to ear rot by Fusarium verticillioides in a tropical field maize and popcorn core collection. Crop Sci 58:564–578 Collard B, Jahufer Z, Brouwer JB, Pang E (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142:169–196 Collins N, Drake J, Ayliffe M, Sun Q, Ellis J, Hulbert S, Pryor T (1999) Molecular characterization of the maize RP1-D rust resistance haplotype and its mutants. Plant Cell 11:1365–1376 de Jong G, Pamplona AKA, Von Pinho RG, Balestre M (2018) Genome-wide association analysis of ear rot resistance caused by Fusarium verticillioides in maize. Genomics 110(5):291–303

74

R. U. Zunjare et al.

De Souza IRP, Schuelter AR, Guimarães CT, Schuster I, De Oliveira E, Redinbaugh M (2008) Mapping QTL contributing to SCMV resistance in tropical maize. Hereditas 145(4):167–173 Dhakal R, Windham GL, Williams WP, Subudhi PK (2016) Quantitative trait loci (QTL) for reducing aflatoxin accumulation in corn. Mol Breed 36(12):1–11 Di Renzo MA, Bonamico NC, Díaz DG, Ibañez MA, Faricelli ME, Balzarini MG, Salerno JC (2004) Microsatellite markers linked to QTL for resistance to Mal de Río Cuarto disease in Zea mays L. J Agric Sci 142(3):289–295 Ding JQ, Wang XM, Chander S, Yan JB, Li JS (2008) QTL mapping of resistance to Fusarium ear rot using a RIL population in maize. Mol Breed 22(3):395–403 Duble CM, Melchinger AE, Kuntze L, Stork A, Lubberstedt T (2000) Molecular mapping and gene action of Scm1 and Scm2, two major QTL contributing to SCMV resistance in maize. Plant Breed 119(4):299–303 Frey M, Schullehner K, Dick R, Fiesselmann A, Gierl A (2009) Benzoxazinoid biosynthesis, a model for evolution of secondary metabolic pathways in plants. Phytochemistry 70:1645–1651 Frey TJ, Weldekidan T, Colbert T, Wolters PJCC, Hawk JA (2011) Fitness evaluation of Rcg1, a locus that confers resistance to Colletotrichum graminicola (Ces.) GW Wils. Using near-­ isogenic maize hybrids. Crop Sci 51:1551–1563 Galiano-Carneiro AL, Kessel B, Presterl T, Miedaner T (2021a) Intercontinental trials reveal stable QTL for Northern corn leaf blight resistance in Europe and in Brazil. Theor Appl Genet 134(1):63–79 Galiano-Carneiro AL, Kessel B, Presterl T, Gaikpa DS, Kistner MB, Miedaner T (2021b) Multi-­ parent QTL mapping reveals stable QTL conferring resistance to Gibberella ear rot in maize. Euphytica 217(1):1–13 Ganiger PC, Yeshwanth HM, Muralimohan K, Vinay N, Kumar ARV, Chandrashekara K (2018) The occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr Sci 115(4):25 Gao X, Shim WB, Gobel C, Kunze S, Feussner I, Meeley R, Balint-Kurti P, Kolomiets M (2007) Disruption of a maize 9-lipoxygenase results in increased resistance to fungal pathogens and reduced levels of contamination with mycotoxin fumonisin. Mol Plant-Microbe Interact 20(8):922–933 Gao X, Brodhagen M, Isakeit T, Brown SH, Göbel C, Betran J, Feussner I, Keller NP, Kolomiets MV (2009) Inactivation of the lipoxygenase ZmLOX3 increases susceptibility of maize to Aspergillus spp. Mol Plant-Microbe Interact 22(2):222–231 Garcia-Lara S, Khairallah MM, Vargas M, Bergvinson DJ (2009) Mapping of QTL associated with maize weevil resistance in tropical maize. Crop Sci 49(1):139–149 Garcia-Lara S, Burt AJ, Arnason JT, Bergvinson DJ (2010) QTL mapping of tropical maize grain components associated with maize weevil resistance. Crop Sci 50(3):815. https://doi. org/10.2135/cropsci2009.07.0415 Gimenez E, Salinas M, Manzano-Agugliaro F (2018) Worldwide research on plant defense against biotic stresses as improvement for sustainable agriculture. Sustainability 10(2):391 Giomi GM, Kreff ED, Iglesias J, Fauguel CM, Fernández M, Oviedo MS, Presello DA (2016) Quantitative trait loci for Fusarium and Gibberella ear rot resistance in Argentinian maize germplasm. Euphytica 211(3):287–294 Glauser G, Marti G, Villard N, Doyen GA, Wolfender JL, Turlings TC, Erb M (2011) Induction and detoxification of maize 1, 4-benzoxazin-3-ones by insect herbivores. Plant J 68(5):901–911 Gong F, Yang L, Tai F, Hu X, Wang W (2014) “Omics” of maize stress response for sustainable food production: opportunities and challenges. Omics: J Integr Biol 18(12):714–732 Groh S, González-de-León D, Khairallah MM, Hoisington DA (1998) QTL mapping in tropical maize: III. Genomic regions for resistance to Diatraea spp. and associated traits in two RIL populations. Crop Sci 38(4):1062–1072 Handrick V, Robert CA, Ahern KR, Zhou S, Machado RA, Maag D, Glauser G, Fernandez-Penny FE, Chandran JN, Rodgers-Melnik E, Schneider B, Buckler ES, Boland W, Gershenzon J, Jander G, Erb M, Köllner TG (2016) Biosynthesis of 8-O-methylated benzoxazinoid defense compounds in maize. Plant Cell 28(7):1682–1700

Dissection of QTLs for Biotic Stress Resistance in Maize

75

He W, Yang L, Leng Y, Zhang B, Yang J, Li L, Chen Y, Kang J, Tang H, Deng L, Wu Y (2018) QTL mapping for resistance of maize to grey leaf spot. J Phytopathol 166(3):167–176 Hegde S, Kumar M, Mahantesh G, Meenakshi GN (2018) Mapping QTLs for resistance to Northern Leaf Blight in Tropical Maize (Zea mays L.). Int J Curr Microbiol App Sci 7(6):1940–1946 Hossain F, Boddupalli PM, Sharma RK, Kumar P, Bahadur Singh B (2007) Evaluation of quality protein maize genotypes for resistance to stored grain weevil Sitophilus oryzae (Coleoptera: Curculionidae). Int J Trop Insect Sci 27:114–121 Huang X, Han B (2013) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol. https://doi.org/10.1146/annurev-­arplant-­050213-­035715 Huffaker A, Kaplan F, Vaughan MM, Dafoe NJ, Ni X, Rocca JR, Alborn HT, Teal PEA, Schmelz EA (2011) Novel acidic sesquiterpenoids constitute a dominant class of pathogen-induced phytoalexins in maize. Plant Physiol 156(4):2082–2097 Hulbert SH (1997) Structure and evolution of the rp1 complex conferring rust resistance in maize. Annu Rev Phytopathol 35:293–310 Hurni S, Scheuermann D, Krattinger SG, Kessel B, Wicker T, Herren G, Fitze MN, Breen J, Presterl T, Ouzunova M, Keller B (2015) The maize disease resistance gene Htn1 against northern corn leaf blight encodes a wall-associated receptor-like kinase. Proc Natl Acad Sci USA 112(28):8780–8785 Jakhar DS, Singh R, Singh SK (2021) Assessment of genetics for turcicum leaf blight resistance in maize (Zea mays L.). Bangladesh J Bot 50(1):195–198 Jamann TM, Luo X, Morales L, Kolkman JM, Chung CL, Nelson RJ (2016) A remorin gene is implicated in quantitative disease resistance in maize. Theor Appl Genet 129(3):591–602 Jampatong C, McMullen MD, Barry BD, Darrah LL, Byrne PF, Kross H (2002) Quantitative trait loci for first-and second-generation European corn borer resistance derived from the maize inbred Mo47. Crop Sci 42(2):584–593 Jensen SG, Lane LC, Seifers DL (1996) A new disease of maize and wheat in the High Plains. Plant Dis 80:1387–1390 Jiang JF, Han Y, Xing LJ, Xu YY, Xu ZH, Chong K (2006) Cloning and expression of a novel cDNA encoding a mannose-specific jacalin-related lectin from Oryza sativa. Toxicon 47(1):133–139 Jimenez-Galindo JC, Ordas B, Butrón A, Samayoa LF, Malvar RA (2017) QTL mapping for yield and resistance against mediterranean corn borer in maize. Front Plant Sci 8:698 Jindal J (2013) Incidence of insect pests and management of shoot fly, Atherigona spp. in spring sown maize. Doctoral dissertation, PAU Ludhiana Johal GS, Briggs SP (1992) Reductase-activity encoded by the Hm1 disease resistance gene in maize. Science 258:985–987 Ju M, Zhou Z, Mu C, Zhang X, Gao J, Liang Y, Chen J, Wu Y, Li X, Wang S, Wen J (2017) Dissecting the genetic architecture of fusarium verticillioides seed rot resistance in maize by combining QTL mapping and genome-wide association analysis. Sci Rep 7(1):1–11 Jung M, Weldekidan T, Schaff D, Paterson A, Tingey S, Hawk J (1994) Generation-means analysis and quantitative trait locus mapping of anthracnose stalk rot genes in maize. Theor Appl Genet 89(4):413–418 Kannan M, Ismail I, Bunawan H (2018) Maize dwarf mosaic virus: from genome to disease management. Viruses 10(9):492 Kaur M, Vikal Y, Kaur H, Pal L, Kaur K, Chawla JS (2019) Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea mays L.). J Phytopathol 167(10):591–600 Keno T, Azmach G, Gissa DW, Regasa MW, Tadesse B, Wolde L, Deressa T, Abebe B, Chibsa T, Mahabaleswara S (2018) Major biotic maize production stresses in Ethiopia and their management through host resistance. Afr J Agric Res 13(21):1042–1052 Khairallah MM, Bohn M, Jiang C, Deutsch JA, Jewell DC, Mihm JA, Melchinger AE, González-­ De-­León D, Hoisington DA (1998) Molecular mapping of QTL for southwestern corn borer resistance, plant height and flowering in tropical maize. Plant Breed 117(4):309–318 Kibe M, Nair SK, Das B, Bright JM, Makumbi D, Kinyua J, Suresh LM, Beyene Y, Olsen MS, Prasanna BM, Gowda M (2020) Genetic dissection of resistance to gray leaf spot by combining

76

R. U. Zunjare et al.

genome-wide association, linkage mapping, and genomic prediction in tropical maize germplasm. Front Plant Sci 11:1602 Krakowsky MD, Brinkman MJ, Woodman-Clikeman WL, Lee M (2002) Genetic components of resistance to stalk tunneling by the European corn borer in maize. Crop Sci 42(4):1309–1315 Kump KL, Holland JB, Jung MT, Wolters P, Balint-Kurti PJ (2010) Joint analysis of near-isogenic and recombinant inbred line populations yields precise positional estimates for quantitative trait loci. Plant Genome 3(3) Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, Holland JB (2011) Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet 43:163–168 Leng P, Ji Q, Asp T, Frei UK, Ingvardsen CR, Xing Y, Studer B, Redinbaugh M, Jones M, Gajjar P, Liu S, Li F, Pan G, Xu M, Lubberstedt T (2017) Auxin binding protein 1 reinforces resistance to sugarcane mosaic virus in maize. Mol Plant 10(10):1357–1360 Li ZM, Ding JQ, Wang RX, Chen JF, Sun XD, Chen W, Song WB, Dong HF, Dai XD, Xia ZL, Wu JY (2011) A new QTL for resistance to fusarium ear rot in maize. J Appl Genet 52(4):403–406 Li YX, Chen L, Li C, Bradbury PJ, Shi YS, Song Y, Zhang D, Zhang Z, Buckler ES, Li Y, Wang T (2018) Increased experimental conditions and marker densities identified more genetic loci associated with southern and northern leaf blight resistance in maize. Sci Rep 8(1):1–12 Li N, Lin B, Wang H, Li X, Yang F, Ding X, Yan J, Chu Z (2019) Natural variation in Zm FBL41 confers banded leaf and sheath blight resistance in maize. Nat Genet 51(10):1540–1548 Liu C, Hua J, Liu C, Zhang D, Hao Z, Yong H, Xie C, Li M, Zhang S, Weng J, Li X (2016a) Fine mapping of a quantitative trait locus conferring resistance to maize rough dwarf disease. Theor Appl Genet 129(12):2333–2342 Liu L, Zhang YD, Li HY, Bi YQ, Yu LJ, Fan XM, Tan J, Jeffers DP, Kang MS (2016b) QTL mapping for gray leaf spot resistance in a tropical maize population. Plant Dis 100(2):304–312 Liu Q, Deng S, Liu B, Tao Y, Ai H, Liu J, Zhang Y, Zhao Y, Xu M (2020) A helitron-induced RabGDIα variant causes quantitative recessive resistance to maize rough dwarf disease. Nat Commun 11(1):1–14 Liu Y, Hu G, Zhang A, Loladze A, Hu Y, Wang H, Qu J, Zhang X, Olsen M, San Vicente F, Crossa J (2021) Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm. Crop J 9(2):325–341 Lopez-Zuniga LO, Wolters P, Davis S, Weldekidan T, Kolkman JM, Nelson R, Hooda KS, Rucker E, Thomason W, Wisser R, Balint-Kurti P (2019) Using maize chromosome segment substitution line populations for the identification of loci associated with multiple disease resistance. G3: Genes, Genomes, Genetics 9(1):189–201 Lü X, Li X, Xie C, Hao Z, Ji H, Shi L, Zhang S (2008) Comparative QTL mapping of resistance to sugarcane mosaic virus in maize based on bioinformatics. Front Agric China 2(4):365–371 Luan J, Wang F, Li Y, Zhang B, Zhang J (2012) Mapping quantitative trait loci conferring resistance to rice black-streaked virus in maize (Zea mays L.). Theor Appl Genet 125(4):781–791 Lyimo HJ, Pratt RC, Mnyuku RS (2011) Heritability and gene effect estimates for components of partial resistance to grey leaf spot of maize by generation mean analysis. Plant Breed 130(6):633–639 Mahuku G, Lockhart BE, Wanjala B, Jones MW, Kimunye JN, Stewart LR, Cassone BJ, Sevgan S, Nyasani JO, Kusia E, Kumar PL (2015) Maize lethal necrosis (MLN), an emerging threat to maize-based food security in sub-Saharan Africa. Phytopathology 105(7):956–965 Marti G, Erb M, Boccard J, Glauser G, Doyen GR, Villard N, Robert CAM, Turlings TC, Rudaz S, Wolfender JL (2013) Metabolomics reveals herbivore-induced metabolites of resistance and susceptibility in maize leaves and roots. Plant Cell Environ 36(3):621–639 Martin DP, Shepherd DN (2009) The epidemiology, economic impact and control of maize streak disease. Food Secur 1(3):305–315 Martin DP, Willment JA, Billharz R, Velders R, Odhiambo B, Njuguna J, James D, Rybicki EP (2001) Sequence diversity and virulence in Zea mays of Maize streak virus isolates. Virology 288(2):247–255

Dissection of QTLs for Biotic Stress Resistance in Maize

77

Martins LB, Rucker E, Thomason W, Wisser RJ, Holland JB, Balint-Kurti P (2019) Validation and characterization of maize multiple disease resistance QTL.  G3: Genes, Genomes, Genetics 9(9):2905–2912 Maschietto V, Colombi C, Pirona R, Pea G, Strozzi F, Marocco A, Rossini L, Lanubile A (2017) QTL mapping and candidate genes for resistance to Fusarium ear rot and fumonisin contamination in maize. BMC Plant Biol 17(1):1–21 Mikel MA, D’arcy CJ, Rhodes AM, Ford RE (1981) Yield loss in sweet corn correlated with time of inoculation with maize dwarf mosaic virus. Plant Dis 65(11):902–904 Mitchell NJ, Bowers E, Hurburgh C, Wu F (2016) Potential economic losses to the US corn industry from aflatoxin contamination. Food Addit Contam Part A 33(3):540–550 Moeller DA, Tiffin P (2005) Genetic diversity and the evolutionary history of plant immunity genes in two species of Zea. Mol Biol Evol 22(12):2480–2490 Morales L, Zila CT, Moreta Mejía DE, Montoya Arbelaez M, Balint-Kurti PJ, Holland JB, Nelson RJ (2019) Diverse components of resistance to Fusarium verticillioides infection and fumonisin contamination in four maize recombinant inbred families. Toxins 11(2):86 Morris SW, Vernooij B, Titatarn S, Starrett M, Thomas S, Wiltse CC, Frederiksen RA, Bhandhufalck A, Hulbert S, Uknes S (1998) Induced resistance responses in maize. Mol Plant-­ Microbe Interact 11(7):643–658 Mottaleb KA, Loladze A, Sonder K, Kruseman G, San Vicente F (2019) Threats of tar spot complex disease of maize in the United States of America and its global consequences. Mitig Adapt Strateg Glob Chang 24(2):281–300 Muimba-Kankolongo A (2018) Food crop production by smallholder farmers in Southern Africa: challenges and opportunities for improvement. Academic Muthusamy V, Hossain F, Thirunavukkarasu N, Saha S, Agrawal PK, Gupta HS (2016) Genetic analyses of kernel carotenoids in novel maize genotypes possessing rare allele of β-carotene hydroxylase gene. Cereal Res Commun 44:669–680 Nwosu LC (2018) Maize and the maize weevil: advances and innovations in postharvest control of the pest. Food Qual Saf 2(3):145–152 Oluwafemi S, Bruce TJ, Pickett JA, Ton J, Birkett MA (2011) Behavioral responses of the leafhopper, Cicadulina storeyi China, a major vector of maize streak virus, to volatile cues from intact and leafhopper-damaged maize. J Chem Ecol 37(1):40–48 Ong’amo G, Khadioli N, Le Ru B, Mujica N, Carhuapoma P (2016) Spotted stemborer, Chilo partellus (Swinhoe 1885). In: Kroschel J, Mujica N, Carhuapoma P, Sporleder M (eds) Pest distribution and risk atlas for Africa. Potential global and regional distribution and abundance of agricultural and horticultural pests and associated biocontrol agents under current and future climates. International Potato Center (CIP), Lima. ISBN 978-92-9060-476-1 Onukogu FA, Guthrie WD, Russell WA, Reed GL, Robbins JC (1978) Location of genes that condition resistance in maize to sheath-collar feeding by second-generation European corn borers. J Econ Entomol 71(1):1–4 Ordas B, Malvar RA, Santiago R, Sandoya G, Romay MC, Butron A (2009) Mapping of QTL for resistance to the Mediterranean corn borer attack using the intermated B73 × Mo17 (IBM) population of maize. Theor Appl Genet 119(8):1451–1459 Ordas B, Malvar RA, Santiago R, Butron A (2010) QTL mapping for Mediterranean corn borer resistance in European flint germplasm using recombinant inbred lines. BMC Genomics 11(1):1–10 Papst C, Bohn M, Utz HF, Melchinger AE, Klein D, Eder J (2004) QTL mapping for European corn borer resistance (Ostrinia nubilalis Hb.), agronomic and forage quality traits of testcross progenies in early-maturing European maize (Zea mays L.) germplasm. Theor Appl Genet 108(8):1545–1554 Perera MF, Filippone MP, Ramallo CJ, Cuenya MI, Garcia ML, Ploper LD, Castagnaro AP (2009) Genetic diversity among viruses associated with sugarcane mosaic disease in Tucumán, Argentina. Phytopathology 99(1):38–49

78

R. U. Zunjare et al.

Perez-Brito D, Jeffers D, Gonzales-de-Leon D, Khairallah MM, Cortes-Cruz M, Velazquez-­ Cardelas G, Azpíroz S, Srinivasan G (2001) QTL mapping of Fusarium moniliforme ear rot resistance in highland maize, Mexico. Agrociencia 35:181–196 Piperno DR, Ranere AJ, Holst I, Iriarte J, Dickau R (2009) Starch grain and phytolith evidence for early ninth millennium BP maize from the Central Balsas River Valley, Mexico. Proc Natl Acad Sci USA 106(13):5019–5024 Poland JA, Bradbury PJ, Buckler ES, Nelson RJ (2011) Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize. Proc Natl Acad Sci 108(17):6893–6898 Prasanna BM, Bruce A, Beyene Y, Makumbi D, Gowda M, Asim M, Martinelli S, Head GP, Parimi S (2022) Host plant resistance for fall armyworm management in maize: relevance, status and prospects in Africa and Asia. Theor Appl Genet 135:3897–3916 Pu Z (2013) Dynamic analysis on the disease progression of maize leaf blight between Xianyu335 and improved Xianyu335 in Western region of Heilongjiang Province. J Maize Sci 21(4):119–123 Qi J, Sun G, Wang L, Zhao C, Hettenhausen C, Schuman MC, Baldwin IT, Li J, Song J, Liu Z, Xu G (2016) Oral secretions from Mythimna separata insects specifically induce defence responses in maize as revealed by high-dimensional biological data. Plant Cell Environ 39(8):1749–1766 Ramakrishna W, Emberton J, Ogden M, SanMiguel P, Bennetzen JL (2002) Structural analysis of the maize Rp1 complex reveals numerous sites and unexpected mechanisms of local rearrangement. Plant Cell 14:3213–3223 Ranganatha HM, Lohithaswa HC, Anand SP (2017) Understanding the genetic architecture of resistance to northern corn leaf blight and southern corn rust in maize (Zea mays L.). Indian J Genet Plant Breed 77(3):357–363 Ranganatha HM, Lohithaswa HC, Pandravada A (2021) Mapping and validation of major quantitative trait loci for resistance to northern corn leaf blight along with the determination of the relationship between resistances to multiple foliar pathogens of maize (Zea mays L.). Front Genet 11:1764 Rashid Z, Sofi M, Harlapur SI, Kachapur RM, Dar ZA, Singh PK, Zaidi PH, Vivek BS, Nair SK (2020) Genome-wide association studies in tropical maize germplasm reveal novel and known genomic regions for resistance to Northern corn leaf blight. Sci Rep 10(1):1–16 Rasmann S, Kollner TG, Degenhardt J, Hiltpold I, Toepfer S, Kuhlmann U, Gershenzon J, Turlings TC (2005) Recruitment of entomopathogenic nematodes by insect-damaged maize roots. Nature 434(7034):732–737 Redinbaugh MG, Stewart LR (2018) Maize lethal necrosis: an emerging, synergistic viral disease. Annu Rev Virol 5(1):301–322 Robertson-Hoyt LA, Jines MP, Balint-Kurti PJ, Kleinschmidt CE, White DG, Payne GA, Maragos CM, Molnár TL, Holland JB (2006) QTL mapping for Fusarium ear rot and fumonisin contamination resistance in two maize populations. Crop Sci 46(4):1734–1743 Samayoa LF, Butron A, Malvar RA (2014) QTL mapping for maize resistance and yield under infestation with Sesamia nonagrioides. Mol Breed 34(3):1331–1344 Samayoa LF, Malvar RA, McMullen MD, Butron Gomez AM (2015a) Identification of QTL for resistance to Mediterranean corn borer in a maize tropical line to improve temperate germplasm. BMC Plant Biol 15:265 Samayoa LF, Malvar RA, Olukolu BA, Holland JB, Butron A (2015b) Genome-wide association study reveals a set of genes associated with resistance to the Mediterranean corn borer (Sesamia nonagrioides L.) in a maize diversity panel. BMC Plant Biol 15(1):1–15 Samayoa LF, Cao A, Santiago R, Malvar RA, Butrón A (2019) Genome-wide association analysis for fumonisin content in maize kernels. BMC Plant Biol 19(1):1–11 Santiago R, Reid LM, Arnason JT, Zhu X, Martinez N, Malvar RA (2007) Phenolics in maize genotypes differing in susceptibility to Gibberella stalk rot (Fusarium graminearum Schwabe). J Agric Food Chem 55:5186–5193 Schmelz EA, Kaplan F, Huffaker A et al (2011) Identity, regulation, and activity of inducible diterpenoid phytoalexins in maize. Proc Natl Acad Sci USA 108:5455–5460

Dissection of QTLs for Biotic Stress Resistance in Maize

79

Schon CC, Lee M, Melchinger AE, Guthrie WD, Woodman WL (1993) Mapping and characterization of quantitative trait loci affecting resistance against second-generation European corn borer in maize with the aid of RFLPs. Heredity 70(6):648–659 Shepherd DN, Martin DP, Van der Walt E, Dent K, Varsani A, Rybicki EP (2010) Maize streak virus: an old and complex ‘emerging’ pathogen. Mol Plant Pathol 11(1):1–12 Shi LY, Hao ZF, Weng JF, Xie CX, Liu CL, Zhang DG, Li MS, Bai L, Li XH, Zhang SH (2012) Identification of a major quantitative trait locus for resistance to maize rough dwarf virus in a Chinese maize inbred line X178 using a linkage map based on 514 gene-derived single nucleotide polymorphisms. Mol Breed 30(2):615–625 Shiferaw B, Prasanna B, Hellin J, Banziger M (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur 3:307–327 Signoret PA (2008) Cereal viruses: maize/corn. In: Encyclopedia of virology, pp 475–482. https:// doi.org/10.1016/b978-­012374410-­4.00700-­7 Singh R, Srivastava RP (2015) New quantitative trait loci (QTLs) for turcicum leaf blight in maize. J Biotech Crop Sci 5:35–43 Smith CJ (1996) Accumulation of phytoalexins: defence mechanism and stimulus response system. New Phytol 132:1–45 Smith JS, Williams WP, Windham GL, Xu W, Warburton ML, Bhattramakki D (2019) Identification of quantitative trait loci contributing resistance to aflatoxin accumulation in maize inbred Mp715. Mol Breed 39(6):1–14 Song FJ, Xiao MG, Duan CX, Li HJ, Zhu ZD, Liu BT, Sun SL, Wu XF, Wang XM (2015) Two genes conferring resistance to pythium stalk rot in maize inbred line Qi319. Mol Gen Genomics 290(4):1543–1549 Spangler LM (2008) Impact of lignin and caffeoyl coenzyme a O-methyltransferase 1 on Aspergillus flavus growth in maize cobs. MSc thesis, The Pennsylvania State University, State College, PA, USA Steffenson BJ (1992) Analysis of durable resistance to stem rust in barley. Euphytica 63:153–167 Sun H, Zhai L, Teng F, Li Z, Zhang Z (2021) qRgls1.06, a major QTL conferring resistance to gray leaf spot disease in maize. Crop J 9(2):342–350 Tamiru A, Bruce TJ, Woodcock CM, Caulfield JC, Midega CA, Ogol CK, Mayon P, Birkett MA, Pickett JA, Khan ZR (2011) Maize landraces recruit egg and larval parasitoids in response to egg deposition by a herbivore. Ecol Lett 14(11):1075–1083 Tao Y, Liu Q, Wang H, Zhang Y, Huang X, Wang B, Lai J, Ye J, Liu B, Xu M (2013a) Identification and fine-mapping of a QTL, qMrdd1, that confers recessive resistance to maize rough dwarf disease. BMC Plant Biol 13(1):1–13 Tao Y, Jiang L, Liu Q, Zhang Y, Zhang R, Ingvardsen CR, Lübberstedt T, Xu M (2013b) Combined linkage and association mapping reveals candidates for Scmv1, a major locus involved in resistance to sugarcane mosaic virus (SCMV) in maize. BMC Plant Biol 13(1):1–13 Tembo L, Asea G, Gibson PT, Okori P (2014) Quantitative trait loci for resistance to Stenocarpella maydis and Fusarium graminearum cob rots in tropical maize. J Crop Improv 28(2):214–228 Terefe H, Gudero G (2019) Distribution and association of factors influencing emerging maize lethal necrosis disease epidemics in Southern Ethiopia. Cogent Food Agric 5(1):1683950 Tian Z, Ai T, Deng C, You S, Shi Q, Ma Y, Li Z, Ding J (2018) QTL mapping of resistance to northern corn leaf blight in maize inbred line P178 and effect analysis. J Henan Agric Sci 47(2):73–97 Tippannavar PS, Talekar SC, Mallapur CP, Kachapur RM, Salakinkop SR, Harlapur SI (2019) An outbreak of fall armyworm in Indian subcontinent: a new invasive pest on maize. Maydica 64(1):10 Ullstrup AJ (1972) The impacts of the southern corn leaf blight epidemics of 1970-1971. Annu Rev Phytopathol 10:37–50 United States Department of Agriculture (2020) Retrieved from USDA: https://apps.fas.usda.gov/ psdonline/circulars/production.pdf

80

R. U. Zunjare et al.

Van Inghelandt D, Melchinger AE, Martinant JP, Stich B (2012) Genome-wide association mapping of flowering time and northern corn leaf blight (Setosphaeria turcica) resistance in a vast commercial maize germplasm set. BMC Plant Biol 12(1):1–15 Velasco P, Revilla P, Monetti L, Butron A, Ordas A, Malvar RA (2007) Corn borers (Lepidoptera; Noctuidae; Crambidae) in northwestern Spain: population dynamics and distribution. Maydica 52:195–203 Veyrat N, Robert CAM, Turlings TCJ, Erb M (2016) Herbivore intoxication as a potential primary function of an inducible volatile plant signal. J Ecol 104(2):591–600 Waiss AC Jr, Chan BG, Elliger CA, Wiseman BR, McMillian WW, Widstrom NW, Zuber MS, Keaster AJ (1979) Maysin, a flavone glycoside from corn silks with antibiotic activity toward corn earworm. J Econ Entomol 72(2):256–258 Wang ZH, Fang SG, Xu JL, Sun LY, Li DW, Yu JL (2003) Sequence analysis of the complete genome of rice black-streaked dwarf virus isolated from maize with rough dwarf disease. Virus Genes 27(2):163–168 Wang C, Yang Q, Wang W, Li Y, Guo Y, Zhang D, Ma X, Song W, Zhao J, Xu M (2017) A transposon-­ directed epigenetic change in ZmCCT underlies quantitative resistance to Gibberella stalk rot in maize. New Phytol 215(4):1503–1515 Wang J, Xu Z, Yang J, Lu X, Zhou Z, Zhang C, Zheng L, Tian R, Hao Z, Yong H, Li M, Zhang D, Li X, Weng J (2018) qNCLB7. 02, a novel QTL for resistance to Northern corn leaf blight in maize. Mol Breed 38(5):1–12 Wang X, Yang Q, Dai Z, Wang Y, Zhang Y, Li B, Zhao W, Hao J (2019) Identification of QTLs for resistance to maize rough dwarf disease using two connected RIL populations in maize. PLoS One 14(12):e0226700 Welz HG, Geiger HH (2000) Genes for resistance to northern corn leaf blight in diverse maize populations. Plant Breed 119:1–14 Wen J, Shen Y, Xing Y, Wang Z, Han S, Li S, Yang C, Hao D, Zhang Y (2020) QTL mapping of resistance to Gibberella ear rot in maize. Mol Breed 40(10):1–11 Wen J, Shen Y, Xing Y, Wang Z, Han S, Li S, Yang C, Hao D, Zhang Y (2021) QTL mapping of Fusarium ear rot resistance in maize. Plant Dis 105(3):558–565 Willcox MC, Khairallah MM, Bergvinson D, Crossa J, Deutsch JA, Edmeades GO, Gonzalez-­ de-­Leon D, Jiang C, Jewell DC, Mihm JA, Williams WP, Hoisington D (2002) Selection for resistance to southwestern corn borer using marker-assisted and conventional backcrossing. Crop Sci 42(5):1516 Woloshuk C, Wise K (2010) Gibberella ear rot. Purdue University Extension. Retrieved from: https://www.extension.purdue.edu/extmedia/BP/BP-­77-­W.pdf Woloshuk C, Wise K (2011) Aspergillus ear rot. Purdue University Extension. Retrieved from: https://www.extension.purdue.edu/extmedia/bp/bp-­83-­w.pdf Womack ED, Warburton ML, Williams WP (2018) Mapping of quantitative trait loci for resistance to fall armyworm and southwestern corn borer leaf-feeding damage in maize. Crop Sci 58(2):529–539 Womack ED, Williams WP, Windham GL, Xu W (2020) Mapping quantitative trait loci associated with resistance to aflatoxin accumulation in maize inbred MP719. Front Microbiol 11:45 Wu F, Shu J, Jin W (2014) Identification and validation of miRNAs associated with the resistance of maize (Zea mays L.) to Exserohilum turcicum. PLoS One 9:e87251 Xia X, Melchinger AE, Kuntze L, Lubberstedt T (1999) Quantitative trait loci mapping of resistance to sugarcane mosaic virus in maize. Phytopathology 89(8):660–667 Xia H, Gao W, Qu J, Dai L, Gao Y, Lu S, Zhang M, Wang P, Wang T (2020) Genetic mapping of northern corn leaf blight-resistant quantitative trait loci in maize. Medicine 99(31):e21326 Xing Y, Ingvardsen C, Salomon R, Lubberstedt T (2006) Analysis of sugarcane mosaic virus resistance in maize in an isogenic dihybrid crossing scheme and implications for breeding potyvirus-­resistant maize hybrids. Genome 49(10):1274–1282 Xu ML, Melchinger AE, Xia XC, Lubberstedt T (1999) High-resolution mapping of loci conferring resistance to sugarcane mosaic virus in maize using RFLP, SSR, and AFLP markers. Mol Gen Genet MGG 261(3):574–581

Dissection of QTLs for Biotic Stress Resistance in Maize

81

Xu DL, Park JW, Mirkov TE, Zhou GH (2008) Viruses causing mosaic disease in sugarcane and their genetic diversity in southern China. Arch Virol 153(6):1031–1039 Xu Z, Hua J, Wang F, Cheng Z, Meng Q, Chen Y, Han X, Tie S, Liu C, Li X, Wang Z, Weng J (2020) Marker-assisted selection of qMrdd8 to improve maize resistance to rough dwarf disease. Breed Sci 70(2):183–192 Yang Q, Yin G, Guo Y, Zhang D, Chen S, Xu M (2010) A major QTL for resistance to Gibberella stalk rot in maize. Theor Appl Genet 121:673–687 Yang Q, He Y, Kabahuma M, Chaya T, Kelly A, Borrego E, Bian Y, El Kasmi F, Yang L, Teixeira P, Kolkman J, Nelson R, Kolomiets M, Dang JL, Wisser R, Caplan J, Li X, Lauter N, Balint-Kurti P, Balint-Kurti P (2017) A gene encoding maize caffeoyl-CoA O-methyltransferase confers quantitative resistance to multiple pathogens. Nat Genet 49(9):1364–1372 Ye J, Zhong T, Zhang D, Ma C, Wang L, Yao L, Zhang Q, Zhu M, Xu M (2019) The auxin-­ regulated protein ZmAuxRP1 coordinates the balance between root growth and stalk rot disease resistance in maize. Mol Plant 12(3):360–373 Yuan L, Dussle CM, Melchinger AE, Utz HF, Lubberstedt T (2003) Clustering of QTL conferring SCMV resistance in maize. Maydica 48(1):55–62 Yuan G, Chen B, Peng H, Zheng Q, Li Y, Xiang K, Liu L, Zou C, Lin H, Ding H, Pan G, Zhang Z (2020) QTL mapping for resistance to ear rot caused by Fusarium graminearum using an IBM Syn10 DH population in maize. Mol Breed 40(9):1–12 Zanakis GN, Mariolis NA, Rodiatos AJ, Kesoglou AP, Paraskevakos EP, Dimou KN, Theodosiadis CA, Zampogianis CT (2009) Chemical control of maize pests Ostrinia nubilalis (Lepidoptera: Crambidae) and Sesamia nonagrioides (Lepidoptera: Noctuidae) in Greece. In: Proceedings of 13th Panhellenic entomological congress. Copycity, Thessaloniki, pp 302–303 Zhang Y, Xu L, Fan X, Tan J, Chen W, Xu M (2012) QTL mapping of resistance to gray leaf spot in maize. Theor Appl Genet 125(8):1797–1808 Zhang D, Liu Y, Guo Y, Yang Q, Ye J, Chen S, Xu M (2012a) Fine-mapping of qRfg2, a QTL for resistance to Gibberella stalk rot in maize. Theor Appl Genet 124(3):585–596 Zhang W, Deng S, Zhao Y, Xu W, Liu Q, Zhang Y, Ren C, Cheng Z, Xu M, Liu B (2021) qMrdd2, a novel quantitative resistance locus for maize rough dwarf disease. BMC Plant Biol 21(1):1–11 Zhao Y, Lu X, Liu C, Guan H, Zhang M, Li Z, Cai H, Lai J (2012) Identification and fine mapping of rhm1 locus for resistance to southern corn leaf blight in maize. J Integr Plant Biol 54(5):321–329 Zhu M, Tong L, Xu M, Zhong T (2021) Genetic dissection of maize disease resistance and its applications in molecular breeding. Mol Breed 41(5):1–22 Zila CT, Fernando Samayoa L, Santiago R, Butron A, Holland JB (2013) A genome-wide association study reveals genes associated with fusarium ear rot resistance in a maize core diversity panel. G3: Genes Genomes Genetics 3(11):2095–2104 Zila CT, Ogut F, Romay MC, Gardner CA, Buckler ES, Holland JB (2014) Genome-wide association study of Fusarium ear rot disease in the USA maize inbred line collection. BMC Plant Biol 14(1):1–15 Zunjare RU (2012) Genetic analyses of resistance to stored grain weevil (Sitophilus oryzae) in maize. MSc Thesis submitted to Post graduate School, Indian Agricultural Research Institute, New Delhi India, p 123 Zunjare R, Hossain F, Thirunavukkarasu N, Muthusamy V, Jha SK, Kumar P, Gupta HS (2014) Evaluation of specialty corn inbreds for responses to stored grain weevil (Sitophilus oryzae L.) infestation. Indian J Genet Plant Breed 74:564–567 Zunjare R, Hossain F, Muthusamy V, Jha SK, Kumar P, Sekhar JC, Guleria SK, Singh NK, Thirunavukkarasu N, Gupta HS (2015a) Genetics of resistance to stored grain weevil (Sitophilus oryzae L.) in maize. Cogent Food Agric 1:1075934. https://doi.org/10.108 0/23311932.2015.1075934 Zunjare RU, Hossain F, Muthusamy V, Vishwakarma A, Pandey N, Kumar P, Sekhar JC, Jha SK, Thirunavukkarasu N, Gupta HS (2015b) Analyses of genetic diversity among exotic-and indigenous-maize inbreds differing for responses to stored grain weevil (Sitophilus oryzae L) infestation. Maydica 60:1–7

82

R. U. Zunjare et al.

Zunjare R, Hossain F, Muthusamy V, Choudhary M, Kumar P, Sekhar JC, Guleria SK, Singh NK, Thirunavukkarasu N, Gupta HS (2015c) Popping quality attributes of popcorn hybrids in relation to weevil (Sitophilus oryzae) infestation. Indian J Genet Plant Breed 75:510–513 Zunjare R, Hossain F, Muthusamy V, Jha SK, Kumar P, Sekhar JC, Thirunavukkarasu N, Gupta HS (2016) Genetic variability among exotic and indigenous maize inbreds for resistance to stored grain weevil (Sitophilus oryzae L.) infestation. Cogent Food Agric 2:1137156. https://doi.org/1 0.1080/23311932.2015.1137156 Zunjare RU, Hossain F, Muthusamy V, Baveja A, Chauhan HS, Bhat JS, Thirunavukkarasu N, Saha S, Gupta HS (2018) Development of biofortified maize hybrids through marker-assisted stacking of β-carotene hydroxylase, lycopene-ε-cyclase and opaque2 genes. Front Plant Sci 9:178 Zwonitzer J, Bubeck D, Bhattramakki D, Goodman M, Arellano C, Balint-Kurti PJ (2009) Use of selection with recurrent back-crossing and QTL mapping to identify loci contributing to southern leaf blight resistance in a highly resistant maize line. Theor Appl Genet 118:911–925 Zwonitzer JC, Coles ND, Krakowsky MD, Arellano C, Holland JB, McMullen MD, Pratt RC, Balint-Kurti PJ (2010) Mapping resistance quantitative trait loci for three foliar diseases in maize recombinant inbred line population-evidence for multiple disease resistance. Phytopathology 100(1):72–79

Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize Baljeet Singh, Shabir Hussain Wani and Umesh Goutam

, Sarvjeet Kukreja, Vijay Kumar,

1 Introduction Maize or corn (Zea mays L.) is one of the most valued staple crops grown throughout the globe. It ranked as the third most important cereal crop of India, after rice and wheat. It is known as queen of cereals because of its yield potential and nutritional value. Apart from carbohydrates, protein and fat, the maize kernels are good source of vitamins (vitamin B1, vitamin B3, vitamin B5, vitamin B6, vitamin C, vitamin E and vitamin K) and minerals (phosphorus, potassium, calcium, iron and magnesium) (Shah et al. 2016). Apart from being a major part of human diet, maize also has an economic importance in beverage, paper, textile and pharmaceutical industries (Ranum et al. 2014). It is widely used for animal feed; the maize silage provides high-energy forage to ruminants (Klopfenstein et al. 2013; Mandić et al. 2021). However, due to the climate change, the maize production has been decreased in several regions across the globe (Murray-Tortarolo et al. 2018; Chen et al. 2020). Further, from the last few decades, human population is increasing at alarming rate (Van Bavel 2013). To meet the needs of human population, the development of high-yielding maize varieties with imperative quality traits is today’s need. Therefore, the development of high-yielding varieties has become one of the foremost aims in the current maize breeding programmes. The maize architecture is a B. Singh · V. Kumar · U. Goutam (*) School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India S. H. Wani MRCFC Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir, India S. Kukreja Department of Agronomy, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_4

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chief agronomic trait that plays a pivotal role for determining the yield. Maize grain yield is controlled by yield-associated traits like kernel size, number of ears per plant, number of kernels per row and number of rows per ear (Raut et al. 2017). As maize kernels are the edible part of the plant, which are the major source of carbohydrates, photosynthetic efficiency also plays a major role in determining the yield (Sun et  al. 2019). Over the years, improvements in the agronomic practices and plant breeding have resulted in consistent increase in maize yield (Duvick 2005), but still there is an urgent need for better maize varieties to feed the world. However, the genetics behind these complex agronomic traits is still poorly understood. GWAS has emerged as a powerful tool, which takes the advantage of natural variations to decipher the genetic basis associated with agronomic traits (Tibbs Cortes et al. 2021). During last few years, advancements in the high-throughput phenotyping and genotyping methods have enhanced the efficiency of GWAS significantly. The marker-trait associations (MTAs)/genes/QTLs associated with agronomic trait(s) can further set a platform for developing better maize cultivars using MAS (marker-assisted selection) and breeding or with the help of transgenic approaches.

2 Natural Variations The rates of increase in the yield of major staple crops have been gradually decreasing in several regions globally. Natural variations are the genetic diversity present in single plant species in nature. Some of these variations contribute to phenotypic traits; these can be detected by morphological assessments while others are quantitative and these are studied using molecular markers. These are often associated with QTLs and present in the coding region of DNA (Dittberner et al. 2018). These variations are rich source of useful traits that could be used to develop better crop plants (Maqbool et  al. 2021). However, these variations in the major crops have been exploited since domestication of crop plants. Due to the crop evolution under domestication, most of the modern crop cultivars have narrow genetic basis (Alonso-­ Blanco et al. 2009). Tradition plant breeding programmes were focused to enhance the crop yield and reached near to the plateau. Modern plant breeding is aimed to use the desirable traits present in the crop germplasm to develop high yielding as well as stress-resistant varieties. Several approaches have been established for the genetic studies and mapping of these natural variation. A number of studies using different molecular markers have been conducted in cultivated crops and in their wild relatives (reviewed by Gostimsky et  al. (2005) and Garrido-Cardenas et  al. (2018)). Further, the availability of crop reference genomes enhanced the speed and accuracy of identifying the natural variations at genetic sequence levels.

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3 Natural Variations in Maize In the modern world, the demand of maize has increasing continuously because it is a multipurpose crop. Rosegrant et al. (2009) predicted that by 2050, the demand of maize in the developing countries would increase by 100%. However, presently maize is grown in approximately 100 million hectares, but still its yield is limited because of many factors such as biotic abiotic stress and lack of high-yielding maize varieties for specific environmental conditions (Prasanna 2012). As per the demand, area under maize cultivation is increasing steadily (Shiferaw et al. 2011). However, area expansion is not a solution of it, because it could have adverse effects on other crops, environment, forests and wild diversity. Maize and its wild relatives (aka teosintes) possess tremendous amount of genetic variation with wide climatic adaptability that opened various windows for genetic enhancement regardless of the challenges stated above (Hellin et al. 2014). Some of these natural variations exhibit astonishing phenotypes for morphological, agronomical and reproductive traits, and some respond to unfavourable conditions (Khan et al. 2017; Aci et al. 2018; Nelimor et al. 2020). A wide range of variabilities has been observed in the maize germplasm in terms of yield parameters, ear length, ear girth, number of rows per ear, number of kernels per ear, kernel colour, kernel size (Rahman et al. 2015; Chen et al. 2016; Raut et al. 2017), nutrient content (Menkir et al. 2015; Diepenbrock et al. 2017) and biotic and abiotic stresses (Mammadov et al. 2018; Li et al. 2019). Breeders have been collecting and grouping the diverse maize germplasm and its wild relatives since centuries (Sood et al. 2014). However, the modern maize cultivars have less genetic diversity due to domestication and improvement bottlenecks (Hufford et al. 2012). Further improvements in maize using modern maize varieties with narrow genetic base are challenging. However, the presence of beneficial traits in the wild germplasm can catalyse the maize breeding programmes aimed to improve its agronomic performance.

4 Evolution of Germplasm Characterization Methods in Maize Over the time, several methods have been developed and employed to screen and characterize the crop germplasm. Several molecular markers have been identified for the genetic analysis and mapping of quantitative trait loci (QTLs). In the earlier studies, random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), restriction fragment length polymorphism (RFLP) and simple sequence repeat (SSR) markers have been used extensively for the analysis of genetic diversity and genetic relatedness in maize (Gedil and Menkir 2019; Zebire 2020). AFLP markers are highly useful in maize to study the phylogenetic analysis

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and genetic diversity and to construct genetic maps. These markers are very suitable for the study of variations within the species. In one such study, genetic diversity of a population of 62 maize accessions was studied using 650 highly polymorphic AFLP markers (Beyene et al. 2006). Warburton et al. (2005) suggested that RFLP markers could be employed for estimating genetic relationship in diverse inbred maize lines. The markers involved in an association with phenotypic traits are detected in association mapping (AM). Modern AM studies largely depend upon SSR and single nucleotide polymorphism (SNP) markers. Since twenty-first century, SSR markers have been used extensively for the identification of marker-trait associations (MTAs) and QTLs for different agronomic traits (Xiang et al. 2010; Kim et al. 2017; Sa et al. 2018; Vathana et al. 2019). Choi et al. (2019) reported 15 QTLs for 10 different agronomic traits by screening 121 double haploid maize lines using 200 SSR markers. A number of studies have been performed for QTL analysis and mapping of major QTLs conferring plant architecture traits, such as yield, kernel size, plant height, ear height, stalk diameter and number and length of tassel branches (Zhu et al. 2013; Dong et al. 2015; Wang et al. 2018; Zhao and Su 2019; Wang et  al. 2020; Yang et  al. 2020). The stable QTLs and genes contributing to beneficial agronomic traits could be used for MAS, which will ultimately accelerate the maize breeding programmes by serving as a great platform for the selection of ideal parental lines on the basis of genotypic data.

5 Association Mapping The conventional QTL mapping depends upon biparental populations and therefore specifies the extent of variability in the phenotype linked to a DNA loci. As traditional QTL mapping population is derived from two parents, therefore it is challenging to identify QTLs with high resolution (Burghardt et  al. 2017). Association mapping is another mapping technique complement to linkage-based AM which takes advantage of linkage disequilibrium (LD) to detect productive MTAs. AM is a high-resolution mapping approach for identifying the genes controlling complex traits in random breeding populations, germplasm core collections, multi-parent advanced generation inter-cross lines and nested association mapping populations instead of bi-parental population (Ibrahim et  al. 2020). However, various factors affect the efficiency of AM such as marker density, population type, size and structure. In addition, quality of phenotypic and genotypic data also alters the accuracy of identifying the MTAs. With the help of advance phenotyping and genotyping methods, AM offers a great potential to enhance crop genetic improvements. It is a novel approach for the identification of new makers for MAS breeding. AM is of two types: candidate gene studies and GWAS.

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6 Genome-Wide Association Studies Since the advent of high-throughput next-generation sequencing (NGS) methods, the availability of reference GWAS has come to limelight as a powerful tool to decipher the genetics contributing a complex phenotypic trait. The NGS-based genotyping has greatly reduced the labour works and enhanced the resolution of QTL mapping (Myles et  al. 2009). To investigate the allelic variations in the diverse populations, GWAS is a favourable toolkit, where NGS could play a pivotal role. It was first introduced for human genomics, but due to the availability of more phenotypic variations, it becomes more popular in plants (Brachi et al. 2011). In comparison to traditional bi-parental QTL mapping in plants, GWAS provide higher resolution, and it can use highly diverse populations that play better role for the identification of MTAs for different traits. In recent years, with several modification in the high-throughput phenotyping and genotyping methods, the number of GWAS-­ based publications has increased rapidly. Till now GWAS have been employed to investigate the agronomic traits in major crops such as maize, wheat, rice, barley, soybean and sorghum and in several other crops (Contreras-Soto et al. 2017; Owens et al. 2019; Jamil et al. 2019; Almerekova et al. 2019; Verma et al. 2021; Wang et al. 2021; Tibbs Cortes et al. 2021).

7 Significance of Population Structure for GWAS In the last decade, GWAS has been used extensively to identify the genetic basis behind a complex phenotypic trait. Generally, association studies depend upon standard regression methods with an assumption that the data is identical and distributed independently (Sul et  al. 2018). Usually, the larger association mapping panels have unavoidable distantly connected individuals, which can lead to the detection of false-positive MTAs. To mitigate the detention of false associations, several mixed models have been developed (Yu et  al. 2006; Kang et  al. 2010; Listgarten et al. 2012; Zhou and Stephens 2012). For an instance, Zhao et al. (2007) established a mixed model to reduce the chances of getting false positives, which rely on genome-wide differences in the relatedness by estimated pairwise kinship coefficients. Different types of populations are used to assemble association mapping panels. Based upon kinship and population structure, these can be categorized into five different groups: (1) ideal population having subtle population structure and familial relatedness (kinship), (2) multifamily population, (3) sample having population structure, (4) sample having moderate population structure and familial relatedness and (5) sample having strong population structure and familial relatedness. Among all, most of the association mapping panels use population type four (Zhu et al. 2008).

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8 Phenotyping Highly efficient phenotyping techniques are important prerequisite for the detection of genuine MTAs. The power of GWAS increases by increasing the population size. The GWAS include a large diversity panel representing a highly diversified population sample, with an aim to enhance recombination frequencies. Thus, experiment designs involve replicated trails under uniform agronomy, can reduce the environmental effects and increase the power of association mapping. However, achieving an accurate and detailed phenotypic data is a cumbersome and expensive task. However, advancements in the next-generation phenotyping methods (aka phenomics) enabled researchers to generate and collect large amount of phenotypic data in a cost-effective way. These highly efficient, automated, multifunctional techniques are considered as valuable tools in the breeding programmes for crop improvement. The typical phenomics can be divided into two parts: (1) phenotypic data collection using various automated sensors and (2) phenomic analysis using different algorithms and software (Zhao et al. 2019a). Different data collection tools specific to target phenotypic traits at crop, whole plant and cellular levels are available. The realistic phenotypic data enhance the accuracy of MTAs detected in GWAS.

9 Genotyping In GWAS, the same population sample used for phenotyping is also used for genotyping using different molecular markers that are present throughout the genome. To achieve highly efficient associations, the molecular markers should be distributed at a distance more than 40 centimorgan in the genome (Yu et al. 2011). The locations of these molecular markers in different individuals are used to measure the extent of linkage disequilibrium (LD). Among all molecular markers, SSR and SNPs are widely used in GWAS. The SNP markers are more efficient than the SSR markers due to their high ability to detect LD at low cost. Therefore, nowadays, SNPs are considered as the ideal molecular markers for GWAS in plants.

10 Next-Generation-Based Genotyping The older molecular marker-based genotyping approaches were labourious, expensive and less efficient. However, today with the availability of reference sequences, SNP detection assays (Smith and Maughan 2015; Vos et al. 2015), high-throughput NGS techniques, high-density genotyping arrays (Sim et al. 2012; Cai et al. 2017; Pandey et al. 2017; Sun et al. 2020) and large-scale SNP-based genotyping have cut the cost and increased the efficiency. The NGS allows the re-sequencing of association mapping panel to determine the genetic variations at nucleic acid sequence

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levels present within and between the species (Causse et  al. 2013; Aflitos et  al. 2014; Cheng et al. 2019; Sahu et al. 2020). The reliability of SNP calling increases with the depth of coverage, and at least 10- to 20-fold sequence depth is considered as good for reliable variant calling (D’Agostino and Tripodi 2017). However, it is difficult to achieve this much coverage of depth because whole genome re-­ sequencing is expensive. However, an alternate approach to resolve this problem is to generate a compact representation of the genome with the help of array or liquid hybridization techniques to detect the target genomic regions and alleles (Terracciano et  al. 2016, 2017; Ruggieri et  al. 2017). The modern SNP discovery-based approaches involve three common steps: (1) DNA digestion using restriction enzymes, (2) ligation of digested DNA fragments with sequencing platform-­specific adapters and (3) PCR (polymerase chain reaction)-based amplification. The restriction site-associated DNA sequencing (RAD-Seq) was an earlier technique for SNP discovery (Baird et al. 2008). Later, genotyping by sequencing (GBS) becomes the most promising approach for high-throughput SNP detection by replacing the RAD-­ Seq (Chung et al. 2017; Rowan et al. 2017; Wickland et al. 2017). GBS was first introduced in maize crop to identify the SNPs in a recombinant inbred line (RIL) population (Elshire et al. 2011). Later on, this approach was used in various crops such as wheat, rice, maize, barley and potato for large-scale SNP discovery (Poland et al. 2012a; He et al. 2014). It has proved its potential and efficiency in RIL populations (Boutet et al. 2016; Kong et al. 2018), bi-parental populations (Henning et al. 2016; Celik et al. 2017), mutants (Mishra et al. 2016; Lemay et al. 2019) and genetically diverse populations (Taranto et al. 2016; Baral et al. 2018; Pereira-Dias et al. 2019). Earlier, ApeKI restriction enzyme was used to develop DNA fragments in GBS approach. Currently, most of the GBS experiments involve two restriction enzymes (PstI&MspI) to achieve high SNP density, number of reads per polymorphic site and reads per sample (Poland et al. 2012b; Fu et al. 2016; Wickland et al. 2017). The GBS approach combined with GWAS has provided numerous fruitful MTAs that could pave ways for developing the crops for future.

11 GWAS in Maize Presence of genetic variations, availability of reference genome and rapid LD decay make maize a perfect crop for GWAS (Shikha et al. 2021). Presently maize germplasm maintained in different germplasm banks could be proved as a vital resource for maize genetic improvements. It is important to study the genetic diversity of both cultivated and wild maize species for future maize breeding. NGS-based sequencing of large maize populations can identify elite variants, which can be used as parental lines. Moreover, availability of maize reference genome opened various vistas that can speed up the MAS and breeding. In comparison to other major crops, LD decay is rapid in maize (Xu et al. 2017). To generate high-resolution maps with low LD requires large number of molecular markers, whereas high LD increases the

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chances of detection of markers associated with casual variants (Shikha et al. 2021). In 2008, first GWAS experiment was performed using 553 inbred lines to identify the loci associated with oleic acid levels in the maize kernels (Beló et al. 2008). In the last decade, many GWAS were performed to investigate the genetics linked to different agronomic traits at sequence levels (Table 1). GWAS has appeared to be a potential technique to identify associations between SNP and agronomic traits. A number of QTLs/MTAs/genes have been identified for major traits in maize such as plant height (Zhang et al. 2019), ear-related traits (Zhu et al. 2018), yield-related traits (Zhang et al. 2020) and root structure (Ju et al. 2018) and for some other traits also. Apart from these, GWAS has been performed for several other traits in maize such as biotic and abiotic stress (Shikha et al. 2021). The GWAS-based screening of core collections and diverse inbred lines lead to the identification of various lines with better single or multiple agronomic traits. The further use of these lines in the maize association mapping panel will increase the variations and will enhance the efficiency of GWAS in terms of gene detection. For example, linkage mapping combined with GWAS revealed 100 QTLs and 138 SNPs associated with yield-related traits in maize in different environments (Zhang et  al. 2020). Besides cultivated maize, GWAS has also been applied on teosintes for 22 domestication and agronomic traits (Chen et  al. 2019). The teosinte NAM population (TeoNAM)-based study reported PROSTRATE GROWTH1 gene, which is associated with rice domestication and is also linked with tillering capacity in maize and teosintes. Moreover, QTLs for other traits such as flowering time were also detected for which the teosinte alleles contribute to a phenotype similar to maize (Chen et al. 2019). Therefore, potential candidate genes can be transferred from teosintes to cultivated maize varieties. In general, agronomic traits are multigenic and are also influenced by genotype x environment interactions. These traits have been improved over decades by traditional breeding and reached to a plateau. It is challenging to further improve these traits by traditional breeding. However, emergence of GWAS created various opportunities to screen the SNPs, QTLs and MTAs controlling such complex agronomic traits. Therefore, the genetic factors controlling the grain yield and flowering were studied under heat and drought stress conditions (Yuan et al. 2019). Similarly, 368 maize inbred lines grown in seven different environments were studied for three forage quality traits, which resulted in the detection of 73, 41 and 82 SNPs associated with acid detergent fibre, neutral detergent and in vitro dry matter digestibility (Wang et al. 2016). Hindu et al. (2018) reported 20 and 26 SNPs significantly associated with grain zinc and iron concentrations. The results of such kind of studies are useful to speed up the development of high-yielding, nutrient-rich climate-­ resilient maize cultivars through MAS breeding or genomic selection.

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Table 1  List of GWAS for agronomic traits in maize during recent years Population 412 inbred lines

Traits under study Aboveground dry matter

942 diverse inbred lines 292 inbred lines

Stalk biomass and stalk anatomy Kernel length and width, ear length and diameter and cob diameter Plant architecture, including plant height, leaf angle, leaf length and leaf width Plant height

87 diverse inbred lines

300 maize hybrids

380 maize (Zea mays) inbred lines

Root system architecture

639 maize inbred lines

Kernel row number

508 inbred lines

Husk tightness

350 elite inbred maize lines MAGIC population of 672 recombinant inbred lines (RIL)

13 different agronomic traits Yield, plant height and silking

144 maize inbred lines 250 lines

Kernel moisture content (KMC) at harvest stage Yield-related traits

RIL population of 190 individuals 508 inbred lines

Root traits

368 inbred lines

Four husk traits, that is, number of layers, length, width and thickness Forage quality

Associated SNPs/QTLs/genes 129 highly significant associated SNPs for all dry matter traits 16 candidate genes associated 20 SNPs were found to be associated 36 highly significant associated quantitative trait SNPs were identified for these traits A total of nine significant SNP makers and two candidate genes were identified for plant height SNP-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes 49 genes from the 7 regions were expressed in different maize tissues 27 candidate genes have been identified Discovered a total of 129 significant SNPs 48 QTLs and 60 candidate genes for plant height, 15 QTLs and 24 candidate genes for grain yield and 34 QTLs and 49 candidate genes for silking time 8 SNPs associated with KMC Identified 100 QTLs and 138 SNPs 36 putative quantitative trait loci (QTLs) 9 SNPs significantly associated 82 SNPs were found

Reference Lu et al. (2021) Mazaheri et al. (2019) Zhu et al. (2018) Zhao et al. (2019b)

Zhang et al. (2019)

Zheng et al. (2020)

An et al. (2020) Jiang et al. (2020) Zhang and Qi (2021) Malvar Pintos et al. (2018)

Zhou et al. (2018) Zhang et al. (2020) Ju et al. (2018) Cui et al. (2016) Wang et al. (2016)

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12 Identification of Various Candidate Genes Through GWAS in Maize Many SNPs, QTLs and genes associated with different agronomic traits have been identified in maize (Table 1). However, before incorporating these MTAs in breeding programmes or transgenic studies, their functionality must be confirmed. The reverse genetic approaches can be used to validate the functionality of newly identified candidate genes and MTAs via GWAS. The reverse genetic approaches involve RNAi, VIGS, T-DNA mutagenesis and CRISPR/Cas9 (Singh et al. 2018). Despite that, functionality of only a few number of genes or MTAs has been validated. For example, GWAS on a population of 942 diverse inbred lines identified 16 candidate genes with stalk-related traits. Out of these 16 candidate genes, functionality of a regulatory gene Zmm22 has been tested that was found to be associated with plant height using transgenic approach. This study revealed that elevated expression of Zmm22 gene results in the decreased plant height and tassel branch number (Mazaheri et  al. 2019). In another study, a candidate gene (GRMZM2G098557) associated with ear row number that was identified from GWAS has been functionally validated by PCR amplification followed by sequencing to detect the SNP in the gene (Zhang et  al. 2020). The functional validation of potential candidate genes would be helpful for understanding the molecular pathways underlying the complex agronomic traits in maize.

13 Conclusion Plant breeding focuses on the selection of better progeny and development of new varieties with improved qualitative and quantitative traits. To feed the world, the agronomic performance of maize crop must be improved. As maize germplasm is a rich source of natural variations, it can be assumed that there is no lack of desirable traits. The major challenge is the screening of vast germplasm for the identification of genes and QTLs associated with complex agronomic traits. GWAS is an approach for the genetic dissection of polygenic phenotypic traits. It reduced the time and efforts required in the traditional bi-parental mapping. GWAS is a powerful tool, but its accuracy could be improved by using better and more reliable genotyping and phenotyping methods. It has been employed for the studies of different traits in different crops. In the recent years, the number of GWA studies and publications in maize is increasing every year; therefore, future research should focus on validation of candidate genes detected from GWAS. After that, these should be incorporated into the maize breeding programmes and transgenic studies.

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References Aci MM, Lupini A, Mauceri A, Morsli A, Khelifi L, Sunseri F (2018) Genetic variation and structure of maize populations from Saoura and Gourara oasis in Algerian Sahara. BMC Genet 19(1):1–10 Aflitos S, Schijlen E, De Jong H et al (2014) Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing. Plant J. https://doi.org/10.1111/ tpj.12616 Almerekova S, Sariev B, Abugalieva A, Chudinov V, Sereda G, Tokhetova L et  al (2019) Association mapping for agronomic traits in six-rowed spring barley from the USA harvested in Kazakhstan. PLoS One 14(8):e0221064 Alonso-Blanco C, Aarts MG, Bentsink L, Keurentjes JJ, Reymond M, Vreugdenhil D, Koornneef M (2009) What has natural variation taught us about plant development, physiology, and adaptation? Plant Cell 21(7):1877–1896 An Y, Chen L, Li YX, Li C, Shi Y, Zhang D et al (2020) Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize. BMC Plant Biol 20(1):1–11 Baird NA, Etter PD, Atwood TS et al (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One. https://doi.org/10.1371/journal.pone.0003376 Baral K, Coulman B, Biligetu B, Fu YB (2018) Genotyping-by-sequencing enhances genetic diversity analysis of crested wheatgrass [Agropyron cristatum (L.) gaertn.]. Int J Mol Sci. https://doi.org/10.3390/ijms19092587 Beló A, Zheng P, Luck S, Shen B, Meyer DJ, Li B et al (2008) Whole genome scan detects an allelic variant of fad2 associated with increased oleic acid levels in maize. Mol Gen Genomics 279(1):1–10 Beyene Y, Botha AM, Myburg AA (2006) Genetic diversity in traditional Ethiopian highland maize accessions assessed by AFLP markers and morphological traits. Biodivers Conserv 15(8):2655–2671 Boutet G, Alves Carvalho S, Falque M et  al (2016) SNP discovery and genetic mapping using genotyping by sequencing of whole genome genomic DNA from a pea RIL population. BMC Genomics. https://doi.org/10.1186/s12864-­016-­2447-­2 Brachi B, Morris GP, Borevitz JO (2011) Genome-wide association studies in plants: the missing heritability is in the field. Genome Biol 12(10):1–8 Burghardt LT, Young ND, Tiffin P (2017) A guide to genome-wide association mapping in plants. Curr Protoc Plant Biol 2(1):22–38 Cai C, Zhu G, Zhang T, Guo W (2017) High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. BMC Genomics. https://doi.org/10.1186/ s12864-­017-­4062-­2 Causse M, Desplat N, Pascual L et al (2013) Whole genome resequencing in tomato reveals variation associated with introgression and breeding events. BMC Genomics. https://doi.org/10.118 6/1471-­2164-­14-­791 Celik I, Gurbuz N, Uncu AT et al (2017) Genome-wide SNP discovery and QTL mapping for fruit quality traits in inbred backcross lines (IBLs) of solanum pimpinellifolium using genotyping by sequencing. BMC Genomics. https://doi.org/10.1186/s12864-­016-­3406-­7 Chen J, Zhang L, Liu S, Li Z, Huang R, Li Y et  al (2016) The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS One 11(4):e0153428 Chen Q, Yang CJ, York AM, Xue W, Daskalska LL, DeValk CA et al (2019) TeoNAM: a nested association mapping population for domestication and agronomic trait analysis in maize. Genetics 213(3):1065–1078 Chen X, Wang L, Niu Z, Zhang M, Li J (2020) The effects of projected climate change and extreme climate on maize and rice in the Yangtze River Basin. China Agric Forest Meteorol 282:107867

94

B. Singh et al.

Cheng H, Liu J, Wen J et  al (2019) Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol. https://doi.org/10.1186/ s13059-­019-­1744-­x Choi JK, Sa KJ, Park DH, Lim SE, Ryu SH, Park JY et al (2019) Construction of genetic linkage map and identification of QTLs related to agronomic traits in DH population of maize (Zea mays L.) using SSR markers. Genes Genomics 41(6):667–678 Chung YS, Choi SC, Jun TH, Kim C (2017) Genotyping-by-sequencing: a promising tool for plant genetics research and breeding. Hortic Environ Biotechnol 58(5):425–431 Contreras-Soto RI, Mora F, de Oliveira MAR, Higashi W, Scapim CA, Schuster I (2017) A genome-wide association study for agronomic traits in soybean using SNP markers and SNP-­ based haplotype analysis. PLoS One 12(2):e0171105 Cui Z, Luo J, Qi C, Ruan Y, Li J, Zhang A et al (2016) Genome-wide association study (GWAS) reveals the genetic architecture of four husk traits in maize. BMC Genomics 17(1):1–14 D’Agostino N, Tripodi P (2017) NGS-based genotyping, high-throughput phenotyping and genome-wide association studies laid the foundations for next-generation breeding in horticultural crops. Diversity 9(3):38 Diepenbrock CH, Kandianis CB, Lipka AE, Magallanes-Lundback M, Vaillancourt B, Góngora-­ Castillo E et al (2017) Novel loci underlie natural variation in vitamin E levels in maize grain. Plant Cell 29(10):2374–2392 Dittberner H, Korte A, Mettler-Altmann T, Weber AP, Monroe G, de Meaux J (2018) Natural variation in stomata size contributes to the local adaptation of water-use efficiency in Arabidopsis thaliana. Mol Ecol 27(20):4052–4065 Dong YB, Zhang ZW, Shi QL, Wang QL, Qiang ZHOU, Fei DENG et al (2015) QTL consistency for agronomic traits across three generations and potential applications in popcorn. J Integr Agric 14(12):2547–2557 Duvick DN (2005) The contribution of breeding to yield advances in maize (Zea mays L.). Adv Agron 86:83–145 Elshire RJ, Glaubitz JC, Sun Q et al (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. https://doi.org/10.1371/journal.pone.0019379 Fu YB, Peterson GW, Dong Y (2016) Increasing genome sampling and improving SNP genotyping for genotyping-by-sequencing with new combinations of restriction enzymes. G3 Genes, Genomes, Genet. https://doi.org/10.1534/g3.115.025775 Garrido-Cardenas JA, Mesa-Valle C, Manzano-Agugliaro F (2018) Trends in plant research using molecular markers. Planta 247(3):543–557 Gedil M, Menkir A (2019) An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa. Front Plant Sci 10:1430 Gostimsky SA, Kokaeva ZG, Konovalov FA (2005) Studying plant genome variation using molecular markers. Russ J Genet 41(4):378–388 He J, Zhao X, Laroche A et  al (2014) Genotyping-by-sequencing (GBS), An ultimate marker-­ assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484 Hellin J, Bellon MR, Hearne SJ (2014) Maize landraces and adaptation to climate change in Mexico. J Crop Improv 28(4):484–501 Henning JA, Gent DH, Twomey MC et  al (2016) Genotyping-by-sequencing of a bi-parental mapping population segregating for downy mildew resistance in hop (Humulus lupulus L.). Euphytica. https://doi.org/10.1007/s10681-­015-­1600-­3 Hindu V, Palacios-Rojas N, Babu R, Suwarno WB, Rashid Z, Usha R et al (2018) Identification and validation of genomic regions influencing kernel zinc and iron in maize. Theor Appl Genet 131(7):1443–1457 Hufford MB, Xu X, Van Heerwaarden J, Pyhäjärvi T, Chia JM, Cartwright RA et  al (2012) Comparative population genomics of maize domestication and improvement. Nat Genet 44(7):808–811 Ibrahim AK, Zhang L, Niyitanga S, Afzal MZ, Xu Y, Zhang L et al (2020) Principles and approaches of association mapping in plant breeding. Trop Plant Biol 13:212–224

Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize

95

Jamil M, Ali A, Gul A, Ghafoor A, Napar AA, Ibrahim AM et al (2019) Genome-wide association studies of seven agronomic traits under two sowing conditions in bread wheat. BMC Plant Biol 19(1):1–18 Jiang S, Zhang H, Ni P, Yu S, Dong H, Zhang A et al (2020) Genome-wide association study dissects the genetic architecture of maize husk tightness. Front Plant Sci 11:861 Ju C, Zhang W, Liu Y, Gao Y, Wang X, Yan J et al (2018) Genetic analysis of seedling root traits reveals the association of root trait with other agronomic traits in maize. BMC Plant Biol 18(1):1–15 Kang HM, Sul JH, Service SK et al (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet. https://doi.org/10.1038/ng.548 Khan B, Nawab NN, Qamar M, Abbas M, Haroon M, Intikhab A et al (2017) Genetic variability in different maize (Zea mays L.) genotypes for comparative yield performance under local conditions of Rawalakot, Azad Jammu and Kashmir. Int J Biosci 11(3):102–107 Kim OG, Sa KJ, Lee JR, Lee JK (2017) Genetic analysis of maize germplasm in the Korean Genebank and association with agronomic traits and simple sequence repeat markers. Genes Genomics 39(8):843–853 Klopfenstein TJ, Erickson GE, Berger LL (2013) Maize is a critically important source of food, feed, energy and forage in the USA. Field Crop Res 153:5–11 Kong WQ, Kim C, Zhang D et  al (2018) Genotyping by sequencing of 393 sorghum bicolor BTx623 × IS3620C recombinant inbred lines improves sensitivity and resolution of QTL detection. G3 Genes, Genomes, Genet. https://doi.org/10.1534/g3.118.200173 Lemay MA, Torkamaneh D, Rigaill G et al (2019) Screening populations for copy number variation using genotyping-by-sequencing: a proof of concept using soybean fast neutron mutants. BMC Genomics. https://doi.org/10.1186/s12864-­019-­5998-­1 Li N, Lin B, Wang H, Li X, Yang F, Ding X et al (2019) Natural variation in Zm FBL41 confers banded leaf and sheath blight resistance in maize. Nat Genet 51(10):1540–1548 Listgarten J, Lippert C, Kadie CM et al (2012) Improved linear mixed models for genome-wide association studies. Nat Methods 9(6):525–526 Lu X, Wang J, Wang Y, Wen W, Zhang Y, Du J et al (2021) Genome-wide association study of maize aboveground dry matter accumulation at seedling stage. Front Genet 11:1679 Malvar Pintos RA, Jiménez Galindo JC, Butrón Gómez AM, Santiago Carabelos R, Ordás López B (2018) GWAS for resistance to Mediterranean corn borer and agronomic traits in a MAGIC population of maize. Maize Genetics Conference, March Mammadov J, Buyyarapu R, Guttikonda SK, Parliament K, Abdurakhmonov IY, Kumpatla SP (2018) Wild relatives of maize, rice, cotton, and soybean: treasure troves for tolerance to biotic and abiotic stresses. Front Plant Sci 9:886 Mandić V, Bijelić Z, Krnjaja V, Đorđević S, Brankov M, Mićić N, Stanojković A (2021) Harvest time effect on quantitative and qualitative parameters of forage maize. J Anim Plant Sci 31(1) Maqbool A, Bakhsh A, Aksoy E (2021) Effects of natural variations on biofortification. In: Wild germplasm for genetic improvement in crop plants. Academic Press, pp 115–138 Mazaheri M, Heckwolf M, Vaillancourt B, Gage JL, Burdo B, Heckwolf S et al (2019) Genome-­ wide association analysis of stalk biomass and anatomical traits in maize. BMC Plant Biol 19(1):1–17 Menkir A, Rocheford T, Maziya-Dixon B, Tanumihardjo S (2015) Exploiting natural variation in exotic germplasm for increasing provitamin-A carotenoids in tropical maize. Euphytica 205(1):203–217 Mishra A, Singh A, Sharma M et al (2016) Development of EMS-induced mutation population for amylose and resistant starch variation in bread wheat (Triticum aestivum) and identification of candidate genes responsible for amylose variation. BMC Plant Biol. https://doi.org/10.1186/ s12870-­016-­0896-­z Murray-Tortarolo GN, Jaramillo VJ, Larsen J (2018) Food security and climate change: the case of rainfed maize production in Mexico. Agric For Meteorol 253:124–131

96

B. Singh et al.

Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21(8):2194–2202 Nelimor C, Badu-Apraku B, Nguetta SP, Tetteh AY, Garcia-Oliveira AL (2020) Phenotypic characterization of maize landraces from Sahel and Coastal West Africa reveals marked diversity and potential for genetic improvement. J Crop Improv 34(1):122–138 Owens BF, Mathew D, Diepenbrock CH, Tiede T, Wu D, Mateos-Hernandez M et  al (2019) Genome-wide association study and pathway-level analysis of kernel color in maize. G3: Genes, Genomes, Genetics 9(6):1945–1955 Pandey MK, Agarwal G, Kale SM et  al (2017) Development and evaluation of a high density genotyping “Axiom-Arachis” Array with 58 K SNPs for accelerating genetics and breeding in groundnut. Sci Rep. https://doi.org/10.1038/srep40577 Pereira-Dias L, Vilanova S, Fita A et al (2019) Genetic diversity, population structure, and relationships in a collection of pepper (Capsicum spp.) landraces from the Spanish centre of diversity revealed by genotyping-by-sequencing (GBS). Hortic Res. https://doi.org/10.1038/ s41438-­019-­0132-­8 Poland J, Endelman J, Dawson J et  al (2012a) Genomic selection in wheat breeding using genotyping-­by-sequencing. Plant Genome. https://doi.org/10.3835/plantgenome2012.06.0006 Poland JA, Brown PJ, Sorrells ME, Jannink JL (2012b) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS One. https://doi.org/10.1371/journal.pone.0032253 Prasanna BM (2012) Diversity in global maize germplasm: characterization and utilization. J Biosci 37(5):843–855 Rahman S, Mia MM, Quddus T, Hassan L, Haque MA (2015) Assessing genetic diversity of maize (Zea mays L.) genotypes for agronomic traits. Res Agric Livest Fish 2(1):53–61 Ranum P, Peña-Rosas JP, Garcia-Casal MN (2014) Global maize production, utilization, and consumption. Ann N Y Acad Sci 1312(1):105–112 Raut SK, Ghimire SK, Kharel R, Kuwar CB, Sapkota M, Kushwaha UKS (2017) Study of yield and yield attributing traits of maize. Am J Food Sci Health 3(6):123–129 Rosegrant MR, Ringler C, Sulser TB, Ewing M, Palazzo A, Zhu T et al (2009) Agriculture and food security under global change: prospects for 2025/2050. International Food Policy Research Institute, Washington, DC, pp 145–178 Rowan BA, Seymour DK, Chae E et  al (2017) Methods for genotyping-by-sequencing. In: Genotyping. Humana Press, New York, pp 221–242 Ruggieri V, Anzar I, Paytuvi A et al (2017) Exploiting the great potential of sequence capture data by a new tool, SUPER-CAP. DNA Res. https://doi.org/10.1093/dnares/dsw050 Sa KJ, Hong TK, Lee JK (2018) Genetic diversity and association analyses of Canadian maize inbred lines with agronomic traits and simple sequence repeat markers. Plant Breed Biotechnol 6(2):159–169 Sahu PK, Sao R, Mondal S et  al (2020) Next generation sequencing based forward genetic approaches for identification and mapping of causal mutations in crop plants: a comprehensive review. Plants 9(10):1355 Shah TR, Prasad K, Kumar P (2016) Maize—a potential source of human nutrition and health: a review. Cogent Food Agric 2(1):1166995 Shiferaw B, Prasanna BM, Hellin J, Bänziger M (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur 3(3):307–327 Shikha K, Shahi JP, Vinayan MT, Zaidi PH, Singh AK, Sinha B (2021) Genome-wide association mapping in maize: status and prospects. 3. Biotech 11(5):1–10 Sim SC, Durstewitz G, Plieske J et al (2012) Development of a large snp genotyping array and generation of high-density genetic maps in tomato. PLoS One. https://doi.org/10.1371/journal. pone.0040563

Genome-Wide Association Studies (GWAS) for Agronomic Traits in Maize

97

Singh B, Kukreja S, Goutam U (2018) Milestones achieved in response to drought stress through reverse genetic approaches. F1000Res 7:1311. https://doi.org/10.12688/f1000research.15606.1 Smith SM, Maughan PJ (2015) SNP genotyping using KASPar assays. Methods Mol Biol. https:// doi.org/10.1007/978-­1-­4939-­1966-­6_18 Sood S, Flint-Garcia S, Willcox M, Holland J (2014) Mining natural variation for maize improvement: selection on phenotypes and genes. In: Tuberosa R, Graner A, Frison E (eds) Genomics of plant genetic resources. Springer, Dordrecht. https://doi.org/10.1007/978-­94-­007-­7572-­5_25 Sul JH, Martin LS, Eskin E (2018) Population structure in genetic studies: confounding factors and mixed models. PLoS Genet 14(12):e1007309 Sun J, Gao J, Wang Z, Hu S, Zhang F, Bao H, Fan Y (2019) Maize canopy photosynthetic efficiency, plant growth, and yield responses to tillage depth. Agronomy 9(1):3 Sun C, Dong Z, Zhao L et al (2020) The Wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnol J 18(6):1354–1360 Taranto F, D’Agostino N, Greco B et al (2016) Genome-wide SNP discovery and population structure analysis in pepper (Capsicum annuum) using genotyping by sequencing. BMC Genomics. https://doi.org/10.1186/s12864-­016-­3297-­7 Terracciano I, Cantarella C, D’Agostino N (2016) Hybridization-based enrichment and next generation sequencing to explore genetic diversity in plants. In: Dynamics of mathematical models in biology. Springer, Cham Terracciano I, Cantarella C, Fasano C et al (2017) Liquid-phase sequence capture and targeted re-­ sequencing revealed novel polymorphisms in tomato genes belonging to the MEP carotenoid pathway. Sci Rep. https://doi.org/10.1038/s41598-­017-­06120-­3 Tibbs Cortes L, Zhang Z, Yu J (2021) Status and prospects of genome-wide association studies in plants. Plant Genome 14(1):e20077 Van Bavel J (2013) The world population explosion: causes, backgrounds and -projections for the future. Facts Views Vis ObGyn 5(4):281–291 Vathana Y, Sa KJ, Lim SE, Lee JK (2019) Genetic diversity and association analyses of Chinese maize inbred lines using SSR markers. Plant Breed Biotechnol 7(3):186–199 Verma RK, Chetia SK, Dey PC, Rahman A, Saikia S, Sharma V et  al (2021) Genome-wide association studies for agronomical traits in winter rice accessions of Assam. Genomics 113(3):1037–1047 Vos PG, Uitdewilligen JGAML, Voorrips RE et al (2015) Development and analysis of a 20K SNP array for potato (Solanum tuberosum): an insight into the breeding history. Theor Appl Genet 128:2387–2401. https://doi.org/10.1007/s00122-­015-­2593-­y Wang H, Li K, Hu X, Liu Z, Wu Y, Huang C (2016) Genome-wide association analysis of forage quality in maize mature stalk. BMC Plant Biol 16(1):1–12 Wang B, Liu H, Liu Z, Dong X, Guo J, Li W et al (2018) Identification of minor effect QTLs for plant architecture related traits using super high density genotyping and large recombinant inbred population in maize (Zea mays). BMC Plant Biol 18(1):1–12 Wang G, Zhao Y, Mao W, Ma X, Su C (2020) QTL analysis and fine mapping of a major QTL conferring kernel size in maize (Zea mays). Front Genet 11:603920 Wang A, Jiang Y, Shu X, Zha Z, Yin D, Liu Y et  al (2021) Genome-wide association study-­ based identification genes influencing agronomic traits in rice (Oryza sativa L.). Genomics 113(3):1396–1406 Warburton ML, Ribaut JM, Franco J, Crossa J, Dubreuil P, Betrán FJ (2005) Genetic characterization of 218 elite CIMMYT maize inbred lines using RFLP markers. Euphytica 142(1):97–106 Wickland DP, Battu G, Hudson KA et  al (2017) A comparison of genotyping-by-sequencing analysis methods on low-coverage crop datasets shows advantages of a new workflow. BMC Bioinformatics, GB-eaSy. https://doi.org/10.1186/s12859-­017-­2000-­6 Xiang K, Yang KC, Pan GT, Reid LM, Li WT, Zhu X, Zhang ZM (2010) Genetic diversity and classification of maize landraces from China’s Sichuan basin based on agronomic traits, quality traits, combining ability and SSR markers. Maydica 55(1):85–93

98

B. Singh et al.

Xu Y, Li P, Yang Z, Xu C (2017) Genetic mapping of quantitative trait loci in crops. Crop J 5(2):175–184 Yang J, Liu Z, Chen Q, Qu Y, Tang J, Lübberstedt T, Li H (2020) Mapping of QtL for grain yield components based on a DH population in maize. Sci Rep 10(1):1–11 Yu J, Pressoir G, Briggs WH et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. https://doi.org/10.1038/ng1702 Yu LX, Lorenz A, Rutkoski J et  al (2011) Association mapping and gene-gene interaction for stem rust resistance in CIMMYT spring wheat germplasm. Theor Appl Genet. https://doi. org/10.1007/s00122-­011-­1664-­y Yuan Y, Cairns JE, Babu R, Gowda M, Makumbi D, Magorokosho C et al (2019) Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Front Plant Sci 9:1919 Zebire DA (2020) Applications of molecular markers in genetic diversity studies of maize. Niger J Biotechnol 37(1):101–108 Zhang X, Qi Y (2021) Genetic architecture affecting maize agronomic traits identified by variance heterogeneity association mapping. Genomics 113(4):1681–1688 Zhang Y, Wan J, He L, Lan H, Li L (2019) Genome-wide association analysis of plant height using the maize F1 population. Plan Theory 8(10):432 Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y et al (2020) A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet 133(10):2881–2895 Zhao Y, Su C (2019) Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize. Sci Rep 9(1):1–10 Zhao K, Aranzana MJ, Kim S et  al (2007) An Arabidopsis example of association mapping in structured samples. PLoS Genet. https://doi.org/10.1371/journal.pgen.0030004 Zhao C, Zhang Y, Du J et al (2019a) Crop phenomics: current status and perspectives. Front Plant Sci 10:714 Zhao Y, Wang H, Bo C, Dai W, Zhang X, Cai R et al (2019b) Genome-wide association study of maize plant architecture using f 1 populations. Plant Mol Biol 99(1–2):1–15 Zheng Z, Hey S, Jubery T, Liu H, Yang Y, Coffey L et al (2020) Shared genetic control of root system architecture between Zea mays and Sorghum bicolor. Plant Physiol 182(2):977–991 Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet. https://doi.org/10.1038/ng.2310 Zhou G, Hao D, Xue L, Chen G, Lu H, Zhang Z et al (2018) Genome-wide association study of kernel moisture content at harvest stage in maize. Breed Sci 68(5):622–628 Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome J 1:5. https://doi.org/10.3835/plantgenome2008.02.0089 Zhu LY, Chen JT, Li D, Zhang JH, Huang YQ, Zhao YF et al (2013) QTL mapping for stalk related traits in maize (Zea mays L.) under different densities. J Integr Agric 12(2):218–228 Zhu XM, Shao XY, Pei YH, Guo XM, Li J, Song XY, Zhao MA (2018) Genetic diversity and genome-wide association study of major ear quantitative traits using high-density SNPs in maize. Front Plant Sci 9:966

Genomic Selection in Maize Breeding Vishal Singh and Amita Kaundal

1 Introduction Maize is one of the most important crops after rice and wheat and has numerous industrial uses. It is a diploid species from the tribe Maydae of the Poaceae family, having 2n = 20 chromosome numbers, with its primary center of origin in Mexico and Central America. Since its domestication, maize has undergone artificial and natural selection for centuries. Selection for morphological traits has been the foundation of crop improvement. Thousands of years of conscious selection by the farmers have led to the development of landraces adapted to specific climatic conditions harboring valuable alleles for various traits related to quality and yield. In the early 1900s, efforts for systematic corn breeding to develop hybrids started (East 1908; Shull 1909). The selection was practiced even before that time, providing several open-pollinated cultivars through mass selection. This improved germplasm acted as a sourced germplasm for deriving inbred lines for hybrid breeding (Hallauer et al. 1988). Europe observed a tremendous expansion of corn area in some countries, aided by selection for early maturity (Trifunovic 1978). During the earlier phases of crop improvement, unconscious selection was practiced for a few loci, followed by selecting many loci through mass selection. After the invention of Mendelian genetics, selection for a few loci resulted in the improvement of disease resistance and the development of dwarf wheat varieties enabling the green revolution. Later, the development of the breeder’s equation (Lush 1937) and linear mixed model (Henderson et  al. 1959) methods provided new tools to plant breeding. With the advent of sequencing technologies, genomic data could be associated with phenotype data which helped identify causal genomic regions through QTL (quantitative trait loci) and association mapping. It facilitated the transfer of QTLs using markerassisted selection (MAS) approach. A detailed description of all these plant V. Singh (*) · A. Kaundal Utah State University, Logan, UT, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_5

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breeding phases is provided elsewhere by Ramstein et al. (2019) and recommended for more information. The major drawback of MAS was its unsuitability for quantitative traits where QTLs have minor effects only, thus having huger QTL × environment interactions. Also, QTL and association mapping strategies are challenged by the difficulty in identifying rare and small effect QTLs for important traits. The use of genome-wide markers was proposed to predict the breeding value of genotypes (Meuwissen et al. 2001) and is called genomic selection (GS). GS is contrary to MAS, where few loci with significant effects are targeted. The goal of GS is to predict the breeding and/or genetic values of the genotypes. Although getting genotypic information is still a significant financial bottleneck for implementing GS in breeding programs, reducing cost of genotyping is a strong motivation for adopting this advanced tool in crop improvement. Statistically, GS has supervised learning where a set of individuals (inbreds, hybrids, or segregating genotypes) acts as a training set having both genotypic and phenotypic information. A suitable prediction model is applied to this training set, and a fitted model is used to predict the breeding values of unknown samples using only the sequence data. A common misunderstanding about GS is that it predicts phenotypic value, which it does not. Instead, it predicts genomic estimated breeding values (GEBVs), which do not directly represent phenotypic values. Despite this, it may be used to rank genotypes for a trait that is used to fit the model. A correlation between the actual phenotypic value and GEBVs might give a good idea about the accuracy of the predictions by such an approach. The selection of an appropriate prediction model is an essential aspect of GS. Several models have been proposed for GS considering different statistical factors (Crossa et al. 2017). GS has several complexities which need to be addressed to obtain acceptable prediction accuracies. One of these complexities is a huge number of markers (p) compared to population size (n). It makes least squares estimates for marker effects to be less practical to compute. Solutions to these complexities include dimensionality reduction, penalized regression, and variable selection, to mention a few. Another critical challenge is to consider genotype × environmental interaction in GS models. GS methods can be classified based on different criteria. A major classification scheme divides GS models into parametric, semiparametric, and nonparametric models. Among many, parametric models include ridge regression BLUP (rrBLUP), genomic BLUP (gBLUP), compressed BLUP (cBLUP), and super BLUP(sBLUP), collectively called BLUP models (Endelman 2011; Pérez and de Los Campos 2014; Wang et al. 2018). Another group of models in the parametric category is Bayesian models comprising Bayesian ridge regression, Bayesian LASSO, and Bayes alphabets A, B, and C (Pérez and de Los Campos 2014). The principal components can be integrated into parametric models to account for the population structure in the GS models (Merrick and Carter 2021). Semiparametric methods include reproducing Kernel Hilbert spaces regression (Gianola and van Kaam 2008), abbreviated as RKHS. RKHS is supposed to capture complex gene interaction. Random Forest and Support Vector Machine regressions are among the nonparametric methods for GS. The choice of model depends on various factors, including the composition of the training population. Epistatic interaction also

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affects prediction accuracies and can be improved slightly using specific models that capture epistasis, such as EG-BLUP.  The predictive abilities of models also depend on crop and trait genetics.

2 GS in Maize for Biomass, Yield, and Yield-Related Traits Yield is a complex phenotype governed by several loci with small to medium effect sizes in maize (Chen et  al. 2017). Prediction of yield using GS may be made at several levels depending on the study’s objectives. Since hybrids are the main cultivar types in maize, GS can be employed to identify high-yielding parents in segregating or double haploid populations to predict hybrids’ yield to narrow down candidates for field trials.

2.1 Prediction of Per Se and Hybrid Performance in Segregating Generations The development of parental lines for a hybrid breeding program is a continuous process in a maize breeding program. Pedigree breeding is a popular method of developing new inbreds where two or more selected parents are crossed to generate segregating population to select desired segregants based on their per se performance or combining ability with chosen testers. Early generation testing helps determine better lines by reducing the cost of evaluation and advancing many lines from a cross. GS may be applied at early generation testing to reduce the cost of evaluation of test crosses. In one such attempt, the shelling percentage was predicted with higher accuracy than the yield in test crosses of an F2 population obtained with the primary aim of improving the shelling percentage (Sun et  al. 2019). To improve grain yield and stover quality traits, the genome-wide selection was implemented on a testcross population from 223 recombinant inbreds. The results were compared with that of marker-assisted recurrent selection (MARS) in the same population (Massman et al. 2013). GS resulted in significantly higher realized gains than MARS for yield + stover index. GS was employed to predict the hybrid performance in test crosses to predict GCA for grain yield (Burdo et al. 2021). Genomic-­ estimated GCA for the inbred lines was computed and was found to have a higher correlation to testcross values than phenotypic GCA. The primary motivation was to identify the best combiner lines in the early generations of inbred line development to avoid an exhaustive and practically unfeasible scheme to test all the parental line candidates. Such an approach for GS-assisted line selection can save a significant amount of finances by narrowing down the best candidates for field-based testcross evaluations for yield.

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2.2 GS in Inbred Lines Inbred lines are homogenous and homozygous populations used as parents in maize hybrid breeding. GS selection in maize inbred populations primarily aims at the parental selection and studying the feasibility of GS for a specific crop-trait scenario. Stalk strength is an important agronomic trait in maize and is related to stalk lodging and grain yield. A set of inbred lines belonging to two RIL populations were subjected to GS for rind penetrometer resistance (RPR), an indicator of stalk strength in maize. High prediction accuracy for RPR was observed when a multivariate model was used and when QTLs were taken as a fixed effect in the model (Liu et al. 2020). The authors explained that fixed and multivariate models might better capture the genetic variance of the trait and probably both the additive and nonadditive interaction effects. The husk is a part of the maize ear that indirectly affects grain yield by reducing susceptibility to ear rot (Warfield and Davis 1996), providing limited photosynthesis and acting kernel dehydration after physiological maturity. Suitable husk characteristics are important for obtaining optimum yield in specific agroecologies. On an association mapping panel of 498 inbred lines, GS models were used to predict husk-related traits (Cui et al. 2020). The highest prediction accuracy was observed for husk thickness. Diverse association mapping panels are highly likely to have the presence of subpopulations. While predicting husk-­ related traits, subpopulation-level training of models showed higher prediction accuracies than when modeling across the subpopulations, provided that the subpopulation size is large enough. Similarly, the kernel oil trait was predicted with good prediction accuracy (0.68) in a set of maize inbred lines to assess the feasibility of GS for this trait (Hao et al. 2019).

2.3 GS for Double Haploid-Based Breeding Programs In commercial maize breeding programs, double haploid (DH) line development has become a routine scheme. The convenience of generating a large number of DH lines in a much shorter time compared to the traditional pedigree method has allowed generating thousands of DH lines every year. The phenotypic evaluation of this vast novel germplasm resource is a very challenging and costly task. GS may help narrow down good candidates for field evaluation for specific traits, thus saving substantial financial resources. Scientists at CIMMYT (International Maize and Wheat Improvement Center) evaluated a scheme to predict yield and agronomic traits for a set of 3068 tropical DH lines at the early stages of the pipeline (Beyene et al. 2019). The experiment was conducted over multiple years, and it suggested that the inclusion of 10–30% of lines from the following year to the existing training set of the previous year can significantly increase the prediction accuracies and save high costs compared to testcross formation and multilocation testcross evaluation. A comparative study at CIMMYT showed that the performance of testcrosses from

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DH lines selected from GEBVs has an advantage over DH lines selected based on phenotypic values for yield and yield-related traits (Beyene et al. 2019). The gain from GS was realized in terms of a 32% cost reduction and time savings. A good reference for different phases of GS in the breeding program is reported elsewhere (Fu et al. 2022).

2.4 Rapid Cycling Genomic Selection Time is an essential factor in the breeder’s equation. The time taken to complete a breeding cycle depends on factors like crop species and the availability of off-­season nurseries. Breeders have to optimize their program to incorporate GS in such a way that reduces breeding cycles. Maize is a crop that can be grown in multiple seasons in tropical climates, providing opportunities to utilize off-season nurseries. Several studies have reported efforts to shorten the generation interval to increase genetic gain per unit of time (Beyene et al. 2015; Gaynor et al. 2017; Massman et al. 2013; Vivek et al. 2017). Rapid cycling genomic selection (RCGS) was implemented in a multi-parental tropical maize population, using 18 founder lines for 4  cycles (2 cycles per year) for selecting grain yield (Zhang et al. 2017). A slight reduction in genetic diversity was reported after four cycles compared to the base population. The authors suggested that RCGS can be adopted in tropical maize breeding programs without rapidly losing genetic diversity and achieving higher genetic gains in a short period. A good compilation of information on RCGS is available elsewhere (Volpato et al. 2021).

3 GS for Abiotic and Biotic Stress Tolerance Abiotic stresses are a few critical challenges of today’s climate change era. Maize faces substantial loss in biomass and yield when subjected to environmental stresses such as drought, heat, salt, and waterlogging. The evaluation of breeding material takes twice as much effort as the same set of germplasm is evaluated both under control and stressful environments compared to yield and yield-related traits. Implementing GS may save time and resources in stress tolerance-oriented breeding programs, thus increasing genetic gain per unit of time. The genomic selection was conducted on eight bi-parental populations by CIMMYT to estimate genetic gain for grain yield under managed drought conditions (Beyene et al. 2015). The study suggested the superiority of the GS approach over conventional pedigree-based phenotypic selection for increasing genetic gains for yield in a drought environment. In another attempt to improve drought tolerance in tropical maize using GEBV-based selection, researchers at CIMMYT (Vivek et al. 2017) demonstrated the superiority of genomic selection over phenotype-based selection in two

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bi-parental populations. Markers were used to generate a stable source population by selecting drought-tolerant alleles without selecting under drought stress. Possible erratic rainfall pattern due to climate change poses a risk of drought and waterlogging within the same crop growth period. It is important to simultaneously improve drought and waterlogging tolerance in the same genetic background. It is theoretically possible due to potential links between molecular mechanisms imparting tolerance to moisture-deficit and excess environments. A rapid cycling genomic selection was implemented to select combined stress tolerance in a multi-parent yellow synthetic population (Das et al. 2020). The study enables the development of a breeding population through molecular markers even in the absence of the target stress in one season. No yield penalty under optimal moisture conditions was observed while selecting yield for drought and waterlogging environments. Not much efforts have been made to improve cold and heat tolerance using genomic selection. Salinity stress is another critical environmental stress causing substantial yield reduction to maize yield. In a first attempt to predict salinity tolerance for biomass-related traits in maize, GS was implemented on a set of diverse inbred lines for the shoot and root-related traits (Singh et  al. 2019). With leading efforts by CIMMYT, good progress in improving abiotic stress tolerance using genomic selection is expected. Plant diseases pose severe threats to global food security, and with climate change, pathogens are expected to evolve rapidly, overthrowing the existing tolerance of crops for them. Efforts to map R genes have been undertaken in the past (Carson et al. 2004; Collins et al. 1998; Kuki et al. 2018). Despite such efforts, not many large-scale marker-assisted selection (MAS)-based gene pyramiding studies are available to transfer these mapped genomic regions. Genomic selection can be a good approach where there are no or very few major QTLs available for quantitative disease resistance. For selecting genotypes with reduced disease severity against northern lead blight (NLB) caused by Setosphaeria turcica, the G-BLUP model was implemented (Technow et  al. 2013) with prediction accuracies up to 0.706 (dent corn) and 0.690 (flint corn). High prediction accuracies using GWAS-detected SNP markers were observed for Fusarium ear rot (FER), another destructive fundal disease of maize (Liu et al. 2021). The feasibility of GS has been studied for a few other maize diseases, such as tar spot complex (Cao et  al. 2021), lethal necrosis (Gowda et al. 2015), and Gibberella ear rot (Riedelsheimer et al. 2013). With careful design of strategy to implement, GS may yield higher genetic gains saving time and resources as it does for any other trait.

4 GS for Pre-breeding The genetic basis of elite cultivars in major crops suffers from a narrow genetic base. It poses a risk of the inability to cope with the new challenges of climate change. Corp wild relatives harboring valuable alleles for the traits of interest are an essential resource for crop improvement, and introgression of these alleles into

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cultivation background, popularly known as pre-breeding, is a challenging task. Despite difficulties, several attempts to introgress valuable alleles in the active breeding germplasm have been made for a few traits in some crops (Barrantes et al. 2016; Dutra et al. 2018; Fulop et al. 2016; Grewal et al. 2020; dos Santos et al. 2022; Singh et al. 2021; Wang et al. 2017). Most marker-assisted introgressions for pre-­ breeding have been successful for qualitative traits and traits for which major alleles are present. For quantitative traits, the traditional introgression approaches are not an idea. A novel origin-specific genomic selection (OSGS) scheme was proposed (Yang et al. 2020) where in a bi-parental derived population, separate marker effects were predicted for favorable exotic alleles based on their origin (wild vs. elite). The scheme aims at increasing the contribution of favorable exotic alleles in a bi-­parental cross between wild and elite lines. The scheme was validated on two nested association mapping populations of barley and maize. In another interesting study, a proposed design was evaluated, which aimed at initiating a pre-breeding program to harness polygenic variation from landraces using genomic selection (Gorjanc et al. 2016). The study suggests the introgression of favorable alleles from landraces in a phased manner. Thus, genomic selection may help broaden the genetic base of breeding populations.

5 Take-Home Message Genomic selection is a powerful tool gaining popularity in maize breeding programs to predict the breeding values of individuals. Genomic selection is a practically proven tool and can give remarkable success for maize improvement if used wisely. With the reducing prices of DNA sequencing, an upward trend is expected in its adoption as an essential breeding tool. However, for developing countries in general and public sector breeding programs in particular, DNA sequencing of many maize inbreds is still not feasible. Hence, an appropriate strategy for selecting various parameters, such as the number of markers and population size, in the context of specific traits is recommended.

References Barrantes W, López-Casado G, García-Martínez S, Alonso A, Rubio F, Ruiz JJ, Fernández-Muñoz R, Granell A, Monforte AJ (2016) Exploring new alleles involved in tomato fruit quality in an introgression line library of Solanum pimpinellifolium. Front Plant Sci 7:1172. https://doi. org/10.3389/FPLS.2016.01172/BIBTEX Beyene Y, Semagn K, Mugo S, Tarekegne A, Babu R, Meisel B, Sehabiague P, Makumbi D, Magorokosho C, Oikeh S, Gakunga J, Vargas M, Olsen M, Prasanna BM, Banziger M, Crossa J (2015) Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci 55(1):154–163. https://doi.org/10.2135/ CROPSCI2014.07.0460

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Beyene Y, Gowda M, Olsen M, Robbins KR, Pérez-Rodríguez P, Alvarado G, Dreher K, Gao SY, Mugo S, Prasanna BM, Crossa J (2019) Empirical comparison of tropical maize hybrids selected through genomic and phenotypic selections. Front Plant Sci 10. https://doi. org/10.3389/FPLS.2019.01502 Burdo B, de Leon N, Kaeppler SM (2021) Testcross vs. randomly paired single-cross progeny tests for genomic prediction of new inbreds and hybrids derived from multiparent maize populations. Crop Sci 61(5):3425–3435. https://doi.org/10.1002/CSC2.20545 Cao S, Song J, Yuan Y, Zhang A, Ren J, Liu Y, Qu J, Hu G, Zhang J, Wang C, Cao J, Olsen M, Prasanna BM, San Vicente F, Zhang X (2021) Genomic prediction of resistance to tar spot complex of maize in multiple populations using genotyping-by-sequencing SNPs. Front Plant Sci 12:1438. https://doi.org/10.3389/FPLS.2021.672525/BIBTEX Carson ML, Stuber CW, Senior ML (2004) Identification and mapping of quantitative trait loci conditioning resistance to southern leaf blight of maize caused by Cochliobolus heterostrophus Race O. Phytopathology 94(8):862–867. https://doi.org/10.1094/PHYTO.2004.94.8.862 Chen L, An Y, Li YX, Li C, Shi Y, Song Y, Zhang D, Wang T, Li Y (2017) Candidate loci for yield-­ related traits in maize revealed by a combination of metaQTL analysis and regional association mapping. Front Plant Sci 8:2190. https://doi.org/10.3389/FPLS.2017.02190/BIBTEX Collins NC, Webb CA, Seah S, Ellis JG, Hulbert SH, Pryor A (1998) The isolation and mapping of disease resistance gene analogs in maize. Mol Plant Microbe Interact 11(10):968–978. https:// doi.org/10.1094/MPMI.1998.11.10.968 Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22(11):961–975. https:// doi.org/10.1016/J.TPLANTS.2017.08.011 Cui Z, Dong H, Zhang A, Ruan Y, He Y, Zhang Z (2020) Assessment of the potential for genomic selection to improve husk traits in maize. G3: Genes Genomes Genet 10(10):3741–3749. https://doi.org/10.1534/g3.120.401600 Das RR, Vinayan MT, Patel MB, Phagna RK, Singh SB, Shahi JP, Sarma A, Barua NS, Babu R, Seetharam K, Burgueño JA, Zaidi PH (2020) Genetic gains with rapid-cycle genomic selection for combined drought and waterlogging tolerance in tropical maize (Zea mays L.). Plant Genome 13(3). https://doi.org/10.1002/TPG2.20035 dos Santos IG, Verzegnazzi AL, Edwards J, Frei UK, Boerman N, Tonello Zuffo L, Pires LPM, de La Fuente G, Lübberstedt T (2022) Usefulness of temperate-adapted maize lines developed by doubled haploid and single-seed descent methods. Theor Appl Genet 135(6):1829–1841. https://doi.org/10.1007/S00122-­022-­04075-­2/TABLES/5 Dutra WF, Guerra YL, Ramos JPC, Fernandes PD, Silva CRC, Bertioli DJ, Leal-Bertioli SCM, Santos RC (2018) Introgression of wild alleles into the tetraploid peanut crop to improve water use efficiency, earliness and yield. PLoS One 13(6):e0198776. https://doi.org/10.1371/ JOURNAL.PONE.0198776 East EM (1908) Inbreeding in corn. In Connecticut Agric. Exp Stn. Rep for 1907 p 419–428 Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4(3):250–255. https://doi.org/10.3835/PLANTGENOME2011.08.0024 Fu J, Hao Y, Li H, Reif JC, Chen S, Huang C, Wang G, Li X, Xu Y, Li L (2022) Integration of genomic selection with doubled-haploid evaluation in hybrid breeding: from GS 1.0 to GS 4.0 and beyond doubled-haploid technology faces a great challenge for hybrid breeding. Mol Plant 15:577–580. https://doi.org/10.1016/j.molp.2022.02.005 Fulop D, Ranjan A, Ofner I, Covington MF, Chitwood DH, West D, Ichihashi Y, Headland L, Zamir D, Maloof JN, Sinha NR (2016) A new advanced backcross tomato population enables high resolution leaf QTL mapping and gene identification. G3: Genes Genomes Genetics 6(10):3169–3184. https://doi.org/10.1534/G3.116.030536/-­/DC1 Gaynor RC, Gorjanc G, Bentley AR, Ober ES, Howell P, Jackson R, Mackay IJ, Hickey JM (2017) A two-part strategy for using genomic selection to develop inbred lines. Crop Sci 57(5):2372–2386. https://doi.org/10.2135/CROPSCI2016.09.0742

Genomic Selection in Maize Breeding

107

Gianola D, van Kaam JBCHM (2008) Reproducing Kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178(4):2289–2303. https://doi. org/10.1534/GENETICS.107.084285 Gorjanc G, Jenko J, Hearne SJ, Hickey JM (2016) Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations. BMC Genomics 17(1):1–15. https://doi.org/10.1186/S12864-­015-­2345-­Z/FIGURES/7 Gowda M, Das B, Makumbi D, Babu R, Semagn K, Mahuku G, Olsen MS, Bright JM, Beyene Y, Prasanna BM (2015) Genome-wide association and genomic prediction of resistance to maize lethal necrosis disease in tropical maize germplasm. TAG Theor Appl Genet 128(10):1957–1968. https://doi.org/10.1007/S00122-­015-­2559-­0 Grewal S, Hubbart-Edwards S, Yang C, Devi U, Baker L, Heath J, Ashling S, Scholefield D, Howells C, Yarde J, Isaac P, King IP, King J (2020) Rapid identification of homozygosity and site of wild relative introgressions in wheat through chromosome-specific KASP genotyping assays. Plant Biotechnol J 18(3):743–755. https://doi.org/10.1111/PBI.13241 Hallauer AR, Russell WA, Lamkey KR (1988) Corn breeding. http://lib.dr.iastate.edu/ agron_pubs/259 Hao Y, Wang H, Yang X, Zhang H, He C, Li D, Li H, Wang G, Wang J, Fu J (2019) Genomic prediction using existing historical data contributing to selection in biparental populations: a study of kernel oil in maize. Plant Genome 12(1):180025. https://doi.org/10.3835/ PLANTGENOME2018.05.0025 Henderson CR, Kempthorne O, Searle SR, von Krosigk CM (1959) The estimation of environmental and genetic trends from records subject to culling. Biometrics 15(2):192. https://doi. org/10.2307/2527669 Kuki MC, Scapim CA, Rossi ES, Mangolin CA, do Amaral AT, Barth Pinto RJ (2018) Genome wide association study for gray leaf spot resistance in tropical maize core. PLoS One 13(6). https://doi.org/10.1371/JOURNAL.PONE.0199539 Liu X, Hu X, Li K, Liu Z, Wu Y, Wang H, Huang C (2020) Genetic mapping and genomic selection for maize stalk strength. BMC Plant Biol 20(1):1–16. https://doi.org/10.1186/ S12870-­020-­2270-­4/FIGURES/7 Liu Y, Hu G, Zhang A, Loladze A, Hu Y, Wang H, Qu J, Zhang X, Olsen M, San Vicente F, Crossa J, Lin F, Prasanna BM (2021) Genome-wide association study and genomic prediction of Fusarium ear rot resistance in tropical maize germplasm. Crop J 9(2):325–341. https://doi. org/10.1016/J.CJ.2020.08.008 Lush J (1937) Animal breeding plans. Collegiate Press Inc. Massman JM, Jung HJG, Bernardo R (2013) Genomewide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53(1):58–66. https://doi.org/10.2135/CROPSCI2012.02.0112 Merrick LF, Carter AH (2021) Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs. Plant Genome 14(3). https://doi.org/10.1002/ TPG2.20158 Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829. https://doi.org/10.1093/ GENETICS/157.4.1819 Pérez P, de Los Campos G (2014) Genome-wide regression and prediction with the BGLR statistical package. Genetics 198(2):483–495. https://doi.org/10.1534/genetics.114.164442 Ramstein GP, Jensen SE, Buckler ES (2019) Breaking the curse of dimensionality to identify causal variants in Breeding 4. Theor Appl Genet 132(3):559–567. https://doi.org/10.1007/ s00122-018-3267-3 Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink J-L, Melchinger AE (2013) Genomic predictability of interconnected biparental maize populations. Genetics 194(2):493–503. https://doi.org/10.1534/genetics.113.150227 Shull GH (1909) A pure line method of corn breeding. Amer Breed Assoc Rpt 5:51–59

108

V. Singh and A. Kaundal

Singh V, Kaundal A, Sandhu D (2019) Genomic prediction of salinity stress tolerance in maize (Zea mays L.). ASA, CSSA, SSSA International Annual Meeting, Salt Lake City, UT. https:// scisoc.confex.com/scisoc/2021am/meetingapp.cgi/Paper/139065 Singh KP, Kumari P, Yadava DK (2021) Introgression and QTL mapping conferring resistance for Alternaria brassicae in the backcross progeny of Sinapis alba + Brassica juncea somatic hybrids. Plant Cell Rep 40(12):2409–2419. https://doi.org/10.1007/S00299-­021-­02785-­3 Sun Q, Wang P, Li W, Li W, Lu S, Yu Y, Zhao M, Meng Z (2019) Genomic selection on shelling percentage and other traits for maize. Breed Sci 69(2):266–271. https://doi.org/10.1270/ JSBBS.18141 Technow F, Bürger A, Melchinger AE (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3 (Bethesda, Md.) 3(2):197–203. https://doi.org/10.1534/G3.112.004630 Trifunovic V (1978) Maize production and maize breeding in Europe. In DB Walden (ed) Maize breeding and genetics. John Wiley and sons NY. p 41–57 Vivek BS, Krishna GK, Vengadessan V, Babu R, Zaidi PH, Kha LQ, Mandal SS, Grudloyma P, Takalkar S, Krothapalli K, Singh IS, Ocampo ETM, Xingming F, Burgueño J, Azrai M, Singh RP, Crossa J (2017) Use of genomic estimated breeding values results in rapid genetic gains for drought tolerance in maize. Plant Genome 10(1). https://doi.org/10.3835/ PLANTGENOME2016.07.0070 Volpato L, Bernardeli A, Gomez F (2021) Genomic selection with rapid cycling: current insights and future prospects genomic selection with rapid cycling: current insights and future prospects ARTICLE. Crop Breed Appl Biotechnol 21(S):394721–394735. https://doi.org/10.1590/1984 Wang B, Draye X, Zhuang Z, Zhang Z, Liu M, Lubbers EL, Jones D, May OL, Paterson AH, Chee PW (2017) QTL analysis of cotton fiber length in advanced backcross populations derived from a cross between Gossypium hirsutum and G. mustelinum. TAG Theor Appl Genet 130(6):1297–1308. https://doi.org/10.1007/S00122-­017-­2889-­1 Wang J, Zhou Z, Zhang Z, Li H, Liu D, Zhang Q, Bradbury PJ, Buckler ES, Zhang Z (2018) Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits. Heredity 121(6):648–662. https://doi.org/10.1038/s41437-­018-­0075-­0 Warfield CY, Davis RM (1996) Importance of the husk covering on the susceptibility of corn hybrids to fusarium ear rot. Plant Dis 80(2):208–210. https://doi.org/10.1094/PD-­80-­0208 Yang CJ, Sharma R, Gorjanc G, Hearne S, Powell W, Mackay I (2020) Origin specific genomic selection: a simple process to optimize the favorable contribution of parents to progeny. G3: Genes Genomes Genet 10(7):2445–2455. https://doi.org/10.1534/G3.120.401132 Zhang X, Pérez-Rodríguez P, Burgueño J, Olsen M, Buckler E, Atlin G, Prasanna BM, Vargas M, Vicente FS, Crossa J (2017) Rapid cycling genomic selection in a multiparental tropical maize population. G3: Genes Genomes Genet 7(7):2315–2326. https://doi.org/10.1534/ G3.117.043141

Transcriptional Factor: A Molecular Switch to Adapt Abiotic Stress Mechanisms in Maize Muhammad Qudrat Ullah Farooqi, Sanathanee Sachchithananthan, Muhammad Afzal, and Zahra Zahra

1 Introduction Abiotic stress comprises of nonliving factors that negatively affect living organisms in a defined environment. The nonliving variables affect the physiology of the organism beyond normal environmental conditions (Zorilla and Vidriero 2017). Some of the variables that affect maize are drought, high and cold temperatures, the toxicity of minerals, and the salinity of the soil. These are the major causes of reduced yields worldwide. Abiotic stress on maize harms the economy as maize is an important food crop. Some plants have been able to adapt to newer environments due to evolutionary changes. Some species have demonstrated to be relatively resistant to harsh environmental elements than others (Brahmane 2017). Maize, which is scientifically known as Zea mays, is a major food crop that is cultivated worldwide. Maize production has significantly heightened from 1968 to 2017  – yielding over a million tons. Production of maize has steadily grown in developed and developing countries. Africa and South America are the leading in the cultivation of maize worldwide. Maize is consumed by human beings, utilized in the manufacture of animal feeds and production of corn syrup (Stephanie 2017). M. Q. U. Farooqi (*) UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia S. Sachchithananthan School of Molecular Science, The University of Western Australia, Perth, WA, Australia M. Afzal College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia Z. Zahra Department of Civil & Environmental Engineering, University of California-Irvine, Irvine, CA, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_6

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In the past years, maize has been identified as a resource in the biofuel and bioenergy industries. Abiotic stress such as drought, high and cold temperatures, toxicity of minerals, and salinity of the soil have adversely affected maize production (Beals and Byl 2013). Accumulation of metabolites has enabled the maize to respond positively to the abiotic and biotic factors. Metabolic reprogramming of plants is the initial response triggered to mitigate changes in ions, accumulation of callose. The mitogen-activated protein kinase (MAPK) is another way associated with the abiotic stress response. Activation of MAPK cascades with the identification of arousal initiating the dominance of stress ways of the plant. Abscisic acid is another pathway used in abiotic stress control. Increased accumulation of abscisic acid triggers a stress response under abiotic conditions (Des Marais and Juenger 2010). Numerous gee transcripts and proteins are altered in abiotic stress as the plant tries to regulate gene expression and turnover of protein; available genomic information, data and tools are widely used to increase maize production.

2 Transcriptional Factors The fundamental TF families, particular TF qualities, and regulons that are concerned with abiotic stress guidelines are momentarily discussed. Incredible accentuation is applied on maize abiotic stress advancement all through the survey, albeit different models from maize will be utilized. Two groups classify the abiotic stress-induced genes. One group is represented by gene coding that triggers cells to reject stress from the environment like enzyme synthesis proline, betamine, osmotic regulatory protein, and late embryogenesis abundant. The other group comprises of gene-regulating proteins that indicate transduction networks like molecular chaperones, TF, kinases, and functional proteins. Plant gene regulation by transcription is restrained using a network of TFs that contain transcriptional factor binding sites (Tran and Mochida 2015). TFs consist of two domains which are the activation domain and a DNA binding domain. Binding of the TF element through the support of the binding domain through the promoter region of a stress-induced plant enables the activation of the gene which is made possible by the binding of the binding domain. Gene regulation is achieved through TF activation which suppresses the activity of RNA polymerase. Various family species have been classified through DNA-DB transcriptional factors. A complex gene regulatory network controls response to stress from abiotic factors through a complex pathway at the transcriptome level. Abiotic stress in plants is majorly regulated by a gene regulatory system (Azevedo and Mazzafera 2013). Abscisic acid functions to coordinate the abiotic stress the plants face when exposed to environmental factors. Receptive oxygen species, for example, hydrogen peroxide and superoxide which are delivered because of oxidative anxieties, hinder photosynthesis and cause huge cell obliteration. ROS (reactive oxygen species) are ordinarily taken out

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quickly by antioxidative instruments, albeit this evacuation can be obstructed by the anxieties prompting an expansion in fixation in the cellular elements and expanding harm that was induced (Azevedo and Mazzafera 2013). Alternative ways associated with stress reactions in plants is the mitogen-enacted protein kinase (MAPK) falls. MAPK falls are enacted following the acknowledgment and impression of stress boosts and control the pressure reaction pathways. They are exceptionally moderated and are liable for transduction of signal in different cell measures in various abiotic stress reactions. The primary part in the blend of abiotic stress is assumed by pressure reactions that are associated with MAPK (Kumar and Singh 2017). The distinctive pressure, resilient, and responsive TFs regularly work freely although there is a likelihood that some degree of cross-connect happens between these TFs. Numerous examinations depicted ABA-autonomous and ABA-­ subordinate paths that could meet at a few sudden focuses. These places of assembly address enablers and transcriptional muffle cooperating by implication and straightforwardly with DRE rehash and a reactive ABA component and consequently start interactive associations among the ABA reaction and pressure (Ramachandiran and Pazhanivelan 2016). The previously articulated TF families’ feature concentrated in various significant model frameworks of plants and food crops. Much advancement has been accomplished in the comprehension of the transcriptional guideline, transduction of the signal, and quality articulation in reactions to abiotic stresses. In maize, for instance, overexposure of a NAC transcriptional family encryption quality, SNAC1, came about in expanded harvests and resilience in direction of dry spell in plants that are transgenic (Feng 2017). Overexposure of NAC TF which is a glycine assigned as GsNAC019 Arabic genus brought about plants which were lenient to soluble pressure at the germination and development phases, albeit plants that are transgenic that bore diminished affectability. Essentially, practical investigation of a NAC transcriptional family quality assigned as PbeNAC1 uncovered quality that is engaged with some guideline of dry and cold season pressure resistance. Moreover, a chickpea stress-related TF, CarNAC4, connected with decreased MDA substance and pressure from water levels in light of a dry and salty season pressure separately. Vescio et al. (2020) depicted the exposure of a finger millet bZIP TF quality EcbZIP17 in tobacco plants brought about an increase in germination and biomass expansion, with expanded endurance levels in plants. Moreover, tobacco plants similarly showed amplified production of seeds contrasted with the check plants. Exposure of OsMYB55 which is a rice MYB encryption quality in maize of transgenic nature brought about better plant development which diminished damaging impacts on the dry spell, and increased temperature has shown that CiMYB3 and CiMYB5 derived from Cichorium genus were associated with the fructan path debasement because of different stresses (Guerra-Peraza and Nguyen 2011). Exposure of bananas of an MYB TF quality assigned as MpMYBS3 altogether increased resilience to pressure in the plants. Somewhere else, an MYB TF quality assigned as MtMYBS had the option of upgrading salt and dry spell resistance in transgenic Arabic genus by increasing the essential development of the roots.

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In the WYKY TF quality family, OsWRKY71 was found to go about as a confident controller to cold pressure resilience by managing a few current qualities like WSI76 and OsTGFR. Infection-actuated quality quieting of the GhWRKY6 quality from sunflower prompted expanded affectability to different abiotic stresses in the hushed plants. SIDRW1 and SLWRKY39 that are WRKY transcriptional family presented abiotic pressure resilience in carrots by actuating abiotic stress and disease-­ridden qualities (Mohammed and Mohammed 2019). Regulons and transcriptional factors and associated with abiotic guidelines from other transcriptional factors have additionally recognized and portrayed. For example, in Euphrates, popular, external articulation of PeHLH35 having a place with the bHLH TF brought a huge increase in retention of water, resilience via alterations in a few logical cycles, stomatal thickness, and record rate (Thomson et al. 2007). In tomatoes, exposure of a cycling D of component that is assigned as CDF3 brought about expanded mass creation with better returns in tomato plants exposed to a salty environment.

3 TFs and the Specific Target Genes Involved in Abiotic Stress Tolerance in Maize 3.1 MYC and MYB Regulon MYB and MYC groups of a transcriptional factor of proteins are assorted capacities and found in flora and fauna MYB, and MYC transcriptional factors take part in ABA-subordinate paths associated with abiotic strain flagging. Primary MYB transcriptional factor quality in flora was recognized in maize. It was assigned as C1, which represents c-MYB-like transcriptional factor which is engaged in the synthesis of water-soluble pigments (Avin-Wittenberg 2018). Each transcriptional factor comprises of a MYB space having 1–3 defective rehashes and is composed of 53 amino corrosive buildups which contains a helix-helix adaptation that intervenes the significant sections of DNA. MYB and MYC transcriptional factors are generally engaged with building up normal regulons that are referred to as MYB and MYC. In the maize genome (Vescio et al. 2020), detailed 72 MYB-related proteins dissected the articulation of 46 MYB qualities of maize, in light of different abiotic strains which discovered 23 qualities which reacted to a distinctive pressure condition. Moreover, 16 qualities were incited because of at least two anxieties. These outcomes proposed that these qualities could partake in transduction of signal paths engaged with abiotic strain reactions. Furthermore, the capacity of ZmMYB30 that was essentially managed in dry season, salty, and ABA difficulties was additionally examined.

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Abiotic stress-related TF families, in conjunction with specific TFs in maize Family DREB/ CBF

MYB/ MYC

bZIP

Cis-element TFs in maize recognition ZmDREBA2A DRE TACCGACAT ZmDBP3 DRE TACCGACAT ZmDREB1A DRE CRT G/ ACCGAC ZmMYB30 MYBR TAACNA/G ZmMYB31 N/A ZmbZIP60

ACGTGGC

ZmbZIP17

N/A

WRKY

ZmWRKY17

HD ZIP

Zmhdz10

W-box TTGACC/T CAATAATTG

Stress response Heat, salt, drought and cold

Downstream genes Rd29A, rd29B and ZmGOLS2

Cold and salt Drought and cold Salt, drought and ABA Sensitivity to UV radiation Dehydration, high salinity, drought, ABA, and heat stress Drought, ABA, heat and salt Drought and salt Salt and ABA

KIN1, KIN2, CORISA, etc. RD20, RD29A and RbohD ZmF5H and ZmCOMT ZmF5H and ZmCOMT ZmF5H and ZmCOMT Bhlh92, KIN1, and DREB1F AB11, RD22, and P5C51

Exogenous formulation of ZmMYB30 in the mouse ear animated resilience in salty condition and raised the outflow of seven abiotic strain-relating qualities RD29A (ABF3, AB15, DREB2A, MYB2, RD20, RD29B, and ATGolS2) empowering plants more leniency in unfavorable ecological environment. Also, six qualities (LEA14, RAB18, P5CS1 RD22, RbohF, and RbohD) were unaltered and marginally raised in the transgenic mouse ear plant (Finnegan and Brown 2013). Alternative type of maize MYB transcriptional factor, ZmMYB31, was discovered to curb synthesis of C15H16O9 – prompting expanded affectability to ultraviolet light and dwarfism in plants (Fornalé et al. 2010). Besides, ZmMYB31 actuated various pressure-responsive qualities (ZmF5H, C3H, ZmActin, and ZmCOMT) in maize and 4CL1 and COMT qualities in transgenic mouse ear plant. The jobs of maize MYB-associated qualities because of dry season stress were inspected dependent on microarray information. The maize has 18,000 GeneChip in which 26 test groups appeared to compare to 32 MYB-associated qualities (5 tests addressed a single quality). Advanced examination of the exceptionally comparative grouping information uncovered that most qualities related to MYB were communicated at abject degree seven though some appearance was because of particular pressure (Fornalé et al. 2010). Elsewhere, a quality investigation between two maize assortments, a dry spell delicate (Ye478) assortment and a dry season lenient (Han21) assortment, was discovered to be practically the same.

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4 The AP2 and EREBP Regulons AP2 and EREBP (ET-responsive component restricting protein) is composed of an enormous plant gathering explicit transcriptional factors which are described in front of an exceptionally monitored AP2 and ET-reactive component restricting element (ERF). It communicates straightforwardly with the GCC and potentially DRE-rehash component (CRT) which is the advertisement showcasing objective qualities. AP2/EREBP transcriptional factors assume fundamental parts exposed to pressure reactions, formative cycles like cell expansion, plant chemical reactions, and abiotic stress (Stephen 2018). Given the similitude areas, AP2 and EREBP transcriptional factors are assembled in four fundamental categories: ERF, RAV (identified with AB13/VP1), DREB, and AP2.

5 NAC Transcriptional Factors and Regulons Transcriptional factors in the NAC family address the biggest flora-explicits. In the principal crop species, countless NAC transcriptional factors were dissected and queued in the genome level. It incorporates a 100 individuals in carrots and 116 in mouse ear plant (Essien and Stoeckert 2010), 200 individuals in the Chinese cabbage, and 152 individuals in maize. The TFs having a place with NAC family dividing a significantly saved N end comprised of amino corrosive buildups, comprising a DNA domain bind that conveys five spaces from A to E which is a differing C-terminal.

6 bZIP TFs: AREB/ABF Regulon This regulon is a maintained security-acting component limited to the essential leucine zipper domain (bZIP) transcriptional factors. ABA initiated qualities initially were contented with Abre. The bZIP TFs have a place in the biggest and expanded transcriptional factor groups in plants (Fujita et al. 2012). The regulons are divided into ten categories dependent on the essential district grouping similitudes and the existence of themes.

7 Alternative TFs and Their Regulons The five principles of transcriptional factor categories depicted various transcriptional factors that participate in different jobs in fauna (Essien and Stoeckert 2010). This includes managing reactions in biotic and in abiotic burdens in different

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development and advancement measures. As of late, broad exploration has revealed pressure-relieving parts of various transcriptional factors whose reactions to abiotic strains were already obscured. Homeodomain-leucine zipper (HD-Zip) building blocks address an enormous transcriptional factor which is explicit to plants. Proteins have been recreated by portraying a few significant yields and in plants such as rice and mouse ear (Singh and Laxmi 2015).

8 Designing of TFs New disclosure of transcriptional factors as possible instruments of control and designing in numerical qualities, for example, dry season and saltiness, have touched off the advancement of novel advances dependent on TFs and profiting quality disclosure as well as harvest improvement. Designing TF action was a significant objective in this process, a bearing that guarantees a better future by adjusting the metabolic paths. For instance, exposure of DREB2 brought about little pressure resistance in improvement since they are made out of spaces that curtail the enlistment of their objective qualities (Mazzafera 2013).

9 Post-genomics and Current Approaches Abiotic strains address a blend of different characteristics comprising a quantitative example of legacy. To productively comprehend the plant’s reaction to distinctive stresses at the subatomic phase (Mazzafera 2013), more profound comprehension of frameworks associated with record guidelines is required. Quality planning, practical portrayal, genomic choice, quick DNA and RNA genotyping devices, and making advances in different stages are presently utilized in investigating hereditary instruments of various stresses including dry and cold seasons and salty conditions with an end goal to accelerate the rearing interaction in maize.

10 Conclusion A projection on the worldwide population by the year 2050 is projected to be nine billion persons. Environmental changes being experienced currently have accelerated the improvement of food crops so as to get better yields. The subatomic system and qualities of stress response to abiotic reactions to plants are of importance. Advancement of technology has propagated development of resilient crops that are able to withstand adverse environmental conditions.

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References Avin-Wittenberg T (2018) Autophagy and its role in plant abiotic stress management. Plant Cell Environ 42(3):1045–1053. https://doi.org/10.1111/pce.13404 Azevedo R, Mazzafera P (2013) Publishing new and valuable information on abiotic stress responses in plants. Ann Appl Biol 163(3):319–322. https://doi.org/10.1111/aab.12062 Beals C, Byl T (2013) Chemiluminescent examination of abiotic oxidative stress of plants. Environ Toxicol Chem. https://doi.org/10.1002/etc.2314 Brahmane M (2017) Impact of rearing temperatures on maize growth, development and gene expression. J Environ Biol 38(6):1261–1266. https://doi.org/10.22438/jeb/38/6/mrn-­440 Des Marais D, Juenger T (2010) Pleiotropy, plasticity, and the evolution of plant abiotic stress tolerance. Ann N Y Acad Sci 1206(1):56–79 Essien K, Stoeckert C (2010) Conservation and divergence of known apicomplexan transcriptional regulons. BMC Genomics 11(1). https://doi.org/10.1186/1471-­2164-­11-­147 Feng W (2017) The study of physiological reaction of GPDH in maize seedlings under abiotic stress. Sci Discov 5(4):293 Finnegan P, Brown G (2013) Transcriptional and post-transcriptional regulation of RNA levels in maize mitochondria. Plant Cell 2:71–83. https://doi.org/10.1105/tpc.2.1.71 Fornalé S, Shi X, Chai C, Encina A, Irar S, Capellades M, Fuguet E, Torres JL, Rovira P, Puigdomènech P, Rigau J, Grotewold E, Gray J, Caparrós-Ruiz D (2010) ZmMYB31 directly represses maize lignin genes and redirects the phenylpropanoid metabolic flux. Plant J 64(4):633–644. https://doi.org/10.1111/j.1365-313X.2010.04363.x Fujita K, Morisaki A, Takaoka A, Maeda T, Hori Y (2012) Incidental memory in dogs (Canis familiaris): adaptive behavioral solution at an unexpected memory test. Anim Cognit 15(6):1055–1063. https://doi.org/10.1007/s10071-012-0529-3 Guerra-Peraza O, Nguyen H (2011) ZmCOI6.1, a novel, alternatively spliced maize gene, whose transcript level changes under abiotic stress. Plant Sci 176(6):783–791. https://doi. org/10.1016/j.plantsci.2009.03.004 Kumar A, Singh K (2017) Maize production under abiotic stress conditions: an empirical analysis. J AgriSearch 4(2) Mazzafera P (2013) Publishing new and valuable information on abiotic stress responses in plants. Ann Appl Biol 163(3):319–322. https://doi.org/10.1111/aab.12062 Mohammed S, Mohammed M (2019) Effect of abiotic stress on irrigated maize forage yield as compared to sorghum. J Hortic Plant Res 6:27–36. https://doi.org/10.18052/www.scipress. com/jhpr.6.27 Ramachandiran K, Pazhanivelan S (2016) Abiotic factors (nitrogen and water) in maize: a review. Agric Rev 37(4) Singh D, Laxmi A (2015) Transcriptional regulation of drought response: a tortuous network of transcriptional factors. Front Plant Sci 6. https://doi.org/10.3389/fpls.2015.00895 Stephanie A (2017) Molecular strategies for development of abiotic stress tolerance in plants. Cell Cellular Life Sci J 2(2). https://doi.org/10.23880/cclsj-­16000113 Stephen A (2018) Role of AP2/EREBP transcription factor family in environmental stress tolerance. Cell Cellular Life Sci J 3(1). https://doi.org/10.23880/cclsj-­16000120 Thomson J, Mundree S, Farrant J (2007) The development of genetically modified maize for abiotic stress tolerance. S Afr J Bot 73(3):494–495. https://doi.org/10.1016/j.sajb.2007.04.033 Tran L, Mochida K (2015) Identification and prediction of abiotic stress responsive transcription factors involved in abiotic stress signaling in soybean. Plant Signal Behav 5(3):255–257. https://doi.org/10.4161/psb.5.3.10550 Vescio R, Abenavoli M, Sorgonà A (2020) Single and combined abiotic stress in maize root morphology. Plan Theory 10(1):5. https://doi.org/10.3390/plants10010005 Zorilla F, Vidriero L (2017) Regulation of root system behavior by abiotic stress. SDRP J Plant Sci 1(1). https://doi.org/10.25177/jps.1.1.3

Physiological and Biochemical Responses in Maize under Drought Stress Suphia Rafique

1 Introduction Global warming led to rise in temperatures and severe water scarcity or drought stress, which drastically hamper plant growth, development, and yield every year in major crops of the world. Mostly, the drought obstructs plant growth and development and reduces biomass accumulation (Farooq et al. 2009). The other main consequences of drought stress are reduction in CO2 assimilation rate (Kaiser 1987), reduced chlorophyll content (Rong-hual et al. 2006), reduction in leaf size due to reduced cell expansion and cell division (Verelst et al. 2013; Tardieu et al. 2000) and stomatal closure (Cochard 2002), and low water use efficiency (WUE) (Hatfield and Dold 2019). The other massive physiological changes in plants are reduction in shoot growth (Pace et  al. 1999), delayed flowering time (Pantuwan et  al. 2002; Angus and Moncur 1997; Dwyer and Stewart 1987), and fresh/dry weight (Liu et al. 2011). Drought also affects crop phenology (Bindiger et al. 1987) and induces early transition from the vegetative to the reproductive phase (Desclaux and Roumet 1996) leading to altered crop growth cycle. The essential phytohormone abscisic acid (ABA), which is generated in response to drought stress and is crucial for plants to respond to stress (Yamaguchi-Shinozaki and Shinozaki 2006; Cui et al. 2017), increased ABA level leads to stomatal closure (Wilkinson and Davies 2010) and prevent water loss, and increase water use efficiency (Chaves et al. 2009)thus water deficiency signals perceived by ABA receptors (PYR/RCAR) (He et al. 2018). As the plants received the water deficiency signals, it initiates the physiological and biochemical adaptation strategies. The drought resistance mechanism can be grouped into three categories: (i) drought escape (complete life cycle before drought affects its survival), (ii) drought avoidance (endurance with increased internal water S. Rafique (*) Department of Biotechnology, Faculty of Chemical and Life Sciences, Jamia Hamdard, New Delhi, Delhi, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_7

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content and preventing tissue damage), and (iii) drought resistance (endurance with low internal water content, whereas sustaining growth over the drought period) (Maiti and Satya 2014; Bänziger et al. 2000; Song et al. 2016). Therefore, the first cellular water loss by drought stress promotes the drought signals to produce stress-­ protected metabolites such as proline, trehalose, sugars, and alcohols and also triggers production of enzymatic antioxidant system (CAT, SOD, ascorbic peroxidase) and nonenzymatic antioxidant (vitamin C, glutathione, and tocopherols) to prevent acute cellular damage and to maintain membrane integrity and redox homeostasis (Mittler 2002; Ashraf 2009; Halimeh et al. 2013; Waseem et al. 2015; Kosar et al. 2015). Wheat, rice, and maize are the three primary crops grown globally (FAOSTAT 2010). Maize usually requires 500–800 mm during life cycle (Critchley and Klaus 1991). The main effect of water scarcity throughout its life cycle is to reduce its water and N use efficiencies, resulting in substantial yield losses (Ashraf et  al. 2016). Heisey and Edmeades (1999) estimated that 20–25% of the global maize planting area is affected by drought yearly. In maize, grain yield reduction caused by drought ranges from 10% to 76% depending on the severity and stage of occurrence (Bolaños et al. 1993). All vegetative and yield parameters were significantly affected by water shortage. At vegetative growth period, short-duration water deficits caused 28–32% loss of final dry matter weight. In a similar way, single irrigation omission during tasselling and cob formation stages (sensitive stages) may cause 30% and 40% grain yield loss during dry years (Cakir 2004). As the water availability decreases, the biomass production also decreases. Kamara et al. (2003) revealed that water deficit imposed at various developmental stages of maize reduced total biomass accumulation at silking by 37%, at grain-filling period by 34%, and at maturity by 21%. Thus, drought stress severely hampers the growth and productivity of maize (Liu et al. 2010; Ge et al. 2012; Talaat et al. 2015). It triggers different changes in crop plants through various morphological, physiological, and biochemical responses (Ahammed et al. 2020; Jan et al. 2019; Hussain et al. 2020). Anjum et  al. (2011a) indicated that drought stress in maize led to considerable decline in net photosynthesis (33.22%), transpiration rate (37.84%), stomatal conductance (25.54%), water use efficiency (50.87%), intrinsic water use efficiency (11.58%), and intercellular CO2 (5.86%) as compared to control plants. Adverse conditions in maize plants evoked various mechanisms to deal with the stressful environment such as antioxidant capabilities, osmotic adjustment, reduction in photosynthetic rates, and ABA accumulation (Gong et al. 2014; Sah et al. 2016). The varied physiological, morphological secondary traits have been identified for improving the drought tolerance in maize (Edmeades et al. 1997; Bänziger et al. 2000; Monneveux et al. 2008). Thus, drought-associated physiological and metabolic modifications could be used as stress indicators for measuring the susceptibility or tolerance of a plant in response to water stress environments (Alharby and Fahad 2020). This chapter contains information on physio-biochemical aspects of drought tolerance mechanism of maize crop in particular. The maize leaves have special anatomy (Kranz anatomy), the PCR cycle is operative in bundle sheath (BS) chloroplast, and PCR cycle enzymes are very sensitive to H2O2. The Bundle Sheath tissues sustain the majority of the oxidative damage due to insufficient antioxidant

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defense in this tissue. The drought affects the vegetative and reproductive phase. However, after drought, stress recovery periods play an important role in drought adaptation. The effects of drought stress can be reduced through priming and application of beneficial microorganism. However, in present climate change scenario and for long-term food security, there is a need to develop the crop varieties which can sustain growth and sustainable production in drought conditions.

2 Morpho-physiological Changes Under Drought Stress Drought stress affects the morpho-physiology of maize at both the cellular and whole-plant levels. The primary effects of drought include decreased plant height, reduced leaf elongation, and induced leaf withering (Habben et al. 2014), perturb root system, leaf rolling, reduction in photosynthetic rate, stomatal conductance, assimilate translocation, all these reduced growth rate and plant dry biomass (Bänziger et al. 2000; Yin et al. 2010; Zhang et al. 2011). Maize crop is extremely sensitive to drought stress (Farre et al. 2000). Early studies exhibited that the various morpho-physiological characteristics showed distinctive responses to drought stress, such as root development, stomatal activity, osmotic adjustment, abscisic acid, and proline levels in the whole plant (Li and Van Staden 1998a, b; Selmani and Wasson 2003). In a study of eight maize cultivars, drought stress significantly decreased shoot fresh and dry weights, root fresh and dry weights, and chlorophyll pigments (a and b) (Shafiq et al. 2019). The proportional decrease of shoot biomass was greater than the proportional decrease in root biomass, leading to an increase in the root/shoot ratio as water deficit stress increased at all growth stages (Benjamin et  al. 2014). In six different maize hybrids under drought stress, drought affects plant height and chlorophyll content (except for one NPE4) and causes a rise in leaf temperature in all hybrids (Witt et al. 2012). On comparing the two cultivars for growth response and some physiological characteristics, O’Regan et  al. (1993) observed that drought-resistant cultivar had a higher growth rate and deeper rooting, higher transpiration rate and lower diffusive resistance during the onset of water stress, and higher relative water content and levels of abscisic acid and proline throughout the period of water stress than the drought-sensitive cultivar. In maize, water deficit shows clearly a concerted downregulation of NR activity and photosynthesis (Foyer et al. 1998).

2.1 Priming Improved Physiological Process Priming is one of the most important physiological methods which improves the seed performance and provides faster and coordinated germination (Nawaz et al. 2013). The seeds have been primed to reduce the time between seed sowing and seedling emergence, thus to synchronize emergence processes (Parera and Cantliffe

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1994). However, the response of tolerant and sensitive maize cultivars was not remarkable differentiae when foliar spray of α-tocopherol (0 mmol and 50 mmol) at vegetative stage on two maize cultivars (Agaiti-2002 and EV-1098) improved the growth of both cultivars, besides improvements in photosynthetic pigment, water relations, antioxidative mechanism, and better nutrient acquisition in root and shoot along with tocopherol contents and a decrease in lipid peroxidation (Ali et al. 2020). Furthermore, after foliar application, the tocopherol level increases in roots because of basipetal translocation and thus induction of drought tolerance of maize associated with tissue-specific improvements in antioxidative defense mechanism through its translocation. Likewise, the seedlings of a drought-tolerant (NK8711) and drought-sensitive (P1574) maize hybrid were foliar sprayed with various SNP (sodium nitroprusside) doses (0, 50, 100, 150, and 200 μM) under drought stress conditions. Foliar spray of 100 μM markedly improved water status and chlorophyll contents and alleviated drought-induced oxidative damages through increased antioxidant (catalase, ascorbate peroxidase, and superoxide dismutase) activities in both maize hybrids (Majeed et  al. 2018), whereas maize seed presoaking in ascorbic acid (AsA) or salicylic acid (SA) solutions resulted in massive increase in growth parameters, chlorophyll contents, osmo-protectants (soluble sugars, free amino acids, and soluble proteins), antioxidant enzyme activity [ascorbate peroxidase (APOX) and superoxide dismutase (SOD)], and nonenzymatic antioxidants [carotenoids and glutathione (GSH)] content as compared to control. Conversely, proline, catalase (CAT), and malondialdehyde (MDA) content were decreased significantly (Loutfy et al. 2020). Application of melatonin with the root-irrigation method and the leaf-spraying method on maize seedlings improved the photosynthetic activities and alleviated the oxidative damages under the drought stress. Compared with the leaf-spraying method, the root-irrigation method was more effective on enhancing drought tolerance (Huang et al. 2019). In contrast, some of the studies indicated distinct response of tolerant and susceptible genotypes on pretreatment of seeds. Two maize cultivars drought resistance PAN (6043) and drought sensitive (SC 701) in different concentrations of uniconazole, brassinolide, and methyl jasmonate enhanced and maintained a higher relative water content and diffusive resistance and decreased the relative conductivity and transpiration rate in the seedlings of the drought-resistant cultivar, PAN 6043, whereas treatments have the opposite effect on seedlings of the drought-sensitive cultivar (which decreased the relative water content) (Li and Van Staden 1998a, b). In maize, the bio-­stimulant Kappaphycus alvarezii seaweed extract (KSWE) was applied foliarly only once at the grain-filling stage in moderate and severe stress. There was lesser degree of oxidative stress in KSWE-treated plants. There is a decrease in lipid peroxidation and increase in activities of antioxidant enzymes and nonenzymatic antioxidant like GSH (glutathione) and proline (Trivedia et al. 2018).

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2.2 Effect of Drought and Recovery Period on Maize Plant responses to water scarcity are complex processes; plants either involve in adaptive changes or have a deleterious effect. It also depends on the intensity and duration of stress as well as plant species and the stage of development (Chaves et  al. 2003). The physiological responses of the maize (Zea mays L.) cultivars (Doge, Vero and Luce) to drought stress and recovery were determined. Growth of all cultivars was retarded under drought stress conditions and regained speed during the recovery stage. Although many physiological parameters decrease (FM, FV/ FM, FV′/FM′, ϕPSII and qL, and an increase of non-photochemical quenching (NPQ)), they regain in all the three cultivars after recovery period except in Doge, although Doge has the ability to withstand drought with better upregulating its protective mechanisms such as increasing NPQ, chla/b ratio (smaller antenna size), and anthocyanin and proline content decreasing FV′/FM′ compare to other two cultivars. As a result of that, Doge was classified as less drought tolerant but others as tolerant (Efeoglu et al. 2009). Further, natural gradual drought stress was applied to maize inbred lines, and seedlings were observed for growth and various physiological responses for drought stress and recovery. Drought induced decrease in all the physiological parameters (leaf water content, water potential, osmotic potential, gas exchange parameters, chlorophyll content, Fv/Fm and nitrogen content, and increased H2O2 accumulation and lipid peroxidation). However, after recovery, most of these physiological parameters rapidly returned to normal levels. Although drought resistance-related parameters were leaf water potential and chlorophyll content while chlorophyll content and Fv/Fm were associated with drought recovery, hence, drought recovery and drought resistance are the major causes of maize seedling drought adaptation. In addition, leaf water potential, chlorophyll content, and Fv/Fm could be used as efficient reference indicators in the selection of drought-­ adaptive genotypes (Chen et al. 2016). Likewise maize (Zea mays L.) plants of two inbred lines were subjected to two cycles of drought and rewatering. Metabolic pathways in the maize plants returned to their normal status at different rates during recovery. The results provide valuable insight into the growth, biochemical, and metabolic mechanisms used by maize to adapt to cyclic drought (Sun et al. 2016). Also growth limitation depends on the number of drought cycles and either full or partial recovery of growth. Moreover, to overcome from cyclic drought stress, maize plants adjusted their leaf spectral properties and employ growth and biochemical strategies and recover from drought stress after rewatering. However, the extent of plant growth recovery after rewatering may depend on plant genotype and the number of consecutive drying cycles (Sun et al. 2018).

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2.3 Physiological Changes in Vegetative/Reproductive Phase Under Drought Several reports have shown that water demand of maize crop is less at early vegetative growth (Shaw 1977; Cakir 2004). However, Cakir (2004) explained that water deficit decline in plant extension growth and reduction of leaf size at vegetative stage reduced the grain yield up to 40%. Maize hybrids with higher levels of antioxidant enzyme activities both at vegetative and reproductive stages showed higher resistance to drought stress and produced higher yield under stress (Ghahfarokhi et al. 2016). The reproductive stage of maize has higher water requirement (Kranz et al. 2008); water stress just before anthesis, during silking and seed filling periods, reduces the yield potential (NeSmith and Ritchie 1992; Cakir 2004; Musick and Dusek 1980; Moser et al. 2006). The drought stress relied on drought intensity and duration, with more severe drought stress creating more serious effects on maize. The responses of maize (Zea mays L.) in different water deficit conditions were examined; the moderate stress during the silking and blister stages has no significant change in the relative water content (RWC) but significant changes in the relative conductivity (RC) of the leaves. However, severe stress significantly decreases the leaf RWC and increases membrane permeability (leaf relative conductivity). Furthermore, under severe drought stress, antioxidant enzyme activities declined significantly in later stages, namely, for superoxide dismutase (SOD) during the tasseling and blister stages, for peroxidase (POD) during the milk stage, and for catalase (CAT) during the tasseling, blister, and milk stages. Meanwhile, membrane lipid peroxidation (measured as malondialdehyde content) significantly increased in all stages (Bai et  al. 2006). A transgenic maize plant with increased ZmNFYB2 expression shows tolerance to drought based on the responses of a number of stress-­ related parameters, including chlorophyll content, stomatal conductance, leaf temperature, reduced wilting, and maintenance of photosynthesis. These stress adaptations contribute to a grain yield advantage to maize under water-limited environments (Nelson et  al. 2007). Mansouri-Far et  al. (2010) evaluated the yield response of two maize hybrids at vegetative and reproductive stage under water stress and normal conditions. Water stress minimized the characteristics such as leaf greenness, relative water content, grain yield, and 100 kernel weight but maximized proline content; 100 kernel weight was considered highly sensitive to water stress. Bolaños et al. (1993) emphasize the importance of secondary traits in increasing selection efficacy for grain yield under severe drought stress during eight cycles of recurrent full-sib selection in the lowland tropical maize (Zea mays L.) population. Secondary traits like increased relative stem and leaf elongation rate (REL), delayed foliar senescence, reduced canopy temperatures, and reduced ASI increased efficacy of selection for grain yield under drought. However, Bruce et al. (2002) in their review article emphasized that reproductive success can be achieved through better partitioning of biomass to the developing ear results in faster spikelet growth; this in turn reduced the number of spikelet formed on the ear that facilitates overall seed set by reducing carbon and water constrains per spikelet. Multiple, independent

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transgenic hybrids (transgenic gene-silencing approach was used to modulate the levels of ethylene biosynthesis in maize (Zea mays L.)) were tested in field trials at managed drought stress- and rain-fed locations for its effect on grain yield. The yield performance in transgenic hybrids had significantly increased with a 0.58 Mg/ ha (9.3 bushel/acre) increase after a flowering period drought stress, whereas analysis of secondary traits showed that there was a consistent decrease in the anthesis silking interval and a concomitant increase in kernel number/ear in transgenes (Habben et al. 2014). A drought-resistant maize plant is characterized by reduced plant height, smaller tassels, smaller leaves above the ear, erect leaves, larger stem diameter, stay-green, and deeper rooting with less lateral branching and less root biomass, compared to a drought-susceptible phenotype (Ribaut et al. 2008).

3 Biochemical Changes Under Drought Stress 3.1 Metabolic Changes and Oxidative Defense Mechanism for Drought Tolerance Maize plant most sensitive to drought stress is often used as model crop to assess the impact of drought tolerance (Anjum et al. 2011a). In C4 plants, the antioxidative enzymes are distributed between mesophyll and bundle sheath cells (Foyer 2002), whereas H2O2 was found to accumulate only in mesophyll cells (Doulis et  al. 1997). Perhaps the PCR cycle is operative in bundle sheath chloroplast, and enzymes of PCR cycle are very sensitive to H2O2; thus, it is suggested that oxidative damage under stressful conditions in C4 plants is restricted to bundle sheath tissue because of inadequate antioxidant protection in this tissue (Kingston-Smith and Foyer 2000). The key reaction of plants in drought stress is the production of reactive oxygen species (ROS) such as superoxide, hydroxyl radicals, hydrogen peroxide, and singlet oxygen (Jiang and Zhang 2004; Ashraf 2009; Ali and Ashraf 2011). A study shows maize cultivars under water deficit conditions (eight maize cultivars) significantly increased free proline, glycinebetaine (GB), total phenolics, hydrogen peroxide (H2O2), malondialdehyde (MDA) contents, activities of enzymatic antioxidants (CAT, POD, and SOD), and ascorbic acid (AsA) contents (Shafiq et al. 2019). Some recent work have been cited in Table  1. Further, Chugh et  al. (2011) reported drought-tolerant genotypes LM5, and Prakash exhibits the effective antioxidant defense mechanism which is capable to quench H2O2 and superoxide radicles. Also, these genotypes accumulate less MDA and H2O2, together with high ascorbic acid content during stress conditions. These ROS cause oxidative damage to membrane lipid structure, DNA, and proteins (Nair et al. 2008; Ashraf 2009, Anjum et al. 2011a), while tolerant plants accumulate less MDA (malondialdehyde) and increase the regulation of antioxidant enzymes (Teisseire and Guy 2000; Zhang et al. 2007). Similarly, Anjum et  al. (2016) examine two maize cultivars (Rung Nong 35 and Dong Dan 80) under progressive drought stress. Higher tolerance to drought stress

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Table 1  Physio-biochemical responses of maize genotypes in drought stress during the period from 2018 to 2021 Maize genotypes B73 and MO17

Stress stage Physio-biochemical response 27 days before Increase activity of SOD, CAT, GB, flowering proline, and MDA. Enzymatic and nonenzymatic antioxidant and organic osmolytes increase significantly in both lines. SOD activity and GB and proline content are potential biochemical indicators of drought resistance MO17 line Tolerant cultivar Increase in root growth indices, H2O2 (Karoon) and content, and antioxidant enzyme susceptible activity. Tolerant cultivars increase (260) polyamine oxidase activity and putrescine synthesis rate. B104 line Vegetative and Higher photosynthesis rate, antioxidant reproductive enzyme activity, better membrane stability, and less electrolyte leakage Drought-tolerant Seedling roots Tolerant variety strong water retention (Chang 7-2) and capacity, synergistic effect of sensitive antioxidant enzymes, strengthened cell (TS141) wall, osmotic stabilized PM proteins, varieties effective recycling amino acid, and improved lignification Madura local Germinating Higher antioxidant capacities (ABTS), maize variety stage hydroxyl radical scavenging and antioxidant enzyme CAT and APX activities. All plays an important role in germination phase Hybrid At sixth leaf Mild stress adaptive response-activation Shaanke 9 stage antioxidant system and photorespiration Severe stress damage photosynthetic apparatus Hybrid Xida At tasseling Xida 889 greater tolerance, strong 889 and Xida stage 15 days antioxidant defense system, osmolyte 319 accumulation; maintain photosynthesis pigments and nutrient balance Inbred line Seedling stage Drought-tolerant-287M higher SOD DT-287M and (roots) and ascorbate peroxidase activity than DS-753F drought-susceptible-753F Maize Hybrid Seedling to Moderate drought increase in leaf Naudi maturity chlorophyll and carotenoid content and K/Na ratio Eight Two weeks Osmoprotectants like proline and GB Commercial after can be used as stress tolerance indicator cultivars germination

Reference Moharramnejadet al. (2019)

Ahangir et al. (2020)

Wang et al. (2018)

Zeng et al. (2019)

A. R. H Dani and T. A. Siswoyo

Li et al. (2021)

Hussain et al. (2019)

Zheng et al. (2020)

Leila Romdhane et al. (2019) Shafiq et al. (2019)

Abbreviations: SOD superoxide dismutase, CAT catalase, GB glycine betaine, MDA malondialdehyde, APX ascorbate peroxidase, H2O2 hydrogen peroxide

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was observed in Dong Dan 80, and this was associated with higher photosynthetic activity, osmolyte accumulation, and antioxidant activities and lower lipid peroxidation as compared with Run Nong 35. Overall, the cultivar Dong Dan 80 was better able to resist the detrimental effects of progressive drought stress. Plants respond to drought stress through various physiological and biochemical changes. Exposure to drought leads to cellular dehydration; in response, plants increase the production of specific sets of primary and secondary metabolites that act as osmo-protectants and osmolytes (Ashraf and Akram 2009). These accumulated solutes lower the cellular osmotic potential and draw water into the cell to maintain turgor pressure. Moreover, osmo-protectants preserve the cellular apparatus from the damage caused by dehydration, without interfering with the normal metabolic processes at the cellular level (Van Oosten et  al. 2017). Plants also produced several amines (polyamines and glycinebetaine), amino acids (proline), soluble sugars (glucose, sucrose, trehalose), and polyols (mannitol, sorbitol, and inositol) (Singh et  al. 2015). An elite maize inbred line DH4866 was transformed with the beta gene from Escherichia coli encoding choline dehydrogenase (EC 1.1.99.1), a key enzyme in the biosynthesis of glycine betaine from choline. The transgenic maize plants accumulated higher levels of glycine betaine and were more tolerant to drought stress than wild-type plants (non-transgenic) at germination and the young seedling stage. Most importantly, the grain yield of transgenic plants was significantly higher than that of wild-type plants after drought treatment. The enhanced glycine betaine accumulation in transgenic maize provides greater protection of the integrity of the cell membrane and greater activity of enzymes compared with wild-type plants in conditions of drought stress (Quan et al. 2004). However, some crops have low levels of these compounds; to increase the level of the manipulation of genes involved in osmo-protectant biosynthesis pathways is one of the strategies to improve stress tolerance in plants (Reguera et al. 2012). Furthermore, the mechanism of plant acclimation to stress depends on the metabolic plasticity as well as biosynthesis and accumulation of osmo-­protective compounds. A general increase in metabolite levels under drought stress was observed in maize plants, including changes in amino acids, sugars, sugar alcohols, and intermediates of the TCA cycle (Witt et al. 2012). The metabolic pattern of different maize tissues like leaf blade, leaf sheath, ear, husk, and silks showed most contrasting metabolic pattern due to drought stress treatment. Also among all tissues, the leaf blade displaying the most considerable metabolome changes due to water deficiency. The effect of drought stress on water relation has been manifested at cellular as well as whole plant level. Maize leaf provides an excellent experimental model for molecular studies of the developing tissues (Avramova et al. 2015). Tolerant maize hybrids experience a smaller impact of drought on cell division due to a smaller reduction of leaf meristem size and number of dividing cells. The leaf meristems of these hybrids are better protected during the stress, particularly due to a higher activity of the redox-regulating enzymes CAT, POX, APX, and GR, resulting in less H2O2 production in these zones, allowing improved growth under drought conditions (Avramova et al. 2017).

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3.2 Priming Improves Biochemical Mechanism of Drought Tolerance Seed priming is the control of hydration of seeds in water or a solution of low osmotic potential to initiate the germination metabolism without radical protrusion. Seed priming is known to trigger the normal metabolic developments during early stage of germination, before the radicle protrusion (Hussain et al. 2019). Presoaking maize grains with methyl jasmonate (MeJA) led to increases in total carbohydrates, total soluble sugar, polysaccharides, and free amino acids, proline, and total protein content. Moreover, the application of the investigated MeJA significantly improved growth hormone in terms of IAA. In contrast ABA level was markedly declined in maize plant. The activities of oxidative CAT, POX, and SOD were also increased with MeJA (Abdelgawad et al. 2014). Two maize (Zea mays L.) cultivars, that is, Shaandan 9 (S9) and Shaandan 911 (S911), were treated exogenous glycinebetaine (GB). The foliar application of GB increased the concentrations of all osmolytes measured, DM and GY of both cultivars under DS.  These positive responses of exogenous GB spray were more pronounced in S911 as compared to those in S9 (Zhang et al. 2009). The combined pretreatments with exogenous MeJA+SA mitigated the adverse effects of drought-induced oxidative stress, as reflected in lower levels of lipid peroxidation, LOX activity, and H2O2. Exogenous applications of MeJA+SA approximately doubled the activities of the antioxidant enzymes catalase, peroxidase, and superoxide dismutase. The same pretreatment also maintained adequate water status of the plants under drought stress by increasing osmolytes including proline, total carbohydrate content, and total soluble sugars. The results show that seed and foliar pretreatments with exogenous MeJA and/or SA can have positive effects on the responses of maize seedlings to drought (Tayyab et al. 2020). Exogenous application of ascorbic acid in two maize cultivars Agaiti-2002 (tolerant) EV-1098 (sensitive) lowered the drought stress-induced reduction in growth, fresh and dry biomass, and photosynthetic pigments. Application of AsA further enhanced the activity superoxide dismutase (SOD) and peroxidase (POD) enzymes in maize plants. The results indicate that foliar application of AsA alleviated the detrimental effects of drought stress in the maize plants by improving the antioxidative defense system (Noman et al. 2015). In response to drought stress and to counteract reactive oxygen species, leaves and roots showed significant transcriptional upregulation of glutathione synthesis (GSH1) and reduction (GR). However, the growth of leaves arrested due to low flux of sulfur from sulfate into cysteine and glutathione of drought-stressed plants, ultimately resulting in enhanced oxidative stress, which together contribute to growth arrest of leaves. The low flux of sulfur into glutathione is a result of decreased SERAT activity and low sulfate availability. In contrast, roots accumulate sulfate to support sulfide, cysteine, and glutathione formation and maintain growth. The results evidence a significant and organ-­specific impact of drought upon sulfate assimilation in the staple crop maize. We conclude that the antagonistic regulation of sulfur metabolism in leaves and roots enables a successful drought stress response at the whole plant level (Ahmad et  al. 2016). Exogenous application of Spermidine (Spd) on two maize cultivars, Xianyu 335 (drought resistant) and Fenghe 1 (drought susceptible), reduces oxidative damage

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by enhancing the antioxidant components, raising the redox state of ascorbate and glutathione, and altering the polyamine pool; the improvement is much greater in Xianyu 335 (drought resistant) than Fenghe 1 (drought susceptible) (Li et al. 2018).

4 Role of Biotic Factors to Modify Physiological and Biochemical Process in Drought Tolerance Water deficit foremost restricts the plant growth, development, and yield mainly in arid and semiarid regions causing economic loss in agriculture. Plant growth-­ promoting rhizobacteria are habituated in adverse environmental conditions; plants were often inoculated with beneficial microorganisms because they protect plants with drought and promote plant growth (Glick et al. 1997; Timmusk and Wagner 1999; Marulanda et al. 2007, 2008). Bacterial cells under drought stress build up compatible solutes like amino acids, quaternary amines, and sugars that stop deteriorating processes and enhance cell development in unfavorable osmotic conditions (Potts 1994). The PGPR-induced tolerance is termed as “induced systemic tolerance” (IST) (Yang et al. 2009). In a study, the effect of inoculated maize plants with genetically engineered Azospirillum brasilense strains that over-accumulate trehalose was analyzed. Eighty-five percent of maize plants inoculated with transformed A. brasilense survive drought tolerance through increase in leaf and root biomass and higher accumulation of trehalose compared to wild-type A. brasilense that did not accumulate significant levels of the disaccharide (Rodríguez-Salazar et al. 2009). Moreover, seed bacterization of maize with three EPS-producing bacterial strains (Proteus penneri (Pp1), Pseudomonas aeruginosa (Pa2), and Alcaligenes faecalis (AF3)) in combination with their respective EPS showed improved soil moisture contents, plant biomass, root and shoot length, and leaf area. Under drought stress, the inoculated plants showed increase in relative water content, protein, and sugar though the proline content and the activities of antioxidant enzymes were decreased (Naseem and Bano 2014). Similarly, maize seedlings inoculated with Bacillus spp. showed physiological response that could alleviate drought stress-negative effect on osmoregulation by increased proline, sugars, and free amino acids and decreased electrolyte leakage. Inoculation reduced the activity of antioxidant enzymes ascorbate peroxidase, catalase, and glutathione peroxidase (Vardharajula et al. 2011). During infertile or drought conditions, maize is an effective host for arbuscular mycorrhizae (AM) (Boomsma and Vyn 2008). So far few studies have been conducted to examine the morpho-physiological effects of AM infection on maize drought tolerance. The symbiotic relationship between AM and roots increases the productivity of various crops including maize (Sylvia et  al. 1993). AM fungi often alter rates of water influx and efflux in host plants, thus affecting tissue water content and leaf physiology. Other impacts of AM symbiosis involve changes in stomatal conductance (gs) and transpiration (T), typically higher, while conductance (gs) unaffected or greater during drought stress in AM relative to non-AM plants (Augé 2001) also delays reductions in leaf water potential (Cw) during periods of drought stress. The role of biotic factors is listed in Table 2.

Table 2  Inoculation with beneficial microorganism and changes in physio-biochemical responses of maize crop under drought stress Beneficial microorganism G. intraradices (arbuscular mycorrhizal AM) Pseudomonas spp.

Azospirillum lipoferum produced ABA and GA Azospirillum strain Az19 more tolerant than Az39 Pseudomonas putida strain FBKV2 B. thuringiensis or AM fungi or combination of both Pseudomonas putida, Pseudomonas sp., and Bacillus megaterium Azospirillum

Bacterial endophytes-Burkholderia Phytofirmans strain PsJN and Enterobacter sp. FD17 Azotobacter strains (PGPR) Az63, Az69, and Az70 Bacillus pumilus spp. + L-tryptophan Azospirillum lipoferum strain Arbuscular mycorrhizal fungi (AMF) and PGPR Arthrobacter arilaitensis and Streptomyces pseudo venezuelae (actinomycetes) Bacillus spp. strains

Physiological biochemical changes Enhance leaf water potential and RWC and recover in less time after rewatering Improved plant biomass, RWC, leaf ψ, soil/ root tissue ratio, high proline, sugars and free amino acids, decreased electrolyte leakage, and low antioxidant enzyme activity Alleviate water stress effects on maize plants

References Subramanian et al. (1997) Sandhya et al. (2010)

Osmotic stress tolerance

García et al. (2017) Vurukonda et al. (2016)

Better growth in shoot, root length, and dry biomass. Also improved cellular metabolites and stomatal conductance Bt increase accumulation of nutrients. Combination of both (Bt + AM fungi) reduced oxidative damage; AM fungi maintain homeostatic or improved tolerance P. putida and B. megaterium increased osmotic stress tolerance, proline content, shoot and root biomass, and water content. Pseudomonas spp. decrease IAA production Accumulates trehalose

Bacterial inoculation minimize the drought effects; increase in shoot/root biomass, leaf area, chlorophyll content, photosynthesis, and PSII efficiency Increase shoot dry weight; plant height; chlorophyll content; and N, P, and Fe concentrations Increase RWC, osmotic potential, protein content, and photosynthetic pigments Accumulates more free amino acids and sugars. Also increase shoot/root fresh weight, dry weight, and length Improves crop growth, yield, and drought tolerance Protects deleterious effects of drought and increase in physiological parameters

Increases in dry biomass, plant height, and K+ and P+ uptake. Accumulates proline and modulates antioxidant system by decreasing ascorbate and glutathione reductase activity P. putida strain FBKV2 Colonized roots; modulate metabolic, transcriptomic analysis signaling, and stress-responsive genes Endophytic (PGPE) bacteria Elevated the morphological variables, relative Enterobacter cloacae water content, and antioxidant activity 2WC2 strain

Armada et al. (2015)

Marulanda et al. (2009)

Rodríguez-­ Salazar et al. (2009) Naveed et al. (2013)

Shirinbayan et al. (2019) Yasmin et al. (2021) Bano et al. (2013) Zoppellari et al. (2014) Chukwuneme et al. (2020) Moreno-Galván et al. (2020)

Ali et al. (2018) Maqbool et al. (2021)

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5 Conclusions and Future Prospects Water constrains reduced the plant growth, development, and productivity. Plants are sessile; they adapt through avoidance or developing resistance against the drought conditions. Maize plants’ resistance mechanism involves the scavenging of ROS, free radicals, hydroxyl group, and H2O2 and thus maintains the metabolic homeostatic. Although priming has a significant effect on the drought-tolerant mechanism, the major shortcoming of this technique is due to different types of priming agents having different properties and effectiveness. Therefore, each plant species needs to be optimized for the priming solutions (Horii et al. 2007). Similarly, the potential benefits of biotic factors in drought tolerance are restricted to certain drought-prone, infertile regions of the tropics (i.e., portions of Africa and Asia) in which maize production does not commonly involve intensive irrigation and fertilizer application. Therefore, the long-term strategies include the development of drought-tolerant crop varieties. Maize is the staple food crop over the world’s most regions, since long CIMMYT has been engaged to produce climate-resilient maize germplasm for increased tolerance to traits associated with a variable and changing climate, along with yield potential, defensive traits, and consumer-preferred traits. Further, to increase genetic gain and enhance the breeding progress for stress-prone environment, effective integration of modern tools and techniques is required like high throughput and precision, phenotyping, DH technology, and molecular marker-­ assisted breeding. Another way to develop drought-tolerant crops is through biotechnological approach; recently, many genes that are related to plant response to various abiotic stresses have been identified and described. To develop the drought-­ tolerant crop through genetic engineering, manipulation of single gene that affects the specific target can be employed. Overexpression of genes is associated with the accumulation of osmolytes, proteins, and enzymes of antioxidant system; ion transporters can be utilized to develop the novel drought resistance genotype. Therefore, selection of the drought-tolerant germplasm based on the physio-biochemical based may be helpful to develop the drought tolerance varieties.

References Abdelgawad ZA, Khalafaallah AA, Abdallah M (2014) Impact of methyl jasmonate on antioxidant activity and some biochemical aspects of maize plant grown under water stress condition. Agric Sci 5:1077–1088 Ahangir A, Ghotbi-Ravandi AA, Rezadoost H, Bernard F (2020). Drought tolerant maize cultivar accumulates putrescine in roots. Rhizosphere 16:100260 Ahammed GJ et al (2020) The SlWRKY81 transcription factor inhibits stomatal closure by attenuating nitric oxide accumulation in the guard cells of tomato under drought. Physiol Plantarum 172:885–895 Ahmad N, Malagoli M, Wirtz M, Hell R (2016) Drought stress in maize causes differential acclimation responses of glutathione and sulfur metabolism in leaves and roots. BMC Plant Biol 16(1):247

130

S. Rafique

Alharby HF, Fahad S (2020) Melatonin application enhances biochar efficiency for drought tolerance in maize varieties: modifications in physio-biochemical machinery. Agron J 112:2826–2847 Ali Q, Ashraf M (2011) Induction of drought tolerance in maize (Zea mays L.) due to exogenous application of trehalose: growth, photosynthesis, water relations and oxidative defence mechanism. J Agron Crop Sci 197:258–271 Ali SkZ, Vardharajula S, Vurukonda SSKP (2018) Transcriptomic profiling of maize (Zea mays L.) seedlings in response to Pseudomonas putida stain FBKV2 inoculation under drought stress. Ann Microbiol 68:331–349 Ali Q, Javed MT, Haider MZ, Habib N, Rizwan M, Perveen R, Ali S, Alyemeni MN, El-Serehy HA, Al-Misned FA (2020) α-Tocopherol Foliar Spray and Translocation Mediates Growth, Photosynthetic Pigments, Nutrient Uptake, and oxidative defense in maize (Zea mays L.) under drought stress. Agronomy 10(9):1235 Angus JF, Moncur MW (1997) Water stress and phenology in wheat. Aust J Agric Res 28:177–181 Anjum SA, Farooq M, Wang LC, Xue LL, Wang SG, Wang L, Zhang S, Chen M (2011a) Gas exchange and chlorophyll synthesis of maize cultivars are enhanced by exogenously-applied glycine betaine under drought conditions. Plant Soil Environ 57:326–331 Anjum SA, Tanveer M, Ashraf U, Hussain S, Shahzad B, Khan I, Wang L (2016) Effect of progressive drought stress on growth, leaf gas exchange, and antioxidant production in two maize cultivars. Environ Sci Pollut Res 23(17):17132–17141 Ashraf M (2009) Biotechnological approach of improving plant salt tolerance using antioxidants as markers. Biotechnol Adv 27:84–93 Ashraf M, Akram NA (2009) Improving salinity tolerance of plants through conventional breeding and genetic engineering: an analytical comparison. Biotechnology advances, 27(6):744–752 Ashraf U, Salim MN, Sher A, Sabir SR, Khan A, Pan SG et al (2016) Maize growth, yield formation and water-nitrogen usage in response to varied irrigation and nitrogen supply under semi-­ arid climate. Turk J Field Crops 21:87–95 Augé RM (2001) Water relations, drought and vesicular-arbuscular mycorrhizal symbiosis. Mycorrhiza 11:3–42 Avramova V, Sprangers K, Beemster GTS (2015) The maize leaf: another perspective on growth regulation. Trends Plant Sci 20:787–797 Avramova V, AbdElgawad H, Vasileva I, Petrova AS, Holek A, Mariën J, Asard H, Beemster GTS (2017) High antioxidant activity facilitates maintenance of cell division in leaves of drought tolerant maize hybrids. Front Plant Sci 8:84 Armada E, Azcón R, López-Castillo OM, Calvo-PolancoJuan M, Ruiz-Lozano M (2015) Autochthonous arbuscular mycorrhizal fungi and Bacillus thuringiensis from a degraded Mediterranean area can be used to improve physiological traits and performance of a plant of agronomic interest under drought conditions. Plant Physiol Biochem 90:64–74 Bai LP, Sui FG, Ge TD (2006) Effect of soil drought stress on leaf water status, membrane permeability and enzymatic antioxidant system of maize. Pedosphere 16(3):326–332 Bano Q, Ilyas N, Bano A, Zafar N, Akram A, Ul Hassan F (2013) Effect ofAzospirillum inoculation on maize (Zea mays l.) under drought stress. Pak J Bot 45:13–20 Bänziger M, Edmeades GO, Beck D, Bellon M (2000) Breeding for drought and nitrogen stress tolerance in maize: from theory to practice. CIMMYT, Mexico Benjamin JG, Nielsen DC, Vigil MF, Mikha M, Calderon F (2014) Water deficit stress effects on corn (Zea mays L.) Root shoot ratio. Open J Soil Sci 4:151–160 Bindiger FR, Mahalakshmi V, Rao GDP (1987) Assessment of drought resistance in pearl millet (Pennisetum americanum (L.) Leeke). I. Factors affecting yields under stress. Aust J Agric Res 38:37–48 Bolaños J, Edmeades GO, Martinetz L (1993) Eight cycles of selection for drought tolerance in lowland tropical, maize. III. Responses in drought adaptive physiological and morphological traits. Field Crops Res 31:269–286

Physiological and Biochemical Responses in Maize under Drought Stress

131

Boomsma CR, Vyn TJ (2008) Maize drought tolerance: potential improvements through arbuscular mycorrhizal symbiosis? Field Crop Res 108:14–31 Bruce WB, Edmeades GO, Barker TC (2002) Molecular and physiological approaches to maize improvement for drought tolerance. J Exp Bot 53(366):13–25 Cakir R (2004) Effect of water stress at different development stages on vegetative and reproductive growth of maize. Field Crops Res 89:1–16 Chaves MM, Maroco JP, Pereira JS (2003) Understanding plant responses to drought—from genes to the whole plant. Funct Plant Biol 30:239–264 Chaves MM, Flexas J, Pinheiro C (2009) Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell. Ann Bot 103:551–560 Chen D, Wang S, Cao B, Cao D, Leng G, Li H, Yin L, Shan L, Deng X (2016) Genotypic variation in growth and physiological response to drought stress and re-watering reveals the critical role of recovery in drought adaptation in maize seedlings. Front Plant Sci 6:1241 Chugh V, Kaur N, Gupta AK (2011). Evaluation of oxidative stress tolerance in maize (Zea mays L.) seedlings in response to drought. Ind J Biochem Biophys 48:47–53 Chukwuneme CF, Babalola OO, Kutu FR, Ojuederie OB (2020) Characterization of actinomycetes isolates for plant growth promoting traits and their effects on drought tolerance in maize. J Plant Interact 15(1):93–105 Cochard H (2002) Xylem embolism and drought-induced stomatal closure in maize. Planta 215:466–471 Critchley W, Klaus S (1991) A manual for the design and construction of water harvesting schemes for plant production. Available at: http://www.fao.org/docrep/u3160e/4316eo4.htm Cui G, Zhao X, Liu S, Sun F, Zhang C, Xi Y (2017) Beneficial effects of melatonin in overcoming drought stress in wheat seedlings. Plant Physiol Biochem 118:138–149 Desclaux D, Roumet P (1996) Impact of drought stress on the phenology of two soybean (Glycine max L. Merr) cultivars. Field Crops Res 46:61–70 Doulis AG, Debian N, Kingston-Smith AH, Foyer CH (1997) Differential localization of antioxidants in maize leaves. Plant Physiol 114:1031–1037 Dwyer LM, Stewart DW (1987) Influence of photoperiod and water stress on growth, yield and development rate of barley measured in heat units. Can J Plant Sci 67:21–34 Edmeades GO, Bänziger M, Elings A, Chapman SC, Ribaut JM (1997) Recent advances in breeding for drought tolerance in maize. In: Kropff MJ et al (eds) Applications of systems approaches at the field level. Systems approaches for sustainable agricultural development, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-­94-­017-­0754-­1_5 Efeoglu B, Ekmekçi Y, Ciçek N (2009) Physiological responses of three maize cultivars to drought stress and recovery. South Afr J Bot 75(1):34–42 FAOSTAT (2010) Statistical databases and data-sets of the Food and Agriculture Organization of the United Nations. Retrieved from http://faostat.fao.org/default.aspx Farooq M, Basra SMA, Wahid A, Ahmad N, Saleem BA (2009) Improving the drought tolerance in rice (Oryza sativa L.) by exogenous application of salicylic acid. J Agron Crop Sci 195:237–246 Farre I, Van Oijen M, Leffelaar PA, Faci JM (2000) Analysis of maize growth for different irrigation strategies in northeastern Spain. Eur J Agron 12:225–238 Foyer CH (2002) The contribution of photosynthetic oxygen metabolism to oxidative stress in plants. In: Inze D, Montago MV (eds) Oxidative stress in plants. Taylor and Francis Publishers, New York, pp 33–68 Foyer CH, Valadier MH, Migge A, Becker TW (1998) Drought induced effects on nitrate reductase activity and mRNA and on the coordination of nitrogen and carbon metabolism in maize leaves. Plant Physiol 117:283–292 García JE, Maroniche G, Cecilia C, Suárez-Rodríguez R, Ramirez-Trujillo JA, Groppa MD (2017) In vitro PGPR properties and osmotic tolerance of different Azospirillum native strains and their effects on growth of maize under drought stress. Microbiol Res 202:21–29

132

S. Rafique

Ge TD, Sui FG, Bai LP, Tong CL, Sun NB (2012) Effects of water stress on growth, biomass partitioning, and water-useefficiency in summer maize (Zea mays L.) throughout the growth cycle. Acta Physiol Plant 34:1043–1053 Ghahfarokhi MG, Mansouri-Far C, Saeidi M, Abdoli M (2016) Different physiological and biochemical responses in maize hybrids subjected to drought stress at vegetative and reproductive stages. Acta Biologica Szegediensis 60(1):27–37 Glick BR, Liu C, Ghosh S, Dumbroff EB (1997) The effect of the plant growth promoting rhizobacterium Pseudo monas putida GR 12-2 on the development of canola seedlings subjected to various stresses. Soil Biol Biochem 29:1233–1239 Gong F, Yang L, Tai F, Hu X, Wang W (2014) “Omics” of maize stress response for sustainable food production: opportunities and challenges. OMICS J Integr Biol 18(12):1–19 Habben JE, Bao X, Nicholas JB, DeBruin JL, Dennis D, Hasegawa D, Timothy GH, Lafitte RH, Lovan N, Mo H, Reimann K, Schussler JR (2014) Transgenic alteration of ethylene biosynthesis increases grain yield in maize under field drought-stress conditions. Plant Biotechnol J 12:685–693 Halimeh R, Mahlagh G, Maryam P, Pazoki A (2013) Effect of drought interactions with ascorbate on some biochemical parameters and antioxidant enzymes activities in Dracocephalum moldavica L. Middle East J Sci Res 13:522–531 Hatfield JL, Dold C (2019) Water-use efficiency: advances and challenges in a changing climate. Front Plant Sci 10:103 He Z, Zhong J, Sun X, Wang B, Terzaghi W, Dai M (2018) The maize ABA receptors ZmPYL8, 9, and 12 facilitate plant drought resistance. Front Plant Sci 9:422 Heisey PW, Edmeades GO (1999) Maize production in drought stressed environments: technical options and research resource allocation. Part 1 of CIMMYT 1997/98 world maize facts and trends; maize production in drought-stressed environments: technical options and research resource allocation. CIMMYT, Mexico Horii A, McCue P, Shetty K (2007) Seed vigour studies in corn, soybean and tomato in response to fish protein hydrolysates and consequences on phenolic-linked responses. Bioresour Technol 98:2170–2177 Huang B, Chen Y-E, Zhao Y-Q, Ding C-B, Liao J-Q, Hu C, Zhou L-J, Zhang Z-W, Yuan S, Yuan M (2019) Exogenous melatonin alleviates oxidative damages and protects photosystem II in maize seedlings under drought stress. Front Plant Sci. https://doi.org/10.3389/fpls.2019.00677 Hussain S, Hussain S, Khaliq A, Ali S, Khan I (2019) Physiological, biochemical, and molecular aspects of seed priming. In: Hasanuzzaman M, Fotopoulos V (eds) Priming and pretreatment of seeds and seedlings. Springer Nature Singapore Pte Ltd., Singapore, pp 43–62. https://doi. org/10.1007/978-­981-­13-­8625-­1_3 Hussain HA et al (2020) Maize tolerance against drought and chilling stresses varied with root morphology and antioxidative defense system. Plan Theory 9:720 Jan S, Abbas N, Ashraf M, Ahmad P (2019) Roles of potential plant hormones and transcription factors in controlling leaf senescence and drought tolerance. Protoplasma 256:313–329 Jiang M, Zhang J (2004) Abscisic acid and antioxidant defense in plant cells. Acta Bot Sin 46:1–9 Kaiser WM (1987) Effects of water deficit on photosynthetic capacity. Physiol Plant 71:142–149 Kamara AY, Menkir A, Badu-apraku B, Ibikunle O (2003) The influence of drought stress on growth, yield and yield components of selected maize genotypes. J Agric Sci 141:43–50 Kingston-Smith AH, Foyer CH (2000) Bundle sheath proteins are more sensitive to oxidative damage than those of the mesophyll in maize leaves exposed to paraquat or low temperature. J Exp Bot 51:123–130 Kosar F, Akram NA, Ashraf M (2015) Exogenously applied 5-aminolevulinic acid modulates some key physiological characteristics and antioxidative defense system in spring wheat (Triticum aestivum L.) seedlings under water stress. S Afr J Bot 96:71–77 Kranz WL, Irmak S, van Donk SJ, Yonts CD, Martin DL (2008) Irrigation management for co Rn. Neb Guide (G1367-A), University of Nebraska Extension. Available at: http://ianrpubs.unl. edu/live/g1850/build/g1850.pdf

Physiological and Biochemical Responses in Maize under Drought Stress

133

Li L, Van Staden J (1998a) Effects of plant growth regulators on the antioxidant system in callus of two maize cultivars subjected to water stress. Plant Growth Regul 24:55–66 Li L, Van Staden J (1998b) Effects of plant growth regulators on drought resistance of two maize cultivars. S Afr J Bot 64(2):116–120 Li L, Gu W, Li C, Li W, Li C, Li J, Wei S (2018) Exogenous spermidine improves drought tolerance in maize by enhancing the antioxidant defence system and regulating endogenous polyamine metabolism. Crop Pasture Sci 69(11):1076–1091 Li H-J, Wang Y-F, Zhao C-F, Yang M, Wang G-X, Zhang R-H (2021) The quantitative proteomic analysis provides insight into the effects of drought stress in maize. Photosynthetica 59(1):1–11 Liu Y, Li S, Chen F, Yang S, Chen X (2010) Soil water dynamics and water use efficiency in spring maize (Zea mays L.) fields subjected to different water management practices on the Loess Plateau. China Agric Water Manag 97:769–775 Liu H, Wang X, Wang D, Zou Z, Liang Z (2011) Effect of drought stress on growth and accumulation of active constituents in Salvia miltiorrhiza bunge. Ind Crop Prod 33:84–88 Loutfy N, Azooz M, Abou Alhamd MF (2020) Exogenously-applied salicylic acid and ascorbic acid modulate some physiological traits and antioxidative defense system in Zea mays L. seedlings under drought stress. Egypt J Bot 60(1):313–324 Maiti RK, Satya P (2014) Research advances in major cereal crops for adaptation to abiotic stresses. GM Crops Food 5(4):259–279 Majeed S, Nawaz F, Naeem M, Ashraf MY (2018) Effect of exogenous nitric oxide on sulfur and nitrate assimilation pathway enzymes in maize (Zea mays L.) under drought stress. Acta Physiol Plant 40(206):1–12 Mansouri-Far C, Mohammad SA, Sanavy M, Saberali SF (2010) Maize yield response to deficit irrigation during low-sensitive growth stages and nitrogen rate under semi-arid climatic conditions. Agric Water Manag 97:12–22 Maqbool S, Ammna, Maqbool A, Mehmood S, Suhaiba M, Tariq S, Farooq M, Munis H, Shafiq-urRehman, Chaudhary HJ (2021) Interaction of Acc deaminase and antioxidant enzymes to induce drought tolerance in enterobacter cloacae 2wc2 inoculated maize genotypes. Pak J Bot 53(3) Marulanda A, Porcel R, Barea JM, Azcón R (2007) Drought tolerance and antioxidant activities in lavender plants colonized by native drought-tolerant or drought-sensitive Glomus species. Microb Ecol 54:543–552 Marulanda A, Azcón R, Ruíz-Lozano JM, Aroca R (2008) Differential effects of a Bacillus megaterium strain on Lactuca sativa plant growth depending on the origin of the arbuscular mycorrhizal fungus coinoculated: physiologic and biochemical traits. J Plant Growth Regul 27:10–18 Marulanda A, Barea J-M, Azcón R (2009) Stimulation of plant growth and drought tolerance by native microorganisms (AM Fungi and Bacteria) from dry environments: Mechanisms related to bacterial effectiveness. J Plant Growth Regul 28:115–124 Mittler R (2002) Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci 7:405–410 Moharramnejadi S, Sofalian O, Valizadeh M, Asghari A, Shiri MR, Ashraf M (2019) Response of maize to field drought stress: Oxidative defense system, Osmolytes accumulation and Photosynthetic pigments. Pak J Bot 51(3):799–807 Monneveux P, Sanchez C, Tiessen A (2008) Future progress in drought tolerance in maize needs new secondary traits and cross combinations. J Agric Sci 146:1–14 Moreno-Galván A, Romero-Perdomo FA, Estrada-Bonilla G, Meneses CHSG, Bonilla RR (2020) Dry-caribbean Bacillus spp. strains ameliorate drought stress in maize by a strainspecific antioxidant response modulation. Microorganisms 8(6):823 Moser SB, Feil B, Jampatong S, Stamp P (2006) Effects of pre-anthesis drought, nitrogen fertilizer rate, and variety on grain yield, yield components, and harvest index of tropical maize. Agric Water Manag 81:41–58 Musick LT, Dusek DA (1980) Irrigated corn yield response to water. Trans ASAE 23:92–98

134

S. Rafique

Nair AS, Abraham TK, Jaya DS (2008) Studies on the changes in lipid peroxidation and antioxidants in drought stress induced Cowpea (Vignaunguiculata L.) varieties. J Environ Biol 29:689–691 Naseem H, Bano A (2014) Role of plant growth-promoting rhizobacteria and their exopolysaccharide in drought tolerance of maize. J Plant Interact 9(1):689–701 Naveed M, Mitter B, Reichenauer TG, Wieczorek K, Sessitsch A (2013) Increased drought stress resilience of maize through endophytic colonization by Burkholderia phytofirmans PsJN and Enterobacter sp. FD17. Environ Exp Bot 97:30–39 Nawaz J, Hussain M, Jabbar A, Nadeem GA, Sajid M, Subtain MU, Shabbir I (2013) Seed priming a technique. Int J Agric Crop Sci 6(20):1373–1381 Nelson DE, Repetti PP, Adams TR, Creelman RA, Wu J, Warner DC, Anstrom DC, Bensen RJ, Castiglioni PP, Donnarummo MG et al (2007) Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved corn yields on water-limited acres. Proc Natl Acad Sci U S A 104(42):16450–16455 NeSmith DS, Ritchie JT (1992) Short- and long-term responses of maize to a pre-anthesis soil water deficit. Agron J 84:107–113 Noman A, Ali S, Naheed F, Ali Q, Farid M, Rizwan M, Irshad MK (2015) Foliar application of ascorbate enhances the physiological and biochemical attributes of maize (Zea mays L.) cultivars under drought stress. Arch Agron Soil Sci 61(12):1659–1672 O’Regan BP, Cress WA, van Staden J (1993) Root growth, water relations, abscisic acid and proline levels of drought-resistant and drought-sensitive maize cultivars in response to water stress. S Afr J Bot 59(1):98–104 Pace PF, Cralle HT, El-Halawany SHM, Cothren JT, Sensaman SA (1999) Drought-induced changes in shoot and root growth of young cotton plants. J Cotton Sci 3:183–187 Pantuwan G, Fukai S, Cooper M, Rajatasereekul S, Toole JCO (2002) Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands 2. Selection of drought resistant genotypes. Field Crop Res 73:169–180 Parera AC, Cantliffe DJ (1994) Pre-sowing seed priming. Hortic Rev 16:109–148 Potts M (1994) Desiccation tolerance of prokaryotes. Microbiol Rev 58:755–805 Quan R, Shang M, Zhang H, Zhao Y, Zhang J (2004) Engineering of enhanced glycine betaine synthesis improves drought tolerance in maize. Plant Biotechnol J 2:477–486 Reguera M, Peleg Z, Blumwald E (2012) Targeting metabolic pathways for genetic engineering abiotic stress-tolerance in crops. Biochim Biophys Acta 1819:186–194 Ribaut J-M, Betrán J, Monneveux P, Setter T (2008) Drought tolerance in maize. In: Bennetzen JL, Hake SC (eds) Handbook of maize: its biology. Springer, New York, pp 311–344 Rodríguez-Salazar J, Suárez R, Caballero-Mellado J, Iturriaga G (2009) Trehalose accumulation in Azospirillum brasilense improves drought tolerance and biomass in maize plants. FEMS Microbiol Lett 296:2–59 Romdhane L, Leila R, Farooq M, Cortivo CD, Panozzo A, Vamerali T (2019) Morphological and biochemical changes in maize under drought and salinity stresses in a semi-arid environment. Plant Biosyst - An International Journal Dealing with all Aspects of Plant Biology. https://doi. org/10.1080/11263504.2019.1635221 Rong-hual LI, Pei-pol GUO, Baumz M, Grando S, Ceccarelli S (2006) Evaluation of chlorophyll content and fluorescence parameters as indicators of drought tolerance in barley. Agric Sci China 5(10):751–757 Sah SK, Reddy KR, Li JX (2016) Abscisic acid and abiotic stress tolerance in crop plants. Front Plant Sci 7:571. https://doi.org/10.3389/fpls.2016.00571 Sandhya V, Ali SZ, Grover M, Reddy G, Venkateswarlu B (2010) Effect of plant growth promoting Pseudomonas spp. on compatible solutes, antioxidant status and plant growth of maize under drought stress. Plant Growth Regul 62:21–30 Selmani A, Wasson CE (2003) Daytime chlorophyll fluorescence measurement in field-grown maize and its genetic variability under well-watered and water stressed conditions. Field Crop Res 31:173–184

Physiological and Biochemical Responses in Maize under Drought Stress

135

Shafiq S, Nudrat AA, Ashraf M (2019) Assessment of physio-biochemical indicators for drought tolerance in different cultivars of maize (Zea mays L.). Pak J Bot 51(4):1241–1247 Shaw RH (1977) Climatic requirement. In: Sprague GF (ed) Maize and maize improvement. Agron. Monogr. 18. ASA, CSSA, and SSSA, Madison, pp 315–341 Shirinbayan S, Khosravi H, Malakouti MJ (2019) Alleviation of drought stress in maize (Zea mays) by inoculation with Azotobacter strains isolated from semi-arid regions. Appl Soil Ecol 133:138–145 Singh R, Mishra A, Dhawan SS, Shirke PA, Gupta M, Sharma A (2015) Physiological performance, secondary metabolite and expression profiling of genes associated with drought tolerance in Withania somnifera. Protoplasma 252:1439–1450 Song W, Zhao H, Zhang X, Lei L, Lai J (2016) Genome-wide identification of VQ motif-­containing proteins and their expression profiles under abiotic stresses in maize. Front Plant Sci 6:1177 Subramanian, KS, Charest C, Dwyer LM, Hamilton RI (1997) Effects of arbuscular mycorrhizae on leaf water potential, sugar content, and P content during drought and recovery of maize. Can J Bot 75:1582–1591 Sun CA, Gao X, Chen X, Jianqi F, Zhang Y (2016) Metabolic and growth responses of maize to successive drought and re-watering cycles. Agric Water Manag 172:62–73 Sun CX, Li C, Zhang CY, Hao LY, Song M, Liu W, Zhang YL (2018) Reflectance and biochemical responses of maize plants to drought and re-watering cycles. Ann Appl Biol 1:14 Sylvia DE, Hammond LC, Bennet JM, Hass JH, Linda SB (1993) Field response of maize to a VAM fungus and water management. Agron J 85:193–198 Talaat NB, Shawky BT, Ibrahim AS (2015) Alleviation of drought-induced oxidative stress in maize (Zea mays L.) plants by dual application of 24-epibrassinolide and spermine. Environ Exp Bot 113:47–58 Tardieu F, Reymond M, Hamard P, Granier C, Muller B (2000) Spatial distributions of expansion rate, cell division rate and cell size in maize leaves: a synthesis of the effects of soil water status, evaporative demand and temperature. J Exp Bot 51:1505–1514 Tayyab N, Naz R, Yasmin H, Asia N, Keyani R, Sajjad M, Hassan MN (2020) Combined seed and foliar pre-treatments with exogenous methyl jasmonate and salicylic acid mitigate drought induced stress in maize. PLoS One 15(5):e0232269 Teisseire H, Guy V (2000) Copper-induced changes in antioxidant enzymes activities in fronds of duckweed (Lemna minor). Plant Sci 153:65–72 Timmusk S, Wagner EGH (1999) The plant growth promoting rhizo-bacterium Paenibacillus polymyxa induces changes in Arabidopsis thaliana gene expression: a possible connection between biotic and abiotic stress responses. Mol Plant-Microbe Interact 12:951–959 Trivedia K, Vijay KG, Vaghel AP, Ghosh A (2018) Differential growth, yield and biochemical responses of maize to the exogenous application of Kappaphycus alvarezii seaweed extract, at grain filling stage under normal and drought conditions. Algal Res 35:236–244 Van Oosten MJ, Pepe O, De Pascale S, Silletti S, Maggio A (2017) The role of biostimulants and bioeffectors as alleviators of abiotic stress in crop plants. Chem Biol Technol Agric 4:5 Vardharajul S, Ali S Z, Grover M, Reddy G, Venkateswarlu B (2011) Drought-tolerant plant growth promoting Bacillus spp.: effect on growth, osmolytes, and antioxidant status of maize under drought stress. J Plant Interact 6(1):1–14 Verelst W, Bertolini E, De Bodt S, Vandepoele K, Demeulenaere M, Inzé MEPD (2013) Molecular and physiological analysis of growth-limiting drought stress in Brachypodium distachyon leaves. Mol Plant 6(2):311–322 Vurukonda SSKP, Vardharajula S, Shrivastava M, Ali SkZ (2016) Multifunctional Pseudomonas putida strain FBKV2 from arid rhizosphere soil and its growth promotional effects on maize under drought stress. Rhizosphere 1:4–13 Wang B, Li Z, Ran Q, Li P, Peng Z, Zhang J (2018) ZmNF-YB16 overexpression improves drought resistance and yield by enhancing photosynthesis and the antioxidant capacity of maize plants. Front Plant Sci 9:709

136

S. Rafique

Waseem H, Bano R, Khatak B, Hussain I, Yousaf M, David U (2015) Temperature sensitivity and soil organic carbon pools decomposition under different moisture regimes: effect on total microbial and enzymatic activity. Clean Soil Air Water 43:391–398 Wilkinson S, Davies WJ (2010) Drought, ozone, ABA and ethylene: new insights from cell to plant to community. Plant Cell Environ 33:510–5250 Witt S, Galicia L, Lisec J et al (2012) Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. Mol Plant 5:401–417 Yamaguchi-Shinozaki K, Shinozaki K (2006) Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol 57:781–803 Yang J, Kloepper JW, Ryu CM (2009) Rhizosphere bacteria help plants tolerate abiotic stress. Trends Plant Sci 14:1–4 Yasmin H, Rashid U, Hassan MN, Nosheen A, Naz R, Ilyas N, Sajjad M, Azmat A, Alyemeni, MN (2021) Volatile organic compounds produced by Pseudomonas pseudoalcaligenes alleviated drought stress by modulating defense system in maize (Zea mays L.). Physiol Planta 172(2):896–911 Yin L, Wang S, Eltayeb AE, Uddin MI, Yamamoto Y, Tsuji W et  al (2010) Overexpression of dehydroascorbate reductase, but not monodehydroascorbate reductase, confers tolerance to aluminum stress in transgenic tobacco. Planta 23:609–621 Zeng W, Peng Y, Zhao X, Wu B, Chen F, Bin Ren, Zhuang Z, Gao Q, Ding Y (2019) Comparative proteomics analysis of the seedling root response of drought-sensitive and drought-tolerant maize varieties to drought stress. Int J Mol Sci 20(11):2793 Zhang, LX, Li SX, Zhang H, Liang ZS (2007) Nitrogen rates and water stress effects on production, lipid peroxidation and antioxidative enzyme activities in two maize (Zea mays L.) genotypes. J Agron Crop Sci 193:387–397 Zhang LX, Li SX, Liang ZS (2009) Differential plant growth and osmotic effects of two maize (Zea mays L.) cultivars to exogenous glycine betaine application under drought stress. Plant Growth Regul 58:297–305 Zhang RH, Xue JQ, Pu J, Zhao B, Zhang XH, Zheng YJ, Bu LD (2011) Effects of drought stress on plant growth and photosynthetic characteristic of maize seedlings. Acta Agron Sin 37(892):521–528 Zheng H, Yang Z, Wang W, Guo S, Li Z, Liu K, NaSui (2020) Transcriptome analysis of maize inbred lines differing in drought tolerance provides novel insights into the molecular mechanisms of drought responses in roots. Plant Physiol Biochem 149:11–26 Zoppellari F, Malusà E, Chitarra W, Lovisolo C, Spanna F, Bardi L (2014). Improvement of drought tolerance in maize (Zea mays L.) by selected rhizospheric microorganisms. Ital J Agrometeorol 18(1):5–18

Current Biotechnological Approaches in Maize Improvement Moutoshi Chakraborty, Saurab Kishore Munshi, Ashraful Haque, Md. Abul Kalam Azad, Tofazzal Islam, Mobashwer Alam, and Muhammad J. A. Shiddiky

1 Introduction Maize or corn (Zea mays L.) occupies a distinct position in global agriculture being a feed, food, and supplier of a wide range of commercially important goods. Following rice as well as wheat, it is the third most major grain crop (FAO 2021). Maize is an annual, single-stalk, and a C4 monoecious plant belonging to the grass M. Chakraborty (*) Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, Bangladesh S. K. Munshi Department of Microbiology, Stamford University Bangladesh, Dhaka, Bangladesh A. Haque Department of Genetics and Plant Breeding, Bangladesh Agricultural University, Mymensingh, Bangladesh M. A. K. Azad Office of the Director (Administration & Support Service), Bangladesh Institute of Nuclear Agriculture( BINA), Mymensingh-2202, Bangladesh T. Islam Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh M. Alam Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Nambour, Queensland, Australia M. J. A. Shiddiky School of Environment and Science (ESC) and Queensland Micro- and Nanotechnology Centre (QMNC), Griffith University, Brisbane, Queensland, Australia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_8

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family Gramineae and subfamily Panicoideae of Maydae tribe, with an increased photosynthesis activity that contributes to high-yield potential for grain and biomass (Edwards 2011). About 8,000 years back, it was domesticated in Central Mexico from a wild, bushy, short grass species known as teosinte (Hake and Ross-­ Ibarra 2015). It is primarily a cross-pollinating crop, which has led to its wide morphological diversity and regional adaptability (Paliwal et al. 2000). About 4.5 billion consumers of 94 low-income countries, comprising 900 million impoverished individuals, rely on it for approximately 30% of their daily calories, along with rice and wheat (Shiferaw et al. 2011; Babu and Prasanna 2014;Maqbool et al. 2021). Maize is also used extensively in the poultry and livestock industries all over the world (Tanumihardjo et  al. 2019). According to the FAO (Food and Agriculture Organization), 55% of maize production was used for feed, 20% for nonfood purposes, and therefore only 12% for food (FAO 2021). Its demands for animal feed might likely to outpace the need for human consumption, especially in Asia where production is anticipated to be double from 165 million tons (Mt) to around 400 Mt by 2030 (Paliwal et al. 2000). To fulfill this growing need, biotechnology is anticipated to play a major role in maize improvement. The utilization of molecular marker technologies has aided rapid advancement in maize breeding. Several generations of DNA detection technologies have been introduced during the last two decades, including simple sequence repeats (SSRs), single nucleotide polymorphisms (SNPs), restriction fragment length polymorphisms (RFLPs), and amplified fragment length polymorphisms (AFLPs). These methods progressed rapidly from theoretical to practical usage in breeding. The ability to the effective application of molecular markers was also aided by the parallel growth of computational capabilities during this period. The utilization of molecular markers has significantly aided transgenic trait introgression into maize commercial germplasm, allowing for quick integration of transgenic features in well-adapted germplasm worldwide (Brookes and Barfoot 2006a; b). The advancement of genome modification technologies like transformation and genome editing has enabled the production of the designed crop with novel traits. Transformation techniques of maize of the early 1990s increased the use of different transgenic methods for maize yield enhancement. In the mid-1990s, the first insect-­ protected maize crops bearing genes conferring tolerance to the European corn borer as well as other lepidopteran insects were marketed. Since then, maize-­ producing areas across the world have adopted first-generation biotechnological features for herbicide tolerance and pest protection. The usage of maize bearing three stacked genes and/or several characteristics for controlling lepidopteran pests and corn rootworm, as well as herbicide resistance for more effective weed management, has risen substantially in recent years. Transgenic maize has been grown on over 30 million hectares of land in 16 nations by 2007 (Brookes and Barfoot 2006a; b). In addition to increased production, these transgenic hybrids of maize assist to reduce greenhouse gas emissions and pesticide application. While the first generation of maize traits has been widely adopted, the next generation, which is currently being developed, offers even greater potential. These characteristics are intended to help maize thrive in drought circumstances, use nitrogen more effectively, produce

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better yields, increase insect and pest resistances, and enhance grain qualities for feed, food, and biofuels (Duvick and Cassman 1999; Brookes and Barfoot 2006a; b). The most current technological advancement in agriculture that has performed a major part in reshaping agriculture and food production and also meeting needs rapidly and cost-effectively is nanobiotechnology, which is the interface of biotechnology and nanotechnology (Sugunan and Dutta 2008). Its use in biotechnology has resulted in the rapid synthesis of commercial formulations through the utilization of nanoparticles and engineered nanomaterials of various shapes and sizes in agriculture for crop improvement (Zheng et al. 2005). There have been several reports of nanoparticles having beneficial impacts on maize growth and development (Yuvakumar et al. 2011; Morteza et al. 2013; Rizwan et al. 2019). Its novelty also has revolutionized and improved crop improvement programs through genetic modifications and target-specific gene as well as biomolecule (proteins, nucleotides, and activators) delivery systems (Galbraith 2007; Cheng et al. 2016). It is predicted to become a feasible alternative to molecular plant breeding as well as genetic engineering in near future. The current study represents an overview and discusses the recent biotechnology advances in maize research that may open up new avenues for crop improvement.

2 Recent Improvement in Maize Production Global maize cultivation was substantially facilitated by adaptation to temperate conditions, which occurred initially throughout 2000 years after the introduction onto the North American continents around 4000 years ago (Swarts et al. 2017). Although maize has almost equal production regions in the temperate and tropical conditions, temperate environments account for 70% of maize production (Edmeades et al. 2017). World’s maize grain output reached a new height of 1054 Mt in 2016–2017, increasing by around 15 Mt each year since 1961. Nine nations and the European Union produce 85% of the maize grain. In 2017/2018, the USA and China produced 56% of the world’s maize (National Corn Growers Association 2018). Furthermore, more than fourfold to 18 Mt as much silage maize has been produced globally, with 1.4 million hectares (mha) of land under cultivation and a productivity increase of more than twice to 12.8 tons per hectare (t/ha) since 1961 (FAOSTAT 2018).

3 Molecular Marker Technology Researchers working on crop improvement are increasingly utilizing molecular markers as an efficient and suitable tool for addressing a range of applied and basic research aspects related to agricultural production strategies (Mohan et  al. 1997; Nadeem et  al. 2018). Any detectable polymorphism in DNA or proteins has the

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potential to be utilized as a molecular marker. It is feasible to construct a genetic map by genotyping several genetically related individuals of a population using multiple markers. Distinct chromosomal intervals can be associated with traits and phenotypes using molecular markers as well as genetic mapping (Teama 2018). Molecular markers have been used to characterize germplasm, verify pedigree records, assign inbreeds into heterotic groups, understand the basis and predict the heterosis, identify and localize genes, and conduct marker-assisted selection in maize.

3.1 Germplasm Characterization Over the last few decades, advancements in genomics have resulted in the discovery of multiple maize DNA markers, such as simple sequence repeat (SSR), single nucleotide polymorphisms (SNPs), and insertion-deletion (InDel) markers. These PCR-based genetically codominant markers are durable, hypervariable, repeatable, numerous, and evenly distributed throughout plant genomes (Powell et al. 1996). Besides the SNPs and SSRs, a considerable amount of genes influencing diverse factors of plant developmental processes, abiotic and biotic stress tolerance, quality characteristics, and other traits has been characterized and cloned in maize, making them valuable resources for molecular marker-assisted breeding. Currently, SSRs are the most frequently employed molecular markers in maize research because of their availability, accessibility, and efficacy in public domains (MaizeGDB; http://www.maizegdb.org). It is used to investigate linkage disequilibrium, diversity, and evolution (Reif et al. 2005; Stich et al. 2005). InDel markers like SSRs may be utilized in almost every molecular laboratory without requiring significant investments in training or equipment. To use it in maize improvement, more than one million of genomic sequences from maize B73 has been stored in Gene Bank which includes genomic survey sequences (GSSs), bacterial artificial chromosome (BAC) shotgun reads produced by the Consortium for Maize Genomics (CMG), as well as random whole genome shotgun (WGS) sequences produced by the Joint Genome Institute (JGI). These were initially assembled in MAGIs (maize assembled genomic islands) (Palmer et  al. 2003a; b; Whitelaw et  al. 2003a; b; Emrich et al. 2004; Fu et al. 2005). InDels have the benefit of being far more prevalent than SSRs. An SSR is found in around 1.5% ESTs of maize (Kantety et  al. 2002). SSRs and InDels have many advantages over initial molecular markers (RFLPs, AFLPs, and RAPDs (random amplified polymorphic DNAs)), although they also require electrophoresis to be detected (Schlotterer 2004). As a result, they are not suitable for the relatively high-throughput analysis necessary for large-scale genetic research. SNPs, on the other hand, may be identified via hybridization to mass spectrometry or short oligo chips in a relatively high-throughput way (Leushner and Chiu 2000). Although both SNPs and SSRs may be used in a wide spectrum consistently, SNPs are largely automated and provide major benefits for breeding and/or genetic purposes. Contrasted to other cultivated crop genomes, the SNP

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frequency is high in maize and there is one SNP per 28–124 bp (Ching et al. 2002; Yu et al. 2006). For maize, a database of SNP identification and trait dissection has been developed, where phenotype, genotype, and polymorphism data for multiple maize populations and inbreds may be accessed (Zhao et al. 2006a, 2006b: http:// www.panzea.org). Various modern genotyping systems have been generated allowing for the simultaneous and rapid genotyping of approximately a million SNP markers. Furthermore, based on available SNP data for maize, a custom GoldenGate assay comprising 1,536 SNPs has been constructed (Yan et al. 2009). Various mapping populations, such as the intermated B73 × Mo17 population and recombinant inbred lines (IRILs) generated from IBM population, are accessible in maize as the international maize genomic resources (Cone et al. 2002; Lee et al. 2002). Employing IBM population, researchers produced ISU–IBM Map4, a maize genetic map that combines 2,029 present and 1,329 novel InDel markers (Fu et  al. 2006). Another major genetic resource established in recently is the maize nested association mapping (NAM) population which comprises 5,000 recombinant inbred lines (RILs). The NAM population is a novel approach for identifying genes regulating complex characteristics that combines the quantitative ability of QTL mapping and association mapping alongside high chromosomal resolution (Yu et al. 2008). Drought-tolerant (Ki11) and drought-sensitive (Ki3) inbred lines of Suwan (Thailand) as well as eight maize lines are produced at CIMMYT predominantly utilizing tropical germplasm of maize (CML52, CML103, CML69, CML247, CML228, CML277, CML333, CML322). Therefore, global heterogeneity has been recorded in NAM RIL germplasm collection allowing the maize researchers to identify genes implicated in a range of scientific or agronomic traits (Yu et al. 2008).

3.2 Pedigree Records Verification Maize breeders are generally interested in genetic diversity within and across the breeding populations as well as elite germplasms since it has a significant impact on the success of future breeding strategies (Xu et al. 2009; Akhi et al. 2017). Therefore, classifying inbreds in several heterotic groups for optimizing their possible utility in the production of fertile synthetics and hybrids is a crucial preliminary step in any breeding effort. Utilizing general combining ability (GCA), specific combining ability (SCA), and other approaches, breeders usually conduct parental selections for crossings. Since the introduction of molecular markers, the genetic diversity studies have been employed to classify genotypes in different heterotic groups and to choose genetically distinct parental lineages for crosses (Yu et al. 2020). AFLP, RAPD, and SSR markers were mostly exploited in the 1990s to evaluate genetic diversity as well as categorize genotypes depending on the levels of the genetic relatedness (Melchinger et al. 1991; Dubreuil et al. 1996; Pejić et al. 1998; Drinić et al. 2000; Erić et al. 2003; Erić 2004). The prominence of other kinds of markers has declined as modern genotyping technologies like genotyping through sequencing (GBS) have emerged. Maize scientists are studying with a broad germplasm

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pool that contains a range of maturity groups, yellow and white grains, as well as adaptive characteristic donors obtained from wild relatives like Z. diploperennis. To allocate inbred lines to multiple potentially complimentary groups, researchers are utilizing many methodological techniques, comprising phenological characteristics, testcross performances, and pedigree information. The program’s goal is to produce hybrids that combine important traits. For example, Striga resistance is coupled with drought resistance (Mengesha et  al. 2017), drought resistance with reduced soil nitrogen resistance, or drought resistance with heat resistance (Badu-Apraku and Akinwale 2011; Meseka et al. 2018; Ajala et al. 2019). SNP markers have been utilized to evaluate the genetic diversity of 128 droughts and Striga resistance lines, allowing for the selection of effective inbred lines for a hybrid combination depending on genetic variability (Mengesha et  al. 2017). However, integrating pedigree data with the combined ability and genotypic analyses provides more precise heterotic group classifications (Badu-Apraku et al. 2016b). Markers are frequently utilized for identifying heterotic groups in different types of maturity groups to improve the existing approaches of grouping inbred lines based on the genetic traits with phenotypic values that are crucial for producing synthetic cultivars and heterotic populations (Menkir et al. 2015). The finding of a comparative analysis of several approaches applied to categorize inbred lines in several heterotic groups indicated that the SNP-based genetic distance (GD) approaches were efficient for classifying inbred lines in several heterotic groups which were comparable to groups established utilizing SCA- and GCA-based techniques (Badu-Apraku et al. 2015; Menkir et al. 2015;Badu-Apraku et al. 2016a).

3.3 Assigning Inbreeds to Heterotic Groups Genetic diversity information is valuable for describing current heterotic groups, identifying new heterotic groups, selecting parental lines, and predicting hybrid performance, particularly in crops where hybrids are economically important such as maize. The numerous procedures are required in hybrid breeding schemes, including conducting several crosses and testing the varieties for improved performance (Oyetunde et  al. 2020). As production of heterosis is costly, labor-intensive, and time-consuming, if it can be detected before crossing, the number of crossings and progeny to be tested may be significantly decreased. Various researchers are attempting to relate genetic diversity, as measured by molecular markers, to anticipate hybrid viability in multiple hybrid breeding projects since the degree of genetic variability between both parents has been considered as a potential predictor of the heterosis (Hallauer et al. 1988). Smith and co-workers (1990) found a strong correlation between genetic distances depending on heterosis and RFLP (restriction fragment length polymorphism) markers which includes crossings of inbreds of similar and different groups of heterotics. According to Melchineger and co-­workers (1992), the relationship between genetic distance depending on heterosis and RFLP markers is based on the kind of crossings studied. Lanza et  al. (1997) found a

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negative connection of RAPD-based genetic distances and heterosis for yield for 18 inbred lines despite considering the categorization of inbreds in several heterotic groups relying on the markers. Several research teams have investigated the efficacy of SSRs for categorizing lines into heterotic groups and correlating SSR-based genetic distances to hybrid productivity and/or heterotic performances in maize (Yuan et al. 2001; Drinić Mladenovič et al. 2002; Prasanna and Hoisington 2003; Teng et  al. 2004; Xu et  al. 2005; Choukan et  al. 2006; Mohammadi et  al. 2002, 2008; Xie et al. 2007, 2008). Teng et al. (2004) applied SSR data to analyze the heterotic groups and patterns of 84 parent lines of 71 frequently utilized hybrids depending on hybrid plantation areas from 1992 to 2001. The research led to the discovery of seven heterotic groups in maize, as well as evidence that key heterotic groups of maize had shifted positions in the last decade. Lancaster, TangSPT, Reid, E28, and Zi330 were the leading heterotic groups in the early 1990s, whereas Reid, M30, TangSPT, Lancaster, and Tem-tropic I were the major heterotic groups in the early twenty-first century. In another research in China, polymorphic data of 70 loci was used to divide 187 frequently employed maize inbreds into six subpopulations: PB, BSSS, PA, Lan, LRC, and SPT (Xie et al. 2007). Around 40 of the 187 lines with ambiguous and/or inconsistent pedigree data were divided into 6 groups identified through structure analysis (Xie et al. 2008). Nevertheless, as the genetic distance measured by SSR data has had limited effectiveness for maize, the SSR loci might not be helpful for evaluating heterosis across the lines in general (Melchinger 1999;Xie et al. 2007, 2008; Dhliwayo et al. 2009). Breeding lines generated from wild populations, as well as lines obtained from breeding resources with no precise information on their germplasm composition, cannot be able to be divided into distinct heterotic groups (George et al. 2004). Research relying on advanced genotyping techniques and genomic data that utilize functional and more informative markers depending on polymorphic regions in a significant amount of agronomically essential genes may assist to identify heterotic groups when it has been challenging to achieve.

3.4 Understanding the Basis and Heterosis Prediction The forms of gene activities implicated in hybrid performance, as well as their comparative contributions to the development of heterosis, are crucial in determining the effective breeding approach. Overdominance is one of the hypotheses for explaining heterosis. A wide QTL for crop yield was found around the Amp3 marker on chromosome 5 by Stuber and co-workers (1992). In both backcrosses, QTL was substantially linked with crop yield, and in the both cases, heterozygote class of marker was better than the homozygote, showing that heterosis is based on overdominance. A significant impact on crop yield was also detected on chromosome 5 in the same other research of finding QTL in a maize population derived from the crossing between B73 and Mo17. A potential QTL on chromosome 5 that controls maize production was identified by Graham and co-workers (1997). They divided

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the area containing this QTL into two distinct regions. Two QTL with dominant effects may be found at NP1449 and NRZ5. These genetic variables are linked in a repulsion phase, and thereby their impacts complement the heterosis dominance theory. Lu and co-workers (2003) investigated the F2 population of a single cross LH200 × LH216 that has been randomly interbred for three generations by 160 SSR markers. For grain yield, 28 QTLs were discovered with 24 QTLs of them displaying overdominance. They proposed two plausible interpretations for the findings – (1) grain yield QTLs display actual overdominance and (2) grain yield QTLs exhibit partial to total dominance – but their effects are so similar that three generations of randomized mating fail to distinguish their distinct impacts. Mohammadi et  al. (2002) highlighted the importance of the overdominance gene actions in maize for the development of heterosis for grain production and its constituents in their research of gene actions for SSR markers.

3.5 Identification and Localization of Gene Aside from crop yields, most of the agronomically significant characteristics like maturity, quality, and tolerance to a number of biotic and abiotic stresses are quantitative and complex in nature, with numerous genes regulating them (Kumar et al. 2020; Hossain et  al. 2021). Chromosomal locations may be assigned to specific QTLs, the kinds and magnitudes of gene impacts, and which parent has the positive allele at every QTL can be determined using molecular markers. The capacity to detect a correlation between a molecular marker and a QTL is determined by the degree of a QTL’s impact on a trait, the population size under investigation, and the frequency of recombination between the QTL and the marker (Michelmore et al. 1991). The number and position of genes with major effects controlling a trait of interest might be determined using segregating populations (backcross, F2, double haploids, or recombinant inbred lines) derived from crossings between parents which differ in expression of specific traits. A mapping population of crops must be genotyped along with all the markers chosen to map the genomes and phenotyped for characteristics of interest in order to conduct quantitative trait locus studies (Michelmore et al. 1991; Abecasis et al. 2001). Plants from these segregating populations, on the other hand, can be categorized based on phenotypic expression of a characteristic and evaluated for differences in allele frequency among the population bulks: bulk segregant analysis (BSA) (Michelmore et  al. 1991). Individuals from a segregating population that are homozygous for parental alleles over a specific interval are chosen depending on the marker’s genotype across that interval. A wide range of individuals in every pool enhances the chances of a marker indicating that polymorphism across the bulks will be related to the target interval. Segregation analysis can then be used to determine the marker’s exact location (Shaw et  al. 1998). When a strong linkage between a targeted gene and a molecular marker is found, the gene’s inheritance may be tracked in breeding efforts. BSA and molecular markers were employed by Quarrie et al. (1999) to find QTLs linked to yield

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under extreme drought conditions. Following the initial publication on QTLs for yield-related characteristics in maize, the maize researchers all over the world have published several reports on molecular markers that tag QTLs/genes for a range of scientific and agronomic traits. QTLs for numerous crucial traits influencing maize also have been identified in Asia (Stuber et al. 1987). These characteristics include plant heights (Zhang et al. 2007), sugarcane mosaic virus (SCMV) tolerance (Zhang et al. 2003), downy mildew tolerance (George et al. 2003; Sabry et al. 2006), maize dwarf mosaic virus (MDMV) tolerance (Liu et al. 2006), head smut tolerance (Li et  al. 2008), common smut tolerance (Ding et  al. 2008), Fusarium moniliforme causing ear rot tolerance (Zhang et al. 2006), drought tolerance (Xiao et al. 2005; Hao et  al. 2008; Lu et  al. 2006; Prasanna et  al. 2009a), BLSB (banded leaf and sheath blight) tolerance (Zhao et al. 2006a, 2006b; Garg et al. 2009), waterlogging tolerance (Qiu et al. 2007), high oil content (Song et al. 2004), nutrients under nitrogen deficiency (Liu et  al. 2008a, 2008b), CMS-S (Tie et  al. 2006), and popping ability (Li et al. 2006; Babu et al. 2006). Such research has helped researchers to understand the genetic architecture of several traits in maize, including drought tolerance (Tuberosa et al. 2007) and disease resistance (Wisser et al. 2006). CM137, CM139, CM138, CM212, and CM140 are five leading Indian maize lines that have been pyramided for Polysora rust (Puccinia polysora) and turcicum leaf blight (Exserohilum turcicum) resistance (Prasanna et al. 2009b). The discovery of QTLs impacting agronomically significant traits might aid in the development of breeding strategies designed to improve desired traits and allowing for effective marker-­ assisted selection.

3.6 Marker-Assisted Selection Breeding Marker-assisted selection (MAS) is founded on the principle that the presence of a tightly linked marker may be employed to predict the presence of a gene. Owing to the events of double crossover recombination, since the marker and the gene are placed far from each other, the probability of their being transferred together to offspring individuals becomes reduced. As a result, markers must be strongly associated with the targeted gene to be used in such selection. Saturation of areas containing the gene of interest on the genetic linkage maps is required for this purpose. Transferring particular QTLs using MAS is the ultimate application of QTL mapping in a breeding effort. By enhancing selection efficacy, the use of markers in introgression processes can reduce the number of breeding cycles, especially during the early stages. Utilizing maize as a model paradigm, major success has been achieved globally in optimizing MAS to enhance both quantitatively and qualitatively inherited characteristics. The application of opaque2-related SSR markers in the transformation of maize lines into QPM (quality protein maize) lines with increased nutritional quality is one effective example of MAS for maize enhancement that is particularly useful for the developing countries (Prasanna et al. 2001; Morris et al. 2003; Babu et al. 2005a; b). By marker-assisted translocation of the o2

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gene and the phenotypic screening for endosperm enhancers in parental lines (CM212 and CM145), Vivek QPM Hybrid 9, a MAS-derived QPM hybrid, was produced (Babu et al. 2005a; b; Gupta et al. 2009). Same method was utilized to produce QPM variants of many early maturing superior inbred lines acclimated to India’s hilly areas (Gupta et  al. 2009), as well as QPM variants of six superior inbred lines (CM138, CM139, CM137, CM151, and CM150), which were three single-cross hybrid parents, PEHM2 (CM138 × CM137), Parkash (CM140 × CM139), and PEE (CM151 × CM150) (Khanduri et al. 2009). In the field of molecular breeding of crop plants like maize, MAS for provitamin A (PVA) contents is regarded as an appropriate goal. Alterations in nucleotides which affect the carotenoid synthesis in the endosperm of maize were classified in the temperate germplasm of maize in the view of the discovery of main genes engaged in the carotenoid production pathway (Harjes et al. 2008; Yan et al. 2010). The impact of such functional gene markers on the accumulation of PVA in the tropical maize was studied, wherein PCR-based DNA markers produced from three major genes (PSY1, lcyE, and crtRB1) were evaluated for their efficacy in MAS for the content of PVA in 130 maize tropical lines (Azmach et al. 2013). Following that, these 130 lines were extensively explored for genome-wide associations employing GBS to look for other additional genes, comprising germplasm of diverse genetic backgrounds emerging from temperate and tropical regions. Several strong association signals were discovered, the majority of those were being colocalized with existing genes of carotenoid biosynthesis such as ZEP (chromosome 2), lcyE (chromosome 8), and crtRB1 (chromosome 10) (Azmach et al. 2018). Gebremeskel and co-workers (2018) also investigated marker-trait association on other 108 inbred lines set to corroborate the effectiveness of the markers and confirm the impact of Zep1 gene. The study’s findings showed the significant impact and correlation of crtRB1 markers (3'TE and 5'TE) with the accumulation of carotenoid. They discovered, however, that Zep-SNP (801) solely influenced A-carotene accumulation. The basic conclusion derived from these researches is that larger numbers of beneficial alleles in a genotype are related to a higher amount of total content of PVA. Zinc and iron, in addition to VA (vitamin A), are important micronutrients which can be supplemented by breeding (Garcia-Oliveira et al. 2018). CIMMYT and IITA have been working on high-Zn biofortification of maize kernels in collaboration with private and public sector partners. Landraces, inbreds, hybrids, and OPVs were shown to have significant genetic variations for the kernel-Zn (4–96 ppm) within the tropical germplasms of maize (Bänziger and Long 2000; Menkir 2008; Prasanna et al. 2011; Hindu et al. 2018). Unlike rice and wheat, no statistically significant relation in kernel Zn and Fe content was found in maize (Guzmán et al. 2014). In Asia, producing maize cultivars that are tolerant to lower soil nitrogen stress and possess higher nitrogen usage efficacy is becoming more important. In this context, China has recently conducted an association analysis (Xie et  al. 2008; Wu et  al. 2008; Wu et al. 2009). Natural gene variants encoding two members (Gln1-4 and Gln1-3) of the cytosolic glutamine synthetase gene family were studied utilizing phenotyping tests in two settings at three places over two years in a controlled population group of 187 inbreds of maize from China.

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Despite the challenges of enhancing polygenic traits using MAS, there were a handful of notable examples especially MAS for enhancement of the drought endurance in all the tropical inbred lines at CIMMYT (Ribaut et al. 2001; Ribaut and Ragot 2007a; b; Landi et al. 2007; Menkir et al. 2012). Recent attempts have concentrated on drought tolerance technologies that integrate index-based selection and high-density genotyping. The marker-assisted recurrent selection (MARS) is the process of enhancing the F2 population by performing one cycle of the marker-­assisted selection (which depends on marker scores and phenotypic data) accompanied by more than two cycles of the marker-based selection (which only depends on marker scores). Maize product contamination with mycotoxins generated by fungi which induce ear rot in maize impairs the food quality and poses a serious health concern. Scientists have produced maize varieties which are resistant to aflatoxin- and fumonisin-producing fungus (Menkir et  al. 2008). Furthermore, omic-based research focused on the aflatoxin resistance mechanism of crops that has resulted in the detection of multiple genes as well as signal molecules (Fountain et al. 2015). Warburton et  al. (2013) used association mapping to identify a novel source of A. flavus resistance. Researchers developed assays of SNP marker for use in the MAS or marker-assisted backcrossing (MABC) for the introgression of aflatoxin resistance to superior lines using SNP markers associated with these QTLs. MABC has been used in a large number of successful maize MAS research works to incorporate beneficial alleles and accelerate the rest of the genome’s restoration of the recipient genotype (Table 1). Resistance to maize SCMV, parasitic weeds of streak virus (Striga hermonthica), and stem borer pests (e.g., Eldana saccharina and Sesamia calamistis) has been obtained with promising findings in MAS at CIMMYT (Ribaut et al. 2001; Menkir et al. 2012). A single principal gene on chromosome 1 appeared to control the maize streak virus (MSV) (Kyeter 1995). Forward selection has been carried out using SNP markers linked with this key QTL (Nair et al. 2015). Still MSVD resistance is highly dependent on the single key resistance locus (msv1). Despite the ease with which a MAS approach for MSV may be developed, the question of resistance durability remains, since all components of resistance are generated from alleles occurring at the same locus. To solve this problem, varietal diversification, varietal mixtures, and resistance gene pyramiding have been utilized, and MAS for resistance genes may be beneficial for all of these techniques. At four physiological phases (seedlings, anthesis, elongation, and grain filing), three QTLs imparting SCMV resistance were found on chromosomes 10, 5, and 3, whereas a QTL on chromosome 6 was identified at the phase of elongation (Zhang et al. 2003). Three QTLs on chromosomes 10, 5, and 3 are used in MAS for SCMV resistance, allowing for the pyramiding of QTL alleles into a single line. Stem borers, including the sugarcane borer (Eldana saccharina) and the pink stem borer (Sesamia calamistis), are among the several insects that damage maize. An F2 maize population of 238 lines was generated to unravel the genetic basis of resistance to stem borers. Preliminary QTL studies of 238 F3 genotyped lines utilizing SNP markers showed many genomic areas on multiple chromosomes which influence the traits of stem borer resistance, including damage of leaf, leaf feeding, and length of the tunnel during the terminal stage.

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Table 1  Applications of MAS in maize improvement Molecular markers RFLP

RFLP, SSR SSR

Target alleles/ genes Introgression of favorable QTL alleles Early generation test of inbreeding

Traits Yield, plant height, and ear number Yield and stalk lodging percentage Yield and Foreground selection of three earliness loci Southwestern Tracing of five corn borer loci during resistance backcross Introgression of Seedling three QTL alleles emergency Stalk strength Introgression of and beneficial QTL European corn alleles borer resistance Drought Introgression of tolerance beneficial QTL alleles Drought Introgression of tolerance five beneficial QTL alleles Rf3 gene Introgression of selection fertility restorer gene

References Stuber (1995) and Stuber et al. (1999)

Eathington et al. (1997)

Bouchez et al. (2002)

Willcox et al. (2002)

Yousef and Juvik (2002) Flint-Garcia et al. (2003)

Ribaut and Ragot (2007a, b)

Ribaut et al. (2002)

Xia and Zheng (2002)

(continued)

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Table 1 (continued) Molecular markers

Target alleles/ genes Introgression of four QTL alleles Introgression of opaque2 gene

Traits Yield

References Ho et al. (2002)

Quality protein maize (QPM)

Babu et al. (2005a, b), Danson et al. (2006), Magulama and Sales (2009), Jompuk et al. (2011), Gupta et al. (2013), Kostadinovic et al. (2016), Krishna et al. (2017), Tripathy et al. (2017), Hossain et al. (2018), Pukalenthy et al. (2019), and Kaur et al. (2020) Vignesh et al. (2012), Muthusamy et al. (2014), Goswami et al. (2019), and Natesan et al. (2020) Chandran et al. (2019) and Mehta et al. (2020)

Introgression of crtRB1 gene

Quality protein maize (QPM)

Introgression of opaque2 and crtRB1 genes Introgression of qMrdd8 gene

Quality protein maize (QPM)

Rough dwarf disease resistance Introgression of Maize streak msv1 gene virus (MSV) disease resistance Introgression of Northern corn Ht1 gene leaf blight (NCLB) resistance Sorghum downy Introgression of mildew (SDM) SDM resistance resistance QTLs Quality protein Introgression of crtRB1, lcyE, and maize (QPM) opaque2 genes Introgression of Quality protein lpa2-2 allele maize (QPM)

Xu et al. (2020)

Mafu (2013)

Puttarach et al. (2016)

Sumathi et al. (2020)

Zunjare e al. (2018)

Sureshkumar et al. (2014a) and Tamilkumar et al. (2014) (continued)

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Table 1 (continued) Molecular markers

Target alleles/ genes Introgression of lpa2 allele Introgression of qHSR1 gene Introgression of qHO6 locus Introgression of ZmVTE4 gene Introgression of lcyE gene Pyramiding of opaque2 and opaque16 genes Mapping and validation of sugary1 and shrunken2 genes Pyramiding of crtRB1 and lcyE genes into opaque2

Traits Quality protein maize (QPM) Head smut resistance Oil content

References Sureshkumar et al. (2014b)

Quality protein maize (QPM) Provitamin A (proA) content Quality protein maize (QPM)

Feng et al. (2015)

Quality protein maize (QPM)

Hossain et al. (2015)

Quality protein maize (QPM)

Singh et al. (2021)

Zhao et al. (2012) Hao et al. (2014)

Yang et al. (2018) Sarika et al. (2018)

Molecular markers can also be employed in the fast conversion of backcross of superior inbred lines for the activation of new genes inserted by transformation. The MAS can be utilized for the conversion of line, which is the transmission of superior alleles at specific QTLs from the donor lines to recipient lines, in which phenotypic assessment is costly and challenging, such as in a breeding program that included numerous genes, recessive genes, late expression of a gene of interest, resistance gene pyramiding, segregating population selection, and at the initial phase of plant growth (Boopathi 2020). Following are the key components for MAS in the plant breeding: (i) the marker must co-segregate and/or be tightly linked to the preferred characteristics, (ii) a reliable method of testing huge populations for the molecular markers must be accessible, and (iii) an evaluation technique must have elevated reproducibility at all laboratories, must be cost-effective, and must be easy to apply. One issue is that QTLs must be found for each new series of parental resources before MAS can be attempted. Another drawback of marker technologies is a gene marker intended for one cross which may be ineffective in another. Poor experimental design, highly expensive, and the complexity of the qualitative characteristics are among the other drawbacks (Jiang 2013). Although MAS is costly in most cases, the precision of selection provided by DNA markers, as well as the development of new markers, may enable it a more cost-effective selection technique for breeding projects ahead.

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4 Large-Scale Genomics for Trait-Specific Genes Progress in biotechnology and research equipment has led to the fast sequencing of large genomic sections of multiple species. Genomes of above 180 horticultural species have already been sequenced (Chen et  al. 2019), together with several important cereal crops including rice (Huang et al. 2010, 2012a, b), soybean (Fang et  al. 2017), and solanaceous plants (Hardigan et  al. 2017; Qin et  al. 2014). Sequencing the complete genome of a species with a more complicated genome, like maize, is a huge challenge. In terms of genome size, maize is roughly the same size as the human genome. A study on maize genetic variation in which 502 unique loci of eight different accessions were sequenced encompassed maize germplasm’s genetic diversity of about 90% and around 90% of the genetic diversity present in maize germplasm (Bhattramakki et al. 2002). In the United States, two major projects regarding sequencing of maize genome were launched in the early 2000s (Whitelaw et al. 2003a; b; Palmer et al. 2003a; b; Messing et al. 2004; Haberer et al. 2005). Both techniques have been proven to be highly efficient, collecting almost all genes (95%) in test BACs, and are predicted to be more successful than randomized genome-wide shotgun sequencing (Springer et al. 2004; Rabinowicz and Bennetzen 2006). TIGR has assembled maize sequences from different approaches (http:// maize.tigr.org/release4.0/assembly.shtml). Furthermore, the MAGIdb consists of maize assembled genomic islands (MAGIs), which were created with the help of almost four billion genomes survey sequences generated by CMG and could use against other sequences (http://magi.plantgenomics.iastate.edu/) applying basic local alignment search tool (BLAST). A large maize sequencing study was carried out in 2008 (Science Daily June 28, 2008) utilizing bulked of plants from a Mexican popcorn landrace Palomero. The program was focused on major gene-rich areas (http://www.niherst.gov.tt/s-­and-­t/s-­and-­t-­news/). The next-generation DNA sequencing technology may allow us to sequence any targeted maize genotype (Shendure and Ji 2008; Gupta 2008). High-throughput sequencing methods of a new generation have the potential to revolutionize the scientific industry, possibly supplanting array-based methods and offering up a plethora of new opportunities (Kahvejian et al. 2008). Significantly improved sequencing capacity will allow us to find a wide range of unknown genes, to efficiently combine biological data for a complete description of many aspects on an individual basis, and to achieve advancements which we cannot yet anticipate. A genome sequence may also be used for effective map-based gene cloning and to link candidate genes to major agronomic or biological characteristics. A physical map has also been developed relying on the fingerprinting and isolation of huge amounts of BAC clones as well as the sequencing of BAC ends (Bennetzen et al. 2001). This map is required for sequencing the maize genome, and it may be obtained by allocating transcribed sequences of maize

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to their precise genomic location. ESTs offer wide-scale data on maize’s gene complement. Maize NCBI is a repository for EST sequences (http://www.ncbi.nlm.nih. gov/mapview/mapsearch.cgi?taxid=4577). With the introduction of huge datasets of maize ESTs, it is now possible to search such data for maize homologs to genes first identified in other species and to gain some functional knowledge. Several methods, like differential display, RNA blots, reverse transcription PCR, and ribonuclease protection assays, were established to identify alterations in gene expression. While these procedures are successful, their reliance on gel electrophoresis limits the sample number that may be analyzed simultaneously. The ability to measure the expression of tens to thousands genes simultaneously is a major advantage of microarrays (Shena et al. 1995). All microarrays are based on the precise placement of the DNA fragments at a very high density on the solid substrate so that they can function as molecular detectors. Microarrays are divided into two categories that are widely used and differ largely in length: probe cDNA (500–5,000bp long), which is imprinted on a membrane or a glass in DNA microarrays, and oligo-­ based microarrays (DNA chip), which comprise array components made up of small synthetic DNA molecules (less than 20bp) (Leming 2002). About 100,000 ESTs were sequenced from the cDNA libraries derived from various tissue types of maize and utilized in the development of microarrays (Elumalai et al. 2002). The annotation and analysis of ESTs from the Maize Gene Discovery Project (MGDP) of NSF have resulted in the discovery of 22,000 putative distinct genes that are involved in the manufacturing and application of microarrays (http://www.zmdb. iastate.edu/zmdb/microarray/). The DNA microarray technique has two primary applications: gene identification and gene expression level determination. Searching for genes that are up- or downregulated concerning the test conditions is the easiest method to find genes of potential significance. This technique can be beneficial in linking function with genes since genes that are strongly regulated to certain stress, for example, are likely to play essential roles in response to stress (Holloway et al. 2002; Tarca et al. 2006). Identifying gene expression patterns and classifying genes into expression classes might offer a lot more information about their biological process. Microarray technology was utilized by Anelkovic et al. (2003) to investigate gene expression profiling of the maize kernel in responses to heat and water stress. Due to the up- or downregulation in stressful conditions, genes are indicated by various intensities of color, for example, red and green, in addition to clustering together genes having similar patterns via hierarchical grouping. The development of proteomic and genomic technologies has led to a huge amount of information. This flood of data required the development of computerized databases for collecting, organizing, and indexing the data and also specialized devices for accessing and analyzing it (Holloway et al. 2002).

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5 Bioinformatics for Analyzing Genomic Data for Molecular Breeding Bioinformatics refers to the utilization of computer science to store, manipulate, and analyze biological information. This entails the computer administration of a wide range of biological data, including genes and their products, entire organisms, and especially ecological processes. Because of the massive amounts of data generated by genomic and proteomic research, the majority of modern bioinformatics efforts focus on the functional and structural properties of proteins and genes. First, the information is gathered and arranged in databases specific to distinct subjects. Computational tools are required in the following step to analyze the gathered data in the most effective manner. Several bioinformaticists, for instance, are focusing on predicting the biological activities of proteins and genes or sections of them depending on structural information (Abdurakhmonov 2016). The development and management of biological information databases are the most basic tasks in bioinformatics. A biological database is a large, organized compilation of permanent information which is usually supported by computerized software allowing users to modify, search, and recover information from the program. The majority of biological databases are composed of long sequences of nucleotides and/or amino acids. Each nucleotide or amino acid sequence corresponds to a specific protein, or gene, or part of it. Single letter identifiers are used to represent sequences in shorthand. This reduces the amount of space required to store data while increasing analytical processing speed. However, two more aspects were necessary for researchers to profit from all this data: (i) fast access to the sequence data pool and (ii) a method to retrieve just those sequences of relevance to a specific researcher from this pool. Several major databases focusing on data from maize genomic studies have been established in recent years. MaizeGDB is a vast repository of genomic data and informatic toolkits for maize. Others are MaizeNCBI, MAGI (the Maize Assembled Genomic Island site), Gramene, VPhenoDBS, Maizesequence, etc. Table 2 is a list of several maize databases and resources. In plant genomics, multiple software and decision aid tools have been developed for breeding population management, genetic map construction, germplasm evaluation, MAS, GEI analysis, information management, and breeding simulation and design (Dwivedi et al. 2007). QTL meta-analysis, population structure, gene comparative analysis, and association mapping are among the tools listed in Table  3 which are effective in molecular breeding and genomic research of maize. The analysis of sequence data is one of the most important bioinformatic tasks. Computational biology refers to the exact method of processing and interpreting data. Computational biology entails locating genes in DNA sequences from different species, establishing a system to determine the structure and/or function of recently found structural RNA sequences and proteins, grouping sequences of protein into families of relevant sequences, the advancement of protein designs, and related proteins aligning and producing phylogenetic trees to investigate evolutionary kinships. Current sequencing information databases may be utilized to find

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Table 2  Maize-related databases and resources Name MaizeGDB

MaizeDIG

Gramene

MAGI

Descriptions A database for genetic, genomic, sequence, functional characterization, gene product, and person/organization contact information of maize Database of various reference genome assemblies linked with genome browsers to provide custom tracks displaying tagged mutant phenotypic images in the genomic context, as well as custom image tagging to emphasize the phenotype Database of grass genomics, such as protein, maps, genomes, traits, markers, biochemical pathway, genetic diversity, and literature Database of maize genome assembly project

Links http://www.maizegdb.org/

http://maizedig.maizegdb. org/

http://www.gramene.org/

mtmDB

http://magi. plantgenomics.iastate. edu/ http://mtm.cshl.edu/

SAM

http://www. maizesequence.org/index. html http://www.ncbi.nlm.nih. gov/mapview/mapsearch. cgi?taxid=4577 http://sam.truman.edu/

A transposon library for the aim of selecting gene knockouts in maize with precision Maizesequence Database of annotation and sequence of maize genome obtained from genome sequencing project of maize NCBI Maize Database of all maize accessions such as genomics, HTG, EST, GSS, cDNA, and sequences

Maize tilling

Helitons Panzea Grassius

TIGR Maize

MaizePLEX

VPhenoDBS

Database for the expression of maize shoot apical meristems A website for maize tilling program which compiles and disseminates seeds and mutants of maize tilling A catalog of helitrons that have been discovered A database of the maize genome’s functional and molecular diversity Database for transcription factors, promoters, and cis-regulatory components among various grasses, like maize, rice, sorghum, and sugarcane Database comprises linkages to the NSF-funded collaboration for maize genomic program, which contains maize sequence, assembly, and annotation information, as well as maize gene index linkages Database of plant ontology and MIAME-compliant improved expression of maize microarray information A web-based visual phenotypic data management platform which allows users to query phenotypic information by picture sample, sequencing, ontology, physical and genetic map data, and textual annotation simultaneously

http://genome.purdue. edu/maizetilling/ http://genomecluster.secs. oakland.edu/helitrons/ http://www.panzea.org/ http://www.grassius.org/

http://maize.tigr.org/

http://www.plexdb.org/ plex.php?database=Corn http://medbio.cecs. missouri.edu/ VPhenoDBS/

(continued)

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Current Biotechnological Approaches in Maize Improvement Table 2 (continued) Name ZmGDB

MaizecDNA PML ZEAMAP

MaizeNet

Descriptions Database of maize sequence-centered genome representations with a focus on gene structure annotations Database of 30000 FLcDNA from various B73 tissue specimens A database of maize genetic resources designed for analyses of chloroplast biogenesis in maize Database of genomics, transcriptomes, annotations, chromatin interactions, open chromatin regions, genetic variants, phenotypes, metabolomics, genetic maps, population structures, genetic mapping loci, and populational DNA methylation indicators within inbred lines of maize Database of maize genes’ cofunctional networks at the genome scale

Links http://www.plantgdb.org/ ZmGDB/ http://www.maizecdna. org/ http://chloroplast. uoregon.edu/ http://www.zeamap.com/

http://www.inetbio.org/ maizenet/

homologues of newly sequenced and amplified molecules in laboratories. They may be assessed by the alignment of the comparable sections as well as finding similar and dissimilar letters in respective sequences. Proteins or genes which are significantly identical are considered to be homologous to each other, implying that they are related. If a similar molecule exists, any recently found protein may be simulated, which implies that a gene product’s three-dimensional structure may be predicted except by undertaking laboratory experiments.

6 Genetic Modification Technologies for Improvement of Maize Plant biotechnology enables the exchange of single or some desirable genes contrary to traditional plant breeding that involves crossing among several genes. This scientific development facilitates plant breeding experts to design crops comprising of specific beneficial features while avoiding undesired traits (Ulukan 2009). Crossing in plant breeding occurs most often between intra- or inter-species levels (Ulukan 2009). In traditional crossing, the gene pool available for utilization is therefore confined to genes that are present in selected individuals for breeding. Genetic transformation and genome editing technologies allow for the transfer of one or a set of genes between the same or different species, resulting in genetically modified plants with novel features. It is also possible to eliminate an undesired feature by inhibiting the cell’s ability to produce the product expressed by the gene (Gaj et al. 2016; Gosal and Wani 2018).

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Table 3:  Available bioinformatic tools for functional and genomic research of maize Name MetaQTL CMTV

BioMercator

Description A Java program developed to perform field data integration for gene mapping research A combined bioinformatic tool for generating consensus maps and comparing functional genomic information among genomes and investigations QTL integration and genetic mapping

Links http://bioinformatics.org/mqtl/ http://www.ncgr.org/cmtv/

http://cms.moulon.inra.fr/index.php ?option=comcontent&task=view&i d=13&Itemid=43 QTL-Finder A bioinformatic tool for integrating QTLs, http://gqtl.maizecenter.cn comparing them, and identifying potential genes among genomes and investigations GDPC It allows users to access genomic diversity http://www.maizegenetics.net/gdpc/ information including SSRs, SNPs, and index.html sequences, as well as phenotypic data obtained from the field, genetics, or physiological investigations TASSEL A software program for analyzing trait http://www.maizegenetics.net/index. correlations, linkage disequilibrium, and php?page=bioinformatics/tassel/ evolutionary patterns index.html TWINSCAN Enhanced gene prediction efficiency in http://mblab.wustl.edu/query.html rice and maize PowerMarker A collection of statistical techniques for http://statgen.ncsu.edu/ evaluating genetic marker information, powermarker/ specifically developed for SNP and SSR SPAGeDi It is a novel computational software http://www.ulb.ac.be/sciences/ developed for analyzing the mapped ecoevol/spagedi.html individual’s spatial genetic structure or mapped populations utilizing genotype information of all ploidy levels Structure A free software program for studying http://pritch.bsd.uchicago.edu/ population structure employing multilocus structure.html genotyping data RepMiner Utilize graph theory to identify and http://jestill.myweb.uga.edu/ assemble transposable factors from smaller RepMiner.htm DNA fragments generated from sub-­ cloning bacterial artificial chromosome pools TEnest Maize nested transposable elements are http://bak.public.iastate.edu/tenest. automatically annotated and depicted in a html chronological order

6.1 Genetic Transformation Genetic transformation has evolved into a potent research tool for gene validation in key cereal crops and also speeds up or supplements traditional breeding through directly introducing novel genes into the breeding pool. Genetic transformation has

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been widely employed for the improvement in maize as such that can result in generating novel commercial cultivars with pest and herbicide resistance. In recent times, drought tolerance and improvement in grain quality have also been gained in maize through genetic transformation. Initial attempts to convert maize required injecting DNA directly into tissue, but these failed miserably (Coe and Sarkar 1966). After 20 years, with the advancement of biotechnological tools, it was possible to carry out stable maize transformation (Fromm et al. 1986). Also, DNA transfer using Agrobacterium to maize was successful (Grimsley et al. 1987). The first transgenic variety of maize was generated employing protoplast transformation though it was infertile (Rhodes et al. 1988). However, biolistics of embryogenic cell suspension (Gordon-Kamm et  al. 1990) and transformation of the protoplast (Golovkin et al. 1993) resulted in the development of successful fertile transgenic maize. Lately, maize plants transformed with genes that confer resistance to pests and tolerance to the herbicide were distributed in many parts of the world and remain to be a major success of genetic transformation of the previous century. In 2019, genetically transformed maize was planted on 60.9 million hectares around the world (ISAAA 2019). Transformation of genes from Bacillus thuringiensis (Bt) in maize resulted in the production of insecticidal crystal (Cry) proteins that provide protection against several corn borers. This pest-resistant variety of maize has substantial commercial success. On the other hand, transgenic maize plants that show resistance to homopterans as well as impart toxicity to a Lepidoptera variety named Asia corn borer were transformed with gene encodes snowdrop lectin controlled by the phloem-­ specific promoter (Wang et  al. 2005). Novel Bt products with the US rootworm control gene Cry3Bb were launched in 2003. Transgenic plants have increased silk maysin production after being transformed with p1 transcription factor, thereby achieving resistance against corn earworm since accelerated proportion of silk maysin resulted in earworm abiosis (Cocciolone et al. 2005). Herbicides like glyphosate or glufosinate-ammonium tolerance are represented in the major transgenic maize varieties that are herbicide-tolerant. The maize plants ready for use are changed to express glyphosate tolerance. It works by inhibiting 5-enolpyruvyl-shikimate-3-phosphate synthase, a significant contributor in the essential amino acids’ synthesis (Schütte et  al. 2017). Genetically transformed maize carry a class of the enzyme derived from an Agrobacterium tumefaciens strain (CP4). Transformation of maize plant with bacterial gene encoding phosphinothricin acetyl transferase leads to PAT enzyme and herbicide breakdown (Que et al. 2014). More complex characteristics such as quality of the grains and tolerance to abiotic stresses are becoming increasingly important. Naqvi et al. (2009) introduced superior genetically modified inbred maize in South Africa; consequently, three vitamins’ concentrations were increased in particular by simultaneously modifying three individual metabolic pathways in the endosperm. The kernels of transgenic white maize (Cv. M37W) were found to have 169 times greater carotene content, 6 times higher the quantity of ascorbate, and twice the level of folate. Genetically modified maize has been produced with increased protein and higher lysine levels

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(Abeurope 2003). To enhance the proportion of biologically utilizable iron, Drakakaki et al. (2005) constructed genetically modified corn plants that are capable to express Aspergillus phytase and the iron-binding protein ferritin. This strategy is effective in increasing iron availability and absorption. By carrying out genetic transformation in maize with the insertion of an antisense gene encoding pyruvate orthophosphate dikinase (PPDK) that is responsible for chilling and cold tolerance, Ohta et al. (2004) made able the maize withstand 3oC lower temperature than the wild variety. Similarly, constant production of tobacco mitogen-activated protein kinase (Nicotiana NPK1), though at low concentration, in transgenic variety of frost-sensitive maize plants resulted in enhanced freezing tolerance (Shou et al. 2004). Moreover, the same gene (NPK1) recombined with an altered constitutive promoter 35SC4PPDK showed a considerable effect on the rates of photosynthesis in drought conditions (Sheen 1993). The beta A gene, which is responsible for the production of choline dehydrogenase catalyzing the conversion of choline into glycine betaine, has been turned into an outstanding maize inbred line, DH4866. The transgenic plants were found to be more drought-­ resistant along with higher yields than the wild type at both the germination and young seedling stages (Quan et al. 2004). Transgenic maize variety having superior salt tolerance was achieved by introducing AtNHX1 gene, and some lines were capable to survive at sodium chloride concentrations of 0.8% and 1.0% (Yin et al. 2004). Castiglioni et al. (2008) delineated that maize lines genetically modified with bacterial RNA chaperones accelerated the yield of grain and abiotic stress tolerance under water-limited conditions. Plant responses to drought tolerance have resulted from the coordination of Arabidopsis’s and maize’s nuclear factor YB subunit (NF-­ YB) protein. Similar activities were found in an orthologous transcript factor of maize, ZmNF-YB2. Drought resistance in transgenic maize plants as a consequence of increased ZmNF-YB2 expression is found to depend on several stress-related parameters such as the content of chlorophyll, temperature of leaves, stomatal conductance, maintenance of photosynthesis, and decreased wilting where there is limited water activity. The same study demonstrated the highest performing line of transgenic maize claimed about 50% yield enhancement comparatively than controls under relatively adverse conditions. The implementation of this technique has the potential to have a substantial influence on drought-prone maize producing systems (Nelson et al. 2007). The Maize Research Institute “Zemun Polje” used neomycin phosphotransferase (NPTII) with marker gene controlling activity for maize transformation (Konstantinov et  al. 1993). Robust procedures for maize transformation by Agrobacterium (Ishida et al. 1996) and protoplast transformation by polyethylene glycol (PEG) were demonstrated between the mid-1990s and early 2000s (Wang et al. 2000). The genotype of corn transformation methods is quite important. As a result, identifying transformable genotypes was a major challenge. High type II callus production (Hi II) has been found to facilitate the formation of highly utilizable calluses; thereby it turned out to be the most popular way of transforming maize (Zhao et al. 2001; Frame et al. 2002). However, Hi type II as a segregating family makes functional analysis of genes more difficult. Consequently, it was revealed

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that baby boom, as well as Wuschel morphogenic regulators encoded by the overexpressed maize genes capable to refractory genotypes to be transformed into leaf cells (Lowe et al. 2016), is a significant step forward in maize transformation.

6.2 Genome Editing for Precision Breeding The use of genome editing methods in maize is predicted to result in a new genetic variety, which will be useful for both fundamental research and the development of improved commercialized cultivars. To cause DNA double-strand breaks (DSBs), nucleases are currently used in genome editing tools such as the zinc finger nucleases (ZFNs), the transcription activator-like effector nucleases (TALENs), and the clustered regularly interspaced palindromic repeats (CRISPR)-CRISPR-associated (Cas) techniques (Georges and Ray 2017). Seed phytate content was reduced when ZFN technique was used to target the IPK (inositol phosphate kinase) homologues (Shukla et  al. 2009). However, the ZFN technique’s restricted capacity to produce large mutation frequencies makes identifying the altered alleles difficult (Puchta and Hohn 2010). TALENs, like ZFNs, have a nonspecific Fok1 nuclease linked to a DNA binding domain. Developing a highly precise domain of TALE that avoids off-target DNA breakage is the most challenging part of utilizing TALENs. This nonspecific DNA editing could have detrimental effects, making it more difficult to achieve a desirable mutation. The rapid advancement in the methodologies of CRISPR-Cas genome editing has started to revolutionize crop improvement including maize (Haque et al. 2018; Islam 2019; Molla et al. 2020; Paul et al. 2021). The CRISPR method has been utilized to alter characteristics such as male sterility, herbicide resistance, secondary metabolism, lignin production, grain quality, and drought resistance in maize (Chilcoat et  al. 2017). The initial application of Cas9/gRNA for gene editing used PEG-mediated protoplast transformation in maize to target the maize IPK gene (ZmIPK) (Liang et al. 2014). Immature embryos from the B73 inbred line were transformed in similar studies (Xing et  al. 2014). Moreover, employing biolistic maize transformation, CRISPR/Cas9, has been used in maize to generate mutations and add or modify genes (Svitashev et al. 2015). Char and co-workers (2017) found that infecting two Agrobacterium isolates with distinct Cas9/gRNA can lead to transgenic crops having up to 70% mutation frequencies, although CRISPR-Cas technology allowed for the manipulation of many maize features (Svitashev et al. 2015;Svitashev et al. 2016; Char et al. 2017). The native maize GOS2 promoter was used to replace and augment the native ARGOS8 promoter by Shi et  al. (2017). Variants having altered expression of ARGOS8, a negative regulator of ethylene responses, demonstrated improved yields under drought stress, while well-watered circumstances revealed no yield penalty (Shi et al. 2017). Recently, Dong et al. (2019) developed sweet and waxy corns by altering starch production cascade in seed endosperms by inducing mutation in the maize SHRUNKEN2 (SH2) and WAXY gene (WX) through CRISPR/Cas9

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technology. It is expected that the CRISPR technology would be used as a convenient tool for the modification of maize for improvement in yield and quality for addressing abiotic and biotic stresses owing to the shift in the global climate.

6.3 Commercialization of Transgenic Maize and Its Consequences Genetically modified (GM) plants are subjected to federal restrictions and guidelines regulating their confinement, transportation, and release into environment. The Federal Republic of Yugoslavia has adopted regulations concerning genetically modified organisms following European Union directives from May 2001. Methods for detecting GMOs (genetically modified organisms) as or in commodities were developed by that strategy. Detecting transgenic DNA or proteins generated from this DNA can be used to determine the presence of GM.  Qualitative PCR-based tests is used at the Maize Research Institute. In general, PCR-based techniques use distinct transgene sequences, promotor sequences (CaMV P-35S), terminator sequences (T-nos), or protein-coding sequences (cp4 epsps, cry1Ab, or pat) (Engl 2019; Turnbull et al. 2021). Commercial transgenic maize cultivation in the United States began in 1996. Around 25% of US maize has developed transgenic tolerance to several herbicides and insects by the year 2000, which rose to approximately 40% and 52% by 2003 and 2005, respectively (Ortiz-Garc´ıa et al. 2005; Brookes and Barfoot 2006a; b). Argentina and Canada were among the other nations that have allowed the distribution of genetically modified (GM) maize by 1996. The governments of France and Spain allowed the usage of GM maize throughout Europe in 1998. Any seed that is allowed in one EU nation is immediately accepted in all the others, according to European legislation. However, the EU ban on novel GM products placed a five-­ year halt on the procedure of extending authorization for MON810 (European corn borer resistance) outside Spain and France. The European Commission lifted the prohibition in May 2004, enabling the cultivation of MON810  in EU countries. However, Greece has refused to remove the prohibition on genetically modified maize, whereas other countries, including Germany, are only approving it on an individual basis as well as for nonfood purposes (Bertho et al. 2020; Turnbull et al. 2021). Venezuela, a non-EU country, passed the Seed Law in the year 2015, which bans all the GM seeds and plants, including the ones that are utilized for research (Apbrebes 2016; Global Agriculture 2016). Venezuela is still heavily reliant on GM maize imported from Brazil, Argentina, and the United States for feed and food (USDA FAS 2018). There is no regulation in Chile governing the import and domestic usage of GM seeds of maize for feed and food; however, cultivation of GM seeds as a local product is prohibited (Salazar et al. 2019; Sánchez and León 2016). As a first African country, South Africa has approved the direct consumption of genetically modified staple food crops, including white maize. The farming of Bt maize

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was originally allowed in Egypt and Burkina Faso. However, Egypt banned the cultivation of genetically modified crops in 2012 (Gakpo 2019), while in 2016, Burkina Faso banned them (Dowd-Uribe and Schnurr 2016). Commercial cultivation of GM crops is permitted in Asia and the Pacific in the following nations, Bangladesh, India, Pakistan, China, Australia, Myanmar, the Philippines, Vietnam, and Indonesia (ISAAA 2018). At the end of 2019, China released a list of 192 genetically modified crops that are scheduled for biosafety certification, including GM maize (Xiaodong 2020; Cremer 2020). Transgenic maizes that produce Bacillus thuringiensis (Bt) insecticidal toxins are commonly employed to manage pests; however, their effects will be lost once the pests develop resistance (Chilcutt and Tabashnik 2004). To assist the survival of vulnerable pests that may have a competitive advantage over resistant pests, the obligatory high-dose/refuge approach for delaying the establishment of pest resistance involves the production of toxin-free crops alongside Bt crops (www.epa.gov/ pesticides/biopesticides/pips/btbrad.htm). Resistance to a Bt crop, on the other hand, has yet to be documented, suggesting that resistance control strategies have thus far been successful. However, existing techniques for postponing resistance are far from satisfactory (Bates et al. 2005). Nevertheless, enforcing the refugee policy is extremely difficult for governments in terrible countries. Furthermore, when Bt toxin levels were moderate to low in refuge plants of non-Bt maize kernels after pollen-mediated genetic flux from Bt maize, it might compromise the high-dose/ refuge strategy and facilitate the fast development of insect species tolerant to Bt crops (Chilcutt and Tabashnik 2004). As a result, it appears that farmers will have to take steps to restrict gene flow among refuge plants and Bt crops. This will be far more difficult than pioneering the refuge strategy. Alternative evolutionarily and environmentally suitable strategies that are straightforward to adopt in underdeveloped nations are unquestionably necessary. Gene flow between transgenic crops and their wild relatives is another major obstacle to determine the hazards of cultivating a GM crop close to its domestication site (Quist and Chapela 2001; Quist and Chapela 2002; Ortiz-Garc´ıa et al. 2005).

7 Application of Nanobiotechnology Nanobiotechnology is an emerging branch of research that focuses on the distinctive physicochemical and biological features of nanostructures, as well as their uses in different biological fields, including agriculture (Asghari et  al. 2016). It is a potential tool that can bring a new age of precision farming strategies, thereby paving the way for increased agricultural production and crop enhancement (Sugunan and Dutta 2008; Misra et  al. 2016). Its current focus is on target farming using nanoparticles (NPs) such as carbon nanomaterials (CNMs), metal-based nanoparticles (MBNPs), silver nanoparticles (SNPs), mesoporous silica NPs (MSNPs), gold (Au) nanoparticles, engineered nanoparticles (ENPs), etc. (Tarafdar et  al. 2014). Researchers are becoming more interested in the utilization of nanoparticles in

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agriculture due to their numerous positive impacts and features like tiny size, high surface-to-volume ratio, chemical reactivity and improved solubility, and magnetic and optical capabilities (Zheng et al. 2005). Several researches have reported a positive reaction to maize growth (Racuciu and Creang 2007; López-Moreno et  al. 2010; Yuvakumar et al. 2011; Salama 2012; Lahiani et al. 2013; Disfani et al. 2017), as well as improvements in nanoparticle exposure, including increased carotenoids and anthocyanin contents (Morteza et  al. 2013), improved crop biomass (Tiwari et al. 2014), enhanced photosynthesis, antioxidant enzyme activities, and reduced oxidative stress (Rizwan et al. 2019). Engineered nanoparticles (ENPs) are a novel vehicle for delivering bioactive compounds such as DNA, proteins, nucleotides, and activators into plant cells (Torney et al. 2007), allowing for their transient existence that could be utilized for genome alterations and even the development of new species (Etxeberria et al. 2006; Liu et al. 2008b; Liu et al. 2009; Lyons 2010; Huang et al. 2011; Martin-Ortigosa et  al. 2012; Martin-Ortigosa et  al. 2014). In genetic engineering, silicon dioxide nanoparticles were utilized to transmit DNA sequences to the targeted maize plant without causing any undesirable side effects (Galbraith 2007; Cheng et al. 2016). As a result, the time-consuming method of DNA transgenics could be eliminated, and modified characteristics could be transmitted to future generations directly. Insect-­ resistant new crop cultivars are also developed using the NP-assisted delivery method. DNA-coated NPs, for instance, are employed as bullets in gene gun technologies to bombard cells or tissues to deliver specific genes to the targeted plants (Lyons 2010; Vijayakumar et al. 2010). A nano-based delivery method enables transient DNA-free plant genome editing by regulated delivery of different biomolecules (include gene silencing), resulting in the development of edited nontransgenic plants that varies from traditional genetic engineering approaches. A nanoparticle-­ associated delivery method may transport several biomolecules to the target cell at the same time, such as DNA and its activator, DNA and proteins, or even multiple genes (Martin-Ortigosa et  al. 2012). Furthermore, by surface functionalization, ENPs may readily be loaded with biological molecules for precise and selective delivery. The widespread and unregulated usage of nanoparticles has sparked a public debate regarding their possible negative impacts on ecosystems. Many studies on the impact of ENPs in plants have shown contradictory results during the last decade (Judy and Bertsch 2014). Many reports have also shown that particle size influences ENP absorption and phytotoxicity; smaller particles accumulate in higher quantities and are thus harmful than larger counterparts (Slomberg and Schoenfisch 2012). ENP coatings have also been utilized to reduce dissolution traits and toxic ion emission (Yang et al. 2012). Although the danger of environmental exposure has risen as a result of global nanomaterial consumption and production, scientists generally believe that knowledge about nanomaterial interactions with plants and microorganisms is limited. In 2009, the European Food Safety Authority (EFSA) published a guideline that focused on nanoparticles’ possible toxicity. The USEPA (US Environmental Protection Agency) approved HeiQ AGS-20, a nanoparticle-based antimicrobial pesticide in 2010, but regulations for using nanomaterials in other

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agrochemicals have remained elusive (Ganzleben et  al. 2011). A comprehensive evaluation of NPs is required in the agri-food industry for public acceptability so that the obstacles encountered by GMOs (genetically modified organisms) throughout the world might be avoided. Their utilization in crop enhancement must be strictly regulated by the safety-by-design principles and directed by functionalization of NP, nano-delivery technologies, and plant physiology to efficiently deliver bioactive molecules to crops while reducing negative effects on other organisms and the environment.

8 Future Perspectives and Concluding Remarks Maize will likely have a key role in influencing the future of crop production and improvement strategies as a multifunctional crop of interest in all the crop-­producing areas in the world. Production, breeding, and genomic advances in maize would have a significant effect on the livelihoods of a large percentage of the global population (Xu and Crouch 2008a). Biotechnology will undoubtedly revolutionize the future of maize breeding. Marker technology advancements combined with MAS offer a novel method for selecting desired genotypes. By balancing informativeness and cost, PCR-based markers could enable DNA marker technologies more effective and inexpensive. Nearly any trait can be mapped and tagged by using saturated linkage maps. The DNA markers have aided in understanding the genetic basis of complicated traits, as well as studying their mechanism of action and how it is regulated by the environment. Breeders may utilize this information to consistently collect the germplasm and employ the most effective resources in the crossing initiatives since DNA markers can provide an accurate estimation of germplasm associations. MAS will progressively develop into more comprehensive genomic-assisted breeding techniques, owing to advancements in multiple omic domains and the integration of speed breeding and digital phenotyping approaches to reduce the amount of time, effort, and resources required for the selection and genetic improvement of crop cultivars to accelerate the production of high-performing varieties with the trait of interest (Chawade et al. 2019; Wanga et al. 2021). Because maize genomic resources are among the richest of any major crop species, genomic research might have a significant impact on maize improvement, including genetic diversity detection which underlies improved performance and increased breeding efficiency. Genome-wide association analysis and the next generation of genetic mapping populations may help researchers better understand the genetic base of trait variation. Linking traits with genes or gene signatures may aid crop breeding in producing hybrids with the desired traits in a short period (Bevan et al. 2017). Transcriptomics is likely to have significant advancements in the foreseeable future, with growing implications for maize physiological research. The identification of transcript pathways in various genotypes will be facilitated by studying wide-scale changes in transcript profiles (Tuberosa et al. 2002). Genome-­ wide transcript profiling may follow the tracks of genome mapping, which had a

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huge effect on the traits regulated by major QTL or genes but still has challenges for controlling complicated traits. Genetic engineering approaches enable plant breeders to produce novel plants by integrating genetic components from many sources. The emerging CRISPR/Cas9 technology may be employed to produce heritable and stable mutations without modifying existing valuable characteristics (Feng et al. 2014; Pan et al. 2016). The characterization and advancement of novel CRISPR effector proteins could extend the spectrum of biotechnological applications through the CRISPR/Cas technology. Recent advances in tissue engineering as well as engineered nanomaterial-based controlled delivery of the CRISPR/Cas, sgRNA, and mRNA for crop genetic manipulation are a remarkable scientific breakthrough (Ran et al. 2017; Kim et al. 2017). Furthermore, by enhancing the precision and efficiency of CRISPR/Cas methods, nanomaterials can minimize the incidence of off-target mutations. For instance, in cultivated cell lines, ArgNPs (cationic arginine gold nanoparticles) constructed Cas9En (E-tag)-RNP (ribonucleoproteins) delivery of sgRNA which offers almost 30% successful nuclear/cytoplasmic genome editing effectiveness, which will significantly aid future maize improvement research (Mout et al. 2017). The development of bionic plants by introducing nanoparticles into living plant chloroplasts and cells for sensing  elements in their environments, and communicating with each other  via infrared sensors, and even self-powering themselves using light sources has an enormous prospect in precision agriculture (Ghorbanpour and Fahimirad 2017; Kwak et al. 2017). Advances in the nanobionic methods for maize improvement and ecological surveillance might pave the way for future research on functional nanomaterial hybrids of maize plants (Giraldo et al. 2014; Wong et al. 2017). The advancement of biomass-to-fuel conversion could be facilitated by nanobiotechnology. Nanomaterials hold the promise of a new green revolution with lower risks for farming. As a result, raising awareness of the benefits and problems of nanotechnology is necessary for better acceptance by people and society, and considerable research is required to comprehend the mechanisms, cytotoxicity, and environmental impact. Since maize is an important crop in both developed and underdeveloped nations and is widely utilized in diverse ways, the north-south cooperation in maize breeding, genomics, and nanobiotechnology must be encouraged for researchers working in theoretical and practical biotechnological disciplines. Though currently the rapid advances in maize breeding, genomics, and nanobiotechnology centered on temperate maize germplasms, it should also be applied for improving the subtropical and tropical maize that is an essential crop for the food security in underdeveloped nations. By the resolution of a plethora of genetic, practical, and administrative obstacles, such as the development of seed DNA-based genotyping (Gao et al. 2008; Xu and Crouch 2008b), and the continuing advancement of effective decision support devices, genomic- and nanobiotechnology-assisted strategies can be anticipated becoming a routine element of crop improvement initiatives of public and private sectors globally.

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References Abdurakhmonov IY (2016) In: Abdurakhmonov IY (ed) Bioinformatics: Basics, development, and future, bioinformatics - Updated features and applications. IntechOpen. Available from: https:// www.intechopen.com/chapters/50934 Abecasis GR, Cookson WO, Cardon LR (2001) The power to detect linkage disequilibrium with quantitative traits in selected samples. Am J Hum Genet 68:1463–1474 ABEurope (2003) Future developments in crop biotechnology. Issue paper 6. www.ABEurope.info Ajala SO, Olayiwola MO, Ilesanmi OJ, Gedil M, Job AO, Olaniyan AB (2019) Assessment of genetic diversity among low-nitrogen-tolerant early generation maize inbred lines using SNP markers. South Afr J Plant Soil 36:181–188 Akhi AH, Ahmed S, Karim ANMS, Begum F, Rohman MM (2017) Genetic divergence of exotic inbred lines of maize (Zea mays L). Bangladesh J Agril Res 42(4):665–671 Anđelković V, Drinić Mladenović S, Babić M, Delić N, Stanković G (2003) Gene expression profiling in response to heat and water stress in maize kernal. Genetika 35(3):139–147 Apbrebes (2016) The new seed law of Venezuela. Available online at: https://www.apbrebes. org/news/new-­seed-­law-­venezuela?pk_campaign=NL22&utm_content=buffer34dc3&utm_ medium=social&utm_source=facebook.com&utm_campaign=buffer. Accessed 23 June 2021 Asghari F, Jahanshiri Z, Imani M, Shams-Ghahfarokhi M, Razzaghi-Abyaneh M (2016) Nanobiomaterials in antimicrobial therapy || Antifungal nanomaterials. Antifungal Nanomaterials: Synthesis, Properties, and Applications 6:343–383 Azmach G, Gedil M, Menkir A, Spillane C (2013) Marker-trait association analysis of functional gene markers for provitamin A levels across diverse tropical yellow maize inbred lines. BMC Plant Biol 13:227–242 Azmach G, Menkir A, Spillane C, Gedil M (2018) Genetic loci controlling carotenoid biosynthesis in diverse tropical maize lines. G3-Genes Genomes Genet 8:1049–1065 Babu R, Prasanna BM (2014) Molecular breeding for quality protein maize (QPM). In: Tuberosa R (ed) Genomics of plant genetic resources. Springer, pp 489–505 Babu R, Nair SK, Kumar A et al (2005a) Two-generation marker-aided backcrossing for rapid conversion of normal maize lines to quality protein maize (QPM). Theor Appl Genet 111:888–897 Babu R, Nair SK, Kumar A et  al (2005b) Two-generation marker-aided backcrossing for rapid conversion of normal maize lines to quality protein maize (QPM). Theor Appl Genet 111(5):888–897 Babu R, Nair SK, Kumar A, Rao HS et al (2006) Mapping QTLs for popping ability in a popcorn 9 flint corn cross. Theor Appl Genet 112:1392–1399 Badu-Apraku B, Akinwale R (2011) Identification of early-maturing maize inbred lines based on multiple traits under drought and low N environments for hybrid development and population improvement. Can J Plant Sci 91(5):931–942 Badu-Apraku B, Annor B, Oyekunle M, Akinwale RO, Fakorede MAB, Talabi AO et al (2015) Grouping of early maturing quality protein maize inbreds based on SNP markers and combining ability under multiple environments. Field Crops Res 183:169–183 Badu-Apraku B, Fakorede MAB, Gedil M, Annor B, Talabi AO, Akaogu I et al (2016a) Heterotic patterns of IITA and CIMMYT early-maturing yellow maize inbreds under contrasting environments. Agron J 108:321–1336 Badu-Apraku B, Fakorede MAB, Talabi AO, Oyekunle M, Akaogu IC, Akinwale RO et al (2016b) Gene action and heterotic groups of early white quality protein maize inbreds under multiple stress environments. Crop Sci 56:83–199 Bänziger M, Long J (2000) The potential for increasing the iron and zinc density of maize through plant-breeding. Food Nutr Bull 21:397–400 Bates SL, Zhao J-Z, Roush RT, Shelton AM (2005) Insect resistance management in GM crops: past, present and future. Nat Biotechnol 23(1):57–62 Bennetzen JL, Chandler VL, Schnable P (2001) National science foundation-sponsored workshop report. Maize genome sequencing project. Plant Physiol 127:1572–1578

166

M. Chakraborty et al.

Bertho L, Schmidt K, Schmidtke J, Brants I, Cantón RF, Novillo C et al (2020) Results from ten years of post-market environmental monitoring of genetically modified MON 810 maize in the European Union. PLoS ONE 15(4):e0217272 Bevan M, Uauy C, Wulff B, Zhou J, Krasileva K, Clark MD (2017) Genomic innovation for crop improvement. Nature 543:346–354 Bhattramakki D, Dolan M, Hanafey R, Wineland R, Vaske JC, Register JC, Tingey SV, Rafalski A (2002) Insertion-deletion polymorphism in 3' regions of maize genes occur frequently and can be used as highly informative genetic markers. Plant Mol Biol 48:539–547 Boopathi NM (2020) Marker-Assisted Selection (MAS). Genetic mapping and marker assisted selection. Springer, Singapore, In Bouchez A, Hospital F, Causse M, Gallais A, Charcosset A (2002) Marker-assisted introgression of favorable alleles at quantitative trait loci between maize elite lines. Genetics 162(4):1945–1959 Brookes G, Barfoot P (2006a) Global impact of biotech crops: Socio-economic and environmental effects in the first ten years of commercial use. AgBioForum 9(3):139–151 Brookes G, Barfoot P (2006b) GM crops: the first ten years- global socio-economic and environmental impacts. ISAAA Briefs 36 Castiglioni P, Warner D, Bensen RJ et al (2008) Bacterial RNA chaperones confer abiotic stress tolerance in plants and improved grain yield in maize under water-limited conditions. Plant Physiol 147(2):446–455 Chandran S, Pukalenthy B, Adhimoolam K, Manickam D, Sampathrajan V, Chocklingam V, Eswaran K, Arunachalam K, Joikumarmeetei L, Rajasekaran R, Muthusamy V, Hossain F, Natesan S (2019) Marker-Assisted selection to pyramid the Opaque-2 (O2) and β-Carotene (crtRB1) genes in maize. Front Genet 10:859 Char SN, Neelakandan AK, Nahampun H, Frame B, Main M, Spalding MH, Becraft PW, Meyers BC, Walbot V, Wang K, Yang B (2017) An Agrobacterium-delivered CRISPR/Cas9 system for high-frequency targeted mutagenesis in maize. Plant Biotechnol J 15:257–268 Chawade A, van Ham J, Blomquist H, Bagge O, Alexandersson E, Ortiz R (2019) High-throughput field-phenotyping tools for plant breeding and precision agriculture. Agronomy 9(5):258 Chen F, Song Y, Li X, Chen J, Mo L, Zhang X, Lin Z, Zhang L (2019) Genome sequences of horticultural plants: past, present, and future. Hortic Res 6:112 Cheng HN, Klasson KT, Asakura T, Wu Q (2016) Nanotechnology in agriculture. In: Cheng HN, Doemeny L, Geraci CL, Schmidt DG (eds) Nanotechnology: Delivering on the Promise, vol 2. ACS, Washington, DC, pp 233–242 Chilcoat D, Liu Z-B, Sander J (2017) Use of CRISPR/Cas9 for crop improvement in maize and soybean. Prog Mol Biol Transl Sci 149:27–46 Chilcutt CF, Tabashnik BE (2004) Contamination of refuges by Bacillus thuringiensis toxin genes from transgenic maize. Proceedings of the National Academy of Sciences of the UnitedStates of America. 101(20):7526–7529 Ching A, Caldwell KS, Jung M, Dolan M, Smith OS, Tingey S, Morgante M, Rafalski AJ (2002) SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines. BMC Genet 3:19 Choukan R, Hossainzadeh A, Ghannadha MR et al (2006) Use of SSR data to determine relationships and potential heterotic groupings within medium to late maturing Iranian maize inbred lines. Field Crops Res 95:212–222 Cocciolone SM, Nettleton D, Snook ME, Peterson T (2005) Transformation of maize with the p1 transcription factor directs production of silk maysin, a corn earworm resistance factor, in concordance with a hierarchy of floral organ pigmentation. Plant Biotechnol J 3(2):225–235 Coe EH, Sarkar KR (1966) Preparation of nucleic acids and a genetic transformation attempt in maize. Crop Sci 6:432–435 Cone KC, McMullen MD, Bi IV et  al (2002) Genetic, physical, and informatics resources for maize. On the road to an integrated map. Plant Physiol 130:1598–1605 Cremer J (2020) China moves toward commercialization of GMO corn and soy. Alliance Science, Ithaca, NY

Current Biotechnological Approaches in Maize Improvement

167

Danson JW, Mbogori M, Kimani M, Lagat M, Kuria A, Diallo A (2006) Marker assisted introgression of opaque2 gene into herbicide resistant elite maize inbred lines. African J Biotechnol 5(24):2417–2422 Dhliwayo T, Pixley K, Menkir A, Warburton M (2009) Combining ability, genetic distances, and heterosis among elite CIMMYT and IITA tropical maize inbred lines. Crop Sci 49:1201–1210 Ding J-Q, Wang X-M, Chander S, Li J-S (2008) Identification of QTL for maize resistance to common smut by using recombinant inbred lines developed from the Chinese hybrid Yuyu22. J Appl Genet 49:147–154 Disfani MN, Mikhak A, Kassaee MZ, Maghari A (2017) Effects of nano Fe/SiO2 fertilizers on germination and growth of barley and maize. Arch Agron Soil Sci 63:817–826 Dong L, Qi X, Zhu J, Liu C, Zhang X, Cheng B, Mao L, Xie C (2019) Super sweet and waxy: meeting the diverse demands for specialty maize by genome editing. Plant Biotechnol J 17(10):1853–1855 Dowd-Uribe B, Schnurr MA (2016) Burkina Faso’s reversal on genetically modified cotton and the implications for Africa. Afr Aff 115:161–172 Drakakaki G, Marcel S, Glahn RP et al (2005) Endosperm specific co-expression of recombinant soybean ferritin and Aspergillus phytase in maize results in significant increases in the levels of bioavailable iron. Plant Mol Biol 59(6):869–880 Drinić Mladenovič S, Trifunović S, Drinić G, Konstantinov K (2002) Genetic divergence and its correlation to heterosis in maize as revealed by SSR-based markers. Maydicam 47:1–8 Drinić G, Drinić Mladenović S, Barišić N (2000) Genetic relationship among maize inbred lines revealed by protein molecular markers. XVIII International conference on maize and sorghum genetics and breeding at the end of the 20th century, Maize and sorghum Eucarpia, Belgrade, june 4-9. Dubreuil P, Dufour P, Krejci E, Causse M, De Vienne D, Gallais A, Charcosset A (1996) Organization of RFLP diversity among inbred lines of maize representing the most significant heterotic groups. Crop Sci 36:790–799 Duvick DN, Cassman KG (1999) Post-green-revolution trends in yield potential of temperate maize in the north-central United States. Crop Sci 29:1622–1630 Dwivedi SL, Crouch JH, Mackill DJ et al (2007) The molecularization of public sector crop breeding: progress, problems, and prospects. Adv Agron 95:163–318 Eathington SR, Dudley JW, Rufener GK II (1997) Usefulness of marker-QTL associations in early generation selection. Crop Sci 37(6):1686–1693 Edmeades GO, Trevisan W, Prasanna BM, Campos H (2017) Tropical maize (Zea mays L.). In: Campos H, Caligari PDS (eds) Genetic improvement of tropical crops. Springer, New York, pp 57–109 Edwards EJ (2011) Rapid report: New grass phylogeny resolves deep evolutionary relationships and discovers C 4 origins. New Phytol 193:304–312 Elumalai R, Gchachu M, Pierson E, Chandler V, Galbraith DW (2002) Maize cDNA Microarrays. MNL 76:56 Emrich SJ, Aluru S, Fu Y, Wen TJ, Narayanan M, Guo L, Ashlock DA, Schnable PS (2004) A strategy for assembling the maize (Zea mays L.) genome. Bioinformatics 20(2):140–147 Engl (2019) Detection of food and feed plant products obtained by new mutagenesis techniques. Available online at: https://gmo-­crl.jrc.ec.europa.eu/doc/JRC116289-­GE-­report-­ENGL.pdf. Accessed 23 Jan 2021 Erić I (2004) Genetička karakterizacija ZP hibrida kukuruza iz različitih ciklusa selekcije primenom molekualrnih markera. Magistarski rad. Biološki fakultet, Univerzitet u Beogradu Erić I, Drinić Mladenović S, Konstantinov K, Stankovic G (2003) Genetic diversity among ZP maize hybrids from different selection cycles obtained by protein markers. MGNL 77:7–8 Etxeberria E, Gonzalez P, Baroja-Fernandez E, Romero JP (2006) Fluid phase endocytic uptake of artificial nano-spheres and fluorescent quantum dots by sycamore cultured cells: evidence for the distribution of solutes to different intracellular compartments. Plant Signal Behav 1:196–200

168

M. Chakraborty et al.

Fang C, Ma Y, Wu S, Liu Z, Wang Z, Yang R et al (2017) Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean. Genome Biol 18:161 FAO (2021) World Agricultural Production [Internet]. Available from: http://faostat.fao.org/ default.aspx. Accessed on 14 June 2021 FAOSTAT (2018) Crop data. FAO United Nations, Rome. http://www.fao.org/faostat/en/#data/QC Feng Z, Mao Y, Xu N, Zhang B, Wei P, Yang DL et al (2014) Multigeneration analysis reveals the inheritance, specificity, and patterns of CRISPR/Cas-induced gene modifications in Arabidopsis. Proc Natl Acad Sci USA 111:4632–4637 Feng F, Wang Q, Liang C et  al (2015) Enhancement of tocopherols in sweet corn by marker-­ assisted backcrossing of ZmVTE4. 4 206:513–521 Flint-Garcia SA, Darrah LL, McMullen MD, Hibbard BE (2003) Phenotypic versus marker-­ assisted selection for stalk strength and second-generation European corn borer resistance in maize. Theor Appl Genet 107(7):1331–1336 Fountain JC, Khera P, Yang L, Nayak SN, Scully BT, Lee RD et al (2015) Resistance to Aspergillus flavus in maize and peanut: molecular biology, breeding, environmental stress, and future perspectives. Crop J 3:229–237 Frame BR, Shou H, Chikwamba RK, Zhang Z, Xiang C, Fonger TM, Pegg SE, Li B, Nettleton DS, Pei D, Wang K (2002) Agrobacterium tumefaciens-mediated transformation of maize embryos using a standard binary vector system. Plant Physiol 129:13–22 Fromm ME, Taylor LP, Walbot V (1986) Stable transformation of maize after gene transfer by electroporation. Nature 319:791–793 Fu Y, Emrich SJ, Guo L, Wen TJ, Ashlock DA, Aluru S, Schnable PS (2005) Quality assessment of maize assembled genomic islands (MAGIs) and large-scale experimental verification of predicted genes. Proc Natl Acad Sci USA 102(34):12282–12287 Fu Y, Wen T-J, Ronin YI, Chen HD et al (2006) Genetic dissection of intermated Recombinant Inbred Lines using a new genetic map of maize. Genetics 174:1671–1683 Gaj T, Sirk SJ, Shui SL, Liu J (2016) Genome-editing technologies: Principles and applications. Cold Spring Harb. Perspect.Biol 8(12):a023754 Gakpo JO (2019) Egypt poised to again lead Africa in ag biotech innovation. Alliance Science, Ithaca, NY Galbraith DW (2007) Nanobiotechnology  - Silica breaks through in plants. Nat Nanotechnol 2:272–273 Ganzleben C, Pels F, Hansen SF, Corden C, Grebot B, Sobey M (2011) Review of environmental legislation for the regulatory control of nanomaterials.http://ec.europa.eu/environment/chemicals/nanotech/pdf/review_legislation.pdf. Accessed 8 June 2021 Gao S, Martinez C, Skinner D, Krivanek A, Crouch JH, Xu Y (2008) Development of a single-seed based genotyping system for marker assisted selection in maize. MolBreed 22:477–494 Garcia-Oliveira AL, Chander S, Ortiz R, Menkir A, Gedil M (2018) Genetic basis and breeding perspectives of grain iron and zinc enrichment in cereals. Front Plant Sci 9:937 Garg A, Prasanna BM, Sharma RC et  al (2009) Genetic analysis and mapping of QTLs for resistance to banded leaf and sheath blight (Rhizoctonia solani f.sp. sasakii) in maize. In: Proceedings of 10th Asian regional maize workshop (October 20–23, 2008, Makassar, Indonesia). CIMMYT, Mexico DF. (in press) Gebremeskel S, Garcia-Oliveira AL, Menkir A, Adetimirin V, Gedil M (2018) Effectiveness of predictive markers for marker assisted selection of pro-vitamin A carotenoids in medium-late maturing maize (Zea mays L.) inbred lines. J Cereal Sci 79:27–34 George MLC, Prasanna BM, Rathore RS et al (2003) Identification of QTLs conferring resistance to downy mildews of maize in Asia. Theor Appl Genet 107:544–551 George MLC, Regalado E, Li W et al (2004) Molecular characterization of Asian maize inbred lines by multiple laboratories. Theor Appl Genet 109:80–91 Georges F, Ray H (2017) Genome editing of crops: a renewed opportunity for food security. GM Crops Food 8:1–12

Current Biotechnological Approaches in Maize Improvement

169

Ghorbanpour M, Fahimirad S (2017) Plant nanobionics a novel approach to overcome the environmental challenges. In: Ghorbanpour M, Varma A (eds) medicinal plants and environmental challenges. Springer International Publishing, Cham, Switzerland, pp 247–257 Giraldo JP, Landry MP, Faltermeier SM, McNicholas TP, Iverson NM, Boghossian AA, Reuel NF, Hilmer AJ, Sen F, Brew JA (2014) Plant nanobionics approach to augment photosynthesis and biochemical sensing. Nat Mater 13:400 Global Agriculture (2016) Venezuela passes new seed law banning genetically modified crops. Available Online at: https://www.globalagriculture.org/whatsnew/news/en/31519.html. Accessed 31 Mar 2021 Golovkin MV, Abraham M, Morocz S, Bottka S, Feder A, Dudits D (1993) Production of transgenic maize plants by direct DNA uptake into embryogenic protoplasts. Plant Sci 90:41–52 Gordon-Kamm WJ, Spencer TM, Mangano ML, Adams TR, Daines RJ, Start WG, O’Brien JV, Chambers SA, Adams WR Jr, Willets NG, Rice TB, Mackey CJ, Krueger RW, Kausch AP, Lemaux PG (1990) Transformation of maize cells and regeneration of fertile transgenic plants. Plant Cell 2:603–618 Gosal SS, Wani SH (2018) Plant Genetic Transformation and transgenic crops: Methods and applications. In: Gosal S, Wani S (eds) Biotechnologies of crop improvement, vol 2. Springer, Cham. https://doi.org/10.1007/978-­3-­319-­90650-­8_1 Goswami R, Rajkumar Uttamrao Z, Suphiya K, Aanchal B, Vignesh M, Firoz H, Thomas L (2019) Marker-assisted introgression of rare allele of β-carotene hydroxylase (crtRB1) gene into elite quality protein maize inbred for combining high lysine, tryptophan and provitamin A in maize. Plant Breed 138(2):174–183 Graham IG, Wolff D, Stuber C (1997) Characterization of a yield quantitative trait locus on chromosome five of maize by fine mapping. Crop Sci 37:1601–1610 Grimsley N, Hohn T, Davies JW, Hohn B (1987) Agrobacterium mediated delivery of infectious maize streak virus into maize plants. Nature 325:177–179 Gupta PK (2008) Single-molecule DNA sequencing technologies for future genomics research. Trend Biotechnol 26(11):602–611 Gupta Hari S, Babu R, Agrawal PK, Vinay M, Hossain F, Thirunavukkarasu N, Gupta P (2013) Accelerated development of quality protein maize hybrid through marker-assisted introgression of opaque-2 allele. Plant Breed 132(1):77–82 Gupta HS, Agrawal PK, Mahajan V et al (2009) Quality protein maize for nutritional security: rapid development of short duration hybrids through molecular marker assisted breeding. Curr Sci 96:230–237 Guzmán C, Medina-Larqué AS, Velu G, González-Santoyo H, Singh RP, Huerta-Espino J et  al (2014) Use of wheat genetic resources to develop biofortified wheat with enhanced grain zinc and iron concentrations and desirable processing quality. J Cereal Sci 60:617–622 Haberer G, Young S, Bharti AK et al (2005) Structure and architecture of the maize genome. Plant Physiol 139(4):1612–1624 Hake S, Ross-Ibarra J (2015) Genetic, evolutionary and plant breeding insights from the domestication of maize. ELife 4:1–8 Hallauer AR, Russel WA, Lamkay KR (1988) Corn breeding. In: Sprague GF, Dudley JW (eds) Corm and corn improvement, 3rd edn. Am Soc Agron, Madison, pp 463–564 Hao Z, Li X, Xie C et al (2008) Two consensus quantitative trait loci clusters controlling anthesis-­ silking interval, ear setting and grain yield might be related with drought tolerance in maize. Ann Appl Biol 153:73–83 Hao X, Li X, Yang X et al (2014) Transferring a major QTL for oil content using marker-assisted backcrossing into an elite hybrid to increase the oil content in maize. Mol Breed 34:739–748 Haque E, Taniguchi H, Hassan MM, Bhowmik P, Karim MR, Śmiech M, Zhao K, Rahman M, Islam T (2018) Application of CRISPR/Cas9 Genome Editing Technology for the Improvement of Crops Cultivated in Tropical Climates: Recent Progress, Prospects, and Challenges. Front Plant Sci 9:617

170

M. Chakraborty et al.

Hardigan MA, Laimbeer FPE, Newton L, Crisovan E, Hamilton JP, Vaillancourt B et al (2017) Genome diversity of tuber-bearing Solanum uncovers complex evolutionary history and targets of domestication in the cultivated potato. Proc Natl Acad Sci USA 114:E9999–E10008 Harjes CE, Rocheford TR, Bai L et al (2008) Natural genetic variation in Lycopene epsilon cyclase tapped for maize biofortification. Science 319:330–333 Hindu V, Palacios-Rojas N, Babu R, Suwarno WB, Rashid Z, Usha R et al (2018) Identification and validation of genomic regions influencing kernel zinc and iron in maize. Theor Appl Genet 131:1443–1457 Ho JC, McCouch SR, Smith ME (2002) Improvement of hybrid yield by advanced backcross QTL analysis in elite maize. Theor Appl Genet 105(2-3):440–448 Holloway AJ, Van Laar R, Tothill T, Owtell D (2002) Option available from start to finish for obtaining data from DNA microarrays II. Nat genet suppl 32:481–489 Hossain F, Nepolean T, Vishwakarma AK et al (2015) Mapping and validation of microsatellite markers linked to sugary1 and shrunken2 genes in maize (Zea mays L.). J Plant Biochem Biotechnol 24:135–142 Hossain F, Muthusamy V, Pandey N et al (2018) Marker-assisted introgression of opaque2 allele for rapid conversion of elite hybrids into quality protein maize. J Genet 97:287–298 Hossain A, Skalicky M, Brestic M, Maitra S, Ashraful Alam M, Syed MA, Hossain J, Sarkar S, Saha S, Bhadra P, Shankar T, Bhatt R, Kumar Chaki A, EL Sabagh A, Islam T (2021) Consequences and mitigation strategies of abiotic stresses in wheat (Triticum aestivum L.) under the changing climate. Agronomy 11(2):241 Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y et al (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967 Huang X, Stein BD, Cheng H, Malyutin A, Tsvetkova IB, Baxter DV, Remmes NB, Verchot J, Kao C, Bronstein LM, Dragnea B (2011) Magnetic virus-like nanoparticles in N. benthamiana plants: a new paradigm for environmental and agronomic biotechnological research. ACS Nano 5:4037–4045 Huang X, Kurata N, Wang Z-X, Wang A, Zhao Q, Zhao Y et al (2012a) A map of rice genome variation reveals the origin of cultivated rice. Nature 490:497–501 Huang X, Zhao Y, Li C, Wang A, Zhao Q, Li W et al (2012b) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39 ISAAA (2018) Global status of commercialized Biotech/GM crops in 2018: Executive Brief. ISAAA, Ithaca, NY ISAAA (2019) Global status of commercialized biotech/GM crops: ISAAA Brief No. 55. ISAAA: Ithaca, NY Ishida Y, Saito H, Ohta SH, Hiei Y, Komari T, Kumashiro T (1996) High efficiency transformation of maize (Zea mays L.) mediated by Agrobacterium tumefaciens. Nat Biotechnol 14:745–750 Islam T (2019) CRISPR-Cas technology in modifying food crops. CAB Reviews 14:1–16 Jiang G-L (2013) Molecular markers and marker-assisted breeding in plants, Plant Breeding from Laboratories to Fields, Sven Bode Andersen, IntechOpen, Available from: https:// www.intechopen.com/books/plant-­b reeding-­f rom-­l aboratories-­t o-­f ields/molecular markers-and-marker-assisted-breeding-in-plants Jompuk C, Cheuchart P, Jompuk P, Apisitwanich S (2011) Improved tryptophan content in maize with Opaque-2 gene using marker assisted selection (MAS) in backcross and selfing generations. Kasetsart J (Nat Sci) 45:666–674 Judy JD, Bertsch PM (2014) Bioavailability, toxicity, and fate of manufactured nanomaterials in terrestrial ecosystems. Adv Agron 123:1–64 Kahvejian A, Quackenbush J, Thompson JF (2008) What would you do if you could sequence everything? NatBiotechnol 26(10):1125–1133 Kantety RV, La Rota M, Matthews DE, Sorrells ME (2002) Data mining for simple sequence repeats in expressed sequence tags from barley, maize, rice, sorghum and wheat. Plant Mol Biol 48(5–6):501–510

Current Biotechnological Approaches in Maize Improvement

171

Kaur R, Kaur G, Vikal Y et al (2020) Genetic enhancement of essential amino acids for nutritional enrichment of maize protein quality through marker assisted selection. Physiol Mol Biol Plants 26:2243–2254 Khanduri A, Tiwari A, Prasanna BM et  al (2009) Conversion of elite maize lines in India into QPM versions using an integrated phenotypic and molecular marker-assisted selection strategy. In: Proceedings of 10th Asian regional maize workshop (October 20–23, 2008, Makassar, Indonesia). CIMMYT, Mexico DF. (in press) Kim DH, Gopal J, Sivanesan I (2017) Nanomaterials in plant tissue culture: The disclosed and undisclosed. RSC Adv 7:36492–36505 Konstantinov K, Mladenović S, Saratlić G, Gošić S, Tadić B, Šukalović V, Dumanović J (1993) Transgenic maize: disturbance of host genome expression by bacterial gene integration. In: « Breeding and molecular biology: accomplishment and future promises». Proc Maize and sorghum EUCARPIA, june. Bergamo, Italy, pp 6–9 Kostadinovic M, Ignjatovic-Micic D, Vancetovic J, Ristic D, Bozinovic S, Stankovic G et  al (2016) Development of high tryptophan maize near isogenic lines adapted to temperate regions through marker assisted selection - Impediments and benefits. PLoS ONE 11(12):e0167635 Krishna MSR, Sokka Reddy S, Satyanarayana SDV (2017) Marker-assisted breeding for introgression of opaque-2 allele into elite maize inbred line BML-7. 3 Biotech 7(3):165 Kumar S, Purkyastha S, Roy C, Ranjan T, Ranjan RD (2020) Genes for different abiotic stresses tolerance in wheat, plant stress physiology. Akbar Hossain, IntechOpen. Available from: https://www.intechopen.com/books/plant-­s tress-­p hysiology/genes-­f or-­d ifferent-­a biotic­stresses-­tolerance-­in-­wheat Kwak SY, Giraldo JP, Wong MH, Koman VB, Lew TTS, Ell J, Weidman MC, Sinclair RM, Landry MP, Tisdale WA (2017) A nanobionic light-emitting plant. Nano Lett 17:7951–7961 Kyeter DT (1995) Genetic basis of tolerance in maize to maize streak virus using molecular markers. Ohio State University, Ohio, Ph.D.Thesis, 169pp Lahiani MH, Dervishi E, Chen JH, Nima Z, Gaume A, Biris AS, Khodakovskaya MV (2013) Impact of carbon nanotube exposure to seeds of valuable crops. ACS Appl Mater Interfaces 5:7965–7973 Landi P, Sanguineti MC et al (2007) Root-ABA1 QTL affects root lodging, grain yield, and other agronomic traits in maize grown under well-watered and water-stressed conditions. J Exp Bot 58(2):319–326 Lanza LB, Jr De Souza CL, Ottoboni LMM, Vieira MLC, De Souza AP (1997) Genetic distance of inbred lines and prediction of maize single-cross performance using RAPD markers. Theor Appl Genet 94:1023–1030 Lee M, Sharopova N, Beavis WD et al (2002) Expanding the genetic map of maize with the intermated B73 x Mo17 (IBM) population. Plant Mol Biol 48:453–461 Leming SHI (2002) DNA microarray. www.gene-­chips.com Leushner J, Chiu NH (2000) Automated mass spectrometry: a revolutionary technology for clinical diagnostics. Mol Diagn 5(4):341–348 Li Y-L, Dong Y-B, Niu S-Z (2006) QTL analysis of popping fold and the consistency of QTLs under two environments in popcorn. Acta Genetica Sinica 33:724–732 Li XH, Wang ZH, Gao SH et al (2008) Analysis of QTL for resistance to head smut (Sporisorium reiliana) in maize. Field Crops Res 106:148–155 Liang Z, Zhang K, Chen K, Gao C (2014) Targeted mutagenesis in Zea mays using TALENs and the CRISPR/Cas system. J GenetGenom 41:63–68 Liu X, He D, Zhang H et al (2006) QTL mapping for resistance to MDMV2B in maize. J Agric Univ Hebei 29:56–59 Liu X, Xie C, Zhao Q et al (2008a) Establishment of fluorescent SSR technique on detecting allelic frequency in maize (Zea mays L.) populations with bulk sampling strategy in China. Sci Agric Sin 41:3991–3998

172

M. Chakraborty et al.

Liu J, Wang F, Wang L, Xiao S, Tong C, Tang D, Liu X (2008b) Preparation of fluorescence starch-nanoparticle and its application as plant transgenic vehicle. J Central South Uni Technol 15(6):768–773 Liu Q, Chen B, Wang Q, Shi X, Xiao Z, Lin J, Fang X (2009) Carbon nanotubes as molecular transporters for walled plant cells. Nano Lett 9:1007–1010 López-Moreno ML, de la Rosa G, Hernández-Viezcas JA, Peralta-Videa JR, Gardea-Torresdey JL (2010) XAS corroboration of the uptake and storage of CeO2 nanoparticles and assessment of their differential toxicity in four edible plant species. J Agric Food Chem 58(6):3689–3693 Lowe K, Wu E, Wang N, Hoerster G, Hastings C, Cho MJ, Scelonge C, Lenderts B, Chamberlin M, Cushatt J, Wang L, Ryan L, Khan T, Chow-Yiu J, Hua W, Yu M, Banh J, Bao Z, Brink K, Igo E, Rudrappa B, Shamseer PM, Bruce W, Newman L, Shen B, Zheng P, Bidney D, Falco C, Register J, Zhao ZY, Xu D, Jones T, Gordon-Kamm W (2016) Morphogenic regulators Baby boom and Wuschel improve monocot transformation. Plant Cell 28:1998–2015 Lu H, Romero-Severson J, Bernardo R (2003) Genetic basis of heterosis explored by simple sequence repeat markers in a random-mated maize population. Theor Appl Genet 107(3):494–502 Lu GH, Tang JH, Yan J-B et al (2006) Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol 48:1233–1243 Lyons K (2010) Nanotechnology: Transforming food and the environment. Food First Backgr 16:1–4 Mafu NF (2013) Marker-assisted selection for maize streak virus resistance and concomitant conventional selection for downy mildew resistance in a maize population. 177 pp. [MSc Thesis, University of KwaZulu-Natal] Magulama EE, Sales EK (2009) Marker-assisted introgression of opaque2 gene into elite maize inbred lines.USM. R D 17(2):131–135 Maqbool MA, Issa ARB, Khokhar ES (2021) Quality protein maize (QPM): Importance, genetics, timeline of different events, breeding strategies and varietal adoption. Plant Breed 140:375–399 Martin-Ortigosa S, Valenstein JS, Sun W, Moeller L, Fang N, Trewyn BG, Lin VS, Wang K (2012) Parameters affecting the efficient delivery of mesoporous silica nanoparticle materials and gold nanorods into plant tissues by the biolistic method. Small 8:413–422 Martin-Ortigosa S, Peterson DJ, Valenstein JS, Lin VS, Trewyn BG, Lyznik LA, Wang K (2014) Mesoporous silica nanoparticle-mediated intracellular Cre-protein delivery for maize genome editing via lox-P site excision. Plant Physiol 164:537–547 Mehta BK, Vignesh M, Zunjare RU, Aanchal B, Chauhan HS, Chhabra R, Singh AK, Hossain F (2020) Biofortification of sweet corn hybrids for provitamin-A, lysine and tryptophan using molecular breeding. J Cereal Sci 96:103093 Melchinger AE (1999) Genetic diversity and heterosis. In: Coors JG, Pandey S (eds) The genetics and exploitation of heterosis. ASA, CSSA, and SSSA, Madison, pp 99–118 Melchinger AE, Messmer MM, Lee M, Woodman WL, Lamkey KR (1991) Diversity and relationships among U.S. maize inbreds revealed by restriction fragment length polymorphisms. Crop Sci 31:669–678 Melchinger AE, Boppenmaier J, Dhillon BS, Pollmer WG, Herrmann RG (1992) Genetic diversity for RFLPs in European maize inbreds, II. Relation to performance of hybrids within versus between heterotic groups for forage traits. Theor Appl Genet 84:672–681 Mengesha WA, Menkir A, Unakchukwu N, Meseka S, Farinola A, Girma G et al (2017) Genetic diversity of tropical maize inbred lines combining resistance to Striga hermonthica with drought tolerance using SNP markers. Plant Breed 136:338–343 Menkir A (2008) Genetic variation for grain mineral content in tropical-adapted maize inbred lines. Food Chem 110:454–464 Menkir A, Brown RL, Bandyopadhyay R, Cleveland TE (2008) Registration of six tropical maize germplasm lines with resistance to aflatoxin contamination. J Plant Regist 2:246–250

Current Biotechnological Approaches in Maize Improvement

173

Menkir A, Franco J, Adepoju A, Bossey B (2012) Evaluating consistency of resistance reactions of open-pollinated maize cultivars (Del.) Benth under artificial infestation. Crop Sci 52:1051–1060 Menkir A, Kling JG, Anjorin B, Ladejobi F, Gedil M (2015) Evaluating testcross performance and genetic divergence of lines derived from reciprocal tropical maize composites. Maydica 60:1–10 Meseka S, Menkir A, Bossey B, Mengesha W (2018) Performance assessment of drought tolerant maize hybrids under combined drought and heat stress. Agronomy 8:274 Messing J, Bharti AK, Karlowski WM et al (2004) Sequence composition and genome organization of maize. Proceedings of the National Academy of Sciences of the United States of America 101(40):14349–14354 Michelmore RW, Paran I, Kesseli RV (1991) Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci USA 88:9828–9832 Misra P, Shukla PK, Pramanik K, Gautam S, Kole C (2016) Nanotechnology for crop improvement. In: Khodakovskaya M (ed) Kole C, Kumar D. Plant Nanotechnology. Springer, Cham, pp 219–256 Mohammadi SA, Prasanna BM, Sudan C, Singh NN (2002) A microsatellite marker based study of chromosomal regions and gene effects on yield and yield components in maize. Cell Mol Biol Letters 7:599–606 Mohammadi SA, Prasanna BM, Sudan C, Singh NN (2008) SSR heterogenic patterns of maize parental lines and prediction of heterotic performance. Biotechnol Biotech Eq 22:541–547 Mohan M, Nair S, Bhagwat A, Krishna TG, Yano M, Bhatia CR, Sasaki T (1997) Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol Breed 3:87–103 Molla KA, Karmakar S, Islam MT (2020) Wide Horizons of CRISPR-Cas-derived technologies for basic biology, agriculture, and medicine. In: Islam MT, Bhowmik PK, Molla KA (eds) CRISPR-Cas methods. Springer Protocols Handbooks. Humana, New York, NY Morris M, Dreher K, Ribaut J-M, Khairallah M (2003) Money matters (II): costs of maize inbred line conversion schemes at CIMMYT using conventional and marker assisted selection. Mol Breed 11:235–247 Morteza E, Moaveni P, Farahani HA, Kiyani M (2013) Study of photosynthetic pigments changes of maize (Zea mays L.) under nano TiO2 spraying at various growth stages. Springer Plus 2(1):247 Mout R, Ray M, Yesilbag Tonga G, Lee Y-W, Tay T, Sasaki K, Rotello VM (2017) Direct cytosolic delivery of CRISPR/Cas9-ribonucleo protein for efficient gene editing. ACS Nano 11:2452–2458 Muthusamy V, Hossain F, Thirunavukkarasu N, Choudhary M, Saha S et al (2014) Development of b-carotene rich maize hybrids through marker-assisted introgression of b-carotene hydroxylase allele. PLoS ONE 9(12):e113583 Nadeem MA, Nawaz MA, Shahid MQ, Doğan Y, Comertpay G, Yıldız M, Hatipoğlu R, Ahmad F, Alsaleh A, Labhane N, Özkan H, Chung G, Baloch FS (2018) DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol Equip 32(2):261–285 Nair SK, Babu R, Magorokosho C, Mahuku G, Semagn K, Beyene Y et al (2015) Fine mapping of Msv1, a major QTL for resistance to maize streak virus leads to development of production markers for breeding pipelines. Theor Appl Genet 128:1839–1854 Naqvi S, Zhu C, Farre G et  al (2009) Transgenic multivitamin corn through biofortification of endosperm with three vitamins representing three distinct metabolic pathways. Proc Natl Acad Sci USA 106(19):7762–7767 Natesan S, Duraisamy T, Pukalenthy B, Chandran S, Nallathambi J, Adhimoolam K, Manickam D, Sampathrajan V, Muniyandi SJ, Meitei LJ, Thirunavukkarasu N, Kalipatty Nalliappan G, Rajasekaran R (2020) Enhancing β-Carotene concentration in parental lines of CO6 maize hybrid through marker-assisted backcross breeding (MABB). Front Nutr 7:134

174

M. Chakraborty et al.

National Corn Growers Association (2018) World corn production, National Corn Growers Association (sourced from USDA, FAS Grain: World Markets and Trade) http://www. worldofcorn.com/#world-­corn-­production. Accessed 12 Jan 2021 Nelson DE, Repetti PP, Adams TR et al (2007) Plant nuclear factor Y (NF-Y) B subunits confer drought tolerance and lead to improved corn yields on water-limited acres. Proc Natl Acad Sci USA 104(42):16450–16455 Ohta S, Ishida Y, Usami S (2004) Expression of cold-tolerant pyruvate, orthophosphate dikinase cDNA, and heterotetramer formation in transgenic maize plants. Transgenic Res 13(5):475–485 Ortiz-Garc´ıa S, Ezcurra E, Schoel B, Acevedo F, Soberon J, Snow AA (2005) Absence of detectable transgenes in local landraces of maize in Oaxaca, Mexico (2003-2004). Proc Natl Acad Sci USA 102(35):12338–12343 Oyetunde OA, Badu-Apraku B, Ariyo OJ, Alake CO (2020) Efficiencies of heterotic grouping methods for classifying early maturing maize inbred lines. Agron 10(8):1198 Paliwal RL, Granados G, Lafitte HR, Violic AD, Marathee JP (2000) Tropical maize: Improvement and production, food and agriculture organization of the Unites Nations. Rome, Italy Palmer LE, Rabinowicz PD, O’Shaughnessy AL, Balija VS, Nascimento LU, Dike S, de la Bastide M, Martienssen RA, McCombie WR (2003a) Maize genome sequencing by methylation filtration. Sci 302(5653):2115–2117 Palmer LE, Rabinowicz PD, O’Shaughnessy AL et al (2003b) Maize genome sequencing by methylation filtration. Sci 302(5653):2115–2117 Pan CT, Ye L, Qin L, Liu X, He YJ, Wang J et al (2016) CRISPR/Cas9-mediated efficient and heritable targeted mutagenesis in tomato plants in the first and later generations. Sci Rep 6:24765 Paul SK, Akter T, Islam T (2021) Chapter 32 - Gene editing in filamentous fungi and oomycetes using CRISPR-Cas technology. In: Abd-Elsalam KA, Lim K-T (eds) Nanobiotechnology for Plant Protection, CRISPR and RNAi Systems. Elsevier, pp 723–753 Pejić I, Ajmon Marsan P, Morgante M, Kozumplick V, Castoglioni P, Taramini G, Motto M (1998) Comparative analysis of genetic similarity among maize inbred lines detected by RFLPs, RAPDs, SSR and AFLPs. Theor Appl Genet 97:1248–1255 Powell W, Morgante M, Andre C et al (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238 Prasanna BM, Hoisington D (2003) Molecular breeding for maize improvement: an overview. Indian J Biotech 2:85–98 Prasanna BM, Vasal SK, Kassahun B, Singh NN (2001) Quality protein maize. Curr Sci 81:1308–1319 Prasanna BM, Beiki AH, Sekhar JC, Srinivas A, Ribaut J-M (2009a) Mapping QTLs for component traits influencing drought stress tolerance of maize in India. J Plant Biochem Biotech 18:151–160 Prasanna BM, Hettiarachchi K, Mahatman K et al. (2009b) Molecular marker-assisted pyramiding of genes conferring resistance to Turcicum leaf blight and Polysora rustin maize inbred lines in India. In: Proceedings of 10th Asian regional maize workshop (October 20–23, 2008, Makassar, Indonesia). CIMMYT, Mexico DF Prasanna BM, Mazumdar S, Chakraborti M, Hossain F, Manjaiah KM, Agrawal PK et al (2011) Genetic variability and genotype × year interactions for kernel iron and zinc concentration in maize (Zea mays L.). Indian J Agric Sci 81:704–711 Puchta H, Hohn B (2010) Breaking news: plants mutate right on target. Proc Natl Acad Sci USA 107:1165–11658 Pukalenthy B, Manickam D, Chandran S, Adhimoolam K, Sampathrajan V, Rajasekaran R, Arunachalam K, Ganapathyswamy H, Chocklingam V, Muthusamy V, Hossain F, Natesan S (2019) Incorporation of opaque-2 into ‘UMI 1200’, an elite maize inbred line, through marker-­ assisted backcross breeding. Biotechnol Equip 33(1):144–153 Puttarach J, Puddhanon P, Siripin S, Sangtong V, Songchantuek S (2016) Marker assisted selection for resistance to northern corn leaf blight in sweet corn. SABRAO J Breed Genet 48(1):72–79

Current Biotechnological Approaches in Maize Improvement

175

Qin C, Yu C, Shen Y, Fang X, Chen L, Min J et al (2014) Whole-genome sequencing of cultivated and wild peppers provides insights into Capsicum domestication and specialization. Proc Natl Acad Sci U S A 111:5135–5140 Qiu F, Zheng Y, Zhang Z, Xu S (2007) Mapping of QTL associated with waterlogging tolerance during the seedling stage in maize. Ann Bot 99:1067–1081 Quan R, Shang M, Zhang H, Zhao Y, Zhang J (2004) Improved chilling tolerance by transformation with beta gene for the enhancement of glycinebetaine synthesis in maize. Plant Science 166(1):141–149 Quarrie S, Lazić-Jančić V, Kovačević D, Steed A, Pekić S (1999) Bulk segregant analysis with molecular markers and its use for improving drought resistance in maize. J Exp Bot 50:1299–1306 Que Q, Elumalai S, Li X, Zhong H, Nalapalli S, Schweiner M, Fei X, Nuccio M, Kelliher T, Gu W, Chen Z, Chilton M-DM (2014) Maize transformation technology development for commercial event generation. Front Plant Sci 5:379 Quist D, Chapela IH (2001) Transgenic DNA introgressed into traditional maize landraces in Oaxaca, Mexico. Nature 414(6863):541–543 Quist D, Chapela IH (2002) Communications arising: response to criticism of transgenic DNA introgressed into traditional maize landraces in Oaxaca, Mexico. Nature 416:602–603 Rabinowicz PD, Bennetzen JL (2006) The maize genome as a model for efficient sequence analysis of large plant genomes. CurrOpinPlant Biol 9(2):149–156 Racuciu M, Creang DE (2007) Journal of Magnetism and Magnetic Materials 311:291–294 Ran Y, Liang Z, Gao C (2017) Current and future editing reagent delivery systems for plant genome editing. Sci China Life Sci 60:490–505 Reif JC, Hamrit S, Heckenberger M, Schipprack W, Peter Maurer H, Bohn M, Melchinger AE (2005) Genetic structure and diversity of European flint maize populations determined with SSR analyses of individuals and bulks. Theor Appl Genet 111(5):906–913 Rhodes CA, Pierce DA, Mettler IJ, Mascarenhas D, Detmer JJ (1988) Genetically transformed maize plants from protoplasts. Science 240:204–207 Ribaut J-M, Ragot M (2007a) Marker-assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives. J Expe Bot 58(2):351–360 Ribaut JM, Ragot M (2007b) Marker-assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives. J Exp Bot 58(2):351–360 Ribaut JM, Betran J, Dreher K, Pixley K, Hoisington D (2001) Marker-assisted selection in maize: strategies, examples and costs. Plant and animal genome IX conference.13-13.01.2001. California. Ribaut JM, Anziger MB, Betran J et  al (2002) In: Kang MS (ed) Use of molecular markers in plant breeding: drought tolerance improvement in tropical maize in Quantitative Genetics, Genomics, and Plant Breeding. CABI Publishing, pp 85–99 Rizwan M, Ali S, Rehhman MZ, Andrees M, Arshad M, Qayyum MF et al (2019) Alleviation of cadmium accumulation in maize (Zea mays L.) by foliar spray of zinc oxide nanoparticles and biochar to contaminated soil. Environ Pol 248:358–367 Sabry A, Jeffers D, Vasal SK, Frederiksen R, Magill C (2006) A region of maize chromosome 2 affects response to downy mildew pathogens. Theor Appl Genet 113:321–330 Salama HMH (2012) Effects of silver nanoparticles in some crop plants, common bean (Phaseolus vulgaris L.) and corn (Zea mays L.). Int Res J Biotechnol 3(10):190–197 Salazar MP, Valenzuela D, Tironi M, Gutiérrez RA (2019) The ambivalent regulator: the construction of a regulatory style for genetically modified crops in Chile. Tapuya Lat Am Sci Technol Soc 2:199–219 Sánchez MA, León G (2016) Status of market, regulation and research of genetically modified crops in Chile. N Biotechnol 33:815–823 Sarika K, Hossain F, Muthusamy V, Zunjare RU, Baveja A, Goswami R, Bhat JS, Saha S, Gupta HS (2018) Marker-assisted pyramiding of opaque2 and novel opaque16 genes for further enrichment of lysine and tryptophan in sub-tropical maize. Plant Sci 272:142–152

176

M. Chakraborty et al.

Schlotterer C (2004) The evolution of molecular markers – just a matter of fashion? Nat Rev Genet 5(1):63–69 Schütte G, Eckerstorfer M, Rastelli V, Reichenbecher W, Restrepo-Vassalli S, Ruohonen-Lehto M, Saucy AW, Mertens M (2017) Herbicide resistance and biodiversity: agronomic and environmental aspects of genetically modified herbicide-resistant plants. Environ Sci Euro 29(1):5 Shaw SH, Carrasquillo MM, Kashuk C, Puffenberger EG, Chakravarti A (1998) Allele frequency distributions in pooled DNA samples: applications to mapping complex disease genes. Genome Res 8:111–123 Sheen J (1993) Protein phosphatase activity is required for light inducible gene expression in maize. EMBO J 12(9):3497–3505 Shena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with complementary DNA microarray. Sci 270:467–470 Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26(10):1135–1145 Shi J, Gao H, Wang H, Lafitte R, Archibald RL, Yang M, Hakimi SH, Mo H, Habben J (2017) ARGOS8 variants generated by CRISPR-Cas9 improve maize grain under field drought stress conditions. Plant Biotechnol J 15:2017–2216 Shiferaw B, Prasanna BM, Hellin J et al (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Sec 3:307 Shou H, Bordallo P, Fan J-B et  al (2004) Expression of an active tobacco mitogen-activated protein kinase enhances freezing tolerance in transgenic maize. Proc Natl Acad Sci USA 101(9):3298–3303 Shukla VP, Doyon Y, Miller JC, DeKelver RC, Moehle EA, Worden SE, Mitchell JC, Arnold NL, Gopalan S, Meng X, Choi VM, Rock JM, Wu YY, Katibah GE, Zhifang G, McCaskill D, Simpson MA, Blakeslee B, Greenwalt SA, Butler HJ, Hinkley SJ, Zhang L, Rebar EJ, Gregory PD, Urnov FD (2009) Precise genome modification in the crop species Zea mays using zinc finger nucleases. Nature 459:437–441 Singh J, Sharma S, Kaur A et  al (2021) Marker-assisted pyramiding of lycopene-ε-cyclase, β-carotene hydroxylase1 and opaque2 genes for development of biofortified maize hybrids. Sci Rep 11:12642 Slomberg DL, Schoenfisch MH (2012) Silica nanoparticle phyto-toxicity to Arabidopsis thaliana. Environ Sci Technol 46:10247–10254 Smith OS, Smith JSC, Bowen SL, Tenborg RA, Wall SJ (1990) Similarities among a group of elite maize inbreds as measured by pedigree, F1 grain yield, grain yield heterosis and RFLPs. Theor Appl Genet 80:833–840 Song X-F, Song T-M, Dai J-R, Rocheford T, Li J-S (2004) QTL mapping of kernel oil concentration with high-oil maize by SSR markers. Maydica 49:41–48 Springer NM, Xu X, Barbazuk WB (2004) Utility of different gene enrichment approaches toward identifying and sequencing the maize gene space. Plant Physiol 136(2):3023–3033 Stich B, Melchinger AE, Frisch M, Maurer HP, Heckenberger M, Reif JC (2005) Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor Appl Genet 111(4):723–730 Stuber CW (1995) Mapping and manipulating quantitative traits in maize. Trends Genet 11(12):477–481 Stuber CW, Edwards MD, Wendel JF (1987) Molecular marker facilitated investigations of quantitative trait loci in maize. II.  Factors influencing yield and its component traits. Crop Sci 27:639–648 Stuber C, Lincon SE, Wolff DW, Helentjaris T, Lander ES (1992) Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genet 132:823–839 Stuber CW, Polacco M, Senior ML (1999) Synergy of empirical breeding, marker-assisted selection, and genomics to increase crop yield potential. Crop Sci 39(6):1571–1583 Sugunan A, Dutta J (2008) Pollution treatment, remediation and sensing. In: Harald K (ed) Nanotechnology. Wiley-VCH, Weinheim 3:125–143

Current Biotechnological Approaches in Maize Improvement

177

Sumathi K, Ganesan KN, Aarthi P et al (2020) Introgression of QTLs determining sorghum downy mildew (SDM) resistance into elite maize line UMI 79 through marker-assisted backcross breeding (MABC). Aus Plant Pathol 49:159–165 Sureshkumar S, Tamilkumar P, Senthil N, Nagarajan P, Thangavelu AU, Raveendran M, Vellaikumar S, Ganesan KN, Balagopal R, Vijayalakshmi G, Shobana V (2014a) Marker assisted selection of low phytic acid trait in maize (Zea mays L.). Hereditas 151(1):20–27 Sureshkumar S, Paramasivam T, Thangavelu AU, Senthil N, Nagarajan P, Vellaikumar S, Ganesan KN, Balagopal R, Raveendran M, Lübberstedt T (2014b) Marker-assisted introgression of lpa2 locus responsible for low-phytic acid trait into an elite tropical maize inbred (Zea mays L.). Plant Breed 133(5):566–578 Svitashev S, Young JK, Schwartz C, Gao H, Falco SC, Cigan MA (2015) Targeted mutagenesis, precise gene editing, and site specific gene insertion in maize using Cas9 guide RNA. Plant Physiol 169:931–945 Svitashev S, Schwartz C, Lenderts B, Young JK, Cigan MA (2016) Genome editing in maize by CRISPR-Cas9 ribonucleoprotein complexes. Nat Commun 7:13274 Swarts K, Gutaker RM, Benz B, Blake M, Bukowski R, Holland J, Kruse-Peeples M, Lepak N, Prim L, Cinta Romay M, Ross-Ibarra J, de Jesus Sanchez-Gonzalez J, Schmidt C, Schuenemann VJ, Krause J, Matson RG, Weigel D, Buckler ES, Burbano HA (2017) Genomic estimation of complex traits reveals ancient maize adaptation to temperate North America. Sci 357:512–515 Tamilkumar P, Senthil N, Sureshkumar S, Thangavelu AU, Nagarajan P, Vellaikumar S, Ganesan KN, Natarajan N, Balagopal R, Nepolean T, Raveendran M (2014) Introgression of low phytic acid locus (lpa2-2) into an elite Maize (Zea mays L.) inbred through marker assisted backcross breeding. Aus J Crop Sci 8(8):1224–1231 Tanumihardjo S, McCulley L, Roh R, Lopez-Ridaura S, Palacios-Rojas N, Gunaratna N (2019) Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals. Glob Food Sec 25:100327 Tarafdar JC, Raliya R, Mahawar H, Rathore I (2014) Development of zinc nanofertilizer to enhance crop production in pearl millet (Pennisetum americanum). Agric Res 3(3):257–262 Tarca AL, Romero R, Draghici S (2006) Analysis of microarray experiments of gene expression profiling. Am J Obstet Gynecol 195(2):373–388 Teama S (2018) DNA polymorphisms: DNA-based molecular markers and their application in medicine, genetic diversity and disease susceptibility. Yamin Liu, IntechOpen. Available from: https://www.intechopen.com/books/genetic-­diversity-­and-­disease-­susceptibility/ dna-­polymorphisms-­dna-­based-­molecular-­markers-­and-­their-­application-­in-­medicine Teng WT, Can JS, Chen YH et al (2004) Analysis of maize heterotic groups and patterns during past decade in China. Sci Agric Sin 37:1804–1811 Tie S, Xia J, Qiu F, Zheng Y (2006) Genome-wide analysis of maize cytoplasmic male sterility-S based on QTL mapping. Plant Mol Biol Reporter 24:71–80 Tiwari DK, Dasgupta-Schubert N, Villasenor Cendejas LM, Villegas J, Carreto Montoya L, Borjas Garcia SE (2014) Interfacing carbon nanotubes (CNT) with plants: enhancement of growth, water and ionic nutrient uptake in maize (Zea mays) and implications for nanoagriculture. Appl Nanosci 2:577–591 Torney F, Trewyn BG, Lin VS, Wang K (2007) Mesoporous silica nanoparticles deliver DNA and chemicals into plants. Nat Nanotechnol 2:295–300 Tripathy SK, Lenka D, Ithape DM, Maharana M (2017) Development of opaque-2 introgression line in maize using marker assisted backcross breeding. Plant Gene 10:26–30 Tuberosa R, Salvi S, Sanguineti MC, Landi P, Maccaferri M, Conti S (2002) Mapping QTLS regulating morphophysiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Anna Bot 89:941–963 Tuberosa R, Salvi S, Giuliani S et al (2007) Genome-wide approaches to investigate and improve maize response to drought. Crop Sci 47:S120–S141 Turnbull C, Lillemo M, Hvoslef-Eide TAK (2021) Global regulation of genetically modified crops amid the gene edited crop boom – A review. Front Plant Sci 12:630396

178

M. Chakraborty et al.

Ulukan H (2009) The evolution of cultivated plant species: classical plant breeding versus genetic engineering. Plant Syst Evol 280:133–142 USDA FAS (2018) GAIN Report: Agricultural Biotechnology Annual -Venezuela. Available Online at: https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName? fileName=AgriculturalBiotechnologyAnnual_Caracas_Venezuela_12-­20-­2018. Accessed 23 Oct 2020 Vignesh M, Firoz HT, Nepolean SS, Agrawal PK, Guleria SK, Prasanna BM, Gupta HS (2012) Genetic variability for kernel b-carotene and utilization of crtRB1 3’TEgene for biofortification in maize (Zea mays L.). Indian J Genet 72(2):189–194 Vijayakumar PS, Abhilash OU, Khan BM, Prasad BLV (2010) Nanogold-loaded sharp-edged carbon bullets as plant-gene carriers. Adv Funct Mater 20:2416–2423 Wang AS, Evans RA, Altendorf PR, Hanten JA, Doyle MC, Rosichan JL (2000) A mannose selection system for production of fertile transgenic maize plants from protoplasts. Plant Cell Rep 19:654–660 Wang Z-Y, Sun X-F, Wang F, Tang K-X, Zhang J-R (2005) Enhanced resistance of snowdrop lectin (Galanthus nivalis L. agglutinin)-expressing maize to Asian corn borer (Ostrinia furnacalis Guen´ee). JIntegrPlant Biol 47(7):873–880 Wanga MA, Hussein S, Jacob M, Laing MD (2021) Opportunities and challenges of speed breeding: A review. Plant breed 140(2):185–194 Warburton ML, Williams WP, Windham GL, Murray SC, Xu W, Hawkins LK et  al (2013) Phenotypic and genetic characterization of a maize association mapping panel developed for the identification of new sources of resistance to and aflatoxin accumulation. Crop Sci 53:2374–2383 Whitelaw CA, Barbazuk WB, Pertea G, Chan AP, Cheung F, Lee Y, Zheng L, van Heeringen S, Karamycheva S, Bennetzen JL, San Miguel P, Lakey N, Bedell J, Yuan Y, Budiman MA, Resnick A, Van Aken S, Utterback T, Riedmuller S, Williams M, Feldblyum T, Schubert K, Beachy R, Fraser CM, Quackenbush J (2003a) Enrichment of gene-coding sequences in maize by genome filtration. Sci 302(5653):2118–2120 Whitelaw CA, Barbazuk WB, Pertea G, Chan AP, Cheung F, Lee Y, Zheng L, Van Heeringen S, Karamycheva S, Bennetzen JL, SanMiguel P, Lakey N, Bedell J, Yuan Y, BudimanMA RA, Van Aken S, Utterback T, Riedmuller S, Williams M, Feldblyum T, Schubert K, Beachy R, Fraser CM, Quackenbush J (2003b) Enrichment of gene-coding sequences in maize by genome filtration. Sci 302(5653):2118–2120 Willcox MC, Khairallah MM, Bergvinson D, Crossa J, Deutsch J, Edmeades G, González-de-­ León D, Jiang C, Jewell D, Mihm J, Williams W, Hoisington D (2002) Selection for resistance to southwestern corn borer using marker assisted and conventional backcrossing. Crop Sci 42(5):1516–1528 Wisser RJ, Balint-Kurti PJ, Nelson RJ (2006) The genetic architecture of disease resistance in maize: a synthesis of published studies. Phytopathol 96:120–129 Wong MH, Giraldo JP, Kwak S-Y, Koman VB, Sinclair R, Lew TTS, Bisker G, Liu P, Strano MS (2017) Nitroaromatic detection and infrared communication from wild-type plants using plant nanobionics Nat Mater 16:264 Wu Y, Li X, Zhang Z et al (2008) Genomic DNA sequence, gene structure, conserved domains, and natural alleles of Gln1-3 gene in maize. Acta Agrono Sin 34:1114–1120 Wu Y, Li X, Hao Z, Xie C (2009) Genomic DNA sequence, gene structure, conserved domains, and natural alleles of Gln1-4 gene in maize. Acta Agron Sin 35:983–991 Xia JH, Zheng YL (2002) Molecular marker-assisted backcross breeding of maize Rf3 NIL and its efficient analysis. Acta Agrono Sin 28:339–344 Xiao YN, Li XH, George ML, Li MS, Zhang SH, Zheng YL (2005) Quantitative trait loci analysis of drought tolerance and yield in maize in China. Plant Mol Biol Reporter 23:155–165 Xiaodong W (2020) GMO crops set to pass biosafety tests. Available Online at: https://www. chinadaily.com.cn/a/202001/02/WS5e0d28cfa310cf3e35581e9f.html Accessed 28 June 2021

Current Biotechnological Approaches in Maize Improvement

179

Xie C, Zhang S, Li M, Li X, Hao Z, Bai L, Zhang D, Liang Y (2007) Inferring genome ancestry and estimating molecular relatedness among 187 Chinese maize inbred lines. J Genet Genom 34:738–748 Xie CX, Warburton M, Li MS, Li XH, Xiao MJ, Hao ZF, Zhao Q, Zhang SH (2008) An analysis of population structure and linkage disequilibrium using multilocus data in 187 maize inbred lines. Mol Breed 21:407–418 Xing HL, Dong L, Wang ZP, Zhang HY, Han CY, Liu B, Wang XC, Chen QJ (2014) A CRISPR/ Cas9 toolkit for multiplex genome editing in plants. BMC Plant Biol 14:327 Xu Y, Crouch JH (2008a) In: Moore PH, Ming R (eds) Genomics of tropical maize, a stable food and feed across the world in Genomics of Tropical Crop Plants. Springer, London, pp 333–370 Xu Y, Crouch JH (2008b) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48(2):391–407 Xu S-X, Liu J, Liu J-S (2005) The use of SSRs for predicting the hybrid yield and yield heterosis in 15 key inbred lines of Chinese maize. Hereditas 141:207–215 Xu Y, Skinner DJ, Wu H, Palacios-Rojas N, Araus JL, Yan J, Gao S, Warburton ML, Crouch JH (2009) Advances in maize genomics and their value for enhancing genetic gains from breeding. Int J Plant Genomics 2009:1 Xu Z, Hua J, Wang F, Cheng Z, Meng Q, Chen Y, Han X, Tie S, Liu C, Li X, Wang Z, Weng J (2020) Marker-assisted selection of qMrdd8 to improve maize resistance to rough dwarf disease. Breed Sci 70(2):183–192 Yan J, Yang X, Shah T, Sánchez-Villeda H, Li J, Warburton M, Zhou Y, Crouch JH, Xu Y (2009) High-throughput SNP genotyping with the GoldenGate assay in maize. Mol Breed 25:441–451 Yan J, Kandianis CB, Harjes CE, Bai L, Kim E-H, Yang X, Skinner DJ, Fu Z, Mitchell S, Li Q, Fernandez MGS, Zaharieva M, Babu M, Fu Y, Palacios N, Li J, Penna DD, Brutnell T, Buckler ES, Warburton ML, Rocheford T (2010) Rare genetic variation at Zea mayscrtRB1 increases b-carotene in maize grain. Nat Genet 42:322–329 Yang X, Gondikas A, Marinakos S, Auffan M, Liu J, Kim H, Meyer J (2012) Mechanism of silver nanoparticle toxicity is dependent on dissolved silver and surface coating in Caenorhabditis elegans. Environ Sci Technol 46:1119–1127 Yang R, Yan Z, Wang Q, Li X, Feng F (2018) Marker-assisted backcrossing of lcyE for enhancement of proA in sweet corn. Euphytica 214:130 Yin X-Y, Yang A-F, Zhang K-W, Zhang J-R (2004) Production and analysis of transgenic maize with improved salt tolerance by the introduction of AtNHX1 gene. Acta Bot Sin 46(7):854–861 Yousef GG, Juvik JA (2002) Enhancement of seedling emergence in sweet corn by marker-assisted backcrossing of beneficial QTL. Crop Sci 42(1):96–104 Yu J, Pressoir G, Briggs W, Irie VB, Masanori Y, John FD, Michael DM, Brandon SG, Dahlia MN, James BH, Stephen K, Edward SB (2006)A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet38:203–208 Yu J, Holland JB, McMullen MD, Buckler ED (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551 Yu K, Wang H, Liu X, Xu C, Li Z, Xu X, Liu J, Wang Z, Xu Y (2020) Large-Scale analysis of combining ability and heterosis for development of hybrid maize breeding strategies using diverse germplasm resources. Front Plant Sci 11:660 Yuan LX, Fu JH, Zhang SH, Liu X-Z, Peng Z-B, Li X-H, Warburton M, Khairallah M (2001) Heterotic grouping of maize inbred lines using RFLP and SSR markers. Acta Agron Sin 27:149–156 Yuvakumar R, Elango V, Rajendran V, Kannan NS, Prabu P (2011) Influence of nanosilica powder on the growth of maize crop (Zea mays L.). Int J Green Nanotechnol 3(1):180–190 Zhang SH, Li XH, Wang ZH, George MLC, Jeffers DP, Wang FG, Liu XD, Li MS, Yuan LX (2003) QTL mapping for resistance to SCMV in Chinese maize germplasm. Maydica 48:307–312 Zhang F, Wan X-Q, Pan G-T (2006) QTL mapping of Fusarium moniliforme ear rot resistance in maize. 1. Map construction with microsatellite and AFLP markers. J Appl Genet 47:9–15

180

M. Chakraborty et al.

Zhang ZM, Zhao MJ, Ding HP, Ma YY, Rong TZ, Pan GT (2007) Analysis of the epistatic and QTL x environments interaction effects of plant height in maize (Zea mays L.). Int J Plant Prod 2:153–162 Zhao Z-Y, Gu W, Cai T, Tagliani L, Hondred D, Bond D, Schroeder S, Rudert M, Pierce D (2001) High throughput genetic transformation mediated by Agrobacterium tumefaciens in maize. Mol Breed 8:323–333 Zhao M, Zhang Z, Zhang S, Li W, Jeffers DP, Rong T, Pan G (2006a) Quantitative trait loci for resistance to Banded Leaf and Sheath Blight in maize. Crop Sci 46:1039–1045 Zhao W, Canaran P, Jurkuta R, Fulton T, Glaubitz J, Buckler E, Doebley J, Gaut B, Goodman M, Holland J, Kresovich SM, McMullen M, Stein L, Ware D (2006b) Panzea: a database and resource for molecular and functional diversity in the maize genome. Nucl Acids Res 34:D725–D757 Zhao X, Tan G, Xing Y, Wei L, Chao Q, Zuo W, Lübberstedt T, Xu M (2012) Marker-assisted introgression of qHSR1 to improve maize resistance to head smut. Mol Breeding 30(2):1077–1088 Zheng L, Hong F, Lu S, Liu C (2005) Effect of nano-TiO2 on strength of naturally aged seeds and growth of spinach. Biol Trace Element Res 104:83–91 Zunjare RU, Hossain F, Muthusamy V, Baveja A, Chauhan HS, Bhat JS, Thirunavukkarasu N, Saha S, Gupta HS (2018) Development of biofortified maize hybrids through marker-assisted stacking of β-Carotene Hydroxylase, Lycopene-ε-Cyclase and Opaque2 Genes. Front Plant Sci 9:178

Advances in Genome Editing for Maize Improvement Samra Farooq, Asifa Shahzadi, Ali Razzaq, Fozia Saleem, Shabir Hussain Wani , and Karansher Sandhu

1 Introduction Human population is increasing rapidly. According to the European Union, the current world population is 7.9 billion people and estimated to increase by 10 billion in the next 30 years. The FAO estimates that 2577.85 million tons of food were produced during 2016 (McIntosh et al. 2015). Current food production is not fulfilling the required demands of growing population, which leads to food shortage. It is predicted that around 9% of world population that counts for 697 million people are food insecure and around 820 million people are undernourished. For this tremendous increase in population, more food supply is required. Global food production is estimated to double by 2050 and requires a 70–85% increase in crop yield to feed undernourished. Global food security is dependent on both adequate food production and food access, but some factors are limiting the production of food. Food production has been widely affected by abiotic and biotic factors and raised concerns of food security. Agriculture sector is facing serious problems due to negative ecological Samra Farooq, Asifa Shahzadi and Ali Razzaq contributed equally with all other contributors. S. Farooq · A. Shahzadi · A. Razzaq · F. Saleem Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture Faisalabad, Faisalabad, Pakistan S. H. Wani Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu & Kashmir, India K. Sandhu (*) Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_9

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consequences. It imposes serious threats to the economy of a country (Raza et al. 2019). Agriculture-driven growth and food security are at risk due to ecological changes. Furthermore, food security is becoming a major concern around the world due to drought and heat stress that is the leading constraint of food production. Drought conditions and high temperature result in physiological, physical, and biochemical changes of plants that affect the plant growth, development, and production (Fathi and Tari 2016). Abiotic factors are nonliving, whereas biotic factors are living factors that reduced the agricultural production to a greater extent and directly influence food supply (Razzaq et al. 2021d; Wani et al. 2022). Abiotic factors are due to environmental changes that include high or low temperature, salt stress, drought stress, relative humidity, and fluctuation of pH (Arora 2019). However, biotic factors are due to insects and pests that reduce crop yield. The role of these factors in ecosystem regulation has been extensively studied over the years, resulting in the characterization of biotic and abiotic relationships (Dresselhaus and Hückelhoven 2018). These factors are essential for energy flow and nutrient cycling of an ecosystem. Biotic and abiotic factors are mainly due to climate changes that have a significant impact on crop reduction. Temperature rising and yield losses are major challenges of climate change. Extreme weather conditions of heavy rainfall, change in CO2 concentrations, drought, alteration of host-pathogen relationship, and depletion of ozone layer cause significant reduction of crop production and adversely affect global food security (Raza et al. 2019). Some crops are sensitive to saline environment, which develop resistance mechanisms against salt stress. Rather than developmental stages, germination stages of plants are more sensitive to salty environment. Plant uptake of nutrients, for example, nitrogen, calcium, magnesium, potassium, and iron, decreases due to high content of sodium and chloride rhizosphere (Farooq et  al. 2015). Photosynthesis is enhanced by atmospheric CO2 concentrations that reduced crop water usage. Variation of CO2 levels increases global crop water productivity depending on crop type. Global yield loss could be mitigated by the elevated level of CO2 concentrations. It would reduce the consumption of water up to 4–17% (Deryng et al. 2016). Abiotic stresses may have resulted in the aggregation of reactive oxygen species (ROS), which act as signal transduction molecules. These ROS cause cellular damage and inhibit photosynthesis. ROS are removed with antioxidants to prevent extensive cell damage (Dietz et  al. 2016). Yield of staple foods is substantially reduced due to insect pests. There is a significant relationship between temperature, population growth, and metabolic rates of insects that augment grain losses. High temperature will increase insect’s population and their metabolic rates. The production losses of staple food such as rice, wheat, and maize are expected to rise by 10–15% as global warming increases (Deutsch et  al. 2018). Agronomic traits of plants are different under different environmental conditions. Agronomic traits include plant height, plant biomass, harvest index (HI), spike length, and number of spikes. Plant agronomic traits appear during later stages of their growth, based on their interaction with environment (Mochida et al. 2020).

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These factors alone or in combination adversely affect global food production and ultimately affect global food security. In order to overcome ecological consequences and ensure food security, scientists are forced to develop novel breeding techniques to tackle yield loss due to these stresses.

2 Significance of Maize and Global Status Maize (Zea mays L.) is the world’s third dominant and high yielding grain crop. It is the world’s most significant crop, cultivated over more than 118 million hectares with 600 million metric tons annual production. If the world’s population grows to 9 billion people by 2050, global food demand will begin to rise rapidly. In 2020, worldwide maize demand will grow by 45%, compared to 30% for wheat and 32% for rice. This shows that the global need for maize will double, and it will become the most grown crop worldwide by 2050 (Ten Berge et al. 2019). Maize is valuable feed grain as it provides high energy density of 365 kcal/100 g. It contains about 72% starch, 24% carbohydrates, 10% protein, 3–5% vitamins A and B, 4% fats, and 3.7% sugars but has less amount of fiber contents (Ranum et al. 2014). Maize is a raw material and extensively used to make different products. Maize is high source of ethanol production that can be used as biofuel. As maize is a high source of energy and lowest in fiber and protein content, it can be used as livestock feedstock. Maize contributes significantly to global food security by supplying food, feedstock, and energy for the growing human population. It can grow under different environmental conditions, and this quality makes it globally desirable crop (Sanaullah et  al. 2018). Maize originated from a wild grass in central Mexico around 7000 years ago. Three top maize producer countries are the United States, China, and Brazil where maize grown all around the globe. These countries produced maize worth of 563–717  million metric tons per year, grown on 70–100 million acres annually. In 2019, China harvested 41.3 million hectares of maize, 32.9  million hectares in the United States, and 17.5  million hectares in Brazil. The total maize production in the United States was 347 million metric tons, 260 million metric tons in China, and 101 million metric tons in Brazil. In the last 10 years, 40% maize production has increased in the United States. Maize, being the world’s most produced cereal crop, is of great importance in developing countries like Pakistan, where available food supplies are not sufficient for a rapidly growing population. After wheat, cotton, and rice, maize is the fourth most grown crop in Pakistan. It is grown on an area of more than 1.4 million hectares and average grain yield of 51,203 kg/ha. In Pakistan, the harvested area is 1.4 million hectares, with an annual production of 72  million metric tons. A total of 35 and 27  million metric tons of annual maize production have been calculated in Bangladesh and in India, respectively. The Maize extraction rate varies from 60% to 100% in different countries. Although maize is not a native crop in Africa, now 300 million Africans are growing it due to its high nutritional values. Higher extraction rates are observed in South

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Africa, from 62% to 99% depending on the product type. Maize varieties that grow in different seasons have lower extraction rates after the milling process (Ranum et al. 2014). Along with its industrial and nutritional importance, maize is also a model crop for genetic research. So, experiments have been conducted, and genetically modified maize have been introduced since the 1990s and adopted worldwide. To increase maize production, genetic approaches have been applied to improve the agronomic features of the crop. Farmers have shifted from traditional approaches toward modern genetically modified techniques as they can significantly reduce the losses caused by various biotic and abiotic factors by improving agronomic traits. Traditionally, irradiation and mutagenesis were used, but nowadays, gene editing tools are used to modify the genome of maize (Razzaq et al. 2021b).

3 Genome Editing Current crop yield has to be doubled to ensure food security in the era of growing population and developing biotic and abiotic stressors (Ray et al. 2013). To combat food insecurity concerns, it is important to develop stress-tolerant crops. The conventional approaches that are being used enhanced food production in cultivated areas but didn’t focus on stress resistance and nutritional quality. Nature has been changing the genome through natural selection from the beginning of life (Razzaq et al. 2021a). But with the advancement of knowledge, scientists developed artificial selection or selective breeding for desirable traits. Artificial selection has been done in numerous plants, such as modern corn, a descendant of teosinte (Yang et  al. 2019). However, further research is in progress to combat evolving challenges and to produce stress-resistant crops. Recent advancements in plant biotechnology have made it possible to alter the genomes of plants that influence desirable agronomic traits against biotic and abiotic stresses. The current approaches tend to increase crop yield, enhance nutritional qualities, map resistance genes, and regulate various physiological and metabolic pathways against biotic and abiotic stresses (Razzaq et al. 2021a). For nutritional improvement, it provides adequate minerals and vitamins that are beneficial for human health (Farre et  al. 2012). Specific enzymes in metabolic pathways have been manipulated to increase the amount of vitamins and minerals while lowering the levels of antinutrients such as phytic acids and acrylamide-forming amino acids (Mugode et al. 2014). Researchers target specific agronomic traits that cope with stresses to increase yield and production. For this purpose, plant structure is manipulated to introduce specific traits (Tshikunde et al. 2019). Selection of traits such as low and high temperatures, salt stress, drought stress, heavy metals, and other stresses has been targeted as abiotic factors. Resistance to bacterial, viral, and fungal pathogens against biotic stresses has become a growing concern as temperature intensifies the spread of pathogens.

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A significant breakthrough in genetic engineering has focused on addition of DNA sequences into the genome (Datta 2013). Agrobacterium tumefaciens has been extensively used for gene modification in plants (Nester 2015). In 1994, the first GM crop, “Flavor Savor Tomato” was commercialized. Genetic engineering has developed crops with stress-resistant properties, enhanced nutritional content, and improved yield. However, with all the advantages and modifications, this technique has some drawbacks. The major limitation is the insertion of foreign DNA into plants for quantitative and qualitative characteristics without using the plant’s own genetic material. Public concern over GMOs is a major drawback, along with other environmental and ethical concerns. GMOs have been restricted in several countries due to nonspecificity and risk to human health. There is a need for technology that can overcome all hurdles and produce stress-­ tolerant crops. Mario Capecchi established gene targeting technology for genome editing in the 1980s. For example, nonhomologous end joining (NHEJ) can be used to create functional knockout of genes by inserting and deleting a few genes (Weinthal et al. 2013). Genome editing precisely alters the DNA sequence in one specific base with insertions and deletions that develop desirable agronomic traits to cope with the high and low temperatures and insect and pest attack. Functional and nutritional traits of diverse crops have been modified with genome editing (Tan et al. 2020; Sedeek et al. 2019; Razzaq et al. 2019). Genome modification by editing is among the most promising techniques of all time in applied biological and industrial research. Genetically modified plants with foreign DNA in their genomes may prove to be more sustainable for the public than genome editing. Genome editing’s accessibility has overcome all of the limitations of traditional methods (Abdallah et al. 2015). Site-specific recombinases and site-specific nucleases could be used for genome editing. The high-oleic soybean variety was the first approved gene edited product for commercialization in 2019, paving the way for commercialization of other genome edited crops. Traditionally, irradiation and mutagenesis were used for genome modification of maize, but they have numerous limitations. This approach accuracy was limited. That’s why gene editing tools are used to modify the genome of maize to produce stress-resistant varieties with high precision. Accessibility to genomic data of maize sets new insights for genome editing. Meganucleases, zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR/Cas systems have all been found as genome editing tools so far (Curtin et al. 2012). ZFNs and TALENs are protein-designed systems, whereas CRISPR/Cas is RNA-based system that can bind to target DNA. The main mechanism is to make double-stranded breaks (DBS) on specific sites of DNA with nucleases. Various agricultural crops have been modified to produce biotic and abiotic stress-tolerant cultivars. Many agricultural innovations have been created with this novel system.

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4 Meganucleases (MegaN) The discovery of meganucleases sparked the idea of genome editing. Meganucleases are naturally occurring endonucleases, also known as homing endonucleases due to their specific nature. Meganucleases have 12–40  bp recognition sites and make double-­stranded breaks (DSBs) (Gallagher et al. 2014). This specific nature makes them perfect tools for gene editing. LAGLIDADG, GIY-YIG, HNH, His-Cys box, and PD-(D/E)XK are the five families of meganucleases based on their structural motifs (Zhao et al. 2007; Orlowski et al. 2007). LAGLIDADG is the best explored among these numerous families for genome editing. It is estimated that LAGLIDADG recognizes a DNA sequence of 14–40 bp. Gene editing with modified meganuclases has incorporated into Arabidopsis, cotton, and maize. It is very difficult to engineer meganuclease because DNA binding domains and endonuclease catalytic domains are located in the same location and cannot be easily detached. Additionally, naturally occurring meganucleases are less in number and are not enough for potential locus. Construction of site-specific enzymes for meganucleases is time-consuming and expensive. Technically, initial protein modification is required to produce a meganuclease for target genome engineering, and this is very challenging, and patent issues have seen in cultivars of this family (Abdallah et  al. 2015). As manipulation of meganucleases is difficult, additional efforts are needed to improve this approach. Therefore, researcher’s interest has shifted to more efficient and accurate methods, such as ZFNs, TALENS, and CRISPR.

5 Zinc Finger Nucleases (ZFNs) Over the past two decades, new technologies have developed to modify plant genomes. Among these zinc finger nucleases (ZFNs) are designed enzymes that are intended to cleave DNA at certain sites or locations within the genomic structure. ZFNs are first-generation-targeted genome editing techniques that can be used for deletions, additions, and knockout of targeted genes in the genome. ZFNs are made up of a zinc finger DNA binding domain and an endonuclease (Fok1 nuclease) (Davies et al. 2017). Fok1 has distinct binding and cleavage activities. Fok1 belongs to type IIS class of endonuclease enzymes, which are commonly used cleavage domain. The cleavage domain dimerizes to cut DNA at a recognize site. A binding arrangement of four to six zinc finger protein domains has the ability to recognize about 3 bp of DNA (Carroll 2011). Binding to the appropriate targeted sequence, a pair of zinc finger arrays arrange themselves in reverse order. Two ZFAs have binding sites that are around 5–8 bp apart (each 18–24 bp in length). In ZFN design, this gap is crucial because it permits fok1 monomers to dimerize and create a DSB in the desired sequence (Ansari et al. 2020). They then employ the cellular endogenous DSB repair process to alter the target genomic region (Feng et al. 2016). To repair these double-stranded breakage (DSB), nonhomologous end joining (NHEJ) repair

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mechanism is used, which allows for site-specific mutagenesis (Yadava et al. 2017). It is now feasible to target a large number of DNA sequences in the genome with this technique. ZFNs can be employed in agricultural plants to induce alterations in normal genomic structure, which could be more consumer-friendly than genetically modified strains generated via gene insertion. ZFNs can be utilized to re-grow crops such as maize by changing callus or cells in culture (Townsend et al. 2009). This technology is used to modify endogenous loci in maize, but it faces many challenges. This method is time-consuming and expensive as it involves complex steps that require protein engineering. It must be genetically engineered to create double-­ stranded breaks (DSB) at the specific sites. It may attach to any DNA sequence in the genome rather by binding with the target location and cause a high rate of changes that make it difficult to distinguish between changed or altered alleles (Petolino 2015). Several optimizations are needed to improve editing of plant genomes via ZNFs, for example, choice of plant tissue for targeting, the lack of off-­ target mutagenesis, and the introduction of enzyme activity and the constant identification of mutated alleles and specialized skills required for interpretation of results, which have made it less applicable for crop genome editing (Agarwal et al. 2018).

6 Transcription Activator-Like Effector Nucleases (TALENs) TALENs uses Fok1 nucleases in the same way as ZFNs do, but its mechanism for recognizing the target site is totally different. TALEN has emerged as a potential genetic technique for mutagenesis of specific genes. Each of two monomers of TALEN has the TALE DNA binding domain (highly conserved repeats) linked to the Fok1 domain. The DNA binding domain is derived from TALE produced by Xanthomonas spp. that is used to change the gene transcription in host plants (Khan et al. 2017). TALENs belong to first generation genome editing tool and recognize the 14–20 bp recognition site of target DNA (Kumar et al. 2019). TALE domain recognizes the defined specific DNA site, and Fok1 nuclease dimerizes to create DSBs. These DSBs can be repaired by NHEJ and HR (Char et al. 2015). These mechanisms have potential for deletions, insertions, rearrangements, or replacement of chromosomes. NHEJ mostly leads to insertion and deletions into the genome and leads to loss of gene function, while DSBs are accurately repaired in HR repair mechanism by using a DNA template from homologous donor (Sprink et al. 2015). In contrast to ZFN, TALEN has many advantages. It is more efficient, flexible, and less toxic. It is easier to engineer. The main advantage is that they can bind with specific DNA sequence, while in the case of the zinc finger, it chooses a library of fingers that have the necessary binding characteristics. As an efficient tool, TALENs have been applied to create useful traits and qualities in crop plants and make the crops resilient to withstand the enormous biotic and abiotic stresses (Malzahn et al. 2017).

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But still, this technique has many limitations. The creation of TALE repetitions is still a challenge, and the effectiveness of TALEN gene targeting varies. It is time-consuming, expensive, and difficult to handle and can never be used on large scale. There are still some questions that needed to be answered to get desired outcomes, for example, which vector transformation method has to be used and the choice of explant (Khan et al. 2017). Scientists are optimistic that targeted genome engineering will have a bright future and will bring solutions to our issues.

7 CRISPR/Cas9: A Robust Genome Editing Tool Zinc finger nucleases and TALENS were extensively utilized genome editing techniques until 2013. However, with the discovery of CRISPR/Cas from the last few decades, scientific advances have ushered in a revolution in genome editing. CRISPR is more simple, reliable, and robust method of specific mutagenesis than previously used tools. This second-generation tool is simple to use, versatile, accurate, and economical than ZFN and TALEN tools of first generation. CRISPR opens up new opportunities against biotic and abiotic factors and produced high-quality resistant crops and improved yield traits. The implementation of this robust tool has provided great insights in agricultural biotechnology and improved crop production (Razzaq et al. 2019). CRISPR/Cas9 was extracted from immune system of bacteria (Jinek et al. 2012) and initially was classified into three types and ten subtypes (Makarova and Koonin 2015). After that, it grew to include two classes with further five types and sixteen subtypes (Makarova et al. 2015). With two previously known classes, the classification of CRISPR was extended to six types and 33 subtypes (Makarova et al. 2018, 2020). CRISPR systems that can target multiple genes and improve the specificity of the CRISPR toolbox include type II, V, and VI of class 2 (Cong et al. 2013; Tang and Fu 2018). CRISPR repeats range in size from 23 to 55 in different organisms and have average length of 32 bp. In certain animals, each repeat contains a unique nucleotide sequence that is highly conserved and partly palindromic (Karimi et  al. 2018). Streptococcus pyogenes-derived Cas9 (SpCas9) endonuclease has been widely used in crop improvement (Le Rhun et al. 2019) via different mechanisms including sitespecific CRISPR system and precise base editing (Zong et al. 2017). CRISPR can manipulate genome without the insertion of foreign DNA or exogenous via ribonucleoprotein complex (RNP) and thus results in transgene free plants (He and Zhao 2020; Tsanova et al. 2021). The RNA-driven DNA endonucleases that produce double-stranded breaks are used in this gene editing technique (Jinek et al. 2012). A single guide (sgRNA) and Cas9 nuclease combine to make up the CRISPR/ Cas9 system. The linker loop of the guide sequence contains crRNA and tracrRNA (Cong et al. 2013). The guide sequence is recognized with duplex of CRISPR RNA (crRNA) and trans-encoded CRISPR RNA (tracrRNA) by binding while

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engineering. crRNA hybridize to the complementary strand of targeted genome adjacent to PAM sites. DNA cleavage is carried out with crRNA and tracrRNA on guided RNA (gRNA) and enhanced the cleavage frequency (Jinek et al. 2012). The Cas9 endonuclease contains HNH and RuvC domains. sgRNA combine or pair up with cas9 endonuclease and results in the formation of Cas9 complex. Cas9 paired with gRNA on targeted complementary sequence and generate double-­ stranded breaks at three to four nucleotides that are upstream of the PAM site of targeted genomic locus. Thus, with accurate insertion of sgRNAs, Cas9-mediated cleavage has the ability to initiate site-specific genome editing (O’Connell et  al. 2014; Hanna and Doench 2020). DSBs are produced on HNH domain by cleaving the complementary strands, while RuvC domains produce DSBs by cleaving the noncomplementary strands. As a result, these DSBs trigger DNA repair mechanism either through nonhomologous end joining (NHEJ) that stands a chance of error or through homology-directed repair (HDR) for accurate repair. Many outstanding repairs of single base substitution, gene replacement, and targeted knock-in were achieved via HDR (Schiml et al. 2014). Transformation methods are used to deliver the expression cassette of sgRNA and Cas9 into desired cells via PEG-mediated, agrobacterium-mediated, and biolistic-­mediated transformation (Sandhya et al. 2020). This cassette delivery into cell can be stable or involve transient transformation. Ability of CRISPR to cleave DNA at specific site with remarkably efficient targeting makes it a more flexible and robust approach for plant genome engineering than ZFNs and TALENs (Bortesi and Fischer 2015).

8 Applications of CRISPR Cas9 in Maize Improvement CRISPR/Cas has changed genome editing approaches since its discovery. To address the obstacles, this new technique is utilized to produce specific targeted mutations in living organisms. It is a robust method with a lot of potential for crop improvement. CRISPR/Cas has several applications in maize improvement, including biotic/abiotic stress tolerance, and the production of novel agronomic traits to boost yield and nutrition (Zhang et al. 2021). Teng et al. (2020) employed CRISPR/Cas9 to disrupt the Dicer-like 5 (Dcl5) which is supposed to be involved in the generation of secondary small interfering RNAs (phasiRNAs) and has major function in anther and flower development in maize. The mutant lines showed temperature-sensitive male fertility, defective tapetal cells, and short anthers. This suggested that (Dcl5) in crucial in heat tolerance during anthers development and can be targeted to develop heat tolerant maize lines in future. ARGOS8 gene is the negative regulator of ethylene response in maize. The overexpression of the ARGOS8 gene in transgenic plants reduces ethylene sensitivity, resulting in increased grain yield under drought stress. To check the response of maize to overexpression ARGOS8 under drought stress and measure the abiotic

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stress tolerance, CRISPR/Cas9-mediated targeted mutagenesis was performed on about 400 inbred maize lines to produce novel variants. The native maize GOS2 promoter was inserted into the 5′ untranslated region of the native ARGOS8 gene, resulting in a moderate level of constitutive expression. PCR and RNA sequencing revealed genomic DNA alterations at the maize gene locus (ARGOS8). The results revealed that transgenic plants with the GOS2 promoter overexpressed the ARGOS8 gene, indicating an increase in grain production under drought stress conditions as compared to natural cultivars. As a consequence, the findings highlight the necessity of using CRISPR/Cas9 to create novel variations for breeding drought-tolerant maize cultivars (Shi et al. 2017). One of the primary factors of limiting maize production across the world is salinity. Maize is a glycophyte plant that is extremely vulnerable to salt stress (Razzaq et al. 2021c). Maize has two phases of physiological challenges. The first is osmotic, whereas the second is ion toxic. Plant growth is slowed in the osmotic phase due to reduction in environmental water potential. In ion toxic, the sodium ions accumulate in the plant at toxic level and compete with potassium ions. Maintaining plant salt tolerance requires a balance of sodium potassium ions in the cytoplasm and a low concentration of Na ions. Salt tolerance-QTL, ZmHKT1 is linked to sodium ions and is an essential salt tolerance-QTL. Retrotransposon incorporation in maize and function of gene loss provide enhanced Na+ ion concentrations in leaves, which leads to high salt sensitivity. CRISPR-/Cas-mediated genome editing of ZmHKT1 was achieved. The results showed increased concentration of Na+ in xylem that transfer from root to shoot. Hence, it was concluded that ZmHKT1 plays an essential role in maize salinity stress tolerance and produce new varieties of maize with improved salinity tolerance (Zhang et al. 2018). Executed CRISRP-/Cas9-mediated genome editing to target ZmHKT2 and study the salinity tolerance mechanism in maize. The edited lines indicated reduced level of K+ transports and conder salinity tolerance. For this, recombinant inbred lines (RILs) were developed and mapped the novel QTL ZmHKT2, which controls the K+ concentration through HKT transporters under salinity stress. Furthermore, K+ concentrations in xylem sap has been increased in the absence of ZmHKT2, which offer salt tolerance. Schwartz et al. (2020) demonstrated the targeted inversion of specific region of 75.5 Mb on chromosome 2 in superior maize inbred line. This resulted in the opening of a huge chromosomal segment which harbors significant genetic diversity for recombination and can provide excellent platform to develop elite maize lines with desired traits. In another study, Gao et al. (2020) used CRISPR/Cas9 to disrupt the two waxy allele in maize inbred lines to develop waxy corn hybrids that showed superior field performance. This procedure was very quick in contrast to marker-­ assisted selection and backcrossing for conventional trait introgression. The field trails indicated that the engineered waxy hybrid lines produce higher yield and exhibited elite agronomic traits. Developed a novel prime editing system for maize by improving the pegRNA expression. Two genes such as ZmALS1 and ZmALS2 related to herbicide tolerance were mutated by designing the highly efficient pegRNA prime editor vector. The results revealed that the pZ1WS prime editor was very beneficial to develop

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transgene-free multiple herbicide-resistant maize progenies by targeting the two ALS genes. Development of male sterile lines is always of great interest for the plant breeders to produce hybrid seed development. Chen et al. (2018) used a CRISPR/Cas9 vector to create male sterile lines in maize by mutating the MS8 gene via agrobacterium transformation. Male sterile transgene-free lines were captured by screening of sterile population, and male sterility can be achieved in other superior inbred maize varieties for hybrid development.

9 Conclusion and Future Perspectives Maize is the most important staple crop and contributes significantly to global food security by supplying food. It provides higher amount of energy to human, and its global demand is increasing as the human population is increasing. Due to continuous climate change, many biotic and abiotic factors impose serious threats to maize production and globally affect the food security. Crop output must be increased in order to ensure food security in the era of growing population and biotic and abiotic challenges. To control these factors and ensure food security, breeding methods develop to make the crops resistant by targeting specific traits to cope with stresses to increase yield and production. Genetic engineering is used to make genetically modified corps that are resistant to stresses and enhance the agronomic traits, but this approach has drawbacks along with other environmental and ethical concerns. To overcome all these hurdles, other genome editing technologies are used like meganucleases, zinc finger nucleases, and TALENs that are protein based. These techniques provide efficient genome changes by targeted modifications and produce better agronomic traits. But these techniques have also some limitations. These are time-consuming, expensive, and difficult to handle and can never be used on large scale. The most powerful and excellent tool paves its ways to cope with all these stresses is CRISPR/Cas9, which is RNA-based technology. CRISPR technology is used to produce better varieties with remarkably efficient targeting that makes it more versatile and robust technique for plant genome engineering than others. This technique has many applications in maize improvement and used to create stress-­tolerant cultivars and the production of new agronomic traits to increase the yield and nutrition.

References Abdallah NA, Prakash CS, McHughen AG (2015) Genome editing for crop improvement: challenges and opportunities. GM Crops Food 6(4):183–205. https://doi.org/10.1080/2164569 8.2015.1129937 Agarwal A, Yadava P, Kumar K, Singh I, Kaul T et al (2018) Insights into maize genome editing via CRISPR/Cas9. Physiol Mol Biol Plants 24(2):175–183 Ansari WA, Chandanshive SU, Bhatt V, Nadaf AB, Vats S et al (2020) Genome editing in cereals: approaches, applications and challenges. Int J Mol Sci 21(11):4040 Arora NK (2019) Impact of climate change on agriculture production and its sustainable solutions. Environ Sustain 2(2):95–96. https://doi.org/10.1007/s42398-­019-­00078-­w

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S. Farooq et al.

Bortesi L, Fischer R (2015) The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnol Adv 33(1):41–52. https://doi.org/10.1016/j.biotechadv.2014.12.006 Carroll D (2011) Genome engineering with zinc-finger nucleases. Genetics 188(4):773–782 Char SN, Unger-Wallace E, Frame B, Briggs SA, Main M et al (2015) Heritable site-specific mutagenesis using TALEN s in maize. Plant Biotechnol J 13(7):1002–1010 Chen R, Xu Q, Liu Y, Zhang J, Ren D, Wang G, et al (2018) Generation of transgene-free maize male sterile lines using the CRISPR/Cas9 system. Front Plant Sci 9:1180. https://doi. org/10.3389/fpls.2018.01180 Cong L, Ran FA, Cox D, Lin S, Barretto R, Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N et al (2013) Multiplex genome engineering using CRISPR/Cas systems. Science (New York, N.Y.). Science 339(6121):819–823. https://doi.org/10.1126/science.1231143.Multiplex Curtin SJ, Voytas DF, Stupar RM (2012) Genome engineering of crops with designer nucleases. Plant Genome 5(2):42–50. https://doi.org/10.3835/plantgenome2012.06.0008 Datta A (2013) Genetic engineering for improving quality and productivity of crops. Agric Food Sec 2(1):2–4. https://doi.org/10.1186/2048-­7010-­2-­15 Davies JP, Kumar S, Sastry-Dent L (2017) Use of zinc-finger nucleases for crop improvement. Prog Mol Biol Transl Sci 149:47–63 Deryng D, Elliott J, Folberth C, Müller C, Pugh TAM et  al (2016) Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat Clim Chang 6(8):786–790. https://doi.org/10.1038/nclimate2995 Deutsch CA, Tewksbury JJ, Tigchelaar M, Battisti DS, Merrill SC et al (2018) Increase in crop losses to insect pests in a warming climate. Science (80-.) 361(6405):916–919. https://doi. org/10.1126/science.aat3466 Dietz KJ, Mittler R, Noctor G (2016) Recent progress in understanding the role of reactive oxygen species in plant cell signaling. Plant Physiol 171(3):1535–1539. https://doi.org/10.1104/ pp.16.00938 Dresselhaus T, Hückelhoven R (2018) Biotic and abiotic stress responses in crop plants. Agronomy 8(11):8–13. https://doi.org/10.3390/agronomy8110267 Farooq M, Hussain M, Wakeel A, Siddique KHM (2015) Salt stress in maize: effects, resistance mechanisms, and management. A review. Agron Sustain Dev 35(2):461–481. https://doi. org/10.1007/s13593-­015-­0287-­0 Farre G, Gomez-Galera S, Naqvi S, Bai C, Sanahuja G et  al (2012) Biotechnologycrop/croppingbiotechnologyand nutritional Improvementcrop/croppingnutritional improvementof Cropscrop/cropping. In: Meyers RA (ed) Encyclopedia of sustainability science and technology. Springer, New York, pp 1676–1723 Fathi A, Tari DB (2016) Effect of drought stress and its mechanism in plants. Int J Life Sci 10(1):1–6. https://doi.org/10.3126/ijls.v10i1.14509 Feng C, Yuan J, Wang R, Liu Y, Birchler JA et al (2016) Efficient targeted genome modification in maize using CRISPR/Cas9 system. J Genet Genomics 43(1):37–43 Gallagher RR, Li Z, Lewis AO, Isaacs FJ (2014) Rapid editing and evolution of bacterial genomes using libraries of synthetic DNA.  Nat Protoc 9(10):2301–2316. https://doi.org/10.1038/ nprot.2014.082 Gao H, Gadlage MJ, Lafitte HR et al (2020) Superior field performance of waxy corn engi­ neered using CRISPR–Cas9. Nat Biotechnol 38:579–581. https://doi.org/10.1038/s41587020-0444-0 Hanna RE, Doench JG (2020) Design and analysis of CRISPR–Cas experiments. Nat Biotechnol 38(7):813–823. https://doi.org/10.1038/s41587-­020-­0490-­7 He Y, Zhao Y (2020) Technological breakthroughs in generating transgene-free and genetically stable CRISPR-edited plants. aBIOTECH 1(1):88–96. https://doi.org/10.1007/ s42994-­019-­00013-­x Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA et al (2012) A programmable dual-RNA-­ guided DNA endonuclease in adaptive bacterial immunity. Science (80-.) 337(6096):816–821. https://doi.org/10.1126/science.1225829 Karimi Z, Ahmadi A, Najafi A, Ranjbar R (2018) Bacterial CRISPR regions: general features and their potential for epidemiological molecular typing studies. Open Microbiol J 12(1):59–70. https://doi.org/10.2174/1874285801812010059

Advances in Genome Editing for Maize Improvement

193

Khan Z, Khan SH, Mubarik MS, Sadia B, Ahmad A (2017) Use of TALEs and TALEN technology for genetic improvement of plants. Plant Mol Biol Report 35(1):1–19 Kumar R, Kaur A, Pandey A, Mamrutha HM, Singh GP (2019) CRISPR-based genome editing in wheat: a comprehensive review and future prospects. Mol Biol Rep:1–13 Le Rhun A, Escalera-Maurer A, Bratovič M, Charpentier E (2019) CRISPR-Cas in Streptococcus pyogenes. RNA Biol 16(4):380–389. https://doi.org/10.1080/15476286.2019.1582974 Makarova KS, Koonin EV (2015) Annotation and classification of CRISPR-Cas systems. Methods Mol Biol 1311:47–75. https://doi.org/10.1007/978-­1-­4939-­2687-­9_4 Makarova KS, Wolf YI, Alkhnbashi OS, Costa F, Shah SA et  al (2015) An updated evolutionary classification of CRISPR-Cas systems. Nat Rev Microbiol 13(11):722–736. https://doi. org/10.1038/nrmicro3569 Makarova KS, Wolf YI, Koonin EV (2018) Classification and nomenclature of CRISPR-Cas systems: where from here? CRISPR J 1(5):325–336. https://doi.org/10.1089/crispr.2018.0033 Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS et al (2020) Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol 18(2):67–83. https://doi.org/10.1038/s41579-­019-­0299-­x Malzahn A, Lowder L, Qi Y (2017) Plant genome editing with TALEN and CRISPR. Cell Biosci 7(1):1–18 McIntosh RA, Williamson PM, Wrigley CW (2015) The nature, causes, and control of grain diseases in the major cereal species. Encycl Food Grains Second Ed 4–4:74–82. https://doi. org/10.1016/B978-­0-­12-­394437-­5.00183-­2 Mochida K, Nishii R, Hirayama T (2020) Decoding plant–environment interactions that influence crop agronomic traits. Plant Cell Physiol 61(8):1408–1418. https://doi.org/10.1093/ pcp/pcaa064 Mugode L, Ha B, Kaunda A, Sikombe T, Phiri S et al (2014) Carotenoid retention of biofortified provitamin a maize (Zea mays L.) after Zambian traditional methods of milling, cooking and storage. J Agric Food Chem 62(27):6317–6325. https://doi.org/10.1021/jf501233f Nester EW (2015) Agrobacterium: Nature’s genetic engineer. Front Plant Sci 5(JAN):1–16. https:// doi.org/10.3389/fpls.2014.00730 O’Connell MR, Oakes BL, Sternberg SH, East-Seletsky A, Kaplan M et al (2014) Programmable RNA recognition and cleavage by CRISPR/Cas9. Nature 516(7530):263–266. https://doi. org/10.1038/nature13769 Orlowski J, Boniecki M, Bujnicki JM (2007) I-Ssp6803I: the first homing endonuclease from the PD-(D/E)XK superfamily exhibits an unusual mode of DNA recognition. Bioinformatics 23(5):527–530. https://doi.org/10.1093/bioinformatics/btm007 Petolino JF (2015) Genome editing in plants via designed zinc finger nucleases. In Vitr Cell Dev Biol Plant 51(1):1–8 Ranum P, Peña-Rosas JP, Garcia-Casal MN (2014) Global maize production, utilization, and consumption. Ann N Y Acad Sci 1312(1):105–112 Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop Production by 2050. PLoS One 8(6):e66428. https://doi.org/10.1371/journal.pone.0066428 Raza A, Razzaq A, Mehmood SS, Zou X, Zhang X et  al (2019) Impact of climate change on crops adaptation and strategies to tackle its outcome: a review. Plan Theory 8(2). https://doi. org/10.3390/plants8020034 Razzaq A, Saleem F, Kanwal M, Mustafa G, Yousaf S, Imran Arshad HM et al (2019) Modern trends in plant genome editing: an inclusive review of the CRISPR/Cas9 toolbox. Int J Mol Sci 20(16):4045 Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F (2021a) Next-generation breeding strategies for climate-ready crops. Front Plant Sci 12 Razzaq A, Mustafa G, Ali MA, Khan MS, Joyia FA (2021b) 23 CRISPR-mediated genome editing in maize for improved abiotic stress tolerance. In: Molecular breeding in wheat, maize and sorghum: strategies for improving abiotic stress tolerance and yield. CAB International, p 405 Razzaq A, Saleem F, Wani SH, Abdelmohsen SA, Alyousef HA, Abdelbacki AM et  al (2021c) De-novo domestication for improving salt tolerance in crops. Front Plant Sci 1623 Razzaq A, Wani SH, Saleem F, Yu M, Zhou M, Shabala S (2021d) Rewilding crops for climate resilience: economic analysis and de novo domestication strategies. J Exp Bot 72(18):6123–6139

194

S. Farooq et al.

Sanaullah AB, Khan A, Rahman WU, Nasrullah A, Khan MAR et al (2018) Estimating cost and net return: a profitability comparison of maize and potato in district upper Dir of Khyber Pakhtunkhwa, Pakistan. Econ Surv:19 Sandhya D, Jogam P, Allini VR, Abbagani S, Alok A (2020) The present and potential future methods for delivering CRISPR/Cas9 components in plants. J Genet Eng Biotechnol 18(1):25. https://doi.org/10.1186/s43141-­020-­00036-­8 Schiml S, Fauser F, Puchta H (2014) The CRISPR/Cas system can be used as nuclease for in planta gene targeting and as paired nickases for directed mutagenesis in Arabidopsis resulting in heritable progeny. Plant J 80(6):1139–1150. https://doi.org/10.1111/tpj.12704 Schwartz C, Lenderts B, Feigenbutz L, Barone P, Llaca V, Fengler K, Svitashev S (2020) CRISPR-Cas9-mediated 75.5-Mb inversion in maize. Nat Plants 6(12):1427–1431. https://doi. org/10.1038/s41477-020-00817-6 Sedeek KEM, Mahas A, Mahfouz M (2019) Plant genome engineering for targeted improvement of crop traits. Front Plant Sci 10(February):1–16. https://doi.org/10.3389/fpls.2019.00114 Shi J, Gao H, Wang H, Lafitte HR, Archibald RL et  al (2017) ARGOS 8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol J 15(2):207–216 Sprink T, Metje J, Hartung F (2015) Plant genome editing by novel tools: TALEN and other sequence specific nucleases. Curr Opin Biotechnol 32:47–53 Tan YY, Du H, Wu X, Liu YH, Jiang M et  al (2020) Gene editing: an instrument for practical application of gene biology to plant breeding. J Zhejiang Univ Sci B 21(6):460–473. https:// doi.org/10.1631/jzus.B1900633 Tang Y, Fu Y (2018) Class 2 CRISPR/Cas: an expanding biotechnology toolbox for and beyond genome editing 06 biological sciences 0604 genetics. Cell Biosci 8(1):1–13. https://doi. org/10.1186/s13578-­018-­0255-­x Ten Berge HFM, Hijbeek R, van Loon MP, Rurinda J, Tesfaye K (2019) Maize crop nutrient input requirements for food security in sub-Saharan Africa. Glob Food Sec 23:9–21 Townsend JA, Wright DA, Winfrey RJ, Fu F, Maeder ML et al (2009) High-frequency modification of plant genes using engineered zinc-finger nucleases. Nature 459(7245):442–445 Tsanova T, Stefanova L, Topalova L, Atanasov A, Pantchev I (2021) DNA-free gene editing in plants: a brief overview. Biotechnol Biotechnol Equip 35(1):131–138. https://doi.org/10.108 0/13102818.2020.1858159 Tshikunde NM, Mashilo J, Shimelis H, Odindo A (2019) Agronomic and physiological traits, and associated quantitative trait loci (QTL) affecting yield response in wheat (Triticum aestivum L.): a review. Front Plant Sci 10(November):1–18. https://doi.org/10.3389/fpls.2019.01428 Wani SH, Samantara K, Razzaq A, Kakani G, Kumar P (2022) Back to the wild: mining maize (Zea mays L.) disease resistance using advanced breeding tools. Mol Biol Rep:1–17 Weinthal DM, Taylor RA, Tzfira T (2013) Nonhomologous end joining-mediated gene replacement in plant cells. Plant Physiol 162(1):390–400. https://doi.org/10.1104/pp.112.212910 Yadava P, Abhishek A, Singh R, Singh I, Kaul T et al (2017) Advances in maize transformation technologies and development of transgenic maize. Front Plant Sci 7:1949 Yang CJ, Samayoa LF, Bradbury PJ, Olukolu BA, Xue W et  al (2019) The genetic architecture of teosinte catalyzed and constrained maize domestication. Proc Natl Acad Sci U S A 116(12):5643–5652. https://doi.org/10.1073/pnas.1820997116 Zhang M, Cao Y, Wang Z, Wang Z, Shi J et al (2018) A retrotransposon in an HKT1 family sodium transporter causes variation of leaf Na+ exclusion and salt tolerance in maize. New Phytol 217(3):1161–1176 Zhang D, Zhang Z, Unver T, Zhang B (2021) CRISPR/Cas: a powerful tool for gene function study and crop improvement. J Adv Res 29:207–221 Zhao L, Bonocora RP, Shub DA, Stoddard BL (2007) The restriction fold turns to the dark side: a bacterial homing endonuclease with a PD-(D/E)-XK motif. EMBO J 26(9):2432–2442. https:// doi.org/10.1038/sj.emboj.7601672 Zong Y, Wang Y, Li C, Zhang R, Chen K et  al (2017) Precise base editing in rice, wheat and maize with a Cas9-cytidine deaminase fusion. Nat Biotechnol 35(5):438–440. https://doi. org/10.1038/nbt.3811

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize (Zea mays L.) Seema Sheoran, Manisha Saini, Vinita Ramtekey, Mamta Gupta, Mohd Kyum, and Pardeep Kumar

1 Introduction Maize is considered as a most valuable and highly productive cereal crop together with wheat and rice, supplying almost 60% of global food energy (Ross-Ibarra et al. 2017; Jiao et al. 2017). To accomplish the world’s food requirement, which is estimated to touch 10 billion figures in the middle of the century, is challenging due to the changing climatic conditions (Tigchelaar et  al. 2018). The rising demand for diverse germplasm to compete with global hunger is further threatened by various biotic and abiotic stresses. The major biotic factors include pathogens and insect pests, while abiotic factors such as drought, waterlogging, salinity, temperature, etc. cause a huge impact on maize yield over the world (Restrepo-Diaz et al. 2021). The detrimental effect on crop losses is highest and uncontrollable in the case of association of both biotic and abiotic factors (Josine et al. 2011). It is alarming the scientific community rapidly introduce maize cultivars having the ability to withstand adverse climatic conditions (Masuka et  al. 2012; Dresselhaus and Hückelhoven 2018). Extensive efforts had been made in the fabrication of maize genotype through conventional breeding methods to maintain the yield potential for the previous six decades. The achievement of sustainable productivity requires the art of utilizing S. Sheoran ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, Punjab, India ICAR-Indian Agricultural Research Institute, Regional Station, Karnal, Haryana, India M. Saini ICAR-Indian Institute of Sugarcane Research, Lucknow, Uttar Pradesh, India V. Ramtekey ICAR-Indian Institute of Seed Science, Mau, Uttar Pradesh, India M. Gupta · M. Kyum · P. Kumar (*) ICAR-Indian Institute of Maize Research, PAU Campus, Ludhiana, Punjab, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 195 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_10

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the natural variability to isolate the desired genotypes and further their application in stress-tolerant breeding programs (Yadav et al. 2015). Nevertheless, traditional methods helped to exploit the variations and further their introgression in cultivated maize varieties, but alternative strategies are required to meet the criteria of specific growing conditions (Haroon et al. 2020). Moving forward with conventional techniques is not convincing due to limited genetic stocks and inefficient selection measures for the plant stresses, which is more redundant in the case of abiotic factors (Bedada et al. 2016). To overcome these continuously evolving problems, the development of alternative strategies is mandatory to be adopted in the changing environment. Genetic engineering (GE) aims to create specific alterations in the plant genome to introduce a novel functionality for the concerning trait. The GE approaches rely on the foreign gene expression and correlation with regulatory and signaling pathways of target plant species to generate or encode stress-specific metabolites (Anwar and Kim 2020). In the previous two decades, GE displayed phenomenal developments in the modification of the maize genes for triggering the defense mechanism. After the first event of genetic transformation back in 1996, maize emerged as a prioritized crop among the field crops at a commercial scale (Yadava et al. 2017; Raman 2017). Agrobacterium-directed delivery of genetic information gained the attention of plant breeders to engineer the maize genome because of its stable integration mechanism and fertile plant production, which follows typical Mendelian inheritance (Bedada et al. 2018). It is very important to enhance our understanding of molecular, biochemical, and cellular changes in response to a diverse range of stresses. The remarkable opportunities have been given by GE to conventional plant breeders for a better understanding of target traits (Ilyas et al. 2021). GE helped to identify the candidate genes, microRNAs (miRNAs), and transcription factors in maize associated with the tolerance mechanism and subsequently utilized them in stress-resistant breeding (Wu et al. 2019; Muppala et al. 2021). In recent years, the climatic variations disturbed the plant growth environment greatly due to drought conditions and rise in temperature, significantly reducing the plant yield potential even after effective agronomical and biotic stress management (Restrepo-Diaz et al. 2021). Abiotic stress causes osmolyte accumulation, stomatal closure, reduced photosynthesis, and activation of stress-responsive genes. The soil productivity is further negatively influenced by salinity which becomes devastating in hot temperate areas, ultimately reducing the cultivation land (Turan et al. 2012). The combination of both biotic and abiotic stresses disorganizes the physiological, molecular, and biochemical framework of plants that leads to low photosynthesis and reduced water uptake ability, which failing crop productivity. The introduction of genes related to hormonal and enzymatic balance, ROS stabilizers, and helper in ion transporters significantly aids in maintaining the source-sink relationship during the adverse condition in maize (Landi et al. 2017). GE is more preferred for resistance genotype development which is only focused on target gene integration. On the other hand, it is not possible through plant breeding methods due to cross incompatibility and undesirable consequences of genomic background (Turan et al. 2012). Several orthologous genes have been identified and transferred into maize and vice

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versa using GE from related plant species against biotic and abiotic stresses (Yin et al. 2004; Shou et al. 2004a, b; Turan et al. 2012; Bo et al. 2020). Currently, GE through genome editing techniques creates fundamental insight into the biology of crop plants ultimately revolutionizing the agricultural sector at a commercial scale (Chen et al. 2019). The future of stress resistance GE in maize looks promising with CRISPR/Cas9 technique, although substitution of every novel technique needs to be simultaneously addressed to avoid any delay for the betterment of agricultural sciences. Furthermore, a combined approach of conventional techniques along with recent advancements in GE can accelerate the introduction of stress resistance varieties at low costs in maize. This chapter highlights the current scenario of GE techniques and status in biotic and abiotic stress resistance breeding in maize along with their challenges and new approaches with future perspectives.

2 Present Status of Genetically Modified Maize Since the introduction of GE technology, the maize crop has received attention in the agricultural sector. Initially, the Food and Drug Administration (FDA) approved the production of genetically modified (GM) Bt corn developed by Ciba-Geigy in 1995 (Bawa and Anilakumar 2013). Further glyphosate-resistant corn variety, Roundup Ready corn, has been commercialized by Monsanto in 1999 (Monsanto 2013). Subsequently, the crystalline proteins, namely, Cry1 and Cry2 to target lepidopteran pests and Cry3 proteins for coleopteran pests attacked on maize have been identified (Schnepf et al. 1998). Therefore, maize was engineered with these toxic Cry proteins. Initially, the GM maize conferred either herbicide or insecticidal resistance, and further single cultivar has been engineered for stacked traits. In 2009, Monsanto and Dow AgroSciences produced a stacked GM maize, Genuity® SmartStax containing genes (cp4 epsps, cry1Fa2, cry2Ab2, cry34Ab1, cry35Ab1, cry3Bb1, cry1A.105, pat) to provide glyphosate tolerance and resistance against Helicoverpa zea (corn earworm), Diatraea grandiosella (southwestern corn borer), Ostrinia nubilalis (European corn borer), Diabrotica virgifera virgifera (western corn rootworm), Diabrotica virgifera zeae (northern corn rootworm), and Diabrotica barberi (Mexican corn rootworm), which was approved for cultivation in the United States (Moglia and Portis 2016; ISAAA 2021). The ISAAA (2019) report states that GM maize is the second most important crop after GM soybean which has been cultivated globally on 60.9 mha area representing 32% of the total area under GM crops globally (190.4 mha). The maize has a maximum number (146) of approved events among all GM crops in 35 countries. Among the top ten GM events, seven are maize events, namely, NK603, GA21 (herbicide-tolerant), MON810, MON89034 (insect-resistant), TC1507, Bt11, and MON88017 (insect-resistant and herbicide-tolerant). The herbicide-tolerant event NK603 of maize has maximum approvals in 28 countries followed by soybean event GTS 40-3-2 for herbicidal tolerance. The GM maize has been grown in 15 countries, namely, the United States, Brazil, Argentina, Canada, Paraguay, South

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Africa, Uruguay, Philippines, Mexico, Spain, Colombia, Vietnam, Honduras, Chile, and Portugal. The highest GM maize growing countries are the United States (33.17  mha) followed by Brazil (16.3  mha), Argentina (5.9  mha), Philippines (0.9 mha), and Vietnam (0.1 mha). According to ISAAA database, there is a total of 240 approved GM events available in maize across various countries, and out of these, 60 events are commercialized with various trade names. These 240 events include 212 events for tolerance to herbicides, 208 events for resistance against various insect pests, 13 events for product quality modifications, and 6 events for pollination control (ISAAA 2021). GM maize has a great contribution in saving million-dollar money for farmers by reducing the usage of pesticides and herbicides (Raman 2017). In North America, GM corn seed covers >90% of the corn seed market (Morder intelligence blog 2021). The soluble protein Vip3A from Bacillus thuringiensis gain popularity these days to control pests that are resistant to Cry proteins due to its different action mechanisms. The Agrisure®Viptera™ having Vip3Aa20 protein which is effective against lepidopteran pests has been developed. The 34 events across different countries containing Vip3A proteins encoding gene along with other genes responsible for insecticidal and herbicidal resistance have been developed. The Bayer company also developed Trecapta technology™ to incorporate three genes Cry1A.105, Cry2Ab2, and Vip3Aa20 having different modes of action to impart tolerance against corn borers, corn earworms, black cutworm, fall armyworm, and western bean cutworm (https://traits.bayer.com/). Various events having approved trade names are Agrisure® Viptera™ 2100, Agrisure® Viptera™ 3110, Agrisure® Viptera™ 3111, Agrisure® Viptera™ 4, Agrisure® Viptera™ 3220, Agrisure® Viptera™ 3100, Agrisure® Duracade™ 5222 by Syngenta, and Power Core™ x MIR162 x Enlist™ by Dow Agro Sciences LLC (ISAAA database; Gupta et al. 2021). In 2013, the first drought-tolerant GM maize Genuity® DroughtGard™ of Monsanto has been released and commercialized in the United States, which contained the gene encoding cold shock protein B (CSPB) isolated from Bacillus subtilis (Moglia and Portis 2016; ISAAA database 2021). Maize has also been engineered for other traits, namely, modified product quality (phyA2 for conversion of phytase phosphorus into inorganic phosphorous for its consumption in animal feed; cordapA for high lysine content with trade names Mavera™ Maize and Mavera™ YieldGard™ Maize; amy797E to increase the thermostability of amylase for enhancement in bioethanol production with trade name Enogen™), pollination control (ms45 to restore fertility with trade name 32,138 SPT maintainer; zm-aa1 for pollen sterility; barnase for male sterility/ trade name InVigor™ Maize), and increased ear biomass by targeting the bHLH TF athb17 (Kumar et al. 2020; ISAAA database 2021). In this series, the Bayer’s SmartStax™ Pro x Enlist™ (cp4 epsps, cry2Ab2, cry1A.105, cry1F, pat, cry34Ab1, cry35Ab1, dvsnf7, aad-1) and Bayer’s SmartStax® Rib Complete® Corn Blend for herbicidal and insecticidal tolerance will be available in 2022 (ISAAA database 2021, https://traits.bayer.com/).

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3 Acceptance and Impact of Genetically Modified Maize The first genetically modified (GM) crop was commercialized in 1996s (Snow and Palma 1997; Benbrook 2012), and since then, it has been rapidly adopted in several countries (FAO 2015). In the recent decades, popularization of transgenic crops has considerably increased the crop yields by 22%, which has led to an approximately 68% increase in farmer profits (Klümper and Qaim 2014). Globally, in the past 22 years, the area of transgenic crops has increased considerably from 1.7 mha in 1996 to 191.7 mha in 2018, that is, around 113-fold increases (ISAAA 2018a, b). Presently, the cultivation of GM crops is dominated by soybean (∼50%), maize (∼30%), cotton (∼13%), and canola (∼5%) crops (ISAAA 2020). The 190 mha of GM crops have been majorly grown by 26 countries of which 46% was contributed by five industrial countries only, that is, the United States, Canada, Australia, Spain, and Portugal (ISAAA 2018a, b, 2020), demonstrating their role in the agricultural economy (Cao et al. 2011). The traits like herbicide tolerance and insect resistance have been mainly targeted to introduce into major crops like soybean, maize, canola, and cotton comprising about 53% and 14% of total GM area, respectively, and about 33% of total GM area for both traits staked in a crop (ISAAA 2016). Extensive research has been conducted to develop GM crops and has been widely accepted in many countries. But still, nearly 38 countries across the world have prohibited their cultivation due to human and environmental safety concerns (ISAAA 2016). Among GM crops, the highest numbers of GE events have been undertaken in maize for single or staked traits. After soybean, GM maize is the second largest crop to be globally adopted (Aldemita et al. 2015). As of 2015, a total 53.6 mha of GM maize has been cultivated globally, representing almost 28% of the 190.4mha of total GM crop cultivation (Statista 2021). Furthermore, GM maize has the highest potential of expansion due to its comparatively lower rate of adoption (30% of the global maize in 2015), and a huge number are under cultivation (ISAAA 2016). The acceptance of transgenic crops has been an issue for many years in many countries due to several human health and environmental concerns. It has been a concern that transgenics can cause allergic and carcinogenic reactions in people, although no evidence has been found yet (Ferber 1999). Furthermore, it can develop resistance to antibiotics that lead to the generation of super bugs. In addition, the digestion of foreign DNA from other sources like a virus or bacteria is also a question to consider, but still, no evidence has been found in any digestion difference from conventional DNA. Another big concern on acceptance of transgenics is the damage to the environment. The pollen from transgenic crops having toxins can be harmful to many nontarget insects such as Monarch butterfly larvae killed due to bacterial toxins in transgenics pollen (Losey et al. 1999). The other biggest concern is the hybridization of transgenic crops with weeds, which can cause super weeds development that will be resistant to herbicides. Genes utilized to develop insect/pest and diseases resistance in plants can benefit weed populations also allowing them to survive under harsh conditions too. But to date, these are just theoretical predications with little evidence to support them (Crawley et  al. 2001). Several studies have been

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carried out to analyze the impact of GM crops on agronomic, economic, and environmental aspects (Ortego et al. 2009; Burachik 2010; Arthur 2011; Xu et al. 2013; Wang et  al. 2014; Nicolia et  al. 2014; The National Academies Press 2016). However, these studies were not much useful to draw unambiguous conclusions. To answer the major questions for GM maize adoption, few meta-analyses have also been attempted to address the concerns related to yield, cost-benefit ratio (Marvier et al. 2007; Areal et al. 2013; Klümper and Qaim 2014), pesticide use (Klümper and Qaim 2014), and effects on nontarget (NT) invertebrates (Marvier et  al. 2007; Wolfenbarger et al. 2008; Naranjo 2009; Comas et al. 2014). However, there are still some key issues such as effect on grain quality and nutrition, toxin values (Ercoli et  al. 2007, 2011) in GM maize production, and its effect on important agro-­ ecosystem services including soil organic matter decomposition. According to Pellegrino et al. (2018), a meta-analysis was carried out for the agronomic, environmental, and toxicological traits of GM maize such as yield, grain quality, NT organisms, target organisms, and soil biomass decomposition. The results depict that GM maize performed better than its near-isogenic line in terms of grain yield (5.6–24.5%) with lower concentrations of mycotoxins (−28.8%), fumonisin (−30.6%), and thricotecens (−36.5%). It was analyzed that NTOs were not affected by GM maize, except Braconidae, a parasitoid of European corn borer due to BT maize. Biogeochemical cycle parameters like lignin content in stalks and leaves also did not fluctuate, while the biomass decomposition was higher in GM maize. Many GM crops for pest control are engineered by using BT toxins, crystal protein from the bacterium Bacillus thuringiensis. The US Environmental security company has analyzed that these toxins don’t pose any hazard to human well-being. The endotoxins are insecticidal and show low environmental persistence by degrading quickly. Although these endotoxins are harmful to bugs, a few studies supported that they are harmless to wild mammals, birds, pets, and people. The use of Bt corn has saved 1.7 billion dollars from the European corn borer damage in US states while 10% yield increment. It has been estimated that by growing 50% of GM crops likes maize, oil seed rape, sugar beet, and cotton would decrease 14.5 million kg of pesticide use in a year sparing 7.5 mha from spray, saving 20.5 million liters of diesel, and avoiding roughly 73,000 lots of carbon dioxide being launched into the atmosphere. From 1997 to 2009, a decrease in 13  million kg of pesticide has been recorded in corn and soybean fields, adopting the GM versions. In the United States, the decrease in pesticide use has been projected approximately 2.5 million pounds a year (Madhusudhan 2016). It is a very well-known fact that genetically engineered crops have accelerated yields, increased taste of meals, and decreased the application of pesticides. Alternatively, these crops also pose some serious concerns related to human wellness and threaten environmental safety by the creation of super weeds, novel pest, negative effects on nontarget species, and the disturbance of ecosystem services. The countries adopted for transgenic crops have gained economic development through increased production and saving chemical and labor costs, in addition to preventing gigantic ecological damage. Slowly, many more developing countries are also accepting transgenic crops as they are gaining profits compared to earlier. Despite the large-scale cultivation of GM maize and its impact

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assessment studies on agro-environmental aspects, the benefits and risks related to GM maize are still being argued, and safety concerns persist.

4 Genetic Engineering Approaches to Develop Transgenic Maize There are several biotechnological platforms used to develop transgenic maize (Fig. 1). Several elements are required for genetic engineering such as the development of gene constructs possessing genes of interest (GOI), promoter, terminator, enhancer, and intron sequences, selectable markers, reporter genes, and binary and alternative vectors. A variety of plant transformation methods are developed to introduce the gene construct to regulate gene expression via suitable approaches such as overexpression, gene stacking, RNA interference (RNAi), and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein-­9 nuclease (Cas9)-mediated genome editing.

4.1 Development of Gene Construct (a) Gene of interest: With the advancement in DNA sequencing tools, crop plant research utilizing genetic engineering approaches has been revolutionized in past few years (Shendure et  al. 2017). Through whole-genome sequencing, huge information has been generated that is to be utilized in association with powerful bioinformatic tools and sophisticated molecular biology methods. It helps to detect the functions of every gene associated with different biotic, abiotic, and agronomic traits. By utilizing different transgenic approaches, specific genes can be targeted to improve/modify their functions through their overexpression or knockdown with desirable phenotypes to improve targeted traits in plants. A few criteria need to be followed to improve a GOI such as expression level, gene structure, presence of conserved domains, GC content, and codon usage optimization for improved translation efficiency (Barahimipour et  al. 2015). To construct the expression cassette, only the protein-coding region, that is, exons, should be inserted for plant transformation except in some cases where endogenous cis-regulatory elements or enhancer sequences are present that are essential for their expression, translation, or stability (Gao et al. 2015a, b; Zhang et al. 2018). (b) Transcriptional promoter sequence: Promoters are DNA sequences located upstream of the 5′-UTR of the gene containing several regulatory elements to regulate transcription initiation (Yamamoto et al. 2007). Several important transcription factors (TFs) (e.g., DREB and ABRE, MYC/MYB TFs for abiotic stresses) play important role in transcriptional regulation by interacting with

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Fig. 1  Different genetic engineering elements and approaches to regulate maize gene expression for biotic and abiotic stress tolerance/resistance development

promoter sequences (Yamaguchi-Shinozaki and Shinozaki 2006; Ambawat et al. 2013). The selection of promoters directly affects the efficiency of new biotechnological tools (NBT) and the accessibility of powerful traits. There are several plants and viral and synthetic promoters that are available with constitutive, stress-induced (biotic and abiotic), tissue-specific, and developmental stage-specific features to regulate the overexpression of GOI in several crops (Basso et al. 2020). (c) Transcriptional terminator sequence (TTS): TTS are conserved sequences present downstream of the protein-coding region and are recognized by the transcriptional machinery as transcription stop signals and consequently induce

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decoupling of this machinery from the DNA (Loke et al. 2005). The most commonly utilized TTS in plants are as T-nos and T-ocs from the nopaline synthase and octopine synthase gene of A. tumefaciens, respectively, T-35S from the Cauliflower mosaic virus 35S terminator, rbcS1 or rbcS-E9 from the ribulose-­1,5-bisphosphate carboxylase gene, small subunit, of Pisum sativum. (d) Intron-mediated enhancement and enhancer sequences: Introns are the noncoding sequences present in primary transcripts that are removed before the translation of the exons, that is, coding sequence. In addition, some introns also act as intron-mediated transcription enhancers and improve the translation efficiency that are useful in genetic engineering (Laxa 2017). The introns like Adh1, Sh1, Bz1, Hsp82, Act1, and GapA1 from maize or rice genes are most commonly used introns to enhance the transcription levels in monocots, while the rbcS, ST-LS1, Ubq3, Ubq10, PAT1, and atpk1 introns from petunia, potato, or A. thaliana genes are the most common in dicots (Gallegos and Rose 2015; Laxa 2017). In contrast to introns, enhancers are (also noncoding DNA sequences) commonly present within the promoter sequence upstream of the TSS or in the 5′or 3′-UTR.  They bind many TFs to trigger the expression of genes sited upstream or downstream. In addition, they also regulate RNA expression, chromatin accessibility, and histone modifications and reduce DNA methylation levels (Weber et  al. 2016); for example, in maize, an enhancer Hepta-repeat located 100 kb upstream of the booster1 gene improves its expression (Belele et al. 2013). Therefore, the introns and enhancers have huge potential in genetic engineering and need more validation studies to support the use of these sequences in specific crops. (e) Selectable markers: The major challenge of genetic transformation is to insert the GOI into the genome of the cell and then to select this transformed cell with regeneration ability. It is feasible by the addition of selective agents, for example, hygromycin, kanamycin, geneticin, glyphosate, glufosinate-ammonium, and imazapyr and hormones used in the in vitro culture medium. There are two methods of selection, that is, via positive selection where non-transformed cells are unharmed without causing injury or death, while in negative selection, either the growth is inhibited or death of non-transformed cells. For positive selection, uidA/gus (β-glucuronidase), manA (phosphomannose isomerase), xylA (xylose isomerase), PTXD (phosphite oxidoreductase), and DOGR1 (2-deoxyglucose-6-phosphate phosphatase), genes isolated from microorganisms are mainly utilized in plant tissue culture (Izawati et al. 2015; Nahampun et al. 2016). For negative selection, the nptII, hptII, and CmR genes are used as selectable markers that confer resistance to antibiotics (geneticin/kanamycin, hygromycin, and chloramphenicol, respectively) blocking ribosome activity and finally inhibit protein synthesis. (f) Exogenous and endogenous reporter genes: Reporter genes are efficient tools to monitor the efficacy of gene delivery vehicles and gene expression. It is cloned downstream of a regulatory region (e.g., promoter/enhancer), which generally controls the expression of a specific gene. Hence, introducing a reporter gene driven by a promoter of interest into the target cell can indirectly monitor the

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expression of the gene. Using the reporter genes, various aspects of gene expression like promoter/regulatory elements, inducible promoters, and endogenous gene expression (Grandaliano et al. 1995; Ikenaka and Kagawa 1995) can be studied. It is an inexpensive, rapid, and sensitive assay to study gene delivery and gene expression, avoiding the development of a specific probe to assess the expression of every new GOI.  The most commonly used exogenous reporter genes are chloramphenicol acetyltransferase, β-galactosidase (GAL), β-glucuronidase (uidA/GUS), β-lactamase, firefly luciferase, and Renilla luciferase; yellow fluorescent protein (YFP); green fluorescent protein (GFP); and red fluorescent protein (RFP). However, phytoene desaturase (PDS) is mostly used as an endogenous reporter gene to assess the RNAi assays in plants (Sundaresan and Gambhir 2002). (g) Binary and alternative vectors: For decades, Agrobacterium-mediated genetic transformation has been widely used to generate transgenic plants. Initially, this technology involves complex microbial genetic methodologies to introduce GOI into the transfer DNA (T-DNA) of large tumor-inducing plasmids (Ti-­ plasmids, ∼200 kb in length) making it complicated to delete or insert any DNA at specific sites. To make it easy, scientists developed more efficient binary vectors by splitting the T-DNA region and virulence (vir) genes into two replicons, which have enhanced the genetic transformation efficiency in crop plants. The superbinary vectors with supplementary vir genes, ternary vectors, and helper plasmid with an augmented number of vir genes have illustrated significant results (Che and Anand 2018; Anand et al. 2018). To enhance the transformation efficiency and stability of transgenes, it was crucial to optimize components with reduced T-DNA length. Therefore, vectors with multiple cloning sites or restriction enzyme sites adjoining the key transcription units are being engineered. For example, the pCAMBIA, pSITE, pGD, pMSP, pGPTV, and pRT100 vectors are amended binary vectors for plant transformation. The traditional vectors have some drawbacks of either non-optimization of their components, or they lack ideal components for a specific trait such as promoter, terminator, selection marker, or reporter gene. To resolve these limitations, new and optimized simple vectors have been synthesized for each explicit case.

4.2 Plant Transformation Methods Plant transformation is the process to introduce the DNA segment into any species genome to alter its genetic constitution to achieve desired gene expression. In crop plants, the transformation was first described in tobacco in 1984, and since then, many plant transformation techniques have been developed (Paszkowski et  al. 1984). Transformation methods to introduce diverse genes into plant cells include the indirect gene transfer through Agrobacterium tumefaciens (Rhizobium radiobacter)-mediated transformation (Sun et  al. 2006), direct gene transfer into protoplasts (Karesch et al. 1991), and particle bombardment (Yao et al. 2006).

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(i) Agrobacterium-mediated T-DNA transfer: It is the most common and widely used transformation protocol to introduce the GOI. A compatible interaction between host plant and A. tumefaciens consequences in T-DNA transfer facilitated by the T4SS into plant cells. The Agrobacterium harbor the tumor (Ti)- or root (Ri)-inducing plasmid from which the T-DNA is transferred into the plant genomic DNA through random integration by recombination. The T-DNA sequence has two borders, that is, left and right borders of 25 bp direct repeats, which are essentially required to perceive the T-DNA by the virD and virE proteins. TheT-DNA is introduced into the plant nucleus by single-stranded DNA (ssDNA)-associated virulence proteins encoded by Agrobacterium (Gelvin 2010). The T-DNA has been engineered into a binary vector by substituting the tumor-causing genes with promoters, GOI, and TTS. Due to the high rate of single transgenic events, this method has become most popular transformation tool among researchers. By using A. tumefaciens strains with varying degrees of virulence such as EHA105, LBA4404, GV3101, C58C1, and AGL1 in addition to better adaptation to a plant species with higher tolerance to recalcitrant tissues can further enhance the efficiency of this method. (ii) Biolistic-mediated transformation: This method (particle bombardment or gene gun) was developed in 1987 as an alternative to the direct gene transfer through protoplast transformation. In this method, the DNA sequence is directly introduced into the plant genome, complexed with small gold or tungsten particles of 0.6–1 μM diameter. This method has shown better results to deliver foreign DNA into cell/tissue/organelle surpassing the barriers. These microcarriers with higher velocity were deposited on the membranes and bombarded against totipotent plant tissue. The major advantage of this method is that irrespective of plant species, it directly transforms tissues like embryo, pollen grain, meristems, and morphogenic cell cultures. In addition, a large number of transgenes can be attempted with this method, but very long DNA sequences cause a risk of DNA breakage during delivery, and insertion of many copies results in instability over successive generations. (iii) Agrolistic-mediated plant transformation: This method combines the advantages of both A. tumefaciens with high-efficiency DNA delivery by biolistic. It has been mostly applied in recalcitrant plants, such as in cotton and soybean. The GOI is integrated into vector sequence as in T-DNA inserts to control the copy number. In addition, biolistic using microcarrier particles without DNA can also be utilized to cause superficial/minor injuries. The injured tissue can be co-cultivated with the suitable A. tumefaciens strain. However, biolistic methods being difficult, other alternative methods such as thermal shock before co-inoculation, needle injury, vacuum infiltration, co-cultivation in petri dishes containing co-culture medium or hydrated filter paper, or tissue sonication can be adopted (Dong et al. 2014). (iv) Chloroplast genome transformation: The transformation of the chloroplast genome offers greater advantages over that of the nuclear genome in genetic engineering (Adem et al. 2017). This method has been enormously exploited to yield biopharmaceutical products such as vaccines, peptides, proteins,

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human serum albumin, and antigens, in addition to resistance/tolerance against insect/pests, herbicide, drought, and pathogens in economically important crops. For chloroplast genome transformation, a typical vector contains the GOI, a selectable marker, an organelle-specific, and 5′- and 3′-UTRs that augment transcription and translation. The expression cassette must be skirted on the left and right borders by two genomic regions for site-specific insertion by homologous recombination (Verma and Daniell 2007). There is still a need to optimize this method for transformed and homoplasmic cell selection, and improved regeneration efficiency in many crop plants. (v) Alternative plant transformation methods: To induce elite transgenic events, some of the desirable features are high transformation efficiency, inexpensive, and ease with reduced somaclonal variation to fulfill the current demand of agricultural production. Hence, alternative methods such as plant transformation methods free from tissue culture and mediated by A. tumefaciens using different explants like axillary buds, stem cuttings, or seeds have been standardized (Manickavasagam et al. 2004; Mayavan et al. 2015). Likewise, the plant transformation via pollen tubes has also shown the merits of being genotype-­independent and tissue culture-free, with elevated efficiency and higher probability to generate selectable markers free events (de Oliveira et al. 2016). Similarly, other methods for in planta transformation have also demonstrated higher efficiency using carrier nanoparticles to efficiently deliver multiple cloning sites (Grossi-de-Sa). The A. rhizogenes-mediated root transformation and hairy root induction have been successfully used as a model for gene expression studies and function in several plant species (Daspute et al. 2019). However, due to the requirement of special handling, these methods are hardly utilized at present. (vi) Clean-Gene technology: It is a safer way to develop genetically modified crops, that is, free from selectable marker genes, which may be undesirable from a biosafety point of view. It is a process to transform plants utilizing two different vectors, one carrying the transgene (GOI) and the other with the selectable marker or reporter gene (Kumar et al. 2010). Through Agrobacterium tumefaciens, these two vectors are integrated at different locations in the plant genomes, which can be segregated from each other at the next generation. So, it is an easy and efficient way to genetically modify plants safely. However, being easy and efficient to develop genetically modified plants, it has been rarely used due to low co-transformation efficiency, and different crop plants show different segregation patterns as in sugarcane and grapevine. Thus, to overcome these demerits, several strategies have been developed based on sitespecific recombination systems or nucleases that mediate site-specific cleavage (Yau and Stewart 2013) to retain the GOIs. To generate marker-free plant, various site-specific recombination systems such as the Cre/Lox (Du et al. 2019), CINH/RS2 (Moon et  al. 2011), FLP/FRT (Hu et  al. 2008), and GIN/GIX (Onouchi et al. 1991) have been successfully used showing high efficiency in DNA excision. In addition, to generate transgene-free elite events has been also feasible by ribonucleoproteins (Cas9 nuclease plus a guide RNA)-based

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genome editing, plant regeneration in nonselective medium, and screening of plant bulks using PCR (Liang et al. 2017).

4.3 Regulation of Gene Expression There are a number of approaches to regulate gene expression from transcription initiation to RNA processing and to the posttranslational modification of a protein to modify the gene product (RNA or protein) (Fig. 1). Among those, gene overexpression is one of the widely utilized strategies to detect the gene function either inactivating (loss-of-function) or activating (gain-of-function) mechanisms. Several GOIs for economic agronomic traits have already been overexpressed in many crops. The overexpression of GOIs induced under different biotic and abiotic stresses have generated highly valuable phenotypes with reduced yield penalty (Wang et al. 2018a). Gene stacking strategy is another important technique to pyramid multiple abiotic and biotic stresses simultaneously, through which two or more GOIs in a single expression cassette have been utilized successfully to improve multiple economic traits in plants (Aznar et al. 2018). It is a powerful strategy to overcome the frequent breakdown of resistance by facilitating the long-term management of insect pests or pathogens. Another very important technique, that is, RNAi-mediated gene silencing has been extensively used to regulate the gene expression of agronomically important traits. Presently, a number of RNAi-based studied have been carried out to downregulate the essential genes related to economically important traits (Rosa et al. 2018). Another class of genes, that is, MIR genes, are the plant micro RNAs (miRNAs) which are typically 21–24 nucleotides in length and are transcribed in the nucleus from non-protein-coding genes. The differential expression of these genes upregulate or downregulate their target mRNAs associated with any phenotype (like growth, flowering, and senescence) or stress conditions (salinity, drought, and nutritional scarcity) (Hackenberg et  al. 2015). Thus, the fine-tuning of these specific MIR genes by genetic engineering is a powerful genetic engineering strategy to improve key agronomic traits (Teotia et al. 2016). Since the last decades, CRISPR/Cas9 or optimized nucleases such as CRISPR/ Cpf1 or CRISPR/Csm1have been utilized successfully in plant genome editing (Wang et  al. 2018a). It is a simple two-component system (guide RNA and Cas nuclease protein) that allows precise editing of target sequence(s) in the genome of an organism. In this process, a guide RNA (gRNA) recognizes the target sequence, which is complementary to it, and the CRISPR-associated endonuclease (Cas) cuts this targeted sequence (Liu et al. 2019). CRISPR-Cas9 induces the double-strand breaks (DSBs) at the targeted DNA site that are repaired either through nonhomologous end joining (NHEJ) or homology-directed repair (HDR) (Liu et  al. 2019). Earlier techniques such as meganucleases, zinc finger nucleases (ZFNs), and transcription activator-like effector nucleases (TALENs) begin this new era. However, genome editing came into the limelight after the entry of clustered regularly

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interspaced short palindromic repeats (CRISPR)/CRISPR-associated nuclease 9 (CRISPR/Cas9) (Chen et al. 2020). CRISPR/Cas9 technique is being mostly utilized in plant breeding programs for the sake of its high efficiency, easy performance, and high flexibility in comparison to earlier techniques. The CRISPR-Cas system has been successfully practiced to engineer or edit several plant genomes. At present, CRISPR-Cas has multiplex editing capability as it can edit more than one gene at a time (Donohoue et al. 2018); in addition, it can target not only the open reading frame (ORF) (Liang et al. 2018) and untranslated region of a coding gene (Mao et al. 2018) but also noncoding RNAs (ncRNAs) (Li et al. 2018a) and microRNAs (Chang et al. 2016) as well as promoter regions (Seth 2016). The CRISPR/Cas9 and CRISPR/Cpf1 systems were engineered to control the expression of the GOIs (Tang et al. 2017) using a typical sgRNA into promoter sequence through deactivated Cas9 nuclease (dCas9) (lacks the HNH and RuvC domains) to produce DSB and is fused at the C-terminus to transcriptional activator or repressor domains (Lowder et al. 2017). There is another approach of CRISPR/ Cas13a-mediated RNA editing in which Cas13a nuclease is utilized to target and cleave single-stranded RNA. It has been successfully developed in plant and mammalian cells to knock down any exogenous or endogenous RNA (Aman et al. 2018). Several advanced versions have been developed in CRISPR-based technology for DNA or RNA editing in plants. Among these, CRISPER-ribonucleoprotein (RNP)based DNA/RNA editing technology has been considered the most important for the acquisition of novel traits in plants. The RNPs are accumulated in  vitro and directly transferred into protoplasts or immature embryos followed by cell repair mechanisms that lead to mutations at the desired target site (Liang et al. 2018).

5 Genetic Engineering of Maize for Stress Tolerance 5.1 Genetic Engineering to Improve the Biotic Stress Tolerance in Maize Biotic stresses are a significant threat to global food security. It comprises the damage brought about by living organisms like bacteria, viruses, fungi, insects, nematodes, and weeds to the plants. Due to the occurrences of climate variation, abiotic stresses appeared recently, whereas these biotic stresses were of historical significance. Previously, there are several incidences of biotic stresses that result in complete failure of the crops, causing famine, for example, potato blight in Ireland, maize leaf bight in the United States (Ullstrup 1972), the Great Bengal Famine in 1943 (Padmanabhan 1973), and coffee rust in Brazil (Rogers 2004). Globally, biotic stresses cause major yield losses resulting in around 800  million people being underfed and further intensifying the challenge of food security as 70% more food will be required by 2050 (Christou and Twyman 2004).

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With the origin of new races of insects, pests, and pathogens with time, breeding for biotic stress-resistant crops is the principal challenge in front of plant breeders. Previously, traditional breeding strategies or classical breeding methods were undertaken and utilized the varietal germplasm, interspecific or intergeneric hybridization, induced mutations, and somaclonal variation of cell and tissue cultures for the creation of genetic variation but met with only bounded success. Although, as due to the complete breakdown of resistance, the speed of the evolution of the new races of pathogens could not get along with using these time-consuming tedious methods. In the same way, modern varieties are more susceptible to biotic and abiotic stresses in comparisons to their wild relatives and available land races, as during the time of development and selection for high yield loss of useful genes in terms of genetic erosion took place (Portis et al. 2004; Reif et al. 2005). Hence, for the development of effective and efficient resistance in a shorter duration, various transgenic approaches have been implemented by researchers to induce one or more useful characters, like herbicide tolerance, insect/pests, and disease resistance (Table 1). Herbicide-Tolerant Transgenic Maize Weed is an unwanted and undesirable plant that competes for nutrients, water, sunlight, and space with the crop plant, causing potential yield losses. Management of weeds by herbicides is one of the potent strategies, but almost all the weeds are herbaceous, and while protecting the crop plant, selective killing of the weeds is not always possible. Hence, the development of herbicide tolerance character in the main crop is a promising solution that can recommend the liberal use of robust nonselective and broad-spectrum herbicides. For weed control, based on selective and nonselective actions, two different types of herbicides are available. Among them, glyphosate and glufosinate, which are nonselective types of herbicides, are largely used. Hence, most of the herbicide-tolerant (HT) transgenic plants have been targeted to develop tolerance to these herbicides. The application of glyphosate inhibits 5-enolpyruvyl shikimate3-phosphate synthase (EPSPS) enzyme, which is involved in the shikimate pathway of aromatic amino acid biosynthesis. Glufosinate, which competitively inhibits glutamine synthetase enzyme (Lea et al. 1984), takes part in the conversion of glutamate and ammonia into glutamine. Inhibition of this enzyme by glufosinate leads to accumulation of ammonia, which constrains photosystem I and II reactions (Tachibana et al. 1986; Sauer et al. 1987). In maize, the development of herbicide tolerance transgenic constitutes the major portion of the GE field. Maize varieties having herbicide tolerance for glyphosate, chlorsulfuron, imazethapyr, phosphinothricin, etc. have been extensively adopted for cultivation, which ultimately benefits the environment as well as the farmers (Cao et al. 2011; Yadava et al. 2017). Few important genes, namely, epsps, als, ahas, pat, bar, etc., from bacteria and plants have been incorporated for the herbicide tolerance, which also play an important role in the selection of transgenic events (Yadava et al. 2017). Two different bacterial genes, namely, pat and bar, from Streptomyces spp. were

Target gene P450

Aryloxyalkanoatedioxygenase-­ 1(AAD-1) Phosphinothricin N acetyltransferase (PAT) Bialaphos resistance gene (BAR)

2,4-D herbicidal tolerance

MEPSPS

Glyphosate herbicidal tolerance

Glyphosate herbicidal tolerance

Glyphosate herbicidal tolerance

GAT4601 Glyphosate N-acetyltransferase CP4EPSPS 5-enolpyruvylshikimate-3-­ phosphate synthase 2MEPSPS

Glyphosate herbicidal tolerance

Glufosinate herbicidal tolerance

Glufosinate herbicidal tolerance

Aceto lactate synthase (ALS)

Sulfonylurea herbicidal tolerance

Imidazolinone herbicidal tolerance Acetohydroxy acid synthase gene (AHAS)

Target trait Sulfonylurea herbicidal tolerance

–

Zea mays

Zea mays

A double mutant version (T102I/P106S) of EPSPS mutation A modified EPSPS (two amino acid substitutions)

Agrobacterium tumefaciens – strain CP4

Bacillus licheniformis

Streptomyces – viridochromogenes Streptomyces hygroscopicus –

Genetic transformation Donor organism technique Soil bacterium Streptomyces Ectopic expression or a griseolus promoter specific for the tapetum Targeted modification of endogenous genes using chimeric RNA/DNA introduced single point mutation Cas9-gRNA causes single point mutation in the ALS gene from proline to serine Delftia acidovorans Transformation

Table 1  Improvement of biotic stress tolerance in maize via genetic engineering

Han and Kim (2019)

Han and Kim (2019)

Han and Kim (2019) Green and Owen (2011) Han and Kim (2019) Han and Kim (2019) Han and Kim (2019)

Chilcoat et al. (2017)

Zhu et al. (2000)

References Werck-Reichhart et al. (2000)

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chalcone isomerase 3-like (CI3) Fusarium ear rot Fusarium graminearum, Fusarium proliferatum, and Fusarium verticillioides, insects and plant pathogenic fungi European corn borer cry1Ab, cry1Ab

Cloning and transformation

Cloning and transformation

Bacillus thuringiensis (Bt)

Cloning and transformation

Fusarium ear rot resistant inbred

Bacillus thuringiensis (Bt)

cry1Ac

Pectinophora gossypiella

Heliothis zea

Spodoptera exigua, Harmonia axyridis Insect resistance Corn borer (Sesamia cretica, Ostrinia nubilalis, Chilo agamemnon Smut resistance

Insect resistance Insect resistance Rhopalosiphum padi insect resistance

Zea mays

Targeted mutagenesis by CRISPR/Cas9 dsRNA-spray Lepidopteran RNA interference dvvgr dvbol Corn root warm D. virgifera RNA interference ß-1-3glucanase Zea mays CRISPR/Cas9 lead to the reduction of callose deposition in maize sieve tubes cry1Ab/cry2Aj Bacillus thuringiensis (Bt) Agrobacterium-mediated transformation using cotyledonary node explants Spodoptera littoralis chitinase gene Insect chitinase cDNA from Cloning and transformation cotton leaf worm (Spodoptera littoralis) Lipoxygenase 3(Lox 3) Zea mays Site-directed mutagenesis by CRISPER/Cas9 cry1Ab Bacillus thuringiensis (Bt) Cloning and transformation

ALS2 P165S

Chlorsulfuron herbicidal tolerance

(continued)

Sanchis (2011)

Pathi et al. (2020) Koziel et al. (1993) Armstrong et al. (1995) Buschman et al. (1998) Dowd et al. (2018)

Osman et al. (2015)

Chang et al. (2017)

Svitashev et al. (2015) Li et al. (2015) Niu et al. (2017) Kim et al. (2020)

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Target trait Western bean cutworm, European corn borer, black cutworm, fall armyworm Western Corn Rootworm Western, Northern, and Mexican Corn Rootworm European corn borer, corn rootworm Western bean cutworm, European corn borer, black cutworm, fall armyworm, Western/Northern/ Mexican corn rootworm Diabrotica virgifera and other coleopteran insects

Table 1 (continued) Donor organism Bacillus thuringiensis (Bt)

Bt subsp. kumamotoensis

Bacillus thuringiensis (Bt) Bacillus thuringiensis (Bt)

–

Target gene cry1F

cry3Bb1 cry34/35Ab1

cry1Ab + cry3Bb1

cry1F + cry34/35Ab1

Genes encoding proteins

RNA interference

Transformation as electroporation, or particle bombardment

Cloning and transformation

Cloning and transformation Cloning and transformation

Genetic transformation technique Cloning and transformation

Baum et al. (2007)

Ellis et al. (2002)

Sanchis (2011)

Sanchis (2011) Sanchis (2011)

References Sanchis (2011)

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used for creating the glufosinate-resistant crops. Both of these genes encode the phosphinothricin acetyl transferase (PAT) enzyme, which detoxifies this herbicide by acetylation. From1996 to 2018, a total of 351 HT events have been approved for cultivation (ISAAA 2019). Out of these, the maximum number of 210 HT events has been commercialized in maize, followed by Argentine canola (34), soybean (33), potato (4), carnation (4), rice (3), sugar beet (3), and wheat (1). Among the commercialized transgenic crops, HT transgenic crops inhabit the largest area. Next to the abovementioned herbicides, HT transgenic maize crop specific to other herbicides, such as 2,4-D (Han and Kim 2019), dicamba, isoxafutole, mesotrione, oxynil, and sulfonylurea (Chilcoat et al. 2017), has been commercialized recently as mentioned in Table 1. Insect-Resistant Transgenic Maize Among the biotic stresses, major crop loss is caused by insect pests and diseases. All around, about 67,000 insect species are causing severe losses to important crops. Previously, farmers mainly depend on the expensive chemically synthesized insecticides as a control measure of insect pests, but these chemicals increase the economic burden on the farmers as well as the environment unfriendly. Hence, to get the better of these pitfalls of insecticide use, genetic engineering of crops to develop insect resistance has gained popularity. Insect-resistant transgenic crops have the second largest area under cultivation, that is, 23.3  mha in 2017 (ISAAA 2017). Globally for cultivation, 304 transgenic events have been accepted, among which maximum 208 events comprising various insect resistance genes in maize have been accepted for cultivation. Generally, a distinct variant of cry gene as insecticidal genes and very few events of vip gene, which manage the harmful insects attacking the crops, has been transferred to most of the commercial crops (Kereša et al. 2008). The cry genes, which are isolated from Bacillus thuringiensis (Bt) (a soil bacterium), are among the widely utilized genes to develop insect-resistant transgenic crops. The cry genes from different isolates of B. thuringiensis offer resistance against a wide range of insect pests, that is, lepidopterans, coleopterans, and dipterans (McPherson et al. 1988). Several cry gene variants have been reported and utilized in gene stacking to develop stable insect resistance (Sanchis 2011; Chang et al. 2017). An additional advantage of the cry gene application is the nontoxicity of the cry protein to mammals. The insect-pest resistance in maize through GE demonstrated the potential of preventing environmental degradation, consumer acceptance, and cost-effectiveness to the farmers. Disease-Resistant Transgenic Maize Diseases caused by the pathogens, such as fungi, bacteria, viruses, and nematodes, result in substantial crop yield loss. Plant diseases are commonly managed by the application of agrochemicals, but hazardous effects caused by the use of

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agrochemicals on the environment permit the investigation of other strategies to handle the plant diseases. Additionally, there might be the possibility of the emergence of chemical-resistant pests due to the undiscriminating use of chemicals. So, to get over the issues imposed by plant pathogens, there is a need for the development of inherent disease resistance in crop plants. For this, identification of genes responsible for disease resistance and transferring the same to plants through breeding or biotechnological approaches. As yet, globally 29 transgenic events possessing resistance against several diseases have been commercialized. Most of the virus-resistant transgenic crops have been developed via gene silencing techniques, such as co-suppression/RNAi and antisense RNA targeted against viral genes (Fei et al. 2007). In maize, various disease resistance such as smut resistance (Pathi et al. 2020) and Fusarium ear rot (Dowd et al. 2018) have been targeted through a transgenic approach as mentioned in Table 1. Parallel to herbicide and insect-pest resistance via GE, the development of disease-resistant varieties has been less focused on maize. GE promises enhanced disease resistance against various pathogens without affecting the beneficial microbes (Hilder and Boulter 1999; Wally and Punja 2010).

5.2 Genetic Engineering to Improve the Abiotic Stress Tolerance in Maize Recently, climatic factors like temperature and rainfall are becoming arbitrary resulting in the transpose of temperature from the optimal state, alteration in precipitation pattern, perpetual drought, and heat negatively affecting crop production and productivity. In the last few decades, due to erratic climate changes, plants are becoming more vulnerable to abiotic stress, which threatens global food security issues (Bhusal et al. 2021). The maize production has been hampered by prolonged drought, heat, cold, variable precipitation, and increase salinity in the soil. Therefore, it is the need of the hour to develop a variety that can show tolerance to abiotic stress and able to sustain crop production. Conventional plant breeding strategy has not proved its success in addressing abiotic stress at a notable level. Genetic engineering provides innumerable applications in crop improvement via direct transfer of closely or distantly related genes of interest with desirable traits (Parmar et  al. 2017). This resultant in the development of significant tolerance to abiotic stress in crops in a shorter period in comparison to conventional plant breeding techniques (Datta 2013; Marco et al. 2015). Here, we briefly describe abiotic stress tolerance in maize improved via genetic engineering, which was able to reduce losses due to climatic changes (Table 2).

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Table 2  Improvement of abiotic stress tolerance in maize via genetic engineering

Target trait Drought

Heat

Gene expression technique References Target gene TsCBF1 Overexpression Zhang et al. (2010) TsVP and T. halophile A. tumefaciens Overexpression Wei et al. BetA and E. coli strain LBA4404 (2011) ZmPLC1 Z. maize A. tumefaciens Overexpression Wang et al. strain LBA4404 (2008) rpk and nced -A. tumefaciens Overexpression Muppala strain EHA105 et al. (2021) beta Escherichia A. tumefaciens Overexpression Quan et al. coli strain LBA4404 (2004) ZmVPP1 Zea maize A.tumefaciens Overexpression Wang et al. (GV3101 + pSoup) (2016a, b) LOS5 Arabidopsis A. tumefaciens Overexpression Lu et al. strain EHA105 (2013) Zm-Asr1 and Z. maize A. tumefaciens Overexpression Jeanneau C4–PEPC et al. (2002) ZmLEA14tv Z. maize A. tumefaciens Overexpression Minh et al. strain EHA105 (2019) ZmVPP1 Z. maize – Overexpression Jia et al. (2020) ZmNAC111 Z. maize A. tumefaciens Overexpression Mao et al. strain LBA4404 (2015) Overexpression Li et al. SbER1–1 and S. bicolor A. tumefaciens (2019) SbER2–1 strains EHA105 and GV3101 AnVP1 A. nanus A. tumefaciens Overexpression Yu et al. strain LBA4404 (2021) TPS1 S. A. tumefaciens Overexpression Liu et al. cerevisiae strain LBA4404 (2015) ZmTIP1 Z. maize A. tumefaciens Overexpression Zhang GV3101 et al. (2019) ARGOS8 Z. maize Particle CRISPR/Cas Chilcoat bombardment gene editing et al. (2017) Z. maize A. tumefaciens Overexpression Gu et al. ZmHSFA2 (2019) and ZmHSBP2 Genetic transformation Donor method organism T. halophila A. tumefaciens strain LBA4404

(continued)

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Table 2 (continued)

Target trait Salinity

Donor Target gene organism ZmWRKY114 Z. maize ZmHAK4

Cold

Waterlogging

Z. maize

Genetic transformation method A. tumefaciens strain GV3101 A. tumefaciens

ZmWRKY104 Z. maize

–

ZmHKT1;5

Z. maize

SAG4 and SAG6 ZmHKT1;1

Z. maize

A. tumefaciens strain LBA4404 A. tumefaciens

Z. maize

Floral dip

ZmCPK11

Z. maize

A. tumefaciens strain EHA105

ZmEREB20

Z. maize

AnAFP

A. nanus

A. tumefaciens strain GV3101 A. tumefaciens

ZmSEC14p

Z. maize

ZmLEA3

Z. maize

ZmMYB48

Z. maize

ZmASR

Z. maize

HaOXR2

H. annuus

6-BA

–

A. tumefaciens strain LBA4404 –

Spermidine

–

–

A. tumefaciens strain EHA105 A. tumefaciens strain EHA105 A. tumefaciens strain LBA4404 A. tumefaciens

Gene expression technique References Overexpression Bo et al. (2020) Overexpression Zhang et al. (2019) Overexpression Yan et al. (2021) Overexpression Jiang et al. (2018) Overexpression Luo et al. (2019) Overexpression Ren et al. (2015) Overexpression Borkiewicz et al. (2020) Overexpression Fu et al. (2021) Overexpression Zhang et al. (2020b) Ectopic Wang et al. expression (2016a, b) Overexpression Liu et al. (2016) Overexpression Wang et al. (2017) Overexpression Li et al. (2018a, b) Overexpression Torti et al. (2020) – Hu et al. (2020) – Liu et al. (2014) (continued)

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Table 2 (continued)

Target trait Drought+salt

Drought+heat

Target gene SsNHX1

Donor organism Suaeda salsa

ZmmiR156

Z. maize

ZmSCE1e

Z. maize

ZmBES1/ BZR1-5

Z. maize

ZmPIP1;1

Z. maize

ZmPIF3

Z. maize

ZmWRKY106 Z. maize

Genetic transformation method A. tumefaciens strain LBA4404 A. tumefaciens strain EHA105 A. tumefaciens strain LBA4404 A. tumefaciens (Floral dip method) A. tumefaciens strain EHA101 A. tumefaciens strain EHA105 –

ZmHsf06

Z. maize

ZmNF-YA3

Z. maize

A. tumefaciens strain GV3101 –

Z. maize

–

Drought+salt+heat ZmERF1

Gene expression technique References Overexpression Huang et al. (2018) Overexpression Kang et al. (2020) Overexpression Wang et al. (2019) Overexpression Sun et al. (2006) Overexpression Zhou et al. (2018) Overexpression Gao et al. (2015a, b) Wang et al. (2018a, b) Overexpression Li et al. (2015) – Su et al. (2018) – Shi et al. (2016)

Drought Tolerance Transgenic Maize Maize being a drought-sensitive crop is highly affected at the seedling stage, which is the most critical stage of the growth period. At vegetative growth period particularly during V1 to V5, drought resulted in a reduction in crop growth, elongation of the vegetative growth period, and shrinkage of the reproductive growth period (Aslam et al. 2013; Aslam et al. 2015) consequently affecting the overall development of plant throughout their life cycle. Drought for a shorter duration resulted into reduction of 28–32% dry weight during the vegetative growth stage, while dry weight was reduced to 66–93% at the time of reproductive growth stages especially during tasseling and ear formation, respectively (Cakir 2004). Moreover, extended drought period hinders tassel and silk development, which leads to reduction of productivity by 15–25% (Nesmith and Ritchie 1992). Wang et al. (2019) observed the reduction in ear elongation and kernel size along with moderation in carbohydrate metabolism and plant growth hormone regulation under drought. Henceforth, there is an urgent need for the development and improvement of varieties with enhanced drought tolerance to achieve maximum maize production and productivity with less water requirement. There are reports on the development and

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improvement of drought-tolerant maize varieties by conventional and molecular breeding approaches (Gedil and Menkir 2019). But the main drawback with these methods is lesser crop improvement in longer time duration coupled with dependent on the availability of gene-pool or germplasm (Anwar and Kim 2020). Therefore, genetic engineering as an alternative technique can be employed to improve drought tolerance more rapidly with reduced time (Zhang et al. 2000). There are a number of natural and synthetic genes and transcription factors whose incorporation can improve drought tolerance to maize via genetic engineering techniques (Parmar et  al. 2017). In general, dehydration-responsive element binding (DREB), late embryogenesis abundant (LEA) proteins, proline accumulators, polyamines, and mitogen-activated proteins play a key role in improving drought tolerance to crops (Bidhan et  al. 2011). Among all the factors, DREB proteins (a subfamily of APETALA 2/ethylene-responsive element binding factor (AP2/ERF) family) and mitogen-activated proteins are mostly targeted for maize drought tolerance. Shou et al. (2004a, b) reported the enhanced drought tolerance by incorporation of nicotiana protein kinase (NPK1) in transgenic maize via genetic transformation. The NPK1 is a tobacco mitogen-activated protein kinase kinase kinase (MAPKKK) enzyme that induces heat shock proteins (HSPs) and glutathione-S-transferases (GSTs) to protect photosynthetic machinery during drought stress. The function of LEA proteins has been briefly explained by Amara et al. (2014) in maize during dehydration state. Amara et  al. (2013) improved drought tolerance in transgenic maize plants by overexpression of group 5 LEA Rab28 candidate gene during drought. This increases the accumulation and stability of Rab28 protein, which leads to enhance water stress tolerance. Likewise, Du et  al. (2015) identified 59 trihelix TFs (GT factors) via in silico approach, which was distributed on maize chromosomes 1 to 10 (11, 8, 5, 9, 9, 2, 1, 4, 3, and 7 genes, respectively). These GT factors exhibit spatial-temporal expression toward drought tolerance in maize. Out of 59, 17 GTs were upregulated, while three were downregulated in response to drought. He et  al. (2018) reported that overexpression of ZmPYL3, ZmPYL9, ZmPYL10, and ZmPYL13 played a significant role in imparting drought resistance in transgenic plants by enhancing ABA signaling, proline, and other drought-related marker genes. Likewise, several drought-responsive genes and TFs have been identified such as TsCBF1 from T. halophila (Zhang et al. 2010); TsVP and BetA from T. halophile and E. coli (Wei et  al. 2011); ZmPLC1, ZmVPP1, ZmTIP1, and ZmNAC111 maize (Wang et al. 2008; Wang et al. 2016a; Zhang et al. 2020a; Mao et al. 2015); LOS5 from Arabidopsis (Lu et al. 2013); SbER1–1 and SbER2–1 from sorghum (Li et al. 2019); AnVP1 from AnVP1 (Yu et al. 2021); and many more, whose overexpression result in drought-tolerant in maize (Table 2). In a nutshell, the identified TFs and drought-responsive genes may serve as potential markers for drought improvement in maize via a transgenic approach.

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Heat Tolerance Transgenic Maize Being a tropical rainfed crop, maize is highly influenced by sporadic heat stress in the Asiatic region. Maize grown in subtropical areas are severely affected by higher temperature during early reproductive and grain filling stages (Prasanna 2011). By looking into the increasing demand for maize, another spring season has been added to take one more cropping season where it is exposed to extremely hot summer period of the year (February to May) especially during late vegetative and reproductive growth stages, which are unfavorable for crop growth resulted into yield loss (Lobell and Burke 2010). Siebers et al. (2017) observed a significant drop in production when heat waves warmed the canopy during silking stage for continuously 3 days. Maize encounters drastic physiological drought due to increased vapor pressure deficit connected to the higher temperature and less humidity under heat stress led to a relatively higher reduction in yield as compared to drought stress (Pavani et  al. 2019). Therefore, the development of heat-tolerant varieties is required to sustain maize production and productivity under heat-stressed conditions. Since the heat sensitivity is tremendously fluctuating throughout the developmental growth stages of the plant, the development and improvement of heat tolerance is a challenging task by conventional plant breeding methods (Driedonks et al. 2016). To avoid environmental interaction and influence, genetic engineering can be the best option for the improvement of heat tolerance in maize. There are several reports that explain the role of HSPs, a kind of transcription factor that activates HSPs to interact with signal transduction via calcium and reactive oxygen species to provide thermo-tolerance to plants in response to cytoplasmic heat stress (Li and Howell 2021). Ribeiro et al. (2020) engineered the maize plants with WPGD1 and WPGD2 transgenes coupled with an endosperm-specific promoter, which increases the activity of 6PGDH (6-phosphogluconate dehydrogenase) thereby being able to redeem defective pgd3-defective kernel phenotype consequently escalate heat tolerance. Overexpression of OsMYB55 gene upregulates the HSPs in maize which reduces negative impacts of high temperature and improves tolerance to heat stress (Casaretto et  al. 2016). According to Zhao et  al. (2021), overexpression of abscisic acid-­ induced calcium-dependent protein kinase ZmCDPK7 in transgenic maize lines resulted in the regulation of heat stress tolerance by upregulation and downregulation of the respiratory burst oxidase homolog RBOHB and phosphorylation of HSp sHSP17.4. This alteration by ZmCDPK7 enhances thermostability, photosynthetic rates, and antioxidant enzyme activity, while it downregulates H2O2 and malondialdehyde (MDA) contents under heat stress. Transformation of Arabidopsis thaliana trehalose phosphate synthase gene (AtTPS1) enhances heat tolerance in maize by involving in trehalose biosynthesis (Almeida et al. 2003). Ko et al. (2007) identified that thermotolerance (TTO6) gene is responsible for heat shock stress tolerance in maize clones, which is 69% similar to GASA4 gene from Arabidopsis thaliana. Similarly, ZmHSFA2 and ZmHSBP2 from maize have significantly contributed to heat tolerance (Gu et al. 2019). The above studies suggest that the identified domains can be employed to develop long-term heat-tolerant genotypes in maize through transgenic approaches.

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Salinity Tolerance Transgenic Maize Globally, around 50% of irrigated and 20% cultivated land is highly affected by salinity stress, which influences the production and productivity of crops (Wang et al. 2017). Maize is moderately sensitive to salinity, which adversely affects its growth and development. Salinity diminishes shoot growth by suppressing leaf growth rate, internode growth, number, and rate of elongation cells. At the molecular level, salinity stress results in membrane damage, protein denaturation, accumulation of oxidative substances, and reduction of relative water content in leaves. These all result in the reduction of photosynthetic rate consequently yield loss in maize (Szalai and Janda 2009). Maize undergoes several biochemical and physiological changes to adapt under salinity stress. The focal point of genetic engineering is to identify key genes/factors associated with the molecular, physiological, and biochemical pathway of salinity stress to improve salinity tolerance by overexpression of identified genes. Typical transcription factors (TF) like DREB, MAPK, and MYB (myeloblastosis) can enhance heat response by actively participating in signal transduction pathways in transgenic plants. MYB TFs were firstly identified in maize, and till today, around 200 MYB families are known to be responsible for abiotic stress response (Du et al. 2012). Wu et al. (2019) studied the effect of a transcriptional activator, that is, ZmMYB3R, whose overexpression results in enhancement of salinity tolerance in transgenic lines by modifying the root architectural system and hormone regulation. ZmMKK4 belongs to MAPKK gene family, which encodes for group C in maize and resides inside the nucleus. Overexpression of ZmMKK4 results in salt tolerance in transgenic Arabidopsis. These transgenic Arabidopsis lines exhibit higher germination rate, lateral root numbers, chlorophyll content, catalase, and peroxidase activity (Kong et  al. 2011). The class of deubiquitinating enzymes (DUBs) and ubiquitin-specific proteases (UBPs) is engaged in the growth and development of the plant. The proteins UBP15, UBP16, and UBP19 are homologs of UBP16 of Arabidopsis, which actively participate in signal transduction during salt stress in maize. Overexpression of ZmUBP15, ZmUBP16, and ZmUBP19 led to rescue ubp16-1, which impart salinity tolerance in transgenic Arabidopsis (Kong et al. 2019). Wang et al. (2007) performed cloning and functional characterization of ZmCBL4, a putative homolog of Arabidopsis calcineurin B-like protein/ salt overly sensitive CBL4/SOS3 protein with unique features. Constitutive expression of ZmCBL4 showed enhanced tolerance to salinity in transgenic lines of Arabidopsis, which is similar to SOS3 in the salt signaling pathway. These transgenic lines exhibit better shoot and root development under salt stress. Besides all these TFs, there are various other genes, and TFs have been diagnosed such as ZmHKT15, ZmHAK4, ZmCPK11, ZmWRKY114, ZmEREB20, andZmWRKY104 (Jiang et al. 2018; Zhang et al. 2019; Borkiewicz et al. 2020; Bo et al. 2020; Fu et al. 2021; Yan et al. 2021) whose transformation has resulted into better performance under the saline condition in maize. The detailed information concerning donor organism, transformation method, and genetic engineering approach has been given in Table 2. The identified functional genes can be useful for gene stacking against salinity tolerance by genetic engineering with high precision in a shorter period.

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Cold Tolerance Transgenic Maize Among abiotic stresses, low temperature (chilling and freezing temperature) is another major stress, which limits the growth and development of plants thereby reducing the production and productivity of maize (Meng and Sui 2019). Chilling and freezing stress is responsible for catastrophic events of the biological and physiological process like membrane destruction, ion leakage, stomata opening, the release of toxic substances, reduced photosynthesis and respiration, reduction in unsaturated fatty acids, increased level of reactive oxygen species (ROS) production, seed germination, and seedling establishment which adversely affect vegetative and reproductive growth stages (Einset et al. 2007). To improve cold tolerance, it is necessary to maintain structural integration of lipid membrane and their dynamic transitional ability from a liquid crystalline to the gel phase. Plants have developed some enzymatic or nonenzymatic adaptive mechanisms like antioxidants, superoxide dismutase (SOD), catalase, ascorbate peroxidase (APX), etc. Conventional plant breeding takes a long time to improve cold tolerance. Moreover, most of the time, the improved lines exhibit nonsignificant results in low temperature. To narrow down the pros of conventional breeding methods, genetic engineering can play a significant role in tolerance toward chilling and freezing. Genetic engineering alters transfer and expresses the targeted gene (antifreeze proteins and TFs) in transgenic lines to reduce the effects of cold stress (Goel and Madan 2014). There are several enzymes such as glycine betaine, antioxidants, catalase, peroxidase, etc. whose integration results in cold improvement under stress conditions. TFs involved in cold stress are MYB, MAPK, LEA, CBL (calcineurin B-like proteins), DREB, ZIP (basic leucine zipper), NAC {NAM (no apical meristem), ATAF (Arabidopsis transcription activation factor), and CUC Cup Shaped Cotyledon)}, CBFs (cold-inducible master transcription factors), etc. (Shou et al. 2004b). Meng and Sui (2019) investigated the effect of ZmMYB-IF35 gene in response to cold stress in transgenic lines of Arabidopsis. The transgenic lines with the integration of ZmMYB-IF35 gene result in chilling tolerance by increasing the activity of SOD and APX enzymes. These enzymes act as a scavenger and result into the protection of chloroplast membrane and maintain the integrity of lipid membrane. ZmMKK1 belongs to MAPK group in maize for cold tolerance. Cai et al. (2014) performed isolation and functional characterization of ZmMKK1. Overexpression of ZmMKK1can show enhanced chilling tolerance in transgenic tobacco. The transgenic lines exhibited increased seed germination, early seedling establishment, longer root growth, lower production of malon-dialdehyde and relative electrolyte leakage, and increased level of soluble sugar, proline content, and inhibition of ROS production. Constitutive expression of tobacco MAPKKK (NPK1) at a low level increased the freezing tolerance in transgenic maize by mimicking the effect of H2O2 signaling, increasing soluble sugar and osmolytes, which act as cryoprotectants and stabilize the membrane integrity (Shou et al. 2004b). Similarly, AnAFP from A. nanus (Zhang et al. 2020b) and ZmSEC14p, ZmLEA3, and ZmASR from Z. maize (Wang et al. 2016b; Liu et al. 2016; Li et al. 2018b) have been identified and briefly explained in Table 2. These different studies suggest that overexpression

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of identified CBFs and other TAFs in transgenic plants can induce cold and freezing tolerance in maize. Therefore, these detected genes/TFs can be used in the development of cultivars tolerant to cold and freezing stress. Waterlogging Tolerance Transgenic Maize Maize being a dry land crop is highly sensitive to waterlogging stress (Zaidi et al. 2010). Excessive water severely hampers the growth and development of plants and limits the quantity and quality of products (Liang et al. 2020). Approximately 10% of the world’s arable land is highly affected by waterlogging stress. The annual maize yield was reduced to about 25–30% in India due to waterlogging (Zaidi et al. 2010). Since the solubility and diffusion rate are very low in waterlogging stress, it leads to a reduction in the amount of available oxygen to plants. To overcome the effects of flooding, plants exhibit some morphological, physiological, and biological adaptation. These adaptations include the development of adventitious root, lysigenous aerenchyma tissue, and hydrophobic surface formation to improve diffusion rate and decrease radial oxygen loss for reduction of oxygen leakage from the rhizosphere (Pan et al. 2020). But the adaptation to waterlogging stress is slightly different in maize from other cereals and marshland plants, as generally it does not develop aerenchyma cells under excessive water (Gong et al. 2019). The genomic regions associated with aerenchyma formation have been studied by Gong et  al. (2019) after the introgression of maize with wild relative (Zea nicaraguensis) to improve tolerance of oxygen deficiency. But to develop improved lines with these conventional methods is not up to the mark and required an alternate strategy such as genetic engineering. This technology has the potential to enhance waterlogging tolerance in maize. Du et  al. (2010) isolated and functionally characterized zmzf (zea maize zinc finger) promoter from Mo17 inbred line, which is waterlogging inducible promoter and highly specific to root traits. The transformation of zmzf can result into the development of lines with waterlogging tolerance in maize. A group of VII ethylene response factors (ERFVIIs) plays a significant role in waterlogging tolerance in plants. However, in the case of maize, ZmERFVIIs is a nonresponsive gene toward waterlogging. In light of this, Yu et al. (2019) identified tightly linked gene ZmEREB180 (waterlogging-responsive gene) with ZmERFVIIs, which is upregulated by ethylene under excessive water. Du et al. (2015) reported 59 trihelix TFs, which were differentially expressed under excessive water stress. Out of 59, 14 GTs were upregulated during waterlogging. These GTs are associated with the primary and secondary structure of proteins, amino acid composition, solubility, and folding state of protein, which serves as valuable information during stress. Overexpression of ZmEREB180 in transgenic lines of maize is able to enhance the survival rate via adventitious root formation, modulation of ROS, and antioxidant levels under submergence conditions. A gram-negative aerobic bacterium, Vitreoscilla, contains a type of hemoglobin Vitreoscilla hemoglobin (VHb), which contributes to waterlogging tolerance in plants. Overexpression of VHb gene in maize through particle bombardment exhibits waterlogging tolerance under submergence conditions. The transgenic lines resulted into an increment of the activity

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of alcohol dehydrogenase, peroxidase enzyme, which ultimately improves primary root length, lateral root number, root dry weight, and shoot dry weight (Du et al. 2016). Likewise, other genes and TFs like HaOXR2 from H. annuus (Torti et al. 2020), 6-BA (Hu et al. 2020) and Spermidine (Liu et al. 2014) have significantly enhanced maize improvement through their overexpression under waterlogging (Table 2). Henceforth, the identified genomic regions or TFs responsible for waterlogging tolerance can be utilized by genetic engineering approaches to develop tolerant and resistant cultivars in maize.

6 Conclusion and Future Perspectives Currently, with changing climatic conditions and food security challenge, employment of new biotechnological tools facilitates wider range of solutions. Hence, the development of genetically modified crops is the hottest topic ever and has been grown as the fastest agricultural technology in the world. In the international market, there is an issue for the acceptance of GM food crops due to its adverse effect on health, but it can be tackled by isolating harmless genes from specific sources or by inducing toxicity properly. To resolve the concerns regarding GM crops, there is the need to opt strict legislation in addition to rigorous technical assessments considering the societal values and demands. The development of transgenic crop conventionally through introduction of foreign gene raise the concerns of toxicity and risks to humans, environment, natural biodiversity, and other nontarget organisms. Hence, to bypass such concerns, the endorsement of alternative technologies like cis-genesis and intra-genesis has come in limelight where transformation belongs from sexually compatible gene pool. Different versions of novel genome editing techniques through using clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system enable precise editing of endogenous gene and site-specific insertion of a GOI. The adoption of these genome editing techniques has the potential to discourse many regulatory issues associated with transgenes and resolve the uncertainty and inefficiency related with conventional random mutagenesis and transgenesis. Some of these methods can also develop crop plants free from any foreign gene, which might help it to fetch higher consumer acceptance in comparison to the transgenic crops and would get quicker regulatory approvals. The regulation of genetically modified crops varies across nations. By identifying the shared facts, opinions, technical expertise, and experiences of various competing interests like researchers, bureaucrats, politicians, and societal interests should harmonize the regulation of transgenics development. Some countries have widely supported the cultivation of transgenic crops due to their increased yields, decreasing the use of pesticides that save the environment and the cost of pesticides and the production of crops with increased nutritional value. Hence, to fit in the climatic changing scenario, the concept of producing transgenic crops is a powerful tool in the current era. To spread the use of transgenic crops at large requires more research at field level considering each aspect of human and environmental safety. It will help to get clarity to fully take the potential advantage of this useful invention.

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References Adem M, Beyene D, Feyissa T (2017) Recent achievements obtained by chloroplast transformation. Plant Methods 13:30. https://doi.org/10.1186/s13007-­017-­0179-­1 Aldemita RR, Reano IME, Solis RO, Hautea RA (2015) Trends in global approvals of biotech crops (1992–2014). GM Crops Food 6(3):150–166 Almeida AM, Araújo S, Cardoso LA et al (2003) Genetic engineering of maize towards desiccation tolerance: electroporation with the trehalose gene. Genetika 35(2):111–121 Aman R, Ali Z, Butt H et al (2018) RNA virus interference via CRISPR/Cas13a system in plants. Genome Biol 19:1. https://doi.org/10.1186/s13059-­017-­1381-­1 Amara I, Capellades M, Ludevid MD et al (2013) Enhanced water stress tolerance of transgenic maize plants over-expressing LEA Rab28 gene. J Plant Physiol 170(9):864–873 Amara I, Zaidi I, Masmoudi K et al (2014) Insights into late embryogenesis abundant (LEA) proteins in plants: from structure to the functions. Am J Plant Sci 5(22):3440 Ambawat S, Sharma P, Yadav NR et al (2013) MYB transcription factor genes as regulators for plant responses: an overview. Physiol Mol Biol Plants 19:307–321. https://doi.org/10.1007/ s12298-­013-­0179-­1 Anand A, Bass SH, Wu E et  al (2018) An improved ternary vector system for Agrobacterium-­ mediated rapid maize transformation. Plant Mol Biol 97:187–200. https://doi.org/10.1007/ s11103-­018-­0732-­y Anwar A, Kim JK (2020) Transgenic breeding approaches for improving abiotic stress tolerance: recent progress and future perspectives. Int J Mol Sci 21(8):2695 Areal FJ, Riesgo L, Rodríguez-Cerezo E (2013) Economic and agronomic impact of commercialized GM crops: a meta-analysis. J Agric Sci 151:7–33 Armstrong CL, Parker GB, Pershing JC et al (1995) Field evaluation of European corn borer control in progeny of 173 transgenic corn events expressing an insecticidal protein from Bacillus thuringiensis. Crop Sci 35(2):550–557 Arthur GD (2011) Benefits and concerns surrounding the cultivation of genetically modified crops in Africa: the debate. Afr J Biotechnol 10:17663–17677 Aslam M, Zamir MSI, Afzal I et al (2013) Drought stress, its effect on maize production and development of drought tolerance through potassium application. Cercetări Agronomice în Moldova 46(2):99–114 Aslam M, Maqbool MA, Cengiz R (2015) Drought stress in maize (Zea mays l.) effects, resistance mechanisms, global achievements and Biological Strategies for Improvement, Springer $ briefs in agriculture. Springer Aznar A, Chalvin C, Shih PM et  al (2018) Gene stacking of multiple traits for high yield of fermentable sugars in plant biomass. Biotechnol Biofuels 11:2. https://doi.org/10.1186/ s13068-­017-­1007-­6 Barahimipour R, Strenkert D, Neupert J et al (2015) Dissecting the contributions of GC content and codon usage to gene expression in the model alga Chlamydomonas reinhardtii. Plant J 84:704–717. https://doi.org/10.1111/tpj.13033 Basso MF, Lourenco-Tessutti IT, Busanello C et al (2020) Insights obtained using different modules of the cotton uceA1.7 promoter. Planta 251:56. https://doi.org/10.1007/s00425-­020-­03348-­8 Baum JA, Bogaert T, Clinton W et  al (2007) Control of coleopteran insect pests through RNA interference. Nat Biotechnol 25(11):1322–1326 Bawa AS, Anilakumar KR (2013) Genetically modified foods: safety, risks and public concerns—a review. J Food Sci Technol 50(6):1035–1046 Bedada LT, Seth MS, Runo SM et al (2016) Drought tolerant tropical maize (Zea mays L.) developed through genetic transformation with isopentenyltransferase gene. African. J Biotechnol 15(43):2447–2464 Bedada LT, Seth MS, Runo SM (2018) Tropical maize (Zea mays L.) genotypes respond differently to agrobacterium-mediated genetic transformation. Ethiop J Agric Sci 28(2):1–22

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize

225

Belele CL, Sidorenko L, Stam M et al (2013) Specific tandem repeats are sufficient for paramutation-­ induced trans-generational silencing. PLoS Genet 9:e1003773. https://doi.org/10.1371/journal. pgen.1003773 Benbrook CM (2012) Impacts of genetically engineered crops on pesticide use in the U.S. – the first sixteen years. Environ Sci Eur 24:24 Bhusal B, Poudel MR, Rishav P et al (2021) A review on abiotic stress resistance in maize (Zea mays L.): effects, resistance mechanisms and management. J Biol Today’s World 10(2):1–3 Bidhan R, Noren SK, Mandal AB et al (2011) Genetic engineering for abiotic stress tolerance in agricultural crops. Biotechnology 10(1):1–22 Bo C, Chen H, Luo G et al (2020) Maize WRKY114 gene negatively regulates salt-stress tolerance in transgenic rice. Plant Cell Rep 39(1):135–148 Borkiewicz L, Polkowska Kowalczyk L, Cieśla J et al (2020) Expression of maize calciumdependent protein kinase (ZmCPK11) improves salt tolerance in transgenic Arabidopsis plants by regulating sodium and potassium homeostasis and stabilizing photosystem II.  Physiol Plant 168(1):38–57 Burachik M (2010) Experience from use of GMOs in Argentinian agriculture, economy and environment. New Biotechnol 27:588–592 Buschman L, Sloderbeck P, Guo Y et al (1998) Corn borer resistance and grain yield of Bt and non­Bt corn hybrids at Garden city, Kansas, in 1997. Progress Report-814, agricultural experiment station and co-operative extension service, Kansas State University, pp 34–38 Cai G, Wang G, Wang L et al (2014) ZmMKK1, a novel group a mitogen-activated protein kinase kinase gene in maize, conferred chilling stress tolerance and was involved in pathogen defense in transgenic tobacco. Plant Sci 214:57–73 Cakir R (2004) Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Res 89(1):1–16 Cao M, Sato SJ, Behrens M et al (2011) Genetic engineering of maize (Zea mays) for high-level tolerance to treatment with the herbicide dicamba. J Agric Food Chem 59(11):5830–5834 Casaretto JA, El-Kereamy A, Zeng B et  al (2016) Expression of OsMYB55  in maize activates stress-responsive genes and enhances heat and drought tolerance. BMC Genomics 17(1):1–15 Chang H, Yi B, Ma R et al (2016) CRISPR/cas9, a novel genomic tool to knock down microRNA in vitro and in vivo. Sci Rep 6(1):1–12 Chang X, Lu Z, Shen Z et al (2017) Bitrophic and tritrophic effects of transgenic cry1Ab/cry2Aj maize on the beneficial, nontarget Harmonia axyridis (Coleoptera: Coccinellidae). Environ Entomol 46(5):1171–1176 Che P, Anand A (2018) Developing a flexible, high-efficiency Agrobacterium-mediated sorghum transformation system with broad application. Plant Biotechnol J 16:1388–1395. https://doi. org/10.1111/pbi.12879 Chen K, Wang Y, Zhang R et al (2019) CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu Rev Plant Biol 29(70):667–697 Chen G, Zhou Y, Kishchenko O et  al (2020) Gene editing to facilitate hybrid crop production. Biotechnol Adv 5:107676 Chilcoat D, Liu ZB, Sander J (2017) Use of CRISPR/Cas9 for crop improvement in maize and soybean. Prog Mol Biol Transl Sci 149:27–46 Christou P, Twyman RM (2004) The potential of genetically enhanced plants to address food insecurity. Nutr Res Rev 17(1):23–42 Comas C, Lumbierres B, Pons X et  al (2014) No effects of Bacillus thuringiensis maize on non-­target organisms in the field in southern Europe: a meta-analysis of 26 arthropod taxa. Transgenic Res 23:135–143 Crawley MJ, Brown SL, Hails RS et  al (2001) Transgenic crops in natural habitats. Nature 409:682–683 Daspute AA, Yunxuan X, Gu M et  al (2019) Agrobacterium rhizogenes-mediated hairy roots transformation as a tool for exploring aluminum-responsive genes function. Futur Sci OA 5:FSO364. https://doi.org/10.4155/fsoa-­2018-­0065

226

S. Sheoran et al.

Datta A (2013) Genetic engineering for improving quality and productivity of crops. Agric Food Sec 2(1):1–3 de Oliveira RS, Oliveira-Neto OB, Moura HF et  al (2016) Transgenic cotton plants expressing Cry1Ia12 toxin confer resistance to fall armyworm (Spodoptera frugiperda) and cotton boll weevil (Anthonomus grandis). Front Plant Sci 7:165. https://doi.org/10.3389/fpls.2016.00165 Dong S, Delucca P, Geijskes RJ et  al (2014) Advances in Agrobacterium-mediated sugarcane transformation and stable transgene expression. Sugar Tech 16:366–371 Donohoue PD, Barrangou R, May AP (2018) Advances in industrial biotechnology using CRISPR-­ cas systems. Trends Biotechnol 36(2):134–146 Dowd PF, Berhow MA, Johnson ET (2018) Enhanced pest resistance and increased phenolic production in maize callus transgenically expressing a maize chalcone isomerase-3 like gene. Plant Gene 13:50–55 Dresselhaus T, Hückelhoven R (2018) Biotic and abiotic stress responses in crop plants. Agronomy 8:267 Driedonks N, Rieu I, Vriezen WH (2016) Breeding for plant heat tolerance at vegetative and reproductive stages. Plant Reprod 29(1):67–79 Du H, Zhang Z, Li J (2010) Isolation and functional characterization of a waterlogging-induced promoter from maize. Plant Cell Rep 29(11):1269–1275 Du H, Feng BR, Yang SS et al (2012) The R2R3-MYB transcription factor gene family in maize. PLoS One 7(6):e37463 Du H, Huang M, Liu L (2015) The genome wide analysis of GT transcription factors that respond to drought and waterlogging stresses in maize. Euphytica 208(1):113–122 Du H, Shen X, Huang Y et al (2016) Overexpression of Vitreoscilla hemoglobin increases waterlogging tolerance in Arabidopsis and maize. BMC Plant Biol 16(1):1–11 Du D, Jin R, Guo J, Zhang F (2019) Construction of marker-free genetically modified maize using a heat-inducible auto-excision vector. Genes (Basel) 10:374. https://doi.org/10.3390/ genes10050374 Einset J, Winge P, Bones A (2007) ROS signaling pathways in chilling stress. Plant Signal Behav 2(5):365–367 Ellis RT, Stockhoff BA, Stamp L et  al (2002) Novel Bacillus thuringiensis binary insecticidal crystal proteins active on western corn rootworm, Diabrotica virgifera virgifera LeConte. Appl Environ Microbiol 68(3):1137–1145 Ercoli L, Masoni A, Pampana S et al (2007) Allelopathic effects of rye, brown mustard and hairy vetch on redroot pigweed, common lambsquarter and knotweed. Allelopath J 19:249–256 Ercoli L, Lulli L, Arduini I et al (2011) Durum wheat grain yield and quality as affected by S rate under Mediterranean conditions. Eur J Agron 35:63–70 FAO (2015) FAO statistical pocketbook 2015. FAO Fei Y, Wenwei Z, Hong X et al (2007) Transgenic hairpin RNA wheat shows resistance to barley yellow dwarf virus. Heredity 29(1):97–97 Ferber D (1999) Risks and benefits: GM crops in the cross hairs. Science 286:1662–1666 Fu J, Zhu C, Wang C et al (2021) Maize transcription factor ZmEREB20 enhanced salt tolerance in transgenic Arabidopsis. Plant Physiol Biochem 159:257–267 Gallegos JE, Rose AB (2015) The enduring mystery of intron-mediated enhancement. Plant Sci 237:8–15. https://doi.org/10.1016/j.plantsci.2015.04.017 Gao W, Liu W, Zhao M et al (2015a) NERF encodes a RING E3 ligase important for drought resistance and enhances the expression of its antisense gene NFYA5 in Arabidopsis. Nucleic Acids Res 43:607–617. https://doi.org/10.1093/nar/gku1325 Gao Y, Jiang W, Dai Y et al (2015b) A maize phytochrome-interacting factor 3 improves drought and salt stress tolerance in rice. Plant Mol Biol 87(4–5):413–428 Gedil M, Menkir A (2019) An integrated molecular and conventional breeding scheme for enhancing genetic gain in maize in Africa. Front Plant Sci 10:1430 Gelvin SB (2010) Plant proteins involved in Agrobacterium-mediated genetic transformation. Annu Rev Phytopathol 48:45–68

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize

227

Goel S, Madan B (2014) Genetic engineering of crop plants for abiotic stress tolerance. In: Emerging technologies and management of crop stress tolerance. Academic, pp 99–123 Gong F, Takahashi H, Omori F et al (2019) QTLs for constitutive aerenchyma from Zea nicaraguensis improve tolerance of maize to root-zone oxygen deficiency. J Exp Bot 70(21):6475–6487 Grandaliano G, Choudhury GG, Abboud HE (1995) Transgenic animal models as a tool in the diagnosis of kidney diseases. Semin Nephrol 15:43–49 Green JM, Owen MD (2011) Herbicide-resistant crops: utilities and limitations for herbicide-­ resistant weed management. J Agric Food Chem 59(11):5819–5829 Gu L, Jiang T, Zhang C et al (2019) Maize HSFA 2 and HSBP 2 antagonistically modulate raffinose biosynthesis and heat tolerance in Arabidopsis. Plant J 100(1):128–142 Gupta M, Kumar H, Kaur S (2021) Vegetative insecticidal protein (Vip): a potential contender from Bacillus thuringiensis for efficient management of various detrimental agricultural pests. Front Microbiol 12:1139 Hackenberg M, Gustafson P, Langridge P et al (2015) Differential expression of microRNAs and other small RNAs in barley between water and drought conditions. Plant Biotechnol J 13:2–13. https://doi.org/10.1111/pbi.12220 Han YJ, Kim JI (2019) Application of CRISPR/Cas9-mediated gene editing for the development of herbicide-resistant plants. Plant Biotechnol Rep 13(5):447–457 Haroon M, Zafar MM, Farooq MA et al (2020) Conventional breeding, molecular breeding and speed breeding; brave approaches to revamp the production of cereal crops. Preprints. https:// doi.org/10.20944/preprints202011.0667.v1 He Z, Zhong J, Sun X et al (2018) The maize ABA receptors ZmPYL8, 9, and 12 facilitate plant drought resistance. Front Plant Sci 9:422 Hilder VA, Boulter D (1999) Genetic engineering of crop plants for insect resistance–a critical review. Crop Prot 18(3):177–191 Hu Q, Kononowicz-Hodges H, Nelson-Vasilchik K et  al (2008) FLP recombinase-­ mediated site-specific recombination in rice. Plant Biotechnol J 6:176–188. https://doi. org/10.1111/j.1467-­7652.2007.00310.x Hu J, Ren B, Dong S et  al (2020) Comparative proteomic analysis reveals that exogenous 6-­benzyladenine (6-BA) improves the defense system activity of waterlogged summer maize. BMC Plant Biol 20(1):1–19 Huang Y, Zhang XX, Li YH et al (2018) Overexpression of the Suaeda salsa SsNHX1 gene confers enhanced salt and drought tolerance to transgenic Zea mays. J Integr Agric 17(12):2612–2623 Ikenaka K, Kagawa T (1995) Transgenic systems in studying myelin gene expression. Dev Neurosci 17:127–136 Ilyas M, Nisar M, Khan N et  al (2021) Drought tolerance strategies in plants: a mechanistic approach. J Plant Growth Regul 40(3):926–944 ISAAA (2016) Global status of commercialized biotech/GM crops: 2016. ISAAA Brief No. 52 ISAAA (2017) Global status of commercialized biotech/GM crops in 2017: biotech crop adoption surges as economic benefits accumulate in 22 years, ISAAA brief no. 53. ISAAA, Ithaca. https://www.isaaa.org/resources/publications/briefs/53/ ISAAA (2018a) Global status of commercialized biotech/GM crops in 2018: biotech crops continue to help meet the challenges of increased population and climate change, ISAAA brief no. 54. ISAAA, Ithaca. https://www.isaaa.org/resources/publiccations/briefs/54/executivesummary/pdf/B54-­ExecSum-­English.pdf ISAAA (2018b) Global status of commercialized biotech/GM crops in 2018: executive brief. ISAAA, Ithaca ISAAA (2019) Global status of commercialized biotech/GM crops in 2019b: biotech crops drive socio-economic development and sustainable environment in the new frontier, ISAAA brief no. 55. ISAAA, Ithaca ISAAA (2020) ISAAA brief 55-2019: Executive Summary. Available Online at: https://www.isaaa. org/resources/publications/briefs/55/executivesummary/default.asp. Accessed 20 Jan 2021

228

S. Sheoran et al.

ISAAA database (2019) GM approval database retrieved on 17 August 2021. https://www.isaaa. org/gmapprovaldatabase/default.asp ISAAA database (2021) Accessed on 17 Aug 2021. https://www.isaaa.org/gmapprovaldatabase/ Izawati AM, Masani MY, Ismanizan I et  al (2015) Evaluation on the effectiveness of 2-­deoxyglucose-6-phosphate phosphatase (DOG(R)1) gene as a selectable marker for oil palm (Elaeis guineensis Jacq.) embryogenic calli transformation mediated by Agrobacterium tumefaciens. Front. Plant Sci 6:727. https://doi.org/10.3389/fpls.2015.00727 Jeanneau M, Gerentes D, Foueillassar X, Zivy M, Vidal J, Toppan A, Perez P (2002) Improvement of drought tolerance in maize: towards the functional validation of the Zm-Asr1 gene and increase of water use efficiency by over-expressing C4–PEPC. Biochimie 84(11):1127–1135 Jia TJ, Jing-jing LI, Wang LF et  al (2020) Evaluation of drought tolerance in ZmVPP1-­ overexpressing transgenic inbred maize lines and their hybrids. J Integr Agric 19(9):2177–2187 Jiang Z, Song G, Shan X et al (2018) Association analysis and identification of ZmHKT1; 5 variation with salt-stress tolerance. Front Plant Sci 9:1485 Jiao Y, Peluso P, Shi J et al (2017) Improved maize reference genome with single-molecule technologies. Nature 546(7659):524–527 Josine TL, Ji J, Wang G et al (2011) Advances in genetic engineering for plants abiotic stress control. Afr J Biotechnol 10(28):5402–5413 Kang T, Yu CY, Liu Y et al (2020) Subtly manipulated expression of ZmmiR156 in tobacco improves drought and salt tolerance without changing the architecture of transgenic plants. Front Plant Sci 10:1664 Karesch H, Bilang R, Scheid O et  al (1991) Direct gene transfer to protoplasts of Arabidopsis thaliana. Plant Cell Rep 9:571–574 Kereša S, Grdiša M, Barić M et al (2008) Transgenic plants expressing insect resistance genes. Sjemenarstvo 25(2):139–153 Kim SY, Bengtsson T, Olsson N et al (2020) Mutations in two aphid-regulated β-1, 3-glucanase genes by CRISPR/Cas9 do not increase barley resistance to Rhopalosiphum padi L. Front Plant Sci 11:1043 Klümper W, Qaim M (2014) A meta-analysis of the impacts of genetically modified crops. PLoS One 9(11):e111629 Ko CB, Woo YM, Lee DJ et  al (2007) Enhanced tolerance to heat stress in transgenic plants expressing the GASA4 gene. Plant Physiol Biochem 45(9):722–728 Kong X, Pan J, Zhang M et al (2011) ZmMKK4, a novel group C mitogenactivated protein kinase kinase in maize (Zea mays), confers salt and cold tolerance in transgenic Arabidopsis. Plant Cell Environ 34(8):1291–1303 Kong J, Jin J, Dong Q et  al (2019) Maize factors ZmUBP15, ZmUBP16 and ZmUBP19 play important roles for plants to tolerance the cadmium stress and salt stress. Plant Sci 280:77–89 Koziel MG, Beland GL, Bowman C et  al (1993) Field performance of elite transgenic maize plants expressing an insecticidal protein derived from Bacillus thuringiensis. Biotechnology 11(2):194–200 Kumar S, Arul L, Talwar D (2010) Generation of marker-free Bt transgenic indica rice and evaluation of its yellow stem borer resistance. J Appl Genet 51:243–257. https://doi.org/10.1007/ BF03208854 Kumar K, Gupta M, Singh A et  al (2020) Frontier technologies in maize improvement. Maize research in India: retrospect and prospect. New India Publishing Agency, New Delhi, pp 541–563. isbn:978-93-89992-00-7 Landi S, Hausman JF, Guerriero G et al (2017) Poaceae vs. abiotic stress: focus on drought and salt stress, recent insights and perspectives. Front Plant Sci 8:1214 Laxa M (2017) Intron-mediated enhancement: a tool for heterologous gene expression in plants? Front Plant Sci 7:1977. https://doi.org/10.3389/fpls.2016.01977 Lea PJ, Joy KW, Ramos JL et  al (1984) The action of 2-amino-4-(methylphosphinyl)-butanoic acid (phosphinothricin) and its 2-oxo-derivative on the metabolism of cyanobacteria and higher plants. Phytochemistry 23:1–6

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize

229

Li Z, Howell SH (2021) Heat stress responses and thermotolerance in maize. Int J Mol Sci 22(2):948 Li H, Guan R, Guo H et al (2015) New insights into an RNAi approach for plant defence against piercing sucking and stemborer insect pests. Plant Cell Environ 38(11):2277–2285 Li R, Fu D, Zhu B et  al (2018a) CRISPR/Cas9 mediated mutagenesis of lncRNA1459 alters tomato fruit ripening. Plant J 94(3):513–524 Li X, Li L, Zuo S et al (2018b) Differentially expressed ZmASR genes associated with chilling tolerance in maize (Zea mays) varieties. Funct Plant Biol 45(12):1173–1180 Li H, Han X, Liu X et al (2019) A leucine-rich repeat-receptor-like kinase gene SbER2–1 from sorghum (Sorghum bicolor L.) confers drought tolerance in maize. BMC Genomics 20(1):1–15 Liang Z, Chen K, Li T et  al (2017) Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes. Nat Commun 8:14261. https://doi.org/10.1038/ ncomms14261 Liang Z, Chen K, Zhang Y et al (2018) Genome editing of bread wheat using biolistic delivery of CRISPR/Cas9 in vitro transcripts or ribonucleoproteins. Nat Protoc 13:413–430. https://doi. org/10.1038/nprot.2017.145 Liang K, Tang K, Fang T et  al (2020) Waterlogging tolerance in maize: genetic and molecular basis. Mol Breed 40(12):1–13 Liu MY, Sun J, Wang KY et al (2014) Spermidine enhances waterlogging tolerance via regulation of antioxidant defence, heat shock protein expression and plasma membrane H+ATPase activity in Zea mays. J Agron Crop Sci 200(3):199–211 Liu Y, Han L, Qin L et al (2015) Saccharomyces cerevisiae gene TPS1 improves drought tolerance in Zea mays L. by increasing the expression of SDD1 and reducing stomatal density. Plant Cell Tissue Organ Cult 120(2):779–778 Liu Y, Liang J, Sun L et al (2016) Group 3 LEA protein, ZmLEA3, is involved in protection from low temperature stress. Front Plant Sci 7:1011 Liu M, Rehman S, Tang X et  al (2019) Methodologies for improving HDR efficiency. Front Genet 9:691 Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to climate change. Agric For Meteorol 150(11):1443–1452 Loke JC, Stahlberg EA, Strenski DG et al (2005) Compilation of mRNA polyadenylation signals in Arabidopsis revealed a new signal element and potential secondary structures. Plant Physiol 138:1457–1468. https://doi.org/10.1104/pp.105.060541 Losey JE, Rayor LS, Carter ME (1999) Transgenic pollen harms monarch larvae. Nature 399(6733):214 Lowder LG, Paul JW, Qi Y (2017) Multiplexed transcriptional activation or repression in plants using CRISPR-dCas9-based systems. Methods Mol Biol 1629:167–184. https://doi. org/10.1007/978-­1-­4939-­7125-­1_12 Lu Y, Li Y, Zhang J et al (2013) Overexpression of Arabidopsis molybdenum cofactor sulfurase gene confers drought tolerance in maize (Zea mays L.). PLoS One 8(1):e52126 Luo X, Wang B, Gao S et al (2019) Genomewide association study dissects the genetic bases of salt tolerance in maize seedlings. J Integr Plant Biol 61(6):658–674 Madhusudhan L (2016) Transgenic crops and the environment. J Ecosys Ecography 6:218. https:// doi.org/10.4172/2157-­7625.1000218 Manickavasagam M, Ganapathi A, Anbazhagan VR et al (2004) Agrobacterium-mediated genetic transformation and development of herbicide-resistant sugarcane (Saccharum species hybrids) using axillary buds. Plant Cell Rep 23:134–143. https://doi.org/10.1007/s00299-­004-­0794-­y Mao H, Wang H, Liu S et  al (2015) A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nat Commun 6(1):1–13 Mao Y, Yang X, Zhou Y et al (2018) Manipulating plant RNA-silencing pathways to improve the gene editing efficiency of CRISPR/Cas9 systems. Genome Biol 19(1):1–15 Marco F, Bitrián M, Carrasco P et al (2015) Genetic engineering strategies for abiotic stress tolerance in plants. In: Plant biology and biotechnology. Springer, New Delhi, pp 579–609

230

S. Sheoran et al.

Marvier M, McCreedy C, Regetz J et al (2007) A meta-analysis of effects of Bt cotton and maize on nontarget invertebrates. Science 316:1475–1477 Masuka B, Araus JL, Das B et al (2012) Phenotyping for abiotic stress tolerance in maize F. J Integr Plant Biol 54(4):238–249 Mayavan S, Subramanyam K, Jaganath B et al (2015) Agrobacterium-mediated in planta genetic transformation of sugarcane setts. Plant Cell Rep 34:1835–1848. https://doi.org/10.1007/ s00299-­015-­1831-­8 McPherson SA, Perlak FJ, Fuchs RL et  al (1988) Characterization of the coleopteran–specific protein gene of Bacillus thuringiensis var. tenebrionis. Biotechnology 6(1):61–66 Meng C, Sui N (2019) Overexpression of maize MYB-IF35 increases chilling tolerance in Arabidopsis. Plant Physiol Biochem 135:167–173 Minh BM, Linh NT, Hanh HH et al (2019) A LEA gene from a Vietnamese maize landrace can enhance the drought tolerance of transgenic maize and tobacco. Agronomy 9(2):62 Moglia A, Portis E (2016) Genetically modified foods. In: Encyclopedia of food and health. Academic Press is an imprint of Elsevier, Kidlington; Waltham, pp 196–203 Moon HS, Abercrombie LL, Eda S et al (2011) Transgene excision in pollen using a codon optimized serine resolvase CinH-RS2 site-specific recombination system. Plant Mol Biol Rep 75:621–631. https://doi.org/10.1007/s11103-­011-­9756-­2 Morder intelligence blog (2021) Maize seed market – growth, trends, covid-19 impact, and forecasts (2021–2026). Available at https://www.mordorintelligence.com/industry-­reports/maize-­ corn-­seed-­market. Accesses on 25 Aug 2021 Muppala S, Gudlavalleti PK, Malireddy KR et al (2021) Development of stable transgenic maize plants tolerant for drought by manipulating ABA signaling through agrobacterium-mediated transformation. J Genet Eng Biotechnol 19(1):1–14 Nahampun HN, Lopez-Arredondo D, Xu X et  al (2016) Assessment of ptxD gene as an alternative selectable marker for Agrobacterium-mediated maize transformation. Plant Cell Rep 35:1121–1132. https://doi.org/10.1007/s00299-­016-­1942-­x Naranjo SE (2009) Impacts of Bt crops on non-target invertebrates and insecticide use patterns. CAB Rev:11. https://doi.org/10.1079/PAVSNNR20094011 Nesmith DS, Ritchie JT (1992) Effects of soil water-deficits during tassel emergence on development and yield component of maize (Zea mays). Field Crops Res 28(3):251–256 Nicolia A, Manzo A, Veronesi F et al (2014) An overview of the last 10 years of genetically engineered crop safety research. Crit Rev Biotechnol 34:77–88 Niu X, Kassa A, Hu X et al (2017) Control of western corn rootworm (Diabrotica virgifera virgifera) reproduction through plant-mediated RNA interference. Sci Rep 7(1):1–13 Onouchi H, Yokoi K, Machida C et al (1991) Operation of an efficient site-specific recombination system of Zygosaccharomyces rouxii in tobacco cells. Nucleic Acids Res 19:6373–6378. https://doi.org/10.1093/nar/19.23.6373 Ortego F, Pons X, Albajes R et al (2009) European commercial genetically modified plantings and field trials. In: Ferry N, Gatehouse AMR (eds) Environmental impact of genetically modified crops. CAB International, pp 327–343 Osman GH, Assem SK, Alreedy RM et al (2015) Development of insect resistant maize plants expressing a chitinase gene from the cotton leaf worm, Spodoptera littoralis. Sci Rep 5(1):1–11 Padmanabhan SY (1973) The great Bengal famine. Annu Rev Phytopathol 11(1):11–24 Pan J, Sharif R, Xu X et  al (2020) Mechanisms of waterlogging tolerance in plants: research progress and prospects. Front Plant Sci 11:627331. https://doi.org/10.3389/fpls.2020.627331 Parmar N, Singh KH, Sharma D et al (2017) Genetic engineering strategies for biotic and abiotic stress tolerance and quality enhancement in horticultural crops: a comprehensive review 3. Biotechnology 7(4):1–35 Paszkowski J, Shillito RD, Saul M et  al (1984) Direct gene transfer to plants. EMBO J 3(12):2717–2722 Pathi KM, Rink P, Budhagatapalli N et al (2020) Engineering smut resistance in maize by site-­ directed mutagenesis of LIPOXYGENASE 3. Front Plant Sci 11:1559

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize

231

Pavani N, Kuchanur PH, Patil A et al (2019) Stability analysis of stress-resilient maize (Zea mays L.) hybrids across stressed and non-stressed environments. Int J Curr Microbiol App Sci 9:252–260 Pellegrino E, Bedini S, Nuti M (2018) Impact of genetically engineered maize on agronomic, environmental and toxicological traits: a metaanalysis of 21 years of field data. Sci Rep 8(1):1–12 Portis E, Acquadro A, Comino C et al (2004) Effect of farmers’ seed selection on genetic variation of a landrace population of pepper (Capsicum annuum L.), grown in north-West Italy. Genet Resour Crop Evol 51(6):581–590 Prasanna BM (2011) Maize in the developing world: trends, challenges, and opportunities. In: Addressing climate change effects and meeting maize demand for Asia-B. Extended summaries of the 11th Asian maze conference. Nanning, China, pp 26–38 Quan R, Shang M, Zhang H et  al (2004) Engineering of enhanced glycine betaine synthesis improves drought tolerance in maize. Plant Biotechnol J 2(6):477–486 Raman R (2017) The impact of genetically modified (GM) crops in modern agriculture: a review. GM Crops Food 8(4):195–208 Reif JC, Zhang P, Dreisigacker S et al (2005) Wheat genetic diversity trends during domestication and breeding. Theor Appl Genet 110(5):859–864 Ren Z, Liu Y, Kang D et al (2015) Two alternative splicing variants of maize HKT1; 1 confer salt tolerance in transgenic tobacco plants. Plant Cell Tissue Organ Cult 123(3):569–578 Restrepo-Diaz H, Chávez-Arias CC, Ligarreto-Moreno GA et  al (2021) Maize responses challenged by drought, elevated daytime temperature and arthropod herbivory stresses: a physiological, biochemical and molecular view. Front Plant Sci 12:1512 Ribeiro C, Hennen-Bierwagen TA, Myers AM et al (2020) Engineering 6-phosphogluconate dehydrogenase improves grain yield in heat-stressed maize. Proc Natl Acad Sci 117(52):33177–33185 Rogers DL (2004) Genetic erosion no longer just an agricultural issue. Nat Plants J 5(2):112–122 Rosa C, Kuo YW, Wuriyanghan H et  al (2018) RNA interference mechanisms and applications in plant pathology. Annu Rev Phytopathol 56:581–610. https://doi.org/10.1146/ annurev-­phyto-­080417-­050044 Ross-Ibarra J, Sawers R, Hufford MB (2017) Maize diversity and climate change. University of California e-Scholarship Working Papers Roundup Ready System. Monsanto. Archived from the original on 2 April 2013 Sanchis V (2011) From microbial sprays to insect-resistant transgenic plants: history of the biospesticide Bacillus thuringiensis. A review. Agron Sustain Dev 31(1):217–231 Sauer H, Wild A, Rühle W (1987) The effect of phosphinothricin (glufosinate) on photosynthesis II. The causes of inhibition of photosynthesis. Zeitschrift für Naturforschung C 42(3):270–278 Schnepf E, Crickmore NV, Van Rie J et al (1998) Bacillus thuringiensis and its pesticidal crystal proteins. Microbiol Mol Biol Rev 62(3):775–806 Seth K (2016) Current status of potential applications of repurposed Cas9 for structural and functional genomics of plants. Biochem Biophys Res Commun 480(4):499–507 Shendure J, Balasubramanian S, Church GM et al (2017) DNA sequencing at 40: past, present and future. Nature 550:345. https://doi.org/10.1038/s41586-­019-­1120-­8 Shi Q, Dong Y, Zhou Q et al (2016) Characterization of a maize ERF gene, ZmERF1, in hormone and stress responses. Acta Physiol Plant 38(5):126 Shou H, Bordallo P, Wang K (2004a) Expression of the Nicotiana protein kinase (NPK1) enhanced drought tolerance in transgenic maize. J Exp Bot 55(399):1013–1019 Shou H, Bordallo P, Fan JB et al (2004b) Expression of an active tobacco mitogen-activated protein kinase kinase kinase enhances freezing tolerance in transgenic maize. Proc Natl Acad Sci 101(9):3298–3303 Siebers MH, Slattery RA, Yendrek CR et al (2017) Simulated heat waves during maize reproductive stages alter reproductive growth but have no lasting effect when applied during vegetative stages. Agric Ecosyst Environ 240:162–170 Snow AA, Palma PM (1997) Commercialization of transgenic plants: potential ecological risks. BioScience 47:86–96

232

S. Sheoran et al.

Statista (2021). Available at: https://www.statista.com/statistics/263292/acreage-­of-­genetically-­ modified-­crops-­worldwide/. Accessed on 25 Aug 2021 Su H, Cao Y, Ku L et al (2018) Dual functions of ZmNF-YA3 in photoperiod-dependent flowering and abiotic stress responses in maize. J Exp Bot 69(21):5177–5189 Sun HJ, Uchii S, Watanabe S et al (2006) A highly efficient transformation protocol for micro-tom, a model cultivar for tomato functional genomics. Plant Cell Physiol 47(426):431 Sundaresan G, Gambhir SS (2002) Radionuclide imaging of reporter gene expression. In: Brain mapping: the methods. Academic, pp 799–818 Svitashev S, Young JK, Schwartz C et al (2015) Targeted mutagenesis, precise gene editing, and site-specific gene insertion in maize using Cas9 and guide RNA. Plant Physiol 169(2):931–945 Szalai G, Janda T (2009) Effect of salt stress on the salicylic acid synthesis in young maize (Zea mays L.) plants. J Agron Crop Sci 195(3):165–171 Tachibana K, Watanabe T, Sekizawa Y et al (1986) Accumulation of ammonia in plants treated with bialaphos: action mechanism of bialaphos (part 2). J Pestic Sci 11(1):33–37 Tang X, Lowder LG, Zhang T et  al (2017) A CRISPR-Cpf1 system for efficient genome editing and transcriptional repression in plants. Nat Plants 3:17018. https://doi.org/10.1038/ nplants.2017.103 Teotia S, Singh D, Tang X et  al (2016) Essential RNA-based technologies and their applications in plant functional genomics. Trends Biotechnol 34:106–123. https://doi.org/10.1016/j. tibtech.2015.12.001 Tigchelaar M, Battisti DS, Naylor RL et al (2018) Future warming increases probability of globally synchronized maize production shocks. Proc Natl Acad Sci 115(26):6644–6649 Torti P, Raineri J, Mencia R et al (2020) The sunflower TLDc-containing protein HaOXR2 confers tolerance to oxidative stress and waterlogging when expressed in maize plants. Plant Sci 300:110626 Turan S, Cornish K, Kumar S (2012) Salinity tolerance in plants: breeding and genetic engineering. Aust J Crop Sci 6(9):1337–1348 U.S. National Academies of Sciences (2016) Engineering, and medicine genetically engineered crops: experiences and prospects. Academic Ullstrup AJ (1972) The impacts of the southern corn leaf blight epidemics of 1970–1971. Annu Rev Phytopathol 10(1):37–50 Verma D, Daniell H (2007) Chloroplast vector systems for biotechnology applications. Plant Physiol 145:1129–1143. https://doi.org/10.1104/pp.107.106690 Wally O, Punja ZK (2010) Genetic engineering for increasing fungal and bacterial disease resistance in crop plants. GM Crops 1(4):199–206 Wang M, Gu D, Liu T et al (2007) Overexpression of a putative maize calcineurin B-like protein in Arabidopsis confers salt tolerance. Plant Mol Biol 65(6):733–746 Wang CR, Yang AF, Yue GD et al (2008) Enhanced expression of phospholipase C 1 (ZmPLC1) improves drought tolerance in transgenic maize. Planta 227(5):1127–1140 Wang F, Peng S, Cui K et al (2014) Field performance of Bt transgenic crops: a review. Aust J Crop Sci 8:18–26 Wang X, Wang H, Liu S et al (2016a) Genetic variation in ZmVPP1 contributes to drought tolerance in maize seedlings. Nat Genet 48(10):1233–1241 Wang X, Shan X, Xue C et al (2016b) Isolation and functional characterization of a cold responsive phosphatidylinositol transfer-associated protein, ZmSEC14p, from maize (Zea may L.). Plant Cell Rep 35(8):1671–1686 Wang Y, Wang Q, Liu M et al (2017) Overexpression of a maize MYB48 gene confers drought tolerance in transgenic arabidopsis plants. J Plant Biol 60(6):612–621 Wang B, Li Z, Ran Q et al (2018a) ZmNF-YB16 overexpression improves drought resistance and yield by enhancing photosynthesis and the antioxidant capacity of maize plants. Front Plant Sci 9:709. https://doi.org/10.3389/fpls.2018.00709 Wang CT, Ru JN, Liu YW et al (2018b) Maize WRKY transcription factor ZmWRKY106 confers drought and heat tolerance in transgenic plants. Int J Mol Sci 19(10):3046

Genetic Engineering to Improve Biotic and Abiotic Stress Tolerance in Maize

233

Wang H, Wang M, Xia Z (2019) Overexpression of a maize SUMO conjugating enzyme gene (ZmSCE1e) increases Sumoylation levels and enhances salt and drought tolerance in transgenic tobacco. Plant Sci 281:113–121 Weber B, Zicola J, Oka R et  al (2016) Plant enhancers: a call for discovery. Trends Plant Sci 21:974–987. https://doi.org/10.1016/j.tplants.2016.07.013 Wei A, He C, Li B et al (2011) The pyramid of transgenes TsVP and BetA effectively enhances the drought tolerance of maize plants. Plant Biotechnol J 9:216–229 Werck-Reichhart D, Hehn A, Didierjean L (2000) Cytochromes P450 for engineering herbicide tolerance. Trends Plant Sci 5(3):116–123 Wolfenbarger LL, Naranjo SE, Lundgren JG et al (2008) Bt crop effects on functional guilds of non-target arthropods: a meta-analysis. PLoS One 3:e2118 Wu J, Jiang Y, Liang Y et al (2019) Expression of the maize MYB transcription factor ZmMYB3R enhances drought and salt stress tolerance in transgenic plants. Plant Physiol Biochem 137:179–188 Xu Z, Hennessy DA, Sardana K et al (2013) The realized yield effect of genetically engineered crops: U.S. maize and soybean. Crop Sci 53:735–745 Yadav OP, Hossain F, Karjagi CG et al (2015) Genetic improvement of maize in India: retrospect and prospects. Agric Res 4(4):325–338 Yadava P, Abhishek A, Singh R et al (2017) Advances in maize transformation technologies and development of transgenic maize. Front Plant Sci 7:1949 Yamaguchi-Shinozaki K, Shinozaki K (2006) Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol 57:781–803. https://doi.org/10.1146/annurev.arplant.57.032905.105444 Yamamoto YY, Ichida H, Matsui M et al (2007) Identification of plant promoter constituents by analysis of local distribution of short sequences. BMC Genomics 8:67. https://doi.org/10.118 6/1471-­2164-­8-­67 Yan J, Li J, Zhang H et al (2021) ZmWRKY104 positively regulates salt tolerance by modulating ZmSOD4 expression in maize. Crop J. https://doi.org/10.1016/j.cj.2021.05.010 Yao Q, Cong L, Chang JL et al (2006) Low copy number gene transfer and stable expression in a commercial wheat cultivar via particle bombardment. J Exp Bot 57(14):3737–3746 Yau YY, Stewart CN (2013) Less is more: strategies to remove marker genes from transgenic plants. BMC Biotechnol 13:36. https://doi.org/10.1186/1472-­6750-­13-­36 Yin XY, Yang AF, Zhang KW et  al (2004) Production and analysis of transgenic maize with improved salt tolerance by the introduction of AtNHX1 gene. Acta Bot Sin 46:854–861 Yu F, Liang K, Fang T et  al (2019) A group VII ethylene response factor gene, ZmEREB180, coordinates waterlogging tolerance in maize seedlings. Plant Biotechnol J 17(12):2286–2298 Yu H, Qu J, Guo X et al (2021) Overexpression of vacuolar H+pyrophosphatase (H+PPase) gene from Ammopiptanthus nanus enhances drought tolerance in maize. J Agron Crop Sci. https:// doi.org/10.1111/jac.12504 Zaidi PH, Maniselvan P, Srivastava A et al (2010) Genetic analysis of water-logging tolerance in tropical maize (Zea mays L.). Maydica 55(1):17–26 Zhang J, Klueva NY, Wang Z et al (2000) Genetic engineering for abiotic stress resistance in crop plants. In Vitro Cell Dev Biol Plant 36(2):108–114 Zhang S, Li N, Gao F et al (2010) Over-expression of TsCBF1 gene confers improved drought tolerance in transgenic maize. Mol Breed 26(3):455–465 Zhang S, Jiao Z, Liu L et al (2018) Enhancer-promoter interaction of SELF PRUNING 5G shapes photoperiod adaptation. Plant Physiol 178:1631–1642. https://doi.org/10.1104/pp.18.01137 Zhang M, LiangX WL et al (2019) A HAK family Na+ transporter confers natural variation of salt tolerance in maize. Nat Plants 5(12):1297–1308 Zhang X, Mi Y, Mao H et al (2020a) Genetic variation in ZmTIP1 contributes to root hair elongation and drought tolerance in maize. Plant Biotechnol J 18(5):1271–1283 Zhang Y, Cao Y, Zheng H et  al (2020b) Ectopic expression of antifreeze protein gene from Ammopiptanthus nanus confers chilling tolerance in maize. Crop J 9(4):924–933

234

S. Sheoran et al.

Zhao Y, Du H, Wang Y et al (2021) The calciumdependent protein kinase ZmCDPK7 functions in heat stress tolerance in maize. J Integr Plant Biol 63(3):510–527 Zhou L, Zhou J, Xiong Y et al (2018) Overexpression of a maize plasma membrane intrinsic protein ZmPIP1; 1 confers drought and salt tolerance in Arabidopsis. PLoS One 13(6):e0198639 Zhu T, Mettenburg K, Peterson DJ et al (2000) Engineering herbicide-resistant maize using chimeric RNA/DNA oligonucleotides. Nat Biotechnol 18(5):555–558

Genetic Improvement of Specialty Corn for Nutritional Quality Traits Firoz Hossain, Rajkumar U. Zunjare, Vignesh Muthusamy, Ashwani Kumar, Jayanthi Madhavan, Gopinath Ikkurti, Ashvinkumar Katral, Zahirul A. Talukder, Rashmi Chhabra, Gulab Chand, Vinay Bhatt, Irum Gul, Subhra J. Mishra, Hriipulou Duo, Suman Dutta, Nisrita Gain, Priyanka Chauhan, Shalma Maman, Shashidhar B. Reddappa, and Ravindra Kumar Kasana

1 Introduction Malnutrition leads to increased morbidity, disability, and abnormal physical and mental health and contributes to poor socioeconomic development (Yadava et al. 2022). Two billion people were seriously affected from concern of malnutrition in the developing and underdeveloped countries (Global Nutrition Report 2020). Nearly, 2.37 billion global population do not have access to adequate food, of which 768 million are undernourished. Undernutrition causes ~45% death among children (2 mg/100 g (FW) from base level of 0.2–0.3 mg/100 g (FW). Yang et al. (2018) introgressed lycopene-ε-­ cyclase (lcyE) gene to increase proA from 1.55 to 3.95 ppm, while pyramiding of Purple1 (Pr1), Coloured1 (C1), and o2 gene into single genotype has led to the improved sweet corn genotypes with ten-fold increased anthocyanin and 30% increased tryptophan content (Jompuk et al. 2020). Elite sweet corn cultivars were also improved to a level of 4.5-fold increase in α- and γ-tocopherols by marker-­ assisted breeding (MAB) of vte4 gene (Xiao et  al. 2020). Mehta et  al. (2020a) improved proA to 18.98  ppm from 3.12  ppm, lysine to 0.39% from 0.23% and tryptophan to 0.10% from 0.06% through MAB of o2 and crtRB1 in two sweet corn hybrids (PSSC-2 and ASKH-2). Baveja et al. (2021) combined crtRB1, lcyE, and o2 genes for increasing nutrient composition of sweet corn hybrids through molecular breeding. The introgressed sweet corn hybrids possessed increased lysine (0.390%), tryptophan (0.082%), and proA (21.14 ppm). Chauhan et al. (2021) analyzed the biofortified inbreds and revealed higher α-tocopherol (2.03-fold), γ-tocopherol (1.19-fold), α−/γ-T (1.71-folds), β-carotene (6.21-fold), β-cryptoxanthin

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Table 1  List of biofortified specialty corn germplasm developed worldwide

Recipient Donor S. No. genotype genotype SY999 1. M01, M14, K140, and K185

2.

Hybrix5

3.

BK-3 M01, M14, K140, and K185 sh2 sweet Purple o2 corn waxy corn

4.

HZ

Gene(s) selected for nutritional quality vte4

–

lcyE

Pr1, C1 and o2

5.

Yuetian 9, Yuetian 22 and Yuetian 28

XIANM-1, vte4 TIANZ-1, 1132, 1022, 794

6.

SWT-16, SWT-17 and SWT-18

PMI-PV-5 and PMI-PV-8

7.

PMI-PV5, SWT-147 PMI-PV6, PMI-PV7 and PMI-PV8

crtRB1 and o2

o2, crtRB1 and lcyE

Nutritional trait(s) improved ProE (γ-tocopherol from 29.11 to 51.45 μg/g and total tocopherols from 36.63 to 63.34 μg/g) Zeaxanthin (0.2– 0.3 mg/100 g FW to >2.0 mg/100 g FW) β-carotene (1.54 μg/g); ProA (1.85 μg/g) 30% increase in tryptophan, tenfold increase in anthocyanin content ProE (4.5-fold increase in α-tocopherol level) ProA (3.12 μg/g to 18.98 μg/g), Lysine (0.19% to 0.37%) and Tryptophan (0.05% to 0.08%) Lysine (0.390%), tryptophan (0.082%) and proA (21.14 ppm)

Specialty corn types Country Reference(s) Sweet Republic Feng et al. corn of China (2015)

Sweet corn

Australia O’Hare et al. (2015)

Sweet corn

China

Sweet corn

Thailand Jompuk et al. (2020)

Sweet corn

China

Xiao et al. (2020)

Sweet corn

India

Mehta et al. (2020a)

Sweet corn

India

Baveja et al. (2021)

Yang et al. (2018)

(continued)

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Table 1 (continued)

Recipient S. No. genotype 8. SWT16, SWT-17 and SWT-18 9. P3, P2, P9, and P5

Donor genotype HP465–41

Gene(s) selected for nutritional quality sh2, crtRB1 and vte4

o2 Tx807, CML154Q, CML154Q and K0326Y AgQ53 o2

10.

Kwi1 and Kwi9

11.

QCL5008 QCL3024

o2

12.

QCL5019 TAIXI19 (o2o2) and QCL3021 (o16o16) Zhao QPM OP-6 CA339 QCL5013 Tai19 (o2o2)

o2 o16

13. 14.

15.

16.

17.

HKI161, HKI163, HKI193– 1, and HKI193–2 Zheng58 and Chang7–2 S4 Red sweet corn

o2 o2

MGU-102-­ o2 wx1

By804

dgat1–2

Purple o2 waxy corn

o2

Nutritional trait(s) improved ProA (>19 ppm), ProE (20 ppm] Lysine (increment of 1.45- to 2-fold)

Specialty corn types Country Sweet India corn

Popcorn

United States

Tryptophan (increased by 0.5- to 1.2-fold) and lysine (increased by 2- to 2.5-fold) Increment in lysine content of 18–28% Lysine (0.33–0.62%)

Waxy corn

Thailand Sinkangam et al. (2011)

Waxy corn

China

Yang et al. (2013)

Waxy corn

China

Zhang et al. (2013)

QPM (lysine: 0.31–0.47%) Lysine (0.32 to 0.37–0.38%) Lysine: 0.384% and tryptophan: 0.102%

Waxy corn Waxy corn

China

Zhou et al. (2016) Wang et al. (2019)

Waxy corn

India

Talukder et al. (2022a)

27–37% increment in oil content 25% increased Tryptophan; 259– 557 mg/100 g Anthocyanins

High oil corn

China

Chai et al. (2011)

Red Sweet corn

Thailand Inplean et al. (2020)

China

Reference(s) Chauhan et al. (2022)

Ren et al. (2018)

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(4.14-­fold), and total provitamin-A (proA) (5.49-fold) compared to their original versions of inbreds. Chauhan et  al. (2022) also introgressed vte4 and crtRB1 in sweet corn inbreds through MAB.  The improved sweet corn inbreds possessed 2.03-fold higher α-tocopherol, 1.19-fold γ-tocopherol, 1.71-fold α−/γ-tocopherol, and 5.49-fold proA as compared to original inbreds. Development of markers for sh2 and su1 has helped in combining these nutritional traits in sweet corn genetic background (Hossain et al. 2015; Chhabra et al. 2019a, b, 2020, 2021).

1.2 Popcorn Popcorn is an extreme flint-type corn forming large expandable aromatic and delicious puffy flakes at high temperature. It is an all-time popular snack leading in cinema theaters and currently booming also as microwave popcorn in home consumption. Popcorn is also used in the preparation of many traditional dishes (Zunjare et al. 2015). Heating of kernels creates vapour pressure from kernel moisture and pericarp holds the pressure upto a threshold level. On reaching a threshold pressure, it ruptures causing starch gelatinization. Soft flakes are formed from differential expansion of kernel’s soft and hard endosperm regions (Hoseney et  al. 1983; da Silva et al. 1993). The global ready-to-eat popcorn market was valued at US$ 4068.7  million in 2020. COVID-19 impact analysis reported a compound annual growth rate (CAGR) of 3.2% during 2021. However, the market is expected to boom to US$ 6406.3 million at a CAGR of 6.7% by 2027 (https://www.verifiedmarketresearch.com). The consumer demand, satisfaction, and market value are primarily decided by popping expansion volume (PEV) of kernels. PEV is defined as the ratio of volume of popped kernels (cm3) to weight of unpopped kernels (g). Hence,  the research focus has been  shifted towards improving  popping quality traits rather than inferior agronomic traits through germplasm diversity (Metzger et al. 1989; Sweley et al. 2013). Additionally, high PEV with desirable flake texture including fluffiness, tenderness, and aroma are the preferable attributes at consumer’s end. Popcorn flake shape is classified based on flake morphology. “Butterfly” flakes have bidirectional appendages spread on near opposite sides. Some flakes with multiple flake appendages are called “multilateral” and contribute to better PEV. “Mushroom-’shaped flakes are more spherical in appearance, hence less broken during processing (Sweley et al. 2011). Mushroom type is usually preferred for confectionary and spicy coatings and sold at higher price. Several quantitative trait loci (QTLs) for popping-related traits have been mapped using QTL mapping approach. Backcross populations like BC1S1(Lu et al. 2003) and BC2F2 (Li et al. 2009) as well as F2:3 populations (Babu et al. 2006; Li et al. 2007; Dhaliwayo 2008) and recombinant  inbred lines (RILs) (Dong et  al. 2012) were included to identify QTLs. Some common genomic regions were reported across studies for different popping- and yield-related traits harboring meta-QTLs mostly identified on chromosome 1 (Dong et al. 2012; Kaur et al. 2021). A total of

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nine QTLs for flake size (Li et al. 2007) with highest R2 value of 15.6%, four for flake volume (Babu et  al. 2006), three for kernel weight (Dhaliwayo 2008), 14 QTLs for PEV (Babu et al. 2006; Li et al. 2008; Dhaliwayo 2008) with highest R2 of 30% in F2:3 and 24.4% in RILs, four QTLs for popping fold (Dong et al. 2012) with highest R2 of 24.5%, 15 QTLs for popping rate (Babu et  al. 2006; Li et  al. 2009; Dong et al. 2012), and 14 QTLs for popping volume (Lu et al. 2003; Li et al. 2006; Li et al. 2007; Dong et al. 2012) were detected. Bulked segregant analysis on F2:3 seeds derived from high and low popping volume parents showed high association of three SSRs (bnlg1331, bnlg1520 and bnlg1836) with PEV explaining 78% of phenotypic variance (Thakur et  al. 2021). Application of association mapping identified several popping traits associated SNPs and related candidate genes (Senhorinho et al. 2019; Li et al. 2021). Association mapping for PEV in CIMMYT inbred lines identified 15 SNPs within already reported QTLs, and description of related candidate gene were available for four QTLs. Following gene ontology analysis of candidate genes affecting popping expansion traits, genes encoding lycoyltransferase, N-acetyllactosaminide 3-alpha-galactosyltransferase, starch composition determinants, pentatricopeptide repeat protein, phosphoenolpyruvate carboxylase, photosystem-II 11 kDa protein, regulatory protein viviparous-1, and cryptochrome-1 have been identified. The introgression of opaque2 (o2) present on chromosome 7 to popcorn genotype resulted in 20–65% increased tryptophan (Adunola 2017). Quality protein maize (QPM) conversion of popcorn by Ren et al. (2018) was successful in maintaining popcorn kernel shape  with vitreous endosperm and popping expansion (Table 1). The results showed recovery of fully poppable (90% popping rate) introgressed lines containing two-fold increase in lysine. Parsons et  al. (2020, 2021) reported the development of several quality protein popcorn (QPP) hybrids using popcorn inbreds having o2 gene. The selection of Pr1 and Booster1 (B1) in popcorn breeding has succeeded the development of anthocyanin-enriched popcorn variety (Lago et al. 2013).

1.3 Waxy Corn Waxy maize possesses higher amylopectin (~95–100%) as against normal maize (~70–75% amylopectin) (Zhou et al. 2016; Talukder et al. 2022a, b, c; Reddappa et al. 2022). It is an important part of the human diet in South-East Asian countries (Devi et al. 2017; Hossain et al. 2019a). Fresh waxy cobs are becoming increasingly popular as a breakfast food around the world. Due to higher amylopectin level, waxy maize is easily digested in the human gut (Fukunaga et al. 2002; Lu and Lu 2012). For professional athletes, waxy maize is a preferable food (Roberts et  al. 2011). Among the poultry birds, waxy maize has shown to increase weight and digestibility with better feed conversion efficiency (Kimura and Robyt 1995; Collins et al. 2003). Amylopectin concentrate is a desirable product in the textile, adhesive, and paper sectors due to its pasting characteristics (Bao et  al. 2012). Quick

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hydrolysis of starch in waxy maize provides better efficiency in converting it  to ethanol (Yangcheng et al. 2013). China (26% of the waxy grains produced) was the largest exporter worth of US$  244  million, while Malaysia (6.78% of the waxy grains produced) was the largest importer worth of US$ 74 million in the year 2019. In the year 1935, Emersonk and colleagues discovered the mutant waxy1 (wx1) gene on the long arm of chromosome 9 (Neuffer et al. 1997). Subsequently, Sprague et al. (1943) discovered that the maize wx1 mutant lacks amylose. The 3718 bp long Wx1 gene is made of 14 exons and 13 introns (Huang et  al. 2010). Mutant wx1 alleles produced through variety of mutations in Wx1 gene caused by insertions of transposons in exons and untranslated regions inhibit the function of wild-type Wx1 gene causing lower amylose and high amylopectin in grains (Wessler et al. 1986; Liu et al. 2007; Zhang et al. 2013). Moreover, despite mutant wx1 gene, there was a moderate variation of 95–99% amylopectin across waxy germplasm. This could be explained by different modifier loci or QTL that govern the accumulation of different starch fractions in maize (Lin et al. 2019; Talukder et al. 2022a, b). Considering the significance of waxy corn in diet, nutritional quality enhancement of waxy maize is imperative (Table 1). Sinkangam et al. (2011) introgressed o2 gene into Kwi1 and Kwi9 waxy lines. Yang et al. (2013) introgressed recessive opaque16 (o16) gene on chromosome 8 into genetic background of two Chinese waxy lines, QCL5019 and QCL5008, from donor QCL3024, and observed improvement of 20% higher lysine content. Zhang et al. (2013) pyramided o2 and o16 in a waxy genotype and observed genotype with genetic constitution of wx1wx1/o2o2/ o16o16 possess 11% increased lysine compared o2o2 genotypes. Zhou et al. (2016) have also improved waxy inbreds by introgression of o2 gene and reported that 51.6% greater lysine content than the original waxy line (Zhao-OP-6/O2O2). Wang et al. (2019) also introgressed o2 gene into waxy line, QCL5013. Talukder et al. (2022a) have successfully stacked recessive wx1 and o2 genes in the parents of four popular QPM hybrids (HQPM1, HQPM4, HQPM5, and HQPM7) using genomics-­ assisted breeding approach. They reported that new hybrids thus developed had high lysine (0.384%), tryptophan (0.102%), and amylopectin (98.84%) in the endosperm.

1.4 High Amylose Maize Traditional maize possesses up to 30% of amylose, but high amylose maize has more than 50% of amylose. Resistant starch (RS) is one of the central applications of high amylose starch. RS takes more than 2  hours for its digestion as it is not digested in the small intestine, but fermentation by probiotic bacteria in the large intestine produces short-chain fatty acids (SCFA), mainly butyrate with some gases (Hu et  al. 2016). Because of very slow digestion, it has low calorific value of 8  Kilojoules/gram (kJ/g) when compared to completely digestible starch 15  kJ/g (Liversey 1994). “Diabetes mellitus-type2” is one of the most widespread lifestyle diseases affecting more than 366  million people and expected to affect

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552  million  people by 2030 across the world (Sami et  al. 2017). High-amylose starches have very low glycemic index (GI), which reduces postprandial blood glucose, thereby playing an important role in controlling type II diabetes (Reader et al. 1997). The GI of the high amylose maize is only 44 when compared to traditional maize with GI of 81 (Ai and Jane 2016). High amylose starches have the activity of hypocholestrolemic effect, which reduce total lipids, total cholesterol, and triglycerides (Hashimoto et al. 2006). Lowering insulin reduces not only cholesterol but also bilestone formations (Malhotra 1968). RS is also considered as prebiotic as it selectively stimulates the growth and activity of certain beneficial bacteria like Lactobacilli, Bifidobacteria, and Streptococci populations in the gastrointestinal tract (Silvi et al. 1999). General uptake of RS is only about 4 g/day, but 60–80 g of non-digestible starch is needed in a day to sustain beneficial microbe in bowel (Topping et al. 2003). Besides, RS has its role in absorption of minerals, reducing the gastrointestinal discomfort (laxative effect) and cardiovascular diseases, reducing diseases like shigellosis,  and also other wide applications in food industry (Lopez et al. 2001; Maki et al. 2009). Among the several genes listed in the starch biosynthesis pathway, the recessive amylose extender1 (ae1) located on chromosome 5 is regarded as the key gene that would double the amylose content to 50%. Homozygous recessive ae1 mutants lack activity of SBEIIb and are distributed over 17  kb region with 22 exons and 21 introns (Kramer et al. 1956; Kim et al. 1998). Due to such a large size, there are number of ae mutants like ae1.1 (Vineyard and Bear 1952), ae1.2 (ae1-Elmore) (Liu et al. 2012), ae-1-AE11 (Chen et al. 2013; Han et al. 2021), and a dominant ae1 mutant Ae1–5180 (Stinard et al. 1993). Maize starch with amylose content of more than 60% is a result of high-amylose modifier (HAM) genes in the maize ae1 background. One such modifier locus (Sbe1a) was mainly due to the loss of SBE1a activity present on chromosome 5 (Sidebottom et  al. 1998). It is also located on chromosome 5 on the short arm with a size of 9 kb. High amylose maize possesses special uses, but it is not under wide cultivation. However, considering its low GI, high amylose maize can serve as a suitable breakfast cereal especially for the diabetics. So far, there are no published reports on biofortification for high amylose maize. However, genes like o2, crtRB1, lcyE, and vte4 can be combined well with ae1 to develop high amylose maize with higher lysine, tryptophan, proA, and provitamin-E (proE) (Hossain et  al. 2021, 2022). Recently, research efforts at ICAR-IARI, New Delhi, have led to the development of high amylose maize inbreds with higher concentration of lysine, tryptophan, and proA. Enriching high amylose maize for anthocyanins will also help in providing desirable antioxidant activity required for healthy lifestyle.

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1.5 High Oil Maize Maize oil is an emerging commodity in commerce, due to its unique fitness aids such as a potential curative agent for inflammation and obesity (Dupont et al. 1990; Chai et al. 2011). It also plays irrefutable role in the reduction of blood pressure, oxidative damage, and most importantly in preventing cancer (Li et al. 2019). The global import of maize oil is valued US$ 663.27  million as compared to export value of US$ 641.31  million (FAOSTAT 2019). The production of maize oil increased from 2  mt in 2004 to 3.54  mt in 2019 (http://faostat.fao.org). America leads in the maize oil production with 2.17 mt (FAOSTAT 2019). The calorific value of maize oil is 2.25 times greater than that of starch on dry weight basis (Weber 1987; Alexender 1988). Therefore, feed efficiency and livestock productivity can be improved with increasing oil content in the maize (Han et al. 1987; Benitez et al. 1999; Lambert et al. 2004). Oil is also considered to be one such factor that enhances the bioavailability of fat-soluble vitamin A (Welch and Graham 2002) and vitamin E (Yang et al. 2017). Maize oil is well known for its high amount of polyunsaturated fatty acids (PUFAs), mainly high content of linoleic acid (C18:2) and oleic acid (C18:1) (Olmos et al. 2018). As a mixture, maize oil is composed of 80.5% unsaturated fatty acids, mostly oleic acid (C18:1: 28.3%) and linoleic acid (C18:2: 50.6%), while saturated fatty acids constitute 19.5% of the total proportion with palmitic acid (12.1%) as a major one (Lambert 2001; Kim et al. 2009; Dauqan et al. 2011; Zheng et al. 2014). High stability of maize kernel oil is attributed to low level of linolenic acid (1.2%) relative to 47.4% in linseed oil, 13.0% in conventional canola oil, and 7.8% in soybean oil (White and Weber 2003; Kim et al. 2009; Shen et al. 2010). Higher amount of ubiquinone and vitamin E enhance shelf life of corn oil and improve the quality of oil by reducing the absorption of cholesterol from the food (Dupont et al. 1990; Val et al. 2009). Generally, modern maize cultivars possess 3–4% of oil, whereas high oil corn contains >6% of oil (Lambert 2001). Maize oil is mainly confined to the germ, that is, 85% of total kernel oil (Shende and Sidhu 2014). The remaining oil quantity is accumulated in the endosperm and hull. Further, high oil maize has also  increased concentration of carotenoids, which are considered important for poultry industry (Singh et al. 2016). Maize oil content and quality are highly heritable traits, but these are governed by complex and polygenic inheritance (Dudley 1977). Previous investigations reported  that high oil trait is highly associated with size of embryo and elevated embryo oil content (Lambert 2001; Dudley and Lambert 2004). QTL analysis by various researchers across the globe reported that more than 50 QTLs were involved in the oil biosynthesis, each with small, similar, and cumulative effects (Dudley 1977; Clark et al. 2006). The majority of the QTLs for oil content and quality is located on chromosomes 2, 5, 6, and 9 (Langade et al. 2013). A major QTL (qHO6) affecting maize seed oil and oleic acid content has been reported by Zheng et al. (2008). Using RIL population, dgat1–2 gene (chromosome 6) under a QTL affecting seed oil and oleic acid content has been identified (Zheng et al. 2008; Yang et al.

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2010). The mutant dgat1–2 causes an increment of 41% of seed oil content and 107% of oleic acid content. Biparental mapping studies indicated that three QTLs for oleic acid, namely, ole6–1 (bin 6.04), ole6–2 (bin 6.04), and ole6–3 (bin 6.05), have been mapped proximal to dgat1–2 (Yang et  al. 2010; Olmos et  al. 2018). Another major QTL affecting oil composition (QTL-Pal9) was mapped to 90-kb region on chromosome 9 in maize using biparental segregating population, and it explains 42% of the phenotypic variation for palmitic acid content in maize grains (Yang et al. 2010). Further, fine mapping via linkage and association analyses for this QTL led to identification of fatb gene, which encodes acyl-ACP thioestersase (Li et al. 2011). Recently, Katral et al. (2022a) identified wide genomic variability in fatb among the inbreds. Other genes, namely, ZmSAD1, ZmLEC1, ZmWR11a, ZmGE2, Zmfad2, ACP, COPII, and LACS, were also responsible for oil content and composition. Since oil enhances the bioavailability of vitamin A and vitamin E, introgression of crtRB1, lcyE, and vte4 genes would enhance these vitamins. Chai et al. (2011) reported increment of oil content by 37% through the introgression of DGAT1-2 in to elite lines Zheng58 and Chang7-2. Katral et  al. (2022b) recently have introgressed dgat1-2 and fatb genes into the elite maize parents having o2, crtRB1, lcyE,  and vte4 genes. These inbreds with high oil, lysine, tryptophan, proA, and proE would play an important role in providing healthy diets to humans as well as poultry birds.

1.6 Colored Corn Color has a positive significant impact on the acquirement of food as they show a significant role in desirability, marketing, and consumption of products. Maize grain naturally displays spectrum of kernel colors among the collections of landraces (Calzada and Padilla 2009). However, white and yellow color predominates over orange, blue, red, purple, brown, or black color among the Mexican genotypes (Trujillo et  al. 2009). Recently, colored corn, besides fruits and vegetables, has emerged as an additional source of anthocyanins, polyphenols, and related phytochemicals (Paulsmeyer et al. 2017). Maize with yellow endosperm and dark seed coat containing both carotenes and anthocyanins was proposed as an improved version of purple colored maize and considered as an alternate organic superfood (Peniche-Pavia and Tiessen 2020). In addition, these colors are preferred for their aesthetics, which led to its popularity among researchers and companies as they explore alternate novel food for maize consumers (Toufektsian et al. 2008; Petroni et al. 2014). Among the colored corns, purple corn is widely consumed and emerging as a special cultivar in Andean countries especially in Peru (Aoki et al. 2002; Jones 2005). Colored corns contain phytochemicals including anthocyanin, phenolics, and carotenoids, which provide health benefits with antioxidant and bioactive properties (Heinonen et al. 1998; Setchell and Aedin 1999). Intake of these compounds was reported to have healthy benefits such as anticancer and

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anti-­inflammatory effects (Norberto et  al. 2013). Purple-, blue-, and red-colored maize have free radical scavenging activities and potentially inhibit colorectal cancer cell proliferation in human by encouraging apoptosis and negatively regulating angiogenesis (Hagiwara et al. 2001; Aoki et al. 2002; Mazewski et al. 2017). Recent study also that dark purple-colored kernels and red pink-colored kernels contain cyanidin-­based anthocyanin and pelargonidins as the most predominant components, respectively (Peniche-Pavia and Tiessen 2020). Anthocyanins were found to be anti-inflammatory and antidiabetic. Purple-colored maize had the highest antioxidant capacity as compared to red, yellow, and white maize. It was found that antioxidant capacity and germination speed were negatively correlated (Deng et al. 2015). Among 12 genotypes, purplish black-colored waxy corn genotype kernel was found to have highest anthocyanin content and antioxidant activity at both milky stage and mature stage (Harakotr et  al. 2014). In maize, anthocyanins are most often accrued either in pericarp or aleurone of the pigmented seeds. Therefore, the corn can be categorized as “aleurone-pigmented” or “pericarp-pigmented” corn based on accumulation of the pigment at particular position in the seed (Chatham et al. 2019). Biosynthesis of the anthocyanidin glucosides and their linkage with different structural genes have been well characterized. It has been determined that A1 gene in maize is important in the synthesis of anthocyanins (Holton and Cornish 1995). Leucoanthocyanidins can be transformed to trans flavan-3-ols or converted to anthocyanidins by the anthocyanidin synthase encoded by A2 gene in maize. The E183K mutation in maize chalcone synthase C2 located on chromosome 4 was identified as the cause of colorlessness using the MutMap approach (Dong et al. 2021). Purple aleurone1 (Pr1) on chromosome 5 is considered most vital, which involved in synthesis of eriodictyol and dihydroquercetin (Sharma et al. 2011). The activity of pr1 has received special attention in the food industry as they largely regulates the color of anthocyanin extracts. pr1 locus mutations result in the accretion of almost entirely pelargonidin-based anthocyanins in maize aleurone as functional Pr1 is crucial for synthesis of cyanidin-based derivatives (Chatham et al. 2019). The C locus located on chromosome 9 of maize controls the color of aleurone tissue of kernels. Dominant alleles CI and C are responsible for colorless and colored, respectively, while the recessive c allele produces colorless aleurone (Coe Jr 1962). Mutation in Bronze1 on chromosome 9 and Bronze2 on chromosome 1 in maize results in the accumulation of bronze-colored products (Marrs et al. 1995). In maize, C1 (Colorless1) on chromosome 9 is conditioned by activity in aleurone and scutellum, whereas Pl1 (Purple plant1) on chromosome 6 is recorded in vegetative tissues as the site for anthocyanin biosynthesis (Petroni and Tonelli 2011). The bHLHs (basic helix-loop-­ helix) present in the factors, namely, R1 (Red color1) on chromosome 10 and B1 (Booster1) on chromosome 2, generally regulates the accumulation of anthocyanin in the seed and plant tissues, respectively, with some overlapping expression in other parts of the tissues (Chatham et  al. 2019). Mutation in the Pale aleurone colour1 (Pac1) gene on chromosome 5 in the aleurone is required for total activation of anthocyanin biosynthesis genes without altering pigmentation in vegetative organs (Selinger and Chandler 1999). Another locus that conditions aleurone and

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the seedling color is R locus on chromosome 10. R’ allele conditions red seedling with colored aleurone and its modified forms Rst and Rmb conditions stippled aleurone (Brink 1956) and marbled aleurone (Brink and Weyers 1957), respectively. As many as ten genes were hypothesized to be involved in controlling the blue color aleurone trait (Betran et al. 2000). Another sort of pigmentation caused by phlobaphenes was reported to be under monogenic control of Pericarp1 (P1) gene located on chromosome 1 (Cassani et al. 2017). Colored sweet corn has been improved for kernel tryptophan by combining o2 with genes responsible for anthocyanin coloration (Inplean et al. 2020). The tryptophan was about 1%, while total anthocyanin content ranged from 259–557 mg/100 g in red kernel genotypes. The pelargonidin-3-glucoside was the anthocyanin component. A tryptophan range of 0.88–0.98% and total anthocyanin range of 144–364  mg/100  g were observed among F1 hybrids, which are significantly increased than check hybrids (Inplean et al. 2020). Jompuk et al. (2020) developed anthocyanin- and tryptophan-rich sweet corn by combining Pr1, C1, and o2 genes. The purple sweet corn possessed 30% higher tryptophan content and a ten-fold higher anthocyanin content than yellow kernel maize.

1.7 Baby Corn Baby corn is unpollinated form of ear where young, unfertilized, and tender cobs (80% moisture) are harvested (Pal et al. 2020). It is harvested at silk emergence stage, and dehusked cobs are consumed either as fresh or canned vegetable. Nutritionally, it possesses higher levels of folate; vitamins B, A, and C; and minerals such as P, K, Ca, Zn, and Fe, besides it is a richest source of P (Babu et al. 2020). Its by-products like tassel, silk, husk, and green stalk are used as an important cattle feed (Singh et al. 2019). Thailand is the largest baby corn exporter with 80% of world exports of the product. Thailand exports more canned baby corn than fresh baby corn. The major importers are United States, Japan, Netherlands, and Taiwan. The presence of more than one ear per plant is called “prolificacy” is the important breeding objective (Singh et al. 2019). Numerous factors, namely, initiation of axillary primordia, growth of axillary bud, branch elongation, female floret development, and ear development, are responsible for prolificacy, which in turn are controlled by polygenes (Prakash et al. 2019). Out of the eight QTLs identified by Wills et al. (2013), one QTL (prol1.1) located on chromosome 1 accounted for the highest phenotypic variance (36.7%). Fine mapping of prol1.1 to a 2.7  kb revealed the “causative region” present 7.5 kb upstream of a grassy tillers1 (gt1) on short arm of chromosome 1 (Wills et al. 2013). In India, “Sikkim Primitive” is a prolific maize landrace with five to nine ears per plant. Recently, using bulked-segregant analysis approach, Prakash et  al. (2021) have identified and validated a novel QTL (bin: 8.05) “qProl-SP-8.05” explaining 31.7% phenotypic variation governing the prolificacy in this landrace. Six candidate genes responsible for prolificacy were also

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identified. Besides, teosinte branched1 (tb1) has also been identified as the key gene conferring prolificacy trait in teosinte (Doebley et al. 1995, 1997; Doebley 2004). Haplotype analysis between maize and teosinte revealed that insertion of Hopscotch retro-transposon is the key to the difference in prolificacy (Vann et al. 2015). Thus, tb1 allele of teosinte without insertion of Hopscotch is a suitable candidate for enhancing the prolificacy in maize. Male sterility in baby corn saves the process of manual detasseling and thus saves lots of resources. Cytoplasmic male sterility (CMS) systems can be exploited to develop male sterile baby corn. CMS is a trait with maternal inheritance that governs the production of nonfunctional pollen grain or inability of pollen production (Rhoades 1933). There are three types of CMS systems available in maize, namely, CMS-T (Texas), CMS-C (Charrua), and CMS-S (USDA) (Schnable and Wise 1998). In maize, the genes responsible for the male sterility are present in mitochondria and lead to breakdown of the functional pollen production (Bohra et  al. 2016). Chimeric copies of Turf13 gene, atp6-C, and a repeated DNA region “R” containing two orfs in mtDNA are the male sterility causative regions of CMS-T, CMS-C, and CMS-S systems, respectively (Zabala et al. 1997). Few investigations were available dealing with the nutritional profiling of baby corn worldwide. Each 100 g of baby corn ears is comprised of calcium (28.00 mg), phosphorus (86.00  g), iron (0.10  mg), thiamine (0.05  mg), riboflavin (0.08  mg), ascorbic acid (11.00 mg), and niacin (0.03 mg) (Yodpet 1979). Bar-Zur and Schaffer (1993) analyzed total sugar content in baby corn ears and observed a range of 20–30  mg/g FW across the genotypes. The reducing sugars such as glucose and fructose were present in about equal amounts (10–15 mg/g FW), while sucrose was present at silking stage in minute quantity. Moreover, the presence of 17 amino acids with 9 essential amino acids has been identified in the baby corn (Yu et al. 1993; Hooda and Kawatra 2013). Prasanthi et al. (2017) reported that 100 g raw baby corn ears possessed 1.68 g protein, 0.67 g fat, and 1.14 g carbohydrate. With the discovery of genes/QTLs for many of these nutritional traits, it would be straightforward to integrate the genetic improvement of nutritional quality in baby corn breeding program.

2 Challenges and Future Prospects Biofortified specialty corn has tremendous implication for food and nutritional. However, there are many tasks for successful adoption and popularization of biofortified maize on large scale (Gupta et al. 2015, 2019; Yadava et al. 2018; Hossain et  al. 2019b, c, 2021, 2022). The genetic base of biofortified maize needs to be widened by developing more diverse hybrids adapted to diverse ecology of the country. Under continuous emerging biotic and abiotic stresses, biofortified maize should sustain the high grain yield; thus, development of climate resilient biofortified maize is very essential. Popularization of biofortified maize varieties through large-scale demonstrations at farmers’ field needs to be undertaken in a big way.

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Participatory seed production program for large-scale quality seed production of biofortified hybrids must be undertaken to meet the increasing demand of quality seeds (Hossain et al. 2021, 2022). Awareness campaigns also need to be organized to highlight the importance of biofortified food crops. This would play crucial role in commercialization of crops and enhancing farmers’ income who are growing the biofortified crops. Belief of low yield of biofortified maize needs to be altered. The yield potential of biofortified maize varieties is at par with normal varieties and, therefore, adoption of biofortified maize needs to be encouraged. The health benefits of biofortified maize have been well documented through scientific investigations. Hence, extension organizations should reach to the villagers/consumers for promotion of biofortified maize technologies and its adoption. Sensitization of family heads for acceptance of biofortified maize needs to be encouraged to alleviate malnutrition. Similarly, preference of biofortified varieties as animal feed for poultry birds and pigs is very essential to enhanced for quantitative and qualitative meat and egg production and its subsequent net returns. Linkages with the poultry sector, policy intervention to popularize biofortified crops, supporting industries through subsidies and loans, premium price to biofortified produce over available traditional produce, and inclusion of biofortified foods in different government sponsored schemes are some of the measures to enhance the production and consumption of biofortified maize (Hossain et al. 2022).

References Adunola PM (2017) Introgression of opaque2 gene into the genetic background of popcorn using marker assisted selection. Master’s thesis, Federal University of Technology, Akure Ai Y, Jane J (2016) Macronutrients in corn and human nutrition. Compr Rev Food Sci Food Saf 15:581–598 Alexender DE (1988) Breeding special nutritional and industrial types. In: Sprague GF, Dudley JW (eds) Corn and corn improvement, 3rd edn. Crop Science Society of America, Madison, pp 869–880 Aoki H, Kuze N, Kato Y et al (2002) Anthocyanins isolated from purple corn (Zea mays L.). Foods Food Ingred J Japan 199:41–45 Babu R, Nair S, Kumar A et al (2006) Mapping QTLs for popping ability in a popcorn × flint corn cross. Theor Appl Genet 112:1392–1399 Babu S, Raghavendra S, Avasthe RK et al (2020) Impact of land configuration and organic nutrient management on productivity, quality and soil properties under baby corn in Eastern Himalayas. Sci Rep 10:16129 Bao JD, Yao JQ, Zhu JQ et al (2012) Identification of glutinous maize landraces and inbred lines with altered transcription of waxy gene. Mol Breed 30:1707–1714 Bar-Zur A, Schaffer A (1993) Size and carbohydrate content of ears of baby corn in relation to endosperm type (Su, su, se, sh2). J Am Soc Hortic Sci 118:118–141 Baveja A, Muthusamy V, Panda KK et al (2021) Development of multinutrient-rich biofortified sweet corn hybrids through genomics-assisted selection of shrunken2, opaque2, lcyE and crtRB1 genes. J Appl Genet 62(3):419–429 Baveja A, Chhabra R, Panda KK et  al (2022) Expression analysis of opaque2, crtRB1 and shrunken2 genes during different stages of kernel development in biofortified sweet corn. J Cereal Sci 105:103466

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F. Hossain et al.

Benitez J, Gernat A, Murillo J et  al (1999) The use of high oil corn in broiler diets. Poult Sci 78:861–865 Betran FJ, Bockholt AJ, Rooney LW (2000) Blue corn. In: Specialty corns. CRC Press, pp 305–314 Bohra A, Jha UC, Adhimoolam P et al (2016) Cytoplasmic male sterility (CMS) in hybrid breeding in field crops. Plant Cell Rep 35:967–993 Boyer C, Hannah L (2001) Kernel mutants of corn. In: Specialty corns, 2nd edn. Hallauer, pp 1–32 Brink RA (1956) A genetic change associated with the R locus in maize which is directed and potentially reversible. Genetics 41:872 Brink RA, Weyers WH (1957) Invariable genetic change in maize plants heterozygous for marbled aleurone. Proc Natl Acad Sci U S A 43:1053 Calzada JP, Padilla J (2009) The Mexican landraces: description, classification and diversity. In: Handbook of maize: its biology. Springer, pp 543–561 Cassani E, Puglisi D, Cantaluppi E et al (2017) Genetic studies regarding the control of seed pigmentation of an ancient European pointed maize (Zea mays L.) rich in phlobaphenes: the “Nero Spinoso” from the Camonica valley. Genet Resour Crop Evol 64:761–773 Chai Y, Hao X, Yang X et al (2011) Validation of DGAT1-2 polymorphisms associated with oil content and development of functional markers for molecular breeding of high-oil maize. Mol Breed 29:939–949 Chatham LA, Paulsmeyer M, Juvik JA (2019) Prospects for economical natural colorants: insights from maize. Theor Appl Genet 132(11):2927–2946 Chauhan HS, Chhabra R, Rashmi T et al (2021) Impact of vte4 and crtRB1 genes on composition of vitamin-E and provitamin-a carotenoids during kernel-stages in sweet corn. J Food Compos Anal 105:104264 Chauhan HS, Zunjare RU, Rashmi T et al (2022) Accelerated development of vitamin-a and vitamin-­E rich sweet corn hybrids through marker-assisted introgression of crtRB1 and vte4 genes. In: Abstracts of 43rd annual meeting of PTCA(I) & international symposium on “advances in plant biotechnology and nutritional security” APBNS-2022, p 217, 28–30 Apr 2022 Chen TT, Ning LH, Liu X et al (2013) Development of functional molecular markers of SbeI and SbeIIb for the high amylose maize germplasm line GEMS-0067. Crop Sci 53:482–490 Chhabra R, Hossain F, Muthusamy V et al (2019a) Mapping and validation of Anthocyanin1 pigmentation gene for its effectiveness in early selection of shrunken2 gene governing kernel sweetness in maize. J Cereal Sci 87:258–265 Chhabra R, Hossain F, Muthusamy V et al (2019b) Development and validation of breeder-friendly functional markers of sugary1 gene encoding starch-debranching enzyme (DBE) involved in enhancing kernel sweetness in maize. Crop Pasture Sci 70:868–875 Chhabra R, Hossain F, Muthusamy V et al (2020) Development and validation of gene-based markers for shrunken2-reference allele and their utilization in marker-assisted sweet corn (Zea mays sachharata) breeding programme. Plant Breed 139:1135. https://doi.org/10.1111/pbr.12872 Chhabra R, Muthusamy V, Gain N et al (2021) Allelic variation in sugary1 gene affecting kernel sweetness among diverse-mutant and wild-type maize inbreds. Mol Gen Genomics 23:1–18 Clark D, Dudley JW, Rocheford TR et al (2006) Genetic analysis of corn kernel chemical composition in the random mated 10 generation of the cross of generations 70 of IHO × ILO. Crop Sci 46:807 Coe EH Jr (1962) Spontaneous mutation of the aleurone color inhibitor in maize. Genetics 47:779 Collins NE, Moran ET, Stilborn HL (2003) Performance of broilers feed normal and waxy corn diets formulated with chick and rooster derived apparent metabolizable energy values for the grains. J Appl Poult Res 12:196–206 da Silva W, Vidal B, Martins M et al (1993) What makes popcorn pop. Nature 362(6419):417 Das AK, Gowda MM, Muthusamy V et al (2021) Development of maize hybrids with enhanced vitamin-E, vitamin-A, lysine and tryptophan through molecular breeding. Front Plant Sci 12:659381 Dauqan EM, Abdullah A, Sani HA et al (2011) Natural antioxidants, lipid profile, lipid peroxidation, antioxidant enzymes of different vegetable oils. Adv J Food Sci Technol 3:308–316

Genetic Improvement of Specialty Corn for Nutritional Quality Traits

251

Deng B, Yang K, Zhang Y et al (2015) The effects of temperature on the germination behavior of white, yellow, red and purple maize plant seeds. Acta Physiol Plant 37(8):1–11 Devi EL, Hossain F, Muthusamy V et  al (2017) Microsatellite marker-based characterization of waxy maize inbreds for their utilization in hybrid breeding. 3 Biotech 7:316. https://doi. org/10.1007/s13205-­017-­0946-­8 Dhaliwayo T (2008) Genetic mapping and analysis of traits related to improvement of popcorn. Ph.D. dissertation, Iowa State University, Ames, Iowa Doebley JF (2004) The genetics of maize evolution. Annu Rev Genet 38:37–59 Doebley J, Stec A, Gustus C (1995) Teosinte branched1 and the origin of maize: evidence for epistasis and the evolution of dominance. Genetics 141(1):333–346 Doebley J, Stec A, Hubbard L (1997) The evolution of apical dominance in maize. Nature 386:485–488 Dong Y, Zang Z, Shi Q et al (2012) Quantitative trait loci mapping and meta-analysis across three generations for popping characteristics in popcorn. J Cereal Sci 56:581–586 Dong H, Li H, Xue Y et al (2021) E183K mutation in Chalcone synthase C2 causes protein aggregation and maize colorless. Front Plant Sci 12:679654. https://doi.org/10.3389/fpls.2021.679654 Dudley JW (1977) Seventy-six generation of selection for oil and protein percentage in maize. In: Pollak E (ed) Proceedings of international conference on quantitative genetics. Iowa State University Press, Ames, pp 459–473 Dudley JW, Lambert RJ (2004) 100 generations of selection for oil and protein in corn. Plant Breed Rev 24:79–110 Dupont J, White PJ, Carpenter MP et al (1990) Food uses and health effects of corn oil. J Am Coll Nutr 9:438–470 FAO, IFAD, UNICEF, WFP and WHO (2021) The state of food security and nutrition in the world 2021. In: Transforming food systems for food security, improved nutrition and affordable healthy diets for all. FAO, Rome FAOSTAT (2019) Trade/crops and livestock products. http://www.fao.org/faostat/en/#data/TP FAOSTAT (2020) Trade/crops and livestock products. http://www.fao.org/faostat/en/#data/TP FAS-USDA (2021) Foreign agricultural service-USDA. Office of global analysis. https://ipad.fas. usda.gov Feng ZL, Liu J, Fu FL et al (2008) Molecular mechanism of sweet and waxy in maize. Int J Plant Breed Genet 2:93–100 Feng F, Wang Q, Liang C (2015) Enhancement of tocopherols in sweet corn by marker-assisted backcrossing of ZmVTE4. Euphytica 206. https://doi.org/10.1007/s10681-­015-­1519-­8 Fisher MB, Boyer C (1983) Immunological characterization of maize starch branching enzymes. Plant Physiol 72:813–816 Fukunaga K, Kawase M, Kato K (2002) Structural variation in the Waxy gene and differentiation in foxtail millet [Setaria italica (L.) P. Beauv.]: implications for multiple origins of the waxy phenotype. Mol Genet Genomics 268:214–222 Global Hunger Index (2021) Hunger and food systems in conflict settings. Welthungerhilfe/ Concern Worldwide, Bonn/Dublin Global Nutrition Report (2018) Shining a light to spur action on nutrition. Development Initiatives, Briston, UK Global Nutrition Report (2020) Action on equity to end malnutrition. Development Initiatives, Bristol, UK Gupta HS, Hossain F, Muthusamy V (2015) Biofortification of maize: an Indian perspective. Indian J Genet 75:1–22 Gupta HS, Hossain F, Muthusamy V et al (2019) Marker-assisted breeding for enrichment of provitamin a in maize. In: Qureshi AMI (ed) Quality breeding in field crops. Springer, Basel, pp 139–157 Hagiwara A, Miyashita K, Nakanishi T et  al (2001) Pronounced inhibition by a natural anthocyanin, purple corn color, of 2-amino-1-methyl-6-phenylimidazo [4, 5-b] pyridine (PhIP)associated colorectal carcinogenesis in male F344 rats pretreated with 1, 2-dimethylhydrazine. Cancer Lett 171:17–25

252

F. Hossain et al.

Han Y, Parsons C, Alexander D (1987) Nutritive value of high oil corn for poultry. Poult Sci 66:103–111 Han N, Li W, Xie C et al (2021) Breeding potential of maize germplasm line GEMS-0067 for high amylose proportion. Preprints. https://doi.org/10.20944/preprints202105.0560.v1 Harakotr B, Suriharn B, Tangwongchai R et al (2014) Anthocyanins and antioxidant activity in coloured waxy corn at different maturation stages. J Functl Food 9:109–118 Hashimoto N, Ito Y, Han KH et al (2006) Potato pulps lowered the serum cholesterol and triglyceride levels in rats. J Nutri Sci Vitaminol 52:445–450 Heinonen IM, Meyer AS, Frankel EN (1998) Antioxidant activity of berry phenolics on human low-density lipoprotein and liposome oxidation. J Agric Food Chem 46:4107–4112 Holton TA, Cornish EC (1995) Genetics and biochemistry of anthocyanin biosynthesis. Plant Cell 7:1071–1083 Hooda S, Kawatra A (2013) Nutritional evaluation of baby corn (Zea mays). Nutri Food Sci 43:68–73 Hoseney RC, Zeleznak K, Abdelrahman A (1983) Mechanism of popcorn popping. J Cereal Sci 1:43–52 Hossain F, Nepolean T, Vishwakarma AK et al (2015) Mapping and validation of microsatellite markers linked to sugary1 and shrunken2 genes in maize. J Plant Biochem Biotechnol. https:// doi.org/10.1007/s13562-­013-­0245-­3 Hossain F, Chhabra R, Devi EL et al (2019a) Molecular analysis of mutant granule-bound starch synthase-I (waxy1) gene in diverse waxy maize inbreds. 3 Biotech 9:3 Hossain F, Konsam S, Muthusamy V et al (2019b) Quality protein maize for nutritional security. In: Qureshi AMI et al (eds) Quality breeding in field crops. Springer, Switzerland, pp 217–237. https://doi.org/10.1007/978-­3-­030-­04609-­5_11 Hossain F, Muthusamy V, Zunjare RK, Gupta HS (2019c) Biofortification of maize for protein quality and provitamin-a content. In: Jaiwal PK, Chhillar AK, Chaudhary D, Jaiwal R (eds) Nutritional quality improvement in plants. Springer, pp  115–136. https://doi. org/10.1007/978-­3-­319-­95354-­0_5 Hossain F, Rakshit S, Kumar B et al (2021) Molecular breeding for increasing nutrition quality in maize: recent progress. https://doi.org/10.1079/9781789245431.0021 Hossain F, Zunjare R, Muthusamy V et al (2022) Biofortification of maize for nutritional security. https://doi.org/10.1007/978-­981-­16-­3280-­8_6 Hu L, Zheng Y, Peng Y et al (2016) The optimization of isoamylase processing conditions for the preparation of high-amylose ginkgo starch. Int J Biol Macromol 86:105–111 Huang BQ, Tian ML, Zhang JJ et al (2010) Waxy locus and its mutant types in maize (Zea mays L). Agric Sci China 9:1–10 Inplean C, Jompuk C, Chai-Aree W et al (2020) Improved sugar content in a sweet corn grain mutant with high quality protein and anthocyanin. Agric Nat Resour 54:553–558 Jompuk C, Jitlaka C, Jompuk P et al (2020) Combining three grain mutants for improved quality sweet corn. Agric Environ Lett:5. https://doi.org/10.1002/ael2.20010 Jones K (2005) The potential health benefits of purple corn. Herb Gram 65(2):46–49 Katral A, Muthusamy V, Zunjare RU et al (2022a) Allelic variation in Zmfatb gene defines variability for fatty acids composition among diverse maize genotypes. Front Nutr 9:845255. https://doi.org/10.3389/fnut.2022.845255 Katral AK, Muthusamy V, Hossain F et  al (2022b) Enrichment of kernel oil through marker-­ assisted introgression of dgat1 and fatb genes in elite multi-nutrient rich maize inbreds. 43rd annual meeting of PTCA(I) & international symposium on “advances in plant biotechnology and nutritional security” APBNS-2022, 28–30 Apr 2022 Kaur S, Rakshit S, Choudhary M et  al (2021) Meta-analysis of QTLs associated with popping traits in maize (Zea mays L.). PLoS One 16(8):e0256389 Kim KN, Fisher DK, Gao M et al (1998) Molecular cloning and characterization of the amylose extender gene encoding starch branching enzyme IIB in maize. Plant Mol Biol 38:945–956

Genetic Improvement of Specialty Corn for Nutritional Quality Traits

253

Kim J, Kim DN, Lee SH et al (2009) Correlation of fatty acid composition of vegetable oils with rheological behavior and oil uptake. Food Chem 118:398–402 Kimura A, Robyt JF (1995) Reaction of enzymes with starch granules; kinetics and products of the reaction with glucoamylase. Carbohydr Res 277:87–107 Kramer HH, Whistler RL, Anderson EG et al (1956) A new gene interaction in the endosperm of maize. Agron J 48:170–172 Lago C, Landoni M, Cassani E et al (2013) Study and characterization of a novel functional food: purple popcorn. Mol Breed 31(3):575–585 Lambert RJ (2001) High-oil corn hybrids. In: Hallau AR (ed) Special corn. CRC Press, Boca Raton, pp 131–153 Lambert RJ, Alexander D, Mejaya I (2004) Single kernel selection for increased grain oil in maize synthetics and high-oil hybrid development. Plant Breed Rev 24:153–175 Langade DM, Shahi JP, Agrawal VK et al (2013) Maize as emerging source of oil in India: an overview. Maydica 58:224–230 Lertrat K, Pulam T (2007) Breeding for increased sweetness in sweet corn. Int J Plant Breed 1:27–30 Li YL, Dong YB, Niu SZ (2006) QTL analysis of popping fold and the consistency of QTLs under two environments in popcorn. Acta Genet Sin 33:724–732 Li Y, Dong Y, Niu S et  al (2007) QTL for popping characteristics in popcorn. J Plant Breed 126:509–514 Li YL, Dong YB, Niu SZ et al (2008) The genetic relationship between popping expansion volume and two yield components in popcorn using unconditional and conditional QTL analysis. Euphytica 162:345–351 Li Y, Dong Y, Niu S et al (2009) Identification of QTL for popping characteristics using a BC2F2 population and comparison with its F2:3 population in popcorn. Agric Sci China 8(2):137–143 Li L, Li H, Li Q et  al (2011) An 11-bp insertion in Zea mays fatb reduces the palmitic acid content of fatty acids in maize grain. PLoS One 6(9):e24699. https://doi.org/10.1371/journal. pone.0024699 Li H, Thrash A, Tang JD et al (2019) Leveraging GWAS data to identify metabolic pathways and networks involved in maize lipid biosynthesis. Plant J 98:853–863 Li J, Li D, Espinosa C et al (2021) Genome-wide analyses reveal footprints of divergent selection and popping-related traits in CIMMYT’s maize inbred lines. J Exp Bot:72. https://doi. org/10.1093/jxb/eraa480 Lin F, Zhou L, He B et al (2019) QTL mapping for maize starch content and candidate gene prediction combined with co-expression network analysis. Theor Appl Genet 132:1931–1941 Liu J, Rong T, Li W (2007) Mutation loci and intragenic selection marker of the granule-bound starch synthase gene in waxy maize. Mol Breed 20:93–102 Liu F, Ahmed Z, Lee EA et al (2012) Allelic variants of the amylose extender mutation of maize demonstrate phenotypic variation in starch structure resulting from modified protein-protein interactions. J Exp Bot 63:1167–1183 Liversey G (1994) Energy value of resistant starch. Proceedings of the concluding plenary meeting of Euresta, Wageningen, The Netherlands, pp 56–62 Lopez HW, Verny MA, Coudray C et al (2001) Class 2 resistant starches lower plasma and liver lipids and improve mineral retention in rats. J Nutr 131:1283–1289 Lu D, Lu W (2012) Effects of protein removal on the physico-chemical properties of waxy maize flours. Starch-Starke 64:874–881 Lu HJ, Bernardo R, Ohm HW (2003) Mapping QTL for popping expansion in popcorn with simple sequence repeat markers. Theor Appl Genet 106:423–427 Maki KC, Sanders LM, Reeves MS et al (2009) Beneficial effects of resistant starch on laxation in healthy adults. Int J Food Sci Nutr 60:296–305 Malhotra SL (1968) Epidemiological study of cholelithiasis among rail road workers in India. Gut 9:290–295 Marrs KA, Alfenito MR, Lloyd AM (1995) A glutathione S-transferase involved in vacuolar transfer encoded by the maize gene Bronze-2. Nature 375(6530):397–400

254

F. Hossain et al.

Mazewski C, Liang K, de Mejia EG et  al (2017) Inhibitory potential of anthocyanin-rich purple and red corn extracts on human colorectal cancer cell proliferation in vitro. J Fun Foods 34:254–265 McAuliffe S, Ray S, Fallon E et al (2020) Dietary micronutrients in the wake of COVID-19: an appraisal of evidence with a focus on high-risk groups and preventative healthcare. BMJ Nutr Prev Health 3:93. https://doi.org/10.1136/bmjnph-­2020-­000100 Mehta BK, Hossain F, Muthusamy V et al (2017a) Analyzing the role of sowing and harvest time as factors for selecting super sweet (sh2sh2) corn hybrids. Indian J Genet Plant Breed 77:348–356 Mehta BK, Hossain F, Muthusamy V et al (2017b) Analysis of responses of novel double mutant (sh2sh2/su1su1) sweet corn hybrids for kernel sweetness under different sowing-and harvest-­ time. Indian J Agric Sci 87:1543–1548 Mehta B, Hossain F, Muthusamy V et al (2018) Microsatellite-based genetic diversity analyses of sugary1-, shrunken2- and double mutant- sweet corn inbreds for their utilization in breeding programme. Physiol Mol Biol Plants 23(2):411–420 Mehta BK, Muthusamy V, Zunjare RU et  al (2020a) Biofortification of sweet corn hybrids for provitamin-a, lysine and tryptophan using molecular breeding. J Cereal Sci 96:103093 Mehta BK, Muthusamy V, Baveja A et al (2020b) Composition analysis of lysine, tryptophan and provitamin-a during different stages of kernel development in biofortified sweet corn. J Food Compos Anal 94:103625 Metzger DD, Hsu KH, Ziegler KE et al (1989) Effect of moisture content on popcorn popping volume for oil and hot-air popping. Cereal Chem 66:247–248 Muthusamy V, Hossain F, Thirunavukkarasu N et al (2014) Development of β-carotene rich maize hybrids through marker-assisted introgression of β-carotene hydroxylase allele. PLoS One 9. https://doi.org/10.1371/journal.pone.0113583 Neeraja CN, Babu VR, Ram S et al (2017) Biofortification in cereals: progress and prospects. Curr Sci 113:1050–1057 Neuffer MG, Coe EH, Wessler SR (1997) Mutants of maize. Cold Spring Harbor Laboratory Press, New York, p 63 Norberto S, Silva S, Meireles M et al (2013) Blueberry anthocyanins in health promotion: a metabolic overview. J Fun Foods 5:1518–1528 O’Hare TJ, Fanning KJ, Martin IF (2015) Zeaxanthin biofortification of sweet-corn and factors affecting zeaxanthin accumulation and colour change. Arch Biochem Biophys 572:184–187 Olmos SE, Trejo JM, Percibaldi NM et al (2018) Genetic diversity near the DGAT1-2 gene for high oleic acid content and kernel trait variation in a maize breeding collection. Mol Breed 38:1–15 Pal S, Zunjare RU, Muthusamy V et al (2020) Influence of T-, C- and S- cytoplasms on male sterility and their utilisationin baby corn hybrid breeding. Euphytica 216:146 Parsons L, Ren Y, Yobi A et al (2020) Production and selection of quality protein popcorn hybrids using a novel ranking system and combining ability estimates. Front Plant Sci 11:698 Parsons L, Ren Y, Yobi A et al (2021) Final selection of quality protein popcorn hybrids. Front Plant Sci 12:658456 Paulsmeyer M, Chatham L, Becker T et al (2017) Survey of anthocyanin composition and concentration in diverse maize germplasms. J Agric Food Chem 65:4341–4350 Peniche-Pavia HA, Tiessen A (2020) Anthocyanin profiling of maize grains using DIESI-MSQD reveals that cyanidin-based derivatives predominate in purple corn, whereas pelargonidin-­ based molecules occur in red-pink varieties from Mexico. J Agric Food Chem 68:5980–5994 Petroni K, Tonelli C (2011) Recent advances on the regulation of anthocyanin synthesis in reproductive organs. Plant Sci 181(3):219–229 Petroni K, Pilu R, Tonelli C (2014) Anthocyanins in corn: a wealth of genes for human health. Planta 240:901–911 Pfeiffer WH, McClafferty B (2007) HarvestPlus: breeding crops for better nutrition. Crop Sci 47:S88–S105 Prakash NR, Zunjare RU, Muthusamy V et al (2019) Genetic analysis of prolificacy in ‘Sikkim Primitive’-a prolific maize (Zea mays L.) landrace of North-Eastern Himalaya. Plant Breed 138:781–789

Genetic Improvement of Specialty Corn for Nutritional Quality Traits

255

Prakash NR, Zunjare RU, Muthusamy V et al (2021) A novel quantitative trait loci governs prolificacy in ‘Sikkim Primitive’ – a unique maize (Zea mays) landrace of North-Eastern Himalaya. Plant Breed 140:400–408 Prasanna BM, Palacios-Rojas N, Hossain F et  al (2020) Molecular breeding for nutritionally enriched maize: status and prospects. Front Genet 10. https://doi.org/10.3389/fgene.2019.01392 Prasanthi PS, Naveena N, Rao MV et al (2017) Compositional variability of nutrients and phytochemicals in corn after processing. J Food Sci Technol 54(5):1080–1090 Reader D, Johnson ML, Hollander P et al (1997) Response of resistant starch in a food bar vs. two commercially available bars in persons with type II diabetes mellitus. Diabetes 46:1254A Reddappa SB, Chhabra R, Talukder ZA et al (2022) Development and validation of rapid and cost effective protocol for estimation of amylose and amylopectin in maize kernels. 3 Biotech 12:62 Ren Y, Yobi A, Marshall L et  al (2018) Generation and evaluation of modified opaque2 popcorn suggests a route to quality protein popcorn. Front Plant Sci 9. https://doi.org/10.3389/ fpls.2018.01803 Rhoades MM (1933) The cytoplasmic inheritance of male sterility in Zea mays. J Genet 27:71–93 Roberts DM, Lockwood C, Dalbo JV et al (2011) Ingestion of a high-molecular-weight hydrothermally modified waxy maize starch alters metabolic responses to prolonged exercise in trained cyclists. Nutrition 27:659–665 Sami W, Ansari T, Butt NS et al (2017) Effect of diet on type 2 diabetes mellitus: a review. Int J Health Sci 11:65–71 Sarika K, Hossain F, Muthusamy V (2018) Marker-assisted pyramiding of opaque2 and novel opaque16 genes for further enrichment of lysine and tryptophan in sub-tropical maize. Plant Sci 272:142–152 Schnable PS, Wise RP (1998) The molecular basis of cytoplasmic male sterility and fertility restoration. Trends Plant Sci 3:175–180 Selinger DA, Chandler VL (1999) A mutation in the pale aleurone color1 gene identifies a novel regulator of the maize anthocyanin pathway. Plant Cell 11(1):5–14 Senhorinho H, Coan M, Marino T et al (2019) Genomic-wide association study of popping expansion in tropical popcorn and field corn germplasm. Crop Sci 59:2007. https://doi.org/10.2135/ cropsci2019.02.0101 Setchell KD, Aedin C (1999) Dietary isoflavones: biological effects and relevance to human health. J Nutr 129:758S–767S Sharma M, Cortes-Cruz M, Ahern KR (2011) Identification of the Pr1 gene product completes the anthocyanin biosynthesis pathway of maize. Genetics 188(1):69–79 Shen B, Allen WB, Zheng P et al (2010) Expression of ZmLEC1 and ZmWRI1 increases seed oil production in maize. Plant Physiol 153:980–987 Shende D, Sidhu GK (2014) Methods used for extraction of maize (Zea mays L.) germ oil-a review. Indian J Sci Technol 2:48–54 Shiferaw B, Prasanna BM, Hellin J et al (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Sec 3:307–327 Sidebottom C, Kirkland M, Strongitharm B et al (1998) Characterization of the difference of starch branching enzyme activities in normal and low amylopectin maize during kernel development. J Cereal Sci 27:279–287 Silvi S, Rumney CJ, Cresci A et al (1999) Resistant starch modifies gut microflora and microbial metabolism in human flora-associated rats inoculated with faeces from Italian and UK donors. J Appl Microbiol 86:521–530 Singh N, Vasudev S, Yadava DK et al (2016) Oil improvement in maize: potential and prospects. In: Maize: nutrition dynamics and novel uses 2014. Springer, New Delhi, pp 77–82 Singh SP, Neupane MP, Sravan US et al (2019) Nitrogen management in baby corn: a review. Curr J App Sci Technol 34(5):1–11 Sinkangam B, Stamp P, Srinives P et al (2011) Integration of quality protein in waxy maize (Zea mays L.) by means of SSR markers. Crop Sci 51:2499–2504 Sprague GF, Brimhall B, Hixon RM (1943) Some effects of the waxy gene in corn on properties of the endosperm starch. J Am Soc Agron 35:817–822

256

F. Hossain et al.

Stinard PS, Robertson DS, Schnable RPS (1993) Genetic isolation, cloning, and analysis of a Mutator-induced, dominant antimorph of the maize amylose extender1 locus. Plant Cell 5:1555–1566 Sweley JC, Rose DJ, Jackson DS (2011) Composition and sensory evaluation of popcorn flake polymorphisms for a select butterfly-type hybrid. Cereal Chem 88:321–327 Sweley JC, Rose DJ, Jackson DS (2013) Quality traits and popping performance considerations for popcorn (Zea mays Everta). Food Rev Intl 29(2):157–177 Tako E, Hoekenga OA, Kochian LV et al (2013) High bioavailability iron maize (Zea mays L.) developed through molecular breeding provides more absorbable iron in vitro (Caco-2 model) and in vivo (Gallus gallus). J Nutr 12:3–11 Talukder M, Muthusamy V, Chhabra R et al (2022a) Combining higher accumulation of amylopectin, lysine and tryptophan in maize hybrids through genomics-assisted stacking of waxy1 and opaque2 genes. Sci Rep 12:1. https://doi.org/10.1038/s41598-­021-­04698-­3 Talukder ZA, Muthusamy V, Chhabra R et al (2022b) Enrichment of amylopectin in sub-­tropically adapted maize hybrids through genomics-assisted introgression of waxy1 gene encoding granule-bound starch synthase (GBSS). J Cereal Sci 105:103443. https://doi.org/10.1016/j. jcs.2022.103443 Talukder ZA, Muthusamy V, Zunjare RU, Chhabra R, Reddappa SB, Mishra SJ, Prakash NR, Gain N, Chand G, Hossain F (2022c) Pollen staining is a rapid and cost-effective alternative to marker-assisted selection for recessive waxy1 gene governing high amylopectin in maize. Physiol Mol Biol Plants 28:1753–1764 Thakur S, Kumar R, Vikal Y et  al (2021) Molecular mapping of popping volume QTL in popcorn (Zea maize L.). J Plant Biochem Biotechnol 30:1–8. https://doi.org/10.1007/ s13562-­020-­00636-­y Topping DL, Anthony MF, Bird F (2003) Resistant starch as prebiotic and synbiotic: state of the art. Proc Nutr Soc 62:171–176 Toufektsian MC, De Lorgeril M, Nagy N et  al (2008) Chronic dietary intake of plant-derived anthocyanins protects the rat heart against ischemia-reperfusion injury. J Nutr 138:747–752 Trujillo EE, Castillo MDCM, Gonzalez FC et  al (2009) Anthocyanin accumulation in pericarp and aleurone layer of maize kernel and their genetic effects on native pigmented varieties. Rev Fiotec Mex 32:303–309 Val LD, Schwartz SH, Kerns MR et  al (2009) Development of a high oil trait for maize. In: Molecular genetic approaches to maize improvement. Springer, Berlin, pp 303–323 Vann L, Kono T, Pyhajarvi T et al (2015) Natural variation in teosinte at the domestication locus teosinte branched1 (tb1). Peer J 3:e900 Vineyard ML, Bear HP (1952) Amylose content. Maize Genet Coop Newsl 26:5 Wang W, Niu S, Dai Y et al (2019) Molecular mechanisms underlying increase in lysine content of waxy maize through the introgression of the opaque2 allele. Int J Mol Sci 20:684 Weber EJ (1987) Lipids of the kernel. In: Watson SA, Ramsted PE (eds) Corn: chemistry and technology. American Association of Cereal Chemists, St. Paul, pp 311–349 Welch RM, Graham RD (2002) Breeding crops for enhanced micronutrient content. In: Food security in nutrient-stressed environments: exploiting plants’ genetic capabilities. Springer, Dordrecht, pp 267–276 Wessler S, Baran G, Varagona M et al (1986) Excision of Ds produces waxy proteins with a range of enzymatic activities. EMBO J 5:2427 White PJ, Weber EJ (2003) Lipids of the kernel. In: White PJ, Johnson LA (eds) Corn: chemistry and technology, 2nd, vol 10. American Association of Cereal Chemists Inc, St. Paul, pp 355–395 Wills DM, Whipple CJ, Takuno S et al (2013) From many, one: genetic control of prolificacy during maize domestication. PLoS Genet 9. https://doi.org/10.1038/362417a0 Xiao Y, Yu Y, Li G (2020) Genome-wide association study of vitamin E in sweet corn kernels. Crop J 8:341. https://doi.org/10.1016/j.cj.2019.08.002

Genetic Improvement of Specialty Corn for Nutritional Quality Traits

257

Yadava DK, Hossain F, Mohapatra T (2018) Nutritional security through crop biofortification in India: status & future prospects. Indian J Med Res 148:621–631. https://doi.org/10.4103/ijmr. IJMR_1893_18 Yadava DK, Hossain F, Choudhury PR et al (2022) Biofortified varieties: sustainable way to alleviate malnutrition, 4th edn. Indian Council of Agricultural Research, New Delhi, p 106 Yang X, Guo Y, Yan J et al (2010) Major and minor QTL and epistasis contribute to fatty acid compositions and oil concentration in high-oil maize. Theor Appl Genet 120:665–678 Yang L, Wang M, Wang W et al (2013) Marker-assisted selection for pyramiding the waxy and opaque-16 genes in maize using crosses and backcross schemes. Mol Breed 31:767–775 Yang Y, Xiao H, McClements DJ (2017) Impact of lipid phase on the bioavailability of vitamin-E in emulsion-based delivery systems: relative importance of bioaccessibility, absorption, and transformation. J Agric Food Chem 65:3946–3955 Yang R, Yan Z, Wang Q et al (2018) Marker-assisted backcrossing of lcyE for enhancement of proA in sweet corn. Euphytica 214:130 Yangcheng H, Jiang H, Blanco M et al (2013) Characterization of normal and waxy corn starch for bioethanol production. J Agric Food Chem 61:379–386 Yodpet C (1979) Studies on sweet corn as potential young cob corn (Zea mays L.). Ph.D. thesis. University of the Philippines, Philippines Yu WZ, Sun SL, Zhou DQ et al (1993) An elite maize cultivar for baby corn. Chin Veg 6:52 Zabala G, Gabay-Laughnan S, Laughnan JR (1997) The nuclear gene Rf3 affects the expression of the mitochondrial chimeric sequence R implicated in S-type male sterility in maize. Genetics 147:847–860 Zhang WL, Yang WP, Wang MC et al (2013) Increasing lysine content of waxy maize through introgression of opaque-2 and opaque-16 genes using molecular assisted and biochemical development. PLoS One 8:e56227 Zheng P, Allen WB, Roesler K (2008) A phenylalanine in DGAT is a key determinant of oil content and composition in maize. Nat Genet 40:367–372 Zheng P, Babar MD, Parthasarathy S et al (2014) A truncated FatB resulting from a single nucleotide insertion is responsible for reducing saturated fatty acids in maize seed oil. Theor Appl Genet 127:1537–1547 Zhou Z, Song L, Zhang Li X et al (2016) Introgression of opaque2 into waxy maize causes extensive biochemical and proteomic changes in endosperm. PLoS One. https://doi.org/10.1371/ journal.pone.0158971 Zunjare R, Hossain F, Muthusamy V (2015) Popping quality attributes of popcorn hybrids in relation to weevil (Sitophilus oryzae) infestation. Indian J Genet 75:510–513 Zunjare RU, Hossain F, Muthusamy V et  al (2018) Development of biofortified maize hybrids through marker-assisted stacking of β-carotene hydroxylase, lycopene-ε cyclase and opaque2 genes. Front Plant Sci 9:178. https://doi.org/10.3389/fpls.2018.00178

Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate Resilience P. S. Basavaraj, Jagadish Rane, M. D. Prathibha, K. M. Boraiah, and Mahesh Kumar

1 Introduction Maize is known for its versatile uses and adopted to different geographical regions. Globally, maize production surpassed that of major cereals such as wheat and rice (FAO 2020). Major maize growing countries includes the United States, China, Brazil, Mexico, and India. Globally, 1147.7  million MT maize is produced from 193.7 M ha (FAO 2020). More than 61% of maize produced is used as feed and 22% for industrial purposes such as starch and biofuel production. Thus, due to its versatile applications in food, feed, and industrial raw material, maize has attained status of industrial crop (Shiferaw et al. 2011). Its contribution to food security of human being is highly significant. Nevertheless, this crop substantially contributed to food security of poor countries (Shiferaw et al. 2011), while it is the key ingredients used in feed preparation that contributes to animal-based food sources, which are continuously dominating in the international trade. This can be attributed to surge in global livestock consumption driven by population expansion, increased income levels, and urbanization and changed consumption patterns (Herbert 2017). Over the past 50 years, intensive efforts of plant breeding coupled with optimum production technologies have enhanced cereal yields (Pingali 2012). With the current growth rate of 1.3% per year in 40% of the land sown, cereal production has now reached yield plateau. Therefore, to meet the projected food demand, crop productivity must be doubled by 2050 with the enhanced rate of 2.4% per year (Ray et al. 2012). Hence, in the recent past, deliberate attempts have been made to integrate trait-based genomic approaches to complement conventional approaches of crop

P. S. Basavaraj (*) · J. Rane · K. M. Boraiah · M. Kumar ICAR-National Institute of Abiotic Stress Management, Baramati, India M. D. Prathibha ICAR-Indian Institute of Horticulture Research, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 259 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_12

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improvement. However, the success of these genomics approaches is mainly based on phenotypic expression of a trait; hence, phenotyping is one of the key components of plant breeding. Genetic dissection of key traits and its molecular mechanism involves extensive phenotyping of large set of population which ranges from 200 to 10,000 lines. Screening of such a huge population is with traditional methods, which is expensive and tedious and consumes a lot of time. In addition, plant phenotypes are the results of interaction between genotype × environment × management (G × E × M); thus, they are complex in nature (Dwivedi et al. 2020). This interaction has an impact on the presence as well as expression of genes associated with specific structural and functional traits at the cell, tissue, organ, and plant levels. Performance of plants traits such as morphology, biomass, and yield is the results of interaction between structural and functional phenotypes (Houle et  al. 2010; Dhondt et al. 2013). In comparison with genomic tools available and genomic data generated for breeding population, phenotyping is still a limiting factor for understanding the genetic basis of complex traits contributing to productivity, biotic stress, abiotic stress, and quality. To overcome these phenotyping barriers and increase the competence of marker-assisted breeding, there is a need of dependable, precise, automatic, and high-throughput phenotypic technologies (Lorenz et al. 2011). Recent developments in phenomics led to the improvement in phenotyping protocol that is more powerful than ever to dissect complex traits into easily scorable traits. In addition, this helps in uncovering underlying genetic mechanism for trait expression. This chapter aimed at describing phenotyping tools available for plant scientist to fast track their maize improvement in precise and robust manner for climate resilient agriculture.

2 Phenotype A plant’s phenotype is defined by its morphological, biochemical, physiological, and molecular attributes. To describe these features, various quantitative and qualitative traits are measured. The term phenotype was coined by Wilhelm Ludvig Johannsen (1911), who found large diversity in quantitative traits among genetically similar material. He suggested the term “phenotype” to annotate the diversity in particular trait contributed by G × E interaction, and thus he was successful in demonstrating that variation for a trait is entirely not under the control of genes.

2.1 Phenotyping Phenotyping, which often refers to quantification of a particular trait or its components, it dates back with human civilization, when a man started selecting best individuals for domestication of crop plants (Diamond 1997). Subsequently, the

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selection of the best genotype for key agronomic traits was driven by detectable phenotypic expression and using the selected genotype in hybridization programs to produce new improved cultivars in plant breeding (Pearson et al. 2008). Subsequently, trait-based phenotyping approaches emerged for examining phenotypic traits of diverse species either in the field (Reich et al. 1992) or in the laboratory (Poorter et al. 1990). However, traits other than yield and yield components were tedious to measure due to underlying complexity particularly for early stage of selection in the breeding program. This necessitated advances in phenotyping techniques for enhancing the genetic gain in crop improvement (Phillips 2010). Since the development of new improved varieties or hybrids depends on the extent of heritable phenotypic variation, persistent efforts were made to link genomic and phenomic features of plant (Bhat et al. 2016). Genomic information is now readily, quickly, and cheaply available due to rapid advances in high-throughput genotyping technologies. Now, there is large scope to employ these technologies to deal with prevailing large set of mapping population (RILS, NILs, BILs, MAGIC, CSSL, NAM, and AB-NAM) and diversity panels for genome-wide association mapping (McMullen et al. 2009). Nevertheless, phenotyping is highly crucial component in trait discovery, QTL mapping, and genomics-assisted breeding (GAB) (Jannink et al. 2010). Further, phenotyping is inevitable to generate proof of concept through promising events in transgenic studies (Gaudin et  al. 2013). Since the power of prediction of association between the gene and the trait largely depends on the size of samples assessed for genotypic and phenotypic features, scientists have to phenotype large numbers of accessions accurately and rapidly. Conventional methods of phenotyping are laborious, destructive, and less precise. Hence, high-tech robust, automated phenomic tools gained immense importance in phenotyping of large set samples in a robust and accurate manner in advance controlled environmental facility and also in the field (Fig. 1, Table 1).

Fig. 1  Time line of development in phenomics

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Table 1  Milestones in developments in phenomics Year Development 1911 Johannsen proposed concept of phenotype and genotype 1997 Nicholas Schork proposed concept of phenomics corresponding to genomics in disease research 1998 CropDesign Company of Belgium developed high-­ throughput phenotyping facility 2007 Creation of Australian Plant Phenomics Facility 2011 Furbank identified bottlenecks in plant phenotyping 2012 Algorithms for analyzing root, stalk, and seed traits using RootAnalyzer, VesselParser, etc. 2012 Establishment of the European Plant Phenotyping Network (EPPN) 2013 Mccoueh proposed concept of next-generation phenotyping 2016 First field high-throughput plant phenotype platform (Scanalyzer Field) by Lemna Tec 2016 World largest phenotyping center “The International Plant Phenotyping Network (IPPN) was registered” 2016 Establishment of National Phenotypic Facility at ICAR-­ National Institute of Abiotic Stress Management, Baramati, India 2017 Establishment of Nanaji Deshmukh Plant Phenomics Centre” at ICAR-IARI, New Delhi, India 2017 Multiscale phenomics involving big data, bioinformatics, and OMICs data was proposed by Francois Tardieu and Malcolm Bennett for phenotypic traits identification and information extraction 2020 Establishment of European plant phenotyping network

Reference Johannsen (1911) Schork (1997) http://www.cropdesign.com; Reuzeau et al. (2005); Reuzeau (2007) https://www.plantphenomics. org.au Furbank and Tester (2011) Burton et al. (2012) https://emphasis.plant-­ phenotyping.eu Cobb et al. (2013) http://www.lemnatec.com Virlet et al. (2017) https://www.plant-­ phenotyping.org/; Carrolla et al. (2019) http://www.niam.res.in

www.iari.res.in Tardieu et al. (2017)

https://emphasis.plant-­ phenotyping.eu

2.2 Types of Phenotyping Plant phenotyping is a major determinant for identification and selection of superior genotype, functional gene analysis, and forward and reverse genetic analysis. Plant breeders often uses different set of materials such as germplasm collections, breeding lines, pre-released cultures, mutant population, and different mapping population in a breeding program in a large scale. Such efforts aim at identification of key traits and mechanisms governing it through both forward and reverse phenotyping approaches.

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Forward Phenotyping Forward phenotyping aims at identifying the most promising accessions by phenotyping huge number of plants. In this approach, the trait of interest can be recognized even at early growth stage of plants avoiding a long waiting up to maturity of crop in the field. However, the speed and accuracy of phenotyping depends on the complexity in measurement of traits (Reynolds et al. 2020). Reverse Phenotyping The reverse phenotyping is applied when the information about the best genotype is already available with key desirable traits through previous observations, and it aims at unravelling the mechanisms contributing to superiority of the genotype. This involves a series of process like dissection of a physiological trait to biochemical or bio-physical components that eventually lead to the discovery of novel gene(s) (Reynolds et al. 2020).

3 Scope for Phenotyping Plant Responses 3.1 Phenotyping for Productivity In crops like maize, grain yield is regarded as valuable trait for a plant breeder. Expression of this trait was influenced by genetic makeup of an individual, management practices, and growth environment (Baye et al. 2011). Besides grain yield, a set of component traits that are called as secondary traits play crucial role in crop performance. These plant attributes are too influenced by G × E × M, which adds further dimension for breeding for targeted environment (Reynolds and Langridge 2016). Traditionally, yield and yield attributes including plant height and biomass of plants are measured manually, which is time- and labor-intensive and error-prone. For instance, plant height of maize is measured manually at maturity; however, plants at this stage are generally tall. Ensuring the precision in this task becomes challenging when a large number of plants are to be measured and such situation demand deployment of high-throughput phenotyping tools (Wang et  al. 2019a). Estimation of growth rate of plants rather than biomass at any given point of time is crucial to understand plant responses to environmental stimuli over time. In conventional approach, this task is accomplished by periodic harvest of plant at different stages of crops through destructive approach. Any phenotyping technique that enables non-destructive approaches can be immensely useful, and hence, there is ample scope for high-throughput phenotyping. In crop like maize, measurement grain yield components per cob require a lot of time and labor. This also widens the scope of high-throughput phenotyping for improvement of crop productivity (Lydia et al. (2021).

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3.2 Phenotyping for Biotic Stress Crop plants are affected by pest and diseases, which cause economic loss to the production of many crops including maize (Tandzi and Charles 2020). Development and deployment of resistant cultivar is most economic and plausible strategies to curtail economic losses (Basavaraj et al. 2021). It is indispensable to phenotype large number of plants and different set of genotypes (breeding lines, hybrids, germplasm, mutants, etc.) for identification of resistance sources and hence development of resistant cultivars. However, it is time-consuming and labor-intensive. In addition, traditionally phenotyping for disease resistance is carried out by visual screening, and it is based purely on skills of the individual, and thus it varies greatly with person to person. Thus, any phenotyping technique that enables rapid detection of disease symptoms without biasness can be immensely useful, and hence there is ample scope for high-throughput phenotyping for biotic stress tolerance.

3.3 Phenotyping for Abiotic Stress Abiotic stresses are unpredictable in terms of time and frequency of occurrence. Among various approaches to manage abiotic stress, identification and development of stress-tolerant varieties is most plausible economic approach. Many breeding programs are focusing on the development of novel germplasm that is more tolerant to stress. In the three broad mechanisms associated with abiotic stress resistance, namely, escape, tolerance, and resistance (Levitt 1972), only few of the traits such as early flowering has been exploited for improvement of yield under stress conditions. Traits associated with water relations under soil moisture-deficit environment or extreme temperatures are very difficult to measure with traditional and destructive methods. This provides scope for employment of high-throughput techniques. For instance, water stress directly affects intercellular organs and cell turgidity and thus influences shoot and root architecture, which is difficult to quantify through traditional methods and can be accomplished only by high throughput-phenotyping tools (Schlemmer et al. 2005).

3.4 Phenotyping for Quality Traits Maize grains are composed of several biochemical compounds such as carbohydrates, proteins, different fatty acids, and flavonoids. In addition, kernel hardness/ texture is the key trait for farmers ad millers. Measurement of kernel hardness through manual method is time-consuming and destructive method. Nondestructive

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and rapid measuring methods are in high demand. Besides these maize kernels are known to be affected by aflatoxin-producing fungi (Fusarium and Aspergillus), which are harmful for human and animal. Conventionally, mycotoxin is quantified by ELISA and RT-PCR, which is time taking and a smaller number of samples can be tested at one go (Glen and O’Hare 2017). In starch industry, quantifying starch structure is the most essential component of product development process. High-­ throughput phenotyping tools can play an immense role in making the process less expensive and rapid without compromising with quality.

4 Phenomics The development in sequencing technologies advances in plant phenotyping techniques especially dealing with large set of data, and imaging and advances in sensor technology have given rise to new sciences called “phenomics.” The term “phenomics” is often used parallelly to genomics; however, they are different. In genomics, it is possible to completely characterize the entire genome, whereas it is difficult or impossible to completely characterize phenome because of the heterogeneity in phenotypic expression across environments (Houle et  al. 2010). The term “phenome” refers to the entire phenotype, that is, the expression of the phenotype for a trait in a specific environment, whereas “phenomics” refers to the large-scale gathering of multidimensional phenotypic data of an organism at many levels of plant organization, such as cell, tissue, organ, and whole plant (Soul 1967). In the year 2002, Gerlai coined the term phenomics to describe imaging techniques that allow researchers and scientists to learn about plants at the root or whole-plant level. The phenomics necessary includes precision and accuracy with which a target trait can be measured across the plants, population, and replications or in other words can be able to identify both spatial and temporal variation in plant response to environmental stimuli. In addition, phenomics ensures speed, which refers to number of plants characterized per unit time for a trait of interest or relevance to target traits while screening large number of genotypes to enhance power prediction between association of trait and genes. Further, nondestructive nature enables phenomics tools to quantify traits without physical damage and thus can help in generating time-lapse measurements for assessing the process rather than mere event represented by one-­ time measurements (Basavaraj and Rane 2021).

5 Phenomic Tools Phenomics includes tools that are engaged for measurement of environmental stresses or stimuli, plant responses to environmental factors, and those that are used to translate images into data and finally computational and statistical tools to

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manage data and analyze them for better interpretations (Table 2). At present, high-­ throughput phenotyping that employs imaging technology has emerged as powerful tool in phenomic science for noninvasive characterization of responses of large set of genotypes to particular environmental stimuli (Berger et al. 2010). These imaging technologies employ a variety of sensors that capture specific regions of electromagnetic spectrum to reveal features of plant. For example, visible fraction of electromagnetic spectrum captured by conventional and high-resolution camera allows scientist to measure plant growth, plant architecture, phenology, and health status of leaves. On the other hand, imaging systems that capture near-infrared range of electromagnetic spectrum allow quantification of tissue water and soil water content noninvasively. The imaging systems that are configured to sense far infrared range are designed to reveal dynamics of temperature of plant canopy/ leaf (Liu et al. 2011; Zia et al. 2013). Chlorophyll fluorescence imaging system has now emerged a very popular tool for monitoring physiological state of photosynthetic machinery. While plant responses are measured in high-throughput manner by employing highly sensitive sensors, quantification of stress like soil moisture deficit has been made easy by system that allows automated weighing and watering (Furbank and Tester 2011) particularly under controlled environmental conditions. Further, advanced phenomics technique has also been demonstrated for drought screening under field condition. Another proven optical technique is hyperspectral reflectance spectroscopy that employs radiometric measurement or imaging system for noninvasive measurement of plant responses to stress in both controlled environmental or field phenomics platforms (Jones and Vaughan 2010). The large set of data generated by these machines now relies on emerging techniques in machine learning and artificial intelligence for better interpretation. Digital imaging is a cost-effective, simple, and commonly used approach for measuring stress tolerance quantitatively. It is the most widely used method for in situ crop phenotyping in controlled environments. It is employed to measure a quantitative change in growth and development under stress by capturing images over a period, and changes at each stage of crop can be quantified through image analysis. In addition, digital images recorded in the visible wavelength regions allow for the monitoring color of the plants, which can be employed to quantify senescence due to different stresses. For instance, germanium toxicity is quantified in barley mapping population (Schnurbusch et al. 2010) and identified a QTL (Jefferies et  al. 1999). In addition, Harris et  al. (2010) assessed water use efficiency in plants. Canopy temperature is another important trait that is indirect measure of photosynthetic efficiency and water use efficiency, and handheld thermopile-based infrared thermometers can be deployed in measuring canopy temperature in many crops including maize (Han et al. 2016). Photosynthetic function is a good indicator of health status of plants, which changes under the influence of biotic and abiotic stresses. Chlorophyll fluorescence was successfully deployed in assessing health status of Nicotiana tabacum, Brassica napus, cotton, wheat, soybean, mung bean (Baker 2008; Woo et al. 2008) under abiotic stresses. In addition, digital imaging is employed to investigate projected leaf area and growth rate

Yield

Canopy temperature

Vegetative index

IR thermometer

NDVI meter

SPAD chlorophyll Leaf level of greenness meter High-throughput phenotyping tools Drone Plant height, spectral Larger-scale filed signatures for different features Phenocart Plant height, spectral Large-scale field phenotyping signatures for different features Aircraft Spectral signatures for Field phenotyping measurement of different traits

No restriction for pay load capacity High payload, fast

Faster, nondestructive

Measurement of transpiration cooling under different stresses Robust, easy and quick Seedling establishment, seedling vigor, leaf nitrogen, canopy cove and greenness, stay green Indicates leaf N status, Robust to weather chlorophyll content

High accuracy and dependability Handy to use, low cost

Less payload, prone to wind problems Difficult to use under wet soil Needs training and license to operate and low resolution associated with image captured

High costs

Influenced by prevailing weather parameters High costs

High maintenance cost

Standardization

Standardization

Plot harvester

Handy, low cost

Easy to use, low cost

Weighing balance

Scale

Quick and easy method Error prone, differs person to person

Disease scoring, characterization of qualitative traits Minimum data set for agronomic acceptability Grain yield, straw’s yield biomass measurement Yield measurement

Visual characters such as phenology, lodging, disease resistance/susceptibility Plant height, leaf length, panicle length etc. Grain, straw, kernel weight

Limitations

Advantages

Application

Trait measured

Tools/techniques Traditional tools Eyes

Table 2  Different types of phenotyping tools for trait measurement

(continued)

Wang et al. (2019b) Crain et al. (2016) Wang et al. (2019a)

Ghimire et al. (2015)

Xia et al. (2016)

Chawade et al. (2019) Chawade et al. (2019) Araus et al. (2012) Fuchs (1990)

Chawade et al. (2019)

References

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Measurement of morphological, canopy architectural traits. Measuring dynamics of growth Canopy architecture

Image analysis/data processing Spectral indices Traits associated with composition of pigments, water content of tissue and soil, nutrient status, disease detection

LiDAR

Digital cameras (RGB)

Table 2 (continued) Tools/techniques Trait measured Blimp Spectral signatures for measurement of different traits in addition, thermography RGB cameras, LiDAR can be employed to capture various additional traits Gantry Spectral signatures for measurement of different traits in addition, thermography RGB cameras, LiDAR can be employed to capture various additional traits Sensors Infrared Dynamics of canopy temperature Multi- and Spectral indices for versatile hyperspectral traits

Plant health status under different stress, dynamics of biochemicals under different stress

Canopy structure

Generation of voluminous data

Fast, high resolution, cheap

Simple processing

Precision

Robust, cheaper

Influenced by prevailing weather, low resolution Costlier, handling data is challenging

Rapid screening tool

Measures canopy health status under different stress Pigment composition, radiation absorbance, pathogen infection Pigment composition, radiation absorbance, pathogen infection, plant color

Usually affected by view geometry

Challenges in data processing and handling

Static to one place

Ability to carry high payload

Intensive research areas

Limitations Prone to wind problems

Advantages Carry high payload

Application Aerial phenotyping

Ma et al. (2018)

Jin et al. (2020)

Li et al. (2021b)

Li et al. (2017) Xia et al. (2019)

Li et al. (2021a)

References Chawade et al. (2019)

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Data processing is challenging Robust, rapid, precision Slow, requires an oven

Data processing is challenging

Dissection of complex traits Easy measurement

Data processing is challenging

Comprehensive information

Rapid identification of donors for high radiation and water use efficiency, and other key physiological traits Quantum yield, health status of High cost leaves

Plant health status under different stress, dynamics of biochemicals under different stress

Photoinhibition, physical damage in photosystems, recovery from shade Measurement of soluble Stem reserves Inexpensive/sample Capital investment, carbohydrates of plant tissue resolution Gas exchange parameters Measures the photosynthetic Gives several gas High cost and time taking rate, stomatal conductance and exchange parameters in for large number of samples transpiration efficiency a single go Measurement of leaf area index Estimation of LAI Rapid, Depends on clear skies and plot uniformity Stem and root lodging Lodging risk estimation after Reasonably cheaper, Slow (∼10 plots/day/ probability anthesis lodging risk can be person) assessed even in absence

Health status of plant leaves under different stresses (PS-II)

Modified from Reynolds et al. (2020)

Dynamometer, caliper, ruler + protractor

Ceptometer

IR gas analysis

NIR reflectance

Fluorescence

Traits associated with composition of pigments, water content of tissue and soil, nutrient status, disease detection Feature Counting number of spikes, recognition phenology study, disease recognition LiDAR and digital Canopy structure imaging Full growth Measurement of growth analysis dynamics, efficiency of ration use, assimilate partitioning

Full spectrum analysis

Pokovai and Fodor (2019)

Zimmer et al. (1990) Ogbaga et al. (2017)

Chawade et al. (2019)

Jin et al. (2020) Li et al. (2021a)

Jiang et al. (2018)

Feng et al. (2013)

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(Barbagallo et  al. 2003), early detection of plant responses to pathogen attack (Swarbrick et al. 2006; Chaerle et al. 2009). In addition to imaging system, tools such as laser image detection and ranging (LiDAR) gaining popularity among researchers throughout the world. This extremely dynamic instrument takes three-dimensional measurements of plant canopy, subcanopy, and terrain. As a result, it allows for topographic mapping and measurement of plant height, ground cover, and canopy architecture (Weiss and Biber 2011; Deery et al. 2014).

5.1 Post-Harvest Phenotyping Tools Seed and milling industries employ imaging and sensor technologies to monitor quality aspects routinely. Gegas et al. (2010) used 2D image capture coupled with feature extraction phenotyping pipeline in wheat germplasms and unraveled the genetic architecture of grain. This method tool and protocol can be applied to most of the grain crops, and it can be extended to any parts of the plants if it is refined. In rice to phenotype spikelets, an integrated automated machine was designed and tested, and it is now possible to measure 1440 plants per day with more than 95% precision (Duan et al. 2011). Candidate genes associated with vascular bundle and rind traits in maize kernels (Miller et al. 2017), stalks (Mazaheri et al. 2019), and tassels (Gage et  al. 2017) were also identified. An Android-based SeedCounter mobile application precisely estimates grain size and grain number (Komyshev et al. 2016). In addition, tools to capture the morphological and other complexity of seed and related traits evolved over a time. “PhenoSeeder” measures individual seeds three-dimensionally, and parameters derived can be correlated with vigor-­ related traits in early seedling stage (Jahnke et al. 2009). Wallays et al. (2009) used hyperspectral imaging to measure clean grains, whereas Singh et al. (2010) used this technique for identification of insect damaged grains in wheat as well as protein content in grain (Wang et al. 2004; Sun et al. 2019). To unravel the genetic architecture of key agronomic traits, it is essential to combine large data set of phenotyping data with genomic approaches. For instance, protein content in rice grain (Sun et al. 2019) and panicle architecture in sorghum (Zhou et al. 2019) were dissected using these combined approaches. Warman and Fowler (2020) developed a high-­ throughput image-based phenotyping systems for scanning maize ear. This system clearly distinguishes GFP and anthocyanin seed markers. As presented in Table 3, recently emerged phenomics tools promise greater speed with precision in phenotyping plant responses as compared to traditional tools. Many of these tools have now become integral part of phenomics platforms ranging from controlled environment to natural field conditions.

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5.2 Pocket Phenotyping Tools Advances in the field of smart phone technologies coupled with rapid developments in sensors, computing enabled its applications in diverse fields. For instance, PocketPlant3D a smart phone-based canopy measurement tool available for maize (Confalonieri et al. 2017). Another portable mobile based app uses machine learning algorithm to diagnose leaf pot disease caused by Cercospora in sugar beet (Hallau et  al. 2018). Similarly, vitisBerry is an android based app measures the number of berries in grapevine (Aquino et al. 2018).

5.3 Root Phenotyping Tools Inspection, measurement, and sampling are generally easy to perform in aboveground parts of the plants, whereas the location and the function of root are confined to subsurface within soil, which makes direct observation difficult. Hence, root phenotyping remained as a great bottleneck for a long time despite the fact that roots are vital organs in plant growth and development with key role in uptake of soil moisture and nutrients (Reynolds et al. 2012; Atkinson et al. 2019). Conventional method that involves trenching is a slow and labor-intensive and is not economical for large-­ scale phenotyping. With advances in root system architecture (RSA) research, several methods have emerged over time, such as the use of translucent medias such as hydroponic, aeroponic, and gel-base. PlaRoM, connected with RGB cameras, facilitate to measure root growth dynamics and hair development, (Yazdanbakhsh and Fisahn 2009). Simple screening systems capable of identifying robust maize brace roots as well as root lodging resistance might serve as nondestructive selection tools for breeders associated with selection for soil moisture stress tolerance in plants. In maize, the development of brace roots helps reduce moisture and heat stress. X-ray, CT, and 3D imaging applications are used to visualize the inner 3D structures based on the differences in the x-ray attenuation from roots and soil. Alternatively, magnetic resonance imaging (MRI), positron emission tomography (PET), and a combination of both can be used to measure 3D RSA in soil (Atkinson et al. 2019). The PET can scan plant roots down to 82 mm deep and dynamically screen carbon (photo assimilate) transportation over a prolonged time period (Garbout et al. 2011). Additionally, hyperspectral system was combined to measure root chemical components like water status and lignin content (Bodner et al. 2018). Some of the phenomics tools and software deployed in root phenotyping are presented in Table 3.

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Table 3  Phenomic tools and software for root phenotyping Tools Rhizoslides

Description Robust, nondestructive, faster analysis of RSA in maize based on paper growth system. Works best with “GiaRoots” software for image segmentation and analysis GROWSCREENRhizo Auto captures images of both roots and shoots of plants from rhizotrons with a throughput of 60 rhizotrons per hour Rhizoponics Offers simultaneous monitoring and measurement of RSA and shoot development from seedling to adult stages RADIX Enables simultaneous monitoring and measurement and monitoring of RSA and shoot development throughout. In addition, estimates the effects of N distribution to parts of the root system RhizoTubes Both root and shoots can be measured nondestructively entire life cycle of crops Software GiARoots Semiautomated root image analysis software RootReader3D A 3D root phenotyping software platform for rice seedlings RootTrace SmartRoot, EZ-Root-VIS RooTrak NMRooting

3D root system architecture in soil using X-rays Coupled with MRI measures changes in root morphological traits during Cercospora beticola infestation in sugar beet

Crop Maize

Reference Le Marie et al. (2014)

Barley and Nagel et al. maize, (2012) Arabidopsis, wheat and cotton Arabidopsis Mathieu et al. (2015)

Maize

Le Marié et al. (2016)

Pea, Medicago, Brassica, grape, wheat

Jeudy et al. (2016)

Rice

Galkovskyi et al. (2012) Clark et al. (2011) French et al. (2009) Pound et al. (2013) Shahzad et al. (2018) Mairhofer et al. 2012 Schmittgen et al. (2015)

6 Phenomics Platforms 6.1 Controlled Environments Now, it is possible to phenotype thousands of plants in robust manner under controlled condition. In controlled phenomics, plants are moved through conveyor belt and a chamber equipped with automated imaging systems such as RGB, infrared, or

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3D cameras (Li et al. 2021b). The pots are labelled with barcodes or radio tags such that the system identifies plants with intriguing characteristics in a pot. The chosen plants can then be grown to yield seed, which can be used for further research and breeding. This platform collects massive phenotypic data from thousands of plats throughout the growth period. Using this system, minimum of hundreds of plants can be phenotyped daily for various traits. It is a powerful tool enabling us to measure crop growth, yield, and its component traits in a noninvasively, precisely, and rapidly. Subsequently, in combination with molecular breeding approaches like QTL mapping, genome-wide association studies (GWAS), genomic selection (GS), it achieves product development through genomics-assisted breeding, thereby helping famers to produce sustainable crop yield (Fahlgren et  al. 2015). Dodig et  al. (2021) monitored dynamics of vegetative growth and quantified drought adaptability of 20 maize inbreds employing image-based phenomic tools under controlled conditions. Images are captured through RGB camera, and 13 biomass-related and morphophysiological traits were extracted. Zhang et al. (2017) employed rice automatic phenotyping platform to phenotype growth-related traits of maize RIL population. Data obtained from controlled phenotyping is further utilized for QTL mapping that resulted in detection of QTLs associated with dynamics of genetic architecture of plant growth. Very recently, 30 maize inbred lines were screened for drought tolerance at National Plant Phenomic facility (ICAR-NIASM), Baramati, India. Different image-derived parameters, namely, pixel area, plant aspect ratio, convex hull ratio, and calliper length, were assessed to use them as surrogate traits for rapid nonestructive phenotyping for drought tolerance in maize. In addition to aboveground plant traits, roots play vital role in plant responses to environmental stimuli. “Shovelomics” is a least complicated and less resource-­ intensive platform for root phenotyping of maize under controlled condition (Trachsel et al. 2011). It provides information about number of roots, root angle, and branching patterns of crown and brace roots. However, this technique lacked desired level of precision for detection of associate genes and QTL with high confidence levels. GROWSCREEN-Rhizo is also one of the root phenotyping platforms under controlled condition. It captures images from shoots and roots in transparent soil-filled rhizotrons simultaneously. It allows integrating additional sensors (i.e., hyperspectral) to estimate root chemical changes, such as root water and lignin content under drought conditions (Bodner et al. 2018).

6.2 Field Phenotyping Often, controlled environmental experimental results are not reflecting the actual filed condition. Thus, development and deployment of high-throughput field phenotyping is alternative to controlled facility. Ideal field phenotyping platforms should be capable of integrating tools and software for large-scale data acquisition, scoring, and processing, as well as remote sensing methods, with automated data collection at an affordable cost. Evidences from the study suggest that phenotyping,

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particularly at the field level, is the main limiting factor that contributes to poor efficiency of conventional breeding in capturing full potential of molecular breeding approaches (Araus et  al. 2008; Cabrera-Bosquet et  al. 2011; Cairns et  al. 2012; Cobb et al. 2013; Araus and Cairns 2013). However, with rapid advances in phenomic science, particularly cutting-edge technologies like sensors, aeronautics, and high-performance computing are paving the way to improve precision, throughout in dissecting trait complexity. Su et al. (2019b) deployed ground-based laser scanner to measure key traits (plant height, plant area index, and projected leaf area PLA) in different maize varieties under drought stress with substantial accuracy of measurement for plant height (96%) and projected leaf area (92%) in comparison to plant area index. Biomass is a key yield contributing trait in maize, which was predicted in different maize genotypes using terrestrial LiDAR. Jin et al. (2020) demonstrated the application of LiDAR in measurement of plant height weed dynamics and leaf area in maize and sorghum (Andújar et al. 2013: Thapa et al. 2018). Montes et al. (2011) developed and deployed HTTP platform consisting of light curtains (LC) and spectral reflectance (SR) sensors to measure biomass of maize plants under field conditions. However, phenotyping of large number of germplasms under field condition is challenging task. In addition, soil heterogeneity, differences in agronomic practices, and aberrant weather parameters are imposing greater challenges in field phenotyping. To overcome this bottleneck, both ground-based and aerial field phenotyping platforms are being increasingly employed in field phenotyping of crop plants. Ground-Based Phenomics Tools Various approaches have been proposed to evaluate targeted traits. For example, a field Scanalyzer system for field phenotyping developed by Rothamsted Research station fitted with sensors, RGB camera, laser scanner, IR camera, hyperspectral cameras, a chlorophyll fluorescence sensor, and a CO2 sensor. This platform was used to investigate dynamics of canopy development in wheat crop (Sadeghi-Tehran et al. 2017; Virlet et al. 2017). Similarly, another platform Crop3D measures plant architectures three-dimensionally in addition to canopy temperature (Guo et  al. 2018). Yet some of the drawbacks associated with open field phenotyping system include vulnerability to errors arising out of variation in quantity and quality of light, the cost of maintenance, and difficulties in analyzing terabyte data in the form of hyperspectral images and laser-scanned images (Vadez et al. 2015). To overcome some of these problems, “BreedVision system” mounted multiple sensors like hyperspectral imaging, lasers, and RGB sensors to phenotype traits such as plant height, yield, tiller number, water content, canopy color, and dry biomass nondestructively (Busemeyer et al. 2013; Fanourakis et al. 2014). Further INRA group based in Montpellier, France, has an automated protocol to monitor silk development in maize over a space and time (Brichet et  al. 2017). High-throughput rice phenotyping facility (HRPF) equipped with X-ray CT, automatic controls, and an image analysis pipeline has been successfully demonstrated for measurement of 15

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traits in rice, and this has been also demonstrated for assessing genetic variation for growth-related traits in maize for GWAS study (Zhang et al. 2017). However, establishing high-throughput conveyor-based phenotyping systems is expensive, and maintaining function and adaptability requires skills in engineering and computational sciences. Because the unit cost of such platform is proportional to the throughput, such systems can only be justified at major research institutions or corporations. The most effective features for field phenotyping combine the crop’s performance in terms of capturing resources and how efficiently these resources are used in time and space (Araus et al. 2002, 2008). Arial Platforms Ground-based HTPPs may capture data at the plot level, which is straightforward and requires little postprocessing efforts for interpretation of data. Additionally, this technology allows us to use closed multispectral imaging systems, which eliminate the influences of wind and sunlight (Svensgaard et al. 2014). On the other hand, ground-based HTTPs are limited by their inability to simultaneously measure all plots within a trial (Busemeyer et  al. 2013). A smart and alternative solution to ground-based phenotyping is to exploit remote sensing of plant response sensors mounted on drones or unmanned aerial vehicle (UAV), which provide a versatile platform for quickly collecting data across environments in robust manner. This technology can be facilitated by data analytical packages like machine vision, deep learning, and AI that can handle millions of remote sensing images with greater speed and precision (Bauer et al. 2019). Thus, drone-facilitated remote sensing has become a popular tool for tracking drought stress responses; assessing nutrient status and growth, dynamics, and weed and pathogen detection; predicting yield (Maes and Steppe 2019); and identifying relevant QTLs (Wang et al. 2019a). A UAV-­based imaging system has been utilized to estimate leaf area, identification of wheat ears (Madec et al. 2019) and weeds (Hung et al. 2014), and assessment of seedling vigor (Zhao et  al. 2018). In addition, crop nutrition status and monitoring of various stresses can be carried out with the aid of tools that sense visible or NIR bands fixed in UAVs. RGB camera is often a good tool that can be used to assess the leaf N status of genotypes under stress conditions (Elazab et al. 2016) and the incidence and intensity of diseases (Vergara-Diaz et al. 2015). Further, IR and NIR provide detailed spectral information, which increases crop growth monitoring accuracy. Detailed information on uses of UAV-based trait measurement is reviewed by Aasen et  al. (2018) and Maes and Steppe (2019). Wang et  al. (2019a, 2019b) analyzed plant height of diverse maize inbred lines at different growth stages employing UAV high-throughput phenotypic platforms (UAV-HTPPs) that ensured 95% accuracy relative to manual measurement. Further, GWAS identified 68 unique quantitative trait loci (QTLs) for seven plant height (PH)-related traits, and 35% of the QTLs coincided with those previously reported to control PH. Dynamics of maize growth under nitrogen and drought stress can be measured using RGB camera. A total of 13 component traits were derived from RGB-captured images associated with growth

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of maize (Dodig et al. 2021). Su et al. (2019a) measured plant height, canopy leaf area index, and one lodging area using UAV-based and photogrammetric method to identify lodging-tolerant donors in maize. Han et al. (2018) developed a semiautomated, UAV-based HTTP fitted with RGB camera and multispectral sensors for measuring plant height, canopy cover, and normalized vegetation index (NDVI) to analyze growth dynamics of maize inbreds. Results of this experiment revealed that plant height, the most heritable trait, can be measured precisely in comparison to manual method, whereas NDVI trait was the most easily detectable among the traits influenced by the external environment in maize. Wang et  al. (2017) used hyperspectral data to estimate biomass in maize. Measurement of leaf water and nitrogen status under moisture stress is limited by phenotyping bottlenecks. With rapid evolution in current phenotyping tools and remote sensing approaches, hyperspectral measurements provided information about leaf N, water status, and also grain yield under different growth stages (Osborne et al. 2002). Trachsel et al. (2019) predicted yield data of maize DH lines using hyperspectral data in combination with genotyping data under heat stress. Different regression approaches (partial least squares regression, random forest, ridge regression, and Bayesian ridge regression) were applied for accurate prediction of yield data, among which BayesB model predicted grain yield of maize very accurately based on physiological genomic estimated breeding values. Though at macro levels, climate and land scape play an important role in determining the crop yield of maize, at micro levels, soil moisture and nutrient deficiencies, insect pest, and diseases directly influence plant response to environmental stimuli (Zhao et  al. 2003). Plant infested with pathogen undergoes a series of changes at cell as well as tissue levels as a result of defensive modifications in biochemical constituents, which can be captured through spectral signatures (Tables 2 and 4) (Xu et al. 2007; Sudbrink et al. 2003). A study conducted on performance and resistance to Tar Spot Complex (TSC) at CIMMYT’s Agua Fria Experimental Station revealed that remote sensing could be employed as an alternate tool for assessment of disease resistance and prediction of potential yield loses incurred due to TSC in large population (Fig. 2) (Loladze et al. 2019).

7 Data Management and Analysis Tools Increased number of open-source tools for image analysis has offered development and deployment of digital camera-based protocols for phenotyping. Software such SmartGrainestimates seed size and shape (Tanabata et al. 2012) and GrainScan provides information about grain size and color-related traits (Whan et al. 2014). An Android-based “SeedCounter” mobile application precisely estimates grain size and grain number (Komyshev et al. 2016).

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Table 4  Mobile-based applications for disease detection in crop plants Sl. No. Application 1. CropsAI

2.

Plantix

3.

Leaf doctor

4.

Crop doctor

5.

Purdue tree doctor

6.

Leaf Plant Tech

Developer Spacenus

Compatibility Specific role IOS Employed for disease detection in crops like apple, blueberry, cherry, corn, grape, orange, peach, pepper, potato, raspberry, soybean, squash, strawberry, and tomato PEAT GmbH Android Identification of disease in crop plants also suggest the management practice either chemical or biological Cornell University IOS/android Identification of disease and calculate and the University the percentage of disease infestation of Hawaii at Manoa Govt. of India Android Specific for rice, wheat, and maize starts from management practices to disease detection Purdue university IOS App. Contains a database of 1000 high-resolution plant disease images, farmer has to match the symptoms accordingly with that of database Jaguza Tech IOS Interpretation of spectral data generated Uganda Limited by RS using drone and giving appropriate alert to farmers in a region about disease incidence

Fig. 2  Flow chart elucidating disease detection employing remote sensing

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8 Integration of High-Throughput Phenotyping and Genomic Approaches in Maize Genetic studies integrated with high-throughput phenotyping provided better insights into traits and their inheritance. Several studies deployed the trait dissection with the aid of high-throughput phenotyping and genetic mapping approaches that include biparental population and diverse genotypes for genome-wide association studies (GWAS) for mapping QTL in many crops including maize. Leaf architecture traits (Condorelli et al. 2018), root anatomical traits (Xie et al. 2017), biomassor growth-related traits (Zhang et  al. 2017), drought-related and salinity response-related traits (Guo et al. 2018)), and yield-related traits (Zhou et al. 2019) have been mapped in many crops. Leiboff et  al. (2015, 2016) employed high-­ throughput phenotyping for maize shoot apical meristem (SAM) traits in diverse genotypes and backcross population. GWAS and QTL analyses coupled with phenotyping data revealed that maize seedling SAM morphology of maize seedlings can predict phenotypes of adult maize plants. Plant leaves play crucial role in photosynthesis and transpiration processes (Wang et al. 2011). The yield potential of a crop is determined by plant growth, biomass, canopy architecture, and canopy greenness (Wang et  al. 2015). Zhang et  al. (2017) generated 22 leaf architecture traits in RILS of maize using HRPF platform, and results revealed that leaf angle and leaf length distribution can be used as yield prediction. In a similar study, Muraya et al. (2017) assessed the dynamics of growth-related traits in a set of 252 diverse inbred lines of maize nondestructively. Further, these phenotyping data were utilized for GWAS and GS to dissect complexity of biomass trait at specific developmental stage. Exchange of gas via stomatal conductance is central to photosynthesis. Prado et  al. (2018) noninvasively measured whole stomatal exchange parameters using high-throughput phenotyping platform in 254 maize hybrids. Further, they have identified 16 QTLs by GWAS and colocalized with transpiration and biomass QTLs. Wang et  al. (2019a, b) measured plant height of 252 diverse tropical and temperate maize lines noninvasively by employing UAV-­ HTTP. Accuracy of UAV measured was more than 95% relative to manually measurements. Further data from UAV was utilized for GWAS that resulted in 68 QTLs of which seven were unique QTLs for plant height-related traits and remaining were associated with previously reported QTLs for plant height (Table 5).

9 Conclusion and Future Prospectives Maize is a versatile crop used for diverse products. As it is a model C4 plant for pant genetic studies, it is also being explored for developing phenomic tools. Monitoring plant responses to environment is at the core of maize breeding and management recommendation practices. Due to the time- and labor-intensive nature of manual phenotyping, the phenotyping efforts have been typically focused only at the

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Table 5  Phenotyping tools/phenomic platform available for maize Phenomic tools/ platforms Maize-IAS (maize image analysis software Zeppelin NT aircraft PlantU-net Phenomobile Laser scanner LiDAR

High precision laser scanning system Hyperspectral reflectance Shovelomics

UAV-HTPPs Multispectral drone imagery RGB and hyperspectral imaging Thermography Spectro-radiometer (NDVI) Robot-assisted imaging pipeline Carbon isotope DeepCob Photometry

Traits measured Leaf sheath, number of leaves, internode length, diameter of stem and plant height Early vigor, leaf length, plant density, vigor rate and senescence rate, and leaf area index Crop growth, seedling condition, shoot coverage Row spacing and plant height Height of plants, plant area index and projected leaf area, stem and leaf traits, biomass Plant heigh, biomass, leaf area, weed dynamics

References Zhou et al. (2021b) Liebisch et al. (2015) Li et al. (2021b)

Plant growth and architectural traits

Qiu et al. (2019) Su et al. (2019b); Jin et al. (2018, 2020) Andújar et al. (2013); Thapa et al. (2018) Paulus et al. (2014)

Biochemical and photosynthetic traits

Craig et al. (2017)

Root angles, branching density of brace and Samuel et al. (2011) crown roots, number of crown and brace roots, etc. Plant height Wang et al. (2019a, b) Water use efficiency Thorp et al. (2018) Projected plant area, shoot fresh and dry weight, leaf area, indirectly WUE, plant growth dynamics, plant color Adoption to water stress

Ge et al. (2016),  Aguate et al. (2017),

Green biomass

Giuseppe et al. (2011) Ge et al. (2016)

Silk and ear growth

Brichet et al. (2017)

Measurement of WUE Cob geometry Measurement of immature ears

Philippe et al. (2007) Lydia et al. (2021) Li et al. (2016)

terminal stages of crop growth for measurement of traits such as plant height, ear height, and yield. The development of robust, rapid, and low-cost phenomics methods can allow the assessment of key morphological features throughout the growing season. This is beneficial in a breeding context as it allows breeders to understand genetic variation in environmental responses, which is the prerequisite to develop varieties resilient to extreme weather events. Recent technological advances in drones, sensors, and computational tools can help accomplish this task. However, there is a need of optimization of protocols for each of the tools and traits for

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realizing the desired impact of phenomics and genomics for every crop including maize. The advances in optimized analysis procedures for extracting traits of agronomic importance using unmanned aerial systems. These procedures will be applied to a relatively homogeneous field with variable genotypes, akin to a corn breeding nursery to evaluate plants on a plot basis for variable responses to environmental conditions. In addition, sensors have high spectral resolution to evaluate plants under a number of stress conditions to identify the unseen signatures of stress that proceed visible phenotypes, allowing for earlier management interventions. Detecting and understanding nonvisible early symptoms will also provide valuable information for future physiological and genetic work to understand the specific mechanisms plants use to respond to their environment so that we can create more resilient plants for the future.

References Aasen H, Honkavaara E, Lucieer A, Zarco-Tejada PJ (2018) Quantitative remote sensing at ultra-­ high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows. Remote Sens 10:1091 Aguate FM, Trachsel S, Pérez LG, Burgueño J, Crossa J, Balzarini M, Gouache D, Bogard M, de los Campos G (2017) Use of hyperspectral image data outperforms vegetation indices in prediction of maize yield. Crop Sci 57:2517–2524 Andújar D, Dionisio, Escolà A, Alexandre, Joan RR, César F, José D (2013) Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops. Comput Electron Agric 92:11–15 Aquino A, Barrio I, Diago MP, Millan B, Tardaguila J (2018) vitisBerry: an Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis. Comput Electron Agric 148:19–28 Araus JL, Cairns JE (2013) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61. https://doi.org/10.1016/j.tplants.2013.09.008 Araus JL, Slafer G, Reynolds M, Royo C, Cobb JA (2002) Plant breeding and drought in C3 cereals: what should we breed for? Ann Bot 89:925–940 Araus JL, Slafer GA, Royo C, Serret MD, Dolores Serret M (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27:377–412 Araus JL, Serret MD, Edmeades GO (2012) Phenotyping maize for adaptation to drought. Front Physiol 3:305. https://doi.org/10.3389/fphys.2012.00305 Atkinson JA, Pound MP, Bennett MJ, Wells DM, Cobb JC (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol 55:1–8 Baker NR (2008) Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu Rev Plant Biol 59:89–113 Barbagallo RP, Oxborough K, Pallett KE, Baker NR (2003) Rapid, non-invasive screening for perturbations of metabolism and plant growth using chlorophyll fluorescence imaging. Plant Physiol 132(485–493):37 Basavaraj PS, Rane J (2021) Avenues to realize potential of phenomics to accelerate crop breeding for heat tolerance. Plant Physiol Rep 25:594–610 Basavaraj PS, Muralidhara B, Manoj CA, Anantha MS, Rathod S, Damodar Raju CH et al (2021) Identification and molecular characterization of high-yielding, blast resistant lines derived from Oryza rufipogon Griff. In the background of ‘Samba Mahsuri’ rice. Genet Resour Crop Evol 8:1905–1921. https://doi.org/10.1007/s10722-­020-­01104-­1

Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate…

281

Bauer A, Bostrom AG, Ball J, Applegate C, Cheng T, Laycock S, Rojas SM, Kirwan J, Zhou J (2019) Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: a case study of lettuce production. Hortic Res 6:70 Baye TM, Abebe T, Wilke RA (2011) Genotype-environment interactions and their translational implications. Pers Med 8(1):59–70. https://doi.org/10.2217/pme.10.75 Berger B, Parent B, Tester M (2010) High throughput shoot imaging to study drought responses. J Exp Bot 61:3519–3528 Bhat JA, Ali S, Salgotra RK, Mir ZA, Dutta S, Jadon V, Tyagi A, Mushtaq M, Jain N, Singh PK, Singh GP, Prabhu KV (2016) Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Front Genet 7:221. https://doi.org/10.3389/fgene.2016.00221 Bodner G, Nakhforoosh A, Arnold T, Leitner DJ (2018) Hyperspectral imaging: a novel approach for plant root phenotyping. Plant Methods 14:1–17 Brichet N, Fournier C, Turc O, Strauss O, Artzet S, Pradal C, Welcker C, Tardieu F, Cabrera-­ Bosquet L (2017) A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods 13:96. https://doi.org/10.1186/ s13007-­017-­0246-­7 Burton AL, Williams M, Lynch JP, Brown KM (2012) Rootscan: software for high-throughput analysis of root anatomical traits. Plant Soil 357:189–203 Busemeyer L, Mentrup D, Moller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Wurschum T, Muller J (2013) Breed vision–a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13:2830–2847 Cabrera-Bosquet L, Molero G, Stellacci A, Bort J, Nogués S, Araus JJ (2011) NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. J Integr Plant Biol 39:147–159 Cairns JE, Sonder K, Zaidi P, Verhulst N, Mahuku G, Babu R, Nair S, Das B, Govaerts B, Vinayan MJ (2012) Maize production in a changing climate: impacts, adaptation, and mitigation strategies. Adv Agron 114:1–58 Carrolla AA, Clarke J, Fahlgrenc NA, Gehanc MJ, Lawrence-Dilld C, Lorencee L (2019) NAPPN: who we are, where we are going, and why you should join us! Plant Phenom J 2:180006 Chaerle L, Lenk S, Leinonen I, Jones HG, Van Der Straeten D, Buschmann C (2009) Multi-sensor plant imaging: towards the development of a stress catalogue. Biotechnol J 4:1152–1167 Chawade A, Joost V, Hanna B, Alexandersson E, Rodomiro O (2019) High-throughput field-­ phenotyping tools for plant breeding and precision agriculture. Agronomy 9(5):258 Clark RT, MacCurdy RB, Jung JK, Shaff JE, McCouch SR, Aneshansley DJ, Kochian LV (2011) Three-dimensional root phenotyping with a novel imaging and software platform. Plant Physiol 156:455–465 Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126:867–887. https://doi. org/10.1007/s00122-­013-­2066-­0 Condorelli GE, Maccaferri M, Newcomb M, Andrade-Sanchez P, White JW, French AN, Sciara G, Ward R, Tuberosa R (2018) Comparative aerial and ground based high throughput phenotyping for the genetic dissection of NDVI as a proxy for drought adaptive traits in durum wheat. Front Plant Sci 9:893 Confalonieri R, Paleari L, Foi M, Movedi E, Vesely FM, Thoelke W, Agape C, Borlini G, Ferri I, Massara F, Motta R, Ravasi AR, Tartarini S, Zoppolato C, Baia LM, Brumana A, Colombo D, Curatolo A, Rossini L (2017) PocketPlant3D: analysing canopy structure using a smartphone. Biosyst Eng 164:1–12 Craig RY, Tiago Tomaz T, Christopher M, Youyuan C, Alison M, Patrick JB, Lauren M, Andrew DB, Ainsworth EA (2017) High-throughput phenotyping of maize leaf physiological and biochemical traits using hyperspectral reflectance. Plant Physiol 173(1):614–626. https://doi. org/10.1104/pp.16.01447

282

P. S. Basavaraj et al.

Crain JL, Yong W, Barker J, Sean M, Phillip AD, Reynolds M, Zhang N (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56(3):965. https://doi. org/10.2135/cropsci2015.05.0290 Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4:349–379. https://doi. org/10.3390/agronomy4030349 Dhondt S, Wuyts N, Inzé D (2013) Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci 18:428–439. https://doi.org/10.1016/j.tplants.2013.04.008 Diamond J (1997) Guns, germs, and steel: the fates of human societies. Norton and Company, New York Dodig D, Božinović S, Nikolić A, Zorić M, Vančetović J, Ignjatović-Micić D, Delić N, Weigelt-­ Fischer K, Altmann T, Junker A (2021) Dynamics of maize vegetative growth and drought adaptability using image-based phenotyping under controlled conditions. Front Plant Sci 11(12):652116. https://doi.org/10.3389/fpls.2021.652116 Duan L, Yang W, Huang C, Liu Q (2011) A novel machinevision- based facility for the automatic evaluation of yield-related traits in rice. Plant Methods 7:44 Dwivedi SD, Goldman I, Ceccarali C, Ortiz R (2020) Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding. Adv Agron 162:89–142 Elazab A, Ordonez RA, Savin R, Slafer GA, Araus JL (2016) Detecting interactive effects of N fertilization and heat stress on maize productivity by remote sensing techniques. Eur J Agron 73:11–24 Fahlgren N, Gehan MA, Baxter I (2015) Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24:93–99. https://doi.org/10.1016/j. pbi.2015.02.006 Fanourakis D, Briese C, Max JF, Kleinen S, Putz A, Fiorani F, Ulbrich A, Schurr U (2014) Rapid determination of leaf area and plant height by using light curtain arrays in four species with contrasting shoot architecture. Plant Methods 10:9 FAO (2020) Statistical database. http://faostat3.fao.org/home/E Feng R, Zhang Y, Yu W, Hu W, Wu J, Ji R, Zhao X (2013) Analysis of the relationship between the spectral characteristics of maize canopy and leaf area index under drought stress. Acta Ecol Sin 33(6):301–307. https://doi.org/10.1016/j.chnaes.2013.09.001 French A, Ubeda-Tomás S, Holman TJ, Bennett MJ, Pridmore TJ (2009) High-throughput quantification of root growth using a novel image-analysis tool. Plant Physiol 150:1784–1795 Fuchs M (1990) Infrared measurement of canopy temperature and detection of plant water stress. Theor Appl Climatol 42:253–261. https://doi.org/10.1007/BF00865986 Furbank RT, Tester M (2011) Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644. https://doi.org/10.1016/j.tplants.2011.09.005 Gage JL, Miller ND, Spalding EP, Kaeppler SM, de Leon N (2017) TIPS: a system for automated image-based phenotyping of maize tassels. Plant Methods 13:21 Galkovskyi T, Mileyko Y, Bucksch A, Moore B, Symonova O, Price CA, Topp CN, Iyer-Pascuzzi AS, Zurek PR, Fang SJ (2012) GiA roots: software for the high throughput analysis of plant root system architecture. BMC Plant Biol 12:116 Garbout A, Munkholm LJ, Hansen SB, Petersen BM, Munk OL, Pajor R (2011) The use of PET/ CT scanning technique for 3D visualization and quantification of real-time soil/plant interactions. Plant Soil 352:113–127 Gaudin AC, Henry A, Sparks AH, Slamet-Loedin IH (2013) Taking transgenic rice drought screening to the field. J Exp Bot 64:109–117 Ge TB, Stoerger G, Vincent S, James C (2016) Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Comput Electron Agric 127:625–632. https://doi.org/10.1016/j.compag.2016.07.028 Gegas VC, Nazari A, Griffiths S, Simmonds J, Fish L, Orford S, Sayers L, Doonan JH, Snape JW (2010) A genetic framework for grain size and shape variation in wheat. Plant Cell 22:1046–1056

Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate…

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Ghimire B, Timsina D, Nepal J (2015) Analysis of chlorophyll content and its correlation with yield attributing traits on early varieties of maize (Zea mays L.). J Maize Res Dev 1(1):134–145 Giuseppe R, Zia S, Speer W, Sanchez C, Cairns J, Araus JL, Müller J (2011) Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput Electron Agric 79(1):67–74. https://doi.org/10.1016/j.compag.2011.08.011 Glen FP, O’Hare TJ (2017) Analyzing maize grain quality. Burleigh Dodds Science Publishing Limited, pp 1–18. https://doi.org/10.19103/AS.2016.0001.14 Guo Q, Wu F, Pang S, Zhao X, Chen L, Liu J, Xue B, Xu G, Li L, Jing H (2018) Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping. Sci China Life Sci 61(3):328–339 Hallau L, Neumann M, Klatt B, Kleinhenz B, Klein T, Kuhn C, Rohrig M, Bauckhage C, Kersting K, Mahlein AK (2018) Automated identification of sugar beet diseases using smartphones. Plant Pathol 67:399–410 Han M, Zhang H, DeJonge KC, Comas LH, Trout TJ (2016) Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agric Water Manag 177:400–409. https://doi.org/10.1016/j.agwat.2016.08.031 Han L, Yang G, Yang H, Xu B, Li Z, Yang X (2018) Clustering field-based maize phenotyping of plant-height growth and canopy spectral dynamics using a UAV remote-sensing approach. Front Plant Sci 9:1638. https://doi.org/10.3389/fpls.2018.01638 Harris BN, Sadras VO, Tester M (2010) A water-centred framework to assess the effects of salinity on the growth and yield of wheat and barley. Plant Soil 336:377–389 Herbert KD (2017) Assessment of maize (Zea mays) as feed resource for poultry. In: Poultry science. Milad Manafi, Intech Open. https://doi.org/10.5772/65363 Houle D, Govindaraju DR, Omholt S (2010) Phenomics: the next challenge. Nat Rev Genet 11:855–866. https://doi.org/10.1038/nrg2897 Hung C, Xu Z, Sukkarieh S (2014) Feature learning-based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV. Remote Sens 6:12037–12054 Jahnke S, Menzel MI, van Dusschoten D, Roeb GW, Buhler J, Minwuyelet S, Blumler P, Temperton VM, Hombach T, Streun M (2009) Combined MRI-PET dissects dynamic changes in plant structures and functions. Plant J 59:634–644 Jannink JL, Lorenz AJ, Iwata HJ (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177 Jefferies SP, Barr AR, Karakousis A, Kretschmer JM, Manning S, Chalmers KJ, Nelson JC, Islam AKMR, Langridge P (1999) Mapping of chromosome regions conferring boron toxicity tolerance in barley (Hordeum vulgare L.). Theor Appl Genet 98:1293–1303 Jeudy C, Adrian M, Baussard C, Bernard C, Bernaud E, Bourion V, Busset H, Cabrera-Bosquet L, Cointault F, Han SJ (2016) RhizoTubes as a new tool for high throughput imaging of plant root development and architecture: test, comparison with pot grown plants and validation. Plant Methods 12:1–18 Jiang B, Wang P, Zhuang S, Li M, Li Z, Gong Z (2018) Detection of maize drought based on texture and morphological features. Comput Electron Agric 151:50–60 Jin S, Su Y, Wu F, Pang S, Gao S, Hu S, Tianyu L, Qinghua G (2018) Stem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial lidar data. IEEE Trans Geosci Remote Sens 57:1–11. https://doi.org/10.1109/TGRS.2018.2866056 Jin S, Su Y, Song S (2020) Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level. Plant Methods 16:69. https://doi. org/10.1186/s13007-­020-­00613-­5 Johannsen W (1911) The genotype conception of heredity. Am Nat 45(531):129–159 Jones HG, Vaughan RA (2010) Remote sensing of vegetation: principles, techniques and applications. Oxford University Press, Oxford Komyshev E, Genaev M, Afonnikov D (2016) Evaluation of the seed counter, a mobile application for grain phenotyping. Front Plant Sci 7:1990

284

P. S. Basavaraj et al.

Le Marie C, Kirchgessner N, Marschall D, Walter A, Hund A (2014) Rhizoslides: paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis. Plant Methods 10:13 Le Marié C, Kirchgessner N, Flütsch P, Pfeifer J, Walter A, Hund AJ (2016) RADIX: rhizoslide platform allowing high throughput digital image analysis of root system expansion. Plant Methods 12:1–15 Leiboff S, Li X, Hu HC, Todt N, Yang J, Yu X, Muehlbauer GJ, Timmermans MC, Yu J, Schnable PS (2015) Genetic control of morphometric diversity in the maize shoot apical meristem. Nat Commun 6:8974 Leiboff S, DeAllie CK, Scanlon MJ (2016) Modeling the morphometric evolution of the maize shoot apical meristem. Front Plant Sci 7:1651 Levitt J (1972) Responses of plants to environmental stresses. Academic, New  York, San Francisco, London Li B, Fan R, Huang S (2017) Far infrared imaging, an effective way to screen maize seedling mutants for drought stress response. Biologia 72:1010–1016. https://doi.org/10.1515/ biolog-­2017-­0111 Li G, Hanselman TA, Hockenson J, Loussaert DF, Moriarty TM, Woods R (2016) Immature ear photometry in maize. US patent number. US 2016/022.3507 A1. Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A (2021a) High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field. Front Bioeng Biotechnol 8:623705. https://doi.org/10.3389/fbioe.2020.623705 Li Y, Wen W, Guo X, Yu Z, Gu S, Yan H et al (2021b) High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network. PLoS One 16(1):e0241528. https://doi.org/10.1371/journal.pone.0241528 Liebisch F, Kirchgessner N, Schneider D, Walter A, Hund A (2015) Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods 11(1):9. https://doi. org/10.1186/s13007-­015-­0048-­8 Liu Y, Subhash C, Yan J, Song C, Zhao J, Li JJ (2011) Maize leaf temperature responses to drought: thermal imaging and quantitative trait loci (QTL) mapping. J Exp Bot 71:158–165 Loladze A, Rodrigues FA Jr, Toledo F, San Vicente F, Gérard B, Boddupalli MP (2019) Application of remote sensing for phenotyping tar spot complex resistance in maize. Front Plant Sci 10:552 Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123 Lydia K, Abondano MC, Blas R, Schmid R (2021) DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics. bioRxiv 2021.03.16.435660. https://doi.org/10.1101/2021.03.16.435660 Ma BR, Zhang PS, Wu L (2018) Spectral identification of stress types for maize seedlings under single and combined stresses. IEEE 6:13773–13782. https://doi.org/10.1109/ ACCESS.2018.2810084 Madec S, Jin X, Lu H, De Solan B, Liu S, Duyme F, Heritier E, Baret F (2019) Ear density estimation from high resolution RGB imagery using deep learning technique. Agric For Meteorol 264:225–234 Maes WH, Steppe K (2019) Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci 24:152–164 Mairhofer S, Zappala S, Tracy SR, Sturrock C, Bennett M, Mooney SJ, Pridmore TJ (2012) RooTrak: automated recovery of three-dimensional plant root architecture in soil from X-ray microcomputed tomography images using visual tracking. Plant Physiol 158:561–569 Mathieu L, Lobet G, Tocquin P, Périlleux CJ (2015) Rhizoponics: a novel hydroponic rhizotron for root system analyses on mature Arabidopsis thaliana plants. Plant Methods 11:1–8 Mazaheri M, Heckwolf M, Vaillancourt B, Gage JL, Burdo B, Heckwolf S, Barry K, Lipzen A, Ribeiro CB, Kono TJY (2019) Genome-wide association analysis of stalk biomass and anatomical traits in maize. BMC Plant Biol 19:45

Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate…

285

McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, Flint-Garcia S, Thornsberry J, Acharya C, Bottoms CJS (2009) Genetic properties of the maize nested association mapping population. Science 325:737–740 Miller ND, Haase NJ, Lee J, Kaeppler SM, de Leon N, Spalding EP (2017) A robust, high-­ throughput method for computing maize ear, cob, and kernel attributes automatically from images. Plant J 89:169–178 Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE (2011) High-throughput non-­ destructive biomass determination during early plant development in maize under field conditions. Field Crop Res 121(2):268–273. https://doi.org/10.1016/j.fcr.2010.12.017 Muraya MM, Chu J, Zhao Y, Junker A, Klukas C, Reif JC, Altmann T (2017) Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non-invasive phenotyping. Plant J 89:366–380 Nagel KA, Putz A, Gilmer F, Heinz K, Fischbach A, Pfeifer J, Faget M, Blossfeld S, Ernst M, Dimaki CJ (2012) GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. J Exp Bot 39:891–904 Ogbaga CC, Miller ME, Athar H, Johnson GN (2017) Fourier transform infrared spectroscopic analysis of maize (Zea mays) subjected to progressive drought reveals involvement of lipids, amides and carbohydrates. Afr J Biotechnol 16(18):1061–1066 Osborne SL, Schepers JS, Francis DD, Schlemmer MR (2002) Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen-and water-stressed corn. Crop Sci 42:165–171 Paulus S, Schumann H, Kuhlmann H, Jens L (2014) High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst Eng 121:1–11. https://doi.org/10.1016/j.biosystemseng.2014.01.010 Pearson CH, Ernst SM, Barbarick KA, Hatfield JL, Peterson GA, Buxton DR (2008) Agronomy journal turns one hundred. Agron J 100:1–8 Philippe M, Sheshshayee MS, Akhter J, Ribaut J (2007) Using carbon isotope discrimination to select maize (Zea mays L.) inbred lines and hybrids for drought tolerance. Plant Sci 173(4):390–396. https://doi.org/10.1016/j..2007.06.003 Phillips RL (2010) Mobilizing science to break yield barriers. Crop Sci 50:S-99–S-108 Pingali PL (2012) Green revolution: impacts, limits, and the path ahead. Proc Nat Acad Sci 109:12302–12308 Pokovai K, Fodor N (2019) Adjusting Ceptometer data to improve leaf area index measurements. Agronomy 9:866. https://doi.org/10.3390/agronomy9120866 Poorter H, Remkes C, Lambers H (1990) Carbon and nitrogen economy of 24 wild species differing in relative growth rate. Plant Physiol 94(2):621–627 Pound MP, French AP, Atkinson JA, Wells DM, Bennett MJ, Pridmore TJ (2013) RootNav: navigating images of complex root architectures. Plant Physiol 162:1802–1814 Prado SA, Cabrera-Bosquet L, Grau A, Coupel-Ledru A, Millet EJ, Welcker C, Tardieu F (2018) Phenomics allows identification of genomic regions affecting maize stomatal conductance with conditional effects of water deficit and evaporative demand. Plant Cell Environ 41:314–326 Qiu Q, Sun N, Bai H, Wang N, Fan Z, Wang Y, Meng Z, Li B, Cong Y (2019) Field-based high-throughput phenotyping for maize plant using 3d lidar point cloud generated with a “Phenomobile”. Front Plant Sci 10:554. https://doi.org/10.3389/fpls.2019.00554 Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3:1–7 Reich PB, Walters MB, Ellsworth DS (1992) Leaf life-span in relation to leaf, plant, and stand characteristics among diverse ecosystems. Ecol Monogr 62:365–392 Reuzeau C (2007) TraitMill (TM): a high throughput functional genomics platform for the phenotypic analysis of cereals. In Vitro Cell Dev Biol Anim 43:S4 Reuzeau C, Pen J, Frankard V, de Wolf J, Peerbolte R, Broekaert W (2005) TraitMill: a discovery engine for identifying yield-enhancement genes in cereals. Mol Plant Breed 1:1–6

286

P. S. Basavaraj et al.

Reynolds M, Langridge P (2016) Physiological breeding. Cur Opin Plant Biol 31:162–171. https:// doi.org/10.1016/j.pbi.2016.04.005 Reynolds M, Pask A, Mullan D (2012) Physiological breeding I: interdisciplinary approaches to improve crop adaptation. CIMMYT Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno DNL, Sukumaran S (2020) Breeder friendly phenotyping. Plant Sci:110396. https://doi.org/10.1016/j. plantsci.2019.110396 Sadeghi-Tehran P, Sabermanesh K, Virlet N, Hawkesford MJ (2017) Automated method to determine two critical growth stages of wheat: heading and flowering. Front Plant Sci 8:252 Samuel T, Shawn MK, Kathleen MB, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341(1–2):75–87. https://doi. org/10.1007/s11104-­010-­0623-­8 Schlemmer MR, Francis DD, Shanahan J, Schepers JS (2005) Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron J 97:106–112 Schmittgen S, Metzner R, Van Dusschoten D, Jansen M, Fiorani F, Jahnke S, Rascher U, Schurr U (2015) Magnetic resonance imaging of sugar beet taproots in soil reveals growth reduction and morphological changes during foliar Cercosporabeticola infestation. J Exp Bot 66:5543–5553 Schnurbusch T, Hayes JE, Sutton TJ (2010) Boron toxicity tolerance in wheat and barley: Australian perspectives. Breed Sci 60:297–304 Schork NJ (1997) Genetics of complex disease: approaches, problem, and solutions. Am J Respir Crit Care Med 156:S103–S109 Shahzad Z, Kellermeier F, Armstrong EM, Rogers S, Lobet G, Amtmann A, Hills A (2018) EZ-Root-VIS: a software pipeline for the rapid analysis and visual reconstruction of root system architecture. Plant Physiol 177:1368–1381 Shiferaw B, Prasanna BM, Hellin J (2011) Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Sec 3:307. https://doi. org/10.1007/s12571-­011-­0140-­5 Singh CB, Jayas DS, Paliwal J, White NDG (2010) Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging. Comput Electron Agric 73:118–125 Soul M (1967) Phenetics of natural populations I. Phenetic relationships of insular populations of the side-blotched lizard. Evolution 21:584–591 Su W, Zhang M, Bian D, Liu Z, Huang J, Wang W, Wu J, Guo H (2019a) Phenotyping of corn plants using unmanned aerial vehicle (UAV) images. Remote Sens 11(17):2021. https://doi. org/10.3390/rs11172021 Su Y, Wu F, Ao Z (2019b) Evaluating maize phenotype dynamics under drought stress using terrestrial lidar. Plant Methods 15:11. https://doi.org/10.1186/s13007-­019-­0396-­x Sudbrink DL, Harris FA, Robbins JT, English PJ, Willers LL (2003) Evaluation of remote sensing to identify variability of cotton plant growth and correlation with larval densities of beet armyworm and cabbage looper (Lepidoptera: Noctuidae). Fla Entomol 86:290–294 Sun D, Cen H, Weng H, Wan L, Abdalla A, El-Manawy AI, Zhu Y, Zhao N, Fu H, Tang J (2019) Using hyperspectral analysis as a potential high throughput phenotyping tool in GWAS for protein content of rice quality. Plant Methods 15:54 Svensgaard J, Roitsch T, Christensen S (2014) Development of a mobile multispectral imaging platform for precise field phenotyping. Agronomy 4(3):322–336. https://doi.org/10.3390/ agronomy4030322 Swarbrick PJ, Schulze-Lefert P, Scholes JD (2006) The metabolic consequences of susceptibility and the activation of race specific or broad-spectrum resistance pathways in barley leaves challenged with the powdery mildew fungus. Plant Cell Environ 29:1061–1076 Tanabata T, Shibaya T, Hori K, Ebana K, Yano M (2012) SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. Plant Physiol 160:1871–1880

Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate…

287

Tandzi NL, Charles MS (2020) Factors affecting yield of crops. In: Agronomy – climate change & food security. Intech Open, Amanullah. https://doi.org/10.5772/intechopen.90672 Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017) Plant phenomics, from sensors to knowledge. Curr Biol 27:R770–R783. https://doi.org/10.1016/j.cub.2017.05.055 Thapa S, Zhu F, Walia H, Yu H, Ge Y (2018) A novel LiDAR-based instrument for high-­throughput, 3D measurement of morphological traits in maize and sorghum. Sensors 18:1187 Thorp K, Thompson A, Harders S, Andrew F, Richard W (2018) High-Throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sens 10(11):1682. https://doi.org/10.3390/rs10111682 Trachsel S, Kaeppler SM, Brown KM, Lynch JP (2011) Shovelomics: high throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil 341:75–87 Trachsel S, Dhliwayo T, Gonzalez Perez L, Mendoza Lugo JA, Trachsel M (2019) Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.). PLoS One 14(3):e0212200. https://doi.org/10.1371/journal. pone.0212200 Vadez V, Kholova J, Hummel G, Zhokhavets U, Gupta SK, Hash CT (2015) LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. J Exp Bot 66:5581–5593 Vergara-Diaz O, Kefauver SC, Elazab A, Nieto-Taladriz MT, Araus JL (2015) Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions. Crop J 3:200–210 Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford MJ (2017) Field Scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44(1):143. https://doi.org/10.1071/fp16163 Wallays C, Missotten B, Baerdemaeker JD, Saeys W (2009) Hyperspectral waveband selection for on-line measurement of grain cleanness. Biosyst Eng 104:1–7 Wang Z, Wang J, Liu L, Huang W, Zhao C, Wang C (2004) Prediction of grain protein content in winter wheat (Triticum aestivum L.) using plant pigment ratio (PPR). Field Crop Res 90:311–321 Wang P, Zhou G, Yu H, Yu S (2011) Fine mapping a major QTL for flag leaf size and yield-related traits in rice. Theor Appl Genet 123:1319–1330 Wang Q, Xie W, Xing H, Yan J, Meng X, Li X, Fu X, Xu J, Lian X, Yu S (2015) Genetic architecture of natural variation in rice chlorophyll content revealed by a genome-wide association study. Mol Plant 8:946–957 Wang C, Nie S, Xi X, Luo S, Sun X (2017) Estimating the biomass of maize with hyperspectral and lidar data. Remote Sens 9:11–22 Wang J, Badenhorst P, Phelan A, Pembleton L, Shi F, Cogan N, Spangenberg G, Smith K (2019a) Using sensors and unmanned aircraft systems for high-throughput phenotyping of biomass in perennial ryegrass breeding trials. Front Plant Sci 10:1381. https://doi.org/10.3389/ fpls.2019.01381 Wang XQ, Zhang RY, Song W, Han L, Liu XL, Sun X, Luo MJ, Chen K, Zhang YX, Yang H (2019b) Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV). Sci Rep 9:3458 Warman C, Fowler JE (2020) Custom built scanner and simple image processing pipeline enables low-cost, high-throughput phenotyping of maize ears. bioRxiv 780650. https://doi. org/10.1101/780650 Weiss U, Biber P (2011) Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot Auton Syst 59(5):265–273. https://doi.org/10.1016/j.robot.2011.02.011 Whan AP, Smith AB, Cavanagh CR, Ral JP, Shaw LM, Howitt CA, Bischof L (2014) GrainScan: a low cost, fast method for grain size and colour measurements. Plant Methods 10:23 Woo N, Badger MR, Pogson BJ (2008) A rapid non-invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence. Plant Methods 4:27

288

P. S. Basavaraj et al.

Xia T, Miao Y, Wu D, Shao H, Khosla R, Mi G (2016) Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sens 8:605. https://doi.org/10.3390/rs8070605 Xia S, Yang S, Huang M, Zhu Q, Guo Y, Qin J (2019) Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis. Infrared Phys Technol:103077. https:// doi.org/10.1016/j.infrared.2019.103077 Xie Q, Fernando KMC, Mayes S, Sparkes DL (2017) Identifying seedling root architectural traits associated with yield and yield components in wheat. Ann Bot 119:1115–1129 Xu HR, Ying YB, Fu XP, Zhu SP (2007) Near-infrared spectroscopy in detecting leaf miner damage on Tomato leaf. Biosyst Eng 96:447–454 Yazdanbakhsh N, Fisahn JJ (2009) High throughput phenotyping of root growth dynamics, lateral root formation, root architecture and root hair development enabled by PlaRoM. Funct Plant Biol 36:938–946 Zhang X, Huang C, Wu D, Qiao F, Li W, Duan L, Wang K, Xiao Y, Chen G, Liu Q (2017) High-­ throughput phenotyping and QTL mapping reveals the genetic architecture of maize plant growth. Plant Physiol 173:1554–1564 Zhao D, Reddy KR, Kakani VG, Read JJ, Carter GA (2003) Corn (Zea mays L.) growth, leaf pigment concentration, photosynthesis and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant Soil 257:205–217 Zhao B, Zhang J, Yang C, Zhou G, Ding Y, Shi Y, Zhang D, Xie J, Liao Q (2018) Rapeseed seedling stand counting and seeding performance evaluation at two early growth stages based on unmanned aerial vehicle imagery. Front Plant Sci 9:1362 Zhou Y, Srinivasan S, Mirnezami SV, Kusmec A, Fu Q, Attigala L, Salas Fernandez MG, Ganapathysubramanian B, Schnable PS (2019) Semiautomated feature extraction from RGB images for sorghum panicle architecture GWAS. Plant Physiol 179:24–37 Zhou Q, Huang W, Liang D, Tian X (2021a) Classification of aflatoxin B1 concentration of single maize kernel based on near-infrared hyperspectral imaging and feature selection. Sensors 21(13):4257. https://doi.org/10.3390/s21134257 Zhou S, Chai X, Yang Z (2021b) Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. Plant Methods 17:48. https://doi.org/10.1186/ s13007-­021-­00747-­0 Zia S, Romano G, Spreer W, Sanchez C, Cairns J, Araus JL, Müller J (2013) Infrared thermal imaging as a rapid tool for identifying water stress tolerant maize genotypes of different phenology. J Agron Crop Sci 199(2):75–84 Zimmer E, Gurrath PA, Paul C (1990) Near infrared reflectance spectroscopy analysis of digestibility traits of maize stover. Euphytica 48:73–81. https://doi.org/10.1007/BF00028963

Maize Improvement Using Recent Omics Approaches Gopal W. Narkhede

and K. N. S. Usha Kiranmayee

1 Introduction Maize (Zea mays L.) is a major food and feed crop around the world. In terms of area and production, maize is the third most important food crop after rice and wheat, and India is the world’s fifth largest producer, accounting for 3% of total global production. Most people in developing countries are overly reliant on maize as a staple food due to economic necessity. It provides 50% of dietary protein for humans and can account for 70% of protein intake for people in developing countries (Deutscher 1978). In Africa and some Asian countries, nearly 90% of maize grown is for human consumption, accounting for 80–90% of total energy intake. Together with rice and wheat, it accounts for at least 30% of the food calories consumed by more than 4.5 billion people in 94 developing countries. Maize has long served as a model species in genetics, developmental biology, physiology, and, more recently, genomic research. Genetic research on Zea mays L. began with Edward East’s 1908 report on inbreeding depression and hybrid vigor, and the 1940s saw a cytogenetic breakthrough, such as transposable elements (TEs) by Barbara McClintock (Walbot 2008). The accumulated cytogenetic and genetic data, as well as the vast sequence data derived from maize genomic studies, have provided a wealth of information on the structure, function, and evolution of the maize genome. We discuss multi-omics approaches, their applications, and anticipated implementations in maize improvement to improve crop yields and biotic and abiotic stress tolerance in this book chapter.

G. W. Narkhede Kalash Seeds Pvt. Ltd, Jalna, Maharastra, India K. N. S. Usha Kiranmayee (*) Nuziveedu Seeds Pvt. Ltd, Hyderabad, Telangana, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 289 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_13

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2 Genomics The study of genes and genomes is known as genomics, and it focuses on the structure, function, evolution, mapping, epigenomic, mutagenomic, and genome editing aspects (Muthamilarasan et al. 2019). Genomics can play an important role in elucidating genetic variation, which can improve crop breeding efficiency and lead to genetic improvement of crop species. Structural genomics includes sequence polymorphism and chromosomal organization, and it allows plant biologists to create physical and genetic maps to identify traits of interest. Functional genomics, on the other hand, provides insights into the functions of genes in relation to the regulation of the trait of interest. Epigenomics refers to the phenomenon of epigenetic changes occurring at the genomic level in the form of histone modifications, DNA, or small RNA methylations. Mutagenomics is concerned with the mutational events that orchestrate genetic modification in mutant traits. Mutagenomics and pangenomics are two recent omics approaches in crop sciences that focus on mutagenesis and the pangenome, respectively (Golicz et al. 2016; Muthamilarasan et al. 2019).

3 Structural Genomics Structural genomics is reliant on molecular markers, which can be used to tag and map genes of interest before being used in crop breeding programs. There are different types of marker techniques. The first is non-PCR techniques such as restriction fragment length polymorphisms (RFLP). Restriction fragment length polymorphism detects DNA polymorphism by hybridizing a labeled DNA probe to a Southern blot of restriction enzyme digested DNA, resulting in a different DNA fragment profile (Agarwal et  al. 2008). The second is PCR-based techniques for detecting markers such as random amplified polymorphic DNA (RAPD), amplified fragment length polymorphisms (AFLP), and single nucleotide polymorphisms (SNPs) (Williams et al. 1990; Vos et al. 1995). The RAPD markers are created by PCR-based amplification of random DNA segments with a single primer of any nucleotide sequence (Rabouam et al. 1999). Amplification of restriction fragments from a total digest of genomic DNA is also a PCR-based technique that performs selective PCR amplification of restriction fragments (Vos et al. 1995). Single nucleotide polymorphisms are single nucleotide variations in an individual’s or organism’s genome. Sequencing of genomic PCR products derived from various individuals can be used to detect SNPs (Appleby et  al. 2009). In contrast, diversity array technology (DArT) is a high-throughput technique based on microarray hybridization that involves genotyping of numerous polymorphic loci spread across the genome (Jaccoud et al. 2001). With the advent of NGS, it became possible to identify and use SNPs. Quantitative trait loci (QTL) mapping and genome-wide association studies are two approaches used to understand and study the multiple traits in crops (GWAS). Quantitative trait loci mapping is a statistical method for connecting two types of

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data, namely, complex phenotypes and genotypes. Molecular markers such as SNPs and AFLPs are commonly used to map QTLs, which can then be correlated with observed phenotypic data (Kearsey 1998; Challa and Neelapu 2018). GWAS, on the other hand, has the potential to identify variants associated with traits. Based on SNPs in the sequence data, genome-wide association studies may also identify correlations between genetic variants/phenotypes in any organism’s population (Challa and Neelapu 2018). GWAS identified 48 QTLs associated with maize crop yield under heat and water stress (Millet et al. 2016). Furthermore, numerous SNPs associated with drought-responsive TFs have been identified using maize crop GWAS (Shikha et  al. 2017). Furthermore, structural variants (SVs) play an important role in the genetic control of agronomically important traits in crops. Breeders can now improve hybrid breeding by combining marker-assisted selection (MAS) with genotyping by sequencing (GBS) to improve crop quality and yield (He et al. 2014). Multiparent mapping, specifically multiparent advanced generation intercrosses (MAGIC) and nested association mapping (NAM) in model plants and crops (Yu et al. 2008; Kover et al. 2009), has revealed the vast amount of phenotypic diversity that can be achieved through experimental studies. The MAGIC population is excellent for breeding improvement. Analyses of the relationships between genotypes and phenotypes can identify QTLs, which can then be validated using functional genomics approaches.

4 Functional Genomics and Muta-Genomics Functional genomics will eventually make use of the vast resources and information provided by structural genomics. Hieter and Boguski (1997) define functional genomics as the development of global experimental approaches to assess gene function. Numerous biotechnological tools have been developed to identify and isolate genes of interest, clone and characterize those genes, and generate overexpression or knockout lines for functional transgenic studies (Muthamilarasan et  al. 2019). Prior to genome sequencing methods, identifying candidate genes required time-consuming procedures such as suppression subtractive hybridization (SSH), expressed sequence tag (EST), and cDNA-AFLP-sequencing. As a result of the introduction of NGS, the tediousness of these approaches has decreased (Muthamilarasan et al. 2019). The availability of crop genome sequencing has led to the identification of genes involved in disease resistance, stress resistance, and yield determination. Furthermore, using genome editing tools such as the clustered regularly interspaced short palindromic repeats (CRISPR/Cas9 system) and transcription activator-like effector nuclease (TALEN) and authentic genome engineering has been proposed to improve crops (Rinaldo and Ayliffe 2015). Genome editing tools that do not require the insertion of foreign DNA could potentially increase yield in genetically modified crops by introducing pest and disease resistance. A bread wheat mildew

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resistance locus o (TaMlo) mutant was created using TALEN and CRISPR/Cas9 technologies (Wang et al. 2014). Similarly, the same technique was used to create a SlMlo mutant in a tomato crop (Nekrasov et al. 2017). Numerous important crops, including soybean, rice, maize, and sorghum, have already had their genomes edited using the CRISPR/Cas9 system (Jiang et al. 2013; Lawrenson et al. 2015; Li et al. 2015; Svitashev et al. 2015). Several mutants related to crop growth, development, and stress tolerance in rice, maize, wheat, and barley have been identified using comparative genomics (Talukdar and Sinjushin 2015). TILLING has also been used to detect mutations in rice (Suzuki et al. 2008), maize (Till et al. 2004), wheat (Dong et al. 2009), barley (Caldwell et al. 2004), tomato (Minoia et al. 2010), and soybean (Cooper et al. 2008). Mutagenomics has enabled the investigation of gene function by silencing and interrupting candidate genes using reverse genetic approaches. Specific reverse genetic techniques used to screen/induce crop mutations include RNA interference (RNAi) and (VIGS). When mutant alleles are not available, reverse genetic techniques can be used to knockdown or silence the phenotype of a gene, allowing for gene function analysis (Talukdar and Sinjushin 2015). Furthermore, reverse genetic approaches such as RNAi and gene silencing technologies have been used to screen for mutations in maize (Dwivedi et  al. 2008; Tomlekova 2010). As a result, both functional genomics and mutagenomics have been shown to be beneficial in terms of crop growth, yield, and stress resistance.

5 Epigenomics The term epigenetics refers to heritable changes that are not caused by changes in the DNA sequence. These epigenetic changes are caused by DNA methylation and histone posttranslational modification (PTM) (Strahl and Allis 2000; Novik et al. 2002). The combination of epigenetics and genomics is known as epigenomics, and it has emerged as a new omics technique to better understand genetic regulation and its role in cellular growth and stress responses (Callinan and Feinberg 2006). In contrast to genomics, epigenomics can be influenced by environmental factors such as abiotic and biotic stress. Nonetheless, genome-wide studies could be conducted to investigate these epigenetic events at any developmental stage or to assess abnormalities caused by plant disease (Muthamilarasan et  al. 2019). This method was found to be useful in one epigenomic study for identifying histone modifications associated with photosynthesis in maize (Offermann et al. 2006).

6 Pangenomics The pangenome concept refers to a species’ entire genomic makeup, which can be divided into core and dispensable genes. The core gene sets are shared by all individuals, whereas the dispensable gene sets (also known as accessory genes) are individual-specific and/or present in some but not all individuals (Tettelin et  al.

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2005). Advances in sequencing technology and analysis tools have enabled the sequencing of multiple crop species accessions (Golicz et  al. 2016). A wave of pangenomic studies in maize (Hirsch et  al. 2014) has revealed that dispensable genes play important roles in crop diversity and quality improvement.

7 Transcriptomics Transcriptomics is concerned with the transcriptome, which is the complete set of RNA transcripts produced by an organism’s genome in a cell or tissue (Raza et al. 2021). Transcriptome profiling is a dynamic technique that has emerged as a promising technique for analyzing gene expression in response to various stimuli over time (Duque et  al. 2013; El-Metwally et  al. 2014). This strategy enables the researcher to observe the differential expression of genes in vitro in order to comprehend the first layer function of a specific gene. Initially, transcriptome dynamics were studied using traditional profiling techniques such as cDNAs-AFLP, differential display-PCR (DD-PCR), and SSH, but the resolution was low (Nataraja et al. 2017). Following the introduction of robust techniques, RNA expression profiling using microarrays, digital gene expression profiling, NGS, RNAseq, and SAGE became possible (Kawahara et al. 2012; De Cremer et al. 2013; Duque et al. 2013). Furthermore, RNA-seq studies in maize have been conducted to identify drought stress-responsive genes (Kakumanu et al. 2012). Another method for understanding differential expression profiles in response to stress in different crop species is comparative transcriptomics. In response to heat stress, comparative transcriptomic analysis identified 16 common genes in rice, wheat, and maize compared to those in switch grass (Ding et al. 2013; Li et al. 2013). To generate multiple transcripts in response to abiotic stress conditions, an alternative splicing (AS) transcriptomics approach was launched (Laloum et al. 2018). In response to heat and drought stress, this method has been used in crops such as rice, maize, and sorghum (Zhang et al. 2015). As a result, AS transcriptomic analyses revealed the importance of splicing factors in controlling abiotic stress responses in crops. All of these transcriptomic techniques, taken together, have the potential to play a critical role in the regulation of gene expression, resulting in crop species improvement.

8 Proteomics Proteomics is a technique that involves profiling total expressed protein in an organism and is classified into two types. There are four distinct parts: sequence, structural, functional, and expression proteomics (Mosa et al. 2017; Aizat and Hassan 2018). The amino acid sequence is determined by proteomics. Sequences that are typically identified in a sequential

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manner using high liquid chromatography with high performance (HPLC; Twyman 2013). Structural proteomics is concerned with the structure of proteins. Comprehend their ostensible functions Structural proteomics can help be analysed using a variety of methods, including computer based modelling, as well as experimental methods such as nuclear crystallisation, electron microscopy, magnetic resonance (NMR), and protein crystal X-ray diffraction (Sali et al. 2003; Woolfson 2018). Protein extraction and separation advances have contributed to the rapid advancement of plant proteomic research at both the sample and genome-wide scales (Nakagami et al. 2012). Traditional proteomics techniques include exchange chromatography (IEC), size exclusion chromatography (SEC), and affinity chromatography. However, western blotting and enzyme-linked immunosorbent assay (ELISA) could be used to analyze specific proteins. Later, more advanced techniques such as SDS-PAGE, two-dimensional gel electrophoresis (2-DE), and two-dimensional differential gel electrophoresis (2D-DIGE) were developed and used for protein separation using gel-based techniques. Simultaneously, protein microarrays/chips for detection of small amounts of protein sample have been developed for rapid protein expression analysis. SDS-PAGE and two-dimensional gel electrophoresis are required to identify proteins and measure quantitative protein content parameters, respectively (Eldakak et al. 2013). The identified proteins are now used to determine the molecular mass of peptides using mass spectrometry (MS), ion trap-mass spectrometry (IT-MS), or liquid chromatography (LC; Fournier et al. 2007). MALDI-TOF, electrospray ionization (ESI) and collision-induced dissociation (CID) have also been used to determine the molecular weights of proteins (Tanaka et al. 1988; McLuckey and Stephenson Jr. 1998; Baggerman et al. 2005).

9 Metabolomics Transcriptomics, proteomics, and metabolomics also offer opportunities to decipher and understand the molecular basis of stress tolerance. The use of proteomics and metabolomics-based metabolite markers can serve as an efficient selection tool as a substitute for phenotype-based selection. This review covers the molecular mechanisms for salinity stress tolerance, recent progress in mapping and introgressing major gene/QTL (genomics), transcriptomics, proteomics, and metabolomics in major cereals, namely, rice, wheat, and maize (Kumar et al. 2022). Breeding for drought-tolerant crops depends on omics-based approach enabling accelerated maize breeding for biotic and abiotic stress tolerance trait in crop breeding program. Increased nutrient uptake leads to increased growth as well as yield. Plant parts play a major role in nutrient uptake; majorly, nitrogen usage efficiency is mostly dependent on root traits. Maize root traits were well studied under

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nitrogen stress conditions. Several secondary metabolites and amino acids play important role in root biomass growth. Several transcriptomic-based experiments under nitrogen-deficient conditions increased maize root biomass production with the help of phosphatidylcholine and phosphatidyl glycerol metabolites (Chowdhury et al. 2022). All together integrated omics is an efficient approach to enable the stress tolerance. Breeding for drought-tolerant crops depends on omics-based approach enabling accelerated maize breeding for biotic and abiotic stress tolerance trait in crop breeding program. Advanced changes in proteins and metabolites during different environmental conditions and biotic and abiotic stress conditions affect the physiological process and growth. Frequent timely alteration of proteins and metabolites in maize plants will improve growth in hybrids. Heterosis in hybrids shows that metabolites alter physiological changes for increased hybrid vigor (Li et al. 2020). Both biotic and abiotic stresses along with timely growth and physiological and biochemical occur in maize during developmental stages. Various proteins and metabolites released vary during different developmental changes and the diverse proteins and metabolites captured during different developmental stages. Everything is interrelated. The altered genes will produce altered proteins, which combine to form altered metabolites. Different developmental stages and different genes encode for different proteins as well as metabolites. Methods used GC-LC chromatography, confocal microscopy, high-performance liquid chromatography along with ion trap tandem mass spectrometry, HPLC liquid chromatography, and genotype to phenotype prediction using genomics are not always possible for traits. The end product in the cellular regulatory processes might be a combination of gene to gene interactions, and modification leads to physiological changes. More than 200,000 metabolites including primary and secondary metabolites were identified in plants. Primary metabolites are involved in necessary plant growth and developmental activities, and the secondary metabolites are derived from primary metabolites and are involved in plant defense mechanism and biotic and abiotic stresses. Primary metabolites include carbohydrates, lipids, proteins, vitamins, and amino acids, whereas secondary metabolites are alkaloids, phenolics, sterols, steroids, lignins, and essential oils. Maize grains have highest polyphenol content and can be well studied in metabolomics and phytochemicals. Polyphenols are known for its anticancer properties and antioxidant properties. Modifications in the metabolites are the major outcome of phenotypic outcome. Localization of nutritional phytochemicals in plant tissues is a significant information for metabolomics. Maize grains have different phytochemical substances like anthocyanins in aleurone layer and 56 other compounds including oxylipins 13-trihydroxy-octadecenoicacid and 9, 12, 13-trihydroxy-­trans-­10-octadecenoic acid. The genes involved in the synthesis of these substances might be expressed only in certain tissues. Combination of different methods allows more information about variable metabolites (Razgonova et al. 2022). Genetic regulatory mechanism of phenotypic traits can be well understood with metabolome studies.

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In maize, the grain is the major commercial source, and improving the kernel quality not always depends on genomics but also in different metabolites. Metabolites from parental lines and hybrids were assessed, and they have different metabolites, which were clustered differently in PCA and clustering. ANOVA of the metabolites showed that 163 metabolites were exhibited significant difference between hybrids and parents. Utilizing metabolites from hybrids and parental lines heterosis was also studied, which indicates two third of all metabolites displayed 36% positive over dominance for hybrids compared to parental lines (Xu et al. 2021). Seven metabolic markers were also identified associated with multiple traits. Maize leaf base and tip samples were subjected for metabolome analysis across diverse population of inbreed lines, and this shows that their metabolite differences are due to their tissues but not due to occurrence rate. Large number of metabolite transcripts contain housekeeping genes involved in primary metabolism as well as important cellular functions. Complex genetic architecture of the maize seedling leaves metabolome was studied using metabolome GWAS (mGWAS) and identified significant SNPs associated for the important metabolome on chromosome. Maize has different metabolite accumulation like phenylpropanoid, benzoxazinoid and flavonoids. Metabolomic difference were observed between tissue types and subpopulations clearly (Zhou et al. 2019). Metabolomics can be used as a tool for defining biosynthetic pathways and other maize physiological questions. Metabotyping of maize hybrids under early sowing conditions could determine the metabolites responsible for chilling tolerance at vegetative stage. There are different methods for metabolomic profiling. A specialized method is named reversed-­ phase liquid chromatography (LC)–mass spectrometry (MS). Maize ear, late cob, leaf, stem, and tassel metabolites were studied. Specialized spectral metadata including structural characterization of candidate substrate-product pair (CSPP) network identified several new phenyl propanoids in all organs, and other metabolic classes are organ specific. Oligolignols are abundant in LS-MS profile of stem, hydroxyl fatty acids are found in late cob and leaf extracts, and benzoxazinoids are mostly present in tassels, auxin-related compounds in late cob and tassels. Interplay of glycosylations and acylations leads to mixed glycosides present in single type of tissue. The characterized compound and varied compounds are involved in metabolite discovery and systems biology research. The spectral meta data is available in a database (DynLib spectral database, https://bioit3.irc.ugent.be/dynlib/) (Desmet et al. 2021). Abiotic stress causes major yield loss in maize breeding. Major abiotic stresses involved are drought, heat, salt, and cold stresses. Prolonged stress conditions lead to retarded growth, biomass, and yield. Leaf metabolome of B73 inbred plants grown under long-term nonlethal drought, heat, and salt stress conditions shows that leaf metabolites are affected strongly when compared with controlled plants. Multiple amino acids like serine, threonine, tryptophan, histidine, glutamate, lysine, tyrosine, and ornitine accumulated during salt stress conditions. Several secondary metabolites like quinic acid and pipecolic acid and two unidentified phenolic compounds were also accumulated. Both salt stress and heat stress show accumulation of raffinose and its precursor galactinol. But sugar alcohol lactitol was accumulated more, and citrate and trans-3-caffeoyl quinic acid were depleted under heat stress.

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In case of drought stress, hexoses were accumulated along with raffinose. In stressed leaves, multiple raffinose biosynthesis genes were upregulated and are involved in dehydration tolerance along with oxidation prevention. All together it’s shown that biosynthesis of raffinose series sugars is the major protective mechanism for all abiotic stresses including cold tolerance. Another important amino acid is a proline, which is familiar as a protective osmolyte and antioxidant during stress conditions. Proline is derived from arginine and ornithine where stress leads to upregulation genes involved in arginine and ornithin pathways. During stress, GABA accumulation is a common response, which affects tricorboxylic acid intermediates that leads to shift in carbon and nitrogen metabolism in stressed leaves (Joshi et al. 2021). Metabolites associated with maize chilling tolerance during vegetative stage (eight leaf visible leaf stage) were identified using untargeted metabolomic approach of 30 diverse maize hybrids. Marker metabolite correlation with aerial biomass of mature plants was not affected by early sowing. Due to early sowing, the leaf metabolites in field were affected, and the metabolites involve both primary and specialized metabolism. For leaf metabolites, the balance between sugars and organic acids has higher carbohydrates (sucrose, fructose, starch, and glucose) and lower organic acids (malate, succinate) in early sowing than normal sowings. Tryptophan, shikimic acid, and quinic acid are in high contents during early sowings. Raffinose, a stress metabolite accumulation, is less in early sown hybrids (Lamari et al. 2018). Early sown hybrids showed negative correlation between aerial biomass and raffinose. Heat stress in maize is a major constrain for maize grain development. In order to understand the maize heat tolerance mechanism, a heat-tolerant hybrid ZD309 derived from female H39_1 and male M189 were tested in heat stress environments by transcriptomic and metabolomic approaches (Liu et al. 2022). Under heat stress, growth of hybrid and its parents was deteriorated by 6 days of heat treatment compared to plants in control conditions. Plant hormone signal transduction, cystine and methionine metabolism, and alpha linolenic acid metabolism play major roles in maize heat tolerance. The genes involved in these mechanism can be utilized in maize breeding for heat tolerance. Maize response to aphid feeding is revealed by transcriptomic and metabolomic assays. Sucking pests like corn leaf aphid directly damage plants by sucking phloem nutrients and transmit plant viruses. B73 plant leaves were infected with aphids and observed two different responses with both transcriptional and metabolic changes. Increased jasmonic acid levels increase the accumulation of benzoxazinoids. It was observed that there was a predominant effect of salicylic acid regulation and altered gene expression for prolonged induction of oxylipins (Tzin et al. 2015).

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10 Conclusion Omics analysis has been critical in identifying genetic processes, growth, development, and stress tolerance in maize. In crop science, several omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, have used high-throughput techniques to interpret functional analysis, molecular mechanisms of genes, and gene networks. Furthermore, combining GWAS with metabolomics, transcriptomics, and proteomics has shown to be a promising tool for elucidating biochemical processes and abiotic stress tolerance in some model crops including maize. The studies demonstrated how combining several omics approaches could be advantageous for identifying potential candidate genes and their pathways. The integration of some omics approaches in crop sciences has become possible thanks to advances in high-­ throughput technologies and computational tools. The panomics platform, which includes integrated multi-omics such as genomics, epigenomics, transcriptomics, proteomics, proteomics, and metabolomics, would make it easier to build models to predict agronomically important traits in order to improve crops through precision breeding. Importantly, combining systems biology and complex omics datasets has improved our understanding of molecular regulator networks for crop improvement. G–P–E interactions in crops have been discovered through research. Following that, through the “genotype to phenotype” concept, integration of functional genomics with trancriptomics, proteomics, and metabolomics may result in apparent crop quality phenotypic traits under certain stresses.

References Agarwal M, Shrivastava N, Padh H (2008) Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep 27:617–631. https://doi.org/10.1007/ s00299-­008-­0507-­z Aizat WM, Hassan M (2018) Proteomics in systems biology. In: Aizat W, Goh HH, Baharum S (eds) Omics applications for systems biology, Advances in experimental medicine and biology. Springer, Cham, pp 31–49. https://doi.org/10.1007/978-­3-­319-­98758-­3_3 Appleby N, Edwards D, Batley J (2009) New technologies for ultrahigh throughput genotyping in plants. In: Gustafson JP, Langridge P, Somers DJ, Totowa NJ (eds) Methods in molecular biology, plant genomics. Humana Press, New  York, NY, pp  19–39. https://doi. org/10.1007/978-­1-­59745-­427-­8_2 Baggerman G, Vierstraete E, De Loof A, Schoofs L (2005) Gel-based versus gel-free proteomics: a review. Comb Chem High Throughput Screen 8:669–677. https://doi. org/10.2174/138620705774962490 Caldwell DG, McCallum N, Shaw P, Muehlbauer GJ, Marshall DF, Waugh R (2004) A structured mutant population for forward and reverse genetics in barley (Hordeum vulgare L.). Plant J 40:143–150. https://doi.org/10.1111/j.1365-313X.2004.02190.x Callinan PA, Feinberg AP (2006) The emerging science of epigenomics. Hum Mol Genet 15(Suppl_1):R95–R101. https://doi.org/10.1093/hmg/ddl095

Maize Improvement Using Recent Omics Approaches

299

Challa S, Neelapu NR (2018) Genome-wide association studies (GWAS) for abiotic stress tolerance in plants. In: Wani SH (ed) Biochemical, physiological and molecular avenues for combating abiotic stress tolerance in plants. Academic, Cham, pp  135–150. https://doi. ­ org/10.1016/B978-­0-­12-­813066-­7.00009-­7 Chowdhury NB, Schroeder WL, Sarkar D, Amiour N, Quilleré I, Hirel B, Maranas CD, Saha R (2022) Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. J Exp Bot 73(1):275–291. https://doi.org/10.1093/jxb/erab435 Cooper JL, Till BJ, Laport RG, Darlow MC, Kleffner JM, Jamai A, El-Mellouki T, Liu S, Ritchie R, Nielsen N, Bilyeu KD (2008) TILLING to detect induced mutations in soybean. BMC Plant Biol 8(1):1–10. https://doi.org/10.1186/1471-2229-8-9 De Cremer K, Mathys J, Vos C, Froenicke L, Michelmore RW, Cammue BPA et al (2013) RNAseq-­ based transcriptome analysis of Lactuca sativa infected by the fungal necrotroph Botrytis cinerea. Plant Cell Environ 36:1992–2007. https://doi.org/10.1111/pce.12106 Desmet S, Saeys Y, Verstaen K, Dauwe R, Kim H, Niculaes C, Fukushima A, Goeminne G, Vanholme R, Ralph J, Boerjan W (2021) Maize specialized metabolome networks reveal organ-preferential mixed glycosides. Comput Struct Biotechnol J 1(19):1127–1144 Deutscher D (1978) The current status of breeding for protein quality in corn. Adv Exp Med Biol 105:281–300 Ding X, Li X, Xiong L (2013) Insight into differential responses of upland and paddy rice to drought stress by comparative expression profiling analysis. Int J Mol Sci 14:5214–5238. https://doi.org/10.3390/ijms14035214 Dong C, Dalton‐Morgan J, Vincent K, Sharp P (2009) A modified TILLING method for wheat breeding. The Plant Genome 2(1). https://doi.org/10.3835/plantgenome2008.10.0012 Duque AS, Almeida AM, da Silva AB, da Silva JM, Farinha AP, Santos D et al (2013) Chapter 3: Abiotic stress responses in plants: unraveling the complexity of genes and networks to survive. In: Vahdati K, Leslie C (eds) Abiotic stress: plant responses and applications in agriculture. INTECH Open, Rijeka, pp 49–102. https://doi.org/10.5772/45842 Dwivedi S, Perotti E, Ortiz R (2008) Towards molecular breeding of reproductive traits in cereal crops. Plant Biotechnol J 6:529–559. https://doi.org/10.1111/j.1467-­7652.2008.00343.x Eldakak M, Milad SI, Nawar AI, Rohila JS (2013) Proteomics: a biotechnology tool for crop improvement. Front Plant Sci 4:35. https://doi.org/10.3389/fpls.2013.00035 El-Metwally S, Ouda OM, Helmy M (2014) Next generation sequencing technologies and challenges in sequence assembly, 1st edn. Springer, New  York, NY. https://doi. org/10.1007/978-­1-­4939-­0715-­1 Fournier ML, Gilmore JM, Martin-Brown SA, Washburn MP (2007) Multidimensional separations-­ based shotgun proteomics. Chem Rev 107:3654–3686. https://doi.org/10.1021/cr068279a Golicz AA, Batley J, Edwards D (2016) Towards plant pangenomics. Plant Biotechnol J 14:1099–1105. https://doi.org/10.1111/pbi.12499 He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z (2014) Genotyping- by- sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484. https://doi.org/10.3389/fpls.2014.00484 Hieter P, Boguski M (1997) Functional genomics: it’s all how you read it. Science (New York, NY) 278:601–602. https://doi.org/10.1126/science.278.5338.601 Hirsch CN, Foerster JM, Johnson JM, Sekhon RS, Muttoni G, Vaillancourt B et  al (2014) Insights into the maize pan-genome and pantranscriptome. Plant Cell 26:121–135. https://doi. org/10.1105/tpc.113.119982 Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29:25. https://doi. org/10.1093/nar/29.4.e25 Jiang W, Zhou H, Bi H, Fromm M, Yang B (2013) Demonstration of CRISPR/Cas9/sgRNA-­ mediated targeted gene modification in Arabidopsis, tobacco, sorghum and rice. Nucleic Acids Res 41:e188. https://doi.org/10.1093/nar/gkt780

300

G. W. Narkhede and K. N. S. Kiranmayee

Joshi J, Hasnain G, Logue T, Lynch M, Wu S, Guan J-C, Alseekh S, Fernie AR, Hanson AD, McCarty DR (2021) A core metabolome response of maize leaves subjected to long-duration abiotic stresses. Meta 11:797 Kakumanu A, Ambavaram MR, Klumas C, Krishnan A, Batlang U, Myers E et al (2012) Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-­ seq. Plant Physiol 160:846–867. https://doi.org/10.1104/pp.112.200444 Kawahara Y, Oono Y, Kanamori H, Matsumoto T, Itoh T, Minami E (2012) Simultaneous RNA-­ Seq analysis of a mixed transcriptome of rice and blast fungus interaction. PLoS One 7:e49423. https://doi.org/10.1371/journal.pone.0049423 Kearsey MJ (1998) The principles of QTL analysis (a minimal mathematics approach). J Exp Bot 49:1619–1623 Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD et al (2009) A multiparent advanced generation inter-cross to fine map quantitative traits in Arabidopsis thaliana. PLoS Genet 5:e1000551. https://doi.org/10.1371/journal.pgen.1000551 Kumar P, Choudhary M, Halder T et  al (2022) Salinity stress tolerance and omics approaches: revisiting the progress and achievements in major cereal crops. Heredity 128:497. https://doi. org/10.1038/s41437-­022-­00516-­2 Laloum T, Martín G, Duque P (2018) Alternative splicing control of abiotic stress responses. Trends Plant Sci 23:140–150. https://doi.org/10.1016/j.tplants.2017.09.019 Lamari N, Zhendre V, Urrutia M, Bernillon S, Maucourt M, Deborde C, Prodhomme D, Jacob D, Ballias P, Rolin D, Sellier H (2018) Metabotyping of 30 maize hybrids under early-sowing conditions reveals potential marker-metabolites for breeding. Metabolomics 14(10):1–5 Lawrenson T, Shorinola O, Stacey N, Li C, Ostergaard L, Patron N et al (2015) Induction of targeted, heritable mutations in barley and Brassica oleracea using RNA-guided Cas9 nuclease. Genome Biol 16:258. https://doi.org/10.1186/s13059-­015-­0826-­7 Li YF, Wang YI, Tang Y, Kakani VG, Mahalingam R (2013) Transcriptome analysis of heat stress response in switch grass (Panicum virgatum L.). BMC Plant Biol 13:153. https://doi.org/1 0.1186/1471-­2229-­13-­153 Li Z, Liu ZB, Xing A, Moon BP, Koellhoffer JP, Huang L et al (2015) Cas9-guide RNA directed genome editing in soybean. Plant Physiol 169:960–970. https://doi.org/10.1104/pp.15.00783 Li Z, Zhu A, Song Q, Chen HY, Harmon FG, Chen ZJ (2020) Temporal regulation of the metabolome and proteome in photosynthetic and photorespiratory pathways contributes to maize heterosis. Plant Cell 32(12):3706–3722. https://doi.org/10.1105/tpc.20.00320 Liu J, Zhang L, Huang L, Yang T, Ma J, Yu T, Zhu W, Zhang Z, Tang J (2022) Uncovering the gene regulatory network of maize hybrid ZD309 under heat stress by transcriptomic and metabolomic analysis. Plan Theory 11(5):677 McLuckey SA, Stephenson JL Jr (1998) Ion/ion chemistry of high-mass multiply charged ions. Mass Spectrom Rev 17:369–407. https://doi.org/10.1002/(SICI)1098-­2787(1998)17:63.0.CO;2-­J Millet EJ, Welcker C, Kruijer W, Negro S, Coupel-Ledru A, Nicolas SD et al (2016) Genome-wide analysis of yield in Europe: allelic effects vary with drought and heat scenarios. Plant Physiol 172:749–764. https://doi.org/10.1104/pp.16.00621 Minoia S, Petrozza A, D’Onofrio O, Piron F, Mosca G, Sozio G, Cellini F, Bendahmane A, Carriero F (2010) A new mutant genetic resource for tomato crop improvement by TILLING technology. BMC Res Notes 3(1):1–8. https://doi.org/10.1186/1756-0500-3-69 Mosa KA, Ismail A, Helmy M (2017) Omics and system biology approaches in plant stress research. In: Mosa KA, Ismail A, Helmy M (eds) Plant stress tolerance: an integrated omics approach. Springer, Cham, pp 21–34. https://doi.org/10.1007/978-­3-­319-­59379-­1_2 Muthamilarasan M, Singh NK, Prasad M (2019) Multi-omics approaches for strategic improvement of stress tolerance in underutilized crop species: a climate change perspective. Adv Genet 103:1–38. https://doi.org/10.1016/bs.adgen.2019.01.001 Nakagami H, Sugiyama N, Ishihama Y, Shirasu K (2012) Shotguns in the front line: phosphoproteomics in plants. Plant Cell Physiol 53:118–124. https://doi.org/10.1093/pcp/pcr148

Maize Improvement Using Recent Omics Approaches

301

Nataraja KN, Madhura BG, Parvathi SM (2017) Omics: modern tools for precise understanding of drought adaptation in plants. In: Zargar SM, Rai V (eds) Plant OMICS and crop breeding. Apple Academic Press, Palm Bay, FL, pp 289–320 Nekrasov V, Wang C, Win J, Lanz C, Weigel D, Kamoun S (2017) Rapid generation of a transgene-­ free powdery mildew resistant tomato by genome deletion. Sci Rep 7:482. https://doi. org/10.1038/s41598-­017-­00578-­x Novik KL, Nimmrich I, Genc B, Maier S, Piepenbrock C, Olek A et  al (2002) Epigenomics: genome-wide study of methylation phenomena. Curr Issues Mol Biol 4:111–128 Offermann S, Danker T, Dreymuller D, Kalamajka R, Topsch S, Weyand K et al (2006) Illumination is necessary and sufficient to induce histone acetylation independent of transcriptional activity at the C4-specific phosphoenolpyruvate carboxylase promoter in maize. Plant Physiol 141:1078–1088. https://doi.org/10.1104/pp.106.080457 Rabouam C, Comes AM, Bretagnolle V, Humbert JF, Periquet G, Bigot Y (1999) Features of DNA fragments obtained by random amplified polymorphic DNA (RAPD) assays. Mol Ecol 8:493–503. https://doi.org/10.1046/j.1365-­294X.1999.00605.x Raza A, Tabassum J, Kudapa H, Varshney RK (2021) Can omics deliver temperature resilient ready to-grow crops? Crit Rev Biotechnol 2021:1–24. https://doi.org/10.1080/0738855 1.2021.1898332 Razgonova M, Zinchenko Y, Pikula K, Tekutyeva L, Son O, Zakharenko A et al (2022) Spatial distribution of polyphenolic compounds in corn grains (Zea mays L. var. Pioneer) studied by laser confocal microscopy and high-resolution mass spectrometry. Plants 11(5):630 Rinaldo AR, Ayliffe M (2015) Gene targeting and editing in crop plants: a new era of precision opportunities. Mol Breed 35:40. https://doi.org/10.1007/s11032-­015-­0210-­z Sali A, Glaeser R, Earnest T, Baumeister W (2003) From words to literature in structural proteomics. Nature 422:216–225. https://doi.org/10.1038/nature01513 Shikha M, Kanika A, Rao AR, Mallikarjuna MG, Gupta HS, Nepolean T (2017) Genomic selection for drought tolerance using genome-wide SNPs in maize. Front Plant Sci 8:550. https:// doi.org/10.3389/fpls.2017.00550 Strahl B, Allis C (2000) The language of covalent histone modifications. Nature 403:41–45. https://doi.org/10.1038/47412 Suzuki T, Eiguchi M, Kumamaru T, Satoh H, Matsusaka H, Moriguchi K, Nagato Y, Kurata N (2008) MNU-induced mutant pools and high performance TILLING enable finding of any gene mutation in rice. Mol Genet Genomics 279(3):213–223. https://doi.org/10.1007/ s00438-007-0293-2 Svitashev S, Young JK, Schwartz C, Gao H, Falco SC, Cigan AM (2015) Targeted mutagenesis, precise gene editing, and site-specific gene insertion in maize using Cas9 and guide RNA. Plant Physiol 169:931–945. https://doi.org/10.1104/pp.15.00793 Talukdar D, Sinjushin A (2015) Cytogenomics and mutagenomics in plant functional biology and breeding. In: Barh D, Khan M, Davies E (eds) Plant omics: the omics of plant science. Springer, New Delhi, pp 113–156. https://doi.org/10.1007/978-­81-­322-­2172-­2_5 Tanaka K, Waki H, Ido Y, Akita S, Yoshida Y, Yoshida T (1988) Protein and polymer analyses up to m/z 100000 by laser ionization timeof- flight mass spectrometry. Rapid Commun Mass Spectrom 2:151–153. https://doi.org/10.1002/rcm.1290020802 Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL et  al (2005) Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial ‘pan-genome’. Proc Natl Acad Sci U S A 102:13950–13955. https://doi.org/10.1073/ pnas.0506758102 Till BJ, Reynolds SH, Weil C, Springer N, Burtner C, Young K et al (2004) Discovery of induced point mutations in maize genes by TILLING.  BMC Plant Biol 4:12. https://doi.org/10.118 6/1471-­2229-­4-­12 Tomlekova NB (2010) Induced mutagenesis for crop improvement in Bulgaria. Plant Mutat Rep 2:4–27 Twyman RM (2013) Principles of proteomics. Garland Science Press, Abingdon

302

G. W. Narkhede and K. N. S. Kiranmayee

Tzin V, Fernandez-Pozo N, Richter A, Schmelz EA, Schoettner M, Schäfer M, Ahern KR, Meihls LN, Kaur H, Huffaker A, Mori N (2015) Dynamic maize responses to aphid feeding are revealed by a time series of transcriptomic and metabolomic assays. Plant Physiol 169(3):1727–1743 Vos P, Hogers R, Bleeker M, Reijans M, Lee T, Hornes M et al (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23:4407–4414. https://doi.org/10.1093/nar/23.21.4407 Walbot V (2008) Maize genome in motion. Genome Biol 9(4):303 Wang Y, Cheng X, Shan Q, Zhang Y, Liu J, Gao C et  al (2014) Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat Biotechnol 32:947–951. https://doi.org/10.1038/nbt.2969 Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531–6535. https://doi.org/10.1093/nar/18.22.6531 Woolfson M (2018) The development of structural x-ray crystallography. Phys Scr 93:1–32. https://doi.org/10.1088/1402-­4896/aa9c30 Xu Y, Ma Y, Wang X, Li C, Zhang X, Li P et al (2021) Kernel metabolites depict the diversity of relationship between maize hybrids and their parental lines. Crop J 9(1):181–191 Yu J, Holland JB, McMullen MD, Buckler ES (2008) Genetic design and statistical power of nested association mapping in maize. Genetics 178:539–551. https://doi.org/10.1534/ genetics.107.074245 Zhang C, Yang H, Yang H (2015) Evolutionary character of alternative splicing in plants. Bioinf Biol Insights 9:47–52. https://doi.org/10.4137/BBI.S33716 Zhou S, Kremling KA, Bandillo N, Richter A, Zhang YK, Ahern KR, Artyukhin AB, Hui JX, Younkin GC, Schroeder FC, Buckler ES (2019) Metabolome-scale genome-wide association studies reveal chemical diversity and genetic control of maize specialized metabolites. Plant Cell 31(5):937–955

Fungal Pathogen-Induced Modulation of Structural and Functional Proteins in Zea mays L. Ankit Singh, Shalini Sharma, Gourav Choudhir, and Sushil Kumar

1 Introduction Food is one of the most basic needs for human beings. The qualitative and quantitative aspects of the crop are undermined by an array of plant pathogens, namely, fungi, bacteria, and viruses. In some food crops, loss reaches up to 50% or more, putting agriculture under a higher pressure to feed the ever-increasing population. In its worldwide production, maize (Zea mays) acquires the third position as wheat and rice remained first and second, respectively (Rehman et al. 2021). The indigenous people of Southern Mexico are believed to be the agriculturists who domesticated maize for the first time about 10,000 years ago (Benz 2001). Zea mays bears male and female ears named as pollen and ovuliferous inflorescences. Fruit kernels or grains are produced in ovuliferous inflorescences. Maize area of cultivation and production is increasing in certain parts of the world where it exceeds wheat and rice. Despite being consumed directly by humans, it is usually used as a base material for ethanol manufacturing, making animal feed and corn starch or syrup, etc. (Murdia et al. 2016). Mainly six different types of maize are cultivated around the world, namely, dent, flint, pod, pop, pod, flour, and sweet corns. Besides being consumed as food and fodder, maize is used for the production of many industrial

Ankit Singh, Shalini Sharma, Gourav Choudhir and Sushil Kumar contributed equally with all other contributors. A. Singh · G. Choudhir Centre for Rural Development and Technology Delhi, Hauz Khas New Delhi, India S. Sharma Department of Biotechnology, Meerut Institute of Engineering and Technology, Meerut, India S. Kumar (*) Department of Botany, Shaheed Mangal Pandey Govt Girls PG College, Meerut, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 303 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_14

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products such as corn ethanol, biofuels, corn starch, corn syrup, corn oil, and beverages like bourbon whiskey (Ali et al. 2020). Biotic and abiotic stresses lead to many diseases and disorders that resulted in great losses if not managed properly. Disease-causing agents are pernicious to the wealth and menace the food supply all over the world particularly in the areas where maize is the primary source of nutrition for the local population. Fungi are major pathogens to which maize is susceptible followed by bacteria, viruses, and mycoplasma (Cobo-Diaz et  al. 2019. After infection, fungal pathogens multiply and accumulate various mycotoxins, which are detrimental to the plant as well as the consumers like humans and other animals. Mycotoxins like aflatoxins produced by Aspergillus flavus (Aspergillus ear rot in maize) are highly carcinogenic in nature (McKean et al. 2006). Deoxynivalenol and zearalenone produced by Fusarium graminearum (Gibberella ear rot) are noxious for both humans and domestic animals. Fumonisins produced by F. verticillioides causing Fusarium ear and stalk rot are perhaps the most common contaminants of maize products (Fandohan et al. 2003). Fumonisins are extremely toxic and carcinogenic in nature (Stockmann-Juvala and Savolainen 2008). Various fungal pathogens have been reported to beget great harm to maize crops and affect mankind directly. A few diseases caused by a fungal pathogen in maize are summarized with major symptoms in Table 1. There are two types of defense mechanisms displayed by plants. PTI (pathogen-­ associated molecular pattern triggered immunity), that is, the first line of defense in the form of pre-existing physical and biochemical barriers and effector-triggered immunity (ETI), that is, second line of defense (Dodds and Rathjen 2010; Jones and Dangl 2006). Pathogen-associated molecular patterns are specific and well-­ conserved molecular structures, for example, chitin containing fungal cell walls or flagellin in bacterial cell walls (Dodds and Rathjen 2010; Boller and Felix 2009). Pattern recognition receptors (PPRs) recognize these conserved microbial structures. PPRs trigger a signaling cascade that activates PTI and check the further progress of the pathogen. This is a non-host broad-spectrum resistance against non-­ adapted microbes (Lanubile et al. 2014; Wu et al. 2015). When these barriers are overcome by the pathogen, it leads to an inducible defense response involving a complex cascade of signaling pathways (Dangl et  al. 2013). This resulted in the formation of a variety of structural and functional proteins performing as physical barriers, a variety of enzymes, and factors, respectively. These enzymes produced as a result of induction due to pathogens accelerate reactions in different biochemical pathways to produce the effectors against the pathogen. Pathogens intervene with host metabolism through intake of sugar and other metabolites for its requirements and in return interfere with host metabolism by producing an array of compounds. There is an evolutionary tug-of-war between the host and the fungus in which the former tries to check the latter’s approach nutrients and induce defense responses. Meanwhile, pathogens evolve mechanisms to outcome plant defense mechanisms and utilize their nutrients (Boller and He 2009; Chen et al. 2010). Omics are a group of technologies that traverse the roles, relationships, and activities of different types of molecules that constitute the cells of an organism. It

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Table 1  Major diseases of maize caused by fungal pathogens S.N. Disease 1 Brown spot

2 3 4

Crazy top downy mildew Brown stripe downy mildew Green ear disease

Fungal pathogen Physoderma maydis

Major symptoms On leaf blades, chlorotic spots, dark brown, and circular spots appear on midribs, presence of brown lesions over nodes and internodes, stalk rot, and lodging Sclerophthora On leaves and leaf sheaths chlorotic striping, macrospora dwarfing of the tassel, baring pollen, and Sclerophthora rayssiae inflorescence formation. Narrow, thick, and abnormally erect leaves var. zeae

7

Java downy mildew Philippine downy mildew Common rust

Sclerospora graminicola Peronosclerospora maydis Peronosclerospora philippinensis Puccinia sorghi

8

Polysora rust

Puccinia polysora

9

Tropical rust

Physopellazeae

10

Tar spot complex

Phyllachora maydis and Monographella maydis

11

Turcicum leaf blight

Setosphaeria turcica

12

Maydis leaf blight Cochliobolus heterostrophus

13

Anthracnose leaf blight

5 6

14

15

16

Colletotrichum graminicola b Glomerella graminicola Yellow leaf blight Mycosphaerella zeae-maydis b Phyllosticta maydis a Banded leaf and Rhizoctonia solani f. sheath blight sp. sasakii (Corticium sasakii) Leptosphaeria leaf Leptosphaeria spot michotii a

Small, elongated, powdery pustules appear on both sides of the leaves Powdery pustules are smaller in size, light orange in color, and comparatively more circular Round or oval pustules at the center pale yellow with a small opening Black, raised, shiny spots by only Phyllachora maydis and necrotic tissue in close vicinity to the tar spot complex may amalgamate, resulting in the complete burning of leaves Small water-soaked oval spots grow into elongated, spindle-shaped necrotic lesions and lead to the complete burning of foliage At early stages diamond-shaped, small lesions became elongated followed by rectangular ones. These coalesce leading to the complete burning of leaves During seedling phase irregular, oval-to-­ elongated lesion on leaves with a distinct margin of yellow to a reddish-brown color similar in advance stages Parallel to the veins narrow and necrotic lesions are there. Blighting near the tip Concentric spots on leaves and husk, rotting ears, small, round, and black sclerotia, etc. At early stages, small lesions are there and these later grow into larger concentric patches to cover more laminar area (continued)

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Table 1 (continued) S.N. Disease 17 Phaeosphaeria leaf spot 18 19

Curvularia leaf spot Gray leaf spot

20

Septoria leaf blotch

21

Fusarium stalk rot

22

Head smut

23

False head smut

24

Black Bundle Disease Anthracnose stalk rot

25

26

Charcoal stalk rot

27 28

Stenocarpella stalk rot Penicillium ear rot

29

Aspergillus ear rot

30

Fusarium and Gibberella ear rot

31

Ergot, Horse’s tooth Nigrospora ear rot

32

Fungal pathogen Major symptoms Phaeosphaeria maydis Small pale green lesions became bleached and necrotic lesions with dark brown margins Curvularia lunata A tiny chlorotic or necrotic patch having a light-colored halo Cercospora Lesions on the leaf can be identified as tiny, zeae-maydis regular, elongated brown-gray necrotic spots. Symptoms advances side by side to the veins leading to drastic leaf senescence Septoria maydis Minor patches on the leaves are generally light-green-to-yellow colored. These spots combine and result in necrosis and drastic blotching a The lowest internodes are badly affected due Fusarium moniliforme to the appearance of tiny, dry, and dark-­ b Gibberella fujikuroi brown lesions. The wilted plant remained standing upright Sphacelotheca reiliana Abnormal development of the tassels, black masses of spores in male florets Ustilaginoidea virens Greatly reduced number of male florets isolated from the tassel show spores Cephalosporium Ears with underdeveloped, shrunken kernels, acremonium brown vascular bundles a Lesions appeared on the stem in the form of Colletotrichum narrow elongated dark spots. Plants show graminicola b early wilting, vandalization of vascular Glomerella bundles and pith graminicola Macrophomina Atypical drying of tissue on the upper leaf phaseolina surface. Shredding of vascular, sclerotia in vascular bundles and black kernels Stenocarpella maydis Browning of the pith, pycnidia on the surface of internodes Penicillium oxalicum Dust of typical light blue-green color appears in between grains and on the cob exterior Aspergillus flavus Powdery dark-colored masses of spores that A. niger cover both kernels and cob (A. niger), yellow-green masses of spores (A. flavus) Infection to the inflorescence starts from the Fusarium tip to downward as white mycelium appears graminearum and Fusarium moniliforme in the same direction. Mycelium changes reddish-pink in kernels and white cottony growth on pericarp and cob surface Claviceps gigantea Infected grains change into compact fungal structures, the sclerotia a Nigrospora oryzae Chaffy and lightweight ears, kernels b Khuskia oryzae discolored, different parts of cob, and kernel tips contain a tiny black clump of spores (continued)

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Table 1 (continued) S.N. Disease 33 Gray ear rot

Fungal pathogen Physalospora zeae

34

Common smut

Ustilago maydis

35

Cephalosporium kernel rot Hormodendrum ear rot

Acremonium strictum

36

Hormodendrum cladosporoides

Major symptoms Black mycelium and sclerotia spread throughout the cob White galls replace individual kernels containing the mass of spores White streaks were observed on the pericarp of infected kernels The physical injury appeared on infected kernel tips while dark brown-green stripes on kernels start at the kernel and cob bases

Anamorph, bTeleomorph (CIMMYT 2004) (www.cimmyt.org)

a

includes genomics, proteomics, transcriptomics, glycomics, metabolomics, lipidomics, etc. Proteomics is the large-scale study of proteomes. The term “proteomics’ was first used by Marc Wilkins in 1996. By definition, proteome is the totality of proteins that can be expressed by a genome in a particular cell, tissue, or organism under certain conditions or stages of development. Besides structural and functional proteins act as functional molecules to catalyze various biochemical reactions. Any change in genome directly affects protein structure and function and in turn to the cell or organism as a whole. Proteomics is a magnificent strategy for studying changes in metabolism under different stresses and developmental stages. Proteomics opened avenues to find molecular mechanism of disease development and related changes. Maize remained the model plant species to understand the molecular changes when infected by a pathogen. An in-depth understanding of host response against a pathogen at the molecular level is necessary to explain the genetic basis of host resistance (Schnable et al. 2009). In this chapter, we thoroughly review the contemporary status of proteomics studies undertaken by different workers to explore the molecular mechanisms of maize defense against fungal pathogens as well as the latter’s sidesteps to escape it.

2 Maize Proteomics Against Fungal Infection A lot of researchers are searching for the molecular mechanism of disease development as well as the elicitation of the plant to protect them from invading pathogens. In response to infection cellular machinery of the host, the plant undergoes a disturbance as foreign invaders secrete several toxic molecules inside it. It modulates the way and magnitude of gene expression by host cells and resulted in the production of an array of proteins. These proteins may be structural or functional. Structural proteins produced under such circumstances act as barriers to check further spreading of pathogen, while functional proteins in the form of enzymes accelerate the generation of molecules that neutralize the toxic effects of pathogen and restrict or kill the pathogen. Each type of pathogen elicits the production of a different set of

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proteins. By analyzing and quantifying such proteins, scientists unveiled the hostpathogen interaction and opened new avenues to manage diseases. When mass spectroscopy (MS) was applied in proteomics, several researchers shifted their attention toward maize proteome. Many fungal pathogens are known to infect maize plants and leads to heavy losses. The proteomics of Zea mays has evolved due to continuous improvements in high-throughput technologies, protein databases, EST, and genomic databases (Schnable et al. 2009). Maize proteome includes many kinds of proteins in healthy and infected plants (Fig. 1). The plant has its recognition system of any pathogenic infection, and then it responds in its downstream defenses. These are complex steps that require series of changes that take place in plant metabolic reactions and involves the synthesis of molecules associated with signaling, changes in cell wall as reinforcements, rearrangement of cytoskeleton, multifold synthesis of induced proteins, and secondary metabolites. Primary metabolic pathways provide power to all these high-energy demanding processes (Berger et al. 2007; Bolton 2009; Rojas et al. 2014; Van Loon 1987). In this chapter, summary of proteome-based research of maize interaction with its diversified fungal pathogen and its defense mechanism will be discussed. Numerous potential proteins and associated biochemicals play an important role against different fungal diseases in maize. Any methodology, that is, either conventional breeding or genetic engineering techniques, can rely upon these selective markers for the development of resistant elite maize varieties (Pechanova and Pechan 2015). Maize proteins involved in fungal defense can be categorized as follows.

Fig. 1  Categories of maize proteins modulated by fungal pathogens

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2.1 Storage Proteins Plant storage proteins can be classified into two classes: (a) The later stages of seed development can be identified by the accumulation of seed storage proteins (SSPs). These SSPs help in the germination of seeds by functioning as a nutritional source after their degradation and reallocating these amino acids for the development of seedlings. SSPs are the major plant proteins in grains and one of those that are the most abundantly consumed by human beings. Some seed storage proteins are reported to play a role in defense mechanisms against fungal infection in maize as these include late embryogenesis abundant proteins (LEA3, LEA 14) and globulin 1 and 2. Peethambaran et al. (2010) reported the presence of these proteins in resistant varieties in comparison to susceptible ones. Relative protein profiling of silk from maize genotype resistant to Aspergillus flavus (i.e., Mp313E, Mp420) and susceptible to Aspergillus flavus (SC212m, Mp339) via 2-Dimensional Electrophoresis identified that hydrolytic Hydrolytic enzymes PRm3 chitinase, chitinase I, and chitinase A are constitutively expressed proteins synthesized at higher levels in silks of resistant maize varieties as the defense mechanism of the plant. (b) Vegetative storage proteins (VSPs) are species-specific proteins that accumulate in any vegetative tissues of different parts such as leaves, stems, or tubers in the vegetative when ample resources are available. VSPs serve as a temporal reserved resource of amino acids that will help in growth and development of plant at various stages (Fujiwara et al. 2002).

2.2 Detoxifying Enzymes The plant infection stage is followed by pathogen recognition, which is the most critical event that is further followed by an oxidative burst produce reactive oxygen species (ROS) (Bolwell and Wojtaszek 1997; Mittler et al. 2004). The diverse group of ROS mainly consists of superoxide, hydroxyl radical, and hydrogen peroxide, which are essential for the activity of various defense mechanisms that determine the outcomes of host-pathogen interaction. ROS helps in the oxidative cross-linking of glycoproteins, which improves the strength of the host cell wall. ROS also stimulates the molecular and physiological activities of a plant cell by acting as secondary messengers. The short-term activation of extremely reactive ROS is highly regulated because if not controlled properly, they can be led to adverse oxidative damages in the host cell, which can ultimately lead to necrosis. Therefore, detoxification of ROS is essential for the survival of cells. The ROS-scavenging systems found in plants are integral for controlling the activity of ROS.  The detoxifying enzymes present in ROS-scavenging systems help to maintain the constant level of ROS in cell organelles. Plant cells accommodate following important detoxifying enzymes:

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(a) APX (Pechanova et  al. 2011; Mittler et  al. 2004; Hiraga et  al. 2001; Jiang et al. 2016) (b) Glutathione peroxidase (GPX) (Huang et  al. 2009a, b; Mittler et  al. 2004; Kurama et al. 2002) (c) SOD (Pechanova et al. 2011; Campo et al. 2004; Mittler et al. 2004; Jiang et al. 2016; Kurama et al. 2002; Guan and Scandalios 1998) (d) Catalase (Neucere 1996; Campo et  al. 2004; Lanubile et  al. 2015; Wu et  al. 2013a, b; Li et al. 2011; Magbanua et al. 2007; Kurama et al. 2002) (e) Peroxiredoxin (PER) (Chen et al. 2004a, b; Chen et al. 2007; Chen et al. 2002; Wu et al. 2013a, b; Mittler et al. 2004; Dietz et al. 2006) (f) Thioredoxin (Pechanova et al. 2011; Wu et al. 2013a, b; Mittler et al. 2004) and glutaredoxin (Mittler et al. 2004; Gutsche et al. 2015) (g) Glutathione reductase (Mittler et al. 2004; Jiang et al. 2016; Chugh et al. 2013) (h) Dehydroascorbate reductase (Li et al. 2011; Mittler et al. 2004) In the maize plant, the induction and repression of ROS-detoxifying enzymes was the main characteristic feature followed by exposure to fungi like F. verticillioides (Campo et al. 2004), F. graminearum (Mohammadi et al. 2011), A. flavus (Pechanova et al. 2011; Magbanua et al. 2007), C. lunata (Huang et al. 2009a, b). RBSDV (Li et al. 2011), and SMV (Wu et al. 2013a, b) (Table 2).

Table 2  Detoxifying enzymes against pathogenic attacks Enzymes protecting S. No. maize 1 Catalase 2 and SOD 2 3 4

5

Cumulative action of GPX and APX Catalase activity

Fungal disease/ fungi Fusarium ear rot (F. verticillioides) C. lunata in both resistant varieties A. flavus

Protection Embryo from oxidative damage Supporting maize defense response In immature embryos of resistant maize In aflatoxin-­ Expressed resistant maize constitutively at higher varieties (A. flavus) levels in multiple plant tissues

Antioxidant enzymes SOD (Cu–Zn) 4AP, peroxiredoxin, and thioredoxin-like protein 5 Detoxifying enzymes F. verticillioides SOD, catalase 2, and glutathione-S-transferase (GST)

Protect embryos during seed germination from oxidative damage and xenobiotics

Reference Campo et al. (2004) Huang et al. (2009a, b) Magbanua et al. (2007) Chen et al. (2007) and Pechanova et al. (2011) Campo et al. (2004)

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2.3 Stress-Related Proteins When plants are exposed to higher levels of various types of stresses within the cell, then a group of diverse proteins is expressed, which are known as stress-related proteins, and they actively participate in defense mechanisms. A strong correlation is observed between stress tolerance and disease resistance since proteins that contribute toward the tolerance of heat and drought in maize are also helpful in protecting against the effects of aflatoxin produced by A. flavus. According to Chen et al. (2002), there are some stress-related proteins such as aldose reductase (ALD), osmotic stress-related proteins WSI18, peroxiredoxin antioxidant (PER1), cold regulated protein, anionic peroxidase, glyoxalase I protein (GLX I), and several small heat shock proteins (HSP) that are stress-related proteins with a significant role in defense against fungal diseases in maize. (a) The LEA proteins present in maize kernels impart aflatoxin resistance in addition to protecting the higher plants from effects of dehydration, which might arise from environmental abiotic stress like drought (Hong-Bo et al. 2005). (b) Similarly, the GLX1 protein found in infected maize kernel provides resistance for aflatoxin by directly and actively controlling methylglyoxal, which is the main substrate as is evident in infected maize susceptible genotypes that exhibit a significant increase in methylglyoxal content (Chen et al. 2004a, b). (c) A trial was conducted by Pechanova et  al. (2010 and 2011) by using two-­ dimensional difference in gel electrophoresis (2D-DIGE) to understand the role of stress proteins in aflatoxin resistance among 21-day-old inbred maize for four genetically distinct aflatoxin-susceptible SC212m, B73, resistant Mp313E, and Mp420 maize varieties. The resistant rachis analysis showed a higher level for 44 abiotic stress-related proteins like antioxidant enzymes, heat shock proteins, ascorbate peroxidase (APX), thioredoxin, and other diversified stress-­ related proteins. (d) Closely related results were derived by Chen et al. (1998) who identified a correlation between abiotic stress tolerance and aflatoxin-resistance. The higher expression abiotic stress-related proteins, that is, two small HSPs, helped resistant rachis to deal better with heat and drought challenges. The resistant rachis had 11-fold higher expression of HSP as compared to susceptible rachis, which also protected A. flavus/aflatoxins (Pechanova et al. 2011). Thus, these findings relate the stress tolerance and disease resistance power of plants that must be implored in the future as a significant new approach for resistance in maize against fungal diseases.

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2.4 Proteins Involved in Protein Synthesis, Folding, and Stabilization According to central dogma of the cell, the information present in DNA must be encoded into mRNA followed by a translation into polypeptide chains consisting of sequences of amino acids. However, the protein thus synthesized has to undergo further modifications to make itself functional, which includes changes in specific three-dimensional configurations together with cleavage and the covalent bonding between carbohydrates and lipids. These changes are essential for the functioning and accurate protein targeting inside the cell. In some cases, multiple polypeptide chains combine to form a functional complex. The molecular chaperones and chaperonins are proteins that are found in both prokaryotic as well as eukaryotic cells that help in the folding of other proteins (Cooper 2000). Some common examples of chaperonin assisting in protein folding are heat shock proteins such as Hsp 70 and Hsp 60. The challenge of germinating maize embryos with F. verticillioides helped in understanding the role of this category of proteins in terms of defense responses (Campo et al. 2004). The comparative two-dimensional electrophoresis of proteins from fungus-infected embryos after 24  hours incubation with those from sterile embryos identified an abundance of proteins like small HSP 17.2, cyclophilin, and peptidylprolyl cis-trans isomerase in fungus-challenged embryos. The mentioned proteins actively participate in the process of protein synthesis, folding, and stabilization. Some common examples of proteins such as eukaryotic translation initiation factor 5A (eIF-5A), multiple HSPs, peptidylprolyl cis-trans isomerases, chaperonins, and cyclophilin participate significantly in maize-pathogen interaction (Pechanova et al. 2011; Mohammadi et al. 2011; Campo et al. 2004; Chivasa et al. 2005; Huang et al. 2009a, b; Wu et al. 2013a, b). The functions such as initiation of eukaryotic cellular protein biosynthesis and synthesis of proteins needed for pathogen-responsive defense are assisted by eukaryotic translation initiation factor 5A (eIF-5A). In model organisms such as Arabidopsis, eIF-5A is known to act as a key defensive element in regulating the induction of programmed cell death as a response to infection with a virulent pathogen (Hopkins et al. 2008). The essential functional conformation of proteins is maintained by chaperones that help in the functional activities of the cell and its survival during stress conditions (Sun et al. 2002). The HSPs that are the intra-molecular chaperons are stress proteins that are induced under heat and other stressful environmental conditions (Andreeva et al. 1991). These proteins inhibit the assembling of newly formed proteins and the clustering of proteins damaged by various stresses (Hendrich and Hartl 1993). The HSPs are one of the most highly expressed proteins, and the 11-fold higher expression levels in rachis are associated with aflatoxin-resistant maize genotypes, which makes them particularly useful for inducing resistance in maize crops against A. flavus and aflatoxins (Pechanova et al. 2011). Due to its ability to alleviate heat stress, the HSPs control aflatoxin production.

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There are some other proteins identical to HSPs as cyclophilins and Peptidylprolyl cis-trans isomerases (PPIs) which are upregulated under stressful conditions to induce folding or refolding of nascent or damaged proteins, respectively as reported by Kim et al. (2012), Sekhar et al. (2010), Sharma and Singh (2003), and Trivedi et al. (2013).

2.5 Antifungal Proteins During their life cycle, plants face various pathogenic attacks including those from fungi. Therefore, plants have developed an array of defense mechanisms for protection, which includes the synthesis of proteins with antifungal properties and low molecular-weight compounds. To date, approximately 17 peptidyl groups have been identified, which include pathogenesis-related (PR) proteins and trypsin inhibitors that are involved in either providing direct or induced resistance against fungal attack (Selitrennikoff 2001). Trypsin Inhibitor The kernel protein extract analysis of maize seeds from seven aflatoxin-resistant inbred lines found that 14-kDa trypsin inhibitor was widely distributed in them but absent in susceptible genotypes of maize (Chen et al. 1998). By antifungal assay, it was analyzed and concluded that kernels, where trypsin inhibitor was found in the high amount, contributed to resistance in plants by rupture of spores, inhibition of fungal α-amylase, and abnormal hyphal growth of A. flavus. (Chen et al. 1998). Pathogenesis-Related Proteins The PR proteins comprise of most widely distributed and diverse collection of proteins that are responsible for resistance in maize. While some of these PR proteins are constitutively expressed in healthy plants as part of regular growth and development mechanisms, others exhibit stimulus-based responses depending on a variety of signals such as salicylic acid, jasmonic acid, ethylene signaling, or as a defense mechanism against any pathogenic attack. To provide overall protection to plants, PRPs are distributed throughout the plant rather than begin present just at the site of infection (Van Loon et al. 2006). PRPs display a wide range of enzymatic activity with the aim of reviving the plant by fighting back against pathogen attacks and are divided into 17 families. They are found in all members of the plant kingdom, and some common examples include PR-10, xylanase inhibitors (XIP), thaumatin-­like protein, zeamatin precursor, and GLPs that play role in silk defense as identified via the 2DE approach (Chen et al. 1998).

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PR proteins are expressed in maize plant against fungal diseases arising from infection with F. verticillioides (Campo et al. 2004), F. graminearum (Mohammadi et al. 2011), and A. flavus (Chen et al. 2006; Pechanova et al. 2011; Sajjadi et al. 2016; Peethambaran et al. 2010). PR proteins exhibit an array of enzymatic activities like antifungal hydrolytic enzymes and generally work in synchronization with others to fight against pathogen attacks including isoforms of chitinase, β-1,3-­ glucanases chitinases; PR 6b, β-1.3-glucanase, GLP subfamily 1 member 17; and catalase 3 and peroxidase. The activity of these hydrolytic enzymes is based on the degradation of major structural polysaccharides of the fungal cell wall, arthropodal, and nematode exoskeletons, which are made up of chitin and glucans (Bowles 1992; Kasprzewska 2003; Leah et al. 1991; Schlumbaum et al. 1986). A high level of differences was observed in the resistant rachis of maize as compared to susceptible genotypes in response to A. flavus infections. In resistant varieties, the proteins responsible for resistance are already pre-present in their rachis part, but for susceptible varieties, a defense mechanism is the inducible type wherein the expression is multifold upregulated, and the same is the case with antifungal protein activity. A group of 15 different PR proteins including glucanases, chitinases, permatin, protein P21, PRm3, PRm6b PR-1, and PR-5 have expressed up to three- to tenfold. A significantly different level of expression was observed for PRm3 among all the proteins related to the anti-pathogenic activity. As a class III chitinase type protein, PRm3 accumulated at 40-fold higher level during maturation stages of resistant rachis in comparison to 7.3-fold increase for the susceptible line. Proteins related to defense were identified and mapped near glucanases, chitinases, permatin, protein P 21, PRm3, PRm6b PR-1, and PR-5 that are present on several chromosomes related to resistance in the rachis of Zea Mays including the following: (a) Asr protein (b) Abscisic stress ripening protein 1 (c) Abscisic acid (ABA)-responsive protein (d) Auxin-binding protein 1 (e) Caffeoyl-CoA 3-O-methyltransferase 1 (f) And remain as these Pechanova and Pechan (2015) defined mechanisms of maize-pathogen interactions and assessed them at the proteome level. He identified defense-associated proteins and summarized how they play a crucial role in maize against various agriculturally important pathogens. (a) The F. verticillioides-infected maize embryo produced PR-5 protein P23, which is highly basic in nature (Campo et al. 2004), while rachis infected with A. flavus produced highly acidic protein P21 (Pechanova et al. 2011). (b) F. graminearum infected silks and kernels react by upregulating the production of zeamatin and thaumatin, respectively. Zeamatin belongs to PR-5 protein family. Due to close commonality with sweet-tasting protein thaumatin, it is also named asthaumatin-like proteins (TLs). Zeamatin makes the cell wall of

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fungus highly permeable, which leads to rapid cell lysis and reduction in pathogen load (Vigers et al. 1991). (c) The fungal germination activities of other defense-associated proteins are impaired by a dual trypsin/α-amylase inhibitor (Richardson et al. 1987). A cell-­ suspension culture of maize was reported to constitutively secrete an array of PRPs including antifungal proteins such as zeamatin (P33679) against a clinical strain of the human pathogenic yeast Candida albicans comparable with one reported for the same from an extract of maize seeds. (d) Some other PRPs such as 9  kDa lipid transfer protein and 26  kDa xylanase inhibitor proteins are also expressed. (e) Another important PRP present in seeds of maize is called zeamatin-like protein (ZLP). They are included in TLPs, which are known to have multigenic activity depending on series of responsible/induced genes against pathogenesis (Pechanova et al. 2010, 2011; Fandohan et al. 2003). (f) Class III peroxidases are another type of fungus-responsive pathogen, which bears close resemblance with the PR-9 group example ZmPrx16 that increases in response to the infection of F. graminearum (Fandohan et al. 2003). These enzymes are well recognized for their association with defense mechanisms and are located in plant cell walls and vacuoles (Passardi et al. 2005; Almagro et al. 2009). These enzymes function by increasing the rate of lignification or suberization, which results from an increased rate of H2O2-dependent cross-linking of cell wall components, thus creating a physical barrier in host tissue to stop the pathogenic invasion. (g) A screening and classification study for differentially expressed proteins (DEPs) identified many defense-related proteins such as defense marker proteins along with proteins related to the phenylpropanoid lignin biosynthesis, benzoxazinone biosynthesis, and jasmonic acid signal pathway. All these proteins are involved in the defense responses of maize to S. turcica infection in maize (Liu et al. 2009). Early induction of GST proteins also helps in developing resistance for S. turcica. Other than protein-protein interactions, a network of defense-­ related proteins also play important role in defense against S. turcica infection. An example of this can be ZmGEB1. (h) The antifungal protein PR-10 imparts resistance to the host kernel. The hyphal growth of A. flavus and its conidial germination both are inhibited by overexpression of the ZmPR-10 gene (Chen et  al. 2006). By using the RNAi gene silencing approach, it was identified that pr10-silenced transgenic kernels have higher fungal colonization and aflatoxin accumulation, whereas callus lines with pr10-silencing have a higher sensitivity to heat stress treatments along with reduced pr10 transcription. (i) Infection of heterothallic ascomycete fungus Setosphaeria turcica leads to Northern corn leaf blight (NCLB) disease. A comparative proteomic study of maize aimed at the identification of molecular mechanisms underlying the defense responses revealed that defense-related proteins such as glucosidase, SOD, polyamines oxidase, HSC 70, and PPIases were upregulated, while those related to photosynthesis and metabolism were downregulated when plants

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were inoculated with S. turcica (Zhang et  al. 2014). The outcomes of the research identified the synergistic functioning of several defense mechanisms wherein they directly come from defense proteins, modulate the primary metabolism, and control photosynthesis and metabolism of carbohydrates with aim of protecting the maize plant against fungi attacks.

2.6 Proteins Involved in Secondary Metabolism An important role of secondary metabolites is protection against attacks of pests and pathogens. Several strategies are implored for it, which might include deterrence/antifeedant activity, toxicity, or involvement of secondary metabolites in physical defense systems. An enormous variety of secondary metabolites are “induced” by infection and derived from either shikimic acid or aromatic amino acids, etc. produced predominantly by the phenylpropanoid, the isoprenoid, and the alkaloid pathways (Iriti and Faoro 2009). As a response to stress or signaling molecules or various elicitors, these metabolites accumulate and are heavily involved in aiding the adaptation of the plant to its environmental niche and response to stress conditions. These secondary metabolites may function in many ways including impediment of fungal spread as an outcome of increased cell wall lignification. A common mechanism for resistance against fungal and viral diseases in plants would be the fortification of cell walls by an increased rate of lignin biosynthesis. (a) The secondary metabolites in form of phenolics are involved in the formation of secondary cell walls, which are responsible for providing strength and mechanical rigidity (Boerjan et al. 2003). (b) PAL that is a key regulator for phenylpropanoid pathway along with caffeoyl-­ CoA 3-O-methyltransferase1 and chalcone–flavanone isomerase works in synchronization to inhibit the spread of A. flavus in the ears of resistant maize genotypes. Another example of cell wall reinforcing protein that is involved in increasing the rate of a branch of phenylpropanoid synthesis would be cinnamyl alcohol dehydrogenase (Pechanova et al. 2011). It also helps to increase the cell wall lignification rate (Magbanua et al. 2007). It was identified that this enzyme tends to preferentially accumulate in leaves of plants diseased with RBSDV (Li et al. 2011) as well as with SCMV (Wu et al. 2013a, b) and is expressed only after viral infection. (c) Enzymes from secondary metabolism pathways especially those from phenylalanine ammonia-lyase (PAL) including enzymes such as caffeoyl-CoA-3-Omethyltransferase1 and chalcone flavanone isomerase are differentially secreted in developing maize kernels infected with F. graminearum (Mohammadi et al. 2011). (d) Identical outcomes were also observed in the case of Gibberella ear rot wherein several important enzymes from secondary metabolism especially phenylpropanoid pathway (PAL) such as cinnamyl alcohol dehydrogenase, 4-coumarate-­

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CoA ligase, and phenolic O-methyltransferase were more widely distributed in the resistant genotype CO441. (e) Enzymes such as hydroxymethylbutenyl 4-diphosphate synthase (HDS) from isoprenoid methylerythritol phosphate (MEP) pathway as well as terpenoid biosynthetic pathway were found to be most responsive for F. graminearum infection in both susceptible as well as resistant genotypes. Two chalcone flavonone isomerases that are part of flavonoid biosynthesis are also among those which exhibited higher levels of many defense-associated proteins. A transcriptomic study conducted by Alessandra Lanubile et al. (2017) with the aim of outlining the intracellular signaling sequence taking place in maize cells infected with F. verticillioides considering it as a model organism identified that: (a) Recognition receptors such as receptor-like kinases and R genes are involved in pathogen perception along with several other receptors. (b) These receptors are triggered by mitogen-associated protein kinases and subsequently activate downstream signaling networks. (c) The signal transmission is mainly governed by hormones such as salicylic acid, auxin, abscisic acid, ethylene, and jasmonic acid in synchronization with calcium signaling. (d) The target for these signal molecules is multiple transcription factors. (e) Activation of transcription factor initiates the subsequent activation genes that are part of defense mechanisms such as those responsible for detoxification processes, phenylpropanoid, and oxylipin metabolic pathways. (f) It helps in the inhibition of fungal entry into the host cells due to cell wall lignification or other mechanisms.

2.7 Proteins Involved in Energy-Producing Carbohydrate Metabolic Pathways The essential physiological functions of plant such as signaling and gene regulation (Tsukaya et al. 1991) involves usage of simple sugars. A plant’s ability to trigger necessary defensive responses during abiotic stress conditions is directly or indirectly dependent on the concentration of sugars in plant cells. As per the maize plant, the proteins required in the metabolism of carbohydrates also play an integral part in pathogen interactions. Various essential physiological pathways such as glycolysis, gluconeogenesis, pentose phosphate pathway, TCA cycle, as well as associated mitochondrial electron transport and ATP biosynthesis (Pechanova et al. 2011; Mohammadi et  al. 2011; Campo et  al. 2004; Chivasa et  al. 2005; Huang et  al. 2009a, b; Li et al. 2011; Wu et al. 2013a, b) are also dependent on response-associated proteins that act as enzymes. These proteins may act the following: (a) Energy provider (b) Provide signal molecules that act as trigger molecules for activation of other metabolic pathways

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(c) Provide a signal to upregulate or lower down the expression of some other protein to fight against fungi One example of this can be plantglyceraldehyde-3-phosphate dehydrogenase (GAPDH) consist unusual functions that go beyond the usual role of energy production during glycolysis (Rojas et al. 2014). Cytosolic GAPDH get reversibly inactivated by H2O2 and reactivated by reduced glutathione, which helps in signaling involving ROS (Hancock et al. 2005; Henry et al. 2015). Maize plant reacts to A. flavus infection by upregulating the levels of GADPH (Pechanova et al. 2011); meanwhile, in cases of F. verticillioides infection, its expressions are lowered (Campo et al. 2004). F. verticillioides infection changes several groups of proteins as well as carbohydrate metabolism in embryos by changing the expressed form of two cytosolic GAPDH and fructose-bisphosphate aldolase. The physiological processes like glycolysis are repressed, and gluconeogenesis is activated by F. verticillioides. RBSDV viral agent infection in maize causes alternation in glucose phosphate isomerase, ADP-glucose pyrophosphorylase, UDP-glucose pyrophosphorylase, GAPDH, fructose-bisphosphate aldolase, and transketolase, which might lead to immense changes in maize physiology (Li et  al. 2011). Sugarcane mosaic virus infection in maize leads to a similar pathogenic response leading to a compromised state of major energy-producing pathways involved in carbohydrate metabolism (Wu et al. 2013a, b). More research is required to explore how the changes in metabolism of carbohydrates affect the successful establishment of fungus, disease development, and resistance in maize.

3 Concluding Remark and Future Prospects The chapter presents the current and comprehensive status of plant defense research between maize and different fungal pathogens that cause heavy losses. Plant-­ microbe interactions are highly interwoven complex molecular events and pose difficult to deal with. Proteomics studies using advanced technologies made an avenue to go further. This is well known that proteins are the workhorses of cell machinery and can be used to understand the molecular events during disease development and plant defense. Accumulation and activation of particular kinds of proteins indicate their involvement in plant defense. This is expected that most of the plant disease resistance traits are quantitative polygenic and regulated by environmental factors. Since maize-fungal pathogen interactions are much complex, the complete unveiling is a difficult task and needs a systems biology approach to deal with huge data. In the future, this is anticipated that differential expression proteomics and interactomics in combination with other high-throughput omics technologies will open new avenues for geneticists and breeders to develop resistant maize varieties.

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References Ali A, Adnan M, Abbas A, Javed A, Safdar ME, Asif M, Imran M, Iqbal T, Rehman FU, Ahmad R (2020) Comparative performance of various maize (Zea mays L.) cultivars for yield and related attributes under semi-arid environment. Agric Biol Res 36(4):63–66 Almagro L, Gomez Ros LV, Belchi-Navarro S, Bru R, Ros Barcelo A, Pedreno MA (2009) Class III peroxidases in plant defence reactions. J Exp Bot 60:377–390 Andreeva L, Heads R, Green CJ (1991) Cyclophilins and their possible role in the stress response. Int J Exp Pathol 80:305–315 Benz BF (2001) Archaeological evidence of teosinte domestication from Guilá Naquitz, Oaxaca. Proc Natl Acad Sci 98(4):2104–2106. https://doi.org/10.1073/pnas.98.4.2104 Berger S, Sinha AK, Roitsch T (2007) Plant physiology meets phytopathology: plant primary metabolism and plant-pathogen interactions. J Exp Bot 58:4019–4026 Boerjan W, Ralph J, Baucher M (2003) Lignin biosynthesis. Annu Rev Plant Biol 54:519–546 Boller T, Felix G (2009) A renaissance of elicitors: perception of microbe-associated molecular patterns and danger signals by pattern-recognition receptors. Annu Rev Plant Biol 60:379–406 Boller T, He SY (2009) Innate immunity in plants: an arms race between pattern recognition receptors in plants and effectors in microbial pathogens. Science 324:742–744 Bolton MD (2009) Primary metabolism and plant defense—fuel for the fire. Mol Plant-Microbe Interact 22:487–497 Bolwell GP, Wojtaszek P (1997) Mechanisms for the generation of reactive oxygen species in plant defence-a broad perspective. Physiol Mol Plant Pathol 51:347–366 Bowles DJ (1992) Defense-related proteins in higher plants. Annu Rev Biochem 59:873–907 Campo S, Carrascal M, Coca M, Abian J, San Segundo B (2004) The defense response of germinating maize embryos against fungal infection: a proteomics approach. Proteomics 4:383–396 Chen ZY, Brown RL, Lax AR, Guo BZ, Cleveland TE, Russin JS (1998) Resistance to aspergillus flavus in corn kernels is associated with a 14-kDa protein. Phytopathology 88:276–281 Chen ZY, Brown RL, Damann KE, Cleveland TE (2002) Identification of unique or elevated levels of kernel proteins in aflatoxin-resistant maize genotypes through proteome analysis. Phytopathology 92:1084–1094 Chen ZY, Brown RL, Cleveland TE (2004a) Evidence for an association in corn between stress tolerance and resistance to Aspergillus flavus infection and aflatoxin contamination. Afr J Biotechnol 3:693–699 Chen ZY, Brown RL, Damann KE, Cleveland TE (2004b) Identification of a maize kernel stress-­ related protein and its effect on aflatoxin accumulation. Phytopathology 94:938–945 Chen ZY, Brown RL, Rajasekaran K, Damann KE, Cleveland TE (2006) Identification of a maize kernel pathogenesis-related protein and evidence for its involvement in resistance to Aspergillus flavus infection and aflatoxin production. Phytopathology 96:87–95 Chen ZY, Brown RL, Damann KE, Cleveland TE (2007) Identification of maize kernel endosperm proteins associated with resistance to aflatoxin contamination by Aspergillus flavus. Phytopathology 97:1094–1103 Chen LQ, Hou BH, Lalonde S, Takanaga H, Hartung ML, Qu XQ, Guo WJ, Kim JG, Underwood W, Chaudhuri B et  al (2010) Sugar transporters for intercellular exchange and nutrition of pathogens. Nature 468:527–532 Chivasa S, Simon WJ, Yu XL, Yalpani N, Slabas AR (2005) Pathogen elicitor-induced changes in the maize extracellular matrix proteome. Proteomics 5:4894–4904 Chugh V, Kaur N, Grewal MS, Gupta AK (2013) Differential antioxidative response of tolerant and sensitive maize (Zea mays L.) genotypes to drought stress at reproductive stage. Indian J Biochem Biophys 50:150–158 Cobo-Diaz JF, Baroncelli R, Le Floch G, Picot A (2019) Combined metabarcoding and co-­ occurrence network analysis to profile the bacterial, fungal and Fusarium communities and their interactions in maize stalks. Front Microbiol 10:261. https://doi.org/10.3389/fmicb.2019.00261 Cooper GM (2000) The cell: a molecular approach, 2nd edn. Sinauer Associates, Sunderland, MA

320

A. Singh et al.

Dangl JL, Horvath DM, Staskawicz BJ (2013) Pivoting the plant immune system from dissection todeployment. Science 341(6147):746–751 Dietz KJ, Jacob S, Oelze ML, Laxa M, Tognetti V, de Miranda SM, Baier M, Finkemeier I (2006) The function of peroxiredoxins in plant organelle redox metabolism. J Exp Bot 57:1697–1709 Dodds PN, Rathjen JP (2010) Plant immunity: towards an integrated view of plant–pathogen interactions. Nat Rev Genet 1:539 Fandohan P, Hell K, Marasas WFO, Wingfield MJ (2003) Infection of maize by Fusarium species and contamination with fumonisin in Africa. Afr J Biotechnol 2:570–579 Fujiwara T, Eiji N, Kazutoshi Y, Derek B. Arabidopsis Book. 1: e0020.Goto, and Satoshi Naito, 2002; Storage Proteins Published online 2002 Sep 30. https://doi.org/10.1199/tab.0020 Guan L, Scandalios JG (1998) Two structurally similar maize cytosolic superoxide dismutase genes, Sod4 and Sod4A, respond differentially to abscisic acid and high osmoticum. Plant Physiol 117:217–224 Gutsche N, Thurow C, Zachgo S, Gatz C (2015) Plant-specific CC-type glutaredoxins: functions in developmental processes and stress responses. Biol Chem 396:495–509 Hancock JT, Henson D, Nyirenda M, Desikan R, Harrison J, Lewis M, Hughes J, Neill SJ (2005) Proteomic identification of glyceraldehyde 3-phosphate dehydrogenase as an inhibitory target of hydrogen peroxide in arabidopsis. Plant Physiol Biochem 43:828–835 Hendrich JP, Hartl FU (1993) Molecular chaperone functions of heat shock proteins. Annu Rev Biochem 62:349–384 Henry E, Fung N, Liu J, Drakakaki G, Coaker G (2015) Beyond glycolysis: GAPDHs are multi-­ functional enzymes involved in regulation of ROS, autophagy, and plant immune responses. PLoS Genet 11:e1005199 Hiraga S, Sasaki K, Ito H, Ohashi Y, Matsui H (2001) A large family of class III plant peroxidases. Plant Cell Physiol 42:462–468 Hong-Bo S, Zong-Suo L, Ming-An S (2005) LEA proteins in higher plants: structure, function, gene expression and regulation. Colloids Surf 45:131–135 Hopkins MT, Lampi Y, Wang TW, Liu Z, Thompson JE (2008) Eukaryotic translation initiation factor 5A is involved in pathogen-induced cell death and development of disease symptoms in Arabidopsis. Plant Physiol 148:479–489 Huang X, Liu L, Chen J, Zhai Y (2009a) Comparative proteomic analysis of the response in resistant and susceptible maize inbred lines to infection by Culvularialunata. Prog Nat Sci 19:845–850 Huang X, Liu L, Zhai Y, Liu T, Chen C (2009b) Proteomic comparison of four maize inbred lines with different levels of resistance to Culvularialunata (wakker) boed infection. Prog Nat Sci 19:353–358 Iriti M, Faoro F (2009) Chemical diversity and defence metabolism: how plants cope with pathogens and ozone pollution. Int J Mol Sci 10:3371–3399 Jiang C, Cui Q, Feng K et al (2016) Melatonin improves antioxidant capacity and ion homeostasis and enhances salt tolerance in maize seedlings. Acta Physiol Plant 38:82. https://doi. org/10.1007/s11738-016-2101-2 Jones JDG, Dangl JL (2006) The plant immune system. Nature 444:323 Kasprzewska A (2003) Plant chitinases—regulation and function. Cell Mol Biol 8:809–824 Kim SK, You YN, Park JC, Joung Y, Kim BG, Ahn JC, Cho HS (2012) The rice thylakoid luminal cyclophilin OsCYP20-2 confers enhanced environmental stress tolerance in tobacco and Arabidopsis. Plant Cell Rep 31:417–426 Kurama EE, Fenille RC, Rosa VE Jr, Rosa DD, Ulian EC (2002) Mining the enzymes involved in the detoxification of reactive oxygen species (ROS) in sugarcane. Mol Plant Pathol 3:251–259 Lanubile A, Ferrarini A, Maschietto V, Delledonne M, Marocco A, Bellin D (2014) Functional genomic analysis of constitutive and inducible defense responses to Fusarium verticillioides infection in maize genotypes with contrasting ear rot resistance. BMC Genomics 15(1):710 Lanubile A, Maschietto V, de Leonardis S, Battilani P, Paciolla C, Marocco A (2015) Defense responses to mycotoxin-producing fungi Fusarium proliferatum, F. subglutinans, and

Fungal Pathogen-Induced Modulation of Structural and Functional Proteins in Zea

321

Aspergillus flavus in kernels of susceptible and resistant maize genotypes. Mol Plant Microbe Interact 28:546–557 Lanubile A, Maschietto V, Borrelli VM, Stagnati L, Logrieco AF, Marocco A (2017) Molecular basis of resistance to Fusarium ear rot in maize. Front Plant Sci 81774. https://doi.org/10.3389/ fpls.2017.01774 Leah R, Tommerup H, Svendsen I, Mundy J (1991) Biochemical and molecular characterization of three barley seed proteins with antifungal properties. J Biol Chem 266:1564–1573 Li K, Xu C, Zhang J (2011) Proteome profile of maize (Zea mays L.) leaf tissue at the flowering stage after long-term adjustment to rice black-streaked dwarf virus infection. Gene 485:106–113 Liu T, Liu LX, Huang XL, Jiang X, Zhou PH (2009) A new furanoid toxin produced by Culvularialunata, the causal agent of maize Curvularia leaf spot. Can J Plant Pathol 31:22–27 Magbanua ZV, de Moraes CM, Brooks TD, Williams WP, Luthe DS (2007) Is catalase activity one of the factors associated with maize resistance to Aspergillus flavus? Mol Plant-Microbe Interact 20:697–706 McKean C, Tang L, Billam M, Tang M, Theodorakis CW, Kendall RJ, Wang JS (2006) Comparative acute and combinative toxicity of aflatoxin B1 and T-2 toxin in animals and immortalized human cell lines. J Appl Toxicol 26:139–147 Mittler R, Vanderauwera S, Gollery M, van Breusegem F (2004) Reactive oxygen gene network of plants. Trends Plant Sci 9:490–498 Mohammadi M, Anoop V, Gleddie S, Harris LJ (2011) Proteomic profiling of two maize inbreds during early gibberella ear rot infection. Proteomics 11:3675–3684 Murdia LK, Wadhwani R, Wadhawan N, Bajpai P, Shekhawat S (2016) Maize utilization in India: an overview. Am J Food Nutr 4(6):169–176. https://doi.org/10.12691/ajfn-­4-­6-­5 Neucere JN (1996) Inhibition of aspergillus flavus growth by silk extracts of resistant and susceptible corn. J Agric Food Chem 44:1982–1983 Passardi F, Cosio C, Penel C, Dunand C (2005) Peroxidases have more functions than a swiss army knife. Plant Cell Rep 24:255–265 Pechanova O, Pechan T (2015) Academic editors: Setsuko Komatsu and Zahed Hossain maize-­ pathogen interactions: an ongoing combat from a proteomics perspective 2. Int J Mol Sci 16:28429–28448. https://doi.org/10.3390/ijms161226106 Pechanova O, Pechan T, Ozkan S, McCarthy FM, Williams WP, Luthe DS (2010) Proteome profile of the developing maize (Zea mays L.) rachis. Proteomics 10:3051–3055 Pechanova O, Pechan T, Williams WP, Luthe DS (2011) Proteomic analysis of the maize rachis: potential roles of constitutive and induced proteins in resistance to Aspergillus flavus infection and aflatoxin accumulation. Proteomics 11:114–127 Peethambaran B, Hawkins L, Windham GL, Williams WP, Luthe DS (2010) Anti-fungal activity of maize silk proteins and role of chitinase in Aspergillus flavus resistance. Toxin Rev 29:27–39 Rehman F, Adnan M, Kalsoom M, Naz N, Husnain MG, Ilahi H, Ilyas MA, Yousaf G, Tahir R, Ahmad U (2021) Seed-bornefungal diseases of maize (Zea mays L.): a review. Agrinula: Jurnal Agroteknologi dan Perkebunan 4(1):43–60 Richardson M, Valdes-Rodrigues S, Blanco-Labra A (1987) A possible function for thaumatin and TMV-induced protein suggested by homology to a maize inhibitor. Nature 327:432–434 Rojas CM, Senthil-Kumar M, Tzin V, Mysore KS (2014) Regulation of primary plant metabolism during plant-pathogen interactions and its contribution to plant defense. Front Plant Sci 5:17 Sajjadi AM, Bahsiri T, Saeed S, Iqbal M, Ahmad S, Islam S (2016) Zeamatin like protein (zlp) gene is associated with resistance against A. niger in maize (Zea mays L.). Cercetări Agronomiceîn Moldova XLIX 2(166):29–39 Schlumbaum A, Mauch F, Vogeli U, Boller T (1986) Plant chitinases are potent inhibitors of fungal growth. Nature 324:365–367 Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115

322

A. Singh et al.

Sekhar K, Priyanka B, Reddy VD, Rao KV (2010) Isolation and characterization of a pigeon pea cyclophilin (CcCYP) gene, and its over-expression in Arabidopsis confers multiple abiotic stress tolerance. Plant Cell Environ 33:1324–1338 Selitrennikoff CP (2001) Antifungal proteins. Appl Environ Microbiol 67(7):2883–2894. https:// doi.org/10.1128/AEM.67.7.2883-­2894.2001 Sharma AD, Singh P (2003) Effect of water stress on expression of a 20 kD cyclophilin-like protein in drought susceptible and tolerant cultivars of sorghum. J Plant Biochem Biotechnol 12:77–80 Stockmann-Juvala H, Savolainen K (2008) A review of the toxic effects and mechanisms of action of fumonisin B1. Hum Exp Toxicol 27:799–809 Sun W, van Montagu M, Verbruggen N (2002) Small heat shock proteins and stress tolerance in plants. Biochim Biophys Acta 1577:1–9 The CIMMYT Maize Program (2004) Maize diseases: A guide for field identification. 4th edition. Mexico, D.F.: CIMMYT. Trivedi DK, Ansari MW, Tuteja N (2013) Multiple abiotic stress responsive rice cyclophilin: (OsCYP-25) mediates a wide range of cellular responses. Commun Integr Biol 6:e25260 Tsukaya H, Ohshima T, Naito S, Chino M, Komeda Y (1991) Sugar-dependent expression of the CHS-A gene for chalcone synthase from Petunia in transgenic Arabidopsis. Plant Physiol 97:1414–1421 Van Loon LC (1987) Disease induction by plant viruses. Adv Virus Res 33:205–255 Van Loon LC, Rep M, Pieterse CM (2006) Significance of inducible defense-related proteins in infected plants. Annu Rev Phytopathol 44:135–162 Vigers A, Robert W, Selitrennikof C (1991) A new family of plant antifungal proteins. Mol Plant-­ Microbe Interact 4:315–323 Wu L, Han Z, Wang S, Wang X, Sun A, Zu X, Chen Y (2013a) Comparative proteomic analysis of the plant-virus interaction in resistant and susceptible ecotypes of maize infected with sugarcane mosaic virus. J Proteome 89:124–140 Wu L, Wang S, Chen X, Wang X, Wu L, Zu X, Chen Y (2013b) Proteomic and phytohormone analysis of the response of maize (Zea mays L.) seedlings to sugarcane mosaic virus. PLoS One 2013(8):e70295 Wu XJ, Li X, Zhao PF, Li N, Wu L, He Y, Wang SC (2015) Comparative transcriptome proling of two maize near-isogenic lines differing in the allelic state for bacterial brown spot disease resistance. J Integr Agric 14(4):610–621 Zhang X-l, SI B-w, Fan C-m, LI H-j, Wang X-m (2014) Proteomics Identification of Differentially Expressed Leaf Proteins in Response to Setosphaeria turcica Infection in Resistant Maize. J Integr Agri 13(4):789–803. S2095311913605134. https://doi.org/10.1016/S2095-3119(13)60513-4

Role of Plant Growth-Promoting Rhizobacteria Mitigating Drought Stress in Maize Shifa Shaffique, Muhammad Imran, Shabir Hussain Wani, Anjali Pande, Waqas Rahim, Muhamad Aaqil khan, Sang-Mo Kang, and In-Jung Lee

Abbreviations

ABA abscisic acid. CK cytokinin. IA indole-3-acetic acid. PGPRs plant growth-promoting rhizobacteria

1 Introduction Plants are sessile in nature due to which they get exposed to various kinds of stresses in their life. As a result, they develop different kinds of approaches to survive and combat the stress. Various ecological stresses such as change in temperature, light, pH, water, heavy metal stress, and salinity are the leading factors that cause adverse effects on plant productivity (Rejeb et al. 2014; Gull et al. 2019; Peck and Mittler 2020). Among all the abiotic stresses, drought is the leading cause of morbidity and mortality in plants. They cause 50% of the crop losses globally every year (Mishra and Singh 2010). Drought is an osmotic stress caused due to the shortage of water (Farooq et al. 2009; Fang and Xiong 2015). Global warming is the greatest challenge for agriculture industry. Industrialization also has a negative impact on global warming (Masson-Delmotte et  al. 2018). According to a recent report of IPCC, S. Shaffique · M. Imran · M. Aaqil khan · S.-M. Kang · I.-J. Lee (*) Department of Applied Biosciences, Kyungpook National University, Daegu, South Korea e-mail: [email protected] S. H. Wani MRCFC Khudwani, 192101, Shere-e- Kashmir University of Agriculture Sciences and Technology, Srinagar, Jammu and Kashmir, India A. Pande · W. Rahim Laboratory of Plant Molecular Pathology and Functional Genomics, Department of Plant Biosciences, School of Applied Biosciences, College of Agriculture and Life Science, Kyungpook National University, Daegu, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 S. H. Wani et al. (eds.), Maize Improvement, https://doi.org/10.1007/978-3-031-21640-4_15

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global temperature increases every year by 2 degrees and leads to increase in global warming due to which the problem of agricultural drought arises (Harvey 2018). In general, drought is a complex phenomenon as it affects the physiological, biochemical, and molecular processes, thereby reducing the yield of crops (Dai 2013; Trenberth et al. 2014). Maize is an important cereal crop utilized globally as a staple food consumed by humans, livestock, and dairy animals as well. Zea mays (corn) is important because of its nutritional status (Ribaut et al. 2009; Cairns et al. 2013). It contains carbohydrates, fibers, vitamin B, vitamin C, and folic acid. This crop requires an integrated water supply because it is the most sensitive toward water scarcity. In addition, it is estimated that 20% of maize crop is lost due to drought stress every year (Zinselmeier et al. 1995; Monneveux et al. 2006; Daryanto et al. 2016). To meet the food requirements of the increasing population of the world, there is a need to enhance the existing conventional strategies to cope with the stress; however, these strategies are expensive. Conventional strategies that are being practiced have failed to produce a significant crop yield under stress conditions with minimal side effects (Mengistu 2016; Rosero et al. 2020). Inoculation of beneficial microorganisms as biostimulants is the newest technique with promising effects, which enable plants to withstand drought stress (Shaffique et al. 2022). Various studies are supporting the fact that the biostimulants enhance the yield under drought stress. Use of beneficial microbes is an eco-friendly technique to manage disease control as well as to promote plant growth and development. Various recent researches are available on the use of beneficial microbes (PGPRs, fungi, endophytes, and phyllosphere) in stress tolerance (Kour et  al. 2020, Singh, Singh et  al. 2020). Plant microbial interaction enhances the production of phytohormones, osmolytes, ACC deaminase, mineral uptake, nitrogen fixation, and genetic expression (Kuiper et al. 2004; Pathak and Nallapeta 2014). In response to stress, the plants develop adaptation mechanisms that modulate the physiological and biochemical changes through the production of phytohormones and chemical metabolites produced due to plant microbial interactions (Verma et al. 2017; Govindasamy et al. 2018).

2 Plant Growth-Promoting Rhizobacteria Soil is rich in microscopic organisms such as bacteria, viruses, fungi, algae, and protozoans. Bacteria that have the impeding potential to survive and colonize the host plants in a symbiotic relationship along the rhizospheric axis are known as the PGPRs (Lugtenberg and Kamilova 2009; Ahemad and Kibret 2014). There are various types of PGPRs such as Azospirillum, Pseudomonas, Enterobacter, and Arthrobacter. Inoculation of beneficial microorganisms (PGPRS) enhances overall plant growth and development (Van Loon 2007; Singh 2013). When PGPRs interact with plants, these plants show various mechanistic approaches to boost the drought tolerance by enhancing cell membrane protection, plant growth, photosynthesis,

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antioxidant metabolism, and carbohydrate metabolism through modulation of Krebs cycle (Naseem and Bano 2014; Zhang et al. 2020).

3 Mechanism of Drought Tolerance by PGPRs 3.1 PGPRs and Phytohormones Plant microbial interactions modulate stress tolerance through production of various phytohormones and chemical metabolites. Plant microbial interaction enhances the production of the phytohormones such as auxin, gibberellins, cytokinin, and abscisic acid. Phytohormones are signaling molecules that are transported throughout the plant (Hardtke et al. 2007; Overvoorde et al. 2010). Auxin is a phytohormone that enhances the growth of roots and shoot under drought stress. Abscisic acid, also known as the stress hormone, is involved in the maintenance of turgor pressure in plant cells and provides osmoprotection to plants under stress conditions (Gururani et al. 2015; Egamberdieva et al. 2017). Cytokinins are known as the master regulators in response to the stress, and their functions are antagonistic to ABA. Gibberellin is produced under stress conditions and enhances the plant defense through the activation of antioxidants through enzymatic and nonenzymatic pathways (Khan et al. 2012, Khan et al. 2020).

3.2 PGPRs and Mineral Uptake Plant microbial interaction inhibits the production of organic compounds, which rehabilitate the nutritional status of the plant under stress. Sugars, vitamins, and organic acids are taken up by the microbial community from the host plant. Beneficial microbes, such as PGPRs, promote the mineral uptake through phosphate solubilization, nitrogen fixation, and production of the siderophores (Adesemoye and Kloepper 2009). For example, phosphate is available to the microorganisms in the form of inositol phosphate, phosphotriester, and phosphomonoester (Dong and Lu 2012; Sharma et  al. 2020). PGPRs solubilize these complex biomolecules into lower molecular weight compounds, such as citric acid and gluconic acid. In case of limited iron in the soil, siderophores are produced by microbial interaction with the host plant. It enhances the mineral uptake during abiotic stress (Adesemoye and Kloepper 2009). Potassium mobilization involves hydrolysis, due to which the bacteria release gluconic acid, citric acid, and oxalic acid, thereby increasing the chelation of the cations, which are bound to potassium (Teotia et al. 2016).

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3.3 PGPRs and Secondary Metabolites When a microbe comes in contact with the plant, it modulates the production of secondary metabolites such as proline, organic acid, glycine betaine, amino acids, and sugars (Mona et al. 2017). Proline is an osmolyte, which also acts as an antioxidant to scavenge free radicals. It helps the plants to survive under stress and protects the plant from further stress (Ghorai et al. 2021). Besides proline, glycine betaine also acts as an osmolyte under various stresses. It stabilizes the cellular integrity, cellular pressure, and enzymatic pathways under stress conditions. Polyamines and trehalose also act as osmoprotectants, which are the metabolites that protect the plant under stress conditions (Ruiz et al. 2010; Bacon et al. 2015).

3.4 PGPRs and Antioxidant Machinery Reactive oxygen species is one of the by-products of photosynthesis and respiration. During abiotic stress such as in case of drought stress, it also accumulates in the plant cell and causes extra damage to the plant cell. It is very toxic to the plant cell (Cruz de Carvalho 2008; Verma et al. 2019). PGPRs have an essential role in stress elevation through mitigation of free radicals or oxygen species. Production of several enzymes due to microbial interaction such as superoxide dismutase (SOD), peroxidases, ascorbate peroxidases (APOX), and catalases (CAT) minimizes the oxidant stress level in plants (Ansari and Ahmad 2019; Jochum et al. 2019; Lephatsi 2020) (Fig. 1).

4 Role of PGPRs in Maize Plants Under Drought Condition The PGPR strains Pseudomonas aeruginosa, Enterobacter cloacae, Achromobacter xylosoxidans and Leclercia adecarboxylata were identified and inoculated in a glass jar experimental setup with the maize seedlings. The results demonstrated that inoculation of these microorganisms enhances drought tolerance by lowering the ethylene and improving the growth characteristics, such as photosynthetic pigmentation, and fresh and dry biomass (Danish et al. 2020a, b). In 2017, an in vitro study was conducted on 35 isolates of Azospirillum strain to determine the osmotic tolerance in maize under PEG-induced drought stress. Plant growth-promoting characteristics were checked, including the production of IAA, siderophores and exopolysaccharides, and trehalose. The results suggested that microbe-inoculated plants have more tolerance toward drought stress than non-­ inoculated plants. Among these microbe-inoculated plants, AZ19 has more potential toward water stress tolerance through the production of osmoprotectants. It helps the maize seedlings to withstand water-deficit conditions (García et al. 2017).

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Fig. 1  Microbial mitigation stragetoies of drought stress in maize plant

In a pot experiment, three microbial strains were inoculated with maize seeds to study drought tolerance. Among the three microbial strains, Pa2 was more efficient in mitigating the drought stress, through the production of EPS. Inoculated plants minimize the drought tolerance and improve the relative water content, leaf area, and biomass in the plants. Inoculation of PGPRs enhances soil moisture content. The mechanism of action is to minimize the drought stress by reducing the reactive oxygen species and therefore enhancing drought tolerance in plants (Naseem and Bano 2014). In another study, three bacterial isolates were studied – F2, YL2, and YX2 were isolated and inoculated into the seeds of maize cultivar Zhengdan 958 to study the osmotic tolerance. The results suggested that inoculation of PGPRs enhances the osmotic regulation via the production of glycine betaine and choline. All the bacterial isolates significantly show polyethylene glycol tolerance. The YX2 strain showed more competence (Gou et al. 2015). Five PGPRs were isolated from rhizospheric soil in Pakistan and inoculated into the maize seeds for screening drought tolerance. The results suggested that endogenous production of the phytohormones (IAA, GA, ABA) and osmoprotectants, such as proline, enhances drought tolerance. All the rhizospheric strains are efficient in the mitigation of drought stress. When these are co-inoculated with tryptophan, they significantly enhance drought mitigation in the maize plants. The study also suggested that PGPRs, Bacillus pumilus, can act as a good biofertilizers in maize plants (Yasmin et al. 2017).

Host plant Zea mays

Zea mays

Zea mays

Maize cultivar Zhengdan 958

Zea mays

PGPR strain Pseudomonas aeruginosa, Enterobacter cloacae, Achromobacterxylosoxidans and Leclerciaadecarboxylata

Azospirillum strains AZ19 AZ39

Proteus penneri (Pp1), Pseudomonas aeruginosa (Pa2), and Alcaligenes faecalis (AF3)

Klebsiella variicola F2 Raoultellaplanticola YL2) and Pseudomonas fluorescens YX2

Proteus sp. and Pseudomonas sp. B. pumilus

Mechanism of action ↑RWC, ↑Chlorophyll ↑Stomatal conductance ↑Nitrogen fixation ,↑IAA, ACC deaminases, siderophores ↑EPS, ↑Sugars, proline, RWC, biomass, soil moisture content and leaf area ↑Secondary metabolites ↑Glycine betaine, choline, biomass, relative water contents ↑Phytohormones (ABA,IAA,GA) RWC, proline, antioxidant enzymes

Table 1  PGPR strains used for mitigating drought stress in maize References 2020,PakistanI (Danish et al. 2020a, b)

2017,Pakistan, (Yasmin et al. 2017)

Rhizospheric soil from Pakistan

Pot experiment

2014,Pakistan,(Naseem and Bano 2014)

Rhizospheric soil of 2015, China,(Gou et al. rainfed field area 2015).

Ghotki Sindh and Kallarsayedan

Rhizospheric soil of 2017,Argentina, Argentina (García et al. 2017)

Isolated from Rhizospheric soil, Multan, Pakistan

Pot experiment

Pot experiment

In vitro and in growth chamber

Experimental setup Glass jar experiment

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Zea mays

Pseudomonas fluorescens PA28, Pseudomonas borealis PA29, Bacillus subtilis BA41 Cupriavidus necator sp. 1C2 (B1) and Pseudomonas fluorescens S3X (B2) Zea mays

Host plant Zea mays

PGPR strain Pseudomonas aeruginosa, Enterobacter cloacae, Achromobacterxylosoxidans and Leclerciaadecarboxylata

Mechanism of action ↑ACC deaminase, ↑photosynthetic pigment, ↑stomatal conductance ,↑NPK ↑Mineral content ↑Chlorophyll ↑Gaseous exchange ↑Nitrogen and phosphorus contents Biomass Ariel biomass ↑Siderophore, IAA, ↑WHC Senegal

Environmental degraded area

Green house

Isolated from Rhizospheric soil from Multan Pakistan

Green house

Experimental setup Research area of the department

2020, Portugal,(Monneveux et al. 2006)

Italy,2014,(Zoppellari et al. 2014)

References 2020,Pakistan,(Danish et al. 2020a, b)

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Four PGPRs having ACC deaminase potential were inoculated into maize plants to determine drought tolerance. Among all the bacterial isolates, E. cloacae/A. xylosoxidans significantly enhanced drought tolerance through the improved production and maintenance of relative water content, photosynthetic pigments, and stomatal conductance (Danish et al. 2020a, b). In another greenhouse experiment, three rhizospheric bacterial strains were isolated and inoculated with the maize plants under osmotic stress. All physiological and biochemical parameters were measured. The results suggested that bacterial strains efficiently enhance the stomatal conductance and parameters that regulate the gaseous exchange, even under stress conditions. The bacterial strains enhance the nitrogen content in the plants through nitrogen fixation. Inoculation also improves the biomass and drought tolerance in maize plants. In conclusion, the study suggested that the inoculation of PGPRs is one of the best suitable agronomical practices with minimal ecological disturbance (Zoppellari et al. 2014). Two PGPRs isolated from Portugal were inoculated into maize plants under drought stress to study the osmotic tolerance in bacteria. The results suggested that inoculation of individual and combined bacterial isolates improved the biomass of the plants and rehabilitated the nutritional status in them. In this manner, even under stress conditions, bacterial isolates improve the nutritional status of plants for survival (Pereira et al. 2020). Table 1 summarizes the inoculation of PGPRs in mitigation of drought stress in maize crop plant.

5 Conclusion Overall, the beneficial microorganisms (PGPRs) and their interaction with the plants help in maintaining the sustainable yield and growth of maize plants under abiotic stresses, such as drought stress. Plant microbial interactions enhance the production of phytohormones and biochemical metabolites to provide the necessary adaptation and tolerance under stress conditions. Only a few studies are reported on the plant microbial interaction and the exact mechanism by which microbes mitigate stress is not known. Further microbial communists need to be explored, and attention must be paid on the mechanisms of stress tolerance. We anticipate that the next-generation agricultural tools will be based on the smart integration of microbial isolates.

References Adesemoye AO, Kloepper JW (2009) Plant–microbes interactions in enhanced fertilizer-use efficiency. Appl Microbiol Biotechnol 85(1):1–12 Ahemad M, Kibret M (2014) Mechanisms and applications of plant growth promoting rhizobacteria: current perspective. J King Saud University-Science 26(1):1–20

Role of Plant Growth-Promoting Rhizobacteria Mitigating Drought Stress in Maize

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Ansari FA, Ahmad I (2019) Alleviating drought stress of crops through PGPR: mechanism and application. In: Microbial interventions in agriculture and environment. Springer, pp 341–358 Bacon CW, Palencia ER, Hinton DM (2015) Abiotic and biotic plant stress-tolerant and beneficial secondary metabolites produced by endophytic bacillus species. Plant microbes symbiosis: applied facets. Springer, pp 163–177 Cairns JE, Crossa J, Zaidi P, Grudloyma P, Sanchez C, Araus JL, Thaitad S, Makumbi D, Magorokosho C, Bänziger M (2013) Identification of drought, heat, and combined drought and heat tolerant donors in maize. Crop Sci 53(4):1335–1346 Cruz de Carvalho MH (2008) Drought stress and reactive oxygen species: production, scavenging and signaling. Plant Signal Behav 3(3):156–165 Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3(1):52–58 Danish S, Zafar-Ul-Hye M, Hussain S, Riaz M, Qayyum MF (2020a) Mitigation of drought stress in maize through inoculation with drought tolerant ACC deaminase containing PGPR under axenic conditions. Pak J Bot 52(1):49–60 Danish S, Zafar-ul-Hye M, Mohsin F, Hussain M (2020b) ACC-deaminase producing plant growth promoting rhizobacteria and biochar mitigate adverse effects of drought stress on maize growth. PLoS One 15(4):e0230615 Daryanto S, Wang L, Jacinthe P-A (2016) Global synthesis of drought effects on maize and wheat production. PLoS One 11(5):e0156362 Dong H, Lu A (2012) Mineral–microbe interactions and implications for remediation. Elements 8(2):95–100 Egamberdieva D, Wirth SJ, Alqarawi AA, Allah EFA, Hashem A (2017) Phytohormones and beneficial microbes: essential components for plants to balance stress and fitness. Front Microbiol 8:2104 Fang Y, Xiong L (2015) General mechanisms of drought response and their application in drought resistance improvement in plants. Cell Mol Life Sci 72(4):673–689 Farooq M, Wahid A, Kobayashi N, Fujita D, Basra S (2009) Plant drought stress: effects, mechanisms and management. Springer, Sustainable agriculture, pp 153–188 García JE, Maroniche G, Creus C, Suárez-Rodríguez R, Ramirez-Trujillo JA, Groppa MD (2017) In vitro PGPR properties and osmotic tolerance of different Azospirillum native strains and their effects on growth of maize under drought stress. Microbiol Res 202:21–29 Ghorai AK, Patsa R, Jash S, Dutta S (2021) Microbial secondary metabolites and their role in stress management of plants. Elsevier, Biocontrol Agents and Secondary Metabolites, pp 283–319 Gou W, Tian L, Ruan Z, Zheng P, Chen F, Zhang L, Cui Z, Zheng P, Li Z, Gao M (2015) Accumulation of choline and glycinebetaine and drought stress tolerance induced in maize (Zea mays) by three plant growth promoting rhizobacteria (PGPR) strains. Pak J Bot 47(2):581–586 Govindasamy V, George P, Raina SK, Kumar M, Rane J, Annapurna K (2018) Plant-associated microbial interactions in the soil environment: role of endophytes in imparting abiotic stress tolerance to crops. Advances Crop Environ Inter Springer:245–284 Gull A, Lone AA, Wani NUI (2019) Biotic and abiotic stresses in plants. Abiotic and biotic stress in plants: 1–19 Gururani MA, Mohanta TK, Bae H (2015) Current understanding of the interplay between phytohormones and photosynthesis under environmental stress. Int J Mol Sci 16(8):19055–19085 Hardtke CS, Dorcey E, Osmont KS, Sibout R (2007) Phytohormone collaboration: zooming in on auxin–brassinosteroid interactions. Trends Cell Biol 17(10):485–492 Harvey LD (2018) Global warming. Routledge Jochum MD, McWilliams KL, Borrego EJ, Kolomiets MV, Niu G, Pierson EA, Jo Y-K (2019) Bioprospecting plant growth-promoting rhizobacteria that mitigate drought stress in grasses. Front Microbiol 10:2106 Khan NA, Nazar R, Iqbal N, Anjum NA (2012) Phytohormones and abiotic stress tolerance in plants. Springer Science & Business Media

332

S. Shaffique et al.

Khan N, Bano A, Ali S, Babar M (2020) Crosstalk amongst phytohormones from planta and PGPR under biotic and abiotic stresses. Plant Growth Regul 90(2):189–203 Kour D, Rana KL, Yadav AN, Sheikh I, Kumar V, Dhaliwal HS, Saxena AK (2020) Amelioration of drought stress in foxtail millet (Setaria italica L.) by P-solubilizing drought-tolerant microbes with multifarious plant growth promoting attributes. Environ Sustain 3(1):23–34 Kuiper I, Lagendijk EL, Bloemberg GV, Lugtenberg BJ (2004) Rhizoremediation: a beneficial plant-microbe interaction. Mol Plant Microbe Interact 17(1):6–15 Lephatsi MM (2020) Epigenetic and targeted metabolic changes in microbial biostimulant-treated maize plants under drought stress conditions. University of Johannesburg (South Africa) Lugtenberg B, Kamilova F (2009) Plant-growth-promoting rhizobacteria. Annu Rev Microbiol 63:541–556 Masson-Delmotte V, Zhai P, Pörtner H-O, Roberts DC, Skea J, Shukla PR, Pirani A, Moufouma-­ Okia W, Péan C, Pidcock R, Connors S, Matthews JBR, Yang C, Zhou X, Steg L (2018) Global warming of 1.5 °C: Summary for policy makers. IPCC  - The Intergovernmental Panel on Climate Change. 32 p Mengistu D (2016) Impacts of drought and conventional coping strategies of Borana community, southern Ethiopia. Res Humanit Soc Sci 6(23):29–37 Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1-2):202–216 Mona SA, Hashem A, Allah EFA, Alqarawi AA, Soliman DWK, Wirth S, Egamberdieva D (2017) Increased resistance of drought by Trichoderma harzianum fungal treatment correlates with increased secondary metabolites and proline content. J Integr Agric 16(8):1751–1757 Monneveux P, Sanchez C, Beck D, Edmeades G (2006) Drought tolerance improvement in tropical maize source populations: evidence of progress. Crop Sci 46(1):180–191 Naseem H, Bano A (2014) Role of plant growth-promoting rhizobacteria and their exopolysaccharide in drought tolerance of maize. J Plant Inter 9(1):689–701 Overvoorde P, Fukaki H, Beeckman T (2010) Auxin control of root development. Cold Spring Harb Perspect Biol 2(6):a001537 Pathak KV, Nallapeta S (2014) Plant-microbial interaction: a dialogue between two dynamic bioentities. Springer, Agricultural Bioinformatics, pp 259–272 Peck S, Mittler R (2020) Plant signaling in biotic and abiotic stress. Oxford University Press UK 71:1649–1651 Pereira S, Abreu D, Moreira H, Vega A, Castro P (2020) Plant growth-promoting rhizobacteria (PGPR) improve the growth and nutrient use efficiency in maize (Zea mays L.) under water deficit conditions. Heliyon 6(10):e05106 Rejeb IB, Pastor V, Mauch-Mani B (2014) Plant responses to simultaneous biotic and abiotic stress: molecular mechanisms. Plan Theory 3(4):458–475 Ribaut J-M, Betran J, Monneveux P, Setter T (2009) Drought tolerance in maize. Handbook of maize: its biology. Springer, pp 311–344 Rosero A, Granda L, Berdugo-Cely JA, Šamajová O, Šamaj J, Cerkal R (2020) A dual strategy of breeding for drought tolerance and introducing drought-tolerant, underutilized crops into production systems to enhance their resilience to water deficiency. Plan Theory 9(10):1263 Ruiz B, Chávez A, Forero A, García-Huante Y, Romero A, Sánchez M, Rocha D, Sánchez B, Rodríguez-Sanoja R, Sánchez S (2010) Production of microbial secondary metabolites: regulation by the carbon source. Crit Rev Microbiol 36(2):146–167 Shaffique S, Khan MA, Imran M, Park Y, Wani SH, Lee I-J (2022) Research progress in the field of microbial mitigation for drought stress in plants. Front Plant Sci 13:870626 Sharma V, Salwan R, Tawfeeq L (2020) Molecular aspects of plant beneficial microbes in agriculture. Academic Press Singh JS (2013) Plant growth promoting rhizobacteria. Resonance 18(3):275–281 Singh DP, Singh V, Gupta VK, Shukla R, Prabha R, Sarma BK, Patel JS (2020) Microbial inoculation in rice regulates antioxidative reactions and defense related genes to mitigate drought stress. Sci Rep 10(1):1–17

Role of Plant Growth-Promoting Rhizobacteria Mitigating Drought Stress in Maize

333

Teotia P, Kumar V, Kumar M, Shrivastava N, Varma A (2016) Rhizosphere microbes: potassium solubilization and crop productivity–present and future aspects. Springer, Potassium solubilizing microorganisms for sustainable agriculture, pp 315–325 Trenberth KE, Dai A, Van Der Schrier G, Jones PD, Barichivich J, Briffa KR, Sheffield J (2014) Global warming and changes in drought. Nat Clim Change 4(1):17–22 Van Loon L (2007) Plant responses to plant growth-promoting rhizobacteria. New perspectives and approaches in plant growth-promoting Rhizobacteria research. Springer, pp 243–254 Verma P, Yadav AN, Kumar V, Singh DP, Saxena AK (2017) Beneficial plant-microbes interactions: biodiversity of microbes from diverse extreme environments and its impact for crop improvement. Plant-microbe interactions in agro-ecological perspectives. Springer, pp 543–580 Verma G, Srivastava D, Tiwari P, Chakrabarty D (2019) ROS modulation in crop plants under drought stress. In: Reactive oxygen, nitrogen and sulfur species in plants: Production, metabolism, signaling and defense mechanisms. John Wiley & Sons, pp 311–336 Yasmin H, Nosheen A, Naz R, Bano A, Keyani R (2017) L-tryptophan-assisted PGPR-mediated induction of drought tolerance in maize (Zea mays L.). J Plant Inter 12(1):567–578 Zhang M, Yang L, Hao R, Bai X, Wang Y, Yu X (2020) Drought-tolerant plant growth-promoting rhizobacteria isolated from jujube (Ziziphus jujuba) and their potential to enhance drought tolerance. Plant Soil 452(1):423–440 Zinselmeier C, Lauer M, Boyer J (1995) Reversing drought-induced losses in grain yield: sucrose maintains embryo growth in maize. Crop Sci 35(5):1390–1400 Zoppellari F, Malusà E, Chitarra W, Lovisolo C, Spanna F, Bardi L (2014) Improvement of drought tolerance in maize (Zea mays L.) by selected rhizospheric microorganisms. Ital J Agrometeorol 18(1):5–18