Genomic Designing of Climate-Smart Fruit Crops 3319979450, 9783319979458

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Chittaranjan Kole Editor

Genomic Designing of Climate-Smart Fruit Crops

Genomic Designing of Climate-Smart Fruit Crops

Chittaranjan Kole Editor

Genomic Designing of Climate-Smart Fruit Crops

123

Editor Chittaranjan Kole Raja Ramanna Fellow Department of Atomic Energy Government of India ICAR-National Institute for Plant Biotechnology New Delhi, India

ISBN 978-3-319-97945-8 ISBN 978-3-319-97946-5 https://doi.org/10.1007/978-3-319-97946-5

(eBook)

© Springer Nature Switzerland AG 2020, corrected publication 2023 This work is subject to copyright. All rights are reserved 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

Dedicated to

Prof. Albert Glenn Abbott Formerly Emeritus Professor of the Department of Genetics & Biochemistry, & Robert and Lois Coker Endowed Chair in Molecular Genetics in the Clemson University, USA— working with whom as a visiting professor I learnt the art of dedicating equal importance to science, students and colleagues, and the society as a whole—a genuine example of a ‘friend, philosopher and guide’ to all people at all levels of orbits revolving around him!

Preface

The last 120 years have witnessed a remarkable evolution in the science and art of plant breeding culminating in quite a revolution in the second decade of the twenty-first century! A number of novel concepts, strategies, techniques and tools have emerged from time to time over this period and some of them deserve to be termed as milestones. Traditional plant breeding, immediately following the rediscovery of laws of inheritance, has been playing a spectacular role in the development of innumerable varieties in almost all crops during this entire period. Mention must be made on the corn hybrids, rust-resistant wheat and obviously the high-yielding varieties in wheat and rice that ushered the so-called green revolution. However, the methods of selection, hybridization, mutation and polyploidy employed in traditional breeding during this period relied solely on the perceivable phenotypic characters. But most, if not all, of the economic characters in crops are governed by polygenes which are highly influenced by environment fluctuations, and hence, phenotype-based breeding for these traits has hardly been effective. Historical discovery of DNA structure and replication in 1953 was followed by a series of discoveries in the 1960s and 1970s that paved the way for recombinant DNA technology in 1973 facilitating the detection of a number of DNA markers in 1980 onwards and their utilization in construction of genetic linkage maps and mapping of genes governing the simply inherited traits and quantitative trait loci controlling the polygenic characters in a series of crop plants starting with tomato, maize and rice. Thus, new crop improvement technique called as molecular breeding started in later part of the twentieth century. On the other hand, genetic engineering made modification of crops for target traits by transferring alien genes, for example, the Bt gene from the bacteria Bacillus thuringiensis. A large number of genetically modified crop varieties have thus been developed starting with the commercialization of ‘flavr Savr’ tomato in 1994. Meantime, the manual DNA sequencing methodology of 1977 was being improved with regard to speed, cost-effectiveness and automation. The first-generation sequencing technology led to the whole genome sequencing of Arabidopsis in 2000 and followed by rice in 2002. The next-generation sequencing technologies were available over time and used for sequencing of genomes of many vii

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other models and crop plants. Genomes, both nuclear and organellar, of more than 100 plants have already been sequenced by now, and the information thus generated is available in public database for most of them. It must be mentioned here that bioinformatics played a remarkable role in handling the enormous data being produced in each and every minute. It can be safely told that the ‘genomics’ era started in the beginning of the twenty-first century itself accompanying also proteomics, metabolomics, transcriptomics and several other ‘omics’ technologies. Structural genomics have thus facilitated annotation of genes, enumeration of gene families and repetitive elements and comparative genomics studies across taxa. On the other hand, functional genomics paved the way for deciphering the precise biochemistry of gene function through transcription and translation pathways. Today, genotyping by sequencing of primary, secondary and even tertiary gene pools; genomewide association studies; and genomics-aided breeding are almost routine techniques for crop improvement. Genomic selection in crops is another reality today. Elucidation of the chemical nature of crop chromosomes has now opened up a new frontier for genome editing that is expected to lead the crop improvement approaches in near future. At the same time, we will look forward to the replacement of genetically modified crops by cisgenic crops through transfer of useful plant genes and atomically modified crops by employing nanotechnology that will hopefully be universally accepted for commercialization owing to their human-friendly and environment-friendly nature. I wish to emphatically mention here that none of the technologies and tools of plant breeding is too obsolete or too independent. They will always remain pertinent individually or as complimentary to each other and will be employed depending on the evolutionary status of the crop genomes, the genetic resources and genomics resources available and, above all, the cost–benefit ratios for adopting one or more technologies or tools. In brief, utilization of these crop improvement techniques would vary over time, space and economy scales! However, as we stand today, we have all the concepts, strategies, techniques and tools in our arsenal to practice genome designing, as I would prefer to term it, of crop plants not just genetic improvement to address simultaneously food, nutrition, energy and environment security, briefly the FNEE security, I have been talking about for the last five years at different platforms. Addressing FNEE security has become more relevant today in the changing scenario of climate change and global warming. Climate change will lead to greenhouse gas emissions and extreme temperatures leading to different abiotic stresses including drought or waterlogging on the one hand and severe winter and freezing on the other. It will also severely affect uptake and bioavailability of water and plant nutrients and will adversely cause damage to physical, chemical and biological properties of soil and water in cropping fields and around. It is also highly likely that there will be emergence of new insects and their biotypes and of new plant pathogens and their pathotypes. The most serious concerns are, however, the unpredictable crop growth conditions and the unexpected complex interactions among all the above stress factors leading to drastic reduction in crop yield and

Preface

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quality in an adverse ecosystem and environment. Climate change is predicted to significantly reduce productivity in almost all crops. For example, in cereal crops the decline of yield is projected at 12–15%. On the other hand, crop production has to be increased at least by 70% to feed the alarmingly growing world population, projected at about 9.0 billion by 2050 by even a moderate estimate. Hence, the unpredictability of crop growing conditions and thereby the complexity of biotic and abiotic stresses warrant completely different strategies of crop production from those practiced over a century aiming mostly at one or the few breeding objectives at a time such as yield, quality, resistance to biotic stresses due to disease pests, tolerance to abiotic stresses due to drought, heat, cold, flood, salinity, acidity, or improved water and nutrient use efficiency, etc. In the changing scenario of climate change, for sustainable crop production, precise prediction of the above limiting factors by long-term survey and timely sensing through biotic agents and engineering devices and regular soil and water remediation will play a big role in agriculture. We have been discussing on ‘mitigation’ and ‘adaptation’ strategies for the last few years to reduce the chances of reduction of crop productivity and improve the genome plasticity of crop plants that could thrive and perform considerably well in a wide range of growing conditions over time and space. This is the precise reason for adopting genomic designing of crop plants to improve their adaptability by developing climate-smart or climate-resilient genotypes. Keeping all these in mind, I planned to present deliberations on the problems, priorities, potentials and prospects of genome designing for the development of climate-smart crops in about 50 chapters, each devoted to a major crop or a crop group, allocated under five volumes on cereal, oilseed, pulse, fruit and vegetable crops. These chapters have been authored by more than 250 of eminent scientists from over 30-plus countries including Argentina, Australia, Bangladesh, Belgium, Brazil, Canada, China, Egypt, Ethiopia, France, Germany, Greece, India, Ireland, Japan, Malaysia, Mexico, New Zealand, Kenya, Pakistan, Philippines, Portugal, Puerto Rico, Serbia, Spain, Sri Lanka, Sweden, Taiwan, Tanzania, Tunisia, Uganda, UK, USA and Zimbabwe. There are huge number of books and reviews on traditional breeding, molecular breeding, genetic engineering, nanotechnology, genomics-aided breeding and gene editing with crop-wise and trait-wise deliberations on crop genetic improvement including over 100 books edited by me since 2006. However, I believe the present five book volumes will hopefully provide a comprehensive enumeration on the requirement, achievements and future prospects of genome designing for climate-smart crops and will be useful to students, teaching faculties and scientists in the academia and also to the related industries. Besides public and private funding agencies, policy-making bodies and the social activists will also get a clear idea on the road travelled so far and the future roadmap of crop improvement. I must confess that it has been quite a difficult task for me to study critically the different concepts, strategies, techniques and tools of plant breeding practiced over the last 12 decades that also on a diverse crop plants to gain confidence to edit the chapters authored by the scientists with expertise on the particular crops or crop

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groups and present them in a lucid manner with more or less uniform outline of contents and formats. However, my experience gained over the last seven years in the capacity of the Founding Principal Coordinator of the International Climate Resilient Crop Genomics Consortium (ICRCGC) was highly useful while editing these books. I have the opportunity to interact with a number of leading scientists from all over the world almost on regular basis. Organizing and chairing the annual workshops of ICRCGC since 2012 and representing ICRCGC in many other scientific meetings on climate change agriculture offered me a scope to learn from a large number of people from different backgrounds including academia, industries, policy making and funding agencies and social workers. I must acknowledge here the assistance I received from all of them to keep me as a sincere student of agriculture specifically plant breeding. This volume entitled Genomic Designing of Climate-Smart Fruit Crops includes 10 major crops including almond, apple, apricot, banana, cherries, citrus, coconut, grape, peach and rosaceous berries. These chapters have been authored by 61 scientists from 12 countries including Argentina, Belgium, France, Germany, Greece, India, Italy, Lithuania, Mexico, Spain, Tanzania and USA. I place on record my thanks for these scientists for their contributions and cooperation. My own working experience on genomics and breeding in fruit crops is, in fact, limited to only five years since 2007 during my visit to the program of Prof. Albert Glenn Abbott, Professor, later on Emeritus Emeritus Professor in the Department of Genetics and Biochemistry, and Robert and Lois Coker Endowed Research Chair on Plant Molecular Genetics in the Clemson University, USA. It is only due to him, a field crop scientist like me had got an opportunity to work on genomics and breeding in two important fruit crops, peach and apricot. Although a Visiting Professor to Clemson, I also got an opportunity to work voluntarily as the Director of Research of the Institute of Nutraceutical Research under his Directorship. An outstanding scientist himself, Prof. Abbott, had an excellent way of leading from the front but allowing enough physical facilities and intellectual freedom to his students and colleagues from his laboratory, the university, the country and from the entire world. Above all, Prof. Abbott has heartiest feelings and concern for people at all levels of orbits revolving around him. I feel myself immensely happy and proud to dedicate this book volume to Prof. Albert Glenn Abbott—a ‘friend, philosopher and guide’ to me and to all his students and colleagues! New Delhi, India

Chittaranjan Kole

Contents

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Genomic Designing for New Climate-Resilient Almond Varieties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Angela S. Prudencio, Raquel Sánchez-Pérez, Pedro J. Martínez-García, Federico Dicenta, Thomas M. Gradziel and Pedro Martínez-Gómez Challenges and Strategies for Developing Climate-Smart Apple Varieties Through Genomic Approaches . . . . . . . . . . . . . . . . . . . . . Anastassia Boudichevskaia, Gulshan Kumar, Yogesh Sharma, Ritu Kapoor and Anil Kumar Singh Genomic Designing for New Climate-Resilient Apricot Varieties in a Warming Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose A. Campoy, Jean M. Audergon, D. Ruiz and Pedro Martínez-Gómez

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91

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Breeding Climate-Resilient Bananas . . . . . . . . . . . . . . . . . . . . . . . . Allan Brown, Sebastien C. Carpentier and Rony Swennen

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Toward Development of Climate-Resilient Citrus . . . . . . . . . . . . . . 117 Supratim Basu

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Genomic Designing of Climate-Smart Coconut . . . . . . . . . . . . . . . . 135 S. V. Ramesh, V. Arunachalam and M. K. Rajesh

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Genetic and Genomic Approaches for Adaptation of Grapevine to Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Serge Delrot, Jérôme Grimplet, Pablo Carbonell-Bejerano, Anna Schwandner, Pierre-François Bert, Luigi Bavaresco, Lorenza Dalla Costa, Gabriele Di Gaspero, Eric Duchêne, Ludger Hausmann, Mickaël Malnoy, Michele Morgante, Nathalie Ollat, Mario Pecile and Silvia Vezzulli

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Contents

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Genomic-Based Breeding for Climate-Smart Peach Varieties . . . . . 271 Yolanda Gogorcena, Gerardo Sánchez, Santiago Moreno-Vázquez, Salvador Pérez and Najla Ksouri

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Development of Climate-Resilient Varieties in Rosaceous Berries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 Rytis Rugienius, Birutė Frercks, Ingrida Mažeikienė, Neringa Rasiukevičiūtė, Danas Baniulis and Vidmantas Stanys

10 Genomics Opportunities and Breeding Strategies Towards Improvement of Climate-Smart Traits and Disease Resistance Against Pathogens in Sweet Cherry . . . . . . . . . . . . . . . . . . . . . . . . 385 Antonios Zambounis, Ioannis Ganopoulos, Filippos Aravanopoulos, Zoe Hilioti, Panagiotis Madesis, Athanassios Molassiotis, Athanasios Tsaftaris and Aliki Xanthopoulou Correction to: Genomic-Based Breeding for Climate-Smart Peach Varieties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yolanda Gogorcena, Gerardo Sánchez, Santiago Moreno-Vázquez, Salvador Pérez and Najla Ksouri

C1

Abbreviations

d13C 101-14 1103 P 110R 140Ru 2D 3309C 41B A/T ABA ADH AEGIS AFLP AFPM AGL ALMT AM AMF AMY APX AREB ARF ASR Aux Avr BAC BAM BC BD bHLH

Carbon isotope discrimination Rootstock 101-13 Millardet et de Grasset Rootstock 1103 Paulsen Rootstock 110 Richter Rootstock 140 Ruggeri Two-dimensional gel electrophoresis Rootstock 3309 Couderc Rootstock 41B Millardet et de Grasset Instantaneous water-use efficiency Abscisic acid Alcohol dehydrogenase A European Genebank Integrated System Amplified fragment length polymorphism Autonomous fruit picking machine Agamous-like Aluminum-activated malate transporter Association mapping Arbuscular mycorrhizal fungi alfa-amylase Ascorbate peroxidase ABA-responsive element binding protein Auxin response factor Abscisic acid and ripening Auxin Avirulence gene Bacterial artificial chromosome Beta-amylase Backcross Bloom date Basic helix-loop-helix

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BIMS BLAST Botd8 BR BRENDA Brix BSA bZIP CaMV CAPS Cas9 CBF CBTs CCD CDPK CEBAS CG CHS CIRAD CK cM CMO CNV CO2 COST CoGe COR cpDNA CPK CPVO CR CRISPR CRLK1/2 CS CSIC CT50 CU Cu/Zn-SOD CUT1 cv DAM DAMP DAP

Abbreviations

Breeding Information Management System Basic local alignment search tool Peach fungal gummosis resistance gene Brassinosteroid The Comprehensive Enzyme Information System Refractive index of plant juices Bulked segregant analysis Basic leucine zipper Cauliflower mosaic virus Cleaved amplified polymorphic sequence CRISPR-associated 9 protein C-repeat binding factor Conventional breeding technologies Charge-coupled device Calcium-dependent protein kinase Centro de Edafología y Biología Aplicada del Segura Candidate gene Chalcone synthase Center for International Cooperation in Agricultural Research for Development Cytokinin Centimorgan Common Market Organisation Copy-number variation Carbon dioxide European Cooperation in Science and Technology Comparative genomics Cold-responsive gene Chloroplast DNA Calcium-dependent protein kinase Community Plant Variety Office Chilling requirement Clustered regularly interspaced short palindromic repeats Calcium/calmodulin-regulated receptor-like kinase Climate smart Consejo Superior de Investigaciones Científicas Critical temperature corresponding to ion leakage equivalent to 50% lethality Chill/chilling unit Copper, zinc superoxide dismutase Cuticular protein 1 Cultivar Dormancy-Associated MADS-Box Damage-associated molecular pattern Days after planting

Abbreviations

DB DDBJ DEG DFR DNMT DREB DUS DXS E EBI eBSV EC ECO2 EEA EEAD EEC EMBL epi-GBS EPPO ERF EST ERF4 ET ET ET0 ETI ETS EU evg F1 F2 F3H F6P FACE FAIR FAO Fd/FD FDP Flb FLC FT FW G
M GXE

xv

Database DNA databank of Japan Differentially expressed gene Dihydroflavonol 4-reductase DNA methyltransferase Dehydration-responsive element binding Distinctness, uniformity and stability Deoxy-xylulose synthase Transpiration European Bioinformatics Institute Endogenous banana streak viruses European Commission Elevated carbon dioxide concentration Estación Experimental Agropecuaria Estación Experimental de Aula Dei European Economic Community European Molecular Biology Laboratory Epi-Genotyping by Sequencing European and Mediterranean Plant Protection Organization Ethylene response factor Expressed sequence tag Ethylene-responsive factor Elevated temperature Ethylene/evapotranspiration Reference evapotranspiration Effector-triggered immunity Escuela Técnica Superior European Union Evergrowing gene First filial generation Second filial generation Flavonoid 3′5′-hydroxylase Fructose 6-phosphate Free atmospheric carbon enrichment Findable, accessible, interoperable and reusable Food and Agriculture Organization of the United Nations Flowering date Fruit development period Fleshless berry Flowering Locus C Flowering locus T Fruit weight Genotype
management Genotype X environment

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G G6PDH GA GAB GBLUP Gbrowse GBS GC-MS GDD GDH GDR GEBV GEO GFF GM GMO GMOG GO GPI GPU GRIN GS gs Gs GSI GSTF GT GUI GWAS G
M h2 Ha HD HI HIR HKT HR HR HRM HS HSF HST IAA IBD IBMP

Abbreviations

Group Glucose-6-phosphate dehydrogenase Gibberellic acid Genomics-assisted breeding Genomic best linear unbiased prediction Gene browse Genotyping by sequencing Gas chromatography–mass spectrometry Growing degree days Growing degree hours Genome Database for Rosaceae Genomic estimated breeding value Gene Expression Omnibus General feature format Genetically modified Genetically modified organism Global mixture of Gaussians Gene ontology Geographical protected indication Graphics processing unit Germplasm Resources Information Network Genomic selection Stomatal conductance Stomatal conductance Gametophytic self-incompatibility Glutathione S-transferase Glucosyltransferase Graphical user interface Genomewide association study Genotype
Management Narrow-sense heritability Hectare Harvest date Harvest index Hypersensitive-induced reaction High-affinity potassium transporter Heat requirements Hypersensitive response High resolution melting Highly susceptible Heat shock transcription factor Host-specific toxin Indole acetic acid Identity by descent 2-Methoxy-3-isobutylpyrazine

Abbreviations

ID IFR IGGP IIoT ILs INAO InDel INRA INTA IPCC IPGI IPSC IRSC JA JDs KEGG KOG LACS2 Lb LD LEA LG lncRNA LPR LRR LT50 LTR MA Ma MAB MABC MADS-box MAF MAGIC MAI MAPK MAS MASS mC MD MDA Mf MIAME miRNA MPSS

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Identifier Isoflavone reductase International Grape Genome Program Industrial Internet of things Interspecific lines Institut National des Appellations d’Origine Insertion/deletion Institut national de la recherche agronomique Instituto Nacional de Tecnología Agropecuaria Intergovernmental Panel on Climate Change International Peach Genome Initiative International Peach SNP Consortium International RosBREED SNP consortium Jasmonic acid Julian days Kyoto Encyclopedia of Genes and Genomes EuKaryotic Orthologous Groups Long-chain acyl-CoA synthetase 2 Late blooming Linkage disequilibrium Late Embryogenesis Abundant Linkage group Long non-coding RNA Low phosphate resistant Leucine-rich repeat Semi-lethal temperature Long terminal repeat Malic acid content RKN resistance gene Marker-assisted breeding Marker-assisted backcrossing Protein containing the conserved MADS motif Minor allele frequency Multiparent advanced generation inter-cross Marker-assisted introgression Mitogen-activated protein kinase Marker-assisted selection Marker-assisted seedling selection Methylated cytosine Maturity date Malondialdehyde RKN resistance gene Minimum information about a microarray experiment microRNA Massive parallel signature sequencing

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MS MSAP MYBA1 NA NaE NAM NBS NBS-LRR NBTs NCBI NGR NGS NHEJ NHX1 NILs NMR NPBTs NPGS NRT OA OIV OMT ORCAE OSCAR OTC PA PAL PANTHER PAR PBM PCA PCR PDB PDO PEBP PFAM PGDD PGI PI PkMi PlantGDB PlantTFDB PMR PN40024 PPV

Abbreviations

Mass spectrometry Methylation Sensitive Amplification Polymorphism Avian myeloblast virus Naphthaleneacetic acid Na+ exclusion Nested association mapping Nucleotide-binding site Nucleotide binding site-leucine-rich repeat New breeding techniques National Center for Biotechnology Information National Grape Register Next-generation sequencing Non-homologous end joining Sodium–hydrogen exchanger Near-isogenic lines Nuclear magnetic resonance New plant breeding technologies National Plant Germplasm System Nitrate transporter Osmotic adjustment Organization Internationale de la Vigne et du Vin O-methyltransferase Online Resources for Community Annotation of Eukaryotes National Observatory for the Deployment of Resistant Cultivars Open-top chamber Predictive ability Phenylalanine ammonia-lyase Protein analysis through evolutionary relationships Photosynthetic active radiation Process-based simulation model Principal component analysis Polymerase chain reaction Protein Data Bank Protected Designation of Origin Phosphatidylethanolamine-binding protein Protein families Plant genome duplication database Protected geographic indication Pistillata RKN resistance gene Plant genome database Plant transcription factor database Powdery mildew-resistance gene Pinot noir 40024 Plum pox virus

Abbreviations

PR PRIDE ProDom PROSITE PRR PTI PTM qRT-PCR QTL QTLs R-Gene RAD RAD-seq RAPD REC Ren RFLP RGA RGM RILs Rjap RKN Rm RMia RNAseq ROS Rpv RRL RS-GIS RT-PCR Run RxD SAMPL SBP/SPL SCAR SDI SERK SLAF SLAH SMART SNP SNV SO4 SOD SRAP

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Pathogenesis-related Proteomics identifications database Protein domain families Protein domains, families and functional sites Pattern recognition receptor PAMP-triggered immunity Posttranslational histone modification Quantitative reverse transcription PCR Quantitative trait locus Quantitative trait loci Resistance gene Restriction site-associated DNA Restriction site-associated DNA sequencing Random amplified polymorphic DNA Relative electrical conductivity Resistance to Erysiphe necator Restriction fragment length polymorphism Resistance gene analog Riparia Gloire de Montpellier Recombinant inbred lines RKN resistance gene Root-knot nematode Green peach aphid resistance gene RKN resistance gene RNA sequencing Reactive oxygen species Resistance to Plasmopara viticola Reduced-representation library Remote sensing and geographic information system Reverse transcription PCR Resistance to Uncinula necator R1000 x ‘Desmayo Largueta’ Selective amplified microsatellite polymorphic loci SQUAMOSA promoter binding protein-like Sequence characterized amplified region Seed development inhibitor Somatic embryogenesis receptor kinase Specific length amplified fragment sequencing Slow-type anion channel Simple modular architecture research tool Single-nucleotide polymorphism Single-nucleotide variants Rootstock selection Oppenheim Superoxide dismutase Sequence-related amplified polymorphism

xx

SS S-SAP SSC SSCP SSN SSR STS SUT T
E TA TALEN TB TDN TE TF TMT TPE TSS TxE UNFCCC UniProt UPOV USDA USDA-ARS UV VEP VIVC VMC Vr VTCDB Vv/Vvi WABP WGRS WGS WTS WUE

Abbreviations

Total soluble solids Sequence-specific amplified polymorphism Soluble solid content Single-strand conformation polymorphism Site-specific nuclease Simple sequence repeat Stilbene synthase Sucrose transporter Texas
Earlygold Titratable acidity Transcription activator-like effector nuclease Tempranillo Blanco 1,1,6-Trimethyl-1, 2-dihydronaphthalene Transposable element Transcript factor Tonoplast membrane transporter Target population of environments Total soluble sugars ‘Texas’ x ‘Earlygold’ United Nations Framework Convention on Climate Change Universal protein resource International Union for the Protection of New Varieties of Plants United States Department of Agriculture USDA Agricultural Research Service Ultraviolet light Variant effect predictions Vitis International Variety Catalogue Vitis Microsatellite Consortium Powdery mildew-resistance gene Grapevine co-expression database Vitis vinifera Washington State University Apple Breeding Program Whole genome re-sequencing Whole genome shotgun Whole transcriptome sequencing Water-use efficiency

Chapter 1

Genomic Designing for New Climate-Resilient Almond Varieties Angela S. Prudencio, Raquel Sánchez-Pérez, Pedro J. Martínez-García, Federico Dicenta, Thomas M. Gradziel and Pedro Martínez-Gómez

Abstract During the falling temperatures of autumn, temperate tree-crop species, including almond [Prunus dulcis (Miller) Webb], activate a winter survival strategy called endodormancy to protect against unfavorably cold temperatures. Trees cease vegetative growth and form structures called buds to protect enclosed meristems from the unfavorable environmental conditions, including low temperature and desiccation. Chill accumulation allows the progression from flower bud endodormancy stage to flower bud ecodormancy, a type of dormancy regulated by heat accumulation. Environmental stresses including those caused by climate change significantly affect global crop production. Consequently, climate-resilient crops that can withstand an array of climate changes and environmental perturbations will be required to maintain production. In this context, the adaptation of temperate tree-crop species such as almond to expected future warming climates will depend on the breeding of climate-resilient varieties able to complete endodormancy under warmer and more variable climates. Major breeding challenges will be the appropriate phenotyping and selection of elite seedlings from the required large breeding populations. Genomics, transcriptomics, and epigenetics provide useful tools for the development of improved breeding strategies that are particularly useful when trait evaluation such as endodormancy and ecodormancy is expensive or time-consuming. This chapter reviews the genomic designing for new climate-resilient almond varieties, including promising genetic, genomic, transcriptomic and epigenetic approaches. Keywords Prunus dulcis · Breeding · Phenology · Flowering · Breeding · Molecular Markers · Genomics · Transcriptomics · Epigenetics

A. S. Prudencio · R. Sánchez-Pérez · P. J. Martínez-García · F. Dicenta · P. Martínez-Gómez (B) Departamento de Mejora Vegetal Grupo de Mejora Genética de Frutales, CEBAS-CSIC, Espinardo, Murcia, Spain e-mail: [email protected] T. M. Gradziel Department of Plant Sciences, University of California, Davis, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_1

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A. S. Prudencio et al.

1.1 Introduction The cultivated almond [Prunus dulcis (Miller) Webb] is a tree species whose domestication and spread has closely paralleled the rise of Eurasian civilizations. This tree-crop species is mainly planted for its edible seeds (Gradziel and MartínezGómez 2013). Today, almonds are cultivated in more than 50 countries (http:// faostat.fao.org), with approximately 95% produced in California, Australia, and the Mediterranean Basin. Almonds are not only used as a fresh and processed fare but also as a functional food with both nutritional and medical properties including, anti-inflammatory, and hypocholesterolemia properties (Poonam et al. 2011; Musa-Velasco et al. 2016). With a value approaching USD 2.30 billion in 2016, almond has become the largest specialty crop export in the USA and the largest agricultural export ($4532 M$) for California (Almond Board of California 2016). California accounted for over 80% of the 2017 global production of 92,9864 tons with over 38,0405 ha in production. Australia is now the second largest producer of almonds with 79,461 tons and 39,662 ha (29,358 ha bearing, 6758 ha non-bearing and 3546 ha new plantings). Spain, the third largest producer and has the largest area under cultivation, estimated at over 544,518 ha. The remaining world production comes from about 20 countries including Tunisia, Morocco, Italy, Turkey, Chile, and Iran. Limited almond production extends into the Balkan Peninsula including areas of Bulgaria, Romania, and Hungary. Additional plantings exist in central and southwestern Asia including Syria, Iraq, Israel, Ukraine, Tajikistan, Uzbekistan, Afghanistan and Pakistan, and extending into western China (Almond Board of Australia 2017; https://www. australianalmonds.com.au/) (Table 1.1). The almond kernel is consumed either in the natural state or processed. Because of its good flavor, crunchy texture, and good visual appeal, it has many important food Table 1.1 Global almond kernel production (Almond Board of Australia 2017)

Country

Production (mt)

California (USA)

929,864

Australia

79,461

Spain

50,954

Tunisia

15,000

Iran

15,000

Turkey

13,000

Chile

14,000

Morocco

11,000

Italy

7500

Greece

3000

Others

30,000

1 Genomic Designing for New Climate-Resilient Almond Varieties

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uses. As an ingredient in many manufactured food products, kernels may be dryroasted or roasted in oil followed by salting with various seasonings. The processed product is mainly sold as either blanched or unblanched kernels. Blanching removes the pellicle (‘skin’) using hot water or steam. Almond kernels can be sliced or diced for use in pastry, ice cream, breakfast cereals, and vegetable mixtures. The kernels are also ground into a paste to be used in bakery products and in the production of marzipan. The flavor and texture of almonds can be intensified or moderated through proper selection of cultivar, origin, moisture content, and processing and handling methods (Kester and Gradziel 1996). Almond cultivation in the Mediterranean countries is mostly non-irrigated with kernel yields of approximately 400–500 kg/ha, which is much lower than the 2300 kg/ha yields of California, where the production is much more intensive and routinely irrigated (http://faostat.fao.org). Regional almond production also plays important roles in supporting community and social resiliency including retention of families and so reduced emigration. However, in spite of the high oil quality and nutraceutical content found in the varieties grown in these Mediterranean areas, crop sustainability is negatively affected by the low rainfall and drought-limited production. The objective of this chapter is an assessment of the genomic designing for new climate-resilient almond varieties including promising genetic, genomic, transcriptomic, and epigenetic approaches.

1.2 Designing Climate-Resilient Almond Varieties Tree-crop breeding presents unique conditions for plant breeding such as its plurennial woody character, its long juvenile period, and its propagation by vegetative clones. These conditions can make the genetic improvement processes long and tedious. Consequently, it is necessary to have access to the best information and technology possible for the design of new varieties within the typical twelve-year variety development cycle (Fig. 1.1a). The requirements for new varieties must be anticipated 12 years in advance, as this is the average time from the original cross to the release new pre-crop variety (Fig. 1.2a). A critical decision is the choice of the parents to be used. Subsequent crosses can be of the complementary type (when we cross two varieties with complementary characteristics to obtain a new variety that integrates the good aptitudes of both varieties), or of the transgressive type, where two varieties are crossed with good aptitudes in order to obtain progeny performance even better than either parent (Martínez-Gómez et al. 2003). From the economic perspective, a crucial determination is the number of progeny to be generated and how rapidly the few elite individuals can be identified and selected from those populations. The breeder thus needs to identify the critical endogenous (internal) as well as exogenous (external) variables influencing genotype

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Fig. 1.1 a Scheme for an Almond breeding program based on the realization of genetically diverse crosses and subsequent selection of elite individuals. b Internal and external variables affecting the viability and establishment of a new almond varieties obtained through classical breeding. Application of molecular (DNA, RNA, and epigenetic) markers could affect feasibility, efficiency, and viability of these breeding programs

performance. Both types of variables need to be subjected to scientific evaluation (Fig. 1.1b). The primary internal variables to be considered in almond breeding are derived from the plant traits that are considered primary objectives. The most important characteristics in these breeding programs are as follows (Martínez-Gómez et al. 2003): (a) Tree: Floral self-compatibility, time of flowering and maturation, productivity, and resistance to pests and diseases (Sánchez-Pérez et al. 2004). (b) Seed (kernel) quality: Organoleptic quality, seed size, seed shape, seed flavor, and hardness of the shell (Socias i Company et al. 2007). Detailed knowledge of these internal, genetic variables will determine the suitability of the proposed breeding design. Prunus species, such as almond, are characterized by a high level of interspecific cross-compatibility making it relatively easy to perform interspecific gene introgression. A major constraint for tree-crop breeding is a relatively narrow gene pool. In self-compatible species such as peach or apricot, the low genetic diversity is further exasperated by progeny populations often resulting from self-pollinations. Even in self-incompatible and so outcrossing crops such as

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Fig. 1.2 a Almond bud dormancy progression and flowering; b estimated increase of temperatures during the period 2070–2100 (https://theglobalclimate.net/temperature-increase/). Almond production zones are labeled

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almond and cherry, the limited parental species gene pool limits breeding options (Gradziel and Martínez-Gómez 2013). In this context of reduced genetic diversity, interspecific gene introgression is very useful for the development of new germplasm conferring new traits (Gradziel et al. 2001; Sorkheh et al. 2009) particularly when the required trait, such as self-fruitfulness or very late flowering, is not present within the crop species. Internal variables also include the methodologies available for use in the selection of individuals. These methodologies are closely related to the level of knowledge available at the meso- and micro-levels, particularly the availability of effective markers for the selection traits (Gradziel et al. 2001; Martínez-Gómez et al. 2003). Finally, we can include various economic factors within the production framework, (i.e., the goals, processes, and outcomes). The effectiveness and viability of the genetic improvement program will depend on appropriate methodological developments for the given objectives. These two terms (effectiveness and viability) are related in the economic context to the relationship between science and economics. The evaluation of effectiveness and feasibility can lead to the abandonment of the new design if found to be inappropriate and a new breeding design formulated in a continuous, reiterative process. In almond breeding, it is also necessary to consider a number of external variables including environmental interactions over a period of years to decades. There are many traits such as flowering time and floral self-compatibility which must be evaluated for at least three years following initial seedling maturity. Due to these constraints, the use of molecular marker-assisted selection methods is of particular interest in breeding programs. These environmental conditions will determine the appropriateness of breeding objectives and consequently, variety sustainability in a given environment. The adaptation of new designs or new almond varieties to changing climatic conditions is initially determined by their winter cold adaptability (Campoy et al. 2011) and in the case of rootstocks, by their compatibility with different soil conditions. As previously described, almonds flower in response to an established pattern of low (cold needs) followed by high (heat needs) temperatures after overcoming endodormancy (Sánchez-Pérez et al. 2014). These needs of cold and heat guarantee that in each zone, the flowering will take place at a favorable time for pollination and subsequent seed set. This pollination will be viable in a context of floral selfcompatibility (fertilization of the ovule by pollen from the same variety occurs) (Gradziel et al. 2002; Gradziel and Martínez-Gómez 2013), whereas in a context of floral self-incompatibility, this flowering will require pollen of another variety since an isolated variety will not produce (Sánchez-Pérez et al. 2004). Later flowering varieties are particularly desirable to avoid spring frosts (Sánchez-Pérez et al. 2014). In other Prunus tree crops such as peach or apricot, where flowering is not so early, what is sought is earlier fruiting to obtain better market prices. The knowledge of these external variables, including associated biotic and abiotic stresses, will indicate their appropriateness with respect to the proposed design objectives. Feasibility will thus also require germplasm with the desired genes for resistance to relevant biotic or abiotic stresses.

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Consumer and market requirements are also important external variables. New designs must have the appropriate characteristics to meet such demands. In addition, the availability of effective techniques for analysis and selection represent additional external variables. Similarly, we can include various economic factors, provided that they are considered within the framework of production: the phase of objectives, processes, and results. This includes external economic variables, which are those that affect their market value. The level of knowledge for each variable will determine the accuracy of the selection indices utilized in the design strategy for new varieties as well as the ultimate feasibility, effectiveness, and appropriateness of the final traits.

1.3 Prioritizing Climate-Smart (CS) Agronomic Traits 1.3.1 Flowering Time During autumn, almonds activate a survival strategy called endodormancy, to protect against unfavorable winter chill conditions. Trees cease vegetative growth and form structures called buds that protect meristems from the unfavorable winter environmental conditions, including desiccation and low temperatures (Campoy et al. 2011; Prudencio et al. 2018a). Chill accumulation also allows the gradual transition from flower bud endodormancy to flower bud ecodormancy where development is regulated by subsequent heat accumulation (Egea et al. 2003; Sánchez-Pérez et al. 2012). Flowering times in Prunus species are mainly determined by chilling requirements required to overcome endodormancy (Fig. 1.2a). Individual chilling requirements are a cultivar specific trait, typically correlated with species or cultivar origin (Tabuenca et al. 1972; Egea et al. 2003). Insufficient cold accumulation under the warmer winter temperatures predicted by many climate change scenarios would lead to irregular and insufficient flowering, with a subsequent loss of production (Erez 1995; Luedeling 2012). Because of its economic importance, dormancy release is being studied in several different tree species, yet knowledge remains scarce and a common mechanism has not been identified. This is particularly interesting since commercial Prunus cultivars display a wide range of flowering and ripening times. Recent reports suggest an increasing vulnerability of agro-ecological systems to ongoing warming trends and associated shifting in precipitation seasons (Hatfield et al. 2018). These changes are expected to have serious implications for food production. Adaptation of Prunus tree crops to new climatic conditions in a warming context (Fig. 1.2b) will depend on the capacity for dormancy breaking and subsequent flower development to fertilization and seed set and has become one of the most prioritized traits in Prunus breeding programs including almond (Dicenta et al. 2005, 2017; Martínez-Gómez et al. 2017). Because of its wide geographical distribution, almond germplasm displays extensive variability in flowering time. Martínez-Gómez et al. (2017) concluded that the

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adaptation of almonds from the warmer Mediterranean Basin to colder regions of Northern Europe and North and South America has been achieved mainly through delayed flowering times. Late-flowering cultivars have typically been developed through quantitative selection within each ecological region. The use of molecular markers for the early selection of genes conferring late-flowering also facilitated the development of additional late cultivars including ultra-late cultivars flowering as late as April as well as numerous bud sport mutations conferring the late flowering. In addition, because of the importance of the flowering time to regional breeding programs, many studies have been reported concerning flowering time distribution from seedlings of selected breeding parents (Kester 1965; Sánchez-Pérez et al. 2007a). In a few cases, the heritability of the trait has been calculated (Kester and Asay 1973; Kester et al. 1977, Dicenta et al. 1993). Kester and Asay (1973) and Kester et al. (1977) obtained, by regression, heritability values of close to 0.8 under the conditions found in the almond growing regions of California. These authors also determined that the primary control of flowering was by additive genes. Dicenta et al. (1993) estimated the heritability of flowering time over a period of three years by regression and variance components utilizing 2483 individuals from 51 families. The values for h2 varied depended on the method and year, but were usually very high (between 0.8 and 1.0). This high heritability shows that the most efficient way to obtain late-flowering progenies is by crossing late-flowering progenitors. The greater influence of the general combining ability (GCA = 85–93%) versus the specific combining ability (SCA = 7–15%) in the transmission of flowering time has been reported (García et al. 1994). Flowering time appears to be controlled by nuclear DNA as no maternal effect has been observed. Dicenta and García (1993) studied the flowering time of 826 individuals from nine families, and their reciprocal crosses over three years and found no significant differences between the direct and reciprocal crosses. These authors therefore concluded that the direction of crossing does not affect the flowering time of descendants. Some authors have studied the flowering time of descendants of the ‘Tardy Nonpareil’ almond (extra-late-flowering bud sport mutation of ‘Nonpareil’) and have observed a bimodal distribution (Kester 1965; Grasselly 1978; Socias i Company et al. 1999; Ballester et al. 2001; Sánchez-Pérez et al. 2007a, 2012). This fact has been explained by the presence of a major gene that is quantitatively modified by other minor genes. Finally, Sánchez-Pérez et al. (2012), studying the flowering time as a function of chilling and heat requirements in 167 seedlings of R1000 × ‘Desmayo Largueta’, found that the segregation of flowering time was similar to that of chilling requirements. Furthermore, descendants generally segregated to flowering times between the parents.

1.3.2 Cold Tolerance Cold tolerance is also an important trait to develop climate-smart almond. The decreased seed yields caused by low temperatures have been attributed to abortion of

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flowers and inadequate production (Afshari et al. 2011). To expand almond production area, cold tolerance is a key trait, as it is essential for almond cultivars to adapt low temperature in spring and sudden cold shock at the reproductive stages during summer in the northern parts of the planet, such as northern Europe and central Asia. To increase yield in these northern areas with short growing seasons, efforts need to be made to develop varieties. Further efforts are needed to characterize the genetic elements controlling these traits and utilize them in the soybean breeding programs, especially for the northern areas. The cold tolerance at the reproductive stages is usually evaluated as quantification of kernels or direct measurement of kernel yield (Afshari et al. 2011). These almond genetic resources tolerant to freezing provide potential to improve cold tolerance of new cultivars and knowledge on the loci involved and their allelic status in breeding lines would facilitate the use of molecular markers to assist in the development of cold-tolerant varieties at the maturity (Moheb et al. 2018). Among the key transcription factors, members of DOFF family were the central regulators of genes in the constructed network. In line with this observation, the Pddof4 transcript had significant over-expression in the tolerant genotypes compared to the sensitive genotypes during cold stress. These findings may highlight the importance of Pddof4 in conferring frost tolerance to almond. PdMIR7122-3p was predicted as a negative regulator of PdHOS1. PdMIR7122-3p/PdHOS1 was found to be the only post-transcriptional regulator of genes in the network. Taken together, our network model will be useful to unravel the mechanisms involved in almond tolerance to cold stress and to highlight the role of different regulatory factors and their interactions in response to this commonly occurring environmental condition (Alisiltani et al. 2015). These results were also corroborated using RNA-Seq technology evidencing a high number of genes involved in the response to freezing stress (Mousavi et al. 2014). Hosseinpour et al. (2010) identified 863 upregulated genes and 555 downregulated genes linked to cold tolerance in almond.

1.3.3 Drought Resistance Climate variability and water availability require the fast development of production systems able to cope with risk and uncertainty. In this sense, drought resistance in almond, linked to the efficient use of water (Yadollahi et al. 2011), together with the ability of the root system to access water is important breeding targets. These materials allow a more sustainable production, particularly in the marginal areas of harsh climate conditions found around the Mediterranean Basin (Gouta et al. 2010, 2011, 2019). Therefore, rusticity and flexibility of the different components of the production systems (including cultivars and rootstocks) should be improved. Clearly, the plantation and management of new sustainable agro-systems in the Mediterranean Basin for almond production, designed to last long periods, must consider the impact of climatic conditions when selecting new cultivars and rootstocks. In this sense, drought is

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one of the biggest problems for non-irrigated culture across the Mediterranean countries. Studies about drought resistance have been conducted on wild and cultivated almonds (Palasciano et al. 2014), but these are scarce in the case of native germplasm from other areas. Some of these studies conducted by the different research groups in Morocco, Tunisia, and Spain allowed preliminary results on drought resistance of almond cultivars and hybrid rootstocks (Esmaeli et al. 2017) and also the discovery of new genetic diversity from these regions (Gouta et al. 2012; Kodad et al. 2008). Under drought conditions, plants may find strategies to escape the stress (accelerating the life cycle) or to avoid it (controlling stomatal conductance, investing in the development of the root system, reducing canopy, etc.), or still to activate strategies of osmotic adjustment to increase tolerance to low tissue water potential (for instance, accumulating compatible solutes). The efficiency of photosynthetic carbon gain relative to the rate of water loss can be used as indicator (Jiménez et al. 2013). In the seasonally dry and variable environment of the Mediterranean region, the ability of species like almond to cope with water scarcity is not only dependent on the variety, but also very dependent on the rootstock on which it is grafted. The characterization of drought resistance in almond cultivars and the rest of fruit crops is therefore linked to efficiently use of water (Yadollahi et al. 2011), together with the ability of the root system to access water. Collecting materials for future research and breeding are helpful tools to reduce drought losses, but this is a lengthy process. However, the development of improved production systems using droughtresistant almonds may be possible utilizing native germplasm. These materials allow a more sustainable production, particularly in the marginal areas of harsh climate conditions found around the Mediterranean Basin (Gouta et al. 2010, 2011, 2019). This starting material has proven to be more efficient and resilient than wild species that, moreover, show poorer agronomical behavior (Sorkheh et al. 2009). However, an integrated approach evaluating new materials, having good yield, better nutraceutical quality and highly resistant to drought has not been developed as far as we know.

1.3.4 Chemical and Nutritional Values Among all nuts, almonds are one of the most nutritive, considered as a good source of essential fatty acids, vitamins, and minerals (Kodad et al. 2006, 2013; Socias i Company et al. 2007) (Table 1.2). A single ounce of almonds (approximately 20– 25 kernels) contains 15% of the recommended daily value of phosphorus, 37% of the recommended daily value of vitamin E, and 21% of magnesium. Almonds also represent a convenient source of fiber and folic acid. Historical uses of sweet and/or bitter almond ointment included the treatment of asthma, pattern baldness, and as a soothing salve for burns (Socias i Company et al. 2007; Gradziel and MartínezGómez 2013). Almond kernels are an important source of high-quality oil, composing over 50% of the kernel dry weight. This oil is primarily composed of the more stable oleic acid, making it desirable from ancient times to the present for use as a base for various

1 Genomic Designing for New Climate-Resilient Almond Varieties Table 1.2 Nutrient composition of the almond kernel per 100 g fresh weight of edible portion (Adapted from Socias i Company et al. 2007)

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Nutrient

Value

Energy

578 kcal

Protein

21.26 g

Carbohydrate

19.74 g

Fiber, total dietary

11.8 g

Glucose

4.54 g

Starch

0.73 g

Calcium

248 mg

Magnesium

275 mg

Phosphorus

474 mg

Potassium

728 mg

Sodium

1 mg

Folate, total

29 mcg

Vitamin E

25.87 mg

Saturated fatty acids

3.88 g

Monounsaturated fatty acids

32.16 g

Polyunsaturated fatty acid

12.21 g

ointments and pharmaceuticals. The high level of this monounsaturated fat in almond kernels may be partly responsible for the observed association between frequent nut consumption and reduced risk of coronary heart disease (Lovejoy et al. 2002). The almond is appreciated for the processed food industry but also as a functional food with both nutritional and medical (nutraceutical) properties including nutrients, vitamins, healthy blood lipids, or anti-inflammatory and hypocholesterolemic properties (Kodad et al. 2008, 2011; Musa-Velasco et al. 2016). The implementation of methodologies to phenotype the nutraceutical properties of new CS cultivars and breeding populations are new goals in almond breeding programs. Understanding the influence of diverse stresses such as drought and heat stresses that can be modified the composition of the beneficial components in kernels is an important goal in almond breeding. New strategies combining food science knowledge and breeding approaches must be incorporated into the actual breeding programs to obtain better food for the following generations (Kodad 2017).

1.4 Marker-Assisted Breeding for CS Traits Development and application of molecular (DNA, RNA, and epigenetic) markers could affect feasibility, efficiency, and viability of these breeding programs (Fig. 1.1b). For this reason, almond breeding programs are incorporating these new molecular technologies.

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1.4.1 Development and Application of DNA Markers The use of mapping populations segregating for the characters of interest has been the principal approach for the development of marker-assisted selection (MAS) strategies in almond. MAS would allow the early selection of a large number of plants, which would then have to undergo a final evaluation to certify the desirable traits using traditional methods. For instance, the accurate phenotyping of the parents and offspring is a key to identify the molecular markers linked to disease resistance (Fig. 1.3). The analysis of co-segregation among markers and characters allows establishing the map position of major genes and quantitative trait locus (QTL) responsible for

Fig. 1.3 Almond QTLs linked to traits of interest: blooming date (Bd), chilling requirements (CR), heat requirements (HR), productivity (P), self-incompatibility (S), double kernels (Dk), kernel length (Ln), kernel length/width (Ln/Wn), shell hardness (D), kernel size (Sz), spherical index (Sin), kernel thickness (Tn), kernel thickness/length (Tn/Ln), kernel weight (Wgn), kernel width (Wn), leafing date (Lf), ripening time (rp), kernel geometric diameter (GDn), amygdalin hydrolase (AH), Glucosyl transferase (GT), kernel taste (bitterness/sweet) (Sk), linoleic acid (Linoleic), mandelonitrile lyase (MDL), palmitic acid (Palmitic), palmitoleic acid (Palmitoleic), oil seed content (Oil), oleic acid (Oleic), prunasin hydrolase (PH), stearic acid (Stearic), tocopherol homologs (T-), total seed protein (Protein), germination date (GD), juiciness (jui), and almond fruit type (alf). A tentative scale of the map is performed in cM using as framework bin map of reference in Prunus indicating each bin in red. Between branches the references, 1: Ballester et al. (1998); 2: Ballester et al. (2001); 3: Silva et al. (2005); 4: Sánchez-Pérez et al. (2007b); 5: Sánchez-Pérez et al. (2010); 6: Fernández i Martí et al. (2011); 7: Font i Forcada et al. (2012); 8: Sánchez-Pérez et al. (2012); 9: Fernández i Martí et al. (2013); 10: Rasouli et al. (2014); 11: Donoso et al. (2016). A tentative scale of the map is given in cM using as a framework the Prunus reference bin map in (Howad et al. 2005) with each bin numbered in black

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their expression. Genetic linkage analysis was initially performed in almond by combining different molecular makers including RFLPs, CGs (candidate genes), SSRs, and SNPs. Regarding the genetic structure of almond mapping populations, the typically assayed populations were of type F1 or F2. These different population types used for mapping have advantages and disadvantages. The work developed in F1 populations is more extensive because these populations are easier to develop than F2, given the longer period of juvenile growth. F2 populations are common in interspecific crosses in the case of peach and almond species (Donoso et al. 2016). During the last two decades, many QTLs mapping studies have been carried conducted. The genetic dissection of the most important agronomic traits has resulted in the identification of several and different QTLs across the genome of almond (Ballester et al. 1998, 2001; Sánchez-Pérez et al. 2007b, 2010, 2012; Fernández i Martí et al. 2011, 2013; Font i Forcada et al. 2012; Rasouli et al. 2014; Donoso et al. 2016) (Fig. 1.3). Although, in general the QTLs are distributed in all the LGs, a high number of QTLs can be observed in LG1 of almond, having LG8 the lower number of QTLs. The application of these results in marker-assisted selection has been developed for blooming time and kernel taste (Sánchez-Pérez et al. 2010). In the case of blooming date, different publications report the use of using SSR markers in a F1 population between a seedling of ‘Tardy Nonpareil’ (‘R1000’) × ‘Desmayo Largueta’ (R × D) and also confirmed the location of late-blooming gene (Lb) in G4 while identifying

Fig. 1.4 Relative gene expression of PdDAM6 evaluated by qPCR in ‘Desmayo Largueta’ and ‘Penta’ during the seasons 2015–2016 and 2016–2017 with relative flower morphology Adapted from Prudencio et al. (2018b)

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other QTLs for flowering time in G1 and G6 (Sánchez-Pérez et al. 2007b; Rasouli et al. 2014) (Fig. 1.3). Flowering time is a complex trait, and different regions of the genome have been identified as being involved in its control (Sánchez-Pérez et al. 2014). The first studies in which quantitative trait loci (QTLs) were found to be related to flowering time in almond were done with isozymes (Asins et al. 1994). In this study, a 123-seedling population was analyzed over three years. The authors found a relationship between some isozymes and flowering time, suggesting that these isozymes could be used to perform early marker-assisted selection. Subsequently, DNA-level studies with random amplified polymorphic DNAs (RAPDs) were performed in a ‘Felisia’ × ‘Bertina’ population (‘Felisia’ is a seedling of ‘Titan’ × ‘Tuono’) using bulked segregant analysis (BSA). It was found that flowering time was also controlled by a major gene called Lb or Late blooming. It was suggested that the Lb gene had probably been inherited from ‘Tardy Nonpareil,’ the parent of ‘Titan.’ In total, three RAPDs and Lb gene related to flowering time were found in linkage group (G4) (Ballester et al. 2001). Moreover, Silva et al. (2005) described other QTLs linked to flowering time in an interspecific family between almond x peach in G1, G2, G3, G5, G6, and G7 (Fig. 1.3). More recently, the localization of the Lb gene in G4 has been confirmed in other studies using simple sequence repeat markers (SSRs) in the population R1000 × ‘Desmayo Largueta’ (R1000 is a French selection resulting from ‘Tardy Nonpareil’ × ‘Tuono’). In this study, other QTLs linked to flowering time were found in G1, G6, and G7 (Sánchez-Pérez et al. 2007b, 2012) (Fig. 1.3). The authors found that the SSR UDP-96003 was located very close to the Lb gene in G4, explaining between 56 and 86% of the variance in R1000 and between 54 and 67% of the variance in the R1000 × ‘Desmayo Largueta’ family. Silva et al. (2005) and Sánchez-Pérez et al. (2012) described different QTLs in G1, G3, G4, G5, G6, and G7 linked to chilling requirements (endodormancy) and in G2 and G7 linked to heat requirements (ecodormancy), confirming the polygenic control of final flowering time (Fig. 1.3). Moreover, homologous genes previously characterized in Arabidopsis have been used to study 10 candidate genes (CGs) from peach and almond (PrdLFY, PrdMADS1, PrpAP1, PrpFT, PrpAGL2, PrpFAR1, PrdTFL, PrdGA20, PrpAP2, and PrpCO) (Silva et al. 2005). These ten genes were localized in the Prunus reference map (‘Texas’ × ‘Earlygold’, TxE) in G1, G2, G3 G5, G6, and G7. However, none of these genes colocalized with the Lb gene in G4. One of the reasons for this could be that the mechanisms to control flowering in annual plants are different from those in perennial plants (Sánchez-Pérez et al. 2014). The confirmation of the respective allelic association with phenotype must still be obtained using a larger number of accurately phenotyped varieties and progenies. This validation would be required to determine whether a marker could be effectively used in routine MAS screening. These DNA markers together with an accurate phenotyping will help develop effective breeding designs for almond (Haimovich et al. 2015) including under changing climates (Kole et al. 2015). RNA-based markers might also provide value for the analysis of flower bud dormancy and flowering under different environments.

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On the other hand, other traits including in-shell weight, leafing time, shell hardness, kernel length, nut width, nut length, kernel length, palmitoleic acid, or tocopherol homologs were described as linked to specific molecular markers although until know the application of these results is still limited. Also, the confirmation of the respective allelic association with phenotype must still be obtained using a large number of correctly phenotyped cultivars and progenies. This validation would indicate whether or not a marker could be used in routine screening for MAS.

1.4.2 Development and Application of RNA Markers The transcriptome refers to both coding and non-coding RNAs and thus represents an enormous capacity for adjusting developmental needs of living organisms under varying environments. RNA can easily change quantitatively and/or qualitatively, thus having a tremendous potential impact on final phenotype. RNA analysis techniques can be applied for gene functional characterization and development of new markers for specific traits. These RNA markers also have a great potential for validating DNA markers. In addition, these RNA markers are of great interest in monitoring complex process such as flowering (Prudencio et al. 2018b). In the case of almond, targeted gene expression studies have been conducted to study bud dormancy, flower development, and cold acclimation (Barros et al. 2012; Prudencio et al. 2018b). Expression of two homeotic genes related to floral organ development, PdMADS1 and PdMADS3, where found to gradually increase during ecodormancy (Barros et al. 2012), particularly after the green-tip-dormancy-break stage. Expression of an almond GA20-oxidase gene with a predicted role in gibberellin biosynthesis was detected in floral buds before the initiation of endodormancy (Silva et al. 2005) and was shown to decrease during growth resumption (Barros et al. 2012). The opposite pattern was found for almond GA2-oxidase, involved in GA catabolism, which increased during flower bud break. Based on these results, Barros et al. (2012) proposed that a change in GA metabolism occurs during late stages of almond flower bud break. Interestingly, gibberellins are involved in the regulation of cell elongation in stamen filament as well as in cellular development in anthers, in the model plant Arabidopsis thaliana (Cheng et al. 2004). Gene expression analysis of cold-responsive PdCBF1, PdCBF2, and PdDHN1 genes along with seasonal development patterns in flower buds and shoot internodes suggested a shutdown of the CBF-mediated regulatory pathway prior to bloom, in agreement with the decreased frost tolerance observed in flowers (Barros et al. 2012). Interestingly, CBF-specific CTR/DRE cis-elements in promoters of peach PpDAM5 and PpDAM6 genes were also found, suggesting their association with a CBF regulon (Barros et al. 2012). Recently, Prudencio et al. (2018b) also studied the differential expression of other genes (PdDREB2c, PdAWPM19, and PdDAM5) to determine dormancy break in two contrasting varieties and reported a peak in expression correlating with bud break.

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Finally, analysis of almond flower bud morphology allowed a preliminary association of anther maturation with the expression of PdDAM6, since transcript levels of this gene showed a decline when anthers were approaching full maturity (Prudencio et al. 2018c) (Fig. 1.4). Monitoring bud transition from endodormancy to ecodormancy should be of great interest in terms of the use and optimization of biostimulants to promote or delay flowering in fruit tree species (Ionescu et al. 2017a, b) including as an option for mitigating the effects of climate change. The time of application of these biostimulants is critical to success and depends on the endodormancy stage of the bud including its transition to ecodormancy (Erez 1995, 2000) as well as the bud-forcing strategies utilized (Kauffman and Blake 2018). Treatments with these biostimulants need to be applied at the optimum time for breaking bud dormancy, as they can be null or even toxic if applied at the wrong time (Erez 1995). Monitoring almond flower bud dormancy through gene expression might be useful for determining the appropriate moment to apply these biostimulants. An improved understanding of transcription regulations linked to endodormancy is required for the development of effective selection tools, including molecular markers. Prudnecio et al. (2018d) reported transcriptomes of flower buds in different stages, from dormancy to active state using, RNA-seq technology and informatics analysis. Experimental design included total RNA from flower bud pools of three almond cultivars with different requirements for endodormancy and ecodormancy. A total of 22,833 transcript sequences were identified and between 850 and 1710 transcripts were revealed as differentially expressed in comparisons among samples. These findings should improve our knowledge about the transcriptional network of flower bud dormancy in tree-crop species, including differences in regulation for early and late almond cultivars. This information may then be used to optimize agronomical production or more efficiently breed useful late-flowering almond cultivars (Prudencio et al. 2018d).

1.4.3 Development and Application of Epigenetic Marks Epigenetic changes consist of chemical modifications affecting DNA or structural proteins (histones) within the chromatin. Two types of epigenetic modifications have been described: DNA methylation (5´-cytosine methylation, 5mC) in plants and post-translational histone modifications (PTMs), which include the acetylation and methylation of histones. Epigenetic changes are part of the transcriptional regulation machinery of genomes (Rios et al. 2014). The combination of processes, including histone modifications and DNA methylation, that regulate genome expression are referred to as the epigenomes. DNA methylation is the covalent addition of methyl groups to DNA bases leading to gene silencing. 5-Methylcytosine (5mC) catalyzed by DNA methyltransferases (DNMTs) is the most common form of DNA methylation. DNA methylation is associated with cell status, stability, and regulation of expression. DNA methylation occurs in three sequence contexts: CG and CHG (where H = A, C or T), which are

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found in promoter and coding regions, and CHH (where H = A, C or T), found in non-coding regions and transposable elements (TEs). In almond, recent studies showed that DNA methylation (5mC) pattern is generally genotype-dependent rather than dormancy-dependent state. In a recent study, several DNA-methylated genes changed between the dormant and active state of the flower buds and the results provided candidate epialleles linked to dormancy release and flowering time (Prudencio et al. 2018e). Earlier results had also shown that DNA methylation is one of the mechanisms participating in the regulation of MADS-box genes controlling bud dormancy in other Prunus sècies (Rothkegel et al. 2017). Prudencio et al. (2018e) have applied the epi-GBS protocol (epi-Genotyping by Sequencing) to almond DNA. The technical potential is evident in the discovery of epigenetic variants, based on 5mC, that are genotype-dependent. In addition, the DNA methylation (5mC) pattern is generally genotype-dependent rather than dormancy-dependent state. Comparative DNA methylation studies of recent almond varieties released from breeding programs with more traditional varieties will improve our knowledge of methylation variants and possibly provide candidate epialleles linked to agronomic traits. Such polymorphisms could then be used to screen large populations using next-generation sequencing (NGS) to confirm the locus methylation state associated with a given character of interest. Results showed the identifications of different genes where DNA methylation state changed between the dormant and active state of the flower buds. This was possible in both the traditional early-flowering genotype ‘Desmayo Largueta’ as well as the extralate-flowering genotype ‘Penta’ from the CEBAS-CSIC almond breeding program. Additional, common genes were identified in the analysis (Prudencio et al. 2018e). Several studies have described the role of epigenetics including histone modifications in the regulation of dormancy in Rosaceae fruit species. In peach, a genomewide pattern of the PTM H3K27me3 during bud dormancy release was identified (de la Fuente et al. 2015; Lloret et al. 2017), found a relationship between gene expression, PTMs, and sorbitol synthesis during bud dormancy progression and final release.

1.5 Concluding Remark and Future Prospects Human activities are producing a significant increase in global temperatures referred to as climate change. According to the ‘Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report,’ the average global temperature has increased by 0.74 °C over the last century and is expected to rise between 1.1 and 6.0 °C in this century (IPCC 2007). This climate change is affecting all life processes on earth, including food crop production. Increases in temperature are modifying the growth stages of plants, especially those in temperate zones that are adapted to seasonal changes in solar radiation, temperature, and humidity. Originating in the diverse environments of Central and Southwest Asia, almond has shown tremendous ecological adaptability with an extensive geographical range beyond its center of origin.

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Similarly, it has become an important tree-crop worldwide with extensive plantings in California, Australia, and the Mediterranean Basin. Genomic designing for climate-resilient varieties is a breeding priority in this species. In this context different genomic, transcriptomic and epigenetic markers have been developed to facilitate selection of new almond climate-resilient genotypes. These molecular markers together with increasingly accurate phenotyping will facilitate the breeding of new climate-resilient varieties as well as the possible development of cultural management options such as biostimulants and biorepressants to extend the productivity of current varieties. Acknowledgements This study has been supported by Grants Nº 19308/PI/14 and Nº 19879/GERM/15 of the Seneca Foundation of the Region of Murcia and the Almond Breeding project of the Spanish Ministry of Economy and Competiveness and by the European project ‘Nut4Drought: Selection and characterization of drought resistant almond cultivars from the Mediterranean basin with high nutraceutical values’ from an ERA-NET Action financed by the European Union under the Seventh Framework Program for research called ARIMNet2 (Coordination of Agricultural Research in the Mediterranean; 2014–2017; www.arimnet2.net).

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Ionescu IA, López-Ortega G, Burow M, Bayo-Canha A, Junge A, Gericke O, Moller BL, SánchezPérez R (2017b) Transcriptome and metaboliote changes during hydrogen cyanamide-induced floral bud break in sweet cherry. Front Plant Sci 8:1233 IPCC (2007) Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge Jiménez S, Dridi J, Gutiérrez D, Moret D, Moreno MA, Gorgocena Y (2013) Physiological and molecular responses in four Prunus submitted to drought stress. Tree Physiol 33:1061–1075 Kauffman H, Blanke M (2018) Substitution of winter chilling by spring forcing for flowering using sweet cherry as model crop. Sci Hort 244:75–81 Kester DE (1965) Inheritance of time of bloom in certain progenies of almond. Proc Amer Soc Hort Sci 87:214–221 Kester DE, Asay RN (1973) Correlation among chilling requirements for germination, blooming and leafing in almond. Genetics 74:5135 Kester DE, Raddi P, Asay RN (1977) Correlations of chilling requirements for germination blooming and leafing within and among seedling population of almond. J Amer Soc Hort Sci 102:145–148 Kester DE, Gradziel TM (1996) Almonds. In: Janick J, Moore JN (eds) Fruit Breeding, vol III. Nuts. John Whiley & Sons, New York, pp 1–97 Kodad O (2017) Chemical composition of almond. In: Socias i Company R, Gradizel TM (eds) Almond: botany, production and uses. Editorial CABI. Boston (USA), pp 428–449 Kodad O, Estopañán G, Juan T et al (2013) Protein content and oil composition of almond Moroccan seedlings. J Am Oil Chem 90:243–252 Kodad O, Estopañán G, Juan T, Socias I Company R (2011) Tocopherol concentration in almond oil. J Agri Food Chem 59:6137–6141 Kodad O, Socias I Company R (2008) Variability of oil content and of major fatty acid composition in almond. J Agri Food Chem 56:4096–4101 Kodad O, Socias i Company R, Prats MS, López Ortiz MC (2006) Variability in tocopherol concentrations in almond oil and its use as a selection criterion in almond breeding. J Hort Sci Biotechnol 81:501–507 Kole C, Muthamilarasan M, Henry R, Edwards D, Sharma R et al (2015) Applicationofgenomicsassistedbreedingforgenerationofclimateresilientcrops: progressandprospects. Front Plant Sci 6:563 Lloret A, Martínez-Fuentes A, Agustí M, Badenes ML, Ríos G (2017) Chromatin-associated regulation of sorbitol synthesis in flower buds of peach. Plant Mol Biol 95:507–517 Lovejoy JC, Most M, Lefevre M, Greenway FL, Rood JC (2002) Effect of diets enriched in almonds on insulin action and serum lipids in adults with normal glucose tolerance or type 2 diabetes. Am J Clin Nutr 76:1000–1006 Luedeling E (2012) Climate change impacts on winter chill for temperate fruit and nut production: a review. Sci Hort 144:218–229 Martínez-Gómez P, Sozzi GO, Sánchez-Pérez R (2003) New approaches to Prunus tree crop breeding. J Food Agri Environ 1:52–63 Martínez-Gómez P, Prudencio AS, Gradziel TM, Dicenta F (2017) The delay of flowering time in almond: a review of the combined effect of adaptation, mutation and breeding. Euphytica 213:197 Moheb MB, Imani A, Shamili M (2018) The evaluation of almond progenies of cold-susceptible and cold-tolerant parents (Filippo-Ceo xShahrood-12). Sci Hort 234:176–183 Mousavi S, Alisoltani A, Shiran B (2014) De novo transcriptome assembly and comparative analysis of differentially expressed genes in Prunus dulcis Mill. In: Response to freezing stress. PLoS One9:e104541 Musa-Velasco K, Paulionis L, Poon T, Lee HY (2016) The effect of almond consumption on fasting blood lipid levels: a systematic review and meta-analysis of randomised controlled trials. J Nutr Sci 5:e34 Palasciano M, Logoluso V, Lipari E (2014) Differences in drought tolerance in almond varieties grown in Apulia region (Southeast Italy). Acta Hort 1028:319–324

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Poonam V, Raunak G, Kumar CS et al (2011) Chemical constituents of the genus Prunus and their medical properties. Curr Med Chem 18:3758–3824 Prudencio AS, Martínez-Gómez P, Dicenta F (2018a) Evaluation of breaking dormancy, flowering and productivity of extra-late and ultra-late flowering almond cultivars during cold and warm seasons in South-East of Spain. Sci Hort 235:39–46 Prudencio AS, Dicenta F, Martínez-Gómez P (2018b) Monitoring flower bud dormancy breaking in almond through gene expression analysis. Acta Hort 1219:93–98 Prudencio AS, Dicenta F, Martínez-Gómez P (2018c) Monitoring dormancy transition in almond [Prunus dulcis (Miller) Webb] during cold and warm Mediterranean seasons through the analysis of a DAM (Dormancy-Associated MADS-Box) gene. Horticulturae 4:41 Prudencio AS, Dicenta F, Martínez-Gómez P (2018d) Gene expression analysis of flower bud dormancy breaking in almond using RNA-Seq. Acta Hort 1219:119–124 Prudencio AS, Werner O, Martínez-García PJ, Dicenta F, Ros RM, Martínez-Gómez P (2018e) DNA methylation analysis of dormancy release in almond using epi-Genotyping by Sequencing. Intl J Mol Sci 19:3542 Rasouli M, Fatahi R, Zamani Z et al (2014) Identification of simple sequence repeat (SSR) markers linked to flowering time in almond by bulked segregant analysis (BSA). Acta Hort 936:53–56 Ríos G, Leida C, Conejero C, Badenes ML (2014) Epigenetic regulation of bud dormancy events in perennial plants. Front Plant Sci 5:247 Rothkegel K, Sánchez E, Montes C, Greve M, Tapia S, Bravo S, Almeida AM (2017) DNA methylation and small interference RNAs participate in the regulation of MADS-box genes involved in dormancy in sweet cherry. Tree Physiol 37:1739–1751 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2004) Identification of S-alleles in almond using multiplex-PCR. Euphytica 138:263–269 Sánchez-Pérez R, Ortega E, Duval H, Martínez-Gómez P, Dicenta F (2007a) Inheritance and correlation of important agronomic traits in almond. Euphytica 155(3):381–391 Sánchez-Pérez R, Howad D, Dicenta F, Arús P, Martínez-Gómez P (2007b) Mapping major genes and quantitative trait loci controlling agronomic traits in almond. Plant Breed 126:310–318 Sánchez-Pérez R, Dicenta F, Martínez-Gómez P (2012) Inheritance of chilling and heat requirements for flowering in almond and QTL analysis. Tree Genet Genomes 8:379–389 Sánchez-Pérez R, Del Cueto J, Dicenta F, Martínez-Gómez P (2014) Recent advancements to study flowering time in almond and other Prunus species. Fron Plant Sci 5:334 Sánchez-Pérez R, Howad W, García-Mas J, Arús P, Martínez-Gómez P, Dicenta F (2010) Molecular markers for kernel bitterness in almond. Tree Genet Gen 6:237–247 Silva C, García-Mas J, Sánchez AM, Arús P, Oliveira MM (2005) Looking into flowering time in almond (Prunus dulcis (Mill.) D.A. Webb): the candidate gene approach. Theor Appl Genet 110:959–968 Socias i Company R, Felipe AJ, Gómez Aparisi J (1999) A major gene for flowering time in almond. Plant Breed 118:443–448 Socias i Company R, Kodad O, Alonso JM, Gradziel TM (2007) Almond quality: a breeding perspective. Hort Revi 33:1–33 Sorkheh K, Shiran B, Asadi E, Jahanbazi H, Moradi H, Gradziel TM, Martínez- Gómez P (2009) Phenotypic diversity within native Iranian almond (Prunus spp.) species and their breeding potential. Genet Resour Crop Evol 56:947–961 Tabuenca MC, Mut M, Herrero J (1972) Influence of temperature in the date of flowering of almond cultivars. Anal Estación Exp Aula Dei 11:378–395 Yadollahi A, Arzani K, Ebadi A, Karimi S (2011) The response of different almond genotypes to moderate severe water stress in order to screen drought tolerance. Sci Hort 129:403–413

Chapter 2

Challenges and Strategies for Developing Climate-Smart Apple Varieties Through Genomic Approaches Anastassia Boudichevskaia, Gulshan Kumar, Yogesh Sharma, Ritu Kapoor and Anil Kumar Singh Abstract Apple is one of the most consumed and traded fruit crops in the world, and its health benefits as well as economic importance cannot be overestimated. Being a temperate fruit crop, it requires winter chilling to release its seasonal dormancy and complete the phenological cycle through fruit production. Since climatic conditions significantly influence the quality and yield of the fruit, understanding the effect of climatic variations on apple cultivation is of prime importance. In addition, characterization of available genetic resources is required to develop climate-resilient apple cultivars with high quality and yield. In the recent decades, large amount of transcriptomic, genomic, genetic, and breeding data has been generated; their structuring and digitalization should provide support to the breeders and agricultural biotechnologists in their daily duties with effective solutions. Such coordination of information along with data exchange, data transfer, and improved analytical capacity is those prerequisites that make possible ‘information-driven’ breeding process. We review the current state of generated knowledge and its digitalization for apple plant breeding, highlight the availability of precise and robust detection systems enabling automation in apple breeding, and conclude with the discussion of future prospects. Keywords Apple · Climate change · Chilling · Databases · Bioinformatics tools · Computer vision

A. Boudichevskaia Research Group Chromosome Structure and Function, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstraße 3, 06466 Stadt Seeland, Germany G. Kumar · Y. Sharma · R. Kapoor National Agri-Food Biotechnology Institute, Mohali, Punjab 140306, India R. Kapoor Department of Biotechnology, Panjab University Chandigarh, Chnadigarh 160014, India A. K. Singh (B) School of Genetic Engineering, ICAR-Indian Institute of Agricultural Biotechnology, Ranchi 834010, India e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_2

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2.1 Introduction Fruits are considered as part of a balanced and healthy diet, since they are rich source of vitamins and minerals. In the fresh fruit production, banana, apple, and grapes are among the top fresh fruit commodities. According to the world fruit map, 2018, banana and apple are the top most traded fresh fruit commodities with over 150 metric tons of trade flow, separately (https://research.rabobank.com/far/en/sectors/regionalfood-agri/world_fruit_map_2018). Apple is the largest temperate fruit produce in the world, followed by grapes and oranges. Therefore, apple cultivation has socioeconomic importance in apple-growing regions. In the current scenario, China is the largest producer of apple fruit and alone accounts for nearly half of the world’s total apple production. Among other apple-producing countries, USA, Poland, Turkey, India, Italy, and several other European countries with temperate climate are the major ones. The apple cultivation was originated in Central Asia and Europe and then spread throughout the world where climatic conditions are suitable for apple cultivation. In most of the plants, transition from vegetative to reproductive development is controlled by environmental signals, such as photoperiod and temperature. In plants of temperate regions, the distinct low-temperature requirement during winters is an adaptive response to withstand the harsh winter conditions and to ensure flower development in favorable spring season. In most plants of the Rosaceae family, the winter dormancy affects the period of flowering time by influencing the timing of dormancy release. Similarly, the dormant buds of apple tree require certain amount of chilling units (CUs; temperature ≤ 7 °C for one h) during winter, before dormancy release at the onset of spring. The requirement of chilling units is genotype dependent in apple (Heide and Prestrud 2005). However, insufficient chilling during winter results in inevitable physiological disorders, which adversely affect the productivity and yield of the crop. The major physiological disorders include irregular and temporal flowering leading to anomalous growth and non-uniform crop development, which eventually result in loss of fruit yield and quality (Chandler 1942; Petri and Leite 2004).

2.2 Impact of Climate Change on Apple Apples were initially produced in the cold and dry climatic regions of Western Asia and Southern Europe (Lu 1980). The climatic factors, which determine the apple spatial and temporal distribution, have significantly changed with the increased climate change (Sugiura et al. 2005; Sharma et al. 2014; Fang et al. 2016). For cereals and other annual crops, studying the impact of climatic variations is relatively more feasible as compared to the perennial tree crops. Therefore, potential impact of climate change on fruit trees is less known. Since perennial tree plants produce fruits after 10–15 years of plantation and require significant investment for the establishment

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of new orchards, it makes sense to consider the future growing environment to seek maximum productivity from the orchard. It is difficult to determine how and when forthcoming impact of warm temperature will affect the apple production, since there is uncertainty in the climatic projections and associated seasonal climatic variations. Although, sensitivity of apple cultivars towards temperature change is indicated by environment-induced variation in the flowering time of cultivars and their pollinisers, and the difference in fruit quality. There are some important factors, which need to be considered for better crop management: (i) cultivar and rootstock combinations for the different growing climate with reference to chilling and heat; (ii) chilling requirement and its effect on apple production in different apple cultivars; (iii) effect of chilling period and heat period on flowering, fruit set, and fruit quality; (iv) effect of poor bud burst, due to inadequate chilling, on fruit development, tree health and growth; and (v) effect of extreme heat on post-harvest shelf life. According to a study on assessment of climate change on California agriculture (Baldocchi and Wong 2006), it is predicted that there will be 50% decrease in chill hours from 1950 to 2100, which will drastically reduce availability of chilling for many fruit tree species in California. The modeling results showed that for apple cultivars requiring more than 1000 chilling hours, as well as other temperate fruit crop with higher chilling requirement, only few locations have been left to exist today with safe chilling level and worryingly none will exist by mid-century (Luedeling et al. 2009). Similarly, in the Himalayan region of India, climatic variations resulted in the reduction of available chilling required for flowering of many temperate crops, including apple (Vedwan and Rhoades 2001; Rai et al. 2015; Kumar et al. 2016a). The warm climate during winter season, with reduced available chilling, affects timing of bud burst and flowering, which adversely influence the quality fruit set and yield.

2.3 Need for Adaptation to Climate Change The accelerated rate of industrialization resulted in high atmospheric levels of greenhouse gases, viz. carbon dioxide, methane, nitrous oxide, etc., which increase the average mean temperature and consequently lead to climatic variations (Singh 2010). It is estimated that by the end of the twenty-first century, the carbon dioxide concentration will be 100% more than that observed in pre-industrial era and the global temperature will rise by 6 °C (IPCC 2007). In this scenario, it is unlikely that present agroclimatic conditions will remain suitable for sustainable agriculture in future. The trending climatic conditions will lead to reduced crop productivity through loss of vigor, reduced fruit bearing ability and fruit size, in addition to adverse effects on other important agronomical fruit quality traits. Moreover, due to high rate of ripening at elevated temperature, the shelf life of apple will be reduced (Else and Atkinson 2010). The high temperature conditions in the spring will also increase the reproduction and invasion of insect pests with subsequent low yield and poor-quality apple crop (Patterson et al. 1999; Gautam et al. 2013; Jangra and Sharma 2013), and

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therefore, studies related to identification of disease resistant genes are significant (Arya et al. 2014). In addition, apple cultivars are cross-pollinated to develop fruits, and therefore, blooming time of polliniser plants and fruiting plants should coincide. The changing climatic conditions may lead to the change in flowering time of polliniser and fruiting plants, which may consequently lead to low fruit set and eventually lower crop yield. Therefore, replacement of currently cultivated tree species with new climate-resilient cultivars is urgently required in the near future. The introduction of low chill cultivars is the most feasible solution to the problem of insufficient chilling during winters. Plant breeding is one of the possible ways to develop low chill cultivars. However, breeding is the time-consuming and difficult process. With the advent of modern biotechnological approaches, molecular mapping of genetic determinants of chilling requirement will boost the breeding process to develop the climate-resilient apple cultivars.

2.4 Efforts to Develop Climate-Smart Apple Varieties 2.4.1 Dormancy and Dormancy Release 2.4.1.1

Genomics Approaches

Phenology is the key trait which is most affected by the climatic conditions. The cyclic seasonal climatic changes, such as change in photoperiod (day-length) and temperature or combination of both, regulate the time of growth cessation during dormant period and active meristem growth under favorable environmental conditions. The chilling requirement of plants is genotype-dependent, and therefore, their spatial distribution and survival as well as their productivity is affected by climatic conditions (Chuine and Beaubien 2001). Precisely, insufficient chilling results in inevitable physiological disorders which consequently lead to yield and quality compromised fruit crop. Therefore, it is important to understand the winter dormancy release which plays a crucial role in the growth and developmental aspects of the temperate perennial plants, including apple. The dormancy-related changes in gene expression using transcriptomics approach have been reported in several perennial plants, including poplar, Populus tremula × Populus alba and Populus trichocarpa (Rohde et al. 2007; Ruttink et al. 2007; Ko et al. 2011); white spruce, Picea glauca (El Kayal et al. 2011); leafy spurge, Euphorbia esula (Horvath et al. 2006, 2008; Dogramaci et al. 2013) and other perennial horticultural plants, viz. peach, Prunus persica (Jimenez et al. 2010; Leida et al. 2010, 2012); pear, Pyrus pyrifolia (Liu et al. 2012; Bai et al. 2013); Japanese apricot, Prunus mume (Yamane et al. 2008; Zhong et al. 2013); blackcurrant, Ribes nigrum (Hedley et al. 2010); raspberry, Rubus idaeus (Mazzitelli et al. 2007); kiwifruit, Actinidia deliciosa (Walton et al. 2009); blueberry, Vaccinium (Dhanaraj et al. 2007;

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Rowland et al. 2012) and grapewine, Vitis riparia and Vitis vinifera (Halaly et al. 2008; Mathiason et al. 2009; Ophir et al. 2009; Diaz-Riquelme et al. 2012). Besides these studies, five studies related to transcriptome profiling during bud-beak in apple have been reported, so far. To study the differential expression of genes in apple, cultivars with contrasting chilling requirements, namely ‘Gala’ standard and ‘Castel Gala’ were used for suppression subtractive hybridization (Falavigna et al. 2014). The authors identified 1019 unigenes. Subsequent qRT-PCR analysis of 28 selected genes showed differential expression of 17 genes. More importantly, the steady-state mRNA level of these genes remained longer in cultivars with high chilling requirement. Among the differentially expressed genes (DEGs), Dormancy-Associated MADS-box (DAM), dehydrins, NAC, HTA, and RAP2 could be considered as potential candidate genes to study dormancy release process in apple. The effect of various environmental factors, including seasonal and day-to-day variation as well as the effect of tree-to-tree variation on the transcriptional changes during growth to dormancy transition and during initiation of flower development was studied by Pichler et al. (2007). In this study, seasonal variations were found to be the dominant cause of variance among gene expression differences. Using microarray with 57,000 genes, Porto et al. (2015) have suggested the possible role of auxin transport and photosynthesis-related genes in regulating the dormancy release in apple. The genes related to photosynthesis and flavonoid biosynthesis were repressed in response to cold exposure. The involvement of small non-coding RNAs in regulating the DEGs during dormancy process was corroborated with differential expression pattern of sense and antisense transcripts. Surprisingly, the expression of DAM genes, the most promising regulators of dormancy, was not found to be differentially expressed in this study. In addition, FLOWERING LOCUS C-LIKE (FLC-like) and MADS AFFECTING FLOWERING (MAF) genes were reported to be induced during dormancy. These genes have chromosomal location near the major quantitative trait locus (QTL) for the timing of bud break. However, their expression pattern was not in accordance with downregulation of arabidopsis FLC and MAF genes in response to prolonged low temperature exposure. The study considered the effect of cold treatment, genotype difference, and harvesting date on dormancy status of apple in two contrasting apple genotypes with different chilling requirements. Although this study unravels several important transcriptional changes during distinct dormancy status in apple, the transcriptional profiling of buds in the same genotype under low and high chilling in field conditions was not performed. In our own study (Kumar et al. 2017), the transcriptome profiling during bud dormancy release and initial fruit set under low and high chill conditions in the same genotype was performed using RNA-seq. The high chill conditions were found to be associated with high number of DEGs during bud break and fruit set as compared to that of low chill conditions, which indicates that chilling availability reorganizes the transcriptional dynamics. The comparative analysis also reveals the differential expression of genes involved in phytohormone metabolism, particularly involvement of abscisic acid, gibberellic acid, ethylene, auxin, and cytokinin. The expression of flowering time regulatory genes, such as DAM, FLC-like, Flowering Locus T-like,

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and Terminal Flower 1-like genes was found to be modulated under differential chilling. In addition, the enrichment of post-embryonic development related genes and co-expression gene modules specific to the high chill conditions were identified using the co-expression network analysis. Moreover, the hub genes for these high chillspecific modules were identified, which included Early flowering 7, RAF10, ZEP4, and F-box, suggesting them as potential regulators of chilling-mediated dormancy release and fruit set. Recently, Takeuchi et al. (2018) have applied the RNA-seq analysis of endo- and ecodormant apple buds under field and controlled conditions and identified DEGs during the transition from endodormant to ecodormant apple buds. The upregulated and chilling-correlated expression of transcription factor genes including PIF4, FLC-like, APETELLA2/ETHYLENE RESPONSIVE 113, and MYC2 suggests their role in chilling-mediated endodormancy release.

2.4.1.2

Candidate Gene Approach

In Rosaceae family, the genetic analysis of evergreen peach mutant was an important study to suggest the role of tandem arrayed six genes at EVERGROWING (EVG) locus in dormancy regulation (Rodriguez et al. 1994; Werner and Okie 1998; Bielenberg et al. 2004; Bielenberg et al. 2008), which were later described as DAM genes (Li et al. 2009). In apple, DAM genes appear to be arranged in tandem on chromosomes 8 and 16, in regions syntenic to the peach chromosome 1, where six peach DAM genes are located (Bielenberg et al. 2008). In addition, several studies investigated the effect of individual gene on bud dormancy release in apple. The MADS-box genes play a major role in plant growth and development. The genes similar to arabidopsis SHORT VEGETATIVE PHASE (SVP), subclass of MADS-box gene family, have been associated with regulation of flowering in annual plants and dormancy in perennial plants. Two SVP orthologs are located on apple linkage group (LG) 4 and LG11, respectively (Illa et al. 2011; Mimida et al. 2015; Porto et al. 2016). In addition, the differential seasonal expression of DAM and SVPlike genes during development of apical bud suggests their role in bud dormancy (Mimida et al. 2015; Kumar et al. 2016b; Porto et al. 2016). The ectopic expression of MdDAMb and MdSVPa affects the seasonal dormancy cycle; however, it does not impact the flower and fruit development. The constrained axillary shoot growth and significant delay in bud break during growth favoring spring season were observed in transgenic MdDAMb and MdSVPa lines, as compared to control plants, which indicates their role in the maintenance of dormancy (Wu et al. 2017). The bHLH transcription factors are known to play diverse biological roles, including responses to abiotic stresses (Feng et al. 2012). One of the bHLH gene family members in apple, MdCIbHLH1 was selected on the basis of its induced expression under low temperature. The overexpression studies of MdCIbHLH1 in arabidopsis, tobacco, and apple showed enhanced cold tolerance in transgenic plants (Feng et al. 2012). The expression analysis of MdCIbHLH1, using semi-quantitative RT-PCR, during apple bud dormancy release showed that MdCIbHLH1 transcripts were highly upregulated in dormant buds and then gradually downregulated during bud dormancy

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release, followed by low-level expression maintained during the green tip emergence after the bud-break stage. In addition, the expression analysis of MdCIbHLH1 in stratified apple seeds showed similar pattern to that of apple bud dormancy release (Yiran et al. 2016). All these studies suggested the relation between MdCIbHLH1 and apple bud- and seed-dormancy release. The PEBP gene family members play an important role in seed germination and floral initiation and have been well characterized in the model plant arabidopsis (Wang et al. 2015; Yu et al. 2019). The members of this family can be classified into three defined clades, namely MOTHER OF FT AND TFL1 (MFT ), FLOWERING LOCUS T (FT ), and TERMINAL FLOWER1 (TFL1) in case of angiosperms. In apple, two copies of genes from each clade are present due to duplication. The expression analysis of MdTFL-1 and MdTFL-1revealed similar spatio-temporal expression in meristematic zone of shoot apical meristem during vegetative growth, before it drastically declined during the transition from committed vegetative meristem into inflorescence meristem (Mimida et al. 2009, 2011). The RNA silencing of MdTFL1 in transgenic apple lines showed perpetual flowering phenotypes, which indicates the crucial role of the MdTFL1 gene in maintaining vegetative growth by repressing transition to reproductive meristem (Kotoda et al. 2006; Flachowsky et al. 2012). The FT protein in arabidopsis works as florigen which is produced in leaves and transferred to shoot apical meristem through phloem, where it interacts with FLOWERING LOCUS D (FD) to promote flowering. The induced expression of FT gene is resulted from integration of flower-inducing signals from photoperiodic, autonomous, and vernalization pathways, which control the time of flowering in coordination with floral integrator genes SUPRESSOR OF CONSTANS 1 (SOC1) and LEAFY (LFY ). In several other plant species, the homologs of FT were also found to act as floral promoter, which suggest their functional conservation (Hsu et al. 2006; Zeevaart 2008; Kotoda et al. 2010; Mimida et al. 2011; Shen et al. 2012). For instance, in apple, two homologs of FT gene, namely MdFT1 and MdFT2, exhibit expression in shoot apex but not in the leaves. The MdFT1 expression level was gradually increasing in shoot apical meristems and showed peak during the floral bud induction, which indicates its role as floral inducer (Kotoda et al. 2010; Mimida et al. 2011). In addition, the transgenic apple constitutively expressing MdFT1 or MdFT2 showed precocious flowering phenotype. However, the flowering signal was not transmitted across grafts from transgenic to wild-type plants (Tränkner et al. 2010; Kotoda et al. 2010), which does not corroborate that MdFT1 and MdFT2 work as florigen in apple. Further phylogenetic and expression analyses allowed to conclude that FT-like genes in apple and other members of Rosaceae might have diverged evolution in function and expression (Mimida et al. 2013). The early conversion of the vegetative phase to the adult phase is a desirable agronomic trait in apple, since it decreases the time period to get fruit production from newly planted apple orchards. The involvement of miR156 via SBP/SPL has been known to regulate the conversion of vegetative phase to adult phase in higher plants (Wu and Poethig 2006). In apple genome, pre-miR156 is encoded by 31 putative MdMIR156 genes, where seven of these genes were found to be dominantly expressed (Jia et al. 2017). However, the downregulated expression was only

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observed for MdMIR156a5 and MdMIR156a12 during vegetative phase conversion, which was also consistent with mature miR156 level. Further analysis in relation to redox status showed that vegetative phase conversion is controlled by regulatory network of MdMIR156a5 and MdMIR156a12. All the studies discussed in this section are aimed to identify the important genes involved in the regulation of winter dormancy in apple. Several genes have been found and validated for their role in winter dormancy release. However, dormancy is one of the complex physiological processes where interaction among the several cellular pathways is expected which makes it more complex to understand.

2.4.2 Abiotic Stress 2.4.2.1

Cold Tolerance

The low temperature is required for the winter dormancy release in apple and is a major influencing factor, which constraint the apple cultivation worldwide. Indeed, the plants should be cold tolerant to withstand the harsh winter conditions. Therefore, various studies have been performed to identify the genes and pathways involved in the cold tolerance mechanism. Du et al. (2015) studied the gene expression in the leaves of cold-tolerant apple rootstock, where majority of DEGs were found to be involved in the biological pathways related to metabolism, plant–pathogen interaction and signal transduction. In particular, the dehydration-responsive elementbinding protein/C-repeat factor (DREB/CBF) gene was found to be highly expressed during initial period of cold treatment. In another study by Wang et al. (2018a),10 elite apple dwarfing rootstocks were investigated for relative electrical conductivity (REC), anthocyanin content, protein content, soluble sugar content, soluble starch content, proline content, malondialdehyde (MDA) content, superoxide dismutase activity, and peroxidase activity under different low-temperature stress conditions (0, −15, −20, −25, −30, and −35°C). The results showed a significant correlation between LT50 , MDA content, soluble starch, and anthocyanin content at different temperature regimes. Based on these results, author ranked the rootstocks according to their cold tolerance, where GM256 and SH6 were among the top two cold tolerant apple rootstocks, while T337 and M9 were among the least cold-tolerant apple rootstocks. Using a candidate gene approach, Xie et al. (2018) identified the genes which might be involved in providing cold hardiness to the transgenic apple. The MYB transcription factors, namely MYB88 and MYB124, were found to positively regulate cold-responsive genes and consequently imparted freezing tolerance in transgenic apple plants by C-REPEAT BINDING FACTOR (CBF)-dependent and CBFindependent pathways. In addition, the MdMYB88 and MdMYB124 promoted anthocyanin accumulation and H2 O2 detoxification in response to cold. The similar findings were observed by An et al. (2018), where apple MdMYB23, which was notably

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induced in response to cold stress, was found to enhance the cold tolerance in transgenic apple calli overexpressing MdMYB23. Authors suggested that the cold tolerance regulatory activity was due to direct binding of MdMYB23 to the promoters of MdCBF1 and MdCBF2 and their subsequent activation. In addition, interaction of MdMYB23 with MdANR has been suggeseted to play role in promoting pro-anthocyanidin accumulation and reactive oxygen species (ROS) scavenging.

2.4.2.2

Drought Stress

It has been forecasted that global warming and high urban use of water will reduce the availability of water for agricultural use (Cosgrove and Loucks 2015). Therefore, the development of plant varieties with improved water-use efficiency and drought tolerance could reduce agricultural water use without compromising yield or quality. In perennial fruit crops, drought stress causes wilting, leaf fall, and premature fruit ripening and associated fruit drop. Young apple trees need plenty of water to grow well. The different rootstocks behave differently to the water availability and drought stress. The dwarf or semi-dwarf rootstocks can tolerate the drought stress relatively better than standard-sized trees because of more fruit per leaf area which reduces the transpirational water loss. On contrary, too much water also damages the root system by creating hypoxic conditions in the soil. The waterlogging causes poor growth and mineral absorption, and eventually plant death. To compute the tree water requirement, Kullaj et al. (2017) developed a functional model that can be used as promising tool for early evaluation of drought tolerance capacity of apple genotypes. This model is based on tree field transpiration rate, actual transpiration using sap flow measurement, and stomatal conductance. This model provides insight into rapid response to humidity and light saturation level by different cultivars. Kowitcharoen et al. (2018) observed that drought stress treatment to the apple tree before fruit harvesting leads to induction of antioxidant activity and accumulation of sugars in fruit. This increased antioxidant activity was found to enhance the post-harvest storage life of apple fruit. The identification of drought stress marker is important to study the level of stress and to take precautionary measures in advance. Among biochemical markers for drought stress, Sircelj et al. (2007) found that zeaxanthin and glutathione were the best drought stress markers in apple tree, while ascorbate and sorbitol can be used in case of moderate drought only. In addition, authors also emphasised the use of biochemical markers for determination of drought stress intensity, in cases where low relative air humidity affects stomatal conductance and connected physiological parameters (Sircelj et al. 2007). The investigation of carbohydrate composition in the leaves of several apple cultivars during fruit development was performed by (Nemeskeeri et al. 2015), to establish its use as stress indicator during drought stress. It was observed that the sucrose content of leaves can be considered as stress indicator, while during temporary dry periods, change in the glucose and fructose content of leaves could be used as stress indicators. In an investigation of morphological drought-resistant traits, such as leaf size, and stomata size and their arrangement, Bassett et al. (2011) found no substaintial

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difference in Malus domestica (‘Royal Gala’) and 34 accessions of apple wild relative Malus sieversii. This morphological similarity makes the involvement of genetic variation more apparent toward better water-use efficiency in apple cultivars. Further, the transcriptome profiling of drought stressed ‘Royal Gala’ (relatively more water-use efficient) showed the upregulation of genes related to stress-response and genes associated with photosynthesis, in root and leaf tissue, respectively. It has been previously reported that genes involved in photosysnthesis get upregulated in stress response (Boominathan et al. 2004) to develop source function at smaller leaf area during stress condition (Schurr et al. 2000). Plant root architecture plays an important role in determining drought tolerance. The members of MYB transcription factor family, namely MdMYB88 and MdMYB124, were found to regulate apple tree root morphology (Geng et al. 2018). In addition, these genes were suggested to be involved in adaptive drought tolerance through maintenance of root hydraulic conductivity under long-term drought conditions. Also, the expression of MdMYB88 and MdMYB124 was found to regulate the deposition of cellulose and lignin in root cell walls in response to drought in apple (Geng et al. 2018). In another study, the overexpression of autophagy gene, MdATG18a, resulted in improved drought tolerance in transgenic apple (Sun et al. 2018a). The transgenic lines also exhibited higher photosynthesis rate and antioxidant capacity. In addition, the other important autophagy genes were found to show higher expression in transgenic plants as compared to wild-type plants. Authors also observed that transgenic plants had lower percentage of insoluble and oxidized proteins which might have resulted in more autophagosomes being formed in transgenic plants under drought conditions. This study also showed importance of autophagy in drought response. In one of our own studies, the overexpression of MdDREB76 gene in tobacco was found to enhance the tolerance of transgenic tobacco toward salt and drought stresses (Sharma et al. 2018). The activation of antioxidant system was found to be responsible for the development of tolerance in transgenic tobacco. This study provides an important candidate gene for the development of climate-resilient apple cultivars using advance biotechnological approaches.

2.4.3 Biotic Stress The pollination is indispensable in the cross-pollinated plant species for the reproduction, where the pollinators play a key role. However, these pollinators also serve as vector for some pathogens, such as Erwinia amylovora. In a study by Cellini et al. (2019), the ability of honeybees to discriminate the flowers based on olfactory signals was studied. Two groups of trained honeybees (one group trained to forage on the E. amylovora-infected flowers and another one, trained on healthy flowers), were assessed for their foraging preferences. The results revealed that both groups preferred healthy flowers over the infected ones, as a consequence of several volatile organic compounds released by infected flowers. This influence of floral

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scent increases the visit rate of honeybees on healthy plants that may facilitate the secondary bacterial spread, since honeybees may occasionally visit the infected flowers also. Apple cultivars with durable resistance are needed for sustainable management of fire blight, the most destructive bacterial disease of apple. Several studies have identified genetic resistance to fire blight in both, wild and cultivated apples. The phenotypic and transcriptional profiling of two apple cultivars ‘Empire’ and ‘Gala’ was performed under E. amylovora infection, causing fire blight (Silva et al. 2019). The disease progression in ‘Empire’ was found to reduce more rapidly after 4th day of infection. The transcriptome profiles of ‘Empire’ and ‘Gala’ at three time points (24, 48, and 72 h) after fire blight infection identified higher number of DEGs in ‘Gala’ as compared to ‘Empire’ at initial infection stages (24 and 48 h). However, at later stage of infection (72 h), a more number of genes were found to be differentially expressed in ‘Empire’ than ‘Gala’, along with reduction in disease progression. The gene ontology and co-expression network analysis of DEGs revealed enrichment of genes related to plant defense and response to stress. The DEGs were found to be localized in the previously identified QTL regions for fire blight resistance on LG7 and LG12 and can serve as functional candidates for future research. Balan et al. (2018) performed meta-analysis of transcriptomic data related to fungal, viral, and bacterial attacks in Malus x domestica. The analysis revealed the differential expression of genes involved in sugar alcohol metabolism and ethylene response genes in case of bacterial infection and brassinosteroids signaling genes during fungal infection (Balan et al. 2018). The gibberellins- and jasmonates-related genes were found to be strongly repressed by the fungal and viral infections. Moreover, the involvement of WRKY transcription factors was highlighted through protein–protein interaction network analysis in response to different pathogens. This study has provided with the set of specific common molecular features linked with biotic stress responses. In another study, the RNA-seq anlaysis of E. amylovora treated and mock-treated apple flowers allowed identification of 1080 differentially expressed transcripts after two days of treatment (Kamber et al. 2016). Among the DEGs, the enrichment analysis highlighted the involvement of putative disease resistance, stress-related, pathogen-related and phytohormone-related genes in response to E. amylovora infection. In order to identify the set of genes specifically expressed in response to E. amylovora, transcriptome profiling in response to Pantoea vagans and Pseudomonas syringae was also performed. The authors found that members of peroxidase gene superfamily were downregulated, specifically in the E. amylovora challenged plants, and suggested downregulation of peroxidases as a suspetible response to the E. amylovorain apple. Venturia inaequalis, is a devastating fungal pathogen causing apple scab disease, which is detrimental for quality and yield of apples. In an investigation, the treatment of ‘Florina’ (scab resistant cultivar) and ‘Vista Bella’ (scab susceptible cultivar) cell cultures with V. inaequalis elicitor and further GC-MS-based metabolomic analysis led to the identification of total 21 metabolites that are significantly altered in ‘Florina’ cell culture. Among them, three new specialized metabolites, namely aucuparin, noraucuparin, and eriobofuran were present only in the fungal elicitor

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challenged ‘Florina’ cell culture (Sarkate et al. 2018). These three metabolites were found to inhibit germination of V. inaequalis conidia. Moreover, the expression levels of genes involved in secondary metabolite biosynthesis were correlated with the metabolite level. The developed method showed its sensitivity and specificity to analyze metabolites from apple cell cultures. The study provides insights into the metabolic basis of scab resistance in apple at effector level and can be used as a basis in further studies to decipher specific metabolites associated with the scab resistance in apples. Zhu et al. (2019), performed the transcriptome analysis of two contrasting apple rootstocks after their inoculation with the soil-borne necrotrophic pathogen Pythium ultimum. The apple rootstock genotypes used in the study were the susceptible Bud 9 (B.9) and the resistant Geneva® 935 (G.935). The trancriptome profile of B.9 roots after 48 h of infection revealed downregulated expression of genes involved in cellular processes, which suggested the severe suppression of cellular processes in B.9. While in case of resistant rootstock G.935, transcriptome profile showed early and strong defense response through differential expression of genes related to kinase receptors, MAPK signaling, Jasmonic acid (JA) biosynthesis, transcription factors, and transporters. This early and stronger defense activation led to effective inhibition of necrosis progression in G.935 roots. The valuable resources, in the form of DEGs, provided in this study can be validated for their potential association with resistance traits. In the previous section, we discussed the involvement of autophagy related genes in providing drought resistance in apple tree. The autophagy also plays a key role in pathogen resistance. In a study, Sun et al. (2018b) showed that overexpression of MdATG18a (autophagy related) enhances resistance to Diplocarpon mali infection, which causes marssonina apple blotch. In MdATG18a overexpressing transgenic apple lines, the accumulation of salicyclic acid and high expression of chitinase, β-1,3-glucanase in the leaves of transgenic apple were suggested to be crucial for the enhanced disease resistance. In addition, the transgenic apple also exhibited high expression of other MdATG genes, which may be involved in the increased autophagy activities in transgenic plants to impart disease resistance. This finding provides important information on autophagy-mediated disease resistance that could be used for devising strategies to minimize the impact of pathogens on apple production. One of the significant constrains to the plantation of new apple orchard on sites where apple was previously cultivated is the apple replant disease (ARD). The Pythium ultimum, an oomycete, is a significant component of the ARD pathogen complex in root system (Tewoldemedhin et al. 2011). In the foliar system, the defense responses mediated through ethylene (ET) and JA are well studied (Broekaert et al. 2006; Robert-Seilaniantz et al. 2011). In order to investigate the presence of similar defense response during interaction between perennial plant root system and soilborne pathogens, Shin et al. (2014) identified and analyzed the expression of genes related to ET/JA biosynthesis, MdERF (ethylene response factor) anda pathogenesisrelated (PR) gene and a target of ERF, CHITINASEB. The qRT-PCR analysis in response to P. ultimum and exogenous ET and/or JA treatment showed that the

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majority of genes were significantly upregulated. These findings suggest that ET/JAmediated defense pathways are also functional in the root system of perennial tree species in response to soil-borne pathogens (Shin et al. 2014).

2.4.4 Fruit Quality Sugiura et al. (2013) studied the long-term effect of climate change on the taste and textural attributes of apple fruit over the 30–40 years. This study concluded that the taste and textural attributes of apple have changed due to decrease in acid concentration and fruit firmness and in some cases increase in soluble solids concentration. These changes in the quality attributes of apple are induced by climate change through earlier blooming and higher temperatures during maturation period. To study the effect of position of fruits within the tree canopy, Feng et al. (2014), harvested fruits from the interior and exterior of tree canopy. Three cultivars of apple, namely ‘McIntosh,’ ‘Gala,’ and ‘Mutsu’ were investigated to gain insight into the influence of canopy position on fruit quality. The levels of primary and secondary metabolites were studied in the peel and flesh of fruit. The fruits from outer region of canopy were found to have comparable higher fresh weight, higher soluble solid contents, soluble sugars and sugar alcohols, but had lower starch content as compared to the fruits from inner region of canopy. However, the inner canopy fruits were found to be rich in amino acids. The phenolic compounds were found to be higher in outer canopy fruits. The inner- and outer-canopy fruits received variable light exposure and therefore showed different levels of primary and secondary metabolite accumulation. This study demonstrated the importance of light exposure during apple fruit development. The climate conditions considerably affect the apple fruit quality. Therefore, Chagné et al. (2014) studied the fruit quality traits, including fruit maturation timing, firmness, and dry matter content, in large population of ‘Royal Gala’ × ‘Braeburn’ at three climatically diverse apple-growing regions in New Zealand. This led to the detection of 190 QTLs, which control these traits regardless of the environmental conditions. Of 190 QTLs, the environmentally stable loci are present in two regions on LG10 and LG16. These loci along with the closely associated markers can be used for marker-assisted breeding purposes to generate cultivars with firmer fruits. However, the loci for fruit dry matter content and late maturing phenotypes were found to be closely associated and remain unintentionally selected during marker-assisted selection (MAS). Moreover, the identification of 113 new QTLs with a smaller effect suggests the underlying complex control of traits which determines the fruit quality (Chagné et al. 2014). The enhanced storability of apple fruit is one of the important targets for apple breeding or improvement program. During cold storage, the scald development is the major concern that degrades the quality of the fruit. McClure et al. (2016) used genotyping-by-sequencing (GBS) to generate the QTL map and identify soft scaldassociated markers. Two QTLs associated with the soft scald were found in one of

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the biparental populations. This study is one of the examples how application of nextgeneration technologies can contribute to the mapping of QTLs controlling complex traits such those related to the apple storability. Takos et al. (2006) isolated a R2R3 MYB transcription factor, MdMYB1 from apple (cv Cripps’ Pink) which was found to regulate the transcription of apple genes involved in anthocyanin biosynthesis. The expression of MdMYB1 correlated with anthocyanin level in red skin regions of apple fruit and its expression was comparatively higher in non-red skin cultivars. The polymorphisms were identified in the promoter region of MdMYB1 gene. Based on the polymorphism, a cleaved amplified polymorphic sequence (CAPS) marker was designed that allowed differentiation of skin color in progeny of cross between red skin apple (a sibling of Cripps’ Pink) and non-red skin cultivar ‘Golden Delicious’. These finding suggests the regulation of anthocyanin pathways through MdMYB1 in apple skin. Similarly, Lin-Wang et al. (2011), investigated the effect of heat on anthocyanin accumulation in fruit peel. They found that anthocyanin biosynthesis is temperature-sensitive and primarily caused by altered transcript levels of the anthocyanin regulatory complex. The high temperature resulted in repression of R2R3 MYB (MYB10) expression, while low temperature induced the expression of MYB10, which regulates anthocyanin biosynthesis pathway. Indeed, the candidate genes that can repress anthocyanin biosynthesis were not found to be responsible for reduction in anthocyanin content. In crab apple, the similar involvement of McMYB genes, McMYB12a and McMYB12b, was observed in regulating pro-anthocyanidin and anthocyanin biosynthesis (Tian et al. 2017). The transient overexpression of these genes in apple fruit showed enhanced accumulation of proanthocyanidin and anthocyanin. In particular, the McMYB12a preferentially binds to the promoter of anthocyanin biosynthesis genes, while the McMYB12b preferentially binds to the promoter of genes involved in pro-anthocyanidin synthesis. This study provides the mechanism of coordinated regulation of pro-anthocyanidin and anthocyanins through an autoregulatory balance involving McMYB12a and McMYB12b expression. On the cytosolic surface of endoplasmic reticulum, biosynthesis of anthocyanin takes place, from where anthocyaninis transported to the vacuole for storage with the help of glutathione S-transferase (Sun et al. 2012). The expression of apple glutathione S-transferase, MdGSTF6, was found to be positively correlated with the anthocyanin content during apple fruit development (Jiang et al. 2019). Authors observed that the knockdown of MdGSTF6 affects anthocyanin accumulation in apple fruit. Moreover, the MdMYB1 was found to interact with the promoter of MdGSTF6 to regulate its expression. These findings suggest that MdMYB1 regulates biosynthesis and transport of anthocyanin in apple. The exposure to UV light is one of the environmental signals which affect the flavonoid pathway and regulate the level of pro-anthocyanin, anthocyanin, and flavonols (Zoratti et al. 2014). In an investigation by Henry-Kirk et al. (2018), the apple trees were grown under different UV exposures using spectral filters to solar light. The fruit analysis showed that under low UV exposure, the fruit size, anthocyanin and flavonol levels were decreased in addition to delayed ripening. The UV exposure was found to regulate the genes involved in the biosynthesis of anthocyanin

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and flavonols. This study has implication on determining the quality of apple fruit produced around the world with varying degree of UV exposer. In an investigation, Soppelsa et al. (2018) used several biostimulants to study their effects on apple tree performance. Authors found that the use of macro-seaweed extract, among other biostimulants, positively regulates tree growth potential, yield performances, and apples quality. The macro-seaweed extract, B-group vitamins and alfalfa protein hydrolysate were able to significantly enhance the intensity and extension of red coloration of apple fruit through higher accumulation of anthocyanin content. These findings highlight the potential influence of these biostimulants on the synthesis of secondary metabolites in apple. In higher plants, the jasmonate ZIM-domain (JAZ) interacts with bHLH transcription factors to negatively regulate the biosynthesis of anthocyanins (Qi et al. 2011). To identify the role of MdJAZ2 in negative regulation of anthocyanin biosynthesis, Ke-Qin et al. (2017) identified the interacting partners of MdJAZ2 using Y2H system. The screening and further interaction analysis revealed the interaction of MdJAZ2 with hypersensitive induced reaction (HIR) proteins, MdHIR2 and MdHIR4. The functional analysis of MdHIR4 indicated its role in negative regulation of genes involved in anthocyanin biosynthesis and fruit coloration. In another study, Wang et al. (2018b) investigated the regulatory effect of auxin signaling on anthocyanin metabolism through overexpression ofMdIAA121 and MdARF13 in transgenic redfleshed apple calli. The transgenic calli showed weak inhibitory effect of MdARF13 on anthocyanin biosynthesis through interaction of MdIAA121 (Aux/IAA repressor) and MdARF13. The exogenous application of auxin induced degradation of MdIAA121 and consequent release of MdARF13, a negative regulator of anthocyanin metabolic pathway. Further, molecular analysis revealed that MdARF13 directly binds to the promoter of MdDFR, which is an anthocyanin pathway structural gene. This study provides insights into the auxin-mediated control of anthocyanin accumulation in apple fruit. The analysis of MdMYB1 upstream genomic region from anther-derived homozygous apple lines showed the presence of long terminal repeat (LTR) retrotransposon in red-skinned apple phenotype (Zhang et al. 2019). Further structural genomics analysis of anther-derived homozygous apple lines detected 18,047 deletions, 12,101 insertions and 14 large inversions as compared to reference genome of ‘Golden Delicious’ apple. These findings reveal the molecular basis of red coloration in apple fruit and also highlight the importance of high quality genome assembly for the functional genomics and marker-assisted breeding program. Sugar content is a fundamental component of fruit quality, and derives the consumer preference by improving the fruit sensory quality. Zhen et al. (2018) investigated the role of SWEET sugar transporter genes for accumulation of sugar in apple fruit. Of the total 25 MdSWEET genes, the molecular markers for highly expressed 9 genes were developed and used for genotyping of 188 apple cultivars. The association mapping of polymorphic MdSWEET genes with soluble sugar content in mature fruit showed that three genes, MdSWEET2e, MdSWEET9b, and MdSWEET15a were significantly associated with fruit sugar content. Moreover, the MdSWEET9b

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and MdSWEET15a accounted for the most of the phenotypic variation in sugar content. Hence, MdSWEET9b and MdSWEET15a are likely candidates for regulating fruit sugar accumulation in apple. This study provides the valuable molecular tools for genetic improvement of fruit quality in apple breeding programs. In another investigation, Ma et al. (2017) performed the expression profiling of several sugar transporter genes and observed induced expression of MdTMT1, MdTMT4, MdSUT1, MdSUT2, MdSUT4 MdAMY1, MdAMY3, MdBAM1, and MdBAM3, in response to ABA treatment on apple fruit. In addition, the transient expression of MdAREB2 resulted in higher expression of MdSUT2 (involved in sugar transport) and higher accumulation of soluble sugar and sucrose content in fruit tissue. This finding suggests the positive regulation of sucrose and soluble sugar accumulation by MdAREB3 through regulation of sugar transporter genes. The plant sugars are synthesized in the leaves and get transported to the developing fruits, for accumulation. The aldose-6-phosphate reductase is the key enzyme of sorbitol synthesis. In the study of Li et al. (2018), antisence suppression of aldose-6phosphate reductase, significantly modified the composition of accumulated sugars in apple fruit tissue, through higher accumulation of glucose. Moreover, the activity of sugar metabolizing enzymes, including sorbitol dehydrogenase, fructokinase sucrose phosphate synthase, sucrose synthase, and hexokinase was found to be altered in developing transgenic apple fruits. This study provides insights into the regulation of sugar metabolism and accumulation in sorbitol-synthesizing plant species.

2.4.5 Root Stock Characterization The high-density planting and high yield index through reduced scion vigor are some of the advantages of dwarfing rootstock, and therefore, its use in commercial apple production is widely accepted. In addition, dwarf rootstocks have other advantages over standard rootstocks, which include enhanced drought tolerance (Fernandez et al. 1997). To identify the QTL responsible for the dwarf phenotype, a population of progeny derived from cross between ‘M9’ × ‘Robusta5’ (non-dwarfing) was used by Pilcher et al. (2008). A major QTL, Dw1was identified on LG5 that has a significant influence on dwarfing of the scion. Also, another QTL, Dw2 with similar effect on scion dwarfing was identified on LG11 by Fazio et al. (2014). The location of the major QTL Dw1 on LG5 and its significant influence on dwarfing of the scion were further confirmed by Foster et al. (2015). In this study, the second dwarfing-related QTL Dw2 located on LG11, however, showed moderate influence on dwarfing. The genotypic analysis of 41 rootstock accessions revealed that the most of them carried the Dw1 and a half of them had in addition Dw2. The authors demonstrated that combination of Dw1 and Dw2 strongly influenced rootstock-induced dwarfing whereas the QTL Dw2 alone could not induce dwarfing. From the association study of Dw1 and Dw2 linked markers with dwarfing phenotype of dwarfing and semi-dwarfing rootstock accessions, authors suggested the common genetic origin of most of these apple dwarfing rootstocks (Foster et al. 2015).

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In another study, the RNA sequencing of dwarf rootstocks, M27 and M9, and vigorous rootstock M793 was performed to identify the vascular-enriched genes, responsible for rootstock-induced growth restriction (Foster et al. 2017). In dwarfing rootstocks, the genes involved in the metabolism of amino acid and lipids were found to exhibit altered expression, where anabolic genes were downregulated, while catabolic genes upregulated. Interestingly, the starch biosynthesis-related genes were found to be upregulated and showed good correlation with starch content in scion grafted on M9 rootstock. However, the concentration of glucose and fructose was found to be much lower. These results suggested the sugar depletion and reduced cellular activity in the dwarfing rootstocks, despite the availability of large starch reserves. The RNA-seq data analysis allowed authors to conclude that the downregulation of auxin transporters, MdAUX1 and MdLAX2, in dwarfing rootstocks may reduce the polar auxin transport (Foster et al. 2017). The plant CBF transcription factors have been reported to be involved in chilling and drought resistance (Haake et al. 2002; Welling and Palva 2008). The ectopic expression of peach PpCBF1 gene conferred phenotype with early onset of dormancy and delayed spring budburst in transgenic M.26 line (T166), as compared to nontransgenic M.26 rootstock (Wisniewski et al. 2011, 2015). In a subsequent study, Artlip et al. (2016) found that overexpression of the peach PpCBF1 gene in an apple rootstock could alter growth and flowering in the scion but did not influence cold hardness, the onset of dormancy, and budbreak. In particular, no transcript expression of PpCBF1 was found in the phloem or cambium of the scion ‘Royal Gala’ (RG) grafted on transgenic M.26 rootstock (T166). This was in contrast to the situation in the own-rooted T166 trees, which suggested no graft transmission of transgene mRNA. However, the shoot extension and overall height were reduced in RG/T166 trees, as compared to RG/M.26 (nontransgenic rootstock) trees. Interestingly, the number of flowering trees, number of flower bearing shoots, and flower cluster per shoot were significantly less in RG/T166 as compared to RG/M.26. Furthermore, the elevated level of the DELLA gene expression, a negative regulator of GA signaling, in RG/T166 and T166 trees allowed authors to conclude that the DELLA gene might be responsible for reduced growth. In this study, authors provided insights into the influence of rootstock on the juvenility and flower abundance in the grafted scion. In the above sections, we discussed the findings on various aspects of apple phenology, bud dormancy, winter hardiness, fruit quality, and biotic and abiotic stress responses. These studies have added valuable resources toward current understanding of underlying physiological and molecular mechanisms involved in basic biology and climate-resilient response, and however, more efforts are required to understand the crosstalk among different responses. In this direction, the development of genuslevel crop model will facilitate the use of modern biotechnology tools in molecular genetics studies. In addition, the development of methodologies such as rapid screening for induced mutations, rapid breeding, and targeted genome engineering (CRISPER/Cas system) accompanied by reduced juvenile phase will be worthwhile for the development of climate-smart apple cultivars for sustainable crop production.

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2.5 Molecular Mapping of Genes/QTLs The initiation of crop domestication, back in ancient times, involved the unconscious selection of plants, followed by subsequent breeding for intentional selection based on useful traits. However, the use of conventional breeding methods is timeconsuming process, especially in perennial species with a long juvenile phase such as apple. For several decades, the apple breeding involves as a valued supplement the molecular breeding that applies the DNA markers and QTLs closely linked to phenotypic traits to assist in a selection procedure for a desirable trait. Table 2.1 summarizes the achievements in the identification of the QTLs related to the important apple diseases, quality traits as well as growth-related traits. In different breeding programs, the application of marker-assisted breeding enables the selection of favorable genotypes at a very early seedling stage. Exploitation of structural genomics using the whole genome information generated through the next-generation sequencing techniques has substantially contributed to the identification of these QTLs. In apple, numerous studies that have been performed on QTLs related to vegetative bud release or bud dormancy release are discussed below. Based on the co-localization of candidate genes, responsible for dormancy release in apple, four regions have been identified for chilling perception and dormancy release. One of the candidate genes, MDP0000126259, an FLC-like gene, was found to be closely associated with a major QTL at LG9. The FLC-like gene has been shown to be differentially expressed during dormancy release in different varieties with different chilling requirements, which support its role in repression of bud growth during ecodormancy (Porto et al. 2016). Close to the same genomic region, MDP0000143531, a homolog to AGL24, a MADS-box transcription factor, was also predicted (Wells et al. 2015). Since AGL24 is a floral promoter, regulated by an FLC-independent vernalization pathway in arabidopsis (Michaels and Amasino 1999), this suggests complementary roles of these homologous genes in chilling perception and endodormancy regulation in apple. The apple genome has four genes annotated as DAM genes, three of them are colocalized with two QTLs on LG8 (MdDAMc and MdDAMd) and LG15 (MdDAMa). These QTLs have been found to play a role in the establishment of endodormancy and its maintenance in apple (Mimida et al. 2015). The construction of genetic linkage map is of prime importance in breeding programs. To determine the major QTLs involved in initial bud break in apple after the winter season, the genetic linkage maps in two F1 crosses were constructed for low chilling cultivar ‘Anna’ (common male partner) and higher chilling requiring cultivars ‘Golden and Sharpe’s’ ‘Golden Delicious’ and ‘Sharpe’s Early’ as female parents using simple sequence repeat (SSR) markers and series of expressed sequence tag(EST)-derived microsatellite (EST-SSR) markers (van Dyk et al. 2010). In this study, authors identified 18 putative QTLs on LG11 showing close association with initial vegetative bud break and suggested that these QTLs might be responsible for regulation of initial vegetative bud break. Another QTL on LG9 was also found to be associated with time of initial vegetative bud break (van Dyk et al. 2009, 2010). It

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Table 2.1 Summary of QTLs for disease resistance, fruit quality-related traits, and morphological traits identified in Malus Trait

Mapping type

Marker type

Author

Apple scab

Biparental

AFLP, RAPD, SCAR, SSRs

Liebhard et al. (2003a, b)

Fire blight

Biparental

Isozyme, AFLPs, RFLPs, RAPD, SSRs

Calenge et al. (2005)

Powdery mildew

Biparental

Isozyme, AFLPs, SSRs

Calenge and Durel (2006)

Fire blight

Biparental

AFLP, RAPD, SSRs

Khan et al. (2006)

Fire blight

Biparental

SSRs

Peil et al. (2007)

Fire blight

Biparental

SSRs

Durel et al. (2009)

Leaf miner

Biparental

AFLP, RAPD, SSRs

Stoeckli et al. (2009)

Fire blight

Biparental

SSRs, SNPs

Gardiner et al. (2012)

Fire blight

Biparental, association mapping

SNPs

Khan et al. (2013)

Bitter pit

Biparental

SSRs

Buti et al. (2015)

Soft scald

Biparental

GBS markers

McClure et al. (2016)

Fire blight

Biparental

SSRs, SNPlex, HRM, GBS markers

Desnoues et al. (2018)

Soft scald, soggy breakdown

Related full-sib apple families

SNPs

Howard et al. (2018)

Growth, blooming, fruit harvest

Biparental

AFLP, RAPD, SCAR, SSRs

Liebhard et al. (2003a, b)

Architectural traits

Biparental

SSRs

Segura et al. (2007)

Vegetative budbreak

Biparental

SSRs

van Dyk et al. (2010)

Vegetative budbreak, floral budbreak

Biparental

SSRs, SNPs

Celton et al. (2011)

Flowering date

Biparental

SSRs

Kunihisa et al. (2014)

Budbreak and flowering time

Related full-sib apple families

GBS markers

Allard et al. (2016)

Rootstock-induced dwarfing

Biparental

SNPs

Harrison et al. (2016)

Rooting from apple hardwood cutting

Biparental

SSRs

Moriya et al. (2015)

Water-use efficiency

Biparental

SNPs

Wang et al. (2018)

Budbreak date

Biparental

SNPs

Miotto et al. (2019)

Volatile organic compounds

Biparental

AFLPs, SSRs

Dunemann et al. (2009)

Fruit firmness

Biparental

AFLPs, SSRs

Costa et al. (2010) (continued)

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Table 2.1 (continued) Trait

Mapping type

Marker type

Author

Polyphenolic composition

Biparental

SNPs

Chagné et al. (2012)

Phenolic compounds

Biparental

AFLPs, DArT, RAPDs, SSRs, NBS-LRR markers

Khan et al. (2012)

Fruit texture physiology

Biparental

SSRs, SNPs

Longhi et al. (2012)

Fruit acidity

Biparental

SSRs

Zhang et al. (2012)

Fruit maturity, red leaf/red flesh

Biparental

SSRs

Morimoto et al. (2013)

Volatile organic compounds

Biparental

AFLPs, RAPDs, SSRs, SCAR

Costa et al. (2013)

Fruit firmness

Pedigreed families

SSRs

Bink et al. (2014)

Fruit maturation, dry matter, firmness

Biparental

SNPs

Chagné et al. (2014)

Ethylene production

Biparental

SSRs, SNPs, PCR markers

Costa et al. (2014)

Russet, red color of fruit skin, preharvest fruit drop, sugar content, juciness, firmness, weight, soluble solids, acidity, juice browning

Biparental

SSRs

Kunihisa et al. (2014)

Fruit juice browning, fruit acidity

Biparental

SSRs

Morimoto and Banno (2014)

Fruit circumference, diameter, length, weight, total soluble solids, total titratable acids

Biparental

SSRs

Potts et al. (2014)

Biosynthesis of esters

Biparental

SSRs

Souleyre et al. (2014)

Polyphenol content

Biparental

SSRs, SNPs

Verdu et al. (2014)

Fruit size, shape

Biparental

SSRs

Chang et al. (2014)

Skin color

Biparental

GBS markers

Gardner et al. (2014)

Apple skin russeting

Biparental

SNPs

Falginella et al. (2015)

Individual sugars and soluble solids content

Advanced selections, elite cultivars

SNPs

Guan et al. (2015)

Fruit quality

Biparental

SNPs

Sun et al. (2015)

Fruit texture

Pedigreed families, collection of apple accessions (GWAS)

SNPs

Di Guardo et al. (2017)

Fruit acidity

Pedigreed families

SNPs

Verma et al. (2019)

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was validated in different genetic backgrounds and at different developmental stages. It was found in more than one population and was consistent in tree and seedling stages. Genotyping of low chilling trait for time of initial vegetative bud break will help in the apple breeding to develop low chilling requirement cultivars using MAS. In two F1 apple progenies, Celton et al. (2011) performed QTL analysis for the identification of specific genomic regions responsible for the date of green tip emergence, vegetative and flowering bud break, in response to climatic conditions. The results indicated strong correlation between QTLs and requirement of heat and chilling. In addition, the effect of QTL variation on the phenological variables suggests the strong capability for adapting the diverse climatic conditions. These QTLs were found to be present on LG9 and LG1 for chilling requirement and LG8 for heat requirement. Further, the putative candidate genes underlying one major QTL on LG9 were identified and they were found to be key genes involved in cell cycle control, phytohormone signaling, and metabolism. This study revealed the interaction between climatic conditions and QTLs involved in bud break and subsequent active growth. The DEGs with similar GO annotation (include cell cycle genes) were also identified by Kumar et al. (2017) during transcriptomic analysis of dormancy release in apple under differential chilling conditions. In another study, five full-sib apple families were considered in pedigree-based analysis for the determination of QTLs linked to chilling requirement and heat requirement (Allard et al. 2016). In addition, the association of QTLs and candidate genes, involved in dormancy regulation, was also studied. From the QTL mapping, based on the integrated genetic map containing 6849 single nucleotide polymorphosms (SNPs), four major QTLs on LG7, LG10, LG12, and LG9, in addition to other small effect QTLs on other linkage groups, were found to be responsible for trait variance. The co-localization of traits suggested common genetic determinant for chilling and heat requirement. Further analysis of candidate genes revealed close association of AGL24 and FT with QTLs present on LG9 and LG12, respectively, while DAMs were closely associated with QTLs present on LG8 and LG15 (Allard et al. 2016). Trainin et al. (2016) identified a unique haplotype for early bud-break trait from the genotyping of 73 apple accessions including old, modern, and hybrid accessions. This haplotype was found in approximately 190 Kb region on LG9 of early bud-break genotypes accessions, while it was found to be absent in late bud-break accessions. Numerous candidate genes for bud-break time have been predicted within this haplotype region namely, two copies of FLC-like genes (MDP0000167381 and MDP0000126259); a MADS-box gene family gene (MDP0000296123); a WRKY transcription factor gene (MDP0000154734) and genes involved in chromatin modifications (MDP0000317368 and MDP0000237694). All these studies suggested that the apple bud break and chilling requirement are complex polygenic traits and involve the complex interplay of several genetic determinants. The genome-wide association study (GWAS) is a powerful approach to strongly associate the genetic marker with the loci controlling the traits of interest in a population of unrelated individuals, which can be further utilized for MAS in perennial fruit crops. The GWAS needs generation of high-density genetic map and phenotyping of

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a trait of interest. Recently, the genotyping of 1200 apple accessions using Affimytrix Axiom_Apple480K array (487,000 SNPs) was performed, where significant markertrait association was obtained for flowering period and harvesting date on LG9 and LG3, respectively (Muranty et al. 2017). In another GWAS of 1168 different apple genotypes, using the same Affimytrix Axiom_Apple480K array, two SNPs for flowering period (LG9) and six for ripening period (four on LG3, one on LG10, and one on LG16) have been found that accounted for a total 8.9 and 17.2% of the phenotypic variation, respectively (Urrestarazu et al. 2017). Although above-mentioned studies do not relate to apple bud dormancy release, these studies demonstrate the potential of association genetics in unraveling the genetic control of important traits in apple. The GWAS and application of the genome-scanning tools like, high-density array Axiom®Apple 480 K SNP could be considered as a step forward to the identification of reliable SNP markers and QTLs associated with traits of interest. Thus, the use of GWAS approach could be one of the promising approaches to identify the loci/marker strongly associated with the regulation of dormancy release in apple as well as other traits, important for the development of climate-resilient apple cultivars.

2.6 Digital Breeding for Development of Climate-Smart Apple Varieties Digital technologies have spread worldwide and affected practically all sectors of the economy, including agriculture. Challenges in agriculture such as population growth, global warming, and emerging of plant diseases force to implement new approaches in order to ensure a sustainable future for the next generations. In recent years, the adoption of digital technologies in agriculture has been increasing that reflects $4.6 billion the industry’s investment in technology, according to the 2015 (Newman 2018). To stay relevant and successful in a challenging environment, plant breeding companies have to adopt modern tools that promote more efficient breeding of new cultivars (smart cultivars) with fewer inputs. Large amount of genomic, genetic, and breeding data is already digitalized, structured, and available for the breeders. This section deals with a review of digital tools/instruments suitable for apple plant breeding and Malus science research (Fig. 2.1). The features of such digital tools are defined and advantages are highlighted. In addition, we summarize how digital transformation can be effective in the phenotypic selection of apple emphasizing the tools based on digital image analysis.

2.6.1 Apple Databases There are a number of successful attempts to structure the wealth of genomic, phenotypic, and genetic data from Malus. Currently, several public databases specific

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Fig. 2.1 Resources for ‘information-driven’ breeding process

for Rosaceae and related to the breeding process are known. One of the earliest attempts to create a database containing information both about genetic and phenotypic data on the level of individual genotypes is the AppleBreed DataBase (Antofie et al. 2007). It was created within the European project HIDRAS (Gianfranceschi and Soglio 2004). Organized as a relational database, the AppleBreed DataBase included large-scale data about genotype of the tree (plant material, accession number), molecular markers (allele, linkage group, markers, maps), phenotypic data (instrumental analysis and sensorial analysis of the fruit quality, and external analysis of fruits), and data about pedigree, growth sites, and organization. This database was specially adapted for the plant breeders and geneticists. As a combination of molecular marker data and phenotypic data, the AppleBreed DataBase was a helpful platform in the identification of the candidate genes validated by geneticists. Information about SSR markers incorporated to the AppleBreed DataBase is still accessible via HIDRAS SSR database (SSRdb) on the website http://www.hidras.unimi.it/. The SSRdb provides detailed information about 300 SSR markers, their position on the apple linkage maps, sequence information and conditions about PCR amplification. The structure, functions, and the experience developed for the AppleBreed DataBase were integrated with the apple database FruitBreedomics.

2.6.1.1

FruitBreedomics

As the next step toward improvement of the Apple database, there is the database developed within the European project FruitBreedomics (Laurens et al. 2012). Recent advances in high-throughput technology enabled to come to the next level of database

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complexity and information capacity. The integrated approach and the main principles used in the FruitBreedomics are described in the recent paper of Laurens et al. (2018). The large European project FruitBreedomics (2011–2015) joining 28 European research institutes and private companies has been initiated to fill gap between genomic and breeding and to improve the efficiency and speed of current breeding programs (Laurens et al. 2012). The Web site of the FruitBreedomics project (http://www.fruitbreedomics.com/) includes valuable information for the breeders. It informs about pre-breeding seedling material that can be used in future breeding programs. In addition, information about resistance to diseases (scab, powdery mildew, fire blight, and rosy apple aphid), fruit quality traits, fruit size and shape, shelf life, fruit texture, and flavor is publicly available. Within the project FruitBreedomics, a series of SNP markers for apple have been developed and approved to be suitable in marker-assisted breeding (MAB). During the project, FruitBreedomics developed protocols and tools for the standardized phenotyping of apple breeding material among different research institutes. This information is available on the Web site. Learning material covering many topics of interest for the plant breeders, like tools and methods for fruit breeding programs, genotyping, usage of pedigree-based analysis of breeding material, is freely accessible. FruitBreedomics offers also MAB services on a commercial basis for the breeding companies. The idea behind is to provide breeders with support taking the advantages of the latest molecular technologies. The services include, for example, genotyping of plant material, its analysis, interpretation of genotyping results, and planning for using MAB in the breeding programs. FruitBreedomics website informs about consulting service, regular meetings, and collaborations. Such new way of communication allows breeders to be more aware of new trends and possibilities offered by new technologies. Within the FruitBreedomics project, the Axiom Apple Genotyping Array (Axiom® Apple480K Chip) was produced, based on the Affymetrix technology (Bianco et al. 2016). The 96-format-array includes information about 480,000 SNP markers. The markers were identified based on the whole genome sequence data from 63 Malus domestica cultivars and two double haploid accessions. This array is the largest ever produced for a fruit plant and among the largest for higher plants (Laurens et al. 2018). The Axiom® Apple480K (Applied Biosystems Inc., Foster City, CA, USA) has been commercially released both for public and proprietary use. This prominent tool was already successfully utilized for apple GWAS on 1324 apple accessions from six European germplasm collections for the construction of four high-density parental maps (Laurens et al. 2018). One of the high-priority goals of the FruitBreedomics project was the development of bioinformatics infrastructures to provide access to phenotypic data and genome sequence variation information. As a part of the FruitBreedomics, a new managed Web-based relational data bank Fruitbreedomics@PTP was specifically developed for apple and peach data (http:// bioinformatics.tecnoparco.org/fruitbreedomics/). It incorporates information about phenotypic traits, molecular markers (SNPs), known genes, and their position on the map. Sequence data for 16 apple cultivars and breeding families based on the Illumina platform are available upon request.

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The two tools, SSR explorer and SNPChip explorer, are completely integrated in the Web site. The first tool contains genotypic SSR data for apple accessions, based on the 16 SSR markers. These markers are suitable for the diversity analysis in apple breeding material. The SNPChip explorer contains information about SNP markers (Apple 20 K SNP) for the particular cultivar/genotype. Information could also be easily downloaded as excel- or csv file. In addition, the database provides information about fully informative multi-allelic haploblock markers (Apple haploblock dataset 2015-02-06) that can be used in QTL studies. The information about plant breeding material is accessible through the tool Breeder’s interface. The navigation bar assists the user in selecting of the apple genotype of interest as well as desirable traits. The automated SNP scoring software ASSIsT, developed within the project FruitBreedomics, allows users to analyze different types of experimental populations, namely Cross Pollinated (CP—F1), Back Cross (BC), F2 and collections of unrelated individuals (Germplasm). The program is compatible with the output files from GenomeStudio (Illumina Inc., San Diego, CA, USA) and the resulting files can be used for downstream analyses with software FlexQTL, HapMap, GAPIT, JoinMap, PLINK, Structure, and Tassel. The program ASSIsT together with supporting material is available for downloading. Laurens et al. (2018) described other useful tools for genetic studies in Malus developed or significantly improved within the project FruitBreedomics. Initially, the software FlexQTL explored SSR markers for QTL analysis with an emphasis on the known pedigree of a population (Bink et al. 2014). With appearance of the SNP markers, the FlexQTL was adapted to the new marker type. In addition, the Graphical User Interface Visual FlexQTL has been developed for persons without programming skills. Both tools are publicly available for research applications at https://www.flexqtl.nl. Another tool performing QTL analysis more efficiently, by using less memory and computation time is the PediHaplotyper (Voorrips et al. 2016). The idea of the software is to reduce the complexity of high-density SNP data by combining closely linked SNPs into single genetic locus called haploblock. Such strategy allows working with missing and incorrect SNP data and significantly reduces computation time. The program is publicly available and has already been approved in a series of studies (reviewed in Laurens et al. 2018).

2.6.1.2

NPGS Apple Collection

The USDA-ARS (https://www.ars.usda.gov/) National Plant Germplasm System (NPGS) includes information about 5004 unique accessions in the field and 1603 seed accessions representing M. x domestica, 33 Malus species, and 15 hybrid species (Volk et al. 2015). The NPGS supports apple production by conserving apple germplasm, its evaluation, documentation as well as distribution. The USDA has the most diverse collection of Malus species ex situ in the world (Volk et al. 2015). The NPGS apple collection of 2500 accessions is being characterized phenotypically using a 28-trait descriptor set (see supporting file of Volk et al. 2015). The important traits for breeding process like those, related to disease resistance, tree vigor, harvest

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season, flowering time, fruit traits have been scored. Such information is generated to help breeders in apple variety improvement. All passport and phenotypic data are represented in the Germplasm Resources Information Network (GRIN) database (https://www.ars-grin.gov/) and its updated version GRIN-Global. GRIN-Global is a system designed for gene banks to store and share information about plant genetic resources globally. Apple breeders and researches around the world can use this platform and request information about apple germplasm. Thus, it is a platform allowing collaborations with international programs. The apple collection is being characterized genotypically. Information about gene, sequence, marker, diversity, and trait locus data for Malus is transferred to the Genome Database for Rosaceae (www. rosaceae.org). The genetic information together with phenotypic information about the large collection is valuable for breeding as well as for fundamental scientific purposes.

2.6.1.3

Genome Database for Rosaceae (GDR)

This online database was designed to integrate breeding data from the Washington State University Apple Breeding Program (WABP) with the existing genomic and genetic data of the Rosaceae family. Since its inception in 2003, the GDR database has grown to incorporate large-scale phenotypic and genotypic data along with publication data, pedigree data from the WABP, and search tools for the Rosaceae genomics research community. The updated GDR database integrates also public datasets of the USDA-NIFA SCRI-funded RosBREED project (Iezzoni et al. 2010). The database description and its analytical capabilities are given in detail by Evans et al. (2013), Jung and Main (2014) and Jung et al. (2008, 2014, 2018). The database contains secured private breeding data and public data. Being a central repository for the genomic, genetic and breeding data of Rosaceae crops, it contains a wealth of information about apple. The overview page (https:// www.rosaceae.org/organism/Malus/all-species) guides users through the database. The structured information about the database features, like Genes, Genetic maps, Genomes, Markers, Publications, Sequences, SNP array, Trait Loci, Transcripts as well as link to the analytical tools, such as BLAST, JBrowse, and Sequence Retrieval (will be discussed below) are incorporated. Thus, users have different options to query and analyze data in GDR. For example, the GDR hosts genotypic data in the form of SNPs (3707) and SSR markers (4003) for Malus. Information about 108 apple maps is accessible for the apple breeders. Each map is supported by the information about parents of cross, population type, number of QTL, description of the QTLrelated trait, expert contact information and related publication. The database allows searching for a QTL associated with an important agricultural trait such as plant vigor, growth, and development, stress, sterility or fertility, plant morphology, and yield. The GDR includes annotated whole genome sequences of Malus. In addition, transcript data are accessible through the website. The genome-related files are in GFF and FASTA formats and can be downloaded. The GDR incorporates analytical tools facilitating the bioinformatics work. For example, it offers such tools as

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BLAST + (BLASTN, BLASTX, TBBLASTN, and BLASTP). The target databases include information about Malus x domestica or other Rosaceae species. All applications are available on the Web site. Breeder’s toolbox is being developed to query and visualize breeding data within the database. There are several searching options, namely search by different names and traits; search by parentage. It is possible to query based on the variety/marker or marker/allele. In addition, search based on haplotype information is possible. The GDR supplies breeders with the information about descriptors used to classify/phenotype varieties. This is information about the traits and corresponding units used to evaluate appearance, flavor, productivity, and texture. Thus, Search by trait allows breeders to query for an individual seedling with desirable trait set, like specific appearance, flavor, texture, and production trait. A new interface of the Breeder’s toolbox, called Breeders Information Management System (BIMS) is under development. It will allow breeders with a GDR account to create a new breeding program within the GDR frame. In addition, some new online tools related to BIMS will be available. These are, for example, Input file for pedimap (a tool to prepare an input file based on the pedigree of the genotype for the subsequent usage in the Pedimap program); Marker Converter (a tool to facilitate design of new markers related to the QTL), and QTL Validator. Several other tools might be extremely useful for breeders. For instance, the Technology Portfolio v.1.0 is an online tool allowing breeders to find suitable service providers for their genetic screening needs. The Technology Portfolio v.1.0 offers such information as prices, volume, contact details, and protocols. Another tool is Seedling Select v1 that serves to facilitate cost estimation for marker-assisted seedling selection (MASS) in Rosaceae species. The advantage of this tool is its accurate prediction of the cost-efficient scheme. The GDR database is characterized by the direct access to such analytical tools as Primer3web, JBrowse, GDRCyc, Sequence retrieval and Synteny viewer, MapViewer. Let us briefly describe each of the tools. Primer3, a well-known tool for the primer development, is embedded in the GDR Web site. MapViewer is suitable for the visualization of the genetic maps. Clicking a particular linkage group opens a more detailed view. JBrowse is a fast, scalable genome browser allowing scrolling, zooming, and selecting a region of interest. Being embedded in the Web site, it allows visualization of the Malus x domestica genome (GDDH13 v1.1). The predicted genes from the whole apple genome sequences were used to construct AppleCyc (metabolic pathway database). Currently, information about 63,541 protein-encoding genes is available in Malus. AppleCyc incorporates information about pathways (520), GO Terms (5067), and transporters (660). AppleCyc Pathways Database allows to query genes, proteins, RNA, pathways, enzymes and metabolites, and growth media. Other useful options of the AppleCyc are comparative analysis, omics data analysis, display of full metabolic maps or individual pathways. The whole Malus genome assembly and its annotation can be retrieved with the online tool Sequence Retrieval. This tool enables downloading of information about nucleotide, protein sequences, chromosomes, scaffolds, genes, mRNAs, transcript coding sequences, protein, and unigene contigs. The GDR database represents information about many Rosaceae species. In case of interest to integrate data from Malus

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and other publicly available Rosaceae species, the tool Synteny Viewer is the proper one. It allows viewing synteny among Rosaceae genomes, i.e., it allows visualization of conserved syntenic regions among different species. The GDR provides with information about synteny among different Rosaceae species and reference. The GDR is a database with the mission to join Rosaceae community. It announces upcoming conferences, provides with conference reports, and abstracts from previous conferences. The GDR database has infrastructure build with the software Chado and Drupal. From one side, this makes it possible to combine complex large-scale phenotypic and genotypic data. From another side, such infrastructure is compatible with the infrastructure of other databases like TreeGene sand FruitBreedomics. It enables cross-database communication and connection based on the Tripal Gateway. In such case, if follow community-standards it becomes possible to share, store, and visualize complex biological data between different projects.

2.6.2 Other Digital Resources Available for the Apple Breeders Plant breeding explores a range of molecular methods and generates genomic and genetic information that enables breeders to design efficient breeding strategies and facilitate development of the smart cultivars. With the advances in sequencing and genotyping technology, the bioinformatics resources become available that accelerate the breeding process (Hu et al. 2018). Some of them are suitable in the Malus research and breeding (Table 2.2). Within the last ten years, a number of exciting reviews describing bioinformatics resources for the Rosaceae research community were published. For example, the first book on Rosaceae genomics (Folta and Gardiner 2009) summarizes progress in genomic research as well as gives examples of the application of genomics technologies for crop development. Jung and Main (2014) reviewed the database resources for the Rosaceae family. In this paper, analytical tools for proteomics and metabolomics, as well as comparative genomics resources, and community resources are described in detail. Yamamoto and Terakami (2016) have compiled in their review the recent advances in genomics studies and their practical applications for Rosaceae, including Malus. A number of Web-based plant databases including information about Malus or suitable for the comparative analysis are available and will be described here shortly. JGI’s Phytozome (Goodstein et al. 2012) is the Web-based plant comparative genomics portal that integrates 93 assembled and annotated genomes, from 82 Viridiplantae species. Integration of different genomes allows comparative genomics studies. Phytozome includes information about Malus genome (Malus domestica v1.0). All genes of the database have been annotated with KOG, KEGG, ENZYME, Pathway, and the Interpro family of protein analysis tools. Query for name of the

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Table 2.2 URLs of some important tools and resources for Malus research studies and breeding Databases/digital tools

URL

Description

FruitBreedomics

http://www.fruitbreedomics. com/

Apple breeding material, publications, SNP markers, MAB Services, learning material

Fruitbreedomics @ PTP

http://bioinformatics. tecnoparco.org/ fruitbreedomics/

Phenotypic traits, molecular markers, maps, sequencing data, bioinformatics tools

Genome Database for Rosaceae (GDR)

www.rosaceae.org

Phenotypic and genotypic data, publications, maps, pedigree data, analytical tools, services

GRIN (USDA-ARS National Plant Germplasm System)

https://npgsweb.ars-grin.gov/ gringlobal/search.aspx

Apple accessions, phenotypic traits

CRIN-Global database

https://www.grin-global.org/

Open-source software for gene banks’ data, germplasm request

A European Genebank Integrated System (AEGIS)

http://www.ecpgr.cgiar.org/ aegis/

Conservation and access to unique germplasm in Europe

The European Search Catalogue for Plant Genetic Resources (EURISCO)

https://eurisco.ipkgatersleben.de/apex/f?p= 103:1:0

Provides information about Malus accessions preserved ex situ

GreenPhylDB

http://www.greenphyl.org/ cgi-bin/index.cgi

Functional genomics

CoGe (The Place to Compare Genomes)

https://genomevolution.org/ coge/

Comparative genomics

Phytozome

https://phytozome.jgi.doe. gov/pz/portal.html

Comparative genomics portal, analytical tools

PLAZA 3.0

https://bioinformatics.psb. ugent.be/plaza/versions/ plaza_v3_dicots/

Plant comparative genomics

NCBI Taxonomy browser

https://www.ncbi.nlm.nih. gov/Taxonomy/Browser/ wwwtax.cgi

Genomic information about Malus and BLAST tools

NCBI’s Gene Expression Omnibus (GEO)

https://www.ncbi.nlm.nih. gov/geo/

Large-scale molecular data

Kyoto Encyclopedia of Genes and Genomes (KEGG)

https://www.genome.jp/keggbin/show_organism? org=mdm

Genomic, chemical and systemic functional information related to Malus

Uniprot

https://www.uniprot.org/

Protein sequence and functional information

Plant CAZyme

http://cys.bios.niu.edu/ plantcazyme/download.php

Carbohydrate-active enzymes in Malus (continued)

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Table 2.2 (continued) Databases/digital tools

URL

Description

PlantTFDB (Plant Transcription Factor Database)

http://planttfdb.cbi.pku. edu.cn/

Transcription factor families in Malus

CATH database

http://www.cathdb.info/ search?q=Malus

Protein function, structure, evolution

Europe PMC

https://europepmc.org/

Sciences articles, books, and patents related to Malus

gene or BLAST returns information about the transcript and protein sequences for a particular gene. GreenPhylDB (Rouard et al. 2011) serves for the comparative genomic analysis of full genomes (protein sequences), including Malus. The database allows phylogenomics analysis and gene family classification. The database integrates a number of tools allowing BLAST searching and identification of homologous sequences. Orthologs’ searching based on the protein sequences from other plant species is available. CoGe (The Place to Compare Genomes) is an open-access Web platform (Lyons and Freeling 2008) that is designed to study publicly available and private genomic data of species across all domain of life, including Malus. A number of analysis and visualization tools are integrated into the Web tool to assist in comparative genomics studies. PLAZA 3.0 (Van Bel et al. 2012) is a hub for plant comparative genomics that allows users to perform evolutionary analyses and data mining, exploring the power of integrated sequence data. Currently, structural and functional annotation for Malus is available. The web platform enables visualization of the physical clustering of functionally related genes in Malus and Malus enriched gene families. Plant Transcription Factor Database (PlantTFDB) hosts 165 species, including Malus, and provides comprehensive functional and evolutionary information for the transcription factor families (Jin et al. 2017). Gene annotation of Malus x domestica is based on the GDR. Information about 3119 transcription factors representing 58 families is available for research studies. Within the PIplantTFDB, there is a new portal PlantReqMap including analytical tools for regulation data, binding site prediction, and functional enrichment analysis. In addition, Malus sequences can be analyzed with the tool transcription factor prediction, as well as with BLAST. All information of the Web platform is available for downloading. Plant CAZyme database (Ekstrom et al. 2014) collects information related to the carbohydrate-active enzymes. Carbohydrate-active enzyme annotation for 2220 genes of Malus is available. Such information can be useful to the plant cell wall and plant carbohydrate research communities. Kyoto Encyclopedia of Genes and Genomes (KEGG) is an online platform providing with the information about the functions of the biological system based on the molecular information like genome sequencing data (Kanehisa et al. 2016; Kanehisa,

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2017). Currently, information about Malus domestica is represented in the KEGG. The Web-based tool provides with the links to the genomic, chemical, and systemic functional information related to the Malus allowing integration and interpretation of large-scale molecular data. The biological and biochemical processes in apple are described with 134 KEGG PATHWAY (134 links) and KEGG MODULE (132 links), respectively. The KEGG collects information about Malus genes based on the publicly available resources, mostly NCBI RefSeq and GenBank. Daily updated database with free access, KEGG GENES includes information about 54,401 genes. Information about a collection of genes from large-scale metagenomics studies is represented in the KEGG MGENES (521 hits). KEGG summarizes information about Malus biological system based on 9 databases (56,225 hits). Information on the KEGG is daily updated and is freely available for downloading. National Center for Biotechnology Information (NCBI) browser provides access to genomic information of Malus and BLAST tools. NCBI Taxonomy browser summarizes comprehensive information about Malus based on different databases, such as GEO, UniGene, and PubMed Central. This represents Malus-related proteins (64,195), genes (62,858), nucleotide sequences (81,048), nucleotide EST (326,941), primer-probe sets (29,280), and crystal structure of some Malus genes (10). In addition, 1108 experiments based on the next-generation sequencing technology in Malus are available for downloading via a link to the SRA experiments. Gene Expression Omnibus (GEO) is a public repository that hosts and freely distributes large-scale data, grouped into expression profiling by array, non-coding RNA profiling by array, and expression profiling by high-throughput sequencing. Presently, GEO includes information about 774 experiments related to the Malus x domestica. The increase in the number of publications makes it time-consuming and tedious to query for particular information. The Europe PMC is a repository, providing access to worldwide life sciences articles, books, and patents. Currently, the Europe PMC repository contains 3360 free full text, 2649 open access, 394 reviews, 142 patents, and 9 preprints by searching for Malus. The information could also be queried based on the ORCID record of the articles or Grant ID.

2.6.3 Data Repositories for the Gene Banks Vegetatively propagated crops, such as apple need their preservation for further generations, breeding, and research purposes. Maintaining apple gene banks is not trivial under the conditions when more or less cutting down publically funded apple breeding programs occurs (Nybom and Garkava-Gustavsson 2009). For the apple breeders as well as researchers, it is very important to know about availability of gene banks with genetically distinct accessions. Volk et al. (2015) reviewed Malus resources, considering the information from the FAO database (see Online Resource 10 in Volk et al. 2015). According to the FAO, Malus collections are present in 58 countries. The largest collection of Malus x domestica trees is in Switzerland (6617), followed

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by Italy (4403), Russian Federation (3586), Austria (2731), and France (2648). The largest collection of the wild-type Malus is maintained in the USA (5051) followed by Italy (710) and Japan (678). Gene banks accumulate large-scale breeding data along with genomic and genetic data and develop data repositories. Integrating data from different data resources with existing Malus databases based on the common database schema like Chado can strongly improve online data repositories. One of the examples of the gene banks’ data integration is the GRIN-Global project (Postman et al. 2010). An open-source software developed by the GRINGlobal meets demands of the gene banks. It includes such tools as Curator Tool, Search Tool, Admin Tool, Pubic Web site with Shopping Cart and GG Updater. The users are able to store the information, manage it, and exchange it between the GRIN-Global database and order germplasm from the gene bank. The flexible format of GRIN-Global allows displaying information in any language (English default). Moreover, customers can choose the views, forms, and wizards. The GRIN-Global database platform is tested or already in use in more than 20 institutes around the world (https://www.grin-global.org/). Another example of the platform integrating gene banks is A European Genebank Integrated System (AEGIS). Founded in 2009, AEGIS hosts 34 member countries and 58 associate Member Institutions, according to the 2016. Membership in this system is open for all European countries that are members of the European Cooperative Programme for Plant Genetic Resources (ECPGR). Among the selected accessions that are maintained by the participating institutions, there are 32,878 accessions of Malus, according to the European Search Catalogue for Plant Genetic Resources (EURISCO). The genetic material is available through a standard material transfer agreement. All users of the AEGIS, including apple breeders, researchers, and farmers, have access under common terms to the genetically unique and important Malus accessions for Europe. Information about the breeding material diversity, based on the genotypic and phenotypic characterization as well as proper documentation of the data, its storage and accessibility are those prerequisites that make possible ‘information-driven’ breeding process. In turn, this will allow faster development of smart apple cultivars with improved quality traits as well as resistance to diseases.

2.6.4 Digital Image-Based Phenotyping Phenotypic selection is one of the key steps in the breeding process. It includes manual measurements and visual ratings of the trait values in breeding material, such as disease symptoms, flowering time, color, quality grading of fruits (size, surface spottiness, color, shape), and yield estimation. The standard approaches to score phenotypic traits are highly labor-intensive and inefficient in terms of both economy and time and hamper selection of superior genotypes. Any technology improving efficiency of the phenotyping and reducing expenses associated with breeding process is in high demand. This chapter presents image-based phenotyping methodologies

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developed to estimate fruit loading and disease symptoms. The emphasis will be on research papers describing applications of digital tools in apple.

2.6.4.1

Computer Vision System in Automatic Apple Harvesting

To decrease harvesting cost, a number of approaches based on the machine vision were developed within the three decades. The vision system serves as a basis in the automatic fruit harvesting system, enabling recognition of the location and distance to the fruit. This is an alternative to the mechanized solutions since it recognizes size and maturity of fruit and can make decision about fruit picking without human having to be involved. Ideally, such automated system should provide the same or better quality of fruit harvesting. The first research describing application of the computer vision system for detecting apples was published in 1977 (Parrish and Goksel 1977). A single grayscale camera with a red optical filter has been applied in order to differentiate red apples among green-colored leaves. In the 1980s (D’Esnon 1985; D’Esnon et al. 1987; Sistler 1987, Harrell et al. 1989), it was recognized that machine vision is an important asset for a robot/mechanical device since it can successfully recognize fruits on the tree. In 2000, Jiménez et al. (2000) reviewed computer vision approaches and discussed the difficulties that seriously affected the feasibility of future harvesting robots. Challenges in localization of spherical fruits (orange) for robotic harvesting were discussed in (Plebe and Grasso 2001). Since that time a number of approaches to detect fruits were introduced. Bulanon et al. (2001) offered a machine vision algorithm to detect the location of apples (‘Fuji’ cultivar) on the tree. A color CCD camera was explored to get images under natural lighting conditions. The proposed algorithm allowed classification of fruits, leaves, and branches. Proper location of the fruit with a success rate above 80% could be demonstrated (Bulanon et al. 2001). Stajnko et al. (2004) introduced a new method for estimating the number of apple fruits and measuring their diameter in orchard. They applied a thermal camera that was able to capture images of apple trees in the late afternoon and calculate the fruit load. Based on a fruit detection algorithm, high correlation between the manually measured fruit number and the estimated number was achieved (R2 from 0.83 to 0.88). As for the fruit’s diameter, the values of R2 were up to 0.70. Zhao et al. (2005) used stereo vision to accurately locate apples in an orchard based on the color and texture. This approach allowed recognition of 18 apples from 20 visible apples. Another approach was introduced by Tabb et al. (2006). A proposed method allows evaluation of the real-time video images for locating apples. The developed algorithm Global Mixture of Gaussians (GMOG) enabled correct identification up to 96% of red and yellow apples in laboratory environment. This research was the first to demonstrate recognition of apple fruit using video sequences (Tabb et al. 2006). A next attempt to develop a fast and reliable fruit detection system was performed by Wang et al. (2013). The researches represented a pipeline for accurate yield estimation based on a set of carefully designed rules (color, distinctive specular reflection

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pattern, and the average size of apples). A computer vision-based system used a twocamera stereo rig and performed image acquisition at nighttime in order to reduce the variance of natural illumination. To automate data collection, an autonomous orchard vehicle was applied. The introduced by Wang et al. (2013) algorithm allowed detection of red and green apples from acquired sequential images and fruit counting. This study represents a step forward to the development of a fast and reliable fruit detection system, although it cannot be a universal solution for yield estimation due to the necessity in redesigning the rules in case of the new application. Cohen et al. (2011) have developed an algorithm for estimating the number of apples on trees by combining fruit color, shape, and texture analyses. Such colorimaging system developed for the standard color CCD camera allowed automatic estimation of the fruit loading on a tree and correct identification more than 85% of the apples in the images. Gongal et al. (2015) presented a comprehensive review about the studies related to the area of fruit detection and localization in tree canopies. Various techniques based on sensors (black and white cameras, color cameras, spectral cameras, and thermal cameras) together with different image processing techniques were described. Gongal et al. (2015) summarized the most important issues for the successful application of machine vision system for fruit harvesting. In case of accurate detection, the major challenges were fruit occlusions, clustering, and different lighting conditions. For robotic fruit harvesting, such issues as high cost, complexity of overall system, and manipulation speed of robotic arms were crucial. Recent achievements in machine learning and artificial neural networks algorithms allowed further improvement of object detection and classification. Machine learning is a computational way of detecting patterns in a given image dataset based on the ‘experience’ through an extensive training process of a neural network. Sufficiently trained neural network models can recognize features of the unseen images very quickly and precisely. Puttemans et al. (2016) used the advantages of the machine learning approach and improved fruit detection. The method has been validated on apples in orchards. It offers significant opportunities for enabling autonomy of robotic harvesting system since the recognition system is able to identify objects in unseen before images. Sa et al. (2016) applied deep convolutional neural networks (faster R-CNN model) for fruit yield estimation and automated harvesting. Both color and near-infrared images can be used to train a system. The introduced approach represents real-time fruit detection that has improved accuracy and a rapid training. Mehta and Burks (2016) proposed to solve a problem of fruit localization using multiple cameras. They introduced a localization algorithm based on pseudo-stereo vision approach that allows improve fruit localization efficiency. Availability of the accurate and fast fruit detection system adapted to different types or size of fruits as well as the different environmental conditions is an important step forward in the application of the fully automated robotic harvesting systems. A number of semi- and automatic fruit harvesters were described in Li et al. (2011). Baeten et al. (2008) introduced an autonomous fruit picking machine (AFPM) for robotic apple harvesting. A newly designed flexible gripper allowed a firm grip without damaging the fruit. The integrated inside of the gripper camera ensured proper

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localization of apple and avoided the necessity for thorough calibration. The functionality of the AFPM was proved in the orchards. Extensive experiments demonstrated that about 80% of the apples (with diameter range from 6 cm to 11 cm) could be detected and harvested. Bulanon and Kataoka (2010) introduced a fruit harvesting robot that is able to recognize apples in the foliage in a real-time mode and detach a fruit from the tree without damaging. The researchers applied machine vision for fruit recognition in combination with a laser ranging sensor for the distance estimation. The introduced method was validated in the field conditions. The results showed ‘more than 90% success rate in detaching the fruit with average time use of 7.1 s’ (Bulanon and Kataoka 2010). Comparatively low-cost platforms are available now in the agri-food sector. Emmi et al. (2014) have reviewed the resources for the configuring autonomous agricultural systems, their reliability as well as cost related to the development of the hardware and software. The future of robotic agriculture is discussed in the comprehensive UK-RAS White Paper of Duckett et al. (2018). The state-of-the-art robotics and autonomous systems are reviewed and potential future directions for research and development are highlighted. In the era of Industrial Internet of Things (IIoT), also known as Industry 4.0, implementation of robotics system will become a common practice in agriculture too. It will be possible due to the availability of the fast, precise and robust detection systems from one side, and a new range of flexible agricultural equipment from another side. Higher degree of automation in apple breeding will allow replacing hard human labor in orchards related to the inspection of fruit quality and maturity of fruits. Moreover, implementation of the robotic technology will enable selective harvesting (i.e., harvesting of desired quality fruits), as well as fruit harvesting in general, reducing cost. Finally, the data collected by the robotic systems including information about fruit loading, location, as well as measurements of the environment, can be automatically interpreted to give better overview about the breeding material.

2.6.4.2

Disease Recognition

Computer vision and object recognition, in particular, have enormous potential in recognition of plant diseases. Typically, phenotypic analysis of visually observable disease symptoms in Malus is labor-intensive often leads to human errors during scoring (for example, lesion number/size and area infected) and time-consuming, taking into account the extremely large areas of orchards. To solve these problems, a number of digital tools based on fruit image processing and automatic recognition were developed within the last decade. Barbedo (2013) reviewed such digital tools enabling quantifying and classifying diseases in different plant species. For example, a freely available software called Scion Image was used to quantify fungal infection (powdery mildew, rust, anthracnose, and scab) in apple leaves (Wijekoon et al. 2008; Goodwin and Hsiang 2010). A conference paper of (Dubey and Jalal 2012) describes a method for the detection and classification of fruit diseases (apple blotch, apple rot, and apple scab) based on the image processing. The three steps of

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the analysis, namely segmentation using K-means clustering, feature extraction, and classification with a multi-class support vector machine are applied. The experimentally validated procedure allows proposing the significance of the method since the automatic classification accuracy for the solution is achieved up to 93%. Recently, plant disease identification based on deep learning approaches was introduced. The largest repository of open access information on over 150 crops and over 1800 diseases is collected within the PlantVillage project (Hughes and Salathe 2015) and is accessible on the same Web site (https://plantvillage.psu.edu/). The information about plant diseases is written by plant pathology experts and is intended for food growers. The Malus collection includes more than 3000 images. Along with images, the information about the disease symptoms and management is represented on the Web site. The collection of images will continue to grow over the coming months and years (Hughes and Salathe 2015). With the appearance of the datasets including thousands of images of healthy and diseased crop plants, it becomes possible to explore the power of the neural networks in plant disease diagnosis. Such a digital approach can be supplemental to the laboratory tests. For example, Mohanty et al. (2016) used 54,306 images of the PlantVillage dataset with convolutional neural network approach to classify 26 diseases in 14 crop species, including Malus, with the accuracy of 99.35%. Even the training of the neural network was very time-consuming (multiple hours on a highperformance GPU cluster computer), the classification itself took only less than a second on a CPU (Mohanty et al. 2016). This offers significant opportunities for the implementation of the recognition system on a smartphone. Discussing the achievements based on the neural network recognition Mohanty et al. (2016) highlights the limitations of the technology. For example, the accuracy of recognition is reduced substantially, if the images used for training differ from the images under analysis. To improve performance, the authors of the paper propose to expand data collection and use variable data. Recently, the data related to the apple black rot caused by the fungus Botryosphaeria obtusa were used to find the optimal deep convolutional neural network model allowing to overcome a disease severity classification problem with few training data (Wang et al. 2017). The image dataset from the PlantVillage related to the apple black rot was further annotated by botanists according to the disease severity and divided into training and test sets. Based on the strategy described in paper, it was possible to identify a neural network model ensuring an overall accuracy of prediction of 90.4% on the test set. Liu et al. (2018) could successfully identified apple leaf diseases (mosaic, rust, brown spot, and alternaria leaf spot) based on deep convolutional neural networks. Using a dataset of 13,689 images of diseased apple leaves, Liu et al. (2018) were able to train a neural network to recognize disease symptoms with the accuracy of 97.62%. Apart from that, the proposed model used less computational resources to build the model. This research is a further step forward to the reliable and robust recognition of apple leaf diseases. Khan et al. (2018) described utilization of the machine vision technology that is able not only to detect the diseases (apple scab, apple rot) at their early stages but also classify them. Combination of correlation coefficient-based segmentation method with deep

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pre-trained models allowed outperforming several existing methods in classification accuracy. The neural network models were trained on the publicly available datasets—PlantVillage and the Internal Feeding Worm (IFW) datasets. Khan et al. (2018) could demonstrate great precision of the method (classification accuracy of 98.60%). The next step in the phenotyping of apple trees can be an application of the unmanned aerial vehicles (drones) that are able to capture numerous plant images in an orchard. A number of algorithms developed for drones are already tested in such applications as occurrence of potato disease, weed detection, water stress of crops, and estimation of leaf area index (reviewed in Yamamoto et al. 2017). In a recent paper, Yamamoto et al. (2017) introduced an approach based on deep convolutional neural networks that has potential to accelerate phenotyping and vigor diagnosis using drones and image analysis technologies.

2.7 Concluding Remark and Future Prospects In the current scenario of accelerated climatic variations, to make apple cultivation sustainable in near future, it is important to develop climate-resilient apple cultivars. Therefore, understanding the fundamental mechanisms of abiotic and biotic stress response is the utmost. The characterization of different genotypes for different environmental cues and their further analysis for associated genomic regions will be helpful in accelerating the process of breeding to generate the climate-resilient apple cultivars. The future of plant breeding and apple breeding in particular depends on its digital transformation. Availability of the genomics and bioinformatics resources in digit format provides new opportunities for improving the selection of the apple varieties required to ensure sustainable and secure fruit production. Integrating largescale data resources across gene banks can support apple plant breeders to assess the vast amount of valuable germplasm and estimate the diversity held in gene banks worldwide. Implementation of open global standards in the recording, storing, and sharing of data will allow cross-database communication and connection. The rapid accumulation of big plant breeding data will force not just for technology, but also for analysis and advisory services since digitalization means smart use of large-scale digitized data. Currently, the FruitBreedomics and the GDR provide such smart services and make digitalized information work for plant breeders. Digital transformation takes the advantages of digitalization to create completely new business concepts. In this review, we described image-based phenotyping methodologies developed to estimate fruit loading and apple disease symptoms. Higher degree of automation in apple breeding will lead to reduction of cost related to monitoring of trees and fruit harvesting. Taking the advantages of the digital technologies, plant breeders and farmers will be able to manage apple production in new and efficient way.

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Chapter 3

Genomic Designing for New Climate-Resilient Apricot Varieties in a Warming Context Jose A. Campoy, Jean M. Audergon, D. Ruiz and Pedro Martínez-Gómez

Abstract Flowering time is one of the most important traits in fruit trees including apricot (Prunus armeniaca L.), both in natural populations and orchards. Adaptation to climatic conditions largely depends on an adequate flowering time. Flowering time also correlates with chilling requirement and harvesting time. The former is relevant to avoid problems related to insufficient chilling or spring frost, and the latter highly determines the market price of temperate fruits, including apricot. However, the mechanism controlling flowering time in these species could be conserved as suggested by the colocalization of QTLs and the identification of common genes. In this chapter, we provide a review of the state of the art for the genomic designing of climate-resilient apricot varieties. Keywords Prunus armeniaca · Breeding · Phenology · Flowering · Molecular markers · Genomics · Transcriptomics · Epigenetics

3.1 Introduction The cultivated apricot (Prunus armeniaca L.) is a domesticated tree species that has coevolved with human civilization. This fruit tree species is mainly used for its edible fruits (Bailey and Hough 1975; Mehlenbacher et al. 1991) (Fig. 3.1). Apricot is one of the most important and desirable of the temperate tree fruits, with

J. A. Campoy Department of Plant Developmental Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany e-mail: [email protected] J. M. Audergon Unité de Génétique et Amélioration des Fruits et Légumes, INRA, Avignon, France e-mail: [email protected] D. Ruiz · P. Martínez-Gómez (B) Departamento de Mejora Vegetal Grupo de Mejora Genética de Frutales CEBAS-CSIC, Espinardo, Murcia, Spain e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_3

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Fig. 3.1 Cultivated apricot tree (a) with detail of flowers (b), leaf (c) and fruit morphology (d)

total world production reaching around 4.25 million tons (Mt) and 536,072 cultivated ha (FAO 2017; http://www.fao.org/faostat/en/#data). Main producers include Turkey (0.985 Mt), Uzbekistan (0.532 Mt), Italy (0.266 Mt), Algeria (0.256 Mt), Iran (0.239 Mt), Pakistan (0.178) and Spain (0.162 Mt). Fruit quality is fundamental for the acceptance of apricot cultivars by consumers, especially due to the current situation of high competition in the markets with the presence of numerous new cultivars and other fruits crops. Fruit quality is defined as the conjunction of physical and chemical characteristics which give good appearance and acceptability to the consumable product. Fruit quality is a human concept which includes sensory properties (appearance, texture, taste and aroma), nutritional values, chemical compounds, mechanical properties and functional properties. Consumers appreciate the beauty and aromatic flavor of high-quality apricots, while other parameters, such as size, resistance to manipulation and good conservation aptitude, are especially taken into account by the apricot industry (Ruiz and Egea 2008). On the other hand, climate change is considered to be one of the main environmental problems of the twenty-first century. According to the Intergovernmental Panel on Climate Change (IPCC) fourth assessment report, global average surface temperature increased by 0.74 ± 0.18 °C in the last century, and an increase of a further

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1.1–6.0 °C is projected to occur in this century (Fig. 3.2a). This increase may put at risk the fruit production in the coming decades in various temperate and subtropical regions throughout the world. This risk is associated with the lack of adaptation of the dormancy/growth cycle to future climatic conditions, mainly related to a higher frequency of spring frost and insufficient fulfillment of chilling requirement events (Campoy et al. 2011a). Thus, high- and low-chill requirements are important objectives for the adaptation of this species in different areas guarantying the stability of the production. The objective of this chapter is an assessment of the genomic designing for new climate-resilient apricot varieties by employing genetic, genomic, transcriptomic and epigenetic approaches.

Fig. 3.2 a Apricot bud dormancy progression and flowering; b estimated increase of temperatures during the period 2010–2070 (https://theglobalclimate.net/temperature-increase/). Approximated apricot production areas are highlighted

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3.2 Design of New Climate-Resilient Apricot Varieties Apricot breeding presents clear specificities within plant breeding such as its perennial woody character, its long juvenile period or its multiplication by grafting. These specificities make the breeding process long and tedious. Therefore, it is necessary to have the most rigorous information possible for designing new varieties. The new varieties must be planned 12 years in advance, which is the average time for the application of science to obtain a new variety of fruit (Fig. 3.3). One of the most important inputs for the breeding is the choice of the parents which should be used for crossing. These crosses can be of the complementary type, when we cross two varieties with complementary characteristics to obtain a new variety that integrates the good aptitudes of both varieties, or of the transgressive type, where two varieties are crossed with good characteristics to obtain another even better variety than both (Ruiz et al. 2011). To develop suitable ideotypes, it is necessary to correctly define the relevant traits that will support the success of new varieties in the coming decades. It is thus necessary to examine the endogenous (internal) or exogenous (external) variables influencing the phenomenon in question. Both types of variables must be subjected to scientific evaluation. The most important characteristics in these breeding programs are as follows (Ruiz et al. 2011): Tree: Floral self-compatibility, flowering and ripening time, productivity and resistance to pests and diseases (mainly virus). Fruit quality: organoleptic quality, fruit size, shape.

Fig. 3.3 Scheme of an apricot breeding program based on the realization of crosses and the later selection and application of molecular markers

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Secondly, internal variables also include the current methodologies available for use in the selection of individuals. These methodologies are closely related to the level of knowledge available especially in relation to the development of molecular markers for the selection of individuals and will give us an idea of the efficacy and feasibility of designs of new varieties. Finally, we can include various economic factors within the production framework, i.e., the goals, processes and outcomes. The effectiveness and viability of the improvement program will depend on these methodological developments dedicated to the evaluation. These two terms (effectiveness and viability) are related to the economic context to the relationship between science and economics. The evaluation of this feasibility, effectiveness and feasibility can lead to the abandonment of the new design if it were not feasible for agronomic production. We would have to proceed with the design of a new variety with new crosses and new variables to evaluate within the program. In any case, this evaluation process is continuous (Martínez-Gómez 2017). On the other hand, in apricot breeding, it is necessary to consider a number of external variables including interactions with the environment over a period of several years. There are many characteristics such as flowering time and floral compatibility which must be evaluated for at least three years, from the time trees produce the first flowers. Due to these drawbacks associated with juvenility and multi-year evaluation, the use of molecular marker-assisted selection (MAS) methods is of particular interest in breeding programs. These environmental conditions will determine the feasibility of the objectives and the new variety in a given environment. The adaptation of new varieties to specific climatic conditions will be highly conditioned by their chilling requirements for dormancy release (Campoy et al. 2011a) and by their adaptation to different soil conditions in the case of rootstocks. As we indicated before, apricot flowering time is determined by the fulfillment of chilling and heat requirements (Campoy et al. 2011a). These requirements of cold and heat guarantee that in each zone the flowering will take place at a favorable time for pollination, which is a key process for outcrossing cultivars. The modifications in temperature regimes due to climate change have already shown uncoupling of flowering time in traditionally pollinators tandems in the case of self-incompatible cultivars. These differences in the flowering date of inter-pollinators can drastically reduce the pollination rate and productivity (Campoy et al. 2011a). Satisfying consumer demands is also an important external variable. New cultivars must have the appropriate characteristics to meet such demands. In addition, the availability of different techniques for analysis and selection is another important external variable. The social acceptance of these new varieties will depend on the criteria of environmental sustainability. On the other hand, we can include various factors of an economic nature, provided that they are considered within the framework of production: the phase of objectives, processes and results. This affects external economic variables, which are those that affect their market value. The result of a new genetic variety presents a durability in terms of year of production and involves a market price conditioning the objectives of the technological innovation. The degree of knowledge of the variables determines the quality of the prediction in the design of new varieties of apricot in addition to its effectiveness, feasibility

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and suitability. New designs must meet the appropriate characteristics to meet those requirements. External variables are also the availability of the different analysis and selection techniques available at each moment, since they have varied throughout history.

3.3 Prioritizing Climate-Smart (CS) Agronomic Traits 3.3.1 Flowering Time Apricot is a temperate fruit species grown in climates with well-differentiated seasons. Dormancy and freezing tolerance are the main mechanisms developed against low winter temperatures and frost damage and, although they could be independent, freezing tolerance cannot be developed adequately without growth cessation, which marks the onset of dormancy. Upon the accumulation of a chill temperature, the tree fulfills its chilling requirements needed for flowering. However, in the context of climate change, many producing areas may not accumulate enough chilling to assure the production of temperate fruits (Fig. 3.2b; Luedeling 2012). The knowledge of the chilling requirement of a cultivar has significant practical and economic impacts on the control, maintenance and production of woody plants (Fennell 1999) and is necessary for cultivating apricot cultivars in the most suitable areas (Campoy et al. 2011a). In this way, if a cultivar is established in an area where its chilling requirements are not satisfied adequately, the vegetative and productive behavior of the cultivar will be affected negatively. On the contrary, in the case of cultivars with low-chilling requirements (i.e., early flowering cultivars) growing in cold-winter areas, blooming happens too early because chilling requirements are quickly satisfied, and low temperatures can induce a significant loss of yield by frost (Campoy et al. 2011a). On the other hand, in mild areas, the early ripening of apricot has special economic importance, and this could be enhanced with the use of chemical breaking agents which bring forward the date of dormancy breaking (Ruiz et al. 2005). Chilling requirements for the breaking of dormancy of apricot cultivars have to be fully satisfied for obtaining the desired vegetative growth and the best fruit-bearing capacity. Depth of rest, and consequently the chilling requirement, is apparently a specific parameter of each cultivar. Important differences between cultivars have been reported (Ruiz et al. 2007; Campoy et al. 2012). The first chilling requirement data available for the apricot species, which is one of the least-studied temperate fruit with regard to chilling requirements, show that the chilling requirements in most apricot cultivars range from 800 to 1200 chill units, with extreme values of 500 and above 1400. More recently, Ruiz et al. (2007) described in the apricot species a range of chilling requirements (chill units, CU) between 596 CU (‘Currot’) and 1266 CU (‘Orange Red’), though most of the cultivars showed chilling requirements between 800 and 1200 CU. In addition, the heat requirements for flowering, which represent the thermal integral required for flowering after breaking

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of dormancy, ranged between 4078 and 5879 GDH (growing degree hour). The apricot cultivars assayed by these authors showed important differences concerning the flowering date, and the results indicate in apricot a high positive correlation between chilling requirements and flowering date, as well as a negative correlation between chilling requirements for breaking of dormancy and heat requirements for flowering (Ruiz et al. 2007). However, flowering date seems to be determined basically by chill requirements. Olukolu et al. (2009) in a molecular study described several quantitative trait loci (QTLs) linked to the chilling requirement trait (see ‘Genetic linkage analysis’ section below) which corroborated the quantitative control of this trait. In temperate fruit trees such as apricot, the dormancy/growth cycle is synchronized to environmental and climatic conditions. Temperate tree species have developed a strategy to adapt to alternating well-differentiated seasons based on bud dormancy, which helps to protect the bud from winter cold, ensuring that flowering occurs under optimal conditions. In temperate fruit tree species, exposure to cold in winter (fulfillment of chilling requirements for overcoming endodormancy) followed by a warm period (fulfillment of heat requirements) in spring is essential for flowering. Consequently, climate warming during winter and spring is responsible for several disruptions already manifested in temperate fruit trees (Luedeling 2012). Advances in bud break and blooming dates are reported for many species, increasing the risk of frost damage. Frost damage, in the USA, causes more economic losses than any other weather-related phenomenon. Moreover, climate warming will induce an incomplete dormancy release in many temperate and subtropical regions (Luedeling 2012) that could lead to bud burst delay, low bud burst rate and lack of uniformity of leafing and bloom. This would lead to higher flower bud drop and morphological abnormalities that could have important impacts on fruit production. This would be especially important in species with scarce low-chill commercial cultivars, such as apricot. A diverse germplasm has been described in apricot related to flowering time in accordance with the wide distribution around the world. In addition, due to the importance of the flowering time in breeding programs, many works have studied the transmission of the trait using data recorded during the selection of seedlings (Ruiz et al. 2007).

3.3.2 Drought Resistance Climate variability and water availability require the fast development of production systems able to cope with risk and uncertainty. In this sense, drought resistances in apricot, linked to the efficient use of water, together with the ability of the root system to access water are important breeding targets. Therefore, rusticity and flexibility of the different components of the production systems (including cultivars and rootstocks) should be improved. Clearly, the plantation and management of new sustainable agro systems in the Mediterranean Basin for apricot production, designed to last long periods, must consider the impact of climatic conditions when selecting new cultivars and rootstocks. In this sense, drought is one of the biggest problems

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for non-irrigated culture across the Mediterranean countries. Studies about drought resistance have been conducted on wild and cultivated apricots but these are scarce in the case of native germplasm from other areas. Under drought conditions, plants may find strategies to escape the stress (accelerating the life cycle) or to avoid it (controlling stomatal conductance, investing in the development of the root system, reducing canopy, etc.), or still to activate strategies of osmotic adjustment to increase tolerance to low tissue water potential (for instance accumulating compatible solutes). The efficiency of photosynthetic carbon gain relative to the rate of water loss can be used as an indicator (Jiménez et al. 2013). In the seasonally dry and variable environment of the Mediterranean region, the ability of species like almond to cope with water scarcity is not only dependent on the variety, but also very dependent on the rootstock on which it is grafted. In apricot, different studies have been performed in the adaptation of water stress. The stomatal closure and epinasty observed in response to water stress represented adaptive mechanisms to drought, allowing the plants to regulate water loss more effectively and prevent leaf heating (Ruiz-Sanchez et al. 2000; Perez-Pastor et al. 2009). In addition, Zaurov et al. (2013) described valuable apricot germplasm from Central Asia that expresses new and valuable characteristics such as drought, cold and salt tolerance.

3.3.3 Fruit Quality and Nutritional Values A major varietal renewal process is currently underway in apricot in order to satisfy consumers and industry demands. Consumers appreciate the beauty and aromatic flavor of high-quality apricots, those with orange ground color, intense red blush, orange and flesh juicy (Fig. 3.1d). Fruit quality is fundamental for the acceptance of apricot cultivars by consumers, especially due to the current situation of high competition in the markets with the presence of numerous new cultivars and other fruits and foods. Consumers appreciate the beauty and aromatic flavor of high-quality apricots, while other parameters, such as size, resistance to handling and good conservation aptitude, are especially taken into account by the apricot industry (Infante et al. 2008, 2011). New apricot cultivars must be characterized by fruit quality attributes which satisfy the consumers. However, an evolution of the apricot fruit quality parameters has been observed in the last decades such as the increase of the fruit size, firmness, attractiveness of the blush color, soluble solids and titratable acidity. Numerous pomological traits influence the fruit quality in apricot including the size, color, firmness, resistance to handling, taste, aroma and texture as the fundamental quality attributes in apricot fruit (Salazar et al. 2013). A high genetic diversity has been observed in the apricot species regarding some quality parameters, fundamentally due to different genetic origins of the cultivated apricot cultivars. However, there is limited information on the global evaluation of fruit quality attributes in apricot and on the relationship among pomological traits

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linked to the fruit quality. Ruiz and Egea (2008) studied fruit quality attributes for two consecutive years in 43 apricot cultivars and selections grown in a Mediterranean climate. Authors described high variability in physical (weight, size, flesh and skin color, percentage of blush, firmness and percentage of dry matter), chemical parameters (total soluble solids content and acidity) and sensory parameters (attractiveness, taste, aroma and texture). Year-by-year variations were observed for some pomological traits such as harvest date, flesh color, fruit weight, firmness and soluble solids content. A high correlation was found among some apricot quality attributes. In addition, principal component analysis (PCA) made it possible to establish similar groups of genotypes depending on their quality characteristics as well as to study relationships among pomological traits in the set of apricot genotypes evaluated. More recently, the implementations of methodologies to phenotype the nutraceutical properties of new CS apricot cultivars and breeding populations are new goals in breeding programs including total phenolics, total carotenoids, β-cryptoxanthin and β-carotene (Kafkaletou et al. 2019). Other interesting nutraceutical properties include important antioxidant properties with positive effects against many diseases such as cancers and diabetes (Moustafa and Cross 2019).

3.4 Marker-Assisted Selection A key point for apricot breeding is to maintain production and consumers’ confidence by assuring acceptable production and quality levels through the introduction of new apricot cultivars on the market. Until now, apricot cultivars have been mainly generated through controlled crosses and open-pollination. Additional advantages encouraging the utilization of the new biotechnologies to apricot breeding include a reduction in sequencing costs, high levels of synteny between genomes and a well-established international network of cooperation among researchers. In this sense, future works regarding marker-assisted selection (MAS) of apricot breeding must include the comparative mapping of different progenies and the implementation of genomic selection. New sequencing technologies are easing the discovery of genomic regions of interest in quality selection in apricot (Fig. 3.3). More recent efforts are being oriented to the genome sequencing of several Prunus species (Verde 2013; Zhang et al. 2012; Shirasawa et al. 2017) to develop efficient molecular markers applicable to assisted selection in breeding programs. Finally, the increasing availability of biotechnological techniques such as genetic transformation further complements in vitro culture opportunities. In this regard, at this time several apricot genotypes genetically modified are being assayed although to date there is no commercial varieties (Burgos L; personal communication). Selection by molecular markers is particularly useful in fruit tree crops such as apricot with a long juvenile period and when the expression of the gene is recessive or the evaluation of the character is otherwise difficult, as with resistance to biotic or abiotic stress. If sufficient mapping information is known, MAS can dramatically shorten the number of generations required to remove the undesired background

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genes of the donor in backcrossing programs. Marker loci linked to major genes can be used for selection, which is sometimes more efficient than direct selection for the target gene (Rubio et al. 2014). In addition, the reduction in sequencing costs may open the avenue of implementing the genomic selection in non-model species such as apricot.

3.4.1 Development and Application of DNA Markers The use of mapping populations segregating for the characters of interest has been the principal approach for the analysis of marker-trait association in apricot (Fig. 3.4). The analysis of cosegregation among markers and characters allows establishing the map position of major genes and QTLs responsible for their expression. In this sense, several genetic linkage maps have been developed using different molecular markers to identify major genes and QTLs associated with flowering time in apricot.

Fig. 3.4 QTL identified linked to flowering date (Fd), chill requirements (CR) and heat requirements (HR) in apricot (green) and other Prunus species including peach (red), almond (blue) and cherry (violet). References are shown in brackets (1: Dirlewanger et al. 1999; 2: Quarta et al. 2000; 3: Yamamoto et al. 2001; 4: Verde et al. 2002; 5: Quilot et al. 2004; 6: Silva et al. 2005; 7: Ogundiwin et al. 2009; 8: Fan et al. 2010; 9: Eduardo et al. 2011; 10: Romeu et al. 2014; 11: Bielenberg et al. 2015; 12: Sánchez-Pérez et al. 2007; 13: Sánchez-Pérez et al. 2012; 14: Olukolu et al. 2009; 15: Campoy et al. 2011b; 16: Salazar et al. 2013; 17: Salazar et al. 2016; 18: Wang et al. 2000; 19 Castède et al. 2014; 20: Castède et al. 2015; 21: Rasouli et al. 2013). A tentative scale of the map is performed in cM using as a framework bin map of reference in Prunus (Howad et al. 2005) indicating each bin numbered in black. Shaded in red are indicated the main regions of the Prunus genome involved in flowering time determination

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Finally, the availability of genomes and the lower cost of new sequencing technologies allow for the development of high number of markers for application of genomic selection strategies which can boost the breeding process in species with high juvenility such as apricot. Substantial progress has been made in identifying QTLs for this trait in Prunus species: almond (Sánchez-Pérez et al. 2012, 2014; Silva et al. 2005), apricot (Campoy et al. 2011b; Salazar et al. 2013, 2016), sour cherry (Wang et al. 2000), sweet cherry (Castède et al. 2014, 2015) and peach (Dirlewanger et al. 2012). Recently, the dissection of flowering date into chilling and heat requirements has been reported in apricot (Olukolu et al. 2009), in peach (Fan et al. 2010) and sweet cherry (Castède et al. 2014, 2015). In addition, Olukolu et al. (2009) performed a cross between two cultivars with high- (‘Perfection’) and low (‘A1740’)-chilling requirements to conduct a molecular study for QTL linkage analysis. A high-density map was constructed in this study using around 500 simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers spanning 537 cM with marker interval of 0.87 cM. In this map, several QTLs linked to chilling requirements were found in linkage groups (LGs) 1, 5 and 7 (Fig. 3.4). On the other hand, genome editing technologies using engineered nucleases have been developed as effective genetic engineering methods to target and digest DNA at specific locations in the genome. This technology has been satisfactorily used for editing the genome of several plants, animals, bacteria and yeast. However, its applications in woody species are scarce and limited to those having whole genome sequences and efficient transformation systems (Fernandez í Marti and Dodd 2018). Belonging to this group of species are Populus (Fan et al. 2015), Citrus sinensis (Jia and Wang 2014), Duncan grapefruit (Jia et al. 2016) and Malus domestica (Nishitani et al. 2016). While the genome sequence of the majority of species is available (Verde 2013; Zhang et al. 2012; Shirasawa et al. 2017; Baek et al. 2018) or will be available soon thanks to the reduction in sequencing technologies, the transformation and regeneration of these species are the most difficult aspect. Actually, only Prunus domestica has been successfully transformed (Petri et al. 2018). The new advances in transformation, with the development of better protocols, will help to reduce the complexity of genetic engineering application in Prunus species. In the future, new genetic transformation approaches will have the potential of success across species of Prunus and will serve a solid basis for the future implementation of genome editing of Prunus species to improve traits more rapidly than ever. In this context, Balogh et al. (2019) identified and characterized the apricot homologs of three dormancyrelated genes, namely the ParCBF1 (C-repeat binding factor), ParDAM5 (dormancyassociated MADS-BOX) and ParDAM6 genes. Genomics resources and knowledge linked to breaking dormancy and flowering in apricot could be used for these new genome editing technologies for the design of new more adapted varieties. However, classical breeding assisted by molecular markers continues being the most extended methodology in this new design. Another interesting method is the registration of spontaneous mutants. Recently, Ruiz et al. (2019) described two early flowering somatic mutants discovered in commercial apricot cultivars.

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Regarding fruit quality traits, the polygenic nature of most of the traits related to fruit quality, with genes distributed throughout the entire genome, makes it very difficult to develop linked markers. As a result, several researchers have focused on the study and characterization of such polygenic traits in different Prunus species and the identification of linked QTLs (Salazar et al. 2014). Many of the QTLs linked to fruit quality traits have been identified in scaffolds 3 and 4 in several Prunus species, including peach and Japanese plum (Salazar et al. 2014).

3.4.2 Development and Application of RNA Markers The transcriptome is represented by coding and non-coding RNAs and is characterized by a high capacity of adjusting to the developmental needs and environmental requirements of living organisms. RNA can easily generate a high diversity thus having a tremendous impact on phenotype expression. RNA analysis techniques can be applied for gene functional characterization and development of new markers for specific traits. These RNA markers have a great potential as validation of DNA markers. In addition, these RNA markers are of great interest in monitoring process such as flowering. In the case of apricot, targeted gene expression studies have been conducted to study bud dormancy, flower development and cold acclimation. Expression of two homeotic genes related to floral organ development, MADS1 and MADS3, was found to gradually increase during ecodormancy break, particularly after the green tip stage (Trainin et al. 2013). More recently, in order to elucidate the molecular bases and to identify candidate genes associated with fruit quality traits and ripening process in apricot, a genetic, genomic and transcriptomic integrated approach is being developed at CEBAS-CSIC breeding program. In the first stage, two segregating progenies were phenotyped and genotyped by using SSRs and single nucleotide polymorphisms (SNPs), allowing the construction of genetic linkage maps and the identification of QTLs associated with the most important quality traits. In the second stage, the transcriptomes of two contrasting genotypes from the same segregating progeny were sequenced at three developmental stages during fruit ripening. These RNA-seq results allowed the identification of key candidate genes involved in the main differential expressed pathways. In addition, monitoring the expression of those key genes in reference, apricot varieties allowed the establishment of a correlation between expression patterns and their specific phenotypes. With this integrated genomic and transcriptomic approach, we have identified the candidate genes responsible for white/orange flesh color, anthocyanin content, softening progression and carbohydrates metabolism. These candidate genes could be very useful to develop efficient MAS strategies to be applied in apricot breeding programmes (García-Gómez et al. 2019).

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3.4.3 Development and Application of Epigenetic Markers On the other hand, epigenetic changes consist of chemical modifications affecting DNA or structural proteins (histones) within the chromatin. Two types of epigenetic modifications have been described: DNA methylation (5 -cytosine methylation, 5mC) in plants and posttranslational histone modifications (PTMs), which include the acetylation and methylation of histones. Epigenetic changes are part of the transcriptional regulation machinery of genomes (Ríos et al. 2014). The combination processes including histone modifications and DNA methylation that regulate genome expression are referred to as the epigenomes. Stress tolerance, dormancy and flowering processes are thus physically and temporarily restricted to a bud, and consequently forced to interact at the regulatory level. Lloret et al. (2018) highlighted the role of epigenetic modifications, phytohormones and common regulatory factors involved in stress tolerance, dormancy and flowering processes in different Prunus species. Recently, the contribution of epigenetic variation to adaptation in flowering time in Arabidopsis thaliana has been shown (Schmid et al. 2018). In this study time, significant differences in flowering time, associated with changes in methylation, were maintained over two generations. Thus, these results encourage to further study the possibility of applying epigenetic selection for flowering time. Nonetheless, this emerging field would need the identification of stable epialleles in apricot through meiosis, for breeding and/or mitosis, for grafting.

3.5 Concluding Remark and Future Prospects According to the ‘Intergovernmental Panel on Climate Change (IPCC) fourth assessment report,’ the average global temperature has increased by 0.74 °C over the last century and is expected to rise between 1.1 and 6.0 °C in this century (IPCC 2007). This climate change is affecting temperate fruit crops, especially in phenology-related traits such as bud dormancy release (Campoy et al. 2011a; Luedeling 2012). Increases in temperature are modifying the growth stages of plants, especially those in temperate zones that are adapted to seasonal changes in solar radiation, temperature and humidity. Originated in Central Asia, apricot was easily spread all over through seeds, becoming an important worldwide grown crop. The genomic designing for new climate-resilient apricot varieties is a priority in this species. In this context, different genomic and transcriptomic markers have been developed and validated for the selection of new apricot climate-resilient varieties. Although, the identification and validation of epigenetic markers for stable phenotypes across cell divisions are still missing. Validated molecular markers together with suitable phenotyping will help breeders to design suitable apricot genotypes with an adequate response to the climatic conditions. Acknowledgements This study has been supported by Grants Nº 19308/PI/14 and Nº 19879/GERM/15 of the Seneca Foundation of the Region of Murcia and the Apricot Breeding project

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of the Spanish Ministry of Economy and Competitiveness. JAC was supported by the Alexander von Humboldt foundation.

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Chapter 4

Breeding Climate-Resilient Bananas Allan Brown, Sebastien C. Carpentier and Rony Swennen

Abstract The impact of global climate change is expected to have the most significant effects on small-scale farmers in subtropical and tropical regions of the world without access to irrigation where banana is grown as an important staple crop. As a consequence, banana breeding needs to re-evaluate priorities and dedicate resources toward producing drought-tolerant cultivars. Specific challenges to breeding bananas for drought stress include the plants’ perennial nature, non-seasonal flowering, physical size, and reproductive barriers to hybridization that complicates selection of superior genotypes in improvement schemes. While considerable efforts to obtain drought-tolerant bananas through transformation strategies have been undertaken in the past decade, they have not to date led to widely accepted cultivars. A number of recommendations for future breeding are presented including the targeted evaluation of genetic variability, an expansion of efforts toward acquiring and distributing novel material, and the greater coordination of physiological and molecular research with active breeding programs to develop a pipeline for evaluation that integrates the strengths of each in a synergistic manner toward a common goal: developing cultivars with high yield potential under drought stress condition that does not detrimentally affect plant’s performance in non-stressed environments.

A. Brown (B) · R. Swennen International Institute of Tropical Agriculture (IITA), Nelson Mandela African Institution of Science and Technology (NM-AIST), PO Box 447, Arusha, Tanzania e-mail: [email protected] R. Swennen e-mail: [email protected] S. C. Carpentier Department of Biosystems, KU Leuven, Leuven, Belgium e-mail: [email protected]; [email protected] Genetic Resources, Bioversity International, Leuven, Belgium R. Swennen Laboratory of Tropical Crop Improvement, Division of Crop Biotechnics, KU Leuven, 3001 Leuven, Belgium Bioversity International, Willem de Croylaan 42, 3001 Heverlee, Belgium © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_4

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Keywords Banana · Drought-tolerance · Climate resilience · Climate change

4.1 Introduction In the coming decades, the global climate is expected to undergo serious modifications in the form of increased atmospheric CO2 , elevated temperatures, and altered precipitation events (Rosenzweig et al. 2014). The consequences of these changes are expected to impact agriculture on a global scale but are likely to have the most significant effects on small-scale farmers in subtropical and tropical regions of the world without access to irrigation. A number of limited models have been utilized to predict anticipated changes in banana production (Ramirez et al. 2011; Van den Bergh et al. 2012; Machovina and Feeley 2013; Calberto et al. 2015; Ranjitkar et al. 2016). While these models differ in their methodologies, underlying parameters, and geographic focus, they are in general agreement that even conservative estimates of a global rise in temperature of 3–5 °C will disturb current production practices in most regions of the world. Breeding efforts to improve banana will need to address these disruptions to production and food security by placing a greater emphasis on identifying sources of genetic stock material adapted to warmer and drier climes and, while at the same time, taking advantage of genomic approaches and molecular techniques recently utilized with successful outcomes in other crop species. The work will need to be coordinated, as breeding efforts in banana often occur at geographic locations removed from where more sophisticated laboratory-based efforts are used to investigating the molecular basis of stress tolerance. In addition, due to limited resources, a prioritization of the various threats of climate-induced challenges to banana production which in turn needs to be integrated into a long-term breeding strategy of overall crop resilience. These challenges include alterations in ambient temperature, increased water demand, and changes in the occurrence and distribution of pests and diseases whose host range and reproductive cycles may be altered by the same changes to the common environment they share with banana. Turner and Lahav (1983) reported that the optimal temperature for vegetative growth in banana occurs at 25°/18 °C (day/night temperatures) with cold damage occurring at 17°/10 °C and heat injury occurring at 37°/30 °C. While these temperatures are likely genotype specific, locations with temperatures are expected to exceed these upper limits for extended periods of time (3 months) and could become unsuitable for banana production as early as 2050 (Machovina and Feeley 2013; Calberto et al. 2015). As high temperatures have not generally been seen as a limiting factor in banana production, the existing research on temperature in bananas has rather focused on effects of lower temperatures in subtropical regions in efforts to expand production beyond its traditional range (Yang et al. 2015). The global rise in temperature, however, will also be accompanied by fluctuations in precipitation patterns and a general increase in water demand that has been predicted to be as high as 12–15% by 2050. Higher evapotranspiration rates, elicited

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by rising temperatures, are likely to exacerbate an already tenuous situation where irrigation is viewed as a major limitation to production (Turner 1995). Global climate models also suggest that the occurrence and severity of drought events are likely to increase in the coming decades (Feller and Vaseva 2014) which will challenge production systems that are exclusively rain-fed in nature. In particular, components of banana yield such as total bunch fresh weight, finger length and diameter, and delays in fruit maturity are all likely to be impacted by reduced water availability (Robinson and Alberts 1986; Mahouachi 2007). An outstanding review of banana/water relations is available (Carr 2009), and readers are referred there for more specifics. Changes in the distribution and frequency of diseases and other pests to banana should also be expected as is the occurrence of new novel biotypes that may render existing host plant resistance ineffective (Pautasso et al. 2012), but the focus on this chapter will be on addressing drought stress, which is likely to be the most serious component of a changing environment, and hence, the priority for banana breeding efforts.

4.2 The Structure and Physiology of Banana Bananas (including plantains) are large herbaceous, perennial monocotyledons grown as both a vital staple food crop and as an export crop that provides significant foreign currency to developing nations. Over 125 Mt of bananas are produced annually in approximately 135 countries (FAO 2017), and production occurs in almost all tropical and many subtropical regions of the world. Bananas are grown between latitudes 30° N and S of the equator and at altitudes up to about 1500 m. Exceptions are found in East Africa where some local clonal types are grown up to 2000 m (Stover and Simmonds 1987). Almost all cultivated (seedless) bananas that constitute the bulk of world consumption are triploid plants (AAA, AAB, or ABB) which have resulted from hybridizations among the diploid (AA) subspecies of Musa accuminata (‘A’ genome originating in South East Asia) or from hybridizations between M. accuminata and Musa balbisiana (‘B’ genome originating in mainland Asia) (Simmonds and Shepherd 1955; Janssens et al. 2016). It has been suggested M. balbisiana could be a source of genetic resources for drought tolerance in banana, and controlled environment studies with a limited number of triploid representatives of varying genomic constitutions (AAA, AAB, and ABB) appear to support this (Thomas et al. 1998; Vanhove et al. 2012; Delfin et al. 2016). However, in natural germplasm, there are few ABB triploid cultivars that have good palatability and high productivity (Santos et al. 2018), suggesting that the genome may also provide considerable undesirable characteristics as well. Bananas are characterized by a subterranean stem (corm) from which aboveground growth occurs through a structure known as a pseudostem that is comprised of the rolled and compressed portions of a large number of leaves. Leaves rise from a terminal growth point of the corm (meristem) and a peduncle (floral structure) also will emerge from this meristem when the pseudostem reaches it maximum height

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(12–20 ft tall in the variety ‘Gros Michel’) after a characteristic number of leaves that is in large part genetic in nature (Barker and Dickson 1961; Barker and Steward 1962). The timing of floral initiation and fruit maturity depends greatly on variety and environmental conditions (Karamura et al. 2011). Flowers are essentially unisexual, with female flowers emerging first from protective bracts and male flowers occurring near the terminal end of the peduncle. Perfect or neutral flowers can occur between these two. Nodal clusters of female flowers form ‘hands,’ and the fruit of individual flowers is referred to as ‘fingers’. During the development of the pseudostem, lateral shoots (suckers) develop from buds on the corm and these can produce their own leaves and inflorescences. In commercial plantations, most suckers are removed. Due to the poor sexual fertility of most cultivated bananas, the suckers (or the corm) are the principle way most bananas are propagated. The area of individual leaves of some cultivars can be up to 2–3 m2 and the total leaf area of a plant 17–25 m2 (Stover and Simmonds 1987). The rate of appearance of new leaves is governed by genetics and environmental conditions such as drought and temperature, but the number of functional leaves remains somewhat constant with new leaves appearing every 7–10 days (Purseglove 1972). It has been estimated that under standard planting schemes, about 90% of the incoming solar radiation is intercepted by the leaf canopy (Turner et al. 2007) and the optimum rate of leaf emergence occurs at 28.5 °C in subtropical climates. In Western Australia, Hoffmann and Turner (1993) found that the elongation rate of leaves emerging from the pseudostem (up to 80 mm d−1 ) of container grown plants (cv. ‘Williams’ AAA Cavendish subgroup) was the most sensitive measure of the response of the plant to a drying soil (range soil water potential −10 to −80 kPa). It has been suggested that leaves less than 10 cm wide are less subject to critical heat stress and have lower water loss and higher ratios of photosynthesis to water expended than do leaves of greater width (Taylor and Sexton 1972); however, this may reduce total photosynthetic area. Banana leaves are able to fold along the midrib as incoming light intensity increases and splitting of the lamina along the veins is common, but vascular connections between the mid-rib and the leaf margin remain intact. The folding of banana leaves is influenced by water status, but laminae will fold even in well-irrigated plants as this reduces photochemical damage during the day caused by higher photon flux density (Thomas and Turner 2001). It was suggested that tearing of leaves may increase the cooling effects of wind under hot dry conditions and could be associated with increased leaf water efficiency (Taylor and Sexton 1972). Eckstein et al. (1996), however, have pointed out that in subtropical South Africa, severe leaf tearing can also result in smaller plants and leaf areas, with a subsequent reduction (22%) in fruit mass compared to plants with less severe leaf damage. The free exudation of latex from wounded banana plants makes conventional methods for studying leaf water potential impracticable or questionable. The latex method for laticifers developed by Milburn et al. (1990) has been validated by others for measuring leaf water potential in banana (Thomas and Turner 2001). Clonal variability in leaf stomata size and density has been noted in banana (Ekanayake et al. 1994, 1998).

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Banana leaves produce an epicuticular wax that may play a role in resistance to biotic pests as well as providing drought tolerance (Ortiz et al. 1995; SampangiRamaiah et al. 2016). Epicuticular waxes serve to limit non-stomatal water loss (Raven and Edwards 2004) and were found to be positively correlated to leaf water retention capacity in banana (Sampangi-Ramaiah et al. 2016). Plant waxes are organic solvent-extractable complex mixtures of hydrophobic lipids, consisting mostly of very-long-chain fatty acids (VLCFAs) and their derivatives. These VLCFAs include alkanes, wax esters, branched alkanes, primary alcohols, secondary alcohols, aldehydes ketones, and unsaturated fatty alcohols, as well as cyclic compounds, including terpenoids and metabolites such as sterols and flavonoids (Tuberosa 2012; Yeats and Rose 2013). Drought induces changes in the content and composition of the epicuticular wax (Fich et al. 2016). As a major component of cuticle, cuticular wax is the outermost hydrophobic layer, serving as a barrier to restrain uncontrolled non-stomatal plant gas exchange (Xue et al. 2017). In Arabidopsis studies, the total amount of wax per unit leaf area increased by 80% under water deficit and resulted in a 49% increase in cuticle thickness. Long-chain alkanes (C29, C31, and C33) are accounted for 93% of the increases in total wax content (Kosma et al. 2009). In a further study, in Arabidopsis, it was shown that ABA induced upregulation of 10 out of 25 cuticle-associated genes, including acetyl-CoA carboxylase 1 (ACC1), CER1, CER2, CER5, CER6, CER60, CYP86A2 (cytochrome P450, family 86, subfamily A, polypeptide 2), KCS1, long-chain acyl-CoA synthetase 2 (LACS2), and WAX2/YRE (Kosma and Jenks 2007). The content of epicuticular waxes is impacted by growth and developmental cues as well as environmental factors (e.g., light, temperature, and humidity). With fewer than three dry months and greater than 150 mm/month of rainfall, banana grows well year-round without irrigation (Calberto et al. 2015), although this is dependent on environmental conditions. The timing of irrigation is important with greatest benefit of supplemental irrigation occurring during flowering and fruit fill stages, as opposed to early vegetative stages of growth (Holder and Gumbs 1982). Banana roots can extend to depths of 1.0–1.5 m, but the effective depth of rooting is usually taken to be around 0.40 m with up to 88% of roots found within the upper 60 cm (Araya et al. 1998). Breeding for plants with optimal root architecture (deeper and/or auxin regulated asymmetric root growth) is often seen as an approach to breeding for drought tolerance as considerable variation has been observed in a limited number of studies on banana (Uga et al. 2013). The total excavated fresh weight of banana roots has varied from 0.8 kg for cv. ‘Valery’ (AAA, Cavendish subgroup) to more than 3.5 kg for cv. ‘Yangambi km5’ (AAA), when observed in the same environment (Araya et al. 1998). The challenges of root excavations of such large plants as banana have limited broader assessments, but two non-destructive strategies have been proposed: correlations of root growth with aboveground growth parameters and core sampling to estimate both total mass and also the more detailed architecture involved in lateral growth and root hair density (Blomme 2000; Blomme et al. 2005).

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4.3 Plant Physiological Responses to Water Deficit There are numerous detailed reviews that outline the physiological changes that occur in plants when exposed to osmotic stress, and the topic will only be covered briefly here (Bartels and Sunkar 2005; Yamaguchi-Shinozaki and Shinozaki 2006; Lipiec et al. 2013; Cabello et al. 2014). The plant hormone abscisic acid (ABA) is recognized as a key endogenous messenger in response to plant stresses (Christmann et al. 2007). During soil drying, ABA is synthesized in the roots and transported to the leaves, where it inhibits leaf expansion and induces stomatal closure (Dodd 2005). High concentrations of ABA around guard cells in the leaf result in stomata closure and rapidly function to minimize the loss of water through transpiration (Davies et al. 2005). Stomatal closure also limits CO2 uptake and reduces photosynthetic capacity. Drought-induced ABA-dependent signaling pathways impact the expression of numerous genes even under moderate levels of stress. In Arabidopsis, ABA regulates nearly 10% of the protein-coding genes, a greater percentage than other plant hormones (Fujita et al. 2011). Differential regulation of transcription factors (TFs), including representatives of the myelocytomatosis oncogene (MYC), myeloblastosis oncogene (MYB), basic leucine zipper (bZIP), NAM, ATAF, and CUC (NAC), and the dehydration responsive element binding (DREB) families have been observed in response to drought and in some cases to applications of exogenous ABA (Argarwal and Jha 2010). These transcription factors regulate various stress-inducible genes directly or cooperatively and likely constitute gene networks (Yamaguchi-Shinozaki and Shinozaki 2006). In addition to TFs, both ABA-dependent and ABA-independent signaling pathways interact with protein kinases and phosphatases—enzymes involved in phospholipids metabolism, and potentially other signaling molecules such as calmodulinbinding protein and 14-3-3 proteins. ABA interacts with genes through cis-regulatory, ABA-responsive elements (ABREs), upstream of gene promoters (Bartels and Sunkar 2005; Davies et al. 2005). There is also compelling evidence that additional signaling occurs in response to drought stress in plants through interactions with reactive oxygen species (ROS), salicylic acid, jasmonic acid, brassinosteroids, and ethylene (Miller et al. 2010; Gepstein and Glick 2013). Regulation of plant response to osmotic stress also occurs at post-transcriptional and post-translational levels with alternative splicing, RNA silencing, and ubiquitination all been implicated in regulating response (Staiger and Brown 2013). As a consequence of drought stress, numerous changes are observed including reductions in the rate of photosynthesis, likely due to reduced internal CO2 , and the inhibition of photosynthetic enzymes (RuBisCO and others) (Lipiec et al. 2013). Decreased stomatal conductance for avoiding excessive water loss from the plants reduces the internal CO2 concentrations in the plant leading to rapid depletion of the oxidized NADP (final acceptor of electrons in photosystem I) and an increase in the leakage of electrons to O2 , leading to formation of ROS and concomitant peroxidation of thylakoid membrane lipids (Abogadallah 2010). As a consequence, the activation of scavenger enzymes, such as SOD, CAT, APX, POD, DREBs and others, involved

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in the detoxification of ROS (e.g., singlet oxygen, superoxide radical, hydrogen peroxide, hydroxyl radicals) that cause membrane injuries and protein degradation, is observed in water-stressed plants (Mahajan and Tuteja 2005). Plants respond to dehydration stress by synthesis of protective proteins and by the degradation of irreversibly damaged proteins by proteases. Chaperones participate in refolding of denatured proteins to their native conformation and in removal of non-functional and potentially harmful polypeptides. Late embryogenesis abundant (LEA) proteins are a class of major proteins expressed in response to the water-deficit conditions (Tunnacliffe and Wise 2007). Group 2 LEA proteins, also called dehydrins, are by far the most well-studied putative dehydration protective proteins in higher plants (Hara 2010). Dehydrins protect proteins from denaturation under various types of abiotic stresses and restore the denatured proteins. Under water stress, they protect the hydrophobic domains of enzymes from exposure to the solvent, help maintaining the high-ordered water molecules surrounding the proteins, and prevent structural changes. Aquaporins (AQPs) are intrinsic proteins that comprise a large family of transmembrane channels that facilitate the transport of water, CO2 , and other small molecules (glycerol, boron, and others) across plant membranes (Maurel 2007). It is presumed that they interact with ABA to mediate fast transmembrane transport of water during plant growth and development processes, such as seed germination, cell elongation, stomatal movement, phloem loading and unloading, and reproductive growth (Kaldenhoff et al. 2008). Osmotic adjustment (OA) is a tolerance mechanism through which plants compensate for water deficit by accumulating a number of osmotically active substances (organic solutes and inorganic ions) in the cell (Blum 2017). Through modified transcription, and subsequent modifications, the synthesis of sugars (sucrose, trehalose and sorbitol), sugar alcohols (mannitol), amino acids (proline), and amines (glycine betaine and polyamines) performs numerous tasks including the maintenance of cellular homeostasis and the improved hydration and turgor at the cellular level by creating a negative osmotic potential during drought events (Chen and Jiang 2010; Sanders and Arndt 2012). Compatible solutes also stabilize key macromolecules and membranes from damage by scavenging free radicals, thereby protecting the integrity of metabolic functions. Proline, for example, may sometimes have a slight quantitative contribution to OA, but its major effect is in the protection of cellular functions and organs (Shabala and Shabala 2011). Under severe water stress, higher OA capacity may help plants withstand a prolonged drought spell and recover more promptly upon rehydration. However, plants with higher OA capacity are likely to have slower growth, and hence lower biomass production, due to the metabolic requirements of osmolyte biosynthesis (Palta et al. 2007).

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4.4 Molecular Approaches to Drought Stress in Banana The release of the genomic sequence of the Musa acuminata, double haploid (AA) ‘Pahang’ and the subsequent availability of a draft sequence of Musa Balbisiana ‘Pisang Klutuk Wulung’ (BB), has opened up considerable opportunities to explore the genetic regulation of physiological changes associated with drought stress in banana in far greater detail than previously possible (D’Hont et al. 2012; Davey et al. 2013). Prior to the availability of these genomic resources, research into the biochemical basis of drought response in banana was conducted primarily by utilizing sequence information from a limited set of publically available expressed sequence tag (EST) sequences and the homology of these sequences to known homologous genes and gene families from model crops (Santos et al. 2005; Davey et al. 2009). The occurrence of drought or osmotic stress invokes a considerable number of physiological and biochemical responses within the plant when available water in the soil is reduced and atmospheric conditions favor increased water loss through transpiration or evaporation. To successfully ameliorate these stresses, plants trigger a cascade of events that are initiated with stress signal perception, followed by several parallel transduction pathways that eventually lead to the synthesis of transcription factors and subsequently the differential regulation of genes responsible for synthesis of effector proteins and metabolites which partake in stress tolerance (Shinozaki and Yamaguchi-Shinozaki 2000). Understanding the comparative molecular responses of bananas considered drought tolerant and susceptible has formed the basis of much of the considerable research generated over the past two decades. Transcriptomic, proteomic, and metabolomic approaches have been combined with computational analysis and plant transformation to provide a wealth of information on putative candidate genes, gene families, and pathways involved in alleviating detrimental impacts of osmotic stress (Carpentier et al. 2007; Davey et al. 2009; Henry et al. 2011; Ravishankar et al. 2011; Shekhawat et al. 2011a, b; Msogoya and Grout 2012; Shan et al. 2012; Sreedharan et al. 2012, 2013; He et al. 2013; Shekhawat and Ganapathi 2013; Cenci et al. 2014; Mahouachi et al. 2014; Muthusamy et al. 2014, 2015, 2016; Xu et al. 2014; Feng et al. 2015; Hu et al. 2015a, b, 2016, 2017, 2018; Negi et al. 2015; Vanhove et al. 2015; Yang et al. 2015; Zorrilla-Fontanesi et al. 2016; Goel et al. 2016; Zorrilla-Fontanesi et al. 2016; Miao et al. 2017a, b; Mattos-Moreira et al. 2018; Negi et al. 2018; Song et al. 2018; Jangale et al. 2019). Variation in the banana proteome and the transcriptome has been noted in response to osmotic stress in the leaves, roots, rhizomes, and meristematic tissue (Carpentier et al. 2007; Davey et al. 2009; Ravishankar et al. 2011; Muthusamy et al. 2016; Zorrilla-Fontanesi et al. 2016; Hu et al. 2017; Mattos-Moreira et al. 2018). Transcriptomic changes in leaves (Davey et al. 2009; Ravishankar et al. 2011; Muthusamy et al. 2016) and root tips (Zorrilla-Fontanesi et al. 2016) of osmotically stressed bananas have been observed. Davey et al. (2009) investigated drought-responsive transcript expression of the cultivars ‘Mwazirume’ (AAA) and ‘Cachaco’ (ABB)

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with Affymetrix Rice GeneChip® Genome Array, despite the considerable evolutionary distance between rice and banana was able to identify 2910 differentially expressed transcripts. These transcripts included recognized members of several gene families associated with abiotic stress including functional and regulatory members of the ERF, DREB, MYB, bZIP, and bHLH families. Ravishankar et al. (2011) used a subtractive hybridization approach to identify differentially expressed genes in leaves of the drought-tolerant cultivar ‘Bee Hee Kela’ (BB) during water stress and control conditions. Stress was induced by withholding water for 8 days. Differential expression was noted for transcripts identified as lipoxygenase, RuBisCO activase, glycine dehydrogenase, catalase, and ethylene responsive factor (ERF). Zorrilla-Fontanesi et al. (2016) examined differential root transcriptomics in an analysis of root tip tissue in response to osmotic stress induced by 5% PEG. Differences were noted between stressed and control conditions as well as genotypicspecific differences between the AAA cultivars ‘Grande Naine’ and ‘Mbwazirume.’ Over-representation was noted in classes of genes associated with respiration, glycolysis, and fermentation; it was hypothesized that in fast-growing and oxygendemanding tissues, mild osmotic stress leads to a lower energy level, which induces a metabolic shift toward oxidative respiration, alternative respiration, and fermentation. Muthusamy et al. (2016) in greenhouse/pot study higher observed differential gene expression in 2268 and 2963 expressed sequences in the drought-tolerant cultivars ‘Saba’ (ABB) and ‘Grand Naine’ (AAA), respectively. Differential expression was observed in genes involved in protein modifications, lipid metabolism, alkaloid biosynthesis, carbohydrate degradation, glycan metabolism, and biosynthesis of amino acid, cofactor, nucleotide sugar, hormone, terpenoids, and other secondary metabolites. In the drought-tolerant ‘Saba,’ two wax biosynthetic transcripts, cuticular protein-1 (CUT1) and long-chain acyl-CoA synthetase 2 (LACS2), were overexpressed when compared to drought-susceptible ‘Grand Naine.’ The drought-induced expression of genes responsible for cuticular wax production supported the findings of Surendar et al. (2013a, b) who reported higher wax content in banana-tolerant genotypes, ‘Saba’ under drought stress. It is difficult to draw conclusions from the studies due to the use of different tissues, genotypes, and methods of incurring osmotic stress, but common themes are apparent. Osmotic stress induces differential expression in almost 3000 genes in the banana genome. Davey et al. (2009) noted a greater than twofold differential expression in 2910 Musa gene homologues when comparing stressed and non-stressed leaf tissue of the cultivar ‘Cachaco’ (ABB). Muthusamy et al. (2016) observed 2268 and 2963 statistically significant, functionally known, non-redundant differentially expressed genes from tolerant and sensitive libraries of the cultivars (‘Saba’, ABB—drought tolerant) and ‘Grand Naine,’ AAA drought sensitive), respectively. As the banana genome represents 36,542 predicted protein-coding genes (D’Hont et al. 2012), these numbers suggest that perhaps 8% of all banana genes are altered in their expression pattern when the plant encounters osmotic stress. As one might predict, these differential regulated genes represent numerous gene candidates’ gene families and putative quantitative trait loci (QTL) associated with

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drought tolerance in model crops. Representatives of 46 transcription factor networks, including members of the MYB, bHLH, bZIP, ERF, NAC, and WRKY families, were noted (Muthusamy et al. 2016). Differential expression was noted for transcripts with involvement in both primary and secondary metabolism and included such processes as CO2 fixation, glycolysis, respiration, carbon fixation, fermentation, structural wall modification, ethylene response, and the mediation of reactive oxygen species (ROS). In light of post-transcriptional and post-translational events, proteomics provides additional information not available at the level of plant expression (Carpentier et al. 2008). Alternative splicing and RNA-mediated silencing control the amount of specific transcripts, while ubiquitin and SUMO modify activity, subcellular localization, and half-life of proteins (Guerra et al. 2015). It has been estimated that 20–70% of expressed genes have been shown to undergo alternative splicing depending on the plant species (Staiger and Brown 2013). Conserved alternative splicing across monocots (including bananas) has been observed as well as their likely involvement in regulating signaling pathways (Mei et al. 2017). The induction of both microRNAs and long non-coding RNAs (LncRNAs), in response to osmotic stress in banana, has also been noted, further emphasizing the need to understand transcript processing beyond the level of expression (Muthusamy et al. 2007, 2015). Carpentier et al. (2007) demonstrated that sucrose-mediated osmotic stress resulted in differential expression between treated and untreated meristematic tissues of the AAA cultivar (‘Mwazirume’) and the ABB cultivar, ‘Cachaco’ with a 2-DE proteomics approach. Based on the availability of sequence information at the time, a number of proteins with differentially expression patterns were identified. Proteins up-regulated were those associated with energy, cell rescue, defense, cell death and aging, protein destination, metabolism, cellular organization, transcription, transport facilitation, and cellular communication and signal transduction, while downregulated genes tended to be those associated with cell rescue, defense, cell death and aging, metabolism, protein synthesis, and protein destination. Also observed was genotype-specific expression of certain proteins (isoforms) involved in energy metabolism. Further work by the same group (Vanhove et al. 2012) utilizing five varieties representing different genomic constitutions in banana (AAA, AAB, and ABB) was subjected to osmotic stress and concluded that the ABB cultivar showed the smallest stress-induced growth reduction and identified 24 differential proteins which were involved in respiration and metabolism of ROS and also included several dehydrogenases involved in NAD/NADH. Mattos-Moreira et al. (2018) also utilized a (2-D) electrophoresis approach in tandem with a Q-Tof/UPLC, to examine stress responses in cultivars with the same genomic constitution (AAB), but belonging to different clonal subsets. Prata Anã (AAB, pome) and BRS Tropical (AAB silk) are described as susceptible and tolerant to drought, respectively. Noted were 23 differentially expressed proteins found in the tolerant genotype (BRS Tropical) under water deficit, with proteins involved in metabolism, defense, and transport. It should be noted that most of the cultivars examined in these studies were seedless triploids (2n = 3x = 33) of the genomic constitutions AAA, AAB, and ABB. These triploids are the results of limited, natural hybridizations involving unreduced

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gametes of the respective diploid progenitor species (2n = 2x = 22) (Brown et al. 2017). As the distribution, ecological range, and genetic diversity of the diploids far exceed their triploid progeny, it would be expected that far greater variation could be expected by screening the diploid level. In addition, limiting analysis to the triploid level complicates the breeding improvement schemes, as most triploid bananas contain only residual fertility. An examination of the variation at the diploid level also presents the possibility of developing segregating populations that could lend themselves to better understanding the inheritance, relative importance, and potential interactions of a broad spectrum of putative genetic factors associated with drought tolerance. The availability of the reference genomic sequences has allowed for the in silico and sometimes functional characterization of multiple gene families within Musa to identify potential candidates of family members involved in biotic or abiotic stress. These have included four abscisic acid and ripening (ASR) proteins (Henry et al. 2011), 24 PYL, 87 PP2C, and 11 SnRK2 genes in the abscisic acid pathway (Hu et al. 2017), 162 potentially functional NAC TFs (Cenci et al. 2014), 11 sequences belonging to HSP70 subfamily (Vanhove et al. 2015), 12 superoxide dismutase (SOD) genes (Feng et al. 2015), 47 aquaporin genes (Hu et al. 2015a, b), 121 bZIP TFs in 11 subfamilies (Hu et al. 2016), 147 WRKY TFs (Goel et al. 2016), 43 heat-shock TFs (Wei et al. 2016), 25 SWEET genes in four subfamilies (Miao et al. 2017a), eight AGPase genes (Miao et al. 2017b), 91 U-Box E3 ubiquitin–protein ligase genes, and 96 DREB genes (Jangale et al. 2019). Transformation of model crops or banana has been accomplished with an SK3type dehydrin (MusaDHN-1) (Shekhawat et al. 2011a, b), an A20/AN1 zinc-finger domain-containing stress-associated protein (MusaSAP1) (Sreedharan et al. 2012), two aquaporin genes (MusaPIP1; 1) and (MusaPIP1; 2) (Xu et al. 2014; Sreedharan et al. 2013), and two NAC TFs (MusaVND1) (MusaNAC042) (Negi et al. 2015; Tak et al. 2017). All of these transformations have reported enhanced drought tolerance through one or more metric, but to our knowledge, field testing and multi-site yield evaluations (or potential yield penalties) have not been reported. Transformation is a promising approach to developing varieties tolerant to drought and abiotic stress, but constitutive overexpression of genes (including transcription factors) (TFs), in some cases, has resulted in undesirable side effects such as stunted growth and reduced yield in several transgenic crops (Kasuga et al. 1999, 2004; Hsieh et al. 2002; Youm et al. 2008).

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4.5 Breeding 4.5.1 Defining the Scope of the Breeding Program High yield potential under drought stress conditions is often the stated target of many crop improvement programs (Wimalasekera 2016); however, specific welldefined objectives and related metrics to measure progress toward meeting these targets are required by breeders. Central to these considerations is that selection to improve performance in stressed environments does not detrimentally affect plant’s performance in non-stressed environments. Considerable cross talk occurs among the previously discussed regulatory, metabolic, and developmental pathways which enhance the potential for undesired pleiotropic effects such as growth handicaps and developmental alterations that can ultimately result in significant yield penalties (Cabello et al. 2014). For a breeder, this means dissecting the component traits of what we will refer to as ‘drought tolerance’ and objectively weighing the cost/benefit ratio of each. What complicates these considerations is that (like yield itself) stress tolerance is generally not the product of simple genetic control, but rather the result of multiple physiological and biochemical processes each regulated potentially at multiple levels. With complex genetic traits, it is generally the component traits or parts that define the whole. Banana yield, for example, is defined not only by total bunch weight, but also by such traits as the number of hands (rows) or fingers (fruits) and the diameter and width of the individual fruits (Brown et al. 2017). Generally, components of drought tolerance in most plant improvement schemes can include an array of component traits such as stomatal conductance/leaf temperature, photosynthetic capacity, stay green/leaf senescence, single plant leaf area, rooting depth and structure, epicuticular wax and leaf surface roughness, osmotic adjustment, membrane composition, antioxidative defense, accumulation of stress-related proteins, and other morphological and physiological traits. The genetic variability and heritability of most of these component traits is to a large degree, lacking in banana breeding. Many of these traits are also co-regulated by common factors (e.g., ABA), which suggest that there will be significant co-variances among these traits that will either simplify or complicate the breeding selection process depending on the degree and the direction of the co-variance (Juenger 2013). Selecting for one or more of these component traits is a form of indirect selection which is most effective when the component traits have high heritability and the genetic correlations between them and the principle trait of interest (yield) are also high (Hallauer and Miranda 1981). For a breeder to be comfortable with these correlations, they need to be established with an appropriate experimental design, utilizing relevant target populations and environments. Selecting for one or more of the component traits also presupposes that a model for optimal drought tolerance (without concurrent yield penalties) is available to breeders. Donald (1968), however, cautions that ‘The definition of a model is potentially hazardous, in that it will narrow the spectrum of a breeding program, rather

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than permit the emergence of the highest yielding segregates without prejudice by the breeder as to the most desirable plant form.’ Results from crop simulation modeling suggests that greater progress toward meeting objectives may be achieved by pyramiding multiple traits so that potential negative effects of individual traits can be neutralized (Condon et al. 2004). This approach may also be of benefit when environmental variability in frequency, timing, and severity of climatic stresses is unpredictable as a combination of multiple traits may confer drought tolerance at different phenological stages (Ceccarelli et al. 2010). One breeding strategy that circumvents many of these considerations is based on the observations that selection for plants with higher yields under favorable conditions is sometimes also observed to have higher yields under stressed conditions (Richards 1992; Cattivelli et al. 2008). A considerable number of examples of documented success in grain crops utilizing this approach have been provided by Blum (2011). It is also recognized, however, that significant genotype x environment interaction (including crossover interactions) has been observed in crops when comparing selection efficiency between optimal and stressed environments (Pantuwana et al. 2002; Pidgeon et al. 2006; Messmer et al. 2009) which suggests that concurrent selection in stressed and favorable environments is likely unavoidable. In general, when genetic correlation between yields in stressed and well-watered sites is positive and significant, selection in non-stressed environments can simplify the breeding scheme (Bänziger et al. 2000). Donald (1968) defined two strategies toward generalized plant breeding. In the first, the emphasis is on defect elimination and in the second, the emphasis is on selection for yield. In banana breeding, an example of the former would include selecting for a thicker pseudostem to prevent lodging during intemperate weather conditions. This is an indirect method of increasing yield but focuses on correcting a specific defect in currently grown cultivars. According to Donald, ‘In plant breeding programs based on selection for yield, there is no incorporation of a designated physiological or morphological character, but only an intent to improve yield, without consideration of the why or wherefore of that greater yield.’ Often, physiological studies of select genotypes are published which purport to be of benefit toward plant improvement, but as Jackson et al. (1996) noted that the endpoint of many physiological studies appears to be with recommendations or suggestions for improved methodology as possible selection criteria, but he observed that few objective comparisons were conducted to demonstrate the benefit and utility of these suggestions. The authors stressed the need to work with relevant genetic populations, the importance of close integration of physiological research with an active breeding program, and the dangers of following a pre-defined or narrow focus in physiological research. Blum (2011) also noted the greatest likely of this work supporting and enhancing plant breeding came when it was accomplished within the context, limitations, and framework of an active plant breeding program.

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4.5.2 Challenges Specific to Banana Breeding Banana breeding involves several challenges not faced by most crops, including physiological and reproductive barriers that limit sexual recombination and hinder plant improvement (Ortiz and Swennen 2014; Brown et al. 2017). The difficulties of breeding a seedless crop plant from seed-bearing relatives or producing seed from seedless genotypes is well described (Menendez and Shepherd 1975). The poor fertility of most seedless, triploid genotypes, which constitute the majority of consumed bananas in the world, necessitates a complicated breeding scheme that involves utilizing preferred triploids as female parents and introducing new traits from diploid donors used as males. The results of these hybridizations can include progeny with multiple levels of ploidy (2x = 22, 3x = 33, and 4x = 44), but in general, the tetraploid progeny (4x) is then crossed to a diploid parent to recover the original genetic composition (3n). Other factors that complicate this process includes recovering parthenocarpy, poor seed germination and viability, irregular meiotic behavior, long generation times, time delays due to clonal propagation, and diverse genomic configurations. It can often take more than a decade to evaluate and release a new cultivar (Tenkouano et al. 2019). The potential of these currently utilized diploid and tetraploid parents toward the improvement of drought tolerance in banana is not known, but would logically provide a baseline measurement for what is achievable in the short term. The improvement of banana depends to a large degree on the use of improved diploid parents to introduce new traits of interest and while pre-breeding efforts utilizing exotic material can result in significant achievements, it lengthens the timeline for releasing new cultivars, as desired traits from wild relatives need first to be introgressed into an elite parental background. Perhaps nothing illustrates the disconnect between physiological and molecular approaches in banana and the efforts toward conventional plant improvement more than the focus of physiological and molecular studies to plants at the triploid level, which in many respects represents a genetic dead end for banana breeders due to minimal fertility of these plants.

4.5.3 Phenotyping Perhaps no greater challenges to breeding for drought tolerance in banana are those considerations that are associated with phenotyping and experimental design. Table 4.1 provides a partial list of potential components of drought tolerance, or associated metrics that could be utilized to select indirectly for enhanced drought tolerance. While a number of these traits have been pursued in laboratory, screen house, and field conditions (Taylor and Sexton 1972; Freeman and Turner 1985; Swennen et al. 1986; Mekwatanakarn and Turner 1989; Turner 1990, 1995; Lu et al. 2002; Aguilar et al. 2003; Ravi et al. 2013; Kissel et al. 2015; Zait et al. 2017; van Wesemael et al. 2019), studies to establish correlations with yield in optimal and

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Table 4.1 Potential component traits contributing to drought tolerance and targets for indirect selection in breeding Potential component traits

Environment

References

Canopy cover

Laboratory

Van Wesemael et al. (2019)

Gas exchange

Laboratory

Van Wesemael et al. (2019)

Water use efficiency

Laboratory

Van Wesemael et al. (2019)

Transpiration efficiency (TE)

Laboratory/screen house

Kissel et al. (2015)

Plant biomass

Laboratory

Van Wesemael et al. (2019)

Root hydraulic conductivity

Laboratory

Aguilar et al. (2003)

Leaf turgor pressure

Laboratory

Zait et al. (2017)

Transpiration

Laboratory

Van Wesemael et al. (2019)

Cycle time

Field

Turner (1995)

Day from flowering to harvest

Field

Turner (1995)

flowering date

Field

Turner (1995)

Semi-dwarf habit/plant height

Field

Pseudostem girth

Field

Leaf emergence

Field

Functional leaf number

Field

Number of fingers

Field

Average finger circumference in mm

Field

Average finger external length in mm

Field

Bunch weight

Field

Leaf turgor pressure

Field

Sap flow

Field

Lu et al. (2002)

Root hydraulic conductivity

Field

Aguilar et al. (2003)

Roots’ architecture

Field

Swennen et al. (1986)

Leaf wax content

Field

Freeman and Turner (1985)

Harvest index

Field

Turner (1990)

Carbon isotope discrimination

Screen house

Kissel et al. (2015)

Canopy temperature (CT)

Field

Taylor and Sexton (1972)

Mekwatanakarn and Turner (1989)

Zait et al. (2017)

stressed environments are limited, likely due to the practical difficulties of optimizing large-scale drought conditions for a crop like bananas that are large statured and with such a long duration to maturity (12–20 months) (Ravi et al. 2013).

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4.5.4 Defining the Appropriate Environment for Phenotyping Defining the target population of environments (TPE) is a constant for all breeding endeavors and of paramount importance with respect to drought tolerance. Germplasm evaluation is highly dependent on how relevant the screening environments are compared to the overall TPE (Comstock 1977). Characterizing the frequency of drought stress occurrence in the TPE allows breeders to identify relevant environments and allocate needed resources to where it can have the greatest impact in the selection process. Drought stress timing also needs to be considered. Timing of stress in relation to plant development is an important as stress which is occurred during early establishment or vegetative phases of growth can have different impacts than stress encountered during flowering or fruit filling. Stress during early vegetative stages in most crop plants can slow growth (cell division and expansion) but not necessarily reduce yield of seed crops, while stress during reproductive development can considerably, and potentially irreversibly, diminish productivity (Mickelbart et al. 2015). The perennial nature and non-seasonal flowering of banana make field evaluations of drought tolerance in banana challenging as the timing and duration of drought stress events as well as the physiological growth stage of each plant in screening trials needs to be factored into selection criteria. Statistical models that fail to account for these sources of variability are unlikely to be informative. Rain-out shelters commonly utilized for other crops (Hoover et al. 2018) would require a significant investment in infrastructure given the physical size and perennial nature of the plant. Rajaram et al. (1996) suggested a simultaneous evaluation of the germplasm under near optimum conditions (to utilize high heritability and identify lines with high yield potential) and under stress conditions (to preserve alleles for drought tolerance) with the adoption by farmers as the decisive criteria of success. Zaman-Allah et al. (2016) make a number of useful suggestions for designing dedicated drought stress evaluation sites including the need for reliable irrigation and drainage and spatial analysis of fields to reduce variability in trials.

4.6 Recommendations There is a need for targeted evaluations of genetic variability associated with drought tolerance in currently available germplasm holdings and a concurrent expansion of efforts toward acquiring and distributing novel material from regions where collection efforts have been restricted due to bureaucratic challenges or geographic inaccessibility. In particular, these efforts need to be emphasized within the diploid AA subspecies of M. acuminata and M. balbisiana. Research to date provides limited support for claims that the B genome (M. balbisiana) contains a greater degree and variability of abiotic stress-related genes, but there are concerns about utilizing this material in breeding programs. The B genome is known to possess endogenous

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viral sequences of the Banana streak viruses (eBSVs) which can induce infections under certain environmental conditions (Gayral et al. 2008; Chabannes et al. 2013), and this has prevented seed banks from freely distributing much of this material. The B genome is also recognized as a source of other undesirable characteristics such as seeded and starchy fruit which has limited its use in most breeding programs. Cultivars with the genome constitution of ABB often produce yields that are about half of what can be expected from Cavendish (AAA) (Turner and Hunt 1984). Given that the current approaches to breeding bananas (Ortiz and Swennen 2014; Brown et al. 2017) do not readily facilitate multiple generations of recurrent selection, the utility of this material will depend on considerable pre-breeding efforts to bear fruit. Efforts toward genetic transformation, if in service of breeding, should be directed at the diploid level in banana. It may seem like a rather obvious point, but to date all of the genetic transformation of banana has involved AAA, AAB, or ABB bananas. Elite triploid bananas are utilized only in the initial 3n × 2n hybridization due to their generally poor fertility. Pre-breeding efforts such as those previously described can only be practically obtained at the diploid level. Hence, efforts such as genetic transformation, gene editing, and other techniques should focus on seedless, productive diploids which will greatly facilitate their transfer into elite backgrounds. In particular, the parthenocarpic and productive ‘Mchare’ bananas of East Africa that have recently been recognized as the unreduced (2n) gamete source of ‘Cavendish’ ‘Gros Michel’ and ‘Pome’ dessert bananas (Perrier et al. 2019) would appear to be a suitable candidate for such efforts. Focusing on research at the diploid level also allows for the creation of segregating populations containing multiple mechanisms of drought tolerance to evaluate their relative impact on field response and economic productivity. Marker-based strategies are also easier to implement at the diploid level in banana allowing for indirect selection and genomic investigation of factors that combine drought tolerance with a minimal reduction of important agronomic considerations. Phenotyping, as discussed earlier, remains the greatest constraint to breeding bananas for drought tolerance and the solution will likely require components of both controlled and field evaluation to adequately address current bottlenecks to improvement. While a number of promising high throughput phenotyping strategies have been proposed, the environment the banana faces in the field (variable temperature, humidity, canopy cover, soil borne organisms; and soil fertility, compaction, composition, and depth) cannot truly be duplicated in a growth room or a greenhouse. Conversely, field evaluation of drought tolerance presents its own set of challenges. Banana is an almost non-seasonal crop and within any given field, bananas (even of the same cultivar) may not necessarily be at the same developmental stage when they encounter a stress event. Also, given the size of the mature banana plant, and the amount of required field space of a single plant (3 M3 ), conventional rain-out shelters for drought evaluation are only practical for a limited number of accessions. Precise measurements that need to be conducted at specific times of the day are also not practical under field conditions. All this suggests that a greater coordination is required between laboratory-based efforts and breeding programs to develop a

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pipeline for evaluation that integrates the strengths of each in a synergistic manner toward a common goal.

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Chapter 5

Toward Development of Climate-Resilient Citrus Supratim Basu

Abstract In this era of extreme urbanization and mass deforestation, agriculture is highly affected owing to increase in the prevalence of diseases and global climate change. Citrus is one of the most common fruit crops known worldwide not only for its health benefits but also for its economic importance to the industry. But it has been severely affected by the increased risk of pathogen-induced diseases and abiotic stresses like salt, drought, and temperature. Citrus is one of the difficult plant species to develop new varieties by conventional breeding programs. With recent advancement in the technologies, scientists all over the world have adopted them to respond to the challenges posed by the citrus plant system. Here in this current review, we have talked about the developments in linkage mapping, identification of QTL regions, and how the omics approach have been used to circumvent the critical problems. In the recent years, researches have shown a significant progress toward overcoming the hardships of citrus research. Besides, citrus researchers are building international collaborations and are very optimistic that they will be able to globally sustain the productivity of this highly nutritious and economic crop in the future. Keywords Abiotic stress · Breeding · Citrus · Disease resistance · Genomics · Huanglongbing · Proteomics

5.1 Introduction In the world, citrus is one of the most widely cultivated fruit crops especially in the America followed by Asia and the Mediterranean basin (FAO). Citrus production is primarily limited to tropical and subtropical regions where the conditions are congenial for the trees to survive the extremities of temperature with abundance of water and suitable soil conditions. In addition, to its importance as fresh fruit products citrus is also essential as processed food thereby adding a high economic importance to its produce. Citrus not only serves as a source of food or beverage but also some wild species of citrus are grown as an important resource for medicinal S. Basu (B) 100 Entrada Drive, Los Alamos, NM 87544, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_5

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and sanitation purposes (Gmitter and Hu 1990). In spite of the wide diversity of citrus fruit types, still sweet oranges are the predominantly produced citrus fruit. The cultivars that are used as rootstock or as scion have been a result of methodical and targeted breeding. A critical reason behind the low impact of conventional breeding in improving the genetic make-up of citrus owes to the particularities of reproductive biology and the taxonomic relationships between the cultivars (Gmitter et al. 1992). The juvenile phase ranges from 1 to 20 years though they will essentially flower during 3–7 years depending on the species. A major effect of juvenility is the lag in the selection of the hybrid characteristics and a secondary effect is the necessity for large area to grow the individual hybrids and consequently increasing the cost and reducing the number of families and number of plants within the families that can be grown simultaneously. Commercial citrus varieties produce polyembryonic seeds through nucellar embryony that eventually produce seedlings that are clones of the parent. These embryos are produced autonomously before anthesis, and their development follows the normal course of pollination and endosperm development (Wakana and Uemoto 1987). One point to ponder upon is that the varieties that are considered economically important are not biologically defined species but result of mutations acquired over centuries (Gmitter 1995). Increased urbanization has led to the loss of arable land and enhancement of detrimental citrus diseases that is increasing at an alarming rate, thereby threatening the worldwide citrus industry. Global climate change has led to increase in environmental atrocities such as the prevalence of abiotic stresses like drought, extremes of temperature along with biotic stresses which has subsequently presented greater challenges to tree growth and production. The availability of genetic resources can help meet these challenges. Insufficient knowledge about the regulatory mechanisms governing these traits as well as the genetic challenges are the major obstacle for genetic improvisation and designing of apt strategies for development of improved citrus fruits for sustainable citrus production for the future. It is of relevance to point out that the advent of improved strategies incorporating genomics and computational biology is coming into prominence for developing improved citrus varieties. Citrus fruits evolved from small berries several millions of years ago. Plant chloroplast mapping-enabled tracing of time rewinded back to several billions of years where the ancestors of citrus split into Poncirus (such as the trifoliate orange) and citrus. Mandarin orange, pomelo, and citron are the three ancestral species in the citrus genus that are associated with the modern cultivars of citrus. In the recent years, all the commercially important citrus species (sweet oranges, lemons, grapefruit, limes, and so on) are hybrids developed through crosses between these three species or their progenies or another wild citrus species. The present chapter will provide insights into the advancement that has been achieved thus far toward development of improved climate-resilient citrus varieties by making use of these available new tools and provide a direction for future research in this regard.

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5.2 Prioritizing Climate-Smart (CS) Traits 5.2.1 Role of Exogenous and Endogenous Factors in Flowering The primary factors responsible for prolonging the breeding cycle and production of fruits in citrus industry are juvenility and alternate bearing. These two phenomenon are mostly related to flowering in citrus wherein subduing of flowering results in juvenility while fruit production induced inhibition of flowering causes alternate bearing. Researches are being carried out to find a solution to these problems. The flowering in citrus trees is influenced by several factors like temperature, water availability, etc. Like in other tropical and subtropical trees, the flowering in citrus is induced by temperatures lower than 25 °C as has been observed for satsuma mandarin (Nishikawa et al. 2007). In addition, it has been observed that drought and more specifically severe drought stress induce flowering and even when grown at a temperature of 25 °C which under normal circumstances is an inhibitor of flowering (Nishikawa 2013). Exposure of citrus trees to extreme cold temperature results in defoliation and hence inhibits flowering (Nishikawa et al. 2013). To enhance flowering, girdling and ringing are treatments that have been used in citrus industry that subsequently suppressed the growth of vegetative roots and shoots. Again another important aspect to consider is that fruit removal, or thinning of fruit promotes flowering. In other words, it can be said that number of fruits in the trees is inversely related to flowering. Defoliation of the plants leads to reduction in carbon source and hence negatively impacts flowering and subsequently production of fruits. Phytohormones like gibberellin (GA), auxin, and abscisic acid have also been shown to be a key player in influencing flowering. It has been shown that GA is a negative regulator of flowering, while exogenous application of auxin (NAA) is reported to promote flowering. Cytokinin on the contrary is not directly related to flowering, but it has been reported to influence the breaking of buds.

5.2.1.1

Identification of Flowering-Related Genes

APETALA (AP) 1, 2, and 3, PISTILLATA, and AGAMOUS are several MADS-box genes in Arabidopsis that have been reported to determine identity of the floral organs (Robles and Pelaz 2005). They are in turn regulated by AP1 and LEAFY (LFY ), which also negatively regulates TERMINAL FLOWER 1 (TFL1) that controls plant growth by continuing the indeterminacy of shoot apex (Jack 2004). In addition, MADS-box gene, SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1), FLOWERING LOCUS T (FT ) gene and a rice homolog, Hd3a have also been shown to regulate flowering either by integrating environmental and autonomous signals or by acting as a mobile flowering signal (Corbesier et al. 2007; Tamaki et al. 2007). Researches have shown that homologs of these genes are conserved in citrus species and the expression of CsLFY and CsAP1 were higher in the reproductive tissues

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of navel orange (Pillitteri et al. 2004a, b). Moreover, it has also been observed that the overexpression of CiFT, CsLFY, CsAP1, or CsSLs in Arabidopsis results in early flowering, while CsTFL overexpression in Arabidopsis leads to delayed flowering. In addition, when AP1 or LFY from Arabidopsis was ectopically overexpressed in citranges (hybrid of sweet orange and trifoliate orange) resulted in early flowering and fruiting in comparison with the untransformed wild-type plants (Peña et al. 2001). Similar results of early flowering during the plant growth were observed when CiFT was overexpressed under the control 35S promoter. These transgenic plants showed distinct phenotypes of periodic flowering during the course of different seasons which was not observed in 35S: LFY or 35S:AP1 transgenic plants.

5.2.2 An Insight into the Responses of Citrus to Different Abiotic Stresses Unlike the huge repository of transcription profiling datasets available for other crops like rice, wheat, maize, etc., there is a dearth of knowledge about gene expression of citrus in response to different abiotic stresses like drought, low temperature, salinity, etc. Consistent with previous observations from other plants, it has been shown that citrus plants on exposure to salt stress accumulate more chloride ions than sodium (Romero-Aranda et al. 1998; Moya et al. 2003). Screening of a cDNA expression library for salt stress identified genes like Lea5, glutathione S-transferases, olesins, superoxide dismutases, etc. However, these genes are induced by oxidative stresses rather than specifically to salt stress (Beeor-Tzahar et al. 1995; Lo Piero et al. 2006). In addition, it has also been observed that salt tolerance in citrus rootstocks is associated with metabolic alterations, while for flooding it is mainly due to enhancement of oxidative stress. Commercial citrus varieties are sensitive to low temperatures, but due to the absence of a tolerant variety Poncirus trifoliata (L.) an infertile citrus relative tolerant to low temperatures is being used for identifying genes involved in tolerance. These studies identified induced expression of genes like COR19, C-repeat-binding factor (CBF), dehydrins like COR11, COR15, and COR9 (Cai et al. 1995). In addition, to the dehydrins which are different from the conventional LEA proteins due to the presence of angiosperm-type K-segment, gene expression profiling also showed induction of an AP2 transcription factor, CTL, and a RING-H2 finger gene. It has been shown that damage due to chilling injury can be lowered by exposing them to short heat treatments periodically that can trigger the molecular responses (Sanchez-Ballesta et al. 2003; Sapitnitskaya et al. 2006). To conclude, it can be said that high-throughput gene expression analysis in response to different abiotic stresses in citrus is under progress and will eventually lead to the discovery of several novel targets that can be used for manipulation of abiotic stress tolerance.

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5.2.3 Molecular Mechanism of Citrus–Xylella Fastidiosa Interactions Revealed by Transcriptome Characterization Citrus industry is severely affected by citrus variegated chlorosis (CVC), a disease caused by X. fastidiosa with no cure other than replacing the infected orchards, chemical control of the vector (sharpshooters), or removing the infected leaves and branches (Bové and Ayres 2007). X. fastidiosa is a gram-negative xylem inhabiting bacteria that blocks water transport from xylem and evokes symptoms very similar to drought stress (Almeida et al. 2001). Citrus reticulate and some other hybrids are resistant to the infection probably due to the diameter of the xylem vessels or owing to an active defense response (Coletta-Filho et al. 2007). Analysis of gene expression in C. sinensis with and without disease symptoms showed significant changes in expression of genes like downregulation of genes responsible for photosynthesis justifying the chlorosis phenotype, while genes related to cell wall biosynthesis, pathogen-related (PR) proteins, and stress-inducible genes were upregulated indicating an active defense response (Campos et al. 2007). Though an active defense system is present, it was not enough to combat the pathogen and another important aspect to consider here is that the pathogen recognition system was not active, which may become active once the pathogen is established in the host. A plausible explanation for the activated defense machinery may be that these genes were triggered probably as a secondary stress response like nutrient deficiency or osmotic shock arising as a consequence of pathogen invasion (de Souza et al. 2007a). A study conducted by de Souza et al. (2007b) on CVC-tolerant mandarin variety Ponkan has shown induction of genes involved in perception of pathogen, transduction, and defense machinery. One of the genes to be activated was CC-NBS-LRR-like disease resistance protein along with MAPK(s), ethylene responsive transcription factor, a key enzyme in jasmonic acid (JA) biosynthesis lipoxygenase. Another key enzyme that was induced is S-adenosyl-l-methionine:salicylic acid methyltransferase, an essential component of defense response mediated by salicylic acid (Deng et al. 2005; Park et al. 2007). Additionally, genes responsible for ROS detoxification as well as genes associated with antimicrobial compounds like miraculin, PR 6, and PR 17 were also upregulated suggesting hormonal crosstalk as well as effector triggered immunity (ETI; Tsuda and Katagiri 2010). PTI response is also activated as X. fastidiosa is able to produce PAMPs like proteins that can degrade cell walls, surface adhering proteins, etc. The defense response in conferring resistance is a complex process as there are several unanswered questions like if it is mediated by PTI then what is the necessity for CC-NBS-LRR proteins or if mediated by ETI what effectors from X. fastidiosa are responsible in the absence of type III secretion system. These are intriguing questions that are interesting subjects for research in years to come.

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Proteomics Provides an Insight into a Sustainable Solution for Huanglongbing Disease of Citrus

Huanglongbing (HLB) disease (a.k.a. citrus greening), caused by unculturable gramnegative bacteria Candidatus liberibacter spp., is the most devastating disease of citrus. The disease is transmitted by Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) which when feeds on the leaves transmits the bacteria into the phloem and thereby inhabits and colonizes the sieve tubes. HLB infection in citrus results in blotchy leaves and reduced and sour fruits (da Graça et al. 2015). In spite of some limes, lemons or some citrus species show some resistance against liberibacter infection, but in reality they are all susceptible to the deadly pathogen (Stover et al. 2010). Under the current scenario, it is the need of the hour to develop an accurate and reliable method for diagnosing infected trees that will eventually help in designing an effective strategy for management of HLB. Assays carried out with a polyclonal antibody raised against the outer membrane protein of C.Las (OmpA) were able to detect the presence of the bacteria in the phloem but was not able to detect the bacterial distribution in the diseased trees. Secreted proteins in bacteria plays an important role in disease pathogenesis, and genome analysis revealed the presence of these proteins with a signal peptide at the N-terminus and differentially expressed in the citrus and the psylids suggesting their role as effectors (Duan et al. 2009; Yan et al. 2013). Sec-delivered effectors (SDEs) have been shown in previous researches to move to different parts of the plants just not being restricted to the phloem and thereby spread the infection but simultaneously can also serve as a marker for disease detection. Researches carried out by Pagliaccia et al. (2017) have used SDE1 (Sec-delivered effector 1) as a marker for HLB detection in citrus and have also reported that the antibody raised against SDE1 can be used effectively for disease detection using serological approach. To further characterize the mechanism of infection by C.Las, Clark et al. (2018), performed yeast two hybrid assays using SDE1 and found out that they inhibit papain-like cysteine proteases (PLCPs) that are involved in pathogen resistance. This was confirmed in transgenic citrus overexpressing SDE1 where they have observed reduced expression of PLCPs. Using similar approach, we have performed pull-down assays with total protein from both healthy and Las-infected citrus leaves using two putative effector proteins (designated as LasP235 , LasE3 ) and putative citrus targets using LC MS/MS. Among the identified interactors, we selected Aspartyl protease (AP), glycosyl hydrolases, LTP (lipid transfer proteins), and SOD (superoxide dismutase) for further validation. Using a similar approach, we also identified Kunitz-type trypsin inhibitor protein (KTI), ascorbate peroxidase, alcohol dehydrogenase, and photosystem II proteins. Further biochemical assays, including characterization of AP, KTI, SOD, and hydrolases, antimicrobial activity of LTPs, etc., in the presence and absence of effectors, will be presented in detail. Our understanding of the mechanism of between Liberibacter effectors and citrus proteins should lead to the development of much-needed HLB therapies (S. Basu et al. data unpublished).

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5.3 Linkage Mapping and Quantitative Trait Loci (QTL) Analysis There exists a degree of compatibility between citrus and its close relatives which are essentially diploids with a few occasional tetraploids and triploids and have relatively small genome size (Arumuganathan and Earle 1991). All the researches encompassing genetic mapping or physical mapping over the years on citrus improvement have focused on disease resistance for P. trifoliata through intergenic hybrids. Due to the aforementioned reason, other essential stress tolerance traits like tolerance to cold, disease resistance (Xanthomonas axonopodis pv. citri, Liberibacter asiaticus, Citrus tristeza virus, Tylenchulus semipenetrans) have not been identified in citrus but can be readily identified in other related genera by phenological screening. Traditional breeding approaches have relied on phenotypes and hence are inefficient and timeconsuming methodology and consequently have forced the geneticists and breeders to have linkage maps dependent on molecular markers that can be scored easily. First ever report of linkage mapping in citrus done with leaf isozymes between citrus and Citrus × Poncirus family identified five markers and was also the first report of linkage distortion (Torres et al. 1985). Using a combination of restriction fragment length polymorphism (RFLP) and isozyme methodology, breeders were able to identify 35, 52, and 35 markers in 8, 11, and 10 linkage groups, respectively, using a citrus backcross, intergenic backcross, and a population obtained from the crossing of intergeneric F 1 hybrids (Liou 1990; Durham et al. 1992; Jarrell et al. 1992). Initially, researches carried out for identification of molecular markers with random amplified polymorphic DNAs (RAPDs) followed by intersimple sequence repeats (ISSRs) enabled identification of 310 markers in Durham’s map. A very similar results was observed for Jarrell’s map carried out through the use of simple sequence repeat (SSR) and ISSR where the number of markers was raised to 156 (Kijas et al. 1997; Sankar and Moore 2001). Eventually over the course of years of research, several whole genome maps and maps pertaining to specific traits have been developed that has subsequently enabled identification of quantitative trait loci (QTLs) and gene(s) of significance (Chen et al. 2007). It can be concluded that with the advent of traitspecific mapping progress has been made toward genetic improvement of citrus that includes identification of abiotic stress-tolerant and disease-resistant hybrids. In spite of the ease of access of RAPD (Random Amplification of Polymorphic DNA), AFLP (amplified fragment length polymorphism), and ISSR techniques, they have been used less in comparative genomics and marker-assisted selection (MAS) obviously because of their lack of transferability among populations and the dominant nature of the markers. CAPS (Cleaved amplified polymorphic sequence), RFLPs (Restriction fragment length polymorphism), SCARs (Sequence Characterized Amplified Region), SNPs (Single Nucleotide Polymorphism), and SSRs are being preferred by citrus researchers for their broader applications. Deng et al. (1997) developed SCAR markers using RAPD markers linked to Ctv from P. trifoliate, a gene for resistance against CTV (Deng et al. 1997). To overcome the limitations of SCAR markers, CAPS markers were successfully utilized by García et al. (1999)

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for mapping genes in citrus and Poncirus that were associated with apomixes. In spite of the broad range of applications of these markers, they were of limited use for QTL mapping due to the absence of a genome or transcriptome sequences and hence many of the markers were anonymous to genes. Development of sequences has enabled generation of large datasets of expressed sequence tag (EST) libraries for citrus under varied conditions as well as bacterial artificial chromosome (BAC) sequencing of clones. Using 131 CAPS markers derived from these EST libraries, Omura et al. (2001) were able assign these markers to nine linkage groups spanning a coverage of 685 cM along with their portability to other populations. Several ESTSSRs in citrus have been identified owing to the rapid increase in EST database (Chen et al. 2006), and the result was published on sweet orange and P. trifoliata in 2007. Several international collaborations coordinated by the International Citrus Genome Consortium (ICGC) are underway for EST-SSR mapping with the aim of full-length sequencing of citrus genome and eventually integrating it with genetic and physical maps obtained from BAC sequencing that can subsequently lead to mapping of traits and hence genetic improvement of citrus.

5.3.1 Genomics-Aided Breeding for CS Traits Though a large collection of citrus ESTs have been obtained and deposited, but they were mainly derived from Clementine (C. clementina, 22.2%), sweet orange (C. sinensis, 38.4%), trifoliate orange (P. trifoliata L. Raf, 10.6%), and mandarin (C. reticulata, 9.45%). NCBI (http://www.ncbi.nlm.nih.gov/unigene?term=citrus) and tfGDR have developed UniGene datasets for sweet orange, trifoliate orange, and Clementine. Using clustering analysis, Shimizu et al. (2016) reported 99,000 assembled sequences from all the available EST sequences in public databases. Even though the assembled sequence was larger than the expected genes in citrus genome, a major proportion of them were deduced from orthologs in Arabidopsis and rice. The assembled genome sequence showed significant similarity (86.1%) to shotgun sequence of sweet orange whole genome that was performed by JGI in 2007. Gene ontology analysis classified these genes into three potential categories: i. biological process, ii. cellular components, and iii. biological function (Ashburner et al. 2000). Another analysis showed that these genes can be extrapolated to known metabolic pathway with reference to KEGG ontology terminology and they also belonged to known gene and transcription factor families in Arabidopsis. Once the assembled gene sequences were mapped into several metabolic pathways, researches were being performed for expression profiling and these efforts have been successful owing to the development of different microarrays (Fuji et al. 2008; Terol et al. 2007). Massive parallel signature sequencing (MPSS) has been performed in a sweet orange mutant with redflesh to identify gene expression profiles related to lycopene accumulation (Xu et al. 2010). Though deep sequencing enables identification of

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novel transcripts, splice variants, allelic discrimination, it is not advantageous in comparison with microarrays as it is time-consuming and expensive but it can well be concluded that these can well be used for complementation.

5.3.2 New Insight on Polyploid Citrus Genome Expression Polyploidization is a frequent event in the citrus seedling population with a 1–20% frequency of female gametes (2n) arising probably due to abortion in the megaspore of the first meiotic division (Chen et al. 2008) or second meiotic division (Luro et al. 2004). Tetraploidization resulting as a consequence of chromosome doubling is a feature in apomictic genotypes of citrus. In addition, somatic hybridization has come into prominence with the aim of developing tetraploid parents to be used for triploid or tetraploid rootstock breeding (Grosser and Gmitter 2010). Recent researches have reported that genomic and phenotypic expression in polypoid plants is affected by allopolyploidization (Wendel and Doyle 2005; Flagel and Wendel 2010). In this context, it is important to point out that allopolyploidization affected gene expression can be studied using the model of somatic hybrids that enable combining genomes without sexual recombination. Analysis of volatile compounds in allotetraploid hybrid of six Citrus species with Citrus deliciosa (‘Willowleaf’ mandarin) as the parent have shown the dominance of the mandarin trait-like low amount of sesquiterpene alcohols or the absence of monoterpene aldehydes and alcohols (Gancel et al. 2003). A similar result was observed in proteome analysis of allotetraploid somatic hybrids of C. deliciosa with C. aurantifolia and Fortunella margarita where only protein spots corresponding to the mandarin parent was present and proteins specific to the nonmandarin parent were silenced in the hybrid (Gancel et al. 2006). A transcriptome analysis of allotetraploid between C. reticulata cv ‘Willowleaf’ mandarin + C. limon cv ‘Eureka lemon’ identified downregulation of the differentially expressed genes in either of the parents with more similarity to the mandarin parent, thereby providing an evidence of transgressive overexpression as well (Bassene et al. 2010). In addition, Bassene et al. (2009) demonstrated the potential effect of non-additive gene expression in modulating the phenotype of somatic hybrids by estimating expression of genes from carotenoid/ABA biosynthesis pathway as well as the carotenoid and ABA content.

5.4 Brief on Genetic Engineering for CS Traits Essentially, transformation in citrus is done by using Agrobacterium following conventional protocol, but the regeneration efficiency is very low, and the easy way to overcome this problem is to transform the adult material (Cervera et al. 1998). Transformation is considered to be an alternative to breeding, but not many reports have been published illustrating its use in functional genomics. Researches carried out

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in tobacco overexpressing GA 20-oxidase from citrus resulted in increased content of gibberellin and enhanced shoot growth (Vidal et al. 2001). Later this observation was confirmed when transgenic Carrizo rootstocks were generated overexpressing the same gene (Fagoaga et al. 2007). An interesting point to consider here was that the cell elongation was less affected than cell division (Talón et al. 1991). Several other examples of citrus transgenics include generation of antisense construct for ACC synthase and pectin methylesterase gene (Cs-PME4) that suppressed the accumulation of ACC and juice cloud separation, respectively (Guo et al. 2005). In another example, where LEAFY (LFY) or APETALA1 (AP1) genes from Arabidopsis were overexpressed in Carrizo seedlings that resulted in reduced flowering which correlated positively with transcript accumulation of CsTFL and negatively with levels of LEAFY (LFY) or APETALA1 (AP1) (Pillitteri et al. 2004a, b). Similarly, transgenic Poncirus overexpressing CiFT homolog to FLOWERING LOCUS T showed reduced flowering time, but it was accompanied by pleiotropic effects (Endo et al. 2005). Owing to its importance in industry majority of studies have focused on developing stress tolerance primarily to biotic stresses and also a little bit to abiotic stress. Carrizo rootstocks overexpressing 1 -pyrroline-5-carboxylate synthetase exhibited enhanced tolerance to drought stress (Molinari et al. 2004). Researches carried out by Alvarez-Gerding et al. (2015) have shown that Carrizo rootstocks overexpressing glyoxalase genes (BjGlyI and PgGlyII) showed increased tolerance to salt stress as observed through increased shoot biomass and less chlorosis. Overexpression of a 600 bp upstream region from CuLea5 in Arabidopsis showed stress-inducible gene expression and expression of fruit specific genes (Kim et al. 2011). Very similar results of osmotic stress tolerance were obtained on overexpression of phytoene synthase (PSY3) from grape fruit in tobacco evidenced through increased biomass and ABA accumulation (Cidade et al. 2012). Similarly, overexpression of a PR protein from tomato, p23 showed increased antifungal activity against P. citrophthora (Fagoaga et al. 2006). On a similar ground overexpression of transgenes p23 and p25 from citrus tristeza virus (CTV) have shown increased tolerance via pathogenderived resistance (PDR) (Domínguez et al. 2002). Researches are being carried out all over the world to find a solution to the deadly Huanglongbing disease of citrus but with little or no results. Under this situation, transgenic citrus has been developed overexpressing NPR1 gene from Arabidopsis under the control of CamV35S promoter or a phloem-specific promoter. Both transgenic citrus varieties ‘Hamlin’ and ‘Valencia’ showed enhanced tolerance by modulating the defense signaling pathways (Dutt et al. 2015). Though these studies focused on improving disease resistance by engineering foreign genes, recent study carried out by Hao et al. (2016) has shown that overexpression of a modified thionin from citrus showed enhanced resistance against citrus canker and HLB.

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5.5 Recent Concepts and Strategies Developed 5.5.1 Gene Editing for a Sustained Immunity in Plants Using resistant crop varieties identified from screening of natural germplasm has been the most preferred solution for developing pathogen resistant commercial varieties. Unfortunately, these varieties have not shown expected results under field conditions. Hence under the present scenario, it is more beneficial to combine new breeding techniques (NBTs) with genome engineering to develop a climate-smart high yielding crop. NBT is a very broad term that expands from precision gene editing, speed-breeding to high-through phenotyping and genotyping platforms (Li et al. 2018; Watson et al. 2018). However, the most preferred form of gene editing makes use of site-specific nucleases (SSNs) to insert DNA lesions at a target region that can consequently lead to induction of defense pathways in the host. A significant disadvantage is that error-prone NHEJ (non-homologous end-joining) can aid the DNA repair, thereby resulting in a non-functional allele and a subsequent alteration in the desired trait. Over the years, CRISPR has developed as the most acceptable platform for gene editing due to higher effectiveness, precision and has been successfully applied in several crop species like rice and wheat (Zaidi et al. 2018) for developing resistance to diseases, or abiotic stresses (Sun et al. 2016; Zhao et al. 2016). Various methods like Cre/loxP and FLP/FRT have been implemented previously for generating ‘transgene-free’ crops, but it was ineffective. With the emerging technology of CRISPR, it has become convenient to develop disease resistance and has been successfully demonstrated in Arabidopsis (Pyott et al. 2016), but still it has its limitations for crops that are propagated vegetatively through scions or rootstocks. As an alternative researches have demonstrated the use of a complex of Cas9 and guide RNA (gRNA) [ribonucleoprotein (RNP) complex] for generating mutants by particle bombardment, or using viral vectors (Zhang et al. 2016; Zaidi and Mansoor 2017). Though these methodologies have technical limitations, it will remove the hassle for GMO regulations and has been already implicated in USA and efforts for commercialization in other crops like soybean, maize are in progress (Waltz 2018). It has been observed in several crops of economic importance like citrus that if the S gene is disrupted, it can confer disease resistance. One of the most devastating diseases of citrus is citrus canker that is caused by Xanthomonas citri subsp. citri (Xcc) has resulted in severe yield losses worldwide. CRISPR–Cas9 mediated silencing of CsLOB1 (LATERAL ORGAN BOUNDARIES1) responsive for growth of the pathogen and pustule formation resulted in disease resistance (Jia et al. 2017). In another research conducted by Peng et al. (2017) have targeted the EBE (effector binding elements) region in the promoter of CsLOB1 and shown that it can increase the resistance of citrus against canker. The phenotype of both the mutants and the wild-type plants was very similar suggesting CsLOB1 as a potential candidate for engineering disease resistance in commercial citrus varieties (Yin and Qiu 2019). In another study, it has been demonstrated that expression of Cas9 under the Arabidopsis YAO promoter resulted in highly efficient transformation which was successfully

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studied for mutating PDS (phytoene desaturase) gene in citrus (Zhang et al. 2017). In addition, it has also been shown SaCas9/sgRNA, derived from Staphylococcus aureus can be used as an alternative tool for engineering mutation in citrus as has been shown by Jia et al. (2017) where they have successfully mutated CsPDS and Cs2g12470 genes. With the successful implementation of CRISPR in conferring resistance against canker, researchers have started to use this platform for developing resistance against HLB disease of citrus like mutating the DMR6 orthologs in Carrizo (Zhang et al. 2018). Though the solution seems very simple, the effective implementation is very difficult owing to the complexity of diseases like HLB that is multigenic and identifying the likely candidate is very tricky. Another aspect of hindrance is the genome sequence for citrus. In spite of the fact that Clementine genome sequence is very clear (good coverage, less transposable elements and duplicate gene sequences), it is not complete and perfect and hence designing guide RNA molecule with higher obstacle is very challenging. Besides those mentioned previously another important point to consider is that sequences of the target genes might be different between different citrus species and also the transformation efficiency is very low. In addition, many genes from different families have very similar sequences so the effectiveness of the guide will be reduced but the brighter side of all these pitfalls is that continuous research is under progress and very soon these difficulties will be resolved.

5.5.2 Role of Nanotechnology in Precision Agriculture Nanotechnology, the recent advancement in the field of science, is an interdisciplinary approach (Duhan et al. 2017). Previously over the years, a lot of researches have been conducted to use nanotechnology in improving yield of crop plants (Mishra et al. 2016). The advent of green revolution has resulted in increased usage of pesticides which has led to loss of biodiversity and led to the development of microbial resistance. Under the current scenario, it is worth mentioning that nanoparticle-mediated delivery in plants and use of biosensors for precision farming can be done only by using nanoparticles or nanochips. The major advantage for using nanoparticlemediated delivery is that the availability of the nutrients or agro-chemicals is regulated and it is only up to a certain dose that it becomes available to the plants. Researchers in wine industry are making use of nanotechnology-based viral detection kits for detection of leaf roll or red blotch viruses. These nanoparticles are synthesized from metals or metal oxides and their potential role in protection against biotic or abiotic stresses or in regulating plant growth and development is being assessed. Silicon-based nanoparticle has been used to develop tolerance to abiotic stresses via improvement of nutrient uptake, enhancement of antioxidant enzyme activity (Liang et al. 2007; Saxena et al. 2016). A research group at University of Florida has developed nanotechnology-enabled Cu formulations that have potential

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in combating citrus canker due to their antimicrobial properties. They have applied these successfully in field trials and are in the process of commercializing them. Zinkicide SG4 and SG6 have been tested for their efficacy against X. citri, the causal agent of citrus canker. They were found to very effective in comparison to copper and zinc oxide formulations (Graham et al. 2016). On similar grounds, essential oils extracted from citrus peels have been used as emulsions in polyethylene glycol (PEG) nanoparticles with a very positive effect on tomato pest Tuta absoluta (Camplo et al. 2017). Ghosh et al. (2018) demonstrated the efficacy of 2S albumin-Nano-ZnO formulation against C.Las. It was observed that they were very effective in suppressing C.Las and could be used as novel therapeutic strategy for mitigating HLB in citrus.

5.6 Brief Account on Role of Bioinformatics as a Tool The preliminary annotated assemblies for sweet orange genomes both haploid and diploid for Clementine can be accessed through tfGDR (https://www. citrusgenomedb.org/) and phytozome.net. In addition, Wu et al. (2018) have submitted whole genome shotgun-sequencing data at NCBI (BioProject PRJNA414519). Besides there are other sequencing datasets available at NCBI under the BioProject with accession numbers PRJNA320985, PRJNA321100 for mandarins and oranges, respectively, while other datasets are available at the NCBI Sequence Read Archive with accession codes SRX372786 (sour orange), SRX372703 (sweet orange), SRX372702 (low-acid pummelo), SRX372688 (Chandler pummelo), SRX372685 (Willowleaf mandarin), SRX372687 (W. Murcott mandarin), SRX372665 (Ponkan mandarin), and SRX371962 (Clementine mandarin). Besides, some draft genome sequences are also available from http://www.citrusgenome.jp with accessions BDQV01000001 to BDQV01020876. These web portals have their specific tools for browsing the genomes or utilizing them for specific targeted search. These web portals will also allow access to NCBI citrus genome resources that is not limited to EST, SSRs, or BES of Clementine. Further, more genome information can be accrued from HarvEST:Citrus v 1.32 (harvest.ucr.edu) that houses 469,618 ESTs and 141 libraries from Poncirus and Citrus. Besides genome transcriptome data are also available at Sequence Read Archive (SRA) database in NCBI under the accession number of PRJNA387319 and also from http://bioinfo.ibmcp.upv.es/genomics/cfgpDB/ workplan/transcriptome.html. To conclude, it can be said that as more researches are being carried out these public repositories will get populated and their use will grow exponentially.

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5.7 Brief Account on Social, Political, and Regulatory Issues 5.7.1 Concerns and Compliances About Gene Editing and Genetically Modified Crops The advent of new biotechnology techniques has provided a range of options for improving nutrient contents, tolerance to environmental stresses, and improved yield. Though these techniques and genetically modified (GM) crops have gained popularity, it has raised tremendous uproar leading to social and ethical disputations between the producers, researchers, policy makers, and the consumers. Though, USA and some other parts of the world have accepted genetically modified food crops like soybean, corn, etc. as well as non-food crops like cotton, but Europe has raised concerns about cultivation of GM crops (Maghari and Ardekani 2011). An important point that needs to be considered here is that there is a difference between gene editing and GM where it needs to be clarified that GM primarily caters to the insertion of a foreign gene into the host from another sources, while gene editing essentially introduces mutation in the host genome. Gene editing is similar to the preexisting concept of ‘mutation breeding’ but with more precision and accuracy. In USA, Environmental Protection Agency (EPA) and Food and Drug Administration (FDA) are looking into these processes and are the main factors controlling the implementation of genetically edited food crops. Recently, it is becoming clear that USDA will not be regulating the gene-edited plants.

5.8 Conclusion and Future Directions Here in this review, we have focused on the different facets of researches being carried out in citrus encompassing genomics, transcriptomics, proteomics, and breeding strategies. In the course of this discussion, we have presented several problems that are coming in the way and presented some plausible solutions for the same to increase the value of citrus to the fruit industry and also for incorporating the knowledge into developing new plants. In the recent past, citrus research community is coming together to improve the quality of research as it is impossible for one group to address the global problems that this fruit crop is facing. Citrus is not only an essential fruit crop with benefits to humanity, industry, but it also possesses some unique biological characteristics essential for research. The data generated from these international groups will be of immense help in advancing citrus researches in the future by overcoming the obstacles in its path.

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Chapter 6

Genomic Designing of Climate-Smart Coconut S. V. Ramesh, V. Arunachalam and M. K. Rajesh

Abstract Coconut (Cocos nucifera L.), belonging to the family Arecaceae, has earned the epithet ‘Kalpavriksha’ (“Tree of Life”) because of its multitude of uses. Changing and future climate scenarios have imposed severe constraints on the production of coconut with simulation studies revealing that productivity will be severely affected. Nevertheless, plant genomics has offered many novel approaches to meet the exigencies of climate change by identifying novel genetic sources for future climatic conditions and developing suitable cultivars to tolerate increased drought, high temperature, pests, and diseases’ complex. The perennial and open-pollinated nature of the crop and its long gestation period has hampered the application of genomics in coconut improvement. Next-generation sequencing (NGS) technologies have helped in the generation of enormous genomic and transcriptomic sequence information at a relatively low cost. Also, breeders’ toolbox has been improved with the availability of various techniques such as molecular marker kits, association mapping, and genomic selection. This chapter describes the impact of climate change in coconut, progresses made in the field of coconut genomics, and various approaches being followed to develop climate-smart coconut. Keywords Cocos nucifera L. · Genomics · Molecular markers · Climate change · Transcriptomics · Palms

6.1 Introduction Palms supply various products such as food, fiber, fuel, and wood, and they constitute an economically important group of plants for human well-being, next only to cereals and pulses. Botanically, palms are classified under the family Arecaceae, a member of S. V. Ramesh · M. K. Rajesh (B) ICAR-Central Plantation Crops Research Institute (ICAR-CPCRI), Kasaragod 671124, Kerala, India e-mail: [email protected] V. Arunachalam ICAR-Central Coastal Agricultural Research Institute (ICAR-CCARI), Ela, Old Goa 403402, Goa, India © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_6

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order Arecales of Angiosperms in the Viriplantae group. Among the palms, coconut (Cocos nucifera L.) is an economically valuable crop of humid tropics that offers food, edible oil, coir (fiber), and mineral-rich refreshing drinks, namely tender nut water and inflorescence sap. It is inextricably linked to the lives of people of South, South-East Asia, and the Pacific Islands. Coconut generally grows between 23° N and 23° S latitudes (Kumar and Aggarwal 2013). Even though the crop thrives at 26° N latitude, temperature is a major limiting factor that hinders the growth of the crop beyond the north of these latitudes (Kumar and Aggarwal 2013). The most favorable weather parameters required for the growth and economic yield of coconut comprise an evenly distributed rainfall of 130–230 cm/annum, copious sunlight in the range of 250–350 Wm−2 along with minimum sunshine period of 120 h/month. Also, a mean annual temperature of 27 °C and humidity of above 60% favor optimal growth and yield of the crop (Child 1974; Murray 1977). The extent of genetic variability in coconut is very high; hence, it possesses high morphological, physiological, and biochemical variations. Undoubtedly, the numerous applications of various parts of coconut trees have earned the epithet “Tree of Life”. Further, the antimicrobial property of coconut husk and rich nutrient contents of coconut water has also been recognized. Despite the importance of coconut in tropical ecosystems, the potential of the crop has not been realized. There are a number of barriers such as non-availability of quality planting materials and true to type progeny seed nuts, biotic and abiotic factors, and fluctuating prices hinder the realization of the production potential of the crop (Buschena and Perloff 1991). Coconut accessions are a separate set of inter-breeding gene pool as their wild relatives are unknown yet. Further, molecular investigations have revealed that coconut was brought into cultivation in two different geographical locations namely the Indian Ocean and the Pacific Ocean basins (Gunn et al. 2011). In general, enrichment of gene pool by a collection of germplasm (both indigenous and exotic) and classical breeding techniques such as selection, hybridization, etc., are being utilized in the productivity enhancement of coconut (Arunachalam and Rajesh 2008, 2017). Being a perennial crop with a long gestation period, coconut requires enormous land and other resources for experimentation. Nonetheless, breeding approaches have yielded improved cultivars of coconut for specific traits and productivity levels (Nair et al. 2016). Some of the instances of successful breeding efforts are the development of coconut hybrids involving dwarfs and talls that have yielded intermediate forms, which are early bearing, disease resistant, and give high economic yields. Introgression of lethal yellowing disease resistance in the coconut palms grown in Latin America and Caribbean islands by crossing the Malayan dwarf forms of coconuts from Malaysia with the indigenous talls is worth mentioning (Been 1981). Spontaneous mutants of coconut (‘Makapuno’), which results in the development of a jelly-like endosperm (Zuñiga 1953), are governed by a single recessive gene and such nuts possess high commercial value. Coconut is characterized by chromosome number of 2n = 32 with an estimated genome size of 2950 million base pairs (Mbp) (Gunn et al. 2015).

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6.2 Coconut Genome The availability of genome sequence information of a crop provides a great impetus to the genetic improvement programs as it aids in the development of molecular markers linked to important traits, in addition to the discovery of novel genes, rare alleles, and other regulatory elements. Non-model and non-cereal crops like coconut could not reap the fruits of the first wave of genome sequencing projects which were based on Sanger sequencing technique. However, the relatively cheap next-generation sequencing (NGS) technologies resulted in the discovery of genetic elements unique to palm species such as coconut (Unamba et al. 2015), oil palm, and date palm. Among the palms, the genomes of oil palm, date palm, and coconut are available in the public domain (Al-Dous et al. 2016; Al-Mssallem et al. 2013; Dussert et al. 2013; Singh et al. 2013; Xiao et al. 2017). The chloroplast and mitochondrial genomes of coconut have been sequenced (Huang et al. 2013; Aljohi et al. 2016). The haploid genome size of coconut (C-value) corresponds to the quantum of DNA in its gametic nucleus (pollen or sperm) and is represented in picograms (pg) or in base pairs (bp) (Greilhuber et al. 2005). The preliminary analysis of genome size and ploidy levels of cultivated coconuts are imperative for embarking upon coconut genome sequencing efforts as it helps to decide the optimal depth of reads required for optimal assembly and annotation of the coconut genome (Gunn et al. 2015). Flow cytometry analysis of 23 coconut cultivars, including dwarfs, talls, and hybrids, has estimated that the average genome size of coconut is 5.966 ± 0.111 pg (Gunn et al. 2015). Further, it was deduced that intraspecific variation observed was associated with the process of domestication and tall cultivars showed wide genetic variation compared to the dwarf cultivars. A draft genome of coconut was reported by Alsaihati et al. (2014) based on the seven libraries of paired-end and mate-pair genome sequencing using Illumina’s HiSeq platform. In addition, it was also estimated that 50–70% of the genome was repetitive sequences with the size of the genome standing at ~2.6 Gb. Various genome assembly and annotation techniques and tools were utilized (ALLPATHS-LG and SOAPdenovo2), and ultimately a quality draft genome, with a coverage of 94.5%, was assembled using a custom bioinformatics pipeline. This assembly has only a gap of around 10%. Whole genome sequence of coconut cultivar Hainan Tall was obtained by Xiao et al. (2017). This sequencing effort generated a scaffold length of 2.2 Gb, representing over 91% of the estimated genome of coconut. Interestingly, the coconut genome has been predicted to encode 28,039 proteins, whereas the genetically related palms such as Phoenix dactylifera (variety PDK30), Phoenix dactylifera (variety DPV01), and Elaeis guineensis were predicted to encode 28,889, 41,660, and 34,802 proteins, respectively. Also, it was deduced that the major chunk of coconut genome (72.75%) comprises of transposable elements, among which longterminal repeat elements (LTRs) contribute to over 92% of transposable elements (Xiao et al. 2017). Evolutionary molecular genomic analysis using the Bayesian molecular clock revealed that divergence between coconut and oil palm happened around 46.0 (25.4–83.3) million years ago (Xiao et al. 2017). Comparative genomics divulged that coconut has acquired many ion channel/transporter gene families such

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as Na+ /H+ antiporters, carnitine/acylcarnitine translocases, potassium channels, and potassium-dependent sodium-calcium exchangers. These gene families have greatly helped coconut in its environmental adaptations to salt stress, accumulation of fatty acids, and potassium. To complement these efforts, the genome sequence of dwarf coconut variety, ‘Catigan Green Dwarf’ (CATD), was made available recently. Comparative genomics of tall and dwarf coconut genomes revealed 58,503 variants (Lantican et al. 2019). Genome sequence data of coconut, thus, is an invaluable resource for the development of high-density linkage maps, molecular maps, genome-wide SSRs, identification of quantitative trait loci (QTLs), association mapping, and molecular breeding efforts to develop varieties that are resilient to climate change, pests, and disease outbreak and improved biochemical features.

6.3 Organelle Genomes of Coconut The genome sequence analysis of a subcellular organelle, chloroplast, of a dwarf form of coconut revealed that its size is 154,731 bp and harbors 130 genes and four pseudogenes (Huang et al. 2013). Event though the genome of coconut was smallest among the palms, genome organization, gene content, and repeat structures display colinearity with other palms (Huang et al. 2013). However, some of the unique features of the chloroplastic genome include rps19-like gene pseudogenization and relatively high RNA editing sites. Coconut mitochondrial genome (Oman Local Tall cultivar) was found to be around 679 kbp with an estimated GC content of 45.5%. The mitochondrial genome encodes 72 proteins, nine pseudogenes, 23 tRNAs, and 3 rRNAs (Aljohi et al. 2016). Interestingly, the other mitochondrial genome of family Arecaceae is that of date palm (Phoenix dactylifera) (Fang et al. 2012). Comparative analysis showed that chloroplast-derived regions of coconut mitochondria are just 5.07% of the total assembly, whereas 93.5% of the mitochondrial DNA of the date palm is derived from chloroplast (Fang et al. 2012).

6.4 Coconut and Climate Change According to the United Nations Framework Convention on Climate Change (UNFCCC), climate change is defined as “A change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods”. Thus, UNFCCC clearly distinguishes the climate change phenomenon from ‘climate variability’, which is the result of natural causes. Among the climatic factors that influence the growth and development of crops, rainfall, temperature, and CO2 are predominant factors. On the one hand, higher temperatures directly interfere with the process of photosynthesis, affect the accumulation of photosynthates in the sink (Kumar et al. 2008; Asseng et al. 2015; Hatfield and

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Prueger 2015), and shorten the growth period. On the other hand, low-temperature conditions damage the tissues and slow down the metabolic process, thereby affecting the growth and development of crops. The projections of frequent droughts and heavy precipitations suggest that perennial crops such as plantations will be exposed to multiple stresses even during the same growing season (Kumar et al. 2012). The drought situations will be further aggravated by increased light conditions causing photo-oxidative damages to the cell membranes of plants leading to leaf scorching and yield reduction (Kumar and Kasturi Bai 2009). Nevertheless, increased CO2 concentrations in the atmosphere will benefit the C3 crops such as coconut to accumulate relatively more biomass (Ainsworth and Long 2005). Under nitrogen-limiting environmental conditions, the accumulation of protein might decline. Hence, it is imperative to supply the required quantum of water and nutrients to reap the benefits of elevated CO2 [ECO2 ] conditions. Analysis has also revealed that projected yield levels (in the 2030s) of crops such as maize, mustard, wheat, rice, and sorghum will witness a decline of 2.5–12%. Nevertheless, yield levels of coconut in the west coast and northeastern regions of India are projected to increase (Kumar and Aggarwal 2013). Anatomical, morphological, and biochemical features such as stomatal density, root system density, and stored sugars play a role in drought tolerance of coconut palm (Gomes and Prado 2007). Coconut seedlings displayed increased levels of heat-stable protein fractions in response to stress by high temperature, flooding, and high irradiance (Kumar et al. 2007). Epicuticular wax content of leaves increased in coconut during drought (Kurup et al. 1993) in Tall, T x D, and D x T hybrids. Deposition of wax aids in adaptation to drought stress in plants. QTL mapping of wax composition in coconut leaves was attempted (Riedel et al. 2009), where several simple sequence repeat and amplified fragment length polymorphism (AFLP) markers associated with the wax compositional trait were identified.

6.5 Projected Effects of Climate Change on Coconut Over 90 countries spread across tropical regions of Asia, America, East Africa grow coconut. Among them, Indonesia and the Philippines together contribute around 56% of the global coconut production which is followed by India and Brazil (Burton 2019). Major coconut growing states of India are Kerala, Karnataka, Tamil Nadu, Andhra Pradesh, Telangana, Maharashtra, West Bengal, and Assam. Coconut plantations generally provide sustenance to the millions of farming communities in these regions. Interestingly, over 70% of coconut production in India is confined only to the 20 districts of Kerala, Tamil Nadu, and Karnataka making it more susceptible for changes in climatic factors. Thus, coconut plantations are predominantly grown in ecologically sensitive zones such as coastal regions and in island ecosystems. Also, coconut being a perennial crop faces the effects of climate change even during a single generation or in the standing population. In all likelihood, coconut will encounter increased CO2 , elevated temperatures, frequent droughts and flood situations, and

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biotic stresses during its 60 years of economic yielding period. Coconut, being a perennial crop, is vulnerable for drought or high-temperature stress for a relatively long period. Therefore, elevated temperature and drought stresses during the critical period of inflorescence development affect not only the nut yield of current year but also three more ensuing years (Kumar et al. 2002; Rajagopal and Kasturi Bai 2002). Also, the physiological and biochemical effects of water-deficit stress and the productivity of coconut have been studied in detail (Gomes et al. 2008). Nevertheless, coconut has many adaptive strategies to withstand the stress conditions (Kasturi Bai et al. 2009). Leaf anatomical and morphological features have been linked to drought tolerance in coconut. Epicuticular wax on the upper epidermis of the leaves and scalariform thickenings in the tracheids are the major traits that define drought tolerance in coconut (Kumar et al. 2000). Also, dwarf cultivars consume more water owing to high stomatal frequency (number of stomata/unit leaf area), low epicuticular wax, and poor stomatal regulation (Rajagopal et al. 1990). Contrarily, tall varieties are found to have conservative water utilization pattern (Voleti et al. 1993).

6.6 Approaches to Study the Impact of Climate Change in Coconut A multi-pronged approach has been developed to study the impact of climate change on coconut plantations. These strategies include (a) analyzing the response of coconut seedlings to elevated CO2 (550 ppm and 700 ppm) and elevated temperature (2 °C), (b) phenotyping for water use efficiency and drought tolerance, (c) surveys, and (d) simulation analysis using InfoCrop-COCONUT model.

6.6.1 Effect of Elevated CO2 [ECO2 ] Exposure of coconut seedlings to elevated CO2 (550 and 700 ppm) conditions has shown that it benefited growth and development of seedlings owing to increased assimilation of CO2 and greater photosynthetic rates, thereby resulting in increased shoot and root biomass. Biochemical analysis of coconut leaves exposed to elevated CO2 showed increase in photosynthates such as soluble sugars, free amino acids, reducing sugars, and starch. Nonetheless, elevated CO2 has reduced the total phenols, activity of polyphenol oxidase enzyme, root and shoot C: N ratio suggesting that climate change phenomenon might make coconut susceptible to pests and diseases due to reduced leaf polyphenol content (Sunoj et al. 2013). Open-top chamber (OTC) experiments have revealed that biomass of coconut seedlings grown at 550 and 700 ppm CO2 increases up to 8% and 25%, respectively, compared to 380 ppm CO2 under ambient atmospheric conditions (Hebbar et al. 2013a).

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6.6.2 Effect of Elevated Temperature [eT] Exposure of coconut seedlings to elevated temperature conditions of 3 °C above 31 °C has adverse effects on their growth and development due to reduced photosynthesis, reduced leaf area growth, and reduction in chlorophyll content. Greater reduction in photosynthates (sugars, reducing sugars, and starch) and polyphenols were observed. However, elevated temperature conditions have induced greater deposition of epicuticular wax on the surface of coconut leaves (Hebbar et al. 2013b). Effect of elevated temperature (ET) on the quality of the copra, oil composition, and nut yield of coconut grown in various agroclimatic conditions has also been studied in detail (Kumar 2005). Atmospheric temperature exerted greater influence on the ratio of saturated to unsaturated fatty acids. This ratio showed upward trend with the increase in minimum temperature, whereas it showed a slight decline decrease with the increase in maximum temperature (Kumar 2005). Increase in maximum temperature caused reduction in copra yield and or nut yield; however, oil percentage showed an incremental trend. Genotypic variations in the cardinal temperatures (T min , T opt , and T max ) for pollen germination and growth of pollen tube were observed in coconut. In vitro pollen germination studies revealed that tall genotypes such as West Coast Tall (WCT), Laccadive Ordinary Tall (LCT), Federated Malayan Straits Tall (FMST), dwarf cultivar Chowghat Orange Dwarf (COD) and hybrids (COD X WCT and MYD X WCT) exhibited adaptability to high temperature while the dwarf genotype Malayan Yellow Dwarf (MYD) was found to be least adaptable (Hebbar et al. 2018). Biochemical changes associated with high temperature divulge that a reduction of 20% of soluble protein and inverse relationship between superoxide dismutase (SOD) enzyme activity and pollen germination. Thus, genotypic variations for in vitro pollen germination and pollen tube growth allowed categorization of coconut genotypes: Category 1 varieties: relatively high T opt and low T min and high T max and thus have wider adaptability and these genotypes also had high germination (e.g., WCT, LCT, COD, and hybrids), Category 2: though sensitive to T min but exhibited relative tolerance to T max hence are moderately adaptable for hightemperature conditions [e.g., Philippines Ordinary Tall (PHOT), Cochin China Tall (CCNT), Gangabondam Green Dwarf (GBGD), Chowghat Green Dwarf (CGD), and CRD (Cameroon Red Dwarf)], Category 3: these genotypes showed high T min and low Tmax , coupled with poor germination, and are thus least adaptable to temperature variations (e.g., MYD) (Hebbar et al. 2018). Predicted climate change effects in the selected sites of Sri Lanka showed that an increase in maximum temperature would be an important yield-limiting factor for coconut (Pathiraja et al. 2017). Coconut hybrids have been classified into different heat-tolerant groups based on their pollen germination percentage and tolerance to high temperature. Thus, SLGD × Sri Lanka Tall and Sri Lanka Brown Dwarf × The SLGD Tall were identified as tolerant to ET (Ranasinghe).

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6.6.3 Statistical Analysis A comprehensive study by Peiris (2006), using statistical simulation models, has shown that changes in monsoon rainfall pattern and maximum atmospheric temperature are the two major determinants of coconut production in the changed climatic conditions. Further, it was also deduced that coconut production in the 2040s with the changed climatic parameters, provided all the other external factors remain constant, is projected to remain insufficient to meet the consumption demands (Peiris 2006). Economic analysis of the cultivation of coconut in Sri Lanka deduced that 60% of the variations in the yield could be attributed to the changes in the climatic parameters (Fernando et al. 2007).

6.6.4 Simulation Models InfoCrop-COCONUT model is a comprehensive tool that helps to analyze the future climate scenarios along with the management practices so as to analyze the regional impacts of climate change, vulnerability and to devise adaptation measures. In addition, the combination of crop simulation models and remote sensing and geographic information system (RS-GIS) offers an opportunity for better land use planning, yield, and calamity forecasting and effective monitoring. Simulation analysis predicted that an increase in productivity of up to 4% (in 2020), and 20% (in 2080) over current yields. In the west coast regions of India, a yield increase of up to 39% is predicted (in 2080), whereas, in east coast regions, a decline in yield of 31% (in 2080) over the yield levels of 2009 was projected (Kumar and Aggarwal 2009). Also, analysis of the potential gains due to adaptation strategies has found an increase in coconut productivity by 4.3–6.8% for the years 2030 and 2080, respectively, over the 2000–2005 productivity levels provided the current management practices are followed (Kumar and Aggarwal 2013). Coconut production forecasting estimates in Sri Lanka revealed that the annual rainfall pattern during the months of January to March determines the yield levels. Among the other weather parameters, ambient temperature and relative humidity significantly contribute to the nut production (Peiris et al. 2004). The process-based models such as InfoCrop-COCONUT could not be applied effectively in the context of Sri Lanka because of the paucity of data. However, analytic hierarchy process (AHP)-based expert system modeling approach has been utilized in climate change prediction and adaptation studies as a decision support tool (Pathiraja et al. 2017).

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6.7 Strategies for Developing Climate-Smart Coconut 6.7.1 Genetic Resources The vast resource of germplasm accessions available in coconut can be screened for tolerance toward various biotic and abiotic stress factors. Tolerant traits such as heat tolerance, viability of pollen at high temperatures, nut retention at elevated temperatures are some of the important selection criteria to obtain climate-resilient varieties (Hebbar et al. 2016). Incorporation of genotypes characterized with improved in situ drought tolerance in crop improvement programs is an important and viable approach to design resilient coconut genotypes for changing climatic conditions. For instance, such an approach in coconut population improvement programs has yielded many drought-tolerant varieties, namely Chandra Kalpa, Kalpatharu, Kera Keralam, Kalpa Mitra, Kalpa Dhenu, Kera Sankara, and Chandra Laksha (Kasturi Bai et al. 2009). Drought and heat or excessive temperature are the two important factors that affect yield especially during critical stages of plant growth including pollination, flowering, and fruit development. Water use efficiency (WUE) has been shown to vary among varieties/genotypes and considered as one of the important traits to identify and select drought-tolerant genotypes. WUE in coconut is found to be regulated by both efficient water uptakes by root systems as well as controlled water loss through better regulation of stomatal movement (Hebbar et al. 2016). Tall genotypes Kalpadhenu, FMST, Kalpatharu, etc., exhibited high WUE due to a better root system (Hebbar et al. 2016; Ramesh et al. 2019). Nevertheless, dwarf forms of coconut showed high WUE during water-deficit stress by effective stomatal regulation. Stresstolerant plants in addition to possessing higher photosynthesis accumulated more epicuticular waxes on the leaves for better conservation of moisture. Pollination, being one of the most sensitive phenological stages to heat stress, in vitro pollen germination characteristics of the coconut genotypes has been studied in detail to ascertain their tolerance/susceptibility for high temperature. Tall coconut cultivars such as WCT, LCT, FMST, and the dwarf cultivar COD and hybrids showed relatively good adaptability to high-temperature stress, whereas the dwarf cultivar MYD was the least adaptable (Hebbar et al. 2018). This technique identified temperature tolerant genotypes suitable for climate-vulnerable regions.

6.7.2 Genome-Wide Approaches It is pertinent to utilize molecular markers for genomics-assisted breeding in coconut; hence, marker-trait association and QTL mapping form an important objective for the development of climate-smart coconut. Random amplified polymorphic DNA (RAPD) and simple sequence repeat (SSR) markers linked to eriophyid mite resistance (Shalini et al. 2007), lethal yellowing disease resistance (Cardena et al. 2003) and plant habit (Martinez et al. 2010; Rajesh et al. 2013a, b) have been identified.

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Similarly, the use of molecular markers in QTL mapping for economically important coconut traits such as earliness in flowering (Herran et al. 2000), nut yield (Lebrun et al. 2001), fruit characteristics features (Baudouin et al. 2006), and epicuticular wax composition (Riedel et al. 2009) has been successful. It is also important to saturate the linkage map of coconut with an additional set of molecular markers so that breeding efficiency is improved in a perennial crop like coconut (Rivera et al. 1999). A global strategy for conservation and utilization of coconut genetic resources have unveiled that effective phenotyping should consider the inclusion of phenology and assessment of biomass assessment so as to ascertain the metabolic efficiency at plant scale. Also, a saturated and robust linkage map is being developed by utilizing the core set of single-nucleotide polymorphism (SNP) and SSR markers. Genotyping by sequencing (GBS) approach is being followed for characterizing the mapping population developed from Côte d’Ivoire and the Philippines to saturate the linkage map and for further QTL mapping studies, respectively. Recently, genomic loci governing domestication traits have been identified by Perez et al. (2019) utilizing GBS approach. Population genomics of Northern South American coconut genotypes reaffirmed the genetic differentiation corresponding to the Pacific and Indo-Atlantic oceanic basins (Table 6.1). Table 6.1 Putative QTLs for various traits in coconut Trait(s)

Cross

No. of QTLs identified

Reference

Early germination, early flowering, and yield

Malayan Yellow Dwarf x Laguna Tall

6

Herran et al. (2000)

Number of bunches and number of nuts

Cameroon Red Dwarf x Rennell Island Tall

9

Lebrun et al. (2001)

Fruit and yield components (fruit weight, husk weight, nut weight, shell weight, meat weight, water weight, nut/fruit ratio, shell/nut ratio, meat/nut ratio, water/nut ratio, endosperm humidity)

Cameroon Red Dwarf x Rennell Island Tall

62

Baudouin et al. (2006)

Cuticular wax composition

East African Tall x Rennell Island Tall

46

Riedel et al. (2009)

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6.7.3 Transcriptomic Approaches for Climate-Smart Coconut Transcriptome analysis, using RNA-seq and assembly, is a potent technology being widely employed to study the global gene expression pattern. Furthermore, transcriptome analysis has provided greater insights about hitherto unknown metabolic pathways such as sub-components of secondary metabolism, acquired resistance, and molecular features like epigenetic modifications. It has also facilitated in developing a very valuable genomic resource in crops like coconut that were abandoned when the applications of the first generation of sequencing technologies were reaped in crops like rice, tomato, wheat, and soybean (Unamba et al. 2015). In crops that are devoid of genome sequence information, transcriptome sequencing using RNA-seq approach helps in quantifying the level of transcripts. RNA sequencing (RNA-seq) technologies have helped in the generation of whole transcriptomic sequences of a crop by utilizing bioinformatics tools to assemble shortread sequences (Xia et al. 2011) with improved sensitivity and broad dynamic range. In addition, transcriptome sequencing approaches have yielded fundamental insights into co-expressed genes and their precise role in metabolic processes (Wickramasuriya and Dunwell 2015). The first genome-wide study of transcriptional responses in coconut was conducted by Fan et al. (2013) to study fatty acid biosynthesis and metabolism which identified 347 unigenes associated in this process. Also, it was proposed that the accumulation of medium-chain fatty acids (lauric acid) in coconut could be ascribed to the expression of fatty acyl-ACP thioesterase. One of the most important utility of transcriptomic approaches is to unearth novel candidate genes associated with tolerance/resistance to different abiotic and biotic stresses and genes associated with agronomic traits. Whole transcriptome sequencing (WTS) approaches are a global methodology to study the changes in gene expression using high-throughput sequencing technologies. Hence, until 2012, the whole coconut transcriptome data in the public domain were very scarce as only 774 sequences were available in National Centre for Biotechnology Information database prior to 2012 (Fan et al. 2013). However, on a smaller scale, targeted transcriptomic approaches have yielded valuable results. Expression profiling and cloning of plant resistance (R) genes provide deeper insights into disease progression and even it could identify candidate resistance genes for major diseases. Hence, targeted transcriptomic approaches were employed to gain molecular updates in disease resistance mechanisms in coconut. One of the major components of plant’s defense mechanism is R-genes and studies about R-genes has helped in understanding the mechanism of disease prevention, spread, and progress (Hammond-Kosack and Jones 1997). In general, plant-derived R-genes have functional motifs to recognize and bind the pathogen-derived proteins (expressed from avirulence genes of pathogens) (Scofield et al. 1996). This molecular interaction results in a cascade of signal transduction pathways leading to the expression of plant defense-related genes. These defense strategies include hypersensitive response, or apoptosis, production of phytoalexins, physical barriers such as cell wall formation (Dangl et al. 1996). Comparative genomics approach has been followed to delineate the sequence,

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structure function, and expression dynamics of R genes of coconut using the available genomic sequence of data palm. Thus, resistant gene analogs (RGA) of coconut were studied to gain a molecular understanding of R-gene expression in response to root (wilt) disease. Among the various classes of R-genes, the nucleotide-binding site-leucine-rich repeat (NBS-LRR) class genes are widely characterized (Cannon et al. 2002). The NBS domain possesses several conserved motifs that provide an opportunity to design oligonucleotides and utilize polymerase chain reaction (PCR)based approaches to search for homologous or similar sequences called RGAs in other related genera or species (Kanazin et al. 1996). Based on this principle, conserved regions of NBS-LRR resistance genes of oil palm and date palm were used to design primers to discover RGAs in the coconut genome (Rajesh et al. 2015). This study yielded three putative RGAs in coconut [KC465244 (2211 bp), KF002584 (1165 bp), and KM983337 (616 bp)] with sequence similarity to RGAs of date palm, Oryza, Triticum, Aegilops, Musa, and Brachypodium. Multiple sequence alignment and phylogeny reconstruction showed that coconut RGAs clustered with coiled-coiled-NBS-LRR (CNL)-NBS class sequences. Analysis of amino acid sequences divulged that coconut NBS-LRR-type RGAs have unique TIR or CC motifs in their N-terminus. Further, these putative RGAs showed high expression levels in the leaves of the root (wilt) disease-resistant genotypes. Thus, this study showed that a sequence homology search-based targeted transcriptomics approach could yield potential candidate genes linked to biotic stress tolerance and such candidate genes are a very useful resource in assisting molecular breeding approaches (Rachana et al. 2016).

6.7.3.1

Root (Wilt) Disease

Molecular interactions between host and pathogen provide insights for devising suitable disease control strategies. Of the diseases of coconut palms, root (wilt) disease is a foremost debilitating that seriously hinders productivity. Whole transcriptome sequencing of healthy and diseased Chowghat Green Dwarf (CGD) palms was carried out to study the molecular mechanisms of disease progression and resistance and or susceptibility reactions toward the disease (Rajesh et al. 2013a, b, 2018). RNA-seq analysis yielded over 111 million and 119 million 101 bp clean paired-end reads, respectively, for the healthy and diseased samples (NCBI SRA Accessions are SRX436961 and SRX437650, respectively. Functional annotation using homologybased searches revealed that 37,748 transcripts showing matches to known proteins in UniProt; however, around 36% of transcripts did not show any matches. Further, annotation was carried out using the information obtained from the date palm proteome. This comparative analysis showed that 33,757 (57%) of transcripts could be annotated using date palm proteome. Differential expression analysis between healthy and diseased samples showed a total of 2718 transcripts that were differentially expressed. The gene regulatory network analysis of healthy and diseased

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phenotypes showed significant enrichment of metal ion binding, secondary metabolite biosynthesis, and carbon fixation, etc. Notably, proteins like calmodulin-like 41, WRKY DNA-binding proteins involved in plant–pathogen interactions are found to be upregulated. Based on the leaf transcriptome analysis of diseased and healthy coconut samples, a molecular model has been proposed. This study proposes that the defense response of coconut is triggered by the interaction between membrane receptors and PAMPs or effectors. This interaction initiates the process of signal transduction involving protein kinases and phosphorylations and results in changes in calciumbinding proteins by regulating Ca2+ influxes. To this defense response based on signaling cascade, salicylic acid (SA) is also recruited resulting in the orchestration of genome-wide transcriptional reprogramming with the aid of transcription factors (TFs) such as WRKY and NAC domain-containing proteins. This results in the activation of genes involved in defense responses producing phenylpropanoids and causing resistance reactions in healthy palms. Thus, complex defense responses of the healthy coconut palms to one of the devastating diseases which remained elusive long could be resolved by utilizing transcriptome sequencing approaches. Further, analysis of gene expression patterns and identification of genic markers could greatly aid the plant breeders with candidate resistance genes (Rajesh et al. 2013a, b, 2018).

6.7.3.2

Coconut–Phytoplasma Interactions

Molecular basis of disease progression and cellular response of coconut to the phytoplasma disease would provide insights regarding the host susceptibility- and resistance-conferring factors. Hence, a study was conducted to generate transcriptome data from the healthy and CYD phytoplasma infected leaves of coconut cultivar, Malayan Red Dwarf using Illumina HiSeq™ 2000 sequencer (Nejat et al. 2015). This study yielded a total of 72,019,264 and 70,935,896 reads from the transcriptome of healthy and infected leaves, respectively. Comparative analysis between the healthy and diseased palms indicates 18,013 transcripts were upregulated and 21,860 transcripts were down-regulated in the infected leaves. Further genes involved in defense responses against pathogen invasion were found to be differentially expressed in coconut infected with CYD phytoplasma. Gene ontology analysis identified the upregulation of defense responses, signal transduction pathway, protein phosphorylation among others. Genes involved in pathogenesis-related proteins (PRs) were significantly overexpressed in infected tissues. The study thus provided direct evidence that an active defense mechanism is activated in the wake of phytoplasma attack in the MRD coconut. Further, it was also proposed that phytoplasma could not effectively overcome these defense responses of the host and causes rapid death in palm cultivars, viz. Malayan Tall and Jamaica Tall.

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Fatty Acid Biosynthesis and Metabolism

The first genome-wide transcriptome analysis of coconut was performed by Fan et al. (2013). A total of 347 unigenes corresponding to fatty acid biosynthesis and metabolism were identified, and these unigenes were assigned to the various steps of fatty acid biosynthesis. Most importantly, 20 of these unigenes were predicted to be related to fatty acyl-ACP thioesterase, a crucial enzyme for terminating the elongation of carbon chains. Hence, the observed abundance of medium-chain fatty acids in coconut (lauric acid) could be ascribed to the expression of fatty acyl-ACP thioesterase.

6.7.3.4

Embryogenesis

Somatic embryogenesis (SE), where a single or a group of somatic cells differentiate to form embryonic cells under suitable in vitro conditions, is an ideal system to explore gene expression patterns associated with initial stages of embryo development. The formation of embryos from somatic cells closely resembles the developmental pathway of zygotic embryos (ZEs), and hence, the molecular information generated for the SE pathway could be used to explain the dynamic molecular interactions that take place during early embryogenesis. Rajesh et al. (2016) carried out de novo assembly and characterization of the global transcriptome of coconut embryogenic calli using Illumina paired-end sequencing. Here, transcriptome analysis of coconut embryogenic calli derived from plumular explants of West Coast Tall cultivar was undertaken on Illumina HiSeq 2000 platform. The assembled reads were subjected to annotation, classification, and ontology analysis using BLASTX, Blast2GO, and KEGG programs. Genes known to be involved in somatic embryogenesis, namely protein kinases like receptor-like kinases [somatic embryogenesis receptor kinase (SERK) and CLAVATA 1 (CLV1)], mitogen-activated protein kinase (MAPK), transcription factors [WUSCHEL (WUS), APETALA2/Ethylene-responsive factor (AP2/ERF), PICKLE (PKL), AINTEGUMENTA (ANT ), and WRKY ], extracellular proteins [arabinogalactan protein (AGP), Germin-like protein (GLP), embryogenic cell protein (ECP), and late embryogenesis-abundant protein (LEA)] and glutathione S-transferase (GST ) were identified. Gene ontology (GO) term enrichment analysis identified 8300 transcripts that are associated with biological processes (majority having transcription and regulatory function), 13,193 transcripts with molecular functions (involved in ATP, Zinc ion and metal ion binding processes) and 6076 transcripts associated with cellular components, and the highly expressed one were components integral to membrane followed by the nucleus. Expression analysis indicated that CLV was upregulated in the initial stage of callogenesis. Transcripts of GLP, GST, PKL, WUS, and WRKY were expressed more in the somatic embryo stage. The expression of SERK, MAPK, AP2, SAUR, ECP, AGP, LEA, and ANT was higher in embryogenic callus stage compared to initial culture and somatic embryo stages. This study was an effort to aid in the development of an efficient in vitro production protocol for coconut which is otherwise recalcitrant to in vitro culture.

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149

RNA-Directed DNA Methylation

Huang et al. (2014) performed whole transcriptome analysis to study molecular factors in the seeds and leaves of dwarf coconut palm with a special emphasis on the identification of genetic elements involved in RNA-directed DNA methylation. It was found that small RNA-mediated gene silencing pathways are very active in coconut seeds, including the maturing endosperm. It is a valuable transcriptomic resource and forms the basis for further molecular and functional analysis to assist in molecular breeding and genetic modification. It also has provided a list of highly expressed genes, and hence, suitable tissue-specific promoters could be explored further for use in coconut improvement.

6.7.4 Database and Genomic Resources Data repository and genomic resources of palms are relatively scarce. However, there are online genomic resources especially for coconut and other genetically related palms and plant genomics in general that help in analyzing the voluminous data generated through various genomic approaches (Table 6.2).

6.8 Perspectives and Concluding Remarks The basic understanding of key biological processes and gaining molecular insights are important to help breeders in achieving crop improvement goals. Enormous plant breeding population, high-throughput sequencing technology-based datasets, robust computational tools, followed by basic molecular breeding and gene modification tools are the pillars of modern biotechnology-based climate-smart agriculture. Further, tools such as genome-wide association studies (GWAS), genomic selection (GS), association mapping (AM), and exploration of QTLomics are lacking in coconut owing to limited genomic resources. Also, the development of mapping population and genetic resources such as the multi-parent advanced generation intercross (MAGIC) population and the nested association mapping (NAM) population would help in developing high-resolution QTL mapping in coconut (Fig. 6.1). Transcriptomics studies using RNA-seq could provide an immense wealth of information for genetic manipulation of crop plants so as to make the crops productive and tolerant to biotic and abiotic stresses. Transcriptomics is a relatively cheap and potential technique for harnessing the benefits of biotechnology and to develop better crop phenotypes. The available transcriptome sequences of coconut for various traits would definitely complement the genome sequencing efforts of coconut. Even though many successful instances of the utilization of transcriptomics in coconut and other plantation crops have been conducted, the information obtained

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Table 6.2 Genomic resources and organizations for coconut genomic studies Sl. No.

Organization

Resources

URL

1.

The International Coconut Genetic Resources Network

To promote, collaborate and use coconut genetic resources among the coconut growing nations. Currently, the organization has 39 members

http://www.inibap.org/ cogent/

2.

International Coconut Community

International coconut community formed under the aegis of UN-ESCAP with a vision to improve the socio-economic welfare of farmers and other industry stakeholders

http://www.apccsec.org/

3.

Palms of the World Online (Palmweb)

Online palm encyclopedia which includes morphological descriptors, distribution maps’ images of palms among others

http://www.palmweb. org/

4.

The European Palm Society

It is a non-profit organization that envisages sharing information about palms across Europe. It also houses European Palm Society Database which provides publicly accessible information about palm species

http://www.palmsociety. org.uk/

5.

TropGeneDB

A database that manages information on genetics, genomics, and phenotypic data of tropical crops (Hamelin et al. 2013)

http://tropgenedb. cirad.fr/

6.

Center for International Cooperation in Agricultural Research for Development (CIRAD) Montpellier France

CIRAD actively participates in scientific collaboration with more than 100 countries including the genomics of palms

http://www.cirad.fr

7.

Coconut Research Institute Sri Lanka

A public-funded research organization in Sri Lanka

http://www.cri.lk

8.

ICAR-Central Plantation Crops Research Institute India

A constituent unit of Indian Council of Agricultural Research that undertakes basic, strategic, and applied research in coconut among other crops

http://www.cpcri.gov.in

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Fig. 6.1 Genomics-assisted breeding of climate-smart coconut. The process involves basic science studies, and the information generated thereof will be utilized by molecular breeders using various genomics tools to develop a climate-smart coconut

from host–pathogen interactions is required to be processed further to identify candidate resistance genes. In this regard, the core commonalities emerging from these studies indicate that secondary metabolite synthesis, particularly phenylpropanoid biosynthesis, activation of jasmonate-based defense pathway, NBS-LRR kinases, etc., are potent candidates for searching resistance-conferring genes. Hence, identification of resistance genes would accelerate the biotechnology enabled “fast-forward breeding” approaches to meet climate change-induced exigencies.

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Genome editing technology is a novel approach that accelerates the efforts of plant breeding. It helps in precise and rapid modification of genetic elements for desirable characters such as disease and pest resistance, tolerance to abiotic stressors and in boosting the yield levels. Hence, improved genomic resources of coconut by whole genome sequencing of cultivars targeted resequencing for identification of desired alleles, etc., would add valuable resources for genome editing approaches.

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Peiris TSG (2006) Impact of climate change on coconut industry in Sri Lanka. Paper presented at third international conference on climate impact and assessment (TICCIA), Cairns, Australia Perez JM, Cañas GP, Arias T (2019) Genome-wide diversity of northern South America cultivated coconut (Cocos nucifera L.) uncovers diversification times and targets of domestication of coconut globally. bioRxiv. https://doi.org/10.1101/825398 Rachana KE, Naganeeswaran SA, Fayas TP, Thomas RJ, Rajesh MK (2016) Cloning, characterization and expression analysis of NBS-LRR-type resistance gene analogues (RGAs) in coconut. Acta Bot Croat 75(1):1–10 Rajagopal V, Kasturi Bai KV (2002) Drought tolerance mechanism in coconut. Burot Bull 17:21–22 Rajagopal V, Kasturi Bai KV, Voleti SR (1990) Screening of coconut genotypes for drought tolerance. Oleagineux 45:215–223 Rajesh MK, Fayas TP, Naganeeswaran S, Rachana KE, Bhavyashree U, Sajini KK, Karun A (2016). De novo assembly and characterization of global transcriptome of coconut palm (Cocosnucifera L.) embryogenic calli using Illumina paired-end sequencing. Protoplasma 253(3):913–928 Rajesh MK, Rachana KE, Babu M, Thomas RJ, Karun A (2013a) Characterization of the global transcriptome responsive to root (wilt) disease in coconut using RNA-seq. In: National symposium on ‘Pathogenomics for diagnosis and management of plant diseases’, CTCRI, Thiruvananthapuram, India Rajesh MK, Jerard BA, Preethi P, Thomas RJ, Fayas TP, Rachana KE, Karun A (2013b) Development of a RAPD-derived SCAR marker associated with tall-type palm trait in coconut. Sci Hort 150(4):312–316 Rajesh MK, Rachana KE, Kulkarni K, Sahu BB, Thomas RJ, Karun A (2018) Comparative transcriptome profiling of healthy and diseased Chowghat Green Dwarf coconut palms from root (wilt) disease hot spots. Eur J Plant Pathol 151(1):173–193 Rajesh MK, Rachana KE, Naganeeswaran S, Shafeeq R, Thomas RJ, Shareefa M, Merin B, Karun Anitha (2015) Identification of expressed resistance gene analog sequences in coconut leaf transcriptome and their evolutionary analysis. Turk J Agri For 39:489–502 Ramesh SV, Hebbar KB, Rajesh MK, Archana P (2019) Soil water-deficit differentially modulates the expression of stress associated genes (SAGs) of Cocos nucifera L. seedlings with contrasting water-use efficiency (WUE). In: Plantation Crops Symposium-XXIII, Chikkamagaluru, India Ranasinghe CS. Climate change impacts on coconut production and potential adaptation and mitigation measures: a review of current status. http://www.slcarp.lk/wp-content/uploads/2019/01/ Full-Book-Climate-Change_77.pdf Riedel M, Riederer M, Becker D, Herran A, Kullaya A, Arana-López G, Peña-Rodríguez L, Billotte N, Sniady V, Rohde W, Ritter E (2009) Cuticular wax composition in Cocosnucifera L.: physicochemical analysis of wax components and mapping of their QTLs onto the coconut molecular linkage map. Tree Genet Genom 5:53–69 Rivera R, Edwards KJ, Barker JH, Arnold GM, Ayad G, Hodgkin T, Karp A (1999) Isolation and characterization of polymorphic microsatellites in Cocos nucifera L. Genome 42:668–675 Scofield SR, Tobias CM, Rathjen JP, Chang JH, Lavelle DT, Michelmore RW, Staskawicz BJ (1996) Molecular basis of gene-for-gene specificity in bacterial speck disease of tomato. Science 274:2063–2065 Shalini KV, Manjunantha S, Lebrun P, Berger A, Baudouin L, Pirany N (2007) Identification of molecular markers associated with mite resistance in coconut (Cocos nucifera L.). Genome 50:35–42 Singh R, Ong-Abdullah M, Low ETL, Manaf MAA, Rosli R, Nookiah R, Ooi LCL, Ooi SE, Chan KL, Halim MA, Azizi N (2013) Oil palm genome sequence reveals divergence of interfertile species in old and new worlds. Nature 500(7462):335 Sunoj JVS, Naresh Kumar S, Muralikrishna KS (2013). Variation in total phenols concentration in coconut (Cocosnucifera L.) seedlings under elevated CO2 and temperature in different seasons. In: Sundaresan J, Sreekesh S, Ramanathan AL, Sonnenschen L, Boojh R (eds) Climate change and environment. Scientific Publishers, India, pp 140–149

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Chapter 7

Genetic and Genomic Approaches for Adaptation of Grapevine to Climate Change Serge Delrot, Jérôme Grimplet, Pablo Carbonell-Bejerano, Anna Schwandner, Pierre-François Bert, Luigi Bavaresco, Lorenza Dalla Costa, Gabriele Di Gaspero, Eric Duchêne, Ludger Hausmann, Mickaël Malnoy, Michele Morgante, Nathalie Ollat, Mario Pecile and Silvia Vezzulli Abstract The necessity to adapt to climate change is even stronger for grapevine than for other crops, because grape berry composition—a key determinant of fruit and wine quality, typicity and market value— highly depends on “terroir” (complete natural environment), on vintage (annual climate variability), and on their interactions. In the same time, there is a strong demand to reduce the use of pesticides. Thus, the S. Delrot (B) · P.-F. Bert · N. Ollat UMR EGFV—Bordeaux Sciences Agro, INRA, University of Bordeaux, ISVV, 210 chemin de Leysotte, Villenave d’Ornon 33882, France e-mail: [email protected]; [email protected] J. Grimplet Unidad de Hortofruticultura, Centro de Investigación y Tecnología Agroalimentaria de Aragón, Instituto Agroalimentario de Aragón-IA2 (CITA-Universidad de Zaragoza), Zaragoza, Spain P. Carbonell-Bejerano Department of Molecular Biology, Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076 Tuebingen, Germany L. D. Costa · M. Malnoy Genomic and Advanced Biotechnology Unit, Department of Biology and Genomic of Fruit Plants, Foundation Edmund Mach, Via Mach 1, 38010 San Michele all’Adige, TN, Italy A. Schwandner · L. Hausmann Julius Kühn Institut (JKI), Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, Germany L. Bavaresco Dept. of Sustainable Crop Production, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy G. Di Gaspero · M. Morgante Istituto di Genomica Applicata, 33100 Udine, Italy E. Duchêne UMR SVQV—INRA, University of Strasbourg, 28, rue de Herrlisheim, Colmar 68021, France M. Morgante Department of Agricultural Food, University of Udine, Environmental and Animal Sciences, 33100 Udine, Italy © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_7

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equation that breeders and grape growers must solve has three entries that cannot be dissociated: adaptation to climate change, reduction of pesticides, and maintenance of wine typicity. Although vineyard management may cope to some extent to the short–medium-term effects of climate change, genetic improvement is necessary to provide long-term sustainable solutions to these problems. Most vineyards over the world are planted using vines that harbor two grafted plants’ genomes. Although this makes the range of interactions (scion-atmosphere, rootstock-soil, scion-rootstock) more complex, it also opens up wider possibilities for the genetic improvement of either or both the grafted genotypes. Positive aspects related to grapevine breeding are as follows: (a) a wide genetic diversity of rootstocks and scions that has not been thoroughly explored yet; (b) progress in sequencing technologies that allows high-throughput sequencing of entire genomes, faster mapping of targeted traits and easier determination of genetic relationships; (c) progress in new breeding technologies that potentially permit precise modifications on resident genes; (d) automation of phenotyping that allows faster and more complete monitoring of many traits on relatively large plant populations; (e) functional characterization of an increasing number of genes involved in the control of development, berry metabolism, disease resistance, and adaptation to environment. Difficulties involve: (a) the perennial nature and the large size of the plant that makes field testing long and demanding in manpower; (b) the low efficiency of transformation, regeneration and small size of breeding populations; (c) the complexity of the adaptive traits and the need to define more clearly future ideotypes; (d) the lack of shared and integrative platforms allowing a complete appraisal of the genotype-phenotype-environmental links; (e) legal, market and consumer acceptance of new genotypes. The present chapter provides an overview of suitable strategies and challenges linked to the adaptation of viticulture to a changing environment. Keywords Vitis spp. · Rootstock · Genetic resources · Genotyping · Phenotyping · Climate change · Data bases

7.1 Challenges and Prospects 7.1.1 Environmental, Economical, and Biological Background The grapevine is unique not only because it is a major global horticulture crop but also for its ancient historical connections with the development of human culture. Seeds M. Pecile CREA Research Centre for Viticulture and Enology, Conegliano, Italy S. Vezzulli Grapevine Breeding and Genetics Unit, Foundation Edmund Mach, Research and Innovation Centre, Via E. Mach, 1, 38010 San Michele all’Adige, TN, Italy

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of domesticated grapes dating back to about 8000 years before present (BP) were found in Georgia and in Turkey, and the earliest evidence of wine production was found in Georgia circa 8000–7800 BP (McGovern et al. 2017). Nowadays, worldwide grape production that was 67.4 million tons in 2005, peaked at 77.8 million tons in 2013, and slightly decreased at 73.3 million tons in 2017. The total vineyard surface area worldwide rose slightly from 2010 (7493 kha) to 2017 (7564 kha). The overall increase driven primarily by the development of surface area in North America and Asia is partially offset by continuing decline in surface area in Europe (Organisation Internationale de la Vigne et du Vin (OIV) statistics, www.oiv.int/en/databases-andstatistics). Grapevine is used as fresh (table grapes) or dry fruits (raisin), and for making of wines and spirits and, therefore, has a major economical importance. In spite of its long history and important economical value, viticulture must adapt to the present challenge of climate change. This holds even more true for grapevine than for other crops, because grape berry composition that is a key determinant of wine quality, highly depends both on “terroir” defined as the complete natural environment in which a particular wine is produced, including factors such as the soil, topography, and climate and on vintage, with annual variations of climate. The impact of climate on berry quality and wine production is such that it results in large differences in vintage quality and economical value for a same wine region or estate. According to the 2018 IPCC report, estimated anthropogenic global warming is currently increasing at 0.2 °C (likely between 0.1 and 0.3 °C) per decade due to past and ongoing emissions of greenhouse gases. By the end of this century, CO2 levels are expected to rise up to 700 ppm, with a subsequent increase in temperature up to 4 °C. Warming greater than the global annual average is being experienced in many land regions and seasons. Warming is generally higher over land than over the ocean (IPCC Report 2013). Warming from anthropogenic emissions from the pre-industrial period to the present will persist for centuries to millennia and will continue to cause further long-term changes in the climate system. This means that a mean increase of 2–4 °C may be expected on viticultural areas by the end of the century. In some areas, the temperature rise will be accompanied by a rise of the evaporative demand and a decline in rainfall, both factors contributing to impose more severe water stress to agricultural crops. Interactions of the grapevine plant with the environment are complicated by the fact that, since the end of the nineteenth century, most vineyards over the world were grafted on a rootstock that would confer resistance to phylloxera. The functioning of the grafted plant results more from interactions than from additive physiological functions (water and ion uptake and transport, carbon and nitrogen reduction, and metabolism). These interactions appear as soon as the grafting union is established and may evolve as the plant becomes older. The final composition of the berries thus depends on interactions between the aerial part of the plant (the variety) with the atmosphere (temperature, hygrometry, gas composition, light radiation), and on the interactions of the root system (rootstock) with the soil environment (structure, texture mineral content and availability, water and gas contents, temperature, pH, microorganisms and invertebrates). In addition to interactions with their own environment, the scion, and the rootstock also

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interact with each other through the exchange of nutrients, hormonal, and molecular signals (Fig. 7.1). Most of these interactions are also controlled by grape growers, when they select the genotypes of the scion and of the rootstock, and uses viticultural and oenological practices modulating the environmental effects (Van Leeuwen et al. 2013). These practices include plantation density, row orientation, training system, control of source/sink relationships (through summer and winter pruning), berry exposure to the sun (through the timing and intensity of leaf removal), cluster thinning, irrigation, fertilization, and soil management. The challenge for the vine grower and the wine maker is to maintain the wine style (typicity) linked to a protected vineyard area in spite of the interannual climatic variations, and of the long-term trends due to climate change. As detailed below, climate change has already significantly affected grapevine production since 3–4 decades. Even though viticultural practices or cultivation zones may cope at least in the short term with this impact, long-term strategies involving a change in the rootstock and/or scion genotypes used must be envisaged for some viticultural areas. In the same time, the new genotypes and viticultural practices introduced must take into account the other challenge facing viticulture that is to reduce dramatically the use of pesticides. There is an increasing concern about pesticide residues in food and beverages, including wines, and about short- and long-term pollution of the environment, and the exposure of farmers and citizens to the spraying of pesticides in the field crops where they work or nearby they live. Using different, or even newly Minerals & water uptake

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AdaptaƟon to climate change

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Fig. 7.2 Adaptation of grapevine to climate change must also take into account disease resistance in order to reduce pesticide treatments, and maintain the typicity of the wine to which the consumer is used

generated genotypes, both for scion and rootstock, are potentially a powerful means of adaptation made possible by the genetic diversity available and still unexploited. In addition to the use of the existing clonal and varietal diversities, new genotypes may be created through conventional (traditional and molecular-assisted) breeding or genetic engineering (including genome editing), if this turns out to be more widely accepted by the consumers and producers and if the conditions for realizing and using the products of new breeding technologies will change with a revision of the 2001/18/EC Directive. Finding scion × rootstock × training system combinations able to produce commercial quality wine is a reasonable goal in many grape-growing areas. Thus, the equation to be solved has three entries: adaptation to climate change, reduction of pesticides, and maintenance of wine typicity (Fig. 7.2). None of these terms can be overlooked. Solving this equation is obviously a difficult and long-term task, but it may benefit from a largely unemployed genetic diversity of grapevine varieties and rootstocks.

7.1.2 Ongoing and Expected Effects of Climate Change Climate changes may have dramatic effects on viticulture and wine industry, maintenance of landscape, and promotion of tourism (Hannah et al. 2013). However, the predicted changes of climate are region-specific and adaptation can only be successful considering the regional characteristics with its diverse technical, environmental, economic, and social implications (Schultz 2017). Predicting precisely the impact of climate change on viticulture is difficult for several reasons (a) there are uncertainties on the outputs of the predictive models; (b) models should be adapted to viticulture and should be considered on a small scale (plot or terroir) rather than on a regional scale, because microclimate (altitude, slope, orientation of the vine rows) and soil

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heterogeneity significantly change the scenario predicted at the larger scale (Quénol et al. 2017). Observation and modeling at the finest scale must, therefore, be considered in the development of strategies for mitigating to climate change impacts on vineyard and interactions with terroir (Quénol et al. 2014; Sturman et al. 2017). Although some uncertainties still exist about the exact extent of this impact on global viticulture, this impact has already been experienced in the recent past in a number of cases. Most of the observed changes in phenology may be ascribed to climate change, with possible minor effects of changes in viticultural practices. The past increase of temperatures already led to a well-documented advance of phenology in viticultural regions all over the world (Jones and Davis 2000; Duchêne and Schneider 2005; Petrie and Sadras 2008; Ramos et al. 2008) (Fig. 7.3). Bud break, flowering, and veraison (the onset of ripening) are reached earlier; hence, grapes ripen in warmer conditions not only because of the increase in temperatures (direct effect of climate change), but also because ripening takes place earlier in the season (indirect effect of climate change). The correlation between grapevine phenology and temperature is so tight that phenology, data have even be used to assess temperatures from the past centuries (Chuine et al. 2004) and to develop models accounting for varietal differences (Parker et al. 2011) or predicting the future phonological changes: mathematical models using air temperatures (Webb et al. 2007; Duchêne et al. 2010; Fila et al. 2014) forecast an advance of two to three weeks of the dates of veraison by 2050 when compared to the last 30 years (Webb et al. 2007; Duchêne et al. 2010; Moriondo et al. 2011). 15/11 16/10 16/9

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Climate change may facilitate the expansion of viticulture to regions that were so far too cold for ripening grapes (for example, Southern UK, although grapevines were already grown there in the Roman time and in the Middle Age) or within existing wine regions to higher altitudes. But climate change may particularly affect the viticultural suitability in Mediterranean regions and in areas where water supply is limited such as Australia (Hannah et al. 2013). While some degree of water stress is known to improve berry quality, severe drought may limit yield and alter berry composition at harvest. In viticultural areas that face a diminishing supply of highquality irrigation water and/or predicted rainfall (e.g., Australia), soil salinity is an important issue. When grown on saline soils Vitis vinifera L. wine grape cultivars can suffer from decreased growth and yield, and reduced berry quality due to the high accumulation of chloride ions, and to a lesser degree sodium ions (Dunlevy 2019). In many regions (Australia and New Zealand), reduced water availability is frequently accompanied by increased UV-B radiation levels (Bandurska et al. 2013). Besides water stress, changes in cloudiness may affect the levels of solar radiation, including UV-B, reaching Mediterranean ecosystems in the near future (Giorgi et al. 2008). Excess UV light may also be detrimental for berry quality (Martinez-Luscher et al. 2015). Different components of climate change (higher atmospheric CO2 levels and higher radiation levels) are expected to increase plant biomass production (Bindi et al. 2001; Garcia de Cortazar Atauri 2006; Moutinho-Pereira et al. 2009). However, the increase in total biomass expected in the future might be limited by rainfall distribution and water availability, especially at the end of the growth cycle (Garcia de Cortazar-Atauri 2006). Climate change can have direct effects on grapevine productivity: not only spring frosts can destroy young shoots but higher temperatures around budburst can lower the number of flowers per inflorescence (Petrie and Clingeleffer 2005). As a consequence of elevated CO2, a higher plant vigor and biomass production in the future can likely result in a higher number of inflorescences and flowers per shoot. However, drought during summer can reduce single berry weight in the current season but also lower the number inflorescences per shoot in the following one (Matthews and Anderson 1989). The environmental conditions during ripening attract most of the attention, not only of the scientific community, but also of producers and winemakers. Over the last 30 years, a clear modification has been observed in grape composition at maturity (Fig. 7.4). Temperature increases berry sugar concentration at the expense of malic acid and secondary metabolites, leading to unbalanced wines. The rise in sugar content of wine grapes that is probably the most common change among those observed may be a concern for policy makers and for consumers as it results in wines with higher alcohol content. This trend may be considered as a threat in terms of human health and alcohol abuse. Within a few decades, some wine regions have switched from occasional addition of sugars to the must (chaptalization) to partial sugar or alcohol removal by physical methods such as reverse osmosis. Berry sugar concentrations have increased during the last decades very likely because of the shift of the ripening period toward longer days, and hence higher global radiation interception. Sugar accumulation could, however, be limited in the

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geraniol p6000 different varieties (This et al. 2006; Laucou et al. 2011), although the number of unique genotypes may be substantially lower. Cultivated varieties mostly belong to V. vinifera subsp. vinifera. Some inter-specific hybrids are also used as direct producers since the mid 1800s, and introgression lines have been added later on to national catalogues. Additional useful diversity that can be exploited for grapevine breeding comes from wild populations of V. vinifera subsp. sylvestris and also from wild species within the Vitis genus that are interfertile when crossed with cultivated V. vinifera accessions (Keller 2015; Arroyo-García et al. 2016). Moreover, these wild relatives in most cases are graft-compatible with cultivated forms and are used as rootstocks, directly or after inter-specific breeding (Warschefsky et al. 2016).

7.3.1 Phenotype-Based Diversity Analysis and Phenotyping Issues 7.3.1.1

Phenotyping Experimental Systems

Although grapevine entails a series of developmental features that makes phenotype studies tedious and expensive (This et al. 2012), institutional efforts made in the last years and the advent of new phenotyping technologies are unveiling a great variation on morphological, phenological, production, berry composition, tolerance, and adaptive traits in large collections of either varieties or clones (Liu et al. 2006; Cadle-Davidson 2008; Liang et al. 2008; Shiraishi et al. 2010; Munoz et al. 2014; Tello et al. 2015; Tortosa et al. 2016; Carbonell-Bejerano et al. 2016; Pinasseau et al. 2017; Migicovsky et al. 2017; Bigard et al. 2018; Wu et al. 2018). An example of the large diversity found for major phenotypical traits in a small collection is shown in Table 7.1. Among the limitations found for berry phenotyping, long time is required due to the perennial nature and the long juvenile stage. Large spaces are required for studies at the whole-plant level when comparisons require multiple genotypes and include a sufficient number of replicates. As an outdoor crop, grapevine is exposed to fluctuating environmental conditions in the field and, therefore, the plasticity of

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the phenotype should be tested over a number of years at different locations. To overcome these constraints, different experimental systems and strategies have been established by grapevine researchers and breeders for the screening of genotypes leading to superior phenotypes for target traits. While studies in field conditions are required to reproduce real vineyard environments, an initial assessment of the phenotype under controlled conditions is advisable in many instances for a direct and efficient evaluation of most climate- and environment-smart traits in large-scale screenings (Carvalho and Amancio 2018). The growth of potted vines under outdoor or greenhouse conditions is useful to test physiological responses to controlled doses of temperature, water availability, radiation quality and intensity, CO2 levels, nutrient availability or tolerance to soil composition toxicities (Toselli et al. 2009; Salazar-Parra et al. 2015; Carvalho et al. 2016; Habran et al. 2016). Potted grapevine explants also involve a valuable system to assess for tolerance to some pathogens (Rashed et al. 2013; Toffolatti et al. 2018), whereas explants in hydroponic media proved useful to evaluate responses to herbivorous pests (Diaz-Riquelme et al. 2016). While these cultivation systems enable a rapid characterization of vegetative traits, several growing seasons are still necessary to reach a stable production before a reliable phenotyping of reproductive and fruit traits can be conducted. A smart system that shortens time lapses for reproductive physiology and fruit composition phenotyping is the use of fruitbearing cuttings (Geny et al. 1998; Lebon et al. 2005; Dai et al. 2013). To do so, after cane rooting induction, only one inflorescence per cane is left and leaves are also removed till the first inflorescence appears, so that fruit set is enabled by limiting competition for nutrients in the cutting (Mullins and Rajasekaran 1981; Geny et al. 1998). This system has indeed been useful for assessing the effect of climateand environment-related factors and for inter- and intra-varietal comparisons of fruit traits (Carbonell-Bejerano et al. 2013; Martinez-Luscher et al. 2013, 2015; Arrizabalaga et al. 2018; Salazar-Parra et al. 2018). The study of root development entails an additional level of complexity because the surrounding soil limits the accessibility of the target organ. To overcome this limitation, rhizotrons are glass screens allowing for soil visibility that can be used in potted plants to monitor root growth and architecture in a non-invasive manner (de Herralde et al. 2010). Although in vitro methods are usually less representative of the whole-plant ecophysiology, they are still convenient for a rapid screening of specific features. For instance, inoculation of detached leaves or leaf discs in Petri dishes enables reliable phenotyping for tolerance/resistance of grapevine genotypes to major fungal pathogens such as downy and powdery mildew (Péros et al. 2006; Cadle-Davidson 2008; Peressotti et al. 2011; Calonnec et al. 2013; Cadle-Davidson et al. 2016). In vitro culture of grapevine berries can also be used to assess ripening responses of different genotypes to environmental factors such as light or temperature (Azuma et al. 2012; Dai et al. 2014). As the most reductionist system, cell in vitro culture could be established for the study of grapevine responses to environmental and nutritional factors (Saigne-Soulard et al. 2006; Soubeyrand et al. 2018). Regardless of the phenotyping system applied, for breeding purposes any initial assessment should be followed by the confirmation of selected superior phenotypes

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under a range of field conditions to evaluate for local adaptation to specific viticultural regions. The plasticity of the phenotype under a series of scion-rootstock genotypes should also be tested for selected genotypes (Warschefsky et al. 2016).

7.3.1.2

Phenotyping Methods

Once the experimental system is established, the phenotypic characterization of most pursuable climate- and environmental-smart traits requires laborious morphometric and analytical methods. However, the identification of markers correlating with target traits can be exploited to simplify phenotyping. Based on correlations, quickly evolving non-invasive sensors can be addressed to assist in phenotyping tasks (Grosskinsky et al. 2015). Computer vision and image analysis methods have been developed to estimate vegetative and productive parameters including vine vigor, fertility, number of berries per cluster, bunch structure, and compactness or pathogen growth (Peressotti et al. 2011; Cadle-Davidson et al. 2016; De Bei et al. 2016; Tello et al. 2016a; Divilov et al. 2017; Rist et al. 2018; Tello et al. 2018). Fluorescence, spectrometry, and thermography sensors can be applied to monitor water stress or health statuses of the plant (Bellow et al. 2013; Oerke et al. 2016; Tardaguila et al. 2017; Bianchi et al. 2018; Giovenzana et al. 2018), as well as for non-destructive berry ripening and quality assessment (Barnaba et al. 2014; Nogales-Bueno et al. 2015; Mercenaro et al. 2016). Platforms conducting on-the-go recording from several of those sensors and imaging devices provide the opportunity to reduce phenotyping labors and time in field trials (Kicherer et al. 2017). Under controlled greenhouse conditions, phenomics platforms have already been useful in grapevine for automated phenotyping of WUE-related traits (Coupel-Ledru et al. 2016). For fine determination of fruit composition, high-throughput destructive analytical methods have been developed that enable the quantification of a wide range of metabolites using targeted or non-targeted metabolomics protocols (Degu et al. 2015; Cuadros-Inostroza et al. 2016; Rambla et al. 2016; Pinasseau et al. 2017; Wu et al. 2018). Cost-efficient methods such as infrared and 1 H NMR spectroscopy or enzymatic assays requiring limited sample preparation provide interesting alternatives to rapidly analyze berry composition and quality (Son et al. 2009; Bobeica et al. 2015; Musingarabwi et al. 2016). Besides the development of rapid and cost-efficient phenotyping tools, joining efforts in the constitution of broad phenotyping networks and databases is paramount to understand the phenotype diversity underlying the grapevine germplasm and to optimize breeding.

7.3.2 Genotype-Based Diversity Analysis While the target for crop breeding is only the small fraction of functional genetic variation leading to adaptive and quality phenotypes, most genetic variation that

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accumulates in a species is neutral and does not have any impact on the phenotype. Nevertheless, the study of neutral genetic variation is key to determine the grade of genetic differentiation between individual genotypes and their phylogenetical and evolutionary relationships (Holderegger et al. 2006). For diversity analysis in grapevine, it should be considered that the genetic information is hosted in three different cell compartments, the nucleus, the chloroplast, and the mitochondria. The grapevine nuclear genome is organized in 19 chromosomes (2n = 38), with one chromosome copy inherited from each progenitor and an estimated haploid size of ~487 Mb, whereas chloroplastic and mitochondrial genomes are ~161 and ~773 kb in length, respectively, and multiple clonal copies are present in each cell (Lodhi et al. 1995; Jansen et al. 2006; Jaillon et al. 2007; Goremykin et al. 2009; Tabidze et al. 2017). Chloroplastic genomes are considered to be inherited maternally in grapevine and their diversity has been useful to study parentage, phylogeography, and relationship with wild forms (Arroyo-García et al. 2002, 2006; Hunt et al. 2010). Different sets of molecular markers have been developed for the study of genetic diversity in grapevine. Initially, microsatellite or simple sequence repeat (SSR) markers were developed from the 1990s and allowed for rapid identification of grapevine varieties, which enabled a more efficient molecular management of the germplasm compared to ampelography (Thomas et al. 1994; This et al. 2004; Laucou et al. 2011) (see also Sect. 7.5.2.1). Microsatellites involve the polymorphic repetitions of the same small k-mer, allowing the development of highly informative markers per se due to their codominant and multiallelic nature. Increased number of loci and genotypes studied by using these markers allowed for the first identification of parentage relationships between varieties (Bowers and Meredith 1997; Bowers et al. 1999). Single nucleotide polymorphisms (SNPs) and insertion/deletions (InDels) are much more frequent polymorphisms than microsatellites. Although SNPs are usually biallelic and thus less polymorphic than microsatellites, by the time being SNPs are the molecular markers of choice for many genetic approaches in grapevine because of the possibility of generating large sets of markers, the easiness of genotyping and the clarity and universality of the output (see Sect. 7.5.2.2). The release of a complete reference assembly for the nuclear genome of the highly homozygous PN40024 inbred line and of a draft assembly for the heterozygous cultivar Pinot Noir provided a frame for the design of SNP markers (Jaillon et al. 2007; Velasco et al. 2007). The availability of the reference genome, linked to the development of next-generation sequencing (NGS) technologies, made the identification of SNPs in the grapevine germplasm affordable and enabled the design of SNP chips in which thousands of markers can be analyzed simultaneously (Myles et al. 2011; Laucou et al. 2018). By the time being, with the progressively decreasing cost of the technology, NGS is directly used for genotyping (see Sect. 7.5.2.2). NGS of additional varieties combined with reference genomes allow for genome-wide analysis of SNPs, InDels, and larger genome structural variation (SV), which can lead to the identification of responsible mutations in addition to associated variants (Cardone et al. 2016; Xu et al. 2016; Carbonell-Bejerano et al. 2017; Mercenaro et al. 2017; Tabidze et al. 2017; Zhou et al. 2017; Roach et al. 2018; Royo et al. 2018). More recently, genotyping-by-sequencing (GBS) or restriction-site-associated DNA sequencing (RAD-seq) technologies made

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possible the identification and genotyping of SNPs at a whole-genome scale without the need for reference genomes or any previous information on the sequence (Hyma et al. 2015; Marrano et al. 2017). NGS is also useful for high-throughput studies of variation at specific loci by targeted design of amplicons or capture probes (Tello et al. 2016b; Yang et al. 2016a, b; Marrano et al. 2018).

7.3.3 Relationship with Wild Relatives All grapevines belong to the family of Vitaceae, which in their wild forms are climbing lianas thanks to the development of tendrils derived from modified reproductive organs. Only species within the genus Vitis are considered of agronomic interest, consisting of ~60 and three species in the subgenera Vitis and Muscadinia, respectively (This et al. 2006; Cattonaro et al. 2014). The Vitis genus is a monophyletic group according to a microsatellite analysis of chloroplast DNA that indicated an Asian origin (Péros et al. 2011). Three clades of Vitis species corresponding with their geographical distribution were identified based on chloroplastic SNPs and InDels, the European clade of V. vinifera and separate clades for Asian and American species (Trondle et al. 2010). That study also positioned Asia as the possible center of diversification and showed the possible impact of inter-specific hybridization on the evolution of the genus. Results from another study resequencing 27 nuclear genes are controversial, suggesting a North American origin (Wan et al. 2013). The Vitis genus diversified in the absence of genetic barriers and thus, all species remain mostly interfertile, enabling the utilization of their genetic diversity for the improvement of grapevine scion varieties and rootstocks (Carbonell-Bejerano et al. 2016). In spite of karyotype differences between Muscadinia (generally 2n = 40) and Vitis subgenus (2n = 38), intersubgeneric crosses still can lead to viable offspring if Muscadinia is used as pollen donor (Bouquet 1980, 2011). Although clear proofs are scarce, V. vinifera subsp. sylvestris is undoubtedly considered the living progenitor of most cultivated grapevines, which are classified within the taxon V. vinifera subsp. vinifera. Glacial displacement shaped the current distribution of the wild form, covering in disjoint populations a vast geographical range from the foothills of the Himalayas to Portugal and from the valley of the Rhin river to Northern forests of Tunisia (This et al. 2006). Genotyping of a large collection of accessions with a 9 k SNP chip showed that cultivated vinifera accessions are genetically closer to Eastern sylvestris than to Western sylvestris, positioning the likely origin of domestication in the Near East, with a subsequent dispersion of the crop toward the West (Myles et al. 2011). This finding agrees with archeological remains indicating that domestication likely begun more than 7000 years ago in the area that currently involves countries such as Georgia, Armenia, Azerbaijan, Turkey, and Iran (McGovern 2003; This et al. 2006). The process of grapevine domestication was associated with little developmental modifications in comparison with other crops (This et al. 2006). At least two features might have conditioned the scarcity of differentiation: (i) a likely small number of

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sexual generations between wild progenitors and the founders of the most ancient varieties, which have been vegetatively propagated since the second half of the last millennium (Arroyo-Garcia et al. 2006; Myles et al. 2011) and (ii) an uninterrupted hybridization gene flow between cultivated varieties and their wild relatives, which might have included occasional independent domestication events in Western Europe after the original one in the Near East (Grassi et al. 2003; Arroyo-Garcia et al. 2006; Lopes et al. 2009; Myles et al. 2011; De Andrès et al. 2012; Emanuelli et al. 2013; De Lorenzis et al. 2015). Domestication traits with agronomical significance are associated with increased production in cultivated grapevines. In that manner, cluster and berry size have increased and the mating system has generally changed from dioecy (with individuals developing either male or female flowers in the wild form), to the acquisition of hermaphrodite flowers by cultivated forms, which enables for self-pollination and, therefore, ensures higher fruit set rates (This et al. 2006). Another conspicuous difference with unclear biological or agronomical relevance concerns the change of seedshape between wild and cultivated grapevines (Terral et al. 2010). Outcrossing obligated by the dioecious mating system is related to high levels of heterozygosity and the accumulation of heterozygous deleterious mutations found in wild grapevines (Zhou et al. 2017). Heterozygosity levels have even increased in cultivated varieties, likely due to the inheritance of deleterious variants from the wild progenitors that conditioned the origin of most varieties involving a sexual cross between genetically distant parents (Myles et al. 2011; Lacombe et al. 2013). Additionally, long periods of clonal propagation of old grapevine varieties led to increased levels of heterozygous deleterious variants compared to wild forms due to the absence of purifying selection that occurs during sexual reproduction (Zhou et al. 2017).

7.3.4 Relationship with Geographical Distribution Cultivated grapevine comprises the varieties that are currently used in viticulture along with those much more numerous accessions hosted in germplasm repositories. Geographical, human, and genetic factors itself have conditioned the diversity of the grapevine genetic pools. The study of a large collection of varieties using microsatellite markers identified three major groups corresponding to (i) table grape varieties from Far and Middle East countries, Caucasus, Maghreb and Iberian Peninsula; (ii) wine varieties from the Balkans and East Europe; and (iii) wine varieties from Western and Central Europe (Bacilieri et al. 2013). This structure coincides with the morphological and geographical division in three main groups of proles orientalis, proles pontica, and proles occidentalis, respectively, proposed by the Russian botanist Negrul (Keller 2015). While morphological differences between the groups suggest that grapevine was subject to different selection pressures on each region, such structuration of the diversity likely reflects the dispersion of the crop from the initial domestication center in the Near East toward the West following human

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migrations, which is also genetically supported by the geographical distribution of chlorotypes as well as by archeological remains (McGovern 2003; Arroyo-Garcia et al. 2006). More recently, some additional subgroups were detected using an 18 k SNP chip, indicating further spreading of varieties from the Iberian Peninsula and the Balkans to Italy and from Central to South Europe (Laucou et al. 2018). Furthermore, a great proportion of varieties corresponds to admixture genotypes as a result of intensive human-mediated exchanges between grape-growing regions throughout history and more recent breeding that homogenized the pools (This et al. 2006; Cipriani et al. 2010; Bacilieri et al. 2013; Laucou et al. 2018). Genetic pedigree analyses demonstrated that in fact many grapevine varieties originated after the sexual cross of varieties distributed in the same or in contacting geographical regions. In many cases, few founders representing ancient varieties such as Pinot Noir, Gouais Blanc, Riesling, Traminer, Cabernet Franc, Trebbiano Toscano, Heben, Cayetana Blanca, Muscat a Petit Grains or Sultanina are in the parentage of large numbers of varieties (Bowers et al. 1999; This et al. 2006; Ibáñez et al. 2009; Boursiquot et al. 2009; Myles et al. 2011; Zinelabidine et al. 2012; Lacombe et al. 2013; Cattonaro et al. 2014; Zinelabidine et al. 2015; Laucou et al. 2018; Sunseri et al. 2018). Some of the most frequent founders as Heben coincide with varieties harboring female flowers (Lacombe et al. 2013; Zinelabidine et al. 2015), which eases both spontaneous and deliberate outcrossing and at the same time prevents inbreeding depression. Additional diversity was likely introgressed to cultivated grapevines by spontaneous hybridization with wild plants or secondary domestication, which recurrently took place in different viticultural regions (Grassi et al. 2003; Arroyo-Garcia et al. 2006; Lopes et al. 2009; Myles et al. 2011; De Andrès et al. 2012; Emanuelli et al. 2013; De Lorenzis et al. 2015). Apart from inter-varietal diversity, somatic variation accumulated during the vegetative growth and clonal propagation of grapevine varieties provides an additional source of diversity that is the base for clonal selection and also leads to the emergence of new qualitative traits as it was the case for seedlessness or variation in berry color (Torregrosa et al. 2011). Genome-wide resequencing analyses showed the potential of genetic somatic variation in highly distributed old wine varieties such as Nebbiolo and Chardonnay, but this diversity is several orders of magnitude lower than of inter-varietal diversity (Gambino et al. 2017; Roach et al. 2018). However, different bottlenecks during the history of grapevine, including the phylloxera crisis and the introduction of downy mildew from America in the second half of the nineteenth century, as well as clonal selection and distribution programs initiated in the twentieth century, have constrained both inter- and intra-varietal diversity (This et al. 2006).

7.3.5 Causal Polymorphisms Although genome regions associated to certain climate- and environment-smart traits have been identified, the causal polymorphisms remain largely unknown. Only a handful number of polymorphisms causing phenotype diversity in agronomically

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relevant traits have been discovered in grapevine. These polymorphisms range from single nucleotide variants (SNV) to complex genome rearrangements. For instance, dominant missense SNVs are responsible for the particular fruit aroma in muscat varieties, for seedlessness in table grapes and for dwarfism and floral induction in somatic mutant lines of Pinot (Boss and Thomas 2002; Battilana et al. 2011; Royo et al. 2018). Transposable elements have also frequently been related with the origin of new phenotypes in grapevine. Activated expression caused by the insertion of transposable elements is in the origin of somatic variation for cluster architecture and fruit morphogenesis traits (Fernandez et al. 2010, 2013). The recessive allele leading to white grape varieties comprises a Gret1 retrotransposon insertion in VviMYBA1 transcription factor gene linked to one missense SNV and a frameshift dinucleotide deletion in the neighbor VviMYBA2 gene, whereas Gret1 movement from VviMYBA1 causes somatic recovery of berry color (Kobayashi et al. 2004; Walker et al. 2007). Complex SV comprising the deletion of the chromosome region harboring the functional allele for these MYBA genes is also at the origin of the loss of grape anthocyanin pigmentation capacity in white berry somatic variants (Carbonell-Bejerano et al. 2017). Further research effort is required to identify genetic variation responsible for adaptation to climate and environment. While the identification of associated molecular markers in grapevine cross breeding programs is relevant when the cost of phenotyping surpasses the cost of genotyping, the selection of suitable superior genotypes as starting plant material would reduce the requirement of prebreeding and breeding actions for pyramiding favorable alleles from different loci, easing the generation of improved smart varieties irrespectively of the breeding method used. For GMO and genome editing-mediated breeding, beforehand identification of causal gene variants and polymorphisms is necessary for the targeted introduction of a trait.

7.4 Strategies for Genetic Improvement 7.4.1 Traditional Cross Breeding and Clonal Selection Programs Surprisingly, the most grown grape variety in the world is Kyoho, according to OIV (www.oiv.int, 2018). That is because all grapes are not grown for wine making, which is the most popular use and which Cabernet Sauvignon ranks first for. Some are destined to be table grapes or to be dried (raisins) or to be processed into grape juice. In some cases, the limit of the field of use is not so clear and we talk about miscellaneous varieties. For instance, this is the case for “Ruby Seedless,” “Flame Seedless,” and “Perlette” that are table grape varieties that are occasionally made into raisins (table-raisin aptitude). For the most part, raisin drying is a secondary use for unharvested strips, a market for grapes from young vineyards coming into production, or an alternative market when table grape prices are low. Another example

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is represented by the numerous varieties which have intermediate characteristics and dual wine-table aptitude (e.g., “Cinsaut,” “Chasselas,” “Moscato giallo”). Around the world grapevine cross breeding programs, run by governmental institutions, universities, private companies as well as individual grapevine growers and lovers, are directed to different purposes: (i)

Rootstock, in Germany, Hungary, Italy, France, Eastern Europe, Eastern USA, Western USA, Southern USA, New Zealand, Australia (ii) Wine grape, in Austria, Germany, France, Italy, Spain, Eastern Europe, Brazil, China, Eastern USA, Western USA, Southern USA, Canada (iii) Table grape, in Hungary, Spain, Brazil, China, Eastern USA, Western USA, Southern USA, Canada (iv) Minor although important uses: Raisin in Western USA, and Juice grape in Northern China and Eastern USA (Yun and Park 2007; Reynolds 2015). Each Country aims to have new varieties suited to its territory; for this reason, countries in the New World such as South Africa could soon undertake a breeding program. Unlike cross breeding, clonal selection is performed within existing cultivars in order to keep it phytosanitarily healthy and morphologically stable (Töpfer et al. 2011). Besides controlling virus diseases, clonal selection chooses genotypes on agronomic, viticultural or oenological criteria; moreover, it guarantees to limit the loss of variability and to safeguard the genetic identity at intra-varietal level. Possible explanations of this variability may be the polyclonal origin of cultivars and the accumulation of genetic mutations in their genotypes. On the other hand, virus diseases contribute to increase the phenotypical variability within grapevine populations. How much of the phenotypical variability within a population is due to genotype and how much to virus presence is still not clear (Mannini 2000). Each above-mentioned country running cross breeding programs has its own clonal selection plans led by various realising bodies. Another approach to carry out conservation and selection of grapevine is the methodology followed in Portugal based on the mass conservation/selection of local varieties (Roby et al. 2014). Table 7.2 summarizes the breeding goals and selection achievements in agreement with the different final purposes aimed for the grape/plant (Wei et al. 2002; Fidelibus et al. 2008; Rochfort et al. 2010; Töpfer et al. 2011; Migicovsky et al. 2017; Zyprian et al. 2018). Among them, it is relevant to underline that the goal of eliminating off-flavors (e.g., strawberry-like aroma, furaneol; foxy taste, methyl anthranilate and 2-aminoacetophenone) does not have an absolute value but it is a relative concept depending on the culture and viticultural tradition of each country, either because off-flavors had already been purged from advanced breeding lines in some countries or because the consumers’ taste is more familiar with Isabella-like wine flavors in other countries (i.e., in South America). Finally, if due to climate or market changes the vine will expand into non-traditional growing areas (e.g., area further north) and uses (e.g., oil seed), it will be necessary to pursue new objectives and develop ad hoc programs for cross breeding and clonal selection.

Trunk diseases (e.g., antracnosis) Bacterial diseases

Bacterial diseases

Black root

Trunk diseases (e.g., antracnosis)

Black root

Gray mold (through firm berry, thick berry skin, loose cluster)

Downy mildew

Downy mildew

Gray mold (through firm berry, thick berry skin, loose cluster)

Powdery mildew

Powdery mildew

– Pathogens:

Drosophila: fruit

Drosophila: fruit

– Pathogens:

Phylloxera: leaf

– Pests:

Phylloxera: leaf, root

– Pests:

– Free of off-flavors

Ease of petiole detachment

Phenolic compounds, aroma precursors and others

– Free of off-flavors

Oblong shape

Color

– Taste:

Body

– Berry characteristics:

Flavor Good texture

Good ratio of sugar and acidity

Color

– Taste:

Balanced acidity

– Seedlessness

Table grapes

High sugar

– High wine quality:

Wine grapes

Resistance or tolerance to biotic stress

Quality

Major goal

Bacterial diseases

Trunk diseases (e.g., antracnosis),

Black root

Gray mold (through firm berry, thick berry skin, loose cluster)

Downy mildew

Powdery mildew

– Pathogens:

Drosophila: fruit

Phylloxera: leaf

– Pests:

– Free of off-flavors

Meatiness

Wrinkle presence

Skin persistence

– Berry characteristics:

High sugar

– Taste:

– Seedlessness

Raisins

Nematodes: root

Phylloxera: root

– Pests:

Rootstocks

(continued)

Table 7.2 Plethora of breeding goals according to the different final purposes (from Wei et al. 2002; Rochfort et al. 2010; Töpfer et al. 2011; Migicosky et al. 2017; Zyprian et al. 2018)

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Wine grapes

– Variation in time of ripening, according to market demand

– Maturity preferably medium to late

Others

– Return of frost

– Affinity for grafting – Balanced influence on scion vigor

– Callus formation

– Adequate veraison /maturation

– Balanced vigor

– Adequate veraison/maturation

Drought – Rooting ability

– Upright growth

– Return of frost – Adequate veraison /maturation

– Return of frost

Climate adaptation

– High and stable yield

– Balanced and stable yield

Yield and Maturity – High and stable yield

Acidity Salt

– Sunburn

Lime

– Soil adversities:

Water saturation

– Sunburn

– Heat

– Heat – Drought

Rootstocks

– Sunburn

– Drought

Raisins

– Drought

– Heat

– Cold

Table grapes

– Frost

Resistance or tolerance to abiotic stress

Major goal

Table 7.2 (continued)

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7.4.2 Limitations of Traditional Approaches and Utility of Molecular Genetics In spite of their achievements through the centuries, traditional (or classical) cross breeding and clonal selections have some limitations. Crop peculiarities such as long reproductive cycle, large plant size, and expanded evaluation period do not facilitate these processes. Regarding cross breeding, genetic resource recovery is not straightforward due to limitations in the exchanges of genotypes between countries. In spite of this, large collaborative projects such as Cost FA1003—East-West Collaboration for Grapevine Diversity Exploration and Mobilization of Adaptive Traits for Breeding; FP7 Innovine—Combining innovation in vineyard management and genetic for a sustainable European viticulture; InWiGrape—Increasing the efficiency of conservation of wild grapevine genetic resources in Europe has produced a tremendous effort in securing the existing genetic resources in Europe and neighboring countries. Gaps in grapevine genetics and genomics also still need to be filled. Concerning clonal selection, in order to undertake the best choices, it is essential that the operator has a deep knowledge of the various recent and past culture areas and of the morphological and physiological characteristics of the selected cultivars; if this knowledge is insufficient or even missing, it is essential to carry out preliminary studies (Acquaah 2012). Moreover, there is limited chance of getting new and useful type of variability and the multiplication rate is low. Clonal selection has the major drawback that it impoverishes genetic diversity in commercial vineyards and exposes growers to potential environmental instability of clones because they may derive from chimerical shoot apical meristems. For major grapevine varieties, some genetic diversity is maintained in institutional collections; however, this way of conservation is insufficiently developed, it is expensive and remains fragile. A costeffective way to preserve intra-varietal diversity is to maintain a limited proportion of mass selection in vine propagation. At the beginning of the twenty-first century, as new tools were developed to genetically dissect traits, molecular approaches were introduced into breeding research starting the era of molecular breeding. The advent of molecular markers, genetic mapping and genotype-phenotype association analysis initiated a paradigm change in grapevine genetic improvement from a pure empirical work to the strictly goaloriented design of crosses and of gene management (Töpfer et al. 2011). In particular, the identification of QTLs associated to relevant characteristics—such as phenology, disease resistance and quality—opened the way to more targeted grapevine breeding programmes. In the last decade, genome sequencing projects expedited and enhanced this process, giving insights about trait/gene localization and molecular organization. After the first deciphering of the grapevine genome (PN40024, Jaillon et al. 2007; Pinot Noir, Velasco et al. 2007), nowadays, a discrete number of V. vinifera wholegenome sequences were generated and freely available. These are representative for wine grapes (Tannat, Da Silva et al. 2013; Cabernet Sauvignon, Chin et al. 2016; Chardonnay, Roach et al. 2018 and Zhou et al. 2017), and raisins (Sultanina, Di Genova et al. 2014); pattern of genomic diversity was also recorded in wine and

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table grapes (Migicovsky et al. 2017). Very recently, sequencing efforts unraveled the genomic structure differences among berry color somatic variants (CarbonellBejerano et al. 2017) and other polymorphisms discriminating clones (Pinot Noir: Carrier et al. 2012; Nebbiolo: Gambino et al. 2017; Chardonnay: Roach et al. 2018). Regarding non-vinifera varieties, both private and public centers are carrying on sequencing projects (e.g., Hausmann et al., personal communication). The wealth of this overall genomic information will contribute to the dissection of traits of interest: so far the complete information about responsible genes has been achieved through map-based cloning approaches in a few cases (e.g., Pauquet et al. 2001; Barker et al. 2005; Zhang et al. 2009; Feechan et al. 2013). The identification of genes/molecular markers associated to trait of interest allowed a new boost to refine genetic improvement, becoming a boon to enhance viticulture. Nowadays, markerassisted selection (MAS) is employed in breeding programs for wine grapes, table grapes, and rootstocks to step up and make cultivar development more efficient via early seedling selection and parental choice prior to crossing. Nevertheless, traditional breeding with field evaluations will still play an essential role in developing commercial cultivars: classical breeding will never die. Both based on crossing, traditional, and molecular breeding lie under the hat of the well-established conventional breeding technologies (CBTs), aiming at the creation of new cultivars (EU Explanatory Note, 2017).1 Today, the last frontier of grapevine genetic improvement consists of the debated new breeding technologies (NBTs), allowing the development of new clones by editing already existing varieties (see Sect. 7.10.3).

7.4.3 Marker-Assisted Gene Introgression So far, this strategy has been developed to reach durable resistance against diseases, but not yet adaptation to climate change, although the future varieties bred should meet both expectations. When genes are introgressed, the same varietal genotype cannot be recovered because the two haplotypes are recombined upon segregation in highly heterozygous cultivars. Durable disease resistance is an important aim in each breeding program. Genetically, two basic patterns of resistance are available: qualitative (race-specific, vertical) and quantitative (race-non-specific, horizontal) resistances. Traditional breeding methods are recurrent backcrossing (BC) for introducing single (major) genes, recurrent selection for improving the level of quantitative resistances and multi-stage selection for combining resistances and agronomic traits during cultivar development. Molecular markers allow efficient introduction of qualitative resistances into elite material and to analyze quantitative resistances. During marker-assisted backcrossing (MABC), a major gene can be precisely targeted, the genome of the recurrent parent can be recovered fast, and linkage drag 1 New

techniques in Agricultural Biotechnology, High Level Group of Scientific Advisors, Explanatory Note 02.

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can be reduced. By MAS, major genes or QTLs can be pyramided (Miedaner 2016). Monogenic resistance to highly specialized pathogens is often highly effective when first developed but is generally race-specific and non-durable, especially when characterized by hypersensitive host reactions. Partial resistance is under the control of several to many genes with additive effects and is usually more durable, particularly when it involves morphological or developmental changes in the plant, although might be prone to gradual loss (erosion) in the long term (Stuthman et al. 2007). Pyramiding entails stacking multiple monogenic resistances leading to the simultaneous expression of more than one R-gene in a variety to reinforce the durability of resistance. Gene pyramiding is gaining considerable importance as it would improve the efficiency of plant breeding leading to the development of genetic stocks and precise development of broad spectrum resistance capabilities. The success of gene pyramiding depends upon several critical factors, including the number of genes to be transferred, the distance between the target genes and flanking markers, the nature of germplasm, etc. (Joshi and Nayak 2010). Besides increasing host diversity and complexity, to achieve a higher durability, populations of biotrophic pathogens should be regularly monitored for their virulence frequencies and virulence combinations (Miedaner 2016). In grapevine, MABC and marker-assisted pyramiding programs are currently undertaken for disease resistance traits in France, Italy, Germany, Hungary, and USA. As alternative to the R-loci investigation, a susceptibility (S-) locus to powdery mildew (Sen1) has recently been identified (Barba et al. 2014). Lately, a breakthrough has been achieved by the proposal of combining different S-genes to downy and powdery mildew into a last generation MAB program, according to their homozygous or heterozygous status (Pirrello et al. 2018). Despite the increasing awareness of environmental issues and the consequent importance of disease resistance traits, it is necessary to keep in mind the potential trade-off between the introduction of resilience and resistance traits and the retention of grape and wine quality traits in an introgression line. This is a challenging topic, given the lack of knowledge about the dissection of such polygenic traits and in some cases of detection methodology. Berry color is probably the easiest characteristics to be tracked. In fact, excluding the more complex cases of somatic mutations, color variants can be explained in 95% of the cases by different alleles of the VvMybA1 gene (Lijavevsky et al. 2006; This et al. 2007). In the last decade, QTLs associated with the synthesis of tannins (Huang et al. 2012), flavonols (Malacarne et al. 2015), sugars and acids (Chen et al. 2015; Houel et al. 2015; Yang et al. 2016a) have been identified but these studies need a deepening and a further validation before the results can be exploited in MAS programs (see also Sect. 7.5.4). Concerning the positive selection of aroma compounds both for wine and table grapes, QTLs have been first identified (e.g., Eibach et al. 2003, Doligez et al. 2006b) and a robust candidate gene for terpenol content was discovered (Battilana et al. 2009; Duchêne et al. 2009). Following a dedicated association study, Emanuelli et al. (2014)proposed various user-friendly SNP-based systems, developing functional markers for VvDXS gene that confers muscat flavor in grapevine. By contrast, it would be convenient to perform negative selection of the compounds responsible for the considered off-flavors in most countries, especially for wine grapes. Regarding furaneol and methyl anthranilate, a

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recent study has identified the causal genomic regions along with the slip skin trait into the Catawba genetic background (Braun 2017). Although further fine mapping studies are needed for the thorough dissection of these characteristics, these findings represent a breakthrough toward the definition of “quality chromosomes.” Another trait erroneously associated to bad-quality is the synthesis of anthocyanin (mainly malvidin) 3,5-diglucosides, which occurs in most wild Vitis species and can be inherited in the derivate crosses with V. vinifera. The responsible gene for glycosylation (VvB12H3_5-GT ) has been identified and characterized (Jánváry et al. 2009) and a molecular marker was developed (Welter et al. 2007). The main issue for this trait remains the legislative restriction, since in some EU countries, there is a limit of 15 mg/L to be respected for the marketing of wine; this still represents a limitation to the spread of new resistant genotypes. Finally, seedlessness and hermaphroditism were dissected as traits relevant, respectively, for table grape genetic improvement and MABC programs. Following the discovery of QTLs associated with seedlessness and berry weight (e.g., Doligez et al. 2002), the fine dissection of stenopermocarpic seedlessness has recently culminated into the work by Royo et al. (2018) who discovered the causal missense mutation in the MADS-box gene VviAGL11. Regarding flower sex, the theory of the three M, H, F alleles has been developed and it is now well established (e.g., Dalbó et al. 2000; Battilana et al. 2013).

7.4.4 Limitations and Prospects of Marker-Assisted Selection A compendium of QTLs relevant for breeding is available at Vitis International Variety Cathalogue (VIVC) (www.vivc.de, section “data on breeding and genetics,” 2018). Not all the QTL mapping researches are immediately exploitable for practical purposes or robust enough for MAS practice but they need fine mapping studies and further marker validation in additional populations. In fact, moving from publication to application domain is challenging. The amount of publications on the development and, to a lesser extent, the use of molecular markers in grapevine breeding has increased dramatically during the last decade. However, as in other crop species, most of the publication results derive from investments from funders with a strategic scientific mission or biotech advocacy mandate, leading to scarce emphasis on applied value in breeding programs. Converting successful papers into practical applications requires the resolution of many constraints that are rarely addressed in journal publications. The rate of success is likely to increase due to progress in genebased marker development, improved efficiency in QTL mapping procedures, and reduced cost of genotyping systems. However, some fundamental issues remain to be resolved, particularly regarding complex traits, before MAS realizes its full potential in practical breeding programs. These include the development of high-throughput precision phenotyping systems, improved understanding of genotype by environment interaction and epistasis, and development of publicly available computational tools tailored to the needs of molecular breeding programs (Xu and Crouch 2008).

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To perform a marker-assisted breeding (MAB) program, reliable, efficient and cost-effective molecular markers have to be available. As stable and codominant markers, microsatellites (or SSRs, simple sequence repeats) are currently the marker system of choice (see also Sect. 7.5.2). This type of markers demonstrated to provide robust phenotype correlation with disease resistance (e.g., Eibach et al. 2007; Vezzulli et al. 2008a, b), stenospermocarpy (e.g., Karaagac et al. 2012), and hermaphroditism (e.g., Fechter et al. 2012). Indeed, SSRs have some limitations, such as the need of an expensive equipment for allele sizing through capillary electrophoresis and high mutation rates compared to other DNA markers. The latter tendency generates size variation in DNA regions otherwise identical-by-descent and by their model of evolution, which vice versa produce identical electromorphs via independent mutational events—known as homoplasy—confounding the studies of genetic variation within and among populations (reviewed by Foria et al. 2018). This is confirmed by the recent detection of “resistant SSR alleles,” and exceptionally even haplotypes, in a set of V. vinifera varieties used as a control for pedigree check (Vezzulli S., personal communication). From here comes the need of developing new flanking markers and to convert the original ones into less variable marker types, as the point mutationbased. To date, although few SNPs—also in terms of haplotype block—have been developed for grapevine MAS applications (e.g., Barba et al. 2014; Zyprian et al. 2015), they are becoming more favoured as a marker system, since they are amenable to high-throughput genotyping platform (Mammadov et al. 2012). Lately, to bridge the gap between marker development and MAS implementation, a novel practical strategy with a semi-automated pipeline that incorporates trait-associated SNP discovery, low-cost genotyping through amplicon sequencing and decision making, has been developed (Yang et al. 2016b). Unlike microsatellites and more similar to point mutations, each InDel is a unique and irreversible molecular event, which helps tagging more effectively a given haplotype. For this reason, Foria et al. (2018) have very recently discovered InDel tags for the Rpv3-1 haplotype and proposed them as a significant improvement in terms of marker informative content, ease of allele scoring, and MAS efficiency. So far MAS has been adopted in grapevine to follow qualitative traits or major QTLs. A thorough understanding of quantitative disease resistance would contribute to the design and deployment of durably resistant crop cultivars and more effective utilization of natural resistance alleles. However, the molecular mechanisms that control it remain poorly understood, largely due to the incomplete and inconsistent nature of the resistance phenotype, which is usually determined for its genetic component by many loci of small effect (Poland 2008). Quantitative disease resistance confers a lower reduction of disease symptoms compared to qualitative resistance and has diverse biological and molecular bases as revealed by cloning of loci and identification of the candidate gene(s) underlying them. Increasing our biological knowledge of loci will enhance understanding of how quantitative differs from qualitative resistance and provide the necessary information to better deploy these resources in breeding. Strategies for an optimum deployment of favorable genes require research to understand effects of quantitative disease resistance on pathogen populations over time (St.Clair 2010). In grapevine, most work has been performed on the host side where

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most the identified R-loci carry partial diseases resistances (reviewed in Merdinoglu et al. 2018). Unlike other crops, cross tests between R-loci and different pathogen strains have not routinely been established yet and they constitute the next coming compulsory challenge for MAB programs. For the entire process, efficiency and sustainability, it is obvious that an innovative selection strategy is needed for polygenic traits, that takes advantage of more statistically powerful and accurate predictive methods. The genomic selection approach allows selecting for multiple traits directly in the genome by chip-based, high-throughput genotyping platforms (see Sect. 7.8.3).

7.5 Molecular Mapping of Genes and QTLs 7.5.1 Brief History of Mapping Efforts Genetic mapping is the determination of the loci of genetically inherited variation or polymorphisms in relation to each other. Alfred Sturtevant demonstrated this principle for the first time with six morphological factors of Drosophila flies in 1913. Since then, mapping results accelerated in parallel to the development of new marker types. First genetic maps for higher plants were developed predominantly for model plants and major annual crops in the late 1980s (Tanksley et al. 1989). Grapevine lagged behind many crops, since it is a perennial species with a generation cycle time of 3–4 years that suffers from inbreeding depression, thus it is more difficult to establish appropriate mapping populations. Therefore, it took a few years longer until the first genetic map of grapevine was established (Lodhi et al. 1995). This breakthrough led to an increased interest on mapping different kinds of traits and cloning of the associated genes in grapevine. Today, it is estimated that more than 100 various traits and genetic loci have been mapped in more than 50 different grapevine mapping populations. A table of the most important mapped trait loci for grapevine breeding is available at theV IVC database (www.vivc.de/loci). Breeding of new grapevine varieties relied mainly on empirical work and chance in the past. The success in trait mapping and positional gene cloning initiated a paradigm shift toward knowledge-based and goal-orientated design of crosses and molecular marker selection strategies (Töpfer et al. 2011).

7.5.2 Marker Types: Development from RFLPs to SNPs The evolution of marker types regularly depended on the advent of a few pioneering technical inventions and molecular methods. The Southern blot analysis enabled the restriction fragment length polymorphism (RFLP) technique; the polymerase chain reaction (PCR) is the basis for random amplified polymorphic DNA (RAPD),

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amplified fragment length polymorphism (AFLP), simple sequence repeat (SSR) and some other types of markers; DNA sequencing, especially the NGS technologies, paved the way for the broad application of SNP markers. Grapevine genetics and breeding have greatly benefited from all these marker types.

7.5.2.1

RFLP and PCR-Based Markers

The RFLP marker type was the first DNA marker system developed by Botstein et al. (1980) for linkage mapping in human and was used in the following years in plant breeding to establish the first genetic maps for some major crops (Tanksley et al. 1989). Although RFLP markers presented a breakthrough for the establishment of useful genetic maps, they were barely represented in the very first genetic map of grapevine (Lodhi et al. 1995). Main reasons for this were of technical nature like the requirement of large amounts of high-quality DNA, physical maintenance of the probes and the difficulty to automate the whole process. This changed dramatically with the advent of PCR-based marker techniques in the 1990s. The rather simple RAPD markers use a short universal primer of arbitrary sequence and low amounts of genomic DNA to amplify random sequences segments (Williams et al. 1990). RAPDs provide predominantly dominant markers and can detect a higher level of polymorphisms than RFLPs. They have the advantage of being far less time and cost consuming and need no special laboratory equipment. The first published grapevine genetic maps were mainly (Lodhi et al. 1995; Dalbó et al. 2000) or partly (Fischer et al. 2004) developed with RAPD markers. However, due to the difficulty with the reproducibility of RAPD marker results this marker type disappeared soon while the AFLP technique entered the genetic mapping. AFLP combines the reliability of RFLP with the power of PCR without prior sequence knowledge (Vos et al. 1995). In a single assay, a high number of dominant polymorphisms can be detected that are more reproducible than RAPDs. Therefore, this marker type was more often used for genetic mapping in grapevine in the first decade of this century (Doligez et al. 2002; Grando et al. 2003; Doucleff et al. 2004; Fischer et al. 2004; Cabezas et al. 2006; Troggio et al. 2007; Costantini et al. 2008). Microsatellites or SSRs were the next generation of marker types used for genetic mapping as well as for variety identification and kinship analyses in grapevine. SSRs are short tandemly repeated nucleotide units of 1–7 bp that are interspersed frequently in many eukaryotic genomes (Tautz and Renz 1984). Due to their hypervariability in length, they are very useful for codominant PCR-based markers (Tauz 1989). SSRs are preferentially distributed in non-repetitive genomic regions (Morgante et al. 2002) and their conserved adjacent sequences allow the transferability of them between individuals within the Vitis genus (Sefc et al. 1999; Di Gaspero et al. 2000). Initially, several groups published small sets of 4–22 grapevine SSR markers before the grapevine community developed a large number of 357 SSR markers (Cipriani et al. 2011) in a shared project (Vitis Microsatellite Consortium, VMC). Later, additional 277 SSRs were published (Di Gaspero et al. 2005; Merdinoglu et al. 2005) and almost all marker data are available in public databases like NCBI

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(https://www.ncbi.nlm.nih.gov/probe/). After the release of the first grapevine genome assemblies in 2007 (Jaillon et al. 2007; Velasco et al. 2007), it was possible for every researcher to design his own SSR markers by mining for SSRs in specific genomic regions with various softwares (Sharma et al. 2007; Martins et al. 2009). Further, the genotyping throughput with microsatellite markers can be substantially increased if an automatic DNA sequencer is used for allele size determination and several SSR markers are combined with different allele sizes and fluorescent dyes in a single PCR assay. Therefore, it is not surprising that most of the genetic maps published in the last 10–15 years are based increasingly or solely on SSR markers (e.g., Adam-Blondon et al. 2004; Schwander et al. 2012; Pap et al. 2016). Other related marker types were occasionally used like cleaved amplified polymorphic sequence (CAPS), sequence characterized amplified region (SCAR), single strand conformation polymorphism (SSCP), sequence-specific amplified polymorphism (S-SAP), resistance gene analog (RGA), selective amplification of microsatellite polymorphic loci (SAMPL) or sequence-related amplified polymorphism (SRAP) (Fischer et al. 2004; Cabezas et al. 2006; Di Gaspero et al. 2007; Costantini et al. 2008; Liu et al. 2013).

7.5.2.2

SNP Markers

A SNP represents the ultimate form of a molecular marker since it is an individual nucleotide difference between two DNA sequences of the same genomic locus. Due to their principle biallelic nature, they discriminate clearly between homozygous and heterozygous alleles (Garrido-Cardenas et al. 2018). SNPs are homogeneously distributed throughout the genome with an extremely high frequency of about one every 47–200 bp in grapevine (Chin et al. 2016). Together with the advantage of massive multi-parallel automated analysis at moderate cost per data point, SNPs are very valuable markers for high-density genetic maps. The first plant SNP-based genetic map was developed for Arabidopsis thaliana using the availability of EST collections and random shotgun DNA sequences for SNP calling (Cho et al. 1999). In grapevine, the first SNPs were scored based on rather small sets of sequenced genes and used for proof of concept studies to show their applicability (Salmaso et al. 2004; Lijavetzky et al. 2007). They were then used together with SSR and AFLP markers to increase the resolution of genetic maps to a mean marker distance of 1–2 cM (Troggio et al. 2007; Salmaso et al. 2008; Vezzulli et al. 2008a). However, due to the lower information content of SNPs in comparison to SSRs, two to four times as many SNPs are needed to obtain comparable results (Kruglyak 1997; Schlötterer 2004). In addition, the transferability of SNPs is 31.5% across V. vinifera cultivars and 2.3% among the Vitis genus much lower than that of SSRs (Vezzulli et al. 2008b). Therefore, SSRs are still very helpful to allow crosstalks between maps, especially if biotic resistance traits from non-vinifera species have to be mapped and introgressed in V. vinifera.

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The difficulty to discover huge numbers of genome-wide SNPs was overcome by the development of new massive sequencing methods, known as NGS technologies, which allow resequencing of whole genomes (Davey et al. 2011). This works well for organisms with small genomes but full sequence information is mostly not required. Sequencing costs can be lowered especially for large genomes following a complexity-reduction strategy like reduced-representation library (RRL), restrictionsite-associated DNA (RAD), specific length amplified fragment sequencing (SLAF) or genotyping-by-sequencing (GBS) (Davey et al. 2011; Elshire et al. 2011; Sun et al. 2013). These methods use restriction enzymes to capture reproducibly the same genomic position of a large set of sample DNAs that are then barcoded and pooled for NGS. Myles et al. (2010) used this technique and called SNPs from 17 grapevine accessions, developed a validated 9 K SNP array and demonstrated its genotyping usefulness for assessing population structure and linkage disequilibrium (LD) decay. However, arrays have clear limitations since the transferability of SNPs is rather low, the initial investment high and the SNP chip not flexible once produced. Therefore, for constructing SNP-based genetic maps, the method of choice seems to be the direct genotyping of entire mapping populations following the RAD or RRL sequencing approach (Wang et al. 2012; Barba et al. 2014). Within the US-based Vitis Gen project (www.vitisgen.org), the GBS method was further improved and analytical pipelines elaborated enabling the generation of high-density SNP maps with up to 5185 markers for 17 core mapping populations (Hyma et al. 2015; Demmings et al. 2017).

7.5.3 Population Used for Trait Mapping The most common biparental population used for linkage mapping in plants is a testcross progeny as described by Gregor Mendel. Homozygous parental lines differ from each other regarding their phenotype and/or genotype and are crossed to produce a uniform F1 population. In the case of backcrossing to one of the parents, the marker segregation will be 1:1 in the progeny. While a generated F2 intercross line results in a 1:2:1 (codominant marker) or 3:1 (dominant marker) segregation. The genome of grapevine shows a high level of heterozygosity, since it was domesticated from wild outcrossing ancestral forms. This causes strong inbreeding depression due to accumulated deleterious recessive mutations (Mullins et al. 1992). Together with the long seed to seed cycle the establishment of populations based on selfing or backcrossing (RIL, NIL, F2 , BC1 , etc.) is very problematic. The majority of mapping populations used in grapevine research are, therefore, ordinarily F1 full-sib progenies derived from crosses of two heterozygous parents (Table 7.3). Whereas in the regular Mendelian cross of two homozygous parents, only two alleles of a genetic locus segregate in the F1 progeny, for grapevine this is the case for up to four alleles. Generation of genetic maps for highly heterozygous crops is achieved by the “double pseudo-testcross” strategy (Grattapaglia and Sederoff 1994), first introduced for grapevine by Lodhi et al. (1995). As a consequence of this approach, two separated

118

95 + 114 + 46 +139 + 153

F1

4 × F1 , 1 × S1

F1

F1

F1

S1

F2

S1

Dominga × Autumn seedless

Five mapping populations

(Vrp × Var) × (Vrp × Var/Vcd)

Two mapping populations

Three mapping populations

Muscat Ottonel (selfed)

Storgozia (Buket × Villard Blanc) (selfed)

Regale (selfed)

SSR, SNP, AFLP

94 + 94 + 87

191

98

SSR

SSR

SSR

SSR

46 + 46

121

SSR

SSR

AFLP, SSR, SAMPL

RAPD, RFLP

Marker type

181

60

F1

Cayuga white × Aurore

No of progeny

Population type

Mapping population

181

84

84

1134

420

237

515

595

439

No of markers

Table 7.3 Examples of grapevine genetic maps and mapped traits

948

692

569

1443

1676

1154

1647

1303

1196 /1477

[cM]

Map size

Erysiphe necator R

Monoterpene content

Pierce’s disease R

Seed development inhibitor (Seedlessness)

Trait

Ren5

Mtc

Pdr1

SdI

Symbol

14

5

14

18

4.8

3.8

25.3

23.2–26.9

[Mb]

Genome position Chr

(continued)

Blanc et al. (2012)

Hvarleva et al. (2009)

Duchêne et al. (2009)

Vezzulli et al. (2008a, b)

Di Gaspero et al. (2007)

Riaz et al. (2006)

Doligez et al. (2006a, b)

Cabezas et al. (2006)

Lodhi et al. (1995)

References

7 Genetic and Genomic Approaches for Adaptation … 207

F1

F1

F1

F1

F2

F1

F1

F1

F1

F1

Riesling × Gewuerztraminer

GF.GA-52-42 × Solaris

V3125 × Boerner

F2-35 × V. piasezkii

F1 (V. riparia × Seyval) (selfed)

GF.GA-47-42 × Villard Blanc

MN1264 × MN1214

Chardonnay × V. cinerea B9

C2-50 × Riesling

Red Globe × Shuangyou

149

90

148

147

151

424

277

202

256

188

No of progeny

SLAF

SNP

SNP

SNP

SSR

SNP

SSR

SSR

SSR

SSR

Marker type

7199

367/403

2394/2177

1977

543

1449

208

374

208

No of markers

1929

1587/1706

1275/1293

1853

1324

2424

1005

1365

891

[cM]

Map size

Plasmopara viticola R

Meloidogyne javanica R (root knot nematode)

Diaporthe ampelina R (Phomopsis)

Erysiphe necator R

Véraison

Yeast assimilable nitrogen

Erysiphe necator R

Flowering Time

Plasmopara viticola R

Budbreak-Flowering Interval

Trait

Rpv25

MjR1

Rda1

Ren10

Ver1

YAN

Ren6

FTi

Rpv10

Symbol

15

18

15

2

16

7

9

1

9

14

3.4–3.8

31.8–33.1

19.3–19.6

7.9

15.8

18.8

8.6–9.1

7.3

3.7

24.3

[Mb]

Genome position Chr

Lin et al. (2019)

Smith et al. (2018)

Barba et al. (2018), Hyma et al. (2015)

Teh et al. (2017)

Zyprian et al. (2016)

Yang et al. (2016a, b)

Pap et al. (2016)

Fechter et al. 2014

Schwander et al. (2012)

Duchêne et al. (2012a, b)

References

The selected studies represent the spectrum of the population types and sizes, the used marker types and numbers, the range of map length obtained and the diversity of mapped traits. The position of the trait loci is according to the 12× genome reference sequence of PN40024. Table entries are listed chronologically

Population type

Mapping population

Table 7.3 (continued)

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genetic maps, one from each parent, are obtained. The application of codominant markers like SSRs, often with segregating alleles from both parents, allows the combination of the parental maps into one integrated genetic map with a higher density and better assessment of marker order and distance. The conserved synteny of the microsatellite loci was also used for the development of integrated maps based on data of several different mapping populations all sharing the pseudo-testcross mapping strategy (Doligez et al. 2006a; Di Gaspero et al. 2007; Vezzulli et al. 2008a). An advantage of this approach is that the number of different mapped markers can be increased due to the higher number of parental lines that may exhibit heterozygous states of these loci. A disadvantage of the same approach is that an integrated map shows an artifactual marker order or an inflated marker distance in regions that are rearranged in the parental chromosomes or for markers residing within copy-number variants. Before the availability of the first reference genomes, it also provided the opportunity to gain genome-wide information of marker locations if not one large but several small populations are genotyped with only a limited number of shared markers. The population size and thus the number of meioses as well as the genetic information value of the marker type used are the determining factors of high-resolution fine mapping (Xu 2010). For most QTL analyses in grapevine, the size of mapping populations used ranges typically from around 40–200 individuals (Table 7.3). In some cases, even larger progenies have been established and used for comprehensive studies despite the high maintenance and map construction costs (Doligez et al. 2013; Pap et al. 2016; Yang et al. 2016a). The phenotype of a trait can be influenced by up to four alleles of the QTL within the investigated F1 progeny. A possible interaction between the parental alleles can be avoided by the use of selfing populations. In spite of the above-described difficulties, establishment of a few F2 populations, successful linkage map construction and QTL detection for different traits were achieved (Hvarleva et al. 2009; Yang et al. 2016a). Furthermore, some grapevine S1 populations have been derived by the selfing of a single genotype and successfully applied for trait mapping (Duchêne et al. 2009; Blanc et al. 2012).

7.5.4 Enumeration of Mapping of Climate Smart Traits The generation of linkage maps based on segregating populations derived from two phenotypically differing parents allows the location of agronomical interesting climate smart traits within the grapevine genome. This approach together with the availability of genome sequences represents the first step in the identification of underlying genes, characterization of their function and finally, their positional cloning. In addition, development of genetic markers linked to the trait of interest is achievable for smart breeding. The easiest traits to map are qualitative traits or dominant mutations since they segregate as present or absent within a population. A few important genetic loci were identified for these kinds of traits like sex (Dalbó et al. 2000; Fechter et al. 2012),

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berry color (Doligez et al. 2002; Kobayashi et al. 2004), and seedlessness (Doligez et al. 2002; Cabezas et al. 2006; Costantini et al. 2008). Quantitative traits are more complex since they are polygenic and the expression of the trait is influenced to a variable extent by the environmental conditions. Associated polymorphic loci can be identified with the statistical approach of QTL analysis. This method is based on the availability of a linkage map of a population segregating for the trait of interest and of the corresponding phenotypic data. By far the largest group of mapped traits to date is related to biotic resistance. The majority of mapped resistances are against downy and powdery mildew, which reflects the importance of these two diseases in viticulture and grape breeding. So far, 27 Rpv (resistance Plasmopara viticola) loci have been detected on ten different chromosomes, originating from at least six species (Vitis and M. rotundifolia). For the resistance against Erysiphe necator (Ren, Run), 12 loci on eight chromosomes, originating from at least four species (Vitis and M. rotundifolia), were recorded until now (www.vivc.de/loci). The most recent findings were Ren9 on linkage group (LG) 15 of a F1 population of Regent × Lemberger (Zendler et al. 2017) that was dissected from Ren3 in the same region of the chromosome with enhanced mapping efforts; Ren10 on LG 2 was uncovered using a SNP-based consensus linkage map constructed of the maternal maps of two half-sib F1 families with several different species in their background (Teh et al. 2017). Divilov et al. (2018) discovered five Rpv loci (Rpv17-21) on LGs 6, 7, 8, 11, 14 of two inter-specific F1 populations (V. rupestris B38 × Horizon, Horizon × V. cinerea B9). It was shown that leaf trichomes are associated with QTLs and have an effect on disease resistance. In another study, two novel resistance loci Rpv25 and Rpv26 were mapped both on LG 15 in a F1 progeny of the susceptible Red Globe and the East Asian resistant V. amurensis accession Shuang you (Lin et al. 2019). Further mapped resistance loci are against Phomopsis (Diaporthe ampelina, Barba et al. 2018), black rot (Guignardia bidwellii, Rex et al. 2014), Pierce’s disease (Xylella fastidiosa, Riaz et al. 2008), phylloxera (Daktulosphaira vitifoliae, Zhang et al. 2009), crown gall (Agrobacterium, Kuczmog et al. 2012) and nematodes (Xu et al. 2008; Hwang et al. 2010; Smith et al. 2018). Grapevine phenology is a very important property when considering quality wine production in the context of adaptation to changing environment and climate. Several QTLs were identified for the timing of the key phenological stages: bud break, flowering, onset of berry ripening (véraison) and ripening. Duchêne et al. (2012a, b) detected QTLs for the interval between “15th of February to date of bud break” on LGs 4 and 19 and QTLs for the interval “flowering to véraison date” on LGs 7 and 14. Fechter et al. (2014) report QTLs for flowering time on LGs 1, 4, 8, 10, 11, 14, 16, 17, and 19. Ripening-related QTLs were detected on LGs 2 and 6 (Costantini et al. 2008), LGs 7 und 8 (Fischer et al. 2004), LG 16 (Costantini et al. 2008; Duchêne et al. 2012; Zyprian et al. 2016), LG 17 (Mejía et al. 2007), and LG 18 (Mejía et al. 2007; Duchêne et al. 2012a, b; Zyprian et al. 2016). It is obvious that the phenological traits are polygenic with most QTLs explaining only a rather small portion of the phenotypic variance. There are a few QTLs described associated with fruit composition and aroma metabolites. For the monoterpenes linalool, nerol and geraniol, which make the major

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contribution to muscat flavor, a major QTL was found on LG 5 of three different F1 progenies (MTP2687-85 × Muscat of Hamburg, Italia × Big Perlon, Moscato Bianco × V. riparia Wr63). Additional QTLs were detected for linalool on LGs 2 and 10 and for nerol and geraniol on LG 13 (Doligez et al. 2006b; Battilana et al. 2009; Duchêne et al. 2009). Two QTLs associated with isobutyl-methoxypyrazine (IBMP) biosynthesis, the major methoxypyrazine in grape berries playing an important role in Sauvignon varietal aromas, were identified on LGs 3 and 12 and underlying genes were analyzed (Dunlevy et al. 2013; Guillaumie et al. 2013). Fruit quality traits that affect the fermentation process or wine quality like yeast assumable nitrogen (YAN) were localized by Yang et al. (2016a) on LG 7. They also mapped the malic acid content (MA) and total soluble solids content (SS) both on LG 6. Studies on the genetic background of grapevine responses to abiotic stresses or environmental conditions in general are relatively complex and, therefore, rare. One reason for disturbance of magnesium uptake is inadequate soil condition. Mandl et al. (2006) were able to detect a QTL associated with visual symptoms of magnesium lack and Mg concentration in the leaves of the grape zone on LG 11. Marguerit et al. (2012) report the mapping of traits related to water deficit, in particular scion transpiration controlled by the rootstock. A QTL for tolerance to lime-induced iron deficiency chlorosis on LG 13 was identified (Bert et al. 2013), and Houel et al. (2015) analyzed a microvine F1 population under contrasted temperature conditions and were able to map several QTLs for berry development and quality of leaf area. Several key genes responsible to salinity were identified by global transcriptome analyses and clusters of those genes were detected on chromosomes 2, 5, 6, and 12 (Upadhyay et al. 2018; Das and Majumder 2019). An overview of the schematic location of several mapped trait loci in the grapevine genome is shown in Fig. 7.6. The full names and the short descriptions of the trait abbreviations are listed in Table 7.4.

7.5.5 Map-Based Cloning of Genes and Mutations The success of cloning trait-associated genes or mutations by positional cloning (or map-based cloning) depends highly on the trait itself, its mode of inheritance, the available plant material and applicable methods. The approach usually starts with QTL mapping of a trait in a biparental population. The result is often a confidence interval of a few to 20 cM or even more, which may correspond to 500 kb to some Mb depending on the chromosomal compartment, holding up to several hundred genes. The resolution of the QTL can then be improved by fine mapping using two or more related populations or an enlarged population to increase the number of individuals with recombination around the responsible polymorphism and by increasing the marker density to fine map recombination crossovers. However, in most cases, this strategy is not sufficient to delimit the QTL down to a sequence size corresponding to the causal gene.

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2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

Flb

Ren7

Mb Rpv20 OMT3

FTi gai1

Mtc sex

Rpv4

Lin

Rpv21 Rda2

Ren9 Ufgt

Rpv10

Rpv11

Rpv5

Ren5

5-GT

Rpv8 Rcg1 Rpv12

Ren6

10

Rpv13 Ren10

Rpv7

Ren3 Ren8

Rpv17

BBr

BBr Be Co

Rpv1 Rpv9

Sen1

Rpv18

Run1

Rgb2 Ver1

Ren1

20

Rda1 Rpv14

Rpv6

XiR1

Rdv1

Ver2 FTi

powdery mildew resistance

morphological trait

downy mildew resistance

phenological trait

30

non-mildew pathogen resistance

PdR1 Rgb1 Ren2

Rpv2 Be size Rpv3-1 Rpv3-2 Ren4 SdI Run2.1 Run2.2

Rpv19

metabolic trait

Fig. 7.6 Schematic overview of the genomic positions of mapped grapevine trait loci (Numbering and scale of the 19 chromosomes are according to the 12× reference genome sequence of PN40024)

In grapevine, discrete phenotypes with a dominant monogenic inheritance were the first traits genetically mapped followed by the physically cloning of the corresponding gene or affected mutation site. For example, the berry color segregates in a Mendelian mode and was mapped by several research groups at the same locus on chromosome 2 (Doligez et al. 2002; Fischer et al. 2004). In parallel, a MybA1 gene was found to be involved in anthocyanin regulation and in the following years, this MybA1 gene was sequenced using BAC clones and proofed to be the causal gene that colocalizes with the berry color (Kobayashi et al. 2002, 2004; Walker et al. 2007). The presence of a transposon in the promoter from the MybA1 gene of the “white allele” prevents the transcription of the gene, which, along with one missense and one frameshift mutation in the neighbor MybA2 gene, explains the absence of anthocyanins in white berries. A similar approach has been used for the cloning of the sex locus and several very interesting candidate genes were proposed, but the full elucidation is still awaited (Dalbó et al. 2000; Fechter et al. 2012; Battilana et al. 2013; Picq et al. 2014; Ramos et al. 2017; Coito et al. 2017). In the case of some metabolites, cloning of causal genes was facilitated by the previous knowledge about the pathway in which the gene product performs its catalytic activity. For instance, the gene responsible for the formation of anthocyanin diglucosides was cloned following a candidate gene approach and degenerated PCR primers for anthocyanin 5-glucosyltransferase-like sequences (Hausmann et al. 2009; Jánváry et al. 2009). Further, markers were deduced from genes of the terpene biosynthesis pathway, applied together with anonymous markers for muscat flavor mapping and finally led to a QTL colocalized with the deoxy-xylulose synthase gene on chromosome 5 (Doligez et al. 2006b; Battilana et al. 2009; Duchêne et al. 2009). Finally, a functional

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Table 7.4 Abbreviations and full names of mapped grapevine traits, genetic loci and mutations as shown in Fig. 7.6 Locus name

Full name/description

5GT

5-glucosyltransferase/anthocyanin diglucosides formation

BBr

Bud break

BeCo

Berry skin color

Be size

Berry size

Flb

Fleshless berry

Fti

Flowering time

Gai

Gibberellic acid insensitive/dwarf mutant

Lin

Linalool

MA

Malic acid/malic acid content in grape must

MJR

Meloidogyne javanica resistance/root knot nematode resistance

Mtc

Monoterpene content

OMT

O-methyltransferase/isobutyl-methoxypyrazine formation

PdR

Pierce’s disease resistance/Xylella fastidiosa resistance

Rcg

Resistance Crown gall/Agrobacterium resistance

Rda

Resistance Diaporthe ampelina/Phomopsis resistance

Rdv

Resistance Daktulosphaira vitifolia/phylloxera resistance

Ren

Resistance Erysiphe necator/powdery mildew resistance

Rgb

Resistance Guignardia bidwellii/black rot resistance

Rpv

Resistance Plasmopara viticola/downy resistance

Run

Resistance Uncinula necator/powdery mildew resistance

SdI

Seed development inhibitor/stenospermocarpic seedlessness

Sen

Susceptibility Erysiphe necator/powdery mildew susceptibility

Sex

Sex/sex of flower type

SS

Soluble solids/total soluble solids content in grape must

Ver

Véraison/onset of ripening

XiR

Xiphinema index resistance

YAN

Yeast assimilable nitrogen/YAN in grape must

catalytic role for the associated missense substitution on that gene was confirmed (Battilana et al. 2011). The release of the genome sequence with the annotated genes enabled the systematic search for appropriate candidate genes. This facilitated the identification of VvOMT3 within a QTL for methoxypyrazine aroma as the key gene for the formation of this vegetable-like fragrance (Dunlevy et al. 2013; Guillaumie et al. 2013). There are two known mutations involved in the regulation of the grape berry development. The fleshless berry (Flb) mutation of the variety Ugni Blanc impairs the growth of the mesocarp cells that forms the berry flesh without affecting the

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seed and skin development. The mutation was mapped in the upper telomeric part of chromosome 18 and later refined with a second population to a stretch of 410 kb with 26 putative genes on the physical map (Fernandez et al. 2006, 2013). Gene expression experiments revealed the PISTILLATA-like MADS-box gene (VvPI) as the single differentially regulated gene that is colocalized with the Flb locus. Within the promoter of VvPI, a 1.5 kb transposon insertion was detected and proofed to be the causal mutation. The second mutation, stenospermocarpic seedlessness determined by the SEED DEVELOPMENT INHIBITOR (SdI) locus, is of high interest for table grape breeding. This phenotype was independently mapped with different populations to the lower part of chromosome 18 (Doligez et al. 2002; Cabezas et al. 2006; Mejía et al. 2011; Costantini et al. 2008). Zhang et al. (2017) confirmed this locus with a genome-wide association study and 199 grape accessions, leading to the conclusion that the SdI mutation is present in all seedless grapes. Again, it was possible to restrict the SdI locus with fine mapping and to identify the MADS-box gene VviAGL11 as the only gene within this refined SdI interval that is completely linked with the seedlessness phenotype. RNAseq as well as detailed sequence analysis were applied to elucidate the mutation itself. An InDel in the regulatory region and a SNP, causing an amino acid substitution, in VviAGL11 were proposed as the causal mutations for seedlessness (Mejía et al. 2011; Royo et al. 2018). Targeted sequencing of the gene in a collection of cultivars and in seeded somatic variants of the original seedless cultivar Sultanina showed that only the missense SNP in VviAGL11 was fully associated with seedlessness phenotype, which supports it role as the causal dominant mutation (Royo et al. 2018). Map-based cloning of genes for mildew resistance is maybe one of the most important challenges in grapevine resistance breeding. Since resistances segregate in a biparental population typically as a quantitative trait, QTL analysis is almost always applied for genetic mapping and results in one to a few loci. Mildew resistances are—with a single exception (Hoffmann et al. 2008)—not present in the V. vinifera genetic background but found in individual accessions of other Vitis species pn North America and East Asia. Therefore, the use of the V. vinifera reference genome sequence can be of limited use due to the low synteny in the corresponding QTL sites. The first grapevine resistance genes cloned were from the Run1/Rpv1 locus, originated from Muscadinia rotundifolia and inherited as a single locus. Run1 confers a strong resistance against powdery mildew (Uncinula necator, later renamed to Erysiphe necator) and Rpv1 against downy mildew (Plasmopara viticola). Since at the beginning of the project in 1998, the available genetic resources and markers were limited, the genetic mapping was very tedious and time consuming. For the physical mapping, a bacterial artificial chromosome (BAC) library was constructed and used for a chromosome walking approach as described in detail by Anderson et al. (2011). Sequencing of about 1 Mb of the Run1/Rpv1 locus led to the identification of a cluster with eleven resistance gene analogs (RGAs). The accumulation of RGAs at a single locus is rather common in plant genomes and considerably complicates the final identification of the functional resistance genes. Therefore, for Run1/Rpv1, all RGAs were transformed in susceptible grape varieties and assessed for resistance after artificial inoculation with different isolates of both mildew pathogens (Feechan

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et al. 2013). The two functional RGAs identified, one for each pathogen, are highly homologous and share 86% amino acid identity. Even more difficult seems to be the mapping and cloning of genes related to phenological traits like bud burst, flowering time or onset of ripening (veraison). For these traits, often several QTLs were detected with minor effects that are highly influenced by environmental effects and hence show lower reproducibility. Probably this is the reason why up to now no gene has been cloned from a QTL related to phenology, although understanding the genetic background of these traits is crucial to breed varieties more suited to the forthcoming climate scenarios. For map-based cloning of a phenological trait a suitable QTL is the Ver1 locus on chromosome 16 described by Zyprian et al. (2016), since it is a major QTL located with a small confidence interval of 3 cM explaining up to 70% of the observed phenotypic variance. A few interesting candidate genes were already identified based on the reference genome sequence. Sequencing QTL regions in the parental lines from mapping populations is much easier today due to the strong progress of NGS technologies (Goodwin et al. 2016). Recently, last generation long-read sequencing techniques allowed the de novo and diploid-aware genome assembly of grapevine varieties Cabernet Sauvignon and Chardonnay (Chin et al. 2016; Roach et al. 2018). Thus, although still costly, sequencing in the course of positional cloning will not be that challenging problem anymore. The availability of more and more genome data in the future also promotes the gene discovery in QTLs due to comparative genome analysis.

7.6 Post-genomics Era 7.6.1 History of the Genome Sequencing As an important crop, grapevine was one of the first higher plant species whose genome was sequenced (Jaillon et al. 2007). The International Grape Genome Program (IGGP) decided to sequence a near homozygous cultivar related to Pinot Noir (PN40024) in order to facilitate the sequence assembly by having limited variation between homologous chromosomes. To date, this genome still stands as the reference sequence for the grapevine community. Since then, the sequence and the assembly were updated several times. The sequence coverage was increased from an 8X to a 12X (Adam-Blondon et al. 2011) through the addition of a 4X coverage, including more bacterial artificial chromosome end sequences. However, as for other species, the sequence scaffolding required further improvement as 12.5% of the genome sequence was not correctly anchored on a chromosome. Canaguier et al. (2017) used two strategies to improve the assembly. First, by saturating six parental maps with SNP markers and second, a collection of mate paired sequences generated from 2 kb DNA fragments of V. vinifera cv. Kishmish

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vatkana was used for further scaffolding. This allowed producing the 12X.v2 version of the grapevine genome assembly containing 6.5% of unordered sequences. The percentage of unanchored sequence was 35% in the 8X version. The release of the PN40024 grapevine genome allowed the development of more than 3000 genome-wide transcriptional analyses. Since grapevine diversity makes using a single one-size-fits-all reference genome inadequate for studying the function of non-reference cultivar genomes, additional whole-genome sequencing of a diversity of cultivar is necessary. In order to address the variation in a commercial cultivar, Velasco et al. (2007) sequenced the Pinot Noir genome via Sanger sequencing, providing a high-quality draft of the genome with about 10X coverage. Early attempts to sequence Thompson seedless (Di Genova et al. 2014) and Tannat (Da Silva et al. 2013) genomes using second generation, short readbased, sequencing technologies failed to produce high-quality references using de novo assembly strategies but produced valuable data to study the genetic variation in comparison to the reference sequence. They reported a Thompson seedless genome size of 466 Mb, with 82% of the annotated genes present in the grapevine reference (PN40024) genome. The Thompson seedless raw data were then re-analyzed de novo with different assembly algorithms, improving the quality of the assembly (Patel et al. 2018). As for Thompson seedless, the Tannat comparison revealed that the reference genome assembly lacks genes and that Tannat possesses a set of genes that are not shared with PN40024, as it normally happens in the comparison of any two grapevine individuals. Illumina sequencing can be performed in order to align reads on the reference genome. It is still cheaper, and requires less bioinformatics and computer resources than de novo sequencing. In this way, several other cultivars such as Tempranillo Tinto and Tempranillo Blanco were also sequenced using Illumina technology and aligned to the PN40024 as a reference in order to link berry color variations to chromosomal rearrangements (Carbonell-Bejerano et al. 2017), or to determine the genetic characteristics and variabilities within three Nebbiolo clones (Gambino et al. 2017). Georgian grape cultivars Chkhaveri, Saperavi, Meskhetian green, and Rkatsiteli were in the same manner resequenced for genome comparative analysis (Tabidze et al. 2017) and revealed the pedigree of these grape cultivars. Third-generation sequencing technologies that generate long reads, facilitate assembly and carry the necessary information to phase haplotypes over several kb distances, greatly improving possibilities for de novo assembly. Sequences of Cabernet Sauvignon were first released by Chin et al. (2016) using Canu, FALCON, and FALCON-unzip. FALCON-unzip generated a set of primary contigs (591.4 Mbp in 718 contigs with N50 = 2.17 Mbp) that covers one of the two haplotypes, and a set of correlated haplotigs (367.8 Mbp in 2037 contigs with N50 = 0.80 Mbp). The total p-contig size was larger than the estimated genome size of V. vinifera (~500 Mbp). This suggested that in some cases, FALCON-Unzip underestimated the alternative haplotype sequences because of high heterozygosity between homologous regions. Minio et al. (2017) improved this assembly using optical mapping to scaffold primary contigs to 455.7 Mb and haplotigs to 310.0 Mb. Chardonnay genome sequence was recently assembled also using long reads from PacBio technology (Roach et al.

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2018). The final curated Chardonnay assembly (115X coverage) consists of 854 primary contigs (N50 of 935 kb) and 1883 haplotigs, totaling 490 Mb and 378 Mb, respectively.

7.6.2 Gene Annotation The improvement of the annotation of the reference genome is in constant evolution and the history of the gene annotation follows the release of genome assemblies. Therefore, several unrelated automatic annotations were produced for the reference genome, increasing the complexity of comparing the results of different studies that are using different annotations with poorly correlating gene prediction. With the goal of improving data standardization, a first effort was performed in order to relate the gene prediction released with the initial 8X sequencing with the gene performed on the 12Xv1 by the CRIBI Biotechnology Center (CRIBI v1) (Grimplet et al. 2012). The 12Xv1 was a very popular version of the annotation since the whole-genome microarrays developed for grapevine by NimbleGen were based on this version. Before the advent of the RNAseq technology, the vast majority of the array-based transcriptomic studies were, therefore, performed using this annotation. This work also included a complete curation of the functional annotation of the v1 sequences. The potential of the flexibility in annotation design used for RNAseq, compared to microarray fixed probe design, expanded the variability of possible sources for the annotation and CRIBIv2 and NCBI refseq annotations also became popular among researchers. At that point, the International Grape Genome Program mandated a committee of researchers in order to provide a guideline for an unified and correct nomenclature standard for the grapevine, following the principles adopted for other species (Grimplet et al. 2014). The standardized annotation following the guideline recommendation was released with the 12Xv2 of the genome assembly (Canaguier et al. 2017). Unlike the previous ones, this annotation (VCostV3) did not only involve new automatic gene prediction but was the consensus of the previous gene prediction: CRIBIv1 predicted with JIGSAW and GAZE, NCBI Refseq, predicted with GNOMON and an additional version, predicted with Eugene, that was directly merged in the consensus. This consensus version, therefore, resulted in a much higher number of putative genes, 42,414 gene models when the article was released. Only a third of the genes were detected with the three algorithms and another third only appeared in one of them. The remaining third included much complex cases where several models overlapped between versions. However, comparing gene predictions permitted a significant improvement of the structural annotation of the genes.

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7.6.3 Structural and Functional Genomic Resources This comparative study also highlighted that automated prediction algorithms produce results and genes models with structure that needs curation and cross validation. Therefore, besides a nomenclature of the gene ID structure, a database system allowing manual gene edition and curation is also necessary. The objective of the working group evolved into overseeing the good practices in gene annotation by the grapevine community and integrating curated data into the reference genome annotation. Data in compliance with the guideline could be incorporated into the reference genome annotation in two phases. For the first phase, the curators could choose between two options to provide annotation data to the committee. Curator can send the GFF3 format file containing the curation to a committee member. Data should include descriptive functional annotation data and corrected structural annotation if necessary. The curators also have the possibility to upload their data in the grapevine section of the ORCAE database. In the second phase, when the volume of annotated data would be sufficient, the data will be periodically reviewed by the working group. Reviewed annotation data will be incorporated into the reference genome annotation and submitted to public databases. Handling GFF3 file is more convenient for people with annotation and bioinformatics skill, while the graphical GUI of ORCAE would result easier to people less familiar with the GFF3 format.

7.6.4 SNP Diversity Grapevine is a species showing high level of heterozygosity (Aradhya et al. 2003), being an outbreeding species. In the heterozygous whole-genome sequence, Velasco et al. (2007) identified 2 million SNPs in Pinot Noir, corresponding to one SNP every 250 bases. Comparing RNAseq data from 18 genotypes, Muñoz et al. (2014) observed an average frequency of one every 144 bases (89 in Pinot Noir) in cultivated V. vinifera and 53 in wild species. There were about 2 million single-base differences between Tannat and Corvina cultivar with PN40024. These results concurred with microsatellite data suggesting that Tannat has a lower degree of heterozygosity than other grapevine cultivars (González-Techera et al. 2004). The amount of SNPs in nuclear DNA of Georgian cultivars, in comparison with the Pinot noir genome ranged from 1 million for Meskhetian green to 5 millions for Rkatsiteli (Tabidze et al. 2017). For the three Nebbiolo clones, SNVs identification was performed by comparing the aligned reads to PN40024, resulting in the identification of ~7.2 millions SNVs compared to PN40024 (Gambino et al. 2017). The final set of Nebbiolo variants comprised a total of 665,561 SNVs, substantially in line with that of the table grape “Sultanina” (Di Genova et al. 2014), but much lower than in Chardonnay (Roach et al. 2018).

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Emanuelli et al. (2013) investigated the patterns of molecular diversity at 22 common microsatellite loci and 384 SNPs in 2273 accessions of domesticated grapevine V. vinifera ssp. sativa, its wild relative V. vinifera ssp. sylvestris, inter-specific hybrid cultivars and rootstocks. Despite the large number of putative duplicates and extensive clonal relationships among the accessions, we observed high level of genetic variation. In the total germplasm collection, the average genetic diversity, as quantified by the expected heterozygosity, was higher for SSR loci (He = 0.81) than for SNPs (He = 0.34). The analysis of the genetic structure in the grape germplasm collection revealed several levels of stratification. Diversity within a panel of V. vinifera cultivars (783 different genotypes) was also evaluated using a18k SNP genotyping array. Laucou et al. (2018) obtained a dataset with no missing values for a total of 10207 SNPs and 783 different genotypes. The average inter-SNP spacing was ~47 kbp, the mean minor allele frequency (MAF) was 0.23 and the genetic diversity in the sample was high (He = 0.32). LD extent has previously been estimated in V. vinifera, for both simple sequence repeat (SSR) and SNP markers. Barnaud et al. (2010) reported significant LD values between SSRs extending to 14–17 centiMorgans (cM) in a core collection of cultivars and to less than 1 cM in a wild sample (1 cM corresponding on average in V. vinifera to about 300–400 Kb for a total genome size of 487–504.6 Mb. By contrast, LD decays much more rapidly between SNPs, with r 2 values reaching 0.2 within a few Kb at most (Myles et al. 2011). Nicolas et al. (2016) found a LD extent for a predicted r 2 of 0.2 varied from 9 to 458 Kb according to subgroup and genomic region analyzed from a large panel of germplasms.

7.6.5 Limitations Genome resequencing projects of both prokaryotic and eukaryotic organisms have clearly shown that one genome sequence is insufficient to entirely describe the pangenome of a species (Tettelin et al. 2005; Donati et al. 2010). In order to grasp comprehensive genetic variability and complete gene pools in outcrossing species, such as grapevine, we also need to go beyond the generation of haploid consensus sequences and focus our efforts to begin assembling diploid genome sequences with phased haplotypes. Carbonell-Bejerano et al. (2017) recently gave a nice example of the importance in considering haplotypic structure in trait variation analyses. To understand the mutational mechanisms generating somatic structural variation in grapevine, they compared the Tempranillo Blanco (TB) white berry somatic variant with its black berry ancestor, Tempranillo Tinto. Whole-genome sequencing uncovered a catastrophic genome rearrangement in TB that caused the hemizygous deletion of 313 genes, including the loss of the functional copy for the MYB transcription factors required for anthocyanin pigmentation in the berry skin. Loss of heterozygosity and decreased copy number delimited interspersed monosomic and disomic regions in the right arm of linkage groups 2 and 5.

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7.7 Role of Bioinformatics Bioinformatics plays an increasing role in the study of living organisms including grapevine. High-throughput omics technologies require the development and the implementation of bioinformatics tools within the data production pipelines, generating nearly an infinite amount of data on all the aspects of grapevine genetics and physiology. Therefore, large and heterogeneous datasets are generated, describing genotypes, phenotypes (transcriptome, proteome, metabolome, phenome, development stages, mutant or extreme phenotype), and their interaction to the environment (Adam-Blondon et al. 2016). In addition, valuable information has also been generated in the pre-omics pre-bioinformatics era that should also to be preserved and incorporated to system biology studies in order to compare and integrate data for generating new knowledge. Bioinformatics is also an important component of data analysis, storage, and interpretation. For improving the infrastructure supporting the reuse of scholarly data, the international grapevine community is organizing initiatives so that that published data comply with the FAIR principles (findable, accessible, interoperable and re-usable) (Wilkinson et al. 2016). In this section, we will review the available tools under the scope of the role they play to improve the FAIRness of grapevine data.

7.7.1 Data Should Be Findable and Accessible The key component of these two first items of the FAIR principles are that data and metadata is assigned to a globally unique and persistent identifier, well defined, openly available, in databases complying with globally recognized protocols. Therefore, the IGGP and the COST action FA1106 “Quality fruit” followed by CA17111 “INTEGRAPE” launched several coordinated initiatives in order to standardize nomenclature protocols, certificate databases, and improve the visibility of search tools for published material.

7.7.1.1

Raw Sequences Databases

Genome, gene, and gene products data submission to public databases for grapevine follow a standard procedure identical for any other genome and nucleotide sequences. The data are at minima deposited in any organization of the International Nucleotide Sequence Database Collaboration (NCBI, EMBL, DDBJ, www.insdc.org). Other submission procedures are acceptable, but it is necessary that the information present in these repositories is periodically uploaded to the three mainstream databases. It is also crucial that metadata are provided with as many details as possible for Interoperability and Reusability.

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Data submission in public repository is mandatory for acceptance in a journal article. Therefore, this procedure is ubiquitously performed by the authors for nucleotide sequences. However, there is still room to improve the quality of data and metadata that are registered with the genomes to reach full compliance with the FAIR principles. As example, a common malpractice affecting Findability is the use of outdated gene identifiers for the transcriptomic data. This issue can, however, be fixed by performing sequence alignment or for RNAseq data by re-analyzing the original data but it is unnecessarily time consuming. Data can have outdated ID simply because these ID were the standards at the submission time, but it could also be related to lack of information for the authors on the correct ID to use. Now, these old identifiers have disappeared from the public database, for example, the original gene identifier publisher in Jaillon et al. (2007) cannot be retrieved in GenBank. They are still available on the sequencing consortium webpage, that is still accessible but not maintained anymore and a correspondence table was in (Grimplet et al. 2012), but it does not comply with the Accessibility principle. Fixing these issues was one of the driving forces for implementing and publishing a sustainable gene nomenclature that could carry over major updates of the grapevine reference genome.

7.7.1.2

Quantitative Data. Transcriptome, Proteome, Metabolome, and Phenome Databases

The production of quantitative omics data is performed with a very heterogeneous set of tools whose output data format evolve according to the technological advances. The standards for RNA quantitative data were defined without major obstacle with the MIAME standard (Brazma et al. 2001) when microarray-based technologies were the golden standard for RNA quantification. The emergence of RNAseq simplified further the good practices for complying with the FAIR principle since they are now within the scope of the good practices for nucleotide-based information (www. fged.org/projects/minseqe/). Data are stored as individual reads in fasta or fastainteroperable format, quantification of transcript abundance are obtained a posteriori through processing. For proteomic data, the PRIDE database from the EMBL (www.ebi.ac.uk/pride/ archive/) is admittedly the most universal solution for data submission. It allows the submission of a variety of formats popular in proteomics analysis in particular mzIdentML files, the format from the Proteomics Standards Initiative (www.psidev. info). MS raw results, peak list, 2D gel images, files with quantitative values. There is only 18 datasets for Vitis in the PRIDE database but proteomics is not a field widely investigated for grapevine and most of the articles in the NCBI database have their related data deposited in PRIDE. Other proteomics repositories exist but no grapevine data was deposited there. For metabolite data, standardization and, therefore, setting up unified database are more complicated due to the heterogeneity in compound structures, purification protocols, and analysis methods. Within the recent years, the MetaboLights repository (www.ebi.ac.uk/metabolights/) from the EMBL, however, seems to emerge as

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a well-recognized public repository. So far, only 8 datasets for Vitis are available in this database and further efforts are needed to promote the use of this repository. Phenotyping data are even more heterogeneous that metabolome data and phenotypic data are not currently concentrated in any generic resource. An effort must also be done in order to normalize the metadata and implements the standards of the minimal information about plant phenotyping (www.miappe.org).

7.7.1.3

Comparative Genomics Databases

Whole-genome sequencing of grapevine cultivar has recently emerged and now, a significant list of genomes is available as described in the previous section. Standard formats for genome variation are well established in particular with the vcf format for the genomic variations. The Gramene website (gramene.org) provides valuable tools for comparative genomics. Modules allow performing synteny analysis or comparing polymorphic variation. For grapevine, synteny is available with several plant species (Arabidopsis, peach, rice, cacao and potato), polymorphic variant sites are also available for the data published (Myles et al. 2010). In the near future, we also will require tools for studies at the pangenome level.

7.7.1.4

Tools for Gene Expression Analysis

Initially, microarray-based expression data for grapevine was preferentially stored in PLEXdb, which shut down its activity in 2013, but the data are still accessible on the website (www.plexdb.org). This is an important source of information, since some (meta) data are only available on this website. The plant section of the EBI-EMBL expression atlas is also maintained through the Gramene project (www.ebi.ac.uk/gxa/home). Grapevine is particularly well represented in this database, being amongst the 10 species with more deposited data (fourth plant species). Datasets are divided into “baseline experiments” and “differential experiments.” Baseline experiments consist in RNAseq data that display expression levels of gene products under “normal” conditions. It can also include proteomics data from PRIDE but it is not yet the case for grapevine. “Differential experiments” contain both microarray and RNAseq data that were produced from experiments that compared treatments. Other useful tools exist for visualization of expression data for grapevine. They are still available online but they do not allow input of new data and, therefore, could be considered as static. However, they still provide very useful information on already published data, particularly for older microarray platforms. The Grape eFP Browser (www.bar.utoronto.ca/efp_grape/cgi-bin/efpWeb.cgi) is a very informative tool allowing easy visualization of RNA expression within tissue from the data from the grapevine gene atlas (Fasoli et al. 2012). Vespucchi (Moretto et al. 2016) (www.vespucci.colombos.fmach.it/) is a tool that allows visualization of gene expression amongst the data that were deposited in GEO using the NimbleGen microarrays array. VTCDB (Wong et al. 2013) (www.vtcdb.adelaide.edu.au/

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Home.aspx) allows the visualization of data acquired with affymetrix and NimbleGen platforms. This tool is particularly focused on visualization of co-expression data.

7.7.2 Interoperable Re-Usable The goal of these FAIR principles is that (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. Meta(data) should be richly described with a plurality of accurate and relevant attributes. Applying good practice in description of (meta)data should be enforced within the authors of scientific communications on grapevine. The guidelines within each of the omics fields need to be clearly defined following models of other species more ahead in the process, keeping in mind grapevine uniquenesses (phenology, fruit cluster architecture…) and they should be widely communicated. This is one of the principal objectives of the COST action CA17111 “INTEGRAPE.”

7.7.2.1

Controlled Vocabulary

A common vocabulary is important for data comparison. It involves unequivocal identifiers of genes and genes products (transcript proteins) but also metabolites, plant organs, and phenotypes ranging from status of infection by pathogens, morphological traits, production traits, effects of abiotic stresses… The gene and gene products follow a simple rule, unique identifier is linked to their locus of origin by adapting the LocusID nomenclature for each product (genes are vitvi00g00000; transcripts are vitivi00t00000; proteins are vitivi0p00000) with polymorphic or splicing variant specified in the suffix (vitvi00t00000.t01, vitvi00t0000.cs …). The nomenclature and the controlled vocabulary for the functional names of the genes and genes products should follow rules common for living organisms, which are described in (Grimplet et al. 2014). Controlled vocabulary for metabolites also should follow standards rules for living organisms (www. metabolomics-msi.org/). Ontology of the terms used in phenotyping experiments should also correspond to the ontology defined in the vitis section of cropontology (www.cropontology.org/ontology/VITIS/Vitis).

7.7.2.2

Integration of Different Data

VitisCyc (Naithani et al. 2014), a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes, and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic

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networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes, etc., using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of V. vinifera “Pinot Noir” cultivar with 12X v1 annotations. VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions, and pathways. Currently, VitisCyc harbors 68 super pathways, 361 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy-related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions, and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant’s immediate environment. VitisCyc version 3.19 is available online at www. pathways.cgrb.oregonstate.edu. Many grape researchers rely on another publicly available grape database VitisNet (Grimplet et al. 2012) that allows simultaneous visualization of quantitative transcriptomic, proteomic, and metabolomic data on networks using the Cytoscape software. VitisNet contains metabolic pathways as well as signaling networks. We will be looking for opportunities to coordinate curation efforts especially on signaling pathways and small molecules together with the resources based on gene ontology for visualization like GoMapMan (Ramsak et al. 2014) and Plant Reactome from the Gramene website. Genomics and transcriptomic data should be integrated with other different levels of plant characterization, and with a full description of the environmental and experimental conditions in order to extract the most accurate and complete information from metadata (Fig. 7.7). Such an effort is presently promoted by the COST Action Integrape (www.integrape.eu/index.php).

7.8 Genome-wide Association Studies and Genomic Selection and GS 7.8.1 Extent of Linkage Disequilibrium The potential to discover causative genetic variants of grapevine phenotypic traits using genome-wide association studies (GWAS) is high, compared to the traditional way by which marker-trait associations have been generated in the past (reviewed in Myles 2013). This situation originates from a combination of favorable conditions. The species has a relatively small genome (480 million bp, Jaillon et al. 2007), repetitive DNA accounts for no more than 41% of the total genome length, and individual genomes are diploid and highly heterozygous. Most of the ancestral genetic and phenotypic variations went through weak domestication and improvement bottlenecks, and linkage disequilibrium (LD) decays over short physical distances in modern varieties.

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Gene Transcript

Bioinforma cs Biosta s cs Modelling

Protein

Metabolite Organelle Cell

Metadata

Data bases

Tissue Organ Plant

Plot

Standardized sampling Full descrip on of environmental condi ons

Fig. 7.7 Need for comprehensive databases in grapevine breeding. The correlation between the genotype and the phenotype, and the response of a given rootstock/scion combination to environment may be analyzed at different levels with a wide range of (semi) high-throughput techniques that provide an enormous flow of metadata in various formats. The analysis of these data is only useful if all experimental conditions have been described, and if they are organized in databases that may be analyzed with bioinformatics, biostatistic, and modeling tools

Using forward simulations, Fodor et al. (2014) established that the following demographic scenario best fits the real data of population structure and population genetics parameters observed in the present-day germplasm: (i)

a domestication bottleneck intensity of 10% in the predomestication population of table grapes (ii) an expansion in the domesticated population for approx. 400 generations, before (iii) approx. 9% of migrating individuals founded wine grape populations by mating back to other wild populations (iv) 50–100 further generations of crosses within wine grapes and between wine and table grapes. Under this simulated scenario, Fodor et al. (2014) predicted a LD decay to r 2 values lower than 0.2 within distances ranging from 9 to 13 kb. Real data provided fluctuating estimations of the actual magnitude of LD decay, although all studies converged toward low levels of non-random associations between alleles at nearby marker loci, compared to other outcrossing crops.

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Using NGS of reduced-representation genomic libraries from ten varieties and 16,486 SNPs, Myles et al. (2010) showed that r 2 remains significantly above background levels up to approximately 10 kb, and is no longer significantly distinguishable from the background level at larger inter-SNP distances. Using whole-genome sequencing data from a sample of 14 cultivars, mostly belonging to a single convarietas, Zhou et al. (2017) estimated an r 2 decay below 0.2 within 20 kb. Using amplicon sequencing of gene fragments, Lijavetzky et al. (2007) assessed short-range LD, estimating that r 2 decays below 0.2 within 300 bp in the gene space. The rapid LD decay in V. vinifera is at the same time an advantage and a challenge for GWAS. Rapid LD decay is a requisite for obtaining a high resolution in the identification of the causal gene/variant within a DNA region with significant markerphenotype associations, on one hand, but it requires the use of millions of SNPs for positively identifying the regions containing the causal variants, on the other hand. The low LD at genome-wide level in V. vinifera is, therefore, a favorable factor for GWAS, provided that marker density is not a limiting factor.

7.8.2 Discovery of Variant Sites for GWAS The first step toward GWAS is the discovery of a large set of common variant sites. SNP chips have become available in grapevine since 2010. Two validated arrays incorporate 9 K or 18 K variant sites (Myles et al. 2010; Le Paslier et al. 2013). Myles et al. (2011) and Mahanil et al. (2012) pioneered the use of the 9 K SNP chip for population genomic analyses and for linkage mapping, respectively. The GrapeReSeq 18 K chip was accurately constructed with three quarters of the probes that interrogate pre-ascertained variation in 47 domesticated and undomesticated forms of V. vinifera, additionally balanced for genomic distribution and minor allele frequency classes, and one quarter of the probes targeting variation in 18 accessions of 6 wild species. Many applications proved the GrapeReSeq 18 K chip as an excellent tool for studies of genetic diversity in local and international germplasm (De Lorenzis et al. 2015; Mercati et al. 2016; Laucou et al. 2018; Marrano et al. 2018) and for QTL detection in linkage mapping (Houel et al. 2015). However, every SNP chip (i) relies on hybridization-based genotyping assays, (i) detects individual genotypes at a biased set of pre-ascertained variant sites, and (iii) does not allow for flexibility in marker density. The alternative to hybridization-based genotyping was offered by the advent of restriction-site-associated DNA sequencing (RAD-seq) and the subsequent protocol improvements that led to genotyping-by-sequencing (GBS). RAD-seq and GBS do not require a preliminary step of variant site discovery. With RAD-seq and GBS, variants sites are detected and individual genotypes are called simultaneously in the entire population under study without any ascertainment bias. RAD-seq and GBS use restriction enzymes to subsample a genome fraction in a reproducible manner and sequence pooled and barcoded DNA samples from hundreds of individuals. The fraction of the genome under investigation, and, therefore, the number of potential

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variant sites, can be easily scaled-up by using more frequently cutting restriction enzymes and by increasing the sequencing throughput accordingly, in order to maintain sufficient read depth at each variant site. In the first published application of RAD-seq in grapes, Wang et al. (2012) sequenced DNA stretches of 70 bp at 80,709 genomic locations and detected polymorphic sites within 21,599 of those genomic locations in the parents of a segregating population. Later on, Barba et al. (2014) identified 16,833 polymorphic sites in the parents of another segregating population, with average marker densities ranging between one SNP every 21 and 36 kb on different chromosomes. In the largest comparative GBS experiment ever published for linkage mapping, Hyma et al. (2015) obtained from 12,089 and 17,002 reliable segregating SNPs in the parents of four segregating populations. In all these cases, GBS provided marker densities that largely exceeded the density of genetic bins in segregating populations of tractable size. In our laboratory experience, the use of a ddRAD-seq protocol yielded from approximately 10,500–16,400 high-quality segregating sites per cultivar in five cultivars of vinifera (Fornasiero 2016). Marrano et al. (2017) were the first to use RAD-seq for SNP discovery and population genetics analyses in grape germplasm collections. These authors identified 37,594 nuclear SNPs in 51 cultivated and 44 wild V. vinifera, with average marker densities ranging between one SNP every 10 and 16 kb on different chromosomes. Whole-genome NGS is, therefore, the only sequencing option for SNP discovery that may provide an adequate marker density, suitable for GWAS. To provide a rough estimate, we think that approximately 4 million variant sites, excluding extremely rare variants, or 3 million variant sites with minor allele frequency >0.1 might be detectable in the cultivated grape germplasm across the non-repetitive fraction of the grape genome (G. Di Gaspero, M. Morgante, pers comm).

7.8.3 GWAS of Target Traits and GS All GWAS performed so far in grape could only rely on the availability of low marker density. The constraint posed by the fixed number of probes laid down on an array did not deter Migicovsky et al. (2017) from performing GWAS using SNP chip data. These authors studied the association between 33 phenotypes and 6114 SNPs in 580 accessions from the United States Department of Agriculture germplasm collection. SNP density was in general too poor for detecting significant signals, with the exception of a phenotype related to the domestication syndrome or a phenotype that was targeted by crop improvement. In those limited cases (i.e., hermaphroditism and berry color), GWAS was powerful due to the presence of extended segments of LD and extended haplotype homozygosity around the allele subject to positive selection. As for other crops, GWAS was successful in the detection of marker effects for fruit quality and agronomic traits in species with germplasm structures similar or dissimilar to grapes, such as apple and peach. Using a diversity panel of 85 apple cultivars and 52,440 SNPs, Amyotte et al. (2017) identified SNPs with significant association

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for 6 out of 20 quantitative traits related to fruit quality. These traits included soluble solids content and sensory attributes, i.e., the flavor known as fresh green apple, and texture attributes, i.e., crispness, juiciness, and skin thickness. GWAS efforts of a similar extent in outcrossing crops are, however, largely insufficient to yield any additional biological information, beyond the generation of predictive markers. An European collaborative project with 28 leading research institutes and companies involved in Rosaceae breeding (FruitBreedomics, Laurens et al. 2018) produced a 480 K SNP array for apple, the largest ever developed in a fruit crop. The variant sites were identified through whole-genome resequencing of a panel of 63 highly diverse apple cultivars (Bianco et al. 2016) and used in GWAS of flowering and ripening time. Nevertheless, the low level of LD decay in this species only allowed the authors to list all genes annotated in chromosomal regions corresponding to 95% confidence intervals, which ranged from 150 to 400 kb (Urrestarazu et al. 2017). In peach, Cao et al. (2016) sequenced 129 accessions and generated more than 4 million SNPs to detect marker association for eight fruit traits. The authors restricted the identification of causal genes to a few functional candidates. The latter achievement was possible with a relatively small effort, especially in terms of sample size, because of the low rate of polymorphism in peach, a situation that is not going to happen for GWAS in grape. In species with low nucleotide diversity, causal SNP variants pop up from a low background level of nucleotide variation. In species with high nucleotide diversity, causal variants mingle among the crowd, with a predominant number of sites carrying silent variation in genes and in regulatory regions. Genomic selection (GS) builds on the same germplasm resources and genomic data used for GWAS, but instead of identifying marker-phenotype associations (i.e., loci that explain the genetic variance of trait variation), it only aims at identifying marker effects across the whole genome that collectively predict the phenotype. GS is more useful than alternative approaches for predicting complex traits controlled by the small effect of many loci. The efficacy of GS depends on the accuracy of genomic prediction. Migicovsky et al. (2017) obtained prediction accuracies ranging from r = 0.10 for leaf morphology traits to r = 0.76 for berry traits. As for berries, prediction accuracies were higher for quantitative biometric traits (length, width, size, and shape)—for which the model of genetic control is yet elusive—than for skin color, which is chiefly controlled by a known major locus. GS, therefore, holds the greatest potential exactly for the most challenging traits, those with a complex genetic architecture. Genomic prediction is usually modeled and defined using a training population that represents a biased or unbiased sample of the entire population of cultivated germplasm, and then the predictive equation so obtained is used for genomic selection in candidate populations that derive from particular types of parental combinations. Based on simulated data, Fodor et al. (2014) showed that the predictive ability of GS in estimating phenotypes is highest if the training population is composed of all varieties belonging to same convarietas as the founders of the candidate population. Alternatively, the training population should represent a core collection of 1000 individuals that maximizes the diversity of the entire population (3000 individuals).

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7.8.4 The Limits and Potential for the Application of GWAS GWAS has inherent pros and cons, as does linkage mapping. Linkage mapping succeeded in many cases, revealing loci controlling simple traits and leading to identification of the causal gene in a few cases (Feechan et al. 2013). However, it relies on the generation of large seedling populations and, in several cases, on observations or measurements in mature vines, grown in experimental plots that reproduce exactly commercial wine vineyard conditions. Researchers usually generate segregating populations for the specific scope of a research project, which, therefore, require de novo phenotyping of the trait of interest with limited replication over time and high annual variations. On the contrary, GWAS may use long historical series of phenotypic data, typically collected by curators of germplasm repositories, therefore requiring only genotyping efforts. A striking example of this advantage is provided by the association study performed by Migicovsky et al. (2017), who made use of historical data for 33 biometric, chemical, and phenological parameters collected over a 17-year period. The power of GWAS could not be unleashed in grape yet only because high marker densities were inconvenient to achieve with obsolete technologies. While we expect that this major impediment will be lifted shortly, as the cost of wholegenome resequencing becomes more affordable, at least three suboptimal conditions will remain forever in place, requiring special attention to counteract their potential effects on inflation of test statistics and spurious associations or on lack of power. These three conditions are inherent to the genetic structure of the grape germplasm and to grape genome biology. GWAS works perfectly under the assumption of population homogeneity, but this assumption is normally violated in grape. Any sample and even the entire crop germplasm is far from being a population in Hardy–Weinberg equilibrium, primarily because of non-random mating and secondarily because of selection forces. Genetic diversity in cultivated grapevines is structured in three major groups corresponding to the convarietas orientalis, pontica and occidentalis, earlier described by Negrul (1946), with substantial population stratification (Aradhya et al. 2013). The large majority of cultivated grapevines are also linked one another by close kinship (Myles et al. 2011). These two factors are referred to as population structure and relatedness (or identity-by-descent, IBD). GWAS needs to be performed with compensation for population stratification and IBD. Population structure and kinship matrix, therefore, require advanced modeling and precise estimations. Different types of DNA mutation—other than SNPs—may cause phenotypic variation among grapevines. This frequently occurs due to insertion or excision of transposable elements in the promoter region of genes (i.e., berry color, cluster size and architecture, Walker et al. 2007; Fernandez et al. 2010), copy-number variation (i.e., disease resistance, Foria 2015), and presence–absence of large DNA regions (i.e., berry color, Pelsy et al. 2015). This structural variation is ubiquitous in grape genomes and when it happens to be causative of trait variation, this association is hardly detectable by GWAS using nearby SNPs.

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The third confounding factor in GWAS is caused by grape phenotypes that are the result of somatic mutations or chimerism and by Mendelian inconsistencies in long-lived clonal lineages due to non-germinal SNPs in the parents and postzygotic SNPs in the offspring. The extent of this phenomenon is usually low in other crops and typically overlooked in GWAS. In our sequencing laboratory experience with parent-offspring trios, non-parental SNPs are estimated to occur in grapevine with a frequency of one non-parental SNP every 200–300 kb (G. Di Gaspero, M. Morgante, pers comm).

7.9 Genetic Engineering 7.9.1 Classical Technologies The first manuscript reporting the application of biotechnology in grapevine dates when Morel (1944) achieved in vitro culture of grapevine under aseptic conditions (Bouquet and Torregrosa 2003). From the 1980s, numerous publications have shown the possibility to regenerate shoot meristems from undifferentiated cells. The first somatic regenerations were obtained by adventive organogenesis, i.e., by inducing the development of neo-buds (Barlass and Skene 1978). In parallel, somatic embryogenesis has also been developed for a large number of Vitis genotypes (Martinelli and Gribaudo 2009). The first publication mentioning the successful gene transfer in the grapevine reported the recovery of calli and roots transformed with Agrobacterium tumefaciens derived plasmids (Hemstad and Reisch 1985). The first transgenic grapevines were obtained by Mullins et al. (1990) who coupled the transformation by Agrobacteriumdisarmed vectors with the regeneration of rootstocks by somatic embryogenesis. Over the years, genetic engineering was improved and applied to a range of Vitis species (Bouquet et al. 2009; Torregrosa et al. 2015). Chaib et al. (2010) developed a procedure for the transformation of microvines (Boss and Thomas 2002), a promising model for boosting physiology and genetics studies in grapevine (Torregrosa et al. 2016). However, despite many improvements, the regeneration of non-chimeric transgenic plants is still challenging for many grapevine genotypes. Nevertheless, various alternative techniques using physical vectors or viruses have been developed to obtain ectopic gene expression in individual cells, cell suspension cultures, tissues or non-caulinary organs (Vidal et al. 2010). More recently, genetic modification techniques significantly improved with the development of cisgenesis (Dalla Costa et al. 2016) and genome editing approaches (Osakabe et al. 2018). These new breeding technologies (NBTs) represent interesting prospects for both functional genomics studies and genetic improvement of grapevine (Dalla Costa et al. 2017), although caution is necessary since a recent report described unexpected on-target mutagenesis induced by CRISPR-Cas9 in animal cells, such as large deletions and more complex genomic rearrangements at the targeted sites

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(Kosicki et al. 2018). For this reason, a deep evaluation of the mutational profile induced by Cas9 at the whole-genome level, through the application of NGS and bioinformatics, may be recommended especially if plants are selected for commercial purposes. Genome editing, the most popular NBTs, is detailed in the next section.

7.9.2 Genome Editing Technologies Genetic manipulations based on programmable nucleases (Kim and Kim 2014) allow precision genome editing or engineering. These nucleases are enzymes which cut or double-stranded break (DSB) at specific sites in the genome. Three major classes of programmable nucleases for precision genome editing have been described: zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspersed short palindromic repeats (CRISPR) associatednucleases (Cas). CRISPR/Cas9 is one of the most efficient gene editing techniques available in that it allows for direct sequence modifications in the genome. Originally described as a part of adaptive immunity in Streptococcus pyogenes, the technology operates through guide RNAs (gRNAs) composed of a spacer (20 bp long), complementary to a desired DNA sequence, and a scaffold forming complex with Cas9 (van der Oost et al. 2014). The Cas9/guide RNA complex scans the genome searching for complementarity to the target site and the Cas9 nuclease generates a doublestrand break in a specific position within such 20 bp region, allowing for target specific mutagenesis. This makes the CRISPR/Cas9 system relatively simpler and more straightforward than ZFNs and TALENs. Such simplicity has quickly allowed CRISPR/Cas9 to become the method of choice for precision genome editing (Puchta and Fauser 2014). Two different methodologies of CRISPR/Cas9 delivery have been reported in grapevine based on either the stable integration of the binary vector in the genome via Agrobacterium tumefaciens (Ren et al. 2016; Nakajima et al. 2016; Wang et al. 2018) or the direct delivery of purified Cas9 and gRNAs (Malnoy et al. 2016). Ren et al. (2016) transformed “Chardonnay” embryogenic cell masses with a construct leading to point mutations in the L-idonate dehydrogenase gene, and regenerated whole plants. “Neo Muscat” somatic embryos were transformed with editing constructs targeting the phytoene desaturase gene and transgenic plants with albino leaves were regenerated (Nakajima et al. 2016). Transgenic “Thompson Seedless” plants have also been recently produced with mutated versions of the WRKY52 transcription factor gene under both mono- and biallelic conditions (Wang et al. 2018). Differently, in an attempt to produce non-transgenic edited grapevines, Malnoy et al. (2016) directly delivered the purified Cas9 and the gRNAs targeting the VvMLO7 gene into “Chardonnay” protoplasts. Targeted mutations in protoplasts were detected (editing rate around 0.5%) but it was not possible to regenerate individuals. Osakabe et al. (2018) recently described an improved protocol for the direct transfer of CRISPR/Cas9 components in apple and grapevine protoplasts, and for the regeneration of plants.

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Classical genetic engineering and NBTs are presently used for functional characterization of grapevine genes in different cultivars. Most of the traits studied are related to pest control but also to physiological and reproductive traits, and metabolic engineering. These different traits are briefly described in the following sections.

7.9.3 Physiological and Reproductive Traits Few publications report the functional characterization of physiological and reproductive traits for grapevine. Leida et al. (2017) showed that transgenic grapevines lines overexpressing VviERF045, a berry-specific ethylene responsive factor (ERF) with an important role in triggering the onset of ripening, were affected in phenylpropanoid metabolism and showed a modulation of receptor like-kinases and defencs-related genes resembling a plant immune response. An interesting study has been reported by Malabarba et al. (2018) who showed that the VviAGL11 gene is responsible of the formation of the seeds in grape berries. Indeed, fruits from the seedless “Linda” treated with the VviAGL11-overexpression plasmid showed high expression levels of VviAGL11 and exhibited small seeds that were not found in the untreated control samples. Mature grapes from seeded “Italia” and “Ruby” bunches treated with the VviAGL11-silencing plasmid showed decreased VviAGL11 expression, reduced seeds number and increased number of seed traces (Malabarba et al. 2018).

7.9.4 Biotic and Abiotic Stress Resistance For more than two decades, scientists have been using stable transformation for the functional characterization of key genes putatively involved in the resistance against grapevine pathogens. The strategies adopted to face fungal pathogens have been based on the use of pathogenesis-related proteins (PR) of different classes such as thaumatin like protein (Dhekney et al. 2011), chitinase and glucanase of different sources (NooKaraju and Agrawal 2012). Besides, the ectopic expression of transcription factors or elements triggering high expression of PR genes have been adopted (Merz et al. 2014; Le Henanff et al. 2011) and more recently, an approach based on the silencing of susceptibility genes has been proposed (Pessina et al. 2016). By adopting one of such schemes, transgenic lines of different varieties were obtained which showed different levels of resistance to the main fungal pathogenes affecting grapevine: powdery mildew (Le Henanff et al. 2011; Zhou et al. 2014; Dai et al. 2016, 2017; Pessina et al. 2016: Xie and Wang 2016; Wang et al. 2017a, b), downy mildew (NooKaraju and Agrawal 2012; Feechan et al. 2013; Marchive et al. 2013; He et al. 2017; Jiao et al. 2017; Ma et al. 2018; Su et al. 2018;) and graymold (CoutosThévenot et al. 2001; Rubio et al. 2015; Dabauza et al. 2014; Xie and Wang 2016; Wang et al. 2018). Concerning bacterial pathogens, in order to improve resistance to

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Xylella fastidiosa, the causal agent of Pierce disease, synthetic lytic peptides have been exploited which recognize and cleave bacterial surface proteins (Dandekar et al. 2012; Li et al. 2015) as well as polygalacturonase inhibitor proteins which inhibit the bacterium from breaking down plant cellwall (Aguero et al. 2006; Dandekar et al. 2019). Approaches based on pathogen-derived resistance through the expression of viral sequences have been used to contrast virus diseases (Gambino et al. 2010). Attempts to mitigate insects attack have been also carried out by developing transgenic grapevine with an altered profile of terpenes volatiles attracting the European grapevine moth (Salvagnin et al. 2018). Field trials of genetically engineered grapevines have been conducted worldwide since the mid-1990 mainly to evaluate resistance against fungal pathogens (USA, Canada and Germany) and virus (USA, France) but also to improve quality and yield performances (Italy, USA, Australia) (Gray et al. 2011). Some valuable genes isolated from the Vitis germplasm have been used to improve the tolerance to several abiotic stresses by transgenesis. The functional characterization of a root-specific aquaporin of grapevine showed that the control of water transport under drought conditions is regulated by complex mechanisms involving more than one aquaporin (Penrone et al. 2012). Besides, specific members of the calciumdependent protein kinases (CDPKs) family of wild grapevine Vitis amurensis Rupr. seem to be involved in different abiotic stresses such as cold, drought, and heat (Dubrovina et al. 2016, 2018). Recently, overexpression of the grapevine transcription factor VvWRKY2 in tobacco plants proved its compelling role in salt and water stresses tolerance (He et al. 2018).

7.9.5 Metabolic Engineering Pathways Although considerable efforts have been made for identifying the genes involved in the synthesis of secondary metabolites and in spite of huge progress in gene transfer technology, the manipulation of these biosynthetic pathways is still difficult to obtain due to the complex underlying regulatory mechanisms. Regarding grapevine, major interest has been focused on the phenylpropanoids and isoprenoids metabolisms. The amount of resveratrol, a phytoalexin belonging to the stilbene family with important roles in plant defense as well as with several therapeutic effects for human health, can be strongly increased in transgenic grapevines by overexpressing stilbene synthase genes (Fan et al. 2008; Cheng et al. 2016) or calcium-dependent protein kinases (Aleynova-Shumakova et al. 2014; Dubrovina et al. 2016, 2018). Besides, the overexpression of Vitis labruscana VlmybA1-2 in “Portan” and “Chardonnay”resulted in the production and storage of anthocyanins in all the vegetative tissues of transgenic plants, leading to a very intense red coloration (Cutanda-Perez et al. 2009). Concerning isoprenoids, an example of metabolic engineering is described in the work of Dalla Costa et al. (2018). In this study, the expression of distinct VvDXS1 alleles in the grapevine model system “Microvine” increased the accumulation of monoterpenes in ripe berries compared to control plants.

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7.10 Social, Political and Regulatory Issues 7.10.1 Concerns and Compliances A convenient way to cope with the new challenges faced by viticulture due to climate change is to rely on the proper choice of the grape variety and the rootstock, in order to promote resilience instead of managing risks. Resilience is the best way to minimize impacts from adverse events, aiming at long-term security (Erisman et al. 2015). On the other hand, risk management (cultural practices) aims for short-term security, requires direct intervention and needs continuous monitoring. The growers can rely nowadays on a large number of clones and grape varieties (mostly from the species V. vinifera L.) and rootstocks (normally hybrids) but resilience toward climate change can be even more improved by recovering abandoned varieties or by breeding (producing new varieties/rootstocks). It is moreover important to emphasize that just a single action is not able to provide the ultimate solution to the problem and, therefore, an interaction among strategies is needed. Wine grape varieties seem more affected by climate change than table grapes and grapes for raisins because, as a rule, the latter prefer warm climates. Concerning the management of wine varieties, the most common action is to grow in a certain area affected by climate change (in the sense of rising temperature), a clone (if any) or a late-ripening grape variety not grown there previously. This choice allows to produce grapes which ripe during a less warm period, escaping the negative effects of high temperature on grape (and wine) quality. If for some reason a given growing area becomes wetter, it is possible to grow V. vinifera varieties either less susceptible to downy mildew and graymold or fruiting disease-resistant varieties, resulting in more environmentally friendly grape growing. Another strategy is to use an area (affected by global warming) not yet planted with grapevines, normally a farmland located at higher latitudes or elevations in both hemispheres; according to the pedo-climatic conditions, proper varieties can be grown and the choice is quite large. In the case of table/raisins, grapes global warming can allow the cultivation in higher latitudes/elevations as compared to the current areas. If the objective is to produce a table wine, no regulatory problems occur, except for EU countries and few others in the world, that require the registration of the variety to be grown in the national grape register (NGR). This register was created according to the Directive 68/193/EEC to control the production and marketing of the propagation material, and its classification (in EU countries). In the USA, for instance, there is a similar situation, where a registered list is overseen by the Tax Trade Bureau, and moreover the Foundation Plant Service has a list of what is available as certified. On the other hand, there are other countries where no NGR exists, like for instance Australia, New Zealand, Brasil, Mexico, etc., and other situations in between like for instance Switzerland where there is no NGR but every region can decide which varieties are allowed. Apart from these aspects, the only rule to be followed by the grower is to fulfill the consumers demand and the only judge is the market; in that

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case, the brand and the name of the grape variety (together with the price) are the most likely driving forces from a marketing point of view. If the objective is to produce a Protected Geographic Indication (PGI) or a Protected Denomination of Origin (PDO) wine (in European Union), no regulatory problems arise with clones of allowed varieties, while for further V. vinifera varieties and new resistant individuals, they need to be included in the production protocol, and, therefore, its modification is required. For establishing a new PGI, a protocol has to be written, including the list of the varieties to be grown. The PDO wines follow the same rules as PGI do, but a PDO area cannot be established right away in farmlands not yet used for grape growing. It must be emphasized that for a variety to be included in the PGI and PDO protocol, besides being registered in the NGR, it is mandatory that it is classified as suitable in a certain territory of the country. In other words, the variety has to demonstrate to be able to produce wines of quality considered as satisfactory in a given territory, according to the article 81 of the EU Regulation 1308/2013.2 The classification (possibility to grow a variety for wine production in all EU countries) does not include the following ancient hybrids: Noah, Othello, Isabelle, Jaquez, Clinton and Herbemont. These “hybrids” had a bad reputation because, compared to V. vinifera, they gave rise to wines of low quality, with marked scents of strawberry or foxy and with high methanol content. In general, in the current resistant varieties there are no or little perception of strawberry and foxy and even the methanol content stays within the thresholds fixed by law (the OIV establishes a maximum content of 400 mg/L for red wines and 250 mg/L for white and rosé wines3 ). Concerning PDO in France, INAO recently showed to be flexible and created a new category of grape varieties called “grapes for climate and environmental adaptation.” In the case of table/raisins grape varieties, no growing restrictions are present all over the world, and in the countries where there is a NGR, it is intended for the marketing of the propagating material. In areas where growing conditions are getting warmer and wetter, a possibility is represented by fruiting resistant varieties. There are many options, depending on the countries, as follows: – Countries where there is not a NGR: no regulatory aspects are present and cultivation is free. – Countries where there is a NGR: the varieties must be listed in the NGR and depending on the wine classification rules, they can enter different wine categories. Concerning traditional rootstocks, the most common action to be performed in a given area affected by climate change (i.e., drought stress) is to choose a droughttolerant rootstock for the new plantations. No regulatory requirements occur in the 2 REGULATION

(EU) No 1308/2013 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 17 December 2013 establishing a common organisation of the markets in agricultural products and repealing Council Regulations (EEC) No 922/72, (EEC) No 234/79, (EC) No 1037/2001 and (EC) No 1234/2007 (OJ L 347, 20.12.2013, p. 671). 3 OIV Resolution: ENO 19/2004.

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case of the rootstocks; only the nurseries (in EU) have some rules to follow, because they can propagate only rootstocks registered in the NGR. In the case where ancient wine grape varieties are recovered, the first action for a commercial use is to register them in the NGR (in the countries where this is present). As for the other vine varieties, this registration is done after official trials, according to the Directive 2004/29/CE of 4 March 2004. This requires to verify the distinctness, the stability, and homogeneity of the variety; no evaluation of the wine quality is included. The registration of a given variety in EU countries allows production, trading of the concerned propagating material, and its classification in all EU countries. For the production of table wines with the exception of the classification, no obligation is envisaged and the grower can decide to grow that variety according to his/her forecast of the market demand. Concerning the PGI and PDO wines (in EU), the ancient varieties recovered have to be first classified and, therefore, included in the production protocols which have to be modified. In certain countries like Italy, ancient varieties are spread all over the territory and those having a late ripening (which are the majority) are very interesting in terms of adaptation to global warming. Other possibilities are: to obtain new clones of existing varieties; to breed new V. vinifera varieties or new resistant varieties; and to breed new rootstocks. Clonal selection could aim at later ripening time, drought tolerance, higher acidity, lower sugar accumulation; if the new selection succeeds, the same variety can be grown in the same place and most likely the wine will have more or less the same sensory profile as before. In this case, no changes are needed at regulatory level. It is, therefore, important to exploit the flexibility/adaptability of traditional V. vinifera varieties which is somehow not yet well studied (Van Leeuwen et al. 2013). New V. vinifera varieties and new resistant varieties can be obtained by conventional breeding programs (controlled intra- and inter-specific crosses) or by the NBTs. The former can be easily produced, while the latter is still far away and more difficult to be obtained. In the case of intraspecific crosses, the rules to be followed in EU countries are the same as reported before. For the production of GPI and PDO wines, the production protocol must be modified, while in the countries where there is no wine classification system, the cultivation is free. In the case of resistant varieties for the production of table wine in EU, the only requirement to be fulfilled is the registration in the NGR and the classification. For a PGI wine, the production protocol must also be modified; according to EU 1308/2013, no PDO wines can be produced so far with resistant varieties, but, on this respect, the situation within the Member States is not the same. A different behavior of the EU countries as concerning the registration of new resistant varieties occurs. The Federal Republic of Germany, for instance, has classified them as V. vinifera, unlike Italy and France where they are considered as crosses between V. vinifera and other Vitis species. As a consequence, Germany can produce PDO wines with these varieties, while in other countries (like Italy and France), the article 93 of the Reg. 1308/2013 is applied, which allows the use of varieties coming from “a cross between V. vinifera and other species of Vitis genus” only for table and GPI wines.

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Recently, the EU Commission, by the Implementing Regulation 2018/606 of April 19, 2018,4 allowed the protection of PDO Dons wines (Denmark), that can also be obtained from Cabernet Cortis, Orion, Solaris, Rondo, and Regent (resistant varieties selected in Germany). The Commission has pinpointed the contradiction with the aforementioned article 93 of Reg. 1308/2013 stating that “there is no reference list nor scientific document available from any official competent body, such as OIV, which currently would allow to undisputedly categorize V. vinifera species or a cross between V. vinifera and otherVitis species, neither to distinguish between them.” As a consequence, the Commission decided that this issue is under national responsibility. This position of EU Commission is puzzling, but most likely this is a step forward to allow the cultivation of disease-resistant varieties also for PDO wine production in the future. In the next review of the wine Common Market Organization (CMO), probably clearer provisions will be laid down on this topic. In the case of new resistant varieties for table/raisins grapes, no cultivation restrictions are present all over the world, and in the countries where there is the NGR, their registration is mandatory only for allowing propagation by the nurseries. Concerning the acceptance of new resistant varieties (for wine making) by the market, it is crucial that they produce a wine of excellent organoleptic characteristics, with the disease resistance being a positive side effect. It is moreover important to recognize that this innovation (new variety) has to be shared with and accepted by the other actors of the wine chain (including the consumers); the role of education becomes crucial and in order to be effective, a commitment of resources and time is needed. The scientist has, therefore, to move his/her sphere of influence from private and personal (where there is a small impact) to societal network, organizational, public, and cultural (where there is the largest impact) (Amel et al. 2017). Some scientists are already engaged worldwide in activities other than private and personal, as members of consulting boards of private and/or public bodies/authorities, or think tanks, but we need to do more especially in the public and cultural side. The registration of new rootstocks is under the same requirements as for the fruiting varieties (Directive 2004/29/CE of 4 March 2004). The nurseries (in EU) have some rules to follow, i.e., to propagate genotypes registered in the NGR and the grape growers can use the rootstock they wish, without any restriction.

7.10.2 Patent and IPR Issues A new grape variety can undergo the patent process. In EU, the Community Plant Variety Office (CPVO), a self-financed EU agency is responsible for the management of the Community Plant Variety Rights System, covering the 27 Member States. Located in Angers, France, the CPVO was created by the Council Regulation 4 COMMISSION

IMPLEMENTING REGULATION (EU) 2018/606 of 19 April 2018 conferring protection under Article 99 of Regulation (EU) No 1308/2013 of the European Parliament and of the Council on the name ‘Dons’ (PDO) (O.J. L 101, 20.4.2018, p.37).

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2100/945 and has been operational since April 1995. The grape varieties (including the resistant ones) protected by plant varieties rights are registered and can be found in the CPVO database (https://cpvoextranet.cpvo.europa.eu/Denominations). It is impossible to distinguish the resistant varieties from the other ones, because they are registered as V. vinifera.

7.10.3 Disclosure of Sources of GRs, Access, and Benefit Sharing Disclosure and access to genetic resources is an issue for scientists, while the benefits involve all society. Within CBTs (described above in Sect. 7.4), traditional breeding is time consuming and not cost effective, while molecular breeding is faster but still expensive; that’s why these costs have to be somehow rewarded, in different ways (royalties, club system, etc.). The NBTs are a special case: the most promising NBT is genome editing which has not yet released any commercial variety, but most likely will produce some results in countries other than Europe. In EU, this will not occur in the near future because the Court of Justice has recently (July 25, 2018) released a document stating that living organisms (including plants) modified by the NBT, like genome editing, have to be considered as GMOs and are, therefore, banned for cultivation, according to the EU rules for GMOs (Reg. 2001/18). In the countries where these new individuals (including grapevine) will be allowed, most likely they will be classified as clones. To deal with DNA manipulation for producing improved grapes (especially wine grapes) is a big challenge from a societal point of view, due to concerns arising from the public. Even though many envisioned applications of NBTs (like CRISPR/Cas9) might provide benefits for ecosystems and society, there is the need to move the policy discussion from national or international level to local communities which will be the first to feel the context-dependent impacts of any release, in other words, we need collective oversight (Kofler et al. 2018).

7.11 Conclusions Genetic improvement of grapevine is necessary to face the medium/long-term modifications of the climate. Due to the long time needed between the design of new genotypes and their release on the market, and to the technical bottlenecks still existing, strong and continuous efforts must be amplified for fast release of betteradapted genotypes. Grafted grapevines develop as a result of many interactions (scion-atmosphere, rootstock-soil, scion-rootstock), that open wider possibilities for 5 COUNCIL

REGULATION (EC) No 2100/94 of 27 July 1994 on Community plant varietyrights (OJ L 227, 1.9.1994, p. 1).

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genetic improvement of the two grafted genotypes, but also makes it more difficult to dissect relevant traits and genes, and to define ideotypes that would be the best adapted to the environment. However, there is a wide genetic diversity of rootstocks and scions that has been still poorly explored. Progress in sequencing now allows affordable high-throughput sequencing of full genomes, faster mapping of target traits and easier phylogeny determination. Although significant progress still needs to be done, new breeding technologies potentially allow precise modifications of resident genes and may speed up the design of new genotypes. In spite of their length, conventional breeding strategies must be pursued because they have already proved successful for the release of resistant varieties, and may be more acceptable for the consumer. Furthermore, (semi)-high throughput phenotyping now allow faster and more complete monitoring of many traits on relatively large plant populations issued from crosses. In spite of the difficulties linked to poor transformation efficiency in grapevine, an increasing number of genes involved in the control of development, berry metabolism, disease resistance, and adaptation to environment have been characterized. Systems biology approaches may also be helpful to identify new genes/alleles and gene networks relevant for grapevine adaptation to climate change. To this end, the grapevine scientific community should vigorously develop and use shared and integrative platforms allowing a complete appraisal of the genotype-phenotype-environmental links and of the molecular and metabolic networks. Whatever the efforts and progress made by scientists, a major issue is the legal, market, and consumer acceptance of new genotypes. This issue is particularly important for a cultural, historical and traditional crop. In the long term, given the growth of the population and the rarefaction of land and water resources, viticulture will face an increasingly strong competition with food crops that are more essential for life than wine. Therefore, genetic adaptation of grapevine to design genotypes that are the less demanding possible in terms of water, nutrients, and pesticides is not only a challenge, it is a necessity.

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Chapter 8

Genomic-Based Breeding for Climate-Smart Peach Varieties Yolanda Gogorcena, Gerardo Sánchez, Santiago Moreno-Vázquez, Salvador Pérez and Najla Ksouri

Abstract Improving the performance of peach varieties in the context of climate change requires multiple approaches. Climate change will not only alter plant phenology, but will also drive negative effects of several biotic and abiotic stressors. The challenge is to improve adaptation of peach varieties to a changing environment, while maintaining organoleptic qualities of the fruit. This chapter focuses on the progress in genomics-assisted breeding in peach by breaking the barriers of conventional breeding. Breeding climate-smart (CS) peach trees requires the identification of traits involved in the adaptation to high levels of temperature, CO2, water deprivation, and biotic stresses. Relevant CS traits, such as those that control flowering time (chilling and heat requirements), biotic and abiotic The original version of this chapter was revised. Several sections of this chapter have been removed because of ownership concerns. A Correction to this chapter is available at https://doi.org/10.1007/978-3-319-97946-5_11 Y. Gogorcena (&)
N. Ksouri Laboratory of Genomics, Genetics and Breeding of Fruits and Grapevine, Department of Pomology, Estación Experimental de Aula Dei-CSIC, Avda. de Montañana 1005, 50059 Zaragoza, Spain e-mail: [email protected] N. Ksouri e-mail: [email protected] G. Sánchez Biotechnology Lab. Estación Experimental Agropecuaria (E.E.A) San Pedro, INTA, Ruta N° 9 km 170, 2930 San Pedro, Argentina e-mail: [email protected] S. Moreno-Vázquez Department of Biotechnology—Plant Biology, E.T.S. Ingeniería Agronómica, Alimentaria y de Biosistemas – Universidad Politécnica de Madrid, Avda Puerta de Hierro 2-4, 28040 Madrid, Spain e-mail: [email protected] S. Pérez Centro de Recursos Genéticos y Mejoramiento de Prunus L., Loma Pinal de Amoles 3, Vista Dorada, 76060 Querétaro, Mexico e-mail: [email protected] © Springer Nature Switzerland AG 2020, corrected publication 2023 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_8

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stress tolerance (pests and diseases; water-nutrient efficiency), require prioritization. Here, we review classical mapping and breeding of peach varieties, the progress and limitations of the use of marker-assisted selection and breeding (MAS and MAB, respectively) in expression of traits, such as fruit quality and stress tolerance, and describe the rationale for the use of molecular breeding. Diversity analysis of Prunus germplasm, genome-wide association, molecular mapping, MAB and genomics-assisted breeding for CS traits are also reviewed. MAS and MAB have previously been considered as the optimal solution to plant breeding in the genomics era of plant biology, but genomic selection currently presents a promising alternative. Genomic selection is a marker-based strategy that accounts for quantitative traits controlled by a large number of genes with small effects as many CS traits are governed. The precise phenotypic assessment and appropriate biometric analysis used to identify genotype responses are also discussed. The small but active international peach research community has delivered a high quality sequenced and annotated genome, along with several genomic tools, that potentiate each other in a positive feedback. Bioinformatics and computational biology are currently at the forefront of plant breeding programs, and deal with diverse functional genomics datasets of gene expression, metabolomics and stress physiological responses. Taken together, the existing genomic knowledge and tools may be used to confront the challenges of the development of peach varieties adapted to changing climate scenarios.










Keywords Prunus persica Chilling requirement Flowering Stress tolerance Genetic diversity Marker-assisted breeding Molecular mapping SNPs QTLs GWAS




8.1
















Challenges, Priorities and Prospect of Plant Breeding in Peach

Peach, Prunus persica (L.) Basch, is the third-most globally important fruit tree crop in the Rosaceae, after apples (Malus spp.) and pears (Pyrus spp.). In 2017, world production of peaches and nectarines reached 25 million across a land area of 1.5 million ha. The largest producer is China with 14.3 million tons, followed by Spain, Italy, the USA and Iran (Fig. 8.1; FAOSTAT 2019). Peach is mostly grown in temperate climates, because it requires adequate winter chill to produce economically viable yields. However, climate change is decreasing winter chill units in traditional areas of peach cultivation, thereby threatening production. To minimize impacts of climate change, it is essential that responses of peach tree varieties to temperature are understood and varieties with lower chilling requirements are developed (Luedeling et al. 2011). In this chapter, we review the progress of genomics-assisted breeding, and how novel genomics-based approaches

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Fig. 8.1 Global map of peach and nectarine production for 2017 (FAOSTAT 2019). Source http://www.fao.org/faostat/en/#data/QC/visualize (accessed November 10, 2019)

may break barriers of conventional breeding for climate-smart peach trees. Peach has been selected as a model species in the Rosaceae for genomics studies (Arús et al. 2012) because of its small genome size, short juvenile phase and diploidy. Studies on this model species will facilitate progress in other stone fruit species.

8.1.1

Food, Nutrition, Energy and Environmental Security

As it has been widely reported, global climate change is predicted to impose severe constraints to agricultural productivity worldwide. It is expected that in most regions, climate change effects will impact negatively for fruit production and fruit industry and thereby will challenge food and nutritional security (Kole et al. 2015). Food and Agriculture Organization of the United Nations (FAO) (Turral et al. 2011) estimated that by 2050, based on population growth, the need of water for irrigation will increase up to 11%. Climate change along with population growth may exacerbate water scarcity. Under these conditions, increasing the efficiency of water use is becoming crucial (FAO 2018). Breeders and the agricultural community must address today three opposing demands on crop production to ensure food security in the scenario of climate change: higher food quality, less environmental impacts and limited resources (Mckersie 2015).

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Effects of Climate Change on Fruit Production

Mathematical models predict general increases in the annual mean temperatures and decreases in winter chilling units in most parts of the world (Luedeling et al. 2011). Although not all growing regions would be equally impacted, increases in mean temperatures of up to 2.4ºC by the end of the century (IPCC 2014, 2018) are plausible. In addition to higher temperatures, unpredictable precipitation patterns will directly impact crop production and threaten our food supply (IPCC 2014). Indirect impacts will be a consequence of changes in the dynamics of pest and disease populations (Bradshaw 2017). The atmospheric concentration of CO2 has increased over 40% since the beginning of the Industrial Revolution, and it is expected to double by the end of this century (IPCC 2014). Given that temperature is a major determinant of the timing and duration of key phenological phases (Bahuguna and Jagadish 2015), and CO2 a major determinant of plant growth (Craufurd and Wheeler 2009), climate change is likely to have significant impact on key flowering and fruit maturing processes (Jagadish et al. 2016). For perennials species (Hulme 2011), and in particular for peach (Li et al. 2016), recent data indicate real measurable advancements in flowering time. In warmer regions, the predicted reduction of winter chill units will have severe impacts on flowering and consequently on production in temperate fruit trees.

8.1.3

Limitations of Traditional Peach Breeding

Global warming and climate change impact directly the morphology and physiology of crop plants. Knowledge on the biochemistry and molecular biology of morphological or physiological traits is needed, and a multidisciplinary approach to minimize the effects of climate change is required (Mckersie 2015). Climate-smart peach trees should carry alleles for adaptation to high levels of temperature, CO2 concentration, water deprivation and tolerance to pests and diseases. Identifying alleles that modify and/or regulate the involved phenologicalphysiological traits is a priority but it is not a trivial question because phenotypes depend often on small effects of multiple loci. The challenge of classical breeding is to decipher the underlying genetic architecture of traits of interest; however, the process is slow because horticultural characters are multigenic, with a quantitative genetic control, and in many cases with environmental interactions. In this regard, it is required to conduct phenotypic assessment and appropriate biometric analysis that will assist later in identifying specific genotypic responses, along with the genomic approach that definitely will help to dissect complex traits.

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Prioritizing Climate-Smart (CS) Traits

The more relevant climate-smart traits in peach breeding are those that control flowering time (chilling and heat requirements), and biotic and abiotic stress tolerance (pests and diseases; water-nutrient efficiency). However, a few successful studies have been done to know the effect of environmental factors such elevated CO2 on flowering time (Jagadish et al. 2016) but no such study has been done in peach. Furthermore, the combination of stresses occurring under field conditions will contribute to multiplicity of physiological and molecular responses and interactions that cannot be predicted from individual stressors (Pereira 2016). In these circumstances, for breeding of CS traits, novel platforms have to be developed to mimic field conditions. In our opinion, peach breeding should be better conducted by integrating breeding for a combination of different and complementary CS traits with common responses in a global warming scenario. In this section, we focus on CS traits covered in peach mapping efforts.

8.2.1

Flowering Time

Flowering time is one of the major factors determining the adaptation of plants to climate change (Jagadish et al. 2016). A number of physiological processes that occur during flowering and fertilization are highly sensitive to temperature. Knowledge of the molecular mechanism that regulate flowering time under these changing conditions will help to tailor climate-resilient crops (Jagadish et al. 2016). In temperate fruit tree species, flowering time is a multigenic trait controlled by chilling and heat requirements. In temperate trees, exposure to cold in winter (fulfillment of chilling requirements for overcoming endodormancy) followed by a warm period (fulfillment of heat requirements) in spring is essential for flowering (Castède et al. 2014). Chilling requirement (CR) refers to the minimum duration of cold exposure required before dormant buds will bloom in response to bud break-inducing conditions (Dennis 2003). Peach, as other temperate trees, has a relatively strict winter CR for dormancy breaking, and spring forcing temperature accumulation is needed for blooming. In peach, chill is more determinant of flowering than subsequent heat requirement period (Rodríguez-A et al. 1994; Sánchez-Pérez et al. 2014). CR for breaking dormancy has been well-studied for many peach cultivars (Couvillon and Erez 1985; Scalabrelli and Couvillon 1986; Erez and Couvillon 1987) while the specific heat requirements often remain largely approximations (cited in Martínez-Gómez et al. 2017). A severe threat to the safety of temperate fruit production is the decreasing of winter chill units. A study over the past 30 years in China reported that global warming has advanced spring events, delayed autumn events, and extended growing season in peach (Li et al. 2016). This study reported that bloom date

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(BD) has been advanced by 11.1 days (Li et al. 2016); therefore, it is very essential to anticipate the impacts of climate warming on horticulture. In this scenario, growers and breeders must select cultivars whose CR and BD closely match local climatic conditions in order to avoid crop losses due to poor budbreak (insufficient chilling) or due to late frosts (Luedeling and Brown 2011). Therefore, CR and BD impose a constraint on the introduction and spread of new cultivars with superior agronomic performance and marketability. Source of variation for low chilling requirements is available in wild-related peach genetic resources from China (Byrne et al. 2012; Liu et al. 2012) and in autochthonous peach landraces from Taiwan, Thailand (Byrne et al. 2012), Brazil (Byrne et al. 2000; Thurow et al. 2017), Mexico (Pérez et al. 1993), or Spain (Llácer et al. 2009). For flowering time, several genetic studies were previously done. Many quantitative trait loci (QTLs) have been mapped at linkage group 1 (LG1), LG4, LG6 and LG7 in different peach germplasms (Fan et al. 2010; Dirlewanger et al. 2012; Romeu et al. 2014; Bielenberg et al. 2015), and a few genome-wide association studies (GWAS) have also been reported (Cao et al. 2012; Font i Forcada et al. 2013; Elsadr et al. 2019, See Sects. 8.5 and 8.8). Moreover, a peach mutant showing a nondormant phenotype was found in Mexico (Rodríguez-A et al. 1994). This mutant does not form terminal vegetative buds in response to dormancy-inducing conditions such as short days and low temperatures, the terminal meristems maintain constant growth (leaf addition and internode elongation). This evergrowing trait is genetically controlled by a single recessive gene (evg) (Rodríguez-A et al. 1994) and has been mapped on LG1 (Bielenberg et al. 2008).

8.2.2

Abiotic Stress Tolerance

Peach trees need to be grafted on rootstocks for adaptation to different cultivation areas. Rootstocks are responsible for water and nutrient absorption from soils and may confer stress tolerance to the scion. Numerous evaluation and selection studies have been done in peach rootstocks with priority to the major abiotic stresses (drought, salinity and nutrient deficiencies, among others). Most of these studies dealt with the comparison of a few genotypes, such as tolerant and sensitive, within different genotypes adapted to the differential responses to a single defined abiotic stress. However, a few genetic studies have been done because current phenotyping tools are insufficient to monitor the performance of the associated morphological, physiological and molecular changes. In a climate change scenario, all efforts will be directed to control water consumption. In other words, to decrease water consumption, new cultivars should have lower transpiration rates, higher water-use efficiencies (WUE), better photosynthesis balance, higher osmotic adjustment, better nutrient efficiency, etc. The main goal of peach rootstock programs is to select multi-tolerant rootstocks adapted to different soil and environment conditions and graft-compatible with peach scions. Numerous studies in rootstocks have evaluated abiotic stress

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tolerance to: drought (Jiménez et al. 2013; Bielsa et al. 2016); flooding/soaking (Amador et al. 2012; Arismendi et al. 2015), elevated temperature and elevated CO2 (Dridi 2012; Bedis et al. 2017; Jiménez et al. 2020), iron deficiency (Jiménez et al. 2008, 2011; Gonzalo et al. 2011, 2012) and salinity (Momenpour et al. 2018), among others. Three types of indicators for stress tolerance are commonly used: morphological (root/shoot weight ratio, leaf area, etc.), physiological (osmotic and water potentials, photosynthesis rate, conductance, transpiration rate, water-use efficiency and enzymatic activities, among others) and molecular (water content, chlorophyll content, ion and nutrient balance, content of osmotic protective compounds in plant tissues, etc.). Some of these variables have been used in breeding programs; also, key compounds such as sugars and proline have been proposed to be used as functional markers for tolerance to abiotic stress (Jiménez et al. 2013). Rootstock breeding has been the starting point for introgression of genes from other Prunus spp. to increase stress tolerance in peach cultivars. Interspecific hybridization has been used to produce diversity in order to select genotypes tolerant to abiotic stress under Mediterranean conditions. Hybrids were developed with a prior knowledge of the Prunus germplasm diversity (Moreno et al. 2008; Byrne et al. 2012). P. dulcis((Mill.) D.A. Webb) and P. cerasifera (Ehrh.) and their hybrids with peach have been described as more tolerant to drought stress (Cochard et al. 2008; Jiménez et al. 2013) and chlorosis (Jiménez et al. 2008; Moreno et al. 2008) than P. persica. Moreover, plum-based rootstocks like “Myrobalan 29C” (Prunus cerasifera), “Mariana 2624” (P. cerasifera  Prunus munsoniana) and “Replantac” (P. cerasifera  P. dulcis) exhibit higher tolerance to root hypoxia caused by waterlogging (Pinochet 2010; Amador et al. 2012).

8.2.3

Biotic Stress Tolerance

Peach is threatened by numerous pests and diseases that are different according to the geographical area of cultivation. The main common diseases have been reviewed by Bielenberg et al. (2009) and names are updated according to the US National Fungus Collections Fungal Database USDA-ARS and European and Mediterranean Plant Protection Organization (EPPO): brown rot (Monilinia spp.), powdery mildew (Podosphaera pannosa (Wallr.Fr.) de Bary, Podosphaera clandestina (Wallr.Fr.) Lév.), cytospora canker (Cytospora leucostoma (Pers.) Sacc.), fungal gummosis (Botryosphaeria dothidea (Mong.:Fr.) Ces. and De Not.), leaf curl (Taphrina deformans (Berk.) Tul.), bacterial canker (Pseudomonas syringe pv. syringae van Hall), bacterial spot (Xanthomonas arboricola pv. pruni (Smith) Vauterin, Hoste, Kersters and Swings) and Sharka disease (Plum Pox Virus, PPV). In addition to diseases, pest problems in the focus of most breeding efforts are green aphids (Myzus persicae (Sulzer)), vector of the PPV, peach tree borer (Sanninoidea exitiosa graefi) and several nematodes (Pratylenchus ssp.; Xiphinema ssp.;

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Meloidogyne incognita (Kofold and White) Chitwood; Meloidogyne floridensis Handoo, Nyczepir, Esmenjaud, van der Beek, Castagnone-Sereno, Carta, Skantar and Higgins and Criconemella spp.). Most works on disease resistance have sought for the sources of resistances (Byrne et al. 2012) and modes of inheritance for MAS in breeding programs (see below and Sect. 8.6 for details of genetic advancements). Special priority in peach has been given to brown rot tolerance, caused by Monilinia spp. since the disease is one of the most serious diseases worldwide and associated with up to 80% of the fruit loss after harvest (Obi et al. 2018). Different authors have carried out phenotypic studies in peach evaluating brown rot tolerance (reviewed in Obi et al. 2018). However, large-scale phenotyping is complex (Obi et al. 2019) and the quantitative control of the trait (Martínez-García et al. 2013a; Pacheco et al. 2014; Casals et al. 2015; Fu et al. 2018; Lloret et al. 2018) adds certain difficulty. Phenotyping is based on inoculation under controlled conditions and the main parameters recorded to asses tolerance–susceptibility are lesion diameter, brown rot incidence, and disease and colonization severity (Obi et al. 2018). Sources of resistance have been reported in feral Mexican and Brazilian peach cultivars and tolerance in several descendants of peach (Obi et al. 2018, 2019) peach  almond clone F8,1-42 (Martínez-García et al. 2013a), or P. davidiana (Carrier.) Franch (clone P1908) (Pascal et al. 1998) breeding programs (see also Sect. 8.6). The Brazilian peach cultivar Bolinha has been used traditionally in different breeding programs to introduce resistance to brown rot (Gradziel and Wang 1993; Feliciano et al. 1987, Fu et al. 2018). Sharka disease caused by plum pox virus (PPV) gen. Potyvirus is one of the most important phytosanitary issues in peach. PPV, transmitted by grafting and the green peach aphid, is the most devastating viral pathogen of stone fruits, particularly peach. Immunity or resistance against PPV-M has been reported in the peach-related species P. davidiana, P. dulcis and P. armeniaca L. but a recent GWA study (Cirilli et al. 2017) identified in peach germplasm three major loci in LG2 and LG3 and three highly informative single nucleotide polymorphism (SNP) markers, accounting for most of the phenotypic variability in PPV-M susceptibility that could be useful for MAB or MAS. Nowadays, the introgression of tolerance from P. davidiana clone P1908 to peach (Rubio et al. 2010) is being questioned and change in favor of P. dulcis (Cirilli et al. 2016) Resistance induced by almond to rootstock GF305 has been described (Rubio et al. 2013; Cirilli et al. 2016; Dehkordi et al. 2018). Current programs have selected Del Cid or Garrigues almond cultivars as donors of tolerance (Cirilli et al. 2016). As happened in other studies evaluating response to biotic stress, large-scale phenotyping is a limiting issue. A common concern in evaluating peach response to PPV infection is the lack of an objective method to measure and compare responses among genotypes. Symptoms evaluation through visual inspection and attribution of score classes are affected by a certain degree of subjectivity, which hampers accurate differentiation of the specific response in each accession (Cirilli et al. 2017).

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Genetic Resources and Diversity for CS Traits in Peach

To overcome the effects of climatic change in peach, there are three approaches: 1. Genetic assessment of existing climate-smart traits such as flowering time, maturation date and certain morphological variation (vigor, interaction with rootstocks, etc.) for adaptation to mild winter; 2. To find differences on genetic resources (existing peach cultivars or wild relatives) in response to environmental CO2 concentrations and temperature conditions; and 3. To design breeding strategies in order to repack superior combinations of useful genetic regions into new varieties. We have partially presented in Sect. 8.2 the first two approaches to breed climate-smart trees, prioritizing flowering time as one of the most important CS traits and looking for adaptation to stress using rootstock-scion combinations based on available Prunus spp. genetic resources. However, a number of difficulties inherent to the use of the genetic diversity of peach wild relatives may limit the progress. Sometimes, access and use of novel genetic variation are limited due to the small size of gene pools or due to germplasm access restrictions because of geographical location and or quarantine (Peace 2017). In addition, for peach, it can be difficult to obtain novel recombinant genotypes due to asynchronous flowering parents, self-compatibility, etc. Interestingly, hybridization of peach with other Prunus species, such as plum (P. domestica L.) and apricot (P. armeniaca), is somehow well established in breeding programs, including rescue of embryos (Liu et al. 2012). When wild close relative species such as P. davidiana are used to introgress tolerance to stress, backcrosses with peach are needed. When using distantly related wild species as P. mira Koehne, P. kansuensis Rehd. P. potaninii Batal and P. ferganensis (Kost. et Riab.), Kovalev and Kostov, often infertile hybrids are obtained (Liu et al. 2012). Besides these constraints, main germplasm collections cannot be always phenotypically evaluated at large scale for trait adaptation to future field environments. This is the case of breeding program targeting a high-CO2 future environment (Mckersie 2015). However, at small scale, phenotyping methods have been devised to monitor specific traits relevant in future scenarios (elevated CO2) (Bedis et al. 2017; Jiménez et al. 2020). This could be the first step to study adaptation response to high CO2 inside the existing peach collections. In regard to genotyping, genomics appears to be a promising tool for deciphering the stress responsiveness of tree crop species with adaptation traits or in wild relatives toward identifying underlying genes, alleles or QTLs (Kole et al. 2015). In this direction, the application of genomics to identify and transfer valuable agronomic genes from allied genepools and crop relatives to elite peach cultivars, will increase in pace and assist meeting the challenge of global climate change effect in fruit trees.

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Glimpses on Classical Genetics and Traditional Peach Breeding for CS Traits Classical and Molecular Genetic Mapping

The first genetic map of P. persica was constructed using an F2 progeny derived from the cross “NC174RL”  “Pillar” (Chaparro et al. 1994). This map was built with two morphological traits, one isozyme and 83 random amplified polymorphic DNA (RAPD) markers onto 15 linkage groups. A second map was constructed, just one year later, adding transferable markers, 46 restriction fragment length polymorphisms (RFLPs), 12 RAPDs and seven morphological markers (Rajapakse et al. 1995). Although it was the first map with eight linkage groups, it was incomplete due to 28% of the studied loci remaining unlinked. Other genetic maps based on RFLPs, RAPDs and amplified fragment length polymorphisms (AFLPs) were built but the coverage of the peach chromosomes was quite incomplete (Arús et al. 2012). This problem was solved using F2 progenies from interspecific progenies that were highly polymorphic and resulted in saturated maps (Joobeur et al. 1998; Zeballos 2012 and references therein). The map derived from the almond (cv. Texas)  peach (cv. Earlygold) F2 progeny (T  E) was initially constructed with RFLPs but later enriched with simple sequence repeats (SSRs) and other markers (Dirlewanger et al. 2004; Abbott et al. 2007). This map was adopted as the reference for peach and other Prunus species, providing the terminology and orientation for linkage groups and a large set of transferable markers that were used as anchors for constructing other maps (Arús et al. 2012 and references therein; Gonzalo et al. 2012). Most of the maps developed in different Prunus populations contain a framework of markers in common with T  E, even those constructed later with SNP markers (see Sect. 8.8.1). This fact permits the identification of linkage groups and ensures good coverage and marker spacing of the genome. These maps have usually been developed for genetic analysis of traits with breeding interest. The public Genome Database for Rosaceae displays many of these maps to allow the scientific community to go further on peach breeding (see Sect. 8.9).

8.4.2

Limitation of Classical Endeavors

Breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit) (Iwata et al. 2016). The generation interval in current peach breeding programs takes time and can be up to 5–7 years (Monet and Bassi 2008). Phenotyping is one of the main limitations to go from the phenotype to the genotype (Bazakos et al. 2017). Precision phenotyping still remains a bottleneck

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and is usually labor-intensive, time-consuming, lower throughput, costly and frequently destructive to plants. Although efforts have been made to improve phenotyping efficiency (Zhang et al. 2017 and reference therein), it is far behind genotyping. Shortcuts in evaluating phenotypic diversity for climate-smart traits may lead to erroneous results. In addition to this, the lack of large-scale phenotyping procedures for fruit trees’ tolerance to biotic and abiotic stresses still needs to be developed to accommodate selection and genetic mapping studies. Finding robust phenotyping strategies is a major concern for rosaceous crop breeding programs. Also, standardization of protocols within programs and across programs needs to be done to raise statistical power as was reported for peach and other species (Peace 2017, and references therein). Conventional breeding uses hybridization (intraspecific or interspecific) to genetic base broadening and introgression, and further selection. However, introgression in peach breeding is challenged, on the one hand by the narrow genetic background of commercial cultivars (Scorza et al. 1985), and on the other because of the difficulties to access to and use of new genetic variation (Peace 2017) or desirable genetic diversity. The success and efficiency of classical breeding depend on the heritability, number of influencing trait loci, the effect of alleles and genetic linkages with other traits (Peace and Norelli 2009). Besides this, in many cases, the existence of rootstocks complicates breeding because they produce significant GM (genotypemanagement) interactions (Peace 2017).

8.4.3

Classical Breeding Achievements and Progress of Marker-Assisted Breeding for CS Traits

Traditional peach breeding has been pursued to increase yield, fruit quality and to expand product marketability. One of the main goals targeted by peach breeders was to expand environmental ranges, to reduce chilling requirements, to increase frost tolerances, to increase fruit quality and appearance, to improve shelf-life, to adequate canopy architecture for new harvesting systems and to increase tolerance to abiotic and biotic stresses (Bielenberg et al. 2009). Classical peach breeding was mainly based on phenotypic selection of interspecific or intraspecific crosses, and this process can take more than five years to assess the phenotype in the field. However, the development of molecular markers has allowed the application to assist breeding. Consequently, in the last years, MAS and MAB have turned an option to accelerate breeding in Prunus and other woody crop species. However, MAS in peach remains a practical option only for a few Mendelian trait loci (MTLs) mostly related to fruit attributes, fruit texture (Peace et al. 2005), fruit shape (Picañol et al. 2013), skin pubescence (Vendramin et al. 2014), skin blush (Sandefur et al. 2017), phenology, slow ripening (Eduardo et al. 2015; Meneses et al. 2016), pest, and root-knot nematode resistance (Gillen and Bliss 2005), among others (Vanderzande et al. 2018 and references therein).

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Very few studies have reported successful application of MAS in peach breeding concerning climate-smart traits such as maturity or ripening dates, and RKN tolerance, but it deserves implementation in the changing scenarios of climate change. It is reported that these traits are simpler and more heritable than other horticultural traits. The estimation of heritability for any character requires analysis of phenotypic variation as the result of genetic and environment factors (Cockerham 1963). In general, when variability is large, traits with high heritability values are subject t large genetic gain (Falconer 1989). Concerning flowering traits, studies on 42 peach cultivars estimated quite large narrow-sense heritability for full bloom and date of ripening with h2 = 0.78 and h2 = 0.94, respectively (de Souza and Byrne 1998). In a recent study, using a multi-progeny mapping strategy in 1147 individuals derived from both commercial and noncommercial peach or peach-related accessions, the heritability was higher for flowering date (h2 = 0.92) and also for maturity date (h2 = 0.94) (Biscarini et al. 2017).

8.4.4

Limitations of Conventional Breeding and Rationale for Molecular Breeding

Breeding in Prunus is strongly hampered by the large tree size, long generation and extended juvenile period. These limitations make classical breeding in peach effort, time and resources consuming (Pozzi and Vecchietti 2009). Therefore, research has progressed slowly compared to vegetable crops (Pozzi and Vecchietti 2009; Yamamoto and Terakami 2016). Nowadays, the challenge for peach breeding is the integration of phenomics, metabolomics, proteomics, genetics, genomics, physiologically and molecularly informed breeding. To date, efforts have focused on identifying genetic loci underlying trait variation to characterize genetic potential. For this purpose, genomics may provide shortcut for dissecting the trait to specific elements until genetic approaches can be effective. However, for some approaches, such as QTL cloning, it is needed to employ accurate and high-throughput techniques, such as phenotyping and sequencing for the identification of candidate genes through omics profiling (Kole et al. 2015 and reference therein). Multidisciplinary omics approaches along the integration of diverse functional genomics datasets of gene expression, metabolomics and stress physiological responses (Pereira 2016) are needed to detect target QTLs relevant for climate change scenarios. In foreseen future, progress in peach improvement can also be achieved with other complementary techniques, such as mutation breeding, cisgenesis, transgenesis and gene editing. Although there are some successful protocols for peach transformation (Sabbadini et al. 2015), to our knowledge, these techniques have not been widely developed yet mainly due to technical difficulties associated to transformation and regeneration (Prieto 2011 and references therein). Cisgenesis and gene editing, known as green biotechnologies, have recently emerged as powerful

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tools to substitute classical genetic engineering (Dalla Costa et al. 2017) but today are hardly applicable in peach (Cirilli et al. 2016). These techniques are more acceptable because they mimic spontaneous events and could, overcome many ethical concerns restrains. New plant breeding technologies are displacing genetically modified food crops but still technical and legal aspects are not yet resolved.

8.5

Brief on Peach Diversity Analysis

China has the wider peach genetic diversity and maintains the most extensive collections of germplasm in the world and it includes local cultivars and landraces (Byrne et al. 2012). Around 400–300 BC peaches were spread to Persia and the Mediterranean countries (Hancock et al. 2008). In the sixteenth century peaches were introduced in the Americas by the Spanish and Portuguese (Scorza and Okie 1990; Hancock et al. 2008) and then were spread all over the world (Byrne et al. 2012).

8.5.1

Phenotype-Based Diversity Analysis

Domestication traits in peach are mainly characterized by diverse fruit morphology (size, color, texture, shape, etc.) and self-compatibility (Velasco et al. 2016). Some variations (flat shape, glabrous surface, double flower and colorful anther) exist only in peach but not in other Rosaceae species (Cao et al. 2014). In spite of the 1000 cultivars worldwide having significant phenotypic changes in fruit morphology, most modern cultivars are derived from only a few founders (Li et al. 2013; Akagi et al. 2016). In peach, most studies of phenotype diversity have focused on fruit quality traits (skin or flesh colors, skin blush, sugars, acids and phenolics content, etc.) either in breeding progenies (Cantín et al. 2009, 2010a; Abidi et al. 2015; Zeballos et al. 2016; Laurens et al. 2018) or in broader germplasm collections (Génard and Bruchou 1992; Liu et al. 2012; Font i Forcada et al. 2013, 2014; Abdelghafar et al. 2018). However, there is limited research on the diversity for climate-smart traits mainly due to phenotyping limitations mentioned in Sects. 8.2 and 8.4. On the one hand, the complexity of models to evaluate diversity for the control of flowering time (chilling and heat requirements), and in the other, the current limitations to establish phenotypic assessment at a large scale either for bloom date or tolerance to biotic or abiotic stresses are the main reasons for this gap. Methods for phenotypic assessment using peach-based segregating populations have been proposed for flowering and maturity dates (Dirlewanger et al. 2012; Hernández Mora et al. 2017), blooming and maturity dates (Cantín et al. 2010b); maturity date (Cantín et al. 2010a; Pirona et al. 2013; Nuñez-Lillo et al. 2019), chilling requirement, heat requirement and blooming date (Fan et al. 2010; Romeu

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et al. 2014), among others. Flowering and ripening time have shown to exhibit moderate and high heritability values, respectively (Bielenberg et al. 2009) and are of quantitative nature. A wide diversity was found for chilling requirement, bloom and harvest dates, ranging from 212 to more than 1100 chilling hours, from 40 to 93 Julian days for blooming date (Romeu et al. 2014; Bielenberg et al. 2015) and from 150 to 272 Julian days for harvest date (Cantín et al. 2010b; Romeu et al. 2014). The interval between the earliest and latest blooming date varied with year reflecting of year-to-year variation in chilling accumulation (Bielenberg et al. 2015). However, the fruit development period (FDP, number of days from full bloom to maturity) and maturity date remained yearly stable for each genotype (Cantín et al. 2010b; Dirlewanger et al. 2012). Several studies have evaluated phenological traits using broad peach germplasm collections from different origins, for general genetic analysis (Font i Forcada et al. 2013; Thurow et al. 2017; Elsadr et al. 2019) or to find alleles for adaptation to new growth conditions (Reig et al. 2015; Maulión et al. 2016), but to our knowledge, only a few works have been reported data in a wide range of germplasm concerning climate-smart traits such as flowering time (full bloom), FDP and harvest date (Font i Forcada et al. 2014; Elsadr et al. 2019). The first study analyzed 94 peach germplasm including Spanish landraces (43) and modern cultivars (51). The range of blooming date in these 94 genotypes was only eight days in comparison with 83 days found in a F2 population with 378 different genotypes (Fan et al. 2010). Harvest date ranged from 185 to 275 JDs (Font i Forcada et al. 2014). Font i Forcada and coworkers (2014) found that the modern cultivars showed on average significantly later/delayed full bloom and harvest dates. A recent study (Elsadr et al. 2019) reported less variation (202–272 JD) in 132 peach cultivars for MD (syn. of harvest date). Peach breeding programs in subtropical regions have used low chilling requirement selections adapted to local conditions and hybridized them with genotypes from the southern USA (Pérez 2017). These programs have generated a large number of cultivars with low chilling requirements and widened the peach growing area and harvest season in both the northern and southern hemispheres.

8.5.2

Genotype-Based Diversity Analysis for CS Traits

Different molecular markers have been developed for genotyping and genetic diversity analysis in the genus Prunus like RAPDs, cpDNA and SSRs (Gogorcena and Parfitt 1994; Badenes and Parfitt 1995; Warburton and Bliss 1996; Testolin et al. 2000; Aranzana et al. 2002; Dirlewanger et al. 2002; Aranzana et al. 2003a, Yoon et al. 2006; Bouhadida et al. 2007a; Bouhadida et al. 2007b; Bouhadida et al. 2009; Xie et al. 2010; Bouhadida et al. 2011; Cao et al. 2012; Font i Forcada et al. 2013; Li et al. 2013). All these studies revealed low variability in peach as compared with other related Prunus species, and less heterozygosity in occidental peach cultivars than in oriental ones (Li et al. 2013).

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The greatest progress on the analysis of the genetic diversity and the bases for population genetics in peach began after the release of the reference peach genome by the International Peach Genome Initiative in 2010 (Verde et al. 2013). Thereafter, the development of inexpensive high-throughput technologies for the detection of SNPs superseded other DNA markers as the genetic markers of choice (Ahmad et al. 2011). Different initiatives by sequencing several wild and cultivated peach accessions have provided huge variation at the sequence level including SNPs that have been used as markers to shed light on domestication and facilitated genetic studies (Ahmad et al. 2011; Verde et al. 2012; Fresnedo-Ramírez et al. 2013; Cao et al. 2014, 2016, 2019; Elsadr et al. 2019; Li et al. 2019). Using these markers for population analysis, Cao and coworkers (2014) identified one single domestication event for speciation of P. persica from wild peach and confirmed lower values of heterozygosity in cultivated versus wild peach. In the same vein, using the 9K SNP Infinium® II array for genotyping a broader study was carried out with 1576 peach accessions including bred and landraces maintained in collections available in Spain, Italy, France and China (Micheletti et al. 2015). This germplasm was structured in three main populations: oriental accessions, occidental bred varieties and occidental traditional varieties, which reflect not only the domestication process but also a part of the dispersal history of peach (Micheletti et al. 2015). The oriental accessions formed by Chinese genotypes harbor the original gene pool from domestication, while European ancestral varieties (named occidental traditional varieties) were derived from the first oriental genotypes that entered in the western Europe towards 330 BC (Micheletti et al. 2015). In the occidental pool, most modern varieties, bred in USA in the 20th century from few founders, have a narrow genetic base. These founders were originally from European or Chinese ancestrals and taken to America by European settlers. All these studies revealed that diversity in occidental and oriental regions is complementary and can help to facilitate breeding programs depending on the selection criteria. In peach, either the 9K chip array (Micheletti et al. 2015; Cirilli et al. 2017; Ciacciulli et al. 2018) or SNPs derived from genome-wide sequencing (Cao et al. 2016, 2019; Elsadr et al. 2019; Li et al. 2019) have been successfully used in segregating populations and germplasm collections for genome-wide association studies (GWAS). Micheletti and coworkers (2015) assayed the association mapping of qualitative traits as a proof of concept. They studied seven traits including three traits (flesh color, fruit hairiness and flesh texture) whose candidate genes were cloned, and four traits (titratable acidity, flat fruit shape, flower type and leaf gland type) that were already mapped by linkage analysis. In all cases, SNPs in the expected region were identified by GWAS. In the same way, Cao and coworkers (2016) performed GWAS for 12 horticultural traits of peach using whole-genome sequencing data of 129 accessions including breeding lines, landraces and wild species related to peach. These authors found ten SNPs significantly associated to several qualitative traits including fruit acidity, flesh color, fruit shape, fruit hairiness, flesh adhesion, flower type and kernel taste and quantitative traits including fruit weight and sugar content. Other GWAS studies with CS quantitative traits have been conducted for tolerance to Sharka disease (Cirilli et al. 2017), harvest and

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flowering date (Cao et al. 2012; Font i Forcada et al. 2013; Elsadr et al. 2019), fruit weight (FW), firmness, sugar content, titratable acidity (TA), and content of vitamin C, total phenolics, anthocyanins, flavonoids, sucrose, glucose, fructose, sorbitol and total sugars (Font i Forcada et al. 2013; Cao et al. 2019; Gogorcena unpublished results). A recent wide study has conducted GWAS in an extensive collection of wild and cultivated peach accessions in order to elucidate genetic basis of peach evolution and provide new candidate genes controlling agronomical traits (Li et al. 2019). These authors have provided evidences of control of fruit (flesh color and adhesion, skin color, flesh adhesion, shape, hairiness, texture, weight, soluble solids content (SSC), phenolic content, and chilling requirements (CR) specifically in LG1, LG3, LG7 and LG8. Interestingly, they have provided candidate genes for all traits even though they have developed a DNA-based marker for CR. These previous studies in peach also suggest that caution may be taken to propose functional SNPs from GWAS because two main limitations are derived. On the one hand, the associated trait cannot be assessed when the structural variant that cause the phenotype is not a SNP (Vendramin et al. 2014; Cao et al. 2016). For example, fruit hairiness trait cannot be associated because the recessive nectarine phenotype is due to a retrotransposon insertion that generates a loss-of-function mutation in a MYB gene (Vendramin et al. 2014). On the other hand, other considerations must be considered for some quantitative traits. For example, no consistent SNPs were found for FW and SSC (Cao et al. 2016) and heritability values for MD and FDP (0.09 and 0.11) were unexpectedly low (Elsadr et al. 2019) in comparison with previous studies, pointing out the difficulties of GWAS for quantitative complex traits. Besides all, the power of GWAS to find genomic regions for major genes has been demonstrated and probed. Most of the traits found groups of SNPs (haplotypes) that explain a large fraction of the phenotypes although the association was not completed (Micheletti et al. 2015). These authors emphasized on the power of haplotypes at predicting the phenotypic variation better than a single tightly linked SNP (see Sect. 8.7, Micheletti et al. 2015). We may guess that to perform GWAS for detecting SNPs associated to traits affected by climate change will be difficult, especially for those like harvest date, flowering time and chilling requirements for dormancy release and blooming, for which major QTLs were proposed by linkage analyses in LG4 (Quilot et al. 2004; Cantín et al. 2010a; Eduardo et al. 2011; Dirlewanger et al. 2012; Pirona et al. 2013; Romeu et al. 2014; Nuñez-Lillo et al. 2015; Hernández Mora et al. 2017), LG6 (Dirlewanger et al. 2012; Romeu et al. 2014; Hernández Mora et al. 2017; Nuñez-Lillo et al. 2019) and LG7 (Fan et al. 2010; Romeu et al. 2014). It may be necessary to combine GWAS with other approaches using haplotypes and with consideration of environmental effects and/or epigenetic regulation to propose candidate genes for genomic breeding of these traits.

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287

Brief Account of QTLs and Genes Linked to Resistance to Pests and Diseases Brief History of Peach Mapping Efforts: From Isozymes to SNPs

As it was introduced earlier (Sect. 8.2.3), peach is attacked by numerous pests and diseases that differ depending on the cultivation area and pathogen distribution. The main priority in breeding programs is the selection of cultivars resistant/tolerant to most of the pathogens. However, breeding for disease resistance in peach encounters problems associated with tree evaluation methods and knowledge of the pests and diseases management. Most of the phenotyping methods to assay tolerance to pest and diseases are based on artificial or natural agent exposition and are difficult and time-consuming (Sect. 8.2.3). Several progresses have been made on the genetic knowledge of the major diseases caused by fungi (brown rot, powdery mildew and fungal gummosis), bacteria (bacterial spot), virus (sharka), and of the major insects (aphids) and nematodes (root-knot nematodes) affecting peach (Eduardo I, personal communication). Peach brown rot is caused by several species of the fungus Monilinia spp. Genetic tolerance/resistance to Monilinia spp. has been introduced in peach breeding from a few tolerant/resistant cultivars (reviewed in Oliveira Lino et al. 2016; Obi et al. 2018 and Sect. 8.2.3). For the evaluation process, several phenotyping methods based on artificial inoculation have been used (Obi et al. 2017, 2018; Baró-Montel et al. 2018). Several segregating populations have been used to dissect the genetic basis of brown rot resistance/tolerance and QTLs for tolerance were identified. The source of resistance comes from P. dulcis (Martínez-García et al. 2013a) and P. persica cv. Contender (Pacheco et al. 2014). Sources of tolerance for the main fungal diseases have been found in several cultivars of peach (Pacheco et al. 2014, Pascal et al. 2017), almond (Donoso et al. 2016, Mancero-Castillo et al. 2018), peach  almond hybrids (Martinez et al. 2013a) and other Prunus species like P. davidiana (Foulongne et al. 2003). Single dominant genes conferring resistance were found only for powdery mildew (Donoso et al. 2016; Pascal et al. 2017) and peach fungal gummosis (Mancero-Castillo et al. 2018).

Organism

Fungi

Fungi

Fungi

Fungi

Fungi

Fungi

Bacteria

Virus

Disease

Brown rot

Brown rot

Powdery mildew

Powdery mildew

Powdery mildew

Peach fungal gummosis

Xanthomonas

Sharka

Xanthomonas arboricola pv. pruni Plum pox virus

Podosphaera pannosa var. persicae Podosphaera pannosa var. persicae Podosphaera pannosa var. persicae Botryosphaeria dothidea

Monilinia spp.

Monilinia spp.

Pathogen

Artificial

Artificial

Natural field

Natural greenhouse

Natural field

Natural greenhouse

Artificial

Artificial

Inoculation

P. davidiana (clone P1908)

P. persica cv. Clayton

P. persica Pamirskij 5 (clone S 6146) P. dulcis cv. Tardy Nonpareil

P. dulcis cv. Texas

P. dulcis x persica (clone F8, 1-42) P. persica cv. Contender P. davidiana (clone P1908)

Source of resistance

Tolerance

Tolerance

Resistance

Resistance

Resistance

Tolerance

Tolerance

Tolerance

Resistance/ tolerance

Quantitative

Single dominant gene Single dominant gene Single dominant gene Quantitative

Quantitative

Quantitative

Quantitative

Inheritance

QTLs

QTLs

Botd8

Vr2

Vr3

QTLs

QTLs

QTLs

Gene name

1, 2, 4, 5, 6, 7

1, 4, 5, 6

6 or 8

8

2

1, 2, 4, 6, 8

2, 3, 4

1, 4

LG

(continued)

Rubio et al. (2010)

Yang et al. (2013)

Mancero-Castillo et al. (2018)

Pascal et al. (2017)

Donoso et al. (2016)

Pacheco et al. (2014) Foulongne et al. (2003)

Martinez-Garcia et al. (2013a)

References

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Nematode

Nematode

Nematode

Root-knot nematode

Root-knot nematode

Nematode

Root-knot nematode

Insect

Green peach aphid Root-knot nematode

Nematode

Meloidogyne spp.

Insect

Green peach aphid

Root-knot nematode

Myzus persicae

Insect

Green peach aphid

Meloidogyne spp.

Meloidogyne spp.

Meloidogyne spp.

Meloidogyne spp.

Myzus persicae

Myzus persicae

Plum pox virus

Virus

Sharka

Pathogen

Organism

Disease

(continued)

Artificial

Artificial

Artificial

Artificial

Natural greenhouse Artificial

Natural greenhouse

Natural greenhouse

Artificial

Inoculation

P. dulcis Alnem1

P. dulcis x persica

P. persica (Nemared)

P. salicina

P. persica Weeping Flower Peach P. davidiana (clone P1908) P. cerasifera myrobalan P2175

P. persica cv. Rubira®

P. persica cvs.

Source of resistance

Resistance

Resistance

Resistance

Resistance

Resistance

Tolerance

Induced antixenosis-type GPA resistance Resistance

Tolerance strong

Resistance/ tolerance

Single dominant gene Single dominant gene Single dominant gene Single dominant gene Single dominant gene

Single dominant gene Single dominant gene Quantitative

Quantitative

Inheritance

RMja

RMia

RMiaNem

Rjap

Ma

QTLs

Rm1

Rm2

QTLs

Gene name

7

2

2

7

1, 2, 3, 4, 5, 6, 8 7

1

1

2, 3

LG

(continued)

Van Ghelder et al. (2010)

Saucet et al. (2016)

Claverie et al. (2004b)

Claverie et al. (2004a)

Sauge et al. (2012) Claverie et al. (2004a)

Pascal et al. (2017)

Cirilli et al. (2017) Lambert and Pascal (2011)

References

8 Genomic-Based Breeding for Climate-Smart Peach Varieties 289

Nematode

Root-knot nematode Root-knot nematode Root-knot nematode

Nematode

Nematode

Organism

Disease

(continued)

Meloidogyne spp. Meloidogyne spp. Meloidogyne spp.

Pathogen

Artificial

Artificial

Artificial

Inoculation

P. kansuensis

P. kansuensis

P. kansuensis

Source of resistance

Resistance

Resistance

Tolerance

Resistance/ tolerance

Single gene

Single gene

Major gene

Inheritance

2 Non-mapped

–

2

LG

Mf

PkMi

Gene name

Maquilan et al. (2018b) Maquilan et al. (2018a)

Cao et al. (2011)

References

290 Y. Gogorcena et al.

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Another major topic and complex issue in peach breeding programs is the selection for tolerance to pests. Nevertheless, some studies have identified resistance sources against green peach aphid and root-knot nematodes in several Prunus species, and identified resistance genes in different linkage groups. Two single dominant genes in LG1 were found in peach conferring tolerance to peach green aphids (Lambert and Pascal 2011; Pascal et al. 2017). Several single dominant genes have been described for root-knot nematode resistance, in LG7 from plum (Claverie et al. 2004a), and in LG2 from peach Nemared (RMiaNem, Claverie et al. 2004b), almond (Van Ghelder et al. 2010), almond  peach (Maquilan et al. 2018), and other Prunus species as reported in Saucet et al. (2016). However, it is under discussion if some of the reported genes (Mf, RMia and PkMi) are distinct genes that are tightly linked in a single locus or are part of a single multi-allelic resistance locus (Saucet et al. 2016).

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8.7

Marker-Assisted Breeding for CS Traits: Promises, Progress and Prospects for CS Traits

Molecular markers linked to climate-smart traits will be essential for implementation of MAS and improvement efficiency in CS peach breeding programs. As mentioned previously, breeding for tolerance to biotic and abiotic stresses are among the most challenges in peach programs. However, several issues limit its application, among others, to implement accurate phenotypic methods; to control environmental and management interactions; to understand the complex genetic architecture, the quantitative inheritance, and the epigenetic regulation of the traits; to introgress resistant traits from exotic germplasm; and to develop DNA-based markers linked to the trait. To date, attempts to use MAS in peach breeding programs have remained limited to a few simply inherited traits. QTL discovery does not automatically lead to practical breeding tools (Vanderzande et al. 2018). This disconnection between research and application has been termed “the chasm” (Bliss 2010). The reasons why the application of DNA markers for crop improvement has been developed very scarcely are still under discussion (Vanderzande et al. 2018). For phenological traits, DNA-based test ready to implement MAB were reported only for maturity date (slow ripening) (Eduardo et al. 2015; Meneses et al. 2016) and chilling requirements (Li et al. 2019). In Prunus, some progresses have been made towards mapping of major genes controlling resistance to pests and diseases. MAS is currently used in rootstock breeding programs for selection of tolerant genes to RKN from peach and plum (Gillen and Bliss 2005; Esmenjaud and Srinivasan 2013). R genes with different spectra have been mapped in plums, peach and almond and can be pyramided for durable resistance to RKN in interspecific rootstocks (Saucet et al. 2016). SNPs

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linked to Rm2 and QTLs on LG3 and LG5 have been proposed for MAS of genotypes resistant to green peach aphid and for the powdery mildew resistance gene Vr2 and QTLs on LG6 and LG8 (Lambert et al. 2016). Another strategy to progress in the MAS process is to divide the genome in blocks that contain informative linked markers (haploblocks) as it was proposed for Rosaceae (Peace 2017) and it has been tested to be efficient in peach (Biscarini et al. 2017; Hernández Mora et al. 2017; Laurens et al. 2018). Each block has a set of alleles and all together form the haplotypes that can segregate generating a variation to compute the associated trait effect. For certain characters, MAS strategy may require not only the location of haplotypes closely related to the gene but also identification of other haplotypes that explain the character based on the haplotypes of the parents used in the breeding program (Micheletti et al. 2015). Haploblocking approach assigns missing SNPs or remove genotyping mistakes based on the genetic knowledge accumulated and using specific software (Laurens et al. 2018). This methodology reduces computing time; a complete workflow is described in Vanderzande et al. (2019). In peach the 103 haploblocks with 4005 highly curated phased SNPs and haplotype data sets of germplasm in the Crop Reference Set are available through the Genome Database for Rosaceae (Vanderzande et al. 2019). The genetic knowledge together with the implementation of new breeding approaches and the development of high-throughput phenotyping and genotyping platforms are the passport to cross the bridge toward the implementation of MAB for new CS traits. Consequently, to assess and integrate data from new phenotypic and genotypic platforms, a multidisciplinary network should be created integrating functional and bioinformatics approaches. First, the implementation of phenotypic tools (based on thermal and multispectral imaging and field spectroscopy data) that allow evaluating germplasm collections for tolerance to biotic and abiotic stresses (see Sect. 8.4.2) in different environmental conditions will generate more accurate phenotypic data relevant to explore resilient traits for adaptation. Secondly, the application of novel techniques to elucidate cultivar functional adaptation, such as epigenetic marks (DNA methylation patterns), which are definitively involved in climate-smarts traits, must be prioritized.

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Genomics-Aided Breeding for CS Traits

Improving the performance of peach varieties to face climate change requires multiple approaches since not only phenology traits will be affected but also the interaction with several associated dynamic biotic and abiotic stresses. Moreover, it is necessary to maintain, or even increase, the organoleptic quality of the fruit as the germplasm is improved to be fit in the near-future environment. Multiple approaches that could aid in several ways are desired, it is not realistic to seek for a “Golden hammer”. Genomics tools could address the problem at multiple levels. High-throughput genotyping technologies that can analyze several thousand or even millions of SNPs simultaneously allowed a deep characterization of peach germplasm including wild relatives. Genomics tools and the derived genomic information enable efficient administration of germplasm collections and breeding decisions (e.g., selection of parental genotypes). The small peach genome (230 Mbp) encouraged researchers worldwide to develop a range of genomics tools and genome information that are available for the study of climate change-related traits. This section summarizes the state of the art regarding peach genomics including the platforms established, the knowledge available on genetic resources and the novel strategies for breeding climate change-smart peaches.

8.8.1

Structural and Functional Genomics Resources

Peach definitely entered in the genomics era when its genome was sequenced and released by the IPGI in 2010. The availability of a quality reference genome enables the development of high-throughput genotyping tools. By re-sequencing of 56 accessions, the International Peach SNP Consortium (IPSC) constructed the IPSC peach 9K SNP Infinium® II array v1 (Verde et al. 2012). The array is composed of a prefixed set of 8144 SNPs covering the eight chromosomes of peach with an average distance of 26.7 kb between SNPs. A wide study on 1576 peach accession plus six Prunus-related species proved that 4271 SNPs from the 9K SNP array were polymorphic (Micheletti et al. 2015) which allows to conduct studies at a genome level. This platform boosted genomics studies allowing a deeper understanding of germplasm diversity, the construction of dense genetic maps for QTL analyses and GWAS in peach. Several groups take advantage of the 9K SNP Infinium® II array to construct high density maps covering the eight peach chromosomes (Yang et al. 2013; Frett et al. 2014; da Silva Linge et al. 2015, 2018; Nuñez-Lillo et al. 2015; Zeballos et al. 2016), although in some cases not all the chromosomes were covered (Eduardo et al. 2013; Romeu et al. 2014; Sánchez et al. 2014). Nowadays, the 9K platform is no longer available commercially but the International RosBREED SNP consortium (IRSC) announced the release of novel 16 and 18K array for peach (Genome Database for Rosaceae 2019). Although the details on the novel platforms are still unpublished, it is expected that SNP assortment bias in platform 9K will be improved. An alternative approach that exploited GoldenGate® Genotyping assay to evaluate 1536 in house identified SNPs in two

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mapping populations was undertaken to construct high density maps (Martínez-García et al. 2013b). The SNP set was selected from a total of 6654 SNPs identified by whole-genome sequencing of the three parental genotypes, which segregated in the two progenies, with a combination of Roche 454 and Illumina/Solexa sequencing technologies (Ahmad et al. 2011). Although with some gaps, they were able to reconstruct the eight peach chromosomes with a combined map obtained from the two populations (Martínez-García et al. 2013b). These results highlight the preference of owning sequencing data of the genotypes despite the fact that obtaining whole-genome sequencing is still expensive. Several sequencing methods (RAD, ddRAD, GBS, MSG, etc.), involving minor modification (Andrews et al. 2016), are comprised under the term “RADseq”. RADseq requires generating DNA libraries using one or two restriction enzymes and adapters for sequencing. Each library is tagged with a unique barcode that allows the in silico identification after sequencing. A RADseq emerged as an alternative since it reduces the cost by analyzing a limited portion of the genome and by multiplexing large numbers of individuals on a sequencing lane (Davey and Blaxter 2011). RADseq platform originally developed for maize by digesting DNA with ApekI (Elshire et al. 2011) was applied in peach to genotype peach populations (Bielenberg et al. 2015; Nuñez-Lillo et al. 2019). After sequencing, filtering and removing process, a final set of 410 SNPs and 499 SNPs were maintained covering 8 linkage groups in a F2 population (Bielenberg et al. 2015) and 9 linkage groups in a F1 peach population (Nuñez-Lillo et al. 2019), respectively. Unfortunately, a low proportion of SNP loci in common with the other genotyping platform was obtained (Bielenberg et al. 2015): less than 0.7% with the dataset obtained by whole-genome sequencing/GoldenGate® Genotyping (Martínez-García et al. 2013b) and about 12% with the 9K SNP Infinium® II array (Verde et al. 2012). Indeed, the main drawback of RAD-derived SNPs in constructing genetic maps is the low number of anchoring markers between independent mapping populations (Bielenberg et al. 2015). Different high-throughput genotyping platforms were employed to construct genetic maps, and in all cases the number of available SNPs greatly exceeded the number of SNP map points revealing that the progeny size is the limiting factor. Now, as the genotyping prices are lowering, the cost of generating and maintaining the mapping population on the field becomes more relevant. Recently, a RADseq genotyping platform for peach has been developed that involves DNA digestion with two restriction enzymes and a size selection step (Sánchez et al. unpublished results). First, the digestions produced by 6 pairs of enzymes were analyzed and compared with the digestion generated by ApeKI. It was found that the combination of PstI/MboI retained the highest number of loci establishing this pair for the platform. A peach germplasm collection composed of 191 genotypes was analyzed with this RADseq platform, producing, on average, 1 million of paired-end (2x250bp) sequences per genotype. After sequence analysis, a total of 113,411 SNPs were identified. Only 7565 SNPs were retained after discarding SNPs present in less than 5% of the genotypes and with a minor allele frequency lower than 1%. These results and those reported by Bielenberg et al. (2015) highlight the need of ameliorating the RADseq methods to reach a high overlapping of the region sequenced in each genotype analyzed on the same experiment.

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In summary, several platforms were developed for peach genotyping. Project with aims beyond the genotyping itself could afford whole-genome sequencing of desired genotypes. In other cases, the SNP array platform could be an affordable alternative, especially to detect and analyze SNPs linked to traits of interest in diverse germplasm collections. Finally, the RADseq platform would be helpful in studies involving collections with a certain level of exoticism. Next, we focus on the transcriptomic technologies available for gene expression analysis in peach that could be useful in integrative approaches to discover candidate genes related to climate change traits. Both hybridization and sequence-based methodologies have been developed for transcriptomic studies in peach. In the pre-genomics era of peach, two microarrays were constructed from fruit mesocarp libraries, lPeach 1.0 (ESTree Consortium 2005) and ChillPeach (Ogundiwin et al. 2008). The lPeach 1.0 microarray contains 4806 unigenes of fruit most of them expressed during fruit ripening while ChillPeach is composed with 4261 unigenes enriched with genes expressed during cold storage. Although these platforms were able to decipher candidate genes with some degree of success (Falara et al. 2011; Sánchez et al. 2013), the advent of NGS technologies allows a wider coverage of the transcriptome. A RNA-seq study sequencing cDNA-libraries from leaves (2), flowers (2) and fruits (3) allow a description of the peach transcriptome landscape (Wang et al. 2013). After the analysis of 21.5 Gb of RNA sequences, 24,427 genes were identified from which 2140 were novel transcript not annotated in the peach genome. Moreover, sequencing unveiled that 22% of the genes are proceeded through alternative splicing (Wang et al. 2013). A similar RNA-seq study with 12 RNA libraries from peach allowed the assembly of 22,079 and 17,854 genes associated with root and leaf tissues, respectively. Around 500 differentially expressed genes were identified under drought stress conditions, and functionally annotated (Ksouri et al. 2016). Another benefit of RNA-seq is related to the identification of allelic variants that could complement the expression information. Analyzing transcriptome in seven genotypes, 9587 SNPs and 17,979 SSRs in expressed genes were identified (Wang et al. 2013). Besides coding genes, a set of 1417 long noncoding RNAs (lncRNA) were also discovered (Wang et al. 2013). The lncRNA are transcripts longer than 200 nt without a canonical open-reading-frame that are expressed at low level compared to mRNA in a tissue-specific manner or in response to a stress (Deniz and Erman 2017). Since its discovery, lncRNAs became a hot topic and several functions on plant physiology (Chekanova 2015) and stress responses (Wang et al. 2017) were elucidated mainly in model species. For example, two lncRNAs transcribed from the flowering locus C (FLC) are involved in epigenetic silencing of FLC gene during vernalization of Arabidopsis (Swiezewski et al. 2009; Heo and Sung 2011). It is estimated that there is as much lncRNA as coding genes in eukaryotic genomes (Deniz and Erman 2017). The more functions are revealed, probably the more lncRNA will become a toolkit for peach improvement. miRNAs, the smallest regulatory RNAs of 20- to 24-nucleotides in length, were better studied and were harnessed in biotech plant improvement (Zhang and Wang 2015). In peach, several studies exploited RNA-seq to identify miRNAs in leaves (Li et al. 2017), winter buds (Barakat et al. 2012),

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roots, stems, flower buds and fruits (Zhang et al. 2016). Moreover, miRNAs responding to drought and UV irradiation stresses were identified in other studies (Eldem et al. 2012, Li et al. 2017; Shi et al. 2017). Up to date, RNA-seq technologies allowed a profound description of peach transcriptome but their use as profiling technique was quite restricted as consequence of the deep sequencing needed to analyze several samples (including replicates). As the sequencing cost decreases, RNA-seq technologies will become the method of choice to measure gene transcription due to its high-throughput nature and more importantly because different RNAs (mRNA, lncRNA, miRNA, etc.) and their variants (i.e., alternative splicing) are detected.

8.8.2

Peach Genome Sequencing

The efforts of the international community to obtain the sequence of the peach genome were finally rewarded in 2007 when the Joint Genome Institute (JGI) announced that peach entered in the list of upcoming sequenced plants (Arús et al. 2012). The sequencing phase of the project was completed toward the end of 2009 and by April 1, 2010 the first draft of the peach genome (Peach v1.0) was released by the IPGI under the protection of the Fort Lauderdale Agreement which implies the immediate and freely availability of the sequence data for the scientific community before that data is used for publication. A whole-genome shotgun (WGS) approach combined with Sanger sequencing was used to obtain this first draft (Verde et al. 2013). WGS approach reduces the cost of sequencing compared to the bacterial artificial chromosome (BAC) approach which requires additional steps selecting non-overlapping BACs and preparing shotgun libraries for sequencing and preassembly of the physical map. On the other hand, drawback of the WGS sequencing is the bioinformatics assembly mainly when short read from NGS is used. There exists a risk of misassembly with large regions of repeat elements, duplicated sequences (whole chromosomes or extended portion) and highly heterozygous stretches of DNA leading fragmented genomes with low contiguity. Some inherent characteristics of the peach genome as well as the strategy used allowed the generation of a high-quality first draft. As reference accession, the IPGI selected a doubled haploid obtained from the cultivar Lovell, PLov2-2n (Toyama 1974), which was proved to be a complete homozygous by molecular markers analysis (Arús et al. 2012). Besides, Sanger sequencing was used which increases the assembly quality and contiguity compared to NGS technologies. Additionally, the Prunus reference map T  E (Joobeur et al. 1998) was used to assign scaffolds to chromosomes and check the integrity (Verde et al. 2013). The T  E map is highly saturated due to the bin mapping strategy that allows to place markers in the map genotyping few (6) informative plants (Howad et al. 2005). Nevertheless, since the reference map is generated from a reduced interspecific hybrid (almond  peach) progeny (88 individuals), some regions are not covered by recombination events. The anchoring markers did not provide enough information leaving scaffolds unmapped, and in other cases, scaffolds were anchored but with unknown

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orientation. To overcome these limitations, a set of linkage maps covering wide recombination points in combination with NGS re-sequencing of the reference accession were used to further improve the assembly of the genome leading to a second version of the genome, Peach v2.0 (Verde et al. 2017). The strategy involved the saturation of several high-resolution maps with sequence associated molecular markers (i.e., markers that could be placed in the genome according to their sequence) located on inconsistent genome regions like unmapped scaffolds, randomly oriented scaffolds and uncovered scaffolds ends. The re-sequencing of the Lovell doubled haploid with deeper coverage (43) using NGS sequencing platform (2250 paired-end reads) allows to close Peach v1.0 genome gaps and improve contiguity in the new version (Verde et al. 2017). As result, 99,2% of the sequences could be mapped to on chromosomes, and the Peach v2.0 possesses one of the most deeply assembled genomes compared to other species like apple, citrus, banana, melon, pineapple, grape, watermelon or tomato (Verde et al. 2017). Indeed, besides Arabidopsis and rice that were sequenced by a BAC to BAC approach and represent the golden standard of plant genome (100% of mapped sequences), only the Brachypodium distachyon (L.) Beauv. genome has a higher percentage of sequences mapped (99.8%) among the species sequenced with a WGS approach. The 0.8% (1.7 Mb) of unmapped sequences of the peach genome contains only 62 genes, indicating that almost all of the peach genes could be located on a specific position at the eight chromosomes. At the current state, the Peach v2.0 genome could be considered as “Improved High-Quality draft” according to international standards (Chain et al. 2009). Third-generation sequencing technologies increase sequencing rate, throughput and read length because they involve a simpler and cheaper sample preparation (van Dijk et al. 2018). It is envisaged that as more NGS sequencing projects are developing and/or Third Generation Sequencing (TGS) are applied in peach, soon the peach genome will reach the golden standard.

8.8.3

Impact of Genome Sequencing on Germplasm Characterization and Gene Discovery

Cultivated germplasm worldwide for modern breeding programs derives from a relatively low number of parents and therefore suffers founder effects (Scorza et al. 1985; Li et al. 2013). Due to the relative long juvenile phase of peach, of about 3-4 years, most breeding programs advance no more than one or two generations and usually start from parents already breed. As result, peach varieties show a reduced variability compared to other crops of this genus (Mnejja et al. 2010). Traditionally in peach breeding programs, with the objective of selecting fruit quality traits, landraces and wild cultivars adapted to non-ideal conditions have been ignored and abandoned as donors of utility alleles. Usually, in peach collections, there is an overrepresentation of improved varieties, and therefore, exotic materials or wild materials should be included to enhance the allele richness to face climate change. However, it is not affordable to harbor an infinite number of accessions in active

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collections due to the cost associated to maintain trees in the field in terms of labors and chemical supplies. Therefore, curators need to identify the most suitable core collections according to the breeding (and/or conservation) purposes for an efficient use of economic resources. Nowadays, the availability of a high-quality reference genome along with diverse genomics tools developed for peach enables the genetic characterization of germplasm collections to an unprecedented level. The re-sequencing of 84 accessions, including peach varieties, landraces, ornamental materials and wild relatives, permitted the identification of 4.6 million of SNPs which allow to study the peach domestication at a genome scale (Cao et al. 2014). These authors point out the potential to find novel alleles for breeding purposes in the peach-related wild species analyzed including P. davidiana, P. ferganensis, P. kansuensis and P. mira. The study of a wider germplasm of modern improved and traditional varieties genotyped by 9K SNP array confirmed that another selection event took place through the modern breeding process (see Sect. 8.5.2). As a consequence, most of the modern varieties are related as grandparent-grandchild or half-sibs (Micheletti et al. 2015). But despite this, we can reintroduce some of the lost variability by crossing modern cultivars with ancient European or Chinese materials. A recent genomic study analyzed few Argentinean ferals and Bolivian traditional varieties in the context of a wide number of modern varieties. In this preliminary work, 191 peach accessions were genotyped with the ddRAD platform described in Sect. 8.8.1. Using a reduced dataset of 7565 SNPs (SNPs with MAF >1% and present in 95% of the genotypes materials) and combining phylogenetic, structure and PCA analyses was found that traditional Bolivian varieties developed by the aborigines and Argentinean feral germplasm, both share a common origin that probably goes back to the colony period (Sánchez et al., unpublished results). These genotypes showed high similarity with Italian ancestral varieties which reinforce the hypothesis and show another part of the dispersal of peach through the history. This germplasm as well as other ferals found in America (Pérez et al. 1993) could harbor unexploited sources of variability for breeding since all are adapted to growth in a diverse environment. Moreover, as derived from ancient European materials, these genotypes could contain genes that are lost at present due to the low use of these materials in modern breeding program. Since these feral germplasm pass through a domestication process, it is likely that their mating with modern breeding lines could reach the minimum standard faster compared to the situation where wild relatives are used. Based on the knowledge accumulated until now, genomics approaches allow not only to know domestication process but also to assess diversity studies and conduct fine mapping of traits at the genome level. These features combined with GWAS encourage to find SNPs markers associated with traits affected by climate change and select candidate genes to implement genomics-assisted breeding for target traits.

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Genomics-Assisted Breeding for Addressing Climate Change in Peach: State of the Art and Prospects

The wealth of genomic technologies developed is expected to be harvested by genomic selection (GS) approaches to accelerate peach improvement. There is a high expectation about the potential of GS for saving cost and time in peach breeding. The benefits of an early selection of desirable genotypes (or discard unfavorable ones) in tree breeding are evident. This was the driving force to implement MAS in peach breeding, but at present, only traits controlled by mayor genes have been successfully selected (Laurens et al. 2018, see also Sect. 8.4.3). For quantitative traits controlled by many loci with small effects, MAS is ill-suited. GS promises to overcome this limitation by estimating the effect of all markers simultaneously in order to predict a genetic/breeding value (Heffner et al. 2009). Therefore, dense marker maps, provided by current genomic platforms, are needed to cover the entire target genome so that each QTL could be in LD with at least one marker (Lorenz and Nice 2017). GS also requires accurate phenotyping of the target trait which has ever been the key point for QTL mapping in traditional biparental population. In this approach, the phenotyping is carried out in a genotyped population (training population) that is used to train the GS model to predict the performance of non-phenotyped plants from their marker scores. In this context, the role of phenotyping moves from a selection criterion toward the re-estimation of marker effects for the enhancement of the prediction model (Heffner et al. 2009). GS predicts the phenotypes of a genotyped target population. GS uses a statistical model that combines molecular markers, phenotypic and pedigree data to estimate markers’ effects that allow with another model the prediction of genomic breeding values. The first critical step for GS implementation is the establishment of a proper training population (Jannink et al. 2010) to develop the training model. There are at least three kinds of training populations that contain the variability: (1) a subset of the segregating progeny used as training population to predict the rest of the progeny (target population), (2) a training population that includes a mixture of related and unrelated genotypes to the target population and (3) a diverse germplasm collection (Lorenz and Nice 2017). For the first scenario, high prediction accuracy is obtained with modest population sizes (Lorenzana and Bernardo 2009), therefore, could be the choice for low scale breeding programs with few target ideotypes. Biscarini and coauthors (2017) reported that the second strategy is feasible at least for some traits in peach. It is likely that this strategy requires the cooperation of several breeding programs to rich a sufficient number of genotyped and phenotyped individuals to obtain suitable prediction models. The third option is the most desired for peach breeders since fewer resources are required to build populations. Nevertheless, the prediction accuracy drops when unrelated genotypes are used to train the model (Lorenz and Nice 2017), therefore it is necessary to genotype and phenotype a higher number of individuals compared to the second strategy. The training population is dynamic since selected individuals from the progeny that are

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phenotyped to corroborate the prediction may be added to the training populations to enhance the model. Due to this, dynamism is recommended to use always the same genomic platform and analyze the same markers. The incoming 18K SNP arrays announced by RosBREED consortium will allow testing a standardized set of SNPs which could be used worldwide to generate prediction models for peach. Currently, as we have mentioned, the GS challenge in peach is turning from theory into practice. A pioneer work addresses the application of GS to predict FW, SSC and TA (Biscarini et al. 2017). These authors took advantage of a set of 11 mapping populations (F1, F2 and BCs) and genotyped and phenotyped them to simulate the trait predictions. Data from populations were divided into five aleatory subgroups, four of those were used as training population to predict the phenotype of the individuals of the other group by a genomic best linear unbiased predictions model. Each subgroup was predicted at least one time. The predictive ability measured as the correlation between the observed and the predicted phenotype was analyzed. The predictive ability within the population was variable but reached high values like: 0.84, 0.83 and 0.78 for FW, TA and SSC, respectively (Biscarini et al. 2017), suggesting that the application of GS to peach breeding programs is promising. GS approaches would fit ideally to apply in breeding of quantitative complex traits, highly polymorphic with environmental interactions and epigenetic regulation as those affected by climate change effects. It is said that “GS is the revenge of population geneticist against molecular biologist” since is expected that GS revolutionizes plant improvement with less need of previous work. In the case of peach, the real potential of GS has to be focused on the prediction of phenotypes on phenology-related traits which will be more affected by climate change, CR, HR and flowering and maturity dates are the more affected ones. In order to anticipate the impact of climate, the challenge turns to design the training and target populations taking into account the trait, sources of variability and the ideotype pursuit. Current genomic platforms developed will aid in germplasm characterization to find the best parental genotypes and drive the GS process itself.

8.9

Bioinformatics Tools and Resources for Peach

Climate change, environmental stimulus and biotic stress agents have been recognized as the greatest challenges confronting the agricultural sector. This situation is further exacerbated by an alarming rise in food demand. According to the FAO, global population is expected to reach over 10 billion by 2050, requiring thus an increase of food supplies by 70% (FAO 2018). Definitely, given its limitations in term of time and costs, conventional agriculture is unlikely to handle this situation giving thereby a way to modern alternatives. With the adoption of advanced DNA-technologies and the onset of omic-based approaches (such as genomics, transcriptomics, proteomics and metabolomics), exponential influx of diverse biological data has required a new discipline for data management and interpretation

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(Iquebal et al. 2015). Bioinformatics has emerged as a powerful computer-assisted science for digital data storage, modeling and analysis (Esposito et al. 2016). In other terms, bioinformatics develops and applies suitable mathematical and statistical algorithms to elucidate biological phenomena (Rhee et al. 2006). The term “bioinformatics” has been laid as a working concept by Hogeweg in the beginning of the 1970s (Hogeweg 2011). However, bioinformatics tools began to be used at countless sites by 1980, including the European Molecular Biology Laboratory (EMBL), and the National Center for Biotechnology Information (NCBI). In plants, the introduction of this discipline was initiated with model plants as they are easier to study given their short life cycle and small genome size. Indeed, the release of Arabidopsis thaliana full genome in 2000 represented a great leap toward understanding plant genome organization and functions, deploying computational tools (Initiative 2000). Following this lead, bioinformatics and computational biology have become a forefront of plant breeding programs, driving it to new heights by sweeping away the barriers toward concerted researches and revolutionary plant breeding system. Over the last decades, many plant genome sequencing projects were undertaken starting from rice as the first sequenced crop in 2002 (Yu et al. 2002) and reaching the almond in 2019 (Alioto et al. 2019). Zooming in the Rosaceae family (Fig. 8.2), 18 out of the 3500 species were completely sequenced generating a huge amount of data for the scientific community. Implementation and maintenance of databanks became a mainstay of bioinformatics. In this section, we aim to cover the most relevant public databases of P. persica and we provide readers a summary of their principal features.

Fig. 8.2 Total number of species and sequenced genomes within each taxonomic level. Pink squares indicate the number of total species while green squares represent the number of completely sequenced genomes. Adapted from PLABI database (https://plabipd.de/portal/ sequenced-plant-genomes), consulted in November 2019

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Overview of Biological Databanks

Biological database is a shared repository of organized data, designed to share huge amount of information from a broad spectrum of areas. It is usually associated with computerized software offering an immediate access to a suite of analytical tools necessary to demystify biological questions. Databases can be broadly classified, based on their content, into three categories: (i) primary/archival database: containing raw sequence data derived from experimental results (Genbank from NCBI and EMBL). In this case, the same sequence may be submitted under different names leading to redundancy issue, ((ii) secondary database: also known as curated nonredundant data-houses (Refseq and Uniprot), where multiple entries of the same sequence are merged to create a single sequence with an extensive annotation, (iii) species-specific databases: dealing with particular organisms or groups of organisms (GDR, Graingenes, fungal databases, etc.). Heterogeneity of data types is sometimes gathered in separate databases: nucleotide data, protein sequences, gene expression data, etc. With the increased rate of published sequences, the criteria applicable to database classification have been reviewed considering the heterogeneity of data types. Shedding the light on P. persica, although there is no customized data warehouse for this species, several prominent public databases provide access to peach genomic information. In what follows, we describe a collection of widely used and accessible peach-related databases to help shape a good understanding of these resources.

8.9.2

Gene and Genome Databases

A genome database is a central repository of publicly available genomic data. It encompasses nucleotide sequences with their related biological and bibliographic annotations (Benson et al. 2005). The NCBI in the USA, EMBL in the European Union and the DNA Data Bank of Japan (DDBJ) are considered as the core genomic support of plants. They are updated and synchronized on a daily basis. NCBI is a suite of 37 comprehensive databases (Sayers et al. 2009). For the purpose of this chapter and for an easier information retrieval, we organized these databases into six broad categories (Fig. 8.3). Scrolling the results of P. persica under genomes and genes categories, huge amount of information can be compiled. As illustrated in Fig. 8.4, the batch of genomes category provides a taxonomical report of the species and a full access to genomic sequences and their annotations. Additionally, four assemblies’ accessions of P. persica are supplied, including the recent P. persica v2 published in 2017 (Verde et al. 2017). Data deriving from research projects and high-throughput sequencing platforms are also gathered in BioProject and Short Read Archive

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Fig. 8.3 Schematic skeleton of NCBI’s databases. Each database category is represented by a different color and described in the right side of the figure. Within each category, a collection of databases is listed. Consulted in NCBI on November, 2019

Fig. 8.4 A toolkit offered by genomes (green) and genes (yellow) categories. Each block corresponds to an individual data repository within these categories. Numbers correspond to Prunus persica query search output reported by these different databases. Consulted in NCBI on November 2019

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(SRA) databases, respectively (Sayers et al. 2009). Concomitantly, biosample archive gives a description of the biological material used in these experimental assays (tissue, name, age, geographic stage, etc.). On the other hand, the group of databases corresponding to genes category display curated interface of 27,209 genes with their corresponding ID, position and function. Redundant genes and related sequences are assigned to unigenes and popset libraries, respectively. Information about gene expression experiments is accessible in the Geodataset database. As mentioned previously, an international collaboration has been established between the tripartite nucleotide sequences archives NCBI (Genbank)/EMBL/ DDJB. The main data are daily exchanged among them offering a full data coverage for the research community. Consequently, EMBL and DDBJ offer the same toolkit list offered by NCBI. In other words, they permit retrieve the same information about genes, nucleotide, taxonomy, assembly, SRA experiments. However, the results given by these three databases may not be identical. For instance, the same gene may have different annotations. This may be partially explained by the fact that the original data are only owned by the researchers and do not reach minimal standards or/and guidelines to control the submission quality so that large portion of the stored data may be incorrect or incomplete.

8.9.3

Comparative Genomic Databases and Their Associated Web Portal

The increasing availability of genomic sequences from multiple organisms has provided scientists with a large dataset for comparative genomics studies. CoGe (Castillo et al. 2018), Ensembl (Herrero et al. 2016), GDR (Jung et al. 2004, 2014, 2019), PLAZA (Van Bel et al. 2018), Phytozome (Goodstein et al. 2012), Plant Genome Duplication Database (PGDD) (Lee et al. 2012), Plant GDB (PGDB) (Dong et al. 2004), VISTA (Frazer et al. 2004) are the most curated resources developed to fill this niche for many species including P. persica (Table 8.1). Unlike the genes’ and genomes’ databases that are somehow limiting the users by authorizing only data download tool, comparative web portals are empowered with a set of search and visualization tools allowing better interaction with data. Their strengths and weaknesses are addressed in Fig. 8.5. CoGe is an online comparative platform providing a variety of interconnected tools. EpicCoGe and OrganismView give a genome report information and genomic visualization of a particular organism, respectively. SynFind, SynMap and SynMap3D are employed to identify syntenic regions across whole genomes (SynFind) and generate pairwise (SynMap) and multiple syntenic dot plots (SynMap3D) (Haug-Baltzell et al. 2017). These tools were widely employed to find out syntenic genes between P. persica, P. avium and P. mume (Montardit-Tardá et al. 2018; Ksouri et al. 2020). Moreover, Blast suite tool can be deployed to search

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Table 8.1 List of the general comparative genomics resources covering Prunus persica and their indexed species and available stored genomes (updated on November 2019) Comparative DB

Number of species

Links

CoGe 19,322 genomevolution.org/coge Ensembl Plants v45 67 plants.ensembl.org GDR 6 rosaceae.org PGDD 47 chibba.agtec.uga.edu/duplication Phytozome v12.1.6 93 phytozome.jgi.doe.gov PLAZA dicots v4 55 bioinformatics.psb.ugent.be/plaza VISTA plant 46 genome.lbl.gov/vista DB database, CoGe comparative genomics, GDR genome database for Rosaceae, PGDD plant genome duplication database

Tasks and Databases Annotations

CoGe

Ensembl Plants V45

GDR

Phytozome PLAZA v4 v12.1.6

PGDD

Vista Plant

Functional annotations (Gos) Pathways annotation KEGG

Breeder toolbox Data download Genomic data visualization (Gbrowse) ID and position conversion Load genome and experimental data Maps viewers and Markers Primer design Sequence similarity (Blast) Colinearity analysis Display syntenic blocks Synteny analysis

Multiple syntenic blocks Pairwise syntenic blocks Microsynteny finding Orthologues-paralogues detection

Variants detection and analysis Version 2 of Prunus persica genome

Fig. 8.5 Informative representation of the major toolkits displayed in the comparatives genome databases dealing with Prunus persica. The more relevant tools are shown inside each block

for sequences’ similarities among genome sets. Blast results may be sent further to GEvo tool for micro-synteny analysis of the aligned sequences. Lastly, to ease their tasks, CoGe enables users loading and processing their own genome and next-generation experiments using Load Genome and LoadExp+ (Grover et al. 2017). Ensembl Plants release 45 is a genomic central portal for plant species of scientific interest currently housing 67 species. It attends to found a graphical interface for comparative analysis through a consistent set of computational tools

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(Bolser et al. 2016). These resources permit seeking for DNA and protein sequences’ similarity (Blast), browsing genes’ structure and their corresponding functional annotations (Gbrowse), analyzing and predicting the effect of variants (VEP), protein aligning and homologous detection (HMMER), data mining and batch retrieval of gene-related data (BioMart). To avoid the controversial issue of gene annotation through the genome versions, EnsembPlant employs two great tools: ID history converter to update IDs from previous release to their current equivalents and assembly converter to convert genome coordinates from one version to another. GDR is a comprehensive portal established in 2003 and devoted to the Rosaceae research community including almond, apple, cherry, peach, pear, raspberry, rose and strawberry (Jung et al. 2004, 2014, 2019). It gathers a wide range of bio-tools to pinpoint genomics, genetics and breeding aspects. In addition to Blast and Gbrowse servers, GDR provides also RefTrans to search reference transcriptomes as well as EST datasets for major crop species, PeachCyc to navigate peach pathways, Synteny Viewer to display the syntenic blocks, MapViewer for an interactive 3D visualization and comparison of the genetic maps. Breeders toolbox (BIMS) is also developed within the GDR allowing breeders to store, manage, archive and analyze their private breeding program. Primer3 suite was also integrated for primer design. Moreover, all Rosaceae maps and markers are publicly accessed in this portal. A new friendly “PeachMap” Web platform has been designed integrating specific data for peach from several databases (Ksouri et al. unpublished). Phytozome v12.1.6 is a comparative hub for green plants genomics that integrates a bulk of open components to unravel the evolutionary histories of complex genomes and the forces behind them (Goodstein et al. 2012). Most similar hits to a given query sequence, their equivalent annotations, gene ancestry and possible variations are driven by Blast. Note that Phytozome’s annotation is the most reliable database as it includes GO, KEGG, PFAM, KOG and PANTHER assignment. Uniform genomic views and homologous proteins are hyperlinked, respectively, to Jbrowse and Gene View. Additionally, data of interest and similarly expressed genes are easily downloadable via PytoMine and BioMart. PLAZA v4 is plant-oriented comparative database allowing basically functional and syntenic analysis. It allows visualization of genomic regions with detailed report including GO and KEGG annotations. It includes also a broad range of computational tools suitable for collinearity and pairwise synteny analysis. PGDD and VISTA suite tools are committed more to sequence alignment and visual presentation of pairwise and multiple syntenic blocks across species. However, they are not frequently updated as they only include the peach assembly v1, limiting thereby their readership.

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Genetic Variants Databases

To coordinate genomics and proteomics researches, protein databases have become a crucial part of the modern biology. From the plethora of online plant protein databases, only those displaying better information about peach are discussed. Four classes of protein repositories can be distinguished depending on how the information is organized (Table 8.2). In recent years, with large-scale sequencing, newly developed databases of genetics variants were launched. Peach-VarDB (http://hpc-bioinformatics.cineca.it/ peach) is the first web interface released to store and share peach’s genomic variations. It is a user-friendly portal addressed to bioinformaticians as well as to breeders without bioinformatics background. Peach-VarDB contains a catalog of 4,630,814 and 461,788 annotated SNPs and Indels from a diverse panel of 125 peach accessions and 21 wild-related lines (Cirilli et al. 2018a). Users may access the information applying different query search entries (gene ID, gene features, accessions, regions and sequence similarity). Definitely, this genetic variants database offers great potential for discerning the evolutionary relationship between peach lines and also to perform solid genetics and breeding studies. As a consequence, genome-wide association analysis has been reinforced and breeding programs upgraded to a more satisfactory and effective level.

8.9.5

Protein-Related Databases

To coordinate genomics and proteomics researches, protein databases have become a crucial part of the modern biology. From the plethora of online plant protein databases, only those displaying better information about peach are discussed. Four classes of protein repositories can be distinguished depending on how the information is organized (Table 8.2).

8.9.5.1

Protein Sequence Databases

These databases house a large collection of amino acid sequences with annotations to predict the protein putative functions. NCBI is the basic protein sequence database as the entries are derived from the translation of nucleotides (Apweiler et al. 2004). NCBI’s Entrez Protein (ncbi.nlm.nih.gov/protein) stores 129,626 peach proteins and displays information about identical proteins to the query search (Table 8.2). The universal protein resource (UniProt) is a leader database providing high-quality and well-curated protein sequences and functional annotations. The majority of entries are derived from genome sequencing projects and data are updated every four weeks. UniProt has three components of utmost importance: (i) UniProt Knowledge base (UniProtKB), (ii) UniProt Archive (UniParc) and (iii) UniProt Reference Clusters (UniRef) (Schneider et al. 2005). The UniProtKB is

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Table 8.2 List of protein databases covering Prunus persica Categories

Protein databases

Links

Sequences

NCBI’s Protein ncbi.nlm.nih.gov/protein UniProt uniprot.org Structures NCBI’s structure ncbi.nlm.nih.gov/structure PDB rcsb.org CATH cathdb.info Families InterPro ebi.ac.uk/interpro iProClass pir.georgetown.edu/iproclass Pfam pfam.xfam.org ProDom prodom.prabi.fr PROSITE prosite.expasy.org SMART smart.embl-heidelberg.de Functions BRENDA brenda-enzymes.org PlantTFDB planttfdb.cbi.pku.edu.cn Abbreviations UniProt universal protein resource; PDB protein databank, Pfam protein families, ProDom protein domain families, PROSITE protein domains, families and functional sites, SMART simple modular architecture research tool, BRENDA, the comprehensive enzyme information system; PlantTFDB plant transcription factor database

the central protein portal consisting of two sections: UniProtKB/Swiss-Prot containing reviewed and manually curated sequences and UniProtKB/TrEMBL with unreviewed entries, automatically annotated awaiting full manual correction. In the case of P. persica, 37 proteins are stored in Swiss-Prot while 58,532 records are remaining in TrEMBL, as reported last access in November 2019. On the other hand, UniProt proteomes page provides access to proteomes of fully sequences species. The proteome of P. persica can be found under the ID UP000006882 (https://www.uniprot.org/proteomes/UP000006882) with a set of 38,733 freely downloadable proteins. 8.9.5.2

Protein Structure Databases

Three-dimensional protein structures are of great interest helping to define the biological function and the evolutionary history of these macro-molecules. NCBI's Entrez Protein also encloses a 3D protein structure portal. However, only two items of peach are found corresponding to peach Pru-p3, member of plant nonspecific lipid transfer protein pan-allergens. Protein databank (PDB) is an international repository for 3D structural data determined by crystallography, nuclear magnetic resonance and electron microscopy (Bagchi 2012). It includes large set of proteins derived from all organisms, including plants, animals and humans. Besides, it offers a broad range of options to search by PDB ID, by author, by sequence and by ligands. As given by NCBI’s structure database, PDB contains the same items matching with peach Pru-p3.

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CATH is a free resource for hierarchical classification of protein domain structures into four major levels: Class (C), Architecture (A), Topology (T) and Homology (H) (Bagchi 2012). It displays the same results found in NCBI and PDB. Conclusively, little information about peach’s protein structures is available, hence the need for more experimental researches in this area.

8.9.5.3

Protein Family Databases

Proteins can be classified according to their sequences, structures or functional domains into different families, giving insights into their biological functions. The choice of the classification criteria depends on the users and the availability of data. InterPro, iProClass, Pfam, ProDom, PROSITE, SMART are the most widely used databases for grouping related entries to clans and used for P. persica studies (see Table 8.2).

8.9.5.4

Protein Function Databases

BRENDA and PlantTFDB are the major computational resources giving emphasis on protein function in peach. Indeed, BRENDA is one of the most comprehensive enzymatic and metabolic repositories holding approximately 40,000 enzymes from more than 6900 different organisms (Schomburg et al. 2002). BRENDA stores 150 enzymes from peach displayed with their name and EC numbers. The data are continuously updated and evaluated from the literature. On the other hand, PlantTFDB v 5.0 is a portal for functional and revolutionary studies of plant transcription factors (TFs). It includes 320,370 TFs from 165 species of which 2780 TFs are from P. persica and classified into 58 families (Jin et al. 2017). Both data about enzymes and transcription factors are freely downloadable from http://planttfdb.cbi.pku.edu.cn/index.php?sp=Ppe.

8.10 8.10.1

Future Perspectives Potential for Expansion into Nontraditional Areas

Recent global warming has resulted in local climatic changes that demand redesigning temperate production or searching for new ecosystems to expand peach growing. Subtropical highlands have less chilling accumulation than temperate regions at higher latitudes. Chilling accumulation models were originally designed for specific cultivars grown at high latitudes and may need adjustments when used in the subtropics. These regions, either in America, Africa, or in less extent in Asia,

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may have great possibilities to grow peaches if completely different breeding approaches are used. We briefly present here the possibilities for expansion of peach growing to lower latitudes. Additional climatic variables proposed to identify micro-regions with greatest possibilities for peach growing include chilling accumulation during rest, frost and hail risk, temperatures during the growth cycle for flower bud differentiation, and rains during harvest. This information will guide breeding efforts to: (1) collect a new range of genetic resources with a solid adaptation background, (2) design and develop rootstocks adapted to local ecosystems and (3) breed cultivars producing high-quality fruit from spring to winter.

8.10.2

Breeding for Subtropical Highlands

Traditional peach growing is located in temperate regions extended in both hemispheres between 30° and 45° latitude (Scorza and Sherman 1996) accumulating 650–1000 h of chilling units (CU) during rest (2-8°C), using cultivars and rootstocks mainly from California and ripening from June to September (December to March) in northern hemisphere (Pérez 1992, 2009, Byrne et al. 2000, Byrne 2006). Chilling accumulated during winter months has been the basis for development, testing, recommendation and management of new cultivars, based on models designed in temperate regions of the USA (Byrne 2006). However, these models need to adjust to lower latitudes (between 34°N and S) because all cultivars from northern zones have bud brake problems when planted in areas at lower latitudes or altitudes (Pérez 2017). The expansion of peach growing toward novel ecosystems, connecting the northern to southern hemispheres across the equator in the subtropical highlands may help to reduce the adverse effect of global warming and would allow both, an increase in fruit production and an impressive widening of the harvest season, with fresh a fruit supply from January to December (Pérez 2017). At latitudes (over 35°N and S), there are marked temperature differences between winter and summer, while in areas closer to the equator, these differences are minor and determined mainly by altitude, amount and distribution of rainfall. As an example, the most successful orchards of temperate fruits in Mexico are located in the North, at altitudes of 1600–2000 m. The main limiting production factors in these conditions are late winter-early spring frosts, summer rains during harvest and large distances to the main markets, increasing the cost of production associated with irrigation, frost and hail protection (Pérez 2017). Further south in the northern hemisphere, temperate fruit like peach demands gradually higher elevations for growing, up to 2500 m through the Sierra Madre in Mexico (22°N) and Guatemala (16° N). In Latin America there is a large range of ecosystems where peaches could be grown, starting at 30 degrees of northern latitude and 500 m of altitude in Sonora, Mexico. It continues along the subtropical highlands in Mexico and Guatemala, then across the equator in the Andes, from Colombia to Perú (Pérez

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Table 8.3 Temperate fruit production regions in subtropical highlands Region altitude (m)

Chilling (2–8 ° C)a

Frost risk (0– 4º)b

Rainfall (mm)

Season Bloomc

R1 1600–1800 150–200 0 300–800 0–1 R2 1800–2000 200–400 0–1 500–1000 1–2 R3 1800–2000 400–500 1–2 400–1000 2 R4 2000–2500 500–600 1–4 200–1000 2–3 a Chilling Hours (2–8 °C during rest) b Frost risk From none = 0, to greatest = 4 (every year) c Blooming Very early = 0 (December–early January), to very late = 3 (mid-March) c Harvest From 3 = March to very late = 9 September–October

Harvestc 3–6 4–5 5–7 5–9

2017). Chilling accumulation across this range is from less than 200 up to 600 CU and is correlated with altitude (1500 to 3000 m of elevation). There is a temperature decrease with altitude (0.56ºC per 100 m), resulting in as an estimated chilling increase of 54–61 CU per 100 m of altitude. In order to detect highly competitive new areas for temperate fruit growing in the subtropical areas around the world (Promchot et al. 2008), it will be necessary to integrate additional climate information for sound diagnosis and recommendations (Table 8.3). Other variables such as warm temperatures during flowering may decrease fruit set and influence fruit shape, as well as rain and humidity during blossom and ripening, increasing susceptibility to M. fructicola (G. Winter). This information will allow site selection with higher possibilities for temperate fruit growing: minimum frost and hail risks, 100–300 CU, no rains during harvest, coupled with cultivars and rootstocks designed for each ecosystem, reducing production costs and increased capacity to compete with other growing regions.This study was supported by the Spanish Research Agency [grant AGL2017-83358-R funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”] and the Government of Aragón [grant A09_23R], co-financed with FEDER funds; and the CSIC [grant 2020AEP119] to Y. Gogorcena. N. Kouri was the recipient of a pre-doctoral contract awarded by the Government of Aragon, Spain. To the memory of M. Badenes for her sincere friendship and for sharing her knowledge in peach, who sadly passed away while this chapter was being edited.

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Chapter 9

Development of Climate-Resilient Varieties in Rosaceous Berries Rytis Rugienius, Birut˙e Frercks, Ingrida Mažeikien˙e, Neringa Rasiukeviˇciut˙ ¯ e, Danas Baniulis and Vidmantas Stanys

Abstract Climate change causes many challenges for Rosaceae plants although its impact varies across regions requiring different solutions. Dehardening after temperature fluctuations in winter season, increased frequency of droughts or floods after temperature rise and changes in the structure of precipitation during growing season cause plant growth cessation or injury and yield losses. The appearance of new diseases and pests that are not specific to the region is another challenge. Knowledge of main vectors, first disease symptoms, temperature, moisture requirements for particular fungi and virus spread, helps to apply proper growing conditions for avoiding the disease, but for full control, breeding of new plants with more suitable physiological properties than the existing plant varieties is required. According to recent molecular and genomic data, genes with various functions can be induced by different stresses. Their interactive function suggests the existence of crosstalk between cold and drought, biotic and abiotic stress responses. Knowledge of the structural or mechanical barriers for the pathogens, biochemistry of secondary metabolites and phytohormones as well as linked molecular markers is useful for creating new disease-resistant and adaptable cultivars. Use of biochemical markers such as accumulation of XERO2 like dehydrin, alcohol dehydrogenase, Cu/Zn superoxide dismutase, stachyose, galactinol, succinic acid, aspartic acid and putrescine gives possibility to create and select cold-resistant strawberry and raspberry varieties. It is proven that by inactivation of aquaporins, introduction of osmotin or transcription regulators by genetic engineering, it is possible to develop drought and salt-resistant varieties. Interspecific hybridization using wild relatives of cultivated plants widens the possibilities to introduce valuable traits. In general, scientific knowledge obtained in recent years gives possibility to introduce complex and durable resistance and to develop resilient cultivars featuring sufficient plasticity under changing environment. Keywords Acclimation · Cold · Diseases · Drought · Fragaria · Rubus · Stress

R. Rugienius (B) · B. Frercks · I. Mažeikien˙e · N. Rasiukeviˇci¯ut˙e · D. Baniulis · V. Stanys Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry, Kaunas Str. 30, LT-54333 Babtai, Kaunas, Lithuania e-mail: [email protected] © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_9

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9.1 Introduction In the geological history of the earth, the climate has changed due to natural processes. Generally, known climate change periods caused ice advances and retreats, the demise of the dinosaurs, the Permo–Triassic mass extinction and the Paleocene– Eocene thermal maximum. The last one is described as natural global warming and occurred around 56 million years ago. The climate change observed in the last 200 years is characterized by the fact that the main cause for change is human activity. From the start of the Industrial Revolution, human race began to change the chemical composition of the atmosphere. The various gases emitted by transport, industry and agriculture create a barrier withholding the heat going from the surface of the Earth creating a greenhouse effect. The increase in greenhouse gases is caused by reckless actions of mankind: deforestation, urbanization, intensive agricultural development. In the last 150 years, the average temperature in the world has risen by almost 0.8 °C, in Europe at around 1 °C. According to the model carried out by the Intergovernmental Panel on Climate Change (IPCC) that incorporated data from ocean and atmospheric behaviour the average global temperatures will increase between 1.4 and 5.8 °C by the end of this century. Overcoming this threshold is more likely that changes will be irreversible and possibly catastrophic. Climate change affects all regions of the world; however, the impact is projected to be uneven in the different regions. In southern and central Europe are foreseen to be more frequent heat waves, forest fires and droughts. The rise in temperature and changes in the structure of precipitation are expected to exacerbate the problem of water scarcity in the southern and south-eastern regions. More and more frequent droughts, more frequent and more severe floods are expected throughout Europe. In northern Europe, rainfall is noticeably increasing, and winter floods can turn out to be regular. In the northern and some western European regions, climate change can have even positive effects for some time, especially in agriculture, as plant growth and plant productivity will increase. Agriculture contributes to the promotion of climate change and is itself affected. The report of the IPCC states that climate change in the Nordic countries will inevitably impact on the productivity of agricultural crops. The scenario of climate change indicates the probable increase in plant growing season, often repeated temperature fluctuations in winter season with unusually warm and cold spells. Temperature change induces an early loss of plant hardening and subsequent deaths during the cold period. Mid-winter warming can lead to early budding or bloom, resulting in frost damage when cold winter temperatures return. Yields will be negatively affected if the chilling requirement is not fully satisfied because the flower budding will be low. It will be more difficult to grow crops due to the increase in weeds, insects, other pests and diseases associated with warmer temperatures. Moreover, temperature increases have an impact on the persistence of the occurrence of bacteria, fungi and viruses. Plants suffer more than one biotic and abiotic stress during climate change. Rosaceae family plants are one of the most important plants in the garden. They

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are adversely affected by mild winters when the development of flower buds has shrunk due to insufficient cold weather (Palencia et al. 2009). Rosaceous berries are an economically important berry crop worldwide used for processing and fresh food market. Flooding, for example, can kill plants, reduce fruit yield, total leaf area and weight. Strawberries grown in excess water have lower sugar content. Temperature is one of the most important factors affecting strawberry plant nutrient uptake, flower induction, formation and fruit quality (Palencia et al. 2013). In Florida, at the beginning of the 2015–16 strawberry season in November, hotter-than-normal weather delayed flowering and fruit production on young plants. Study of Bethere et al. (2016) using Regional Climate Models (RCMs) aimed to estimate the timing of strawberry phenological processes for the years 1951–2099 clearly show that strawberry phenological processes can be expected to occur earlier in future, with a significant change in regional patterns. Differences between coastal and inland regions are expected to decrease over time. The authors predict that first fruits will appear two weeks earlier in future. The difference in timing between northern and southern regions is expected to decrease. The data show that in future, the latitude will be more important than the distance to the sea and expected larger increase in temperature over the body of the Baltic Sea than over the adjacent land areas. It was shown that the warmer conditions with a decrease in snow-covering days during winter did not have a positive effect on strawberry fruit yield at Easter in Turkey (Esitken et al. 2009). However, for the same crops, specific local agronomic adaption strategies were found for the different regions (Pathak and Stoddard 2018). Climate change can cause the appearance of new diseases and pests that are not specific to the region. The changing conditions will require new plants with more suitable physiological properties than existing plant varieties. It is necessary to identify the genetic factors that determine the expression and variety of adaptive properties, to create DNA and biochemical markers for plastic climate change for plant selection. Other possible adaptations include migration from growing area and changing crop-growing technology such as irrigation and fertilizer practices.

9.2 Challenges and Prospects of Strawberry and Raspberry Breeding 9.2.1 The Strawberry and Raspberry Pathogens Under Changing Climatic Conditions Plants are influenced by numerous environmental factors. In recent years, the research is focused on understanding plant responses to individual abiotic or biotic stresses (Rejeb et al. 2014). Biotic stress is defined as the damage caused to plants by other living organisms such as parasites and pathogens (Gimenez et al. 2018). Plant pathogen groups include a wide range of fungi, mycoplasma, bacteria, virus, nematode and

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parasitic plant species (Garrett et al. 2016). These groups are diverse and have different plant–host interactions with different mechanisms of response depending on climate change. Climate change is becoming a critical issue nowadays due to inconstant properties of the meteorological factors such as rainfall, humidity and temperature (Nazir et al. 2018). Changeable temperature as compared to the multi-annual average is shifting the agro-climatic zones. It enables pathogens to spread into new geographical areas, survive over the winter season in northern areas and increase the symptoms and susceptibility of the host to infection. The races of pathogen are changing at increasing temperature, thus reducing the durability of host resistance genes (Nazir et al. 2018). The temperature changes influence pathogen growth and development also. Pathogens favoured by changing climate include powdery mildews, Phytophthora species and bacterial pathogens. Colletotrichum species may also increase with changing host cultivation, and viruses can benefit from increasing vector populations. Additionally, climate change causes stress for plants which could make them more susceptible to pests and diseases (Elad and Pertot 2014; Ziska et al. 2017). The climate change is only one of the factors in which environment could move from disease-suppressive to disease-conducive or vice versa (Perkins et al. 2011). According to the classical pathogen triangle model (interactions between host, pathogen and environment), the plant diseases could act as indicators of climate change (Ahanger et al. 2013). However, the most significant consequences of climate warming are expected in the tropics due to narrow temperature growth range (Ghini et al. 2011). Fragaria and Rubus species are affected by many pathogens that are caused by several factors involving a complex interaction between abiotic factors (temperature, soil type and moisture) and biotic factors (one or more of several plant pathogens) that survive in soil, old crowns and roots such as Rhizoctonia spp., Pythium spp., Cylindrocarpon spp., Fusarium spp. and nematodes, particularly root-lesion and root-knot nematodes (Millner 2006). The microscopic biotic factors are crucial to the health of an ecosystem. They appear in higher numbers than other organisms and with the right conditions, multiply quickly (Coakley et al. 1999). These organisms mostly bacteria, fungi and nematodes can influence growth, development and metabolic process in plants. There are biologic vectors of non-cellular pathogens. Microscopic organisms are better able to adapt to unfavourable conditions and react less to environmental (abiotic) factors than horticultural plants do as they can exist in a resting or dormant stage. Therefore, the control of harmful pathogens is a continuous and important process in the production of berry in changing climatic conditions. Strawberry and raspberry fruits have a relative short ripening period. Various rots caused by pathogenic fungi reduce their disease-free period during harvest as well as post-harvest time. Due to fungal contaminations, the crop yield losses can range from 20 up to 40%. Fungal pathogens are mostly not specific only for strawberry and raspberry fruits they also affect other Rosaceous plants during pre- and post-harvest stages (Satin 1996; Wang and Xu 2007; Savary et al. 2012). The plant pathogen extent of damage is the result of the interaction between virulent pathogen, susceptible host and favourable environmental conditions. The

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disease development is influenced by temperature, rain, humidity, wind, fertilization, fungicide, host tolerance and phenology. The increasing temperatures become more suitable for diseases from warmer agro-climatic zones (Pathak et al. 2018). The temperature effect was observed for Botrytis cinerea growth and variability (Rasiukeviˇci¯ut˙e et al. 2017). Also, climate change influences the host, pathogen rate and modified pathogen dynamics. Pathogens are infecting the host at specific development stages like grey mould affects strawberry plants at the flowering time (Fillinger and Elad 2016; Nazir et al. 2018). Additionally, the CO2 concentration influences disease occurrence and severity like Colletotrichum gloeosporioides, and several other pathogens reproduce faster by rising CO2 level (Debela and Tola 2018).

9.2.1.1

Common Fungal Diseases of Strawberry and Raspberry

The main strawberry and raspberry diseases causing pathogens are Colletotrichum spp., Botrytis spp. and Phytophthora spp. The anthracnose causing Colletotrichum species are complex pathogens and infect the whole plant. Yield losses can reach up to 80% (Baroncelli et al. 2015; Feliziani and Romanazzi 2016). It was observed that germination, appressoria and conidia development of C. acutatum are influenced by temperature and humidity, and the optimal temperature for conidial germination is 23.0–27.7 °C (Leandro et al. 2003). For another Colletotrichum species (C. gloeosporioides), the maximum survival temperature was 40 °C (Ansari et al. 2018). However, the optimal temperature for Colletotrichum spp. is 30 °C and morphology changes of the pathogens show the capability to adapt to temperature increase (Pathak et al. 2018). The grey mould caused by Botrytis cinerea Pers: Fr. is an important fruit pathogen destroyer, which influences yield and post-harvest losses. The grey mould is mostly identified during initial infection (Korbin 2011; Savary et al. 2012; Fillinger and Elad 2016; Rasiukeviˇci¯ut˙e et al. 2018). Favourable conditions for B. cinerea are moderate temperature, high humidity and long leaf-wetness period (Fillinger and Elad 2016). Less flower infection by B. cinerea was observed at the temperature below 15 °C or above 25 °C (Carisse 2016). The low temperature was suggested as the prevention of fungal decay (Droby and Lichter 2007). Phytophthora spp. causes crown and root rots with significant yield and economical losses. The pathogen can spread undetected through propagation material, as it is capable to stay in the latent stage for several months. This pathogen is distributed throughout the world, and it is the most destructive disease of raspberries. Outbreaks of this disease were observed in Europe (UK) (Duncan et al. 1987), Germany (Seemüller et al. 1986), Norway (Stensvand et al. 1999), North America and Australia (Wilcox et al. 1993), where Phytophthora fragariae var. rubi was identified as the major causal agent. Recently, a new fungal genus, Neopestalotiopsis, has been identified in association with multiple diseases in strawberry (Nellist 2018), causing crown rot (Chamorro et al. 2016), leaf spot (Rodrigues et al. 2014) and fruit rot (Ayoubi and Soleimani 2016). The distribution and development of other strawberry and raspberry pathogens

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are determined by temperature and humidity. The main strawberry and raspberry fungal diseases are listed in Table 9.1. The soil-borne diseases Phytium spp. destroy the roots of strawberry, and distribution of this pathogen is restricted to cool climate zones (Maas 1998). Strawberry Verticillium spp. causing Verticillium wilt occurs throughout the moderate climate zones and is most destructive in irrigated regions. By contrast, in warm and humid soils, the infection of Verticillium spp. is reduced (Coakley et al. 1999). Moisture excess is favourable for Phytophthora, Pythium, Rhizoctonia solani and Sclerotium rolfsii soil-borne diseases (Mina and Sinha 2008; Sharma et al. 2010). Podosphaera aphanis causing powdery mildew infects the whole plant, leaves, fruit and flowers of strawberry. For powdery mildew, germination and conidia release high relative humidity is required (Maas 1998), while Fusarium spp. causing strawberry leaf and root infections is favoured by high temperatures. Fusarium oxysporum causes strawberry plant death within four weeks by increase of temperature (Fang et al. 2011). The drought has indirect positive influence for some pathogen development through indirect effects on host physiology (Ahanger et al. 2013). The climate change will affect host–pathogen interactions, such as increase of Table 9.1 The main strawberry and raspberry fungal disease Disease

Pathogen

Primarily infected part of a plant

Optimal temperature for disease development, °C

References

Verticillium wilt

Verticillium dahliae; Verticillium albo-atrum

Stem, root and leaves

12–30

Sjulin and Dale (1987), Maas (1998), Coakley et al. (1999)

Grey mould

Botrytis cinerea

Whole plant

15–25

Carisse (2016), Fillinger and Elad (2016), Rasiukeviˇci¯ut˙e et al. (2017)

Anthracnose

Colletotrichum spp. C. acutatum, C. gloeosporioides

Whole plant, mainly fruits

26.7–32.0

Leandro et al. (2003), Baroncelli et al. (2015), Feliziani and Romanazzi (2016)

Root and crown rot

Phytophthora cactorum

Fruit and crown

17–25

Maas (1998)

Red stele/red core root rot

Phytophthora fragariae

Crown and root

17–25

Sjulin and Dale (1987), Maas (1998)

Powderly mildew,

Podosphaera aphanis

Whole plant, leaves, fruit and flowers

25

Sjulin and Dale (1987), Maas (1998)

Fusarium wilt

Fusarium spp.

Leaf and root

25

Maas (1998)

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pathogen generations per year, better overwintering, differences in host susceptibility (Harvell et al. 2002).

9.2.1.2

Main Virus Diseases in Strawberry and Raspberry Crop

Fragaria and Rubus species are propagated vegetative therefore are prone to infection by viruses during development, propagation and fruit production stages. In the last three decades, there are numerous reviews about initial detection and symptoms of more than 30 viruses, virus-like diseases and phytoplasmas affecting Fragaria and Rubus spp. (Converse 1987; Mellor and Krczal 1987; Converse 1991; Gordon et al. 2006). In the last decade, there has been significant work towards the characterization and detection of graft-transmissible diseases, and today, the number of strawberry viruses has more than doubled compared to the number we knew of at the turn of the century (Martin and Tzanetakis 2006; Martin et al. 2013; Sharma et al. 2018). Viruses can infect the plants through mechanically wounded plant tissues or biological vectors such as insects or nematodes (Amil-Ruiz et al. 2011). Strawberry and raspberry viruses are transmitted by aphids, nematodes, whiteflies, mites and trips. The most important strawberry and raspberry viruses and their biotic vectors are listed in Table 9.2. There are four main aphid-borne viruses affecting strawberry worldwide— Strawberry Crinkle Rhabdovirus (SCV), Strawberry Mild Yellow Edge Potyvirus (SMYEV), Strawberry Mottle Caulimovirus (SMoV) and Strawberry Vein Banding Caulimovirus (SVBV) (Thompson and Jelkman 2003). For raspberry, main damaging viruses are Arabis Mosaic Nepovirus (ArMV), Raspberry Ringspot Nepovirus (RpRSV), Tobacco Ringspot Nepovirus (TRSV), Tomato Ringspot Nepovirus (ToRSV) transmitted by nematodes (Converse 1991).

9.2.2 Tolerance to Abiotic Factors Under Changing Climatic Conditions 9.2.2.1

Cold Hardiness Limits

The breeding programs are aimed to supply the market with new well-adapted and productive strawberry cultivars with high berry quality. The need for new varieties adapted to temperate climatic conditions is a major problem for strawberry production in the Northern regions. Strawberry suffers serious cold injury in winter with insufficient snow cover (Yao et al. 2009). Cold hardiness is one of the essential parameters of cultivar evaluation in the Northern countries and is crucial trait for strawberry and raspberry breeding (Nes 1997; Laugale and Bite 2009; Libek 2002; Rugienius et al. 2009; Hall and Kempler 2011).

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Table 9.2 The most important strawberry and raspberry viruses and their biotic vectors Biotic factors

Virus

References

Aphid

Strawberry Mild Yellow Edge (SMYEV) Strawberry Vein Banding (SVBV) Strawberry Crinkle (SCV) Strawberry Mottle (Smov) Strawberry Chlorotic Fleck (SCFV) Strawberry Pseudo-Mild Yellow Edge (SPMYEV) Strawberry Latent C (SLCV)

Martin and Converse (1982), Converse (1987), Tzanetakis (2010), Petrzik et al. (1998), Thompson and Jelkman (2003), Thompson et al. (2003), Tzanetakis et al. (2007), Yoshikawa et al. (1986), Yoshikawa and Inouye (1988), Ratti et al. (2006)

Nematode

Arabis Mosaic (Armv) Raspberry Ringspot (Rprsv) Strawberry Latent Ringspot (Slrsv) Tomato Black Ring (Tbrv) Tomato Ringspot (Torsv)

Martin et al. (2004b), Stankiene et al. (2012), Mazyad et al. (2014), EL-Morsy et al. (2017), Faggioli et al. (2002), Lee et al. (2015), Bargen et al. (2015), Weelink et al. (2000), Loudes et al. (1995)

Whiteflies

Beet Pseudo-Yellows Virus (Bpyv) Strawberry Pallidosis Associated Virus (Spav)

Tzanetakis et al. (2003, 2004, 2006), Martin and Tzanetakis (2013), Wintermantel et al. (2006), Ragab et al. (2009), Constable et al. (2010)

Black Raspberry Necrosis (Brnv) Raspberry Latent (Rplv) Raspberry Leaf Curl (Rplcv) Raspberry Leaf Mottle (Rlmv) Raspberry Vein Chlorosis (Rvcv) Rubus Yellow Net (Rynv)

Halgren et al. (2007), Quito-Avila et al. (2011), Stace-Smith (1987), Besse et al. (2010), McGavin and MacFarlane (2009), Tzanetakis et al. (2007), McGavin et al. (2011), Jones et al. (2002), Martin et al. (2013)

Mites

Raspberry Leaf Blotch (Rlbv)

McGavin et al. (2012)

Nematode

Arabis Mosaic (Armv) Cherry Leaf Roll (Clrv) Raspberry Ringspot (Rprsv) Strawberry Latent Ringspot (Slrsv) Tobacco Ringspot (Trsv) Tomato Black Ring (Tbrv) Tomato Ringspot (Torsv)

Jones and Wood (1978), Stace-Smith (1987), Tzanetakis et al. (2006), Medina et al. (2006), Matus et al. (2008), Stankiene et al. (2012), Scott et al. (2000), Zalloua et al. (1996), Rott et al. (1991, 1995)

Thrips

Impatiens Necrotic Spot (INSV) Strawberry Necrotic Shock (SNSV)

Pappu et al. (2009), Tzanetakis et al. (2004, 2009), Li and Yang (2011), Sharman et al. (2011)

Whiteflies

Beet Pseudo-Yellows (BYPV) Blackberry Yellow Vein-associated (Byvav)

Wisler et al. (1998), Tzanetakis and Martin (2004), Susaimuthu et al. (2007)

Fragaria spp.

Rubus spp. Aphid

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Ploidy might play a role in cold-adaptive processes in diploid and octoploid Fragaria species due to potentially increased cell size with ploidy level, differential expression of cold-induced genes and altered photosynthetic characteristics (Rohloff et al. 2012). However, a high degree of genome co-linearity between diploid and octoploid Fragaria sp. exists (Rousseau-Gueutin et al. 2008), and plant biological processes including metabolic regulation under low-temperature conditions are likely to be highly similar in both species. Compared with diploid genotypes, synthetic polyploids could acquire some desirable horticultural characters, such as adaptability, cold tolerance, disease resistance and other. Hybrid ‘YH15-10’ (2n = 12x = 84) obtained from the interspecific hybridization of F. × ananassa cv. ‘Yuhime’ (2n = 8x = 56) and the wild pentaploid strawberry accession ‘Heilongjiang No. 7’ (2n = 5x = 35) showed sufficient cold resistance under climatic conditions at the northeast of China (Lei et al. 2012). As this hybrid was involved in Fragaria hybridization, 63.3% of hybrids demonstrated higher cold resistance than that of ‘Allstar’ (Luo et al. 2017). Different ploidy level hybrids, including pentaploid (2n = 5x = 35), hexaploid (2n = 6x = 42), octaploid (2n = 8x = 56), enneaploid (2n = 9x = 63) and decaploid (2n = 10x = 70) developed by Luo et al. (2018) could present valuable resources for further strawberry cultivar improvement. Limits of cold hardiness represented by survival temperature range are useful parameter for cold resistance breeding. It allows screening and selection of hardy genotypes and elimination of susceptible ones. This is also important for selecting technological measures for strawberry growing in Northern climates. Crowns of strawberry have been found to be severely injured at −9 °C when unprotected (Galletta and Himmelrick 1990; Nestby et al. 2001; Warmund 1993), meanwhile the lethal temperature for acclimated plants is approximately −12 °C, with some variation by cultivar (Darrow 1966). Similar results were observed by Palonen and Linden (1999), wherein controlled freezing tests, strawberry crowns survived −8 °C without injury, suffered minor injury at −10 °C and severe injury at −12 °C. Raspberry roots are the most cold-susceptible part of the plant during wintering. Cold hardiness of Rubus sp. is highly dependent on genotype as well. The roots of the Canadian cultivars ‘Ottawa’ and ‘Muskoka’ and the Finnish cultivar ‘MaurinMakea’ are among the most cold-resistant and could tolerate almost 10 °C lower temperatures compared to the roots of the Scottish cultivar ‘Glen Ample’ (Palonen et al. 2016). Interspecific crosses among local more adapted species and more susceptible foreign species with high-quality berries give possibility to combine resistance to frost and the quality of berries (Dai et al. 2016). For strawberry, in field conditions, it is important to preserve leaves from cold injury during winter and maintain the photosynthetic activity of existing leaves which would extend the growing season of the crop. Exposure to −3 °C did not significantly reduce CO2 assimilation when compared to plants maintained at 10 °C day/5 °C night. However, leaves exposed to −5 °C for one night had a net CO2 assimilation rate that was 49% of the control. Leaves exposed to −5 °C or −7 °C did not show any recovery over a 28 days monitoring period. These results indicate that protected cultivation

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systems should be managed to maintain strawberry leaf temperatures above −5 °C (Maughan et al. 2015). Cold hardiness of raspberry floral bud depended from blackberry floricane pruning date (Strik 2018). Loss of raspberry bud base vascular tissue hardening capacity coincided with increases in bud water content, maximal water content and water potential in the beginning of April, after diurnal mean temperature had risen above 0 °C (Keinänen et al. 2006). In temperate regions, chilling temperatures during flower development can compromise fruit production, but their adverse effects vary depending on the differing susceptibilities of each developmental stage. Time to reach anthesis from early bud stages was 17–18 days. During this period, four critical periods were detected vulnerable to low temperatures—at stage 8, 9a–9b, 11a and 11b. This was revealed as a significant decrease (up to 82%) in the number of pollen grains and by a four-fold increase of the percentage of non-viable pollen. Female reproductive organs were only affected by chilling 3–6 days before anthesis (stages 11a–11b) when there was up to a 2.4- and 4.8-fold increase in the percentage of aborted ovules or of immature stigmas, respectively (Ariza et al. 2015).

9.2.2.2

Screening for Cold Tolerance in Controlled Conditions

As compared to the variation of wintering conditions in field trials, application of climatic chambers to model the temperature and illumination parameters provides a convenient alternative to study properties of cold acclimation in plants. In addition, cold resistance of plants and tissues could be investigated under controlled conditions in vitro. Electrical conductivity methods are used to monitor ion release from injured tissues. Davik et al. (2013) evaluated low-temperature tolerance of 17 Fragaria vesca, 2 F. nilgerrensis, 2 F. nubicola and 1 F. pentaphylla genotypes. Estimates of temperatures where 50% of the plants survived (LT50) ranged from −4.7 to −12.0 °C among the genotypes. Among the F. vesca genotypes, the LT50 varied from −7.7 to −12.0 °C. Three F. vesca ssp. bracteata genotypes (FDP821, NCGR424 and NCGR502) were most tolerant, while F. vesca ssp. Californica genotype (FDP817) was the least tolerant (LT50-7.7C) (Davik et al. 2013). Exponential extrapolated killing curves indicated: 50% survival of F. ananassa ‘Jonsok’ being more cold-tolerant at approximately −8.3 °C and for cv ‘Frida’ being less cold tolerant at approximately −5.5 °C. The acclimation treatment is crucial for adaptive cold tolerance, and an increase of 5–8 °C in cold tolerance as measured by plant survival was achieved by cold acclimation compared with untreated plants (Koehler et al. 2012). Importance of cold acclimation was confirmed in other studies (Palonen and Buszard 1997; Rugienius et al. 2016a, b). CT50 (critical temperature corresponding to ion leakage equivalent to 50% lethality) for F. vesca, F. virginiana F. moschata and F. × ananassa cultivars ‘Venta’, ‘Melody’ and ‘Dange’ before acclimation was −7.7 °C in average with little variation among the genotypes. However, after 56 days of cold acclimation,

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the average CT50 decreased to −9.0 °C, and differences among genotypes varied from −8.3 °C (cv ‘Elsanta’) to −9.8 °C (cv ‘Melody’) (Rugienius et al. 2016a, b). Discrimination between 11 raspberry cultivars according to cold hardiness in vitro was satisfactory only after acclimation treatment 2 weeks at +15 °C, 2 weeks at +2 °C, 24 h at −2 °C and 3 days at +2 °C on culture medium without growth regulators (Palonen and Buszard 1998). Differences between the various authors may be associated with different freezing techniques. It could be concluded that strawberry as well as raspberry differentiation according to cold hardiness when frozen in vitro reveal itself only sufficient cold acclimation was applied. Basing on in vitro cold-hardiness evaluation, screening technology of cold-hardy strawberry seedling in early stages was developed (Rugienius and Stanys 2001). The data obtained in the study make perquisites for identification of genes responsible for efficient cold acclimation and hardiness.

9.2.2.3

Effect of Heat Stress

The direct consequence of climate change is the elevated environmental temperature that could provoke heat stress in plants. This stress is common in the countries of Southern Europe, but prolonged periods of heat lasting up to several weeks are rather a typical in the temperate regions. Prolonged heat stress negatively affects fruit set and fruit development in strawberry, but the effect of temporary severe heat stress at different stages of flower development is poorly understood. Ledesma and Kawabata (2016) established that strawberry is negatively affected by acute and severe heat stress, but the response varies by floral development stage affected and by cultivar. After 4 h treatment at 42 °C, two heat-sensitive floral development stages were observed cultivars ‘Nyoho’ and ‘Toyonoka’, at 12 and 9 days before anthesis, respectively, and at anthesis for both cultivars. Heat stress makes changes of the content of biochemical compounds in berries, and chlorophyll and anthocyanin contents provide a valuable indicator of the status of plant physiology. Quercetin-3-glucoside levels increased the most in raspberry with elevated temperatures (Bradish et al. 2012). Contrary, in a colder and rainy summer, the content of anthocyanins and the unsaturation level of fatty acids were significantly higher than in a warmer and drier summer (Jaakkola et al. 2012). The higher greenhouse temperature reduced the ratio of cyanidine 3-O-glucoside and increased the proportion of pelargonidine 3-O-glucoside in berries of wild strawberry (Rugienius et al. 2016b).

9.2.2.4

Stress Caused By Increased Salinity

Elevated temperature leads to an increase of evaporation and subsequently to increased salinity. Response to salinity stress also varies among strawberry genotypes. Strawberry cultivars ‘Kurdistan’ and ‘Queen Elisa’ subjected to 40 and 80 mM NaCl treatment for 20, 40 and 60 days showed reduced dry weight of plant organs in

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response to NaCl for both cultivars, except crown weight in cv. ‘Kurdistan’ (Ghaderi et al. 2018). Root to shoot ratio increased due to a greater reduction in above ground biomass under salinity. Strawberry cultivars tended to decrease their stomatal conductance, relative water content (RWC), proline, soluble carbohydrates and proteins during the different evaluation periods. The ‘Queen Elisa’ cv. showed lower tolerance index (45.88%) compared with cv. ‘Kurdistan’ (67.97%). Higher salinity resistance of cv. Kurdistan is probably associated with its ability to maintain higher RWC and higher activity of antioxidant enzymes (Ghaderi et al. 2018). Similar results were obtained in raspberry in vitro: salinity stress significantly decreased morpho-physiological and biochemical characteristics such as RWC, membrane stability index (MSI) and total protein content in regenerated explants and significantly increased the total soluble sugar, proline contents, peroxidase and superoxide dismutase activity in compared to the control (Ghadakchiasl et al. 2017). The levels of chlorophyll and anthocyanin pigments decreased in F. chiloensis in response to increasing salt stress (30 and 60 mM NaCl), and a high positive correlation between the pigment contents was observed (Garriga et al. 2014). The effect of mild stress of plant physiology has rarely been investigated. Galli et al. (2015) showed that mild salt stress did not affect yield but improved strawberry fruit quality. The mild salt stress treatment increased vegetative growth (24%), higher photosynthetic effectiveness and increased activity of phenoloxidase (22%) and polyphenoloxidase (33%), as well as the accumulation of sucrose (5%) and anthocyanins (60%) in the fruit, compared to non-stressed plants. Higher level of mild salt stress increased root growth (30%), the activity of phenylalanine ammonia lyase (68%), the accumulation of total phenolic compounds (14%) and total antioxidant activity (13%) in the fruit, compared to non-stressed plants (Galli et al. 2015). Hydrogen sulphide (H2 S) has been recently found to act as a potent priming agent, helping to control salt stress in plants. Hydroponic pretreatment of strawberry cv. ‘Camarosa’ roots with a H2 S donor, sodium hydrosulphide (NaHS; 100 μM for 48 h) was performed with subsequent exposure to 100 mM NaCl or 10% (w/v) PEG-6000 for 7 days (Christou et al. 2013). Hydrogen sulphide pretreatment of roots resulted in increased leaf chlorophyll fluorescence, stomatal conductance and leaf relative water content as well as lower lipid peroxidation levels in comparison with plants directly subjected to salt and non-ionic osmotic stress, thus suggesting a systemic mitigating effect of H2 S pretreatment to cellular damage derived from abiotic stress factors. Gene expression analysis suggested that H2 S plays a pivotal role in the coordinated regulation of multiple transcriptional pathways. In addition, the ameliorative effect of H2 S was more pronounced in strawberry plants subjected to both stress conditions immediately after NaHS root pretreatment, rather than in plants subjected to stress conditions three days after root pretreatment (Christou et al. 2013). Another metabolite sodium nitroprusside treatments mitigated the impacts of salinity on morphological and physiological characteristics in raspberry shoot-tip explants by increasing the accumulation of proline content, total protein content and total soluble sugar in line with increasing antioxidant enzyme activity under salinity conditions (Ghadakchiasl et al. 2017).

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Water-Deficit Stress

Water deficit is one of the main factors related to climate change that leads to drought and salinity stress in plants. Due to increased periods without precipitation, water deficit would become a problem in strawberry production that is particularly prominent in the southern regions. It is important to develop technologies saving the water and to develop cultivars more tolerant to water-deficit stress. The effect of deficit irrigation (50 ml/day) on three strawberry pre-commercial cultivars (253/29, 279/4 and 279/5) was investigated and compared to plants kept at or near field capacity (200 ml/day) (Giné-Bordonaba and Terry 2016). For cultivar 253/29, the deficit irrigation applied at green stage of fruit development resulted in a considerable reduction in berry size (1.7-fold). Changes in the major sugars and organic acids of strawberry leaves and fruit were cultivar and organ dependent and were associated to an osmotic adjustment strategy within the plant to counteract the effects of drought (Giné-Bordonaba and Terry 2016). Strawberry cv. ‘Elsanta’ fruit from water deficittreated plants had higher levels of abscisic acid (ABA) and accumulated higher concentration of some taste-related (viz. monosaccharides and sugar/acid ratios) and health-related compounds (viz. antioxidant capacity and total phenolics) (Terry et al. 2007). It has been established that higher temperatures and water deficit applied from the second week of cropping season accelerate and sustain berry ripening and increase the yield of F. vesca berries. During the first week of treatment, water stress has been shown to increase the amount of anthocyanin in berries that subsequently decrease at the second part of the experiment when morphological signs of water deficit have emerged (Rugienius et al. 2016b). Zhang et al. (2012) examined the effect of physiological integration on survival, growth and stress indicators such as osmolytes, reactive oxygen intermediates and antioxidant enzymes in a clonal plant, Fragaria orientalis, growing in homogenous and heterogeneous environments differing in patch contrast of water availability. Drought stress markedly reduced the survival and growth of the severed ramets of F. orientalis, especially in high contrast treatments. Support from a ramet growing in benign patch considerably reduced drought stress and enhanced growth of ramets in dry patches. The larger the contrast between water availability, the larger the amount of support the depending ramet received from the supporting one. This support strongly affected the growth of the supporting ramet but not to an extent to cause an increase in stress indicators. The net benefit of physiological integration depends on the environment and integration between ramets of F. orientalis could be advantageous only in heterogeneous conditions with high contrast (Zhang et al. 2012). The connection between two established rosettes prevented death by drought and shade, even when neither rosette could have survived singly. Results suggest that physiological integration of connected rosettes may increase total growth of clones of F. chiloensis through sharing of resources among ramets, especially when resource availability is changeable or patchy (Alpert and Mooney 1986). There was a greater decrease in photosynthetic rates (Pn) and stomatal conductance (Gs) in the disconnected mother ramets than the connected mother ramets upon

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exposure to water stress, indicating that water stress in mother ramets was alleviated by water translocation from the well-watered daughter ramets. It was indicated that: (1) the flux of water translocation between the connected ramets is determined by a water potential gradient; (2) water translocation between connected ramets helps to keep sensitivity of Gs to ABA and water potential in drought-affected ramets, thereby benefit to effectively maintain the homeostasis of leaf water status and (3) the improvements in Pn in water-stressed ramets due to water translocation from well-watered ramets suggest the advantages of physiological integration in clonal plants in environments with heterogeneous water distribution (Mao et al. 2009).

9.2.2.6

Measures to Reduce Drought and Salinity Stress

It was found that ABA can induce a significant depression of mesophyll conductance in raspberry. Proportional increases of mesophyll versus stomatal conductance should improve leaf instantaneous water-use efficiency (rate of CO2 assimilation divided by transpiration rate). If so, improvements in mesophyll conductance may help to improve productivity as well as water-use efficiency of berry crops (Qiu et al. 2018). Immersing the roots of growing strawberry plants in aqueous solutions of 8-hydroxyquinoline sulphate closes the stomata, reduces water loss and increases the time before complete wilting under drought conditions. Under such drought conditions, plant survival and vigour are increased. Prolonged closing of the stomata seems to be the principal mode of action of the chemical (Stoddard and Miller 1962) A study was carried out to assess the protective effects of exogenously applied nitric oxide (NO) in the form of its donor sodium nitroprusside to strawberry seedlings (Fragaria × ananassa cv. Camarosa) grown under iron deficiency, salinity stress or combination of both. Overall, exogenously applied NO was more effective in mitigating the stress-induced adverse effects on the strawberry plants exposed to single stress than those due to the combination of both stresses (Kaya et al. 2019). Proper irrigation conditions help prevent stress associated with water deficit and salinity. Different experiments were conducted in aim to find the best irrigation scheme. The results show the need to adjust crop coefficient (Kc) to cultivar, local weather conditions and growing practices for an appropriate irrigation scheduling. The use of irrigation scheduling based on a time-based Kc function of the days after planting (DAP) or growing degree-days (GDD), could result in significant savings in water and fertilizer without negatively affecting crop yield. The use of GDD may improve the estimate by removing season variations in crop development. It allows farmers to calculate crop water requirements and to set irrigation schedules. It could also significantly reduce environmental impact in terms of diffuse pollution and improve water productivity in strawberry (Lozano et al. 2016). In study of Létourneau et al. (2015), it was revealed that maintaining the soil matric potential lower than −9 kPa could induce stressing conditions for the plants. The best yield and water-use efficiency (WUE) were obtained with an irrigation threshold (IT) of − 8 kPa and suggested that WUE could be further improved by implementing high-frequency irrigation. Considering the results from all sites, an IT

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of −10 kPa appears to be adequate as a starting point for further optimizing irrigation under most field conditions (Létourneau et al. 2015). The consumption of water and other substances by strawberry plants depends on the substrate they grew. It was demonstrated that certain growing media mixes such as peat perlite or the 100% coconut coir possess the needed chemical and hydraulic properties for plant growth (highest water content values at saturation and after free drainage), and the 100% coir could be used as a promising substrate material for open-field production of strawberries (Wang et al. 2016). According to Palencia et al. (2016), plant productivity and berry composition were mainly affected by differences between cultivars (‘Camarosa’, ‘Candonga’ and ‘Festival’) rather than by the nature of the different growing substrates (viz. Agro-textile, coir fibre, perlite and rock wool). However, regardless of the cultivar, plants grown on agro-textile-type substrate produced significantly more fruit (1018.2 g plant−1 ) than those grown in other substrates (average 892.3 g plant − 1). In addition, accompanying greater fruit production, fruit from plants grown on agro-textile generally had the lowest concentrations of the main strawberry anthocyanins perlargonidin-3glucoside (0.74-fold) and pelargonidin derivative 1 (0.85-fold). It was demonstrated that a number of different substrates with different physico-chemical characteristics may be employed during soilless cultivation of strawberry fruit without detrimentally affecting final fruit quality (Palencia et al. 2016).

9.2.2.7

Breeding for Drought Resistance

The first step in breeding for improved water-use efficiency is to determine the extent of adaptation to limited water availability already existing between cultivars. Ten cultivars were compared when irrigated to match 100% (control), or less than 70% (water deficit), of evapotranspiration (Grant et al. 2010). Transpiration rate, numbers and area of new leaves, dry mass of leaves and roots and leaf water potential were significantly reduced under the water-deficit treatment. Root to shoot dry mass ratio was significantly increased. WUE, measured as marketable yield per litre water transpired, increased under water deficit. There were significant cultivar differences for all these variables, and for photosynthetic rate and for transpiration efficiency (TE), measured as biomass per litre water transpired. Total transpiration was significantly correlated with final leaf area and total biomass. Since variation between cultivars in plant canopy and growth and transpiration efficiency was not strongly influenced by environment, genotypic variation in these variables could be utilized in a breeding program for improved drought tolerance in strawberry. On the other hand, a significant interaction of cultivar and irrigation was found for stomatal conductance, midday osmotic potential at full turgor, marketable yield and berry mass, suggesting that mechanisms of response to water deficits are not uniform across cultivars (Grant et al. 2010). Results obtained by Martínez-Ferri et al. (2016) showed that water consumption differs substantially among seven treated short-day cultivars, and these differences were associated with differences in the biomass partitioning into the harvested

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product (i.e. harvest index HI) and in the transpiration efficiency of the standing biomass (TEv) closely related to instantaneous water-use efficiency (A/T). Cultivars were segregated based on the relationship between both parameters, which integrate the differences among cultivars at the physiological (chlorophyll fluorescence, gas exchange, SPAD index, etc.) and at the growth response (fruit production, patterns of carbon allocation, LMA) levels.

9.2.2.8

Flooding

During climate change, not only water deficit but flooding can occur in some places as well. The formation of aerenchyma in adventitious roots is common to both flooding and nutrient deficiency and reduces the energy required for growth and maintenance. In flooded plants, aerenchyma is also crucial for enhancing gas exchange in this lowoxygen environment. When flooding was combined with nutrient uptake studies, it was found that the adventitious roots had higher nutrient uptake ability compared with other root types. This could mean that nutrient-efficient lines, depending on surface adventitious roots, may also have improved flood tolerance (Steffens and Rasmussen 2016).

9.2.2.9

Elevated CO2 Level

One of the factors under climate change that may affect strawberry growth is an elevated level of CO2 . Rising atmospheric CO2 is anticipated to stimulate photosynthesis of C3 plants, possibly increasing growth and yield. However, acclimation to elevated levels of CO2 often occurs, meaning loss of RuBisCo protein and activity, lower chlorophyll (Chl) content, lower Chl a/b ratio, accumulation of carbohydrates (van Oosten and Besford 1996). Quantitative predictions of effects of elevated CO2 on photosynthesis remain uncertain for at least two reasons: variation in the temperature dependence of the short-term response of photosynthesis to elevated CO2 and our current inability to predict either the occurrence or the magnitude of photosynthetic acclimation to elevated CO2 (Bunce 2001). Strawberry plants were grown in field plots at the current ambient CO2 , and at ambient + 300 and ambient + 600 μmol mol−1 CO2 . Stimulation of photosynthetic CO2 assimilation by short-term increases in CO2 increased strongly with measurement temperature. Acclimation of photosynthesis to growth at elevated CO2 was evident from early spring through summer, including the fruiting period in early summer, with lower rates under standard measurement conditions in plants grown at elevated CO2 . The degree of acclimation increased with growth CO2 . However, there were no significant differences between CO2 treatments in total nitrogen per leaf area, and photosynthetic acclimation was reversed one day after switching the CO2 treatments. Tests showed that acclimation did not result from a limitation of photosynthesis by triose phosphate utilization rate at elevated CO2 . Photosynthetic acclimation was not evident during dry periods in

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midsummer when the elevated CO2 treatments conserved soil water and photosynthesis declined more at ambient than at elevated CO2 . Acclimation was also not evident during the fall, when plants were vegetative, despite wet conditions. The data do not support the hypothesis that source–sink balance controls the seasonal occurrence of photosynthetic acclimation to elevated CO2 in this species (Bunce 2001). Plant cultivation plays an important part in deep space manned mission, and the techniques could be used in ground facility agriculture. Two strawberry cultivars ‘Benihoppe’ and ‘Frandy’ were cultivated under high CO2 concentration in a closed ecosystem to establish key techniques for strawberry planting for space life support. It was shown that the two strawberry cultivars grew well during the 105-day-closed experiment, providing 37.4 g fresh berries every day for the crew in the closed system. The yield, harvest index and sugar content of ‘Frandy’ were comparatively higher than those of ‘Benihoppe’, among which the daily dietary allowance of trace elements and amino acids (18 kinds) in the fruits stood out (Yu et al. 2015).

9.2.2.10

Effect of Photoperiod and Growth Regime

Day-neutral cultivars such as ‘Seascape’ are adaptable to a range of photoperiods, including short days that would save considerable energy for crop lighting without reductions in productivity or yield. It was observed that under coolest day/night temperature regime, 16/8 °C, under short 10 h photoperiod tended to produce smaller numbers of larger fruit, however, fruits were more tasty, and the highest scores of sensory attributes related to sweetness, texture, aftertaste as well as physicochemical quality attributes such as Brix, pH and sugar/acid ratio comparing to 16/8 and 20/12 °C temperatures. The intermediate temperature regime (18/10 °C) produced the highest total fresh mass of berries over an entire production cycle. The yield parameters fruit number and size oscillated over the course of a production cycle, with a gradual decline in fruit size under all three temperature regimes. Brix and titratable acidity both decreased over time for all three temperature treatments, but the sugar/acid ratio remained the highest for the cool temperature regime over the entire production period. Periodic rejuvenation or replacement of strawberry propagules may be needed to maintain both quality and quantity of strawberry yield in space (Massa et al. 2015).

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9.3 Prerequisites for Breeding of Climate Smart Strawberry and Raspberry 9.3.1 The Major Pathogen Resistance Mechanisms of the Rosaceous Plants 9.3.1.1

Structural and Mechanical Protection Against Fungal and Bacterial Pathogens

Plants do not have mobile defender cells and a somatic adaptive immune system. Instead, they rely on the innate immunity of each cell, and systemic signals produced and dispersed from infection sites (Chisholm et al. 2006). To fight biotic stress, plants have developed sophisticated defence mechanisms (Gimenez et al. 2018). Biotic stress defence mechanisms in strawberry were described by Amil-Ruiz et al. (2011) but remains marginally understood (Chen et al. 2016). Biotic stress defence mechanisms in raspberry are less investigated. General disease resistance mechanisms in plants are reviewed by Andersen et al. (2018). The resistance of plants is divided into three interlinked stages: pathogen detection, signal transduction and defence response. In first stage, plants must recognize pathogens. The pathogen-associated molecular patterns (PAMPs) are detected by transmembrane pattern-recognition receptors (PRRs) within cell membrane, while damage-associated molecular patterns (DAMPs) are detected by wall-associated kinases (WAKs). Receptors with nucleotide-binding domains and leucine-rich repeats (NLRs), also known as R genes, detect effectors of pathogens Andersen et al. (2018). Pathogenic elicitors produced by bacteria, fungi, insects, nematodes or viruses induce plant receptors to initiate signalling cascades. The structural or mechanical barrier related to changes of enzymatic and non-enzymatic processes occurs in different parts (cell wall, cuticula, trichomes, leaf veins) of plants (Wei and Shirsat 2006; Bustamante et al. 2009; Youssef et al. 2009; Deepak et al. 2010; Mercado et al. 2015). This structural or mechanical protection against fungal and bacterial pathogens (Botrytis cinerea, Colletotrichum fragariae, Colletotrichum acutatum, Tetranychus urticae, Xanthomonas fragariae) is effective in some genotypes of Fragaria spp. (Steinite and Levinsh 2003; Salazar et al. 2007; Osorio et al. 2008, 2011; Guidarelli et al. 2011). A particularly cell wall is a notable factor for strawberry resistance or susceptibility (Amil-Ruiz et al. 2011). During fruit ripening, some physiological changes occur including cell wall expansion, fruit softening, changes in pH and increase in soluble sugars and the susceptibility of fruits to pests and diseases increase (Miles and Schilder 2013). The plant’s ability to synthesize a broad range of secondary metabolites (flavonoids, flavanols, triterpenes, phenolics) is pre-form biochemical barriers and to protect strawberry and raspberry from fungal pathogens and pests (Terry et al. 2004; Hukkanen et al. 2007; Hanhineva et al. 2009; Lee 2010; Slatnar et al. 2016). Strawberry cultivars with a higher level of proanthocyanidins are more resistant to

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pathogens (Hébert et al. 2002). As the fruit cell wall degrades, pathogens can release cell wall degrading enzymes to speed up the process and to overcome plant defence mechanism. This has been reported in cultivated strawberry, where fruit ripening and cell wall changes resulted in higher susceptibility to Colletotrichum acutatum (Guidarelli et al. 2011). However, strawberry cultivars differ in fruit ripening period and softening range. Thus, the susceptibility to pathogens varies also (Chandler et al. 2000).

9.3.1.2

Plant Disease Resistance and Pathogen-Related Proteins

The second level of the plant immune system is plant disease resistance (R) proteins. As primary plant defence response is suppressed through pathogens by expression of effector proteins (Jones and Dangl 2006), R proteins become active and deliver secondary defence response, which suppresses pathogen effects (AmilRuiz et al. 2011). R proteins were identified in strawberry (Zamora et al. 2004, 2008; Salazar et al. 2007) and raspberry (Dossett and Finn 2010; Martin and Tzanetakis 2013). McHale et al. (2006) founded seven distinct families of resistance gene analogues (closely related to R genes from other plants species) in strawberries genomic DNA. However, disease resistance based on a single race-specific resistance (R) gene is not durable in many crop species, as members of the pathogen population emerge that avoid recognition by the plant immune system, requiring the introduction of new resistance traits (Quirino and Bent 2003). Pathogen-related (PR) proteins expression in plants is regulated by mitogenactivated protein kinases (MAPKs), G-proteins, ubiquitin, calcium, hormones, transcription factors (TFs) and epigenetic modifications. They are causing plant defence responses (hypersensitive response (HR), production of reactive oxygen species (ROS), cell wall modification) to prevent further infection (Andersen et al. 2018). HR is planned cell death in the area surrounding an infection. It restrains pathogen from spreading and is an effective response against biotrophs, requiring living tissues of plants. Plant pathogens have host-specific toxin (HST ) genes or avirulence (Avr) genes. If Avr genes are involved, they cause HR. The necrotrophic or hemibiotrophic fungi with HST genes can distinguish protein effectors which interact with host toxin-sensitivity gene to initiate disease (Stukenbrock and McDonald 2009; Ricci et al. 2016). Oxidative burst is defence to the pathogens, characterized by reactive oxygen species response (ROS). ROS activates defence genes, encodes enzymes involved in the synthesis of phytoalexins and is associated with directly defence responses of plants causing the toxicity to pathogens (Ricci et al. 2016). The presence of proteins response to pathogen and pests in strawberries is maintained by van Loon et al. (2006), Khan et al. (2003), Shi et al. (2006), Khan and Shih (2004), Guidarelli et al. (2011).

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Plant Pathogen Detection Regulation Through Hormones

Another level of pathogen detection regulation is different hormones. Salicylic acid (SA), jasmonic acid (JA) and ethylene (ET) are well known as plant response regulators of biotic stresses (Glazebrook 2005). Abscisic acid (ABA), auxin, cytokinin (CK), gibberellic acid (GA), brassinosteroids (BR) and peptide hormones are also part of the hormonal arsenal used by strawberry and raspberry in defence signalling pathways (Bari and Jones 2009; Slatnar et al. 2016). SA plays a central role in local and systemic resistance responses to biotrophic and hemibiotrophic pathogens (Vlot et al. 2009), whereas ET- and JA-induced responses are initiated by necrotrophic pathogens and herbivorous insects (Andersen et al. 2018).

9.3.1.4

Resistance of Rubus Spp. and Ribes Spp. Determined by Genes

The spread of disease is limited by the plant ability to identify the pathogen (Thakur and Sohal 2013). The investigations for resistance to Colletotrichum gloeosporioides in strawberry revealed that clonal propagation shows higher genetic control than the clonal testing (Osorio et al. 2014). The resistance to C. gloeosporioides in strawberry is race-non-specific and quantitative (MacKenzie et al. 2006). The avirulent isolate of C. fragarie provided in strawberry cv.’Pájaro’ against a virulent isolate of C. acutatum activated plant defence response (Salazar et al. 2007). Several reports focused on gene discovery related to C. acutatum defence mechanism (CasadoDíaz et al. 2006; Guidarelli et al. 2011). Lerceteau-Köhler et al. (2005) identified two sequences characterized amplified regions (SCAR) markers for C. acutatum resistance Rca2 gene in strawberry. According to these two SCAR markers, it was possible to identify 81.4% resistant and 62.8% susceptible genotypes. Strawberry resistance to B. cinerea is mainly classified based on visual susceptibility (Bestfleisch et al. 2015). The resistance of strawberry to B. cinerea is quantitative and affected by the environmental factors, plant and fruit firmness. However, grey mould rapidly develops resistance to fungicides possible due to the high genetic diversity of this pathogen (Rasiukeviˇci¯ut˙e et al. 2018). Phytophthora spp. is a destructive hemibiotrophic pathogen. Strawberries are damaged by P. cactorum and P. fragariae species (Nellist 2018). The resistance of strawberry to Phytophthora cactorum is quantitative. Resistance to Phytophthora cactorum 1 locus (RPc-1) was identified comprising 69 potential plant disease resistance genes (Davik et al. 2015a, b). Quantitative trait locus (QTL) for Fragaria × ananassa Resistance to Phytophthora cactorum 2 (FaRPc2) was identified with indication to multiple sources of resistance to P. cactorum in Fragaria (Mangandi et al. 2017). The resistance gene analogues (RGA) were expressing differentially in resistant strawberry genotype ‘Bukammen’ as compared to susceptible FDP821 after infection with P. cactorum (Chen et al. 2016). The resistance to Phytophthora fragariae is based on a gene-for-gene model (Korbin 2011). Primary five resistance genes (R1-R5) interacting with five pathogen avirulence factors (Avr1–Avr5) were

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represented (van de Weg 1997a). For strawberry, resistance to race 2 single dominant gene (Rpf2) was identified (van de Weg 1997b). Random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) markers linked to different Phytophthora fragariae races (Rpf1, Rpf2, Rpf3 and Rpf6) (Haymes et al. 1997, 1998) and SCAR markers Rpf1, Rpf2, Rpf3 and Rpf4 (Rugienius et al. 2006; Sasnauskas et al. 2007) have been developed. Resistance to root rot in red raspberries caused by Phytophthora fragariae var. Rubi is neither monogenic nor extensively quantitative according to observed segregation in red raspberry population. Therefore, the dominant two-gene model was suggested (Pattison et al. 2007). Tolerance of modern cultivars to single virus infections together with much of the cultivation now has done on an annual system have minimized the effect of viruses in the crop. Modern berry plants cultivation has minimized the impact of nematodeborne viruses, but the reduced use of methyl bromide and other soil fumigants may lead to the re-emergence of this group of viruses in future (Tzanetakis 2010).

9.3.1.5

Genetic Markers for Plant Resistance to Pest and Diseases

Several genetic linkage maps are available for raspberry (Pattison and Weber 2003; Graham et al. 2004, Gordon et al. 2006; Sargent et al. 2007; Paterson et al. 2013; Bushakra et al. 2015) which have largely been constructed to identify markers for specific pest and disease resistance. Development of markers for other viral resistance genes was carried out for raspberry leaf spot and raspberry vein chlorosis using the ‘Glen Moy’ × ‘Latham’ cross of Graham et al. (2004). Aphid resistance has been very effective at controlling the aphid transmitted viruses in red raspberry (Jennings 1988; Daubeny and Anderson 1993) but had not been utilized in black raspberry. Investigations into aphid resistance in R. occidentalis germplasm collected from throughout its natural range, the eastern USA and Canada have resulted in the identification of three populations that exhibit resistance to the large raspberry aphid, Amphorophora agathonica (Dossett and Finn 2010). Based on genetic analysis, there are two distinct sources of resistance to this aphid in R. occidentalis (Martin et al. 2013). A single dominant gene for resistance to A. agathonica has been quite effective in North America. Four biotypes of A. idaei have been characterized in Europe, and several genes provide resistance to all four biotypes. The presence of multiple biotypes of the vector aphid will make breeding for resistance more challenging. Aphid resistance is the method of choice for controlling the raspberry mosaic complex. There is resistance to the vector of RpLCV, but very little work has been done with this resistance, and little is known about its inheritance. RVCV immunity is reported but not used so far to develop resistant commercial cultivars. RBDV resistance is the best-studied example of virus resistance in Rubus and is thought to be controlled by a single dominant gene. Several isolates of RBDV have been sequenced (MacLeod et al. 2004), and genetic transformations have been undertaken to produce RBDV-resistant plants (MacLeod et al. 2004; Martin et al. 2004a). However, the resistance gene appears to be linked to negative horticultural

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traits since very few selections from a resistant × susceptible cross have RBDV resistance (Martin et al. 2013). The use of biotechnology to engineer resistance to RBDV is being studied in two laboratories (Jones et al. 1998; Martin and Mathews 2001). ‘Meeker’ raspberry transformed with sequences of the RBDV coat protein, mutated movement protein and non-translatable RNA has been produced. Kennedy et al. (2013) find out that wild accessions of Fragaria × virginiana can be used for successful introgression of powdery mildew resistance into strawberry. Natural sources of strawberry resistance to diseases have been reported among wild species (Harland and King 1957; Gooding et al. 1981; Maas 1998) and also in some varieties of cultivated F. × ananassa (Nelson et al. 1996; Bell et al. 1997; Mori et al. 2005; Particka and Hancock 2005; Zebrowska et al. 2006; Masny and Zurawicz 2009). There are various ways of controlling viral diseases: the use of disease-free planting material but is ineffective for viral diseases transmitted by vectors; adopting cultural practices that minimize epidemics; classical cross-protection, in which a mild strain of the virus is used to infect the crop; use of disease-resistant planting material; engineered cross-protection. This involves integration of pathogen-derived or virus-targeted sequences into DNA of potential host plants and conveys resistance to the virus from which the sequences are derived.

9.3.2 Mechanisms of Cold and Drought Hardiness 9.3.2.1

Signalling Events Triggered by a Cold Exposure

Admittedly low temperatures trigger plasma membrane rigidification which leads, presumably via COLD1-like protein, to the opening of Ca2C channels. An immediate increase in cytosolic calcium (Ca2t) is one of the major signalling events triggered by cold exposure (Dodd et al. 2006). The higher calcium level activates CRLK1/2 (calcium/calmodulin-regulated receptor-like kinase (Wisniewski et al. 2018). A full-length cDNA clone (FaCDPK1) encoding a calcium-dependent protein kinase (CDPK) has been isolated from a strawberry fruit cDNA library. This gene expression was observed in different plant organs including roots, stolons, meristems, flowers and leaves (Llop-Tous et al. 2002). During cold exposure, some proteins are phosphorylated, whereas others are dephosphorylated. These changes in phosphorylation status can affect gene induction. The mitogen-activated kinases (MAPKs) are activated by phosphorylation under the action of MAPK-kinases (MAPKK) which themselves are phosphorylated by MAPKK-kinases (MAPKKK). It is suggested that MAPK modules are upstream of the CBF pathway (Sinha et al. 2011). Wei et al. (2017) identified 12 MAPK genes in the Fragaria vesca genome. Gene transcript profile analysis showed that the majority of the FvMAPK genes were ubiquitously transcribed in strawberry leaves after Podosphaera aphanis inoculation and after treatment with cold, heat, drought, salt and the exogenous hormones abscisic acid, ethephon, methyl jasmonate and salicylic acid.

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Dormancy, Cold Acclimation and Gene Expression

Under cold and drought conditions expression of many genes, changes occur. Those changes relate to cold acclimation and transition to a dormant period. Survival of strawberry and raspberry plants in fluctuable conditions of wintering caused by climate change highly depends on depth and duration of dormancy and abilities to reacclimate after temporary temperature increase in winter. Under 12 and 15 °C in short days, the expression patterns of FaPIN1 (auxin transporter gene), FaNCED1 (ABA biosynthesis gene), FaDRM (DNA methylation) and FaROS1 (DNA demethylation) were coincident with the significant decline in indole-3-acetic acid (IAA) level and increases in abscisic acid (ABA) content and global genomic DNA methylation in young leaves. Interesting that at 12 °C, after treatment for 8 weeks, regardless of day length, potted, runner-derived strawberry plants gradually exhibited morphological traits typical of dormancy. Those changes were similar when plants were exposed at 15 °C and a short-day photoperiod. These results indicated that temperature alone was sufficient to induce strawberry to enter the typical dormant phase, and day length had impact only at higher temperatures (Zhang et al. 2012). Throughout the winter, raspberry canes were hardier than buds. Endodormancy had a greater influence on dehardening and re-hardening in buds than in canes, and cultivars differed in their response (Palonen and Linden 1999). Differential screening of a cDNA library prepared from cold-acclimated strawberry ‘Korona’ plants allowed to isolate several cDNAs showing differential expression at low temperature. Northern analysis showed that the transcript level of Fcor1 (Fragaria Cold-Regulated) peaked after two days of low-temperature exposure while that of Fcor2 peaked after 2 weeks. The level of Fcor1 transcript accumulation is correlated with the freezing tolerance of the strawberry cultivars used in our study. This suggests that Fcor1 may be useful as a molecular marker to select for this trait in resulted species of the Rosaceae family (NDong et al. 1997). Transcript levels in strawberry leaves were determined by microarray after 24 or 74 h of cold treatment, where dozens of cold-associated transcripts were quantitatively characterized, and levels of several potential key contributors (e.g. the dehydrin COR47 and GADb) to cold tolerance were confirmed by qRT-PCR. Among these were several dehydrins, heat shock cognates, cold shock protein, galactinol synthase, glutathione peroxidase and catalase (Koehler et al. 2015).

9.3.2.3

ICE1-Mediated CBF Pathway and Cold Resistance

The best-documented genetic pathway leading to changes in the transcriptome in response to cold is the ICE1-mediated CBF pathway. This pathway has been identified in both cold-resistant and cold-sensitive plants. CBF genes encode AP2/ERF (APETALA2/Ethylene-Responsive Factor)-type transcription factors that specifically bind to the C-repeat (CRT)/dehydration-responsive element (DRE; G/ACCGAC) and regulate the expression of downstream cold-responsive (COR) genes (Sakuma et al. 2006). A protein binding to this motif was first identified in

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Arabidopsis. CBF1 was later found to belong to a small family, together with CBF2 and CBF3. The transcripts of CBF1–3 accumulate in response to cold (Medina et al. 1999) and to a lesser extent, in response to ABA also (Knight et al. 2004). In coldresistant plants and plant able to cold acclimate, the CBF proteins will activate the transcription of genes encoding proteins with major role in cold tolerance and freezing resistance. The CBFs are themselves induced by cold. CBF4 is thought to be primarily associated with desiccation/drought responsiveness (Haake et al. 2002). Genes encoding CBFs have been identified in strawberry (Owens et al. 2002). When regulatory gene RdreB1B1 (CBF1) together with stress-inducible promoter rd29 was introduced to the strawberry, 20 proteins differently expressed between transgenic and non-transgenic strawberries were observed by 2-D PAGE and identified via MALDI-TOF/TOF MS and database (Gu et al. 2015). Eight identified proteins function in energy and metabolism, four in biosynthetic processes, four were stress and defence-related, three spots were identified as cold stress-related expressed sequence tags (ESTs) and two were unknown proteins. The RdreB1BI transgene enhanced RuBisCO and RuBisCO activase synthesis. Thus, the transgene protects RuBisCO from degradation during cold stress. RuBisCO activase increased significantly upon 240 h cold treatment in non-transgenic plants as well (Koehler et al. 2015). Among proteins whose expression increases during cold stress, photosynthetic proteins are the most abundant in other species (Chen et al. 2014; Ruelland et al. 2009). In the study of Gu et al. (2015), overexpression of RdreB1BI in transgenic strawberries promoted the expression of Cu/Zn-SOD, which conferred better ROS scavenge and a higher cold stress tolerance in transgenic strawberries. In the cold-tolerant strawberry cultivar (‘Jonsok’), relative to the less-tolerant cultivar (‘Frida’), increased levels of Cu/Zn-SOD were observed. It is likely a key component in an increased capability of scavenging ROS in low-temperature conditions (Koehler et al. 2012). One of the proteins whose expression increased in transgenic plants was Lea 14-A protein. Lea proteins are related to the stabilization of membranes and enzymes present in their environments, and they obtain stable conformation required for interaction with membranes and other proteins only under dehydration or cold stress (Bremer 2017).

9.3.2.4

DreB Genes and Drought Resistance

Gu et al. (2017) used the same rd29A:RdreB1BI construct and obtained transgenic strawberry lines resistant to drought. This suggests that DreB genes can provide complex resistance to abiotic factors—cold and drought because the frost and drought resistance mechanisms are interconnected. It is therefore reasonable that cold tolerance always encompasses dehydration stress tolerance. Furthermore, according to recent molecular and genomic data, genes with various functions can be induced by both stresses. At the same time, various transcription factors are involved in the regulatory network of both related stresses. Given this, there may be shared gene activity between drought and cold tolerance responses. Cooperative function suggests the existence of crosstalk between cold and drought stress responses. This crosstalk is obtained in other species as well (Swindell 2006; Li et al. 2017). Expression of

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another, NAC transcription factors, that are involved this crosstalk (Nakashima et al. 2012; Nuruzzaman et al. 2013) were significantly higher in the leaves and petioles of transgenic plants under water limiting conditions (Gu et al. 2017). In the case of drought in transgenic plants, under drought stress, the ion leakage was significantly less than in non-transgenic plants, and the water content in the leaves remained higher (Gu et al. 2017). In the transgenic plants, the stomata were larger, but they were narrower. Under drought stress, the activity of POD peroxidase and SOD superoxide dismutase in transgenic plants was three times higher than in non-transgenic plants. This means that transgenic plants have increased free radical binding potential. In the stress state, malonylaldehide in transgenic plants was three times less than non-transgenic plants, and therefore, there was a lower potential for lipid peroxidation. Among all six genes of interest, which were highly up-regulated in transgenic RdreB1BI strawberry plants and which have a DRE cis-acting element in their promoter regions, only the FvPIP2;1 like 1 aquaporin-encoding gene has demonstrated the regulatory activity with RdreB1BI in vitro. It was confirmed by the authors that RdreB1BI binds to the promoter region of FvPIP2;1 like 1 and that RdreB1BI regulates FvPIP2;1 like 1 transcription under drought stress. Analogous gene TaAQP7 provides transgenic tobacco increased drought tolerance by increasing the water content, reducing ROS and membrane damage (Zhou et al. 2012). In the promoter region of gene FvPIP2;1 like 1, it was obtained sequences important for the response to the phytohormones ABA, methyl jasmonate, zein, salicylic acid and auxin. Also, the LTR element for responding to cold stress, as well as MBS, and DRE sequences, are important for responding to drought and dehydration (Gu et al. 2017). Other functional genes are also up-regulated in transgenic leaves and petioles. NCED3, RD22, KIN1 and ABI5 are critical genes involved in drought stress and are significantly up-regulated in drought-tolerant transgenic plants of other species (Peng et al. 2010; Zhu 2016). NCED3 and RD22 are ABA biosynthesis-related genes and could enhance ABA biosynthesis due to their increased abundance of transcripts in transgenic plants. Because ABA has long-lasting effects on the drought stress response and regulates stomatal aperture, ABA may contribute to the reduced stomatal aperture of transgenic plants under drought stress. KIN1 is critical for plants for increasing freezing and drought tolerance (Tähtiharju et al. 1997). HSP90 encodes a molecular chaperone and may contribute to drought tolerance by stabilizing proteins against stress-induced denaturation (Song et al. 2009). Higher expression of drought-related genes observed in transgenic RdreB1BI plants indicates that the DREB1B transcription factor might directly or indirectly control these genes to promote drought tolerance (Gu et al. 2017).

9.3.2.5

NAC, WRKY and HSF Transcription Factors

Another transcription factors—NAC involved plant development and response to various stress stimuli. NAC transcription factors are one of the largest families of transcriptional regulators in plants. Expression profiles derived from quantitative

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real-time PCR suggested that five FvNAC genes responded dramatically to the various abiotic and biotic stresses, indicating their contribution to abiotic and biotic stresses resistance in woodland strawberry. The strawberry NAC genes showed a greater extent responded to the cold treatment than other abiotic stress (Zhang et al. 2018a, b). FvNAC28 shared high similarity with ATAF1/ANAC002, which was previously shown to be involved in abiotic (drought, salt and ABA) and biotic (necrotrophic pathogen B. cinerea) stress responses (Kim et al. 2007). WRKY proteins comprise a large family of transcription factors that play important roles in response to biotic and abiotic stresses and plant growth and development. Weiet et al. (2017) identified 62 WRKY genes (FvWRKY s) in the wild diploid woodland strawberry accession Heilongjiang-3. Expression profiles derived from quantitative real-time PCR suggested that 11 FvWRKY genes responded dramatically to various stimuli at the transcriptional level. Most of WRKY genes tended to be upregulated to a greater degree by drought and NaCl treatment. In contrast, FvWRKY genes tended to be down-regulated to a greater degree by extreme temperatures than drought and NaCl treatment, potentially indicating a positive effect of osmotic stress and a negative effect of temperature stress on FvWRKY genes. FvWRKY42 expression was induced by treatment with powdery mildew, salt, drought, salicylic acid (SA), methyl jasmonate (MeJA), abscisic acid (ABA) and ethylene. The protein interaction network analysis showed that the FvWRKY42 protein interacts with various stress-related proteins. Overexpression of FvWRKY42 in Arabidopsis resulted in cell death, sporulation, slow hypha growth and enhanced resistance to powdery mildew that was concomitant with increased expression of PR1 genes in Arabidopsis. Overexpression also led to enhanced salt and drought stress tolerance, increased primary root length and germination rate, decreased water loss rate, reduced relative electrolyte leakage, and malondialdehyde accumulation, and upregulation of superoxide dismutase and catalase activity. Additionally, FvWRKY42-overexpressing Arabidopsis plants showed increased ABA sensitivity during seed germination and seedling growth, increased stomatal closure after ABA and drought treatment and altered expression of ABA-responsive genes (Wei et al. 2018). Heat shock transcription factors (HSFs) are mainly involved in the activation of response to heat stress as well as other abiotic and biotic stresses. Study of Hu et al. (2015) identified 17Hsfgenes (FvHsfs) in a wild diploid woodland strawberry (F. vesca) and isolated 14 of these genes. Fifteen of the seventeen FvHsf genes exhibited distinct changes on the transcriptional level during heat treatment. Of these 15FvHsfs, 8FvHsfs also exhibited distinct responses to other stimuli on the transcriptional level, indicating versatile roles in the response to abiotic and biotic stresses (Hu et al. 2015). Liao et al. (2016) annotate transcriptome sequences of strawberry cv. Toyonoka. Expression assays revealed that FaTHSFA2a and FaTHSFB1a expression in a strawberry was induced by heat shock and correlated well with elevated ambient temperatures. Overexpression of FaTHSFA2a and FaTHSFB1a resulted in the activation of their downstream stress-associated genes and notably enhanced the thermo tolerance of transgenic Arabidopsis plants. The temperature influences flowering properties also.

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Dehydrins

One of the best-documented responses of plants to cold treatments is the accumulation of hydrophilic proteins predicted to form an amphipathic alpha helix. These proteins include dehydrins, which define the group II of late embryogenesis abundant (LEA) proteins (Bies-Etheve et al. 2008; Puhakainen et al. 2004). Dehydrin accumulation in leaves and crowns of eight F. vesca genotypes was observed at 14 days of cold acclimation and accumulated to much higher levels at 42 days. Bioinformatic analysis identified seven distinct dehydrins. The total dehydrin content after 14 days of cold acclimation of eight F. vesca genotypes was not correlated to the LT50 values; however, a strong correlation (r = −0.81) was evident at 42 days (Davik et al. 2013). This makes the overall dehydrin content a very good candidate for a freezing tolerance protein marker. Dehydrin proteins do appear to accumulate significantly slower in F. vesca crown tissue than what has been observed for other known and well-studied model systems like Arabidopsis. This might be a consequence of the strawberry crown, a largely non-photosynthetic and exceptionally less-studied plant tissue. Since some dehydrins have been shown to have light-regulated accumulation, this factor could conceivably impact dehydrin accumulation in these partially subterranean tissues. This finding reiterates the importance of studying cold responses for specific tissues in different plants (Koehler et al. 2012). Specific antibodies recognize the Arabidopsis dehydrin, COR47, revealed a significant increase in protein levels of a 53 kDa band, designated as FaCOR47 due to its cross-reactivity to the antibody and its appropriate mass. Likewise, another antibody-reactive band (48 kDa) was highly expressed similarly upon cold treatment. This lower band likely represented the non-phosphorylated form of FaCOR47 (Alsheikh et al. 2005). The higher mass but minor band of 82 kDa is likely an aggregate of COR47 often detected in such blots. Increased transcript levels of COR47 and other dehydrins support this observed protein response to low temperatures (Koehler et al. 2015). Houde et al. (2004) reported the transfer of the wheat Wcor410a acidic dehydrin gene in strawberry. WCOR410 proteins accumulate in large quantities in wheat plants. They are localized in the periphery of the membranes and are involved in protecting them from damage during freezing and dehydration (Danyluk et al. 1998). The experiment resulted in the development of three stable transgenic strawberry lines with a high expression of the WCOR410 gene independent of cold hardening. Expression of WCOR410 occurred in all organs examined. The growth of transgenic lines did not differ from the growth of wild-growing plants. The results showed that the LT50 of the leaves of the wild-type cultivar was −13 °C, whereas it was −18 °C for the three transformed lines. These data demonstrate that the phenotypic expression of cold hardiness in transgenic plants increased by 5 °C compared with wild-type. However, when freezing the entire plant, after thawing the plants at −20 °C, the plant began to die. This shows that the individual parts of the plant, most probably the roots, suffered more than leaves (Perras and Sarhan 1989). It is known that the roots are the most sensitive strawberry organ that survives in the winter only because of being on the ground, protected by snow cover. In the study of the functional expression of the

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Arabidopsis gene, ERD14, which is an analogue of the WCOR410 gene, it has been determined that the phosphorylation of the protein encoded by this gene changes its conformation, allowing its association with the calcium (Alsheikh et al. 2003) and promoting binding to the membrane bilayer (Danyluk et al. 1998; Kosova et al. 2014). The hydrophilic nature of WCOR410 means that it is can replace water and stabilize membranes interacting with membrane lipids during cold-induced dehydratation. The data presented above indicate that the WCOR410 protein from wheat has an important impact on cold hardiness of the distant species strawberry. The isolation of the specific kinases responsible for the phosphorylation of WCOR410 could improve our understanding of the mechanism involved in its activation and may help to establish a strategy to improve cold hardiness in crops of agronomical importance (Houde et al. 2004). While transcript levels of the XERO2-like dehydrin increased in both threated Norvegian strawberry cultivars in response to cold, the rate of increase was significantly greater in more cold-tolerant strawberry cultivar ‘Jonsok’ than ‘Frida’. The highest levels occurred at the 42 days after treatment. While the XERO2-like dehydrin shows a consistent increase over the duration of the cold treatment, the increase in levels of the COR47-like dehydrin transcript is greatest after only 1 d of cold. It should also be considered that transcript and protein levels accumulation is not always concomitant (Koehler et al. 2012). Accumulation of XERO2 like dehydrin was observed in our studies in coldacclimated F. vesca. Difference gel electrophoresis of acclimated and control plants of F. vesca revealed three spots that were expressed in the acclimated plants. NanoLC-high resolution analysis of peptides extracted from the gel spots revealed a highly significant match (ten matching peptides) to F. vesca predicted gene 14938-v1.0hybrid, which is homologous to an A. thaliana dehydrin gene Xora 2 (Shulaev et al. 2011). Davik et al. (2013) reported 37 and 25 kDa dehydrins in F. vesca whose expression correlates with freezing tolerance. In our in vitro assay, only the 25 kDa band corresponding Xora 2 dehydrin was observed (Haimi et al. 2017). The fact that same protein was obtained in separate investigations of protein accumulation during cold acclimation shows that this protein really involved in the formation of cold acclimation and hardiness in different Fragaria genotypes. The problem exists to found universal cold acclimation marker because different investigations often found different changes in different genotypes during cold acclimation. It seems that plants of different genotypes use different strategies for cold survival.

9.3.2.7

Other Proteins Accumulating During Cold Acclimation

Along dehydrins, other plant proteins also contribute to the formation of cold response. Using two-dimensional electrophoresis, 135 proteins identified as cold tolerance-associated included molecular chaperones, antioxidants/detoxifying enzymes, metabolic enzymes, pathogenesis-related proteins and flavonoid pathway proteins (Koehler et al. 2012). The greatest proportion of proteins identified falls into the biological process categories of stress-related or stress-responsive proteins. The

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cultivar ‘Frida’ cold response emphasized proteins specific to flavonoid biosynthesis, while the more freezing-tolerant ‘Jonsok’ had a more comprehensive suite of known stress-responsive proteins including those involved in antioxidation, detoxification and disease resistance. Greatest overall differences in the cultivars exist under control and 2-d cold treatments, while the protein expression patterns tend to converge after the long-term cold treatment. The convergence of protein profiles at 42 days can be explained by the observation that many proteins in ‘Frida’ are increasing in abundance due to cold but do not reach levels greater than ‘Jonsok’(and vice versa). This supports a hypothesis in which the differences in cold tolerance between the two cultivars may be significantly linked to differences in protein expression under control conditions or in the initial phase of cold treatment. The molecular basis for ‘Jonsok’enhanced cold tolerance can be explained by the constitutive level of proteins number that provides a physiological stress-tolerant poise. Flavonoid pathway proteins expressed at higher levels in ‘Frida’ than ‘Jonsok’ include three key enzymes in the flavonoid pathway, chalcone synthase (CHS), flavonoid 39-hydroxylase (F3H) and dihydroflavonol 4-reductase (DFR). Isoflavone reductase (IFR) was more abundant in ‘Jonsok’(Koehler et al. 2012). In later studies, using 2DE proteomics, twenty-one proteins were identified in strawberry leaves ‘Korona’ upon cold exposure, many of which were associated with general metabolism or photosynthesis. There were about threefold more downregulated than up-regulated spots. Many chloroplast-associated proteins (twelve of the 21 identified) were affected during cold acclimation. The identified photosynthetic proteins changed a maximum of about fivefold, with the chlorophyll a/b binding proteins having the greatest changes. The significant upregulation of the ATPdependent Zn peptidase is of interest because of its essential role in thylakoid formation and the removal of damaged D1 precursors in monomeric photosystem II reaction centre complexes. The decrease of the F3H, a key enzyme of flavonoid biosynthesis in plants, indicates a distinct down-regulation of secondary metabolism in strawberry leaves ‘Korona’ upon cold exposure. A clear positive correlation between flavanol content and freezing tolerance has been reported (Korn et al. 2008). Thus, it is interesting that regarding the flavonoid pathway, ‘Korona’ appears to have characteristics of a less cold-tolerant cultivar (Koehler et al. 2015). Alcohol dehydrogenase (ADH) can enhance stress survival by ameliorating hypoxic conditions brought on by melting snow or ice encasement. Elevated levels of ADH can prevent the accumulation of toxic end products of anaerobic metabolism, preventing injury and thus increasing winter survival. The LT50 estimates and the expression of ADH and total dehydrins were highly correlated in F. vesca (radh = −0.87) (Davik et al. 2013). In more cold-resistant cultivar, level of ADH was higher than in less cold resistant (Koehler et al. 2012). According to Davik et al. (2013), this protein is thus an excellent candidate as a molecular marker for cold stress tolerance.

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Proteins Related to Accumulation of ROS

Other candidates to molecular markers are a thaumatin-like glucanase, Cu/Zn superoxide dismutase (SOD), ascorbate peroxidase (APX), annexin 1 and L-galactono1,4-lactone dehydrogenase, accumulating in many fold higher amounts comparing to less cold-hardy cultivar. They are key components in an increased capability to directly modulate reactive oxygen species (ROS) levels. Other enzymes involved in redox reactions, aldoketo reductase, 3-ketoacyl-CoA thiolase, IFR and glutathioneS-transferase were also at higher levels or were cold induced in ‘Jonsok’ (Koehler et al. 2012). To alleviate the negative effects of stress and balance the ROS level, the organism would initiate enzymatic and non-enzymatic mechanisms to maintain the cellular redox homeostasis. SOD is involved in dismutation of O2 to H2 O2 , and its activity reflected the ability to adapt to stress in the plant. In study of Zhang et al. (2018), SOD activity increased at the cold treatment in strawberry plants and then decreased, which demonstrated that short-term cold stress could induce antioxidative defence mechanism in the plant. However, the longer the stress time was, the more ROS produced so that antioxidative enzyme activity was impaired and the ROS could not be removed effectively. Subsequently, peroxidation of lipids in the cell membrane results in massive malondialdehyde (MDA) accumulation, which is toxic to plant (Luo et al. 2011). The plasma membrane-located NADPH oxidases have been shown to mainly involve in ROS production and play critical roles in plant development and defence responses. Strawberry Rboh genes coding NADPH oxidases (FvRbohA and FvRbohD) which were specifically expressed in leaves reacted quickly to cold stress by improving transcript levels, followed by an increase in NADPH oxidase activity, leading to O2 accumulation and triggering the antioxidant reaction by the transient increases in SOD activity. This suggested these two genes may be involved in cold stress and defence responses in strawberry (Zhang et al. 2018a, b). It is observed that the enhancement of cold resistance of strawberry, which induced by cold acclimation, related to the significant increase in glucose-6-phosphate dehydrogenase (G6PDH) activity. G6PDH catalyses the first and rate-limiting step of the oxidative pentose phosphate pathway (OPPP), and the expression of this enzyme is related to different biotic and abiotic stresses. Under accumulation of lowtemperature stress, the significant increase in G6PDH activity was found to be closely correlated to the levels of antioxidant enzymes, malondialdehyde (MDA) contents, sugar contents as well as changes of superoxide (O•2− ). G6PDH activates NADPH oxidase to generate reactive oxygen species (ROS) that may be involved in the activation of antioxidant enzymes and accelerates many other important NADPH-dependent enzymatic reactions. This further results in the elevation of membrane stability and cold resistance of strawberry that remains even after placing the plants again under a temperature of 25 °C at least for 1 day (Zhang et al. 2018a, b).

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Proteins Related to Drought and Salinity Stresses

Aquaporins, also called water channels, are integral membrane proteins that form pores in the membrane of biological cells, mainly facilitating the transport of water between cells. PIP aquaporin responses to drought stress can vary considerably depending on the isoform, tissue, species or level of stress. A general down-regulation of these genes is thought to help reduce water loss and prevent backflow of water to the drying soil. Ten Fragaria PIP genes were identified from the woodland strawberry (Fragaria vesca L.) genome sequence and characterized at the sequence level. PIP down-regulation in the root corresponded to the level of drought stress. Moreover, transcript abundance of genes FvPIP1;1 and FvPIP2;1 highly expressed in the root was strongly correlated to the decline in substrate moisture content. The amplitude of diurnal aquaporin expression in the leaves was down-regulated by drought without altering the pattern but showing an intensity-dependent effect. Transcription of PIP aquaporins can be fine-tuned with the environment in response to declining water availability (Šurbanovski et al. 2013). Basic leucine zipper (bZIP) genes are known to play a crucial role in response to various processes in the plant as well as abiotic or biotic stress challenges. Fifty bZIP genes were identified and characterized in the woodland strawberry (Fragaria vesca) genome. Four (mran00393, mrna08566, mrna30280 and mrna1183) bZIP genes showed a significant upregulation after the plants were treated by drought for two days. Heat treatment caused mrna30280 transcription levels to increase in all points of the heat treatment. Elevated expression was observed with different set of genes following drought and heat treatment, which may be caused by the separate response pathway between drought and heat treatments (Wang et al. 2017). Ribosome-inactivating proteins (RIPs), enzymes that are widely distributed in the plant kingdom, inhibit protein synthesis by depurinating rRNA and many other polynucleotidic substrates. Although RIPs show antiviral, antifungal and insecticidal activities, their biological and physiological roles are not completely understood. Increase in RIP activity was found in the leaves of drought-stressed plants (Polito et al. 2013). Authors suggest that RIP expression and activity could represent a response mechanism against biotic and abiotic stresses and could be a useful tool in selecting stress-resistant strawberry genotypes. In the study of Husaini and Abdin (2008), osmotin gene was introduced to strawberry ‘Chandler’ under the control of CaMV 35S promoter. The osmotin protein is produced in response to diseases caused by various biotic and abiotic stresses (Ullah et al. 2018). Osmotin effects proline synthesis as a signal protein that activates MAP kinase and biosynthetic pathway gene expression. Proline synthesis has a multiplier effect by stabilizing proteins, enzymes, macromolecules and organelles, protecting both osmotic and oxidative stress (Zarattini and Forlani 2017; Hakim et al. 2018). This explains why transgenic strawberry plants have retained viability, growth and biomass under stress conditions. Transgenic plants retained growth even in the medium with 150 mM NaCl, while the growth of non-transgenic plants at this concentration stopped completely. Without the stress factor, the transgenic plants

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grew slightly slower and were lower than non-transgenic plants (Husaini and Abdin 2008).

9.3.2.10

Metabolic Profile During Cold Acclimation

Accumulation of different metabolites during cold acclimation is observed in many studies. qGC MS metabolite profiling to assess the reconfiguration of central metabolism and characterize the regulation of selected compatible solutes usually applied. The 17 strawberry genotypes were clustered under the metabolic profile during hardening. At the beginning, they were together but separated in the course of cold acclimation (Davik et al. 2013). It is evident that the metabolic profile of many genotypes has changed during cold acclimation, except for the F. vesca Alta variety, originated from Norwegian far Nord, which remained almost unchanged (Heide and Sønsteby 2007). It shows that different metabolic profiles are associated with distinct genetic and geographical differences. Davik et al. (2013) classified metabolites according to different patterns of leaf metabolic responses across the time points. The first cluster consists of fructose, glucose, succinic acid, malic acid and galactinol. Their content is reduced towards the end of the acclimation period. A second cluster consists of aspartic acid, glutamic acid, citric acid and asparagine. These metabolites are in general accumulated during acclimation. In the third cluster consisting of fumaric acid, galactose, raffinose and sucrose, there is a weak accumulation of the metabolites during acclimation. These general patterns are, however, frequently broken by local peaks or troughs, e.g. the galactose content at day 14 (Davik et al. 2013).

9.3.2.11

Accumulation of Sugars

Usually, monosaccharide and raffinose pathway oligoscharide levels increase during cold acclimation of strawberry and raspberry. Major metabolic changes coincided with a decrease in day length below 14 h in mid-September, and a consistent drop below 10 °C average day temperature by the end of September fructose increased strongly in strawberry cv. ‘Honeoye’ (180 fold compared to start control) towards the end of the acclimation period (Rohloff et al. 2012). Possible sugar metabolism in cold-hardy plants causes decrease of sugar and increase of other more osmotically active sugars—raffinose and galactinol. Galactinol and raffinose have been shown to be involved in plant protection upon oxidative stress (Nishizawa et al. 2008). Moreover, drastically increased levels of hexose phosphates are associated with a targeted biosynthesis of compatible solutes since these compounds exert a higher ROS scavenging capacity (F6P > fructose) compared to non-phosphorylated sugars as reported by Spasojevi´c et al. (2009). Raffinose pathway is affected leading to strongly and transiently increased levels of the precursor galactinol and later the trisaccharide raffinose. Levels of galactinol and raffinose, key metabolites of the cold-inducible raffinose pathway, were drastically enhanced in both leaves and roots

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throughout the cold acclimation period of ten days. Furthermore, initial freezing tests and multifaceted GC/TOF-MS data processing showed that changes in metabolite pools of cold-acclimated F. vesca were clearly influenced by genotype (Rohloff et al. 2012). The proteins and the sugar alcohol galactinol showed the clearest association with cold tolerance and thus the greatest potential to be developed into biomarkers (Davik et al. 2013). Correlation between tetrasaccharide stachyose accumulation and cold hardiness was observed in studies of Rugienius et al. (2016a, b) showing stachyose as potential biochemical marker of cold hardiness. In the case of sucrose as cold acclimation, biomarker opinions are controversial. Sucrose increased 124–165% in raspberry plantlets in vitro and 253–582% in container-grown plants during acclimation and declined rapidly to the level of control plants during deacclimation (Palonen and Junttila 2002). Both sucrose and galactinol in strawberry correlate well with LT50, at least at some of the early time points (Davik et al. 2013). Sucrose levels were steadily increased throughout the cold hardening period with averagely six-fold higher levels in ‘Honeoye’ compared to ‘Polka’, thus underscoring cultivar-dependent differences. However, both varieties showed a decline in sucrose levels after week 47 (Rohloff et al. 2012). The positive correlation between sucrose levels and LT50 at the beginning of the acclimation period indicates that the strawberry genotypes with the lowest sucrose content are the most low-temperature tolerant ones (Koehler et al. 2015). Possible sugar metabolism in cold-hardy plants causes decrease of sugar and increase of other more osmotically active sugars—raffinose and galactinol. Nevertheless, a close correlation between cold hardiness and total sugars, sucrose, glucose and fructose were established—the hardiest raspberry cultivar ‘Festival’ contained more soluble carbohydrates, sucrose and raffinose but less glucose and fructose than the other cultivars. These results demonstrated the importance of soluble carbohydrates, especially sucrose, in cold hardening of red raspberry. It suggests that sugars have more than a purely osmotic effect in protecting acclimated raspberry plants from cold. The rate of sucrose accumulation during cold acclimation in vitro was independent of the medium sucrose level or the initial plant sucrose content in vitro (Palonen and Junttila 2002). Contrary to those findings, exogenous sucrose has positive impact on strawberry cold hardiness in vitro (Rugienius and Stanys 2001; Lukoševiˇci¯ute et al. 2009).

9.3.2.12

Changes in Amino Acid Content

Cold acclimation changes amino acid (glutamic acid, aspartic acid and asparagine) patterns in strawberry (Rohloff et al. 2012). The significant correlations observed for succinic acid to the LT50 estimates are to a large extent caused by the relatively high content of succinic acid in the low-tolerant strawberry genotype (Davik et al. 2013). Metabolic changes in F. vesca included the strong modulation of central metabolism, induction of amino acids (aspartic acid) and amines (putrescine). Putrescine is important for signal transmission, antioxidant system and ABA level changes. In contrast, a distinct impact on the amino acid proline, known to be cold-induced in

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other plant systems, was conspicuously absent (Rohloff et al. 2012). Aspartate and β-alanine were clearly affected as indicated by coordinately increased levels of both metabolites in leaves which agree with other reports describing enhanced alanine and aspartate levels under cold treatment (Allan et al. 2008). An important contributor to stress responses, gamma-aminobutyric acid (GABA) was also strongly accumulated in leaves (Koehler et al. 2015). Overall, the main differences between ‘Alta’ and the less-tolerant F. vesca genotypes were the consistently higher levels of salicylic acid, gluconic acid, inositol conjugates and myo-inositolin ‘Alta’, and the rapid response of this accession to cold exposure (within 3 h) regarding distinct metabolites, namely 2-oxoglutarate, proline, glycine, GABA and gluconic acid. The significant change of amines (putrescine), aspartic acid, N-acetyl-serine was observed, also suggesting regulatory roles of branched-chain amino acids (leucine, isoleucine and valine) in cold responses (Rohloff et al. 2012). Free amino acid changes were more closely linked to senescence and growth processes, while changes in ion content suggested a rapid mobilization of solutes at the onset of freezing temperatures (O’Neil 1983). It is interesting that possible amino acid biosynthesis regulated coordinately but differently in roots and leaves underscoring the evolvement of organ-dependent cold acclimation strategies. Following cold treatment, derivatives of α-ketoglutarate (proline, glutamate, glutamine, histidine and arginine) all accumulate in leaves but decrease in roots.

9.3.2.13

Factors Effecting Metabolite Accumulation and Reaction to Cold Stress

The total number of metabolites with increased levels was obviously higher in the roots in comparison with positively affected leaf metabolites. On the other hand, the total number of compounds with reduced concentration levels in leaves and roots was generally lower compared to the increases (Rohloff et al. 2012; Koehler et al. 2015). Narsai et al. (2010) showed that gene expression in roots at the functional level seems to be more conserved compared to leaves, flowers and seeds. It was found that of all strawberry leaves undergoing osmotic adjustment during cold acclimation, only the younger leaves survived, suggesting presence of an age-dependent component of freezing resistance in leaves. Associated with cold hardening of the overwintering leaves was a twofold increase in the phospholipid content of the leaf membranes with a proportionately smaller increase in free sterols. The large increase in phospholipids presumably is due primarily to the proliferation of a sterol-poor membrane fraction, probably the endoplasmic reticulum (O’Neil 1983). Winter temperature and possibly summer temperature at the plant accession sites might explain some of the metabolic differences between the genotypes. This is where the most northernmost strawberry grows in the summer, almost all the time the sun shines, this can affect the processes of hardening like it is affecting flowering (Rohloff et al. 2012).

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Biotic and abiotic stresses often are interconnected. Moreover, microorganisms sometimes have positive impact on plant adaptivity. It was found that arbuscular mycorrhizal fungi can improve water relations, and colonization by the root symbionts may increase the host tolerance of drought especially when roots have been compromised by herbivory (Boyer et al. 2015). Recently emerged that plant endophytes have often positive effect on plant growth and stress response (Mili¯ut˙e et al. 2015; Tamoši¯un˙e et al. 2018).

9.3.2.14

New Tools for Research and Development of Stress-Resistant Plants

There was also attempts to use gene-editing technologies for strawberry improvement. Zhou et al. (2018) reported high-efficiency genome editing in the wild strawberry Fragaria vesca and its successful application to mutate the auxin biosynthesis gene TAA1 and auxin response factor 8 (ARF8). T1 plants harbouring arf8 homozygous knockout mutations grew considerably faster than wild-type plants. ARF8 in Arabidopsis was found to be a repressor of auxin signalling (Varaud et al. 2011). If it plays a similar role in strawberry, loss of ARF8 function may lead to constitutive auxin responses. FveARF8 may repress auxin signalling in many tissues and developmental stages. Therefore, inactivation of this gene by gene-editing technology makes increase of strawberry plant growth. It is very encouraging that next-generation (T1) JH19-FveTAA1 and JH19-FveARF8 plants continued genome editing with high efficiency (even higher than the T0 generation for JH19-FveTAA1). New types of mutations including large fragment deletions between the two PAM sites were generated in FveTAA1. This feature is especially attractive for CRISPR editing of cultivated strawberry, where continuous propagation of the transgenic plants containing the CRISPR construct may enable eventual editing of all eight alleles in the octoploid genome. Given the allopolyploid nature of the cultivated strawberry, the ability to stack multiple sgRNAs in the same construct each targeting a different allele may also facilitate genome editing in the cultivated strawberry (Zhou et al. 2018). It was shown (Koskela et al. 2016) by a transgenic approach that the silencing of the floral repressor FaTFL1 in the octoploid short-day cultivar ‘Elsanta’ is sufficient to induce perpetual flowering under long days without direct changes in vegetative reproduction. FaFT1 expression in the cultivated strawberry was regulated by temperature, the expression being the highest at intermediate temperature. The study of the adaptability of Rosaceae berry plants to climate change is not possible without broader genomic research. Single nucleotide polymorphism (SNP) array is considered to be one of, high-throughput, relatively cost-efficient and automated genotyping approaches. Davik et al. (2015a, b) used double digest restrictionassociated DNA sequencing (ddRAD) to identify SNPs in a 145 seedling F1 hybrid population. The authors have developed the first linkage map for F. × ananassa using ddRAD and showed that this technique and other related techniques are useful tools for linkage map development and downstream genetic studies in the octoploid strawberry. However, there are significant challenges for SNP identification in complex,

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polyploid genomes, which has seriously slowed SNP discovery and array development in polyploid species. Ploidy is a significant factor impacting SNP qualities and validation rates of SNP markers in SNP arrays, which has been proven to be a very important tool for genetic studies and molecular breeding (You et al. 2018). The cultivated strawberry (F. × ananassa Duch.) is an allo-octoploid considered difficult to disentangle genetically due to its four relatively similar subgenomic chromosome sets. This has been alleviated by the release of the strawberry IStraw90 whole-genome genotyping array (Davik et al. 2015a, b). SNP detection based on reduced genomic sequencing approaches has the potential of providing better coverage in cases where the studied genotypes are only distantly related to the SNP array’s construction foundation. The Affymetrix IStraw90 Axiom array’s high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome and by effectiveness of the employed array design and ploidy-reducing strategies. According Bassil et al. (2015), this array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high-value crop. Sargent et al. (2016) used the IStraw90 array for linkage map development and produced a linkage map containing 8,407 SNP markers spanning 1,820 cm. The array contains a novel marker class known as haploSNPs, which exploit homoeologous sequence variants as probe destabilization sites to effectively reduce marker ploidy. The authors examined these sites as potential indicators of subgenomic identities by using comparisons to allele states in two ancestral diploids. On this basis, haploSNP loci could be inferred to be derived from F. vesca, F. iinumae or an unknown source (Sargent et al. 2016). Genomic selection (GS) is the procedure where by molecular information is used to predict complex phenotypes, and it is standard in many animal and plant breeding schemes. Zingaretti et al. (2019) developed a versatile forward simulation tool, called polyploid sequence-based virtual breeding (pSBVB), to evaluate GS strategies in polyploids; pSBVB is an efficient gene dropping software that can simulate any number of complex phenotypes, allowing a very flexible modelling of phenotypes suited to polyploids. As input, it takes genotype data from the founder population, which can vary from single nucleotide polymorphisms (SNP) chips up to sequence, a list of causal variants for every trait and their heritabilities and the pedigree. Near-isogeniclines (NILs) were used as tools to dissect quantitative morphologic, phenotypic and nutritional characters in F. vesca. The NIL collection can significantly facilitate understanding of the genetics of many traits and provide in sight in to the more complex F. × ananassa genome (Urrutia et al. 2015). The Fragaria Discovery Panel (FDP) containing 287 features was constructed by subtracting the pooled gDNA of nine non-angiosperm species from the pooled gDNA of five strawberry genotypes. This FDP was used for bulk segregant analysis (BSA) to enable identification of molecular markers. This innovative strategy is an efficient and cost-effective approach for molecular marker discovery (Gor et al. 2016). Similar novel approaches were used in metabolomic studies of Vallarino et al. (2018), for identification of locus conferring resistance to Colletotrichum acutatum

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(Salinas et al. 2019), in mapping QTLs associated with Verticillium dahliae resistance in the cultivated strawberry (Antanaviciute et al. 2015).

9.4 Concluding Remarks Rosaceae family plants are one of the most important plants in the horticulture. Climate change is causing many challenges for those plants although its impact varies across regions requiring different solutions. Temperature fluctuations in the winter season, causing dehardening, the rise in temperature and changes in the structure of precipitation during growing season increase frequency of droughts or floods. It can cause the appearance of new diseases and pests that are not specific to the region. Knowledge of main vectors, first pathogen symptoms, temperature, moisture requirements for specific fungi and virus spread, helps to apply proper growing conditions for avoiding the disease but that is not enough. It will require new plants with more suitable physiological properties than existing plant varieties. Moreover, global climate change and the latest achievements in plant science are forcing for ‘global change in mind’. We understand now that instead of relying on one or another resistance gene and single and common resistance mechanism, different adaptive plants of same species use various strategies of resistance and adaptability, based on the whole organism and the harmony of complex features. According to recent molecular and genomic data, genes with various functions can be induced by different stresses. Cooperative function suggests the existence of crosstalk between cold and drought, biotic and abiotic stress responses. Knowledge of the structural or mechanical barriers for the pathogens, biochemistry of secondary metabolites and phytohormones as well as use different molecular markers are useful for creating new disease-resistant and adaptable cultivars. Use of biochemical markers such as accumulation of XERO2 like dehydrin, alcohol dehydrogenase, Cu/Zn superoxide dismutase, stachyose, galactinol, succinic, aspartic acids and putrescine gives the possibility to create and select cold-resistant strawberry and raspberry varieties. It is proven that inactivating of aquaporins, introducing of osmotin or transcription regulators by genetic engineering it is possible to develop drought and salt-resistant varieties. Interspecific hybridization using wild relatives of cultural plants widens possibilities to introduce valuable traits. In general, scientific knowledge obtained in recent years gives the possibility to introduce complex and durable resistance and to develop resilient cultivars featuring sufficient plasticity under changing environment.

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Chapter 10

Genomics Opportunities and Breeding Strategies Towards Improvement of Climate-Smart Traits and Disease Resistance Against Pathogens in Sweet Cherry Antonios Zambounis, Ioannis Ganopoulos, Filippos Aravanopoulos, Zoe Hilioti, Panagiotis Madesis, Athanassios Molassiotis, Athanasios Tsaftaris and Aliki Xanthopoulou Abstract The recent sequencing of many Rosaceae complete genomes, including that of sweet cherry (Prunus avium L.), along with the availability of highthroughput resources offers new challenges and opportunities for cherry breeders in the genomic era towards improvement of climate-smart traits and diseases resistance against the main pathogens, which are consistently plagued the crop. Conventional breeding approaches are laborious, time-consuming and inefficient to fulfil increasing demands, especially in terms of climate change. The advances both in markerassisted and genomics-assisted breeding, high-throughput sequencing technologies and bioinformatics tools should enable the sweet cherry breeding at a faster pace. These genomics technologies will certainly generate a large amount of data, and this new knowledge might be efficiently employed in cherry breeding towards the development of varieties with elevated adaptation to climatic challenges, including A. Zambounis (B) Department of Deciduous Fruit Trees, Institute of Plant Breeding and Genetic Resources, ELGO-DEMETER, 59035 Naoussa, Greece e-mail: [email protected] I. Ganopoulos Institute of Plant Breeding and Genetic Resources, ELGO-DEMETER, 57001 Thessaloniki, Greece F. Aravanopoulos Laboratory of Forest Genetics and Tree Breeding, School of Forestry and Natural Environment, Aristotle, University of Thessaloniki, 54124 Thessaloniki, Greece Z. Hilioti · P. Madesis Institute of Applied Biosciences, CERTH, 57001 Thessaloniki, Greece A. Molassiotis · A. Xanthopoulou Laboratory of Pomology, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece A. Tsaftaris American Farm School, Perrotis College, 57001 Thessaloniki, Greece © Springer Nature Switzerland AG 2020 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_10

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disease resistance against pathogens. The rapidly accumulating genomic resources will enable the development of molecular markers associated with many important quantitative trait loci, deciphering the genomic variations in various germplasms towards the development of climate-smart and disease resistant sweet cherries. Furthermore, an integrated approach based on a full range of plant omics sciences and their outcomes would result in the development of efficient genomics-based trait selection and identification of allelic variations involved in flowering time, dormancy and defence reactions against pathogens. Especially climate change alters dramatically the susceptibility of sweet cherry cultivars to rapidly evolved pathogens, and although the recent advances in genomics resources, there are still only a few reports of genomics applications for diseases resistance evaluation in germplasm collections. In this chapter, we discuss and summarize the advances through genomics-assisted breeding towards improvement of climate-smart traits and diseases resistance in sweet cherry. Keywords Climatic change · Disease resistance · Genetic diversity · Genotyping by sequencing · Genomics-assisted breeding · Pathogens genotyping · Phytopathology

10.1 Introduction The cultivated sweet cherry tree (P. avium L.), a species within the Prunus genus, comprises both the wild cherry natural populations used for timber and the sweet cherry fruit for human consumption. The species is an outbreeding deciduous tree in plant family Rosaceae, sharing a diploid compact genome (2n = 16) (Arumuganathan and Earle 1991). This species is usually common in temperate areas and undoubtedly is among the most important tree crops worldwide (Fernandez i Marti et al. 2012). The list of countries by cherry production in thousand metric tons of the global production (around 3.6 million tons) for 2017, based on data from the Food and Agriculture Organization Corporate Statistical Database is shown in Fig. 10.1. Sweet cherry consumption has potential preventative benefits to diseases such as Alzheimer’s, cancer and inflammation-related diseases (McCune et al. 2010). Due to the economic importance of sweet cherry and its potential to benefit human health, it is vital to research on and improve this crop to maintain global competitiveness. Furthermore, cherries offer unique phytochemicals with important attributes to human health (Kelley et al. 2018). It has been estimated that climate change could lead to a global temperature rise by 11 °C, which might be intensified until 2050 by flooding, drought and disease outbreaks threatening the production and yield of fruit trees (Sarkar et al. 2017). Consequently, appropriate measures and actions must be taken using innovative breeding techniques. Plant breeding and advances in agricultural practices have already contributed to an annual gain in crop productivity equal to 0.8–1.2%. However, this is not enough to meet the food demands for the projected global population in 2050 (the

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Fig. 10.1 List of countries by cherry production in 2017, based on data from the Food and Agriculture Organization Corporate Statistical Database where the estimated total world production for 2017 was 3,643,346 metric tons

2050 challenge) (Ray et al. 2012). The likelihood of extreme weather conditions may increase the incidences of new or more aggressive strains of pests and pathogens, which may result in further yield loss (Watson et al. 2018). In past, plant breeding has undoubtedly improved cherry yield and agronomic traits as well as resistance to biotic and abiotic stresses, but nowadays the main challenge is to continue to increase the cherry yield under the tremendous scenario of climate change. In order to facilitate breeding advances, it is essential to employ and integrate innovative breeding techniques through marker-assisted selection (MAS), and genomics approaches for the production of high-yielding cherries with enhanced adaptation to various environmental stresses, such as attacks by pathogens. Fortunately, we can benefit from the massive amounts of genomic data generated through a variety of technologies, the genomic data mining and the availability of public data repositories to speed up the process of breeding towards the development of biotic and abiotic stress-tolerant varieties. A major tool for enhancing the breeding efforts in a directed way is the development of functional molecular markers. Markerassisted breeding and selection will develop, as our knowledge increases leading to genomics-assisted breeding (GAB) towards disease resistance and adaptation to abiotic stresses. Omics research allowed investigations on structural and functional aspects of plant genomes. More projects for the complete genome and transcriptome sequencing of the major crops and fruit trees are under way. The knowledge generated by these approaches combined with traditional and conventional breeding methods

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will not only enhance the effectiveness of breeding methods but also reduce the time needed. In addition, a new breeding method the so-called genomic selection (GS) has been developed as one of the most promising breeding strategies to increase genetic gain and its advantages over traditional practices have been demonstrated for major crops (Crossa et al. 2017). Genomic selection can be modified by including high-throughput phenotyping for yield-associated physiological traits and functional markers for well-characterized major genes. Sweet cherries display a wide range of phenotypic diversity in flowering time and other agronomic traits (Ganopoulos et al. 2016a). Characterization of genome diversity in sweet cherry cultivars and discovery of genes controlling traits of interest through genotyping-by-sequencing (GBS) and whole-genome re-sequencing (WGRS) will improve the prospects of sweet cherry breeding. The estimated genome size of sweet cherry has been recently sequenced through next-generation sequencing (NGS) (Shirasawa et al. 2017). Recently, chloroplast and mitochondrial genome sequence have also been released (Chen et al. 2018; Yan et al. 2019). Before having its genomic DNA sequenced, many efforts have created linkage maps and molecular markers (Guajardo et al. 2015), which allowed the quick annotation of its genome and helped elucidate its structure. Determining the complexity of sweet cherry genome and its structure and function is pivotal to develop specific, accurate and highly specialized tools for the development of novel elite varieties with high nutritional value and/or abiotic and biotic stress tolerance.

10.2 Genomic Analyses in Sweet Cherry GS has emerged as one effective genomics-aided breeding approach with high accuracy in parallel with the advances of sequencing technology (Crossa et al. 2017). This approach allows breeders to select for superior lines according to the genomicestimated breeding values (GEBVs) of a testing population with mainly genotyping data in combination with phenotyping data (Varshney et al. 2017). Shirasawa et al. (2017) determined the genome sequence of sweet cherry using an NGS technology. They proposed that the total length of the assembled sequences was 272.4 Mb, consisting of 10,148 scaffold sequences with an N50 length of 219.6 kb (Table 10.1). They predicted 43,349 complete and partial protein-encoding genes. Furthermore, they constructed a high-density consensus map with 2382 loci using double-digest restriction site-associated DNA sequencing. Based on their study, it was estimated that the genome size to be 352.9 Mb from the higher peak, which almost agreed with the value (338 Mb) measured by flow cytometry (Arumuganathan and Earle 1991). Comparing the genetic maps of sweet cherry and peach revealed high synteny between the two genomes (Olmstead et al. 2008); thus, the scaffolds were integrated into pseudo-molecules using map- and syntenybased strategies. Finally, whole-genome re-sequencing technology for six modern cultivars (‘Benikirari’, ‘Benisayaka’, ‘Benishuho’, ‘Benitemari’, ‘Beniyutaka’ and ‘Nanyo’) revealed 1,016,866 single nucleotide polymorphisms (SNPs) and 162,402

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Table 10.1 Features of sweet cherry genome assembly as published by Shirasawa et al. (2017) and properly adopted Feature

PAV_r1.0

Sequenced cultivar

Satonishiki

Sequencing strategy

NGS Illumina

Genome coverage

130.34×

Scaffold number total

10,148

Scaffold sequence total

272.4 Mb

Mapped scaffold sequence total

191.7 Mb (70.4%)

Scaffold N50/L50

– /219.6 kb

Contig N50/L50

– /276 bp

Repetitive sequences

119.4 Mb (43.8%)

Protein-coding genes

43,673

Ftp link

ftp://ftp.bioinfo.wsu.edu/species/Prunus_avium/Prunus_ avium-genome.v1.0.a1

insertions/deletions, out of which 0.7% were deleterious (Shirasawa et al. 2017). More recently, Ono et al. (Ono et al. 2018) used large-scale DNA sequencing technologies to identify the pollen-part modifier gene (M) in the ‘Cristobalina’ cultivar. In the same study, genome re-sequencing of diverse sweet cherry genotypes revealed a modifier gene mutation conferring pollen-part self-compatibility. Undoubtedly, considerable resources, including genomics, transcripts, SNPs, simple sequence repeat (SSR) markers and proteomics, will allow us to further elucidate the genetics and evaluation of sweet cherry cultivars for disease resistance and adaptation to abiotic stresses. With the implementation of highly resolution genetic map construction along with the availability of well-annotated reference genomes, resources for association mapping and advances in WGRS, an efficient number of useful quantitative trait loci (QTLs) will hopefully be identified.

10.2.1 Transcriptomics Analyses Recently, using de novo assembly algorithms, RNA-seq experiments for providing a gene expression atlas have been successfully conducted on plants lacking sequenced reference genome such as switchgrass (Zhang et al. 2013), pea (Alves-Carvalho et al. 2015) and black-eyed pea (Yao et al. 2016). De novo assembly of NGS reads allows the discovery of almost all expressed genes in a plant tissue. In sweet cherry, normalized libraries for fruits and mesocarp tissues upon different ripening stages (Alkio et al. 2014; Wei et al. 2015) as well as developing floral buds (Koepke et al. 2012) have been sequenced. However, these experiments were designed for SNP discovery

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and did not produce a reference Unigene set that is easily available to researchers, nor quantitative gene expression profiles, because of library normalization.

10.2.2 Proteomics and Metabolomics Investigations In cherry, there have been fewer proteomics studies than genomics and transcriptomics studies. Proteomics studies include the identification and cataloguing of all proteins produced in a cell (equivalent to whole-genome annotation), the functional annotation of genes based on protein structure and the global analysis of protein–protein interactions (Martinez-Gomez et al. 2012). In sweet cherry, to our knowledge, proteomic approaches were applied only for clinical (Reuter et al. 2005), phytopathological (Chan et al. 2008) or fruit ripening purposes (Prinsi et al. 2016), but no studies were conducted on cultivar-specific proteomic changes. Metabolome analysis provides information on the chemical fingerprints of gene expression and is a crucial component of functional genomics and system biologylevel investigations. It has been known since the beginning of the twentieth century that sweet cherries contain substantial amounts of anthocyanins and polyphenols (Gao and Mazza 1995). Additionally, total and individual contents of phenolic and anthocyanin compounds in sweet cherry cultivars have been previously reported (Usenik et al. 2008); however, the number and type of flavonols and flavan-3-ols present in this tree are less documented. Recently, Crupi et al. (Crupi et al. 2014) presented a comprehensive analytical methodology, based on ‘in-time’ and ‘in-space’ tandem mass spectrometry (MS) techniques, to identify and quantify flavonoid compounds in a typical Italian sweet cherry cultivar cv. Ferrovia. Another study (Smith et al. 2011) evaluated the metabolic changes in primary and secondary metabolic pathways during ripening of sweet cherry fruit treated with ethephon, while Hayaloglu and Demir (2015) investigated the anthocyanin, phenolic and volatile compositions and sensory characteristics of sweet cherry cultivars.

10.2.3 The Epigenetics Interference A new method for detecting epigenetic diversity was created by adapting the amplified fragment length polymorphism (AFLP) technique in order to analyse genomewide sequence-specific methylation status without a priori knowledge of genome sequence (Reyna-López and Ruiz-Herrera 2004). This method is called methylationsensitive amplification polymorphism (MSAP) and has become a powerful and highly informative tool in epigenetics (Cervera et al. 2002). New studies demonstrated that epigenetic modifications could mediate environmentally induced phenotypic variation and adaptation, while epigenetic modifications could be inherited to future generations and have potentially evolutionary effects (Avramidou et al. 2015a). Finally,

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the epigenetic diversity found within and among natural wild cherry populations was uncoupled from genetic variation (Avramidou et al. 2015b).

10.3 Exploring Genetic Resources and Genotype Characterization European laboratories were also actively involved in the development of microsatellite or SSR markers for cherries, which have been widely used for germplasm resources’ characterization (see review Ganopoulos et al. 2016a and references therein). Although at present SSR markers seem to be the best choice for genetics and genomics studies, marker systems with even higher throughput, such as SNPs, have been developed based on whole-genome sequencing data. Using NGS technology, the RosBREED Consortium has developed a 6K SNP array for diploid sweet cherry (P. avium) and allotetraploid sour cherry (P. cerasus) (Peace et al. 2012). NGS sequencing was used to discover SNPs and provide markers for genetic and genomic studies. High-density genetic maps were built using a cherry 6K SNP array (Klagges et al. 2013), which was used to identify genes that may be involved in fruit quality. Noteworthy, in a genetic study (De Franceschi et al. 2013) fruit size was correlated with the presence of two SNPs located within the confidence intervals of major QTLs previously discovered in sweet cherry.

10.3.1 The Self-incompatibility Alleles In general, most of the Prunus species exhibit gametophytic self-incompatibility (GSI) that prevents self-pollination. Breeding of modern cherry cultivars relies extensively on the use of a self-compatible cultivar (‘Stella’), resulting in an intermediate level of genotypic variation compared to other species of the same genus. RNasebased self-incompatibility studies indicate that GSI is the most phylogenetically widespread genetic system that favours outcrossing in plants. A pollen grain sharing one of the self-incompatibility alleles (S-alleles) of the style fails to achieve fertilization, since the pollen tube cannot develop (Franklin-Tong and Franklin 2003). Self-fertilization is therefore impossible, and mating between relatives is reduced. Therefore, commercial cherry cultivars, which share S-alleles, need the presence of conspecific self-compatible genotypes, which will serve as efficient pollinators. The S-incompatibility system is studied by PCR through S-RNase genotyping (Sonneveld et al. 2005; Wiersma et al. 2001). Currently, there is an excess of 20 S-RNases that have been identified leading to the development of 25 incompatibility classes (23 self-incompatibility classes and two classes containing the S 4 -allele. The 23 incompatibility groups include only 10 S-alleles (S 1 –S 7 , S 9 , S 12 , S 13 ) (Schuster et al. 2007). S-allele diversity is generally high (Ganopoulos et al. 2010; Ercisli et al. 2012; Cachi

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and Wünsch 2014). Many geographical centres of origin are characterized by the high frequency of one particular allele, for instance, haplotype S 16 in Sicily (Marchese et al. 2007) and S 1 haplotype in Germany (Schuster et al. 2007). Notably, there are cases where one particular S-allele is completely absent from a defined geographical area, such as S 12 in native Greek populations (Ganopoulos et al. 2012).

10.3.2 Relationship with Other Cultivated Species and Wild Relatives SSR marker-based analysis of wild cherry revealed a higher genetic diversity than sweet cherry. For expected heterozygosity the range of values reported in natural populations is between 0.472 and 0.918 (Avramidou et al. 2010; Schueler et al. 2003). The corresponding values for sweet cherry are reportedly below this range. S-allele diversity in wild cherry is also very high; up to 16 different S-alleles and 29 incompatibility groups have been reported thus far using different methods (Schuster et al. 2007). S-allele diversity of wild cherry is generally concordant to that observed for sweet cherry (Ercisli et al. 2012; Ganopoulos et al. 2012). Ingenetic study of (Ganopoulos et al. 2013b) based on molecular genetic markers (SSRs), it was found that the genetic diversity of wild and sweet cherry in the same general locality of northern Greece is greater compared to the genetic diversity found among distant Greek wild cherry populations.

10.4 Molecular Marker Repertoire and Genotype-Based Diversity Analysis in Sweet Cherry Isoenzymes were the first genetic markers to assess cherry genetic diversity based on the variation detected by the different alleles present in different enzyme systems (Beaver et al. 1995; Granger 2004). Random amplified polymorphic DNA (RAPD) markers were initially used to identify unique genotype profiles with the amount of polymorphism identified to be rather low (Stockinger et al. 1996; Gerlach and Stosser 1997). SSR primers were developed both in sweet and wild cherry (see review by Ganopoulos et al. 2016a and references therein). Initially, SSR primers were transferred from sour cherry and peach (Dirlewanger et al. 2002), followed by specific development of SSRs for P. avium (Lacis et al. 2009; Antonius et al. 2012). The amount of polymorphism identified was rather low probably reflecting a generally narrow genetic base in cultivated sweet cherry germplasm (Wünsch and Hormaza 2002). Reported average values range between N = 2.80–3.30 for the number of alleles per locus, 0.460–0.820 for expected heterozygosity and between 0.450–0.650 for polymorphic information content (see review by Ganopoulos et al. 2016a and references therein).

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Phenotypic and genotypic characterizations of Greek sweet cherry genetic resources have been documented by our group (Ganopoulos et al. 2011, 2016b). However, there are no reports about phenotyping with high-throughput tools. Although at present SSR markers seem to be the best choice for genetics and genomics studies, marker systems with even higher throughput, such as single-SNPs, have been developed based on whole-genome sequencing data. SNP diversity started to be investigated rather recently in cherry (Fernandez i Marti et al. 2012; Ganopoulos et al. 2013a). Fernandez i Marti et al. (2012) and Ganopoulos et al. (2013a) used high resolution melting analysis (HRM) for SNP detection. Expected heterozygosity was around 0.50 in both studies (range of 0.518–0.555) (Campoy et al. 2016) used the RosBREED cherry 6K SNP array v.1, in cherry landraces and cultivars developed by breeding programmes. Notably, they found less diversity in the latter in agreement with earlier similar investigations. Using NGS technology, the RosBREED Consortium has also developed a 6K SNP array for diploid sweet cherry (P. avium) and allotetraploid sour cherry (P. cerasus) (Peace et al. 2012). NGS sequencing was used to discover SNPs, providing markers for genetic and genomic studies.

10.5 Molecular and Genomics-Assisted Breeding for Climate-Smart Traits in Cherries Conventional breeding of fruit trees is restricted mainly by their long juvenile period for seedlings selection (Rikkerink et al. 2007). Cherries possess a GSI system, which prevents self-pollination as well as pollination between incompatible genotypes. Thus, compatible varieties have to be carefully chosen before the establishment of new orchards. Teams from Greece have widely contributed to the development of rapid and efficient molecular techniques for the identification of the GSI alleles of cherry varieties (Ganopoulos et al. 2010, 2012). Genetic mapping and QTLs detection studies constitute the first step towards the implementation of MAS in cherry. Among the three existing sweet cherry genetic maps, two of them were built by European teams (Clarke et al. 2009; Klagges et al. 2013). Moreover, a French group was the first to report QTL detection studies on several important agronomic traits of sweet cherry (Quero-Garcia et al. 2014). Nowadays, advancement in plant genomics allows a cost-effective and highdensity genotyping of genome-wide DNA polymorphisms (Davey et al. 2011) allowing the application of efficient plant breeding approaches (Varshney et al. 2014). Additionally, high-throughput NGS genotyping techniques provide opportunity to obtain the required number of markers on millions of lines, either through SNP genotyping or by implementing GBS approaches (Sonah et al. 2013). These approaches allow the accurate characterization of positions of QTLs among existing cultivars without the development of segregating populations (Khan and Korban 2012). Thus, molecular markers that have significant association with climate-smart traits can be easily used in MAS breeding programmes. Because cherries have quite a long

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Fig. 10.2 Genomics-associated approaches for climate-smart traits and disease resistance in sweet cherry

generation time, the recruitment of MAS approaches, ideally when coupled with approaches of genomics-assisted breeding, increases the selection accuracy and is promising methods for promoting the efficiency of cherry breeding. Genome-wide predictions and related GS utilize genotypic and phenotypic data on a breeding population to calculate the genome-estimated breeding values (GEBVs) (Spindel and McCouch 2016). The accuracy of GEBVs is directly proportional to increase the efficiency of selection (Heffner et al. 2010). Thus, exploitation of available genetic resources in combination with the usage of innovative molecular approaches for genome-wide association studies (GWAS) and application of GS could lead to considerable improvements in sweet cherry to diseases and effects of climate change (Fig. 10.2). High-throughput genomics combined with GS will offer rapid and targeted selection of populations for improved stress-tolerant varieties (Spindel and McCouch 2016). Therefore, innovative approaches in genotyping and phenotyping enable more efficient data for identification of quantitative characters and deciphering the genetic basis of agronomical important traits, such as disease resistance, in sweet cherry under climate change (Fig. 10.2).

10.6 Genomics Approaches for Disease Resistance Selection in Sweet Cherry Sweet cherry is plagued by many pathogens including fungi, bacteria and viruses causing foliar, fruit, vascular and root diseases that cause a significant reduction

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Fig. 10.3 Symptoms of infection of sweet cherry by fungal isolates of Monilinia spp. (a) and Wilsonomyces carpophilus (b) in fruit and leaf, respectively

in yield. These pathogens are major limiting factors in sweet cherry crops production, while long-distance aerial dispersal is an important survival strategy at least for the biotrophic pathogens. Among the most common fungal pathogens are these of Monilinia spp. causing brown rot disease of sweet cherry fruits (Fig. 10.3). Most sweet cherry cultivars grown commercially are highly susceptible (HS) to powdery mildew, caused by Podosphaera oxyacanthae (Kappel et al. 2012), whereas most seedlings are also susceptible to Wilsonomyces carpophilus (Fig. 10.3) showing disease symptoms on their twigs, leaves, buds and petioles (Ahmadpour et al. 2009). During storage, sweet cherry fruits can undergo serious post-harvest decays caused mainly by Monilinia spp. or Botrytis cinerea, and occasionally by Rhizopus stolonifer, Alternaria alternata, Penicillium expansum and Cladosporium spp. (Romanazzi et al. 2001). Vascular wilt and root rot are usually caused by soilborne pathogens including fungi (Fusarium spp.) (Urbez-Torres et al. 2016) and oomycetes of genus Phytophthora (Peronosporales) (Türkölmez and Dervis 2017). Epidemics of these diseases are highly related to environmental factors. Furthermore, in sweet cherry, the bacterial canker is caused by the bacteria Pseudomonas syringae pv. syringae and Pseudomonas syringae pv. morsprunorum (Moore 1988), and their effective control is quite unachievable. Finally, sweet cherry plantations are also affecting by viruses including Prune dwarf (PDV) and Prunus necrotic ringspot (PNRSV) (Song et al. 2013). Plants have evolved sophisticated systems against various invading pathogens. The primary immune response includes the recognition of pathogens by cell-surface pattern-recognition receptors (PRRs) and is collectively referred to as PAMPtriggered immunity (PTI). Perception of microbial elicitors by plants can trigger various cellular responses such as the activation of receptors including FLS2 and EFR (Gomez-Gomez and Boller 2000) at the plasma membrane following by activation of downstream signal transduction pathways such as the MAPK cascade.

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Upon pathogen infection, a rapid increase in the plant cytosolic Ca2+ concentration is commonly observed, which is also a regulator of reactive oxygen species production such as superoxide (O2− ) and hydrogen peroxide (H2 O2 ) and the localized programmed cell death by a hypersensitivity response (HR) (Blume et al. 2000). Effector-triggered immunity (ETI) is a second arm of immune response and functions often in the host cell cytoplasm, either acting directly by detecting pathogen effectors, or acting indirectly by regulating the plant proteins that have been altered by these effectors (Bhattacharjee et al. 2013). Plant defence mechanisms are constantly evolving to respond on time against new diseases-causing treats (Zambounis et al. 2016a), such as evolving pathogen isolates and climate changes (Whitham et al. 2016). Climate change alters dramatically the susceptibility of the plant host, and unfortunately, adaptation of pathogen genome to new environment progresses happens faster than in plant genome in a given pathosystem. Thus, new virulent pathogen isolates can defeat resistance (R) and pathogenesis-related (PR) genes. Effects of climate change on sweet cherry resistance against pathogens have received little attention to date. Development and evaluation of sweet cherry cultivars with elevated disease resistance against the most destructive phytopathogenic organisms of this species is an ongoing activity, worldwide. Therefore, increasing resistance to the major diseases by deciphering the genetic basis of resistance and subsequently creating genotypes with durable disease resistance is one of the basic prerequisites of sweet cherry breeders in the upcoming changes in climate. As a consequence, the assessment of disease resistance in cherry breeding germplasm is a prerequisite in terms of maintaining and enhancing the utility, durability and acceptance of new cultivars, besides the establishment of disease management strategies (Zambounis et al. 2016a). Despite that this procedure is rather a time-consuming approach, previous studies have evaluated continuously the resistance of sweet cherry cultivars to economically important pathogens including the causal agents of powdery mildew and bacterial canker (Olmstead and Lang 2002; Mgbechi-Ezeri et al. 2017). The whole-genome sequencing of sweet cherry and the relevant genetic maps indicate that the structure of genome is rather compact and similar to that of the peach meaning that the positions of QTLs for fungal disease resistances are conserved and overlapping between the two genomes (Shirasawa et al. 2017). In spite of the recent advances in genomics resources, there are still only a few reports of MAS application for diseases resistance evaluation of cherry germplasm collections (Ru et al. 2015). Application of MAS approaches could be effective for improving disease resistance evaluation in cherry, as effective response to main pathogens is controlled by a small number of major genes and large QTLs (Iwata et al. 2016). However, and despite that genetic loci and QTLs for disease resistance have been reported in deciduous trees, as those which are related with scab in apple (Bus et al. 2010), plum pox virus in apricot (Soriano et al. 2008), brown rot in peach (Pacheco et al. 2014) and downy and powdery mildew in grapevine (Riaz et al. 2011; van Heerden et al. 2014), there are only a much less insights in cherries. In any case, mapping of QTLs conferring resistance against fungal pathogens has always been in spotlight, as the majority of these traits are inherited quantitatively. Construction of linkage maps and functional

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molecular markers for linkage analysis and mapping of QTLs associated with disease resistance should be of important value (Miladinovi´c et al. 2015).

10.6.1 Large-Scale Identification of Genes Involved in Sweet Cherry–Pathogens Interactions Breeding for powdery mildew resistance in sweet cherry seems to be quite promising as a single major gene putatively named PMR is responsible for the inheritance of resistance in a dominant manner among progenies from biparental crosses and their reciprocals of commercial sweet cherry cultivars segregation lines (Olmstead and Lang 2002). Main objective towards powdery mildew resistance in sweet cherry is to accurately identify flanking and cosegregating functional markers to this gene. Additionally, Pseudomonas syringae pv. syringae (Pss) and Pseudomonas syringae pv. morsprunorum (Psm), which are the causative agents of bacterial canker, are among the most economically important bacterial pathogens of sweet cherry (Renick et al. 2008). Management potential of this bacterial disease is often difficult because of the lack of an effective chemical control (Kennelly et al. 2007). Breeders’ selection of well-defined sources of resistance alleles and their incorporation into new sweet cherry cultivars would have tremendous impact towards an effective disease management (Mgbechi-Ezeri et al. 2017). Besides, this pathosystem provides an outstanding example of convergent co-evolution of pathogenicity allowing evolutionary questions to be addressed, such as how disease epidemics emerge and which is the ecological processes driving the evolution of pathogenicity (Monteil et al. 2016). Thus, genome-wide analyses of the effector genes were recently used to assess the evolution of pathogenicity among diverse strains of Pseudomonas syringae causing bacterial canker of cherry (Hulin et al. 2018). Besides, transcriptional dynamics of expression profiling of exocarp-associated genes of the developing sweet cherry (Alkio et al. 2014) indicates that resistance to the pathogen infections is strongly associated with the development of the exocarp, which is being specifically engaged in defence responses by the constitutive expression of defence genes (such as metallothioneinlike and lipid transport proteins) in specific contigs such as in Pa_11565 (a Snakin-1 homologue).

10.6.2 Genomics-Assisted Breeding and Evolutionary Concepts Towards Disease Resistance in Sweet Cherry In plants, pathogen recognition is the first step of defence reactions and is often mediated by a plethora of rapidly evolving receptors. The majority of them contain specific ligand-binding and signal transduction domains, such as leucine-rich repeats (LRRs) and NB-ARC domains, respectively, the most highly expanded group of

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genes linked directly to R-genes’ functions (Debener and Byrne 2014; Zambounis et al. 2016a). Besides, in plants, MAS approaches employing innovative molecular mapping technologies, besides traditional breeding practices towards the selection of resistant germplasms were targeted mainly the family of NBS (nucleotide-binding site)/LRRs-containing genes, however, without assigning an exact functional confirmation to individual genes (Dangl et al. 2013). Furthermore, the resistance gene analogues (RGAs) are a crucial resource for the expansion of functional molecular markers employing in numerous disease resistance breeding programs (Perazzolli et al. 2014). The LRR domains of RGAs, the largest class of R-genes, during their evolutionary expansion depict usually new ligand-binding specificities commonly under diversifying selection via a number of different mechanisms, ranging from point mutations at variable residues and variations in repeat numbers to tandem gene duplications and conversions (Zambounis et al. 2016a). Furthermore, the accurate location of these positively selected amino acid residues in the solvent-exposed regions of the LRRs domains is quite crucial for acquiring novel gene functions (Zambounis et al. 2016b). Conclusively, the evolution patterns of NBS/LRRcontaining genes, as of their RGAs counterparts, appear to be rather a complex process (Sekhwal et al. 2015). In cherry genome, it was revealed that positive selective signatures are employed on NBS-LRR-encoding genes and particularly on the RGAs, assuming that successive episodes of diversifying selection might promote to the acquisition of novel pathogens recognition repertoires (Zambounis et al. 2016a). These results could be exploited to the ongoing cherries’ disease resistance breeding approaches in the near future. The employment of NGS approaches, which have highly facilitated the rapid identification of resistance genes throughout genome-wide association studies coupled with biparental QTL mapping, would decipher the phenotypic variations of disease resistance across cherry cultivars and germplasm accessions (Brachi et al. 2011). These approaches along with the recruitment of genomics-assisted breeding approaches might be especially adaptable in cherries for superior genotypes selection during the pre- and post-harvest periods of diseases outbreaks towards their genetic improvement.

10.7 Future Prospects and Conclusions From a biological perspective, the cherry genomics research opportunities lie in the deep understanding of the mechanisms underlying the adaptation of climatesmart traits and disease responses against highly pathogenic and emergent pathogens strains. Nowadays, the strategies for the improvement of the above traits should exploit the related genomics data towards the employment of MAS and GAB tools in the evaluation and selection of superior cherries varieties with elevated disease resistance. The implementation of NGS approaches has been already utilized in various de novo sequencing, WGRS, GBS and RNA-seq analyses in sweet cherry. Furthermore, the technical advances of SNPs identification will facilitate the exploitation

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of high-density array-based genotyping chips. These approaches should focus on the identification of genomic regions where mutations persist across cherry populations using genome re-sequencing, deep knowledge of RNA regulation at the transcriptional and posttranscriptional level, increased protein and metabolite annotation and improved accuracy and standardization for genotype phenotyping. These approaches would enable the identification of genes and genetic loci responsible for agronomic traits, such as flowering time, dormancy and defence reactions against pathogens, through the analysis of the phenome, metabolome, proteome or transcriptome. Despite the importance of these data and the plethora of information that can be gained from each approach, still no integrative approaches have reported in sweet cherry in terms of deepen deciphering our understanding of the complex biological mechanisms for breeding purposes towards improvement of climate-smart traits and disease resistance against pathogens. Acknowledgements This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No. 148.

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Correction to: Genomic-Based Breeding for Climate-Smart Peach Varieties Yolanda Gogorcena, Gerardo Sánchez, Santiago Moreno-Vázquez, Salvador Pérez, and Najla Ksouri

Correction to: Chapter 8 in: C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_8 Several sections of chapter 8 have been removed because of ownership concerns. The correction chapter and the book has been updated with the changes.

The updated version of this chapter can be found at https://doi.org/10.1007/978-3-319-97946-5_8

© Springer Nature Switzerland AG 2023 C. Kole (ed.), Genomic Designing of Climate-Smart Fruit Crops, https://doi.org/10.1007/978-3-319-97946-5_11

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