Rhizosphere Biology: Interactions Between Microbes and Plants [1st ed.] 9789811561245, 9789811561252

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Rhizosphere Biology

Vadakattu V. S. R. Gupta Anil K. Sharma  Editors

Rhizosphere Biology: Interactions Between Microbes and Plants

Rhizosphere Biology Series Editor Anil K. Sharma Biological Sciences, CBSH, G. B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand, India

The Series Rhizosphere Biology, emphasizes on the different aspects of Rhizosphere. Major increase in agricultural productivity, to meet growing food demands of human population is imperative, to survive in the future. Along with methods of crop improvement, an understanding of the rhizosphere biology, and the ways to manipulate it, could be an innovative strategy to deal with this demand of increasing productivity. This Series would provide comprehensive information for researchers, and encompass all aspects in field of rhizosphere biology. It would comprise of topics ranging from the classical studies to the most advanced application being done in the field. Rhizoshpere is a dynamic environment, and a series of processes take place to create a congenial environment for plant to grow and survive. There are factors which might hamper the growth of plants, resulting in productivity loss, but, the mechanisms are not very clear. Understanding the rhizosphere is needed, in order to create opportunities for researchers to come up with robust strategies to exploit the rhizosphere for sustainable agriculture. There are titles already available in the market in the broad area of rhizosphere biology, but there is a major lack of information as to the functions and future applications of this field. These titles have not given all the up-to-date information required by the today’s researchers and therefore, this Series aims to fill out those gaps.

More information about this series at http://www.springer.com/series/15861

Vadakattu V. S. R. Gupta • Anil K. Sharma Editors

Rhizosphere Biology: Interactions Between Microbes and Plants

Editors Vadakattu V. S. R. Gupta Agriculture and Food, CSIRO Urrbrae, Australia

Anil K. Sharma Department of Biological Sciences CBSH, G.B. Pant University of Agriculture & Technology Pantnagar, Uttarakhand, India

ISSN 2523-8442 ISSN 2523-8450 (electronic) Rhizosphere Biology ISBN 978-981-15-6124-5 ISBN 978-981-15-6125-2 (eBook) https://doi.org/10.1007/978-981-15-6125-2 # Springer Nature Singapore Pte Ltd. 2021 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Rhizosphere plant–microbe interactions are diverse, spatially and temporally dynamic, influenced by plant, soil biophysical environment, and are critical to plant health and crop productivity. It is well established that bacteria and fungi around a plant's root in the rhizosphere, the miniature ecosystem around the root, can influence both the root's form and its physiology. The rhizosphere concept, coined in 1904 by Lorenz Hiltner about the roles of soil microorganisms in plant nutrition and health, has initiated a century-long research and discussion about organismic interactions between plants and microbes. More than 50 years ago, Dr Ralph Foster’s (CSIRO, Adelaide) electron micrographs revealed the intricate structure of rhizosphere, rhizoplane, and endosphere environments and the interplay between plant root, microflora, and protozoa. Rhizosphere microbiota provides a valuable potential resource of plant probiotic and growth-promoting functions capable of conjugating crop productivity within sustainable agricultural systems. It is, therefore, important to understand the dynamics of rhizosphere interactions in order to develop practical strategies that would help improving yield and maintain ecosystem health. During the last decade, there has been a renewed interest in exploring the dynamics of the rhizosphere, using omics tools, for its composition and organismal interactions occurring in the complex spatial structuring at the root–soil interface and their key drivers during the crop growth. Recent research has shown structural and functional diversification of root-associated microbial communities of crop varieties, wild and domesticated accessions of barley, maize, canola, peas, and various Arabidopsis accessions, etc. Some of these findings also identified bacterial taxa which were positively correlated with crop performance or yield. This led to an intense effort to identify the plant-based traits that modulate the genetic structure and diversity, gene expression, and functional profile from the outer realms of rhizosphere to inside the root. This new understanding has highlighted an attractive avenue that would help to better harness beneficial outcomes from plant– microbiome interactions. This book is a compilation of the latest knowledge on plant and microbial aspects of rhizosphere biology covering different ecological, molecular, and biochemical characteristics of rhizosphere and endosphere interactions. It contains 15 chapters, each prepared by authors who are internationally recognized for their knowledge and

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expertise in a particular area of rhizosphere interactions. Additionally, it covers the cross-talk between plants and microbes including quorum-sensing signal molecules, plant interactions with abiotic factors, and potential ways rhizosphere microbial composition and functionality could be manipulated for enhanced and efficient benefits. The different chapters cover key areas such as (1) factors driving rhizosphere biology and interactions, (2) diversity of phenotypic and functional groups, (3) functional significances of rhizosphere interactions, and (4) how best to manipulate rhizosphere interactions. A special feature of the papers is that they highlight the benefits of using the latest omics (metagenomics, transcriptomics, and proteomics) and isotopic tools in dissecting the plethora of mechanisms, genes, and metabolites involved in the multitrophic interactions. Endophytic bacteria, those that colonize the internal tissue of the plant showing no external sign of infection or negative effect on their host, have been found in all plants and form a range of relationships including symbiotic, mutualistic, commensalistic, and trophobiotic interactions. This type of plant microbiome is now considered as the second genome of the host plant and concepts such as “holobiont” comprising the plant with its endophytic microbiome as an extended phenotype and a unified system. In the chapter on bacterial endophytes, Tosi et al. (Chap. 1) present a comprehensive summary of the latest knowledge about the diversity and functions of bacterial (including actinobacteria) endophytes, their influence on plant fitness, and the potential to manipulate their functions in agroecosystems. The presence of a taxonomic overlap between endophytic and rhizospheric communities and clear community shifts between these compartments confirms the idea that rhizosphere is a key habitat regulating endophytic communities. Although soil type can have a major influence, strong and significant host genotype effects on the diversity resulting in distinct taxonomic composition of endophytic bacteria have been shown with a variety of plants. However, the observation of differential abundance, core microbiomes, new knowledge on the heritability of the specific taxa and their links to plant genotype through genome-wide association studies is needed in order to develop designer plant–microbiome combinations that maximize beneficial functions. The involvement of root exudates as carbon and nutrient sources in modifying the rhizosphere microbial communities, as proposed by Albert Rovira more than 50 years ago, is now extended to include signal molecules and root architecture influencing the microbiome composition. In Chap. 9, Haichar and Achouak describe how newly introduced and modified genes in plants influence the quality and quantity of root exudates and in turn rhizosphere microbiome. It is increasingly becoming clear that the ability of soil microbes to colonize a particular plant species is fuelled and modulated by the release of signals by either or both partners and are only recognized by the right partner. Taking the knowledge from legume–rhizobia and plant–mycorrhizal symbioses, Antar et al. summarize (Chap. 13) the latest knowledge about the signals involved in other beneficial plant–microbe interactions and microbe–microbe signal interactions. In spite of recent findings about the intricacies of rhizosphere interactions, the exact molecular mechanisms governing the complex root–soil–microbe interactions remain largely unknown. Balasubramanian et al. present (Chap. 14) a review of

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what is known about the strategies for manipulating the rhizosphere region with a focus on engineering the root H+ efflux and organic anions, secondary metabolite composition of root exudates, alterations in root biomass accumulation, and belowground carbon allocations for improved plant performance. The carbon inputs by plant roots not only provide the primary source of organic C into the soil modifying rhizosphere microbiome, but the chemical composition of exudates also strongly influences the metabolic potential of rhizosphere-enriched microbes along with mediating nutrient fluxes in the rhizosphere. In Chap. 2, PettRidge et al. provide evidence showing the downstream effects of rhizosphere dynamics on the colonization of nearby soil minerals, degradation of prior season’s root litter, and the balance of stabilized versus lost soil carbon. Furthermore, this study provides an excellent example illustrating the benefits of using the latest genomic and isotopic techniques to unravel the mechanisms of C flow between growing plant roots, soil microbial communities, and the surrounding mineral matrix. The beneficial effects of these rhizosphere interactions on nutrient fluxes and availabilities could also improve plant nutrition through increased nutrient use efficiency and, as discussed by Paterson and Mwafulirwa (Chap. 3), provide a realistic means of improving plant health and productivity while potentially also mitigating environmental impacts. Also, recent findings about the diazotrophic communities in the rhizosphere and endosphere in terms of their diversity and functional capacity have rejuvenated the old idea of harnessing the biological N fixation in nonleguminous crops through manipulation of this specific functional group; the chapter by Roley (Chap. 4) presents the latest knowledge on this topic. The rhizobia-legume and mycorrhizae–plant interactions are two well-established examples of plant–microbe symbiosis with extensive research and knowledge about the mechanistic aspects of the beneficial interactions. Recent research has shown that the interactions of “Arbuscular Mycorrhizal Fungi” (AMF), the obligate biotrophs, in the rhizosphere are not just restricted to host plant but involve bacteria in the rhizosphere and hyphosphere; Wang and Feng discussed (Chap. 11) new insights into interactions between AM fungi and other organisms in the rhizosphere. Plant health status affected by the presence of pathogens and root disease incidence could be a driver for change in the root microbiome as discussed in the chapter by Barnett (Chap. 8) and it was proposed that microsite-based variation between healthy and diseased niches in the root system could ultimately lead to the development of disease suppressive microbiomes; however, the exact mechanisms for such community changes remain elusive at present. Alternatively, plant–microbe interactions in the rhizosphere can have a significant impact on plant health acting as the first line of defense in the rhizosphere. Therefore, identification of plant genetic traits involved in the recruitment of beneficial microorganisms, i.e. promoting probiotic microbial community, would help improve plant defenses against biotic stresses; the chapter by Chiaramonte et al. (Chap. 7) discusses strategies and potential to explore this option through plant breeding programs. Plant root–biota associations in the rhizosphere involve complex networks and interactions between micro- and macroorganisms across multiple microsites and in intricate spatial structuring that can vary temporally during crop growth from

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seedling to maturity. Recent evidence from genomic and transcriptomic studies clearly indicates that there is a strong association between rhizosphere development and taxonomic makeup including the succession in bacterial community in field environments. This has been demonstrated for several crops including cereals, canola, cotton, and other crops (Richardson et al., Chap. 5). The major predators of microorganisms such as protists, faunal nematodes, and microarthropods can modulate the composition of rhizosphere microbiome through preferential feeding, with functional consequences in plant performance by affecting nutrient cycling, pathogen density, etc. (Geisen and Quest, Chap. 12). As rhizosphere food webs are not universally identical, the functional importance of microbial–faunal interactions is modulated by the soil habitat structure and management. The concept of succession in rhizosphere microbiome dynamics also leads to the idea of “legacy” in that the rhizosphere of crop leaves a footprint in the soil systems affecting the following crops. Since rhizosphere interactions involve modifications in soil physicochemical and biological components, the legacy effects should incorporate physical, chemical, and biological effects that potentially endure beyond the root that created it. In the chapter by Oliver et al. (Chap. 6), they suggest that constraints of destructive sampling can be overcome from the recent advances in micro X-ray computed tomography, but it still requires other complementary techniques to determine the extent of the rhizosphere legacy. It was considered that the most effective form of manipulation of rhizosphere and endosphere microbiomes is through the use of beneficial microorganisms, “bioinoculants” either singly or as consortia for biocontrol to reduce or eliminate plant disease effects or effects of abiotic stresses such as from drought/water-stress or salinity effects. Through the use of latest omics tools (metagenomic, transcriptomics, and metabolomics), it is now possible to describe in-depth the networks of members of rhizosphere microbiomes including the effects of introducing inoculants as well as identifying mechanisms to manipulate and engineer microbiomes (Franco, Chap. 15). For example, recent evidence suggests that rhizobacteria with the capacity to produce ACC deaminase can initiate a cascade of changes in plant physiological and biochemical responses resulting in increased tolerance to abiotic stresses in a broad range of plant species (Sharma et al., Chap. 10). As a result of fast-changing global climate scenario with predictions for reduced rainfall and increased effects of other abiotic stresses across many agricultural regions worldwide, exploitation of such beneficial plant–microbe interactions to alleviate abiotic stress effects in crops should be one of the key approaches to promote resilience and improve global food production. A majority of the recent research on the makeup and dynamics of rhizosphere microbiome until now has concentrated on taxonomic/phylogenetic makeup of the microbiome mainly about who is present and variations with plant type, management, and soil environment. In view of the extensive diversity and the dynamic spatial and temporal structure of the microbiome, interpretation and extrapolation of variations in phylogenetic makeup in terms of their functional potential and resilience have been found to be not straightforward. Hence future research on plant-trait based microbiome interactions requires investigations targeting specific functional groups associated with key

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plant traits to help with the development of management interventions that can improve productivity in agricultural systems. Through a combination of genomic, transcriptomic, and isotopic tools, it should be possible to directly follow the dynamics of specific microbial functional group and link it with associated functional fluxes. Such research would facilitate the identification of key drivers from plant, microbial, and process perspectives, thereby assisting in the development of new designer plant holobionts that utilize native soil microbiome through nextgeneration crop breeding, “syncoms” or synthetic communities and management practices for sustainable and resilient food production systems. Urrbrae, Australia Pantnagar, Uttarakhand, India

Vadakattu V. S. R. Gupta Anil K. Sharma

Acknowledgements

With its complexity and diversity of life it hosts, the topic of rhizosphere attracts the interest of many disciplines. As the co-editors of this book, we acknowledge all the authors for preparing excellent summaries with thought provoking discussions on the various topics of rhizosphere interactions. We extend our sincere appreciation to the various reviewers of the manuscripts for their diligence in helping to improve the contributions. Ms. Ranjita Vadakattu provided invaluable help by providing logistical support through the compilation and revision process keeping track of all the contributions. We also thank our parent organizations CSIRO Agriculture and Food in Australia and G.B. Pant University of Agriculture & Technology in India for their support to our participation in preparing this publication.

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Contents

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Bacterial Endophytes: Diversity, Functional Importance, and Potential for Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Micaela Tosi, Jonathan Gaiero, Nicola Linton, Tolulope Mafa-Attoye, Anibal Castillo, and Kari Dunfield Rhizosphere Carbon Turnover from Cradle to Grave: The Role of Microbe–Plant Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jennifer Pett-Ridge, Shengjing Shi, Katerina Estera-Molina, Erin Nuccio, Mengting Yuan, Ruud Rijkers, Tami Swenson, Kateryna Zhalnina, Trent Northen, Jizhong Zhou, and Mary K. Firestone

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Root–Soil–Microbe Interactions Mediating Nutrient Fluxes in the Rhizosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eric Paterson and Lumbani Mwafulirwa

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Diazotrophic Nitrogen Fixation in the Rhizosphere and Endosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sarah S. Roley

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Root Microbiome Structure and Microbial Succession in the Rhizosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alan E. Richardson, Akitomo Kawasaki, Leo M. Condron, Peter R. Ryan, and Vadakattu V. S. R. Gupta Rhizosphere Legacy: Plant Root Interactions with the Soil and Its Biome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ivanah C. Oliver, Oliver G. G. Knox, Richard J. Flavel, and Brian R. Wilson

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Rhizosphere Microbiome and Soil-Borne Diseases . . . . . . . . . . . . . Josiane Barros Chiaramonte, Lucas William Mendes, and Rodrigo Mendes

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Root Disease Impacts on Root-Rhizosphere Microbial Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stephen Barnett

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Contents

Newly Introduced or Modified Genes in Plants Potentially Modulate the Host Microbiome . . . . . . . . . . . . . . . . . . . . . . . . . . . Feth el Zahar Haichar and Wafa Achouak

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Rhizosphere Plant–Microbe Interactions Under Abiotic Stress . . . . Suvigya Sharma, Dinesh Chandra, and Anil K. Sharma

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Arbuscular Mycorrhizal Fungi Interactions in the Rhizosphere . . . Fei Wang and Gu Feng

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Microbial–Faunal Interactions in the Rhizosphere . . . . . . . . . . . . . Stefan Geisen and Casper W. Quist

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Inter-Organismal Signaling in the Rhizosphere . . . . . . . . . . . . . . . Mohammed Antar, Parghat Gopal, Levini Andrew Msimbira, Judith Naamala, Mahtab Nazari, William Overbeek, Rachel Backer, and Donald L. Smith

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Molecular Mechanisms of Plant–Microbe Interactions in the Rhizosphere as Targets for Improving Plant Productivity . . . . . . . Vimal Kumar Balasubramanian, Christer Jansson, Scott E. Baker, and Amir H. Ahkami

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Inoculation Effects in the Rhizosphere: Diversity and Function . . . Christopher M. M. Franco

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Editors and Contributors

About the Editors Vadakattu V. S. R. Gupta is a Senior Principal Research Scientist at the CSIRO Agriculture and Food unit at the Waite campus in Adelaide, South Australia. He has more than 20 years of experience in fundamental and applied, field-based functional microbial ecology research in soil and water ecosystems in Australia, Canada, and India. His research interests include unraveling the complexities of microbial diversity, functional capability, and resilience of microbe–soil–plant interactions for disease suppression and plant nutrition as a key for developing sustainable agricultural systems. His research has identified changes in soil biology in genetically modified crop systems and the impacts of herbicides on soil biological functions. He has published over 100 refereed articles in scientific journals and books and developed soil biology research investment priorities for government and industry funding bodies in Australia. He was awarded the 2015 Prescott Medal by the Soil Science Society of Australia for his outstanding contribution to Soil Science. Anil K. Sharma is a Professor at the Department of Biological Sciences, CBSH G.B. Pant University of Agriculture & Technology, Pantnagar. He was a Visiting Scientist at the University of Basel, Switzerland from July 2003 to November, 2003, and at the University of Helsinki, Finland in 2013. He completed his postdoctoral studies at GSU, Louisiana, USA, and he has extensive research and teaching experience. He is a reviewer for DBT, DST, and MOEF projects and for journals such as the Biocontrol Journal, International Journal of Agriculture, and Microbiology. He holds three patents in the field of plant biology and microbiology and has received a number of prestigious grants. His laboratory is involved in various international collaborations, and he has published more than 84 research articles, 32 review articles, and two books with renowned publishers. He has presented his research on several internationally acclaimed platforms.

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Editors and Contributors

Contributors Wafa Achouak Aix Marseille Univ, CNRS, CEA, UMR 7265 BVME, LEMIRE, ECCOREV FR 3098, Saint-Paul-lez-Durance, France Amir H. Ahkami Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Richland, WA, USA Scott E. Baker Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Richland, WA, USA Vimal Kumar Balasubramanyam Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Richland, WA, USA Stephen Barnett Medical Biotechnology, Flinders University, Bedford Park, SA, Australia South Australian Research and Development Institute, Hartley Grove, Urrbrae, SA, Australia Anibal Castillo Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Josiane Barros Chiaramonte Embrapa Meio Ambiente, Jaguariúna, SP, Brazil Leo Condron Faculty of Agriculture and Life Sciences, Lincoln University, Christchurch, New Zealand Kari Dunfield Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Feth el Zahar Haichar Univ Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5557, Laboratoire d’Ecologie Microbienne, UMR INRA 1418, Villeurbanne Cedex, France Katerina Estera-Molina Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA Gu Feng College of Resources and Environmental Sciences, China Agricultural University, Beijing, People’s Republic of China Mary K. Firestone Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Livermore, CA, USA Richard J. Flavel Faculty of Science, Agriculture, Business and Law, School of Environmental and Rural Science, The University of New England, Armidale, NSW, Australia Chris Franco Medical Biotechnology, College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia

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Jonathan Gaiero Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Stefan Giesen Department of Terrestrial Ecology, Netherland Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands Christer Jansson Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Richland, WA, USA Akitomo Kawasaki CSIRO Agriculture and Food, Canberra, ACT, Australia Oliver G. G. Knox Faculty of Science, Agriculture, Business and Law, School of Environmental and Rural Science, The University of New England, Armidale, NSW, Australia Nicola Linton Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Tolulope Mafa-Attoye Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Lucas William Mendes Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, SP, Brazil Rodrigo Mendes Embrapa Meio Ambiente, Jaguariúna, SP, Brazil Lumbani Mwafulirwa The James Hutton Institute, Aberdeen, UK Global Academy of Agriculture and Food Security, University of Edinburgh, Midlothian, UK Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK Trent Northen Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Erin Nuccio Physical and Life Sciences, Lawrence Livermore National Laboratory, Livermore, CA, USA Ivanah C. Oliver Faculty of Science, Agriculture, Business and Law, School of Environmental and Rural Science, The University of New England, Armidale, NSW, Australia Eric Paterson The James Hutton Institute, Craigiebuckler, Aberdeen, UK Jennifer Pett-Ridge Physical and Life Sciences, Lawrence Livermore National Laboratory, Livermore, CA, USA Casper W. Quist Department of Terrestrial Ecology, Netherland Institute of Ecology (NIOO-KNAW) and Laboratory of Nematology, Wageningen University and Research Centre (WUR), Wageningen, The Netherlands Alan E. Richardson CSIRO Agriculture and Food, Canberra, ACT, Australia

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Ruud Rijkers Systems Ecology, Department of Ecological Science, VU University, Amsterdam, Netherlands Sarah S. Roley School of the Environment, Washington State University, Richland, WA, USA Peter R. Ryan CSIRO Agriculture and Food, Canberra, ACT, Australia Dinesh Sharma Department of Biological Sciences, CBSH, G.B. Pant University of Agriculture & Technology, Pantnagar, India Suvigya Sharma Department of Biological Sciences, CBSH, G.B. Pant University of Agriculture & Technology, Pantnagar, India Shengjing Shi Science Center, AgResearch Ltd., Christchurch, New Zealand Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA Tami Swenson Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Micaela Tosi Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Guelph, ON, Canada Fei Wang School of Resource and Environmental Sciences, Henan Institute of Science and Technology, Xinxiang, People’s Republic of China Brian R. Wilson Faculty of Science, Agriculture, Business and Law, School of Environmental and Rural Science, The University of New England, Armidale, NSW, Australia NSW Office of Environment and Heritage, University of New England, Armidale, NSW, Australia Mengting Yuan Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA Kateryna Zhalnina Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Livermore, CA, USA Jizhong Zhou Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Livermore, CA, USA Institute for Environmental Genomics, Department of Microbiology and Plant Science, University of Oklahoma, Norman, OK, USA

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Bacterial Endophytes: Diversity, Functional Importance, and Potential for Manipulation Micaela Tosi, Jonathan Gaiero, Nicola Linton, Tolulope Mafa-Attoye, Anibal Castillo, and Kari Dunfield

Abstract

Even though beneficial plant–microbe relationships have been studied for over one century, the recognition of a complex microbiome inhabiting the plant is relatively recent and reveals new opportunities for manipulating plant growth and health. Endophytes, commonly defined as non-pathogenic microorganisms inhabiting the plant interior, constitute an important component of the plant microbiome. Specifically, bacterial endophytes gained research interest only in the past decades, due to their role in plant-growth promotion and their potential use in agriculture. New research is continuously published in this topic, with increasing sophistication provided by new technologies such as omics. For this reason, this chapter aimed to summarize current knowledge on bacterial endophytes focusing on three major aspects: (1) current knowledge on their bacterial endophytic diversity and regulation by plant and soil factors, (2) functional aspects of bacterial endophytes and available tools to study them, and (3) role of bacterial endophytes on plant fitness and potential manipulation tools in agroecosystems. To fit the scope of this book, which is the rhizosphere, the chapter focused on soil-borne facultative endophytes, even though we acknowledge the relevance of obligate vertically transmitted endophytes.

M. Tosi · J. Gaiero · N. Linton · T. Mafa-Attoye · A. Castillo · K. Dunfield (*) Environmental Microbiology of Agro-Ecosystems, School of Environmental Sciences, Alexander Hall, Guelph, ON, Canada e-mail: dunfi[email protected] # Springer Nature Singapore Pte Ltd. 2021 V. V. S. R. Gupta, A. K. Sharma (eds.), Rhizosphere Biology: Interactions Between Microbes and Plants, Rhizosphere Biology, https://doi.org/10.1007/978-981-15-6125-2_1

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M. Tosi et al.

Bacterial Endophytes: Definition, Classification, and Scope of this Chapter

Research on bacterial endophytes began growing exponentially in the late twentieth century, mostly fueled by an interest on their role in plant-growth promotion and potential application in agriculture (Turner et al. 1993; Hallmann et al. 1997; Sturz et al. 2000). The term endophytic was applied to microorganisms that could colonize internal tissues of a plant locally or systemically, residing there latently or actively for at least part of their lifetime without causing damage (i.e., commensal or mutualistic relationships) (Wilson 1995; Hallmann et al. 1997). Later findings reported microbial species with both a pathogenic and a beneficial life form in the plant (Kogel et al. 2006), making this definition controversial and giving birth to a new notion where these categories are extremes in an operational continuum instead of two defined groups (Schulz and Boyle 2005; Partida-Martínez and Heil 2011). To what extent this versatility is widespread is still uncertain, therefore, here we exclusively refer to endophytes as those non-pathogenic microorganisms inhabiting the plant interior. Isolating organisms from the plant interior from those inhabiting plant surfaces, such as the rhizoplane or the phyllosphere, is a challenging step. Typically, plant tissues are surface-sterilized before isolation or nucleic acid extraction but, to date, no standardized methods have been defined for different plant species or tissue types, despite the fact that the chosen methodology can strongly affect the results (Reinhold-Hurek and Hurek 2011) (See Box 1.1). Some beneficial plant–microbe relationships have been studied for more than a century, as is the case for mycorrhizal fungi (Frank 1885), rhizobia–legumes (Beijerinck 1888), and Frankia–actinorhizal plants (Bottomley 1911). However, these studies were focused on just those few known associations, and they were mostly approached as one-to-one interactions between a microorganism and a host plant. It was in the last decades that most endophyte research switched to a community approach, acknowledging the different microorganisms that co-exist, as well as the interactions that occur between them and with the host plant (Andreote et al. 2009; Bulgarelli et al. 2013; Gaiero et al. 2013). New technologies like DNA profiling and sequencing became key in unraveling the complexity of endophytic microbial communities and, nowadays, the increasing number of beneficial traits found suggests that a microbe-free plant would hardly survive under natural conditions (Partida-Martínez and Heil 2011). Endophytic communities are such an essential piece of plant fitness that some authors are considering them the second genome of a plant host, where plant and microbiome work as a meta-organism (Lakshmanan et al. 2014). Similar concepts are now widespread in the literature, like “extended phenotype” (Partida-Martínez and Heil 2011) or “holobiont,” comprising the host organism with its symbiotic microbiome (Vandenkoornhuyse et al. 2015). This change of perspective is in synchrony with animal health, where researchers are also recognizing the host and its microbiome as a unified system (Ramírez-Puebla et al. 2013). Bacterial endophytes are usually classified based on their life strategies: while some of them are considered obligate, which means they need the host plant to fulfill

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their life cycle, others are known as facultative for having a free-living phase in the soil (Hardoim et al. 2008, 2015). These authors also introduced the categories of opportunistic endophytes, as those who thrive as epiphytic but sporadically enter the plant, and passenger or passive endophytes, who enter and inhabit the plant without actively seeking to colonize it (Hardoim et al. 2015). Different life strategies are usually associated with different degrees of dependency with the host plant. Facultative endophytes must colonize the plant from the rhizosphere, for which they will probably go through a mutual signaling phase to then enter through root cracks or wounds, germinating radicles, emerging root hairs, among others (Reinhold-Hurek and Hurek 2011; Santoyo et al. 2016). Obligate endophytes, on the other hand, are thought to be transmitted vertically, via seed (Hardoim et al. 2008; Truyens et al. 2015). Bacterial endophytes can inhabit multiple tissues (e.g., roots, stems, leaves, flowers, fruits, seeds, vascular tissues), their communities fluctuate with plant development, and they carry out many functions that could potentially regulate growth and development of the host plant during its whole lifecycle. These effects could begin as early as seed establishment, as was shown by their indispensable role in primary colonization and rock-dwelling by some cacti species (Puente et al. 2009). Continually, more and more complex interactions are being unmasked, from non-rhizobia bacteria inhabiting legume nodules with the potential for horizontal gene transfer (HGT) (Li et al. 2008) to endophytic fungi hosting symbiotic bacteria (Lackner et al. 2009; Desirò et al. 2015). There is much we do not know about their assembly rules, distribution in the bulk soil-rhizosphere-endosphere continuum, or effects on plant fitness, but past research on widely studied endosymbionts and phytopathogens can act as a useful reference for future studies (Sasse et al. 2018). According to the review by Partida-Martínez and Heil (2011), a key question is how endophytes affect plant physiology, ecology, and, in the long term, evolution. If endophytes can shape the plant’s response to multiple biotic and abiotic factors, and if they can be transmitted from generation to generation, they will most probably affect population dynamics and ecological interactions such as competition, herbivory, or pollination (Friesen et al. 2011). In fact, Friesen et al. (2011) stated that the large populations and short generation times of microorganisms would allow trait mediation to evolve on an ecological time scale. Although the effects of bacterial endophytes at the plant community and ecosystem level are still understudied, with most studies carried out on single plants or monocultures, there is increasing interest in their impact beyond the individual plant level (van der Heijden et al. 2016). For instance, synergistic effects on plant biomass and diversity could be expected from the interaction between different groups of symbionts, likely with outcomes affecting ecosystem functioning (van der Heijden et al. 2008, 2016). Most certainly, these complex communities inhabiting plants have major implications in agroecosystems, as they are widespread in grain, pasture, horticultural, floral, and forestry crops (Baldan et al. 2014). It is also possible that plant breeding has inadvertently modified the microbiome of wild ancestors, which was probably more adapted to marginal soils (Wissuwa et al. 2009; Bulgarelli et al. 2013). But bacterial endophytes are not only important for their impact on plant and ecosystem functioning; they also

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constitute a source of novel metabolites to be applied in medicine or industry (Strobel 2003; Smith et al. 2008). A promising aspect for bioengineering is in their capacity to synthesize, at a higher rate, biologically active substances analogous to those synthesized by the host plant (Gunatilaka 2006; Wang and Dai 2011). Box 1.1 Methodological Constraints in DNA Analyses of Bacterial Endophytes Despite great improvements in the last decades, the study of bacterial endophytes still faces many methodological challenges and inconsistencies that need to be solved to validate and consolidate different findings. Many reviews are discussing the potential and the flaws of studying and manipulating bacterial endophytes, but we still lack standardized methodologies to unify these research efforts. During our literature review, we could identify different areas in need of a critical methodological revision, testing, and development of a standardized protocol. The work by RichterHeitmann et al. (2016) constitutes a useful approach to understanding the effectivity and risks attached to different endosphere isolation methods, while it evidences the variability resulting from root morphology traits. Surface-sterilization. Sodium hypochlorite (NaClO) is the most commonly used agent. Usually, a pre- or post-NaClO treatment with ethanol (70 or 95%) is done to improve penetration and sterilization, although it was suggested it could lead to contraction of the plant tissues (Sieber 2002). For optimal results, concentration, exposure time, and agitation should be tested for different plant species and growth stages (Hallmann et al. 2006). Underexposure to NaClO leads to contamination with surface-dwelling microbes and amplifiable DNA (Reinhold-Hurek and Hurek 2011), but overexposure can damage endophytes (Lundberg et al. 2012). Since residual NaClO can cause DNA mutations and artifacts, we must rinse several times with sterile water, and some authors also included a sodium thiosulfate rinse (Rosenblueth and Martínez-Romero 2004; Pereira et al. 2011). Alternative sterilizing agents, like propylene oxide vapor, hydrogen peroxide, or formaldehyde, are less commonly used (Hallmann et al. 2006; Nassar et al. 2005). Prior to chemical treatment, shaking with sterile glass beads (McClung et al. 1983; Reinhold et al. 1986; Sessitsch et al. 2012) or sonication can be used to physically remove microbes attached to the plant surface. Sonication has been used both complementary to surface disinfection (Conn and Franco 2004) or as the main removal procedure (Lundberg et al. 2012; Bulgarelli et al. 2012). Microscopy studies have shown that physical removal was less efficient to remove rhizoplane microorganisms than NaClO (Richter-Heitmann et al. 2016; Reinhold-Hurek et al. 2015), while sonication in particular could cause root tissue damage (Richter-Heitmann et al. 2016). (continued)

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Box 1.1 (continued) Sterilization control. Usually an aliquot of the last water rinse (or an imprint of the sterilized tissue) is incubated in a general culture medium (e.g., TSA or LB), either liquid or solid. Microbial growth (turbidity or colony growth) indicates incomplete removal of surface bacteria (Hallmann et al. 2006). Culture-dependent techniques might not be suitable if the study has a culture-independent approach, and alternative methods should be examined, like microscopy (Turner et al. 2013), PCR (Wemheuer et al. 2017), or sequencing DNA from the last water rinse, similarly to the “kitome” analysis carried out to check for contamination from DNA extraction kits (Kim et al. 2017; Salter et al. 2014). DNA extraction. Generally carried out directly from plant tissues, whole or ground in liquid nitrogen. On pre-extracted endophytic bacteria, it may overcome some issues of target specificity due to the large amount of plant material (Sessitsch et al. 2012). Extraction methods differ in cell lysis efficiency and removal of PCR inhibitors, biasing downstream analyses, and diversity estimations. Hence, when comparing endophytic and soil microbial communities, there is a compromise between optimizing the procedure for each sample type or treating all samples equally, for example, by using only soil DNA extraction kits (Bulgarelli et al. 2012). Finally, there may exist a trade-off between increased reproducibility and total yield of endophytic diversity, as found when comparing commercial kits and SDS- or CTABbased DNA extraction tests (Maropola et al. 2015). Downstream target specificity. Ideally, PCR primers would cover all target taxa but, as we know, modifications and optimizations are continually made even for the highly conserved bacterial 16S rRNA gene (Caporaso et al. 2012). Another common issue is the co-amplification of non-target DNA (e.g., chloroplasts, mitochondria), since samples will have a high ratio of plant DNA relative to bacterial DNA. Several studies have compared the utility of many bacterial 16S primers for use in endosphere microbiome research (Beckers et al. 2016; Dorn-In et al. 2015; Thijs et al. 2017; Klindworth et al. 2013); 799F-1391R, 335F-769R, and 341F-785R have been suggested due to their high coverage of the domain Bacteria. Relic DNA is often discussed as a potential source of variation in soil microbial diversity analyses, since it may persist for months or years (Carini et al. 2017), but its impact is still controversial (Lennon et al. 2018) and, to our knowledge, it has not been explored in the endosphere. However, propidium monoazide may be used to remove contaminating DNA on the root surface, such as from dead microbial cells (Leff et al. 2017). Understanding the limitations. Even though bacterial endophytes were initially defined as those who could be isolated from surface-sterilized plant tissues, complete removal of surface microbes is challenging. Ideally, (continued)

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Box 1.1 (continued) improved and standardized surface-sterilization alternatives should be developed. But when sterilization is not feasible, and depending on the purposes of the study, less rigid concepts might be more suitable, like root-associated or tightly bound bacteria (Lundberg et al. 2012; Donn et al. 2015). In this chapter, we will discuss current knowledge and prospects on the diversity and function of bacterial endophytes, their influence on plant fitness, and their potential to be manipulated in agroecosystems. The unique symbiotic relationship between legumes and nodulating N-fixing rhizobia, sometimes excluded from the group of endophytic bacteria (Partida-Martínez and Heil 2011), will be mentioned in some examples but detailed information can be found within the large body of published literature (e.g., Poole et al. 2018; Wang et al. 2018). Since this book is mostly dedicated to rhizospheric processes, we emphasize soil-borne facultative endophytes. Obligate endophytes may have a strong influence on plant functioning due to their dependency on their host for survival and reproduction, and that they represent an interesting tool for manipulation, since they are transferred between plant generations via seed (Sachs et al. 2004; Friesen et al. 2011). However, for their distinct behavior and the need to fit the scope of this book, we considered that they deserved a separate analysis. We will also purposely overlook bacteria inhabiting plant surfaces, like the phyllosphere and the rhizoplane, although their influence should not be neglected. Surface-inhabiting communities were shown to affect plant functioning (Oh et al. 2012; Vorholt 2012) and they might be intimately related to the endosphere (Hardoim et al. 2008).

1.2

Diversity of Bacterial Endophytes: What Do We Know?

Studies of bacterial endophytes began with simple systems of one-to-one microbe– host interactions, mostly using traditional techniques like isolation, culturing in synthetic media, and in vitro testing of morphological and physiological features. Many bacterial species have been isolated from different plant tissues and species, as reviewed previously (Hallmann et al. 1997; Sturz et al. 2000; Rosenblueth and Martínez-Romero 2006). Although culturing techniques provide some advantages, like working with isolated strains or working in a controlled environment, they are not amenable to studying the large diversity present in natural environments. In fact, many studies were able to capture more diversity with culture-independent than culture-dependent methods (Araújo et al. 2002; Conn and Franco 2004; Pereira et al. 2011; Qin et al. 2012). While for soils it is estimated that only about 0.001–1% of the microorganisms can be grown in synthetic media (Torsvik and Øvreås 2002), this percentage is unknown for endophytes. Yet, the review by Finkel et al. (2017) stated that the endophytic community encompasses a relatively higher percentage of culturable microorganisms. Supporting this idea, Le Cocq et al. (2017) established

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that, compared to soil microorganisms, most endophytes are fast-growing, with a rapid response to nutrient and carbon substrate availability, thus amenable to culturebased work. So far, a complementary application of culture-dependent and -independent methods, together with an improvement of culturing techniques to capture a higher proportion of the actual diversity (Alain and Querellou 2009; Fierer 2017), seems to be a promising approach for exploring endophytic communities. High-throughput culture-independent methods improved data acquisition, boosting our knowledge of endophytic communities and their ubiquity. First attempts to characterize endophytic bacterial diversity with culture-independent methods were carried out with profiling techniques such as terminal restriction fragment length polymorphism analysis (T-RFLP) and denaturing gradient gel electrophoresis (DGGE) (e.g., Conn and Franco 2004; Seghers et al. 2004; Andreote et al. 2009). More advanced technologies like next-generation sequencing (NGS) allowed more insight into the composition of endophytic communities. For instance, bacteria seem to be the dominant and most ubiquitous taxonomic group in the endosphere, followed by fungi and then, if present, archaea (Hardoim et al. 2015; Krishnaraj and Pasha 2017; Kroll et al. 2017). Studies show that only a few bacterial phyla are consistently dominant (Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes), with higher variability at lower taxonomic resolution levels (Bai et al. 2015; Santoyo et al. 2016; Kroll et al. 2017; Liu et al. 2017a). According to a curated database analyzed by Hardoim et al. (2015), these four phyla comprised ~96% of the total prokaryotic sequences consisting of 21 bacterial and 2 archaeal phyla. These authors reported that Proteobacteria was the most dominant group (54%, and Gammaproteobacteria being the most dominant class), followed by Actinobacteria (20%), Firmicutes (16%), and Bacteroidetes (6%). Bacterial phyla found to have a low abundance in the endosphere are Acidobacteria, Planctomycetes, Verrucomicrobia, and Gemmatimonadetes (Hardoim et al. 2015; Santoyo et al. 2016; Liu et al. 2017a). Several research papers whereby endophytic bacterial communities were analyzed using DNA sequencing studies are listed in the reviews by Bulgarelli et al. (2013) and Liu et al. (2017a). Archaeal phyla have also been detected in plant tissues (Euryarchaeota, Crenarchaeota, and Thaumarchaeota), although they are usually present in low abundance (e.g., 1.96 indicate significant phylogenetic clustering) (Vamosi et al. 2009)

Catenulispora appeared to prefer the rhizosphere to background soil. Overall, positive and negative responses to the root were phylogenetically clustered based on the net relatedness index (NRI) and nearest taxon index (NTI) (Webb et al. 2002), which likely reflect the phylogenetic evenness and clustering within community data. In this study, both indices were significantly positive at all time points (NRI, NTI
1.96), indicating clustering within both deep (NRI) and shallow (NTI)

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Fig. 2.4 The diversity in bulk soil and rhizosphere microbial community associated with A. fatua are indicated by (a) OTU richness and (b) phylogenetic diversity (a measure of biodiversity which incorporates the phylogenetic differences between species) in rhizosphere and bulk soils across the stages of plant growth. Data are presented as mean  standard errors (n ¼ 16). The P values calculated using ANOVA are shown in each figure. Data based on the large phylogenetic tree (Fig. 2.3) were used to calculate phylogenetic diversity (Faith’s PD) using the generalized time reversible model in FastTree with a gamma branch-length correction (Price et al. 2010). The tree topology was constrained using a smaller tree composed of representatives for each family, where an OTU with a closely related full-length 16S sequence (97% similar) was selected for each family in the dataset (Nuccio et al. 2016). Faith’s PD was calculated using alpha_diversity.py (QIIME 1.5dev) for the rhizosphere and bulk soils at weeks 0 (bulk only), 3, 6, 9, and 12

branches of the phylogenetic tree (Vamosi et al. 2009). Both the Shi et al. (2015) study and the Nuccio et al. (2016) study suggest that this phylogenetic coherence between the net positive and net negative root responses indicates an evolutionary adaptation of soil bacteria and the development of traits in individual populations that confer rhizosphere competence. Community ecological factors, such as community assembly, diversity, and interactions, may also be affected by the growth of plant roots. In our studies of Avena spp., we have found that rhizosphere bacterial community assembly coincides with increases in network size and complexity, and a concurrent decrease in richness and diversity (Shi et al. 2016). The positive change in bacterial co-occurrence network complexity indicates that root growth may progressively stimulate interactions within microbial communities or induce the development of shared niches as a plant matures (Shi et al. 2016). We saw some evidence for such interactions in our early Avena spp. studies, which suggest that co-occurring groups (modules) of Alphaproteobacteria interact via quorum signaling with homoserine lactone compounds near mature (12-week-old) roots (DeAngelis et al. 2007). Decreasing bacterial diversity over time with root growth is not surprising; if certain members of an assemblage increase in dominance and a constant mass of DNA is sampled, then the traditional richness (and diversity indices) will decline (Fig. 2.4). Overall, our research using the Avena spp. “wild model” system indicates that rhizosphere microbiomes change in composition, function, and responses to plant exudates as plants mature (Bird et al. 2011; Shi et al. 2015; Zhalnina et al. 2018),

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with increasing microbial network complexity, altered functional potential, and shifting viral–host linkages over time (DeAngelis et al. 2008; Shi et al. 2016; Nuccio et al. 2020; Starr et al. 2019). Together, these results imply that temporal changes in rhizosphere microbial composition and function may impact not only plant–microbe interactions but also the broader soil C cycle.

2.5

Role of Rhizosphere Communities in the Soil Carbon Cycle

It is generally accepted that decomposition of plant litter is mediated by a succession of soil microbial populations (Sylvia et al. 2004); however, the mechanisms underlying rhizosphere community succession and assembly, and their subsequent impact on C cycling are just beginning to be explored and connected. DeAngelis et al. (2009) showed that in the presence of Avena spp. roots, microbial community composition and C utilization patterns are significantly different from those in bulk soil. Subsequent studies assessing the microbial capability to breakdown complex C and N sources (using chitinases and proteases) have demonstrated enhanced activity in the rhizosphere and spatial differences within root zones (DeAngelis et al. 2009; Shi et al. 2015, 2016). An analysis of homoserine lactone signals suggests that density-dependent regulation is partially responsible for the enhanced capacity of the Avena rhizosphere community to break down macromolecular compounds (DeAngelis et al. 2008). Proteomics analyses indicate that rhizosphere bacteria actively synthesize proteins associated with sugar transport and utilization (Pett-Ridge and Firestone 2017), while research on specific root exudates, such as oxalic acid, suggests that some exudates may promote carbon loss by liberating organic compounds from protective mineral associations (Clarholm et al. 2015; Keiluweit et al. 2015). Metatranscriptomic analyses of soil from the A. fatua rhizosphere and near decaying roots indicate the development of distinct carbohydrate depolymerization microbial guilds based on shared gene expression over time, and suggest that a succession of microbial functions occurs as individual roots are colonized, age, and decay (Nuccio et al. 2020). Finally, although little is known about the ecology of bacteriophages or viruses of fungi and other eukaryotes in soil, Starr et al. (2019) found significant composition differences and temporal changes in both hosts and RNA viruses in a comparison of rhizosphere, decaying root and bulk soil habitats. Since viral replication can lead to host cell death and release of soluble carbon, virus-mediated lysis of bacterial and fungal cells may play a role in the redistribution of cellular debris and the ultimate fate of root-derived C. Taken together, these studies provide evidence that plant roots alter both resource availability and the ecology of soil microbial decomposers, and shape how plant C is processed. Several of our studies with Avena spp. specifically address how rhizosphere microbial communities mediate the conversion of plant root litter to either SOM or CO2. Using a broad-brush community characterization approach (13C PLFAphospholipid fatty acid analysis), Bird et al. (2011) followed the decomposition of intact 13C-labeled Avena spp. roots for two subsequent growing periods after plant senescence. The 13C (originating as root carbon) was observed in a succession of

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microbial community components, and with time, different groups of soil organisms acted as the primary decomposers of the decaying root debris. The presence of actively growing root systems stimulated the movement of 13C into Gram-positive and Actinobacteria groups, which are known for their oxidative enzyme capacities (Waldrop and Firestone 2004). In a more recent study, Shi et al. (2018) followed the decomposition of 13C root litter in the presence of an active A. fatua rhizosphere over two growing seasons. In this study, growing roots suppressed the rate of root litter decomposition and significantly affected the bacterial, archaeal, and fungal community composition. Ribosomal RNA gene copy numbers of these microbes were on average 20% higher in the presence of growing roots, affecting the relative abundance of at least nine bacterial phyla. Genetic potential measurements made with GeoChip functional gene arrays (He et al. 2007) showed that microbes living near plant roots had relatively more genes coding for low molecular weight compound degradation enzymes, whereas those from unplanted soil had relatively more macromolecular degradation genes (Shi et al. 2018). To evaluate how community structure, genetic potential, and environmental variables all interacted to control root litter decomposition, Shi et al. (2018) used a Mantel analysis to test for pair-wise correlations. The resulting model suggests that the primary impact of live roots on decomposition appears to result from an alteration of soil microbial functional gene profiles. In a third study on the interaction between growing roots, decaying roots, and soil microbial communities, Nuccio et al. (2020) extracted gene transcripts (metatranscriptomes) from soil near live and decaying roots in microcosms containing A. fatua. Focusing on Carbohydrate-Active Enzymes (CAZyme) functional domains and enzymes involved in the degradation of macromolecular plant compounds, Nuccio et al. used a genome-centric approach to show that carbohydrate depolymerization was carried out by a series of microbial guilds with distinct spatial and temporal response patterns in different soil habitats (rhizosphere and detritusphere). These microbial guilds appear to specialize in their use of the different substrates made available by roots of different ages and decomposition stages. While these root substrates—exudates, mucilage, root hairs, and root biomass—are the initial sources of C that enter belowground food webs, the microbial transformation of this C is what determines whether it is retained as SOM or is returned back to the atmosphere.

2.6

Role of Root Exudates

About 30–60% of C assimilated by plants is transferred to roots (Lynch and Whipps 1990), and up to 50% is exuded into the rhizosphere in a range of forms (Table 2.1; van Dam and Bouwmeester (2016)). Many of the interactions between roots and the surrounding microbial community are accomplished through chemical communication driven by root exudates. These interactions have been implicated in plant defense (Baetz and Martinoia 2014), nutrient acquisition (Khorassani et al. 2011), and the regulation of soil bacterial and fungal community composition (Broeckling et al. 2008; Haichar et al. 2008; Shi et al. 2011). However, the mechanisms that

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Table 2.1 Commonly detected exudates of A. barbata and A. fatua measured from hydroponically grown plants, seedlings, and rhizosphere soil Class Sugars and derivatives (n ¼ 24)

Carboxylic acids and derivatives (n ¼ 12)

Amino acids and derivatives (n ¼ 30)

Aromatic acids and derivatives (n ¼ 15)

Fatty acids and derivatives (n ¼ 12)

Sterols Glycerol and derivatives (n ¼ 3) Nucleosides and nucleotides (n ¼ 12) Plant hormones (n ¼ 4) Betaines (n ¼ 6) Miscellaneous (n ¼ 14)

a

Compound α-D-glucosamine phosphate, arabinose, arbutin, cellotetraose, D-threitol, fructose, galactonic acid, galactose, glucose, inositol, lyxose, maltose, myoinositol, N-acetyl-D-mannosamine, neohesperidin, rhamnose, ribitol, ribose, sorbitol, sorbose, sucrose, threonic acid, xylitol, xylosea 2-Hydroxybutyric acid, 3-hydroxy-3-methylglutaric acid, α-ketoglutaric acid, cis-aconitic acid, fumaric acid, lactic acid, maleic acid, malic acid, malonic acid, oxalic acid, pyruvic acid, succinic acid 2-Aminoisobutyric acid, 5-aminovaleric acid, alanine, arginine, asparagine, aspartic acid, cysteine, gammaamino-n-butyric acid, glutamic acid, glycine, histidine, homoserine, isoleucine, L-citrulline, L-homoserine, Lhydroxyproline, L-pyroglutamic acid, leucine, lysine, methionine, N-acetylaspartic acid, ornithine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, valine 2,3-Dihydroxybenzoic acid, 3-dehydroshikimic acid, 4-hydroxybenzoic acid, 4-hydroxyphenylpyruvic acid, benzoic acid, caffeic acid, cinnamic acid, ferulic acid, nicotinic acid, p-coumaric acid, phthalic acid, quinic acid, shikimic acid, syringic acid, vanillic acid Adipic acid, arachidic acid, elaidic acid, lauric acid, lignoceric acid, linoleic acid, methylhexadecanoic acid, oleic acid, palmitic acid, palmitoleic acid, pelargonic acid, stearic acid Cholesterol Glycerol, glycerol-α-phosphate, glycerol-β-phosphate

Source E, S, Zb

Adenine, adenosine, cytidine, deoxyguanosine, guanine, guanosine, hypoxanthine, inosine, thymidine, uracil, uridine, xanthine Abscisic acid, indole-3-acetic acid, jasmonic acid, salicylic acid Betonicine, carnitine, choline, glycine betaine, stachydrine, trigonelline 1,2,4-Benzenetriol, acetol, biotin, butyrolactam, Dlyxosylamine, dehydroabietic acid, pantothenic acid, riboflavin, sinapyl alcohol, syringylaldehyde, taurine, thiamine, urea, vanillin

E, S, Z

E, S, Z

E, S, Z

E, S, I, Z

E, S, Z

S S

Z Z E, S, I, Z

Exudates were measured by GC–MS, LC–MS, and/or high-performance liquid chromatography (HPLC) b E—Estera (2017); S—Shi (unpublished); I—Iannucci et al. (2012); Z—Zhalnina et al. (2018)

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underlie how root exudates influence microbe-mediated C cycling are complicated and difficult to study within an intact soil matrix. For example, the increased concentration of labile soil C near roots has been shown to both stimulate and repress soil organic carbon mineralization (Kuzyakov et al. 2000; Fontaine et al. 2007), and some studies suggest that exudates are just as likely to persist within soil as root tissue carbon (Sokol et al. 2018). One specific complication is the highly complex nature of root exudate compounds, which vary with plant genotype, root maturity, and in response to environmental stimulations (Jones 1998). Another difficulty is accurate characterization of exudate chemical composition because of the large background signal contributed by soil and microbial components (Kuzyakov and Domanski 2000). Advances in sequencing approaches and high-resolution metabolite analysis have recently made it possible to measure direct links between specific exudate compounds and responses of specific microbial populations. It seems likely that the increased microbial activity and growth in the rhizosphere is fueled by root exudation patterns, which change in composition and abundance as plants grow. Our studies indicate that the chemical landscape of the Avena spp. rhizosphere, comprising osmolytes, fatty acids, senescence hormones, amino acids, sugars, and nucleotides (Table 2.1), changes during plant growth in a successional pattern (Fig. 2.5). Indeed, as community composition, richness, and microbe–microbe interactions are changing during the growth of an Avena plant, plant exudation profiles also shift in a remarkably similar manner (Fig. 2.5, Estera 2017). Recent studies have identified direct predictive links between plant exudate composition and rhizosphere microbiome. Zhalnina et al. (2018) used a combination of comparative genomics and liquid chromatography–mass spectrometry (LC–MS)/ MS exometabolite profiling of Avena root exudate consumption by sequenced bacterial isolates to show that developmental processes in A. barbata generated consistent patterns in root exudate composition. They showed that the chemical succession of Avena root exudates interacted with microbial metabolite substrate preferences (specifically for amino acids, osmolytes, and aromatics) that were predictable from the microbe’s genome sequences. They hypothesized that the combination of plant exudation traits and microbial substrate uptake traits interacted to yield the patterns of microbial community assembly observed in the rhizosphere of this annual grass. Nuccio et al. (2020) show that, around older roots (that have ceased producing exudates and may have begun to senesce), distinct microbial populations (e.g., Streptomycetaceae and Catenulisporales from Actinobacteria) begin to have high d-CAZy gene transcription, expressing many enzymes involved in cellulose and xylose breakdown. Thus, it appears that temporal changes in root exudates over time and space may be directly linked to the successional changes in the rhizosphere microbial community identified by Shi et al. (2015) and may be the key determinants of soil C turnover.

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Fig. 2.5 Plot of partial least squares discriminant analysis (PLS-DA) components 1 and 2 for metabolite samples collected over 9 weeks from a sterile plant growth experiment. Sterilized A. barbata seedlings were planted in sterile plant chambers (SPCs) with sterilized sand, and grown in either 400 ppm (ambient) or 700 ppm (elevated) CO2 conditions. The pore space of the SPCs were fully drained and refreshed with diluted Hoagland solution once a week. SPCs were sampled at weeks 1, 2, 3, 4, 6, and 9 for root exudate profiles analyzed via gas chromatography– mass spectrometry (GC–MS). Metabolite abundances of identified GC-MS peaks were then normalized and analyzed via PLS-DA and ANOVA. Data were normalized from root exudate samples from weeks 1, 2, 3, 4, 6, and 9. There was a significant difference in the metabolic profiles over time, as plants grew, regardless of CO2 treatment. Colors represent the different time points at which the samples were collected and circles represent the individual samples collected. Components 1 and 2 account for 27.3% of the variance in the dataset and are significant predictors of time. Ellipses indicate the 95% confidence interval for each sample grouping (#1–#9)

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Effect of eCO2 and Root Exudates

Elevated CO2 can promote higher rates of photosynthesis and increased allocation of C to roots and various soil C pools (Table 2.2). In Avena spp., eCO2 changes exudate composition and temporal patterns of exudation over time (Fig. 2.6). Hence eCO2 studies provide a unique opportunity to assess the effects of altered root exudation patterns on microbial community succession and function, and in turn, how these population dynamics influence C transformations and stabilization processes. eCO2 concentrations stimulate many plant responses and lead to higher rates of photosynthesis, increased belowground biomass production, and soil deposition of labile C (Hungate 1999; Liu et al. 2009; Phillips et al. 2011) as well as lower transpiration rates and potentially increased soil water content due to reduced stomatal conductance (Hungate 1999). Previous studies suggest that eCO2 disproportionately affects root-associated microbial communities compared to those in the surrounding bulk soil (Drigo et al. 2008, 2009, 2010), and appears to consistently increase fungal populations in rhizosphere soil (Carney et al. 2007; Cheng et al. 2012; Drigo et al. 2013). In one study, eCO2 increased both rhizosphere fungal populations and the activities of carbon decomposition enzymes, resulting in an overall loss of soil carbon (Carney et al. 2007). However, the effect of eCO2 on the temporal variation in soil and rhizosphere microbial communities, and the impact of eCO2 on plant–microbe interactions (Drigo et al. 2010, 2013) remain poorly understood. These interactions may influence plant growth and net primary productivity by altering beneficial microbial colonization and/or pathogen infection. Therefore, it is important to examine the effect of eCO2 on the abundance, composition, and function of rhizosphere microbial communities over time; the integration of such information could greatly improve the predictions of rhizosphere-driven C cycling. From our research on Avena spp., we have found that plants grown under elevated (700 ppm) CO2 increased both C allocated belowground and the amount of rootTable 2.2 Root biomass and plant-derived soil carbon pools after growing Avena spp. for one season under eCO2 and ambient CO2 (aCO2) conditions in 13CO2 growth chambers Treatment Root biomass (g) Total belowground 13C 13 C soil excluding roots 13 C-fLF (μg C/g soil) 13 C-oLF (μg C/g soil) 13 C-HF (μg C/g soil)

aCO2-Planted 0.57  0.03 225.6  20.0 101.1  12.9 79.9  9.2 4.9  1.0 68.2  8.6

eCO2-Planted 0.88  0.10 266.5  22.9 153.1  18.1 103.5  12.9 7.5  1.6 112.5  12.7

P-value 0.039 0.050 0.035 0.275 0.192 0.001

Total belowground 13C is in μg 13C/g soil + roots. 13C soil excluding roots is in μg 13C/g soil. 13C associated with different soil fractions was measured by isotope ratio mass spectrometry (IRMS) following separation of soil into three fractions: free light fraction (fLF), occluded light fraction (oLF), and heavy fraction (HF), according to the established methods (Golchin et al. 1994; Bird et al. 2011). P values shown in bold indicate significant changes between aCO2 and eCO2 treatments (P < 0.05). Data are presented as mean  standard errors (n ¼ 8)

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Fig. 2.6 Heat maps and cluster trees of metabolites from a plant growth experiment where A. barbata was grown in sterile plant chambers (SPC). (a) Heat map of root exudate profiles using the top 25 metabolites that were most important in the projection of the plot from a partial least squares discriminant analysis (PLS-DA). Warm colors reflect a larger abundance of metabolites and cooler colors a decreased abundance. Heat maps and cluster trees were constructed using a Euclidean distance measure and ward clustering algorithm, respectively. Heat maps summarize the root exudate changes in each SPC sample over time. Specifically, root exudates produced during weeks 1, 2, and 3 have lower abundance that those produced during weeks 6 and 9. Conversely, some root exudates produced during weeks 6 and 9 are not produced during the earlier weeks of 1, 2, and 3. (b) Metabolite heat map and cluster tree showing autoscaled abundances for root exudates that are significantly different between eCO2 and aCO2 treatments as analyzed by a two-way ANOVA and Tukey’s HSD (honestly significant difference) with p < 0.05. Out of 125 different metabolites detected from root exudate samples, only 7 were significantly different between the two CO2 treatments. Trees show the degree of similarity among metabolites based on Euclidean distance, and metabolites are clustered to minimize the sum of squares

derived 13C in the mineral-associated fraction of soil (Table 2.2). The increase in C associated with the soil mineral fraction (“heavy fraction”) suggests a potential for increased stabilization of root C under eCO2. In addition, metabolites produced in

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early weeks of plant growth under eCO2 conditions clustered distinctly from later produced metabolites (Fig. 2.6). Since we observed that eCO2 both increased and decreased specific exudate components (Fig. 2.6), additional studies are needed to parse how these changes affect the long-term fate of plant-derived exudate C.

2.8

Role of Soil Moisture

Previous studies have reported a significant interaction between eCO2 and gravimetric soil moisture (as well as N and P availability), possibly due to enhanced plant growth (Hu et al. 1999, 2001). Such eCO2- and soil moisture-induced changes in C sources and soil microenvironments are likely to have a substantial influence on the composition and function of soil microbiota and consequently in mediating the ecosystem processes (e.g., C, N cycling) (Hungate et al. 1997; Cheng and Johnson 1998; Luo et al. 2006; Carney et al. 2007; Phillips et al. 2012). Actively transpiring roots can impact soil C cycling processes by altering nearby soil water content. Castanha et al. (2018) report that Avena spp. caused increased decomposition of soil root detritus early in the growing season, when soil moisture was relatively high; however, as soil moisture levels declined, the plants suppressed decomposition rates of soil litter. In studies of Avena spp. we have found (not surprisingly) that rhizosphere soils have consistently lower soil moisture than unplanted soils (Shi et al. 2018; Nuccio et al. 2020) and this affects the rate of litter decomposition in the root zone versus the surrounding soil. The presence of plant roots also significantly increased the abundance of proV and proW, two common bacterial osmotic stress genes (He et al. 2007; Shi et al. 2018). Altered bacterial community composition and bacterial and fungal functional gene profiles also accompany reduced water in rhizosphere soils (Webb et al. 2002). In CA annual grassland soils where Avena spp. grow, we have found that bacteria and fungi are differentially sensitive to soil moisture; bacteria tend to be substantially more sensitive and responsive to soil moisture than fungi (Barnard et al. 2013). These results suggest that bacterial communities in the rhizosphere may be differentially affected by the water stresses common in Mediterranean climate grasslands, likely impairing their metabolic activities and leading to downstream impacts on decomposition rates and rhizosphere C cycling.

2.9

Downstream Effects on Soil Carbon Stocks and Fluxes

Root-microbial dynamics have significant “downstream” effects on the soil C cycle, altering the amount and types of organic matter that become associated with mineral surfaces (Shi et al. 2018; Whitman et al. 2018), which may persist for long timescales. These effects can be measured by the extent of colonization of nearby soil minerals, decomposition of a prior season’s root litter, and the balance of stabilized versus lost soil carbon. In a study where we incubated fresh minerals

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(quartz, ferrihydrite, kaolinite) in the presence of an active Avena spp. rhizosphere, we found that both the quantity and composition of mineral-associated SOM were largely a factor of mineralogy and the influences of nearby roots (Whitman et al. 2018; Neurath, unpublished data). We also found significant differences in microbial community composition (16S rRNA and ITS) on different mineral types (Whitman et al. 2018). Because different microbial populations have different inherent ecophysiological traits (cell wall biochemistry, carbon use efficiency, growth rate) that can affect soil C persistence, the colonization patterns and habitat preferences of individual microbial populations may be foundational to the persistence of C entering soil via plant roots.

2.10

Conclusions

Interactions between plants and soil microorganisms are of primary importance to terrestrial ecosystem functions and particularly C cycling. Drawing heavily on the results from a “wild model” system, the common grass Avena spp. (wild oat) grown in CA annual grassland soils where it is ubiquitous, we summarize the important aspects of root–microbial interactions that have been commonly underappreciated, and provide the rough outlines of a mechanistic roadmap for how plant root C enters microbial and mineralized soil pools. Most of the root C entering soils returns to the atmosphere as CO2, but a small portion becomes stabilized as longer-lived SOM. The actual path taken by each photosynthetically fixed plant C atom is a result of its consumption and use by bacteria, archaea, fungi, and viruses that make up the rhizosphere microbiome. Our results suggest that the sum of soil microbial ecophysiological traits (shaped by their phylogeny and defined by their genomes and gene expression) predict the fate of root C in soils when interpreted in the physicochemical soil–root environment. However, creating a predictive roadmap for the pathways taken by plant C as it enters the soil continues to be a long-term challenge for soil scientists. Acknowledgments This study is based on research supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research Genomic Science Program under Award Numbers DE-SC0014079, DE-SC0010570, and DE-SC0016247 to MKF. Part of this work was performed at the University of Oklahoma, funded by the DOE under UC-subcontract number 00008322. J. Pett-Ridge and E. Nuccio contributed under the auspices of the US Department of Energy at LLNL under Contract DE-AC52-07NA27344 and US DOE Genomics Science program awards SCW1039, SCW 1632, SCW1589, and SCW1421. The study performed at the Lawrence Berkeley National Laboratory was supported by the DOE, Office of Science, Office of Biological and Environmental Research through Contract No. DE-AC02-05CH11231. We thank the current and past members of the DOE Genomic Science Carbon Cycling “Cradle to Grave” research team for their support on the multiple projects conducted as part of this research.

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3

Root–Soil–Microbe Interactions Mediating Nutrient Fluxes in the Rhizosphere Eric Paterson and Lumbani Mwafulirwa

Abstract

Plant roots have both direct and indirect effects on nutrient availabilities and fluxes in rhizosphere soil. Direct effects include impacts that are a consequence of root growth, water/nutrient uptake and secretion of compounds that promote solubility of poorly available elements such as phosphorus and iron. Indirect effects are largely a consequence of plant–microbe interactions, mediated by the release of organic compounds from roots that both shape rhizosphere microbial community structure and promote microbial nutrient cycling activity. In recent years, significant advances have been made in the quantification of root-mediated impacts on soil biogeochemical processes, demonstrating the importance of these interactions for nutrient cycling to support plant productivity and as a critical control point for the response of soils to environmental change. This is now supplemented with an appreciation that there is a strong element of regulation, both plant and microbial, in how the underlying interactions are established and maintained. This raises the exciting possibility that management of root– microbiota interactions could be a realistic means of improving plant health and productivity, while potentially also mitigating environmental impacts. This chapter discusses progress in quantifying root impacts on soil processes and parallel advances in characterising the specificity of the plant-driven selection of associated microbiota. A clear opportunity for future research is to combine E. Paterson (*) The James Hutton Institute, Aberdeen, UK e-mail: [email protected] L. Mwafulirwa The James Hutton Institute, Aberdeen, UK Global Academy of Agriculture and Food Security, University of Edinburgh, Midlothian, UK Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK # Springer Nature Singapore Pte Ltd. 2021 V. V. S. R. Gupta, A. K. Sharma (eds.), Rhizosphere Biology: Interactions Between Microbes and Plants, Rhizosphere Biology, https://doi.org/10.1007/978-981-15-6125-2_3

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these approaches, functional -omics technologies and bioinformatics to guide next-generation crop breeding that targets both the plant and its associated microbiota (i.e. the holobiont), for productivity and resilience in sustainable agricultural systems.

3.1

Introduction

There is well-established recognition that plant roots have significant influences over soil microbial community composition, metabolic activity and biogeochemical process rates (Haichar et al. 2008). These influences define the narrow zone of soil surrounding roots, the rhizosphere, as a distinct niche within the soil, selecting for organisms adapted to the specific conditions imposed by roots, and representing a dynamic hotspot of biological activity (Uren 2007). More recently, an appreciation has developed that, in addition to these broad impacts of roots on microbial communities, there are also functional consequences of these interactions that may have significant impacts on nutrient cycling, plant productivity and ecosystem responses to environmental change (Ahkami et al. 2017; Garcia and Kao-Kniffin 2018). In this chapter, we aim to provide an overview of root–microbe interactions in the context of element cycling in soil. First, we consider the broad constraints on plant and microbial productivity, and second, we explore the developing understanding that plant and microbial partners in these interactions have evolved strategies to optimise their mutualistic associations, and what implications this may have for their management to promote ecosystem services.

3.2

The Rhizosphere Effect

Plant roots have a number of impacts on soil conditions (Fig. 3.1) that directly or indirectly affect microbial communities and nutrient element availabilities. As the primary organs for water uptake, roots impact soil water potentials, typically coupled to diurnal cycles of stomatal opening that regulate mass flow through transpiration (Hinsinger et al. 2009). Therefore, in unsaturated soils, rhizosphere microbial communities experience larger variations in water potential than those in bulk soil, representing a selective pressure influencing community structure (Nessner et al. 2013). However, another factor is that in dry soils, root water uptake from deeper layers can mitigate soil matric potentials limiting to microbial activity in surface layers, a process termed hydraulic lift (Caldwell et al. 1998), which effectively maintains conditions supporting microbial nutrient cycling activity around roots. Root water uptake also influences fluxes of soluble elements to the rhizosphere in mass flow from bulk soil, and subsequent selective uptake of nutrients by roots results in there being a distinct chemical environment for microbiota in rootassociated soil (Hinsinger et al. 2009). A generally significant influence of nutrient uptake is the consequence of rhizosphere pH. For nitrogen, uptake of ammonium

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Fig. 3.1 Impacts of plant roots on soil conditions that have direct and/or indirect impacts on root– microbe interactions mediating soil processes in the context of nutrient element availability, based on the literature discussed in the main text

(NH4+) results in net acidification, whereas uptake of nitrate (NO3 ) results in net alkalisation of the rhizosphere. This is a consequence of root extrusion of H+ or OH ions to maintain charge balance and can result in a 2 unit pH change in the rhizosphere (Marschner and Romheld 1983), having cascading effects on solubilities of other elements. Plant roots also influence nutrient availabilities directly through the release of compounds affecting element solubilities, or specific biological process rates. The release of carboxylic acids (particularly citric acid) has a significant influence on phosphorus mobilisation (Johnson and Loeppert 2006) and phytosiderophores increase the availability of poorly soluble iron in rhizosphere soil (Nozoye et al. 2011). Release of nitrification and denitrification inhibitors from roots has also been demonstrated (Bardon et al. 2014), promoting the availability of nitrate in the rhizosphere. In addition, root growth has direct impacts on soil physical structure, both in penetrating compacted soil layers (Jin et al. 2013), and through localised compression of soil around roots during a radial expansion (Kolb et al. 2012). These impacts affect the movement of water and gases, through alteration of soil pore networks, each of which impacts the physicochemical environment experienced by root-associated biota. These impacts of roots on the soil environment are largely a direct consequence of growth and physiological activity essential for plant establishment and productivity,

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or function to directly influence physiochemical conditions in the rhizosphere. Roots are also plastic in their growth strategies in response to the soil environment. This is seen at the level of biomass allocation between roots and above-ground tissues, functioning to balance C-fixation capacity and nutrient acquisition from soil (Rao et al. 2016). Therefore, plant biomass allocation can be responsive to changes in above-ground conditions affecting assimilation (e.g. light and CO2 concentration), or the availability of nutrients in the soil (e.g. fertilisation, or atmospheric deposition). Generally, these allometric responses of plants function to balance acquisition of resources (i.e. C from the atmosphere and nutrients from the soil, Weiner 2004). For roots, this may also be apparent in the plasticity of architectural development, for example, the proliferation of root length density within resource-rich patches of the soil environment (Hodge 2004). An important consequence of allometric and architectural plasticity in roots is that the directed allocation of photoassimilate underpinning these responses is also associated with increased deposition of organic C from roots to soil (Paterson et al. 2006), and as we now go on to consider, this consequence is integral to processes mediating element cycling in the rhizosphere.

3.3

Limitations to Plant and Microbial Productivity

From a perspective of resource acquisition, plants and soil microbial communities often experience contrasting, potentially complementary, limitations on growth. Given sufficient light and water, plants have unrestricted access to C from the atmosphere and productivity is most commonly restricted by the availability of mineral nutrients. On the other hand, in oxic mineral soils, microbial communities are generally limited by the availability of organic substrates (Struecker and Joergensen 2015; Sanaullah et al. 2016). This C-limitation is the result of soil organic matter (SOM) stabilisation on clay surfaces (organo-mineral complexes, Kleber et al. 2007) and physical protection (occlusion) within aggregates (von Lutzow et al. 2006; Dungait et al. 2012), but also because the complexity and recalcitrance of much of SOM mean that it is not readily used as microbial substrate. More precisely, although all organic matter is ultimately decomposable, molecules with random, non-repeating structures may require many enzymatic steps to process through to CO2 loss from soil. This implies that greater microbial investment in enzyme production, at a cellular or community level, is required to utilise complex SOM substrates. This relationship between SOM quality and microbial substrate use has been usefully considered in the context of thermodynamic principles, where turnover of SOM substrates is inversely proportional to the number of enzymatic steps required to mineralise organic matter to the irreversible end point of CO2 production (Bosatta and Ågren 1999). It is increasingly recognised that this diversity and complexity of SOM chemical composition is a consequence of it being formed primarily of microbial products, as opposed to recalcitrant fractions of primary plant inputs (Cotrufo et al. 2013). These microbial products are further subject to abiotic processes (e.g. condensation reactions), meaning that SOM is characterised by random, non-repeating chemical structures resistant to enzymatic breakdown

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(Jiang et al. 2017). Consequently, the net energy yield from chemically recalcitrant SOM is low, potentially to the extent that it may represent a non-viable C-substrate for microbial communities. However, this chemically recalcitrant SOM, but potentially available substrate, contains a large proportion of the N-stocks in soil, having a much lower C-to-N ratio in comparison with primary inputs from plants (Cheng 1999). Rates of SOM mineralisation in bulk soil (i.e. soil not under the current and direct influence of plant roots) have been demonstrated to be relatively insensitive to microbial community composition or abundance (Cheng and Coleman 1990; Kemmitt et al. 2008). This finding implies that although SOM mineralisation is a consequence of biological processes, the capacity of microbial communities to mediate these processes does not alone limit SOM turnover. However, evidence of biological control of SOM mineralisation rates comes from scenarios where there is availability of labile C substrates (Kuzyakov et al. 2000; Garcia-Pausas and Paterson 2011), such as exudate compounds released from roots into the rhizosphere (Paterson 2003). Such rhizosphere priming effects (RPE) can have very significant impacts (300 fold increase) on SOM mineralisation rates (Cheng et al. 2003), demonstrating both the potential availability of SOM and physiological capacity of microbial communities to utilise it as substrate. These root–soil interactions strongly contribute to the rhizosphere representing a key zone in soil, where it is estimated that 50% or more of organic matter turnover and nutrient cycling occurs (Finzi et al. 2015). Therefore, it is clearly important to understand root–microbe interactions mediating soil processes in the contexts of productivity of systems (e.g. agroecosystems) and feedbacks to environmental change. One conceptual view is to consider rhizosphere interactions in the context of complementary resource limitations of plants and microbes, where plant roots can stimulate microbial mineralisation activity through investment in rhizodeposition that alters the stoichiometry of resource availability for microbial growth. That is, the high C-to-N ratio of rhizodeposition (relative to that required for balanced microbial biomass production, Cheng 1999) shifts microbial activity (e.g. exo-enzyme production) towards the acquisition of nutrients (such as nitrogen and phosphorus) from SOM (Paterson 2003). In essence, this conceptual view represents one current explanation of RPE, nutrient mining (Dijkstra et al. 2013), which has the consequence of transforming N (and other nutrients) from stabilised SOM forms to forms utilised by microbial communities. Evidence for nutrient mining as a mechanism of RPE has been generated by combining 13C and 15N stable isotope approaches to quantify fluxes associated with SOM decomposition, and comparing these fluxes when affected, or not, by plant-derived inputs. These experiments have demonstrated that the C-to-N ratio of the additional mineralisation of SOM, induced by the input of labile C to soil (compared with the mineralisation flux from control soil), is much lower than the composition of SOM as a whole (e.g. Murphy et al. 2015). This is consistent with the concept of enzymatic stoichiometry (Sinsabaugh and Shah 2012), where the expression of microbial activities is closely coupled to the acquisition of limiting resources, i.e. enzyme synthesis/activities associated with the acquisition of specific nutrient elements.

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In the context of nutrient returns supporting plant productivity, we also have to consider temporal and spatial aspects of the interaction. First, if rhizodeposition functions to switch microbial resource limitation towards availability of mineral nutrients, the expectation is that this leads to immobilisation of nutrients in the microbial biomass (Griffiths et al. 2012), at least in the short term. However, the associated microbial growth also results in increased abundance of microbial predators (protozoa and nematodes, Song et al. 2015), that when consuming microbial cells also release NH4+. This microbial loop (Coleman 1994) for cycling of C and N in the rhizosphere contributes to the flux of nutrients potentially available to roots, and the longevity of roots relative to microbes allows them, over time, to access nutrients mobilised from SOM pools that they cannot access directly. That these processes are occurring in the rhizosphere, means that even for plants in competition, it would be expected that investment in rhizodeposition, that initially results in net microbial immobilisation could act analogously to an external store of nutrients that is accessed progressively over time. In the broad context of co-evolution of plants and associated soil communities, since the colonisation of land 500 million years ago, it could be argued that it would be surprising if such mutualistic associations had not developed, given the potential for them to do so. What remains less clear is the extent to which these interactions have been ‘fine-tuned’ at the genetic level (e.g. through directed plant-mediated microbiome selection for beneficial functions, see Sect. 1.5), or the extent to which the interactions are adaptive to environmental cues. Some insights into environmental, or context-specific, control of RPE can be gained from the variability of responses reported in the literature. For example, there are some studies that have reported reduced RPE when mineral N is abundant (Blagodatskaya et al. 2007; Murphy et al. 2017), as would be expected for RPE driven by an N-mining response. Indeed, some studies have demonstrated negative RPE in response to C-inputs to soil (Carrillo et al. 2014; Yin et al. 2018), measured as reduced mineralisation of native SOM to CO2 under C-input to soil compared to control soil. This can be interpreted as preferential substrate utilisation (Dijkstra et al. 2013), where labile C-inputs typical of rhizodeposition are used in preference to SOM-derived forms, when there are sufficient mineral nutrients to support balanced microbial growth. Understanding these interactions and their controls has important implications for critical soil functions including nutrient cycling and greenhouse gas (GHG) balances, but this understanding is currently incomplete.

3.4

Methodology for Quantification of RPE

It is important to note that there are conflicting results from published studies of RPE that are not readily resolved in the context of microbial nutrient mining responses. For example, some studies (Zhu et al. 2014; Mason-Jones et al. 2018) have shown direct relationships between the magnitude of RPE and microbial activity, not related to an imbalance in C and nutrient availability (e.g. stimulation of RPE following supply of amino acids: labile substrates, but with a low C-to-N ratio). This type of

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response has been described as activation of microbial communities (Dijkstra et al. 2013; Zhu et al. 2014), leading to a general increase in microbial functions, including SOM mineralisation. Similarly, positive priming effects have been identified in response to the addition of relatively recalcitrant substrates, where it is proposed (e.g. on the basis of measured enzyme activities, Fang et al. 2018) that SOM is co-metabolised with the added substrate due to their chemical homology. Consequently, it seems that there are a number of mechanisms that contribute to measured RPEs, which operate under different circumstances, or in parallel. Approaches to differentiate these mechanisms will be important in increasing understanding of, and predictive power relating to, root–soil interactions. To this end, it is useful to consider that the most commonly used approach for quantification of RPE is to use isotopic labelling and partitioning to differentiate C-input and native SOM-derived sources of respiration (Shahzad et al. 2015). This is a powerful approach that can be used non-invasively to quantify RPE over time. However, it is important to recognise that the CO2 efflux from soil represents an end point for a sequence of processes: utilisation of C-inputs to soil; increased microbial activity including the production of exo-enzymes; uptake of SOM-derived compounds; their anabolic/catabolic metabolism and respiration of a proportion of SOM-derived compounds as CO2. A direct implication of this sequence of events is that there is time-dependency in the measurement of RPE following a discrete C-input to the soil. This relates to both the time period over which RPE should be quantified and the contribution of different soil C-pools to CO2 flux. For example, as discussed by Blagodatskaya and Kuzyakov (2008), during the initial phase of microbial utilisation of C-inputs, what is measured as soil-derived CO2 will include contributions from pool-substitution effects, as input-derived C is incorporated into the microbial biomass (termed apparent priming). Although this pool-substitution effect can be quantified (Garcia-Pausas and Paterson 2011), and becomes insignificant where inputs are continuous (as is the case for rhizosphere soil), it represents a complication in interpretation of soil CO2 efflux RPE measurements, particularly if in addition, several RPE mechanisms are operating simultaneously. We suggest that the contribution of different processes to C-fluxes measured as RPE is one reason that it is necessary to apply methods that are complementary to isotopic labelling/partitioning approaches to resolve mechanisms of these root–soil interactions. More broadly, the question of whether RPE represents ‘directed and controlled’ interactions has important implications for whether these interactions, or rhizosphere microbiomes (see below), can be manipulated in the context of management for enhanced plant productivity and soil functioning, and we consider this in following sections.

3.4.1

The Concept of Plant Microbiomes

The importance to higher organisms of their associated microbiota has become a topical and rapidly advancing field of research, notably with respect to human health (Le Chatelier et al. 2013). For example, in recent years, an appreciation of the

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importance of human gut microbial communities (microbiota) to nutrition and wider aspects of human health has developed (Kinross et al. 2011). Analogous research within the plant sciences has also identified that there is considerable specificity in microbiomes that develop in association with roots (Turner et al. 2013), and that this can have important implications for plant health and productivity (Berendsen et al. 2012). Often, plant microbiomes are classified based on each habitat that the plant provides, for example, the rhizosphere, phyllosphere (i.e. above-ground plant surfaces) and endosphere (i.e. plant internal tissues) (Berg et al. 2016). Plant hosts and associated microbiomes are collectively termed ‘holobionts’, recognising their combined influence on, and interaction with, the environment (Alegria-Terrazas et al. 2016). Among these habitats for microbiota, the rhizosphere may be the most studied. This is primarily because of its immense potential for plant nutrition and health (Berendsen et al. 2012; Finzi et al. 2015), and now increasingly for resilience to abiotic stresses (such as drought) and implications for sustainable agriculture (e.g. Bérard et al. 2015). Over recent years, advances in microbial -omics technologies (such as genomics, transcriptomics, proteomics and metabolomics) have helped to reveal structural and functional diversity of plant microbiomes, and to establish that these microbial characteristics are closely linked to plant identity (species and genotype, Turner et al. 2013; Levy et al. 2018). This has included tracing of plant-derived carbon substrates to specific microbial groups and genomes, through analyses targeting microbial biomarkers. This allows identification of the most active members of plant-associated microbiota, and their relative dependencies on plant-derived C for growth and activity. Isotopic labelling of plants coupled to microbial phospholipid fatty acid (PLFA) analysis provides relatively coarse-resolution of microbial community structure but is highly sensitive for quantification of incorporation of plantderived C (Paterson et al. 2007; Garcia-Pausas and Paterson 2011). In contrast, tracing 13C into DNA fractions (stable isotope probing, Radajewski et al. 2000) has the potential for species-level resolution of plant-derived C in microbiota, but its sensitivity is constrained by the physical (density centrifugation) separation of 13 C-enriched fractions. An analogous approach is to target RNA rather than DNA, which has the advantage that it can be related to general (rRNA) or functionally specific (mRNA) activity (Moller et al. 1995; Turner et al. 2013). In view that root exudates (and other rhizodeposits such as sloughed border cells and mucilage) play a major role in the regulation of microbial diversity and activity in the rhizosphere, it is suggested that plant regulation of these inputs to soil may mediate broad-scale selection of rhizosphere microbiota to their benefit (Haichar et al. 2008). For example, benefits derived through altering the balance of microbial growth requirements as previously discussed (Sect. 1.3), or mediating shifts in relative abundance of microbial groups. At a finer level of detail, the importance of specific, low-abundance exudate compounds (signal molecules) in the formation of mutualistic symbioses with rhizobia and mycorrhizas are well-studied and understood (Harrison 2005; Shaw et al. 2006). Indeed, these associations share common genetic elements (symbiotic signalling pathway, SYM, Banba et al. 2008), suggesting related evolutionary development. However, the extent to which there

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is such genetic crosstalk between roots and free-living organisms that mediate nutrient cycling in the rhizosphere is largely unknown. However, examples such as the selective recruitment of bacteria antagonistic to soil-borne pathogens, via secretion of compounds from infected roots (Berendsen et al. 2012), provides evidence of mechanisms underlying specific root–microbe interactions that can shape rhizosphere community structure. More broadly, there is now a large body of evidence that, not only do different plant species select for distinct microbiomes, but that there is also strong genetic (plant genotype-specific) selection of microbial communities. Initially, this was demonstrated for accessions of the model plant Arabidopsis thaliana (e.g. Micallef et al. 2009), but this has now been shown for a range of species, including important crop plants (Bouffaud et al. 2014; Mendes et al. 2014). This has raised the intriguing possibility of explicitly including plant microbiome recruitment as a selectable trait within plant breeding programmes, recognising that defining how plants function and interact with the environment should be inclusive of their associated microbiomes (Kroll et al. 2017; Toju et al. 2018). This is particularly the case if root traits mediating interactions with soil microbial communities have been unintentionally lost from modern elite cultivars, through selection for high yield under high input conditions (York et al. 2015). That is, with high nutrient availability from fertiliser inputs, the benefit of coupled plant–microbe interactions would be largely negated, and potentially selected against if this had a significant plant resource cost. Therefore, the suggestion is that a consequence of crop plant domestication is the evolution of heritable plant–microbiome associations (i.e. holobionts), that are relatively ill-adapted to the transition of agriculture towards lower inputs and sustainable production. This mechanism, also linked to the overall loss of plant genetic diversity and, specifically, loss of traits beneficial for root–soil interactions, has been suggested across many modern crop plants (e.g. Wissuwa et al. 2009; Schmidt et al. 2016). To improve root–soil–microbe interactions via plant breeding, there is an opportunity in exploring both the phenotypic and genetic diversity within landraces and wild relatives of crop plants, as well as the variability retained within the existing breeding programs (Pérez-Jaramillo et al. 2018). Indeed, crop wild relatives have already been shown to be a valuable resource for breeding for drought tolerance and nutrient-use efficiency (Nevo and Chen 2010; Hawkesford 2017), traits closely linked to root–soil–microbe interactions. Ideally, the plant genetic loci influencing beneficial rhizosphere microbiome functions will be identified, a goal that is encouraged by an existing understanding of the genetic bases of several traits regulating root system development and functions (Table 3.1). It seems likely that traits related to soil–microbe interactions will be complex, potentially involving a large number of host (i.e. plant) genes (Ahkami et al. 2017), and that there will be strong genotype x environment dependency of genetic interactions. Therefore, research efforts to elucidate the genetic bases of plant–microbe interactions will require leveraging of modern molecular breeding approaches and sequencing techniques that help to target complex, multiple plant traits. For example, quantitative trait locus (QTL) mapping (i.e. identification of molecular markers that are

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Table 3.1 Root phenotypes or traits (and the respective plant species) of which their roles and plant genetic bases have been characterised, although mostly not in the context of soil–microbe interactions Genetically dissected root phenotype Exudate composition Quantity of exudation Root biomass and root-to-shoot ratio

Root diameter and/or fine roots Root branching Root length Root recalcitrance Root anatomy Root angle and deep rooting Root hairs

Plant species Arabidopsis thaliana Rice Wheat Lentil Rapeseed Radish Rice Maize Rice Maize Cottonwood Maize Sorghum Rice Common bean

Reference for plant genetic basis Mönchgesang et al. (2016) Yamamoto et al. (2016) Iannucci et al. (2017) Idrissi et al. (2016) Zhang et al. (2016) Tsuro et al. (2008) Courtois et al. (2009) Zimmermann et al. (2010) Chaitra et al. (2006) Hund et al. (2011) Ranjan et al. (2010) Burton et al. (2015) Lopez et al. (2017) Uga et al. (2011) Yan et al. (2004)

This demonstrates our long-standing and increasing ability to genetically dissect plant root traits. Elucidation of these root traits and their plant genetic control in the context of soil–microbe interactions, as well as identification of new plant genes or loci influencing these root traits and other phenotypes related to microbial interactions, could promote the breeding of crop plants with greatest potential to shape root–microbe interactions for desired soil or rhizosphere functions

linked to specific traits) has been applied to identify relevant plant genes using recombinant inbred lines (RILs) from genetically and phenotypically diverged parents, including landrace and wild plant genotypes (Matus et al. 2003; Kover et al. 2009).

3.4.2

Selection for Microbiome Function

Recent developments in sequencing technologies and bioinformatics approaches have resulted in rapid progress in plant microbiome studies. This has facilitated the identification of key microbial taxa that are commonly enriched in plant microbiomes and the progression of theoretical frameworks (e.g. via network analysis) that also consider the importance of microbe–microbe interactions in the establishment of stable plant microbiomes. For example, the concepts of microbial hubs (microbial species that hold key topological positions within the microbial networks, Agler et al. 2016) and core microbiomes (the sets of microbes that form cores of interactions that can be used to optimise microbiome functions at individual plant and ecosystem levels, Toju et al. 2018) have recently been suggested. Indeed, this microbe-centric approach could be viewed as a basis to optimise the selection of

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microbial inoculants for application to soil or seeds, to promote biocontrol or biofertilisation, if commonly encountered issues of poor establishment of introduced inocula (Streeter 1994; Wallenstein 2017) can be overcome. However, for both plant-driven selection and inoculant strategies, it is recognised that practical implementation also requires fuller integration of functional measures of microbiomes that link to the patterns identified in microbiome composition and structure (Garcia and Kao-Kniffin 2018; Oyserman et al. 2018). In principle, functional information on microbiome functioning can be obtained by integration of transcriptomic, proteomic and metabolomic approaches with compositional analysis of root-associated microbiomes. While this integration of -omics approaches clearly offers very strong potential to understand fundamental functioning, particularly if this is also related to plant gene expression to elucidate bases of interactions and controls, the current state-of-the-art (particularly for soil microbial communities) limits how comprehensive such a functional analysis could be. This issue likely becomes increasingly important when considering plant-selected microbiomes across different soil types, where functional measures would have to be independent of variation in microbial community composition (i.e. comprehensive functional annotation of sequence data). It is recognised that the indigenous microbial community structure has a strong influence on the composition of plant-selected microbiota (Pérez-Jaramillo et al. 2016), but it remains an open question as to the extent to which nutrient cycling and other functions are dependent on the absolute composition, or whether the large diversity and functional redundancy of soil microbial communities enable distinct microbiomes with equivalent function. This is a question that can also be framed as whether a cultivar that selects an optimal microbiome in one soil is also effective in different soil, or whether crop breeding needs to be highly context specific (e.g. individually tailored to specific soil types and management practices). Therefore, for research on the impacts of plant microbiomes on nutrient cycling, there is an important role for methodologies that directly quantify element fluxes in plant–soil systems. The combination of microbiome compositional and functional analyses for nutrient cycling in common studies is not yet well-developed, but substantial links between rhizosphere microbiome communities and their functions could be drawn from recent studies. For example, rhizosphere microbiome structure and functions (e.g. functions related to nutrient cycling and plant adaptation) have been shown to be closely linked in crop plants such as barley (Bulgarelli et al. 2015), maize (Bouffaud et al. 2014; Szoboszlay et al. 2015), rice (Lucas et al. 2013), sugarcane (Souza et al. 2016) and soybean (Mendes et al. 2014). Bouffaud et al. (2014) and Szoboszlay et al. (2015), for instance, observed differences in the abundance and composition of rhizosphere bacterial and fungal communities and their activities between maize varieties. In the study by Szoboszlay et al. (2015), one variety (i.e. the wild ancestor of modern maize) was shown to have markedly higher bacterial abundance and diversity, which are associated with resilience and other functional processes. Studies have also shown that more diverse microbial communities tend to be better able to maintain ecological functions under stress (i.e. resistance) or recover

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function when the stressor is relieved (e.g. Girvan et al. 2005). In addition, intraspecies variation in rhizosphere priming effects has been found to be consistent with the heritability of plant-mediated impacts on microbial functions (e.g. Mwafulirwa et al. 2016; Pausch et al. 2016). Further, considering that a recent meta-analysis showed that root-mediated SOM mineralisation could account for up to one-third of the total carbon and nitrogen mineralised in soil (Finzi et al. 2015), there seems to be a very strong rationale to further explore the exploitation of the rhizosphere microbiome for plant nutrition.

3.5

Concluding Remarks

Recent years have seen significant advances in understanding and appreciating the quantitative importance of root–soil interactions in mediating rates of soil nutrient cycling processes, supporting the productivity of plants in natural and managed ecosystems. It is now clear that there is considerable specificity in these interactions demonstrated by plant species and genotype variation in the magnitude of root–soil interactions regulating nutrient cycling in the rhizosphere. In parallel, the very rapid development of genomic sequencing and bioinformatic approaches has established not only the plant specificity of microbiome selection but also concepts such as core microbiomes common across genotypes and microbial hubs that are suggested to be central to microbe–microbe interactions shaping community assembly. Given that there is also recognition that there is significant plant genetic variation available to alter plant–microbiota interactions, particularly in wild relatives of crop plants, there seems to be very significant potential for cultivar selection directed towards optimised and resilient plant–microbe holobionts. We suggest that realisation of this goal will be most strongly supported by bringing together the parallel strands of research focused on plant–soil interactions mediating nutrient cycling functions, and research directed towards understanding bases of rhizosphere microbiome assembly. Acknowledgements The James Hutton Institute receives funding from RESAS (Rural & Environmental Science & Analytical Services) of the Scottish Government. LM is a Research Fellow in a BBSRC-supported project (BB/P022936).

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Diazotrophic Nitrogen Fixation in the Rhizosphere and Endosphere Sarah S. Roley

Abstract

A wide variety of nitrogen (N)-fixing Bacteria and Archaea inhabit plant rhizospheres and endospheres. This diazotrophic community is often dominated by α-Proteobacteria, although many other taxa are also present. Diazotrophic community composition is influenced by physicochemical factors, including land use, geography, climate, and soil type. Similarly, soil N fixation rates are predicted by short-term soil physicochemical conditions (e.g., moisture, temperature, nutrient concentrations). Microbial community composition may also influence N fixation rates, but it is unclear if it does so through taxon-specific N fixation abilities, or if the physicochemical template influences the microbial community and N fixation rates similarly. In agricultural ecosystems, rhizosphere and endosphere N fixation is often assumed to be minimal, but recent mass balance data suggest that N fixation contributes nearly 25% of crop N requirements. In natural ecosystems, N fixation inputs are highly variable and uncertain, exceeding those of legumes in some systems, and barely detectable in others. Critical knowledge gaps and priorities for research include better estimates of rhizosphere and endosphere fixation rates, defining the role of diazotrophic community composition in influencing fixation rates, and elucidating the relationship between N-fixing microbes and plants.

S. S. Roley (*) School of the Environment, Washington State University, Richland, WA, USA e-mail: [email protected] # Springer Nature Singapore Pte Ltd. 2021 V. V. S. R. Gupta, A. K. Sharma (eds.), Rhizosphere Biology: Interactions Between Microbes and Plants, Rhizosphere Biology, https://doi.org/10.1007/978-981-15-6125-2_4

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Introduction

Nitrogen (N) is required by all organisms, but most N on Earth is present as dinitrogen gas (N2), which is virtually inert because of its triple bond. Biological N fixation is the microbial conversion of N2 to a biologically available form, ammonia (NH3). In the absence of human activity, most N inputs to terrestrial ecosystems are from biological N fixation; it is critically important to the food web (Cleveland et al. 1999; Vitousek et al. 2013). N fixation requires the nitrogenase enzyme, which is encoded by the nifH, nifD, and nifK genes (Howard and Rees 1996), with most genomic and phylogenetic studies relying on nifH primers (Young 2005). The nif genes are present only in Bacteria and Archaea, but spread easily through horizontal transfer and as a result, the ability to fix N is widespread across these domains (Kechris et al. 2006). N fixers, also called diazotrophs, exist across a wide variety of habitats but are of particular interest in the rhizosphere and endosphere, where they can influence N availability for plants. N fixation can occur in symbiosis with actinorhizal and leguminous plants, whereby the diazotrophs inhabit root nodules and provide N directly to the plant in exchange for carbon (C). But most N fixers are free-living, inhabiting soil, sediments, and aquatic habitats without a plant symbiont (Gaby and Buckley 2011). In soil, these free-living diazotrophs are sometimes referred to as “associative” N-fixers, a relationship in which the microbe and the plant exchange materials, but each can live satisfactorily without the other (Postgate 1998). Associative and free-living N fixers are hard to distinguish from one another because the plant-microbial relationships are largely undefined. N-fixers can also exist as plant endophytes, in which they colonize the aboveground and belowground tissues of plants. This chapter will focus on free-living and associative microbes, which will collectively be referred to as “rhizosphere” or “endosphere” N fixers.

4.2

Rhizosphere and Endosphere Diazotroph Communities

Nitrogen-fixing genes can be found in soils in greater numbers than in other habitats (Gaby and Buckley 2011), although they often make up a small proportion of N cycling genes (Mackelprang et al. 2018; Liang et al. 2016). Diazotrophic taxa exist throughout a wide range of taxonomic groups, but in the rhizosphere, a majority of the diazotrophs are typically Proteobacteria, including representatives from alpha (α), beta (β), gamma (γ), and delta (δ) groups, but especially the α group (Gaby and Buckley 2011; Jesus et al. 2016; Wang et al. 2016). Within the α-Proteobacteria, Order Rhizobiales is particularly well-represented (Collavino et al. 2014; Jesus et al. 2016; Mackelprang et al. 2018; Mao et al. 2013; Wang et al. 2013; Bahulikar et al. 2014; Roley et al. 2019). Cyanobacteria are also a dominant group in some ecosystems, particularly in desert soil crusts and poorly drained taiga (Wang et al. 2016, 2013; Yeager et al. 2012). In soil samples, most taxa are represented by just one or two sequences, however, suggesting that much of the diazotrophic diversity has yet to be discovered (Gaby and Buckley 2011; Mendes et al. 2013).

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The rhizosphere diazotroph community is shaped by an array of physicochemical drivers. Geographic patterns are often observed, with soil type and climate inferred to be driving those differences (Jesus et al. 2016; Liang et al. 2016; Mao et al. 2013; Wang et al. 2016). Sometimes, the diazotrophic community in a general geographic area will have a dominant genus. For example, Geobacter represented >60% of sequences in former agricultural soils converted to perennial grasses in Michigan, USA (Jesus et al. 2016). It is unclear which components of soil type and climate influence community composition and how these components interact with the ecological dynamics (i.e., predation, competition, dispersal) that shape microbial communities. Nonetheless, the presence of geographic patterns suggests that diazotrophic communities are not assembled randomly, but are influenced by physicochemical and biological drivers that are particular to a geographic region. One important component of soil type and climate is nutrient availability. The relationship between nutrient concentrations and nifH abundance is not straightforward, however. In agricultural rhizospheres, nifH abundance and diversity declined as N and P concentrations increased (Collavino et al. 2014; Ouyang et al. 2018; Feng et al. 2018), but diversity and abundance were maintained or increased if those fields also received organic matter inputs (Feng et al. 2018; Ouyang et al. 2018; Liao et al. 2018). In other agricultural systems, nifH abundance increased with soil N (Mao et al. 2013; Liao et al. 2018). Nutrient additions can also influence soil pH, which is a strong driver of microbial community diversity and composition (Feng et al. 2018; Fierer and Jackson 2006; Ouyang et al. 2018). Thus, microbial community responses to fertilizer addition are mediated by soil organic matter content and soil pH. Furthermore, inconsistent responses to fertilizer may reflect the fact that the nifH gene is present across a wide array of taxa. These taxa have other metabolic abilities and their presence in a particular place may be related to other functions as well as (or instead of) N fixation. Land use, especially agricultural land use, also shapes the diazotroph community (Calderoli et al. 2017; Collavino et al. 2014; Jesus et al. 2016; Liang et al. 2016; Mackelprang et al. 2018; Mirza et al. 2014; Hsu and Buckley 2009). It is challenging to predict how agricultural land use changes diazotroph communities because conversion to agriculture can simultaneously change soil organic matter content, N and P availability, pH, overlying plant community, soil disturbance, and soil drainage. In the North American Corn Belt, N-fixing genes are more abundant beneath perennial grasses than beneath nearby cultivated croplands (Jesus et al. 2016; Mackelprang et al. 2018; Mao et al. 2013; Morales et al. 2010). The N-fixing community is altered, as well, with Bradyrhizobiaceae more abundant in remnant prairies (Mackelprang et al. 2018) and Hyphomicrobium and Geobacter more common in perennial grass crops (Jesus et al. 2016) than in nearby cultivated maize. But in the Argentinean Pampas and tropical forest systems, conversion to agriculture had different effects. Conversion to row crops did not affect the N-fixing community, and leguminous monocultures (i.e., soybeans) increased nifH abundance (Collavino et al. 2014), as did the conversion from forest to pasture (Mirza et al. 2014). These effects were linked to declines in soil C, N, and P from cultivation and increased C:N ratios in pasture soils. Thus far, diazotroph abundance, diversity,

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and community composition cannot be predicted from univariate physicochemical or land-use categories but require a more nuanced understanding of the factors shaping microbial communities. In addition, the agricultural legacy is broader than physicochemical and plant effects. A reforested agricultural site in the northeastern USA retained a distinct diazotrophic community, with more α- and β-Proteobacteria than a nearby old-growth forest, 100 years after agriculture was abandoned. These distinct communities were retained despite a similar overlying plant community and a return to pre-cultivation soil physicochemical conditions (Izquierdo and Nusslein 2015). Such differences imply a role for ecological interactions that result in community stability. They also imply that community composition is not deterministic and that there are multiple communities that can occupy a given space. Diazotrophs are among the plant growth-promoting endophytes, and a wide diversity can be found within plant tissues (Rothballer et al. 2009; Bahulikar et al. 2014; Kumar et al. 2017; Rodrigues et al. 2017; Xu et al. 2018; Baldani et al. 1997; Carrell and Frank 2014). Diazotrophic endophytes are part of the larger plant microbiome, and they may function cooperatively with other endophytes to induce fixation (Minamisawa et al. 2004). A large proportion of endophytic diazotrophs are Proteobacteria (Bahulikar et al. 2014; Kumar et al. 2017), but endophyte communities have not been widely sampled; distinct taxonomic patterns may emerge as more endophyte data are collected. So far, the endophyte diazotrophic community appears to be distinct from the rhizosphere diazotrophic community (Kumar et al. 2017; Keymer and Kent 2014; Roley et al. 2019) and have either lower richness and diversity (Kumar et al. 2017) or equal diversity (Roley et al. 2019). Plant species, cultivar, and plant growth stage can all influence the endophytic diazotrophic community (Kumar et al. 2017; Rodrigues et al. 2017; Keymer and Kent 2014). The distinct endophyte community may be a function of the plant’s direct influence on the endophytes and taxon-specific preferences for endophyte habitat.

4.3

Functionality of the Rhizosphere Diazotrophic Community

The presence of diazotrophs is a prerequisite for biological N fixation, but the presence of nifH genes is not a guarantee that N fixation occurs; it merely means that organisms can fix N if conditions allow and it is advantageous to do so. N fixation rates are directly influenced by a suite of physicochemical and biological variables, including nutrient concentrations, soil moisture, temperature, and, possibly, diazotrophic community composition (Fig. 4.1). Many of the direct controls on fixation are related to the particular requirements of the nitrogenase enzyme. Nitrogenase comes in different forms, but all contain phosphorus and iron. Most also require either molybdenum or vanadium (Burgess 1990), although some can use iron instead (Gollan et al. 1993). As a result, N fixation can be limited by P (e.g., Reed et al. 2008; Eisele et al. 1989; Benner et al. 2007) and by metals (Dynarski and Houlton 2018; Silvester 1989; Barron et al. 2009). As with

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Diazotrophic Nitrogen Fixation in the Rhizosphere and Endosphere

Plant community Physicochemical conditions nutrients, pH, moisture, oxygen

short timescale (hours)

Fig. 4.1 Conceptual representation of relationships between soil physicochemical conditions, plant and microbial communities, and their influence on N fixation rates

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N fixation rates

Microbial community composition nifH abundance

other microbial processes, fixation rates generally increase with soil moisture (Brouzes et al. 1969; Roper 1983) and with temperature (Houlton et al. 2008; Roper 1985). In addition to providing necessary water to the diazotrophs, soils with a high water content have lower oxygen tension, which generally promotes fixation (Kondo and Yasuda 2003; Chang and Knowles 1965; Brouzes et al. 1969; O'Toole and Knowles 1973). In addition to meeting material requirements for nitrogenase, fixation requires a tremendous amount of energy—at least 16 moles of ATP per mole of N2 fixed (Gutschick 1981). As a result, fixation often has a positive relationship with soil C availability (heterotrophic fixation; Chang and Knowles 1965; Brouzes et al. 1969; Kondo and Yasuda 2003; O'Toole and Knowles 1973; Roper 1983) and a positive relationship with light (cyanobacterial fixation; Kondo and Yasuda 2003). Because of the large energetic cost, microbes avoid fixing N if it is readily available in the soil and so N fixation is often inhibited by soil mineral N (e.g., Roley et al. 2018; Dynarski and Houlton 2018; Oelze 2000). Univariate relationships between physicochemical factors and fixation relationships are generally well known, but field patterns do not always fit neatly with these schemas. For example, rhizosphere fixation in tropical forests often exhibit high N fixation rates even when N is abundant (Zheng et al. 2018), a pattern also observed in the desert (Wang et al. 2016) and arctic hummock-hollow soil crusts (Stewart et al. 2011). A more useful approach to predicting nutrient-fixation relationships may be substrate stoichiometry. Mineral N is not inhibitory when C and P concentrations are also high; fixation rates increase with C:N and decrease with N:P (Zheng et al. 2018; Eisele et al. 1989). Although patterns of diazotrophic community composition are emerging, it’s not yet clear what effect the community composition has on ecosystem function. Fixation rates often increase with nifH gene abundance (e.g., Lindsay et al. 2010; Wang et al. 2018) and with diversity and evenness of the diazotroph community (Hsu and Buckley 2009). However, it is not clear if nifH abundance and diazotroph diversity cause higher fixation rates or if the abundance of nifH genes is a consequence of physicochemical conditions that also promote fixation. Furthermore, it is not clear if the community composition influences rates; is nifH gene abundance sufficient to

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predict N fixation rates? Or are there taxa or consortia that are more adept at N fixation than others? Few studies have been able to directly assess the role of microbial community composition on fixation rates. Typically, N fixation rates and community composition are measured simultaneously, without experimental manipulations that allow for determination of cause and effect. But one study observed that when physicochemical conditions are kept constant, hotspots of fixation occurred in places with distinct microbial communities (Reed et al. 2010), suggesting that some communities do fix N at higher rates than others. Similarly, Hsu and Buckley (2009) found that diazotrophic richness and diversity explained more of the variation in fixation rates than did physicochemical drivers. On the other hand, very few taxa are transcribing nifH at any given time (Bahulikar et al. 2014; Burgmann et al. 2005), so perhaps the overall diazotrophic community is less important than the presence of a few key taxa. The functional links between microbial community composition and process rates remain tenuous and deserve further investigation, perhaps via experimental manipulations of biogeochemical conditions and/or the microbial community.

4.4

Contribution of Rhizosphere and Endosphere Fixation to Plant N Nutrition

Rhizosphere and endosphere N fixation occur in every biome (Reed et al. 2011) but methodological challenges and variability make generalizations about rates or importance difficult. In agricultural systems, the estimated global contribution from nonlegume fixation in croplands ranges from