The Plant Microbiome: Methods and Protocols [1st ed.] 9781071610398, 9781071610404

Citation preview

Methods in Molecular Biology 2232

Lilia C. Carvalhais Paul G. Dennis Editors

The Plant Microbiome Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

The Plant Microbiome Methods and Protocols

Edited by

Lilia C. Carvalhais Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia

Paul G. Dennis School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia

Editors Lilia C. Carvalhais Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation The University of Queensland St Lucia, QLD, Australia

Paul G. Dennis School of Earth and Environmental Sciences The University of Queensland Brisbane, QLD, Australia

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1039-8 ISBN 978-1-0716-1040-4 (eBook) https://doi.org/10.1007/978-1-0716-1040-4 © Springer Science+Business Media, LLC, part of Springer Nature 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 Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Plants are the main primary producers in terrestrial ecosystems and are intimately associated with diverse communities of microorganisms that inhabit virtually all above- and belowground compartments, inside and out. These microbes engage in complex webs of interactions that influence host fitness, with consequences for the provision of ecosystem goods and services. While understanding of plant microbiomes is ever increasing, our ability to manage these communities or predict their responses to environmental change remains limited. This book contains useful methods and short reviews that are of relevance to new and experienced plant microbiome researchers alike. We begin, perhaps fittingly, with a description of methods used to investigate microbiomes associated with seeds (Chapter 1). Chapters 2–4 then consider how to sample microbiomes from different plant compartments and tissues. Arbuscular mycorrhizal fungi form symbioses with approximately 80% of vascular plant families; hence, methods to sample and characterize these important organisms are provided in Chapter 5. Chapter 6 deals specifically with culture-based methods to study cereal endophytes, and then Chapter 7 presents tools to screen isolates for plant growth promoting traits. Despite their important ecological roles within plant microbiomes, protist communities have received less attention than bacterial or fungal communities. To address this, Chapter 8 describes a culture-independent metabarcoding method for plant-associated protists. Chapter 9 compiles protocols used for the isolation of plant-associated fungi, production of bioactive compounds from pure cultures, and characterization of these compounds. The microbiomes of woody tissues can be particularly challenging to study due to difficulties associated with DNA extraction and purification. Effective methods to obtain DNA and perform metabarcoding of wood-associated fungal communities are presented in Chapter 10. A key constraint to metabarcoding of endophytic communities is co-amplification of host DNA during polymerase chain reaction (PCR) of phylogenetic marker genes. Chapter 11 details a protocol to block PCR amplification of host DNA using peptide nucleic acid clamps, which facilitates effective metabarcoding of endophytic bacterial communities associated with wheat. Molecular methods to characterize the function of plant microbiomes include, but are not limited to, quantitative PCR (qPCR) assays targeting functional genes and shotgun sequencing of environmental DNA (metagenomics). Chapter 12 describes a qPCR-based method to quantify the ammonia oxidizing bacteria involved in the first step of nitrification, using the amoA gene. Chapter 13 then provides an in silico protocol to deplete unwanted sequences (e.g., host sequences) in metagenomic datasets. The protocol relies on alignment of query sequences to reference genomes and provides an example code. In addition to traditional measures of alpha and beta diversity, it may be useful to consider the ‘social characteristics’ of each population (e.g., how many other taxa does each population interact with) and the emergent structural properties of communities (e.g., modularity), which can be inferred using network analysis as described in Chapter 14. Chapter 15 provides a method to edit specific genes in Bacillus genomes using a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system, which is useful for the investigation of gene functions in plant–bacteria interactions. In

v

vi

Preface

Chapter 16, protocols for sample preparation for profiling of phyllosphere bacteria in a culture-independent manner are provided, as well as for isolating and screening of phyllosphere bacteria as potential biocontrol agents against foliar diseases. Continuing with the plant health theme, Chapter 17 presents a method to investigate the “soil-borne legacy,” a term used to describe the phenomenon in which stress triggers plant protection by soil microbes in subsequent host generations. Chapters 18 and 19 review Streptomycetes and plant microbial indicators of bioenergy plant health, respectively. Then Chapter 20 presents a range of traditional methods to determine the activity and functional diversity of soil microbiomes. Chapters 21–23 focus on rhizodeposits, which play important roles in plant–microbe communication and community assembly. Chapter 21 details a novel “bait-trap” assay that facilitates identification and enumeration of microbes that exhibit chemotaxis toward root exudates. Chapter 22 presents a review of methods for root exudate collection and analysis; and in a similar vein, Chapter 23 outlines a method for collecting root exudates under axenic conditions from plants challenged with foliar pathogens. St Lucia, QLD, Australia Brisbane, QLD, Australia

Lilia C. Carvalhais Paul G. Dennis

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

v ix

1 Studying Seed Microbiomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Birgit Wassermann, Daria Rybakova, Eveline Adam, ¨ ller, Christin Zachow, Maria Bernhard, Maria Mu Riccardo Mancinelli, and Gabriele Berg 2 Sampling Microbiomes Associated with Different Plant Compartments. . . . . . . . 23 Henry W. G. Birt, Anthony B. Pattison, and Paul G. Dennis 3 Sampling of Bacteria Associated with Plant Vascular Tissues . . . . . . . . . . . . . . . . . . 31 Anthony J. Young 4 Sampling of Plant Material to Study Endophytes in Small, Large, and Woody Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Jed Calvert 5 Preparation of Samples for Characterization of Arbuscular Mycorrhizal Fungi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Alberto Guille´n 6 Culture-based Methods for Studying the Bacterial Root Microbiome of Wheat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Rebekah J. Robinson, Vanessa N. Kavamura, Penny R. Hirsch, Ian M. Clark, and Tim H. Mauchline 7 Fast Screening of Bacteria for Plant Growth Promoting Traits . . . . . . . . . . . . . . . . 61 Bruna D. Batista, Maria Letı´cia Bonatelli, and Maria Carolina Quecine 8 Protists in the Plant Microbiome: An Untapped Field of Research . . . . . . . . . . . . 77 Kenneth Dumack and Michael Bonkowski 9 Plant-associated Fungi: Methods for Taxonomy, Diversity, and Bioactive Secondary Metabolite Bioprospecting. . . . . . . . . . . . . . . . . . . . . . . . . 85 Mariana Costa Ferreira, Camila Rodrigues de Carvalho, Marina Bahia, De´bora Luiza Costa Barreto, Rafaela Nogueira Azevedo, Betania Barros Cota, Carlos Leomar Zani, Ana Raquel de Oliveira Santos, Carlos Augusto Rosa, and Luiz Henrique Rosa 10 Extraction and Purification of DNA from Wood at Various Stages of Decay for Metabarcoding of Wood-Associated Fungi . . . . . . . . . . . . . . . . . . . . . 113 Jeff R. Powell, Michaela Blyton, Brad Oberle, Gillian L. Powell, Jessica Rigg, Darcy Young, and Amy E. Zanne 11 Peptide Nucleic Acid (PNA) Clamps to Reduce Co-amplification of Plant DNA During PCR Amplification of 16S rRNA Genes from Endophytic Bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Akitomo Kawasaki and Peter R. Ryan

vii

viii

12

13

14 15

16

17

18

19

20 21

22

23

Contents

Quantification of Ammonia Oxidizing Bacterial Abundances in Environmental Samples by Quantitative-PCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aimeric Blaud, Abadie Maı¨der, and Ian M. Clark Removing Host-derived DNA Sequences from Microbial Metagenomes via Mapping to Reference Genomes. . . . . . . . . . . . . . . . . . . . . . . . . . Yun Kit Yeoh Inference and Analysis of SPIEC-EASI Microbiome Networks . . . . . . . . . . . . . . . Henry W. G. Birt and Paul G. Dennis Gene Knockout of Beneficial Plant-associated Bacillus spp. Using the CRISPR-Cas9 Double Plasmid System . . . . . . . . . . . . . . . . . . . . . . . . . . . Everthon Fernandes Figueredo and Maria Carolina Quecine Sample Preparation for Culture-Independent Profiling and Isolation of Phyllosphere Bacteria to Identify Potential Biopesticides . . . . . . . . . . . . . . . . . . Eugenie Singh, Peer M. Schenk, and Lilia C. Carvalhais Soil-Borne Legacies of Disease in Arabidopsis thaliana . . . . . . . . . . . . . . . . . . . . . . Gilles Vismans, Jelle Spooren, Corne´ M. J. Pieterse, Peter A. H. M. Bakker, and Roeland L. Berendsen Bioactive Streptomycetes from Isolation to Applications: A Tasmanian Potato Farm Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nina R. Ashfield-Crook, Zachary Woodward, Martin Soust, and D. I˙pek Kurtbo¨ke Methods to Identify Soil Microbial Bioindicators of Sustainable Management of Bioenergy Crops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acacio Aparecido Navarrete, Rita de Ca´ssia Bonassi, Juliana Heloisa Pineˆ Ame´rico-Pinheiro, Gisele Herbst Vazquez, Lucas William Mendes, Elisaˆngela de Souza Loureiro, Eiko Eurya Kuramae, and Siu Mui Tsai Functional Soil Biological Measurements to Support Healthy Soils . . . . . . . . . . . Hazel R. Lapis-Gaza and Anthony B. Pattison A Bait-Trap Assay to Characterize Soil Microbes that Exhibit Chemotaxis to Root Exudates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katherine V. Weigh, Bruna D. Batista, and Paul G. Dennis Methods for Root Exudate Collection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . Hugo A. Pantigoso, Yanhui He, Michael J. DiLegge, and Jorge M. Vivanco Collection of Sterile Root Exudates from Foliar Pathogen-Inoculated Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Song, Corne´ M. J. Pieterse, Peter A. H. M. Bakker, and Roeland L. Berendsen

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

135

147 155

173

193 209

219

251

265

283 291

305

319

Contributors EVELINE ADAM • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria JULIANA HELOISA PINEˆ AME´RICO-PINHEIRO • Graduate Program in Environmental Sciences, Brazil University (Universidade Brasil), Fernandopolis, SP, Brazil NINA R. ASHFIELD-CROOK • GeneCology Research Centre and the School of Science and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD, Australia RAFAELA NOGUEIRA AZEVEDO • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Nu´cleo de Pesquisas em Cieˆncias Biologicas, Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil MARINA BAHIA • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil PETER A. H. M. BAKKER • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands DE´BORA LUIZA COSTA BARRETO • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Nu´cleo de Pesquisas em Cieˆncias Biologicas, Universidade Federal de Ouro Preto, Ouro Preto, MG, Brazil BRUNA D. BATISTA • School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia; Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia; Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of Sa˜o Paulo, Piracicaba, Sa˜o Paulo, Brazil ROELAND L. BERENDSEN • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands GABRIELE BERG • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria MARIA BERNHARD • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria HENRY W. G. BIRT • School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia AIMERIC BLAUD • School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK MICHAELA BLYTON • Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia MARIA LETI´CIA BONATELLI • Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of Sa˜o Paulo, Piracicaba, Sa˜o Paulo, Brazil MICHAEL BONKOWSKI • Cluster of Excellence on Plant Sciences (CEPLAS), Terrestrial Ecology Group, Institute of Zoology, University of Cologne, Ko¨ln, Germany JED CALVERT • Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia LILIA C. CARVALHAIS • Centre for Horticultural Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia IAN M. CLARK • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK

ix

x

Contributors

BETANIA BARROS COTA • Quı´mica de Produtos Naturais Bioativos, Instituto Rene´ Rachou, FIOCRUZ, Belo Horizonte, MG, Brazil CAMILA RODRIGUES DE CARVALHO • Quı´mica de Produtos Naturais Bioativos, Instituto Rene´ Rachou, FIOCRUZ, Belo Horizonte, MG, Brazil RITA DE CA´SSIA BONASSI • Graduate Program in Environmental Sciences, Brazil University (Universidade Brasil), Fernandopolis, SP, Brazil ANA RAQUEL DE OLIVEIRA SANTOS • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil ELISAˆNGELA DE SOUZA LOUREIRO • Graduate Program in Agronomy, Federal University of Mato Grosso do Sul, Chapada˜o do Sul, MS, Brazil PAUL G. DENNIS • School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia MICHAEL J. DILEGGE • Center for Rhizosphere Biology and Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA KENNETH DUMACK • Cluster of Excellence on Plant Sciences (CEPLAS), Terrestrial Ecology Group, Institute of Zoology, University of Cologne, Ko¨ln, Germany MARIANA COSTA FERREIRA • Quı´mica de Produtos Naturais Bioativos, Instituto Rene´ Rachou, FIOCRUZ, Belo Horizonte, MG, Brazil EVERTHON FERNANDES FIGUEREDO • Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of Sa˜o Paulo, Piracicaba, Sa˜o Paulo, Brazil ALBERTO GUILLE´N • ERIBiotecMed and Department of Plant Biology, University of Valencia, Burjassot, Spain YANHUI HE • Center for Rhizosphere Biology and Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA; Xi’an Key Laboratory of Textile Chemical Engineering Auxiliaries, School of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an, China PENNY R. HIRSCH • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK VANESSA N. KAVAMURA • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK AKITOMO KAWASAKI • CSIRO Agriculture and Food, Canberra, ACT, Australia EIKO EURYA KURAMAE • Department of Microbial Ecology, Netherlands Institute of Ecology, Wageningen, The Netherlands D. I˙PEK KURTBO¨KE • GeneCology Research Centre and the School of Science and Engineering, University of the Sunshine Coast, Maroochydore DC, QLD, Australia HAZEL R. LAPIS-GAZA • Department of Agriculture and Fisheries, Centre for Wet Tropic Agriculture, South Johnstone, QLD, Australia ABADIE MAI¨DER • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK RICCARDO MANCINELLI • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria TIM H. MAUCHLINE • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK LUCAS WILLIAM MENDES • Cell and Molecular Laboratory, Center for Nuclear Energy in Agriculture, University of Sa˜o Paulo, Piracicaba, SP, Brazil MARIA MU¨LLER • Department of Plant Sciences, Institute of Biology, NAWI Graz, University of Graz, Graz, Austria

Contributors

xi

ACACIO APARECIDO NAVARRETE • Graduate Program in Agronomy, Federal University of Mato Grosso do Sul, Chapada˜o do Sul, MS, Brazil; Graduate Program in Environmental Sciences, Brazil University (Universidade Brasil), Fernandopolis, SP, Brazil BRAD OBERLE • Division of Natural Sciences, New College of Florida, Sarasota, FL, USA; Department of Biological Science, George Washington University, Washington, DC, USA HUGO A. PANTIGOSO • Center for Rhizosphere Biology and Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA ANTHONY B. PATTISON • Department of Agriculture and Fisheries, Centre for Wet Tropic Agriculture, South Johnstone, QLD, Australia CORNE´ M. J. PIETERSE • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands GILLIAN L. POWELL • Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia JEFF R. POWELL • Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia MARIA CAROLINA QUECINE • Department of Genetics, “Luiz de Queiroz” College of Agriculture, University of Sa˜o Paulo, Piracicaba, Sa˜o Paulo, Brazil JESSICA RIGG • Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia; NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Menangle, NSW, Australia REBEKAH J. ROBINSON • Department of Sustainable Agriculture Sciences, Rothamsted Research, Harpenden, Herts, UK CARLOS AUGUSTO ROSA • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil LUIZ HENRIQUE ROSA • Departamento de Microbiologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil PETER R. RYAN • CSIRO Agriculture and Food, Canberra, ACT, Australia DARIA RYBAKOVA • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria PEER M. SCHENK • Plant-Microbe Interactions Laboratory, School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia EUGENIE SINGH • Plant-Microbe Interactions Laboratory, School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD, Australia YANG SONG • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands; Jiangsu Provincial Coordinated Research Center for Organic Solid Waste Utilization, Nanjing Agricultural University, Nanjing, People’s Republic of China MARTIN SOUST • Terragen Biotech Pty. Ltd., Coolum Beach, QLD, Australia JELLE SPOOREN • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands SIU MUI TSAI • Cell and Molecular Laboratory, Center for Nuclear Energy in Agriculture, University of Sa˜o Paulo, Piracicaba, SP, Brazil GISELE HERBST VAZQUEZ • Graduate Program in Environmental Sciences, Brazil University (Universidade Brasil), Fernandopolis, SP, Brazil GILLES VISMANS • Plant-Microbe Interactions, Department of Biology, Science4Life, Utrecht University, Utrecht, The Netherlands JORGE M. VIVANCO • Center for Rhizosphere Biology and Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, CO, USA

xii

Contributors

BIRGIT WASSERMANN • Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria KATHERINE V. WEIGH • School of Earth and Environmental Sciences, The University of Queensland, Brisbane, QLD, Australia ZACHARY WOODWARD • Terragen Biotech Pty. Ltd., Coolum Beach, QLD, Australia YUN KIT YEOH • Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China; Centre for Gut Microbiota Research, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China ANTHONY J. YOUNG • School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia DARCY YOUNG • Department of Biological Science, George Washington University, Washington, DC, USA CHRISTIN ZACHOW • Austrian Centre of Industrial Biotechnology, Graz, Austria CARLOS LEOMAR ZANI • Quı´mica de Produtos Naturais Bioativos, Instituto Rene´ Rachou, FIOCRUZ, Belo Horizonte, MG, Brazil AMY E. ZANNE • Department of Biological Science, George Washington University, Washington, DC, USA

Chapter 1 Studying Seed Microbiomes Birgit Wassermann, Daria Rybakova, Eveline Adam, Christin Zachow, Maria Bernhard, Maria Mu¨ller, Riccardo Mancinelli, and Gabriele Berg Abstract Recent studies indicate that seed microbiomes affect germination and plant performance. However, the interplay between seed microbiota and plant health is still poorly understood. To get a complete picture of the system, a comprehensive analysis is required, comprising culture-dependent and culture-independent techniques. In this chapter, we provide a combination of methods that are established and optimized for the analysis of the seed microbiome. These include methods to: (1) activate and cultivate dormant seed microbiota, (2) analyze microbiota in germinated seeds (with and without substrate), (3) quantify microbial DNA via real-time PCR, (4) deplete host DNA for amplicon and metagenome analysis, and (5) visualize seed endophytes in microtomed sections using fluorescent in situ hybridization (FISH) and confocal laser scanning microscopy (CLSM). A deep understanding of the seed microbiome and its functions can help in developing new seed treatments and breeding strategies for sustainable agriculture. Key words Seed microbiome, Microbiota, Germination, Real-time PCR, Peptide nucleic acid, FISHCLSM

1

Introduction Seeds are small embryonic plants that regulate the reproduction of gymnosperms and angiosperms and represent a remarkable phase in a plants’ live cycle [1]. Seeds have to feature resilience towards a diversity of biotic and abiotic stressors; likewise, their native microbiota is highly specialized [2]. Recent multi-omics-based analyses revealed that seeds host highly diverse and abundant microbiota, which is plant genotype-specific and is affected by the environment and the management practice [2]. Up to two billion bacterial cells, constituting >9000 species, comprise the seed microbiome and a beneficial impact on plant health and performance is explicitly suggested [2–8]. Besides bacteria and fungi, archaea were recently discovered as native members of the seed microbiome [9]. Microbes can enter seeds via horizontal transmission from the surrounding environment or via vertical transmission from the mother plant

Lilia C. Carvalhais and Paul G. Dennis (eds.), The Plant Microbiome: Methods and Protocols, Methods in Molecular Biology, vol. 2232, https://doi.org/10.1007/978-1-0716-1040-4_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

1

2

Birgit Wassermann et al.

[6]. The latter is considered as prenatal care for the successive plant generation by enhancing seed vigor, germination, and resilience via the induction of plant defense mechanisms and antagonism towards plant pathogens [10]. The challenges in cultivating seed microbiota led to the long-time assumption that seeds are sterile and that the emerging seedling is colonized mainly by microbes from its surrounding environment, with soil being the main source [1, 6, 11]. Thus, our knowledge of the seed microbiome is still at the initial stage and its potential for the promotion of host health and performance is largely unexplored. Understanding the specificity and efficiency of microbiota transmission from one plant generation to the next, as well as the transmission from seeds to roots and other plant organs might provide new opportunities for the development of healthy, resilient, and high-quality seeds for agriculture [2]. Plants have evolved manifold strategies to produce seeds, and seed morphology, size, and structure are accordingly highly diverse. Moreover, seeds contain plant species-specific secondary metabolites. Therefore, it is essential to adapt the methods to study seed microbiomes for each plant seed accordingly. Additionally, seed microbiome researchers should consider the following facts: (1) seeds are composed of endophytes, which represent the microbial community within seeds, and of epiphytes, colonizing the seed surfaces; (2) inside the seeds, endophytes can colonize different niches: e.g. seed coat, endosperm envelope, cotyledons, and the root hypocotyl embryo [12]; (3) seed microbiota is very often in a dormant state until the seeds are exposed to a certain stimulus, mostly water; and last but not least, (4) the spermosphere, representing the zone surrounding germinating seeds, where seed microbiota interacts with soil microbial communities [1, 13]. Herein, we describe different methods for studying relationships between seeds and its associated microbiota in situ, in vitro, and in silico, and provide some examples of how these methods can be combined for an exhaustive study of the seed microbiome (Fig. 1). Description of bioinformatic analyses of the seed microbiota would go beyond the framework of this chapter due to the ongoing change of standards.

2

Materials

2.1 Preparation and Activation of Seeds

1. Laminar flow hood. 2. Sterile distilled water. 3. Laboratory platform shaker. 4. (optional) Sodium hypochloride (e.g. for oilseed rape seeds: 2% in sterile distilled water) for surface sterilization of seeds.

Studying Seed Microbiomes

3

Fig. 1 Overview of the methods is described in this chapter

2.2 Cultivation of Native Seed Microbiota and Seed Germination

1. Sodium chloride (0.85% in sterile distilled water). 2. Mortar and pestle (sterile). 3. Diverse solid media to determine microbial abundance and to isolate microorganisms (e.g. prepared nutrient media Nutrient Broth No.2 (Sigma-Aldrich) and Luria Broth (Roth) for cultivating bacteria and Potato Extract Glucose Broth (Roth) for cultivating fungi and yeasts mixed with agar-agar.). 4. Centrifuge. 5. Eppendorf tubes. 6. FastDNA™ SPIN Kit for Soil (MP Biomedicals, Solon, USA) (can be replaced by any other microbial DNA extraction kit appropriate for isolation of DNA from host-associated microbiota).

4

Birgit Wassermann et al.

7. Germination pouches (Mega International, Newport, MN, USA) for gnotobiotic substrate-free seed germination. 8. 4 L of soil-vermiculite mixture (1:4) and a plastic container with a volume of 5.6 L per replicate (substrate composition and amount and size of plastic containers can be exchanged by adequate alternatives). 9. Greenhouse with controlled conditions. 10. Stomacher laboratory blender (BagMixer, Interscience, St. Nom, France) or sterile whirl-packs® (Sigma-Aldrich, Vienna, Austria). 2.3 Design of Specific Peptide Nucleic Acid (PNA) Clamps for Amplicon Sequencing of Seeds

1. Eppendorf tubes. 2. PCR tubes. 3. Taq&Go™ DNA Polymerase (Master mix 5xC, MP Biomedicals, Solon, USA; can be exchanged by adequate alternatives). 4. Primer pair, specifically amplifying a target gene sequence, including barcodes for amplicon sequencing. 5. MgCl2 (25 mM). 6. Nuclease-free water. 7. PCR thermal cycler. 8. Wizard® SV Gel and PCR Clean-Up System (Promega, Madison, WI, USA; can be exchanged by adequate alternatives). 9. NanoDrop™ 2000c Spectrophotometer (Fisher Scientific), or any appropriate UV-VIS spectrophotometric technology to determine nucleic acid concentration in samples. 10. Customized PNA clamps [19], if necessary including (a) Gamma functional groups: lysine, miniPEG or alanine and glutamic acid, and/or (b) O linker, E linker, X linker, or two lysines to enhance the solubility of PNA probes (PNA Bio, California, USA). 11. (optional) Materials for single-strand conformation polymorphism (SSCP) to check PNA functionality.

2.4 Bacterial Cell Enrichment for Metagenome Analysis

1. Sterile water. 2. Sterile 50 mL tubes. 3. Eppendorf tubes. 4. BCE buffer (bacterial cell extraction buffer): 50 mM Tris HCl pH 7.5, 1% Triton X-100 and 2 mM 2-mercaptoethanol, added prior to usage. 5. 50 mM Tris HCl pH 7.5. 6. Sterile mortar and pestle. 7. Sterile Mesoft® filters.

Studying Seed Microbiomes

5

8. Refrigerated centrifuge appropriate for 50 mL Tubes. 9. Histodenz™ (Merck, Vienna, Austria) solution: 8 g Histodenz dissolved in 10 mL of 50 mM Tris HCl pH 7.5. 10. FastDNA™ SPIN Kit for Soil (MP Biomedicals, Solon, USA) (can be replaced by any other DNA extraction kit, appropriate to extract microbial community DNA from eukaryotic hosts). 2.5 Microbiota Quantification via Real-time PCR (qPCR)

1. Real-time PCR instrument; we use a Rotor-Gene 6000 realtime rotary analyzer (Corbett Research, Sydney, Australia). 2. PurePlus® 0.1 mL RT PCR Tube Strips for Qiagen® RotorGene® (Labcon). 3. Primers for specific quantification of bacterial, archaeal, and/or fungal genes. We routinely use: (a) Bacteria: primer pair 515f—926r [14], 10 μM each. (b) Archaea: Primer pair 344aF—517uR [15], 5 μM each. (c) Fungi: Primer pair ITS1—ITS2 [16], 10 μM each. 4. Peptide nucleic acid clamps (PNAs) [17]: pPNA and mPNA (PNA Bio, California, USA). 5. QuantiTect SYBR® Green PCR kit (QIAGEN GmbH, Hilden, Germany). 6. Nuclease-free water.

2.6 Visualization of Native Seed Microbiota In Situ

1. Leica CM 3000 cryostat (GMI, USA) supplied with a stainlesssteel rotary microtome. 2. Confocal laser scanning microscope (CLSM), e.g. from Leica Microsystems (Wetzlar, Germany). 3. Water bath. 4. Embedding media: a glycol-based tissue freezing medium (supplied by the manufacturer) that solidifies at low temperatures and binds the tissues to the tissue holder; e.g. EM-400 Embedding Medium (Leica Microsystems, Wetzlar, Germany) can be used. 5. Eppendorf tubes. 6. Ice. 7. Tweezers. 8. 50, 70, 80, and 96% ethanol. 9. 4% Paraformaldehyde (PFA). 10. PBS and ice-cold PBS. For 1 L PBS buffer, add 8 g of NaCl to 800 mL of distilled water, then add 200 mg of KCl, 1.44 g of Na2HPO4, and 240 mg of KH2PO4. Finally, adjust solution to pH of 7.4 and add distilled water until volume is 1 L. 11. Ice-cold double distilled water.

6

Birgit Wassermann et al.

Table 1 FISH probes Probe name

Sequence (50 –30 )

Fluorescent dye

Target

EUB-mix

GCTGCCACCCGTAGGTGT

Cy3

Verrucomicrobiales

ALF968

GGTAAGGTTCTGCGCGTT

Alexa488

Alphaproteobacteria

GAM42a

GCCTTCCCACATCGTTT

Cy5

Gammaproteobacteria

BET42a-comp

GCC TTC CCA CAT CGT TT

/

Gammaproteobacteria

12. Microscopy slides. 13. Lysozyme (Sigma-Aldrich); 1 mg/mL solution. 14. (optional) Calcofluor white (CFW) staining, 0.15%. 15. ProLong Gold antifadent (Molecular Probes, Eugene, USA). 16. Translucent nail polish (any common supplier). 17. (optional) LIVE/DEAD® BacLight™ Bacterial Viability kit, Life technologies, California, USA). 18. Formamide (FA). 19. Fluorescent in situ hybridization (FISH) probes [18]. Probes used in this example are listed in Table 1. 20. Hybridization buffer Hb1 for FISH-CLSM: For 200 μL of Hb1 buffer containing 35% FA, mix 36 μL of 5 M NaCl, 4 μL of 1 M Tris HCl, 1 μL of 2% Sodium dodecyl sulfate (SDS), 70 μL FA and 88 μL ddH2O. Add 1 μL of each chosen probe shortly before you use the solution. In this example, the probes GAM42a, BET42a-competitor and ALF968 were used for the buffer Hb1. Please note that FA concentration in both hybridization and washing buffers is dependent on the probes that you use [18]. 21. Hybridization buffer Hb2 for FISH-CLSM: For 200 μL of Hb2 buffer containing 15% FA, mix 36 μL of 5 M NaCl, 4 μL of 1 M Tris HCl, 1 μL of 2% SDS, 30 μL FA, and add 128 μL ddH2O. Finally, supplement with 1 μL of each chosen probe shortly before you will use the solution. In this example, the probe EUB-mix was used. 22. Washing buffer Wb1 for FISH-CLSM: For 100 μL of Wb1 buffer (FA concentration 35%) mix 14 μL of 5 M NaCl, 20 μL of 1 M Tris HCl, 50 μL of 6.5 M EDTA, and 956 μL ddH2O. 23. Washing buffer Wb2 for FISH-CLSM: For 100 μL of Wb2 buffer (FA concentration 15%) mix 64 μL of 5 M NaCl, 20 μL of 1 M Tris HCl, and 956 μL ddH2O.

Studying Seed Microbiomes

3

7

Methods

3.1 Preparation and Activation of Seeds

All procedures should be carried out under sterile conditions at room temperature, unless otherwise stated. In order to analyze the naturally composed seed microbiota, we recommend using at least four replicates per plant genotype or treatment. 1. To investigate the entire microbial community of the seeds, wash 20 seeds per replicate (or more, dependent on the seed size) three times with sterile distilled water and activate them under agitation (100 rpm) for 4 h in an adequate amount of sterile water (e.g. for the oilseed rape seeds with app. 0.6 g/ 100 seeds, use 2 mL water for 20 seeds. For the pumpkin seeds with app. 22 g/100 seeds, use 13 mL sterile water for 20 seeds) (see Note 1). 2. (optional) Alternatively, to investigate the endophytic seed microbiota, you can either physically remove seed peel under sterile conditions or surface-sterilize seeds by incubating them with sodium hypochlorite solution for 5 min under agitation (we use 2% solution to surface-sterilize oilseed rape seeds). Then, wash the seeds six times with sterile water (see Note 2). 3. (optional) In order to ensure whether the cultivable microorganisms have been inactivated by the surface-sterilization, the seed surface can be printed on solid media, or the final (sixth) wash solution can be plated out. Now, you can either grind the activated seeds directly (Subheading 3.2), or allow the seeds to germinate under sterile conditions and extract microbiota from roots and green parts of the seedlings (Subheading 3.2) (see Note 3).

3.2 Cultivation of Microbiota Directly from Activated Seeds and Microbial DNA Extraction

1. Mortar seeds with 2–10 mL sterile 0.85% sodium chloride (dependent on the size of the seeds) under sterile conditions. 2. Dilute the suspension serially for plating on diverse solid media to isolate microorganisms and determine microbial abundance of the cultivable fraction. The cultivated isolates derived from seeds can be tested in vivo for desired qualities (e.g. activity against some plant pathogens). 3. For the extraction of total microbial DNA, centrifuge mortared seed material at 16,500  g for 20 min at 4  C and store pellets at 70  C or proceed directly to DNA extraction. 4. For DNA extraction, we routinely use the FastDNA™ SPIN Kit for Soil according to the manufacturer protocol.

8

Birgit Wassermann et al.

Fig. 2 (a) Sugar beet seedlings in a germination pouch. (b) Root print of a sugar beet seedling on nutrient agar. © Christin Zachow and Adrian Wolfgang (ACIB GmbH, Graz, Austria) 3.3 Cultivation of Microbiota After Seed Germination with and Without Substrate and Microbial DNA Extraction

1. For cultivation without a substrate, we recommend using germination pouches according to the manufacturer protocol with two or more pouches per replicate (Fig. 2a). 2. For cultivation with a sterile substrate, we routinely use 1 L of soil mixed with vermiculite (4:1) in plastic containers with a volume of 5.6 L. The potting mixture must be autoclaved twice with a 48 h interval in order to inactivate microbial spores present in the soil. We recommend using two or more plastic containers per replicate. 3. Pouches or pots with (sterile) substrate can be incubated under sterile conditions in a greenhouse for 2–4 weeks, dependent on the growth rate of the seedlings. 4. (optional) To visually check the colonization along the plant root, ‘root printing’ on solid media can be performed (Fig. 2b), prior to the determination of microbial abundances or DNA extractions. 5. Separate roots from green parts (optional) and homogenize the plant material under sterile conditions in an adequate amount of sterile 0.85% sodium chloride (NaCl) solution, dependent on the plant size (e.g. use 2 mL of 0.85% NaCl for the roots of 14 oilseed rape seedlings grown for 14 days, or 50 mL of 0.85% NaCl for 7 g of pumpkin seedlings root material grown for 30 days). 6. Homogenization of plant material can be performed using a sterile mortar and pestle or in a Stomacher laboratory blender using sterile whirl-packs® for 3 min. 7. For determining microbial abundances and extracting DNA, process with the homogenized plant material at the same way as described above for the homogenized seeds (Subheading 3.2, steps 3 and 4).

Studying Seed Microbiomes

3.4 Host DNA Depletion I: Design of Specific PNAs for Amplicon Sequencing of Seed Microbiota

9

The peptide nucleic acid (PNA) is a single-strand DNA oligomer, which specifically binds to target DNA causing a selective blocking of PCR synthesis [17]. Ready-to-use pPNA and mPNA can be ordered as catalog items and used with recommended standard protocols to block the mitochondrial and plastid 16S rDNA derived from the host. Nevertheless, host sequences other than mitochondrial and plastid 16S rDNA may be amplified in a PCR reaction in addition to the desired bacterial-derived 16S rRNA gene sequences and therefore, impede microbiome studies. For such cases, specific PNA oligomers can be designed for each study. PCR reaction of the unwanted sequences can be additionally blocked by the means of elongation arrest of polymerase or by competitive binding between the forward or reverse primers and the PNA probe [18] (see Note 4). If your samples show a high proportion of host sequences, find a potential sequence for the PNA design as following: 1. Check a multiple-sequence alignment of host sequences for a site with total base identity of all representative sequences. 2. Prevent that the sequence to be blocked is identical or similar to potential microbial sequences: blast the sequence against the NCBI nucleotide database for highly similar sequences (megablast) with default settings [20, 21]. As PNA probes may also bind even if there is one mismatch in the formed heteroduplex, also different one-mismatch variants should be checked. Sequence hits outside of the DNA regions used for amplicon sequencing (rRNA or ITS regions) can be neglected. 3. Check whether the selected sequence can be found in the database, which will be used in your study (e.g. UNITE reference database for ITS amplicons used by QIIME). Depending on the database and bioinformatic tools used, consider checking sense as well as reverse complement of the sequence. Make sure that the sequence is suitable as a PNA blocking sequence: 4. The optimal length of a PNA oligomer for elongation arrest is between 13 bp and 17 bp [17], nevertheless a range between 12 and 21 bp is also possible [22]. 5. Avoid more than one mismatch between template DNA and PNA probe as it will affect PCR blocking [23]. 6. PNA melting temperature (Tm) has to be higher than that of primers [17]. You can, for example, calculate Tm of your primers with the Tm calculator of New England Biolab available at https://www.nebiolabs.com.au/tools-and-resources/interac tive-tools. PNA annealing temperature has to be higher than the annealing temperature of the primers used in the PCR reaction to be blocked.

10

Birgit Wassermann et al.

7. PNA Tm should be above the temperature of the extension cycle [23]. 8. Further guidelines regarding orientation, selfcomplementarity, purine and guanine content, and distribution can be found in the instructions of the PNA manufacturers. A PNA tool is available at https://www.pnabio.com/support/ PNA_Tool.htm is can be used for assistance in PNA design [22]. 9. In case when problems fulfilling the rules arise, certain modifications of PNA are possible as follows: (a) Addition of Gamma functional groups (lysine, miniPEG or alanine, and glutamic acid) results in a stereogenic center at the γ-carbon atom that can convey advantages such as an increased Tm (5–8  C/substitution), thus providing higher affinity, improved solubility, less selfaggregation, and more stable PNA-DNA duplexes [22, 24]. (b) Addition of solubility enhancers such as O linker, E linker, X linker, or two lysines can enhance the solubility of PNA probes. 10. Example of a 30 μL PCR batch for ITS amplicons including PNA (see Note 5): 6 μL Taq&Go™ DNA Polymerase (Mastermix 5xC). 1.2 μL of each primer including barcodes. 1 μL template DNA (diluted appropriately). 0.9 μL MgCl2 (25 mM). 0.15–0.30 μL PNA (100 μM). 19.40–19.55 μL nuclease-free water. 11. Extend the PCR cycler protocol with the PNA annealing step, considering the rule of the PNA annealing temperature. Be aware that low temperatures in the primer annealing step could cause unspecific binding of PNA. Consult wellestablished protocols (e.g. for using mPNA and pPNA) in order to set the optimal annealing temperature in your specific case. Example of a PCR cycler protocol with a PNA annealing step: 95  C for 5 min, 30 cycles of 95  C for 30 s, 78  C for 5 s (PNA annealing; set temperature 0–2  C below the predicted Tm of PNA including modifications), 58  C for 40 s, (primer annealing; set temperature 0–5  C below the Tm of your primer including the barcode with the lowest Tm) and final elongation at 72  C for 10 min.

Studying Seed Microbiomes

11

12. After PCR amplification, the PCR products are purified using the Wizard® SV Gel and PCR Clean-Up System protocol for centrifugation. 13. DNA extracts can be sent to sequencing after determining purity and the nucleic acid concentration in the samples by UV-VIS spectrophotometric technology, e.g. NanoDrop™ Spectrophotometer. 14. (optional) Before re-sequencing your test samples for validation, you can check PNA functionality by a single strand conformation polymorphisms (SSCP) gel [25]. Use the purified PCR products resulting from various PNA concentrations as well those without PNA (standard PCR) for the SSCP gel. Deviating bands in the lanes of the standard PCR variants without PNA can be excised from the SSCP gel, purified accordingly and sequenced in order to verify that the blocked sequences refer to the targeted-host sequences. 15. For troubleshooting see Note 6. 3.5 Host DNA Depletion II: Bacterial Cell Enrichment for Metagenome Analysis

The main problem when analyzing plant metagenomes are the host sequences that comprise up to 99% of the extracted DNA. To enrich the bacterial cell fraction, we suggest applying a series of centrifugation steps followed by density gradient centrifugation as described in the protocols of Ikeda and colleagues [26] and Tsurumaru and colleagues [27]. We slightly updated the host DNA depletion protocol for the seeds as follows: 1. To receive appropriate amounts of microbial DNA, homogenize up to 10 g of activated seeds in 50 mL BCE buffer using a sterile mortar and pestle. 2. Filter the homogenate through a layer of sterile Mesoft® filter and transfer resulting suspension to a clean tube. 3. Centrifuge at 500  g for 5 min at 10  C and transfer supernatant to a clean tube. 4. Centrifuge at 5500  g for 20 min at 10  C, discard supernatant, and resuspend pellet in 5 mL BCE buffer. 5. To remove insoluble particles, filter suspension through a layer of sterile Mesoft filter, centrifuge filtrate at 10,000  g for 10 min at 10  C discard supernatant, and resolve pellet in 5 mL BCE buffer. 6. Repeat filtration and centrifugation steps two times. 7. Suspend the final filtrate in 6 mL of 50 mM Tris HCl (pH 7.5). 8. Prepare an Eppendorf tube with 4 mL Histodenz™ solution (8 g Histodenz dissolved in 10 mL of 50 mM Tris HCl) and add filtrate via pipetting on the tube bottom below Histodenz solution.

12

Birgit Wassermann et al.

Fig. 3 Seed filtrate following Histodenz density gradient centrifugation. Enriched microbial DNA is visible as a whitish band between the two phases, indicated by a black arrow

9. Centrifuge at 10,000  g for 40 min at 10  C. 10. The bacterial cell fraction is now visible as a whitish band, located at the interface of upper and lower phase (Fig. 3). 11. Collect the whitish band, mix with equal volume of sterile water, and centrifuge at 10,000  g for 1 min at 10  C. 12. In order to obtain appropriate amounts of microbial DNA, you can combine extracts from multiple samples. Store the resulting pellet at 20  C or proceed directly to the microbial DNA extraction. 3.6 Amplicon and Metagenome Sequencing and Bioinformatics Analyses

For appropriate primers and PCR protocols for 16S, ITS and 18S Illumina amplicon sequencing of seed-derived microbiomes, please refer to the suggestions described in the Earth Microbiome Project [28]. Specific adjustments for each study are necessary as the associations of microbial communities with various hosts are diverse. The most prominent bioinformatics tool to analyze host-associated amplicon sequences is the QIIME2 pipeline [29], providing a vast array of commands to depict microbiome composition as well as alpha-(within sample) and beta-(between sample) diversity of microbiota. Recommended databases to assign taxonomy are

Studying Seed Microbiomes

13

UNITE [30] for fungi, and SILVA [31] for bacteria and archaea. For amplicon sequencing, as well as for all other described analyses, an appropriate number of replicates is mandatory. From those replicates, so-called core microbiomes can be constructed, consisting of taxa that are specific for a certain sample. Several online tools for data illustration are available; among others, Krona charts [32] (visualizing multi-level community compositions), Venn-diagrams (picturing shared and unique taxa within the sample pool), METAGENassist [33](to identify positively and negatively correlated taxa), MEGA X [34] (for revealing phylogenetic relationships), and Circos [35] (exploring relationships between samples and associated microbes). Cytoscape program [36], used to visualize microbial interaction networks, and its add-on ‘CoNet’ [37] assisting in inferring putative microbial interactions within their hosts, are valuable tools as well. The analysis of the shotgun metagenomics data is highly dependent on the scientific questioning and the tools for its analyses are numerous. Mentionable are, for example, the open source online application MG-RAST [38] for the automatic phylogenetic and functional analyses of the metagenome, MetaVelvet [39] and metaSPAdes [40] for the assembly of multiple genomes from the mixed sequence reads. In order to bin contigs into whole genomes, CONCOCT [41] can be used, resulting in highly representative metagenome-assembled genomes. 3.7 Microbiota Quantification via Real-time PCR (qPCR)

For quantifying gene copy numbers of bacteria, archaea, and fungi within seeds, qPCR can be performed on the extracted microbial DNA with kingdom-specific primer pairs or alternatively for specific taxa (see Note 7). 1. Prepare the following reaction mixture for one sample: (a) For bacteria: 5 μL QuantiTect SYBR® Green PCR kit, 0.5 μL of each primer, 0.15 μL PNA mix [17], 2.85 μL nuclease-free water, and 1 μL template DNA. (b) For archaea and fungi, prepare the following mixture: 5 μL QuantiTect SYBR® Green PCR kit, 0.5 μL of each primer, 3 μL nuclease-free water, and 1 μL template DNA. 2. We use a Rotor-Gene 6000 real-time rotary analyzer, applying the following cycling conditions: (a) Bacteria: 95  C for 5 min, 40 cycles of 95  C for 20 s, 54  C for 30 s, 72  C for 30 s, and a final melt curve of 72–96  C. (b) Archaea: 95  C for 5 min, 40 cycles of 95  C for 15 s, 60  C for 30 s, and 72  C for 30 s followed by a melt curve of 72–96  C.

14

Birgit Wassermann et al.

(c) Fungi: 95  C for 5 min, 40 cycles of 95  C for 30 s, 58  C for 35 s, and 72  C for 40 s with a melt curve of 72–96  C. 3. Conduct three individual qPCR runs for each replicate and subtract intermittently occurring gene copy numbers that are found in negative controls from the respective sample. 3.8 Preparation Seeds for Microbial Visualization via CLSM

As the seeds are often very hard and small, it is suggested to use the microtome to achieve a very thin slicing of the seeds for in situ visualization of microbiota using CLSM. The seed microbiota on the seed-slices can then be stained and visualized by either using FISH-on-slide method (Subheading 3.9) to differentiate between microbial taxa or by using LIVE/DEAD® BacLight™ Bacterial Viability kit (Subheading 3.10) to visualize the viability of the cells. Figure 4 illustrates the method for preparing and visualizing seed microbiota in situ. 1. Activate the seeds (at least four seeds per condition) for 4 h in water, followed by air-drying them for at least 12 h. 2. Adjust the specimen temperature control of the cryostat cabinet down to the freezing temperature suggested by the manufacturer 30  C (unless other freezing temperature is suggested by the manufacturer) and let it cool down to the desired temperature. 3. (optional) In order to visualize the endophytic microbial communities only, sterilize the seeds’ surface using 80% sterile ethanol three times. If the total seed microbiome (endophytes and epiphytes) is studied, this step can be omitted. 4. Pour the embedding media on the dry surface of the tissue holders. 5. Place the seeds (one seed at a time) on embedding media. 6. Place the chuck (tissue holder) with seed and embedding media in the cryostat cabinet adjusted to a temperature of 30  C (unless other freezing temperature is suggested by the manufacturer). 7. Keep the chuck with the seed and embedding media in the cryostat cabinet for 15 min until the solidification of the embedding media and the seed occurs. 8. Adjust the microtome to obtain 100 μm sections as suggested by the manufacturer. 9. Cut the frozen seeds using the microtome into 100 μm sections. 10. Place the unfolded seed sections on glass slides by separating them from the media using pre-cooled tweezers.

Studying Seed Microbiomes

15

Fig. 4 (a) A schematic of the seed microbiota visualization process. First, the seed is cut in approximately 100 μm thin sections using a microtome, which are then labeled with the FISH probes using FISH-on-slide

16

Birgit Wassermann et al.

11. (optional) Place one slice each in a pre-cooled Eppendorf tube and keep on ice until the fixation procedure, which should occur within maximal 1–2 h after the slicing. 3.9 Staining for CSLM I: FISH-on-slide

The advantages of the on-slide FISH method in comparison to the usual FISH in the tube is that the very thin and delicate sections of plant tissues can be kept directly on the slide and would not be washed away or damaged during repetitive washing and incubation steps. Additionally, much less volume of the buffers is required as compared to the conventional FISH in tube method [5]. 1. Wash the sections with 600 μL PBS in the Eppendorf tube. 2. Add one volume PBS and three volumes 4% PFA to the tube to fix the samples. 3. Incubate the tubes over night at 4  C. 4. Remove the PBS/PFA solution using a pipette tip. 5. Wash the samples three times with PBS in the tube. 6. Place the seed sections on separate glass slides using tweezers and rinse three times with PBS. 7. Incubate the sections with 100 μL lysozyme (1 mg/mL) for 10 min at room temperature to increase the permeability of the bacterial cell wall. 8. Prepare buffer Hb1 containing FISH probes of your choice and pre-warm it at 43  C (see Note 8). 9. Rinse the sections twice with ice-cold PBS on the slide. 10. Add a drop of 50% ethanol directly to the section on a slide so that it covers the seed section and incubate for 3 min. 11. Exchange the ethanol solution with 70% ethanol using the pipette and incubate for 3 min. 12. Exchange again the ethanol solution with 96% ethanol using the pipette and incubate for another 3 min. 13. Rinse the samples with ice-cold PBS on the slide once and incubate with PBS for 3 min at room temperature. 14. Apply 200 μL of pre-warmed (43  C) Hb1 directly to the section so that the tissue is completely covered with the liquid and incubate for 90 min at 43  C in the dark.

ä Fig. 4 (continued) method or using a LIVE/DEAD staining method. (b) CLSM visualization of bacterial colonization patterns in the untreated (1), bio-primed with Pseudomonas brassicacearum CKB26 (2) and Serratia plymuthica HRO-C48 (3) oilseed rape seeds. The strains in (1) and (2) were visualized using FISHCLSM with Alphaproteobacteria-specific ALF968 probe (Alexa488-labeled) and an equimolar ratio of eubacteria probe EUB338, EUB338II, and EUB338III (Cy5-labeled). In (3), BacLight LIVE/DEAD stain was used to visualize alive (green) and dead (red) S. plymuthica HRO-C48 cells in 3D projection. ©Daria Rybakova and colleagues, Graz University of Technology [5]

Studying Seed Microbiomes

17

15. During the incubation time, prepare Hb2 containing FISH probe EUB-mix (Table 1) or other probe of your choice and the washing buffer Wb1 and pre-warm at 43  C and 44  C, respectively. 16. Eliminate Hb1 and rinse the samples with 500 μL of pre-warmed Wb1. Add another 0.5 mL of Wb1 and incubate for 10–15 min at 44  C. 17. Eliminate the washing buffer and add 200 μL of pre-warmed (44  C) Hb2 to the sample, followed by an incubation step at 43  C for 90 min. 18. Remove Hb2 and rinse the samples with pre-warmed (44  C) Wb2. Incubate the samples with additional 0.5 mL of Wb2 at 44  C for 10–15 min. 19. Remove the washing buffer and rinse the samples with ice-cold double-distilled water in order to eliminate any salt residuals. 20. (optional) For the display of the plant structures, stain the samples with CFW, which binds to β-1,3 and β-1,4 polysaccharides as described by the manufacturer: (a) Incubate the samples with 350 μL of 0.15% CFW staining solution for 20–30 min in the dark and rinse afterwards with ice-cold double-distilled water. (b) Add 0.5 mL double-distilled water to the samples and incubate for 5–10 min in the dark. 21. Dry the samples very carefully with soft compressed air on the slide and apply up to 10 μL of ProLong Gold antifadent to the samples. Subsequently, seal the coverslip with nail polish. 22. Leave the samples in the dark at room temperature for 24 h. 23. (Optional) Store the samples at 4  C and darkness until CLSM investigation for maximum of three days. 3.10 Staining for CLSM I: Live/Dead

1. Incubate the sections with 200 μL of 2% LIVE/DEAD® BacLight™ Bacterial Viability kit staining solution for 15 min at room temperature, as suggested by the manufacturer. 2. Remove the solution, cover the sample with the coverslip, and seal with nail polish. 3. Proceed to CLSM immediately.

3.11 Combinations of the Methods for Complex Seed Microbiome Studies

For a thorough representation of the seed microbiome, it is recommended to combine the data gained from the methods described above (Fig. 1). The possibilities are numerous and highly dependent on scientific questioning. 1. Follow a systemic approach by considering the absolute microbial abundance numbers resulting from qPCR for the correct interpretation of relative microbial abundances resulting from

18

Birgit Wassermann et al.

amplicon sequencing. This will allow making a valid comparison between different states of one microbiome or between various microbiomes separated in space. 2. Sequence PCR-amplified 16S rRNA genes from the purified DNA of seed isolates showed desired properties during cultivation-dependent studies. Then, you can track those sequences in the amplicon pool in order to see the distribution of the isolates of interest in the microbial community. 3. Based on the results of qPCR, the amplicon studies or specific properties of isolates in cultivation-dependent studies, select specific FISH probes for verifying co-occurrence and co-localization patterns in situ via CLSM. 4. By using a high number of replicates for cost-effective amplicon sequencing, you can define a seed-specific core microbiome that can be used to determine core functions in the metagenome. 5. Activity of genes of interest in the metagenome can be quantified by cDNA amplification via qPCR by using the designed primers specifically targeting genes of interest.

4

Notes 1. Washing steps remove dust and other abiotic particles from the seeds surface. The majority of microorganisms native to seeds are strongly attached to the seed surface and will not be lost during the washing steps. Soaking dry or washed seeds in sterile water has the aim to activate the inherent but dormant seed microbiota, which are considered to be viable but nonculturable microorganisms. 2. The efficiency of surface sterilization is largely dependent on seed morphology, demanding specific adjustment in sodium hypochloride concentration, and exposure time. Apart from that, we advise against surface sterilization of seeds, as the epiphytic community is a valuable part of the seed microbiome [7]. Alternatively, you can remove seed peel using sterilized dissecting instruments. 3. During seed germination, various plant metabolites are synthesized and secreted that most likely boost the activation of (still dormant) seed microbiota. Seed germination under sterile conditions additionally provides information about what kinds of microorganisms and to which extent seed microbiota can colonize roots and phyllosphere. 4. If the host genome sequence is available, in silico analyses give first insights into the potential interference of the microbiotatargeted PCR with the host-sequences. We recommend to

Studying Seed Microbiomes

19

sequence a smaller part of the seed samples a prior to prepare a comprehensive pool. Pay attention to use several genetically distinct host genotypes for your test run in order to determine whether the PNA functionality is sufficient for all genotypes investigated in your study. 5. Test several concentrations of your PNA in a PCR protocol that usually works well. For example, use a final concentration of 0.5 μM, 0.77 μM, or 1 μM PNA and one without PNA for later validation in your test PNA PCR protocol. 6. If blocking of desired sequences was not sufficient, the protocol can be improved by lowering the annealing temperature of PNA and by increasing the PNA concentration in the PCR batch [42]. If the yielded PCR product is low, then the PNA concentration could be decreased. In the case of very few microbial sequences in the template, higher template amounts might be necessary. In general, there is no upper limit for the amount of the template DNA. A nested PCR [43] can be used in the case of too few microbial sequences in the sample. In a nested PCR, PNA may be used in both PCRs in order to prevent amplification of host sequences. In this case, a lower PNA concentration may be sufficient. 7. The PCR using bacteria-specific primer pair amplifies plant mitochondrial and plastid 16S DNA as well. Blocking amplification of the host sequences can be achieved by adding peptide nucleic acid (PNA) clamps [17] to the qPCR mixture. The disadvantage of this method is that PNAs were originally constructed to block 16S DNA sequences of Arabidopsis plants. Depending on the plant species, other host-plant specific sequences might be similar to primer target sequences, interfering the performance of qPCR. Please refer to Subheading 3.4, describing the method to design host-specific PNAs. 8. Here, we describe labeling of the seed microbiota with the probes ALF968 that target Alphaproteobacteria, GAM42a targeting Gammaproteobacteria, and BET42a-comp, which is a competitor probe that enhances the sensibility of the method [18]. If other microbial taxa need to be visualized, the buffers and probes need to be adjusted as described by Cardinale and coworkers [18]. References 1. Truyens S, Weyens N, Cuypers A, Vangronsveld J (2015) Bacterial seed endophytes: genera, vertical transmission and interaction with plants. Environ Microbiol Rep 7:40–50 2. Berg G, Raaijmakers JM (2018) Saving seed microbiomes. ISME J 12:1167–1170

3. Adam E, Bernhart M, Mu¨ller H, Winkler J, Berg G (2016) The Cucurbita pepo seed microbiome: genotype-specific composition and implications for breeding. Plant Soil 422:35–49

20

Birgit Wassermann et al.

4. Johnston-Monje D, Lundberg DS, Lazarovits G, Reis VM, Raizada MN (2016) Bacterial populations in juvenile maize rhizospheres originate from both seed and soil. Plant Soil 405:337–355 5. Rybakova D, Mancinelli R, Wikstru¨m M, Birch-Jensen A-S, Postma J, Ehlers R-U et al (2017) The structure of the Brassica napus seed microbiome is cultivar-dependent and affects the interactions of symbionts and pathogens. Microbiome 5:104 6. Shade A, Jacques MA, Barret M (2017) Ecological patterns of seed microbiome diversity, transmission, and assembly. Curr Opin Microbiol 37:15–22 7. Nelson EB (2018) The seed microbiome: origins, interactions, and impacts. Plant Soil 422:7–34 8. Bergna A, Cernava T, R€andler M, Grosch R, Zachow C, Berg G (2018) Tomato seeds preferably transmit plant beneficial endophytes. Phytobiomes J 2:183–193 9. Wassermann B, Cernava T, Mu¨ller H, Berg C, Berg G (2019) Seeds of native alpine plants host unique microbial communities embedded in cross- kingdom networks. Microbiome 7 (1):108 10. Vujanovic V, Germida J (2017) Seed endosymbiosis: a vital relationship in providing prenatal care to plants. Can J Plant Sci 97(6):972–981 ˜ izares C, Jorrı´n B, Poole PS, 11. Sa´nchez-Can Tkacz A (2017) Understanding the holobiont: the interdependence of plants and their microbiome. Curr Opin Microbiol 38:188–196 12. Wassermann B, Adam E, Cernava T, Berg G (2019) Understanding the indigenous seed microbiota to design bacterial seed treatments. In: Seed endophytes. Springer, Cham, pp 83–99 13. Schiltz S, Gaillard I, Pawlicki-Jullian N, Thiombiano B, Mesnard F, Gontier E (2015) A review: what is the spermosphere and how can it be studied? J Appl Microbiol 119:1467–1481 14. Parada AE, Needham DM, Fuhrman JA (2016) Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18:1403–1414 15. Probst AJ, Auerbach AK, Moissl-Eichinger C (2013) Archaea on human skin. PLoS One 8: e65388 16. White TJ, Bruns T, Lee S, Taylor JW (1990) Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR

protocols: a guide to methods and applications. Academic, New York, pp 315–322 17. Lundberg DS, Yourstone S, Mieczkowski P, Jones CD, Dangl JL (2013) Practical innovations for high-throughput amplicon sequencing. Nat Methods 10:999–1002 18. Cardinale M, Viera de Castro J, Mu¨ller H, Berg G, Grube M (2008) In situ analysis of the bacterial community associated with the reindeer lichen Cladonia arbuscula reveals predominance of Alphaproteobacteria. FEMS Microbiol Ecol 66:63–71 19. von Wintzingerode F, Landt O, Ehrlich A, Gobel UB (2000) Peptide nucleic acidmediated PCR clamping as a useful supplement in the determination of microbial diversity. Appl Environ Microbiol 66:549–557 20. Morgulis A, Coulouris G, Raytselis Y, Madden TL, Agarwala R, Sch€affer AA (2008) Database indexing for production MegaBLAST searches. Bioinformatics 24:1757–1764 21. Zhang Z, Schwartz S, Wagner L, Miller W (2000) A greedy algorithm for aligning DNA sequences. J Comput Biol 7:203–214 22. PNA Bio Inc. (2018). https://www.pnabio. com/. Accessed 11 June 2019 23. Terahara T, Chow S, Kurogi H, Lee S-H, Tsukamoto K, Mochioka N et al (2011) Efficiency of peptide nucleic acid-directed PCR clamping and its application in the investigation of natural diets of the Japanese eel Leptocephali. PLoS One 6:e25715 24. Manicardi A, Corradini R (2014) Effect of chirality in gamma-PNA: PNA interaction, another piece in the picture. Artif DNA PNA XNA 5:e1131801 25. Rochelle PA (2001) Environmental molecular microbiology: protocols and applications. Horizon Scientific Press, Poole 26. Ikeda S, Kaneko T, Okubo T, Rallos LEE, Eda S, Mitsui H et al (2009) Development of a bacterial cell enrichment method and its application to the community analysis in soybean stems. Microb Ecol 58:703–714 27. Tsurumaru H, Okubo T, Okazaki K, Hashimoto M, Kakizaki K, Hanzawa E et al (2015) Metagenomic analysis of the bacterial community associated with the taproot of sugar beet. Microbes Environ 30:63–69 28. Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ et al (2017) A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551:457–463 29. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA et al (2018) QIIME 2: reproducible, interactive, scalable,

Studying Seed Microbiomes and extensible microbiome data science. PeerJ 6:e27295v2 30. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D et al (2019) The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res 47:D259–D264 31. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P et al (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:D590–D596 32. Ondov BD, Bergman NH, Phillippy AM (2011) Interactive metagenomic visualization in a Web browser. BMC Bioinformatics 12:385 33. Arndt D, Xia J, Liu Y, Zhou Y, Guo AC, Cruz JA et al (2012) METAGENassist: a comprehensive web server for comparative metagenomics. Nucleic Acids Res 40:W88–W95 34. Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35:1547–1549 35. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D et al (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19:1639–1645 36. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of

21

biomolecular interaction networks. Genome Res 13:2498–2504 37. Faust K, Raes J (2016) CoNet app: inference of biological association networks using Cytoscape. F1000Res 5:1519 38. Meyer F, Paarmann D, D’Souza M, Olson R, Glass E, Kubal M et al (2008) The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9:386 39. Afiahayati SK, Sakakibara Y (2015) MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res 22:69–77 40. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA (2017) metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:824–834 41. Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ et al (2014) Binning metagenomic contigs by coverage and composition. Nat Methods 11:1144–1146 42. Orum H (2000) PCR clamping. Curr Issues Mol Biol 2:27–30 43. Niepceron A, Licois D (2010) Development of a high-sensitivity nested PCR assay for the detection of Clostridium piliforme in clinical samples. Vet J 185:222–224

Chapter 2 Sampling Microbiomes Associated with Different Plant Compartments Henry W. G. Birt, Anthony B. Pattison, and Paul G. Dennis Abstract The microbiome is known to influence plant fitness and differs significantly between plant compartments. To characterize the communities associated with different plant compartments, it is necessary to separate plant tissues in a manner that is suitable for microbiome analysis. Here, we describe a standardized protocol for sampling the microbiomes associated with bulk soil, the apical and basal ectorhizosphere, the apical and ectorhizosphere, the rhizome, pseudostem, and leaves of Musa spp. The approach can easily be modified for work with other plants. Key words Sampling, Microbiome, Plant, Sequencing, Bacteria, Fungi, Rhizosphere

1

Introduction Plants live in association with diverse microbial communities (i.e. microbiomes) that influence their growth [1], ability to access nutrients [2], and tolerance to abiotic and biotic stress [3–5]. Different plant compartments (e.g. roots, stems, and leaves) vary in environmental conditions and often host distinct microbial communities [6]. Hence, to more comprehensively characterize the microbiome of a host, it is necessary to encompass multiple plant compartments. For many plants, five key compartments provide distinct functions and therefore, warrant partitioning in investigations: the bulk soil, the endo and ecto rhizosphere, the stem/stalk, and the leaves. Roots may also be further partitioned into apical and basal regions as they have been shown to harbor distinct communities [7–11]. Here, we describe a sampling protocol for banana plants—one of the world’s most important fruit commodities. This chapter presents a simple and tested method for sampling the banana microbiome and is easily adaptable to studies focusing on other plants. This method relies on relatively inexpensive reagents and

Lilia C. Carvalhais and Paul G. Dennis (eds.), The Plant Microbiome: Methods and Protocols, Methods in Molecular Biology, vol. 2232, https://doi.org/10.1007/978-1-0716-1040-4_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

23

24

Henry W. G. Birt et al.

allows a precise partitioning of tissues that can be used for microbiome studies.

2

Materials 1. Large kitchen knife. 2. Small kitchen knife. 3. Scissors. 4. Metal trays. 5. Cutting boards. 6. Household bleach (4% NaClO). 7. Spray bottle. 8. Lab tissue rolls for wiping surfaces. 9. Plastic zip-lock bags. 10. 50 mL tubes. 11. Bin. 12. Bin bags. 13. Small pot for bleaching rhizome. 14. Lunch box for bleaching pseudostem. 15. Timer. 16. Sponges. 17. Dish soap. 18. Lab grade disposable gloves. 19. Marker pen. 20. Toothbrush. 21. Squeeze bottle of sterile distilled water. 22. Sterile distilled water (300 mL per plant). 23. Sterile Phosphate Buffer Solution (PBS) (40 mL per plant). 24. Bunsen burner. 25. Forceps. 26. Centrifuge with 50 mL tube adapters. 27. Ruler (optional). 28. SPAD meter (optional). 29. Camera (optional). 30. Lyophilizer. 31. Aluminum foil. 32. 5 mm steel rods or small screws. 33. 2 mL sample tubes.

Sampling Plant Microbiomes

25

34. Qiagen TissueLyser™. 35. Magnet. 36. 40 mL of phosphate buffered saline (PBS) per root to be sampled (see Note 1).

3

Method

3.1 Preparation of Surfaces

1. At the start and finish of every plant clean all work surfaces, cutting boards, metal trays, knives and scissors with hot soapy water, rinse and then sterilize using bleach (see Note 2). 2. Place the cutting board on the work surface and place next to it a large knife, a small knife, a pair of scissors, a pot with fresh bleach, and a timer. 3. Put a clean bin liner in the bin. 4. Label a 50 mL tube for bulk soil, a 50 mL tube for roots, a bag for leaves, a bag for the rhizome, and a bag for the pseudostem.

3.2

Sampling

3.2.1 Leaves

1. Cut the first three leaves (upper most leaves excluding ” symbol): cat reference_genome_1.fasta reference_genome_2.fasta additional_genomes.fasta > combined_reference.fasta

Index the combined reference file with BWA and proceed with read mapping as described above (Subheading 3.1, step 2). 2. Choose appropriate read mapping tools and settings for aligning reads depending on DNA library and sequencing platform (e.g., Ion Torrent, Illumina paired-end/single-read, PacBio SMRT, Oxford Nanopore Technologies’ IONs). For example, BWA-backtrack and BWA-MEM are different algorithms designed primarily for aligning reads up to 100 base pairs and between 70 and 1000 base pairs, respectively (http://bio-bwa. sourceforge.net/bwa.shtml). 3. A help menu for each tool can be accessed by typing its command name followed by a “-h” or “--help,” for example, samtools -h

4. Users are advised to check for read mapping consistency with an alignment viewer like Tablet [14]. We expect to see even coverage across the host genome, and large coverage peaks in narrow regions are usually indicative of non-specific mappings. 5. If there are many non-specific alignments, mapping specificity can be increased by specifying seed length through the “-k” option in BWA. The default length is 19, input a larger number for increased specificity, for example, 25. 6. The alignment status of a read is stored as a flag value by SAMtools. Users can use these values to specify properties such as unmapped read (flag value 4) and unmapped read pairs (flag value 12), and then choose to retain and/or discard entries according to these properties using the “-f” and “-F” options, respectively, in samtools view. Flag values are described in detail here: https://broadinstitute.github.io/ picard/explain-flags.html 7. Most bioinformatics software can work directly on fastq and fasta files compressed using gzip. To compress files, run gzip filename or pigz filename to use a parallel implementation of gzip (https://zlib.net/pigz/).

Removing Host Contamination in Metagenomes

153

Acknowledgment This work was supported by a seed fund for gut microbiota research provided by the Faculty of Medicine, the Chinese University of Hong Kong. References 1. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E et al (2015) MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 12:902–903 2. Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL et al (2012) Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLOS Comput Biol 8:e1002358 3. Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH (2013) Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 31:533–538 4. Feehery GR, Yigit E, Oyola SO, Langhorst BW, Schmidt VT, Stewart FJ et al (2013) A method for selectively enriching Microbial DNA from contaminating vertebrate Host DNA. PLoS One 8:e76096 5. Marotz CA, Sanders JG, Zuniga C, Zaramela LS, Knight R, Zengler K (2018) Improving saliva shotgun metagenomics by chemical host DNA depletion. Microbiome 6:42 6. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25:1754–1760 7. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079

8. Quinlan AR, Hall IM (2010) BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26:841–842 9. Paungfoo-Lonhienne C, Lonhienne T, Yeoh YK, Donose BC, Webb RI, Parsons J et al (2016) Crosstalk between sugarcane and a plant-growth promoting Burkholderia species. Sci Rep 6:37389 10. Langmead B, Salzberg SL (2012) Fast gappedread alignment with Bowtie 2. Nat Methods 9:357–359 11. Haque MM, Bose T, Dutta A, CVSK R, Mande SS (2015) CS-SCORE: rapid identification and removal of human genome contaminants from metagenomic datasets. Genomics 106:116–121 12. Schmieder R, Edwards R (2011) Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One 6:e17288 13. Czajkowski MD, Vance DP, Frese SA, Casaburi G (2019) GenCoF: a graphical user interface to rapidly remove human genome contaminants from metagenomic datasets. Bioinformatics 35 (13):2318–2319 14. Milne I, Stephen G, Bayer M, Cock PJ, Pritchard L, Cardle L et al (2012) Using Tablet for visual exploration of second-generation sequencing data. Brief Bioinform 14:193–202

Chapter 14 Inference and Analysis of SPIEC-EASI Microbiome Networks Henry W. G. Birt and Paul G. Dennis Abstract Network analysis facilitates examination of the interactions between different populations in a community. It can provide a range of metrics describing the social characteristics of each population and emergent structural properties of the community, which may be used to address novel ecological questions. Using a publicly available dataset, this chapter provides point-by-point code and instructions to infer and analyze a SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) network using free, open source software (R and Gephi). Key words Networks, SPIEC-EASI, Microbiome, Amplicon, Covariance, Interactions

1

Introduction Microbial community structure is influenced by complex interactions between the populations present and their environment over space and time. These interactions are difficult to characterize in any environment, let alone in soils, which are the most diverse of all ecosystems. Due to their diversity, and small size, it is difficult to observe interactions between different microbial populations [1]. Hence, network analysis, which allows biotic interactions to be inferred via the correlation structure between populations, has emerged as an important tool for microbial ecologists [2–4]. The objective of many microbiome studies is to compare the diversity and composition of communities in different samples. These data are typically represented in tables containing the relative abundances of microbial sequence variants (SVs) or operational taxonomic units (OTUs) in each sample. As relative abundances are not independent of one another [5], it is important to use an appropriate method to infer correlations. Pearson’s [4], for example, is less appropriate than methods that compensate for the lack of independence, such as Sparse Correlations for Compositional data (SparCC) [6] and Compositionally Corrected

Lilia C. Carvalhais and Paul G. Dennis (eds.), The Plant Microbiome: Methods and Protocols, Methods in Molecular Biology, vol. 2232, https://doi.org/10.1007/978-1-0716-1040-4_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021

155

156

Henry W. G. Birt and Paul G. Dennis

by REnormalization and PErmutation (CCREPE) [7]. However, these methods do not account for populations that may be indirectly correlated within a network, which manifest as spurious links [5, 8]. SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference) [5] is a method to infer correlations between populations while compensating for the lack of independence and spurious links. Hence, SPIEC-EASI is a relatively robust method to infer microbiome networks. Here we present a step-by-step guide with accompanying code to infer and analyze a SPIEC-EASI microbiome network using free software. This example can be adapted to suit the research needs of a range of projects.

2

Materials

2.1 Microbiome Dataset

2.2

Software

The data used in this chapter are the gut microbiome count data from 1006 Western adults with no reported health complications and has been chosen due to its easy access [9]. The data are available from within R and instructions on how to download the data are provided in the methods section. The data is presented in a matrix where the rows represent samples and the columns represent operational taxonomic units (OTUs). 1. R is a free, open source statistical programming language and is the name given to the software that runs R code. R studio is a desktop application that expands the user environment to increase functionality and makes running R code more user friendly. Code for this analysis is written in R. Version 3.6.1 was run using R studio version 1.1.463. Both R and R studio can be downloaded for free from the following URL: https://www.rstudio.com/products/ rstudio/download/ 2. Gephi is a free, open source network analysis and visualization software package. Gephi version 0.9.2 was used to produce this chapter. Gephi can be downloaded free of charge from the following URL: https://gephi.org/users/download/

3 3.1

Methods Install Packages

1. After installing R, R studio and Gephi there is a need to install R ‘packages’ which contain specific functions. These can all be installed from the R terminal. This step will need to be completed only the first time the code is run on a new machine (see Note 1).

Microbiome Networks

157

install.packages("devtools") install.packages("igraph") install.packages("vegan") install.packages("Matrix") install.packages("reshape2") install.packages("ggplot2") install.packages("dplyr") install.packages("plyr") install.packages("gridExtra") install.packages("grid") install.packages("agricolae") install.packages("BiocManager") BiocManager::install(version = "devel") BiocManager::install("microbiome") library(devtools) install_github("zdk123/SpiecEasi")

3.2 Load Packages and Functions

1. Open RStudio and load the required packages and functions (see Note 2). library(SpiecEasi) library(devtools) library(igraph) library(vegan) library(Matrix) library(reshape2) library(plyr) library(dplyr) library(gridExtra) library(grid) library(microbiome)

158

Henry W. G. Birt and Paul G. Dennis

# load in the following function '%!in%'