Metagenomics of the Microbial Nitrogen Cycle : Theory, Methods and Applications [1 ed.] 9781908230607, 9781908230485

Citation preview

Metagenomics of the Microbial Nitrogen Cycle Theory, Methods and Applications

Edited by Diana Marco

Caister Academic Press

Metagenomics of the Microbial Nitrogen Cycle Theory, Methods and Applications

Edited by Diana Marco CONICET and National University of Córdoba Ciudad Universitaria Córdoba Argentina

Caister Academic Press

Copyright © 2014 Caister Academic Press Norfolk, UK www.caister.com British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-908230-48-5 (hardback) ISBN: 978-1-908230-60-7 (ebook) Description or mention of instrumentation, software, or other products in this book does not imply endorsement by the author or publisher. The author and publisher do not assume responsibility for the validity of any products or procedures mentioned or described in this book or for the consequences of their use. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the prior permission of the publisher. No claim to original U.S. Government works. Cover design adapted from Figure 12.1 and images supplied by Diana Marco

Contents

Contributorsv Prefaceix Forewordxi 1

Functional Assignment of Metagenomic Data: Insights for the Microbial Nitrogen Cycle

Vikas Sharma, Gaurav Chetal, Todd D. Taylor and Tulika Prakash

1

2

Microbial Metagenomics of Oxygen Minimum Zones

17

3

Interactions between Methane and Nitrogen Cycling: Current Metagenomic Studies and Future Trends

33

Quantification of Functional Microbial Nitrogen Cycle Genes in Environmental Samples

65

Stable Isotope Probing the Nitrogen Cycle: Current Applications and Future Directions

87

Frank J. Stewart and Osvaldo Ulloa

Paul L.E. Bodelier and Anne K. Steenbergh

4

David Correa-Galeote, Germán Tortosa and Eulogio J. Bedmar

5

Boris Wawrik

6

Application of Metaproteomics to the Exploration of Microbial N-cycling Communities

111

Functional Molecular Analysis of Microbial Nitrogen Cycle by Microarray-based GeoChip: Insights for Climate Change, Agriculture and Other Ecological Studies

135

Functional and Taxonomic Diversity of the Nitrogen Cycling Guild in the Sargasso Sea Metagenomes

153

Cindy Smith and Florence Abram

7

Kai Xue, Joy D. Van Nostrand, Zhili He and Jizhong Zhou

8

Germán Bonilla-Rosso, Luis Eguiarte and Valeria Souza

iv  | Contents

9

Microbial Nitrogen Cycle: Determination of Microbial Functional Activities and Related N-compounds in Environmental Samples

175

Functional Metagenomics of the Nitrogen Cycle in Freshwater Lakes with Focus on Methylotrophic Bacteria

195

11

The Fungal Contribution to the Nitrogen Cycle in Agricultural Soils

209

12

Biofilms in Nitrogen Removal: Population Dynamics and Spatial Distribution of Nitrifying- and Anammox Bacteria

227

Topic Index

261

Taxonomic Index

265

David Correa-Galeote, Germán Tortosa and Eulogio J. Bedmar

10

Ludmila Chistoserdova

Markus Gorfer, Sylvia Klaubauf, Harald Berger and Joseph Strauss

Robert Almstrand, Frank Persson and Malte Hermansson

Contributors

Florence Abram Functional Environmental Microbiology School of Natural Sciences National University of Ireland Galway Ireland [email protected]

Paul L.E. Bodelier Department of Microbial Ecology Netherlands Institute of Ecology (NIOO-KNAW) Wageningen The Netherlands [email protected]

Robert Almstrand Civil and Environmental Engineering Colorado School of Mines Golden, CO USA

Germán Bonilla-Rosso Laboratory of Experimental and Molecular Evolution Department of Evolutionary Ecology Ecology Institute Mexico National Autonomous University (UNAM) Mexico

[email protected]

[email protected]

Eulogio J. Bedmar Department of Soil Microbiology and Symbiotic Systems Estación Experimental del Zaidín Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) Granada Spain

Gaurav Chetal School of Basic Sciences Indian Institute of Technology (IIT) Mandi Himachal Pradesh India

[email protected]

Ludmila Chistoserdova Department of Chemical Engineering University of Washington Seattle, WA USA

Harald Berger Fungal Genetics and Genomics Unit AIT Austrian Institute of Technology GmbH Bioresources Unit Tulln Austria [email protected]

[email protected]

[email protected]

vi  | Contributors

David Correa-Galeote Department of Soil Microbiology and Symbiotic Systems Estación Experimental del Zaidín Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) Granada Spain [email protected] Luis Eguiarte Laboratory of Experimental and Molecular Evolution Department of Evolutionary Ecology Ecology Institute Mexico National Autonomous University (UNAM) Mexico [email protected] Markus Gorfer Fungal Genetics and Genomics Unit AIT Austrian Institute of Technology GmbH Bioresources Unit and University of Natural Resources and Life Sciences Department of Applied Genetics and Cell Biology Tulln Austria [email protected]

Diana Marco CONICET and National University of Córdoba Ciudad Universitaria Córdoba Argentina [email protected] Frank Persson Water Environment Technology, Civil and Environmental Engineering Chalmers University of Technology Göteborg Sweden [email protected] Tulika Prakash School of Basic Sciences Indian Institute of Technology (IIT) Mandi Himachal Pradesh India; and Laboratory for Integrated Bioinformatics RIKEN Center for Integrative Medical Sciences Yokohama Japan [email protected]

Zhili He Institute for Environmental Genomics University of Oklahoma Norman, OK USA

Vikas Sharma School of Basic Sciences Indian Institute of Technology (IIT) Mandi Himachal Pradesh India

[email protected]

[email protected]

Malte Hermansson Department of Chemistry & Molecular Biology, Microbiology University of Gothenburg Göteborg Sweden

Cindy Smith Marine Microbial Ecology, Microbiology School of Natural Sciences National University of Ireland Galway Ireland

[email protected]

[email protected]

Sylvia Klaubauf CBS-KNAW Fungal Biodiversity Centre Fungal Physiology Utrecht The Netherlands

Valeria Souza Laboratory of Experimental and Molecular Evolution Department of Evolutionary Ecology Ecology Institute Mexico National Autonomous University (UNAM) Mexico

[email protected]

[email protected]

Contributors |  vii

Anne K. Steenbergh Department of Microbial Ecology Netherlands Institute of Ecology (NIOO-KNAW) Wageningen The Netherlands

Osvaldo Ulloa Departamento de Oceanografía Universidad de Concepción Concepción Chile

[email protected]

[email protected]

Frank J. Stewart School of Biology Georgia Institute of Technology Atlanta, GA USA

Joy D. Van Nostrand Institute for Environmental Genomics University of Oklahoma Norman, OK USA

[email protected]

[email protected]

Joseph Strauss Fungal Genetics and Genomics Unit AIT Austrian Institute of Technology GmbH Bioresources Unit and University of Natural Resources and Life Sciences Department of Applied Genetics and Cell Biology Tulln Austria

Boris Wawrik Department of Microbiology and Plant Biology University of Oklahoma Norman, OK USA

[email protected] Todd D. Taylor Laboratory for Integrated Bioinformatics RIKEN Center for Integrative Medical Sciences Yokohama Japan [email protected] Germán Tortosa Department of Soil Microbiology and Symbiotic Systems Estación Experimental del Zaidín Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC) Granada Spain [email protected]

[email protected] Kai Xue Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected] Jizhong Zhou Institute for Environmental Genomics University of Oklahoma Norman, OK USA [email protected]

Current Books of Interest

Antibiotics: Current Innovations and Future Trends2015 Leishmania: Current Biology and Control2015 Acanthamoeba: Biology and Pathogenesis (2nd edition)2015 Microarrays: Current Technology, Innovations and Applications2014 Pathogenic Neisseria: Genomics, Molecular Biology and Disease Intervention2014 Proteomics: Targeted Technology, Innovations and Applications2014 Biofuels: From Microbes to Molecules2014 Human Pathogenic Fungi: Molecular Biology and Pathogenic Mechanisms2014 Applied RNAi: From Fundamental Research to Therapeutic Applications2014 Halophiles: Genetics and Genomes 2014 Molecular Diagnostics: Current Research and Applications2014 Phage Therapy: Current Research and Applications2014 Bioinformatics and Data Analysis in Microbiology2014 The Cell Biology of Cyanobacteria2014 Pathogenic Escherichia coli: Molecular and Cellular Microbiology2014 Campylobacter Ecology and Evolution2014 Burkholderia: From Genomes to Function2014 Myxobacteria: Genomics, Cellular and Molecular Biology2014 Next-generation Sequencing: Current Technologies and Applications2014 Omics in Soil Science2014 Applications of Molecular Microbiological Methods2014 Mollicutes: Molecular Biology and Pathogenesis2014 Genome Analysis: Current Procedures and Applications2014 Bacterial Toxins: Genetics, Cellular Biology and Practical Applications2013 Bacterial Membranes: Structural and Molecular Biology2014 Cold-Adapted Microorganisms2013 Fusarium: Genomics, Molecular and Cellular Biology2013 Prions: Current Progress in Advanced Research2013 RNA Editing: Current Research and Future Trends2013 Real-Time PCR: Advanced Technologies and Applications2013 Microbial Efflux Pumps: Current Research2013 Cytomegaloviruses: From Molecular Pathogenesis to Intervention2013 Oral Microbial Ecology: Current Research and New Perspectives2013 Bionanotechnology: Biological Self-assembly and its Applications2013 Full details at www.caister.com

Preface

The N cycle is one of the most important nutrient cycles in the earth since it allows organisms to use the largely abundant form of nitrogen (gaseous N2). The biological part of the nitrogen cycle is performed by microbial organisms and includes four major processes: nitrogen fixation, mineralization, nitrification and denitrification. During the cycling process some compounds are formed, like the nitrous oxide gas and methane that act as greenhouse gases. Besides, the raising use of nitrogen fertilizers is producing an increase in freshwater nitrate levels, causing drinking water pollution and severe human health problems. So, a better knowledge of the microbial communities that are involved in nitrogen transformations is necessary to understand and eventually counteract the negative effects of nitrogen pollutions. However, many of the microorganisms involved in N cycling are uncultivable and a growing use of metagenomics and other associated ‘omics’ methods is under way. Studies of the distribution and diversity in different habitats of nitrogen cycling microorganisms are increasingly using cultivation-independent techniques targeting functional genes encoding the key enzymes involved in all the steps of the N cycle. Metagenomics methods have allowed, for example, the discovery of the involvement of crenarchaea as important players in the N cycle. The association between metagenomics and other ‘omic’ methods such as metatranscriptomics and metaproteomics is allowing to integrate and model the different

components of the N cycle in habitats like lake waters and marine zones with permanent low oxygen contents. Metagenomics is also increasingly being used in the design and improvement of wastewater and bioremediation treatments. Many other coming applications of metagenomics of the N cycle could be mentioned here. A wealth of literature is being created involving metagenomics and other ‘omics’ approaches to the study of the microbial N cycle. However, there is a strong need of a book providing a comprehensive treatment of the new theoretical, methodological and applied aspects surging from this new intersection of fields, directed to an increasing audience. The book contains 12 chapters including background, methodological and applications contributions from renowned researchers specialized in the most relevant and emerging topics in the field. The chapters cover a broad range of theoretical, methodological and applied aspects, and the focus of the contributions is concentrated on the new integrative and interdisciplinary approach that is increasingly showing the way to the study of such a complex field like the microbial N cycle. I would like to thank all the authors for their invaluable contributions. The foreword so nicely written by Laurent Philippot and colleagues is specially acknowledged. Finally, I thank Caister Academic Press for trusting me again with the edition of a book in an everyday innovative field. Diana Marco Córdoba Argentina

Foreword

Nitrogen, discovered in 1772, is the fourth most common element in many biomolecules essential for life, outranked only by carbon, hydrogen and oxygen. Nitrogen is found in amino acids that form proteins and in the nucleoside phosphates of nucleic acids. Different oxidation states of the nitrogen atom coexist in nature, ranging from reduced compounds (e.g. –3 as in ammonia), to fully oxidized state (e.g. +5 as in nitrate). N2, which represents 78% of our atmosphere, is inert while all biological systems require reactive forms of nitrogen. Nitrogen enters the soil through atmospheric N2 fixation by soil microorganisms, atmospheric nitrogen deposition, as well as exogenous organic and inorganic (fertilization) inputs. The conversions between the different forms of nitrogen through processes that are largely driven by microorganisms form the nitrogen cycle. Nitrogen is the nutrient that is most often limiting for plant growth, and modern agriculture is now significantly affecting its cycle (Rockström et al., 2009). For example, the current input rate of reactive nitrogen produced by the Haber–Bosch process for fertilization is roughly half the global nitrogen fixation rates (Canfield et al., 2010). The fate of reactive nitrogen depends on the transformation pathways it takes and its progress along them, based on biotic and abiotic conditions. Excessive reactive nitrogen, such as nitrate, can pollute groundwater by leaching, with adverse effects on human health, and severely imbalance estuary and coastal ecosystems. It can also be released to the atmosphere and contribute to climate change. Indeed, N2O is not only a potent greenhouse gas but also the dominant stratospheric ozone depleting substance emitted (Ravishankara et al.,

2009), while NOx contributes to acid rain and photochemical smog. Recently, the costs of excessive reactive nitrogen have been estimated to be more than double the financial benefits of using nitrogen fertilizers in Europe (Sutton et al., 2011). When it comes to water pollution, microbial guilds within the N-cycle are already harnessed in waste water treatment plants to remove excessive nitrogen. Studies investigating the ecology of N-cycling microorganisms for a better understanding and eventually management of the corresponding processes started already at the end of the nineteenth century with the pioneer work of Winogradsky, Gayon, Dupetit and later Beijerinck. However, such studies were hampered for decades by our inability to cultivate the large majority of these microorganisms. In the late nineties, the development of PCR altogether with the possibility to extract environmental DNA from microbial communities has revolutionized microbial ecology, and metagenomics became a powerful tool to analyse microbial communities regardless of their culturability. Metagenomics comprises studies targeting a single gene or all environmental genes. Targeted approaches, in which a selected gene is amplified from a community and then sequenced, constitutes the vast majority of studies investigating the ecology of N-cycling microorganisms to date. It has shed light not only on species richness and functional diversity of N-cycling microorganisms but also on their abundances in various environments. It also provided a basis to investigate the dynamics of these communities and the relationships between microbial diversity and functioning

xii  | Foreword

of the N-cycle (Webster et al., 2005; Hallin et al., 2009; Philippot et al., 2013). However, due to a generally high sequence polymorphism, designing primer sets that recognize all orthologues encoding key enzymes in the N-cycle is challenging (Ward et al., 2009, Jones et al., 2013) and is becoming increasingly difficult as the number of more distantly related sequences from complete genome projects or metagenomic studies increases exponentially. In addition, N-cycling gene sequences obtained by sequencing amplicons from various habitats are often not closely related to any cultivated or characterized microorganisms, indicating a significant fraction of heretofore unknown members of these guilds in the environment (Wust et al., 2009; Palmer et al., 2012). Nevertheless, such bottlenecks can be overcome by random shotgun metagenomics, as nicely illustrated by the discovery of ammoniamonoxygenase-like genes associated with archaeal scaffolds in the Sargasso sea (Venter et al., 2004). Shortly after, the isolation of an ammoniaoxidizing marine archaeon (Könneke et al., 2005) confirmed that not only bacteria but also archaea could nitrify, which ended a bacterio-centric view of this N-cycle step. Quantification of archaeal ammonia-monooxygenase genes further revealed that crenarchaea, not bacteria, were the dominant ammonia-oxidizers in many environments (Leininger et al., 2006). There is no doubt that metagenomics will continue to provide insights into the ecology of other overlooked N-cycle players, such as soil fungi. The nitrogen cycle is also linked with other major biogeochemical cycles, as stoichiometric, substrate availability or environmental constraints related to one elemental cycle may affect the physiological capacities of bacteria involved in transformations in another cycle. In the last decade, stable isotope probing (SIP) coupled with metagenomics has not only provided means to distinguish microorganisms that actively metabolize labelled substrate, but also allowed an explicit link between cycles, depending on the downstream functional characterization. Metagenomic approaches are about to reach maturity in terms of standardization of the experimental procedures and performances of the analysis pipelines devoted (Segata et al., 2013).

While metagenomics have already revolutionized our understanding of the N-cycle by establishing snapshots of the genomic potential of microbial communities, it is now crucial to couple these techniques with other meta-omics techniques, such as metatranscriptomics, metaproteomics or metabolomics, to get a grasp on the functioning of microbial communities in different ecosystems and of their responses to human activities and associated climate change. Laurent Philippot, Romain L. Barnard and Aymé Spor; INRA, UMR 1347 Agroecology, Dijon Cedex, France References Canfield, D.E., Glazer, A.N., and Falkowski, P.G. (2010). The evolution and future of Earth’s nitrogen cycle. Science 330, 192–196. Hallin, S., Jones, C., Schloter, M., and Philippot, L. (2009). Relationship between N-cycling bacterial communities and ecosystem functioning in a 50 years old fertilization experiment. ISME J. 3, 597–605. Jones, C.M., Graf, D.R., Bru, D., Philippot, L., and Hallin, S. (2013). The unaccounted yet abundant nitrous oxide-reducing microbial community: a potential nitrous oxide sink. ISME J. 7, 417–426. Könneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B., Waterbury, J.B., and Stahl, D.A. (2005). Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature 437, 543–546. Leininger, S., Urich, T., Schloter, M., Schwark, L., Qi, J., Nicol, G.W., Prosser, J.I., Schuster, S.C., and Schleper, C. (2006). Archaea predominate among ammoniaoxidizing prokaryotes in soils. Nature 442, 806–809. Palmer, K., Biasi, C., and Horn, M.A. (2012). Contrasting denitrifier communities relate to contrasting N2O emission patterns from acidic peat soils in arctic tundra. ISME J. 6, 1058–1077. Philippot, L., Spor, A., Henault, C., Bru, D., Bizouard, F., Jones, C.M., Sarr, A., and Maron, P.A. (2013). Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619. Ravishankara, A.R., Daniel, J.S., and Portmann, R.W. (2009). Nitrous oxide (N2O): the dominant ozonedepleting substance emitted in the 21st century. Science 326, 123–125. Rockström, J., Steffen, W., Noone, K., Persson, A., Chapin, F.S., Lambin, E.F., Lenton, T.M., Scheffer, M., Folke, C., Schellnhuber, H.J., et al. (2009). A safe operating space for humanity. Nature 461, 472–475. Segata, N., Boernigen, D., Tickle, T.L., Morgan, X.C., Garrett, W.S., and Huttenhower, C. (2013). Computational meta’omics for microbial community studies. Mol. Syst. Biol. 9, 666. Sutton, M.A., Oenema, O., Erisman, J.W., Leip, A., van Grinsven, H., and Winiwarter, W. (2011). Too much of a good thing. Nature 472, 159–161.

Foreword |  xiii

Venter, J.C., Remington, K., Heidelberg, J.F., Halpern, A.L., Rusch, D., Eisen, J.A., Wu, D., Paulsen, I., Nelson, K., Nelson, W., et al. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74. Ward, B.B., Devol, A.H., Rich, J.J., Chang, B.X., Bulow, S.E., Naik, H., Pratihary, A., and Jayakumar, A. (2009). Denitrification as the dominant nitrogen loss process in the Arabian Sea. Nature 461, 78–81. Webster, G., Embley, T.M., Freitag, T.E., Smith, Z., and Prosser, J.I. (2005). Links between ammonia

oxidizer species composition, functional diversity and nitrification kinetics in grassland soils. Environ. Microbiol. 7, 676–684. Wust, P.K., Horn, M.A., Henderson, G., Janssen, P.H., Rehm, B.H., and Drake, H.L. (2009). Gut-associated denitrification and in vivo emission of nitrous oxide by the earthworm families megascolecidae and lumbricidae in new zealand. Appl. Environ. Microbiol. 75, 3430–3436.

Functional Assignment of Metagenomic Data: Insights for the Microbial Nitrogen Cycle

1

Vikas Sharma, Gaurav Chetal, Todd D. Taylor and Tulika Prakash

Abstract Nitrogen is an essential component of basic biomolecules. Atmospheric nitrogen is inaccessible to living organisms because of its inert nature but it can be fixed into usable forms by nitrogen-fixing microbial communities. The microbial nitrogen cycle is a complex process which occurs through the coordinated functioning of several microbial genes, many of which have been identified primarily from cultivable microbes. However, for unculturable microbes belonging to the community of a given environment, metagenomics is used as an alternative approach to the classical methods of genomics, including polymerase chain reaction based gene identification and restriction fragment length polymorphism. A few metagenomic studies of terrestrial and aquatic environments, including some under moderate and extreme conditions, have been carried out which focus on nitrogen-fixing microbial communities and their functional diversities. These studies highlighted the roles of the resident microbes and their genes in different steps of the nitrogen cycle. Other studies have shown that the use of nitrogen-based fertilizers in agricultural soil has led to alterations in the microbial populations due to increased nitrogen content in the soil. Recently, scientists have identified novel anammox bacteria which are responsible for the loss of fixed nitrogen from agricultural soil. Presence of anammox along with other non-anammox bacteria indicates a coordinated behaviour of these microbes in the nitrogen-cycling process; however, the complete mechanism of anammox process is not clearly understood. To gain a better understanding of the anammox and other processes of nitrogen-cycling,

metagenomic studies should be combined with metatranscriptomic and functional metagenomic approaches which investigate the functional dynamics of a given community. Introduction Nitrogen (N) is an indispensable nutrient for all organisms as it is a crucial element of proteins, which are the fundamental units of the structural components and functional processes of the organisms. Besides proteins, nitrogen is also present in nucleic acids and other bio-molecules such as adenosine triphosphate (ATP), adenosine diphosphate (ADP), and chlorophyll etc. Earth’s atmosphere contains 78% inert dinitrogen (N2) (Elser, 2011) and most living organisms are unable to use it in this form. Hence, they need it fixed in the form of usable compounds from which it can be easily taken up. This process of converting inert atmospheric N2 into reactive compounds is known as nitrogen fixation (Lin et al., 2000). Fixed nitrogen may be present in different forms including ammonia (NH3), nitrite (NO2–), and nitrate (NO3–), etc. Natural nitrogen fixation is carried out in two ways: abiotic and biological nitrogen fixation. Abiotic nitrogen fixation is a high-energy fixation process which is carried out in the atmosphere with the help of lightning and high-energy radiation. This process involves the combination of dinitrogen (N2) with oxygen (O2) to form nitrogen oxides such as nitrous oxide (NO) and nitrogen oxide (NO2) (Summers et al., 2012). These oxides further react with rainwater to form nitric acid (HNO3) and are carried to the earth’s surface with the rainfall (Singh

2  | Sharma et al.

Betaproteobacteria (Schramm et al., 1998). The second step of nitrification is the oxidation of nitrite to nitrate and is carried out by the bacteria from the genus Nitrobacter of the class Alphaproteobacteria (Starkenburg et al., 2006). Denitrification is the process of conversion of nitrate into dinitrogen and is carried out primarily by bacteria from the genera Rhizobium and Sinorhizobium of the class Alphaproteobacteria, and by bacteria from the genera Pseudomonas and Arcobacter of the class Gammaproteobacteria (Heylen et al., 2006). Anammox is the process of ammonium oxidation under anaerobic conditions, hence it is also called anaerobic ammonium oxidation (Kuenen, 2008). This process is carried out by bacteria from the genera Brocadia, Kuenenia, Scalindua, Anammoxoglobus, and Jettenia of the class Planctomycetia (Hu et al., 2011). In this process ammonia is oxidized to dinitrogen using nitrite as an electron acceptor. Ammonification is the process of conversion of organic nitrogenous products into ammonia (Hanson et al., 2013), and is carried out by bacteria from the genera Wolinella and Sulfurospirillum of the class Epsilonproteobacteria (Strohm et al., 2007). All the above mentioned processes comprise the critical steps of biological nitrogen fixation, 70% of which is carried out by the Rhizobiaceae family of Proteobacteria (ResendisAntonio et al., 2011). Although bacteria are the major players in biological nitrogen fixation, some

and Agrawal, 2008). In contrast, during biological nitrogen fixation atmospheric N2 is fixed into reactive NH3 by various microorganisms with the help of the enzyme nitrogenase (Boyd and Peters, 2013). These microorganisms include freeliving soil bacteria, such as Azotobacter, aquatic organisms, such as blue–green algae and most importantly, legume symbiotic bacteria, such as Rhizobium and Bradyrhizobium (Franche et al., 2009). Thus, nitrogen is continuously recycled, being converted from one form to another as per the requirements for the growth and survival of the organisms in the ecosystem. Combined, these make up the nitrogen cycle in our environment. Microbial nitrogen cycle As shown in Fig. 1.1, the different processes involved in the microbial nitrogen cycle include nitrogen fixation, assimilation, nitrification, denitrification, anammox and ammonification (Bernhard, 2010). Assimilation is the process of conversion of inorganic to organic nitrogen compounds, which can be used as intracellular metabolites (Reitzer, 2003). Nitrification is the formation of nitrite from ammonia and is carried out in two steps. The first step involves the oxidation of ammonia to nitrite and is carried out by bacteria from the genera Nitrosomonas and Nitrosospira of the class

Nitrogen Fixation

Ammonification

Anammox

NH3

Oxi nia mo

NO2–

Am

NO3–

tio n

As

ro

Ae

n

tio

ila

sim

c bi

rifi ca

NO2

–

Nit

ic

ob er

a An

Organic Nitrogen

n

N2O NO

dat io

Denitrification

N2

Nitrite Oxidation

Figure 1.1 Schematic representation of ‘microbial nitrogen cycle’. N2, dinitrogen; NH3, ammonia; NO2–, nitrite; NO3–, nitrate; NO, nitric oxide; N2O, nitrous oxide.

Metagenomics of Nitrogen-cycling Environments |  3

species of fungi including Aspergillus niger and Penicillium glaucum, yeasts such as Saccharomyces elipsoideus and Saccharomyces apiculatus (Lipman, 1911) and some protists including Entamoeba histolytica have also been reported to fix atmospheric nitrogen (Ali et al., 2004). Exploring the nitrogen cycle using genomic and metagenomic techniques The nitrogen cycle is one of the fundamental natural strategies for driving and maintaining life in both terrestrial and aquatic environments in our biosphere. The fixation of atmospheric nitrogen at the microbial level takes place by a cascade of processes involving coordinated regulation of genes and their products, i.e. proteins, of the resident microbes of a given community. Information about many of the microbial genes involved in the various steps of the nitrogen fixation process is available from different types of experimental analysis. We performed an extensive literature search to identify these microbial genes, particularly in terrestrial and aquatic environments, which are shown in Fig. 1.2A and B. There have been several studies to explore the genomic content of nitrogen-fixing microbes with the primary goal of identifying the repertoire of genes that play a significant role in this process. For example, Kaneko et al. (2002) sequenced the whole genome of nitrogen-fixing symbiotic bacterium Bradyrhizobium japonicum USDA110, and the genes blr0680 and blr1755 were found predominantly to be involved in the process of nitrogen fixation (Kaneko et al., 2002). Table 1.1 lists a few other nitrogen-fixing microbes for which whole genome sequences have been obtained (either as finished or draft quality) and the roles of their genes in this process have been explored. Additionally, other techniques such as polymerase chain reaction (PCR) based and restriction fragment length polymorphism (RFLP) based methods have been used to explore the presence of specific genes involved in the nitrogen fixation process in various microbes. For example, the nifH gene for nitrogen fixation was identified in

uncultivable Cyanobacterium UCYN-A using a PCR-based approach (Zehr et al., 2001). Most of the available studies provide useful insights about the nitrogen fixation process at the genomic level in those microbes that can be cultured under laboratory conditions. However, given the fact that nearly 99% of all microbes remain uncultivable (Sharm et al., 2005), our knowledge about nitrogen-fixing microbes is surely limited. Using classical techniques, we are unable to explore the members and genomic contents of these yet uncultivable microbes, which may also be involved in the process of nitrogen fixation. Thus, a topic of active research in the post-genomic era is to explore the structural and functional diversity of microbial communities residing in a wide range of different ecosystems. With the recent advancements in highthroughput sequencing technologies, exploration of yet uncultivable microbes has become possible using metagenomic approaches. As shown in Fig. 1.3, the typical steps involved in metagenomic sequencing of an environment to estimate the number of species and its functional repertoire include DNA or RNA sequencing using next generation sequencers (such as Illumina and Roche 454), sequence assembly, gene prediction, functional and metabolic analysis, taxonomic binning, and comparative analysis of the sequence data using specialized bioinformatics methods and tools. For metagenomic studies, the extracted DNA is fragmented into smaller pieces which are then sequenced using next-generation sequencing (NGS) technologies. These small sequence reads are subjected to quality-filtering steps, generally with the help of quality base-calling programmes such as Phred (Ewing and Green, 1998), to remove the low-quality reads. To improve annotation, small reads can be assembled into longer contigs with the help of sequence-assembly tools such as Phrap (Machado et al., 2011) and Velvet (Zerbino and Birney, 2008). These high-quality longer contigs and remaining singletons are then compared with available reference genomes using sequence similarity-based alignment methods, such as BLAST (Altschul et al., 1990) and HMMER (Eddy, 2011), to functionally annotate the predicted genes. Further functional analysis is

4  | Sharma et al.

A.

nos8

Nitrogen

Nitrous Oxide nor7

NifH1 NifD1 NifK1

Organic Nitrogen

Nitric Oxide

gdh gogat10 10

hzo9

nir6

nasR11

Nitrite

Ammonia amoA2

hao3

Hydroxylamine

Minerals

Nitrite

nxr4

nar5 nap5

Nitrate

Minerals

B.

Nitrogen NifH1 NifD1 NifK1 hzo9

amoA2

hao3

Ammonia

Hydroxylamine

Nitrite nirA4

Aerobic Zone

Organic Nitrogen

Nitrate

nosZ8

Oxygen Minimal Zone

Nitrite

Nitric Oxide

Nitrous Oxide norB7

nirK6

narG5 narK5 narH5

Figure 1.2 (A) Schematic representation of gene–metabolite interactions in terrestrial microbial nitrogen fixation. NifH, NifD, NifK: nitrogen fixation genes; amoA: ammonia-oxidizing gene A; hao: hydroxylamine oxidoreductase; nxr: nitrite oxidoreductase; nar: nitrate reductase; nap: periplasmic nitrate reductase; nir: nitrite reductase; nor: nitric oxide reductase; nos: nitrous oxide reductase; hzo: hydrazine oxidoreductase; gdh: glutamate dehydrogenase; gogat: glutamate synthase. 1, Franche et al. (2009); 2, Zhang et al. (2010); 3, Wu et al. (2012); 4, Poly et al. (2008); 5, Roussel-Delif et al. (2005); 6, Priemé et al. (2002); 7, Garbeva et al. (2007); 8, Stres et al. (2004); 9, Long et al. (2013); 10, Fisher (1989); 11, Goldman et al. (1994). (B) Schematic representation of gene–metabolite interaction in oceanic nitrogen fixation. NifH, NifD, NifK: nitrogen fixation genes; amoA: ammonia oxidizing gene A; hao: hydroxylamine oxidoreductase; nirK: nitrite oxidoreductase K; narG: nitrate reductase G; narK: nitrate reductase K; narH: nitrate reductase H; norB: nitric oxide reductase B; nosZ: nitrous oxide reductase S; hzo: hydrazine oxidoreductase. 1, Goldman et al. (1994); 2, Newell et al. (2011); 3, Vajrala et al. (2013); 4, Bird and Wyman (2003); 5, Xie et al. (2011); 6, Lund et al. (2012); 7, Zehr and Kudela (2011); 8, Scala and Kerkhof (1999); 9, Li et al. (2010).

Metagenomics of Nitrogen-cycling Environments |  5

Table 1.1  List of nitrogen-fixing bacteria for which whole genome sequences are available in the NCBI GenBank database Microorganism

Reference

Anabaena sp. strain PCC 7120

Kaneko et al. (2001)

Azotobacter vinelandii DJ

Setubal et al. (2009)

Bradyrhizobium elkanii 587*

de Souza et al. (2012)

Bradyrhizobium japonicum strain USDA6

Kaneko et al. (2011)

Bradyrhizobium japonicum USDA110

Kaneko et al. (2002)

Burkholderia phenoliruptrix BR3459a (CLA1)

de Oliveira Cunha et al. (2012)

Enterobacter sp. strain R4-368

Madhaiyan et al. (2013)

Enterobacter sp. strain SP1*

Zhu et al. (2012)

Gluconacetobacter diazotrophicus Pal5

Bertalan et al. (2009)

Klebsiella oxytoca M5al*

Bao et al. (2013)

Klebsiella oxytoca SA2*

Chen et al. (2013)

Klebsiella pneumoniae 342

Fouts et al. (2008)

Mesorhizobium loti Strain MAFF303099

Kaneko et al. (2000)

Methylococcus capsulatus (Bath)

Ward et al. (2004)

Nitrobacter hamburgensis X14

Starkenburg et al. (2008)

Nitrosomonas europaea ATCC 19718

Chain et al. (2003)

Nitrosomonas sp. Is79

Bollmann et al. (2013)

Pseudomonas denitrificans ATCC 13867

Ainala et al. (2013)

Pseudomonas stutzeri strain B1SMN1*

Busquets et al. (2013)

Pseudomonas stutzeri strain DSM4166

Yu et al. (2011)

*Bacteria with draft-quality whole genome sequences. 

Environment

Metagenomic DNA

DNA Extraction and Fragmentation

DNA Sequencing

Metagenomic Reads Sequence Quality Filtering

Filtered Sequence Reads Sequence Assembly

Taxonomic Binning

Assembled Contigs and Singletons Predicted Taxonomic Classification Functional Analysis

Genome Analysis and Gene Prediction

Annotated Genes Functional Analysis

Annotated Metagenomic Datasets

Figure 1.3  Flow chart of the steps in metagenomic analysis of an environment.

6  | Sharma et al.

performed by classifying the predicted genes into functional families or orthologous groups using databases such as Pfam (Punta et al., 2012), Gene Ontology (Ashburner et al., 2000), COGs (cluster of orthologous groups) (Tatusov et al., 2001), etc. The functionally annotated genes can then be put in context of the metabolic pathways in which they may be involved by using the available pathway databases including KEGG (Kanehisa et al., 2012), Brenda (Schomburg et al., 2013), etc. There are a few automatic high-throughput sequence analysis pipelines including MG-RAST (Meyer et al., 2008) and IMG/M (Markowitz et al., 2012) which process the metagenomic data similar to the aforementioned workflow. For a comprehensive review on functional analysis of metagenomic data, please see the recent review by Prakash and Taylor (2012). Metagenomic studies provide a description of the genetic and functional diversity of a given community. This provides an opportunity to better understand the coordinated functioning of the resident microbes in a given environment, and the overall metabolic capacities available in that environment. Towards this, several metagenomic projects from environments wherein the microbial nitrogen cycle is known to occur have been carried out, a few representative examples of which are shown in Table 1.2. The main objective of these studies is to explore the presence of genes involved in different steps of the nitrogen-fixation process and to identify the microbial members which harbour them. In this chapter, we discuss the applications of metagenomic techniques to explore the microbial composition and functional diversity of the terrestrial and aquatic ecosystems with respect to the nitrogen cycle.

able to investigate the diverse terrestrial microbial population and their functional potential in different environments.

Terrestrial nitrogen cycle Soil is the most complex and heterogeneous environment on earth, presenting enormous variation in its geochemistry, texture and nutrient content. Thus, soil comprises the most entangled ecosystem with vast microbial diversity. However, many terrestrial microbial species remain unknown, as most of these microbes have yet to be cultured and identified. Through metagenomics, we are

Effect of agriculture on terrestrial microbial and functional diversity In the past few decades, various human activities involving agricultural advancement and industrial growth have elevated the nitrogen content in terrestrial ecosystems, which in turn affects the composition of the resident microbial communities (Fierer et al., 2012). Subsequently, the functional diversity of the given ecosystems may

Nitrogen cycling in extreme terrestrial environments In a study about nitrogen cycling in a thermal soil environment, the nifH gene encoding dinitrogenase reductase, which is responsible for the nitrogen-fixation process, was discovered in the resident microbes including Cyanobacteria, Alphaproteobacteria and Betaproteobacteria. And the nirH gene encoding nitrite reductase was found in Rhodopseudomonas palustris and Sinorhizobium meliloti (Burr et al., 2005). In another study on an Antarctic soil metagenome, 0.28% of the 5000 predicted genes were implicated in nitrate and nitrite ammonification (Pearce et al., 2012); however, the responsible microbes and communities have not yet been identified. In yet another extreme environment, namely glacial ice, archaea were found to be the dominant nitrogen fixers. A total of 1.3% of their genes is involved in nitrogen metabolism, predominantly in assimilatory and nitrification processes (Simon et al., 2009). The assimilation process was previously identified in the archaea, Pyrobaculum aerophilum of Crenarchaeota, which contains the nirA gene encoding ferredoxin-dependent sirohaem nitrite reductase (Cabello et al., 2004). This suggests that the nirA gene may be involved in the archaeal assimilation process. These studies highlight the importance of nitrogen assimilation in the glacial environment. Although all of the aforementioned studies examined microbial communities involved in biological nitrogen fixation in extreme terrestrial environments, none of them could explain the complete nitrogen-cycling process in their native microbial populations.

Metagenomics of Nitrogen-cycling Environments |  7

Table 1.2 List of a few representative metagenomic projects related to the microbial nitrogen cycle Project/paper title

Project ID or Accession No.

References

Sediment microbial communities from Lake Washington for methane and nitrogen cycles

IMG IDs:1377, 1378, 1873, 1879, 1888

Beck et al. (2013)

Comparative metagenomic, phylogenetic, and physiological analyses of soil microbial communities across nitrogen gradients

MG-RAST ID: 4745

Fierer et al. (2012)

Metagenomes from high-temperature chemotrophic systems reveal geochemical controls on microbial community structure and function

–

Inskeep et al. (2010)

Metagenomic analysis of a southern maritime Antarctic soil

–

Pearce et al. (2012)

Ecology of subglacial lake Vostok (Antarctica), based on metagenomic/metatranscriptomic analyses of accretion ice

–

Rogers et al. (2013)

Comparative metagenomics of toxic freshwater cyanobacteria bloom communities on two continents

MG-RAST IDs: 4467029.3, 4467058.3 and 4467059.3

Steffen et al. (2012)

The metagenome of the marine anammox bacterium ‘Candidatus Scalindua profunda’ illustrates the versatility of this globally important nitrogen cycle

JGI IDs: 2017108002 and 2022004002

van de Vossenberg et al. (2013)

Archaeal amoA gene diversity points to distinct biogeography – of ammonia-oxidizing Crenarchaeota in the ocean

Sintes et al. (2013)

Co-occurring anammox, denitrification, and codenitrification in agricultural soils

–

Long et al. (2013)

Distinct soil bacterial communities revealed under a diversely managed agroecosystem

NCBI SRA Accession No. SRA007616

Shange et al. (2012)

Diversity of aerobic and anaerobic ammonia-oxidizing bacteria in marine sponges

GenBank Accession Nos. FJ652464–548 and FJ652549–570

Mohamed et al. (2010)

Diversity of nifH gene sequences in the sediments of South China sea

–

Wu et al. (2011)

Functional gene differences in soil microbial communities from conventional, low input and organic farmlands

–

Xue et al. (2013)

Identification and activities in situ of Nitrosospira and Nitrospira spp. as dominant populations in a nitrifying fluidized bed reactor

–

Schramm et al. (1998)

In situ activity and spatial organization of anaerobic ammonium-oxidizing (Anammox) bacteria in biofilms

GenBank Accession Nos.: AB290144. AB290145, AB290146, AB290147, AB290148, AB302409

Kindaichi et al. (2007)

Observations concerning nitrogen cycling in a Yellowstone thermal soil environment

–

Burr et al. (2005)

Ice metagenome of the northern Schneeferner

CAMERA ID: CAM_PROJ_ IceMetagenome

Simon et al. (2009)

Production of oceanic nitrous oxide by ammonia-oxidizing archaea

GenBank Accession Nos. JF796145–JF796179

Loescher et al. (2012)

The effect of nutrient deposition on bacterial communities in Arctic tundra soil

GenBank Accession Nos. GU339540–GU343711

Campbell et al. (2010)

Spatial distribution of bacteria and archaea and amoA gene copy numbers throughout the water column of the Eastern Mediterranean sea

–

De Corte et al. (2009)

8  | Sharma et al.

also vary. The increased use of nitrogen-based fertilizers generally leads to elevated nitrogen content in agricultural soil. This elevated nitrogen content favours the active and copiotrophic (microbes growing in high-nutrient conditions) microbes, thus affecting the overall microbial population in a given environment. For example, in high-nitrogen environments, copiotrophic bacterial taxa belonging to Alphaproteobacteria, Gammaproteobacteria, Bacteriodetes, and Actinobacteria were found to be more abundant than the oligotrophic (microbes growing in low-nutrient conditions) bacterial taxon, such as those belonging to Acidobacteria. This study also suggested that nitrogen fertilization does not have any significant effect over bacterial diversity, though it has substantial effect on community composition (Fierer et al., 2012). Consistently, in another study on nitrogen-fertilized Arctic tundra soil, Acidobacteria were found to be less abundant than Proteobacteria. Among Proteobacteria, a drastic increase was observed in the population of Dyella spp. (Campbell et al., 2010). Some correlation and dependence was observed between nitrogen availability and the functional genes of nitrogen metabolism in any given community. For example, nitrogen-fixing genes including gdh, which encodes glutamate dehydrogenase, and ureC, which encodes urease-α for ammonification, were found to be dependent on soil-ammonium content (Xue et al., 2013). Fig. 1.2A shows the genes involved in the different steps of the terrestrial nitrogen cycle. Similarly, long-term nitrogen fertilization is found to have a significant impact on the community structure of the ammonia oxidizers, mainly Nitrosospira (Avrahami et al., 2003). These bacteria contain the nitrification-related gene amoA, encoding ammonia monooxygenase, which is responsible for the ammonia oxidation process. The other gene, napA, involved in the denitrification process is found in Shewanella oneidensis MR-1 of Gammaproteobacteria (Cruz-García et al., 2007). Members of Gammaproteobacteria, including Pseudomonas stutzeri and Pseudomonas aeruginosa, have also been shown to harbour two genes, dnrP and dnrS, involved in the denitrification process (Rodionov et al., 2005). However, the specific steps of denitrification in which these

genes are involved are not yet known. These observations indicate that altered microbial diversity due to elevated nitrogen concentrations resulting from the increased use of nitrogen-based fertilizers may also eventually affect the genetic composition of their respective environments either directly or indirectly. Terrestrial anammox process Recently, a new group of anaerobic ammonium -oxidizing bacteria, known as anammox, was found to be predominantly involved in terrestrial nitrogen fixation. The anammox process accounts for 4–37% of the nitrogen loss in agricultural soils, thus highlighting the overall significance of anammox bacteria in the terrestrial ecosystem (Hu et al., 2011). In an earlier study, four soil genera, Brocadia, Kuenenia, Scalindua, and Anammoxoglobus, were found to be primarily responsible for the anammox process (Kindaichi et al., 2007). Subsequently, in addition to Candidatus Brocadia and Candidatus Kuenenia, a third species, Candidatus Jettenia spp., was identified as also being responsible for the anammox process (Humbert et al., 2010). However, in a recent study, only Candidatus Jettenia spp. was found to be primarily responsible for the anammox process in the agricultural soil (Long et al., 2013). This species was also reported to harbour the hzo gene, which encodes hydrazine oxidase (Xue et al., 2013) and is known to be involved in the anammox process (Fig. 1.2A). Together, these studies imply that long-term use of nitrogen fertilizer may affect the diversity of anammox bacteria by favouring one specific genus. Aquatic nitrogen cycle Aquatic ecosystems including oceans, rivers and lakes are one of the most important ecological environments of our biosphere as they act as intermediate links between terrestrial and atmospheric ecosystems. A number of physiochemical and biochemical reactions are carried out in these aquatic ecosystems; thus, they play a major role in maintaining the overall atmospheric gas composition. Many of these processes are carried out by the resident microbial communities of these environments. As nitrogen is a key element of the

Metagenomics of Nitrogen-cycling Environments |  9

aquatic ecosystem, these ecosystems play very crucial roles in biogeochemical nitrogen cycling. Thus, the microbial diversity in aquatic ecosystems is expected to have a profound effect on the atmospheric nitrogen cycle. Nitrogen cycle in marine ecosystems In marine ecosystems, archaea are considered to be more active than bacteria in nitrogen cycling. Most ammonia oxidation in the process of nitrification is known to be performed by archaeal species. It is not surprising then that ammoniaoxidizing archaea were found to be abundant in diverse marine environments (Sintes et al., 2013). Though the gene involved in this process, namely amoA, is found in most marine microbial communities, the abundance and expression level of the archaeal amoA gene was found to be notably higher than the bacterial amoA gene in oxygenlimited marine environments (Loescher et al., 2012). Additionally, the abundance of the archael amoA gene has been found to be dependent upon ocean depth. For example, as the depth increased from 200–500 m to 1000 m, the copy number of archael amoA genes decreased from 4000–5000 copies/ml to 20 copies/ml; however, the bacterial amoA gene was below detection limits (De Corte et al., 2009). This implies that archaea are the major contributors to the oceanic-nitrification process as compared with bacteria. Besides archaea, Planctomyces is a major marine group of aquatic bacteria, including anammox, involved in the nitrogen cycle. For example, Candidatus Scalindua profunda is known to be a dominating anammox member of Planctomyces in marine environments (van de Vossenberg et al., 2013). A metagenomic study targeting the complete genome sequencing of this anammox bacteria identified different nitrogen cycle-associated genes (Fig. 1.2B), including narK, which encodes a nitrite/nitrate antiporter, narG and narH, which encode nitrate reductase, amtB, which encodes an ammonium transporter, and hzsA and hzsB, which encode hydrazine synthase. Among these genes, nark, narG, and narH are the key genes involved in the marine denitrification process, while hydrazine synthase is involved in the condensation of ammonium and nitric

oxide in anaerobic environments. In addition, hzo and hao, which encode the octahaem HZO and octahaem HAO proteins respectively, have been found to be important anammox bacterial genes. These genes act as key catalysts in balancing the hydroxylamine concentration by catalysing hydrazine oxidation to dinitrogen during the anammox process in these bacteria. However, the overall mechanism of the anammox process during nitrogen cycling is not yet clearly understood. This same study also identified non-anammox bacteria containing the narK gene in the same environment. This indicates that both anammox and non-anammox microbes co-exist in the marine environment but they harbour different genes for the nitrogen-fixation process. Sponge-associated bacteria also play a very important role in the marine nitrogen cycle. Ircinia strobilina and Mycale laxissima are two important marine sponges found to inhabit nitrogen cycleassociated microbial communities. M. laxissima colonizes Pirellula, Planctomyces, and anaerobic anammox bacteria while I. strobilina solely colonizes non-anammox Poribacteria of Planctomyces (Mohamed et al., 2010). This supports the evolution of marine anammox bacteria on M. laxissima in deep and anaerobic or oxygen-minimal habitats and non-anammox bacteria on I. strobilina in shallow and aerobic marine environments. This co-existence of anammox and non-anammox bacteria in different oxygen-concentration zones represents their cooperative and coordinated behaviour in nitrogen cycling at the interface of aerobic and oxygen minimum zones of the marine ecosystem. Both anammox and non-anammox bacteria are found to harbour the amoA gene responsible for the nitrification process. This implies the occurrence of similar nitrification metabolism in both anammox and non-anammox bacteria. It will be interesting to further explore how anammox and non-anammox bacteria have diverged from one another during evolution. The gene amoA was consistently identified in other marine bacteria including Betaproteobacteria and Gammaproteobacteria, and archaea including Crenarchaea (Lam et al., 2007). This universal occurrence of the amoA gene in different marine microbial communities indicates towards the significance of the nitrification process in nitrogen

10  | Sharma et al.

cycling in the marine ecosystem. Other than the amoA gene, non-anammox bacteria and archaea from ocean sediments have also been reported to harbour the nifH gene (Wu et al., 2011) involved in the nitrogen-fixation process. This indicates the important role of these two taxonomic domains in the nitrogen fixation process of nitrogen cycling in marine ecosystems. Nitrogen cycle in freshwater ecosystems The bacterial nitrogen cycle in freshwater ecological environments is mainly carried out by Cyanobacteria and Proteobacteria, and some Actinobacteria and Cytophaga. A recent metagenomic study of a freshwater environment identified various genes involved in the nitrogen cycle including nifD, nifH, and nifK for nitrogen fixation, ureA-G for urea metabolism, and nar and nir for nitrate and nitrite reduction (Steffen et al., 2012). Among Proteobacteria, Methylococcaceae, and Methylophilaceae are the two major families involved in freshwater nitrogen fixation (Beck et al., 2013). Isotope probing integrated with metagenomic sequencing revealed that species belonging to these bacterial families are mainly involved in nitrate metabolism under aerobic and micro-aerobic conditions. Under aerobic conditions, Methylococcaceae members are involved in nitrate-mediated methane oxidation, whereas Methylophilaceae members are non-methaneoxidizing methylotrophs. This study presents an example of the well-coordinated linkage of two important biogeochemical cycles, namely the nitrogen and carbon cycles, by freshwater bacterial communities. Nitrogen cycle in extreme aquatic ecosystems In extreme aquatic ecosystems, extremophiles are mainly responsible for biological nitrogen fixation. One study found that nitrogen-fixing extremophiles from an Antarctic lake include both psychrophiles (can tolerate very low temperatures) and thermophiles (can tolerate very high temperatures), signifying the hydrothermal activities within the cold lake. The major taxa of these extremophiles comprise of Actinobacteria, Bacterioidetes, Firmicutes, Alphaproteobacteria,

Betaproteobacteria, and Gammaproteobacteria for thermophiles and a few methanotropic archaea for psychrophiles (Rogers et al., 2013). This indicates the co-existence of both marine and freshwater nitrogen-fixing bacterial communities in the Antarctic lake. Hot springs represent another extreme hightemperature environment in which thermophilic bacteria form the major community responsible for the nitrogen cycle. For example, a thermophilic cyanobacterium, Synechococcus, from the Octopus Spring mat of Yellowstone National Park was found to have a nif gene cluster responsible for nitrogenase synthesis in the nitrogen-fixation process (Steunou et al., 2006). These genes have been shown to become active and fix atmospheric nitrogen only at increased temperatures like those found in hot spring mats. In general, thermophiles from high-temperature chemotrophic systems have been reported to have genes narG and norB encoding nitrate reductase and nitric oxide reductase, respectively, for the denitrification process (Fig. 1.2B) (Inskeep et al., 2010). Deep-oceanic hydrothermal vent chimneys represent another high-temperature extreme environment where the biological nitrogen cycle is known to occur. For example, metagenomic analysis of one such chimney indicated the presence of chemoautotrophic bacteria of class Alpha- and Beta-proteobacteria containing most of the denitrification-process related genes (Xie et al., 2011). In addition, the metagenome was also found to harbour genomic sequences which showed high similarity to the narG, narK, narH, narJ, and narI gene sequences of Thiobacillus denitrificans of Betaproteobacteria. This bacterium is known to gain energy by coupling the oxidation of sulfur to the denitrification process. This indicates the coupling of these two processes in the chimney examined in this study; however, denitrification is not found to be a universal pathway utilized by microbial communities inhabiting all deep sea hydrothermal chimneys (Xie et al., 2011). Conclusions The nitrogen cycle is one of the most wellstudied biogeochemical cycles of our biosphere. Microbial nitrogen cycling is a complex process involving interactions between different

Metagenomics of Nitrogen-cycling Environments |  11

genes of the microbes living in an environment. Several independent studies involving various experimental approaches have identified some of these genes, mainly in cultivable microbes. Recent metagenomic approaches for exploring microbial communities have allowed studies on uncultivable microbes with respect to the nitrogen cycle. Several metagenomic studies from environments involved in the nitrogen cycle have now been carried out but the majority of them have primarily focused on microbial community structure. Unfortunately, there are only a limited number of metagenomic studies which provide useful insights about the functional and metabolic potentials of the environments involved in the nitrogen cycle. Terrestrial and aquatic ecosystems are the major environments responsible for biological nitrogen cycling. Moderate and extreme environments for both these types of ecosystems have been studied using metagenomic techniques. Bacterial populations are known to carry out the majority of nitrogen cycling in terrestrial environments. This is in contrast to aquatic ecosystems, where archaea play a major role in this process. In addition, these metagenomic studies have also helped us to explore the genes involved in various processes of the nitrogen cycle. Nitrogen fixation, nitrification, and denitrification are the three major processes which have been extensively studied so far in both these environments. The other processes, including assimilation, ammonification, and anammox, have not been that well analysed and further studies are necessary to explore the functional potential of these processes in different environments. More detailed studies on extremophiles will allow the identification of important genes involved in the nitrogen cycle which can function under extreme environmental conditions. The increased use of nitrogen-based fertilizers in agriculture practices is found to significantly affect the microbial populations in agricultural soil. As a result, the functional and metabolic capacities of the resident microbial populations have also been found to be affected. Additionally, the presence of the recently discovered anammox bacteria is known to result in a significant loss of fixed nitrogen in agricultural soil. These factors

are known to affect the overall nitrogen-fixation process in agricultural soil, generally leading to increased loss of fixed nitrogen. Complete mechanism of the anammox process is yet unclear; however, the presence of anammox bacteria in deep-sea inhabiting sponges highlights the anaerobic evolution of these microbes. The presence of anammox along with other non-anammox bacteria in different environments indicates a coordinated behaviour of these microbes in the biological nitrogen cycling process. Future scope The microbial nitrogen cycle has been extensively explored in various aquatic and terrestrial ecosystems. For example, terrestrial ecosystem environments with extreme conditions, including thermal soil and glacial ice, have been extensively explored. In addition, agricultural soil from different regions has also been explored for microbial and functional diversity with the main focus being the effect of the use of nitrogen-based fertilizers. Therefore, other smaller ecosystems which may also be involved in nitrogen cycling may be studied using similar metagenomic techniques to gain further insights about novel microbial members and their genetic components involved in this process. Studies targeting the total DNA isolated from a given environment also allow us to investigate the overall functional potential of that environment. To gain a more dynamic understanding, in terms of which functional genes are over- or under- expressed under different conditions, metatranscriptomic studies of these environments are more suitable and should be conducted. Alternatively, functional metagenomic approaches, in which specific functional genes are searched for in community DNA or RNA samples, can also be adopted for this purpose. To better understand the complex microbe–microbe interactions responsible for different steps of the nitrogen cycle, the microbial-associated molecular patterns involved in their cross-talk should be explored in greater detail. Interactions at the biotic and abiotic interface of the nitrogen cycling process need to be explored in terms of microbe–environment interactions. The different biogeochemical cycles, including carbon, sulfur, and nitrogen, are

12  | Sharma et al.

known to interact with each other in a complex manner by sharing certain processes. Therefore, to better understand a given cycle, it is important to explore the processes occurring at the nodal junctions, where coupling of reactions in different biogeochemical cycles takes place. Web resources

Terragenomics: National soil metagenomics project. Available at: www.mcs.anl.gov/project/terragenomicsnational-soil-metagenomics-project Terragenomics: The Argonne pilot project for a comprehensive national soil metagenomics project. Available at: www.mcs.anl.gov/research/project_ detail.php?id=62

References

Ainala, S.K., Somasundar, A., and Park, S. (2013). Complete genome sequence of Pseudomonas denitrificans ATCC 13867. Genome Announc. 1, e00257–13. Ali, V., Shigeta, Y., Tokumoto, U., Takahashi, Y., and Nozaki, T. (2004). An intestinal parasitic protist, Entamoeba histolytica, possesses a non-redundant nitrogen fixation-like system for iron–sulfur cluster assembly under anaerobic conditions. J. Biol. Chem. 279, 16863–16874. Altschul, S.F., Gish, W., Miller, W., Myers, E.W., and Lipman, D.J. (1990). Basic local alignment search tool. J. Mol. Biol. 215, 403–410. Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. (2000). Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29. Avrahami, S., and Conrad, R. (2003). Patterns of community change among ammonia oxidizers in meadow soils upon long-term incubation at different temperatures. Appl. Environ. Microbiol. 69, 6152– 6164. Bao, G., Zhang, Y., Du, C., Chen, Z., Li, Y., Cao, Z.a., and Ma, Y. (2013). Genome sequence of Klebsiella oxytoca M5al, a promising strain for nitrogen fixation and chemical production. Genome Announc. 1, e00074–12. Beck, D.A., Kalyuzhnaya, M.G., Malfatti, S., Tringe, S.G., del Rio, T.G., Ivanova, N., Lidstrom, M.E., and Chistoserdova, L. (2013). A metagenomic insight into freshwater methane-utilizing communities and evidence for cooperation between the Methylococcaceae and the Methylophilaceae. Peer J. 1, e23. Bernhard, A. (2010). The nitrogen cycle: processes, players, and human impact. Nat. Educ. Knowl. 3, 25. Bertalan, M., Albano, R., de Pádua, V., Rouws, L., Rojas, C., Hemerly, A., Teixeira, K., Schwab, S., Araujo, J., Oliveira, A., et al. (2009). Complete genome sequence of the sugarcane nitrogen-fixing endophyte Gluconacetobacter diazotrophicus Pal5. BMC Genomics 10, 450.

Bird, C., and Wyman, M. (2003). Nitrate/nitrite assimilation system of the marine picoplanktonic cyanobacterium Synechococcus sp. strain WH 8103: effect of nitrogen source and availability on gene expression. Appl. Environ. Microbiol. 69, 7009–7018. Bollmann, A., Sedlacek, C., Norton, J., Laanbroeck, H.J., Suwa, Y., Stein, L.Y., Klotz, M.G., Arp, D., SayavedraSoto, L., Lu, M., et al. (2013). Complete genome sequence of Nitrosomonas sp. Is79–an ammonia oxidizing bacterium adapted to low ammonium concentrations. Stand Genomic Sci. 7, 469–482. Boyd, E., and Peters, J.W. (2013). New insights into the evolutionary history of biological nitrogen fixation. Front Microbiol. 4, 201. Burr, M.D., Botero, L.M., Young, M.J., Inskeep, W.P., and McDermott, T.R. (2005). Observations concerning nitrogen cycling in a Yellowstone thermal soil environment. Geotherm. Biol. Geochem. YNP 1, 171–182. Busquets, A., Peña, A., Gomila, M., Mayol, J., Bosch, R., Nogales, B., García-Valdés, E., Bennasar, A., and Lalucat, J. (2013). Draft genome sequence of Pseudomonas stutzeri strain B1SMN1, a nitrogenfixing and naphthalene-degrading strain isolated from wastewater. Genome Announc. 1, e00584–13. Cabello, P., Roldán, M.D., and Moreno-Vivián, C. (2004). Nitrate reduction and the nitrogen cycle in archaea. Microbiology 150, 3527–3546. Campbell, B.J., Polson, S.W., Hanson, T.E., Mack, M.C., and Schuur, E.A. (2010). The effect of nutrient deposition on bacterial communities in Arctic tundra soil. Environ Microbiol. 12, 1842–1854. Chain, P., Lamerdin, J., Larimer, F., Regala, W., Lao, V., Land, M., Hauser, L., Hooper, A., Klotz, M., Norton, J., et al. (2003). Complete genome sequence of the ammonia-oxidizing bacterium and obligate chemolithoautotroph Nitrosomonas europaea. J. Bacteriol. 185, 2759–2773. Chen, M., Lin, L., Zhang, Y., Sun, L., and An, Q. (2013). Genome sequence of Klebsiella oxytoca SA2, an endophytic nitrogen-fixing bacterium isolated from the pioneer grass Psammochloa villosa. Genome Announc. 1, e00601–13. Cruz-García, C., Murray, A.E., Klappenbach, J.A., Stewart, V., and Tiedje, J.M. (2007). Respiratory nitrate ammonification by Shewanella oneidensis MR-1. J. Bacteriol. 189, 656–662. De Corte, D., Yokokawa, T., Varela, M.M., Agogué, H., and Herndl, G.J. (2009). Spatial distribution of Bacteria and Archaea and amoA gene copy numbers throughout the water column of the Eastern Mediterranean Sea. ISME J. 3, 147–158. Eddy S.R. (2011). Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195. Elser, J.J. (2011). A world awash with nitrogen. Science 334, 1504–1505. Ewing, B., and Green, P. (1998). Base-calling of automated sequencer traces using phred. II. Error probablitiles. Genome Res. 8, 186–194. Fierer, N., Lauber, C.L., Ramirez, K.S., Zaneveld, J., Bradford, M.A., and Knight, R. (2012). Comparative metagenomic, phylogenetic and physiological

Metagenomics of Nitrogen-cycling Environments |  13

analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017. Fisher, S.H. (1989). Glutamate synthesis in Streptomyces coelicolor. J. Bacteriol. 171, 2372–2377. Fouts, D.E., Tyler, H.L., DeBoy, R.T., Daugherty, S., Ren, Q., Badger, J.H., Durkin, A.S., Huot, H., Shrivastava, S., Kothari, S., et al. (2008). Complete genome sequence of the N2-fixing broad host range endophyte Klebsiella pneumoniae 342 and virulence predictions verified in mice. PLoS Genet. 4, e1000141. Franche, C., Lindström, K., and Elmerich, C. (2009). Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants. Plant Soil 321, 35–59. Garbeva, P., Baggs, E.M., and Prosser, J.I. (2007). Phylogeny of nitrite reductase (nirK) and nitric oxide reductase (norB) genes from Nitrosospira species isolated from soil. FEMS Microbiol. Lett. 266, 83–89. Goldman, B.S., Lin, J.T., and Stewart, V. (1994). Identification and structure of the nasR gene encoding a nitrate-and nitrite-responsive positive regulator of nasFEDCBA (nitrate assimilation) operon expression in Klebsiella pneumoniae M5al. J. Bacteriol. 176, 5077–5085. Hanson, T.E., Campbell, B.J., Kalis, K.M., Campbell, M.A., and Klotz, M.G. (2013). Nitrate ammonification by Nautilia profundicola AmH: experimental evidence consistent with a free hydroxylamine intermediate. Front. Microbiol. 4, 180. Heylen, K., Vanparys, B., Wittebolle, L., Verstraete, W., Boon, N., and De Vos, P. (2006). Cultivation of denitrifying bacteria: optimization of isolation conditions and diversity study. Appl. Environ. Microbiol. 72, 2637–2643. Hu, B., Shen, L., Xu, X., and Zheng, P. (2011). Anaerobic ammonium oxidation (anammox) in different natural ecosystems. Biochem. Soc. Trans. 39, 1811–1816. Humbert, S., Tarnawski, S., Fromin, N., Mallet, M.-P., Aragno, M., and Zopfi, J. (2010). Molecular detection of anammox bacteria in terrestrial ecosystems: distribution and diversity. ISME J. 4, 450–454. Inskeep, W.P., Rusch, D.B., Jay, Z.J., Herrgard, M.J., Kozubal, M.A., Richardson, T.H., Macur, R.E., Hamamura, N., deM Jennings, R., Fouke, B.W., et al. (2010). Metagenomes from high-temperature chemotrophic systems reveal geochemical controls on microbial community structure and function. PloS One 5, e9773. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., and Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-D114. Kaneko, T., Nakamura, Y., Sato, S., Asamizu, E., Kato, T., Sasamoto, S., Watanabe, A., Idesawa, K., Ishikawa, A., Kawashima, K., et al. (2000). Complete genome structure of the nitrogen-fixing symbiotic bacterium Mesorhizobium loti. DNA Res. 7, 331–338. Kaneko, T., Nakamura, Y., Wolk, C.P., Kuritz, T., Sasamoto, S., Watanabe, A., Iriguchi, M., Ishikawa, A., Kawashima, K., Kimura, T., et al. (2001). Complete genomic sequence of the filamentous nitrogen-fixing cyanobacterium Anabaena sp. strain PCC 7120. DNA Res. 8, 205–213.

Kaneko, T., Nakamura, Y., Sato, S., Minamisawa, K., Uchiumi, T., Sasamoto, S., Watanabe, A., Idesawa, K., Iriguchi, M., Kawashima, K., et al. (2002). Complete genomic sequence of nitrogen-fixing symbiotic bacterium Bradyrhizobium japonicum USDA110. DNA Res. 9, 189–197. Kaneko, T., Maita, H., Hirakawa, H., Uchiike, N., Minamisawa, K., Watanabe, A., and Sato, S. (2011). Complete genome sequence of the soybean symbiont Bradyrhizobium japonicum strain USDA6T. Genes 2, 763–787. Kindaichi, T., Tsushima, I., Ogasawara, Y., Shimokawa, M., Ozaki, N., Satoh, H., and Okabe, S. (2007). In situ activity and spatial organization of anaerobic ammonium-oxidizing (anammox) bacteria in biofilms. Appl. Environ. Microbiol. 73, 4931–4939. Kuenen, J.G. (2008). Anammox bacteria: from discovery to application. Nat Rev Microbiol. 6, 320–326. Lam, P., Jensen, M.M., Lavik, G., McGinnis, D.F., Müller, B., Schubert, C.J., Amann, R., Thamdrup, B., and Kuypers, M.M. (2007). Linking crenarchaeal and bacterial nitrification to anammox in the Black Sea. Proc. Natl. Acad. Sci. U.S.A. 104, 7104–7109. Li, M., Hong, Y., Klotz, M.G., and Gu, J.-D. (2010). A comparison of primer sets for detecting 16S rRNA and hydrazine oxidoreductase genes of anaerobic ammonium-oxidizing bacteria in marine sediments. Appl. Microbiol. Biotechnol. 86, 781–790. Lin, B.-L., Sakoda, A., Shibasaki, R., Goto, N., and Suzuki, M. (2000). Modelling a global biogeochemical nitrogen cycle in terrestrial ecosystems. Ecol Modell. 135, 89–110. Lipman, C.B. (1911). Nitrogen fixation by yeasts and other fungi. J. Biol. Chem. 10, 169–182. Loescher, C., Kock, A., Könneke, M., LaRoche, J., Bange, H.W., and Schmitz, R.A. (2012). Production of oceanic nitrous oxide by ammonia-oxidizing archaea. Biogeosci. Discuss. 9, 2095–2122. Long, A., Heitman, J., Tobias, C., Philips, R., and Song, B. (2013). Co-occurring anammox, denitrification, and codenitrification in agricultural soils. Appl. Environ. Microbiol. 79, 168–176. Lund, M.B., Smith, J.M., and Francis, C.A. (2012). Diversity, abundance and expression of nitrite reductase (nirK)-like genes in marine thaumarchaea. ISME J. 6, 1966–1977. Machado, M., Magalhães, W.C., Sene, A., Araújo, B., FariaCampos, A.C., Chanock, S.J., Scott, L., Oliveira, G., Tarazona-Santos, E., and Rodrigues, M.R. (2011). Phred-Phrap package to analyses tools: a pipeline to facilitate population genetics re-sequencing studies. Invest. Genet. 2. Madhaiyan, M., Peng, N., and Ji, L. (2013). Complete genome sequence of Enterobacter sp. Strain R4–368, an endophytic N-fixing Gamma-proteobacterium isolated from surface-sterilized roots of Jatropha curcas L. Genome Announc. 1, e00544–13. Markowitz, V.M., Chen, I.-M.A., Chu, K., Szeto, E., Palaniappan, K., Grechkin, Y., Ratner, A., Jacob, B., Pati, A., Huntemann, M., et al. (2012). IMG/M: the integrated metagenome data management and

14  | Sharma et al.

comparative analysis system. Nucleic Acids Res. 40, D123-D129. Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E.M., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., et al. (2008). The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 9, 386. Mohamed, N.M., Saito, K., Tal, Y., and Hill, R.T. (2010). Diversity of aerobic and anaerobic ammonia-oxidizing bacteria in marine sponges. ISME J. 4, 38–48. Newell, S.E., Babbin, A.R., Jayakumar, A., and Ward, B.B. (2011). Ammonia oxidation rates and nitrification in the Arabian Sea. Global Biogeochem. Cy. 25, GB4016. de Oliveira Cunha, C., Zuleta, L.F.G., de Almeida, L.G.P., Ciapina, L.P., Borges, W.L., Pitard, R.M., Baldani, J.I., Straliotto, R., de Faria, S.M., Hungria, M., et al. (2012). Complete genome sequence of Burkholderia phenoliruptrix BR3459a (CLA1), a heat-tolerant, nitrogen-fixing symbiont of Mimosa flocculosa. J. Bacteriol. 194, 6675–6676. Pearce, D.A., Newsham, K.K., Thorne, M.A., Calvo-Bado, L., Krsek, M., Laskaris, P., Hodson, A., and Wellington, E.M. (2012). Metagenomic analysis of a southern maritime Antarctic soil. Front. Microbiol. 3, 403. Poly, F., Wertz, S., Brothier, E., and Degrange, V. (2008). First exploration of Nitrobacter diversity in soils by a PCR cloning‐sequencing approach targeting functional gene nxrA. FEMS Microbiol. Ecol. 63, 132–140. Prakash, T., and Taylor, T.D. (2012). Functional assignment of metagenomic data: challenges and applications. Brief Bioinform. 13, 711–727. Priemé, A., Braker, G., and Tiedje, J.M. (2002). Diversity of nitrite reductase (nirK and nirS) gene fragments in forested upland and wetland soils. Appl. Environ. Microbiol. 68, 1893–1900. Punta, M., Coggill, P.C., Eberhardt, R.Y., Mistry, J., Tate, J., Boursnell, C., Pang, N., Forslund, K., Ceric, G., Clements, J., et al. (2012). The Pfam protein families database. Nucleic Acids Res. 40, D290-D301. Reitzer, L. (2003). Nitrogen assimilation and global regulation in Escherichia coli. Annu. Rev. Microbiol. 57, 155–176. Resendis-Antonio, O., Hernández, M., Salazar, E., Contreras, S., Batallar, G.M., Mora, Y., and Encarnación, S. (2011). Systems biology of bacterial nitrogen fixation: high-throughput technology and its integrative description with constraint-based modeling. BMC Syst. Biol. 5, 120. Rodionov, D.A., Dubchak, I.L., Arkin, A.P., Alm, E.J., and Gelfand, M.S. (2005). Dissimilatory metabolism of nitrogen oxides in bacteria: comparative reconstruction of transcriptional networks. PLoS Comput. Biol. 1, e55. Rogers, S.O., Shtarkman, Y.M., Koçer, Z.A., Edgar, R., Veerapaneni, R., and D’Elia, T. (2013). Ecology of subglacial Lake Vostok (Antarctica), based on metagenomic/metatranscriptomic analyses of accretion ice. Biology 2, 629–650. Roussel-Delif, L., Tarnawski, S., Hamelin, J., Philippot, L., Aragno, M., and Fromin, N. (2005). Frequency and

diversity of nitrate reductase genes among nitratedissimilating Pseudomonas in the rhizosphere of perennial grasses grown in field conditions. Microb. Ecol. 49, 63–72. Scala, D.J., and Kerkhof, L.J. (1999). Diversity of nitrous oxide reductase (nosZ) genes in continental shelf sediments. Appl. Environ. Microbiol. 65, 1681–1687. Schomburg, I., Chang, A., Placzek, S., Söhngen, C., Rother, M., Lang, M., Munaretto, C., Ulas, S., Stelzer, M., Grote, A., et al. (2013). BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res. 41, D764– D772. Schramm, A., de Beer, D., Wagner, M., and Amann, R. (1998). Identification and activities in situ of Nitrosospira and Nitrospira spp. as dominant populations in a nitrifying fluidized bed reactor. Appl. Environ. Microbiol. 64, 3480–3485. Setubal, J.C., dos Santos, P., Goldman, B.S., Ertesvåg, H., Espin, G., Rubio, L.M., Valla, S., Almeida, N.F., Balasubramanian, D., Cromes, L., et al. (2009). Genome sequence of Azotobacter vinelandii, an obligate aerobe specialized to support diverse anaerobic metabolic processes. J. Bacteriol. 191, 4534–4545. Shange, R.S., Ankumah, R.O., Ibekwe, A.M., Zabawa, R., and Dowd, S.E. (2012). Distinct soil bacterial communities revealed under a diversely managed agroecosystem. PloS One 7, e40338. Sharma, R., Ranjan, R., Kapardar, R.K., and Glover, A. (2005). ‘Unculturable’ bacterial diversity: an untapped resource. Curr. Sci. 89, 72–77. Simon, C., Wiezer, A., Strittmatter, A.W., and Daniel, R. (2009). Phylogenetic diversity and metabolic potential revealed in a glacier ice metagenome. Appl. Environ. Microbiol. 75, 7519–7526. Singh, A., and Agrawal, M. (2008). Acid rain and its ecological consequences. J. Environ. Biol. 29, 15–24. Sintes, E., Bergauer, K., De Corte, D., Yokokawa, T., and Herndl, G.J. (2013). Archaeal amoA gene diversity points to distinct biogeography of ammonia‐oxidizing Crenarchaeota in the ocean. Environ. Microbiol. 15, 1647–1658. de Souza, J.A.M., Tieppo, E., de Souza Magnani, G., Alves, L.M.C., Cardoso, R.L., Cruz, L.M., de Oliveira, L.F., Raittz, R.T., de Souza, E.M., and de Oliveira Pedrosa, F. (2012). Draft genome sequence of the nitrogen-fixing symbiotic bacterium Bradyrhizobium elkanii 587. J Bacteriol. 194, 3547–3548. Starkenburg, S.R., Chain, P.S., Sayavedra-Soto, L.A., Hauser, L., Land, M.L., Larimer, F.W., Malfatti, S.A., Klotz, M.G., Bottomley, P.J., Arp, D.J., et al. (2006). Genome sequence of the chemolithoautotrophic nitrite-oxidizing bacterium Nitrobacter winogradskyi Nb-255. Appl. Environ. Microbiol. 72, 2050–2063. Starkenburg, S.R., Larimer, F.W., Stein, L.Y., Klotz, M.G., Chain, P.S., Sayavedra-Soto, L.A., PoretPeterson, A.T., Gentry, M.E., Arp, D.J., Ward, B., et al. (2008). Complete genome sequence of Nitrobacter hamburgensis X14 and comparative genomic analysis of species within the genus Nitrobacter. Appl. Environ. Microbiol. 74, 2852–2863.

Metagenomics of Nitrogen-cycling Environments |  15

Steffen, M.M., Li, Z., Effler, T.C., Hauser, L.J., Boyer, G.L., and Wilhelm, S.W. (2012). Comparative metagenomics of toxic freshwater cyanobacteria bloom communities on two continents. PloS One 7, e44002. Steunou, A.-S., Bhaya, D., Bateson, M.M., Melendrez, M.C., Ward, D.M., Brecht, E., Peters, J.W., Kühl, M., and Grossman, A.R. (2006). In situ analysis of nitrogen fixation and metabolic switching in unicellular thermophilic cyanobacteria inhabiting hot spring microbial mats. Proc. Natl. Acad. Sci. U.S.A. 103, 2398–2403. Stres, B., Mahne, I., Avguštin, G., and Tiedje, J.M. (2004). Nitrous oxide reductase (nosZ) gene fragments differ between native and cultivated Michigan soils. Appl. Environ. Microbiol. 70, 301–309. Strohm, T.O., Griffin, B., Zumft, W.G., and Schink, B. (2007). Growth yields in bacterial denitrification and nitrate ammonification. Appl. Environ. Microbiol. 73, 1420–1424. Summers, D.P., Basa, R.C., Khare, B., and Rodoni, D. (2012). Abiotic nitrogen fixation on terrestrial planets: reduction of NO to ammonia by FeS. Astrobiology 12, 107–114. Tatusov, R.L., Natale, D.A., Garkavtsev, I.V., Tatusova, T.A., Shankavaram, U.T., Rao, B.S., Kiryutin, B., Galperin, M.Y., Fedorova, N.D., and Koonin, E.V. (2001). The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 29, 22–28. Vajrala, N., Martens-Habbena, W., Sayavedra-Soto, L.A., Schauer, A., Bottomley, P.J., Stahl, D.A., and Arp, D.J. (2013). Hydroxylamine as an intermediate in ammonia oxidation by globally abundant marine archaea. Proc. Natl. Acad. Sci. U.S.A. 110, 1006–1011. van de Vossenberg, J., Woebken, D., Maalcke, W.J., Wessels, H.J., Dutilh, B.E., Kartal, B., Janssen‐Megens, E.M., Roeselers, G., Yan, J., Speth, D., et al. (2013). The metagenome of the marine anammox bacterium ‘Candidatus Scalindua profunda’ illustrates the versatility of this globally important nitrogen cycle bacterium. Environ. Microbiol. 15, 1275–1289. Ward, N., Larsen, Ø., Sakwa, J., Bruseth, L., Khouri, H., Durkin, A.S., Dimitrov, G., Jiang, L., Scanlan, D., Kang, K.H., et al. (2004). Genomic insights into

methanotrophy: the complete genome sequence of Methylococcus capsulatus (Bath). PLoS Biol. 2, e303. Wu, L., Cui, Y., and Chen, S. (2011). Diversity of nifH gene sequences in the sediments of South China Sea. Afr. J. Microbiol. Res. 5, 5972–5977. Wu, Y., Guo, Y., Lin, X., Zhong, W., & Jia, Z. (2012). Inhibition of bacterial ammonia oxidation by organohydrazines in soil microcosms. Front Microbiol. 3, 10. Xie, W., Wang, F., Guo, L., Chen, Z., Sievert, S.M., Meng, J., Huang, G., Li, Y., Yan, Q., Wu, S., et al. (2011). Comparative metagenomics of microbial communities inhabiting deep-sea hydrothermal vent chimneys with contrasting chemistries. ISME J. 5, 414–426. Xue, K., Wu, L., Deng, Y., He, Z., Van Nostrand, J., Robertson, P.G., Schmidt, T.M., and Zhou, J. (2013). Functional gene differences in soil microbial communities from conventional, low-input, and organic farmlands. Appl. Environ. Microbiol. 79, 1284–1292. Yu, H., Yuan, M., Lu, W., Yang, J., Dai, S., Li, Q., Yang, Z., Dong, J., Sun, L., Deng, Z., et al. (2011). Complete genome sequence of the nitrogen-fixing and rhizosphere-associated bacterium Pseudomonas stutzeri strain DSM4166. J Bacteriol. 193, 3422–3423. Zehr, J.P., Waterbury, J.B., Turner, P.J., Montoya, J.P., Omoregie, E., Steward, G.F., Hansen, A., and Karl, D.M. (2001). Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean. Nature 412, 635–638. Zehr, J.P., and Kudela, R.M. (2011). Nitrogen cycle of the open ocean: from genes to ecosystems. Ann. Rev. Mar. Sci. 3, 197–225. Zerbino, D.R., and Birney, E. (2008). Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829. Zhang, L.-M., Offre, P.R., He, J.-Z., Verhamme, D.T., Nicol, G.W., and Prosser, J.I. (2010). Autotrophic ammonia oxidation by soil thaumarchaea. Proc. Natl. Acad. Sci. U.S.A. 107, 17240–17245. Zhu, B., Chen, M., Lin, L., Yang, L., Li, Y., and An, Q. (2012). Genome sequence of Enterobacter sp. strain SP1, an endophytic nitrogen-fixing bacterium isolated from sugarcane. J. Bacteriol. 194, 6963–6964.

Microbial Metagenomics of Oxygen Minimum Zones Frank J. Stewart and Osvaldo Ulloa

Abstract Marine oxygen minimum zones (OMZs) support complex microbial assemblages with important roles in ocean biogeochemical cycles. The integration of genomic, metagenomic and metatranscriptomic analyses has significantly enhanced our understanding of OMZ microbial communities, revealing a richness of metabolic processes structured along the OMZ redox gradient and previously unrecognized linkages between community members. Specifically, ‘omics studies are clarifying the physiology, in situ activity, and evolutionary history of microbial groups mediating key steps of dissimilatory nitrogen cycling, including OMZ-specific clades of anaerobic ammonium oxidation (anammox) bacteria, aerobic ammonia-oxidizing Thaumarchaeota specialized for high-affinity substrate scavenging, and aerobic nitrite-oxidizing Nitrospina bacteria with adaptations for life under low oxygen conditions. Recent studies have also identified a diverse OMZ community of sulfur-oxidizing autotrophs whose activity appears coupled to reduced sulfur compounds generated by co-occurring sulfatereducing heterotrophs. We discuss these and other OMZ metabolic processes in relationship to key environmental drivers, including water column nutrient and redox gradients and the microscale partitioning of communities between organic particle-associated and free-living microniches. Coupled ‘omic-biogeochemistry studies are critical for understanding how de-oxygenation structures microbial biogeochemistry in the ocean and for identifying key priorities for future OMZ research.

2

Introduction The ocean water column is generally well oxygenated, but in some regions oxygen is scarce or totally absent, due to low ventilation, sluggish circulation, and high aerobic respiration rates fuelled by productive, overlying surface waters. These oxygen minimum zones (OMZs) occur at depths ranging from ~50 m to 1000 m and can extend out far into the open ocean, notably covering vast areas of the Eastern Tropical Pacific Ocean and the Arabian Sea (Fig. 2.1). These zones are dominated by microbial communities phylogenetically and functionally distinct from those of surrounding oxygenated waters (Wright et al., 2012; Ulloa et al., 2012). Diverse metabolic processes by OMZ microorganisms make significant contributions to oceanic biogeochemical cycles, in particular the global marine nitrogen cycle (Lam and Kuypers, 2011). When oxygen disappears in marine waters, nitrate becomes the main terminal electron acceptor for the oxidation of organic matter by microorganisms. Under such conditions, denitrification and anaerobic ammonium oxidation (anammox) contribute to the removal of fixed nitrogen via the production of dinitrogen gas (N2), thereby directly affecting global nutrient budgets. These microbial anaerobic processes leave geochemical signatures in the oxygendepleted part of the water column, including the presence of an inorganic fixed nitrogen deficit relative to phosphorus and the accumulation of nitrite and excess N2. Processes occurring at the boundaries of anoxic OMZ cores also contribute to the production of the potent greenhouse gas

18  | Stewart and Ulloa

Figure 2.1  Global distribution of marine oxygen minimum zones. The map shows the minimum values of water-column dissolved oxygen concentrations (≤ 50 µM) in the global ocean (data from the World Ocean Atlas). The largest and most oxygen-depleted OMZs are found in the Eastern Tropical South and North Pacific (ETSP and ETNP) and the Arabian Sea.

nitrous oxide (N2O), due primarily to the activity of nitrifying microorganisms functioning at low oxygen levels. The predicted expansion of OMZs due to climate change will likely exacerbate the loss of fixed nitrogen from the ocean in addition to increasing N2O production (Codispoti, 2010; Keeling et al., 2010). Molecular taxonomic surveys have shown that some OMZ microorganisms are closely affiliated with groups from diverse seafloor environments (e.g. sub-seafloor sediments, deep-sea hydrothermal vents and cold seeps) and sulfidic (euxinic) basins such as the Black Sea, likely reflecting recurring patterns of niche selection based on convergent environmental conditions (e.g. oxygen depletion, active sulfur cycling) (Stevens and Ulloa, 2008; Walsh et al., 2009; Stewart et al., 2012b; Ulloa et al., 2013). However, OMZ microbial communities are diverse and the precise ecological and biogeochemical roles of the majority of OMZ microbes remain largely unknown. This is due in part to a lack of genomic, metagenomic, and community functional gene expression (metatranscriptomes, metaproteomes) data for diverse marine OMZs. In comparison to other ocean environments, notably the oxygenated photic zone, OMZs

have been the focus of relatively few ‘omic-based characterizations. The small handful of studies that have explored OMZs using community (meta-) ‘omics techniques have provided critical insight into the diversity of microbial taxa and metabolic processes in oxygen-deficient waters, the effects of environmental gradients in structuring OMZ microbial diversity, and the potential for cryptic elemental cycling. Our understanding of OMZ microbial communities has also benefited significantly from genomic studies of single microbial taxa with functions relevant to key OMZ biogeochemical processes (e.g. the gammaproteobacterial sulfur-oxidizing SUP05 lineage), although the bulk of these taxa have not been isolated directly from OMZ environments. Knowledge of the genes and pathways encoded by these microorganisms has enabled more targeted single-gene surveys of OMZs, as well as provided a critical reference for interpreting OMZ metaomic data. Major insights generated from ‘omic studies of OMZs are described below, with a focus on trends from studies of the large anoxic OMZ of the Eastern Tropical South Pacific (ETSP). Key topics for future OMZ ‘omic research, including imperatives for study design and methods development, are discussed in the concluding section.

Oxygen Minimum Zones Metagenomics |  19

Metagenomic insights into OMZ metabolism OMZ metagenomic studies, together with single gene diversity surveys, have provided important insight into how oxygen depletion affects pelagic microbial communities. These analyses have identified a core set of metabolic processes and taxonomic guilds structured vertically along the OMZ redoxcline (Figs. 2.2 and 2.3). In the highly oxygen-depleted OMZ of the Eastern Tropical Pacific, dissolved oxygen concentration falls from near-saturation (~ 250 µM) in surface waters to micromolar (hypoxic) levels just beneath the oxycline, to below current detection limits (1.6 µm, right) bacterioplankton communities. Dashed lines delineate approximate OMZ boundaries. Note: y-axis is not to scale.

20  | Stewart and Ulloa

Figure 2.3 Relative abundances of reference taxa identified as top database matches to metagenome sequences from the ETSP OMZ. Data are from Ganesh et al. (2014). Solid lines: metagenomes from the 0.2–1.6 µm size fraction. Dashed lines: metagenomes from the >1.6 µm size fraction. Approximate dissolved oxygen concentrations at the time of sampling are shown in parentheses; the OMZ extended from ~70 m to 400 m. Abundance: reads matching each taxon as a top match (highest bit score) via BLASTX queries, expressed as a percentage of total protein-coding sequence reads. The putative metabolic strategy of each organism inferred from its reference genome is listed. SUP05: includes sequences matching the SUP05 metagenome (Walsh et al., 2009) and known S-oxidizing symbionts of the SUP05 clade. Note: x-axes vary in scale; y-axis is not to scale. **Re-analysis of a subset of these data suggests that the majority of sequences with top matches to Ca. Nitrospira defluvii in Ganesh et al. (2014) are more closely related to the recently published genome of the marine nitrite oxidizer Nitrospina gracilis 3/211 (Lucker et al., 2013).

Oxygen Minimum Zones Metagenomics |  21

the activities of marine Planctomycete bacteria of the genus Scalindua (Fig. 2.3). Diverse clades of heterotrophic denitrifiers may also contribute to N2 production in this zone ( Jayakumar et al., 2004; Castro-González et al., 2005), notably under conditions of relatively enhanced carbon load. Chemolithotrophic denitrifying bacteria also proliferate within the anoxic OMZ core. This diverse group is typically dominated by gammaproteobacteria of the SUP05 clade, which oxidize reduced sulfur compounds with nitrate to fuel dark carbon fixation. Metagenomic studies also identify other diverse microbial clades that are clearly partitioned along the oxygen gradient or among distinct bacterioplankton size fractions in the OMZ (e.g. sequences matching candidate division OP1, or non-anammox Planctomycetes bacteria, see Fig. 2.3). However, genomic and physiological information is lacking for most groups detected in the OMZ. Below, we highlight recent insight into the distribution and metabolism of key OMZ taxa and their roles in biogeochemical cycling, as inferred from recent -omic studies. Nitrification Aerobic ammonium oxidation Nitrification, the aerobic two-part process of ammonium oxidation to nitrite and nitrite oxidation to nitrate, appears to play an important role in OMZ nitrogen cycling, notably at the peripheries of the most anoxic OMZs. A wealth of recent genetic and biochemical data have demonstrated that aerobic ammonium oxidation in OMZs is mediated primarily by autotrophic archaea. Ammonia-oxidizing archaea (AOA) belong to diverse clades of the recently described phylum Thaumarchaeota (Brochier-Armanet et al., 2008; Pester et al., 2011), with most aquatic lineages falling within the Nitrosopumilus cluster (also called Marine Group 1). The abundance and distribution of AOA in OMZs has been determined primarily through detection of the genes encoding archaeal ammonia monooxygenase (amoABC) or 16S rRNA. These genes have been detected within oxygen-deficient waters of the major OMZs, including in the Eastern Tropical North

and South Pacific and the Arabian Sea (Molina et al., 2010; Newell et al., 2011; Beman et al., 2012; Peng et al., 2013), as well as other oxygen-deficient water columns (Labrenz et al., 2010). However, AOA gene distribution is not always correlated with gene transcription or ammonia oxidation rate (Lam et al., 2009; Beman et al., 2012). For example, metatranscriptome analysis of the anoxic ETSP OMZ showed that transcripts matching the genome of Nitrosopumilus maritimus, an ammoniaoxidizing thaumarchaeote isolated from a marine aquarium (Konneke et al., 2005), dominated the oxycline and upper OMZ depths (oxygen > ~2 µM), constituting up to 20% of total identifiable coding transcripts (Stewart et al., 2012b). At these depths, Thaumarchaeota transcripts were on average 4-fold more abundant in the RNA pool than were their corresponding genes in the DNA pool, suggesting a high level of transcriptional activity by AOA. Notably, transcripts encoding the Nitrosopumilus-like ammonium transporter (Amt) represented the largest single-gene pool in the transcriptome, constituting up to 8% of all coding sequences. High amt expression by AOA may help explain the remarkably high affinity for reduced nitrogen observed in cultured AOA isolates (Martens-Habbena et al., 2009). In the ETSP, AOA transcript and gene abundance become negligible at the anoxic OMZ core (200 m; see N. maritimus in Fig. 2.3), highlighting a strict oxygen requirement for ammonia oxidation (Stewart et al., 2012b). Nonetheless, the distribution and activity of AOA at OMZ peripheries suggest adaptations for life under low oxygen conditions. Knowledge of AOA physiology and genetic diversity has increased substantially in recent years due to an influx of genome data from diverse Thaumarchaeota clades, including the Marine Group 1 cluster as well as groups from estuarine and terrestrial environments (Walker et al., 2010; Blainey et al., 2011; Spang et al., 2012). Although evidence for anaerobic metabolism is lacking for these organisms, genomic comparisons suggest metabolic versatility, with some clades capable of taking up small organic substrates to supplement autotrophic growth (e.g. pyruvate; Tourna et al., 2011) and also using urea and possibly cyanate to generate ammonia for energy metabolism

22  | Stewart and Ulloa

(Spang et al., 2012). It is important to note that no Thaumarchaeota genomes have yet been obtained from OMZ isolates, and therefore the potential for OMZ-specific ecotypes remains unexplored. Intriguingly, recent studies of AOA revealed stratification of distinct amoA or 16S rRNA gene phylotypes with depth near a seasonally hypoxic zone in the northern Gulf of Mexico (Tolar et al., 2013) and in the ETSP OMZ (Belmar et al., 2011). In contrast, a similar analysis reported significant differences in AOA community composition between the Arabian Sea and ETSP OMZs but not across depth zones within an OMZ, suggesting that geography was a stronger driver of AOA genetic variation compared with differing physiochemical environments (Peng et al., 2013). Resolving the relative effects of geography versus environment in structuring AOA diversity and physiology in OMZs will be aided greatly by obtaining whole genome data from OMZ isolates. Aerobic nitrite oxidation Recent genetic, genomic, and process rate measurements also suggest a potentially important role for microaerophilic nitrite oxidation in the upper layers of the most anoxic OMZs. Fussel et al. (2012) measured nitrite oxidation in the coastal OMZ off Namibia at oxygen levels ranging from ~100 nM to 10 µM. Single-gene molecular analyses identified the bacterial genera Nitrospina and Nitrococcus as candidates for nitrite oxidation in this zone, consistent with the role of these bacteria as the primary marine nitrite-oxidizers. Beman et al. (2013) recently measured nitrite oxidation by Nitrospina bacteria in the Eastern Tropical North Pacific (ETNP) OMZ off Mexico, with rates peaking where oxygen concentrations fell below 5 µM. Similarly, metagenomic data from the core of the ETSP OMZ off Chile, where oxygen falls to below the level of detection (~50 nM), revealed indicator genes of nitrite oxidation (nxrB, encoding nitrite oxidoreductase) closely related to those in the sequenced genome of Nitrospina gracilis 3/211 (Lucker et al., 2013), although nitrite oxidizer gene abundance spiked in the oxycline where oxygen concentrations rise to the micromolar range (Ganesh et al., 2014; see data for the

related nitrite oxidizer Ca. Nitrospira defluvii in Fig. 2.3, and note (**) in legend). Interestingly, a preliminary metatranscriptome analysis from these waters captured substantial transcriptional activity by nitrite oxidizers within the OMZ core, where Nitrospina was among the most abundant taxa identifiable in the protein-coding transcript pool (F.J. Stewart, unpublished). These trends, together with data from other low oxygen marine habitats ( Jorgensen et al., 2012), suggest an important contribution to nitrite consumption by these bacteria, which appear well suited to life under very low oxygen tensions. Indeed, the genome of N. gracilis 3/211 includes features suggestive of a microaerophilic lifestyle, including the use of a cbb3-type terminal oxidase with high oxygen affinity terminal oxidases, the lack of traditional mechanisms for defence against reactive oxygen species, and CO2 fixation via the rTCA cycle, which contains enzymes that are typically highly oxygen sensitive (Lucker et al., 2013). This genome also encodes enzymes for gluconeogenesis and the formation of glycogen, which has previously been shown to accumulate in N. gracilis cells (Watson and Waterbury, 1971), perhaps for use as an energy substrate via glycolysis and an oxidative TCA cycle. Genes encoding a Ni–Fe hydrogenase and a dissimilatory nitrite reductase (nirK) were also identified in N. gracilis 3/211. Together, these results suggest the potential for metabolic versatility beyond aerobic nitrite oxidation, although the physiological roles of many genes in N. gracilis have not been confirmed. Interestingly, the N. gracilis genome contains evidence of horizontal gene transfer (HGT) involving anammox Planctomycetes bacteria. Nitrospina and anammox bacteria co-occur in low oxygen environments, including OMZs (Fussel et al., 2012; Ganesh et al., 2014) where they are presumably in direct competition for nitrite. However, the N. gracilis 3/211 was not isolated directly from an OMZ environment. Obtaining genomes for OMZ-specific isolates may reveal additional instances of HGT among OMZ clades. Such genomes would provide valuable templates for mapping metatranscriptome sequences to identify physiological responses to changing oxygen conditions.

Oxygen Minimum Zones Metagenomics |  23

Anaerobic ammonium oxidation (anammox) Genomic data provide important insight into the physiology and evolutionary history of microorganisms mediating anammox. The anammox process is carried out (to the best of current knowledge) exclusively by certain phylogenetic groups of the Planctomycetes phylum, with the candidate genus Scalindua representing the dominant anammox lineage in marine waters. Until recently, genetic insight into OMZ Scalindua was limited to single-gene surveys or to comparisons of environmental metagenomic data to anammox bacteria from freshwater environments, i.e. Ca. Kuenenia stuttgartiensis isolated from wastewater. However, the recent sequencing of the metagenome of Ca. Scalindua profunda, an anammox bacterium isolated from marine sediment, has provided an important reference for OMZ metaomic analyses (van de Vossenberg et al., 2013). The Ca. S. profunda genome, similar to that of Ca. K. stuttgartiensis, is relatively large (~4700 genes). Surprisingly, over 40% of Scalindua genes are absent from Kuenenia, suggesting a high level of genomic novelty in the marine anammox clade. In addition to encoding enzymes for the autotrophic anammox process by which ammonium is oxidized by nitrite to nitrogen gas, the Ca. S. profunda genome contains genes for a diverse array of energy and carbon metabolisms, including the heterotrophic oxidation of simple organic substrates (e.g. acetate, propionate), potentially with nitrate as a terminal oxidant. Intriguingly, it appears possible that Ca. S. profunda reduces nitrate (via a nitrite intermediate) to ammonium, which can then be converted to N2 through the anammox pathway. Quantifying the extent to which this process operates in situ is critical, as N2 generation via this mechanism may be mistakenly interpreted as evidence for denitrification ( Jensen et al., 2011). Environmental metagenome and metatranscriptome data provide important insight into the distribution and in situ physiology of OMZ Scalindua bacteria. Genes and transcripts with top matches to the Ca. S. profunda genome peak within the upper part of oxygen-depleted layers in the ETSP but are essentially absent in the

well-oxygenated waters above and below the OMZ (Fig. 2.3; Ganesh et al., 2014; van de Vossenberg et al., 2013). Key genes of the anammox reaction itself (e.g. hzs and hzo encoding hydrazine synthase and oxidoreductase, respectively) are highly transcribed in the ETSP OMZ, along with genes encoding transporters for both ammonium and nitrite, suggesting that these substrates may be limiting for OMZ anammox bacteria, potentially through competition with co-occurring aerobic ammonium and nitrite oxidizing microbes in the upper OMZ (Stewart et al., 2012b; van de Vossenberg et al., 2013). However, Scalindua genomes from OMZ isolates have not yet obtained and it remains to be determined whether significant OMZ-specific genome content exists for this group. Microheterogeneity (1.6 µm) in the ETSP OMZ revealed an opposing pattern, in which phylogenetic diversity in particle-associated communities peaked within the most oxygen-depleted depths and exceeded that of the free-living community (Ganesh et al., 2014).

26  | Stewart and Ulloa

These studies suggest a complicated relationship between microbial community complexity and dissolved oxygen, with variation among sites, microbial size fractions, and diverse analytical methods. It has been suggested that OMZ depths support the use of a wider range of terminal oxidants, and therefore support diverse microbial niches, compared with non-OMZ layers where oxygen is the dominant electron acceptor (Stevens and Ulloa, 2008). However, observed declines in functional (protein family) richness in the ETSP OMZ suggest a decrease in niche availability associated with oxygen depletion (Bryant et al., 2012). Anoxic, aphotic OMZs presumably have fewer niches associated with light and the degradation of labile organic matter compared with highly productive surface depths. It remains uncertain how the presence of suspended or sinking organic particles contributes to total niche richness in OMZs. Microbial degradation of organic particles can create micro-gradients in substrate availability and composition that presumably help increase niche richness (Stocker, 2012). However, comparisons of taxonomic diversity across microbial size fractions have not identified consistent trends across ocean sites. Indeed, the increase in diversity of particle-associated cell fractions compared with free-living communities in the ETSP OMZ (Ganesh et al., 2014) is consistent with patterns from non-OMZ sites (Eloe et al., 2010; Ganesh et al., 2014), suggesting that factors other than water column oxygen levels (e.g. particle age and organic composition) may be driving the diversity of the particle-associated community in OMZs. Together, these patterns highlight a need for uniform measurements of OMZ diversity and community structure across diverse variables, including the composition, abundance, and size distribution of particles, and the physical and chemical gradients of the bulk water column. Particle association Microbial composition and functional activity vary significantly between free-living and particle-associated communities (DeLong et al., 1993; Grossart, 2010). Surprisingly, the vast majority of marine meta-omic studies, including those in OMZs, focus on the free-living cell fraction, despite major contributions to organic

carbon degradation by microorganisms attached to organic particles (Simon et al., 2002; Stocker, 2012). Particle-associated microbial processes may be particularly important for OMZs, which have been shown to be associated with elevated particulate loads (Pak et al., 1980; Whitmire et al., 2009). Recently, Ganesh et al. (2014) published a metagenomic comparison of size-fractionated microbial communities in the anoxic OMZ off Chile, indicating a partitioning of key genomic variants and metabolisms between free-living and particle-associated microniches. Taxa and genes mediating key steps of dissimilatory nitrogen and sulfur metabolism in the ETSP OMZ were differentially represented (relative abundance) between the smaller, free-living cell fraction (cells between 0.2 and 1.6 µm) and the larger, putatively particle-associated fraction (>1.6 µm). Whereas genes controlling the first steps of denitrification, dissimilatory nitrate and nitrite reduction (e.g. narG and nirK/nirS, respectively), were relatively evenly distributed between cell fractions or did not show a clear fractionspecific pattern, genes controlling the terminal denitrification steps of nitric oxide reduction to nitrous oxide (norB/norZ) and nitrous oxide reduction to dinitrogen (nosZ) were enriched 4-fold in the particle-associated community, in agreement with recent metagenome data from a low-oxygen estuarine site (Smith et al., 2013). Consistent with single-gene surveys of the suboxic zone of the Black Sea (Fuchsman et al., 2012), Scalindua-like anammox genes in the ETSP OMZ were overrepresented (4-fold) in the free-living size fraction. However, Scalindua sequences were also abundant in the particle-associated fraction, which agrees with a prior FISH-based study showing the presence of Scalindua both on and off particles (Woebken et al., 2007). This pattern raises the possibility of life-history plasticity or distinct ecotypes within this organism. Interestingly, sequences matching deltaproteobacteria groups containing heterotrophic sulfate reducers did not differ significantly in abundance among cell fractions. In contrast to oxic water columns where sulfate reduction presumably is localized to reduced microzones on particles (Shanks and Reeder, 1993), sulfate reduction in the OMZ, which has been demonstrated using

Oxygen Minimum Zones Metagenomics |  27

radiolabelling of the bulk water fraction (Canfield et al., 2010), may therefore not be confined to particle-associated microhabitats. Together, these patterns suggest that key steps of microbial elemental cycling may be spatially decoupled at the microscale in oxygen deficient waters and that bulk process rates (e.g. of denitrification) may be related to overall particle load. However, evaluating the ecological significance of particleassociated OMZ communities will require studies that couple meta-omic analysis to process rate measurements across size fractions. Size-fractionated metagenomic analysis of the Chilean OMZ also identified intriguing differences in mobile DNA content between size fractions. Genes involved with moving DNA within and between genomes, notably transposases and viral integrases, were significantly enriched (4- to 6-fold increase in relative abundance) in particle-associated OMZ metagenomes compared with those from the free-living OMZ community. This enrichment was observed across both OMZ and non-OMZ depths but was most pronounced within the anoxic OMZ core, where transposase genes in the particle-associated community were 50-fold more abundant than the single-copy gene encoding RNA polymerase B (rpoB). A similar OMZ-associated increase in relative transposase abundance was observed previously for the free-living microbial community of the ETSP OMZ (Stewart et al., 2012b). Recently, the first metagenomic study of viruses from this OMZ suggested that OMZ phages are predominantly lysogenic, rather than lytic (Cassman et al., 2012), raising the possibility that the transposase and other mobile element genes observed in OMZ microbial metagenomes may originate from lysogenized prophage (Ganesh et al., 2014). Gradual increases in transposase abundance with depth have also been reported in metagenomes spanning a 4000 m depth gradient in the oxygenated and oligotrophic waters of the North Pacific (DeLong et al., 2006; Konstantinidis et al., 2009). It was hypothesized that this increase in transposases, which are typically deleterious for host genomes, was linked to an overall decrease in genetic effective population size with depth. This hypothesis requires further testing, and it remains unclear whether similar constraints may

be operating on OMZ communities. However, the combined metagenomic patterns suggest that OMZs, and specifically particle-associated communities in OMZs, may be hotbeds for DNA mobilization. Challenges The future application of ‘omics technologies to OMZ research must address several key challenges. As described above, few reference genomes are available for individual OMZ-specific bacteria and archaea. Inferences from prior OMZ diversity studies were based largely on sequence comparisons to microbial genomes from non-OMZ systems. While such genomes provide a baseline for interpreting OMZ data, OMZ-specific genomes are critically lacking, and limited primarily to a single functional group, gammaproteobacterial sulfur oxidizers related to the SUP05 lineage (Walsh et al., 2009). OMZ members of this group have functional adaptations distinct from those of non-OMZ relatives, highlighting the potential for novel physiological diversity in OMZs. Identifying this novelty requires additional genome and metagenome data across diverse OMZ systems. In addition, there is a clear need for studies focused on obtaining OMZ isolates, and their associated physiological and genomic characterizations. It is expected that culture-independent, single-cell genomics will help to meet this need, identifying the genomic potential and metabolic gene suites characteristic of OMZ microorganisms. Studies of OMZ viral metagenomes are likely critical for understanding microbial function and genome evolution in these ecosystems. Ocean viruses have global impacts ranging from modulating microbial population dynamics and metabolisms to facilitating horizontal gene transfer and altering biogeochemistry (Suttle et al., 2007). However, OMZ viruses are nearly uncharacterized. A recent study (Cassman et al., 2012) provided the first OMZ viral metagenomes and found high percentages of novel viral groups and hints of elevated selection for lysogeny in the anoxic zone of the anoxic OMZ off Chile. Recent and forthcoming papers utilizing metagenomic data from the viral fraction in the NESAP OMZ

28  | Stewart and Ulloa

document viral seasonality, protein cluster diversity and niche differentiation in and out of the OMZ, and also demonstrate that OMZ viruses appear capable of reprogramming host central carbon metabolism (Hurwitz and Sullivan, 2013; M.B. Sullivan, unpublished). These NESAP viral communities hint at large roles played by viruses in elemental cycling. Viral metagenomes from other OMZ systems will be necessary to help validate the universality of these findings. Sampling microbial communities from anoxic water columns without interjecting bias remains a major challenge in OMZ research. This challenge is particularly significant for metatranscriptomic studies, as oxygen shifts (contamination) during sampling may induce rapid changes in the community RNA pool that do not reflect in situ activity. Indeed, turnover times for microbial mRNA may be on the order of minutes (Deutscher et al., 2006; Steglich et al., 2010), and oxygen exposure during water collection via standard rosette bottles is unavoidable during transit to the surface. Autonomous instruments for filtering and preserving microbial samples in situ, such as the environmental sample processor (ESP; Scholin et al., 2009), have yielded valuable insight into in situ microbial activity in the ocean, but have not been deployed in OMZs. Furthermore, such instruments require considerable resources to develop and deploy, and generally are not available for use by the broader research community. In recent years, several smaller-scale, wire-based instruments for in situ RNA preservation have been developed by individual labs (Feike et al., 2012; Wurzbacher et al., 2012). Notably, a study of microbial communities from the Baltic Sea suboxic zone revealed that abundances of transcripts encoding key nitrogen cycle genes differed by up to 30-fold between samples preserved after filtration aboard ship compared with those preserved in situ using an automatic flow injection sampler that mixed RNA preservative into a water sample at the moment of collection (Feike et al., 2012). Similar devices, along with high-speed pumping systems that can rapidly transport (minutes) OMZ water to the surface (e.g. Stewart et al., 2012a), will be necessary to obtain accurate measurements of community gene expression in OMZs.

Future studies should also enhance the spatial and temporal resolution of OMZ meta-omic surveys. Prior meta-omic surveys have sampled primarily at a vertical resolution of tens of metres (or greater). However, concentrations of key OMZ environmental parameters, notably oxygen, may vary several-fold on the scale of metres (or less), notably during episodic eddy intrusions. The effect of such steep gradients on partitioning microbial taxa and metabolisms, particularly at OMZ edges, is not yet understood. Also, OMZ microbial composition and functional activity can vary significantly between free-living and particleassociated communities (Ganesh et al., 2014). Such variation thus far has been explored only for a single site and for only two size fractions. Sampling metagenomes and metatranscriptomes from additional size fractions across multiple OMZ sites and depths will be necessary to disentangle the combined effects of water column hypoxia and particle-association on OMZ microbial community structure. Time series meta-omic surveys are also lacking for low-oxygen waters, but are critical for understanding ecosystem responses to episodic deoxygenation events. A notable exception is the ongoing sampling programme focused on the seasonal OMZs of Saanich Inlet and the Northeast Subarctic Pacific (NESAP) (see references in Wright et al., 2012). However, these seasonal OMZs differ from other OMZ regions, such as the permanent anoxic OMZs of the Tropical Pacific with regard to vertical structure, physical and chemical forcings, and levels of primary production and oxygen depletion. Additional time-series monitoring at other OMZ sites will be necessary to understand the resilience of microbial community structure and metabolism to oxygen fluctuations. Meeting this challenge, and others described here, will help ensure that the ‘omics field continues to make significant advances in our understanding of OMZ microbial diversity and biogeochemical processes. Acknowledgements We thank Sangita Ganesh for valuable help in compiling and editing this chapter. This work is supported by funding from the National Science Foundation (1151698 to FJS), and the

Oxygen Minimum Zones Metagenomics |  29

Chilean National Commission for Scientific and Technological Research (CONICYT) (Fondecyt 1130784 to OU). We would also like to acknowledge generous support from the Agouron Institute, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation. References

Belmar, L., Molina, V., and Ulloa, O. (2011). Abundance and phylogenetic identity of archaeoplankton in the permanent oxygen minimum zone of the eastern tropical South Pacific. FEMS Microbiol. Ecol. 78, 314–326. Beman, J.M., Popp, B.N., and Alford, S.E. (2012). Quantification of ammonia oxidation rates and ammonia-oxidizing archaea and bacteria at high resolution in the Gulf of California and eastern tropical North Pacific Ocean. Limnol. Oceanogr. 57,711–726. Beman, J.M., Leilei, Shih, J., and Popp, B.N. (2013). Nitrite oxidation in the upper water column and oxygen minimum zone of the eastern tropical North Pacific Ocean. ISME J. doi:10.1038/ismej.2013.96. Blainey, P.C., Mosier, A.C., Potanina, A., Francis, C.A., and Quake, S.R. (2011). Genome of a low-salinity ammonia-oxidizing archaeon determined by singlecell and metagenomic analysis. PLoS One. 6, e16626. Brochier-Armanet, C., Boussau, B., Gribaldo, S., and Forterre, P. (2008). Mesophilic Crenarchaeota: proposal for a third archaeal phylum, the Thaumarchaeota. Nat. Rev. Microbiol. 6, 245 – 52. Brown, M.V., Philip, G.K., Bunge, J.A., Smith, M.C., Bissett, A., Lauro, F.M., Fuhrman, J.A., and Donachie, S.P. (2009). Microbial community structure in the North Pacific Ocean. ISME J. 3, 1374–1386. Bryant, J.A., Stewart, F.J., Eppley, J.M., and DeLong, E.F. (2012). Microbial community phylogenetic and trait diversity decline steeply with depth in a marine oxygen minimum zone. Ecology 93, 1659–1673. Canfield, D.E., Stewart, F.J., Thamdrup, B., De Brabandere, L., Dalsgaard, T., DeLong, E.F., Revsbech, N.P., and Ulloa, O. (2010). A cryptic sulfur cycle in oxygenminimum zone waters off the Chilean Coast. Science. 330, 1375–1378. Cassman, N., Prieto-Davó, A., Walsh, K., Silva, G.G., Angly, F., Akhter, S., Barott, K., Busch, J., McDole, T., Haggerty, J.M., Willner, D., Alarcón, G., Ulloa, O., DeLong, E.F., Dutilh, B.E., Rohwer, F., and Dinsdale, E.A. (2012). Oxygen minimum zones harbour novel viral communities with low diversity. Environ. Microbiol. 14, 3043–3065. Castro-González, M., Braker, G., Farías, L., and Ulloa, O. (2005). Communities of nirS-type denitrifiers in the water column of the oxygen minimum zone in the eastern South Pacific. Environ. Microbiol. 7, 1298–1306. Codispoti, L.A. (2010). Oceans. Interesting times for marine N2O. Science. 327, 1339–1340. Cypionka, H. (2000). Oxygen respiration by Desulfovibrio species. Ann. Rev. Microbiol. 54, 827–848.

Deutscher, M.P. (2006). Degradation of RNA in bacteria: comparison of mRNA and stable RNA. Nucleic Acids Res. 34, 659–666. DeLong, E.F., Franks, D.G., and Alldredge, A.L. (1993). Phylogenetic diversity of aggregate-attached vs freeliving marine bacterial assemblages. Limnol. Oceanogr. 38, 924–934. DeLong, E.F., Preston, C.M., Mincer, T., Rich, V., Hallam, S.J., Frigaard, N.U., Martinez, A., Sullivan, M.B., Edwards, R., Brito, B.R., Chisholm, S.W., and Karl, D.M. (2006). Community genomics among stratified microbial assemblages in the ocean’s interior. Science. 311, 496–503. Dugdale, R.C., Goering, J.J., Barber, R.T., Smith, R.L., and Packard, T.T. (1977). Denitrification and hydrogensulfide in Peru upwelling region during 1976. Deep-Sea Res. 24, 601–608. Eloe, E.A., Shulse, C.N., Fadrosh, D.W., Williamson, S.J., Allen, E.E., and Bartlett, D.H. (2010). Compositional differences in particle-associated and free-living microbial assemblages from an extreme deep-ocean environment. Environ. Microbiol. Rep. 3, 449–458. Feike, J., Jürgens, K., Hollibaugh, J.T., Krüger, S., Jost, G., and Labrenz, M. (2012). Measuring unbiased metatranscriptomics in suboxic waters of the central Baltic Sea using a new in situ fixation system. ISME J. 6, 461–470. Finster, K.W., and Kjeldsen, K.U. (2010). Desulfovibrio oceani subsp oceani sp nov., subsp nov and Desulfovibrio oceani subsp galateae subsp nov., novel sulfate-reducing bacteria isolated from the oxygen minimum zone off the coast of Peru. Anton. Leeuw. Int. J. G. 97, 221–229. Fuchsman, C.A., Staley, J.T., Oakley, B.B., Kirkpatrick, J.B., and Murray, J.W. (2012). Free-living and aggregateassociated Planctomycetes in the Black Sea. FEMS Microbiol. Ecol. 80, 402–416. Fussel, J., Lam, P., Lavik, G., Jensen, M.M., Holtappels, M., Gunter, M., and Kuypers, M.M. (2012). Nitrite oxidation in the Namibian oxygen minimum zone. ISME J. 6, 1200–1209. Galán, A., Molina, V., Thamdrup, B.,Woebken, D., Lavik, G., Kuypers, M.M.M., and Ulloa, O. (2009). Anammox bacteria and the anaerobic oxidation of ammonium in the oxygen minimum zone off northern Chile. DeepSea Res. Pt. II. 56, 1125–1135. Ganesh, S., Parris, D.J., DeLong, E.F., and Stewart, F.J. (2014). Metagenomic analysis of size-fractionated picoplankton in a marine oxygen minimum zone. ISME J. 8, 187–211. Glaubitz, S., Labrenz, M., Jost, G., and Jurgens, K. (2010). Diversity of active chemolithoautotrophic prokaryotes in the sulfidic zone of a Black Sea pelagic redoxcline as determined by rRNA-based stable isotope probing. FEMS Microbiol. Ecol. 74, 32–41. Grossart, H.P. (2010). Ecological consequences of bacterioplankton lifestyles: changes in concepts are needed. Environ. Microbiol. Rep. 2, 706–714. Hurwitz, B.L., and Sullivan, M.B. (2013). The Pacific Ocean virome (POV): a marine viral metagenomic dataset and associated protein clusters for quantitative viral ecology. PLoS ONE. 8, e57355.

30  | Stewart and Ulloa

Jayakumar, D.A., Francis, C.A., Naqvi, S.W.A., and Ward, B.B. (2004). Diversity of nitrite reductase genes (nirS) in the denitrifying water column of the coastal Arabian Sea. Aquat. Microb. Ecol. 34, 69–78. Jensen, M.M., Lam, P., Revsbech, N.P., Nagel, B., Gaye, B., Jetten, M.S., and Kuypers, M.M.M. (2011). Intensive nitrogen loss over the Omani Shelf due to anammox coupled with dissimilatory nitrite reduction to ammonium. ISME J. 5, 1660–1670. Jorgensen, S.L., Hannisdal, B., Lanzen, A., Baumberger, T., Flesland, K., Fonseca, R., Øvreås, L., Steen, I.H., Thorseth, I.H., Pedersen, R.B., et al. (2012). Correlating microbial community profiles with geochemical data in highly stratified sediments from the Arctic Mid-Ocean Ridge. Proc. Natl. Acad. Sci. U.S.A. 109, E2846–E2855. Keeling, R.E., Körtzinger, A., and Gruber, N. (2010). Ocean deoxygenation in a warming world. Ann. Rev. Mar. Sci. 2, 199–229. Kembel, S.W., Eisen, J.A., Pollard, K.S., and Green, J.L. (2011). The phylogenetic diversity of metagenomes. PloS ONE. 6, e23214. Konneke, M., Bernhard, A.E., de la Torre, J.R., Walker, C.B., Waterbury, J.B., and Stahl, D.A. (2005). Isolation of an autotrophic ammonia-oxidizing marine archaeon. Nature. 437, 543–546. Konstantinidis, K.T., Braff, J., Karl, D.M., and DeLong, E.F. (2009). Comparative metagenomic analysis of a microbial community residing at a depth of 4,000 meters at Station ALOHA in the North Pacific Subtropical Gyre. Appl. Environ. Microbiol. 75, 5345–5355. Lam, P., Lavik, G., Jensen, M.M., van de Vossenberg, J., Schmid, M., Woebken, D., Gutiérrez, D., Amann, R., Jetten, M.S., and Kuypers, M.M. (2009). Revising the nitrogen cycle in the Peruvian oxygen minimum zone. Proc. Natl. Acad. Sci. U.S.A. 106, 4752–4757. Lam, P., and Kuypers, M.M.M. (2011). Microbial nitrogen cycling processes in oxygen minimum zones. Ann. Rev. Mar. Sci. 3, 317–345. Lavik, G., Stuhrmann, T., Bruchert, V., Van der Plas, A., Mohrholz, V., Lam, P., Mußmann, M., Fuchs, B.M., Amann, R., Lass, U., and Kuypers, M.M.M. (2009). Detoxification of sulphidic African shelf waters by blooming chemolithotrophs. Nature. 457, 581–586. Lucker, S., Nowka, B., Rattei, T., Spieck, E., and Daims, H. (2013). The genome of Nitrospina gracilis illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27. Madrid, V.M., Taylor, G.T., Scranton, M.I., and Chistoserdov, A.Y. (2001). Phylogenetic diversity of bacterial and archaeal communities in the anoxic zone of the Cariaco Basin. Appl. Environ. Microbiol. 67, 1663–7164. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., and Stahl, D.A. (2009). Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 461, 976U234. Molina, V., Belmar, L., and Ulloa, O. (2010). High diversity of ammonia-oxidizing archaea in permanent

and seasonal oxygen-deficient waters of the eastern South Pacific. Environ. Microbiol. 12, 2450–2465. Newell, S.E., Babbin, A.R., Jayakumar, A., and Ward, B.B. (2011). Ammonia oxidation rates and nitrification in the Arabian Sea. Global Biogeochem. Cy. 25, GB4016, doi:10.1029/2010GB003940 Pak, H., Codispoti, L.A., and Zaneveld, J.R.V. (1980). On the intermediate particle maxima associated with oxygen-poor water off Western South America. DeepSea Res. 27, 783–797. Peng, X., Jayakumar, A., and Ward, B.B. (2013). Community composition of ammonia-oxidizing archaea from surface and anoxic depths of oceanic oxygen minimum zones. Front. Microbiol. 4, 177. Pester, M., Schleper, C., and Wagner, M. (2011). The Thaumarchaeota: an emerging view of their phylogeny and ecophysiology. Curr. Opin. Microbiol. 14, 300– 306. Scholin, C., Doucette, G., Jensen, S., Roman, B., Pargett, D., Marin, R., Preston, C., Jones, W., Feldman, J., Everlove, C., et al. (2009). Remote detection of marine microbes, small invertebrates, harmful algae, and biotoxins using the Environmental Sample Processor (ESP). Oceanography 22, 158–167. Schunck, H., Lavik, G., Desai, D.K., Großkopf, T., Kalvelage, T., Löscher, C.R., Paulmier, A., Contreras, S., Siegel, H., Holtappels, M., et al. (2013). Giant hydrogen sulfide plume in the oxygen minimum zone off Peru supports chemolithoautotrophy. PLoS ONE. 8, e68661. Sheik, C.S., Jain, S., and Dick, G.J. (2013). Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics. Environ. Microbiol. doi: 10.1111/1462–2920.12165. Simon, M., Grossart, H.P., Schweitzer, B., and Ploug, H. (2002). Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211. Smith, M.W., Allen, L.Z., Allen, A.E., Herfort, L., and Simon, H.M. (2013). Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front. Microbiol. 4, 120. Spang, A., Poehlein, A., Offre, P., Zumbrägel, S., Haider, S., Rychlik, N., Nowka, B., Schmeisser, C., Lebedeva, E.V., Rattei, T., et al. (2012). The genome of the ammoniaoxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ. Microbiol. 14, 3122–3145. Steglich, C., Lindell, D., Futschik, M., Rector, T., Steen, R., and Chisholm, S.W. (2010). Short RNA halflives in the slow-growing marine cyanobacterium Prochlorococcus. Genome Biol. 11, R54. Stevens, H., and Ulloa, O. (2008). Bacterial diversity in the oxygen minimum zone of the eastern tropical South Pacific. Environ. Microbiol. 10, 1244–1259. Stewart, F.J., Dalsgaard, T., Thamdrup, B., Revsbech, N.P., Ulloa, O., Canfield, D.E., and DeLong, E.F. (2012a). Experimental incubations elicit profound changes in community transcription in OMZ bacterioplankton. PLoS ONE. 7, e37118.

Oxygen Minimum Zones Metagenomics |  31

Stewart, F.J., Ulloa, O., and DeLong, E.F. (2012b). Microbial metatranscriptomics in a permanent marine oxygen minimum zone. Environ. Microbiol. 14, 23–40. Sunamura, M., Higashi, Y., Miyako, C., Ishibashi, J., and Maruyama, A. (2004). Two bactetia phylotypes are predominant in the Suiyo Seamount hydrothermal plume. Appl. Environ. Microbiol. 70, 1190–1198. Suttle, C.A. (2007). Marine viruses – major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812. Stocker, R. (2012). Marine microbes see a sea of gradients. Science 338, 628–633. Thamdrup, B., Dalsgaard, T., and Revsbech, N.P. (2012). Widespread functional anoxia in the oxygen minimum zone of the eastern South Pacific. Deep-Sea Res. I. 65, 36–45. Tolar, B.B., King, G.M., and Hollibaugh, J.T. (2013). An analysis of thaumarchaeota populations from the northern gulf of Mexico. Front Microbiol. 4, 72. Tourna, M., Stieglmeier, M., Spang, A., Könneke, M., Schintlmeister, A., Urich, T., Engel, M., Schloter, M., Wagner, M., Richter, A., et al. (2011). Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. Proc. Natl. Acad. Sci. U.S.A. 108, 8420–8425. Ulloa, O., Canfield, D.E., DeLong, E.F., Letelier, R.M., and Stewart, F.J. (2012). Perspective: Microbial oceanography of anoxic oxygen minimum zones. Proc. Natl. Acad. Sci. U.S.A. 109, 15996–16003. Ulloa, O., Wright, J.J., Belmar, L., and Hallam, S.J. (2013). Pelagic oxygen minimum zone microbial communities. In The Prokaryotes – Prokaryotic Communities and Ecophysiology, E. Rosenberg, E.F. DeLong, E. Stackebrandt, S. Lory, and F. Thompson, F., eds. (Berlin: Springer-Verlag), pp. 113–122. van de Vossenberg, J., Woebken, D., Maalcke, W.J., Wessels, H.J., Dutilh, B.E., Kartal, B., Janssen-Megens, E.M., Roeselers, G., Yan, J., Speth, D., et al. (2012). The metagenome of the marine anammox bacterium ‘Candidatus Scalindua profunda’ illustrates the versatility of this globally important nitrogen cycle bacterium. Environ. Microbiol. 15, 1275–1289. Walker, C.B., de la Torre, J.R., Klotz, M.G., Urakawa, H., Pinel, N., Arp, D.J., Brochier-Armanet, C., Chain, P.S.,

Chan, P.P., Gollabgir, A., et al. (2010). Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proc. Natl. Acad. Sci. U.S.A.107, 8818–8823. Walsh, D.A., Zaikova, E., Howes, C.G., Song, Y.C., Wright, J.J., Tringe, S.G., Tortell, P.D., and Hallam, S.J. (2009). Metagenome of a versatile chemolithoautotroph from expanding oceanic dead zones. Scienc. 326, 578–582. Watson, S.W., and Waterbury, J.B. (1971). Characteristics of two marine nitrite oxidizing bacteria, Nitrospina gracilis nov. gen. nov. sp. and Nitrococcus mobilis nov. gen. nov. sp. Arch. Mikrobiol. 77, 203–230. Whitmire, A.L., Letelier, R.M., Villagran, V., and Ulloa, O. (2009). Autonomous observations of in vivo fluorescence and particle backscattering in an oceanic oxygen minimum zone. Opt. Express. 17, 21992– 22004. Woebken, D., Fuchs, B.M., Kuypers, M.M., and Amann, R. (2007). Potential interactions of particle-associated anammox bacteria with bacterial and archaeal partners in the Namibian upwelling system. Appl. Environ. Microbiol.. 73, 4648–4657. Woebken, D., Lam, P., Kuypers, M.M., Naqvi, S.W., Kartal, B., Strous, M., Jetten, M.S.M., Fuchs, B.M., and Amann, R. (2008). A microdiversity study of anammox bacteria reveals a novel Candidatus Scalindua phylotype in marine oxygen minimum zones. Environ. Microbiol. 10, 3106–3119. Wright, J.J., Konwar, K.M., and Hallam, S.J. (2012). Microbial ecology of expanding oxygen minimum zones. Nature Rev. Microbiol. 10, 381–394. Wurzbacher, C., Salka, I., and Grossart, H.P. (2012). Environmental actinorhodopsin expression revealed by a new in situ filtration and fixation sampler. Environ. Microbiol. Rep. 4, 491–497. Zaikova, E., Walsh, D.A., Stilwell, C.P., Mohn, W.W., Tortell, P.D., and Hallam, S.J. (2010). Microbial community dynamics in a seasonally anoxic fjord: Saanich Inlet, British Columbia. Environ. Microbiol. 12, 172–191.

Interactions between Methane and Nitrogen Cycling: Current Metagenomic Studies and Future Trends

3

Paul L.E. Bodelier and Anne K. Steenbergh

Abstract Wetlands, lakes, soils and sediments are the most important biological sources as well sinks of the greenhouse gas methane. However, the dynamics, variability and uncertainty in methane emission models from these systems is high necessitating better knowledge of the underlying microbial processes. The impact of nitrogenous fertilizers and atmospheric nitrogen deposition on methane production and consumption in freshwater ecosystems (wetlands, lakes, rice paddies) as well as in upland soils has been the subject of intense research the past decades. However, our mechanistic understanding of the observed effects on methane and nitrogen cycling interactions in these ecosystems is poor which is even more so considering the novel microbial groups and pathways discovered. This chapter gives an overview of the main ways the nitrogen cycle interacts with the microbial methane cycling in freshwater wetlands, soils and sediments and summarizes the main current metagenomic studies that carried out on microbial groups involved. It can be concluded that metagenomic techniques developed and applied have the potential to obtain an integrative view of microbial communities and interactions and bear the potential to discover new pathways and organisms. The way forward is to apply these techniques in replicated, manipulative experimental set ups to obtain mechanistic understanding of methane-nitrogen cycle interactions.

Introduction The global methane cycle Methane (chemical formula CH4) is an odourless and colourless gas that was discovered in 1778 by the Italian physicist Alessandro Volta (http:// en.wikipedia.org/wiki/Methane), who also discovered the flammability of this gas. Nowadays, methane is known as one of the most important fossil fuels on the planet as well as one of the major greenhouse gases (GHG). Next to water vapour and CO2 methane is the third most important greenhouse gas, contributing around 17% to the global radiative forcing (Denman et al., 2007), which is defined as the difference between radiant energy received by the earth and the amount of energy radiated back. A positive radiative forcing value means that the earth warms up, which to a large part is caused by the increase in infrared absorbing gases in the atmosphere. With respect to the latter CH4 has a warming potential 25 times higher than CO2 when expressed on a 100-year time period, taking into account the atmospheric half-life of the gases. The important role of methane in global warming has spurred intensive assessments of atmospheric methane concentrations which have increased from 0.7 ppm in the preindustrial era to 1.8 ppmv at present (http:// cdiac.ornl.gov/pns/current_ghg.html). Recent anomalies, the increased rise from 2007 onwards after a period of stabilization, combined with the uncertainties in global methane budgets (Spahni et al., 2011; Van Amstel, 2012) necessitates

34  | Bodelier and Steenbergh

deeper knowledge on global sources and sinks of methane. Methane can be formed thermogenically under high pressure typically occurring at great depth in oil or gas fields, however, the largest part of methane emitted to the atmosphere is of biogenic origin (Conrad, 2009) and is produced by methanogenic archaea (see section ‘Methane formation’) in anoxic habitats like wetland soils and sediments, the digestive tract of ruminants and insects and in anthropogenically created habitats like landfill and other waste systems (Figs. 3.1 and 3.2). Next to this, substantial amounts of methane are released associated to mining (coal, oil, gas) industrial activities. The single largest source of atmospheric methane is global wetlands, including rice cultivation (Fig. 3.1). Anoxic conditions and the availability of large amounts of plant-derived fermentable carbon promote methanogenesis and methane can escape by diffusion through plant roots and stems into the atmosphere (Bodelier, 2011a). Very recently it has been shown that even trees can facilitate methane escape which is a potentially overlooked methane source in tropical forests (Pangala et al., 2013). Another overlooked

source of atmospheric methane is emissions from lakes accounting up to 6–16% of annual natural methane emissions (Bastviken et al., 2011). Non-microbial methane production, probably originating from chemical reactions, has also been shown for plants (see Wang et al., 2011) and even in aerobic soils ( Jugold et al., 2012). However, since the mechanisms and controls have not been established it is too preliminary to assess the contribution to atmospheric methane budgets. To these developments methane formation by fungi can be added as novel process in global methane formation (Lenhart et al., 2012). Global sinks for methane are the biological consumption and the chemical degradation by OH radicals in the troposphere and stratosphere (see Fig. 3.2) (Van Amstel, 2012). During the latter reaction water and CO2 are formed. The amount of OH radicals in the atmosphere can be lowered by, for example, NOx originating from traffic and industry, which may prolong the lifetime of methane in the atmosphere (http:// en.wikipedia.org/wiki/Atmospheric_methane). The biological consumption of atmospheric methane is predominantly carried out under oxic conditions by methanotrophic bacteria (see

Natural sources: 238 Tg CH4 yr-1 oceans hydrates geological wild animals

termites wetlands

wild fires coal mining

gas, oil, industry

biomass burning

rice agriculture

landfills and waste ruminants

Anthropogenic sources: 336 Tg CH4 yr-1

Figure 3.1  Contribution of natural sources (grey text) and anthropogenic (black text) sources of atmospheric methane. The sizes of the pie pieces represent % of contribution to the total annual methane emission to the atmosphere (i.e. 574 Tg CH4/year) (data derived from Van Amstel 2012).

Interactions of Methane and Nitrogen Cycle |  35

Figure 3.2  Global sinks and sources of atmospheric methane. The sizes of the source and sink compartment represent their relative contribution to atmospheric methane fluxes (data derived from (Van Amstel, 2012)). Methane fluxes are shown as grey arrows, while the microbial processes involved in the production and consumption of methane are given in italics. Note that while aerobic methane oxidation in soils only plays a relatively minor role as a sink for atmospheric methane, microbial methane oxidation in wetlands and oceans prevents the major part of internally produced methane from reaching the atmosphere.

‘Methane consumption’, below) in upland forest, grassland and meadow soils (Dunfield, 2007; Kolb, 2009; Shrestha et al., 2012). In wetlands and lakes, the oxic surface layer of sediments or the oxygenated zone around roots of wetland plants (Bodelier, 2011a; Bodelier et al., 2006) act as ‘mitigating sites’ of internally produced methane rather than a sink of atmospheric methane. It has been estimated that on average 40–60% of the methane produced in wetland habitats is oxidized before it can escape into the atmosphere by diffusion, ebullition and plant-facilitated transport (Megonigal, 2004). Studies in the last decade have clearly demonstrated that methane is also oxidized using other electron acceptors than oxygen (see Joye, 2012; Thauer, 2011). SO42–, Fe3+, Mn4+ and NO2– have been shown to be used by exotic microbes and metabolisms (see ‘Anaerobic methane oxidation’, below) or by as yet uncharacterized microbes. In addition, the extent to which these processes contribute to the global methane budget is unclear. Considering that the major part of sources and sinks involved in net emission of methane to the atmosphere are microbial it is obvious that interactions with other elemental cycles

will be of crucial importance for the regulation of global methane emissions. Given the fact that more than half of the global methane is produced and consumed in wetlands and soils, oxygen and the presence of plants will play an important regulating role. Nitrogen has been identified as a particular influential element since both methane production and consumption can be either inhibited or stimulated by nitrogenous fertilizer additions (see Bodelier, 2011a) with subsequent consequences for methane emission to the atmosphere. In fact, the interaction between methane and nitrogen has been identified as one of the major gaps in carbon-nitrogen cycle interactions (Gardenas et al., 2011). Mechanistically, there is still a poor understanding of nitrogen effects on methane cycling since the microbes involved can potentially use it as nitrogen source (assimilation, fixation), as energy source (NH4+ as well as NO2–), or can be inhibited by it through competition or direct toxic effects (see Fig. 3.5). Next to this, new organisms and pathways have been discovered which have the potential for playing a role in methane-nitrogen cycle interactions (see Figs. 3.2 and 3.5). The goal of this chapter is to give a short overview of processes, microbes

36  | Bodelier and Steenbergh

and regulating factors involved in interactions between methane and nitrogen cycling and what open questions still exist. Considering the other chapters within this book we will restrict our reflections to freshwater wetlands, sediments and soils. Finally, we will assess what metagenomic approaches so far have contributed to this matter and what can be expected from these approaches in future. The large unknowns regarding both microbes and processes involved in methane transformations offer excellent opportunity for metagenomic assessments in this field. Methane formation Within the global carbon cycle a few per cent of the photosynthetically produced carbon is degraded to methane. The production of methane is carried out by microbes belonging to the phylum Euryarchaeota within the domain of the Archaea. The phylogeny, physiology as well as ecology of this group of archaea has extensively been described and for more detailed information the reader is referred to the following excellent reviews: Borrel et al., 2011; Conrad, 2007; Liu and Whitman, 2008; Whitman, 2006. Based on a substantial number of cultivated and described species, six orders of methanogens (Methanomicrobiales, Methanosarcinales, Methanobacteriales; Methanococcales, Methanocellales, Methanopyrales) can be distinguished covering 13 families and 33 genera (Liu and Whitman, 2008; Narihiro and Sekiguchi, 2011). Recently, a seventh order of methanogens, the Methanoplasmales, has been proposed to accommodate a large number of sequences grouped in a deep-branching lineage of uncultivated Euryarchaeota mainly found in intestines of animals (Paul et al., 2012). Enrichment cultures have provided direct evidence that microbes within this lineage that is distantly related to the order of Thermoplasmatales have the ability to produce methane (Paul et al., 2012). Despite the ecological and phylogenetic diversity of methanogens, their way of generating energy is rather non-diverse. Organic matter in anoxic ecosystems (e.g. wetlands, sediments, permafrost, landfills) is degraded by a network of microbes hydrolysing polymers to monomers which subsequently can be fermented to H2, CO2, acetate, formate, methanol and low molecular weight

fatty acids and alcohols (Fig. 3.3). The latter can be further used by fermentative bacteria converting them to CO2 and H2 which can directly be converted to methane. Hence, methanogens have three primary strategies to obtain energy: hydrogenotrophic (H2 + CO2), acetotrophic (acetate) and methylotrophic (methanol, methylamines) with some methanogens being able to utilize secondary alcohols, such as 2-propanol and 2-butanol, which eventually is coupled to the reductive methylation of CO2. The formation of methane is a biochemical complex and unique pathway where a methyl group is transferred through a complex set of enzymes and c-factors (described in detail by Thauer, 2011; Thauer et al., 2008). Irrespective of the upstream electron and methyl-group donors the final step in methane formation is shared by all known methanogens and is catalysed by the enzyme methyl-coenzyme-M reductase (MCR). This enzyme is found in methanogens exclusively and the gene encoding for the α-subunit of this enzyme (mcrA) is highly conserved and congruent with 16S rRNA based phylogeny. The mcrA gene has therefore been used extensively to investigate the environmental ecology of methanogens (Borrel et al., 2011; Narihiro and Sekiguchi, 2011; Steinberg and Regan, 2009). Among the energy-generating pathways as used by methanogens, described above, the hydrogenotrophic pathway is the most common among the known methanogens while the acetotrophic pathway is restricted to members of the genera Methanosarcina and Methanosaeta belonging to the order of the Methanosarcinales (Liu and Whitman, 2008). Together, these two pathways are dominant in natural habitats making it important to understand the controls of these pathways in environmental settings. Using the difference between both pathways in discrimination between light and heavy carbon isotopes (e.g. Conrad, 2005; Conrad et al., 2009, 2012) and the differential sensitivity to inhibitors (e.g. Conrad and Klose, 1999; Daebeler et al., 2013) the contribution of these pathways to methane formation in rice soils and lake sediments was assessed revealing that the contribution depends on ecosystem, competing processes, seasonality as well as type of organic matter. Methane formation in general is controlled and affected

Interactions of Methane and Nitrogen Cycle |  37

Figure 3.3  Schematic representation of biological methane formation. In anoxic habitats complex organic matter derived from dead organisms is hydrolysed by extracellular enzymes mainly from anaerobic bacteria. The produced monomers can be fermented in a primary fermentation step into direct substrates for hydrogenotrophic (H2 + CO2), acetotrophic (acetate) and methylotrophic (methanol) methanogens. Other volatile fatty acids and alcohols can be fermented in secondary fermentation to methanogenic substrates.

mainly by oxygen, alternative electron acceptors (e.g. NO3–, NO2–, Fe3+, SO42–), temperature and amount and type of organic matter which in turn are all regulated by physical factors (such as water table/flooding in wetlands) or other microbes or plants. It is not within the scope of this chapter to discuss all controlling factors in detail and the reader is referred to excellent reviews on these topics (Bridgham, 2013; Conrad, 2007; Megonigal, 2004). Notable, however, are recent findings regarding the oxygen control of methanogenic communities. Methanogens are regarded as oxygen sensitive on account of the co-factors and enzyme complexes involved in the formation of methane (Fetzer et al., 1993; Yuan et al., 2011). However, recently it was demonstrated that methanogens in oxic upland soils and even in desert soils regain activity fast, reaching high methane production potentials (Angel et al., 2011, 2012). Methanogens belonging to the genera Methanosarcina and Methanocella were

found to be almost exclusively present in these soils. In desert soils they even produced methane under an oxic headspace, performing hydrogentrophic methanogenesis and expressing oxygen detoxifying genes (i.e. catalases) (Angel et al., 2011). In addition, even methanogenesis in the oxygenated water column of a freshwater lake has been demonstrated (Grossart et al., 2011). As was already demonstrated by a metagenomic study on a rice soil enrichment (Erkel et al., 2006) and in the genome of the rice soil isolate Methanocella paludicola (Sakai et al., 2011) various enzymes involved in detoxifying oxygen radicals and oxidative stress were found associated to the genus Methanocella. These traits have been proposed as the reason why these methanogens colonize rice roots (Conrad et al., 2008) and why they are responsible for most of the methane produced in rice soils (Lu and Conrad, 2005). Hence, it seems that methanogens can be very well adapted to oxygen exposure.

38  | Bodelier and Steenbergh

The importance of methanogenesis as a process, and the fact that the enzyme systems involved are rather unique, has evoked a high research interest in methanogenic archaea. A large number of cultivated strains are currently available and numerous studies have used the mcrA gene as a phylogenetic marker to determine the environmental distribution and activity of methanogenic communities, especially in wetland soils. All modern molecular biological as well as biogeochemical techniques have been applied in methanogenesis research, which is nicely reviewed by Narihiro and Sekiguchi (2011). Several studies that accurately determine which methanogens are active in situ have revealed clearly that methane from rice soils globally is produced by hydrogenotrophic methanogens associated to the roots of rice that belong to the genus Methanocella (former RC1 cluster) (Conrad et al., 2006). These comprehensive studies have combined stable isotope labelling of DNA, mRNA and PLFA with cloning, profiling techniques (TRFLP, DGGE) and QPCR of 16S as well as mcrA-based detection (Lu and Conrad, 2005). Compared to rice soils, the methanogenic distribution in natural wetlands harbours much more variation, which is dependent on temperature, pH and nutrient status as recently reviewed (Bridgham, 2013). In northern fens and bogs a particular type of hydrogenotrophic methanogens has been identified and isolated belonging to the order of the Methanomicrobiales within the proposed family of the Methanoregulaceae (Cadillo-Quiroz et al., 2009; Sakai et al., 2012), which used to be referred to as the ‘fen-cluster’. Also in lakes methanogens belonging to the Methanoregulaceae are dominant (Borrel et al., 2011) which makes genomic and metagenomic information of these microbes of high value in understanding methane emission of important global sources and their response to environmental change. Methane consumption Microbial methane oxidation is as yet the only known biological sink of methane on earth. Predominantly it is carried out by aerobic bacteria in soils and sediments by either mitigating internally produced methane (e.g. wetlands, lake sediments) or by consuming methane diffusing from

the air into soils. However, in the last decades it has become clear that methane is also degraded under anoxic conditions (see ‘Anaerobic methane oxidation’, below) in marine sediments but also in freshwater sediments and peat soils by organisms generating oxygen out of denitrification. Both aerobic as well as anaerobic methane oxidation has extensively been studied and for detailed information excellent reviews are available (aerobic: Chistoserdova et al., 2009; Conrad, 2007, 2009; Hanson and Hanson, 1996; Semrau et al., 2010; Stein et al., 2012; anaerobic: Knittel and Boetius, 2009; Shen et al., 2012; Smemo and Yavitt, 2011; Thauer, 2011). In this section we will briefly introduce the reader to what is known about aerobic methane-oxidizing bacteria. Aerobic methane-oxidizing bacteria (MOB) generate energy by oxidizing methane into CO2 with a number of intermediate steps all catalysed by a specific enzyme (Fig. 3.4). The first step is catalysed by the methane-monooxygenase enzyme (MMO) which can either be a membrane-bound particulate form (pMMO), present in almost all methanotrophs, or soluble (sMMO) which is present in a more restricted number of species (Table 3.1). The C1 of methane is also utilized as carbon source and is incorporated at the level of formaldehyde (see Fig. 3.4) by the so-called ribulose-monophosphate or serine pathway, making these microbes true C1-utilisers. For many decades MOB have been classified in three types (types I, II and X) on the basis of physiology and morphology (see Hanson and Hanson, 1996 and Table 3.1); however, in the last decades most of the differentiating ‘barriers’ have been removed by the discovery of MOB with deviating signature lipids (i.e. Methylocystis heyeri; Dedysh et al., 2007), lack of typical internal membrane structures (i.e. Methylocella, Methylocapsa; Dedysh et al., 2005), non-monophyletic groups (i.e. Methylohalobius, Methylothermus; Heyer et al., 2005) and the discovery of MOB outside of the class of the Proteobacteria [i.e. Methylacidiphylum (see Op den Camp et al., 2009) and Methylomirabilis (Ettwig et al., 2010)]. Hence, currently it is more useful to refer to types of MOB on the basis of their phylogenetic grouping (see Table 3.1). Next to the mentioned discoveries MOB have been isolated

Interactions of Methane and Nitrogen Cycle |  39

A) ammonia oxidation nitric oxide NO

e

f

b

nitrous oxide N2O

c

d

a

b

ammonia monooxygenase ammonia hydroxylamine NH3 NH2OH

hydroxylamine oxidoreductase nitrite NO2-

B) methane oxidation methane monooxygenase

methane CH4

methanol dehydrogenase

methanol CH3OH

formaldehyde dehydrogenase

formaldehyde CH2O

formate dehydrogenase

formate HCO2H

carbon dioxide CO2

Figure 3.4  Ammonia and methane oxidation pathways (adapted from Stein and Klotz 2011). (A) Ammonia oxidation, showing classical nitrifying pathway (at the bottom of the panel) and additional nitrifying and denitrifying pathways known for both ammonia and methane oxidizers. The first step of ammonia cometabolism in methanotrophic bacteria is catalysed by methane monooxygenase. Not all aerobic methanotrophs possess the full set of genes shown in (A). (B) Methane oxidation. Methane cometabolism by ammonia oxidizers begins with the oxidation of methane by ammonia monooxygenase. The product of this reaction (methanol) cannot be further oxidized by all ammonia oxidizers. Genes encoding enzymes for the oxidation of methanol, formaldehyde, and formate have for example been detected in the genome of Nitrosococcus oceani (Klotz et al., 2006), however, Nitrosomonas europaea and N. eutropha do not possess these genes (Schmidt et al., 2009).

which are facultative and can also grow on acetate and small organic acids and alcohols (Semrau et al., 2011). Despite the fact that the ability to grow on methane is present now in Proteobacteria, Verrucomicrobia and the NC10-phylum the microbes share the same methane oxidizing pathway. The monooxygenases involved are rather unspecific meaning that they can catalyse many other compounds with a similar structure to methane, such as ammonia (see ‘Methane and ammonia co-metabolism by nitrifiers and aerobic methanotrophs’) but also pollutants such as halogenated hydrocarbons (Semrau et al., 2010) which may interfere with the consumption of methane. Similar to methanogens, the pmoA gene reflects the 16S rRNA based phylogeny and is therefore an excellent biomarker for environmental studies as

recently also proposed for the mxaF gene coding for the methanol-dehydrogenase (Lau, 2013). The multitude of molecular techniques available (Luke and Frenzel, 2011; McDonald et al., 2008; Sharp et al., 2012) in combination with limited substrate usage enabling in situ stable isotope labelling has provided a wealth of information on environmental distribution and control of MOB and methane oxidation. Especially the use of signature lipids and labelling profiles (Bodelier et al., 2012; Bodelier, 2009a) has generated information on the identity of active MOB under natural conditions (Shrestha et al., 2008, 2010). The factors that influence methane oxidation in natural environments are numerous (e.g. methane, oxygen, moisture, pH, nitrogen, temperature, grazing, deforestation, plants, agricultural practice) and it

Methylococcaceae+ Crenotrichideae

Methylococcus(x) Methylocaldum(x) Methylobacter Methylomicrobium Methylomonas Methylosarcina Methylosoma Methylosphaera Crenotrix Clonotrix

+/–

+

+

+/–

Family

Genera

sMMO

pMMO

Obligate methanotrophs

N2 fixation RMP

+/–

+

+

–

Methylohalobius Methylothermus

‘Methylothermaceae’

Proteobacteria (g-proteobacteria)

SER

+/–

+/–

+

+/–

Methylocystis Methylosinus

Methylocystaceae

II

Proteobacteria (α-proteobacteria)

SER/CBB

+

+/–

+/–

+/–

Methylocella Methylocapsa Methyloferula

Beijerinckiaceae

Proteobacteria (α-proteobacteria)

CBB

?

?

+

CBB

+/–

+

+

–

‘Methylacidiphilum’

‘Methylomiabilis’

–

‘Methylacidiphilaceae’

Verrucomicrobia

NC10

NC10

Names in brackets are not yet taxonomically validated. RMP, ribulose monophosphate carbon fixation pathway; CBB, Calvin–Benson–Bassham cycle; SER, serine cycle; +/–, variable between species.

RMP/CBB

I + X

‘former type’

C-fixation

Proteobacteria (g-proteobacteria)

Phylum (class)

Table 3.1  Known described aerobic and anaerobic methanotrophs and some of their characteristics (adapted from Stein et al., 2012)

Interactions of Methane and Nitrogen Cycle |  41

falls out of the scope of this paper to discuss this in detail. Again, excellent reviews are available (Conrad, 2007; Hanson and Hanson, 1996; Ho et al., 2013; Semrau et al., 2010). We will focus on the effects of nitrogen in the section ‘Interactions between methane and nitrogen cycling’. Regarding the ecological distribution of MOB in natural habitats patterns are emerging that allows for conceptualizing life strategies for a number of genera as recently proposed (Ho et al., 2013). Gammaproteobacterial MOB belonging to the family Methylococcaceae (i.e. Methylobacter, Methylomonas, Methylosarcina, Methylomicrobium) are active and abundant in eutrophic habitats of high methane source strength and non-extreme pH (e.g. rice soils, floodplains, lake sediments) while α-proteobacterial MOB (Methylocystaceae and Beijerinckiaceaea) are often persistent as inactive cells, activate after disturbances, or occupy low nutrient or low pH habitats such as peat (Dedysh, 2009; Kip et al., 2010). However, recently also γ-proteobacterial MOB were detected in peatlands globally (Kip et al., 2010, 2011b). Hence, regarding the ‘low-affinity’ MOB, consuming non-atmospheric methane concentrations, a wealth of information is available which is in sharp contrast with the microbes responsible for the consumption of atmospheric methane at concentrations in nanomolar range. The oxidation of atmospheric methane in upland (forest, grassland) soils is carried out by an as yet uncultivated group of microbes capable of utilizing methane below 1.8 ppmv in air. This process, and by which microbes it is actually carried out in environmental settings, is still enigmatic. What controls the process (CH4, O2, moisture, ammonium, agricultural practices, land-use change, plants, organic acids and monoterpenes) has been reviewed and described in detail (Dunfield, 2007; Kolb, 2009; Kolb and Horn, 2012; Maurer et al., 2008; Menyailo et al., 2008; Nazaries et al., 2011; Wieczorek et al., 2011). Remarkable is the sensitivity of the process in soils in which it can take decades to restore the uptake capacity after, for example, land-use change (see Levine et al., 2011; Menyailo et al., 2008; Nazaries et al., 2011). Evidence for environmental atmospheric methane consumption to date has only been linked to a number of pmoA-based phylogenetic

lineages of environmental sequences (Kolb, 2009; Levine et al., 2011; Nazaries et al., 2011) classified as ‘upland soil clusters’ distantly related to either Methylocapsa (called USCα) or to Methylococcaceae (USCγ) or were represented by sequences of ambiguous affiliation which can be methanotrophs, nitrifiers or something unknown (i.e. MR1, RA2; Kolb, 2009). Hence, about the physiology of microbes involved information is lacking. However, some Methylocystis species are able to consume atmospheric methane in culture (Knief and Dunfield, 2005) without energy generation and growth. Methylocystis species SC2 displays a copy of the MMO operon (pMMO2) which is expressed only below 600 ppmv methane and which allows the organism to grow at methane levels between 10–100 ppmv (Baani and Liesack, 2008; Dam et al., 2012). Together with the fact that this pMMO copy was observed to be present in a wide-range of α-proteobacterial MOB (Methylocystis as well as Methylosinus) (Yimga et al., 2003) in combination with the high abundance and persistence of Methylocystis species in hydromorphic soils, upland soils and acidic peat has led to the proposal that these species may be involved in consumption of atmospheric methane in these habitats (Belova et al., 2013; Dedysh, 2009; Knief et al., 2006; Kolb, 2009). However, to date this still has to be demonstrated in the environment. A recent finding, however, yielded possible explanations for survival of atmospheric methanotrophs at these low methane concentrations. Members of the USCα-cluster were able to incorporate acetate into their RNA (Pratscher et al., 2011) pointing to the possibility of an alternative carbon source, a fact that has been demonstrated for some cultured Methylocystis species (Belova et al., 2011, 2013). Interactions between methane and nitrogen cycling After having described the methane cycle and main players involved, the proposed most important interactions between the methane and nitrogen cycle are described in the next sections. The levels of interaction are schematically depicted in Fig. 3.5 where we place methane formation (anoxic/ oxic) and consumption (oxic/anoxic) centrally

42  | Bodelier and Steenbergh

with the possible modes of N-cycle interactions fed into methane cycle. The main modes of N-regulation of the methane cycle are through nitrogen as N-source for growth (see ‘N source/competition’, below), energy (see ‘Energy source’, below) or as inhibiting or toxic agent (see ‘Inhibition–toxicity’, below). Again we will focus on soils and sediments mainly and will not go into details regarding processes or microbes that are dealt with in other chapters in this book. N source/competition Methane formation Nitrogen, molecular as well as combined, has not got much attention when it comes to its role as

nutrient in the activity and distribution of methanogens in soils and sediments. In fact, nitrogen has been the focus of many studies either as being an inhibitor or toxicant of methanogenesis in the form of NO3–, NO2–, N2O and NO (see ‘Inhibition–toxicity’, below) or as added ammonium to for example rice fields (Bodelier, 2011a). To our knowledge, neither the necessity of a nitrogen source nor the possible effects of N-limitation of methanogens on the emission of methane from natural systems have been investigated. However, recent studies indicate nitrogen applications to stimulate methanogens on rice roots (Wu et al., 2009), while in a range of natural wetland soils a strong positive correlation was found between abundance of methanogens and soil nitrogen content (Liu et al., 2011), both pointing to

Figure 3.5  Schematic overview of interactions between methane and nitrogen cycles. The possible effects of nitrogen on methane consumption are shown in the top half of the figure, effects on methane production in the lower half. Aerobic and anaerobic processes are shown on the left- and right-hand side of the figure, respectively.

Interactions of Methane and Nitrogen Cycle |  43

N-limitation of methanogens. Other studies observed higher methane emission after nitrogen fertilizer application in salt marsh sediments (Irvine et al., 2012) and in N-enriched poorly drained forest soils (Aronson et al., 2012). The last study also reported on higher methanogen abundance in the N-enriched soils. Roots of rice, natural wetlands soils as well as forest soils can be expected to be N-limited systems. Other systems, such as peat and oligotrophic sediments, may be habitats where N limitation may be expected but no reports on importance of N as nutrient are available from these habitats. Nevertheless, the general increase in methane emission from wetlands following N fertilization (Liu and Greaver, 2009) as is explained by either inhibition of methane oxidation (see ‘Inhibition–toxicity’, below) or increased methanogenesis due to elevated carbon input by plants may be caused by relief of N limitation of methanogens. Although many but not all methanogens have the ability to fix nitrogen (Leigh, 2000; Whitman, 2006) energetically it would be much more favourable to assimilate combined nitrogen. However, despite the fact that there are studies assessing the control of nitrogen fixation vs. assimilation in methanogens (Browne and Cadillo-Quiroz, 2013; Kessler et al., 2001; Weidenbach et al., 2008) applying genomic and transcriptomic approaches on sequenced methanogens, no information is available on the extent to which methanogens fix or assimilate nitrogen under natural conditions. Transcripts of nifH genes were detected in sewage ( Johnston et al., 2010), which is not a direct proof for actual uptake of N2. The only direct proof for nitrogen fixation comes from marine systems where N2-fixation by ANME (see ‘Anaerobic methane oxidation’, below) methanogens was measured using NanoSIMS mass spectrometry (Dekas et al., 2009). When it comes to competition for nitrogen with other microbes nothing is known to date and the role of nitrogen as an N-source in natural soils and sediments calls for much more intense research efforts considering its potential impact on methane emission. Methane consumption What holds for methanogens is even truer for methanotrophs. Numerous studies have been

executed assessing the inhibiting affect of nitrogenous fertilizers on methanotrophs (Bodelier, 2011a; Bodelier and Laanbroek, 2004) (see ‘Inhibition–toxicity’, below), however, the role of nitrogen as N-source in natural environments has been poorly documented. All known methanotrophs to date can grow with combined nitrogen sources (NH4+, NO3–, some species urea, amino acids) but regarding the extent to which nitrogen availability in the environment stimulates or limits their functioning a clear picture is still missing. As reviewed earlier (Bodelier, 2011a; Bodelier and Laanbroek, 2004; Ho et al., 2013) it is clear that in soils and sediment habitats various MOB subtypes respond differentially to the availability of nitrogen. With respect to nitrogen dependence it may be concluded that in a high methane producing, eutrophic habitats (e.g. rice paddies, floodplains, landfills) γ-proteobacterial MOB belonging to the family of the Methylococcaceae (e.g. Methylomonas, Methylobacter, Methylosarcina) are clearly more stimulated and responsive than the α-proteobacterial MOB belonging to the families Methylocystaceae and Beijerinckiaceaea. With respect the Verrucomicrobia and the NC10 phylum MOB no information is available yet concerning environmental response to nitrogen. It must be stated however, that also in less nutrientrich ecosystems (e.g. lake sediments, arctic peat soils) also Methylococcaceae can dominate the methanotrophic community (Beck et al., 2013; Borrel et al., 2011; He et al., 2012; Tveit, 2013) indicating that Methylococcaceae are not restricted to high-N habitats. Recently, Methylococcaceae have even been demonstrated to be present and isolated from nutrient poor acidic peat systems (Kip et al., 2011a) which have been believed to be colonized exclusively with Methylocystaceaea and Beijerinckiaceaea (Dedysh, 2009). In this respect it is highly interesting to what extent MOB in general are capable of fixing molecular nitrogen in N-limited habitats. It was assumed for a long time that the capacity to fix nitrogen was restricted to Methylocystaceae and the genus Methylococcus (Dedysh et al., 2004; Trotsenko and Murrell, 2008) but at the moment it is clear that at least the genetic ability to fix nitrogen is present in many but not all methanotrophs (see Table 3.1 and Semrau et

44  | Bodelier and Steenbergh

al., 2010). For all Methylocystaceae (Methylocystis, Methylosinus) and Beijerinckiaceaea (Methylocella, Methylocapsa, Methyloferula) it may be a potential strategy to survive in very N-depleted systems like acidic peat bogs (Dedysh, 2009). In addition, recent isolates from Sphagnum peat mosses associated to the Methylococcaceae (Methylomonas and Methylosoma) also were capable of growth on nitrogen-free media (Kip et al., 2011a). With respect to the latter it is important to note that the sensitivity of the nitrogenase enzyme for oxygen will be a determinative factor for nitrogen fixation by MOB in natural habitats. From what is known now is that Methylocystaceae and Beijerinckiaceaea have nitrogenases that are less sensitive than those of Methylococcaceae (Khadem et al., 2010). This may explain why Methylococcaceae are predominant in habitats where the active methane-oxidizing zone is at sub-oxic conditions like in lake sediments (Borrel et al., 2011; He et al., 2012). However, fixing nitrogen in cultures or having the genetic repertoire for nitrogen fixation does not mean that these organisms also perform this pathway in natural habitats. Actually, up to date there is only one single study that demonstrates the actual incorporation of molecular nitrogen into MOB biomass in environmental samples (Buckley et al., 2008). By using stable isotope probing, 15N2 was detected in heavy DNA in Methylocystis species in grassland soil. Other than that no direct proof is available that MOB actually fix molecular nitrogen in environmental samples and what controls this process. For that matter, in case N is limiting as combined mineral source, no direct information is available what the competitive abilities of MOB are with other bacteria and plants. This is a completely open area. At least in the rhizosphere of rice it seems that plant uptake of N can lead to a complete cessation of methane oxidation (Kruger and Frenzel, 2003), purportedly caused by N-limitation. The verrucomicrobial MOB have been shown to be able to fix nitrogen (Khadem et al., 2010; Op den Camp et al., 2009) but the environmental relevance has still to be confirmed. It is not yet known whether the NC10 anaerobic methanotrophs can fix molecular nitrogen and to what extent combined or molecular nitrogen in the environment influences the survival and growth.

Considering the role of nitrogen as a nutrient with respect to high-affinity methane consumption in upland soils we can only speculate since no organisms have been isolated that match the active ones in situ. There are indications for USCγ-related MOB to be more dominant in agriculturally used soils (Levine et al., 2011; Maxfield et al., 2011; Nazaries et al., 2011) but this does not give any clue whether this is due to tolerance or dependence. Hence, also here any general conclusion regarding the importance of N-source would be speculative. Energy source Methane and ammonia cometabolism by nitrifiers and aerobic methanotrophs Cometabolism is the process in which microorganisms transform other substrates in addition to their growth substrates. Methanotrophs are known to be able to cometabolize a range of halogenated hydrocarbons, a feature that makes these microorganisms interesting from a bioremediation point of view (Semrau et al., 2010). In addition, methanotrophs are capable of cometabolizing ammonia, and also the reverse – methane cometabolism by nitrifiers – occurs. This form of competitive inhibition takes place due to the structural similarity of the two substrates and the functional homology between the enzymes catalysing these oxidation steps: methane monooxygenase (MMO) and ammonia monooxygenase (AMO). The homology between MMOs and AMOs may result from phylogenetic relatedness between aerobic methane oxidizers and ammonia oxidizers (Stein et al., 2012). Notwithstanding the occurrence of cometabolism, no methanotroph is known to be able to grow with ammonia as sole source of energy (Nyerges and Stein, 2009); similarly ammonia oxidizers are not able to grow with methane as sole energy source ( Jones and Morita, 1983). In the case of co-metabolism of ammonia by methanotrophs, the resulting oxidized nitrogen compound (i.e. hydroxylamine; see Fig. 3.4) is harmful and therefore must be dealt with quickly in order to prevent damage to the cells. It is possibly due to this that more methanotrophs than ammonia oxidizers are known to possess the machinery necessary

Interactions of Methane and Nitrogen Cycle |  45

to further oxidize the products of co-metabolism by their monooxygenases. In this section we will discuss the role of aerobic methanotrophs in ammonia oxidation and of nitrifiers in the oxidation of methane. Ammonia cometabolism by methanotrophic bacteria The first step of methane oxidation is performed by MMO (see Fig. 3.4). This enzyme occurs in two forms that are phylogenetically unrelated: a soluble MMO (sMMO) and a particulate form (pMMO) (Hanson and Hanson, 1996). To quote Hyman and Wood (1983): ‘Methane mono-oxygenase of MOB is an extraordinarily unspecific enzyme’. It is due to this lack of substrate specificity that both pMMO and sMMO are capable of oxidizing ammonia (Colby et al., 1977; Dalton, 1977). As of yet, it is not known whether the MMOs of MOB belonging to the Verrucomicrobia and the candidate division NC10 are also subject to ammonia cometabolism (Vlaeminck et al., 2011). The relative importance of ammonia cometabolism increases with higher ammonia to methane ratios. However, the overall inhibitory effect of ammonia on the inhibition of methane oxidation depends on additional factors. Firstly, all living organisms depend on nitrogen for their growth. Because not all methanotrophs are capable of fixing molecular nitrogen (Semrau et al., 2010), the availability of ammonium can increase methanotroph growth and thereby methane oxidation rates. An example of the importance of ammonium for methanotroph growth is the presence of four predicted ammonium transporters in the genome of Methylococcus capsulatus strain Bath (Ward et al., 2004). Secondly, the extent of the adverse effect of ammonia cometabolism on methanotrophs depends on how well methanotrophic bacteria are equipped to deal with its toxic products. Hydroxylamine, the direct product of ammonia cometabolism, is highly toxic, and also the nitrogen oxides that are formed during further oxidation of hydroxylamine are detrimental. Interestingly, several distinct pathways have been determined for methanotrophs to resolve the physiological stress from ammonia cometabolism (see Fig. 3.4). The genes involved in these

pathways have been subject to lateral gene transfer, leading to an incongruous distribution of these genes across methanotroph phylogeny (Stein and Klotz, 2011). When ammonia cometabolism leads to the production of nitrite, MMO can be additionally inhibited through a mechanism that is thought to be similar to nitrite inhibition of AMO (Nyerges and Stein, 2009). Notwithstanding the negative effects of cometabolism for methanotrophic bacteria, when methane concentrations are high enough to support growth they are likely outweighed by the positive effect of ammonium as a nutrient (Bodelier and Laanbroek, 2004). Owing to these multiple effects of nitrogen on methane oxidation, the net effect of anthropogenic nitrogen loading on methane oxidation can be either an increase or a decrease of methane consumption (Bodelier, 2011a) (see ‘Inhibition–toxicity’, below). In addition, the short-term effect of nitrogen application can be different from the long-term effect (Bodelier and Laanbroek, 2004). Part of these contrasting responses to increased nitrogen availability may be caused by differences in ammonia cometabolism between methanotroph species. Methanotroph species of which the MMO has a higher specificity for methane and which have efficient pathways for dealing with the toxic products of cometabolism will have an advantage in systems with higher N loading (Nyerges and Stein, 2009). In this way, the nitrogen concentration can be a significant forcing factor on methanotroph community structure. As mentioned above, the phylogeny of nitrification and denitrification pathways are not overlapping with general methanotroph phylogeny. It is therefore not possible to predict on the basis of phylogeny whether certain methanotroph taxa will have an advantage over others under high N loads (Nyerges et al., 2010; Nyerges and Stein, 2009). To complicate matters even further, the function of some of the genes involved in these pathways is extremely diverse. As a result of the functional versatility of these genes, it is not possible to determine whether certain processes take place in an organism solely on the basis of genome information (Stein and Klotz, 2011). Ammonia cometabolism is not only an important process at the level of individual

46  | Bodelier and Steenbergh

methanotrophic bacteria, but can also affect climate through the production of nitrous oxide. Nitrous oxide production due to ammonia cometabolism can occur either through the reduction of nitrite, or the oxidation of hydroxylamine (see Fig. 3.4) (Campbell et al., 2011; Stein and Klotz, 2011). As a result, the application of nitrogen fertilizers to systems with active methanotrophic communities can increase the emission of nitrous oxide to the atmosphere (Acton and Baggs, 2011; Lee et al., 2009; Ward et al., 2004). Methane oxidation by ammonia oxidizers The counter process of ammonia cometabolism is the oxidation of methane by nitrifying organisms. The occurrence of this process has been demonstrated for ammonia oxidizing bacteria (AOB; Hyman and Wood, 1983; Jones and Morita, 1983), however, it is not yet known whether methane cometabolism also takes place in ammonia oxidizing archaea (AOA; Thaumarchaeota; see below). Methane cometabolism by the bacterial AMO leads to the formation of methanol. Studies looking into the capacity of AOB to further oxidize methanol show contrasting results. For example, during incubation of Nitrosomonas europaea in the presence of both ammonium and methane, methanol concentrations in the medium increased (Hyman and Wood, 1983), suggesting at best a very limited ability of this AOB to further oxidize methanol. However, in the same year Jones and Morita (1983) showed that N. europaea and a number of other nitrifiers can completely oxidize methane to CO2, and incorporate carbon originating from methane in their biomass. Voysey and Wood (1987) conclude that methanol oxidation to formaldehyde in N. europaea is not catalysed by methanol dehydrogenase (as is the case in aerobic methanotrophs), but is another example of the unspecificity of AMO. When both formaldehyde and hydroxylamine are produced during nitrification, formaldoxime (H2C=NOH) can be formed, which is a severe inhibitor of nitrification (Voysey and Wood, 1987). In addition, formaldehyde is known to react abiotically with organic matter, which can explain the incorporation of methaneC in N. europaea as found by Jones and Morita (1983).

Comparable to methane-oxidizing bacteria, for AOB the effects of cometabolism may depend on which pathways of methane oxidation are available to the separate AOB species. For example, the genomes of Nitrosomonas eutropha and N. europaea do not contain genes encoding formate dehydrogenase (Schmidt, 2009), while pathways for the oxidation of formaldehyde and for formate dehydrogenation are present in the genome of Nitrosococcus oceani (Klotz et al., 2006). The AMO of Thaumarchaeota is structurally homologous to that of AOB, as is the overall reaction stoichiometry of ammonia oxidation (Martens-Habbena et al., 2009). Because the specific affinity of Thaumarchaeota for ammonia (or perhaps ammonium; Stahl and de la Torre, 2012) can be more than 200 times higher than that of AOB (Martens-Habbena et al., 2009), it is possible that the archaeal AMO also has a higher methane affinity (Stein et al., 2012). To complicate matters, the oxidation product of the archaeal AMO has not yet been determined. This product may also very likely be hydroxylamine (Vajrala et al., 2013), but genes encoding for hydroxylamine oxidoreductase are missing from the available genomic information (Stahl and de la Torre, 2012). If methane is cometabolized by archaeal nitrifiers, it has to be determined what the product of this reaction is. Further research is needed to elucidate the potential role of AOA in methane cometabolism, especially when taking in mind the importance of AOA over AOB in global N-cycling. Importance of ammonia and methane cometabolism in natural systems The process of cometabolism gives methanotrophs the potential to be of importance for nitrogen cycling. The contribution methanotrophs to nitrogen cycling, and of nitrifiers to methane transformations, depends not only on the capacity of individual microbial species to catalyse these processes, but also on the ammonia and methane concentration and the size of the active population of these methanotroph and nitrifier species in natural settings. It has been suggested that nitrifying organisms can be responsible for the oxidation of atmospheric methane concentrations. However, as

Interactions of Methane and Nitrogen Cycle |  47

reviewed by Dunfield (2007), a role of nitrifiers in oxidation of atmospheric methane is unlikely. For example, the size of nitrifier communites in soils is too small to be able to account for the observed methane oxidation. Also, if nitrifier communities are responsible for the oxidation of atmospheric methane, one would expect ammonium amendments to have a direct positive effect which is not the case. Isotope fractionation during nitrous oxide formation has been suggested as a way to determine the contribution of methanotrophs to nitrification. If different enzymes are involved in the formation of nitrous oxide in methanotrophs and nitrifiers, this can lead to a difference in the depletion or enrichment of the heavier 15N or 18 O isotopes in nitrous oxide. Indeed, significant differences were detected in both the ∂15N2O and ∂N218O signature of Methylococcus capsulatus Bath and Nitrosomonas europaea (Sutka et al., 2003). However, in this study hydroxylamine was used as substrate instead of ammonium. When ammonium is the starting substrate, the ∂15N2O of methanotrophs and nitrifiers is similar (Mandernack et al., 2009). Owing to the preferential use of the lighter 16O isotope during methane oxidation, high methanotrophic activity can lead to an enrichment of 18O2. As a result, this higher ∂18O signature can be found in nitrous oxide when it is produced in zones of high methanotrophic activity. It can not, however, be used to discern whether nitrous oxide is the result of ammonia cometabolism by methanotrophs (Mandernack et al., 2009). A possible way to determine the extent of cometabolism is to selectively inhibit the activity of either methanotrophic or nitrifying communities in a system and monitor the resulting change in ammonia and methane oxidation. The major difficulty of this technique is caused by the same feature that is responsible for the occurrence of cometabolism: the homology of AMO and MMO causes most inhibitors to affect both enzymes. Selective inhibition is even further complicated by the broad phylogenetic diversity of both enzymes. In the case of MMO for example, compounds that are inhibitory to pMMO may be ineffective in the inhibition of sMMO (Hyman and Wood, 1983). Owing to the relatively recent discovery of

archaeal ammonia oxidizers, the effect of inhibitory compounds on this taxon is largely unknown. Notwithstanding its limitations, in some situations selective inhibition can give insight into the contribution of cometabolism by determining concentrations and combinations of inhibitors that are effective for a certain system (e.g. Bodelier and Frenzel, 1999; Jiang and Bakken, 1999; Lee et al., 2009). The general trend is that while nitrifiers are not considered important for methane conversions, methanotrophs can contribute substantially to nitrification in systems with large active communities of methanotrophic bacteria. Because the product of nitrification by methanotrophs often is nitrous oxide, increased nitrogen inputs in systems with high methanotrophic activity may lead to increased nitrous oxide emissions. For example, the importance of methanotrophs in nitrous oxide production has been suspected in landfill cover soils as the depth of maximal nitrous oxide concentration coincided with that of methane consumption (Mandernack et al., 2009). A possible reason for the asymmetry of cometabolism between methanotrophs and nitrifiers in natural systems is the low solubility of methane in water. Owing to this low solubility, methane concentrations are in general lower than ammonia concentrations. In combination with the lower specific methane oxidation rate of nitrifiers than of methanotrophs, the contribution of nitrifiers to methane oxidation will be negligible. Possible exceptions to this may be situations where ammonia concentrations are low compared to methane. For example, acid peat bogs can be high methane environments in which ammonia concentrations are low. Future research is needed to determine whether the activity of high-affinity (archaeal) nitrifiers in these systems contributes to methane oxidation. Anaerobic methane oxidation In the traditional view of methane transformations, methane is produced under anoxic conditions by methanogens and consumed under oxic conditions by methanotrophs. For a long time, it was considered impossible for methane to be oxidized in the absence of oxygen. Although from a thermodynamic perspective energy can be

48  | Bodelier and Steenbergh

gained from the complete oxidation of methane to CO2 by, for example, nitrate or sulfate, the activation of methane (the cleaving of the first C–H bond) was thought to be impossible under anoxic conditions (Strous and Jetten, 2004). This general concept of methane transformations changed in the 1970s when it was found that methane and sulfate concentrations showed opposite profiles in marine sediments (Reeburgh and Heggie, 1977). A consortium of archaea and sulfate reducing bacteria was found to be responsible for this first discovery of anaerobic methane oxidation (AMO; Knittel and Boetius, 2009; see also Chapter 10). The anaerobic methanotrophic archaea (ANME) involved in this syntrophic interaction are related to methanogenic archaea and are performing the reversed process of methanogenesis. However, a recent discovery shows that in principle, the ANME are capable of performing anaerobic methane oxidation without syntrophic partner, by reducing sulfate to zero-valent sulfur (Milucka et al., 2012). All known ANMEs belong to three nonmonophyletic groups within the Euryarcheaota (ANME-1 to ANME-3). It is estimated that ANMEs are responsible for more than 75% of marine methane oxidation (Strous and Jetten, 2004 and references therein). As there is strong evidence that some ANME phylotypes have nitrogen-fixing capabilities, they can play a significant role in marine nitrogen cycling (e.g. Miyazaki et al., 2009). However, the extent of the role of ANME in nitrogen fixation in marine systems is still unknown. At the moment, only enrichment cultures and no isolates of ANME are available, making it difficult to address questions regarding the physiology of these organisms. After the discovery of the possibility of anaerobic methane oxidation, the quest for methane oxidizers using other oxidants began. In standard conditions the energy gain from anaerobic methane oxidation is higher when nitrate, or oxidized iron (Fe) or manganese (Mn) is used as electron acceptor instead of sulfate. Evidence for both ironand manganese-mediated anaerobic methane oxidation have been found in diverse systems (e.g. an oil-contaminated aquifer, marine methaneseep sediment, and hydrothermal vent sediment; Amos et al., 2012; Beal et al., 2009; Wankel et al.,

2012, respectively); however, it is still not known whether methane oxidation and metal reduction processes are linked directly or indirectly (for example, through sulfur cycling; Beal et al., 2009). Similar to sulfate-dependent anaerobic methane oxidation, no cultures of the responsible microorganisms are available as of yet. Because of this, many questions regarding the ecology of these metal-dependent anaerobic methane oxidizers remain unanswered, for example because specific biomarkers (e.g. PCR primers) are unavailable. Anaerobic methane oxidation coupled to denitrification was first discovered in an enrichment culture from freshwater sediment (Raghoebarsing et al., 2006). Although at first it was thought that this type of anaerobic methane oxidation was performed by a syntrophic consortium similar to that of sulfate-dependent anaerobic methane oxidation, a single bacterial species that has been named ‘Candidatus Methylomirabilis oxyfera’ is capable of performing this process when nitrite is available (Ettwig et al., 2008, 2009; Hu et al., 2011). In natural settings, nitrite may be provided by ammonia oxidizing or denitrification processes. On basis of its 16S rRNA gene sequence, Ca. M. oxyfera has been placed in a bacterial lineage that was first discovered by a metagenomic study in a cave in the Nullarbor region in Australia (labelled Nullarbor Cave lineage 10; NC10; Holmes et al., 2001). Interestingly, the microbial communities in this cave are assumed to gain their energy from the oxidation of nitrite. Although a pure culture of Ca. M. oxyfera is not yet available, its complete genome has been assembled by metagenomic sequencing of two enrichment cultures (Ettwig et al., 2010). From this genome it became apparent that Ca. M. oxyfera lacks genes necessary for complete denitrification (e.g. nitrous oxide reductase), whereas it does possess genes for aerobic methane oxidation. Because of the latter, nitrite-dependent methane oxidation is thought to be an aerobic process, in which oxygen is first produced intracellularly before it acts as electron acceptor in methane oxidation (an intra-aerobic process; Ettwig et al., 2010). This would also explain Ca. M. oxyfera’s incomplete set of genes for canonical denitrification, as nitric oxide can be used for the production of N2 and O2. The putative enzyme for

Interactions of Methane and Nitrogen Cycle |  49

this nitric oxide dismutation reaction to produce oxygen is qNOR (a nitric oxide reductase that uses reduced quinone as its electron donor). This is in agreement with the finding that compared with conventional denitrification, during nitritedependent anaerobic methane oxidation only a fraction of the N2O is produced (Ettwig et al., 2010). Since its discovery, nitrite-dependent anaerobic methane oxidation has been shown to occur in a range of freshwater systems, as well as in soil and wastewater treatment plants (Bannert et al., 2012; Deutzmann and Schink, 2011; Ettwig et al., 2009; Hu et al., 2011; Luesken et al., 2011; Smemo and Yavitt, 2007). In addition, using molecular biomarkers Ca. M. oxyfera and closely related organisms have been detected in natural systems varying from rice paddy fields, freshwater ditch and lake sediment, and in waste water treatment plants (Deutzmann and Schink, 2011; Ettwig et al., 2009; Kojima et al., 2012; Luesken et al., 2011; Shen et al., 2012). Nitrite-dependent anaerobic methane oxidation has not been detected in marine systems, although 16S rRNA sequences of NC10 bacteria have been detected in several marine systems (Zhu et al., 2010). Nitrite-dependent anaerobic methane oxidation may have been a more important process in earth’s geological past, when methane was abundant in the atmosphere but oxygen concentrations were not yet high enough for aerobic methane oxidation (Oremland, 2010). Several sets of primers are available that are specific for members of the NC10 phylum (based on 16S rRNA sequences; Deutzmann and Schink, 2011; Ettwig et al., 2009; Kojima et al., 2012; Luesken et al., 2011) or for close relatives of Ca. M. oxyfera (based on pmoA sequences; Deutzmann and Schink, 2011; Kojima et al., 2012; Luesken et al., 2011). It is possible that other organisms that are unrelated to the known nitrite-dependent anaerobic methane oxidizers are also capable of this reaction. If these organisms are also capable of intra-aerobic methane oxidation, it is likely that they possess qNOR, the enzyme responsible for oxygen production from nitric oxide. The gene encoding qNOR (norZ) would therefore be a good candidate to use as a biomarker in the search for new nitrite-dependent

anaerobic methane oxidizers in natural systems (Shen et al., 2012). Inhibition/toxicity The most studied interaction between the methane and nitrogen cycle is through nitrogenous fertilizers or atmospheric nitrogen deposition, which can both affect methane formation as well as consumption through inhibitory or toxic modes of action. This topic has been substantially reviewed and put into perspective of methane emission from soils and sediments (Bodelier, 2011a; Bodelier and Laanbroek, 2004). The reader is referred to these papers for detailed information and references therein. We will shortly go into the most important findings. Effects of nitrogenous fertilizers on ecosystem methane emission The balance between production of methane and the consumption determines the net methane emission from ecosystems. The multitude of studies that have been performed on the effect of N on net methane emission or consumption can be brought down to a few recent meta-analyses on this topic. Liu and Greaver (2009) used a set of wetland and upland soils covering North and South America, Europe and Asia and concluded that N-fertilization increased methane emission in wetlands and decreased consumption in upland soils. Aronsen and Heliker (2010) performed a similar analysis on upland soils only and observed inhibited consumption but stimulated atmospheric uptake at N-loads of lower than 100  kg/ha/year. Both studies concluded that effects varied with biomes, fertilizer types and land management history. Both studies excluded agricultural wetlands and recently a meta-analysis was performed on rice paddies (Banger et al., 2012). Also in this study an overall increase of methane emission was observed upon fertilizer addition, explained by increase in plant biomass and subsequent carbon availability for methanogens. Coupling of a biogeochemical model with spatially explicit datasets predicted the effect of atmospheric as well as agricultural N-input on GHG balance in China (Lu and Tian, 2013) over a period from 2000 to 2008. Methane emission was generally lowered by 8%. Also here, large variation

50  | Bodelier and Steenbergh

between biomes was observed. Hence, also when assessing methane emission in a comparable way over multiple ecosystems contradictory results are obtained as we concluded in our previous reviews (Bodelier, 2011a). A common conclusion of all these studies was that many of the contradictions and uncertainties in methane emission data and models call for more mechanistic data on the microbes involved. Major controlling factors on ecosystem scale will be plants, timing, dose and type of fertilizer and of course the traits and responses of the microbes (Bodelier, 2011a). Effects of nitrogenous fertilizers on methanogens Nitrogenous fertilizer effects on methanogens will be mainly through direct toxic effects of NO3–, NO2–, N2O or NO or indirect effects because of competition with denitrifiers (see Conrad, 2007). Acetogenic methanogenesis is inhibited in this way because denitrifiers utilize acetate as electron donor for the reduction of nitrate. As oxidant nitrate is least toxic while NO is most potent (Kluber and Conrad, 1998). Urea and ammonium fertilizers can be nitrified to inhibiting compounds and inhibit methanogenesis in this way indirectly. However, most data on this topic have been derived from rice soil laboratory incubations and not been verified on field scale yet. Nitrate uptake by plants will play an important role in keeping nitrate low. Moreover, the dominant methanogens on rice roots have been shown by genomic and metagenomic studies to possess multiple genes that can protect them against oxidative stress (see ‘N-source/competition’, above) (Sakai et al., 2011). However, it remains to be demonstrated to what extent these genes and enzymes protect the organisms in the environment against nitrate-derived toxicants. Fertilizer effects on the fermenting microbes producing methanogenic substrates may also inhibit methanogenic activities indirectly (Conrad, 2007). Effects on nitrogenous fertilizers on methanotrophs Methane oxidizers can be inhibited in various ways by nitrogen-based fertilizers (Bodelier, 2011a; Bodelier and Laanbroek, 2004). Fundamentally, when looking at methanotrophic

cultures the following mechanisms of inhibition have been demonstrated; (i) ammonium can act as a competitive substrate for the methane monooxygenase; (ii) products of nitrification and denitrification (hydroxylamine, NO2–, NO) can be toxic; (iii) osmotic effects of salts used in fertilizer additions. Owing to the lack of atmospheric methane oxidizing isolates we do not even know whether these mechanisms act on these microbes in the same way as the ‘low affinity’ MOB they were tested on. It will be obvious though that in case of competitive inhibition the ratio of CH4/ammonia will be a regulating factor. Up to date however, none of the proposed mechanisms, except for osmotic effects, has been unequivocally proven or explained in natural environments. Moreover, the ecosystem level effects of fertilizer use on methane consumption are rather contradictory and range from short to long-term inhibition and from no effect to stimulation of methane consumption. As we summarized earlier (Bodelier, 2011a) in high methane environments community composition can play a role in the outcome of fertilizer effects. Methylococcaceae were insensitive or were stimulated (Bodelier et al., 2000; Mohanty et al., 2006) while Methylocystaceaea (i.e. Methylocystis) were inhibited by ammonium as well as nitrate based fertilizer. Inhibition by ammonium may be explained by toxic effects of the nitrite produced out of ammonium. It has been shown that MOB strains vary in their sensitivity against nitrite (Nyerges and Stein, 2009) which can explain differential responses to ammonium application in natural habitats. The effect of nitrate is still far from understood. It may be denitrified into toxic intermediates but nitrate inhibition effects have been observed in full oxic incubations (Mohanty et al., 2006). An alternative explanation may be the cation added along with the nitrate is replacing ammonium from the soil adsorption complex which is then turned into nitrite or acts as competitive inhibitor (Mochizuki et al., 2012). Nitrate is also a very potent inhibitor of atmospheric methane consumption. Two recent studies have demonstrated strong inhibition in forest soils. By adding nitrate together with glucose, atmospheric methane consumption was completely inhibited which was attributed to high ammonium release from the soil due to the

Interactions of Methane and Nitrogen Cycle |  51

treatments (Fender et al., 2012). A similar study, with nitrate addition only, demonstrated an exponential decrease of methane uptake by a forest soil with the dose of nitrate, which was also ascribed to ammonium release from the soil complex (Mochizuki et al., 2012). In general the effects of nitrogen on atmospheric methane consumption have been nicely reviewed (Dunfield, 2007; Kolb, 2009). Since these reviews and our assessment of controlling factors (Bodelier, 2011a) a few nice studies have been published that are recommended to take note of. Atmospheric methane consumption was not affected in an agriculturally used grassland upon heavy fertilization with ammonia (Stiehl-Braun et al., 2011). By radiolabelling with spiked methane and applying a cryogenic slicing technique it was demonstrated that the active methane-oxidizing horizon shifted down in the soil profile protecting the MOB from the fertilizer. A nice review by Kolb and Horn (2012) discusses the role of acidic peatlands as sink of atmospheric methane during periods of drought and water table fluctuations and put forward Methylocystis species as atmospheric methane oxidizers in these globally important systems for methane cycling. Regarding the novel methane oxidizers and possible inhibitory effects and its possible consequences for environmental methane oxidation we can only speculate. Up until now there has been only one environmental study of verrucumicrobial methanotrophs (Sharp et al., 2012) in geothermal soils. However, this study does not give any clue towards effect of nitrogen. Transcriptomic studies with one of the described verrucomicrobial MOBs (‘Ca. Methylacidiphylum fumariolicum infernorum SolV’) showed that nitrosative stress genes [hao (hydroxyalamine oxidoreductase), norB and norC (nitric oxide reductase)] were expressed under all conditions tested but were down-regulated under N2-fixing conditions, when ammonium levels in the cell are low (Khadem et al., 2012a). Genome analyses of the one of the other described strains (‘Ca. Methylacidiphylum infernorum V4’) demonstrated the presence of similar detoxifying genes (Hou et al., 2008). The ‘Ca. Methylomiabilis oxyfera’-like organisms have been detected in freshwater wetlands and sewage treatment and their microbiology and ecology have been summarized recently (Shen et

al., 2012). Fairly recently a novel lineage within this NC10 group has been enriched from minerotrophic peatland (Zhu et al., 2012) fed with nitrate rich groundwater. However, the only clue these studies give until now is the dependence of these microbes on nitrite. Current metagenomic studies When considering metagenomics studies in the true sense as introduced by Handelsman (2004), and as it is mostly used currently, there are no studies available that are specifically designed to investigate interactions between methane and nitrogen cycling in soils and sediments. Studies where community or individual genomes are reconstructed from environmental non-amplified DNA are simply not available which are targeting methane–nitrogen interactions in soils and sediments. The only exception focuses on the methane cycle in Lake Washington, but this is the focus of another chapter in this book (Chapter 10). Next to this, comprehensive metagenomic analyses have been executed on anaerobic methane oxidation in marine seep-sediments which we will also not deal with in this chapter. However, there are a handful of studies which focus on the main players as described in the previous sections but focusing on different questions. We will present these and will have a look at studies that use deep-pyrosequencing on communities involved in methane and nitrogen cycling. Metagenomic studies The earliest metagenome studies within methane cycling go back to 2005 where fosmid libraries from forest soils revealed that putative atmospheric methane oxidisers belonging to USCα were related to the described genus Methylocapsa (Ricke et al., 2005). Studies in the same period using similar approaches detected methanotrophs in aquifers (Erwin et al., 2005), obtained complete pMMO operons from forest soil belonging to Methylocystis species (Dumont et al., 2006) and in combination with stable isotope labelling and whole genome multiple displacement amplification it was demonstrated that Methylocystis species were active in acidic peat (Chen et al., 2008). Regarding methanogens the complete

52  | Bodelier and Steenbergh

reconstruction of a genome belonging to the at time uncultivated ‘rice cluster I’ methanogens was established from an enrichment from rice soil (Erkel et al., 2006), demonstrating the hydrogenotrophic metabolism as later confirmed by isolates. Recently, a number of studies have used metagenomic, transcriptomic and proteomic techniques on arctic and antarctic habitats with climate change and the importance of these systems for methane cycling in mind. In Canadian permafrost soil it was demonstrated using a shot gun sequencing approach that methanogen- and methanotroph-related genes were present in the active as well as the frozen part of the soil. In the active part MOB belonging to the Methylococcaceae could be detected while other MOB were not found without the application of targeted QPCR assays (Yergeau et al., 2010). These authors also found a dominance of archaeal ammonia oxidisers (AOA), cooccurring with methanotrophs in equal numbers. A comprehensive study on the effect of thawing on microbial communities in permafrost soils revealed a rapid response of carbon and nitrogen cycling as assessed by random shotgun libraries (Mackelprang et al., 2011). Methane cycling was predominant and the high abundance of methanogens (up to 95% of the archaeal reads) enabled the reconstruction of a draft genome from a methanogen related to the order Methanocellales which were proposed to be performing nitrogen fixation. Methylocystaceae as well as Methylococcaceae were detected and pmoA genes of both groups increased after thaw. In a combined meta-transcriptomic and meta-genomic study of high-arctic peat soils carbon degradation was studied (Tveit, 2013). On the basis of the genes detected the authors concluded that these systems do not differ fundamentally from other climatic zones in terms of carbon degradation and will rapidly respond to warming and act as GHG source with prolonged growing season and associated deeper active layers. Methanogens of the orders Methanobacteriales, Methanomicrobiales and Methanosarcinales increased with depth in the peat. The non-amplified RNA approach used in this study led to SSU rRNA contigs which contained almost full length 16S rRNA sequences closely related to Methylobacter tundripaludum, a

methane oxidizer which was also isolated from the same habitat and of which the genome was recently sequenced (Svenning et al., 2011). Recently, this methanotroph also has been demonstrated to be the active methanotroph in this soil by using stable isotope probing (Graef et al., 2011) confirming the meta-transcriptomic data. This methanotroph dominated the MOB community which comprised up to 4% of all ribo-tags detected. A metaproteogenomic study (Knief et al., 2012) on the phyllosphere and rhizosphere of rice plants revealed only methanogen and methanotroph species in the rhizosphere which are commonly detected in rice root samples (Conrad, 2007). However, the authors suggested that the strong dominance of methanogenic proteins in the rhizosphere may be due to the biased databases which contain many genomes of methanogens. A multiphasic comprehensive study on methanotrophic communities in ponds contaminated with residuals of oil extraction from oilsands combined stable isotope probing with pyrotag sequencing shotgun metagenomic analyses (Saidi-Mehrabad, 2013). The active dominant methanotrophs in the ponds were related to Methylocaldum and Methylomonas species. However, the metagenomic data detected sequences which were not detected by PCR based methods. These sequences belong to the so called PxmA group. This is a group of sequences that can be detected in some methanotrophs and which are distantly related to known pmoA and amoA sequences. Their function is as yet unknown (Tavormina et al., 2011). The metagenome data did not show any sMMO sequences but did contain nitrosative stress genes (e.g. norB) belonging to methanotrophs. In another aquatic study the water around and in a deep-sea hydrothermal plume was dominated by Methylobacter and Methylomonas-related genes which were also very abundant in the transcriptome data (Lesniewski et al., 2012). In this habitat MOB co-occurred with abundant archaeal AOA. Another marine (oil seep sediment) study which is worthwhile mentioning is the one by Havelsrud et al. (2011) which is the only metagenomic study so far detecting verrucomicrobial methanotrophs as well as NC10-related anaerobic MOB in metagenomic datasets.

Interactions of Methane and Nitrogen Cycle |  53

Although, they are the topic of another chapter in this book (Chapter 10), the Lake Washington studies have to be mentioned here since the most recent study is actually the only metagenomic study designed to assess methane-nitrogen cycling interactions (Beck et al., 2013). In this study sediment from Lake Washington is incubated under oxic and micro-aerobic conditions with or without the addition of nitrate to test the possible denitrification capacity of MOB. The active MOB community was selected by stable isotope probing with 13CH4 and subsequently assessed by combined metagenomic and pyrotag (i.e. single gene-based taxonomic profiling) approach. Methane was metabolized both under aerobic and microaerobic conditions by known MOB, predominantly by Methylobacter species. The latter was distinctly stimulated by nitrate addition suggesting that expressed denitrification enzymes were involved in the assimilation of nitrate rather than in energy generation by denitrification. The latter was assumed because as is known to date, MOB do not contain all enzymes for dissimilatory denitrification. Nitrogen fixation genes were detected associated to all groups present but no effect of nitrate or oxygen was seen giving no conclusive evidence whether the MOB were actively fixing N2. Methylocystaceae did not respond to nitrate and were substantially reduced under micro-aerobic conditions. These well sustained findings give support for some of the observed trends and postulated ‘Life-strategies’ for MOB (Ho et al., 2013), that Methylococcaceae are preferably flourishing at oxic–anoxic interfaces (wetland plant roots, surface layer of sediments, oxycline in lakes and oceans) thereby profiting from nitrogen availability while Methylocystaceae are more restricted to aerobic conditions with incidental access to methane (aerobic soils, peat). In this study also NC10 was detected proving that they can assimilate methane in natural habitats albeit at a much lower level than the classical MOB. No response was observed to oxygen or nitrate. Single gene taxonomic profiling Apart from the metagenomic approaches being used, deep-sequencing approaches of specific PCR amplified target genes have been introduced and applied to microbial groups involved

in methane-nitrogen cycling interactions. With respect to methanogens and wetlands, soils and sediments only one study used 16S rRNA pyrosequencing detecting more acetotrophic methanogens in bogs and more hydrogenotrophic methanogens in fens, which is in contrast with what is generally found in these habitats (Lin et al., 2012). No studies targeting the mcrA gene using pyrotag sequencing are available. For methanotrophs several studies have applied pmoA and 16S rRNA-based pyrotag approaches. A comprehensive comparison of pmoA sequences obtained by classical Sanger sequencing and by pyrotag sequencing of pmoA from rice soils revealed congruency between both methods but a higher resolution at the species level when pyrotag sequencing is applied (Luke and Frenzel, 2011). Despite the lower detection level of the method sequences belonging to the atmospheric methane oxidizing USCα and USCγ could not be detected in this soil. In peat mosses it was shown that in contrast to many other studies on peat Methylococcaceae were abundant (Kip et al., 2011b) while recently MOB were studied in the water column of boreal lakes (Peura et al., 2012) and in arctic lakes and sediments (He et al., 2012). Both studies confirmed the general trend observed for Methylococcaceae, mainly Methylobacter species to be the dominant active methane consuming MOB in freshwater systems. Also novel MOB sequence lineages have been discovered using pmoA-based pyrotag profiling in the Zoige Plateau peatlands indicating possible endemic MOB species (Deng et al., 2013). Also recently, the first environmental study was published on the activity and occurrence of verrucomicrobial MOB in geothermal soils (Sharp et al., 2012). This study not only demonstrated the methanotrophic and autotrophic lifestyle of these MOB in situ by using 13CH4 and 13CO2 but also developed the tools to study verrucomicrobial MOB in other habitats. Regarding ammonia oxidizers as interacting group with methane cycling rapid development has taken place in recent years applying a multitude of molecular techniques investigating the ecology of the archaeal ammonia oxidizers (AOA) and their role in the nitrification process in aquatic and terrestrial habitats. For an overview the reader

54  | Bodelier and Steenbergh

is referred to a recent excellent review (Prosser and Nicol, 2012). Pyrotag sequencing has been successfully applied to this group also, and has led to a rather complete overview of the biogeography of AOA (Gubry-Rangin et al., 2011; Pester et al., 2012) and their ecological coherent distribution according to pH of the environment. Excellent contributions with respect to the actual contribution to nitrification and their autotrophic lifestyle have also been made using pyrotag sequencing (Lu et al., 2012; Xia et al., 2011). However, despite the wealth of information collected on AOA as well as ammonia-oxidizing bacteria (AOB) we have still little information how they interaction with MOB. It is obvious that metagenomic techniques have been rarely applied to wetlands, soils and sediments harbouring microbes involved in methane and nitrogen cycling. However, it is obvious that new pathways and organisms can be discovered and can be studied in congruency with the organisms we already know in an integrated way. The biggest advantage seems to be the possibility to study communities in a non-PCR amplified way offering a view on the community as they are composed in the environment. However, an important conclusion on the basis of the studies available now is that the general picture obtained is largely congruent with what is obtained by conventional PCR-based methods, except for the discovery of new organisms and genes. However, none of the studies so far brought us much closer to better understanding of interactions between methane and nitrogen cycling in wetlands, soils and sediments. Future perspective Ecosystems worldwide are impacted by climate change and by nitrogen input via agriculture and atmospheric deposition and there is a great need to understand how this will impact ecosystems and especially carbon cycling (Gardenas et al., 2011). Microbial communities have been demonstrated to be impacted generally by N-input with as yet unknown consequences (Fierer et al., 2012a; Ramirez et al., 2012) for ecosystem functioning. Understanding the effects of climate change and human impact on greenhouse gasses is

of particular importance since global process and emission models have large uncertainties and call for input of mechanistic microbial understanding (Bridgham, 2013; Spahni et al., 2011). Hence, linking microbial diversity and functioning to ecosystem functioning at larger scales is the challenge for the future. In parallel to macro-ecology a functional biodiversity approach based on traits of microbes and microbial communities is proposed as way forward to link communities to ecosystem function and predict community functioning on disturbance (Allison, 2012). Using metagenomic data to assess community traits is already ongoing (Barberan et al., 2012; Fierer et al., 2012b). The limitations and possibilities of meta-omics techniques and their future applications have been excellently put forward by the leading scientist in this field (Knight et al., 2012), hence, the future perspective we want to give is not on these issues. We agree that techniques are largely in place and are being developed to assess what is out there known as well as unknown. For methane and nitrogen cycling already quite a lot of knowledge is available on environmental distribution and when it comes to methanotrophs we have a good understanding of which organism can actually oxidize methane in the environment due to the multitude of techniques available and the application of stable isotope probing (Ho et al., 2013; McDonald et al., 2008). For methanogens it is similar, with many isolates available and the methods to detect the active ones (Conrad, 2007), which even allow for linking communities to ecosystem emissions as demonstrated in peat using mRNA transcripts (Freitag and Prosser, 2009; Freitag et al., 2010). For the recently detected groups (Verrucomicrobial MOB, NC10 anaerobic MOB) we know much less, but detection techniques are available. Hence, it is time to execute more experimental manipulative experiments designed to investigate interaction between methane and nitrogen cycling microbial communities. This can be done with natural communities derived from wetlands, soils and sediment, enrichments as well as pure and defined mixed cultures. Mechanistic information needs factorial designs and replication. The omics tools are there to execute this kind of experiments. It is of great help that many cultures and genomes of important players are

Interactions of Methane and Nitrogen Cycle |  55

available (http://www.genomesonline.org/cgibin/GOLD/index.cgi), which will facilitate the omics annotation in experiments tremendously. Next to this, mechanistic information can be gathered using these cultures and performing whole genome expression studies under different methane and nitrogen conditions, as are already being executed (Khadem et al., 2011, 2012b; Matsen et al., 2013). Crucial, however, will be in manipulative experiments but also in gradient or manipulative studies in natural environments, that the active part of the community is assessed, which is not done by omics only. Therefore combining omics with stable isotope probing techniques will be the

way to go (Chen and Murrell, 2010). In this respect a combination of rRNA microarray analyses with NanoSIMS label detection in every positive probe on the array is a highly promising tool (Mayali et al., 2012) which allows for high-throughput analyses of actively substrate incorporating microbes. Single-cell genomics also offers great possibilities to assess traits of selected cells from manipulative labelling experiments (Blainey, 2013). However, the most important part of understanding the main issues in methane-nitrogen cycle interactions will be to assess interactions between species (competition; mutualistic; inhibitory). Omics can be very helpful there in formulating hypothesis from natural habitats

Figure 3.6  Schematic representation of important elements in elucidating the role of microbial diversity in ecosystem functioning (CH4-Nitrogen cycling). Crucial element is the application of a functional biodiversity concept to link microbial diversity to ecosystem functioning. This approach will facilitate predictive ecosystem modelling and will be fostered by omics techniques. However, to make this conceptual step the mechanistic insight into what is going on in the ‘Black box’ being the structure and functioning of microbial communities and underlying populations, cells and the way they interact, needs to be elucidated. Application of ecological theory, conceptual experimental design, novel methodology and mathematical modelling will be the key to gain access to the knowledge in the ‘Black box’ (from Bodelier, 2011b).

56  | Bodelier and Steenbergh

through network analyses of correlating genes of groups of microbes among each other and in interaction with the environment (Faust and Raes, 2012). These interactions can be the basis for detailed studies in the laboratory. Apart from the omics approaches we have to be bear in mind ‘conventional’ issues which have to be dealt with, like biases in fundamental methods. Basic DNA and RNA extraction is still the start of every project and biases still make comparability of samples even retrieved from the same matrix problematic (Pan et al., 2010). Next to this, when amplification of genomic DNA is necessary biases are almost unavoidable (Bodelier et al., 2009a). Also scale issues are an unresolved question. Recently is has been shown very elegantly that at oxic/anoxic interfaces changes of the active MOB species can occur at mm scales (Reim et al., 2012). Nevertheless, the progress is rapid and the methodology seems in place to start making progress in mechanistic understanding within the microbial ‘black box’ (see Fig. 3.6) and to fill in gaps on the way to linking microbes to ecosystem functioning. It will be the way to go in order to answer the most pressing issues in methane-nitrogen cycling interactions being: role of N in acetotrophic vs. hydrogentrophic methanogens; N2 fixation by methanogens and methanotrophs; nitrification by MOB or AOA/AOB; competition for nitrogen; effect of denitrification intermediates on methanogens and methanotrophs; mechanisms of N-inhibition or high-affinity as well as lowaffinity MOB, just to name the issues related to nitrogen. There are many more issues specific to the microbes involved awaiting solutions which most certainly will be facilitated by the help of meta-omics techniques. Acknowledgments The preparation of this chapter was supported by funds from the Netherlands Organization for Scientific Research (NWO) (Grant number 855.01.150) which was part of the European Science Foundation EUROCORES Programme EuroEEFG. This publication is publication no. 5594 of the Netherlands Institute of Ecology.

References

Acton, S.D., and Baggs, E.M. (2011). Interactions between N application rate, CH4 oxidation and N2O production in soil. Biogeochemistry 103, 15–26. Allison, S.D. (2012). A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058– 1070. Amos, R.T., Bekins, B.A., Cozzarelli, I.M., Voytek, M.A., Kirshtein, J.D., Jones, E.J.P., and Blowes, D.W. (2012). Evidence for iron-mediated anaerobic methane oxidation in a crude oil-contaminated aquifer. Geobiology 10, 506–517. Angel, R., Matthies, D., and Conrad, R. (2011). Activation of methanogenesis in arid biological soil crusts despite the presence of oxygen. PLoS One 6. Angel, R., Claus, P., and Conrad, R. (2012). Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J.6, 847–862. Aronson, E.L., Vann, D.R., and Helliker, B.R. (2012). Methane flux response to nitrogen amendment in an upland pine forest soil and riparian zone. J. Geophys. Res-Biogeosci. 117. Baani, M., and Liesack, W. (2008). Two isozymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp strain SCZ. Proc. Natl. Acad. Sci. U.S.A. 105, 10203–10208. Banger, K., Tian, H.Q., and Lu, C.Q. (2012). Do nitrogen fertilizers stimulate or inhibit methane emissions from rice fields? Glob. Change Biol. 18, 3259–3267. Bannert, A., Bogen, C., Esperschutz, J., Koubova, A., Buegger, F., Fischer, D., Radl, V., Fuss, R., Chronakova, A., Elhottova, D., et al. (2012). Anaerobic oxidation of methane in grassland soils used for cattle husbandry. Biogeoscience 9, 3891–3899. Barberan, A., Fernandez-Guerra, A., Bohannan, B.J.M., and Casamayor, E.O. (2012). Exploration of community traits as ecological markers in microbial metagenomes. Mol. Ecol. 21, 1909–1917. Bastviken, D., Tranvik, L.J., Downing, J.A., Crill, P.M., and Enrich-Prast, A. (2011). Freshwater methane emissions offset the continental carbon sink. Science 331, 50–50. Beal, E.J., House, C.H., and Orphan, V.J. (2009). Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187. Beck, D.A.C., Kalyuzhnaya, M.G., Malfatti, S., Tringe, S.G., Glavina del Rio, T., Ivanova, N., Lidstrom, M.E., and Chistoserdova, l. (2013). A metagenomic insight into freshwater methane-utilising communities and evidence for cooperation between the Methylococcaceae and the Methylophylaceae. PeerJ 1:e23;DOI 10.7717/ peerj.23. Belova, S.E., Baani, M., Suzina, N.E., Bodelier, P.L.E., Liesack, W., and Dedysh, S.N. (2011). Acetate utilization as a survival strategy of peat-inhabiting Methylocystis spp. Environ. Microbiol. Rep. 3, 36–46. Belova, S.E., Kulichevskaya, I.S., Bodelier, P.L.E., and Dedysh, S.N. (2013). Methylocystis bryophila sp. nov., a facultatively methanotrophic bacterium from acidic

Interactions of Methane and Nitrogen Cycle |  57

Sphagnum peat, and emended description of the genus Methylocystis (ex Whittenbury et al., 1970) Bowman et al., 1993. Int. J. Syst. Evol. Microbiol. 63, 1096–1104. Blainey, P.C. (2013). The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol. Rev. 37, 407–427. Bodelier, P.L.E. (2011a). Interactions between nitrogenous fertilizers and methane cycling in wetland and upland soils. Curr. Opin. Environ. Sust. 3, 379–388. Bodelier, P.L.E. (2011b). Toward understanding, managing, and protecting microbial ecosystems. Front. Microbiol. 2. Bodelier, P.L.E., and Frenzel, P. (1999). Contribution of methanotrophic and nitrifying bacteria to CH4 and NH4+ oxidation in the rhizosphere of rice plants as determined by new methods of discrimination. Appl. Environ. Microbiol. 65, 1826–1833. Bodelier, P.L.E., and Laanbroek, H.J. (2004). Nitrogen as a regulatory factor of methane oxidation in soils and sediments. FEMS Microbiol. Ecol. 47, 265–277. Bodelier, P.L.E., Roslev, P., Henckel, T., and Frenzel, P. (2000). Stimulation by ammonium-based fertilizers of methane oxidation in soil around rice roots. Nature 403, 421–424. Bodelier, P.L.E., Frenzel, P., Drake, H.L., Hurek, T., Kusel, K., Lovell, C., Megonigal, P., Reinhold-Hurek, B., and Sorrell, B. (2006). Ecological aspects of microbes and microbial communities inhabiting the rhizosphere of wetland plants. In Wetlands and Natural Resource Management, J.T.A. Verheven, B. Beltman, R. Bobbink, and D.F. Whigham, eds., pp. 205–238. Bodelier, P.L.E., Bar-Gillisen, Marie-Jose, Hordijk, Kees, Sinninghe Damste´, Jaap, S., Rijpstra, W., Irene. C., Geenevasen, Jan, A.J., Dunfield, Peter, F. (2009a). A reanalysis of phospholipid fatty acids as ecological biomarkers for methanotrophic bacteria. ISME J.3, 606–616. Bodelier, P.L.E., Kamst, M., Meima-Franke, M., Stralis-Pavese, N., and Bodrossy, L. (2009b). Wholecommunity genome amplification (WCGA) leads to compositional bias in methane-oxidizing communities as assessed by pmoA-based microarray analyses and QPCR. Environ. Microbiol. Rep. 1, 434–441. Bodelier, P.L.E., Bar-Gilissen, M.-J., Meima-Franke, M., and Hordijk, K. (2012). Structural and functional response of methane-consuming microbial communities to different flooding regimes in riparian soils. Ecol. Evol. 2, 106–127. Borrel, G., Jezequel, D., Biderre-Petit, C., MorelDesrosiers, N., Morel, J.-P., Peyret, P., Fonty, G., and Lehours, A.-C. (2011). Production and consumption of methane in freshwater lake ecosystems. Res. Microbiol. 162, 832–847. Bridgham, S.C., Cadillo-Quiroz, H., Keller, J.K., and Zhuang, Q. (2013). Methane emission from wetlands: biogeochemcial, microbial, and modelling perspectives from local to global scales. Glob. Change Biol. 19, 1325–1346. Browne, P.D., and Cadillo-Quiroz, H. (2013). Contribution of Transcriptomics to Systems-Level Understanding of Methanogenic Archaea. Archaea Article Number: 586369.

Buckley, D.H., Huangyutitham, V., Hsu, S.F., and Nelson, T.A. (2008). N-15(2)-DNA-stable isotope probing of diazotrophic methanotrophs in soil. Soil. Biol. Biochem. 40, 1272–1283. Cadillo-Quiroz, H., Yavitt, J.B., and Zinder, S.H. (2009). Methanosphaerula palustris gen. nov., sp nov., a hydrogenotrophic methanogen isolated from a minerotrophic fen peatland. Int. J. Syst. Evol. Microbiol. 59, 928–935. Campbell, M.A., Nyerges, G., Kozlowski, J.A., PoretPeterson, A.T., Stein, L.Y., and Klotz, M.G. (2011). Model of the molecular basis for hydroxylamine oxidation and nitrous oxide production in methanotrophic bacteria. FEMS Microbiol. Lett. 322, 82–89. Chen, Y., Dumont, M.G., Neufeld, J.D., Bodrossy, L., Stralis-Pavese, N., McNamara, N.P., Ostle, N., Briones, M.J.I., and Murrell, J.C. (2008). Revealing the uncultivated majority: combining DNA stable-isotope probing, multiple displacement amplification and metagenomic analyses of uncultivated Methylocystis in acidic peatlands. Environ. Microbiol. 10, 2609–2622. Chen, Y., and Murrell, J.C. (2010). When metagenomics meets stable-isotope probing: progress and perspectives. Trends Microbiol. 18, 157–163. Chistoserdova, L., Kalyuzhnaya, M.G., and Lidstrom, M.E. (2009). The expanding world of methylotrophic metabolism. Annu. Rev. Microbiol., pp. 477–499. Colby, J., Stirling, D.I., and Dalton, H. (1977). Soluble methane mono-oxygenase of Methylococcus capsulatus (Bath). Its ability to oxygenate n-alkanes, n-alkenes, ethers, and alicyclic, aromatic and heterocyclic compounds. Biochem. J. 165, 395–402. Conrad, R. (2005). Quantification of methanogenic pathways using stable carbon isotopic signatures: a review and a proposal. Org. Geochem. 36, 739–752. Conrad, R. (2007). Microbial ecology of methanogens and methanotrophs. Advances in Agronomy, 96, 1–63. Conrad, R. (2009). The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep. 1, 285–292. Conrad, R., and Klose, M. (1999). How specific is the inhibition by methyl fluoride of acetoclastic methanogenesis in anoxic rice field soil? FEMS Microbiol. Ecol. 30, 47–56. Conrad, R., Erkel, C., and Liesack, W. (2006). Rice Cluster I methanogens, an important group of Archaea producing greenhouse gas in soil. Curr. Opion. Biotech. 17, 262–267. Conrad, R., Klose, M., Noll, M., Kemnitz, D., and Bodelier, P.L.E. (2008). Soil type links microbial colonization of rice roots to methane emission. Glob. Change Biol. 14, 657–669. Conrad, R., Claus, P., and Casper, P. (2009). Characterization of stable isotope fractionation during methane production in the sediment of a eutrophic lake, Lake Dagow, Germany. Limnol. Oceanogr. 54, 457–471. Conrad, R., Klose, M., Yuan, Q., Lu, Y., and Chidthaisong, A. (2012). Stable carbon isotope fractionation, carbon flux partitioning and priming effects in anoxic soils

58  | Bodelier and Steenbergh

during methanogenic degradation of straw and soil organic matter. Soil Biol. Biochem. 49, 193–199. Daebeler, A., Gansen, M., and Frenzel, P. (2013). Methyl fluoride affects methanogenesis rather than community composition of methanogenic Archaea in a rice field soil. PLoS One 8. Dalton, H. (1977). Ammonia oxidation by methane oxidizing bacterium Methylococcus capsulatus strain Bath. Arch. Microbiol. 114, 273–279. Dam, B., Dam, S., Kube, M., Reinhardt, R., and Liesack, W. (2012). Complete genome sequence of Methylocystis sp Strain SC2, an aerobic methanotroph with highaffinity methane oxidation potential. J. Bacteriol. 194, 6008–6009. Dedysh, S.N. (2009). Exploring methanotroph diversity in acidic northern wetlands: Molecular and cultivationbased studies. Microbiol. 78, 655–669. Dedysh, S.N., Ricke, P., and Liesack, W. (2004). Nif H and Nif D phylogenies: an evolutionary basis for understanding nitrogen fixation capabilities of methanotrophic bacteria. Microbiology-(UK) 150, 1301–1313. Dedysh, S.N., Knief, C., and Dunfield, P.F. (2005). Methylocella species are facultatively methanotrophic. J. Bacteriol. 187, 4665–4670. Dedysh, S.N., Belova, S.E., Bodelier, P.L.E., Smirnova, K.V., Khmelenina, V.N., Chidthaisong, A., Trotsenko, Y.A., Liesack, W., and Dunfield, P.F. (2007). Methylocystis heyeri sp nov., a novel type II methanotrophic bacterium possessing ‘signature’ fatty acids of type I methanotrophs. Int. J. Syst. Evol. Microbiol. 57, 472–479. Dekas, A.E., Poretsky, R.S., and Orphan, V.J. (2009). Deep-sea Archaea fix and share nitrogen in methaneconsuming microbial consortia. Science 326, 422–426. Deng, Y., Cui, X., Luke, C., and Dumont, M.G. (2013). Aerobic methanotrophic diversity in Riganqiao peatlands on the Qinghai-Tibetan plateau. Environ. Microbiol. Rep. 5, 566–574. Denman, K.L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P.M., Dickinson, R.E., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D., Lohmann, U., Ramachandran, S., da Silva Dias, P.L., Wofsy, S.C., and Zhang, X. (2007). Couplings between changes in the climate system and biogeochemistry. In Climate Change 2007: The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, D.Q.S. Solomon, M. Manning, Z. Chen, M., Marquis, K.B. Averyt, M. Tignor, and H.L. Miller, ed. (Cambridge, UK, and New York: Cambridge Unversity Press). Deutzmann, J.S., and Schink, B. (2011). Anaerobic oxidation of methane in sediments of Lake Constance, an oligotrophic freshwater lake. Appl. Environ. Microbiol. 77, 4429–4436. Dumont, M.G., Radajewski, S.M., Miguez, C.B., McDonald, I.R., and Murrell, J.C. (2006). Identification of a complete methane monooxygenase operon from soil by combining stable isotope probing and metagenomic analysis. Environ. Microbiol. 8, 1240–1250.

Dunfield, P.F. (2007). The soil methane sink. In Greenhouse Gas Sinks, D.S. Reay, C.N. Hewitt, K.A. Smith, and J. Grace, eds. (Wallingford, Oxfordshire: CABI), pp. 152–170. Erkel, C., Kube, M., Reinhardt, R., and Liesack, W. (2006). Genome of Rice Cluster I archaea – the key methane producers in the rice rhizosphere. Science 313, 370–372. Erwin, D.P., Erickson, I.K., Delwiche, M.E., Colwell, F.S., Strap, J.L., and Crawford, R.L. (2005). Diversity of oxyenase genes from methane- and ammoniaoxidizing bacteria in the Eastern Snake River Plain aquifer. Appl. Environ. Microbiol. 71, 2016–2025. Ettwig, K.F., Shima, S., van de Pas-Schoonen, K.T., Kahnt, J., Medema, M.H., op den Camp, H.J.M., Jetten, M.S.M., and Strous, M. (2008). Denitrifying bacteria anaerobically oxidize methane in the absence of Archaea. Environ. Microbiol. 10, 3164–3173. Ettwig, K.F., van Alen, T., van de Pas-Schoonen, K.T., Jetten, M.S.M., and Strous, M. (2009). Enrichment and molecular detection of denitrifying methanotrophic bacteria of the NC10 phylum. Appl. Environ. Microbiol. 75, 3656–3662. Ettwig, K.F., Butler, M.K., Le Paslier, D., Pelletier, E., Mangenot, S., Kuypers, M.M.M., Schreiber, F., Dutilh, B.E., Zedelius, J., de Beer, D., et al. (2010). Nitritedriven anaerobic methane oxidation by oxygenic bacteria. Nature 464, 543–548. Faust, K., and Raes, J. (2012). Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550. Fender, A.-C., Pfeiffer, B., Gansert, D., Leuschner, C., Daniel, R., and Jungkunst, H.F. (2012). The inhibiting effect of nitrate fertilisation on methane uptake of a temperate forest soil is influenced by labile carbon. Biol. Fertil. Soils 48, 621–631. Fetzer, S., Bak, F., and Conrad, R. (1993). Sensitivity of methanogenic bacteria from paddy soil to oxygen and dessication. FEMS Microbiol. Ecol. 12, 107–115. Fierer, N., Lauber, C.L., Ramirez, K.S., Zaneveld, J., Bradford, M.A., and Knight, R. (2012a). Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017. Fierer, N., Leff, J.W., Adams, B.J., Nielsen, U.N., Bates, S.T., Lauber, C.L., Owens, S., Gilbert, J.A., Wall, D.H., and Caporaso, J.G. (2012b). Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. PNAS 109, 21390–21395. Freitag, T.E., and Prosser, J.I. (2009). Correlation of methane production and functional gene transcriptional activity in a peat soil. Appl. Environ. Microbiol. 75, 6679–6687. Freitag, T.E., Toet, S., Ineson, P., and Prosser, J.I. (2010). Links between methane flux and transcriptional activities of methanogens and methane oxidizers in a blanket peat bog. FEMS Microbiol. Ecol. 73, 157–165. Gardenas, A.I., Agren, G.I., Bird, J.A., Clarholm, M., Hallin, S., Ineson, P., Katterer, T., Knicker, H., Nilsson, S.I., Nasholm, T., et al. (2011). Knowledge gaps in soil carbon and nitrogen interactions – From molecular to global scale. Soil Biol. Biochem. 43, 702–717.

Interactions of Methane and Nitrogen Cycle |  59

Graef, C., Hestnes, A.G., Svenning, M.M., and Frenzel, P. (2011). The active methanotrophic community in a wetland from the High Arctic. Environ. Microbiol. Rep. 3, 466–472. Grossart, H.-P., Frindte, K., Dziallas, C., Eckert, W., and Tang, K.W. (2011). Microbial methane production in oxygenated water column of an oligotrophic lake. PNAS 108, 19657–19661. Gubry-Rangin, C., Hai, B., Quince, C., Engel, M., Thomson, B.C., James, P., Schloter, M., Griffiths, R.I., Prosser, J.I., and Nicol, G.W. (2011). Niche specialization of terrestrial archaeal ammonia oxidizers. PNAS 108, 21206–21211. Handelsman, J. (2004). Metagenomics: Application of genomics to uncultured microorganisms. Microbiol. Mol. Biol. Rev. 68, 669–685. Hanson, R.S., and Hanson, T.E. (1996). Methanotrophic bacteria. Microbiol. Rev. 60, 439–463. Havelsrud, O.E., Haverkamp, T.H.A., Kristensen, T., Jakobsen, K.S., and Rike, A.G. (2011). A metagenomic study of methanotrophic microorganisms in Coal Oil Point seep sediments. BMC Microbiol. 11, 221. He, R., Wooller, M.J., Pohlman, J.W., Quensen, J., Tiedje, J.M., and Leigh, M.B. (2012). Diversity of active aerobic methanotrophs along depth profiles of arctic and subarctic lake water column and sediments. ISME J. 6, 1937–1948. Heyer, J., Berger, U., Hardt, M., and Dunfield, P.F. (2005). Methylohalobius crimeensis gen. nov., sp. nov., a moderately halophilic, methanotrophic bacterium isolated from hypersaline lakes of Crimea. Int. J. Syst. Evol. Microbiol. 55, 1817–1826. Ho, A., Kerckhof, F.M., Luke, C., Reim, A., Krause, S., Boon, N., and Bodelier, P.L.E. (2013). Conceptualising functional traits and ecolological characteristics of methane-oxidizing bacteria as life strategies. Environ. Microbiol. Rep. 5, 335–345. Holmes, A.J., Tujula, N.A., Holley, M., Contos, A., James, J.M., Rogers, P., and Gillings, M.R. (2001). Phylogenetic structure of unusual aquatic microbial formations in Nullarbor caves, Australia. Environ. Microbiol. 3, 256–264. Hou, S., Makarova, K.S., Saw, J.H.W., Senin, P., Ly, B.V., Zhou, Z., Ren, Y., Wang, J., Galperin, M.Y., Omelchenko, M.V., et al. (2008). Complete genome sequence of the extremely acidophilic methanotroph isolate V4, Methylacidiphilum infernorum, a representative of the bacterial phylum Verrucomicrobia. Biology Direct 3, 26. Hu, S.H., Zeng, R.J., Keller, J., Lant, P.A., and Yuan, Z.G. (2011). Effect of nitrate and nitrite on the selection of microorganisms in the denitrifying anaerobic methane oxidation process. Environ. Microbiol. Rep. 3, 315–319. Hyman, M.R., and Wood, P.M. (1983). Methane oxidation by Nitrosomonas europaea. Biochem. J. 212, 31–37. Irvine, I.C., Vivanco, L., Bentley, P.N., and Martiny, J.B.H. (2012). The effect of nitrogen enrichment on c(1)cycling microorganisms and methane flux in salt marsh sediments. Front. Microbiol. 3, 90. Jiang, Q.Q., and Bakken, L.R. (1999). Nitrous oxide production and methane oxidation by different

ammonia-oxidizing bacteria. Appl. Environ. Microbiol. 65, 2679–2684. Johnston, C., Ufnar, J.A., Griffith, J.F., Gooch, J.A., and Stewart, J.R. (2010). A real-time qPCR assay for the detection of the nif H gene of Methanobrevibacter smithii, a potential indicator of sewage pollution. J. Appl. Microbiol. 109, 1946–1956. Jones, R.D., and Morita, R.Y. (1983). Methane oxidation by Nitrosococcus oceanus and Nitrosomonas europaea. Appl. Environ. Microbiol. 45, 401–410. Joye, S.B. (2012). Microbiology. A piece of the methane puzzle. Nature 491, 538–539. Jugold, A., Althoff, F., Hurkuck, M., Greule, M., Lenhart, K., Lelieveld, J., and Keppler, F. (2012). Non-microbial methane formation in oxic soils. Biogeoscience 9, 5291–5301. Kessler, P.S., Daniel, C., and Leigh, J.A. (2001). Ammonia switch-off of nitrogen fixation in the methanogenic archaeon Methanococcus maripaludis: Mechanistic features and requirement for the novel GLnB homologues, Nif I(1) and Nif I(2). J. Bacteriol. 183, 882–889. Khadem, A.F., Pol, A., Jetten, M.S.M., and den Camp, H. (2010). Nitrogen fixation by the verrucomicrobial methanotroph ‘Methylacidiphilum fumariolicum’ SolV. Microbiology-(UK) 156, 1052–1059. Khadem, A.F., Pol, A., Wieczorek, A., Mohammadi, S.S., Francoijs, K.-J., Stunnenberg, H.G., Jetten, M.S.M., and Op den Camp, H.J.M. (2011). Autotrophic methanotrophy in Verrucomicrobia: Methylacidiphilum fumariolicum SolV uses the Calvin– Benson–Bassham cycle for carbon dioxide fixation. J. Bacteriol. 193, 4438–4446. Khadem, A.F., Pol, A., Wieczorek, A.S., Jetten, M.S.M., and Op den Camp, H.J.M. (2012a). Metabolic regulation of ‘Ca. Methylacidiphilum Fumariolicum’ SolV cells grown under different nitrogen and oxygen limitations. Front. Microbiol. 3, 266–266. Khadem, A.F., van Teeseling, M.C.F., van Niftrik, L., Jetten, M.S.M., Op den Camp, H.J.M., and Pol, A. (2012b). Genomic and physiological analysis of carbon storage in the verrucomicrobial methanotroph ‘Ca. Methylacidiphilum Fumariolicum’ SolV. Front. Microbiol. 3, 345–345. Kip, N., van Winden, J.F., Pan, Y., Bodrossy, L., Reichart, G.-J., Smolders, A.J.P., Jetten, M.S.M., Damste, J.S.S., and Op den Camp, H.J.M. (2010). Global prevalence of methane oxidation by symbiotic bacteria in peatmoss ecosystems. Nat. Geosci. 3, 617–621. Kip, N., Ouyang, W., van Winden, J., Raghoebarsing, A., van Niftrik, L., Pol, A., Pan, Y., Bodrossy, L., van Donselaar, E.G., Reichart, G.-J., et al. (2011a). Detection, isolation, and characterization of acidophilic methanotrophs from Sphagnum mosses. Appl. Environ. Microbiol. 77, 5643–5654. Kip, N., Dutilh, B.E., Pan, Y., Bodrossy, L., Neveling, K., Kwint, M.P., Jetten, M.S.M., and den Camp, H.J.M.O. (2011b). Ultra-deep pyrosequencing of pmoA amplicons confirms the prevalence of Methylomonas and Methylocystis in Sphagnum mosses from a Dutch peat bog. Environ. Microbiol. Rep 3, 667–673.

60  | Bodelier and Steenbergh

Klotz, M.G., Arp, D.J., Chain, P.S.G., El-Sheikh, A.F., Hauser, L.J., Hommes, N.G., Larimer, F.W., Malfatti, S.A., Norton, J.M., Poret-Peterson, A.T., et al. (2006). Complete genome sequence of the marine, chemolithoautotrophic, ammonia-oxidizing bacterium Nitrosococcus oceani ATCC 19707. Appl. Environ. Microbiol. 72, 6299–6315. Kluber, H.D., and Conrad, R. (1998). Inhibitory effects of nitrate, nitrite, NO and N2O on methanogenesis by Methanosarcina barkeri and Methanobacterium bryantii. FEMS Microbiol. Ecol. 25, 331–339. Knief, C., and Dunfield, P.F. (2005). Response and adaptation of different methanotrophic bacteria to low methane mixing ratios. Environ. Microbiol. 7, 1307–1317. Knief, C., Kolb, S., Bodelier, P.L.E., Lipski, A., and Dunfield, P.F. (2006). The active methanotrophic community in hydromorphic soils changes in response to changing methane concentration. Environ. Microbiol. 8, 321–333. Knief, C., Delmotte, N., Chaffron, S., Stark, M., Innerebner, G., Wassmann, R., von Mering, C., and Vorholt, J.A. (2012). Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390. Knight, R., Jansson, J., Field, D., Fierer, N., Desai, N., Fuhrman, J.A., Hugenholtz, P., van der Lelie, D., Meyer, F., Stevens, R., et al. (2012). Unlocking the potential of metagenomics through replicated experimental design. Nat. Biotechnol. 30, 513–520. Knittel, K., and Boetius, A. (2009). Anaerobic oxidation of methane: progress with an unknown process. In Annual Review of Microbiology (Palo Alto: Annual Reviews), pp. 311–334. Kojima, H., Tsutsumi, M., Ishikawa, K., Iwata, T., Mussmann, M., and Fukui, M. (2012). Distribution of putative denitrifying methane oxidizing bacteria in sediment of a freshwater lake, Lake Biwa. Syst. Appl. Microbiol. 35, 233–238. Kolb, S. (2009). The quest for atmospheric methane oxidizers in forest soils. Environ. Microbiol. Rep. 1, 336–346. Kolb, S., and Horn, M.A. (2012). Microbial CH4 and N2O consumption in acidic wetlands. Front. Microbiol. 3, 78. Kruger, M., and Frenzel, P. (2003). Effects of N-fertilisation on CH4 oxidation and production, and consequences for CH4 emissions from microcosms and rice fields. Glob Change Biol 9, 773–784. Lau, E., Fisher, M.C., Steudler, P.A., and Cavanaugh, C.M. (2013). The methanol dehydrogenase gene, mxaF, as a functional and phylogenetic marker for proteobacterial methanotrophs in natural environments. PLoS One 8, e56993. Lee, S.W., Im, J., DiSpirito, A.A., Bodrossy, L., Barcelona, M.J., and Semrau, J.D. (2009). Effect of nutrient and selective inhibitor amendments on methane oxidation, nitrous oxide production, and key gene presence and expression in landfill cover soils: characterization of the role of methanotrophs, nitrifiers, and denitrifiers. Appl. Microbiol. Biotechnol. 85, 389–403.

Leigh, J.A. (2000). Nitrogen fixation in methanogens. Curr. Issues Mol. Biol. 2, 125–131. Lenhart, K., Bunge, M., Ratering, S., Neu, T.R., Schuttmann, I., Greule, M., Kammann, C., Schnell, S., Muller, C., Zorn, H., et al. (2012). Evidence for methane production by saprotrophic fungi. Nat. Commun. 3. Lesniewski, R.A., Jain, S., Anantharaman, K., Schloss, P.D., and Dick, G.J. (2012). The metatranscriptome of a deep-sea hydrothermal plume is dominated by water column methanotrophs and lithotrophs. ISME J. 6, 2257–2268. Levine, U.Y., Teal, T.K., Robertson, G.P., and Schmidt, T.M. (2011). Agriculture’s impact on microbial diversity and associated fluxes of carbon dioxide and methane. ISME J. 5, 1683–1691. Lin, X., Green, S., Tfaily, M.M., Prakash, O., Konstantinidis, K.T., Corbett, J.E., Chanton, J.P., Cooper, W.T., and Kostka, J.E. (2012). Microbial community structure and activity linked to contrasting biogeochemical gradients in bog and fen environments of the glacial Lake Agassiz Peatland. Appl. Environ. Microbiol. 78, 7023–7031. Liu, D.Y., Ding, W.X., Jia, Z.J., and Cai, Z.C. (2011). Relation between methanogenic archaea and methane production potential in selected natural wetland ecosystems across China. Biogeosci. 8, 329–338. Liu, L.L., and Greaver, T.L. (2009). A review of nitrogen enrichment effects on three biogenic GHGs: the CO2 sink may be largely offset by stimulated N2O and CH4 emission. Ecol. Lett. 12, 1103–1117. Liu, Y.C., and Whitman, W.B. (2008). Metabolic, phylogenetic, and ecological diversity of the methanogenic archaea. In Incredible Anaerobes: From Physiology to Genomics to Fuels, J. Wiegel, R.J. Maier, and M.W.W. Adams, eds. (Oxford: Blackwell Publishing), pp. 171–189. Lu, C., and Tian, H. (2013). Net greenhouse gas balance in response to nitrogen enrichment: perspectives from a coupled biogeochemical model. Glob. Change Biol. 19, 571–588. Lu, L., Han, W., Zhang, J., Wu, Y., Wang, B., Lin, X., Zhu, J., Cai, Z., and Jia, Z. (2012). Nitrification of archaeal ammonia oxidizers in acid soils is supported by hydrolysis of urea. ISME J. 6, 1978–1984. Lu, Y.H., and Conrad, R. (2005). In situ stable isotope probing of methanogenic archaea in the rice rhizosphere. Science 309, 1088–1090. Luesken, F.A., van Alen, T.A., van der Biezen, E., Frijters, C., Toonen, G., Kampman, C., Hendrickx, T.L.G., Zeeman, G., Temmink, H., Strous, M., et al. (2011). Diversity and enrichment of nitrite-dependent anaerobic methane oxidizing bacteria from wastewater sludge. Appl. Microbiol. Biotechnol. 92, 845–854. Luke, C., and Frenzel, P. (2011). Potential of pmoA amplicon pyrosequencing for methanotroph diversity studies. Appl. Environ. Microbiol. 77, 6305–6309. McDonald, I.R., Bodrossy, L., Chen, Y., and Murrell, J.C. (2008). Molecular ecology techniques for the study of aerobic methanotrophs. Appl. Environ. Microbiol. 74, 1305–1315.

Interactions of Methane and Nitrogen Cycle |  61

Mackelprang, R., Waldrop, M.P., DeAngelis, K.M., David, M.M., Chavarria, K.L., Blazewicz, S.J., Rubin, E.M., and Jansson, J.K. (2011). Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368-U120. Mandernack, K.W., Mills, C.T., Johnson, C.A., Rahn, T., and Kinney, C. (2009). The δ15N and δ18O values of N2O produced during the co-oxidation of ammonia by methanotrophic bacteria. Chem. Geol. 267, 96–107. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre, J.R., and Stahl, D.A. (2009). Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461, 976U234. Matsen, J.B., Yang, S., Stein, L.Y., Beck, D., and Kalyuzhnaya, M.G. (2013). Global molecular analyses of methane metabolism in methanotrophic alphaproteobacterium, Methylosinus trichosporium OB3b. Part I: transcriptomic study. Front. Microbiol. 4, 40. Maurer, D., Kolb, S., Haumaier, L., and Borken, W. (2008). Inhibition of atmospheric methane oxidation by monoterpenes in Norway spruce and European beech soils. Soil Biol. Biochem. 40, 3014–3020. Maxfield, P.J., Brennand, E.L., Powlson, D.S., and Evershed, R.P. (2011). Impact of land management practices on high-affinity methanotrophic bacterial populations: evidence from long-term sites at Rothamsted. Eur. J. Soil Sci. 62, 56–68. Mayali, X., Weber, P.K., Brodie, E.L., Mabery, S., Hoeprich, P.D., and Pett-Ridge, J. (2012). High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use. ISME J. 6, 1210–1221. Megonigal, J.P., Hines, M.E., and Visscher, P.T. (2004). Anaerobic metabolism: linkages to trace gases and aerobic processes. In Biogeochemistry, W.H. Schlesinger, ed. (Oxford, UK.: Elsevier-Pergamon), pp. 317–424. Menyailo, O.V., Hungate, B.A., Abraham, W.-R., and Conrad, R. (2008). Changing land use reduces soil CH4 uptake by altering biomass and activity but not composition of high-affinity methanotrophs. Glob. Change Biol. 14, 2405–2419. Milucka, J., Ferdelman, T.G., Polerecky, L., Franzke, D., Wegener, G., Schmid, M., Lieberwirth, I., Wagner, M., Widdel, F., and Kuypers, M.M.M. (2012). Zerovalent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546. Miyazaki, J., Higa, R., Toki, T., Ashi, J., Tsunogai, U., Nunoura, T., Imachi, H., and Takai, K. (2009). Molecular characterization of potential nitrogen fixation by anaerobic methane-oxidizing archaea in the methane seep sediments at the number 8 Kumano Knoll in the Kumano Basin, offshore of Japan. Appl. Environ. Microbiol. 75, 7153–7162. Mochizuki, Y., Koba, K., and Yoh, M. (2012). Strong inhibitory effect of nitrate on atmospheric methane oxidation in forest soils. Soil Biol. Biochem. 50, 164–166. Mohanty, S.R., Bodelier, P.L.E., Floris, V., and Conrad, R. (2006). Differential effects of nitrogenous fertilizers

on methane-consuming microbes in rice field and forest soils. Appl. Environ. Microbiol. 72, 1346–1354. Narihiro, T., and Sekiguchi, Y. (2011). Oligonucleotide primers, probes and molecular methods for the environmental monitoring of methanogenic archaea. Microb. Biotechnol. 4, 585–602. Nazaries, L., Tate, K.R., Ross, D.J., Singh, J., Dando, J., Saggar, S., Baggs, E.M., Millard, P., Murrell, J.C., and Singh, B.K. (2011). Response of methanotrophic communities to afforestation and reforestation in New Zealand. ISME J. 5, 1832–1836. Nyerges, G., and Stein, L.Y. (2009). Ammonia cometabolism and product inhibition vary considerably among species of methanotrophic bacteria. FEMS Microbiol. Lett. 297, 131–136. Nyerges, G., Han, S.K., and Stein, L.Y. (2010). Effects of ammonium and nitrite on growth and competitive fitness of cultivated methanotrophic bacteria. Appl. Environ. Microbiol. 76, 5648–5651. Op den Camp, H.J.M., Islam, T., Stott, M.B., Harhangi, H.R., Hynes, A., Schouten, S., Jetten, M.S.M., Birkeland, N.K., Pol, A., and Dunfield, P.F. (2009). Environmental, genomic and taxonomic perspectives on methanotrophic Verrucomicrobia. Environ. Microbiol. Rep. 1, 293–306. Oremland, R.S. (2010). NO connection with methane. Nature 464, 500–501. Pan, Y., Bodrossy, L., Frenzel, P., Hestnes, A.G., Krause, S., Luke, C., Meima-Franke, M., Siljanen, H., Svenning, M.M., and Bodelier, P.L.E. (2010). Impacts of Interand Intralaboratory Variations on the Reproducibility of Microbial Community Analyses. Appl. Environ. Microbiol. 76, 7451–7458. Pangala, S.R., Moore, S., Hornibrook, E.R.C., and Gauci, V. (2013). Trees are major conduits for methane egress from tropical forested wetlands. New Phytol. 197, 524–531. Paul, K., Nonoh, J.O., Mikulski, L., and Brune, A. (2012). ‘Methanoplasmatales,’ Thermoplasmatales-Related Archaea in Termite Guts and Other Environments, Are the Seventh Order of Methanogens. Appl. Environ. Microbiol. 78, 8245–8253. Pester, M., Rattei, T., Flechl, S., Grongroft, A., Richter, A., Overmann, J., Reinhold-Hurek, B., Loy, A., and Wagner, M. (2012). amoA-based consensus phylogeny of ammonia-oxidizing archaea and deep sequencing of amoA genes from soils of four different geographic regions. Environ. Microbiol. 14, 525–539. Peura, S., Eiler, A., Bertilsson, S., Nykanen, H., Tiirola, M., and Jones, R.I. (2012). Distinct and diverse anaerobic bacterial communities in boreal lakes dominated by candidate division OD1. ISME J. 6, 1640–1652. Pratscher, J., Dumont, M.G., and Conrad, R. (2011). Assimilation of acetate by the putative atmospheric methane oxidizers belonging to the USC alpha clade. Environ. Microbiol. 13, 2692–2701. Prosser, J.I., and Nicol, G.W. (2012). Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 20, 523–531. Raghoebarsing, A.A., Pol, A., van de Pas-Schoonen, K.T., Smolders, A.J.P., Ettwig, K.F., Rijpstra, W.I.C.,

62  | Bodelier and Steenbergh

Schouten, S., Damste, J.S.S., Op den Camp, H.J.M., Jetten, M.S.M., et al. (2006). A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921. Ramirez, K.S., Craine, J.M., and Fierer, N. (2012). Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob. Change Biol. 18, 1918–1927. Reeburgh, W.S., and Heggie, D.T. (1977). Microbial methane consumption reactions and their effect on methane distributions in freshwater and marine environments. Limnol. Oceanogr. 22, 1–9. Reim, A., Luke, C., Krause, S., Pratscher, J., and Frenzel, P. (2012). One millimetre makes the difference: highresolution analysis of methane-oxidizing bacteria and their specific activity at the oxic–anoxic interface in a flooded paddy soil. ISME J. 6, 2128–2139. Ricke, P., Kube, M., Nakagawa, S., Erkel, C., Reinhardt, R., and Liesack, W. (2005). First genome data from uncultured upland soil cluster alpha methanotrophs provide further evidence for a close phylogenetic relationship to Methylocapsa acidiphila B2 and for high-affinity methanotrophy involving particulate methane monooxygenase. Appl. Environ. Microbiol. 71, 7472–7482. Saidi-Mehrabad, A., He, Z., Tamas, I., Sharp, C.E., Brady, A.L., Rochman, F., Bodrossy, l., Abell, G.C.J., Penner, T., Dong, X., Sensen, C.W., and Dunfield, P.F. (2013). Methanotrophic bacteria in oilsands tailings ponds of northern Alberta. ISME J. 7, 908–921. Sakai, S., Takaki, Y., Shimamura, S., Sekine, M., Tajima, T., Kosugi, H., Ichikawa, N., Tasumi, E., Hiraki, A.T., Shimizu, A., et al. (2011). Genome sequence of a mesophilic hydrogenotrophic methanogen Methanocella paludicola, the first cultivated representative of the Order methanocellales. PLoS One 6. Sakai, S., Ehara, M., Tseng, I.C., Yamaguchi, T., Braeuer, S.L., Cadillo-Quiroz, H., Zinder, S.H., and Imachi, H. (2012). Methanolinea mesophila sp nov., a hydrogenotrophic methanogen isolated from rice field soil, and proposal of the archaeal family Methanoregulaceae fam. nov within the order Methanomicrobiales. Int. J. Syst. Evol. Microbiol. 62, 1389–1395. Schmidt, I. (2009). Chemoorganoheterotrophic growth of Nitrosomonas europaea and Nitrosomonas eutropha. Curr. Microbiol. 59, 130–138. Semrau, J.D., DiSpirito, A.A., and Yoon, S. (2010). Methanotrophs and copper. FEMS Microbiol. Rev. 34, 496–531. Semrau, J.D., DiSpirito, A.A., and Vuilleumier, S. (2011). Facultative methanotrophy: false leads, true results, and suggestions for future research. FEMS Microbiol. Lett. 323, 1–12. Sharp, C.E., Stott, M.B., and Dunfield, P.F. (2012). Detection of autotrophic verrucomicrobial methanotrophs in a geothermal environment using stable isotope probing. Front. Microbiol. 3, 303. Shen, L.-D., He, Z.-F., Zhu, Q., Chen, D.-Q., Lou, L.-P., Xu, X.-Y., Zheng, P., and Hu, B.-L. (2012). Microbiology, ecology, and application of the nitrite-dependent

anaerobic methane oxidation process. Front. Microbiol. 3, 269. Shrestha, M., Abraham, W.-R., Shrestha, P.M., Noll, M., and Conrad, R. (2008). Activity and composition of methanotrophic bacterial communities in planted rice soil studied by flux measurements, analyses of pmoA gene and stable isotope probing of phospholipid fatty acids. Environ. Microbiol. 10, 400–412. Shrestha, M., Shrestha, P.M., Frenzel, P., and Conrad, R. (2010). Effect of nitrogen fertilization on methane oxidation, abundance, community structure, and gene expression of methanotrophs in the rice rhizosphere. ISME J. 4, 1545–1556. Shrestha, P.M., Kammann, C., Lenhart, K., Dam, B., and Liesack, W. (2012). Linking activity, composition and seasonal dynamics of atmospheric methane oxidizers in a meadow soil. ISME J. 6, 1115–1126. Smemo, K.A., and Yavitt, J.B. (2007). Evidence for anaerobic CH4 oxidation in freshwater peatlands. Geomicrobiol. J. 24, 583–597. Smemo, K.A., and Yavitt, J.B. (2011). Anaerobic oxidation of methane: an underappreciated aspect of methane cycling in peatland ecosystems? Biogeosci. 8, 779–793. Spahni, R., Wania, R., Neef, L., van Weele, M., Pison, I., Bousquet, P., Frankenberg, C., Foster, P.N., Joos, F., Prentice, I.C., et al. (2011). Constraining global methane emissions and uptake by ecosystems. Biogeosci. 8, 1643–1665. Stahl, D.A., and de la Torre, J.R. (2012). Physiology and diversity of ammonia-oxidizing Archaea. In Annual Review of Microbiology, Vol. 66, S. Gottesman, C.S. Harwood, and O. Schneewind, eds., pp. 83–101. Stein, L.Y., and Klotz, M.G. (2011). Nitrifying and denitrifying pathways of methanotrophic bacteria. Biochem. Soc. Trans. 39, 1826–1831. Stein, L.Y., Roy, R., and Dunfield, P.F. (2012). Aerobic methanotrophy and nitrification: processes and connections. In eLS (Chichester: John Wiley & Sons, Ltd.). Steinberg, L.M., and Regan, J.M. (2009). mcrA-targeted real-time quantitative PCR method to examine methanogen communities. Appl. Environ. Microbiol. 75, 4435–4442. Stiehl-Braun, P.A., Hartmann, A.A., Kandeler, E., Buchmann, N., and Niklaus, P.A. (2011). Interactive effects of drought and N fertilization on the spatial distribution of methane assimilation in grassland soils. Glob. Change Biol. 17, 2629–2639. Strous, M., and Jetten, M.S.M. (2004). Anaerobic oxidation of methane and ammonium. Annu. Rev. Microbiol. 58, 99–117. Sutka, R.L., Ostrom, N.E., Ostrom, P.H., Gandhi, H., and Breznak, J.A. (2003). Nitrogen isotopomer site preference of N2O produced by Nitrosomonas europaea and Methylococcus capsulatus Bath. Rap. Commun. Mass. Sp. 17, 738–745. Svenning, M.M., Hestnes, A.G., Wartiainen, I., Stein, L.Y., Klotz, M.G., Kalyuzhnaya, M.G., Spang, A., Bringel, F., Vuilleumier, S., Lajus, A., et al. (2011). Genome Sequence of the Arctic Methanotroph Methylobacter tundripaludum SV96. J. Bacteriol. 193, 6418–6419.

Interactions of Methane and Nitrogen Cycle |  63

Tavormina, P.L., Orphan, V.J., Kalyuzhnaya, M.G., Jetten, M.S.M., and Klotz, M.G. (2011). A novel family of functional operons encoding methane/ ammonia monooxygenase-related proteins in gammaproteobacterial methanotrophs. Environ. Microbiol. Rep. 3, 91–100. Thauer, R.K. (2011). Anaerobic oxidation of methane with sulfate: on the reversibility of the reactions that are catalyzed by enzymes also involved in methanogenesis from CO2. Curr. Opin. Microbiol. 14, 292–299. Thauer, R.K., Kaster, A.K., Seedorf, H., Buckel, W., and Hedderich, R. (2008). Methanogenic archaea: ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6, 579–591. Trotsenko, Y.A., and Murrell, J.C. (2008). Metabolic aspects of aerobic obligate methanotrophy. In Advances in Applied Microbiology, Vol. 63, A.L. Laskin, and S. Sariaslani, eds., pp. 183–229. Tveit, A., Schwacke, R., Svenning, M.M., and Urich, T. (2013). Organic carbon transformations in high-Arctic peat soils: key transformations and microorganisms. ISME J. 7, 299–311. Vajrala, N., Martens-Habbena, W., Sayavedra-Soto, L.A., Schauer, A., Bottomley, P.J., Stahl, D.A., and Arp, D.J. (2013). Hydroxylamine as an intermediate in ammonia oxidation by globally abundant marine archaea. PNAS 110, 1006–1011. Van Amstel, A. (2012). Methane. A review. J. Integr. Environ. Sci. 9, 5–30. Vlaeminck, S.E., Hay, A.G., Maignien, L., and Verstraete, W. (2011). In quest of the nitrogen oxidizing prokaryotes of the early Earth. Environ. Microbiol. 13, 283–295. Voysey, P.A., and Wood, P.M. (1987). Methanol and formaldehyde oxidation by an autotrophic nitrifying bacterium. J. Gen. Microbiol. 133, 283–290. Wang, Z.P., Keppler, F., Greule, M., and Hamilton, J.T.G. (2011). Non-microbial methane emissions from fresh leaves: Effects of physical wounding and anoxia. Atmos. Environ. 45, 4915–4921. Wankel, S.D., Adams, M.M., Johnston, D.T., Hansel, C.M., Joye, S.B., and Girguis, P.R. (2012). Anaerobic methane oxidation in metalliferous hydrothermal sediments: influence on carbon flux and decoupling from sulfate reduction. Environ. Microbiol. 14, 2726–2740. Ward, N., Larsen, O., Sakwa, J., Bruseth, L., Khouri, H., Durkin, A.S., Dimitrov, G., Jiang, L.X., Scanlan, D., Kang, K.H., et al. (2004). Genomic insights into

methanotrophy: The complete genome sequence of Methylococcus capsulatus (Bath). PLoS Biol. 2, 1616–1628. Weidenbach, K., Ehlers, C., Kock, J., Ehrenreich, A., and Schmitz, R.A. (2008). Insights into the NrpR regulon in Methanosarcina mazei Go1. Arch. Microbiol. 190, 319–332. Whitman, W.B., Bowen, T.L., and Boone, D.R. (2006). The methanogenic bacteria. In The Prokaryotes A Handbook on the Biology of Bacteria: Ecophysiology and Biochemistry, M. Dworkin, S. Falkow, E. Rosenberg, K.H. Schleifer, and E. Stackebrandt, eds. (New York: Springer), pp. 165–207. Wieczorek, A.S., Drake, H.L., and Kolb, S. (2011). Organic acids and ethanol inhibit the oxidation of methane by mire methanotrophs. FEMS Microbiol. Ecol. 77, 28–39. Wu, L.Q., Ma, K., Li, Q., Ke, X.B., and Lu, Y.H. (2009). Composition of Archaeal community in a paddy field as affected by rice cultivar and N fertilizer. Microb. Ecol. 58, 819–826. Xia, W.W., Zhang, C.X., Zeng, X.W., Feng, Y.Z., Weng, J.H., Lin, X.G., Zhu, J.G., Xiong, Z.Q., Xu, J., Cai, Z.C., et al. (2011). Autotrophic growth of nitrifying community in an agricultural soil. ISME J. 5, 1226–1236. Yergeau, E., Hogues, H., Whyte, L.G., and Greer, C.W. (2010). The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses. ISME J. 4, 1206–1214. Yimga, M.T., Dunfield, P.F., Ricke, P., Heyer, H., and Liesack, W. (2003). Wide distribution of a novel pmoA-like gene copy among type II methanotrophs, and its expression in Methylocystis strain SC2. Appl. Environ. Microbiol. 69, 5593–5602. Yuan, Y., Conrad, R., and Lu, Y. (2011). Transcriptional response of methanogen mcrA genes to oxygen exposure of rice field soil. Environ. Microbiol. Rep. 3, 320–328. Zhu, B., van Dijk, G., Fritz, C., Smolders, A.J.P., Pol, A., Jetten, M.S.M., and Ettwig, K.F. (2012). Anaerobic Oxidization of Methane in a Minerotrophic Peatland: Enrichment of Nitrite-Dependent Methane-Oxidizing Bacteria. Appl. Environ. Microbiol. 78, 8657–8665. Zhu, G.B., Jetten, M.S.M., Kuschk, P., Ettwig, K.F., and Yin, C.Q. (2010). Potential roles of anaerobic ammonium and methane oxidation in the nitrogen cycle of wetland ecosystems. Appl. Microbiol. Biotechnol. 86, 1043–1055.

Quantification of Functional Microbial Nitrogen Cycle Genes in Environmental Samples

4

David Correa-Galeote, Germán Tortosa and Eulogio J. Bedmar

Abstract The nitrogen (N) cycle comprises a large number of oxidative and reductive reactions that are catalysed by wide variety of enzymes. Genes coding for most of the N-cycle enzymes have been shown to be present in a diverse polyphyletic group of microorganisms, including bacteria, archaea and fungi. Therefore, a 16S rRNA phylogeny-based approach to study those microbial populations is not possible. Because cultivation-dependent methods are selective for certain microorganisms, molecular methods have been developed to study the ecology and to assess abundance and diversity composition of nitrogen cycling microorganisms in environmental samples. DNA extraction followed by PCR amplification of genes that encode key functional enzymes of the N-cycle are used to study which genes and/or phylotypes are functionally important in the environment. Methods for DNA isolation and purification from environmental samples will be addressed whilst considering the main functional gene targets used to study the nitrogen fixation, nitrification and denitrification processes within the nitrogen cycle. The fluorescence-based quantitative real-time polymerase chain reaction (qPCR) has proven useful for quantification of nucleic acids in samples obtained from numerous diverse sources. Here, we describe relevant experimental conditions for utilization of qPCR to quantify the 16S rRNA, amoA and nar/nap, nirK/nirS, c-nor/q-nor and nos genes that encode synthesis of key enzymes involved in redox reactions of the N-cycle.

Introduction Nitrogen (N) is a key element for all forms of life as it makes part of essential compounds such as proteins, nucleic acids, hormones, etc. Despite its abundance in the atmosphere (~ 80%), availability of N in a form suitable for plant and animal consumption is a major constraint to life on our planet. Most of the N in the atmosphere is found in the form of dinitrogen gas (N2), which is inaccessible to eukaryotes and many bacteria. In the biogeochemical N cycle, N utilization begins with the conversion of bio-unavailable N2 gas to bio-available ammonia (NH4+) accomplished by the so called diazotrophs, either in free-living or in symbiotic associations (with plants or other organisms). This process is called biological N2-fixation and has a central role in nitrogen availability, and thus in supporting life on earth. N cycle Biological nitrogen fixation The conversion of dinitrogen into ammonia is catalysed in all diazotrophs by the nitrogenase enzyme complex in an ATP-dependent manner. Nitrogenase is composed by two components that are named according to their main functional subunits, dinitrogenase reductase (Fe protein) and dinitrogenase (Mo–Fe protein). The enzyme is encoded by the nif (H, D, K, Y, B, Q, E, N, X, U, S, V, W, Z) genes, of which the nifDK genes are structural genes that encode the Nif D/K (α and β subunits of the dinitrogenase) and the nifH gene codes for the γ2 homodimeric azoferredoxin subunit Nif H of the nitrogenase complex (Dixon and

66  | Correa-Galeote et al.

Kahn, 2004). The nitrogenase enzyme complex is highly sensitive to oxygen, due to the fact that oxygen reacts with the iron component of the proteins. Nevertheless, the free-living aerobic bacteria have developed several mechanisms to overcome such limitation in soils, for example by maintaining a very low level of oxygen in their cells or by producing extracellular polysaccharides (Dalton and Postgate, 1969; Yates, 1970). In the symbiotic N2-fixing organisms, such as Rhizobium, the plant leghaemoglobin regulates the supply of oxygen to the nodule tissues in order to maintain the low oxygen level within the nodules (Fischer, 1994; Downie, 2005; Oldroyd and Downie, 2008). Ammonia produced by biological N2-fixation is subsequently incorporated into cellular biomass mainly via the glutamine synthetase-glutamate synthase (GS-GOGAT) pathway. Alternatively, glutamate dehydrogenase (GDH) may also be involved in aerobic ammonium assimilation. Nitrification In addition to its incorporation into organic nitrogen compounds, ammonia can be oxidized to nitrate (NO3–) by nitrifying (ammonia oxidizing, AO) Archaea (AOA) and Bacteria (AOB) in a two-step process called nitrification. First, the transmembrane Cu-containing enzyme ammonia monooxygenase catalyses the oxidation of ammonium to hydroxylamine taking two electrons directly from the quinone pool. In the second step, a trimeric multihaem c-type hydroxylamine oxidoreductase (HAO) converts hydroxylamine into nitrite in the periplasm. Then, nitrite is oxidized to nitrate by the membrane-associated iron–sulfur molybdoprotein nitrite oxidoreductase (NXR). The amoA, amoB and amoC genes in AOB comprise the functional amo operon (Sayavedra-Soto et al., 1998; Hommes et al., 1998; Norton et al., 2002) and homologue genes have been found in Archaea (Könneke et al., 2005; Treusch et al., 2005), but these genes have low similarity to their bacterial counterparts. A major recent discovery in relation to the nitrification process has been the role of Archaea in ammonia oxidation (Könneke et al., 2005; Treusch et al., 2005; Wuchter et al., 2006), which has led to study the presence of ammonia oxidizing Archaea and Bacteria in many ecosystems

(Leininger et al., 2006; Nicol et al., 2008; Di et al., 2009; Vissers et al., 2013; Peng et al., 2013). Because of the essential role of Amo in ammonia oxidation, Amo-encoding genes are excellent molecular markers to study the occurrence of Archaea and Bacteria in different environmental conditions, the amoA gene being most often used for this purpose (Rotthauwe et al., 1997; Purkhold et al., 2000; Francis et al., 2005). Denitrification Finally, denitrification transforms nitrate into N2 which returns to the atmosphere, thus closing the N cycle in the biosphere. From the biochemical point of view, denitrification is an alternative form of respiration by which nitrate is reduced sequentially to nitrite (NO2–), nitric oxide (NO), nitrous oxide (N2O), and finally nitrogen gas, when oxygen becomes limiting (Zumft, 1997; van Spanning, 2011; Bueno et al., 2012). The denitrification pathway is performed by more than 60 bacterial genera, and there are evidences that some fungi (Takaya et al., 2002; Prendergast-Miller et al., 2011), Archaea (Treush et al., 2005), and Foraminifera and Gromiida (Risgaard-Petersen et al., 2006; Piña-Ochoa et al., 2010; Koho et al., 2011) are also able to denitrify. During denitrification, reduction of nitrogen oxides is coupled to energy conservation and allows cell to grow under microoxic or anoxic conditions (Zumft, 1997; Simon et al., 2008; van Spanning, 2011). Respiratory nitrate reductases The first reaction of denitrification, this is the conversion of nitrate to nitrite, is catalysed by two biochemically different enzymes, a membranebound nitrate reductase (Nar), or a periplasmic nitrate reductase (Nap). Nar enzymes are integral membrane proteins encoded by genes of a well conserved narGHJI operon (see reviews by van Spanning et al., 2007; Richardson, 2011; Bueno et al., 2012). Whereas narGHI encode the structural subunits, narJ codes for a cognate chaperone required for the proper maturation and membrane insertion of Nar. Escherichia coli has a functional duplicate of the narGHJI operon named narZYWV, which physiologically has a function during stress response rather than anaerobic respiration. In some archaea and bacteria the NarGH

Quantifying Microbial N-cycle Genes in Environmental Samples |  67

subunits are on the outside rather than the inside of the cytoplasmic membrane. This enzyme is supposed to be the evolutionary precursor of the Nar system (Martínez-Espinosa et al., 2007). The Nar enzyme couples quinol oxidation with proton translocation and energy conservation. This respiratory function permits cell growth under oxygen-limiting conditions (Potter et al., 2001; Simon et al., 2008). Nap is widespread in all classes of denitrifying and non-denitrifying proteobacteria (reviewed in Jepson et al., 2007; Richardson et al., 2007; Richardson, 2011; Sánchez et al., 2011; Bueno et al., 2012). Nap is a two-subunit enzyme, NapAB, with the catalytic subunit NapA located in the periplasm. Eight different genes have been identified as components for operons that encode Naps in different organisms. Most bacteria studied thus far have the napABC genes in common. Although Nap is also linked to quinol oxidation, it does not synthesize ATP (Simon et al., 2008). Physiological functions for Nap systems include the disposal of reducing equivalents during aerobic growth on reduced carbon substrates and anaerobic nitrate respiration as a part of bacterial ammonification or denitrification pathways (Potter et al., 2001). Respiratory nitrite reductases Two types of respiratory nitrite reductases (Nir) have been described in denitrifying bacteria, NirS and NirK (for reviews see Rinaldo and Cutruzzola, 2007; van Spanning et al., 2007; Rinaldo et al., 2008; Bueno et al., 2012). They are located in the periplasmic space and catalyse the one-electron reduction of nitrite to gaseous nitric oxide, but neither of the enzymes is electrogenic. The cd1 NirS nitrite reductase is encoded by the well-characterized gene clusters from Pseudomonas aeruginosa (nirSMCFDLGHJEN) and Paracoccus denitrificans (nirXISECFDLGHJN). In the model denitrifier Pseudomonas stutzeri there are two nir clusters (nirSTBMCFDLGH and nirJEN). In contrast to the complex organization of the genes encoding the NirS proteins, the Cu-NirK enzyme is encoded by the nirK gene (Rinaldo and Cutrozzola, 2007; Bueno et al., 2012). Although both Nir enzymes are widespread among denitrifiers, there is no evidence that the same species could have both enzymes

(Zumft, 1997), and they are unrelated in terms of evolution (Heylen et al., 2006). Respiratory nitric oxide reductases The third reaction of denitrification is the reduction of the nitric oxide to the nitrous oxide catalysed by nitric oxide reductase (Nor). Three types of Nor have been characterized, cNor, qNor, and qCuANor (reviewed in Zumft, 2005; de Vries et al., 2007; van Spanning et al., 2005, 2007; van Spanning, 2011; Bueno et al., 2012). The cNor is an integral membrane enzyme encoded by the norCBQD operon. The norC and norB genes encode subunit C and subunit B, respectively, and the norQ and norD genes encode proteins essential for activation of cNor. Some denitrifiers have additional norEF genes, the products of which are involved in maturation and/or stability of Nor activity (Hartsock and Shapleigh, 2010). As a unique case, the Nor of Roseobacter denitrificans is similar to cNor, but differs in that it contains copper (Matsuda et al., 2002). The qNor is a single-subunit enzyme that uses quinol or menaquinol as electron donors and is encoded by the qnorB gene. The enzyme has been found not only in denitrifying archaea and soil bacteria (Büsch et al., 2002), but also in pathogenic microorganisms that do not denitrify (Hendriks et al., 2000; de Vries et al., 2003). qNor is proposed to be the ancestor of all nitric oxide reductases and cytochrome oxidases belonging to the superfamily of haem-copper oxidases (de Vries and Schröder, 2002). The qCuANor has been described in the Gram-positive bacterium Bacillus azotoformans (Suharti et al., 2004), but genes encoding qCuANor have not been identified as yet. Respiratory nitrous oxide reductase Periplasmic nitrous oxide reductase enzymes (Nos) catalyse the two-electron reduction of nitrous oxide to N2, the final step in denitrification (reviewed in van Spanning et al., 2005; 2007; Zumft and Kroneck, 2007; van Spanning, 2011; Bueno et al., 2012; Pauleta et al., 2013). The enzyme is encoded by the nos gene clusters, often composed by the nosRZDFYLX genes in members of the Alphaprotebacteria, as nosX is missing in Beta-, Gamma- and Epsiloproteobacteria and

68  | Correa-Galeote et al.

Gram-positive species (Pauleta et al., 2013). The nosZ gene encodes the active subunit of the enzyme, NosZ, and the nosRX genes encode proteins with roles in transcription regulation, activation, and Cu assemblage of Nos. The nosDFYL genes encode proteins that are apparently required for copper assemblage into Nos, although their specific role still remains unknown (Zumft and Kronek, 2007). A comprehensive phylogenetic analysis of the nosZ gene coding nitrous oxide reductase enzymes in genomes retrieved from public databases revealed two distinct clades of nosZ, with one unaccounted for in previous studies investigating N2O-reducing communities. The two clades differ in their signal peptides, indicating differences in the translocation pathway of the enzyme across the membrane. Sequencing of environmental clones of the previously undetected nosZ lineage in various environments showed that it is widespread and diverse and at least as abundant as the other ( Jones et al., 2013). Analysis of N cycle-related functional communities Up to 109 cells/g of bacterial cells have been found in agricultural soils (Sharma et al., 2007; Babic et al., 2008; Dandie et al., 2008; Henry et al., 2008), and cultivation-dependent and -independent methods have shown that functional communities in environmental samples represent up to 5–10% of the total soil bacterial community (Tiedje, 1988; Henry et al., 2006; Herrmann et al., 2008; Jones et al., 2013). Because only a fraction of the bacterial community is cultivable, the culture-dependent isolation techniques are of limited value. Molecular methods that do not require bacterial cultivation have been developed to assess diversity composition of functional communities in environmental samples from soils, waters and sediments. Because the ability to carry out a N cycle-related process (e.g. nitrogen fixation, denitrification, etc.) cannot be associated with any specific taxonomic group, a 16S rRNA phylogenybased approach to study those populations is not possible. Therefore, existing techniques to study the ecology of microbial communities are based on the use of functional genes, or their transcripts,

as molecular markers, and DNA extraction followed by PCR amplification of target gene is currently the most common way to quantify the functional populations in environmental samples (Philippot, 2006; Philippot and Hallin, 2006; Hallin et al., 2007; Smith and Osborn, 2009; Teixeira and Yergeau, 2012). Restriction fragment length polymorphism (RFLP), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE) and terminal restriction fragment length polymorphism (T-RFLP) can been used to analyse the predominant populations related with the N cycle in environmental samples. Alternatively, after amplification of a functional gene, cloning and sequencing of the PCR amplicons offer detailed information, though usually more expensive and time-consuming. Comprehensive reviews on molecular methods to assess diversity of N-cycle related communities have been published (Philippot and Hallin, 2006; Hallin et al., 2007; Sharma et al., 2007). Because fingerprinting techniques are based on the number of peaks, or bands, and on their relative intensity, they give estimates of both richness and evenness, but estimation of the total number of functional populations is neglected. To solve this problem, competitive PCR (cPCR) and quantitative realtime PCR (qPCR) can be used. According to MIQUE guidelines (Bustin et al., 2009), the initials RT-qPCR should be used to refer to reverse transcription-qPCR. cPCR and qPCR Simultaneous amplification of the target DNA and a control DNA with a known concentration, the so-called competitor, is the theoretical base for competitive cPCR assays. Because target and control DNAs compete for the primers during amplifications, and the method assumes that both DNAs have the same amplification efficiency, the mass ratio between the two amplicons can be used to determine the initial amount of target DNA. This ratio is estimated by agarose gel analysis of PCRs of multiple dilutions of the competitor with the target DNA. qPCR does not require a gel migration step, is highly reproducible and sensitive, and is less

Quantifying Microbial N-cycle Genes in Environmental Samples |  69

expensive, laborious and time-consuming than cPCR. Reviews dealing with the advantages and limitations of qPCR have been published (Zhang and Fang, 2006; Smith and Osborn, 2009; Brankatschk et al., 2012; Gadkar and Fillion, 2013). RNA extracted directly from environmental samples can be retro-transcribed to cDNA and used for qPCR, thus providing evidence of gene expression. Using this methodology, N-cycle related genes have been quantified (Nogales et al., 2002; Henderson et al., 2010; Dandie et al., 2011). As for all PCR-based techniques, qPCRs are subjected to well-known biases introduced by e.g. DNA extraction procedures, primer selection, and PCR conditions. qPCR is currently the main technique used for quantification of housekeeping and functional genes after DNA extraction from environmental samples. Both cPCR and qPCR technologies rely on the direct proportionality between the intensity of the fluorescent signal measured during the exponential phase of the PCR reaction and the initial amount of target DNA. The copy number of initial target DNA is thereby determined by comparison to a standard curve constructed using target DNA of a known concentration (see ‘Calculation of the copy number of standard DNA’). Although many functional genes so far studied are present in single copies within bacterial genomes, others such as nifH, amoA, narG and nosZ and the housekeeping 16S rRNA gene can be present in more than one copy (McTavish et al., 1993; Flanagan et al., 1999; Klappenbach et al., 2001; Canfield et al., 2005; Jones et al., 2011). Quantification of functional genes using qPCR DNA extraction and purification, PCR’s inhibition tests, target gene quantification by qPCR, and analysis of the results are conventional steps for quantification of functional bacterial populations in environmental samples. Here, the term environmental sample refers mainly to soil and sediment samples. DNA extraction Pioneer methods for soil DNA isolation from environmental samples used mechanical and

enzymatic lysis, followed by cleaning of the crude extract and DNA precipitation (Muyzer et al., 1993; Zhou et al., 1996), a methodology that was further improved for simultaneous recovery of DNA and RNA (Hurt et al., 2001; Juniper et al., 2001). It was during evaluation of the effectiveness of nine DNA extraction procedures based on the original data of Miller et al. (1999), that a method was developed that provided good quantity and quality DNA (Martin-Laurent et al., 2001). Because this method gave good reproducibility in isolating DNA from different environmental samples, including soils from a range of origins and different physical and chemical characteristics (Philippot et al., 2002; Chèneby et al., 2003; López-Gutiérrez et al., 2004; Martin-Laurent et al., 2004; Čuhel et al., 2010), it was proposed by the Agence Française pour la Normalization (AFNOR) to the International Organization for Standardization (ISO) in 2006. The ISO standard 11063: soil quality method to directly extract DNA from soil samples When the need for an international standard for soil DNA extraction was recognized, twelve different soils were used by fifteen independent European laboratories, including France, Finland, Germany, Italy, Spain and Sweden, to evaluate both the reproducibility of the standardized method and the abundance and genetic structure of the total bacterial community. The method was unanimously approved by the ISO as an international standard method (ISO standard 11063) (Petrić et al., 2011a). Later on, the method has also been used to extract DNA from river sediments and agricultural soils (Bru et al., 2011), polychlorinated biphenyls-contaminated sites (Petrić et al., 2011b), technosols (Hafeez et al., 2012) and constructed wetlands (Correa-Galeote et al., 2013). ISO standards give information on the identity and quality of each compound in the protocol, thus providing a complete quality control for users and avoiding the risks associated to commercial kits. The procedure for the ISO standard 11063 is as follows: 1

Sieve samples to 25 atom%)

Cloning and sequencing possible

Long incubation times; potential for crossfeeding

Targeted PCR possible

High label additions; potential for stimulation

Can target 16S/high phylogenetic resolution

High levels of incorporation needed (>25 atom%)

Cloning and sequencing possible

High label additions; potential for stimulation

Targeted PCR possible

RNA is labile; challenging sample prep

RNA-SIP

Short incubation times possible Protein-SIP Low levels of incorporation needed (>2 atom%) Limited phylogenetic resolution Short incubation times possible

Metagenomic data required

Limited potential for cross-feeding

High cell densities required

Limited potential for stimulation Chip-SIP

Low levels of incorporation needed (>0.5 atom%)

Sequence information required

Short incubation times possible

Can only identify known targets

Limited potential for cross-feeding

Requires chip design

Limited potential for stimulation

Technically challenging/requires NanoSIMS

A comparison of 15N stable isotope techniques is given in Table 5.1. Nitrogen fixation The first targeted field application of the 15N DNA SIP approach were experiments designed to identify diazotrophic bacteria in soil environments (Buckley et al., 2007a, 2008). In many ways, nitrogen fixation provides a best case scenario for 15N-SIP studies. Gradient separation of DNA requires considerable isotopic labelling. This usually means that labelled substrates are added at concentrations that are substantially above what is typically found in the environment. These conditions may result in artefacts because they can stimulate the community at large, encourage cross feeding, or lead to the activation of genetic pathways that would have been silent in the presence of ambient substrate concentrations. 15N-dinitrogen gas, however, can be added at saturating concentrations by replacing the atmosphere in the head space of sealed bottle experiments. Atmospheric gas is already mostly composed of nitrogen gas, which is uncreative under physiologically relevant conditions and will only be assimilated

by diazotrophs. The availability of inorganic, alternative N sources usually inhibits diazotrophy, reducing the potential effect of isotopic dilution when multiple substrates are utilized simultaneously by naturally occurring populations (Buckley et al., 2007a). Historically, nitrogen fixers have been identified in the environment either by cultivation or via the detection of their nitrogenase gene sequences. Nitrogenase genes are particularly useful, because a large proportion of bacteria in the environment cannot be cultivated, and because rRNA gene sequence diversity does not sufficiently reflect the phylogenetic distribution of physiological traits across microbial taxa. Even very closely related microorganisms can sometimes markedly differ in their genomic make-up. For example, when 61 different strains of E. coli were compared, only 6% of the detected genes (993 of 15,741) were shared among all the analysed genomes (Lukjancenko et al., 2010). This indicates that even within a single species up to 90% of genomic content can be variable. Functional genes provide evidence for the presence of microorganisms that actually contain the necessary machinery for requisite geochemical processes. The gene most frequently used as a

94  | Wawrik

proxy for the presence of nitrogen fixers is nifH, which codes for nitrogenase reductase, the electron carrying subunit that couples ATP hydrolysis to interprotein electron transfer during nitrogen fixation (Zehr et al., 2003). The nifH database is one of the largest and most comprehensive DNA sequence databases available, containing sequences from a wide range of cultivated microorganisms and environmental samples. Only about half of the 49 different subgroups of known diazotrophs contain cultivated members (Zehr et al., 2003), however, and it remains unclear which of these organisms (cultivated or not) are relevant to nitrogen fixation in environments such as soil, where a large diversity of nifH sequences can often be observed. The presence of genes typically cannot unequivocally demonstrate their utilization. SIP, however, allows the identification of rRNA gene sequences while simultaneously demonstrating isotope uptake. Buckley et al. (2007a) therefore incubated soils in the presence of 80% 14 N2 or 15N2 gas. 15N2 uptake into soil was assessed via isotope ratio mass spectrometry. After 28 days of incubation, a mean isotopic enrichment of 12.6‰ was observed, indicating significant nitrogen fixation. DNA was extracted and fractionated. Fractions were then assayed via quantitative PCR. Secondary fractionation (Addison et al., 2010) on a targeted subset was used to demonstrate that DNA in these fractions was predominantly 15N containing. Cloning and sequencing of 16S PCR products indicated that Rhodoplanes as well as unclassified betaproteo- and actinobacteria were involved in N fixation. Their in silico predicted terminal restriction fragments (TRFs) were compared with TRF length polymorphism data obtained for all fractions, confirming isotopic labelling of requisite fragments. Overall these data provide evidence for N fixation by three bacterial groups for which this activity had not previously been observed. In many cases it is not possible to cultivate the organisms that are implicated by SIP experiments. This makes it difficult to ascertain what the underlying drivers of observed uptake/activities might be. Even if cultivation dependent verification can be conducted, it removes microbes from their environmental context. In a related study, diazotrophic activity by soil bacteria was therefore

investigated by experimental manipulation (Buckley et al., 2008). The underlying hypothesis was that diazotrophy in soils depends on a heterotrophic process and that such linkages can be experimentally verified via the addition of requisite carbon sources. Soil was incubated in the presence of methane, cellobiose, and p-hydroxybenzoate under a headspace containing 15N2. Significant isotopic enrichment of soil, as determined by isotope ratio mass spectrometry, was only observed in methane treatments, indicating that the presence of methane stimulated diazotrophy. CsCl gradient fractionation coupled to quantitative PCR demonstrated a significant shift of DNA towards higher density, which is consistent with isotopic uptake into DNA. An analysis of nifH gene sequences from targeted fractions implicated the activity of Type II methanotroph closely related to Methylocystis. The work represents the first demonstration of methanotrophic activity by diazotrophs in soils. It also demonstrates how experimental manipulation can be coupled to SIP studies in order to understand the pattern of in situ microbial activity. Biodegradation and bioremediation Powerful energetic compounds such as 2,4,6-trinitrotoluene (TNT) and hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) play important roles in both civilian mining operations and military munitions. The production, storage, and application of these chemicals have resulted in the pollution of soils, groundwater, and marine environments. Understanding their fate and transport in the environment can help in the design and application of appropriate remediation strategies. TNT, for example, is an irritant that can cause liver and kidney damage. It has been observed that TNT can serve as a nitrogen source for some bacteria under both aerobic and anaerobic conditions (Esteve-Núñez et al., 2001), but aspects of ring mineralization are less clear. Gallagher et al. (2010) therefore incubated sediment from Norfolk harbour in the presence of 15N- and 13C-labelled TNT. DNA was extracted as part of a time course study over a five week period and analysed via TRFLP. The study indicating the

Stable Isotope Probing the N Cycle |  95

emergence of a 60-bp peak in ‘heavy’ fractions of CsCl that were located in ethidium bromide stained gradients via the addition of 15N-labelled archaeal DNA. A 16S rRNA gene clone library was screened for the 60-bp peak and sequencing of this clone suggested the presence of Lysobacter sp. DNA. RDX is a non-volatile carcinogenic compound that sorbs poorly to soils and therefore easily forms contamination plumes that can threaten local drinking water supplies (Talmage et al., 1999). The compound degrades both aerobically and anaerobically (Paquet et al., 2011) and a diversity of RDX degraders has been isolated. As with TNT, RDX is frequently utilized as an N source by these strains. The ecology of RDX degradation in the environment, however, remains poorly understood. Roh et al. (2009) therefore applied 15 N SIP to microcosm studies. Soil with a history of RDX exposure was incubated while providing cheese whey or yeast extract as carbons sources. The breakdown of RDX was monitored and DNA was extracted from samples after 25 days of incubation. Two fractions, corresponding to locations in the gradient where light and heavy DNA were expected, were recovered from the CsCl density gradients for the RDX treatments and the controls respectively. DNA from these fractions was characterized by terminal restriction fragment length polymorphism (TRFLP) analysis and by cloning of 16S rRNA gene sequences, indicating the presence of alpha-proteobacteria, actinobacteria, and gammaproteobacteria in the ‘heavy’ DNA from the 15N-RDX treatment. Amplification of the functional gene xplA indicated the presence of bacteria similar to Rhodococcus sp. DN22, a known RDX degrader. A limitation of this study was the use of 15N ring-labelled RDX. RDX contains three N atoms that are not part of the aromatic ring and a maximum labelling efficiency of 50 atom could therefore be achieved. This highlights an important drawback frequently encountered when designing SIP experiments. 15N-labelled substrates are frequently not commercially available and may require custom synthesis. In most cases such synthesis is cost prohibitive, limiting the potential usefulness of 15N-based SIP technology. For substrates that do not contain nitrogen as part of their structure, biostimulation (i.e. the

addition of nitrogenous nutrients) to enhance microbial growth and 15N-label uptake into cellular protein have been used to study microbial hydrocarbon degradation. For example, anaerobic methane oxidation (AMO) has been studied via 15 N-labelling techniques (Krüger et al., 2008). AMO is a key process in marine sediments that regulates the global flux of methane (Raghoebarsing et al., 2006). It is thought that AMO is mediated by a consortium of uncultivated sulfate reducing bacteria and archaea (ANME) (Niemann et al., 2006), but pure cultures have not yet been obtained making physiological investigations challenging. 13C-based SIP studies have not been fruitful, providing inconclusive results due to high CO2 vs. CH4 turnover rates and extensive cross-feeding. The use of 15NH4 provides an opportunity, however, to conduct comparisons between growth conditions, i.e. with and without a respective substrate such as methane. Krüger et al. (2008) hence incubated methane containing marine sediment samples in the presence of 15NH4. Isotopic composition of proteins was determined in treatments containing methane as a growth substrate. After three weeks of incubation, significant 15N labelling of proteins was observed when compared with control treatments, demonstrating methane dependent 15NH4 uptake. Combining these data with cell counts and DGGE profiles allowed for the calculations of AOM microbial doubling times. Biostimulation is also frequently applied in remediation strategies, but in cold environments its effect can be variable between sites. SIP has therefore been applied in an attempt to understand the role nutrients play in the context of arctic soil microbial communities (Bell et al., 2011). In this study, 14N- and 15N-monoammonium phosphate were amended to hydrocarbon contaminated and uncontaminated arctic soils. DNA was extracted after ca. one month of incubation, fractionated, and isotopic labelling of bacterial DNA was demonstrated by quantitative PCR. Fractions across the gradient were then amplified using 16S rRNA and alkane monooxygenase (alkB) primers. Sequence datasets were then generated via bar-coded parallel 454-sequencing of products. Several bacterial families within the Proteo- and Actinobacteria were implicated in petroleum degradation in this

96  | Wawrik

manner. This study is noteworthy because it is the first application of next generation sequencing technology to 15N-SIP and gradient fractionation. The ability to sequence many samples in parallel has emerged in recent years. This has dramatically reduced the cost of generating sequence data (Hamady et al., 2008). Future studies should therefore take advantage of the tremendously increased resolution that next generation sequencing offers. Sequencing all fractions across gradients will make it possible to identify even numerically minor populations that are involved in individual N-uptake processes. Inorganic N cycling Though hydrocarbons are clearly important from an anthropocentric perspective, inorganic

nitrogen transformations play the central role in global N cycling. Dissolved inorganic nitrogen (DIN) can enter cellular metabolism as ammonium and nitrogen oxides (Fig. 5.2), both of which have been the subject of SIP based studies. In marine systems, we have investigated inorganic N uptake for phytoplankton (Wawrik et al., 2009) and bacterioplankton (Wawrik et al., 2012) populations via 15N SIP. Features of the marine N cycle have a large impact on global carbon and nitrogen balances by influencing the rate of C export and sequestration (Falkowski et al., 1998). Historically, it has been perceived that inorganic forms of nitrogen are utilized by phytoplankton, while organic N is primarily a source of nutrients for bacteria. It is now clear that this distinction is too simplistic and that there can be considerable plasticity within a given

Figure 5.2  Shown are the potential pathways by which N can enter cells as well as the fate of cellular N during microbial growth. Nitrate, nitrite, and urea are converted to ammonium via the activities of nitrate reductase, nitrite reductase, and urease respectively. N from dinitrogen gas can enter the cellular ammonium pool via nitrogen fixation (nitrogenase). The resulting ammonium (and ammonium taken up directly) is converted to glutamine via the glutamine synthase (GS). Nitrogen is transferred to 2-oxoglutarate to regenerate glutamate (GOGAT). These amino acids plus amino acids taken up directly are then either incorporated into cellular protein, or N flows into remaining cellular constituents via central metabolism and biosynthesis pathways. The ultimate fate of cellular N includes proteins, aminoglycans, and nucleic. The mode of N entry from the diverse group of poorly described dissolved organic N (DON) sources in the environment is thought to include both extracellular deamination, generating ammonium, and direct uptake and integration into cellular metabolism. However, the role of these processes is poorly understood. In protozoan cells, predation may introduce N from PON into microbial biomass. Grey boxes indicate potential targets for SIP studies.

Stable Isotope Probing the N Cycle |  97

organism’s range of N substrates. It is not clear, however, how phytoplankton and heterotrophic bacteria interact as part of the N cycle when these two groups are forced to compete for limited inorganic and organic N resources. Coastal Gulf of Mexico seawater was therefore incubated in the presence of a range of 15N compounds. DNA was extracted after 24 hours of incubation and fractionated. Quantitative PCR was then utilized to demonstrate that diatoms and Synechococcus incorporated N from 15N-nitrate, 15N-ammonium, and 15N-urea. Synechococcus was also observed to utilize 15N-glutamate as well as a mixture of 15N amino acids (Wawrik et al., 2009). Bacterioplankton uptake of 15N-nitrate was further investigated by TRFLP analysis of all fractions (Wawrik et al., 2012). Molecular evidence suggests that nitrate utilizing bacteria are both abundant and active in marine systems (Allen et al., 2001, 2002), but direct evidence of incorporation of N from nitrate into bacterial biomass had not been reported. Several TRFs with density shifts were identified and comparison with clone library data implicated Thalassobacter and Alteromonadales in nitrate utilization. These data were combined with uptake rate measurements and functional microarray analysis of whole community gene expression. The approach provided a comprehensive perspective of N cycling within complex communities and placed SIP results into a broader context. In general, these studies illustrate how SIP experiments can benefit from ancillary measurement such as the determination of uptake rate measurements or transcriptomic analysis via RNA hybridized to functional gene microarrays or the sequencing of a meta-transcriptomes. Another aspect of inorganic N cycling that has been studied via SIP techniques is ammonium oxidation, which is the rate limiting step in nitrification. The process has received some attention in recent years, because evidence suggests that archaea can carry ammonium oxidase genes (amoA) and that these genes are widespread in the environment (Francis et al., 2005; Jia and Conrad, 2009). Traditionally, ammonium oxidation was thought to be a solely bacterial trait. It was therefore unclear which conditions might favour bacterial or archaeal ammonium oxidation in the environment. SIP-based experiments have

been conducted in lake sediments (Whitby et al., 2001), estuaries (Freitag et al., 2006), and soils ( Jia and Conrad 2009) to address this question. However, ammonium oxidation does not lead to N uptake directly and cannot be studied via 15N incorporation. Instead, autotrophic activity and incorporation of 13C (usually from carbonates that are added to solution) is typically measured. For example, lake sediment was incubated in the presence of 13C-K2CO3 and high levels of ammonium (Whitby et al., 2001). The isotopically labelled CsCl fraction was characterized via culture independent techniques, observing an enrichment of Nitrosomonas sp. Estuarine sediments have been similarly characterized with respect to the ammonium oxidizing betaproteobacteria (Freitag et al., 2006). 16S rRNA gene cloning and denaturing gradient gel electrophoresis (DGGE) analysis suggested that ammonium oxidizers with Nitrospira-like sequences dominated in marine and that Nitrosomonas-like sequences dominated in the freshwater influenced sites respectively. A pattern in amoA gene distribution was not observed, but when sediments were incubated in the presence of 13C-bicarbonate and DNA was analysed by stable isotope probing, AOB Nitrosomonas and the nitrite oxidizer Nitrospira were observed to incorporate label. Tourna et al. (2010) compared DNA and RNA based denaturing gradient gel electrophoresis (DGGE) with SIP in the presence of 13CO2 in cultures of Nitrospira with differing ammonium tolerances after incubations. Comparison of DGGE profiles indicated that DNA-SIP-DGGE was the most direct and sensitive among the tested approaches, providing evidence for uptake before DNA- or RNA-DGGE revealed changes and before NO2/NO3 accumulated. The technique was then applied to demonstrate the dynamics activity of different strains in co-culture. Soil systems have been interrogated specifically to determine whether bacterial or archeal ammonia oxidizers are most relevant ( Jia and Conrad, 2009). amoA genes were monitored via qPCR in soil amended with ammonium bicarbonate or acetylene to simulate and inhibit ammonium oxidation respectively. DNA from treatments with 13C-bicarbonate was analysed via gradient fractionation. qPCR data indicated stimulation of

98  | Wawrik

bacterial, but not archaeal communities, and SIP experiments indicated that only bacterial DNA experienced a shift towards higher density. This demonstrated that bacteria rather than archaea dominate ammonium oxidation in agricultural soil. Similar results were obtained in a subsequent study of agricultural soil (Pratscher et al., 2011). Soil was amended with different concentrations of (NH4)2SO4 and incubated in sealed bottles containing a headspace with 5% 13CO2. Both DNA and RNA SIP were performed. DNA SIP clearly demonstrated isotopic labelling of bacterial but not archaeal amoA DNA. RNA SIP suggested archaeal labelling in amendments with lower concentration of N. These data highlight the utility of RNA SIP. Working with RNA presents special challenges due to its susceptibility to nuclease degradation, but offers potentially greater sensitivity and the ability to apply shorter incubation intervals. RNA molecules turn over rapidly. They are therefore likely to reflect the isotopic composition of the C and N substrate pools long before DNA labelling can be observed. Future SIP would therefore benefit from the application of an RNA based approach, especially when slowly growing populations in the environment are studied. Organic nitrogen Organic nitrogen (ON) represents a key juncture of the global N and C cycles (i.e. it is part of both cycles). For example, in marine systems dissolved organic nitrogen (DON) accounts for 13–83% of the dissolved N pool, going from estuarine to open ocean surface environments (Berman and Bronk, 2003). In these environments, its turnover can be significant, and it is now clear DON likely has a major impact on microbial N and C flow. Precedents of algal, bacterial, archaeal, and protistan utilization of DON have been reported, and in a few cases DON has been implicated in influencing microbial species composition (Berman and Bronk, 2003), yet it remains unclear what the larger role of DON utilization is in the environment. ON therefore presents an important future frontier for stable isotope studies. Little SIP-based work has thus far been conducted on. A notable exception are two studies of soil communities. In the first of these studies, 15N-SIP was

used to assess bacterial plant organic matter decay (España et al., 2011a). 15N-labelled maize and soybean leaf residue were incubated in soils for 15 days. DGGE characterization of 16S rRNA genes from CsCl column fractions and subsequent sequencing of bands were used to indicate which bacterial taxa were actively involved in the decomposition of plant residue. It was concluded that organic matter quality was an important determinant of community composition. In a companion study, fungal 15N uptake was investigated (España et al., 2011b). 18 rRNA genes were characterized across CsCl gradients, indicating that maize promoted slow growing fungal decomposers such as Penicillium spp. and Aspergillus spp., while soybean addition benefited faster growing species including Fusarium spp. and Mortierella spp. The quality of organic matter content was concluded to have a significant impact on fungal soil communities. The way forward Considering the body of literature, it is clear that 15 N DNA SIP is a powerful molecular approach that has unrealized potential for providing detailed, community level resolution of N transformations in the environment. Despite its utility, however, there are significant experimental and methodological limitations of DNA SIP. Foremost, incubations with saturating amounts of N substrates are likely to introduce artefacts via community stimulation as well as potential cross feeding among populations that are involved in different aspects of the N cycle. N transformations are part of a complex and interconnected network and it is likely that label sometimes moves quickly from one reservoir to another. This can confound any attempt to resolve individual uptake signatures. Uptake, transformation, and regeneration rate measurements are therefore clearly useful in this context, but such measurements are labour and technology intensive themselves. Progress has also been hindered by the way microbial taxa are traditionally detected in gradients. Quantitative PCR (qPCR) is highly specific and provides clear and direct evidence of isotopic labelling for a particular DNA sequence or clade of sequences that are captured using degenerate primers. Microbial populations are, however, often composed of

Stable Isotope Probing the N Cycle |  99

thousands of different species. The identities of active organisms and requisite DNA target sequences may not be known. Even if a good DNA sequence database exists, it is unfeasible to design qPCR assays for even a fraction of organisms present in the environment. The technique is therefore only useful for highly targeted hypotheses testing about specific organisms. Other, community-based profiling techniques, such as DGGE, TGGE, TRFLP are frequently used, but without further cloning and sequencing analyses these do not inherently provide taxonomic information. A third important limitation is the time intensive CsCl-based fractionation process. Typically, significant amounts of DNA are needed and large quantities of nucleic acid may not always be available from low biomass, slow growing environments. Given these limitations, future development of SIP technologies should therefore focus on (a) using smaller or even tracer level additions of isotope in order to reduce cross feeding and stimulation artefacts, (b) conducting experiments on shorter time scales by focusing on immediate uptake rather than lengthy bottle incubations, (c) providing greater community level resolutions and phylogenetic data, and (d) on high-throughput approaches that allow the processing of many samples. Important advances are being made in all of these areas and several novel technologies are coming online that are likely to have significant impact on the way SIP studies are being conducted. High community level resolution is, for example, facilitated by the precipitously dropping cost of sequencing. Metagenomic, and metatransciptomic technologies provide unprecedented community genomic information and there is potential for combining these approaches with SIP experiments. In this context, RNA may be particularly appealing target due its rapid turnover rates, hence significantly reducing necessary incubation times. Similarly protein bases SIP may be a way to circumvent traditional substrate addition imitations, as mass spectrometry (MS) approaches mature. Novel ways of combining flow cytometry and MS with microarray technology and isotope detection are also emerging. Some of the recent developments in this context are discussed next.

Metagenomics The term ‘metagenomics’ refers to a range of molecular cloning and sequencing technologies that have collectively been applied to study the genetic properties of complex microbial communities. Historically, such studies were initially conducted by cloning environmental DNA into vectors that can accept large inserts (e.g. BACs or cosmids). In such studies, large numbers of clones were screened for specific molecular signatures such as 16S rRNA genes or functional genes that encode desired biochemical transformations via hybridization or PCR (e.g. Suzuki et al., 2004). Large insert cloning is less common now, in part because it is time and labour intensive, and in large part because it produces only a limited number of potential targets, even when large libraries are screened. Subsequent studies therefore focused on generating small insert shotgun sequencing libraries (e.g. 2-kb pieces that were bidirectionally sequenced). In low complexity environment this has sometimes allowed assembly of partial or even near complete genomes via computational assembly of sequence reads. The principal drawback of this technique has been the staggering cost associated with Sanger sequencing, leaving metagenomic analysis outside of the realm of possibility for most labs. Most environments also contain a very large diversity of microorganism and shotgun sequencing typically only allows assembly of larger genomic fragments for the most abundant species. Abundant cells may not always be involved in the activities that are being studied and rare community members with large impacts on the behaviour of an ecosystem are therefore under-sampled. Having said this, the emergence of affordable and extremely high throughput next generation sequencing technologies has changed this equation. Metagenomic analysis is now much more commonplace, easier to conduct, and offers vast sequencing depth for a fraction of the cost of Sanger reads. This, of course, has created its own challenges including data handling, storage, and analysis, but an increasing number of computational tools are available to the community, making the bio-informatics component of a metagenomic sequencing project less daunting. The declining cost of next generation sequencing technologies has also opened up the

100  | Wawrik

possibility of combining metagenomic sequencing with stable isotope approaches. Even with the tremendous depth that can be achieved with next generation sequencing technologies, it can be expected that most microbial taxa in the environment are still under-sampled by a typical metagenomic survey. It has therefore been argued that gradient fractionation may help to separate DNA from active community member, significantly increasing their proportional contribution in DNA processed for sequencing (Schloss and Handelsman, 2003; Chen and Murrell, 2010). This approach would increase the rate at which desired taxa are observed in libraries. It would also combine the ability to establish a direct link between microbial taxa and their activity with partial reconstruction of their genomic content. Even partial genome information can sometimes provide valuable insights into the physiology of uncultivated organisms. In combination, such data could provide insights about the roles which individual organisms play in ecosystems (Fig. 5.3).

Several studies have utilized a combination of C-SIP and metagenomics. Examples include studies on soils, marine systems, and sediments. A review of these studies is beyond the scope of this chapter, but they are discussed elsewhere (Chen and Murrell, 2010). No studies to date have combined 15N-SIP with metagenomic techniques. In part, this is due to greater technical challenges. The smaller degree of separation that is observed in 15N gradients makes it more difficult to isolate enriched fractions. Given that many studies on 15N-SIP can now be found in the literature, however, it is clear that such experiments are not unfeasible and it can be expected that requisite studies are forthcoming. The approach offers clear advantages. Collection of isotopically enriched DNA can increase the frequency at which N cycling genes are encountered in metagenome libraries. Hence, a deeper understanding of functional gene diversity, as it relates to actual uptake and catabolic activities, can be gleaned, all the while reducing the sequencing depth that is required

13

Figure 5.3  Flow diagram for combining stable isotope probing with metagenomic analysis. After incubation with a 13C- or 15N-isotope labelled substrate, nucleic acids are separated via CsCl density gradient centrifugation. Fractions that contain labelled DNA are identified via one of several methods, e.g. ethidium bromide staining, isotope ratio mass spectrometry, total DNA quantification, qPCR, or fingerprinting analysis. DNA from ‘heavy’ fractions is purified and sequenced using a high-throughput next generation approach and sequence data are assembled to generate contiguous partial genomic fragments. These contigs can then be analysed for the presence of potential protein coding genes, which are assigned function via automated annotation. The resulting metabolic reconstruction is enriched in sequences that originated from active microbial community members. Anchor genes such as 16S rRNA genes can provide phylogenetic context for the observed contigs and can be used as targets for confirmation qPCR experiments.

Stable Isotope Probing the N Cycle |  101

for a good survey of in situ gene diversity. Using metagenomic sequencing, requisite N cycling genes can also be placed into the context of their genomic environment, providing a metabolic scaffold from which geochemical activities can be more clearly understood. This, of course, has pitfalls. Much of the annotation found in reference databases was generated by automated annotation itself, creating significant uncertainties about the accuracy of automated calls, especially when DNA from uncultivated or poorly characterized taxa is encountered. Further complicating this issue is the fact that assemblies of metagenomes can sometimes produce chimeric contigs. The environment often contains populations of very closely related, but genetically non-identical cells, rather than clonal species ( John et al., 2006; Kalyuzhnaya et al., 2008). Sequencing technologies are, however, evolving very rapidly, producing ever greater read length, throughput, and base call accuracy. The current limitations of computational analysis should therefore only be minor obstacles in the future development of SIP technologies. Combining 15N-SIP, metagenomic sequencing and flow cytometry Traditional uptake experiments are conducted on whole communities and in many cases it is not known beforehand who among the large diversity of microbes in an environment is actively contributing to uptake. SIP studies must therefore frequently rely on untargeted broad surveys of microbial populations across fractions from density gradients. This approach is tedious, resource intensive, and slow, resulting in relatively low sample throughput. A technology that offers the potential to increase the efficiency of SIP is flowcytometric (FCM) cell sorting. FCM is used to sort cells based on their optical properties such as size, pigment content, or staining with fluorescent probes (e.g. oligonucleotides or antibodies). Field samples can thus be separated into subpopulations with differing properties and the approach has been use in marine systems for about two decades to assess phytoplankton community composition. FCM has been combined with

isotope ratio mass spectrometry (IRMS). For example, Pel et al. (2004) have combined FCM with IRMS to sort individual phototrophic populations. Lake samples were incubated with 13CO2 and several classes of phytoplankton cells were sorted. IRMS allows estimation of populations specific growth rates for cyanobacteria, green algae, and diatoms. In another study, bacterial and fungal cells were sorted via fluorescence-activated cell sorting (FACS) and their δ13C was determined using IRMS coupled to a custom build wire spooling microcombustion apparatus (Eek et al., 2007). The study demonstrated sorting of specific populations and was able to detect as little as 25 ng of carbon which corresponds to approximately 104 eukaryotic or 107 bacterial cells. This approach makes it possible to use fluorescent oligonucleotide probes or antibodies to sort specific subpopulations after incubation in the presence of 13C or 15N containing substrates and then test if utilization of the isotopically labelled substrate occurred. FCM has also been used to sort phytoplankton from Chesapeake Bay water in order to measure dissolved organic nitrogen and dissolve organic nitrogen uptake (Bradley et al., 2010). 15 N-labelled NH4+, NO3–, dually labelled urea 13 ( C and 15N), and 15N-containing amino acids were added to bay water, and phytoplankton cells were sorted by flow cytometry after incubations. Comparison with traditional uptake rate measurements suggested that sorting may help to reduce artefacts introduced by bacterial contamination of phytoplankton uptake rate measurements. Overall, flow cytometry approaches are still nascent, but offer tremendous potential, especially when combined with molecular targeting and analysis of microbial populations. For example, flow cytometrically sorted cells can be used for metagenome sequencing or DNA extracted from sorted populations could be fractionated using more traditional CsCl gradient approaches. Protein stable isotope probing Another appealing alternative to nucleic acid stable isotope techniques is the detection of isotopic incorporation into protein via peptide mass shifts as determined by mass spectrometry (Fig. 5.4).

102  | Wawrik

Figure 5.4 Flow chart of potential 15N-based stable isotope probing experiments. Nucleic acids are extracted from populations after incubation with labelled isotope, while flow cytometry may serve to separate specific components of communities before extraction. (I) Traditional SIP experiment where nucleic acids are separated via CsCl density gradient centrifugation. Quantitative PCR is typically utilized to demonstrate that a specific DNA target (e.g. species or population) has experienced an increase in the density of its DNA. Quantitative assays are often combined with assays of community composition such as 16S rRNA gene PCR amplification and sequencing. (II). A novel approach is Chip-Stable Isotope Probing (Chip-SIP) in which DNA or RNA from incubations is hybridized to a microarray that contains gene probes for known microbial populations. The array is then scanned via secondary ion mass spectrometer imaging (NanoSIMS) to identify spots containing isotopic label. A combination of quantitative fluorescent dye spot detection and NanoSIMS can serve to provide community wide analysis of isotopic flux. (III) Environmental proteomic analysis combined with stable isotope incubations. The goal is to identify peptide fragments that contain isotopic label and can be identified in metagenomic data sets for more in depth sequence analysis and characterization.

Proteins account for as much as half the dry cell mass of a typical bacterial cell (Bremer and Dennis, 1996) and as much as 95% of cellular energy used for biosynthesis is directed to the synthesis proteins (Cox, 2003). Nitrogen taken up from the environment will therefore predominantly and quickly be incorporated into cellular proteins (Fig. 5.2). Advances in proteomic technologies now allow for the identification of protein fragments that differentially incorporate isotopic label. At first glance, this approach should be superior to nucleic acid SIP, which requires a high degree of labelling (>25%) in order to produce mass differences that can be distinguished between treatments. Mass spectrometric analysis can detect the incorporation of a single heavy atom per peptide and, given the average size of

peptide fragments, it should therefore be possible to detect as little at 1–2% of isotopic incorporation ( Jehmlich et al., 2008a). Overall, this provides an opportunity to perform incubation experiments with lower levels of label and for shorter time intervals, both of which are significant sources of experimental bias via stimulation of growth and cross-feeding in communities. In practice, significant experimental challenges have to be overcome for protein based-SIP work to become routine. For example, the commonly used Mascot software for peptide searches does not account for isotopic labelling ( Jehmlich et al., 2008a), making proper fragment identification difficult. Protein SIP was therefore initially applied to spots isolated from two dimensional SDS-PAGE gels of proteins extracted from P. putida ML2 grown

Stable Isotope Probing the N Cycle |  103

on 12/13C-benzene and 15N ammonium ( Jehmlich et al., 2008a). In this study, matrix-assisted laser ionization/desorption mass spectrometry (MALDI-MS) was used to show 13C or 15 N-content of analysed peptides, demonstrating that this approach is viable for tracing carbon and nitrogen flow in proteins extracted from cultures. Identifying labelled peptides in community protein presents special challenges, because the full protein complement of such communities is usually not known. Efforts were therefore initially directed towards a model system in which a known isolate was added to a consortium of uncharacterized microorganisms ( Jehmlich et al., 2008b). For this experiment Aromatoleum aromaticum EbN1, a well-characterized toluene degrader, was either grown in isolation or in the presence enrichment culture capable of degrading gluconate, providing gluconate or 13C-toluene as sole carbon and energy sources. Since the enrichment culture did not show growth on toluene and EbN1 cannot metabolize gluconate, a model mixed community resulted where a single known community member was capable of metabolizing a substrate of interest (toluene). Two-dimensional SDS-PAGE was used to identity protein spots from samples of EbN1 grown on toluene and the mixed culture grown on 13C-toluene and gluconate. MALDI-MS/MS analysis was then used to determine the isotopic labelling of peptides. 13C incorporation was only observed in proteins from strain EbN1, which displayed an average of 82% label incorporation for 19 analysed EbN1 proteins. These data demonstrate the suitability of proteinbased SIP to microbial communities in order to determine the identity of metabolically active species. In a later study, the level of 15N and 13C isotopic incorporation from ammonium and benzene respectively were estimated for a pure culture of P. putida using MALDI-MS of whole proteins and tryptic peptides ( Jehmlich et al., 2009). The study confirms that MS analysis is a viable approach for detecting isotopic incorporation of both nitrogen and carbon. The identification of species via intact protein mapping and shotgun mass mapping (SMM) has also been investigated. Analysis of mixed communities requires matching isotopically labelled proteins or peptides to the genetic information of specific microorganisms.

A comparison of the two methods indicates that SMM provided lower uncertainty ( Jehmlich et al., 2009), and is useful for the determination of newly synthesized protein. For example, proteinSIP has been applied to demonstrate induction of proteins in P. putida ML2 that was shifted from acetate to benzene as a substrate (Taubert et al., 2011). One dimensional SDS-PAGE was used to resolve protein bands whose isotopic composition was determined via tryptic digest and MS analysis. Regression analysis of incorporation data allowed clear separation of induced and constitutively expressed protein. With respect to environmental samples, significant challenges remain. Foremost, the environment typically contains a very large diversity of microorganisms. Each of these microbes carries a unique and characteristic proteome. Environmental protein-SIP therefore is principally a problem of metaproteomic analysis. A ‘backof-the-envelope’ calculation suggests that that a typical environmental sample could produce approximately 3 × 107 different tryptic peptides, assuming about 1000 different species, each containing an average of 1000 different expressed proteins, which each produce in average about 30 different fragments. Proteins also occur over a very large range of concentrations. Even within a single cell, the concentration range of proteins can span more than five orders of magnitude ( Jehmlich et al., 2010). The experimental requirements for environmental proteome analysis therefore include a need for high-throughput, high sensitivity for peptide detection, a large dynamic range coupled to highly accurate mass determination, as well as the ability to obtain peptide mass fingerprints (Hettich et al., 2012). Mass spectrometry has dramatically improved our ability to resolve proteins within mixed sample in recent years. For example, proteomic analysis has historically been conducted via two dimensional gel electrophoresis and analysis of isolates spots. Recent advances in liquid chromatography coupled to mass spectrometry (LC-MS) have allowed unprecedented shotgun whole proteome resolution without prior gel separation. Mass spectrometry technology is advancing very rapidly, a trend that is likely to continue. It can therefore be expected that improved representation of the environmental

104  | Wawrik

metaproteome can be generated. Unfortunately such data are not useful in the absence of complementing genomic or metagenomic DNA sequence data. Peptide mass fingerprints are ambiguous and only represent short fragments which cannot unambiguously be assigned phylogenetic affiliation. This is important, because it is the identity of organisms involved in requisite processes that is often sought when environmental samples are analysed using stable isotope techniques. The initial application of community proteomic shotgun analysis was therefore limited to low-complexity systems. For example, one of the first large-scale proteomic surveys of a natural community was conducted on a biofilm associated with acid mine drainage (Ram et al., 2005). A metagenomic library had previously been described for this biofilm, which made it possible to reconstruct near-complete genomes of the dominant community members (Tyson et al., 2004). The resulting database of predicted protein-coding genes could be used to map detected peptides to scaffolds to which phylogenetic identity had been ascribed. While this approach was still extremely difficult and costly a decade ago, it is now relatively straightforward. Advances in genomic sequencing technologies have outpaced even optimistic predictions. Community genomic (metagenomic) information can now be generated cheaply and easily, providing unprecedented perspectives of the genetic potential of cultured and environmental microbes. Assembly and meta-analysis of sequencing data allows for the in silico construction of whole community proteome databases against which peptide mass fingerprints can be searched. Metaproteomics therefore has become to a very large degree a challenge of bioinformatical integration of peptide and sequence data sets, which is facilitated by rapid advances in computing and ‘big data’ analysis capabilities. The technology has now been applied to a variety of environments, including communities in acid mine drainage, coastal and open ocean marine system, contaminated soils, and the human gut microbiome (for a review see Hettich et al., 2012). Metaproteomics potential for answering questions about N cycling are extensively reviewed in Chapter 6. As the technology matures it will show it usefulness, especially in the context of

studying specific minor but well characterized populations. A good target, for example, may be nitrogen fixation. A very large and comprehensive database of nitrogen fixation genes exists for many environments because of efforts to characterize diazotrophs by PCR-based amplification, cloning, and sequencing experiments. Diazotrophs often only account for small proportion of cells in the environment, yet it may be possible to match isotopically labelled peptides to corresponding nifH sequences in the database after incubation in the presence of 15N2. Chip stable isotope probing A highly promising and novel technology is chip stable isotope probing (Chip-SIP) (Mayali et al., 2012). Chip-SIP allows researchers to directly interrogate complex communities at the species level with respect to their N and C nutritional preferences. The technique is a highly sensitive, high-throughput approach. It leverages the ability to detect very large numbers of molecular targets in the form of a microarray. Microarray analysis in then combined with the sensitivity of secondary ion mass spectrometer imaging (NanoSIMS) to detect the isotopic labelling of DNA or RNA hybridized to spots on the array. It therefore allows highly targeted and community-wide assessment of resource utilization and partitioning in microbial communities. Incubations can be conducted with substrates containing either stable C or N isotopes (13C and 15N). Microbial populations exhibiting requisite activities can be assumed to produce RNA that reflects the isotopic composition of this substrate pool, while inactive cells or those that utilize different substrates will produce RNA more in line with the isotope’s natural abundances. The technique has been applied to both cultures and environmental samples (Mayali et al., 2012). A comparison of RNA from Pseudomonas stutzeri cultures grown on 12C or 13C-glucose demonstrated clear isotopic incorporation into 60 targets generated for different regions of the P. stutzeri rRNA gene, although isotopic enrichment of spots depended on the efficiency of hybridization on spots. The relative isotopic enrichment of RNA was estimated by determining the slope of the enrichment vs. fluorescence relationship of a

Stable Isotope Probing the N Cycle |  105

probe set. RNA from cultures grown on different levels of 13C-enriched substrate and a comparison between 13C and 15N-labelled substrates indicated good reproducibility as well as robust detection of isotopic incorporation down to a detection limit of 0.5 and 0.1 atom% incorporation for 13C and 15N respectively. In the same study, similar experiments were conducted with seawater samples collected from San Francisco Bay, targeting taxa abundantly found in this environment. Differential incorporation of 13C-glucose and 15N-ammonium were demonstrated, indicating bacterial populations with distinct physiological characteristics can be identified via Chip-SIP (Mayali et al., 2012). Chip-SIP offers distinct advantages over other techniques because label can be added at environmentally relevant or even trace concentrations. The high sensitivity of secondary ion mass spectrometry means that artefacts resulting from cross-feeding or the stimulation of natural communities via isotope addition can be minimized. By targeting RNA, which has short turnover times (on the order of minutes), populations that are highly active can be clearly distinguished from less active or inactive species, even when short incubation times are used. The principal drawbacks of the technique are the considerable cost of equipment and the technical challenges associated with microarray hybridization and nano-SIMS analysis. In many cases it may also be necessary to design a microarray de novo that reflect the phylogenetic composition of samples that are to be analysed. This is not a trivial task and often requires considerable expertise, especially if functional gene targets are added to an array. Microarray platforms that can be deployed without de novo design are available. These include the Phylochip (Brodie et al., 2006), the GeoChip (He et al., 2010), and the MicroTOOLs functional gene array (Shilova, 2014). The Phylochip is based on 16S rRNA gene targets and can be used to identify more than 8000 different bacterial and archaeal strains simultaneously. Advantages of the Phylochip include highly specific species identification, even when target abundances are low, as well as the ability to scale, i.e. add new 16S rRNA gene targets that reflect the species composition of environments that are targeted. The

Phylochip was therefore the chip of choice for the development of the chip-SIP approach. In many cases, however, specific geochemical activities and transformations are of interest. In such cases, it is typically advantageous to target the functional genes that encode the requisite protein machinery (e.g. genes for Nif proteins, which used for nitrogen fixation). A dominant platform in this context is the GeoChip (He et al., 2010). The GeoChip is a functional gene array that contains approximately >28,000 probes for more than 56,500 gene variants. In all, 292 gene families are represented covering carbon, nitrogen, phosphorus, and sulfur cycling. Also included are genes related in energy metabolism, antibiotic resistance as well as metal and organic contaminant remediation. With respect to the nitrogen cycle, gene targets for all relevant inorganic N transformations are represented, including nitrogen fixation, assimilatory and dissimilatory nitrate reduction, and ammonium oxidation (for further details see Chapter 7). While these probes represent a good spread across known sequence diversity, the GeoChip is heavily weighted towards analysis of contaminated environment. The vast majority of genetic diversity in the environment remains to be captured and it is therefore of benefit to utilize arrays tailored towards specific microbial systems (Fig. 5.4). One such an array is the result of the MicroTOOLs initiative, which generated a microarray targeting genes involved in C and N flow associated with coastal marine microbial communities (Shilova, 2014). Chip-SIP therefore provides an adaptable platform that can be tailored to specific environments or even just components of more complex microbial communities. Chip-SIP provides a promising novel approach for stable isotope probing studies. Conclusions It is likely that stable isotope probing techniques will find continued application, especially as novel detection approaches such as proteinSIP, FCM-IRMS, and Chip-SIP mature. Mass spectrometry based techniques are particularly appealing, because they offer the potential for simultaneous utilization of more than one label (e.g. the use of both 15N and 13N labelled

106  | Wawrik

substrates). Recent developments are also likely to yield faster and higher throughput analytical approaches. SIP techniques are therefore likely to continue to play a significant role in the microbial ecology tool box, and yield insights into the functioning of microbial communities, especially in the context of the N cycle where numerically minor populations can often times disproportionate effects on ecosystem function and nutrient dynamics. Acknowledgement Support for the preparation of this manuscript was provided by the National Science Foundation via a grant to apply 15N stable isotope probing in marine environments (Grant # OCE 0961900). References

Aber, J.D., Magill, A., McNulty, S.G., Boone, R.D., Nadelhoffer, K.J., Downs, M., and Hallett, R. (1995). Forest biogeochemistry and primary production altered by nitrogen saturation. Water Air Soil Poll. 85, 1665–1670. Addison, S.L., McDonald, I.R., and Lloyd-Jones, G. (2010). Stable isotope probing: technical considerations when resolving 15N-labeled RNA in gradients. J. Microbiol. Methods 80, 70–75. Allen, A.E., Booth, M.G., Frischer, M.E., Verity, P.G., Zehr, J.P., and Zani, S. (2001). Diversity and detection of nitrate assimilation genes in marine bacteria. Appl. Environ. Microbiol. 67, 5343–5348. Allen, A.E., Howard-Jones, M.H., Booth, M.G., Frischer, M.E., Verity, P.G., Bronk, D.A., and Sanderson, M.P. (2002). Importance of heterotrophic bacterial assimilation of ammonium and nitrate in the Barents Sea during summer. J. Marine Syst. 38, 93–108. Bastida, F., Jechalke, S., Bombach, P., Franchini, A.G., Seifert, J., von Bergen, M., Vogt, C., and Richnow, H.H. (2011). Assimilation of benzene carbon through multiple trophic levels traced by different stable isotope probing methodologies. FEMS Microbiol. Ecol. 77, 357–369. Bell, T.H., Yergeau, E., Martineau, C., Juck, D., Whyte, L.G., and Greer, C.W. (2011). Identification of nitrogen-incorporating bacteria in petroleumcontaminated arctic soils by using 15N DNA-based stable isotope probing and pyrosequencing. Appl. Environ. Microbiol. 77, 4163–4171. Berman, T., and Bronk, D.A. (2003). Dissolved organic nitrogen: a dynamic participant in aquatic ecosystems. Aquat. Microb. Ecol. 31, 279–305. Birnie, G.D. (1978). Centrifugal separations. In molecular and cell biology, D. Rickwood, ed. (Boston, MA: Butterworth-Heinemann Publishers), pp. 169–217. Boesch, D.F. (2002). Challenges and opportunities for science in reducing nutrient over-enrichment of coastal ecosystems. Estuaries 25, 886–900.

Borodina, E., Cox, M.J., McDonald, I.R., and Murrell, J.C. (2005). Use of DNA-stable isotope probing and functional gene probes to investigate the diversity of methyl chloride-utilizing bacteria in soil. Environ. Microbiol. 7, 1318–1328. Bozinovski, D., Herrmann, S., Richnow, H.H., von Bergen, M., Seifert, J., and Vogt, C. (2012). Functional analysis of an anaerobic m-xylene-degrading enrichment culture using protein-based stable isotope probing. FEMS Microbiol. Ecol. 81, 134–144. Bradley, P.B., Lomas, M.W., and Bronk, D.A. (2010). Inorganic and Organic Nitrogen Use by Phytoplankton Along Chesapeake Bay, Measured Using a Flow Cytometric Sorting Approach. Estuar. Coast. 33, 971–984. Bremer, H., and Dennis, P.P. (1996). Modulation of chemical composition and other parameters of the cell growth rate. In Escherichia coli and Salmonella: Cellular and Molecular Biology, F.C. Neidhardt, ed. (Washington, DC: ASM Press), pp. 1553–1568. Brodie, E.L., Desantis, T.Z., Joyner, D.C., Baek, S.M., Larsen, J.T., Andersen, G.L., Hazen, T.C., Richardson, P.M., Herman, D.J., Tokunaga, T.K., et al. (2006). Application of a high-density oligonucleotide microarray approach to study bacterial population dynamics during uranium reduction and reoxidation. Appl. Environ. Microbiol. 72, 6288–6298. Bronk, D.A., Glibert, P.M., and Ward, B.B. (1994). Nitrogen uptake, dissolved organic nitrogen release, and new production. Science 265, 1843–1846. Buckley, D.H., Huangyutitham, V., Hsu, S.F., and Nelson, T.A. (2007a). Stable isotope probing with 15N2 reveals novel noncultivated diazotrophs in soil. Appl. Environ. Microbiol. 73, 3196–3204. Buckley, D.H., Huangyutitham, V., Hsu, S.F., and Nelson, T.A. (2007b). Stable isotope probing with 15N achieved by disentangling the effects of genome G + C content and isotope enrichment on DNA density. Appl. Environ. Microbiol. 73, 3189–3195. Buckley, D.H., Huangyutitham, V., Hsu, S.F., and Nelson, T.A. (2008). 15N2-DNA-stable isotope probing of diazotrophic methanotrophs in soil. Soil Biol. Biochem. 40, 1272–1283. Bürgmann, H., Widmer, F., Von Sigler, W., and Zeyer, J. (2004). New molecular screening tools for analysis of free-living diazotrophs in soil. Appl. Environ. Microbiol. 70, 240–247. Cadisch, G., Espana, M., Causey, R., Richter, M., Shaw, E., Morgan, J.A., Rahn, C., and Bending, G.D. (2005). Technical considerations for the use of 15N-DNA stable-isotope probing for functional microbial activity in soils. Rapid Commun. Mass Spectrom. 19, 1424–1428. Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N., and Smith, V.H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8, 559–568. Cebron, A., Bodrossy, L., Chen, Y., Singer, A.C., Thompson, I.P., Prosser, J.I., and Murrell, J.C. (2007). Identity of active methanotrophs in landfill cover soil as revealed by DNA-stable isotope probing. FEMS Microbiol. Ecol. 62, 12–23.

Stable Isotope Probing the N Cycle |  107

Chen, Y., and Murrell, J.C. (2010). When metagenomics meets stable-isotope probing: progress and perspectives. Trends Microbiol. 18, 157–163. Chen, Y., Vohra, J., and Murrell, J.C. (2010). Applications of DNA-stable isotope probing in bioremediation studies. Methods Mol. Biol. 599, 129–139. Cleveland, C.C., Townsend, A.R., Schimel, D.S., Fisher, H., Howarth, R.W., Hedin, L.O., Perakis, S.S., Latty, E.F., Von Fischer, J.C., Elseroad, A., et al. (1999). Global patterns of terrestrial biological nitrogen (N2) fixation in natural ecosystems. Global Biogeochem. Cy. 13, 623–645. Cox, R.A. (2003). Correlation of the rate of protein synthesis and the third power of the RNA: protein ratio in Escherichia coli and Mycobacterium tuberculosis. Microbiology 149, 729–737. Cupples, A.M. (2011). The use of nucleic acid based stable isotope probing to identify the microorganisms responsible for anaerobic benzene and toluene biodegradation. J. Microbiol. Methods 85, 83–91. Cupples, A.M., Shaffer, E.A., Chee-Sanford, J.C., and Sims, G.K. (2007). DNA buoyant density shifts during 15N-DNA stable isotope probing. Microbiol. Res. 162, 328–334. DeLong, E.F., Wu, K.Y., Prezelin, B.B., and Jovine, R.V. (1994). High abundance of Archaea in Antarctic marine picoplankton. Nature 371, 695–697. Dumont, M.G., and Murrell, J.C. (2005). Stable isotope probing – linking microbial identity to function. Nat. Rev. Microbiol. 3, 499–504. Dumont, M.G., Pommerenke, B., Casper, P., and Conrad, R. (2011). DNA-, rRNA- and mRNA-based stable isotope probing of aerobic methanotrophs in lake sediment. Environ. Microbiol. 13, 1153–1167. Dunford, E.A., and Neufeld, J.D. (2010). DNA stable isotope probing (DNA-SIP). J. Vis. Exp. 42. Eek, K.M., Sessions, A.L., and Lies, D.P. (2007). Carbonisotopic analysis of microbial cells sorted by flow cytometry. Geobiology 5, 85–95. Espana, M., Rasche, F., Kandeler, E., Brune, T., Rodriguez, B., Bending, G.D., and Cadisch, G. (2011a). Assessing the effect of organic residue quality on active decomposing fungi in a tropical Vertisol using 15NDNA stable isotope probing. Fungal Ecol. 4, 115–119. Espana, M., Rasche, F., Kandeler, E., Brune, T., Rodriguez, B., Bending, G.D., and Cadisch, G. (2011b). Identification of active bacteria involved in decomposition of complex maize and soybean residues in a tropical Vertisol using N-15-DNA stable isotope probing. Pedobiologia 54, 187–193. Esteve-Nunez, A., Caballero, A., and Ramos, J.L. (2001). Biological degradation of 2,4,6-trinitrotoluene. Microbiol. Mol. Biol. Rev. 65, 335–352. Falkowski, P.G., Barber, R.T., and Smetacek, V.V. (1998). Biogeochemical Controls and Feedbacks on Ocean Primary Production. Science 281, 200–207. Falkowski, P.G., Scholes, R.J., Boyle, E., Canadell, J., Canfield, D., Elser, J., Gruber, N., Hibbard, K., Hogberg, P., Linder, S., et al. (2000). The global carbon cycle: a test of our knowledge of earth as a system. Science 290, 291–296.

Farnelid, H., and Riemann, L. (2008). Heterotrophic H2-fixing bacteria: overlooked in the marine nitrogen cycle? In Nitrogen Fixation Research Progress, G.N. Couto, ed. (New York: Nova Science Publisher), pp. 409–423. Francis, C.A., Roberts, K.J., Beman, J.M., Santoro, A.E., and Oakley, B.B. (2005). Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc. Natl. Acad. Sci. U.S.A. 102, 14683–14688. Freitag, T.E., Chang, L., and Prosser, J.I. (2006). Changes in the community structure and activity of betaproteobacterial ammonia-oxidizing sediment bacteria along a freshwater-marine gradient. Environ. Microbiol. 8, 684–696. Friedrich, M.W. (2006). Stable-isotope probing of DNA: insights into the function of uncultivated microorganisms from isotopically labeled metagenomes. Curr. Opin. Biotech. 17, 59–66. Gallagher, E., McGuinness, L., Phelps, C., Young, L.Y., and Kerkhof, L.J. (2005). 13C-carrier DNA shortens the incubation time needed to detect benzoate-utilizing denitrifying bacteria by stable-isotope probing. Appl. Environ. Microbiol. 71, 5192–5196. Gallagher, E.M., Young, L.Y., McGuinness, L.M., and Kerkhof, L.J. (2010). Detection of 2,4,6-trinitrotoluene-utilizing anaerobic bacteria by 15N and 13C incorporation. Appl. Environ. Microbiol. 76, 1695–1698. Gruber, N., and Galloway, J.N. (2008). An Earth-system perspective of the global nitrogen cycle. Nature 451, 293–296. Hamady, M., Walker, J.J., Harris, J.K., Gold, N.J., and Knight, R. (2008). Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nature Methods 5, 235–237. He, Z., Deng, Y., Van Nostrand, J.D., Tu, Q., Xu, M., Hemme, C.L., Li, X., Wu, L., Gentry, T.J., Yin, Y., Liebich, J., et al. (2010). GeoChip 3.0 as a highthroughput tool for analyzing microbial community composition, structure and functional activity. ISME J. 4, 1167–1179. Hecky, R.E., and Kilham, P. (1988). Nutrient limitation of phytoplankton in freshwater and marine environments: a review of recent evidence on the effects of enrichment. Limnol. Oceanogr. 33, 796–822. Herrmann, S., Kleinsteuber, S., Chatzinotas, A., Kuppardt, S., Lueders, T., Richnow, H.H., and Vogt, C. (2010). Functional characterization of an anaerobic benzenedegrading enrichment culture by DNA stable isotope probing. Environ. Microbiol. 12, 401–411. Hettich, R.L., Sharma, R., Chourey, K., and Giannone, R.J. (2012). Microbial metaproteomics: identifying the repertoire of proteins that microorganisms use to compete and cooperate in complex environmental communities. Curr. Opin. Microbiol. 15, 373–380. Howarth, R.W., and Marino, R. (2006). Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades. Limnol. Oceanogr. 51, 364–376. Hugenholtz, P., Goebel, B.M., and Pace, N.R. (1998). Impact of culture-independent studies on the

108  | Wawrik

emerging phylogenetic view of bacterial diversity. J. Bacteriol. 180, 4765–4774. Ilikchyan, I., Robidart, J., Tripp, J., Thompson, A., Wawrik, B., Tozzi, S., Turk, K., and Zehr, J. (2012). The design and application of a functional highdensity oligonucleotide microarray for examining ocean biogeochemistry from a microbial perspective (MicroTOOLs project). ISME14 – The 14th International Symposium on Microbial Ecology. Copenhagen, Denmark. Izumi, H., Anderson, I.C., Alexander, I.J., Killham, K., and Moore, E.R. (2006). Diversity and expression of nitrogenase genes (nifH) from ectomycorrhizas of Corsican pine (Pinus nigra). Environ. Microbiol. 8, 2224–2230. Jehmlich, N., Schmidt, F., Hartwich, M., von Bergen, M., Richnow, H.H., and Vogt, C. (2008a). Incorporation of carbon and nitrogen atoms into proteins measured by protein-based stable isotope probing (Protein-SIP). Rapid Commun. Mass Spectrom. 22, 2889–2897. Jehmlich, N., Schmidt, F., von Bergen, M., Richnow, H.H., and Vogt, C. (2008b). Protein-based stable isotope probing (Protein-SIP) reveals active species within anoxic mixed cultures. ISME J. 2, 1122–1133. Jehmlich, N., Schmidt, F., Taubert, M., Seifert, J., von Bergen, M., Richnow, H.H., and Vogt, C. (2009). Comparison of methods for simultaneous identification of bacterial species and determination of metabolic activity by protein-based stable isotope probing (Protein-SIP) experiments. Rapid Commun. Mass Spectrom. 23, 1871–1878. Jehmlich, N., Schmidt, F., Taubert, M., Seifert, J., Bastida, F., von Bergen, M., Richnow, H.H., and Vogt, C. (2010). Protein-based stable isotope probing. Nat. Protoc. 5, 1957–1966. Jia, Z.J., and Conrad, R. (2009). Bacteria rather than Archaea dominate microbial ammonia oxidation in an agricultural soil. Environ. Microbiol. 11, 1658–1671. John, D.E., Wawrik, B., Tabita, F.R., and Paul, J.H. (2006). Gene diversity and organization in rbcL-containing genome fragments from uncultivated Synechococcus in the Gulf of Mexico. Mar. Ecol. Prog. Ser. 316, 23–33. Jones, M.D., Singleton, D.R., Carstensen, D.P., Powell, S.N., Swanson, J.S., Pfaender, F.K., and Aitken, M.D. (2008). Effect of incubation conditions on the enrichment of pyrene-degrading bacteria identified by stable-isotope probing in an aged, PAH-contaminated soil. Microb. Ecol. 56, 341–349. Jones, M.D., Crandell, D.W., Singleton, D.R., and Aitken, M.D. (2011a). Stable-isotope probing of the polycyclic aromatic hydrocarbon-degrading bacterial guild in a contaminated soil. Environ. Microbiol. 13, 2623–2632. Jones, M.D., Singleton, D.R., Sun, W., and Aitken, M.D. (2011b). Multiple DNA extractions coupled with stable-isotope probing of anthracene-degrading bacteria in contaminated soil. Appl. Environ. Microbiol. 77, 2984–2991. Kalyuzhnaya, M.G., Lapidus, A., Ivanova, N., Copeland, A.C., McHardy, A.C., Szeto, E., Salamov, A., Grigoriev, I.V., Suciu, D., Levine, S.R., et al. (2008). Highresolution metagenomics targets specific functional

types in complex microbial communities. Nature Biotechnology 26, 1029–1034. Krüger, M., Wolters, H., Gehre, M., Joye, S.B., and Richnow, H.H. (2008). Tracing the slow growth of anaerobic methane-oxidizing communities by 15N-labelling techniques. FEMS Microbiol. Ecol. 63, 401–411. Larkin, R.P., and Honeycutt, C.W. (2006). Effects of different 3-year cropping systems on soil microbial communities and rhizoctonia diseases of potato. Phytopathology 96, 68–79. Lin, J.L., Radajewski, S., Eshinimaev, B.T., Trotsenko, Y.A., McDonald, I.R., and Murrell, J.C. (2004). Molecular diversity of methanotrophs in Transbaikal soda lake sediments and identification of potentially active populations by stable isotope probing. Environ. Microbiol. 6, 1049–1060. Liou, J.S., Derito, C.M., and Madsen, E.L. (2008). Fieldbased and laboratory stable isotope probing surveys of the identities of both aerobic and anaerobic benzenemetabolizing microorganisms in freshwater sediment. Environ. Microbiol. 10, 1964–1977. Lueders, T., Manefield, M., and Friedrich, M.W. (2004a). Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ. Microbiol. 6, 73–78. Lueders, T., Wagner, B., Claus, P., and Friedrich, M.W. (2004b). Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil. Environ. Microbiol. 6, 60–72. Lukjancenko, O.T.M., Wassenaar, T.M., and Ussery, D.W. (2010). Comparison of 61 sequenced Escherichia coli genomes. Microb. Ecol. 60, 708–720. Martinez-Lavanchy, P.M., Dohrmann, A.B., Imfeld, G., Trescher, K., Tebbe, C.C., Richnow, H.H., and Nijenhuis, I. (2011). Detection of monochlorobenzene metabolizing bacteria under anoxic conditions by DNA-stable isotope probing. Biodegradation 22, 973–982. Mayali, X., Weber, P.K., Brodie, E.L., Mabery, S., Hoeprich, P.D., and Pett-Ridge, J. (2012). High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use. ISME J. 6, 1210–1221. Meselson, M., and Stahl, F.W. (1958). The Replication of DNA in Escherichia coli. Proc. Natl. Acad. Sci. U.S.A. 44, 671–682. Moisander, P.H., Beinart, R.A., Hewson, I., White, A.E., Johnson, K.S., Carlson, C.A., Montoya, J.P., and Zehr, J.P. (2010). Unicellular cyanobacterial distributions broaden the oceanic N2 fixation domain. Science 327, 1512–1514. Neufeld, J.D., Vohra, J., Dumont, M.G., Lueders, T., Manefield, M., Friedrich, M.W., and Murrell, J.C. (2007). DNA stable-isotope probing. Nat. Protoc. 2, 860–866. Neufeld, J.D., Chen, Y., Dumont, M.G., and Murrell, J.C. (2008). Marine methylotrophs revealed by stableisotope probing, multiple displacement amplification and metagenomics. Environ. Microbiol. 10, 1526– 1535.

Stable Isotope Probing the N Cycle |  109

Niemann, H., Losekann, T., de Beer, D., Elvert, M., Nadalig, T., Knittel, K., Amann, R., Sauter, E.J., Schluter, M., Klages, M., et al. (2006). Novel microbial communities of the Haakon Mosby mud volcano and their role as a methane sink. Nature 443, 854–858. Noll, M., Frenzel, P., and Conrad, R. (2008). Selective stimulation of type I methanotrophs in a rice paddy soil by urea fertilization revealed by RNA-based stable isotope probing. FEMS Microbiol. Ecol. 65, 125–132. NRC (2000). Clean Coastal Waters: Understanding and reducing the effects of nutrient pollution. (National Academic Press, Washington, DC). Orr, C.H., James, A., Leifert, C., Cooper, J.M., and Cummings, S.P. (2011). Diversity and activity of free-living nitrogen-fixing bacteria and total bacteria in organic and conventionally managed soils. Appl. Environ. Microbiol. 77, 911–919. Paquet, L., Monteil-Rivera, F., Hatzinger, P.B., Fuller, M.E., and Hawari, J. (2011). Analysis of the key intermediates of RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine) in groundwater: occurrence, stability and preservation. J. Env. Monit. 13, 2304–2311. Pratscher, J., Dumont, M.G., and Conrad, R. (2011). Ammonia oxidation coupled to CO2 fixation by archaea and bacteria in an agricultural soil. Proc. Natl. Acad. Sci. U.S.A. 108, 4170–4175. Radajewski, S., Ineson, P., Parekh, N.R., and Murrell, J.C. (2000). Stable-isotope probing as a tool in microbial ecology. Nature 403, 646–649. Radajewski, S., and Murrell, J.C. (2002). Stable isotope probing for detection of methanotrophs after enrichment with 13CH4. Methods Mol. Biol. 179, 149–157. Raghoebarsing, A.A., Pol, A., van de Pas-Schoonen, K.T., Smolders, A.J., Ettwig, K.F., Rijpstra, W.I., Schouten, S., Damste, J.S., Op den Camp, H.J., Jetten, M.S. et al. (2006). A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921. Ram, R.J., Verberkmoes, N.C., Thelen, M.P., Tyson, G.W., Baker, B.J., Blake, R.C., 2nd, Shah, M., Hettich, R.L., and Banfield, J.F. (2005). Community proteomics of a natural microbial biofilm. Science 308, 1915–1920. Redfield, A.C. (1934). On the proportions of organic derivations in sea water and their relation to the composition of plankton. In James Johnstone Memorial Volume, R.J. Daniel, ed. (Liverpool: University Press of Liverpool), pp. 177–192. Riemann, L., Farnelid, H., and Steward, G.F. (2010). Nitrogenase genes in non-cyanobacterial plankton: prevalence, diversity and regulation in marine waters. Aquat. Microb. Ecol. 61, 225–237. Roh, H., Yu, C.P., Fuller, M.E., and Chu, K.H. (2009). Identification of hexahydro-1,3,5-trinitro-1,3,5triazine-degrading microorganisms via 15N-stable isotope probing. Environ. Sci. Technol. 43, 2505–2511. Schimel, D.S., House, J.I., Hibbard, K.A., Bousquet, P., Ciais, P., Peylin, P., Braswell, B.H., Apps, M.J., Baker, D., Bondeau, A., et al. (2001). Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems. Nature 414, 169–172.

Schindler, D.W., Fee, E.J., and Ruszczynski, T. (1978). Phosphorus input and its consequences for phytoplankton standing crop and production in experimental lakes area and in similar lakes. J. Fish. Res. Board Can. 35, 190–196. Schloss, P.D., and Handelsman, J. (2003). Biotechnological prospects from metagenomics. Curr. Opin. Biotech. 14, 303–310. Shilova, I.N., Robidart, J.C., James Tripp, H., Turk-Kubo, K., Wawrik, B., Post, A.F., Thompson, A.W., Ward, B., Hollibaugh, J.T., Millard, A., et al. (2014). A microarray for assessing transcription from pelagic marine microbial taxa. ISME J. [Epub ahead of print]. Singleton, D.R., Powell, S.N., Sangaiah, R., Gold, A., Ball, L.M., and Aitken, M.D. (2005). Stable-isotope probing of bacteria capable of degrading salicylate, naphthalene, or phenanthrene in a bioreactor treating contaminated soil. Appl. Environ. Microbiol. 71, 1202–1209. Suzuki, M.T., Preston, C.M., Beja, O., de la Torre, J.R., Steward, G.F., and DeLong, E.F. (2004). Phylogenetic screening of ribosomal RNA gene-containing clones in Bacterial Artificial Chromosome (BAC) libraries from different depths in Monterey Bay. Microb. Ecol. 48, 473–488. Talmage, S.S., Opresko, D.M., Maxwell, C.J., Welsh, C.J., Cretella, F.M., Reno, P.H., and Daniel, F.B. (1999). Nitroaromatic munition compounds: environmental effects and screening values. Rev. Environ. Contam. Toxicol. 161, 1–156. Taubert, M., Jehmlich, N., Vogt, C., Richnow, H.H., Schmidt, F., von Bergen, M., and Seifert, J. (2011). Time resolved protein-based stable isotope probing (Protein-SIP) analysis allows quantification of induced proteins in substrate shift experiments. Proteom. 11, 2265–2274. Tillmann, S., Strompl, C., Timmis, K.N., and Abraham, W.R. (2005). Stable isotope probing reveals the dominant role of Burkholderia species in aerobic degradation of PCBs. FEMS Microbiol. Ecol. 52, 207–217. Torsvik, V., Goksoyr, J., and Daae, F.L. (1990). High diversity in DNA of soil bacteria. Appl. Environ. Microbiol. 56, 782–787. Townsend, A.R., Braswell, B.H., Holland, E.A., and Penner, J.E. (1996). Spatial and temporal patterns in terrestrial carbon storage due to deposition of fossil fuel nitrogen. Ecol. Appl. 6, 806–814. Tyson, G.W., Chapman, J., Hugenholtz, P., Allen, E.E., Ram, R.J., Richardson, P.M., Solovyev, V.V., Rubin, E.M., Rokhsar, D.S., and Banfield, J.F. (2004). Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43. Uhlik, O., Jecna, K., Leigh, M.B., Mackova, M., and Macek, T. (2009a). DNA-based stable isotope probing: a link between community structure and function. Sci. Tot. Environ. 407, 3611–3619. Uhlik, O., Jecna, K., Mackova, M., Vlcek, C., Hroudova, M., Demnerova, K., Paces, V., and Macek, T. (2009b). Biphenyl-metabolizing bacteria in the rhizosphere of horseradish and bulk soil contaminated by

110  | Wawrik

polychlorinated biphenyls as revealed by stable isotope probing. Appl. Environ. Microbiol. 75, 6471–6477. Venter, J.C., Remington, K., Heidelberg, J.F., Halpern, A.L., Rusch, D., Eisen, J.A., Wu, D., Paulsen, I., Nelson, K.E., Nelson, W., et al. (2004). Environmental genome shotgun sequencing of the Sargasso Sea. Science 304, 66–74. Wackett, L.P. (2004). Stable isotope probing in biodegradation research. Trends Biotechnol. 22, 153–154. Wawrik, B., Callaghan, A.V., and Bronk, D.A. (2009). Use of inorganic and organic nitrogen by Synechococcus spp. and diatoms on the west Florida shelf as measured using stable isotope probing. Appl. Environ. Microbiol. 75, 6662–6670. Wawrik, B., Boling, W.B., Van Nostrand, J.D., Xie, J., Zhou, J., and Bronk, D.A. (2012). Assimilatory nitrate utilization by bacteria on the West Florida Shelf as determined by stable isotope probing and functional microarray analysis. FEMS Microbiol. Ecol. 79, 400–411.

Whitby, C.B., Hall, G., Pickup, R., Saunders, J.R., Ineson, P., Parekh, N.R., and McCarthy, A. (2001). 13C incorporation into DNA as a means of identifying the active components of ammonia-oxidizer populations. Lett. Appl. Microbiol. 32, 398–401. Zehr, J.P., and Paerl, H.W. (2008). Molecular ecological aspects of nitrogen fixation in the marine environment. In Microbial Ecology of the Oceans. D.L. Kirchman, ed. (Hoboken, New Jersey: Wiley-Blackwell), pp. 481–525. Zehr, J.P., Waterbury, J.B., Turner, P.J., Montoya, J.P., Omoregie, E., Steward, G.F., Hansen, A., and Karl, D.M. (2001). Unicellular cyanobacteria fix N2 in the subtropical North Pacific Ocean. Nature 412, 635–638. Zehr, J.P., Jenkins, B.D., Short, S.M., and Steward, G.F. (2003). Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ. Microbiol. 5, 539–554. Zhang, S., Wang, Q., and Xie, S. (2012). Stable isotope probing identifies anthracene degraders under methanogenic conditions. Biodegrad. 23, 221–230.

Application of Metaproteomics to the Exploration of Microbial N-cycling Communities

6

Cindy Smith and Florence Abram

Abstract The recycling of nitrogen is essential for all organisms on earth and microbial communities play crucial roles in the nitrogen biogeochemical cycle. Constant anthropogenic alterations (both positive and negative) and changing environmental conditions (such as climate change and ocean acidification) have profound effects on the nitrogen cycle. In order to fully elucidate the nitrogen cycle, adequately address the consequences of environmental perturbations and mediate nitrogen pollution, nitrogen transformations need to be thoroughly investigated and ideally, modelled. Systems approaches, typically analysing DNA, RNA, proteins and metabolites together with the corresponding metadata prevailing in the ecosystem under study, ultimately aim at developing models to characterize the ecosystem attributes and predict the consequences of changes in environmental conditions in silico. Therefore, systems approaches hold a lot of potential when applied to the nitrogen cycle. In such context, metaproteomics, defined as the analysis of the proteins collectively expressed by all the organisms present in an ecosystem, becomes a crucial requirement. In this chapter we will discuss how metaproteomics, typically combined with other ‘omics’ technologies, has advanced our understanding of the nitrogen cycle in different environments. We will also discuss proteomic studies of relevant microbial isolates, as well the application of isotope labelling proteomics to the nitrogen cycle.

Introduction Microorganisms make up the main portion of biomass on earth and are found virtually everywhere in the environment. In situ, they coexist in mixed microbial consortia and perform crucial roles sustaining life on our planet such as the recycling of carbon and nitrogen. As an integrative component of proteins, DNA, RNA and numerous other molecules, nitrogen is a critical requirement for life. Nitrogen is often identified as the limiting factor impacting on primary productivity even though it is present in large amounts in the atmosphere (78%) in the form of dinitrogen gas, N2. Dinitrogen, however, is not a bioavailable form of nitrogen for most organisms, which require the conversion of N2 to NH3 (nitrogen fixation) before nitrogen assimilation. The most common process of nitrogen fixation is conducted by microorganisms, specifically bacteria or archaea, equipped with nitrogenase enzyme complex capable of catalysing the conversion of N2 to NH3. An overwhelming increase in nitrogen fixation has occurred in the past decades as a result of anthropogenic activities such as for example the widespread use of fertilizers or the intensive production of nitrogen-fixing crops (e.g. legumes; Compton et al., 2011). As a consequence, the increased availability of nitrogen has led to the alteration of many terrestrial and aquatic ecosystems typically facing amongst other ill effects a reduction in biodiversity (Compton et al., 2011). In order to mediate nitrogen pollution and to fully elucidate the complex cellular networks and biochemical reactions involved in the nitrogen cycle, nitrogen transformations need to be thoroughly investigated with a view to develop predictive

112  | Smith and Abram

models aimed at forecasting the consequences of environmental change on the nitrogen cycle. For this purpose, holistic system approaches typically investigating DNA, RNA, proteins and metabolites in the context of ecosystem metadata are ideal experimental strategies. Even though the investigation of model single organisms to develop such system approaches involving challenging techniques (‘omics’) can be very useful, meaningful data needs to be generated in situ where all the organisms present in the ecosystem (referred to as meta-organism) can be taken into consideration. The investigation of all the DNA molecules present in a given ecosystem involves the use of metagenomics, while RNA, proteins and metabolites produced collectively by the active members of the community are analysed by metatranscriptomics, metaproteomics and meta-metabolomics, respectively. Datasets generated using the above ‘omics’ technologies complement each other and provide information on different aspects of an ecosystem. Metagenomics aims to uncover all the genes that can be potentially expressed by the meta-organism occupying the ecosystem under study, while metatranscriptomics gives some insight into gene expression and therefore informs on the identity of the active members of the consortia and also on the metabolic pathways and functions that could potentially take place within the ecosystem. Metatranscriptomics is still only indicative of potential functions, however, owing to the lack of systematic correlation between transcription and translation mechanisms (Siggins et al., 2012). Metaproteomics together with meta-metabolomics (such as nitrogen cycle rate process data), on the other hand, truly provide insights into ecosystem functioning. Indeed, the identification of proteins and metabolites present in an ecosystem can be directly used to construct metabolic models. Even though metaproteomics and meta-metabolomics complement each other well for the identification of active metabolic pathways, metaproteomics present some valuable advantages over meta-metabolomics. First, all proteins are composed of amino acids, easily identifiable by mass spectrometry, while metabolites vary in their structures, which render their systematic detection quite challenging. Second, proteins can be assigned to specific organisms and

therefore protein identifications instruct not only on what pathways are active within the ecosystem but also on the identity of species involved in specific functions. In that respect, metaproteomics is a powerful tool to link community composition and function. Metabolites, however, cannot provide any information on what organisms are responsible for their production since metabolites are molecularly identical regardless of what species is metabolizing them. In the context of the nitrogen cycle, rate measurements can provide some insight into overall nitrogen transformations while targeting functional genes can identify which members of the community are responsible for specific parts of the process. One of main challenges, however, is to relate functional gene abundances to rate processes. Metaproteomics, by identifying expressed enzymes, can link active microorganisms with specific nitrogen transformations and as such is one step closer to rate processes than detecting and quantifying DNA or mRNA expression. Therefore, even though the detection of a protein does not necessarily imply that it is in its active form, metaproteomic data could be expected to correlate reasonably well with process rates. Metaproteomics is, as mentioned above, an integrative part of system approaches and as such should not be viewed as an isolated method. In addition, the success of metaproteomics is highly dependent on the availability of relevant genome sequences to allow protein identification and therefore metaproteomics is most fruitful when combined with metagenomics (Siggins et al., 2012). Another interesting advantage of using these two ‘omics’ technologies in tandem is the use of protein identification to aid metagenome annotation, a cumbersome process widely recognized as a bottleneck in metagenomics studies (Welsh et al., 2008; Ettwig et al., 2010; Schneider and Riedel 2010; Sherman et al., 2010). Indeed, the detection of peptides with corresponding predicted sequences inferred from consensus DNA can validate metagenome annotation. Robust datasets can then be generated, avoiding doubt when expected gene functions such as for example these involved in denitrification appear to be missing from the metagenome of enrichment cultures performing methane oxidation coupled with

Metaproteomics and N-cycling Microbial Communities |  113

Protein Identification & Data Interpretation

Protein Separation & Mass Spectrometry

Sample Preparation

dinitrogen production (Ettwig et al., 2010). In such instances, without any other data to validate genome annotation, such a surprising observation could be attributed to incorrect assembly. Typically, metaproteomics involves three main experimental stages (Fig. 6.1), namely (i) sample preparation, (ii) protein separation and mass spectrometry analysis and, finally, (iii) protein identification and data interpretation. Metaproteomic approaches have been applied successfully to a wide range of ecosystems such as the human gut (Verberkmoes et al., 2009; Rooijers et al., 2011; Kolmeder et al., 2012), soils (Williams et al., 2010; Wang et al., 2011; Knief et al., 2012), freshwater and marine environments (Morris et al., 2010; Ng et al., 2010; Lauro et al., 2011; Sowell et al., 2011; Williams et al., 2012), as well as bioengineered and acid mine drainage systems (Wilmes et al., 2008; Belnap et al., 2010;

Jemhlich et al., 2010; Bertin et al., 2011; Pan et al., 2011). Even though technical challenges still remain in metaproteomics, most ecosystems have witnessed considerable improvements in the metaproteomic workflow as outlined in Fig. 6.1. This has been largely facilitated by significant advances in mass spectrometry resulting in the high throughput sensitive detection of complex peptide mixtures. As a consequence, two-dimensional gel electrophoresis (2-DGE; O´Farrell, 1975), which was commonly applied for protein separation (Fig. 6.1) prior to mass spectrometry analysis of one protein spot at a time (that could contain a maximum of several proteins), is no longer a requirement for metaproteomics. Even though a useful tool in specific set-ups, 2-DGE presents many disadvantages, such as poor reproducibility and exclusion of membrane proteins and low abundance proteins (Siggins et al., 2012).

Sample collection Recovery of targeted fraction (e.g. microbial fraction or extra cellular components) Protein extraction and quantification

Protein separation and fractionation using gel-based (2-DGE/SDS-PAGE) or gel-free methods

Mass spectrometry analysis (peptide sequence/peptide mass fingerprint)

Databases searches and protein identification

Data interpretation involving the integration of metabolic pathways identified with physicochemical data and other ‘omics’ datasets from ecosystem under study

Figure 6.1 Overview of typical metaproteomic workflow. 2-DGE stands for two-dimensional gel electrophoresis, while SDS-PAGE stands for sodium dodecyl sulfate polyacrylamide gel electrophoresis.

114  | Smith and Abram

Gel techniques and more specifically SDS-PAGE are still largely used in metaproteomic studies for protein separation. This step is typically undertaken to clean the protein samples and to simplify the complex protein mixtures to be analysed by mass spectrometry. In these instances, gel bands can either be analysed individually or more commonly pooled together (Schneider et al., 2012; Kolmeder et al., 2012). In the field of human biology and marine ecosystems, large metagenomics datasets have been generated through the Human Microbiome Project (Qin et al., 2010) and the Global Ocean Sampling (GOS) expedition led by Craig Venter (Rusch et al., 2007; Yooseph et al., 2007), respectively. This in turn provided a great platform to support metaproteomics studies in these environments. Soil ecosystems, however, highly complex in nature and typically involving high species diversity and the presence of inhibitory substances (such as humic acids), have seen slower progress in the application of ‘omics’ technologies to-date. In an effort to overcome this and to gain some further understanding of soil ecosystems, the Terragenome project was formally launched in 2011 to support the generation of soil metagenomic data. As a consequence, many soil metaproteomics studies should be reported in the near future. Initially, bottlenecks in metaproteomics were typically encountered in the first two phases of the metaproteomic workflow outlined in Fig. 6.1, namely (i) sample preparation and (ii) protein separation and mass spectrometry. Limitations have now shifted towards the last phase of metaproteomic analysis, i.e. protein identification and data interpretation with a heavy demand on computer processing time. When combining substrate isotope probing (SIP) with metaproteomics to investigate the flow of 15N in the proteomes of mixed microbial communities, Pan et al. (2011) reported on ~ 2000 processor hours per LC-MS/ MS run (liquid chromatography tandem mass spectrometry). Metaproteomics is by essence an untargeted strategy, therefore when applied to an ecosystem it should theoretically inform on all the metabolic pathways active in situ, at the time of sampling. It is of course possible to target specific organisms, such as microbial fractions (and exclude higher

organisms for example), or to target specific type of proteins such as extracellular proteins, membrane proteins or cytoplasmic proteins, but it is not really feasible to use metaproteomics to specifically target one functional trait of a metaorganism. Therefore, applying metaproteomics in the context of the nitrogen cycle should provide some insights into the recycling of this element in a given ecosystem but such a strategy will not necessarily lead to this information. However, the use of protein-SIP (discussed further below) can help deciphering the flux of nitrogen within an ecosystem. Obviously such approaches cannot be generated in situ and require the set-up of microcosms to reproduce ecosystem conditions in laboratory settings. Most metaproteomic studies report the identification of proteins involved in nitrogen transformations, provided that their coverage is deep enough, but noticeably only few of them actually analyse the data generated in the context of the nitrogen cycle (Lauro et al., 2011; Williams et al., 2012). For the purpose of this chapter we will focus on mixed microbial consortia and how metaproteomics can be used to inform on their participation to the nitrogen cycle in diverse ecosystems. Metaproteomics and the nitrogen cycle Metaproteomic studies have been conducted in the context of aquatic and terrestrial ecosystems, where they have participated to inform on nitrogen transformations in situ and here we discuss the recent advances achieved in these two types of environments. It is important to keep in mind that the best use of technologies such as metaproteomics is in the context of hypothesis-driven research. An excellent example of such a study is the discovery of the pathway of methane oxidation coupled with anaerobic nitrite reduction and oxygen production (Ettwig et al., 2010). When investigating nitrogen cycling in freshwater ecosystems and more specifically the consequences of anthropogenic activities responsible for the increase in nitrate and nitrite concentrations, Ettwig et al. (2010) examined the natural coupling of anaerobic methane oxidation with denitrification. Enrichment cultures, obtained

Metaproteomics and N-cycling Microbial Communities |  115

from sediment of two freshwater ecosystems, could oxidize methane coupled to dinitrogen production. Surprisingly, the two corresponding metagenomes were found to lack genes for complete denitrification, specifically nosZDFY, the genes encoding for the reduction of nitrous oxide (N2O) to dinitrogen gas (N2; Ettwig et al., 2010). In order to facilitate metagenome assembly, both metatranscriptomics and metaproteomics were employed. All datasets (DNA, RNA and proteins) were found to correlate with one another, which indicated that the absence of the nos operon in the metagenomes was not due to technical artefacts. N2 was therefore not being produced by nitrous oxide reduction, and further still N2O was not accumulating within the enrichments. It was initially hypothesized that the observed anaerobic methane oxidation was therefore the result of concerted actions of denitrifying bacteria and methanotrophic archaea (Ettwig et al., 2010). This hypothesis was rejected, however, since the two enrichment culture metagenomes did not show any evidence of archaeal sequences. Strikingly, the anaerobic metagenomes were found to encode all the genes required for aerobic methane oxidation, and corresponding gene transcripts and proteins were detected in both enrichment cultures. It was therefore hypothesized that the formation of N2, was not from the reduction of N2O, but rather from the conversion of two NO (nitric oxide) molecules into O2 and N2. The generated oxygen could in turn be used for the aerobic oxidation of methane. Thus, this latter hypothesis could reconcile the puzzling observations of the coupling of methane oxidation and N2 production with the lack of denitrifying genes encoding nitrous oxide reduction on one hand and the presence of genes encoding the complete pathway of aerobic methane oxidation on the other hand (Ettwig et al., 2010). A rigorous experimental investigation involving the use of isotopic labelling (15N, 13C and 18O) confirmed the authors’ theory and led to the discovery of the new ‘intra-aerobic denitrification’ pathway, the fourth known biological pathway leading to O2 production (Ettwig et al., 2010). This finding is significant from three viewpoints: (i) for the carbon cycle, by providing some insights into the phenomenon of freshwater ecosystems acting as methane sink (ii) for the nitrogen cycle, by

demonstrating a new pathway for N2 production and (iii) for the evolution of our planet, since this suggests that oxygen might have been available to microorganisms prior to photosynthesis. In this study (Ettwig et al., 2010), metaproteomics was used alongside metatranscriptomics and metagenomics to construct a very robust dataset demonstrating the existence of a completely new pathway, which would have been misidentified as traditional denitrification using isotope-pairing or acetylene-block techniques. Therefore the study from Ettwig et al. (2010) not only illustrates the importance of using ‘omics’ technologies in parallel but also demonstrates the power of such approaches to uncover new metabolic pathways that would be otherwise overlooked. Aquatic ecosystems As mentioned above, the GOS expedition has generated a vast amount of metagenomic data, which provided a fantastic platform for metaproteomic investigations in marine environments. In aquatic ecosystems, many studies aimed at analysing microbial communities harvested directly from water (Sowell et al., 2009, 2011; Morris et al., 2010; Ng et al., 2010; Lauro et al., 2011; Williams et al., 2012), while others focused on sponge symbionts (Liu et al., 2012), deep-sea tubeworms symbionts (Gardebrech et al., 2012), microbial communities inhabiting acid mine drainage ecosystems (Ram et al., 2005; Belnap et al., 2010; Bertin et al., 2011), or activated sludge (Wilmes et al., 2008). Since metaproteomics is an untargeted experimental approach, the identification of proteins involved in the nitrogen cycle is highly dependent on the level of coverage of the metaproteome investigated. For example, when the number of proteins identified is limited, the chance of detecting proteins involved in specific metabolic activities such as the nitrogen cycle also becomes limited. In other words, the more successful a metaproteomic study is, the higher the chance of deciphering entire biological pathways. In order to ensure the success of such an approach it is highly recommended to combine metaproteomics with metagenomics (matched or unmatched; Siggins et al., 2012). First, recent advances in the nitrogen cycle by metaproteomics will be discussed in the context of microbial

116  | Smith and Abram

communities recovered from water samples, followed by the investigation of host–symbionts relationships and acid mine drainage biofilms and finally activated sludge systems. Water An excellent example of studies successfully applying metaproteomics to decipher the nitrogen cycle in an entire ecosystem is that of Lauro et al. (2011), who investigated the biogeochemical cycles occurring throughout an entire water column from Lake Antarctica. Combining the analysis of microbial DNA and proteins from water samples taken from six depths of the lake with environmental metadata (dissolved oxygen content, salinity, temperature, reduced nitrogen, nitrate, etc.), they described the carbon, nitrogen and sulfur cycles within the ecosystem. The phylogenetic diversity of the lake, as derived from metagenomics, was found to vary as a function of depth (Lauro et al., 2011). Overall, the Simpson indices of diversity were high for the upper oxic layers of the lake (5 m and 11.5 m depth), even higher for the lower anoxic layers (14 m, 18 m and 23 m depth) and relatively low at the oxycline (12.7 m depth), where green sulfur bacteria (GSB) were found to dominate (Lauro et al., 2011). Nitrogen fixation within the lake ecosystem could potentially be carried out by the dense population of diazotrophic GSB, but at the time of sampling these seemed to assimilate ammonia, most likely found at concentrations inhibiting nitrogen fixation (Ng et al., 2010; Lauro et al., 2011). Indeed, ammonia concentrations were previously shown to be at least ten times that of nitrate and nitrite at the oxycline, where GSB populations thrive (Burton, 1980). GSB genes required for nitrogen fixation and nitrate assimilation were identified in the metagenome but the corresponding enzymes were not detected in the metaproteome (Ng et al., 2010). Both reduced nitrogen (ammonium and amino acids) and total nitrogen concentrations were previously shown to increase with depth (Rankin et al., 1999) with top layers of the lake likely relying on diffusion processes for nitrogen sources availability (Lauro et al., 2011). The detection of glutamate synthases in the metaproteome indicated active nitrogen assimilation from SAR11 and Actinobacteria in the

upper strata of the lake and from GSB at the oxycline. In the lower anoxic layers, mineralization seemed to prevail converting organic nitrogen to ammonium (Lauro et al., 2011). Strikingly, no evidence of nitrification was obtained from either of the ‘omics’ datasets from the lake. Ammonia oxidation genes were found to be absent from metagenomic sequences and only a very limited number of 16S rRNA genes were attributed to known nitrifying microorganisms. The detection of this small fraction of ammonia oxidizers in the absence of nitrification (at the time of sampling) might have resulted from the occasional low input of nitrate within the ecosystem due to snow melt events, which could have selected for this less abundant nitrifying population (Lauro et al., 2011). As nitrate levels were previously observed to be low throughout the year (Rankin et al., 1999), Lauro et al. (2011) suggested that the lack of nitrification together with an observed low level of denitrification could be a strategy to conserve bioavailable nitrogen. Evidence of one potential biological process leading to nitrogen loss was suggested however via the detection of putative hydroxylamine/hydrazine oxidoreductase in the deeper layers of the water column, implying that anaerobic ammonia oxidation (anammox) was occurring at the bottom of the lake (Lauro et al., 2011). Overall, Lauro et al. (2011) successfully described the processes involved in nitrogen cycling within Lake Antarctica and demonstrated the importance of integrating ‘omics’ datasets with environmental metadata to get meaningful insights into ecosystem functioning. Another study emanating from the same group as Ng et al. (2010) and Lauro et al. (2011) led by Ricardo Cavicchioli, elegantly illustrates the power of metaproteomics to provide some insights into nitrogen cycling and specifically to clarify the contribution of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) to nitrification (Williams et al., 2012). In this study, Williams et al. (2012) compared the summer and winter metaproteomes of bacterioplankton from West Antarctic Peninsula surface seawaters. This polar ecosystem is typically exposed to extreme environmental variations with summer periods where continuous solar radiation results in snow melt, contrasting sharply with cold

Metaproteomics and N-cycling Microbial Communities |  117

icy dark winters. In addition, the West Antarctic Peninsula has been identified as experiencing global warming at accelerated rates (Turner et al., 2005). Similarly to Lauro et al. (2011), Williams et al. (2012) employed an experimental strategy involving the collection of physicochemical data combined with metagenomics and metaproteomics. This study identified 1061 proteins amongst which 310 were found to be unique to the summer metaproteome, 349 unique to the winter metaproteome and 402 common to both seasons. In total, over 78% of proteins were assigned to bacteria, 18% to archaea, 3% to eukarya and less than 1% to phage. Protein assignments also revealed seasonal differences in the abundance of specific bacteria and archaea and these phylogenetic profile changes could be correlated to shifts in metabolic activities during summer and winter periods (Williams et al., 2012). For example, proteins assigned to Marine Group I Crenarchaeota and to nitrite oxidizing bacteria were only detected in the winter metaproteome with 30% of the total proteins being expressed by AOA at that time of the year (Williams et al., 2012). Specifically, crenarchaeal proteins involved in ammonia transport and oxidation were identified in the winter metaproteome indicating that AOA play a significant role in nitrification during this season. Overall the metaproteomic data provided evidence of interplay between different microbial groups regarding nitrogen transformations in the surface waters of the West Antarctic Peninsula during the winter months. Briefly, AOA and AOB were found to carry out ammonia oxidation leading to nitrite production, which in turn was oxidized to nitrate by bacterial species belonging to the phyla Nitrospirae and Planctomycetes (Williams et al., 2012). Proteins from Alteromonadales were exclusively detected in the summer metaproteome while the abundance of proteins from Bacteroidetes was found to increase compared with the winter period. Overall, the actual functional categories represented by the expressed proteins did not show great seasonal fluctuations indicating a certain level of stability within this ecosystem. Interestingly, archaeal and bacterial populations were shown to rely on different metabolic pathways to assimilate ammonia (Williams et al., 2012). Specifically, bacteria were found

to express the high substrate affinity enzymes glutamine synthetase and glutamate synthase detected in both summer and winter metaproteomes, while archaeal ammonia oxidation, only limited to winter months, involved the lower substrate affinity enzyme glutamate dehydrogenase. The use of the latter enzyme by AOA could be compensated by a high level of expression of ammonium transporters leading to elevated intracellular ammonium concentrations, which in turn could allow for the use of this low affinity pathway (Stewart et al., 2012; Williams et al., 2012). The observation of different strategies for ammonia assimilation adopted by AOA and AOB supports the hypothesis of niche partitioning of these two microbial groups within the marine environment (Martens-Habbena et al., 2009; Williams et al., 2012). Taurine, naturally present in marine habitat (in tissues of invertebrates and in algae), was identified as a significant source of nitrogen within the polar ecosystem as suggested by the detection of proteins involved in taurine degradation in the summer and winter metaproteomes (Williams et al., 2012). To conclude, this study demonstrated that in the surface waters of West Antarctic Peninsula, AOA participate to nitrification exclusively during the winter months, while AOB appear to be involved in this process predominantly during the summer periods (Williams et al., 2012). The contribution of AOA to nitrification in the marine environment has been the subject of debate since the discovery of this archaeal process (Venter et al., 2004), and has consequently attracted a lot of interest. Another study illustrating the usefulness of metaproteomics to investigate such a topic analysed microbial metaproteomes along a natural gradient, from oligotrophic to nutrient-rich surface waters, in the South Atlantic Ocean (Morris et al., 2010). The protein extraction method employed in this study targeted solely microbial membrane fractions and the metagenomic sequences from the GOS expedition were used for protein identification (Rusch et al., 2007; Yooseph et al., 2007). Bacterial clone library analysis revealed shifts in microbial composition along the nutrient gradient, which were correlated with changes in physicochemical conditions (Morris et al., 2010). For example, Prochlorococcus were exclusively recovered from

118  | Smith and Abram

oligotrophic open ocean waters characterized by high temperature and high salinity, while Bacteroidetes were typically detected in nutrient rich coastal waters where lower temperature and salinity conditions prevailed. SAR 11, however, were found ubiquitously in the South Atlantic Ocean. Urea and ammonia transporters were the most commonly identified transporters from oligotrophic open ocean waters, with urea transporters being one of the most abundant proteins assigned to Prochlorococcus (Morris et al., 2010). Strikingly, no phosphate transporter was detected in any of the metaproteomes analysed, indicating that nitrogen is more limiting than phosphate in the surface waters of the South Atlantic Ocean. Finally, evidence of archaeal nitrification was highlighted by the abundant detection of crenarchaeal ammonium monooxygenase proteins in nutrient-rich coastal waters, suggesting that AOA play an important role in nitrification in this ecosystem (Morris et al., 2010). Another study taking advantage of the vast amount of metagenomic data generated by the GOS expedition reported on targeted metaproteomics in the Sargasso Sea where only SAR11, Prochlorococcus and Synechococcus proteins were analysed (Sowell et al., 2009). This was achieved by selecting specifically the metagenome sequences for these bacterial groups detected in the Sargasso Sea from the GOS expedition database. So even though during the process of protein extraction all the proteins expressed within an ecosystem should be theoretically isolated, it is possible to target specific organisms by tailoring the sequence database used in the final phase of the metaproteomic workflow (Fig. 6.1) accordingly. The Sargasso Sea is an oligotrophic environment where nutrients such as nitrate and nitrite are typically present at low concentrations. Not unexpectedly, the competition for nutrient acquisition appeared to be intense within this ecosystem, as indicated by the large proportion of transporters for substrate uptake in the metaproteome (Sowell et al., 2009). SAR11 were found to assimilate inorganic nitrogen with the detection of ammonium transporters, glutamine synthetase and glutamate synthase proteins, Prochlorococcus appeared to rely on urea as the main nitrogen source, while Synechococcus were shown to utilize

both inorganic and organic nitrogen as indicated by the expression of ammonium and oligopeptide transporters (Sowell et al., 2009). The different strategies in nitrogen acquisition adopted by the three microbial groups analysed might suggest niche partitioning to allow the coexistence of these species within this oligotrophic ecosystem. Even though it might seem limiting perhaps to target only specific organisms when the metagenomic data from the GOS expedition should contain relevant sequences corresponding to the majority of the microbial consortia from the Sargasso Sea, this experimental strategy presents the advantage of honing in on crucial members of the community and therefore facilitating data handling by simplifying the dataset to be analysed. Indeed, SAR11 were previously found to make up as much as 35% of the total prokaryotic populations (Morris et al., 2002), while Prochlorococcus and Synechococcus are considered to be the predominant photosynthetic microorganisms inhabiting the surface waters of the Sargasso Sea (Sowell et al., 2009). Work from the same research group subsequently investigated the metaproteomes from microbial communities of surface waters from the coast of Oregon, also using metagenomic sequences from the GOS expedition. This time, the database constructed for protein identification was reduced in size by selecting the metagenome sequences from geographical locations considered similar to the coast of Oregon in terms of microbial distribution and environmental conditions and therefore data handling was simplified without applying any organisms restrictions (Sowell et al., 2011). Surprisingly, even in the nutrient rich waters from coastal Oregon, competition for substrate acquisition seemed critical for microbial survival as suggested by the abundance of transport proteins identified in the metaproteome. Particularly, the repeated detection of transporters involved in amino acid, nitrate, ammonium and taurine uptake together with the identification of glutamine synthetase proteins indicated that nitrogen is likely limiting in this nutrient rich ecosystem (Sowell et al., 2011). To date, most metaproteomic investigations in the marine environment have focused on pelagic microbial communities but other studies (discussed below)

Metaproteomics and N-cycling Microbial Communities |  119

have used this technology to decipher symbiotic relationships between microbial communities and their marine hosts. Host–symbionts relationships Liu et al. (2012) combined metagenomics and metaproteomics to investigate the interactions occurring between the sponge Cymbastela concentrica and its microbial symbionts. The rigorous experimental set-up employed in this study led to the analysis of a total of 108 mass spectrometry runs involving biological and technical replicates, where microbial communities recovered from three different sponge samples were each analysed in triplicate. Corresponding metagenomic analysis was also performed with the generation of a metagenome from each of the three sponge samples (Liu et al., 2012). Overall 765 nonredundant proteins were identified of which 34% could not be assigned to known species, unveiling the novelty of the sponge microbial communities. Sponges are known to secrete and accumulate ammonium (Liu et al., 2012), which was shown to be taken up and assimilated aerobically by archaeal symbionts via the detection of both ammonia transporters and ammonia monooxygenase proteins assigned to Nitrosopumilus maritimus (Liu et al., 2012). Analysis of the metagenomes revealed the presence of close relatives of nitrogen fixing and denitrifying species both belonging to the Phyllobacteriaceae, as highlighted by the identification of genes involved in nitrate uptake and reduction. The metaproteomes also indicated that denitrification was taking place within the sponge microbial community but the detected nitrate reductase could not be assigned unambiguously to members of the Phyllobacteriaceae. Overall, metaproteomics could demonstrate that both aerobic (nitrification) and anaerobic (denitrification) processes of the nitrogen cycle are being carried out simultaneously by the microbial sponge symbionts in situ, and this consequently could contribute to the removal of ammonium from the host (Liu et al., 2012). Another study investigating host symbionts relationships reported on comparative metaproteomics of microbial communities harvested from two close relatives of deep-sea ocean tubeworms typically thriving in different environmental

conditions (Gardebrecht et al., 2012). Both tubeworms are involved in the sequential process of recolonization of deep-sea vents after volcanic eruptions. In this process, early colonisers Tevnia jerichonana, which thrive under microaerophilic and high sulfide conditions, are eventually displaced by Riftia pachyptila once higher oxygen and lower sulfide concentrations prevail in the environment. Gardebrecht et al. (2012) combined metagenomics and metaproteomics to characterize the microbial communities coexisting within the two above species of tubeworms. Despite the dissimilarity of environmental conditions of the tubeworms habitats, the composition of their microbial endosymbionts was found to be identical. Indeed 16S rRNA sequences from the metagenomes of two R. pachyptila and one T. jerichonana were shown to display 100% similarity. Nitrogen sources within hydrothermal vents are typically found in the form of inorganic nitrogen, which cannot be directly used by tubeworms. Therefore, microbial nitrate assimilation is thought to provide the host with sufficient nitrogen for biosynthesis (Gardebrecht et al., 2012). Metagenomic analysis indicated that the three symbionts investigated were all equipped to perform denitrification with the identification of genes encoding all the reductase enzymes involved in this process (nitrate, nitrite, nitric oxide, nitrous oxide reductases as well as dissimilatory nitrate reductase, NapABC). Nitrate reductase was also detected in the corresponding metaproteomes, suggesting that denitrification is likely to be carried out in situ by the microbial symbionts. Overall protein expression within the two tubeworm species was found to be highly similar, indicating that despite the marked differences in external surrounding conditions, a stable internal environment is likely to prevail inside the host (Gardebrecht et al., 2012). As illustrated by Gardebrecht et al. (2012), comparative metaproteomics is a useful tool to evaluate the level of similarity experienced by microbial communities between different sets of environmental conditions. Belnap et al. (2010) investigating acid mine drainage biofilms, used such an approach to compare the states of mixed microbial cells between laboratory culturing conditions and their natural habitats.

120  | Smith and Abram

Acid mine drainage As mentioned in the introduction, metaproteomics is an untargeted experimental strategy, which can best provide some insights into nutrient fluxes occurring within ecosystems when combined with SIP. The application of such coupled technologies requires the set-up of microcosms striving to replicate natural environments as closely as possible. In that context, Belnap et al. (2010) employed metaproteomics as an indicator of similarities between their laboratory culturing system and the natural habitat of the mixed microbial consortia under study. Acid mine drainage habitats are characterized by extreme environmental conditions, such as low pH and high metal concentrations and as such typically sustain microbial communities of a relatively low diversity. An example of extensively studied acid mine drainage system is the one located at the Richmond mine (Iron Mountain, CA, USA), from which Jillian Banfield’s group exploits the low diversity microbial biofilms as a model system to explore and develop proteomics related technologies. Indeed the first study applying metaproteomics to biofilms from this site is largely considered as a technological breakthrough in environmental proteomics (Ram et al., 2005; Siggins et al., 2012). A mixed microbial consortia culturing system was designed to deepen the understanding of the ecology of acid mine drainage biofilms (Belnap et al., 2010). To ensure that the biofilms were experiencing the same conditions as they were in their natural environment, a comparative metaproteomic approach was employed. The experimental strategy also involved 15N labelling (protein-SIP; discussed further in Chapter 5) for quantitative purposes. Metagenomic sequences are available for the microbial biofilms from this site and were used for protein identification. Fluorescence in situ hybridization (FISH) analysis revealed a similar species organization and morphology between the natural and laboratory cultivated biofilms (Belnap et al., 2010). Overall, metaproteomics indicated a high level of similarity between natural and artificial laboratory samples, with the exception of an increase in stress response proteins correlated with a lower growth rate in the laboratory settings. Subsequent alterations to the culturing conditions led to a decrease in abundance of stress response

proteins together with an increase in growth rate (Belnap et al., 2010). These changes to culturing conditions included a reduced level of ammonia and were also found to result in a decrease in abundance of urea cycle proteins when compared with the natural biofilms. This observation indicated that a high level of fixed nitrogen is likely to be available in the natural environment (Belnap et al., 2010). Acid mine drainage biofilms are known to contain Leptospirillum group III, a nitrogenfixing bacterium, but nitrogen fixation proteins were not detected in the metaproteomes of the natural biofilms. Therefore the authors concluded that a significant amount of fixed nitrogen was likely to be available at the location and time of sampling, released either from upstream communities or decomposition of old biofilms (Belnap et al., 2010). As mentioned earlier, Ram et al. (2005) from the same group as Belnap et al. (2010), conducted the first metaproteomic analysis of a microbial biofilm from the Richmond mine, and identified over 2000 proteins. This was found to correspond to ~ 49% of the predicted proteins from the five most abundant organisms from this ecosystem, which was unprecedented at the time. The biofilm metaproteome was dominated by novel proteins without predicted functions, highlighting the potential of metaproteomics to uncover new metabolic pathways. Strikingly, only one nitrogenase enzyme was detected in the entire dataset and this was assigned to a low-abundance bacterium, Leptospirillum group III (Ram et al., 2005). The dominant biofilm microorganism Leptospirillum group II was found to express proteins involved in nitrogen regulation and ammonia uptake, which suggests that this main colonizer relies on other lower abundance nitrogen fixing bacteria to provide bioavailable nitrogen. Interestingly, two proteins assigned to Leptospirillum group II were found to be encoded in a four-gene operon and showed a high level of similarity with two nitrogen regulatory proteins. This could imply that the other two genes of the four-gene operon encode novel proteins involved in nitrogen metabolism (Ram et al., 2005). Following on from the remarkable observation of the detection of a unique nitrogenase in the metaproteome correlating with the presence of a unique nif operon in the

Metaproteomics and N-cycling Microbial Communities |  121

metagenome, Tyson et al. (2005) proceeded to isolate Leptospirillum group III, the key nitrogen fixing bacterium from the acid mine drainage biofilm community. The logical experimental strategy adopted to isolate the organism was the cultivation of the biofilm in the total absence of a nitrogen source. In addition, the culture medium did not contain any carbon sources which demonstrated that Leptospirillum group III is autotrophic. These two studies (Ram et al., 2005; Tyson et al., 2005) demonstrate how the detailed analysis of ‘omics’ datasets can be used to direct the design of simple targeted culturing strategies for the isolation of uncultivated microorganisms. The biology of an acid mine drainage arsenicrich ecosystem located in Carnoulès (France) has been recently investigated using a combined metagenomic and metaproteomic approach (Bertin et al., 2011). The microbial community recovered from sediments was found to be dominated by seven bacteria and metaproteomics led to the identification of over 500 unique proteins. Two of the seven dominant bacterial strains displayed the ability to fix nitrogen as indicated by the presence of nif operons in the metagenome but the corresponding nitrogenase enzymes were not identified in the metaproteome (Bertin et al., 2011). For one of the nitrogen-fixing bacteria, however, a regulatory protein involved in nitrogen fixation was identified, which might imply that nitrogen fixation was occurring at the time of sampling and that the absence of nitrogenase in the metaproteome might possibly be attributed to the high oxygen sensitivity of this enzyme. Evidence of capacities for nitrate and nitrite respiration as well as urea degradation was found in the metagenome but this did not seem to be confirmed by the metaproteome (Bertin et al., 2011). Nitrogen assimilation was suggested to be occurring at the time of sampling nonetheless by the detection of glutamine synthetase and other nitrogen regulatory proteins in the metaproteome (Bertin et al., 2011). Even though this study identified over 500 proteins, this merely allowed for a glimpse into in situ nitrogen transformations and it is only with more extensive datasets (technically challenging to generate) that deeper insights are achievable. Within the same ecosystem, a different experimental strategy was successfully employed by the same

group of researchers to decipher the metabolic exchanges between the dominant photosynthetic protist Euglena mutabilis and the bacterial community of the acid mine drainage ecosystem (Halter et al., 2012). In that context, proteomics was only applied to the pure culture protist, while metabolomics was used to investigate the protist intracellular metabolites as well as the metabolites present in the aquatic environment of the acid mine drainage. Briefly, Euglena cells were found to express proteins involved in urea cycle as well as in ammonia assimilation and most of the metabolites secreted by the protist were shown to be nitrogen containing compounds, such as amino acid and urea (Halter et al., 2012). As mentioned above, the metagenome of the bacterial community from the same site indicated the presence of genes involved in urea degradation (Bertin et al., 2011), which could suggest a bacterial nitrogen dependence on the protist (Halter et al., 2012). Activated sludge The last ecosystem to be discussed in the context of aquatic metaproteomics and the nitrogen cycle is that of aerobic wastewater treatment and particularly activated sludge systems. Such systems aim amongst other things at reducing the organic matter content of wastewaters before discharge in the environment in order to avoid eutrophication of receiving water bodies. Phosphorus, recognized as a key limiting nutrient in freshwater ecosystems, needs to be removed efficiently before wastewater release. Activated sludge subjected alternatively to aerobic and anaerobic phases was shown previously to promote phosphorus removal (Seviour et al., 2003). Metaproteomics was employed to decipher the metabolic pathways at play within such a system using unmatched metagenomic sequences generated from similar environments for protein identification (Wilmes et al., 2008). This experimental strategy led to the identification of over 1000 non-redundant proteins, amongst which some were involved in denitrification when the sludge was exposed to anaerobic conditions. Previous metagenomic analysis (García Martín et al., 2006) had already indicated the capacity for denitrification of the dominant polyphosphate accumulating bacterium, Accumulibacter phosphatis, prevailing in such activated sludge systems.

122  | Smith and Abram

However, García Martín et al. (2006) could not identify any respiratory nitrate reductase in their metagenome sequences due specifically to the absence of a gene encoding a quinol reductase subunit (napC) of the nap operon. Metaproteomics, however, led to the detection of a NapC homologue shown to be encoded by a gene, which was not part of the nap operon but found elsewhere in the A. phosphatis genome (Wilmes et al., 2008). Therefore, metaproteomics could unveil the disjointed nature of the nap operon from A. phosphatis as well as confirm that denitrification was taking place in situ in activated sludge ecosystems. Further to this observation, Wilmes et al. (2008) suggested the inclusion of anoxic zones during the aerobic phases of phosphate removing activate sludge to promote the growth of A. phosphatis and consequently improve phosphate removal efficiencies. Overall metaproteomics has been applied rather extensively in the context of aquatic environments, where most of the challenges encountered in the technical workflow (Fig. 6.1) have been overcome. This has been supported and paralleled with a relative ease of metagenomic data generation in such habitats. In terrestrial ecosystems, however, and particularly in soil, many technical difficulties are only in the process of being resolved and consequently extensive metaproteomic studies are more seldom. Terrestrial ecosystems Even though metaproteomic approaches have been employed in the context of terrestrial ecosystems and particularly in soil (Siggins et al., 2012), only few studies to date have achieved a level of metaproteome coverage allowing for the identification of proteins involved in the nitrogen cycle. Example of such studies have aimed to elucidate microbial metabolic functions from phyllospheric and rhizopheric environment (Knief et al., 2012) and from wood-termite gut (Burnum et al., 2011). Strikingly, both studies employed experimental strategies involving the combination of metagenomics and metaproteomics. Up until now, only few metagenomic soil datasets were available (Tringe et al., 2005; Mackelprang et al., 2011), and these were surprisingly underused for metaproteomic purposes. The Terragenome

project, however, should lead to the generation of extensive soil metagenome sequences directly relevant to protein identification in this difficult environment. Knief et al. (2012) analysed the metaproteomes from mixed microbial consortia inhabiting the aerial parts (phyllosphere) and the root surface (rhizosphere) of rice plants with the aim, amongst others, to demonstrate the process of microbial nitrogen fixation in situ. Two metagenomic datasets were generated to facilitate protein identification, one from the phyllosphere and one from the rhizosphere. Alphaproteobacteria and Actinobacteria were found to be dominant in the phyllosphere, while the rhizospheric environment displayed a higher level of diversity amongst which Alpha- Beta- and Deltaproteobacteria were the most abundant (Knief et al., 2012). Over 4000 proteins (including redundant proteins) could be identified in total and insights into the relevance of metagenomic data to protein identification by metagenomics was provided. For the phyllosphere 60% of the proteins identified corresponded to metagenomic sequences while the remaining 40% were based on sequences from the general Uniref100 database. For the rhizosphere, however, only a maximum of 25% of proteins could be identified from the corresponding metagenomic data (Knief et al., 2012). The authors reported issues with assembly of the rhizospheric metagenome attributed to the higher level of complexity of this habitat when compared with the phyllosphere. In such instances, when metagenomic data are mainly used to facilitate protein identification, metagenome assembly is best bypassed and algorithms such as the one developed by Rooijers et al. (2011) allow for the use of raw unassembled nonannotated metagenome sequences. Interestingly, the addition of metagenomic data from the phyllosphere did not improve protein identification in the rhizosphere and the reverse was also true for the phyllosphere. This observation highlights the differences in microbial composition from the two habitats (Knief et al., 2012). Metagenomic analysis revealed the presence of genes encoding dinitrogen reductase (nifH) and dinitrogenase (nifD and nifK) in both the phyllosphere and the rhizosphere but these were assigned to different taxa in the two environments. Most commonly,

Metaproteomics and N-cycling Microbial Communities |  123

nifH was found in sequences belonging to Azorhizobium and Rhodopseudomonas in the phyllosphere and to Rhizobium and methanogenic archaea, amongst others, in the rhizosphere (Knief et al., 2012). Metaproteomics, however, only suggested evidence of nitrogen fixation in situ in the rhizosphere and the nitrogenase enzymes detected were not necessarily assigned to taxa from which nifH sequences had been most frequently identified in the metagenome, such as for example in the case of Bradyrhizobium. In the phyllosphere, evidence of nitrogen assimilation was indicated by the detection of peptide, amino acids and ammonium transporters as well as proteins involved in ammonia oxidation (Knief et al., 2012). This study demonstrates not only how metagenomics can assist protein identification but also how genomic inference alone might lead to a distorted prediction of in situ activities since in this case, nitrogen fixation potential was detected in both the phyllosphere and the rhizosphere whereas at the time of sampling this process was only being carried out by the microbiome of the rhizosphere. While investigating the active metabolic pathways carried out by the microbial symbionts within the hindgut of a wood termite (Nasutitermes corniger), Burnum et al. (2011) concluded that nitrogen fixation and assimilation amongst other activities were more important to the symbiotic relationship than cellulose degradation. The combination of metagenomics and metaproteomics led to the detection of nearly 900 proteins from the termite gut microbiota. Evidence of nitrogen fixation and assimilation was highlighted with the detection of nitrogenase, glutamine synthetase and glutamate synthase proteins. Interestingly, pyruvate ferredoxin/flavodoxin oxidoreductase was found to be the most commonly identified protein, which importantly link carbohydrate fermentation with nitrogen fixation (Burnum et al., 2011). This enzyme catalyses the decarboxylation of pyruvate, an intermediate of glycolysis, which results in the generation of electrons directly used by nitrogenase for nitrogen fixation. In this context, metaproteomics revealed the unexpected finding that the microbial symbionts from a wood termite seemed to express more proteins involved in carbohydrate metabolism (other than cellulose)

and nitrogen fixation and assimilation than enzymes directly involved in cellulose degradation. These results were consistent with previous observations indicating that the host termite initiates cellulose degradation before by-products are fully degraded by the gut microbiome (Tokuda and Watanabe, 2007; Burnum et al., 2011). Even though metaproteomics can be very useful for the detection of enzymes involved in the nitrogen cycle, as illustrated by the studies discussed above, protein-SIP presents the advantage of monitoring 15N or 13C incorporation into proteins. As opposed to traditional SIP application, labelled and unlabelled proteins are typically not separated from each other, which allows for the reconstruction of active metabolic pathways both impacted and non-impacted by label uptake. This type of study is most meaningful when protein identification is relatively high and therefore best applied when metagenomic data are available for the ecosystem investigated. Protein-SIP and the nitrogen cycle Protein-SIP can be used for the analysis of carbon and nitrogen fluxes in mixed microbial communities via the incorporation of stable isotopes directly into amino acids. Similarly to metaproteomics, detection of heavy labelled amino acids leads to the identification of the function of the labelled proteins as well as the microorganism responsible for their production. Mass spectrometry is a highly sensitive method for heavy label detection with the added benefit of also measuring the level of isotope incorporation, a direct proxy for activity. Measurements of incorporations of a stable isotope into amino acids involved in the nitrogen cycle in conjunction with rate process data can provide a direct link between metabolic activities and environmental processes. This is an advantage over the more traditional DNA and RNA SIP where the level of stable isotope incorporated into molecules cannot be determined. Furthermore, due to the sensitivity of mass spectrometry detection of the incorporated stable isotope, lower substrate concentrations and shorter incubation times can be used in SIP microcosms. These can in turn emulate more realistically environmental

124  | Smith and Abram

concentrations of the labelled substrate. Moreover, this sensitivity of detection will also allow for the activity and identity of rare members within a mixed microbial community to be detected. The limits of detection of heavy labelled isotopes have been shown to be highly sensitive. Using a pure culture of Pseudomonas fluorescens incubated with 13C-galactose, Taubert et al. (2011) demonstrated sensitivity as low as 0.1% relative isotope abundance (RIA). Based on the detection limits of carbon RIA, they estimated that 0.5% 15N RIA should be possible under similar conditions. Although these experiments were conducted in pure culture and not in complex ecosystems, this level of accuracy should allow meaningful conclusions to be drawn from mixed community incubations. The protein-SIP studies carried out to-date illustrate the level of understanding that can be achieved with such experimental strategy. For example, protein-SIP was used to directly link carbon and nitrogen cycling using Aromatoleum aromaticum, a microorganism that can utilize toluene as an electron donor under denitrifying conditions ( Jehmlich et al., 2008, 2010). 13C7-labelled toluene was supplemented as a carbon source in pure culture and in an artificial mixed consortium. Carbon from the metabolism of the labelled toluene was detected within proteins of A. aromaticum both in pure culture and mixed microbial consortia proving it was the active toluene degrader. In addition to toluene degradation, protein-SIP has been successfully exploited to track the biodegradation of other pollutants, including methyl-tert-butyl ether (MTBE) (Bastida et al., 2010) and benzene (Taubert al., 2012) in enrichment cultures. This method is proving to be a powerful tool to simultaneously detect the metabolically active organisms and trace the carbon or nitrogen flow within complex communities, further linking it to other biogeochemical cycles. An excellent example of this is presented in Taubert et al. (2012), examining benzene degradation under sulfatereducing conditions in ground water microcosms from contaminated aquifers. Combining proteinSIP with metagenomics, the flow of heavy labelled benzene was monitored for 300 days. In total, 688 proteins could be identified and the labelled

carbon was detected in proteins expressed by three major functional groups of microorganisms, each of which were metabolizing different carbon sources within a complex community. Clostridiales were found to be responsible for the initiation of benzene degradation coupled with CO2 fixation. The metabolites released during benzene fermentation were in turn used as carbon sources by sulfate reducing Deltaproteobacteria and finally the detection of low levels of isotope incorporation within proteins assigned to Bacteroidetes and Chlorobi indicated that these bacteria were likely to be scavenging on dead cells. Similar approaches labelling carbon under denitrifying conditions could reveal metabolically active organisms and pathways within the carbon and nitrogen cycle in complex microbial communities. In a similar manner to labelling carbon, heavy isotopes of nitrogen have also been used in protein-SIP studies (for an extensive review see Chapter 5). Pan et al. (2011) in an elegant experiment, used 15N to decipher the organisms and pathways involved in the development and recolonization of acid mine drainage biofilms grown in laboratory reactors. Biofilms were established, from which pieces were removed prior to incubation with three different levels (0.4%, 50% and 98%) of heavy labelled (15NH4)2SO4 as the sole source of nitrogen. Samples from the mature and re-colonized biofilms were taken at two different time points post incubation for protein identification and 15N atom percentage incorporation estimation. Using this approach, over 2400 nonredundant proteins could be identified. While Taubert et al. (2012) reported to have manually searched their mass spectra for 13C incorporation, Pan et al. (2011) developed an algorithm to identify proteins of varying 15N incorporation and accurately estimate 15N percentage incorporation. Overall, the mature biofilm incorporated very low levels of 15N, indicating that there was a low turnover of proteins likely due to no requirement for de novo biosynthesis. In contrast within the early re-growing biofilm, the majority of labelled proteins belonged to Leptospirillium group III. Interestingly, an over-representation of unlabelled proteins from the same group of microorganisms in the later recolonizing biofilm revealed the role of emigration from established biofilm to the newly

Metaproteomics and N-cycling Microbial Communities |  125

forming biofilm. Owing to the sensitive detection limits of heavy-label incorporation, protein-SIP also uncovered members and dynamics within rare biosphere of the biofilm. Of particular note was the identification of proteins from viruses known to infect Leptospirillium (Pan et al., 2011). Protein-SIP offers the distinct advantage of a targeted approach to identify both metabolic activity and level of activity via the accumulation of heavy labelled substrates in proteins. This method, however, requires the establishment of microcosms (e.g. Taubert et al., 2012) or ideally whole-community culturing systems (Pan et al., 2011) under biologically relevant conditions and high quality metagenomic sequences for protein identification as well as high performance computing for the particularly demanding data analysis processes. Proteomics of microbial isolates relevant to the nitrogen cycle Even though metaproteomics and protein-SIP provide insights into in situ metabolic pathways by taking into consideration all the coexisting microorganisms present in an ecosystem, experimental strategies focusing on individual microbial isolates relevant to the nitrogen cycle can also be extremely useful. A few examples of such studies will be discussed here. It should be kept in mind, however, that data generated from pure culture cannot be easily extrapolated to natural habitats, in which isolates are typically faced with intense microbial competition for resources and continuously changing environmental conditions. To avoid generating data in a context too dissimilar to the natural environment of microorganisms, Habicht et al. (2011) adopted the strategy of analysing the proteome of a targeted species in situ. The GSB Chlorobium clathariforme was previously shown to be the predominant phototrophic bacterium in Lake Cadagno (Switzerland) accounting for over 50% of the bacterial population. Proteins were extracted from water samples at four depths (11.5 m, 12 m, 13 m and 17 m) in the lake and a maximum of 1321 proteins from C. clathariforme could be identified, where GSB density was at the highest. Interestingly, 350 and

120 additional proteins could be identified using all Chlorobi genome sequences available and the entire UniProt database for protein identification, respectively (Habicht et al., 2011). Five C. clathariforme proteins (Nif U, Nif H, Nif D, Nif K and Nif U domain protein) involved in nitrogen fixation were detected throughout the water column and protein abundance revealed that C. clathariforme is preferably fixing nitrogen in the upper layer of the lake, where ammonium concentrations are low. Comparable levels of nitrogen assimilation were found to occur at all depths with the detection of ammonium transporters, glutamine synthetase and glutamate synthase (Habicht et al., 2011). The even level of abundance of these nitrogen assimilation proteins throughout the lake column suggests that ammonium is likely the preferred source of nitrogen for C. clathariforme. Interestingly, the nitrogen fixing capacity of C. clathariforme had been previously the subject of debate. Indeed, in pure culture, this microorganism did not show the ability to fix nitrogen despite possessing all the genes required for this process (Overmann and Pfennig, 1989; Madigan, 1995; Halm et al., 2009). The study of Habicht et al. (2011) therefore clarifies the nitrogen fixing role of C. clathariforme, as well as demonstrates how microbial physiology observed in laboratory settings can greatly differ from in situ behaviour. An interesting question that attracted a lot of attention is how certain groups of bacteria can accommodate oxygenic photosynthesis with nitrogen fixation given the high oxygen sensitivity of the nitrogenase enzyme complex (Selao et al., 2008; Aryal et al., 2011; Sandh et al., 2011). Aryal et al. (2011) investigated the compartmentalization mechanisms of these two incompatible processes in the marine unicellular microorganism Cyanothece using proteomics. This cyanobacterium performs photosynthesis in the light and nitrogen fixation in the dark. Using stable isotope labelling with amino acids in cell cultures (SILAC), protein expression was monitored over two 24 h light–dark cycles (Aryal et al., 2011). The direct comparison of heavy and light peptides allowed for the measurement of changes in protein abundance, while incomplete labelling could provide some insights into protein turnover. In total, 721 proteins with temporal changes could

126  | Smith and Abram

be detected, from which 425 were found to correlate with previously identified cycling mRNA transcripts (Stockel et al., 2008; Aryal et al., 2011). This observation implies that the remaining 296 proteins, whose corresponding transcript expression levels were not found to change as a function of the light-dark cycles, are likely to be regulated in a post transcriptional manner. Labelled proteins were identified across diverse functional categories including nitrogen fixation and assimilation as well as CO2 fixation and assimilation. In addition, a large fraction of labelled proteins were found to be of unknown function, potentially playing important roles in novel pathways. Nitrogen fixation proteins were shown to display a higher level of label uptake in the dark when compared with other functional categories with Nif H, Nif D and Nif K not detected during light periods (Aryal et al., 2011). Glutamine synthetase and glutamate synthase abundance was also found to increase during the dark, as well as the level of expression of a terminal oxidase from the respiratory transport chain. This latter observation suggests that high respiratory rates are likely to occur during nitrogen fixation to ensure the scavenging of oxygen molecules from the cytoplasm in order to prevent nitrogenase damage (Aryal et al., 2011). Glycogen synthase was expressed solely during the light periods, while glycogen phosphorylase had a higher level of expression in the dark. Taken together these results indicated that glycogen is actively synthesized in the light during photosynthesis to be metabolized in the dark during nitrogen fixation (Aryal et al., 2011). In addition to glycogen utilization, Cyanothece was also found to express proteins from glycolysis, the pentose phosphate pathway and the phosphoketolase pathway likely to generate the large amounts of both NADH and ATP necessary for the energetically expensive nitrogen fixation process (Aryal et al., 2011). The redirection of carbon metabolism during nitrogen fixation in cyanobacteria has also been reported in the marine bacterium Trichodesmium erythraeum. Sandh et al. (2011) conducted a comparative proteomic investigation of this nitrogen fixing isolate under N2 and NO3– culturing conditions. Over 1000 protein spots could be detected on 2-D gels, amongst which 150 showed differential expression

under the two nitrogen regimes. From these, 94 successfully identified unique proteins were found to be predominantly involved in three main processes, namely energy production and conservation, amino acid transport and metabolism and carbohydrate transport and metabolism. As reported for Cyanothece, nitrogenase and enzymes of the pentose phosphate pathway were expressed at higher levels during nitrogen fixation (Aryal et al., 2011; Sandh et al., 2011). An interesting observation was the up-regulation of an enzyme involved in iron production, whose increase in expression was one of the few proteins to match that of nitrogenase subunits. In the presence of NO3–, enzymes involved in glycogen biosynthesis and the TCA cycle displayed increased levels of expression. This latter observation could possibly correlate with the increase in amino acid biosynthesis observed in NO3– supplemented cultures (Sandh et al., 2011). Overall nitrogen fixation in T. erythraeum was found to coincide with carbon degradation, ammonia assimilation, energy generation and respiration, which is largely consistent with the findings of Aryal et al. (2011). Nitrogen fixation is an energy demanding process, which critically depends on iron availability, as illustrated by the strong up-regulation of an iron producing enzyme in T. erythraeum under nitrogen fixing conditions (Sandh et al., 2011). Indeed, nitrogen fixation distribution in the marine environment has been previously suggested to correlate with iron availability (Saito et al., 2011). Saito et al. (2011) used proteomics to investigate the mechanisms employed by the marine cyanobacterium Crocosphaera watsonii to overcome iron limitation during light/dark cycles. Proteome analysis led to the identification of over 1100 proteins, from which a total of 100 appeared to be differentially regulated over the course of the diel cycle. Amongst these, two main functional groups emerged, proteins involved in photosynthesis on one hand and nitrogen fixation proteins on the other hand. Both had increased level of expression during the day and the night, respectively (Saito et al., 2011). Of particular interest were the drastic changes in expression of nitrogenase iron containing enzymes, which varied from being amongst the

Metaproteomics and N-cycling Microbial Communities |  127

most abundant proteins detected in the dark to undetectable during the light period. These results suggested that C. watsonii synthesizes the required enzymes during the night and orchestrate their degradation during the day. Similarly, iron containing enzymes involved in photosynthesis were shown to be produced during the light period to be further degraded under dark conditions (Saito et al., 2011). The authors could evaluate that such an energetically expensive strategy could lead to the reduction of C. watsonii iron requirement by as much as 40% compared with a virtual situation where all iron-containing enzymes would not be degraded throughout the diel cycle. Taken together, cyanobacteria were shown to have dynamic proteomes whose light– dark cycle shifts are tailored to suit the two incompatible and iron demanding processes of photosynthesis and nitrogen fixation (Aryal et al., 2011; Saito et al., 2011; Sandh et al., 2011). Besides marine microbial nitrogen fixation, proteomic studies have focused on other type of bacterial physiology, such as those of anaerobic and aerobic ammonia-oxidizing bacteria (anammox and AOB, respectively). Both anammox bacteria and AOB play an important role in the nitrogen cycle, anammox, removing nitrogen and AOB, oxidizing ammonia to nitrite and nitrate. A combined genomic, transcriptomic and proteomic investigation of the marine anammox Scalindua profunda led to the identification of 710 proteins expressed in enrichment cultures (van de Vossenberg et al., 2012). Both transcriptomics and proteomics indicated that the most abundant genes transcribed and translated in S. profunda had functions related to nitrogen transformations and carbon metabolism. Hydrazine synthase and hydrazine oxidoreductase proteins were found to be expressed at high level in the enrichment cultures, together with ammonium and nitrite transporters, while nitrate reductase was the most abundant protein detected. Furthermore, nitrite reductase was also expressed at elevated levels, possibly suggesting an important role for this protein in the production of nitrous oxide, a required intermediate for hydrazine metabolism (van de Vossenberg et al., 2012). Overall, the thorough analysis of the genome, the transcriptome and

the proteome led the authors to conclude that S. profunda had a flexible metabolism most likely providing the bacterium with a competitive advantage in the marine environment (van de Vossenberg et al., 2012). In their natural habitats, microorganisms are commonly exposed to feast or famine conditions and their tolerance to starvation is crucial to their survival. The starvation response of Nitrosomonas europea, an AOB commonly found in both aquatic and terrestrial environments was investigated using an experimental strategy involving 15N labelling and proteomics to compare protein expression patterns in actively growing and nitrogen starved cells (Pellitteri-Hahn et al., 2011). Out of 126 proteins identified, 27 were found to be differentially expressed between the two cell states. Overall, not unexpectedly, growing cells seemed to direct the expression of proteins to those involved in biosynthesis and DNA replication while starved-cells appeared to turn on the production of stress response proteins such as those involved in protein protection and degradation or detoxification. Interestingly, the proteome of starved cells was dominated by TonB-dependent receptors involved in nutrient uptake and such proteins were also found to be predominant in situ, where they were ubiquitously identified from both oligotrophic and nutrient-rich waters (Morris et al., 2010). Proteomics has also been employed in the context of terrestrial ecosystems to decipher the symbiotic mechanisms between nitrogenfixing microorganisms and root nodule. Indeed this process is very relevant from an agricultural point of view since it directly impacts on plant productivity. A comparative proteomic approach was conducted on Sinorhizobium meliloti, where bacterial proteomes were analysed in the root nodule and under laboratory culturing conditions (Djordjevic, 2004). Overall, transporters were the most commonly identified proteins and these were differentially regulated under the two conditions investigated; 13 transporters were expressed in situ against 84 under laboratory culturing conditions, suggesting the occurrence of specific nutrient exchange between host and symbiont. Interestingly, 6 transporters out of the 13 detected

128  | Smith and Abram

in the root nodule were involved in amino acid uptake (Djordjevic, 2004). Carbohydrate uptake transport systems were identified under laboratory conditions, while none of the transporters detected in situ had predicted affinity for such substrates, indicating that these might be repressed under nitrogen fixing conditions. Consistent with the expression of amino acid uptake systems in situ, ammonia assimilation proteins were only detected under laboratory conditions, indicating that fixed nitrogen is not assimilated by the symbiont but exported to the host, which in turn might meet the bacterial nitrogen requirement by providing amino acid. In addition, several stress proteins were exclusively expressed in the root nodule, suggesting that the host environment is not optimal for S. meliloti (Djordjevic, 2004). The detection of stress-related proteins during symbiosis was also reported for the nitrogen fixing Bradyrhizobium japonicum and its soybean host (Delmotte et al., 2010). The symbiont was found to express over 2300 proteins in situ, amongst which, not unexpectedly, the most abundant were subunits of the nitrogenase complex. Surprisingly, B. japonicum was found to produce proteins involved in amino acid biosynthesis, which suggests that the fixed nitrogen generated during nitrogen fixation might be directly used by the symbiont (Delmotte et al., 2010), which differs from the observations of Djordjevic (2004). Overall, proteomic strategies in the context of microbial isolates relevant to the nitrogen cycle allow for a detailed look at specific mechanisms including these involved in the orchestration of two biologically incompatible processes by cyanobacteria or legume symbiosis. This level of detail are not really accessible in metaproteomic studies since it would imply, in the case of cyanobacteria for example, the implementation of sampling regimes matching the depth of those used in pure culture context. Obviously, this in turn would lead to an overwhelming number of data, rather difficult to handle. However, metaproteomics and proteomics of relevant isolates should complement each other and ideally results should be integrated with a view to refine predictive models elaborated in the context of system biology aiming to forecast in silico ecosystem responses to environmental stimuli.

Conclusions Overall, recent advances in metaproteomics have allowed this technology to play a central role in the elucidation of in situ microbial community functioning. So far, metaproteomics has been most successful in the context of aquatic ecosystems, including the investigation of acid mine drainage microbial biofilms characterized by a relatively low level of diversity. Indeed, technical challenges inherent to terrestrial ecosystems and particularly soils have somewhat hindered the progress of this technology in these environments. The Terragenome Project, however, should help by generating large amounts of metagenome sequences, which in turn should immediately facilitate protein identification. Above all, metaproteomics presents the unique advantage of linking community composition with function. By definition, metaproteomics is the analysis of the proteins and enzymes expressed in situ by a meta-organism, and as such provides a better evaluation of microbial activity than DNA- or RNA-based technologies. In order to appropriately assess microbial activity, however, an effort should be made to systematically relate metaproteomic data to process rate measurements, the two of which would be expected to correlate reasonably well. As metaproteomics is an untargeted method, specific biological data such as these relevant to biogeochemical cycles can be accessed only when a high level of coverage of the metaproteomes analysed is achieved. This is typically facilitated by the use of relevant metagenomic sequences for protein identification. In addition, meaningful data can only be adequately explored when the corresponding physicochemical characteristics of the ecosystem under study are taken into consideration. In the context of the nitrogen cycle and via the use of diverse experimental strategies, metaproteomics has aided the clarification of many points of debate. An overview of selected studies discussed in this chapter is presented in Table 6.1. For example, metaproteomics together with metagenomics has revealed the existence of a pathway coupling methane oxidation with the production of dinitrogen and oxygen in freshwater ecosystems, which is only the fourth oxygenic biological pathway known to date (Ettwig et al.,

Metaproteomics and N-cycling Microbial Communities |  129

Table 6.1  Overview of selected microbial metaproteomics studies Environment

Main role of metaproteomics

Key findings

Reference

Enrichment cultures from freshwater ecosystems

Aid to metagenome annotation

Novel oxygenic pathway: methane oxidation coupled with oxygen and dinitrogen production

Ettwig et al. (2010)

Aquatic: Lake Antarctica

Identification of active metabolic pathways

Description of the nitrogen, carbon, and sulfur cycles within the water column

Lauro et al. (2011)

Marine: West Antarctic Peninsula

Identification of active metabolic pathways

Contribution to nitrification: AOA exclusively during winter months and AOB mainly during the summer

Williams et al. (2012)

Marine: sponge

Identification of active metabolic pathways

Aerobic nitrification and anaerobic denitrification simultaneously carried out by the microbial community inhabiting the host

Liu et al. (2012)

Marine: tubeworms

Degree of similarity between two environments

Stable internal environment inside two host species typically thriving under different geochemical conditions

Gardebrecht et al. (2012)

Acid mine drainage

Degree of similarity between two environments

High level of fixed nitrogen within the natural habitat, attributed to the decomposition of old biofilms

Belnap et al. (2010)

Acid mine drainage

Identification of active metabolic pathways, and aid to metagenome annotation

Only one nitrogen fixing bacterium Ram et al identified in biofilm, leading to the (2005) design of a clear experimental strategy for its isolation

Acid mine drainage

Combined to SIP for protein identification and 15N incorporation

Identification of species migration from Pan et al. established to newly growing biofilm (2011)

Activated sludge

Identification of active metabolic pathways

Identification of a nitrate reductase subunit homologue revealing the disjointed nature of the nap operon

Terrestrial: rhizosphere and phyllosphere

Identification of active metabolic pathways

Nitrogen fixation only carried out in the Knief et al. rhizosphere while ability to fix nitrogen (2012) was also detected in the phyllosphere

2010). Metaproteomics has also confirmed the contribution of AOA to nitrification in marine ecosystems (Williams et al., 2012) and led to the description of nitrogen transformations, along with other biogeochemical cycles within Lake Antarctica (Lauro et al., 2011). In the context of activated sludge, metaproteomics has also unveiled the disjointed organization of the nap (respiratory nitrate reductase) operon previously predicted as non functional due to the lack of detection of one of its gene members (Wilmes et al., 2008). As illustrated in this chapter, metaproteomics can be employed experimentally to fulfil diverse roles, such as aiding rigorous metagenome annotation (Ettwig et al., 2010) or measuring levels of similarity between two environments (Gardebrecht et al., 2012; Belnap et al., 2010). The application of

Wilmes et al. (2008)

‘omics’ can also lead to the development of clear isolation strategies for the cultivation of previously uncultivated microorganisms, as demonstrated by Tyson et al. (2005), who isolated the key nitrogen fixing bacterium of acid mine drainage biofilms. As metaproteomics is taking into consideration all the interacting microorganisms within ecosystems, it can help defining microbial physiology in situ, which can significantly differ from microbial behaviour observed in the laboratory. For example, the ability of C. clathariforme to carry out nitrogen fixation, which was initially unclear due to the lack of detection of this process under laboratory conditions, was confirmed to occur in the microorganism natural environment using in situ proteomics (Habicht et al., 2011). This observation highlights the necessity to examine microbial

130  | Smith and Abram

behaviour in situ and emphasizes that caution must be taken when evaluating the meaning of data generated in laboratory settings. The combination of SIP methodology with metaproteomics can inform on the fluxes of carbon and nitrogen within mixed microbial communities and therefore render metaproteomic approaches somehow targeted. Even in this context, however, the labelled and unlabelled proteins are typically not separated before mass spectrometry analysis and therefore a snapshot of all the metabolic activities taking place at the time of sampling should theoretically be obtained. In addition, the application of SIP technology requires the operation of mixed microbial culturing systems or microcosms under biologically relevant settings. The conditions experienced in these artificial set-ups should not be assumed to be comparable to these encountered in the natural habitat and the level of similarity between the two environments should be ideally rigorously established using metaproteomics as demonstrated by Belnap et al. (2010). From the point of view of making metaproteomic approaches targeted to hone in on biological pathways of interest, experimental strategies involving the comparison of

metaproteomes under relevant conditions could perhaps lead to the exclusive identification of proteins involved in specific steps of the nitrogen cycle, if analysed using 2-DGE for example. The untargeted and the relatively recent highthroughput nature of metaproteomics, however, seem to constitute some of the main strengths of this technology. Consequently, research groups might be encouraged to start with simple maybe artificial model microbial consortia to facilitate the set-up of such technology rather than only focusing on low throughput methods. Indeed, once the protein extraction protocols are reasonably well established, the best approach would be to analyse all the proteins expressed in simple systems to catch a glimpse of all the detectable metabolic pathways at work and build somehow a rather complete picture of ecosystem functioning. To conclude, metaproteomics applied to the exploration of the nitrogen cycle should ideally be part of a complex and well thought out experimental design, whereby the results of different strategies are integrated to inform as holistically as possible on ecosystem functioning. A recommended experimental strategy is briefly discussed in the next section and an overview of such a workflow is presented in Fig. 6.2.

Natural systems of low level diversity/enrichment cultures

Metagenomics

Metaproteomics

Identification of biomarkers as ecosystem indicators

Ecological significance

Protein - SIP

Deep characterization of model system

Process rate measurements

Physicochemical data collection

Isolation of uncultivated microorganisms

Geographical distribution

Figure 6.2 Recommended experimental strategy for understanding nitrogen cycling microbial communities. SIP stands for stable isotope probing.

Metaproteomics and N-cycling Microbial Communities |  131

Future trends As discussed in this chapter, metaproteomics has been proven a rather powerful tool to examine nitrogen cycling microbial communities. However, this technology should not be viewed as a standalone approach for two main reasons: (i) it is heavily reliant on relevant metagenomic sequences for protein identification and (ii) it is most meaningful when integrated in somewhat holistic experimental set-ups (Fig. 6.2). Based on the review of the literature to-date, a recommended strategy for the characterization of microbial communities involved in the nitrogen cycle is presented here (Fig. 6.2). First, the level of diversity of the ecosystem of interest should be considered, along with the availability of relevant metagenomic data. If the ecosystem is rather complex, the set-up of enrichment cultures is initially recommended. Ideally, metagenomic data should be generated even though metagenomes from similar environments might prove sufficient to support extensive protein identification. The relevance of existing metagenomic data should be assessed as rigorously as possible, using, for example, 16S RNA biomarkers, even if the metagenome sequences have been generated directly from the same environment. Protein extraction protocols should be optimized and metaproteomics conducted. The analyses of carbon and nitrogen fluxes are also highly relevant for the elucidation of ecosystem functioning. These can be accessed with the use of SIP technology, which combined with metaproteomics, can inform on 15N or 13C label uptake, protein identification and protein turnover. Protein-SIP can be directly performed on enrichment cultures, or will necessitate, in the case of ecosystems with low level of diversity (like acid mine drainage), the set up of mixed microbial community culturing systems. To explore optimally the data generated using the technologies mentioned above (Fig. 6.2) and to gain some insights into microbial community structure and function, process rate measurements together with physicochemical data collection become critical. As mentioned earlier, one of the main challenges encountered when investigating the nitrogen cycle using DNAand RNA-based techniques is correlating gene and/or mRNA transcript abundances to process

rates. Due to post-transcriptional regulations, mRNA transcripts and proteins are not expected to show a high level of correlation. However, while the detection of enzymes relevant to nitrogen transformations cannot be always directly related to activity (due to post-translational regulation), proteomics is expected to correlate better with process rates than mRNA transcript analysis. Therefore the correlation between protein abundances and process rates should be systematically examined. The data generated using metagenomics, metaproteomics and protein-SIP, together with process rate measurements and physicochemical data collection should lead to a deep characterization of the model system under investigation, i.e. enrichment cultures or natural habitat of low level diversity. After rigorous analysis, such datasets should guide isolation strategies for the culturing of previously uncultivated key microorganisms, whose ecophysiology can be further characterized in situ and/or under axenic conditions. Another outcome from the analysis of such extensive datasets could be the identification of molecular markers indicative of specific processes. These can then be directly used for the evaluation of the ecological significance and the geographical distribution of the findings identified from the investigation of model systems as recommended by Madsen (2011). For example, the estimation of the occurrence of the coupled methane oxidation and dinitrogen production (Ettwig et al., 2010) in the environment would be the next logical step to assess the significance of this novel pathway, which could have a major impact on the overall carbon and nitrogen cycles. To conclude the experimental strategy presented in Fig. 6.2, should provide great insights into in situ nitrogen transformations and this level of information is a crucial requirement for the adequate management of ecosystems. References Aryal, U.K., Stockel, J., Krovvidi, R.K., Gritsenko, M.A., Monroe, M.E., Moore, R.J., Koppenaal, D.W., Smith, R.D., Pakrasi, H.B., and Jacobs, J.M. (2011). Dynamic proteomic profiling of a unicellular cyanobacterium Cyanothece ATCC51142 across light-dark diurnal cycles. BMC Syst. Biol. 5, 194. Bastida, F., Rosell, M., Franchini, A.G., Seifert, J., Finsterbusch, S., Jehmlich, N., Jechalke, S., Von Bergen, M., and Richnow, H.H. (2010). Elucidating

132  | Smith and Abram

MTBE degradation in a mixed consortium using a multidisciplinary approach. FEMS Microbiol. Ecol. 73, 370–384. Belnap, C.P., Pan, C., VerBerkmoes, N.C., Power, M.E., Samatova, N.F., Carver, R.L., Hettich, R.L., and Banfield, J.F. (2010). Cultivation and quantitative proteomic analyses of acidophilic microbial communities. ISME J. 4, 520–530. Bertin, P.N., Heinrich-Salmeron, A., Pelletier, E., GoulhenChollet, F., Arsene-Ploetze, F., Gallien, S., Lauga, B., Casiot, C., Calteau, A., Vallenet, D., et al. (2011). Metabolic diversity among main microorganisms inside an arsenic-rich ecosystem revealed by meta- and proteo-genomics. ISME J. 5, 1735–1747. Burnum, K.E., Callister, S.J., Nicora, C.D., Purvine, S.O., Hugenholtz, P., Warnecke, F., Scheffrahn, R.H., Smith, R.D., and Lipton, M.S. (2011). Proteome insights into the symbiotic relationship between a captive colony of Nasutitermes corniger and its hindgut microbiome. ISME J. 5, 161–164. Burton, H.R. (1980). Methane in a saline Antarctic lake. In Biogeochemistry of Ancient and Modern Environments, P.A Trudinger, M.R Walter. and B.J. Ralph, eds. (Canberra, Australia: Australian Academy of Science), pp 243–251. Compton, J.E., Harrison, J.A., Dennis, R.L., Greaver, T.L., Hill, B.H., Jordan, S.J., Walker, H., and Campbell, H.V. (2011). Ecosystem services altered by human changes in the nitrogen cycle: a new perspective for US decision making. Ecol. Lett. 14, 804–815. Delmotte, N., Ahrens, C.H., Knief, C., Qeli, E., Koch, M., Fischer, H.-M., Vorholt, J.A., Hennecke, H., and Pessi, G. (2010). An integrated proteomics and transcriptomics reference data set provides new insights into the Bradyrhizobium japonicum bacteroid metabolism in soybean root nodules. Proteomics 10, 1391–1400. Djordjevic, M.A. (2004). Sinorhizobium meliloti metabolism in the root nodule: A proteomic perspective. Proteomics 4, 1859–1872. Ettwig, K.F., Butler, M.K., Le Paslier, D., Pelletier, E., Mangenot, S., Kuypers, M.M.M., Schreiber, F., Dutilh, B.E., Zedelius, J., de Beer, D., et al. (2010). Nitritedriven anaerobic methane oxidation by oxygenic bacteria. Nature 464, 543–548. García Martín, H., Ivanova, N., Kunin, V., Warnecke, F., Barry, K.W., McHardy, A.C., Yeates, C., He, S., Salamov, A.A., Szeto, E., et al. (2006). Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities. Nat. Biotech. 24, 1263–1269. Gardebrecht, A., Markert, S., Sievert, S.M., Felbeck, H., Thurmer, A., Albrecht, D., Wollherr, A., Kabisch, J., Le Bris, N., Lehmann, R., et al. (2012). Physiological homogeneity among the endosymbionts of Riftia pachyptila and Tevnia jerichonana revealed by proteogenomics. ISME J. 6, 766–776. Habicht, K.S., Miller, M., Cox, R.P., Frigaard, N.-U., Tonolla, M., Peduzzi, S., Falkenby, L.G., and Andersen, J.S. (2011). Comparative proteomics and activity of a green sulfur bacterium through the water column of Lake Cadagno, Switzerland. Environ. Microbiol. 13, 203–215.

Halm, H., Musat, N., Lam, P., Langlois, R., Musat, F., Peduzzi, S., Lavik, G., Schubert, C.J., Singha, B., LaRoche, J., et al. (2009). Co-occurrence of denitrification and nitrogen fixation in a meromictic lake, Lake Cadagno (Switzerland). Environ. Microbiol. 11, 1945–1958. Halter, D., Goulhen-Chollet, F., Gallien, S., Casiot, C., Hamelin, J., Gilard, F., Heintz, D., Schaeffer, C., Carapito, C., Van Dorsselaer, A., et al. (2012). In situ proteo-metabolomics reveals metabolite secretion by the acid mine drainage bio-indicator, Euglena mutabilis. ISME J. 6, 1391–1402. Jehmlich, N., Schmidt, F., von Bergen, M., Richnow, H.H., and Vogt, C. (2008). Protein-based stable isotope probing (Protein-SIP) reveals active species within anoxic mixed cultures. ISME J. 2, 1122–1133. Jehmlich, N., Kleinsteuber, S., Vogt, C., Benndorf, D., Harms, H., Schmidt, F., Von Bergen, M., and Seifert, J. (2010). Phylogenetic and proteomic analysis of an anaerobic toluene-degrading community. J. Appl. Microbiol. 109, 1937–1945. Knief, C., Delmotte, N., Chaffron, S., Stark, M., Innerebner, G., Wassmann, R., von Mering, C., and Vorholt, J.A. (2012). Metaproteogenomic analysis of microbial communities in the phyllosphere and rhizosphere of rice. ISME J. 6, 1378–1390. Kolmeder, C.A., de Been, M., Nikkilä, J., Ritamo, I., Mättö, J., Valmu, L., Salojärvi, J., Palva, A., Salonen, A., and de Vos, W.M. (2012). Comparative metaproteomics and diversity analysis of human intestinal microbiota testifies for its temporal stability and expression of core functions. PLoS ONE 7, e29913. Lauro, F.M., DeMaere, M.Z., Yau, S., Brown, M.V., Ng, C., Wilkins, D., Raftery, M.J., Gibson, J.A.E., AndrewsPfannkoch, C., Lewis, M., et al. (2011). An integrative study of a meromictic lake ecosystem in Antarctica. ISME J. 5, 879–895. Liu, M., Fan, L., Zhong, L., Kjelleberg, S., and Thomas, T. (2012). Metaproteogenomic analysis of a community of sponge symbionts. ISME J. 6, 1515–1525. Mackelprang, R., Waldrop, M.P., DeAngelis, K.M., David, M.M., Chavarria, K.L., Blazewicz, S.J., Rubin, E.M., and Jansson, J.K. (2011). Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480, 368–371. Madigan, M.T. (1995). Microbiology of nitrogen fixation by anoxygenic photosynthetic bacteria. In Anoxygenic Photosynthetic Bacteria, R.E Blankenship, M.T Madigan and C.E. Bauer, eds. (Dordrecht, the Netherlands: Kluwer), pp. 49–85. Madsen, E.L. (2011). Microorganisms and their roles in fundamental biogeochemical cycles. Curr. Opin. Biotechnol. 22, 456–464. Martens-Habbena, W., Berube, P.M., Urakawa, H., de la Torre J.R., and Stahl, D.A. (2009). Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature 461, 976-–979. Morris, R.M., Rappe, M.S., Connon, S.A., Vergin, K.L., Siebold, W.A., Carlson, C.A., and Giovannoni, S.J. (2002). SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810.

Metaproteomics and N-cycling Microbial Communities |  133

Morris, R.M., Nunn, B.L., Frazar, C., Goodlett, D.R., Ting, Y.S., and Rocap, G. (2010). Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME J. 4, 673–685. Ng, C., DeMaere, M.Z., Williams, T.J., Lauro, F.M., Raftery, M., Gibson, J.A.E., Andrews-Pfannkoch, C., Lewis, M., Hoffman, J.M., Thomas, T., et al. (2010). Metaproteogenomic analysis of a dominant green sulfur bacterium from Ace Lake, Antarctica. ISME J. 4, 1002–1019. O’Farrell, P.H. (1975). High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250:4007– 4021. Overmann, J., and Pfennig, N. (1989). Pelodictyon phaeoclathratiforme sp. nov., a new brown-colored member of the Chlorobiaceae forming net-like colonies. Arch. Microbiol. 152, 401–406. Pan, C., Fischer, C.R., Hyatt, D., Bowen, B.P., Hettich, R.L., and Banfield, J.F. (2011). Quantitative tracking of isotope flows in proteomes of microbial communities. Mol. Cell. Proteomics 10, M110.006049. Pellitteri-Hahn, M.C., Halligan, B.D., Scalf, M., Smith, L., and Hickey, W.J. (2011). Quantitative proteomic analysis of the chemolithoautotrophic bacterium Nitrosomonas europaea: Comparison of growing- and energy-starved cells. J. Proteomics 74, 411–419. Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K.S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., Yamada, T., et al. (2010). A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65. Ram, R.J., VerBerkmoes, N.C., Thelen, M.P., Tyson, G.W., Baker, B.J., Blake, I.R., Shah, M., Hettich, R.L., and Banfield, J.F. (2005). Community proteomics of a natural microbial biofilm. Science 308, 1915–1920. Rankin, L.M., Gibson, J.A.E., Franzmann, P.D., and Burton, H.R. (1999). The chemical stratification and microbial communities of Ace Lake, Antarctica: a review of the characteristics of a marine-derived meromictic lake. Polarforschung 66, 35–52. Rooijers, K., Kolmeder, C., Juste, C., Dore, J., de Been, M., Boeren, S., Galan, P., Beauvallet, C., de Vos, W., and Schaap, P. (2011). An iterative workflow for mining the human intestinal metaproteome. BMC Genomics 12, 6. Rusch, D.B., Halpern, A.L., Sutton, G., Heidelberg, K.B., Williamson, S., Yooseph, S., Wu, D., Eisen, J.A., Hoffman, J.M., Remington, K., et al. (2007). The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific. PLoS Biol. 5, e77. Saito, M.A., Bertrand, E.M., Dutkiewicz, S., Bulygin, V.V., Moran, D.M., Monteiro, F.M., Follows, M.J., Valois, F.W., and Waterbury, J.B. (2011). Iron conservation by reduction of metalloenzyme inventories in the marine diazotroph Crocosphaera watsonii. Proc. Natl. Acad. Sci. USA 108, 2184–2189. Sandh, G., Ran, L., Xu, L., Sundqvist, G., Bulone, V., and Bergman, B. (2011). Comparative proteomic profiles of the marine cyanobacterium Trichodesmium

erythraeum IMS101 under different nitrogen regimes. Proteomics 11, 406–419. Schneider, T., and Riedel, K. (2010). Environmental proteomics: Analysis of structure and function of microbial communities. Proteomics 10, 785–798. Schneider, T., Keiblinger, K.M., Schmid, E., SterflingerGleixner, K., Ellersdorfer, G., Roschitzki, B., Richter, A., Eberl, L., Zechmeister-Boltenstern, S., and Riedel, K. (2012). Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. ISME J. 6, 1749–1762. Selao, T.T., Nordlund, S., and Norén, A. (2008). Comparative proteomic studies in Rhodospirillum rubrum grown under different nitrogen conditions. J. Proteome Res. 7, 3267–3275. Seviour, R.J., Mino, T., and Onuki, M. (2003). The microbiology of biological phosphorus removal in activated sludge systems. FEMS Microbiol. Rev. 27, 99–127. Sherman, L.A., Min, H., Toepel, J., and Pakrasi, H.B. (2010). Better living through cyanothece – unicellular diazotrophic cyanobacteria with highly versatile metabolic systems. Adv. Exp. Med. Biol. 675, 275–290. Siggins, A., Gunnigle, E., and Abram, F. (2012). Exploring mixed microbial community functioning: recent advances in metaproteomics. FEMS Microb. Ecol. 80, 265–280. Sowell, S.M., Wilhelm, L.J., Norbeck, A.D., Lipton, M.S., Nicora, C.D., Barofsky, D.F., Carlson, C.A., Smith, R.D., and Giovanonni, S.J. (2009). Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 3, 93–105. Sowell, S.M., Abraham, P.E., Shah, M., Verberkmoes, N.C., Smith, D.P., Barofsky, D.F., and Giovannoni, S.J. (2011). Environmental proteomics of microbial plankton in a highly productive coastal upwelling system. ISME J. 5, 856–865. Stewart, F.J., Ulloa, O., and Delong, E.F. (2012). Microbial metatranscriptomics in a permanent marine oxygen minimum zone. Environ. Microbiol. 14, 23–40. Stöckel, J., Welsh, E.A., Liberton, M., Kunnvakkam, R., Aurora, R., and Pakrasi, H.B. (2008). Global transcriptomic analysis of Cyanothece 51142 reveals robust diurnal oscillation of central metabolic processes. Proc. Natl. Acad. Sci. U.S.A. 105, 6156– 6161. Taubert, M., Baumann, S., von Bergen, M., and Seifert, J. (2011). Exploring the limits of robust detection of incorporation of 13C by mass spectrometry in proteinbased stable isotope probing (protein-SIP). Anal. Bioanal. Chem. 401, 1975–1982. Taubert, M., Vogt, C., Wubet, T., Kleinsteuber, S., Tarkka, M.T., Harms, H., Buscot, F., Richnow, H.-H., von Bergen, M., and Seifert, J. (2012). Protein-SIP enables time-resolved analysis of the carbon flux in a sulfatereducing, benzene-degrading microbial consortium. ISME J. 6, 2291–2301. Tokuda, G., and Watanabe, H. (2007). Hidden cellulases in termites: revision of an old hypothesis. Biol. Lett. 3, 336–339. Tringe, S.G., von Mering, C., Kobayashi, A., Salamov, A.A., Chen, K., Chang, H.W., Podar, M., Short, J.M.,

134  | Smith and Abram

Mathur, E.J., Detter, J.C., et al. (2005). Comparative Metagenomics of Microbial Communities. Science 308, 554–557. Turner, J., Colwell, S.R., Marshall, G.J., Lachlan-Cope, T.A., Carleton, A.M., Jones, P.D., Lagun, V., Reid, P.A., and Iagovkina, S. (2005). Antarctic climate change during the last 50 years. Int. J. Climatol. 25, 279–294. Tyson, G.W., Lo, I., Baker, B.J., Allen, E.E., Hugenholtz, P., and Banfield, J.F. (2005). Genome-directed isolation of the key nitrogen fixer Leptospirillum ferrodiazotrophum sp. nov. from an acidophilic microbial community. Appl. Environ. Microbiol. 71, 6319–6324. Venter, J.C., Remington, K., Heidelberg, J.F., Halpern, A.L., Rusch, D., Eisen, J.A., Wu, D., Paulsen, I., Nelson, K.E., Nelson, W., et al. (2004). Environmental Genome Shotgun Sequencing of the Sargasso Sea. Science 304, 66–74. Verberkmoes, N.C., Russell, A.L., Shah, M., Godzik, A., Rosenquist, M., Halfvarson, J., Lefsrud, M.G., Apajalahti, J., Tysk, C., Hettich, R.L., et al. (2009). Shotgun metaproteomics of the human distal gut microbiota. ISME J. 3, 179–189. van de Vossenberg, J., Woebken, D., Maalcke, W.J., Wessels, H.J.C.T., Dutilh, B.E., Kartal, B., JanssenMegens, E.M., Roeselers, G., Yan, J., Speth, D., et al. (2012). The metagenome of the marine anammox bacterium ‘Candidatus Scalindua profunda’ illustrates the versatility of this globally important nitrogen cycle bacterium. Environ. Microbiol., doi:10.1111/j.1462– 2920.2012.02774.x. Wang, H.-B., Zhang, Z.-X., Li, H., He, H.-B., Fang, C.-X., Zhang, A.-J., Li, Q.-S., Chen, R.-S., Guo,

X.-K., Lin, H.-F., et al. (2011). Characterization of metaproteomics in crop rhizospheric soil. J. Proteome Res. 10, 932–940. Welsh, E.A., Liberton, M., Stöckel, J., Loh, T., Elvitigala, T., Wang, C., Wollam, A., Fulton, R.S., Clifton, S.W., Jacobs, J.M., et al. (2008). The genome of Cyanothece 51142, a unicellular diazotrophic cyanobacterium important in the marine nitrogen cycle. Proc. Natl. Acad. Sci. U.S.A. 105, 15094–15099. Williams, T.J., Long, E., Evans, F., DeMaere, M.Z., Lauro, F.M., Raftery, M.J., Ducklow, H., Grzymski, J.J., Murray, A.E., and Cavicchioli, R. (2012). A metaproteomic assessment of winter and summer bacterioplankton from Antarctic Peninsula coastal surface waters. ISME J. 6, 1883–1900. Williams, M.A., Taylor, E.B., and Mula, H.P. (2010). Metaproteomic characterization of a soil microbial community following carbon amendment. Soil Biol. Biochem. 42, 1148–1156. Wilmes, P., Andersson, A.F., Lefsrud, M.G., Wexler, M., Shah, M., Zhang, B., Hettich, R.L., Bond, P.L., VerBerkmoes, N.C., and Banfield, J.F. (2008). Community proteogenomics highlights microbial strain-variant protein expression within activated sludge performing enhanced biological phosphorus removal. ISME J. 2, 853–864. Yooseph, S., Sutton, G., Rusch, D.B., Halpern, A.L., Williamson, S.J., Remington, K., Eisen, J.A., Heidelberg, K.B., Manning, G., Li, W., et al. (2007). The Sorcerer II global ocean sampling expedition: expanding the universe of protein families. PLoS Biol. 5, e16.

Functional Molecular Analysis of Microbial Nitrogen Cycle by Microarray-based GeoChip: Insights for Climate Change, Agriculture and Other Ecological Studies

7

Kai Xue, Joy D. Van Nostrand, Zhili He and Jizhong Zhou

Abstract Nitrogen (N) is a common constraining factor for biological communities, e.g. plants and microbes, in terrestrial ecosystems. Its cycling is mainly mediated by soil microbial communities. Environmental changes may affect functional genes of soil microbial communities involved in N cycling and hence cause substantial changes in related geochemical processes. A microarray-based metagenomics technique, GeoChip, includes a comprehensive set of functional genes involved in almost all processes of N transformation, e.g. N fixation, nitrification, denitrification, etc. GeoChip analysis allows us to monitor the entire N ecological network by a single hybridization to detect changes of all these genes. In this chapter, we will focus on the development and application of GeoChip methodology for analysing functional genes of microbial communities involved in N processes under different treatments in various ecosystems. This chapter will reveal how information obtained from GeoChip enhances our understanding of ecological consequences of climatic changes (e.g. rising temperature and CO) or fertilization treatments. Moreover, the challenges of this technique will also be discussed. Introduction Biological available nitrogen (N) is a common constraining factor in terrestrial ecosystems and critical for ecosystem diversity, dynamics and functioning (Hungate et al., 2003; Reich et al.,

2006b; Vitousek et al., 1997). First, increased N availability has been reported to increase productivity and biomass accumulation, but decrease bio-diversity in affected ecosystems (Bobbink, 1991; Bobbink et al., 1998; Clark and Tilman, 2008; Gough et al., 2000; Reich, 2009). Second, N cycling can affect environmental quality substantially. For example, N leaches in intensively cultivated areas can pollute groundwater, leading to eutrophication in rivers or lakes through runoff and surface waters (Bohlool et al., 1992; Pimentel et al., 2005). N deposition can also acidify soils, which in turn would increase the loss of buffer capacity, leaching of base cations and accumulation of toxic metals in soils (Guo et al., 2010; Van Breemen et al., 1982). In addition, reactive oxides of N may serve as precursors to cause acid rain or photochemical smog (Chameides et al., 1994). Finally, N cycling is crucial for greenhouse gas emissions and global climate change. The global warming potential of long-lived nitrous oxide, derived from the denitrification process, is 310 times higher than that of carbon dioxide (IPCC, 2007). Changes in N cycling can also mediate global C cycling and affect carbon dioxide flux to the atmosphere (Luo et al., 2006a; Vitousek et al., 1997; Zhou et al., 2012). Belowground soil microorganisms are critical in regulating N cycling and other biogeochemical processes in carbon (C), phosphorus (P) and sulfur (S) cycles (He et al., 2012; Jackson et al., 2003; van Bruggen and Semenov, 2000). Specific to N cycling, for example, N mineralization

136  | Xue et al.

is mediated by soil microorganisms through releasing enzymes that are responsible for the extracellular ammonification of organic compounds in soils, like urease and amino acid oxidase (Geisseler et al., 2010); the conversion of atmospheric nitrogen to ammonia, known as N fixation, is performed by free living and symbiotic diazotrophs (Franche et al., 2009); nitrification processes can be performed in aerated soils by specific groups of bacteria and archaea through oxidizing ammonia to nitrate (Myrold and Posavatz, 2007). Microorganisms have the capacity to utilize a wide range of organic and mineral forms of N by developing different uptake mechanisms and assimilating mineral and organic compounds (Marzluf, 1997; Merrick and Edwards, 1995). Regarding the importance of soil microorganisms in N and other biogeochemistry cycling, any changes in soil microbes would have the potential to affect the functions they perform and hence impact the ecological services, like biodiversity sustaining and nutrient cycling in N processes. However, studying microbial communities (e.g. characterization, identification and quantitation of microorganisms) in soil and other natural settings is a great challenge due to their extreme complexity and the fact that the vast majority of microorganisms are not yet cultivated. Soil microorganisms represent the most diverse group of organisms on the planet (He et al., 2012). It has been estimated that 4 × 107 prokaryotic cells exist in 1 g forest soil, while 2 × 109 cells are in 1 g grassland soil. DNA reassociation kinetic studies estimate that 1 g soil contains 2000–18,000 genomes (Torsvik et al., 1996, 1998; Torsvik and Ovreas, 2002). On the other hand, as more than 99% of soil microorganisms are uncultivated, the extent of soil microbial diversity is still largely unknown (Andrew et al., 2012; Delmont et al., 2012; Pignataro et al., 2012). A variety of culture-independent molecular tools have been applied to assess soil microbial communities, including phospholipids fatty acid analysis (PLFA) (Baath and Anderson, 2003; Balser and Wixon, 2009; Zhang et al., 2011; Ziegler et al., 2013), terminal-restriction fragment length polymorphism (T-RFLP) (Hayden et al., 2012; Liu et al., 2012; Xue et al., 2011), denaturing gradient gel electrophoresis (DGGE)

or temperature gradient gel electrophoresis (TGGE) (Muyzer and Smalla, 1998; Muyzer, 1999), Biolog (Guckert et al., 1996; Smalla et al., 1998; Widmer et al., 2001), automated ribosomal intergenic spacer analysis (ARISA) (Deslippe et al., 2012; Fisher and Triplett, 1999; Ranjard et al., 2001), fluorescence in situ hybridization (FISH) (Christensen et al., 1999; Moter and Gobel, 2000), quantitative PCR (Cantarel et al., 2012; Hayden et al., 2012; Liu et al., 2012; Sheik et al., 2011) and 16S rRNA gene cloning-sequencing (Cantarel et al., 2012; Deslippe et al., 2012). Moreover, specific groups of microbes involved in certain N processes, like nitrifiers (Bano and Hollibaugh, 2000; Kowalchuk et al., 1998; Phillips et al., 2000a,b; Purkhold et al., 2000; Rotthauwe et al., 1997), or that possess specific N functional genes, like nirS, nosZ or nifH (Hsu and Buckley, 2009; Morales et al., 2010; Poly et al., 2001; Wallenstein et al., 2006; Zumft, 1997), have been assessed by traditional molecular tools as well. The application of these methods provided numerous insights into our understanding of microbial communities and their responses to various environmental changes. However, the resolution and coverage of these methods are still limited (He et al., 2011), which hampers our ability to characterize soil microbes. For example, it is improper to use PLFA as an index of species diversity (Bossio et al., 1998) because the individual PLFA is not specific to species (Lechevalier, 1977). Moreover, the PCR amplification is required in most of these approaches, which introduces well-known biases (Lueders and Friedrich, 2003; Suzuki and Giovannoni, 1996; Warnecke et al., 1997). Recently emerged metagenomic technologies have revolutionized microbial research through overcoming many of the limitations in conventional molecular approaches and allowing us to assess the diversity, structure and function of soil microbial communities in a much more comprehensive way (Zhou et al., 2012). Among these metagenomic technologies, the functional gene array (FGA) is a high-throughput microarray-based technique focusing on analysing functional genes of soil microbial communities. Though functional genes can also be examined by traditional molecular biology approaches, like quantitative PCR, the advantages of FGAs are the

Analysing Microbial Nitrogen Genes Using GeoChip |  137

ability to measure functional genes that are difficult to design conserved PCR primers because of sequence homology or an insufficient number of sequences. Also, FGAs can hybridize many genes at one time, which would substantially save time and cost. The most comprehensive FGA to date are the GeoChip arrays. For example, GeoChip 3.0 contains more than 10,000 genes in more than 150 functional groups, allowing us to investigate the ecological functions of soil microbial communities involved in C, N, P, S cycles and other processes (He et al., 2007) in various types of terrestrial ecosystems, including grasslands (He et al., 2010b; Zhou et al., 2008, 2012), agricultural lands (Reeve et al., 2010; Xue et al., 2013), and forests (Eisenlord et al., 2013; Garten et al., 2007). In this chapter, we will focus on the development and application of GeoChip for analysing functional genes of microbial communities involved in N processes under different treatments in various ecosystems. Microarray technology and functional gene array Microarray technology is conceptually similar, though reversed, to traditional membrane-based Northern and Southern blots that utilize a labelled probe molecule to hybridize target nucleic acid attached to a membrane. Microarrays are comprised of probes for specific genes, sequences, or genomes on a solid surface which then hybridize labelled DNA or RNA. This technology was first developed to monitor the expression patterns of 45 genes in the model plant Arabidopsis thaliana (Schena et al., 1995). In 1997, a microarray was tested for investigating microbial communities (nitrifying bacteria) and proved to offer a remarkable capacity for studying determinative and environmental microbiology (Guschin et al., 1997). Since then, several types of microarrays have been developed to study microbial communities, including phylogenetic oligonucleotide arrays (POAs), community genome arrays (CGAs), metagenomic arrays (MGAs) and whole-genome open reading frame (ORF) arrays (WGAs), and functional gene arrays (FGAs). POAs, like PhyloChip, contain probes for conserved genes (e.g.

16S rRNA gene) to detect the presence of specific microorganisms in a community and can be used to determine the phylogenetic relatedness or microbial community composition (Brodie et al., 2006; 2007; DeSantis et al., 2007). CGAs use the whole genomic DNA of cultured microorganisms as probes to examine the relatedness of microbial isolates or to detect the presence of specific microorganisms in a community (Wu et al., 2004; Zhang et al., 2004). MGAs contain probes from clone libraries of environmental DNA and can be used as a high-throughput screening method to examine communities (Gresham et al., 2008; Mockler and Ecker, 2005; Rich et al., 2008; Sebat et al., 2003). WGAs contain probes targeting all ORFs in one or more genomes to monitor gene expression of individual microorganisms under different conditions or for comparative genomics (Dong et al., 2001; Murray et al., 2001; Sarkar et al., 2006; White et al., 2008; Wilson et al., 1999). FGAs contain probes for specific functional genes for key proteins or enzymes involved in various processes of interest to provide information on the genes and populations in a community (Gentry et al., 2006; He et al., 2007, 2010a, 2011, 2012; Wu et al., 2001). The first published FGA, the prototype of GeoChip, was constructed with genes involved in N cycling, including nitrite reductase (nirS and nirK) genes, ammonia mono-oxygenase (amoA) genes and methane mono-oxygenase (pmoA) genes, derived from pure cultures and those cloned from marine sediments (Wu et al., 2001). Since the first development, various novel FGAs have been developed either with PCR amplicons or oligonucleotides to target specific functional processes (Bodrossy et al., 2003, 2006; Bontemps et al., 2005; Call et al., 2003; Cleven et al., 2006; Jaing et al., 2008; Kostic et al., 2005; Miller et al., 2008; Palka-Santini et al., 2009; Steward et al., 2004; Taroncher-Oldenburg et al., 2003; Zhang et al., 2007). Focusing on N cycling, several specific arrays have been designed. For example, to examine microbial communities involved in denitrification, N fixation, ammonia oxidation and nitrite reduction, two oligonucleotide arrays covering amoA, nitrogenase (nifH), nirS and nirK genes were developed and applied in a river system (Taroncher-Oldenburg et al., 2003). A nylon

138  | Xue et al.

membrane macroarray was also designed to study the nifH gene by using PCR amplicons as probes and later expanded to investigate Chesapeake Bay diazotrophs ( Jenkins et al., 2004; Steward et al., 2004). Moreover, a microarray targeting the nifH gene was developed and applied in analysing the diversity (based on DNA) and activity (based on mRNA) of diazotrophs in roots of wild rice samples (Zhang et al., 2007), which was found to be highly reproducible and semiquantitative. GeoChip is the most comprehensive FGA reported to date (He et al., 2007; 2010a). It can be applied to study functional genes involved in N cycling and other processes in different environmental niches. The first generation of GeoChip, GeoChip 1.0, has 2006 oligonucleotide probes for N cycling, methane oxidation, sulfate reduction, organic contaminant degradation, and metal resistance. The targeting N functional processes included nitrification (amoA), denitrification (nirS and nirK) and N fixation (nifH). It was the first FGA to cover such a diverse number of functional groups and could be used widely in environmental microbiology studies (Rhee et al., 2004; Tiquia et al., 2004). GeoChip 2.0 was developed to expand the coverage of GeoChip 1.0 by including comprehensive probes (24,243) from 150 gene categories involved in the geochemical cycling of C, N, and P cycling, sulfate reduction, metal reduction and resistance, and organic contaminant degradation (He et al., 2007). A more comprehensive FGA, GeoChip 3.0, was then developed to contain 28,000 probes from 292 gene categories (He et al., 2010a). Not only did the number of covered genes in existing categories increase substantially compared with GeoChip 2.0, but also additional gene categories were added to GeoChip 3.0, including antibiotic resistance, energy processing, and phylogenetic markers (i.e. gyrB). Regarding N cycling, 16 genes were selected in GeoChip 3.0, including (i) nifH (dinitrogenase reductase) for N fixation; (ii) amoA (ammonia monooxygenase) and hao (hydroxylamine oxidoreductase) for nitrification; (iii) gdh (glutamate dehydrogenase) and ureC (urease) for ammonification; (iv) napA (nitrate reductase) and nrfA (ctype cytochrome nitrite reductase) for assimilatory N reduction to ammonium (DNRA); (v) nasA (nitrate reductase) and nirA/nirB (nitrite

reductase) for assimilatory N reduction to ammonium; (vi) narG (nitrate reductase), nirS/nirK (nitrite reductase), norB (nitric oxide reductase), and nosZ (nitrous oxide reductase) for denitrification; and (vii) hzo (hydrazine oxidoreductase) for anaerobic ammonium oxidation (anammox). The latest version of the GeoChip is version 4.0, which uses the NimbleGen format and covers 56,990 sequences from 292 gene families (Tu et al., unpublished). GeoChip 4.0 contains additional functional categories of stress responses, bacterial phages and virulence. GeoChip technology GeoChip design GeoChip design involves major steps of (1) target gene selection, sequence retrieval and verification; (2) oligonucleotide probe design and validation; and (3) array construction. Target gene selection, sequence retrieval and verification Genes are chosen for key enzymes or proteins that are vital to the corresponding functions or processes of interest. For each functional gene, key words are used to search the GenBank Protein Database through the National Center for Biotechnology Information (NCBI) Entrez Programming Utilities (eUtils) and fetch all candidate amino acid sequences. Multiple key words can be chosen in each query, like the name of the target gene/ enzyme, its abbreviation and enzyme commission number (EC), and affiliated domains of bacteria, archaea, and fungi. The retrieved sequences are verified by using the HMMER program (http:// hmmer.wustl.edu/) with seed sequences that are experimentally confirmed for the targeting function. To construct a profile hidden Markov model (HMM) based on the alignment of seed sequences, the ClustalW (Thompson et al., 1994) and hmmbuild (Eddy, 1998) are used to search against all candidate sequences through both HMMER local and global algorithms. All hits with e-value  1.90 for RNA, and A260 to A230 > 1.70 for both DNA and RNA. To achieve these criteria, various purification methods could be adopted, like agarose gel purification followed by phenol–chloroform–butanol extraction (Liang et al., 2011). Generally 1–5 µg DNA or cDNA (reversely transcribed from mRNA) is required for GeoChip hybridization. In some cases, it might be hard to obtain enough DNA or cDNA from environmental samples. If so, additional amplification step will be essential. A whole community genome amplification (WCGA) method has been adopted to amplify a small amount of DNA (1–100 ng) by using the Templiphi 500 amplification kit (phi 29 DNA polymerase, GE Healthcare, Piscataway, NJ) with a modified amplification buffer containing single-stranded binding protein and spermidine (Wu et al., 2006). A whole community RNA amplification uses 50 to 100 ng template (Gao et al., 2007). With or without amplification, the environmental DNA or RNA (reversely transcribed to cDNA) is labelled by a fluorescent dye (e.g. Cy3 or Cy5) through random priming with the Klenow fragment of DNA polymerase for DNA (Wu et al., 2006) or SuperscriptTM II/III RNase H-reverse transcriptase for RNA (He et al., 2005). Then, the labelled DNA/RNA is purified and dried. GeoChip hybridization, image analysis and data preprocessing The dried nucleic acid target labelled with fluorescent dye is suspended in the prepared hybridization buffer (40–50% formamide, 3 × SSC, 0.3% sodium dodecyl sulfate, 0.7 μg/ µl herring sperm DNA, 0.86 mM dithiothreitol). After denaturing at 99°C, the nucleic acid targets are loaded onto the GeoChip array and hybridized at 42°C to 50°C (He et al., 2007). The hybridization stringency, determining the hybridization

140  | Xue et al.

specificity, is controlled by the hybridization temperature and formamide concentration. After hybridization, the GeoChip arrays are scanned with a minimal resolution of 10 μm for inhouse arrays or 2 μm for NimbleGen arrays. The scanned images are analysed by quantifying the pixel density (signal intensity) of each spot using image analysis software, like GenePix Pro (Molecular Devices, Sunnyvale, CA, USA), Gene-Spotter (MicroDiscovery, San Diego, CA, USA), or ImaGene (BioDiscovery, El Segundo, CA, USA). Raw GeoChip data are submitted to the GeoChip data analysis pipeline (He et al., 2010a) to perform positive spot identification and normalization. The positive spot identification is based on signal-to-noise ratio [SNR; SNR  =  (signal mean – background mean)/background standard deviation], or other standards, like signal-to-both standard deviations ratio [SSDR; SSDR = (signal mean – background mean)/(signal standard deviation + background standard deviation)] (He et al., 2005). GeoChip data analysis Data analysis is the most challenging part of GeoChip analysis due to the large amount of data generated, like all other high-throughput technologies. The structure of GeoChip data is multivariate and the number of variables (probe abundance) is much larger than the number of observations (sample number). The microbial community α-diversity based on GeoChip data are characterized by richness, evenness, and indices of Simpson, Shannon and inverse Simpson, etc. The microbial community structure or composition is assessed by ordination techniques (e.g. principal component analysis, PCA; or detrended correspondence analysis, DCA), hierarchical cluster analysis, and non-parametric permutation methods (multiple response permutation procedure, MRPP; permutational multivariate analysis of variance, Adonis; analysis of similarity, anosim). The treatment effects on abundances of single genes or gene groups are tested by t-test, analysis of variance (ANOVA), or response ratio. If environmental data are available, the correlation between microbial functional gene structure and environmental variables could be tested by canonical correspondence analysis (CCA) or

Mantel test. Based on the CCA results, the relative contribution of environmental variables on total variance of microbial community functional gene composition could be determined by variance partitioning analysis (VPA). GeoChip application in investigating N functional genes GeoChip has been utilized to analyse N functional genes in soil microbial communities to address different scientific questions. Some representative studies are highlighted briefly in the following sections. Effects of agricultural management practices Since the Green Revolution in the last century, synthetic fertilizers have become indispensable for modern agriculture as an external input to increase crop yields (Bohlool et al., 1992). However, substantial concerns have been raised globally regarding their adverse impacts on the environment, e.g. soil acidification (Guo et al., 2010; Van Breemen et al., 1982) and water eutrophication (Bohlool et al., 1992), which would inevitably affect crop productivity and damage the sustainability of agriculture (Plucknett and Smith, 1986; Tilman et al., 2002). As a result, interest in alternative crop management systems grows (Pimentel et al., 2005; Smith et al., 2007; Vandermeer, 1995). Using substantially less or no external chemical input (FAO/WHO, 1999) but still maintaining or even enhancing crop yields, alternative managements could be a more sustainable practice for agriculture (Robertson and Swinton, 2005; Smith et al., 2007). They take advantage of legume cover crops or rotated legume crops to provide supplemental N for plant growth (Smith et al., 2007; Snapp et al., 2005). The applications of manure (Edmeades, 2003), compost (Chang et al., 2007), and integrated pest management (Hassanali et al., 2008) are also commonly employed in alternative management systems for replacing chemical fertilizers or pesticides. Soil microbial communities are important biotic indicators for soil health ( Jackson et al., 2003; van Bruggen and Semenov, 2000). They are

Analysing Microbial Nitrogen Genes Using GeoChip |  141

responsible for performing many critical ecosystem functions, like nutrient cycling of N, and are important in sustaining crop productivity. Severity of N input effects on plants has been revealed to depend on soil nutrient status and factors influencing soil nitrification and N immobilization (Bobbink et al., 1998), which are largely mediated by soil microorganisms. Various agriculture management practices may have distinct influences on soil microbial communities and hence impact the ecological services provided by soils, like nutrient cycling and crop protection (Pimentel et al., 2005). Thus, it is necessary to gain a mechanistic understanding of the functional diversity, composition and dynamics of soil microbial communities under various management systems. The W.K. Kellogg Biological Station (KBS) in south-western Michigan, USA, joined the Long Term Ecological Research (LTER) network in 1988 to represent the agricultural or row-crop ecosystem. The Main Cropping System Experiment at the KBS-LTER contains replicated plots of the same soil series that differ in agricultural practices (http://lter.kbs.msu.edu), which is an ideal system for investigating the long-term impacts of agricultural management systems on soil microbial communities. Soil samples were collected from three agricultural systems under conventional (CT), low-input (LI) and organic (ORG) managements at the KBS-LTER site. The soil microbial communities were analysed with GeoChip 3.0 to evaluate changes of functional genes of the soil microbial community, especially those involved in N cycling, under various agricultural systems (Xue et al., 2013). Moreover, the relationship between greenhouse gas fluxes or soil N availability and related functional genes were also evaluated. The long-term treatments of CT, LI, and ORG were established in 1989 at the KBS-LTER site, all with tillage practices. A randomized block design was applied with six blocks and all 1-ha plots were separated by 10-m-wide grassy strips within each block. The CT treatment has been rotated by corn–soybean–winter wheat annually since 1993. The LI and ORG treatments have been rotated by corn–soybean–winter wheat since 1990, and the winter wheat was underseeded with red clover (Trifolium pratense L.) at a rate of 13 kg/ha. The

N fertilizer was applied in the form of NH4NO3 before 1995, and as 28% UAN (solution of urea and ammonium nitrate) thereafter. In the CT treatment, N fertilizer was applied at planting at a rate of 123 kg N/ha for corn and 56 kg N ha for winter wheat. Lime, N, P, and K were applied as needed according to MSU recommendations (Grandy et al., 2006). In the LI treatment, the N fertilizer was applied at planting at a rate of 28 kg N/ha for corn and 34 kg N/ha for wheat, followed by a side-dress application of N fertilizer according to soil test results. In ORG treatment, no chemical fertilizer was applied and the legume green manure was the only source of N, used with winter wheat. All crop varieties adopted were herbicide susceptible. In the CT treatment, herbicides were applied at the rate recommended for the region (Davis et al., 2005). In the ORG treatment, weed management consisted of multiple passes with a cultivator and rotary hoe. In LI, weed management was similar to that in ORG though it received reduced- to full-rate post-emergence herbicide applications, depending on scouting information. No insecticides were applied to any of the treatments. Additional information of the agronomic practices can be found at the KBS LTER website (http://lter.kbs.msu.edu/Data/ DataCatalog.html). Bulk soil samples were collected with bucket augers on 3 September 2008 when maize was planted in all three systems. Six soil cores (3.8 × 10 cm) collected across the row to achieve a sample unbiased by plant proximity were pooled for each replicate plot. Composited soil samples were sieved through a 4 mm sieve in the field and then stored overnight at 4°C. Sub samples were taken on the next day and frozen at –80°C until further analysis. For the entire soil microbial community, a higher functional gene diversity, based on richness (detected probe number in each sample), Shannon–Weaver (H) or Simpson reciprocal (1/D) indices, was observed in LI compared with CT, which might be linked to the higher crop yield stability observed previously in LI. However, there was no significant difference in soil microbial diversity between LI and ORG or CT and ORG. By utilizing the Bray–Curtis index, all three non-parametric tests (MRPP, Adonis, ANOSIM)

142  | Xue et al.

consistently showed that the community compositions in CT were significantly different from those in LI or ORG (P