Applications of Molecular Microbiological Methods [1 ed.] 9781908230690, 9781908230317

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Applications of Molecular Microbiological Methods

Torben Lund Skovhus Sean M. Caffrey Casey R.J. Hubert

Applications of Molecular Microbiological Methods Edited by Torben Lund Skovhus Det Norske Veritas Bergen Norway

Sean M. Caffrey Genome Alberta Calgary, AB Canada

Casey R.J. Hubert School of Civil Engineering and Geosciences Newcastle University Newcastle upon Tyne UK

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: 978-1-908230-31-7 (hardback) ISBN: 978-1-908230-69-0 (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 illustration: Graphics produced by Jennie Ojczyk, JO Design, Ildved, Denmark, [email protected] Printed and bound in Great Britain

Contents Contributorsv Foreword: Towards Engineering and Controlling Microbial Communities by Unlocking the Previously ‘Black Box’  Staffan Kjelleberg

ix

Introduction1 Torben Lund Skovhus, Sean M. Caffrey and Casey Hubert

Part I Industrial Microbiology 1

Molecular Methods in Microbiologically Influenced Corrosion Research, Monitoring and Control Gerard Muyzer and Florence Marty

2

5 7

Using the Power of Molecular Microbiological Methods in Oilfield Corrosion Management to Diagnose Microbiologically Influenced Corrosion23 Victor V. Keasler and Indranil Chatterjee

3

Next-generation Sequencing Approach to Understand Pipeline Biocorrosion33 Hyung S. Park, Jaspreet Mand, Thomas R. Jack and Gerrit Voordouw

4

Molecular Microbiological Methods Applied on Ship Ballast Tank Samples

43

5

Molecular Characterization of Microbial Communities Associated with Accelerated Low-water Corrosion (ALWC) on European Harbour Structures

55

Application of the qPCR Technique for SRB Quantification in Samples from the Oil and Gas Industries

69

Molecular Microbiological Methods Applied to Microbial Methane Production in Oil Reservoirs, Coal Beds, and Shale

77

Anne Heyer, Fraddry D’Souza, Arjan Mol and Hans de Wit

Florence Marty, Mark van Loosdrecht and Gerard Muyzer

6

Mariana Galvão and Márcia Lutterbach

7

Lisa M. Gieg and Karen Budwill

iv | Contents

Part II Medical Microbiology 8

Characterization of Bacterial Communities in Suspected Prosthetic Joint Infections

Yijuan Xu, Henrik C. Schønheyder, Lone H. Larsen, Mogens B. Laursen, Garth D. Ehrlich, Jan Lorenzen, Per H. Nielsen, Trine R. Thomsen and the PRIS Study Group

9

Using the Core and Supra Genomes to Determine Diversity and Natural Proclivities among Bacterial Strains Laura Nistico, Josh Earl, Luisa Hiller, Azad Ahmed, Adam Retchless, Benjamin Janto, J. William Costerton, Fen Z. Hu and Garth D. Ehrlich

Part III Environmental Microbiology 10

105

121

123

The Metabolic Function of Uncultured Microorganisms Assessed Through Single-cell Genomic Techniques

133

Assessing Microbial Activity and Degradation Pathways in the Environment by Measuring Naturally Occurring Stable Isotopes in Organic Compounds

141

Dorthe G. Petersen

12

93

Quantitative PCR and Reverse Transcription Quantitative PCR Applied to Methane-cycling Archaea in the Marine Sediments of the White Oak River Estuary

Karen G. Lloyd

11

91

Martin Elsner, Christian Griebler, Tillmann Lueders and Rainer U. Meckenstock

Part IV Applied Molecular Microbiological Methods

153

13

Metagenomic Analysis of Microbial Communities and Beyond

155

14

Stable Isotope Probing in Environmental Microbiology Studies

171

15

Fluorescence in situ Hybridization (FISH) for the Identification and Quantification of Microorganisms

181

Quantitative Real-time Polymerase Chain Reaction (qPCR) Methods for Abundance and Activity Measures

193

Investigation of Microorganisms at the Single-cell Level using Raman Microspectroscopy and High-resolution Secondary Ion Mass Spectrometry

203

Lars Schreiber

S. Jane Fowler and Lisa M. Gieg

Cristina Moraru and Elke Allers

16

Vibeke B. Rudkjøbing, Tine Y. Wolff and Torben Lund Skovhus

17

Stephanie A. Eichorst and Dagmar Woebken

Index213

Contributors

Azad Ahmed Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA [email protected] Elke Allers Department of Ecology and Evolutionary Biology University of Arizona Life Sciences South Tucson, AZ USA [email protected] Karen Budwill Environment and Carbon Management Alberta Innovates-Technology Futures Edmonton, AB Canada [email protected] Sean M. Caffrey Genome Alberta Calgary, AB Canada [email protected] Indranil Chatterjee Nalco Champion Pune India [email protected]

John William Costerton (deceased) Department of Orthopaedic Surgery Allegheny General Hospital Pittsburgh, PA USA Fraddry D’Souza The Energy and Resources Institute (TERI) Alto-St Cruz, Bambolim Goa India [email protected] Josh Earl Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA [email protected] Garth D. Ehrlich Centre for Genomic Sciences Allegheny-Singer Research Institute Allegheny General Hospital Department of Microbiology and Immunology Drexel University College of Medicine Allegheny Campus Pittsburgh, PA USA [email protected] Stephanie A. Eichorst Division of Microbial Ecology Department of Microbiology and Ecosystem Science University of Vienna Vienna Austria [email protected]

vi | Contributors

Martin Elsner Helmholtz Zentrum München Institute of Groundwater Ecology Neuherberg Germany [email protected] S. Jane Fowler Petroleum Microbiology Research Department of Biological Sciences University of Calgary Calgary, AB Canada [email protected] Mariana Galvão National Institute of Technology Laboratory of Biocorrosion and Biodegradation (LABIO) Brazil [email protected]

Fen Z. Hu Departments of Microbiology and Immunology, and Otolaryngology – Head and Neck Surgery Drexel University College of Medicine Allegheny Campus Pittsburgh, PA USA [email protected] Casey Hubert School of Civil Engineering and Geosciences Newcastle University Newcastle upon Tyne UK [email protected] Thomas R. Jack Biological Sciences University of Calgary Calgary, AB Canada [email protected]

Lisa M. Gieg Petroleum Microbiology Research Department of Biological Sciences University of Calgary Calgary, AB Canada

Benjamin Janto Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA

[email protected]

[email protected]

Christian Griebler Helmholtz Zentrum München Institute of Groundwater Ecology Neuherberg Germany

Victor V. Keasler Nalco Champion Sugar Land, TX USA

[email protected] Anne Heyer M2i Materials Innovation Institute Mekelweg Z Delft Netherlands [email protected] Luisa Hiller Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA [email protected]

[email protected] Staffan Kjelleberg School of Biotechnology and Biomolecular Sciences The University of New South Wales Sydney Australia [email protected] Lone H. Larsen Department of Clinical Microbiology Aalborg University Hospital Aalborg Denmark [email protected]

Contributors | vii

Mogens B. Laursen Department of Orthopaedic Surgery Aalborg University Hospital Aalborg Denmark

Florence Marty Department of Biotechnology Delft University of Technology Delft Netherlands

[email protected]

[email protected]

Karen G. Lloyd Microbiology Department University of Tennessee Knoxville, TN USA

Rainer U. Meckenstock Helmholtz Zentrum München Institute of Groundwater Ecology Neuherberg Germany

[email protected]

[email protected]

Mark van Loosdrecht Department of Biotechnology Delft University of Technology Delft Netherlands

Arjan Mol Department of Materials Science and Engineering Delft University of Technology Delft Netherlands

[email protected]

[email protected]

Jan Lorenzen Danish Technological Institute Life Science Division Denmark

Cristina Moraru Astrobiology Centre National Institute for Aerospace Technology Madrid Spain

[email protected] Tillmann Lueders Helmholtz Zentrum München Institute of Groundwater Ecology Neuherberg Germany [email protected] Márcia Lutterbach National Institute of Technology Laboratory of Biocorrosion and Biodegradation (LABIO) Brazil [email protected] Jaspreet Mand Biological Sciences University of Calgary Calgary, AB Canada [email protected]

[email protected] Gerard Muyzer Department of Aquatic Microbiology Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam Netherlands [email protected] Per H. Nielsen Department of Biotechnology, Chemistry, and Environmental Engineering Aalborg University Aalborg Denmark [email protected]

viii | Contributors

Laura Nistico Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA [email protected]

Torben Lund Skovhus Det Norske Veritas Bergen Norway [email protected]

Hyung S. Park Biological Sciences University of Calgary Calgary, AB Canada

Trine R. Thomsen Department of Biotechnology, Chemistry, and Environmental Engineering Aalborg University Aalborg Denmark

[email protected]

[email protected]

Dorthe G. Petersen Department of Bioscience Center for Geomicrobiology Aarhus C Denmark

Gerrit Voordouw Biological Sciences University of Calgary Calgary, AB Canada

[email protected]

[email protected]

Adam Retchless Centre for Genomic Sciences Allegheny-Singer Research Institute Pittsburgh, PA USA

Hans de Wit Department of Materials Science and Engineering Delft University of Technology Delft Netherlands

[email protected]

[email protected]

Vibeke B. Rudkjøbing Aalborg University Aalborg Denmark

Dagmar Woebken Division of Microbial Ecology Department of Microbiology and Ecosystem Science University of Vienna Vienna Austria

[email protected] Henrik C. Schønheyder Department of Clinical Microbiology Aalborg University Hospital Aalborg Denmark [email protected] Lars Schreiber Department of Bioscience/Centre for Geomicrobiology Aarhus University Aarhus Denmark [email protected]

[email protected] Tine Y. Wolff Danish Technological Institute Aarhus Denmark [email protected] Yijuan Xu Department of Biotechnology, Chemistry, and Environmental Engineering Aalborg University Aalborg Denmark [email protected]

Foreword Towards Engineering and Controlling Microbial Communities by Unlocking the Previously ‘Black Box’

The field of microbial life sciences is undergoing massive transformations following rapid advances made in two parallel advents: the much enhanced understanding of microbial communities as sophisticated complex consortia, or biofilms; and the emergence of next generation sequencing technology enabling high resolution genomic analysis of such microbial assemblages. As highlighted in this volume, these sequencing and related technologies, collectively known as ‘omics’, have revolutionized our capacity to understand the composition and activities of microbial consortia in industrial, medical and environmental settings to the extent where unwanted or deleterious biofilms will be controlled or engineered to enable specific outcomes. To date, our understanding of the microbiological components, and hence the key activity centres of natural, medical and engineered systems, has been poor or non-existent. In many instances, for example wastewater treatment, the required operation and management of a process entirely accommodated by the functions of highly diverse and complex biofilms has relied solely on engineering-based design and principles. Clearly, overlooking the biology of organisms that are central to any natural or engineered system limits our opportunities to fully understand and potentially harness their functions. Moving from the largely inadequate culturebased detection methodologies, to the uptake of increasingly sophisticated high-resolution detection, genomic sequencing, and data analysis provides the key to opening many hitherto biological black boxes and unravelling the hidden intricacies of microbial communities. The uptake

of these so-called ‘next-generation’ sequencing technologies has already delivered an astounding depth of knowledge for even extremely diverse microbiological communities. This allows for very precise questions on systems consisting of thousands of different types of bacteria to be answered: who is present, what are they doing, and how do they interact? As such, we can also comprehensively describe the complex communal metabolic pathways that reflect the actions of the ‘super-organism’ of each species-rich microbial community with increasing sophistication. Microbial consortia in natural, medical and engineered systems can house a remarkable assembly of different metabolic units, and employing molecular methodology to resolve their biology and ecology is expected to not only lead to a much enhanced grasp of the biology of the system, but will also provide the tools for informed and much needed operational interventions and improvements in systems where microbial activity can be harnessed or controlled, such as applications in medical and public health arenas, and natural and engineered systems. The early lessons from this new journey teach us that microbial communities are composed of numerous members and behave communally in previously unanticipated ways. Until very recently, our understanding has been based on a very small subset of specific members within these communities, either from studies of the minute fraction of culturable bacteria, or those observed directly in the system via molecular probes whose design is based on prior but often biased knowledge from a selection of a few microorganisms. With the rapid uptake of next generation genomic sequencing

x | Foreword

platforms we can now characterize microbial communities at very deep, and ultimately, saturation levels. Such studies across a range of natural and engineered or manmade ecosystems reveal new microbial universes composed of hundreds or thousands of species. In many instances few of these align to known genomes in databases, and hence are not similar to organisms previously encountered. In fact, abundant members may not be readily classified, even at a more general taxonomic level. Nevertheless, a comprehensive meta-‘omics’ approach allows the specific functions performed in complex communities to be determined by describing all genes that are expressed. This is achieved by sequencing the pool of transcripts made from all actively expressed genes and assigning these to the organisms that bear them, even if we do not have prior knowledge of the organisms themselves. On that basis a network of the community metabolisms combining metabolic footprints of the entire system can be constructed. In other words, for the first time all members of complex microbial communities in any system can be determined, whether the community is associated with an engineered bioprocess, microbial corrosion of metal pipes, aerobic granulation process for water treatment, or the shifts and function in microbial communities associated with a human host upon colonization and invasion of pathogenic bacteria. The utility of next-generation sequencing technologies now enables massive data sets to be generated with relative ease and affordability. However, the approach incurs potential bottlenecks associated with handling these data. Data handling for complex dynamic systems not only requires clever and user-friendly bioinformatics, and massive computational power, but also the application of a sophisticated systems biology approach to integrate the genomic data sets with all aspects of the dynamic environment to provide meaningful interpretation. Effective systems biology approaches employ cleverly designed sampling regimes and data collection protocol, to maximize spatial and temporal information. To illustrate, the increasingly sophisticated characterization of complex microbial communities as reported in this volume, has spawned a new mode

of sampling design based on a hypothesis-free approach employing indiscriminate sample collection coupled with informed sample processing. As such, samples are routinely and frequently collected over time (for example, in an engineering context at several locations; or in the medical context from many individuals) and stored for subsequent selective processing and sequencing. The simultaneous measurement of all parameters or characteristics relevant to the system (such as flow rate, temperature, pH or nutrient levels in engineered or natural systems; or health-related aspects in medical systems) provides metadata that can be aligned with the molecular and sequence-based information on microbial community structure and function. In this fashion, we are able to identify key time points associated with fluctuations and retrieve only those samples that provide meaningful insights, for example before or after a recorded discrepancy. Community activity can thus be directly correlated with process or other outcomes, and hence provide the specific and sufficiently detailed information needed to greatly enhance the manipulation required to harness or control microbial communities. Finally, looking ahead, the field of modern microbial life sciences can benefit from accessing the rich conceptual framework generated from decades of ecological theory. Microbial biofilms often have highly complex communities, often more diverse than any other community on the planet, and it is exactly these kinds of communities that the conceptual framework of the ecology of higher organisms has been developed for. Thus, the theories from eukaryote ecology that address the structure and dynamics of complex communities are relevant to biofilms, such that we can gain insights into how microbial systems assemble, adapt and respond; the identification and role microbial keystone species; or ecosystem assembly, spatial and temporal stability and resilience. We can now characterize both identity and function for a significant proportion of environmental, engineered and medical microbes, which for the first time gives us the kind of data that allows for rigorous tests of concepts and theories, and ultimately effective applications. We are entering an exciting new phase in microbiology that will continue to pose new challenges

Foreword | xi

as former obstacles are overcome. Innovative and constructive microbiology in this new era will only be fostered by adopting a smart, interdisciplinary strategy that embraces continually evolving technologies, maximizes existing frameworks and takes an informed approach to the potentially overwhelming generation of knowledge. This

new era will witness the harnessing and control of complex microbial communities in a diversity of applications from curbing biocorrosion; managing chronic infections and evasion of a host’s adaptive immune response; to enhancing bioremediation and harnessing fossil energy reserves. Professor Staffan Kjelleberg

Introduction

Torben Lund Skovhus, Sean M. Caffrey and Casey Hubert

Microorganisms are the most abundant and diverse forms of life on Earth. With an estimated number of prokaryotic cells in the order of 1029 (Kallmeyer et al., 2012) prokaryotes outnumber all other organisms by orders of magnitude and represent a major proportion of the total biomass on Earth. With their tremendous diversity and abundance, microorganisms play an essential part in ecological, medical and industrial processes. Microorganisms are involved in mediating major global biogeochemical cycles, including the carbon, nitrogen, and sulphur cycles. Consequently, they are responsible for maintaining the balance of the ecosystems of the Earth and can be used as indicators (markers) of ecosystem health. Microorganisms also play an essential part in the human body and can maintain as well as impair human health. The 1013 microorganisms that inhabit a human body numerically dominate our own 1012 human cells (Blaser, 2006). Although a body’s normal microflora promotes human health, pathogenic bacteria are major causes of illness and mortality. Microorganisms are also crucial to many industrial processes, such as brewing and food processing, and have been so for thousands of years. Today, it is appreciated that microorganisms play a vital part in a wide variety of industries from food to energy production. In each of these industries, microorganisms can either assist processes by producing important bio-products or disrupt processes through various kinds of biofouling and biodegradation processes (Whitby and Skovhus, 2011). There are three primary questions that must be addressed when examining microbial

communities and their interactions with natural environments, human health and industrial processes. They are: (1) which microorganisms are present (2) what is the function of microorganisms and (3) how do the microbial processes interact with the system as a whole. Traditionally, microbiologists have relied on growth- or cultivation-based technologies when approaching these questions. Unfortunately, many microorganisms cannot easily grow in the laboratory and therefore most of the relevant microbial diversity in such systems (typically from 90% to 99%) can be missed by traditional enumeration methods. Furthermore, since microorganisms function in complex networks that often involve speciesto-species interactions, studying isolated pure cultures gives an incomplete picture of a microbial ecosystem. Molecular microbiological methods (MMM) can allow the total diversity of a microbial community to be surveyed and its functions to be investigated (Whitby and Skovhus, 2011). MMMs achieve this by analysing the molecules that microorganisms possess and generate, including DNA, RNA, proteins, lipids and cellular metabolites. In order to understand and/or control the presence and activities of microorganisms a wide range of methods, protocols and procedures are required. Fig. 0.1 shows how different microbiological methods relate to the overall objective of studying and controlling microorganisms. They include defining the microbial diversity within a system (Fig. 0.1A) in order to develop quantitative assays for either cell abundances and/or activities (Fig. 0.1B). Ultimately, the analyses can lead to

2 | Skovhus et al.

A Biodiversity Search of Defined Environment Cloning and Sequencing of relevant target genes • 454 pyro-sequencing for large scale data processing • Data gathering and interpretation for design of quantitative molecular assays •

B

C

Quantitative Molecular Biology Assays • Cell enumeration with FISH and qPCR • Cell activity measured with SIP, FISH, RT-qPCR and metabolites • Develop protocols and procedures for monitoring based on the above

Mitigation, Research & Problem Solving Implement monitoring programs • Test and optimize mitigation protocols • Apply biomarkers for early warring •

Figure 0.1 Overview of qualitative (A) and quantitative (B) molecular microbiological methods (MMM) and how they relate to system control, research and problem solving in microbial life science (C).

an improved understanding of the system and to models that facilitate engineering interventions for mitigation of problems caused by microorganisms (Fig. 0.1C). This book presents several examples that illustrate how these molecular microbiology methods can be applied in microbial ecology, human health and different industrial settings. Twelve case studies are presented in individual chapters and are complemented by five chapters that describe particular methods in greater detail. Overview of chapters Part I presents seven examples of the application of molecular methods in various industries. The first five chapters discuss the use of microbial methods to monitor the microorganisms responsible for accelerating metal corrosion. Muyzer and Marty review the use of microbial methods, such as quantitative polymerase chain reaction (qPCR), fluorescence in situ hybridization (FISH), microarrays and metagenomics, to identify microbial taxa causing metal corrosion. The next two chapters explore the issue of Microbiologically Influenced Corrosion (MIC) in oil field systems. Corrosion damage is one of the largest operating expenses experienced by energy companies. Keasler et al. illustrate the limitations faced by operators that rely on culture-based micro­ biology techniques to evaluate the risk of MIC, and demonstrate that the environmental context the microbes experience must be considered in corrosion risk assessment. Park et al. describe the use of community DNA sequencing to identify methanogenic archaea as unexpected agents of corrosion damage. Chapter four and five focus

on the problem of MIC in marine environments. Heyer et al. use PCR and sequencing to identify the biofilm community members responsible for corroding ship ballast tanks. Marty et al. employ 16S based PCR and DGGE along with sequencing to compare microbial populations growing in areas with low or high corrosion on a steel harbour structure. They suggest that the microbial sulphur cycling led to the production of corrosive sulphur intermediates. The final two industrial case studies explore the microbial communities in fossil fuel deposits. Galvão et al. use real time quantitative PCR to enumerate microbial taxa linked to biofouling, plugging and corrosion in oil field operations. In the final industrial case study, Geig and Budwill combine next generation DNA sequencing with hydrocarbon metabolite analysis to examine the feasibility of using indigenous microorganisms to convert trapped fossil fuels to more easily recoverable methane. Since current extraction technologies typically recover less than 40% of the oil present in a reservoir it is hoped the conversion of the residual oil to methane will vastly improve recovery. Ideally, identifying microorganisms at the root of human infections is a key step towards developing effective treatment. Part II presents two chapters dealing with the identification of microorganisms in medical settings. Xu et al. combine next generation 16S rRNA gene amplicon sequencing, Ibis T5000 biosensor analysis and FISH to identify the microorganisms causing a dangerous infection of a prosthetic joint. The advent of next generation sequencing technologies has caused a rapid proliferation in the number of sequenced bacterial genomes. This has revealed the tremendous genetic diversity inherent in

Introduction | 3

microbial species. In Chapter 9, Nistico et al. describe bacterial species as having a ‘Supragenome’ comprising core and distributed genes. Since 16S rRNA identification uses a small fraction of the total amount of genetic information present in a microorganism and since species can contain substantial genetic diversity, relying on 16S rRNA identification alone to presume function may be misleading. Part III presents three case studies that use molecular methods to assess the structure and function of microbial communities in natural environments. Methane is a powerful greenhouse gas and it is therefore important that we understand the factors that affect methane cycling in the environment. Lloyd presents quantitative PCR analysis of environmental DNA and reverse transcription quantitative PCR analysis of environmental RNA to explore methane cycling in marine sediments. Since the vast majority of microbial species cannot be cultured in the laboratory, our ability to study them is severely limited and in Chapter 11 Petersen reviews several culture independent methods for isolating single cells for whole genome sequencing. This technology allows researchers to identify the functions environmental strains encode and potentially employ. Assessing the latent capacity of an environment for bioremediation is necessary to determine its susceptibility to damage from human disturbance.

In the final chapter of case stories, Elsner et al. use compound-specific isotope analysis to quantify the ability of indigenous microbial communities in several environments to degrade toxic compounds such as hydrocarbons and pesticides. Part IV contains five chapters dealing with many of the molecular methods described in the preceding case studies. These chapters include an overview of metagenomics, stabile isotope probing, fluorescent in situ hybridization, quantitative and revers transcription PCR and single cell methods. These more detailed descriptions are included to help readers evaluate the applicability of various methodological options for approaching questions and case studies of their own. Acknowledgements We would like to thank all the contributors to this book as well as the reviewers for their useful comments and suggestions. References Blaser, J.M. (2006). Who are we? Indigenous microbes and the ecology of human diseases. EMBO Rep. 7(10), 956–960. Kallmeyer, J., Pockalny, R., Adhikan, R.R., Smith, D.C., and Hondt, S.D. (2012). Global distribution of microbial abundance and biomass in subsea floor sediment. PNAS Online. Available at: www.pnas.org Whitby, C., and Skovhus, T.L. (2011). Applied Microbiology and Molecular Biology in Oil Field Systems. Springer Publisher (ISBN 978–90–481– 9251–9).

Part I Industrial Microbiology

Molecular Methods in Microbiologically Influenced Corrosion Research, Monitoring and Control

1

Gerard Muyzer and Florence Marty

Abstract Microbiologically influenced corrosion (MIC) is an enormous problem in different industrial sectors with large economical consequences. Although research has been performed on microorganisms associated with MIC, the causative microbes have not been identified yet. Traditional microbiological techniques have been used, but they only detect 1% of the microbes in nature and so are not suited for this purpose. Molecular methods, based on DNA and RNA, are more efficient to characterize microbial communities in different environments, as they are sensitive and reliable, and not dependent on cultivation. In this chapter we give an overview of nucleic acid-based molecular methods that have been used to study microbial communities associated with MIC and give an outlook on the use of novel, state-of-the art methods that might be applied in the future. Introduction For decades, metal corrosion, and particularly aqueous corrosion, has led to an enormous economic loss, as well as to different safety (e.g. nuclear power plants) and conservation (e.g. cultural heritage) issues. The total costs for metal corrosion in the United States were estimated at $276 billion per year (Schmitt et al., 2009). From these figures, the costs represented by microbiologically influenced corrosion (MIC), or corrosion associated with the action of microorganisms (‘biocorrosion’) would represent 20–30%, affecting many industrial sectors (e.g. gas and oil industry, drinking water industry, wastewater treatment industry, as well as the aviation and maritime sectors).

Theoretically, the occurrence of principally electrochemical reactions on a metal surface can be predicted by thermodynamics laws: the metal (the anode, where oxidation reaction with loss of electrons takes place) in contact with a conductive fluid or vapour (mostly water) and a reducing compound (the cathode, where the reduction reaction takes place, with gain of electrons), usually oxygen or hydroxyl ions depending on the pH of the aqueous solution, will be oxidized, a process called ‘corrosion’. However, the corrosion rates controlled by charge-transfer processes at the metal surface, which scale, multicity and dynamics are largely unknown in natural environments, are still very difficult to predict. The task becomes even more challenging when microbiological factors are involved, such as in MIC. In principle, any microorganism present on a metal surface may cause local electrochemical changes near the metal surface, influencing corrosion kinetics, either passively (their presence producing physical surface heterogeneity with different local potentials) or actively (as the result of physiological properties, e.g. the metal binding nature of exopolymeric substances, and metabolism, e.g. respiration, metal reduction) (Little and Lee, 2007). So far, pioneered by the work of Von Wolzogen Kuhr and Van der Vlugt (1934) sulphidogens, those organisms that can reduce sulphate, thiosulphate, and/or sulphur to sulphide, have been the focus of most studies, and one of the most often MIC key players incriminated (e.g. Larsen et al., 2010; Almahamedh et al., 2011; Lee et al., 2012). This metabolic group, affiliated to the domain Bacteria (in particular to the Deltaproteobacteria and Firmicutes), and

8 | Muyzer and Marty

to the Archaea (Muyzer and Stams, 2008), are physiologically and ecologically versatile owing to their proliferation in different industrial contexts (e.g. harbour structures, gas and petroleum pipelines) (Davidova et al., 2012; Kjellerup et al., 2009; Ravot et al., 2005). Organic sulphur moieties of petroleum compounds have also been assumed to be used as electron acceptors in the absence of inorganic sulphur molecules (Orphan et al., 2003). Nevertheless, bacteria from other phylogenetic groups (e.g. Citrobacter within the Gammaproteobacteria) have been associated with sulphide production and MIC (Agrawal et al., 2010; Angeles-Ch et al., 2002). Moreover, other causative microorganisms have been consistently evidenced and classified as slime-producing bacteria (e.g. Pseudomonas and other bacteria from the Gammaproteobacteria) (Lopéz et al., 2006; (Pavissich et al., 2010), sulphur-oxidizing bacteria (e.g. Thiobacillus within the Betaproteobacteria, Thiomicrospira within the Gammaproteobacteria) (Okabe et al., 2007; Korenblum et al., 2010), metal-oxidizing bacteria (e.g. Gallionella and Leptothrix from the Betaproteobacteria, Mariprofundus from the Zetaproteobacteria (McBeth et al., 2011), and Bacillus from the Firmicutes) and metalreducing bacteria (e.g. Shewanella, Geobacter, Bacillus, respectively from the Gamma-, Deltaproteobacteria, and the Firmicutes) (Teng et al., 2008; Almahamedh et al., 2010) as well as acid-producing bacteria, such as fermenters, acetogens (e.g. Clostridium within the Firmicutes), and even fungi (Oliveira et al., 2011). Additionally, methanogenesis (Zhang et al., 2003), reduction and oxidation of nitrogen compounds (e.g. ammonium, nitrite, nitrate), aerobic respiration/photosynthesis, hydrogenotrophy and hydrogen production have also been associated with MIC events (Little and Lee, 2007). However, it is generally observed that although a single type of organism can lead to MIC, the most severe cases of MIC have been reported in the presence of microbial consortia with different interacting metabolic groups (e.g. Kan et al., 2011). The nature and dynamics of these interactions are however largely unknown. Furthermore, a lot of bacteria are metabolically versatile and therefore may not accelerate corrosion by a single mechanism, but via different pathways depending on the local physico-chemical environment (e.g.

hydrogenotrophy, sulphate and iron reduction for some SRB). Until recently, most of the research and monitoring of microbial populations involved in MIC have been based on culturing approaches intended to select for different metabolic groups. An example of a routinely used method is the MostProbable-Number (MPN) approach in which bacterial numbers in a sample are determined based on positive growth detected over replicate serial dilutions. However, it is well acknowledged that only a limited number (less than 1%) of microorganisms present in the environment are amenable to current culturing approaches making these techniques unreliable for detection and identification of microorganisms involved in MIC (e.g. Price et al., 2010). Furthermore, culturing media are usually not selective enough to target a specific type of bacteria (e.g. Zhu and Kilbane, 2004). In addition, in situ activities, dynamics, and interactions between community members cannot be approached with culturing techniques. Methods based on the detection of molecular markers, such as the 16S rRNA gene or genes encoding proteins (see Table 1.1 for an overview of these ‘functional’ genes), are today more relevant tools for a reliable detection, identification and enumeration of microorganisms in environmental samples, due to better accuracy, reproducibility and time-efficiency. Although these methods are routinely used in microbial ecology and environmental microbiology, their application in monitoring MIC-related processes is still relatively scarce. One of the reasons might be the lack of expertise and/or the relatively high costs for equipment and materials, although the latter might be negligible in comparison to the enormous financial costs caused by MIC. In this chapter we give an overview of the nucleic acid-based molecular methods and associated taxonomic/functional markers that have been used to study microbial communities associated with MIC (focusing on both Bacteria and Archaea) and give an outlook on the use of novel, state-of-the art methods that might be applied in the future (see Fig. 1.1 for a schematic overview). We have grouped the different nucleic acid-based approaches on the basis of three

Methods in Corrosion Research, Monitoring and Control | 9

Table 1.1 Overview of functional genes that might be used in MIC studies Category

Targeted gene

Function

References

cbbL/ cbbM

Calvin cycle

Tourova et al. (2010)

aclB/coxL

Reductive acetyl-CoA pathway

Dunfield and King (2004), Cunliffe et al. (2008)

accA/ pccB

Hydroxypropionate/maly-CoA Weber and King (2011) cycle

Acetogenesis

fhs

Formyltetrahydrofolate synthetase

Xu et al. (2009)

Methanogenesis

mcrA

Methyl co-enzyme M reductase

Luton et al. (2002)

Methane oxidation

mmoX

Methane monooxygenase

Cunliffe et al. (2008)

pmoA

Particular methane monooxygenase

Cunliffe et al. (2008)

nifH

Nitrogen fixation genes

Yan et al. (2011)

amoA

Ammonia monooxygenase

Sahan and Muyzer (2008)

nxrA

Nitrite oxidoreductase

Wertz et al. (2008)

nirS, nirK

Nitrite reductase,

Braker et al. (2000), Nogales et al. (2002), Kandeler et al. (2006)

nosZ

Nitrous oxide reductase

Nogales et al. (2002), Kandeler et al. (2006), Geets et al. (2007)

Dissimilatory nitrate reduction

narG

Nitrate reductase

Nogales et al. (2002), Kandeler et al. (2006)

Anaerobic ammonium oxidation

hao

Hydroxylamide oxidoreductase

Poret-Peterson et al. (2008)

hzo

Hydrazine oxidoreductase

Li and Gu (2011)

Sulphate reduction

dsrAB

Dissimilatory sulphite reductase

Dar et al. (2007)

Sulphide oxidation

soxB

Sulphite oxidase

Meyer et al. (2007), Dang et al. (2011)

S-reduction/Soxidation

aprA

Adenosine 5′-phospho sulphate reductase

Meyer and Kuever (2007)

C-cycle CO2 fixation

N-cycle N2-fixation

Nitrification Denitrification

S-cycle

method principles: (i) PCR-based techniques, (ii) hybridization techniques, and (iii) meta-omic techniques. PCR-based techniques Community fingerprinting PCR-based techniques have often been used in combination with genetic fingerprinting techniques, such as denaturing gradient gel

electrophoresis (DGGE; Muyzer, 1999), single strand conformation polymorphism (SSCP; Lee et al., 1996), and terminal restriction fragment length polymorphism (T-RFLP; Marsh, 1999). In DGGE, a mixture of DNA fragments of identical size is separated based on their difference in melting behaviour (determined by conformational and GC- richness of DNA sequences). The DNA fragments differing in their sequences (presumably corresponding to different community members) are electrophoresed in a polyacrylamide gel

10 | Muyzer and Marty DGGE Community fingerprinting

SSCP T-RFLP

PCR-based techniques

Pyrotag sequencing Clone libraries

MAR

qPCR RSGP

Microbial community analysis NanoSIMS

CARD-FISH

Hybridization techniques SIP

FISH Dot-blot analysis DNA microarrays

Metagenomics Meta-omics

Metatranscriptomics Metaproteomics

Figure 1.1 Schematic overview of the different molecular techniques currently used for the analysis of microbial communities. Different techniques can be combined, such as FISH and MAR, FISH and NanoSIMS, SIP and PCR-based techniques. Abbreviations: DGGE, denaturing gradient gel electrophoresis; FISH, fluorescence in situ hybridization; MAR, micro-autoradiography; NanoSIMS, Nano-Secondary-Ion Mass Spectroscopy; RSGP, reverse sample genome probing; qPCR, quantitative polymerase chain reaction; SIP, stable isotope probing; SSCP, single strand conformation polymorphism; T-RFLP, terminal restriction fragment length polymorphism.

containing a linear gradient of DNA denaturants (i.e. a mixture of urea and formamide). At a certain concentration of denaturants in the gel, the double stranded DNA fragment of one of the community members starts to melt and its mobility is reduced compared to the others, still as double stranded fragments. Fig. 1.2 shows an example of DGGE analysis of different microbial communities. DGGE has the advantage that it is cheap and straightforward. The identity of the community members can be obtained after sequencing the separated DGGE bands. Another option to identify particular community members is hybridization analysis of DGGE profiles with specific oligonucleotide probes. Teske et al. (1996), for instance, used digoxigenin-labelled oligonucleotide probes specific for different sulphate-reducing bacteria to identify these bacteria in water and sediment samples from the Mariager Fjord in Denmark. Interpretation of complex DGGE profiles is difficult, in particular when they

are present on different gels. However, different computer programs have been developed and used to compare DGGE profiles. Marzoratti et al. (2008) wrote an excellent paper on the interpretation of molecular fingerprints using DGGE profiles as an example. DGGE analysis of PCR-amplified 16S rRNA gene fragments has been used in several MICstudies. For instance, Le Borgne and co-workers (2007) used DGGE of PCR-amplified 16S rRNA gene fragments to monitor microbial communities involved in biocorrosion after biocide treatment. They found that after biocide treatment bacterial populations affiliated to Shewanella and Vibrio were persistently present. Hoffmann et al. (2007) used a so-called ‘full-cycle rRNA’ approach (Amann et al., 1995), including DGGE analysis of PCR-amplified 16S rRNA gene fragments, fluorescence in situ hybridization (FISH) and DNA microarray analysis, to monitor microbial communities in oil fields after addition of

Methods in Corrosion Research, Monitoring and Control | 11

1

2

3

4

5

Figure 1.2 DGGE analysis of microbial communities. Genomic DNA was extracted and the 16S rRNA genes were PCR-amplified. Subsequently, the DNA fragments were analysed by DGGE. The number of bands reflects the complexity of the community, while the intensity of the bands is an indication of the abundance of the populations. The identity of the bacteria can be obtained after sequencing the excised bands and comparing the sequences to those stored in databases, such as GenBank.

nitrate (to remove corrosive sulphide favouring nitrate-reducing sulphide oxidizing bacteria), and to study the enrichment of microorganisms in a production system. Larsen et al. (2009) used a similar polyphasic approach (i.e. DGGE, FISH and qPCR) to study sulphate-reducing prokaryotes (SRP) in samples from the Halfdan oil field located in the Danish sector of the North Sea. In another study, Keasler et al. (2010) used PCR-DGGE to study the composition of microbial communities in a complex pipeline network, which transports a mixture of crude oil and water from offshore platforms in Nigeria to an onshore separation facility. Identification of the microbial populations was necessary to improve biocide treatment (Keasler et al., 2011). Machuka et al. (2011) used PCR-DGGE to study the community composition and populations dynamics of microorganisms on coupons of different alloys exposed to seawater. Kan et al. (2011) used

culture-dependent and culture-independent techniques (PCR-DGGE) to study bacterial communities at corrosion sites at Granite Mountain Record Vault in Utah (USA). They found a high diversity of microorganisms belonging to the Alpha-, Beta- and Deltaproteobacteria, Actinobacteria, Firmicutes and Bacteriodetes, but could not identify sulphate-reducing bacteria. DGGE analysis of PCR-amplified 16S rRNA gene fragments was also used by Yang et al. (2011) to study the effect of pipe materials (steel, copper, stainless steel, PVC) on biofilm accumulation and water quality. The steel and copper coupons showed a higher microbial diversity and concomitantly higher corrosion, which negatively influenced the water quality. Another popular fingerprinting technique is terminal restriction fragment length polymorphism (T-RFLP). In T-RFLP, genomic DNA extracted from microbial communities is amplified with primers that are labelled with a fluorochrome at their 5′-end. The resulting DNA fragment is subsequently cut with a restriction enzyme and the restriction fragments are analysed on a capillary sequencer. Only the DNA fragments that are fluorescently labelled will be detected, resulting in a profile of the microbial community. The advantage of T-RFLP is that molecular weight markers labelled with a different fluorochrome can be mixed with the different samples, and so highly reproducible profiles can be obtained. A disadvantage of T-RFLP is that no sequence information is obtained, and that rRNA fragments needs to be cloned and sequenced to obtain this information. Although indirectly related to MIC, Lee and coworkers (2008) used T-RFLP and sequencing of cloned inserts to study the succession of microbial biofilms on three different surfaces (i.e. acrylic, glass and steel), and found, in contrast to earlier results, that some members belonging to the Gammaproteobacteria colonized the surfaces first, followed by different members belonging to the Alphaproteobacteria. Clone libraries In addition to fingerprinting techniques, PCRamplified 16S rRNA gene fragments can also be directly cloned and sequenced. Comparative analysis of these sequences to those stored in public

12 | Muyzer and Marty

databases, such as GenBank, can reveal the identity of the community members. Cloning of 16S rRNA is more labour intensive than fingerprinting, but will reveal more sequence information, which might facilitate the design of specific oligonucleotide probes used in hybridization assays. Lopéz and co-workers (2006) used clone libraries of PCR-amplified 16S rRNA gene fragments to identify bacteria present in a corrosive biofilm in a steel pipeline that was used to inject water into an oilfield in the Gulf of Mexico. They found members belonging to different genera within the Gammaproteobacteria (96.66%), Bacilli (2.67%), and Alphaproteobacteria (0.67%), but surprisingly no sulphate-reducing bacteria. Similar results (i.e. predominance of Alpha- and Gammaproteobacteria) were also found by Korenblum et al. (2010) for water injection systems of Brazilian offshore oil platforms. Almahamedh et al. (2010) used sequencing of clone libraries to identify microorganisms involved in corrosion of oil pipelines made of carbon steel. They found the predominance of different bacteria on different coupons and incubated at different temperatures. Quantitative PCR Real-time quantitative PCR (qPCR) is a common technique to quantify bacteria in environmental samples, and therefore also been used in several MIC-studies. Zhu and co-workers (2006) used real-time qPCR to detect and quantify bacteria, that might be involved in MIC, using general and specific taxonomic and functional markers, i.e. 16S rRNA for Bacteria and Archaea, dsrAB, encoding the dissimilatory sulphite reductase in sulphate-reducing bacteria (SRB), mrcA, encoding the methyl-coenzyme M reductase in methanogens, and nirS, encoding the nitrite reductase in denitrifying bacteria. By using this approach, they could detect and quantify bacteria in general, as well as sulphate reducing bacteria, denitrifiers and methanogens in different gas pipeline samples. Comparative analysis between qPCR and MPN-counts showed that the concentrations of bacteria determined by qPCR were several orders of magnitude higher than those found by MPN (i.e. 2- to 5-log higher for bacteria in general; 2- to 3-log higher for SRB; 1- to 2-log higher for denitrifiers). The authors concluded that their qPCR

is a sensitive and reliable method for the detection and quantification of corrosion-related microorganisms. Larsen et al. (2010; 2011) also used qPCR to detect and quantify sulphate-reducing bacteria (SRB) using dsrAB, and methanogens using mcrA, in samples from oil production facilities of the Halfdan oil field in the North Sea. The authors found high numbers of methanogens in samples close to the metal/scale interface (up to 62% of all Prokaryotes). In addition, apart from sulphate-reducing bacteria, they found sulphatereducing Archaea. Lutterbach and co-workers (2011) used qPCR on dsr to detect SRB in fuel storage tanks. Hybridization techniques Dot-blot hybridization and reverse sample genome probing In these techniques, DNA or RNA is spotted onto a membrane and hybridized with labelled oligonucleotide probes (dot-blot hybridization, e.g. Minz et al., 1999). Peccia and co-workers (2000) used dot-blot hybridization with 32P-labelled oligonucleotide probes on immobilized RNA to test the specificity of these probes for subsequent FISH experiments. The probes were used to detect different acidophilic bacteria (i.e. Acidiphilum and Thiobacillus) involved in concrete corrosion in sewer systems. An alternative technique is ‘reverse sample genome probing’, developed by Voordouw and co-workers (1991; see Greene and Voordouw (2003) for a review), in which genomic DNA of known bacteria is spotted into a membrane and hybridized with labelled environmental DNA to identify the presence of similar bacteria in the environmental samples. These methods, however, have not been used extensively due to their lower sensitivity than PCR-based methods and lower throughputs than DNA microarrays. Only a few MIC studies successfully reported its use (e.g. Voordouw et al., 1993; Voordouw, 1994). Whole-cell hybridization Whole cell hybridization or fluorescence in situ hybridization (FISH; see Amann and Fuchs (2008) for a review) is a powerful technique to

Methods in Corrosion Research, Monitoring and Control | 13

Figure 1.3 FISH analysis of microbial communities. Bacteria were fixed in paraformaldehyde and incubated with a mixture of three oligonucleotide probes specific for the 16S rRNA of (i) Clostridia (red), (ii) sulphate reducing bacteria of the Deltaproteobacteria (green), and (iii) all bacteria (blue). Subsequently, the results were observed with a fluorescence microscope. The different bacteria can easily be distinguished and enumerated.

detect and quantify particular microorganisms microscopically, and to reveal their spatial arrangement. The approach is relatively straightforward; cells are fixed in paraformaldehyde, incubated with an oligonucleotide probe labelled with a fluorochrome and specific for the 16S rRNA, and viewed with a fluorescence microscope (Fig. 1.3). So far, FISH has only been used in a few MIC studies. Price et al. (2010) used FISH to quantify the number of sulphate-reducing bacteria. In combination with scanning confocal laser microscopy (SCLM), it is even possible to determine the three-dimensional distribution of different microorganisms. Kolari et al. (1998) used this approach to study the community structure of biofilms on ennobled stainless steel incubated in Baltic Sea water. McLeod et al. (2002) studied the distribution of Shewanella putrefaciens and Desulfovibrio vulgaris in biofilms on mild steel, and found that Shewanella could out-compete Desulfovibrio indicating that facultative sulphidogenic bacteria could play an important role in biocorrosion as well (see also Lutterbach and Contador, 2011).

Recently, FISH and microautoradiography (FISH-MAR) were combined to identify active microorganisms (Lee et al., 1999). In this approach environmental samples are incubated with radioactively labelled substrate (e.g. [14C]bicarbonate or [3H]-acetate). Subsequently, the samples are incubated with paraformaldehyde to fix the bacteria, placed onto a microscope slide and overlaid with a photographic emulsion. After exposure and development of the emulsion, silver crystals are visible in the vicinity of cells that consumed the substrate. Subsequently, the samples are incubated with fluorescent probes and the active cells can so be identified. Kjellerup and coworkers (2005) used FISH and MAR separately to study biocorrosion in heating systems. In a later study they used both techniques to detect the bacteria that were involved in depolarization and corrosion of stainless steel in an in situ model system (Kjellerup et al., 2008). However, the same authors reported technical difficulties with FISH analysis on carbon steel (Kjellerup et al., 2009) and recommended the use of the more sensitive CARD (Catalysed Reporter Deposition)-FISH method (Pernthaler et al., 2002), with growing applications and validated protocols (e.g. Lupini et al., 2011). Larsen et al. (2005) used a combination of techniques, including FISH-MAR to identify bacteria causing souring and biocorrosion in the Halfdan oil field. DNA microarrays DNA microarrays are glass slides containing immobilized probes that are specific either for the 16S rRNA genes of different microorganisms or for different functional genes. Environmental DNA or PCR products are labelled with a fluorochrome and subsequently incubated on the slide. Homologous sequences will hybridize to the probes and the results can be measured after washing. The advantage of DNA microarrays is their high-throughput; with one chip thousands of different species or genes can be detected. However, there are also limitations, such as the specificity of probes, detection limit, quantification, and reproducibility (Wagner et al., 2007) Currently there are several powerful microarrays available, such as the PhyloChip, a high-density microarray that contains 297,851 probes targeting the 16S

14 | Muyzer and Marty

rRNA gene of up to 8900 species (DeSantis et al., 2007). The authors used this microarray to study bacterial population dynamics during uranium reduction and oxidation (Brodie et al., 2006). Another phylochip, which contain oligonucleotide probes targeting the 16S rRNA genes of all recognized lineages of sulphate-reducing prokaryotes, has been developed by Loy et al. (2002), This microarray has been successfully used to detect and identify SRP in different environments, such as low sulphate, acidic fens (Loy et al., 2004) and river floodplains (Miletto et al., 2008). Zhou and coworkers developed the GeoChip 3.0, which contains 28,000 probes for 57,000 gene variants of 292 functional genes that are involved in the carbon, nitrogen, and sulphur cycle, in energy metabolism and in metal resistance (He et al., 2010). All these chips are excellently suited for the study of microorganisms involved in MIC. However, so far only a few MIC-studies have used DNA microarrays (e.g. Hoffmann et al., 2007). Novel approaches Whole-genome amplification One of the problems in the analysis of microbial communities involved in MIC might be the small sample size or the limited amount of biomass. To solve this problem, whole-genome amplification (WGA) might be a solution. In WGA, genomic DNA is amplified by multiple displacement amplification (MDA). In this technique, genomic DNA is incubated at a constant temperature of 30°C with random hexamer primers, dNTPs and a high-fidelity bacteriophage Phi 29 DNA polymerase. The primers will anneal to the DNA, and the DNA polymerase will elongate the strand up to 10 kb. Subsequently, the strand will displace itself and new primers can anneal. In this way the amount of DNA can be increased 1000-fold within 2–3  h. WGA has been used to amplify genomes from single cells (e.g. Stepanauska and Sieracki, 2007; Yilmaz and Singh, 2011), but also to increase the amount of DNA from low biomass samples (Abulencia et al., 2006). Although powerful, amplification of contaminating DNA is

an enormous problem. Recently, WGA has been used to amplify DNA from corrosion deposits on carbon steel (Marty et al., 2012). Bar-coded 16S rRNA pyrosequencing A relatively novel approach to study microbial communities is bar-coded 16S rRNA pyrosequencing (‘pyrotag’ sequencing). The principle is straightforward: a highly variable part of the 16S rRNA gene is amplified and subsequently sequenced with one of the Next Generation Sequencing approaches (Roche 454; e.g. Lauber et al., 2009) or Illumina (e.g. Caporaso et al., 2011; Degnan and Ochman, 2012). By using barcoded primers, it is possible to analyse many, up to 1544, samples simultaneously (Hamady et al., 2008). The overwhelming amount of sequence data is subsequently analysed with software programs, such as Qiime (Caporaso et al., 2010), Mothur (Schloss et al., 2009) or Pyrotagger (Kunin and Hugenholtz, 2010). This approach of ‘deep’ sequencing makes it possible to detect low abundant community members (‘rare biosphere’; Sogin et al., 2006). Pyrotag sequencing has recently been used to study oil-associated microbial communities that might be involved in biocorrosion. Stevenson and co-workers (2011) used various techniques, including Pyrotag sequencing, to study microbial communities from an oil production facility on Alaska’s North Slope. They found that thermophilic members of the phyla Firmicutes (Thermoanaerobacter and Thermacetogenium) and Synergistes (Thermovirga) dominated the microbial communities. Although not directly related to MIC, Kryachko et al. (2012) used Pyrotag sequencing to identify microorganisms associated with the water and the oil phase in oil-containing production water. Fig. 1.4 shows an example of 16S Pyrotag sequencing results of microbial communities from freshwater, soil and marine environments. One of the disadvantages of 16S Pyrotag sequencing is the detection of contaminating ‘background’ DNA (Biesbroek et al., 2012), and the limited phylogenetic resolution due to a high sequencing error rate, and the short sequence length (Liu et al., 2008).

1

Firmicutes

OD1

OP10

Chlamydiae

Proteobacteria

Chloroflexi

Nitrospirae

Verrucomicrobia

2

3

TM7

Planctomycetes

OP11

4 Cyanobacteria WS3

Actinobacteria

5 Acidobacteria Bacteroidetes

Gemmatimonadetes

6

1

12.41

13.77

6.28

10.51

Deltaproteobacteria

5

10.73

0.58

20.05

5.07

Betaproteobacteria

4

9.15

5.35

6.25

9.71

Gammaproteobacteria

3

7.82

3.29

43.26

2.42

Alphaproteobacteria

2

8.09

7.02

7.56

9.59

6

16.82

1.59

32.92

9.91

Figure 1.4 Surface diagrams showing the relative abundance of dominant (>1%) taxa in samples from freshwater (1 and 2), soil (3 and 4) and marine sediments (5 and 6). The left diagram shows the relative abundance of microorganisms at the phylum level, the right diagram shows the relative abundance of bacteria at the class level within the Proteobacteria.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

16 | Muyzer and Marty

Stable isotope probing (SIP) Another way to measure activity in situ is the use of substrates labelled with stable isotopes, such as [13C]-acetate (see Dumont and Murrel (2005) for a review). As a consequence, all molecules in the bacterium that consumed the substrate will be labelled with the ‘heavy’ carbon. The nucleic acids are extracted and the 13C-labelled DNA (‘heavy’ fraction) can be separated from 12 C-labelled DNA (‘light’ fraction) by caesium chloride centrifugation (Radajewski et al., 2000), while the 13C-labelled RNA can be separated from the 12C-labelled RNA by caesium trifluoroacetate gradient centrifugation (Manefield et al., 2002), because of their difference in mass. Subsequently, the separated DNAs or RNAs can be further analysed using PCR amplification combined with cloning, DGGE or NGS. Ginige et al. (2004) used DNA-SIP, 16S rRNA gene cloning and FISH-MAR to study denitrification in a sequencing batch reactor (SBR) that was fed with [13C]-methanol. They found that the dominant phylotypes in a clone library obtained from the 13C-labelled DNA were closely related to the obligate methylotrophs Methylobacillus and Methylophilus within the order Methylophilales of the Betaproteobacteria, while the dominant clones from the 12C-labelled DNA fraction were closely related to members within the Bacteriodetes. This result was confirmed by using [14C]-methanol and FISH-MAR, and nicely showed that members of the Methylophilales were the dominant denitrifiers in this methanol-fed SBR. Lu and Conrad (2006) used RNA-SIP to identify active methane-producing microbes in the rhizosphere of rice plants. They first labelled full rice plants with 13CO2 and subsequently found that Archaea affiliated to the Rice Cluster 1 were responsible for CH4 production from the plant-derived carbon. Other examples of SIP and other labelling techniques can be found in Neufeld et al. (2007). Although the approach is extremely powerful, there are also some disadvantages and limitations. First of all, it is difficult to predict the time of incubation with the labelled substrate. A too long incubation may lead to labelling of different microorganisms due to cross-feeding. However, this might also be done on purpose to follow the fate of organic material in ecosystems. For instance, Lueders and co-workers

(2006) did an elegant experiment in which they used 13C-labelled E. coli cells to identify micropredators in a soil microbial food web. Another novel approach is the use of NanoSIMS (nano-secondary-ion mass spectroscopy) to identify actively metabolizing cells after incubation with substrates labelled with stable isotopes, like in SIP (see reviews by Wagner, 2009, and by Watrous and Dorrestein, 2011). Labelled cells are sputtered with an energetic primary ion beam, which generates secondary ions that are subsequently analysed by mass spectrometry. Orphan and co-workers (2001) used a combination of FISH and SIMS to study the process of anaerobic methane-oxidation. They found that a specific group of microorganisms affiliated to the Methanosarcinales with the Archaea were strongly depleted in 13C indicating the consumption of methane. Musat et al. (2008) used halogen in situ hybridization–secondary ion mass spectrometry (HISH-SIMS) to identify and quantify the metabolic activities (i.e. H13CO3− and 15NH4+ assimilation) of anaerobic, phototrophic bacteria in a meromictic lake. They found differences of uptake by individual cells of the same species, and that one, less abundant, species contributed to more than 40% of the ammonium and 70% of the carbon uptake in the lake. These novel approaches would be very useful in MIC studies to identify and localize the active cells in corrosion deposits. Functional gene markers The aforementioned methods have been used with 16S rRNA as a molecular marker. However, the monitoring of functional genes in particular can be an important asset to follow specific metabolic activities. Several genes from different metabolic pathways of interest in MIC have been shown to be sufficiently conserved among populations sharing the same pathway to be used as reliable metabolic marker. In particular, the genes encoding the dissimilatory sulphite reductase (dsrAB), a key enzyme in the sulphate-reducing pathway, has been used in different MIC studies (e.g. Price et al., 2010: Lutterbach et al., 2011; Paisse et al., 2012). However, many other functional genes have been used in microbial ecology and environmental microbiology (see Table 1.1 for examples). Therefore, the use of these genes

Methods in Corrosion Research, Monitoring and Control | 17

in MIC studies might reveal the presence and abundance of other functional groups and associated microorganisms involved in MIC as well. Moreover, by targeting the mRNA of functional genes, we can also infer the activity of particular community members (e.g. Nogales et al., 2002; Dar et al., 2007). By detecting the mRNA of the dsrA gene with a reverse transcription quantitative PCR (RT-qPCR), Price et al. (2010) could infer the activity of sulphate-reducing prokaryotes after nitrate injection. Meta-omics Instead of studying one gene at a time, we can also study all genes of a complete microbial community, the ‘metagenome’, at once (e.g. Tringe and Rubin, 2005; Hugenholtz and Tyson, 2008). In this approach the genomes of all microorganisms present in the sample are ‘shotgun’ sequenced with either 454-pyrosequencing or Illumina sequencing. Subsequently, the sequences are assembled in longer sequences (‘contigs’) and eventually into complete genomes. The success of genome assembly depends on the complexity of the community and the relative abundance (Whitaker and Banfield, 2006). Banfield and co-workers were successful to assemble the complete genomes of two members, Leptospirillum and Ferroplasma, of a biofilm from acid mine drainage (Tyson et al., 2004), while for more complex communities only short fragments of the genomes of the different microorganisms could be obtained (e.g. Tringe et al., 2005). However, knowledge of microdiversity, metabolic pathways and interactions at the community level can readily be gained (e.g. Khodadad and Foster, 2012), and perspectives of genomebased ecosystem modelling are currently under development (Vandenkoornhuyse et al., 2010). Similar approaches have been used to study gene expression of community members, metatranscriptomics (Warnecke and Hess, 2009) and metaproteomics (Wilmes and Bond, 2009; VerBerkmoes et al., 2009). Until recently, the meta-omics approach was never applied in biocorrosion studies. However, a metagenomic study performed on a concrete pipeline (GomezAlvarez et al., 2012) uncovered key metabolic networks and physiological functions in biofilm

from the corroded surface, ullustrating the enormous potential of the meta-omics approach with the rapid availability of many microbial genomes. Detection of gene expression with whole-cell hybridization A novel approach in whole-cell hybridization is the in situ detection of gene expression. Recently, Wendeberg and co-workers (2012) studied in situ gene expression of pmoA (encoding subunit A of the particular methane monooxygenase) and aprA (encoding the subunit A of the dissimilatory adenosine-5′-phospho sulphate reductase) in methane- and sulphur-oxidizing symbionts of the hydrothermal vent mussel Bathymodiolus puteoserpentis. They found the highest mRNA expression levels at the ciliated epithelium of the gills, indicating a rapid response of the cells to incoming seawater rich in methane, reduced sulphur compounds and oxygen. Conclusion and perspectives So far, most results in monitoring microorganisms involved in MIC have been obtained with PCR-DGGE and qPCR. However, Pyrotag sequencing would be a powerful alternative, especially because samples can be sent to commercial companies that will perform DNA extraction, PCR amplification, next-generation sequencing and data analysis. This would make it much easier for an operator in the field as he does not need to have detailed knowledge of the molecular biology and a laboratory with specialized equipment. Subsequently, the operator can correlate the Pyrotag data to information on water chemistry, environmental conditions, mineralogy, and electrochemical parameters, which is essential to obtain a comprehensive understanding of the MIC process. Novel stateof-the-art techniques, such as single cell analysis and meta-omics, will continue to be optimized in the coming years, and although this will not be a routine method to monitor MIC, it will certainly contribute to process understanding by identifying the key players, their activities and interactions. We would recommend to study MIC with a system biology approach (Zengler and Palsson, 2012) in the same way as for some

18 | Muyzer and Marty

human diseases, in which microbes are involved, such as cystic fibrosis, obesity and inflammatory bowel disease (Greenblum et al., 2012). Acknowledgements Florence Marty was financially supported by the Research Fund for Coal and Steel of the European Union (project number: RFSR-CT-2008-00018). Gerard Muyzer was financially supported by the University of Amsterdam and the ERC Advanced Grant 322551. References

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Watrous, J.D., and Dorrestein, P.C. (2011). Imaging mass spectrometry in microbiology. Nature Rev. Microbiol. 9, 683–694. Wendeberg, A., Zielinski, F.U., Borowski, C., and Dubilier, N. (2012). Expression patterns of mRNAs for methanotrophy and thiotrophy in symbionts of the hydrothermal vent mussel Bathymodiolus puteoserpentis. ISME J. 6, 104–112. Wertz, S., Poly, F., Le Roux, X., and Degrange, V. (2008). Development and application of a PCR-denaturing gradient gel electrophoresis tool to study the diversity of Nitrobacter-like nxrA sequences in soil. FEMS Microbiol. Ecol. 63, 261–271. Whitaker, R.J., and Banfield, J.F. (2006). Population genomics in natural microbial communities. TRENDS Ecol. Evol. 21, 508–516. Wilmes, P., and Bond, P.L. (2009). Microbial community proteomics: elucidating the catalysts and metabolic mechanisms that drive the Earth’s biogeochemical cycles. Curr. Op. Microbiol. 12, 310–317. von Wolzogen, Kuhr, C.A.H., and van der Vlugt, L.S. (1934). De grafiteering van gietijzer als electrobiochemisch proces in anaerobe gronden. Water 18, 145–165. Xu, K., Liu, H., Du, G., and Chen, J. (2009). Real-time PCS assays targeting formyltetrahydrofolate synthetase gene to enumerate acetogens in natural and engineered environments. Anaerobe 15, 204–213. Yan, Q., Yu, Y., and Feng, W. (2011). Diversity of nifH gene amplified from plankton-community DNA in a shallow eutrophic lake (Lake Donghu, Wuhan, China). Limnology. 12, 245–251. Yang, H.J., Choi, Y.-J., and Ka, J.-O. (2011). Effects of diverse water pipe materials on bacterial communities and water quality in the annular reactor. J. Microbiol. Biotechnol. 21, 115–123. Yilmaz, S., and Singh, A.K. (2011). Single cell genome sequencing. Curr. Op. Biotechnol. 23, 1–7. Zengler, K., and Palsson, B.O. (2012). A road map for the development of community systems (CoSy) biology. Nature Rev. Microbiol. 10, 366–372. Zhang, T., Fang, H.H., and Ko, B.C. (2003). Methanogen population in a marine biofilm corrosive to mild steel. Appl. Microbiol. Biotechnol. 63, 101–106. Zhu X., and Kilbane, II J.J. (2004). Molecular tools in microbial corrosion. In: R. Vazquez-Duhalt, and R. Quintero-Ramírez, eds. Studies in Surface Science and Catalysis, Elsevier. 151, 219–232. Zhu, X.Y., Modi, H., Ayala, A., and Kilbane, II J.J. (2006). Rapid detection and quantification of microbes related to microbiologically influenced corrosion using quantitative polymerase chain reaction. Corrosion 62, 950–955.

Using the Power of Molecular Microbiological Methods in Oilfield Corrosion Management to Diagnose Microbiologically Influenced Corrosion

2

Victor V. Keasler and Indranil Chatterjee

Abstract It is well established that microbes present in oilfield systems can cause several problems for operators such as hydrogen sulphide (H2S) production, microbiologically influenced corrosion (MIC), and/or biofouling. However, it is still being debated as to exactly what types of organisms actually cause these problems and whether their presence alone is enough to prove that the associated problems are going to be encountered. This chapter highlights two different case histories where microbes were previously believed to be of minimal risk based on culture-based enumeration. However, molecular characterization revealed elevated microbial numbers as well as a potential high-risk species in both systems as the predominant sessile organism and a potential significant asset integrity risk. Interestingly, the outcome with regards to localized corrosion was very different between the two systems. This chapter is a reminder that the data obtained through molecular analysis is most valuable when it is correlated back to a system key performance indicator (KPI) to enable smart decisions. Background Microbial growth in oil and gas systems can cause numerous problems that result in production downtime, lost revenue, and safety concerns. Unfortunately for the industry, microbial populations are indigenous to many of these environments or are introduced by contamination and minimizing their impact is challenging. The most common negative impacts caused by

microbes are microbiologically influenced corrosion (MIC), hydrogen sulphide (H2S) production, and biofouling. This chapter will specifically focus on the risk of corrosion. Failures resulting from internal corrosion of pipelines and surface equipment in the oilfield are a costly problem affecting both the producer as well as the environment. There are numerous mechanisms that can lead to internal corrosion of pipelines and equipment including oxygen corrosion, carbon dioxide (CO2) corrosion, sulphide-stress cracking, acidic fluids, underdeposit corrosion, and MIC. One of the keys to minimizing the risk of a corrosion-related failure is proper monitoring to fully understand the corrosion mechanism and the approximate corrosion rate. Although general corrosion rates are often measured, failures almost always occur because of localized (pitting), not general corrosion. In most instances, the rate of localized corrosion is much greater than the rate of general corrosion. Once a system with a high risk of localized corrosion is identified, it is critical that a strategy for minimizing or eliminating the corrosion risk is acted upon. It has been estimated that up to 20% of all corrosion may be influenced by the presence of microorganisms (Flemming, 1996). Although often considered the main culprit for MIC, SRB (sulphate-reducing bacteria) are not the only type of microorganism that is associated with corrosion of metal surfaces. Numerous other microbes are also believed to play an important role in MIC such as sulphate-reducing prokaryotes (SRP; this includes SRB and sulphate-reducing Archaea), acid-producing bacteria (APB), iron-reducing/

24 | Keasler and Chatterjee

oxidizing bacteria (IRB/IOB), and methanogens, amongst others. SRP are able to produce sulphide and/or H2S as by-products of sulphate reduction (King et al., 1973; Postgate, 1979; Pfennig et al., 1981; Hamilton, 1985). These by-products can lead to the formation of iron sulphide and are a concern from a safety perspective as H2S is a toxic gas. Acid producers can secrete organic acids as by-products of their metabolism (Burns et al., 1969). These acids, within the microenvironment of the biofilm, can dramatically reduce the pH of the fluid underneath the biofilm, leading to corrosion. Bacteria can also affect the actual iron ions by oxidizing ferrous ions to ferric ions (IOB), leading to deposition of iron oxides and hydroxides or by reducing iron and/or manganese (IRB) (Obuekwe et al., 1981; Ghiorse, 1984; Gounot, 1994; Little et al., 1997; Dinh et al., 2004). Finally, methanogens are hypothesized to enhance corrosion by removing hydrogen directly from the surfaces of the pipe walls or equipment and/or by producing CO2 as a by-product (Casserly, 2003; Park, 2011). For any petroleum field, it is critical that microbial risk is considered and key performance indicators (KPIs) are identified and measured on a regular basis to understand the impact of microbial growth. For example, if corrosion is identified as a significant risk in a given system, then acceptable corrosion rates must be determined for that system and monitored on a regular interval to ensure that any microbes present are not leading to accelerated corrosion that is outside the KPI. This chapter will discuss two different petroleum fields where corrosion control was considered a KPI and how the presence of microbes impacted the corrosion rate in the system. Methods Prokaryotic DNA extraction and PCR amplification Prokaryotic DNA was isolated from coupon samples that were collected and shipped dry to minimize microbial growth. Once received in the laboratory, solids were removed from the coupons and DNA was extracted from all samples using the UltraClean® Soil DNA Isolation

Kit (MO-BIO) according to the manufacturer’s instructions. Bacterial speciation by denaturing gradient gel electrophoresis (DGGE) and DNA sequencing Eubacterial 16S rRNA gene amplicons were amplified on an Eppendorf Mastercycler using primers 341FGC (GC-clamped) and 518R as described previously (Muyzer et al., 1993; Dar et al., 2005). Thermocycling conditions were: 5 min at 95°C, followed by 20 cycles of 95°C for 40 s, 65°C for 40 s and 72°C for 1 min, followed by 30 cycles of 95°C for 40  s, 55°C for 40  s and 72ºC for 1 min and finally a 10 min extension at 72ºC. The annealing temperature for the initial 20 cycles was decreased ½ of 1 degree each cycle so that the annealing temperature at cycle 20 was 55°C. PCR products were confirmed by agarose gel electrophoresis and then separated by DGGE. DGGE analysis was performed using a D-code 16/16 cm gel system (Biorad, Hercules, CA, USA) maintained at a constant temperature of 60°C in 1× TAE buffer (20 mM Tris-acetate, 0.5 mM EDTA, pH 8.0). Gradients consisted of 30–70% denaturing solution (denaturant is defined as 7  M urea, formamide) formed using 8–10% acrylamide and the gels were run at 60 V for 16 h. Gels were stained using GelRed (manufactured by Biotium). Images were captured by the use of a DyNA Light UV Transilluminator (Labnet). Dominant bands were excised, eluted in 150 μl of sterile water and placed at 4°C overnight and reamplified using the same primers and conditions described above. Using 341F as the primer, products were then sequenced by LoneStar Labs. Chromatogram files that were received from the sequencing facility were then aligned to known DNA sequences using the Ribosomal Database Project and/or the NCBI BLAST genomic database. Any base pair mismatches were verified with the original chromatogram or changed based upon that chromatogram. Quantitative-PCR (qPCR) Real-time PCR was performed on each field sample using oligonucleotides that were designed to target total bacteria (targeted at the 16S rRNA gene), SRB (targeted to the APS gene), and/

Using MMM in Oilfield Corrosion Management | 25

or Archaea (targeted to the 16S rRNA gene). Each reaction contained 12.5  µl SYBR Green Master Mix (Applied Biosystems), forward primer, reverse primer and DNA template from the extracted samples. The PCR conditions for all three tests were as follows: 2 min at 50°C and 10 min at 95°C, followed by 40 cycles of 15 s at 95°C and 1  min at 60°C. A dissociation curve was run at the end of each test. The PCR reaction was carried out in an ABI Prism 7500 Sequence Detection System (Applied Biosystems, Foster City, CA). A calibration curve was obtained by using a serial dilution of a known concentration of positive control DNA. The Ct values that were obtained from each sample were then compared with the standard curve to determine the copy number of prokaryotic DNA present in the tested sample. Corrosion coupon analyses Analysis was performed on corrosion coupons by a third-party laboratory using their internal protocols to determine metal loss, which was reported as mils per year (MPY). Briefly, each corrosion coupon was initially examined to determine if the serial number was present and readable. Then coupons were placed in a beaker containing 25% NaOH which was brought to a boil. After boiling was reached, the coupon was rinsed with water and scrubbed gently with detergent and a soft brush. The coupons were then covered in 10% HCl for 30 s and rinsed off directly after, followed by gentle scrubbing with detergent and a soft brush. Finally, the coupons were rinsed and patted dry with a paper towel and rinsed with acetone. The coupons were placed in a drying oven at 220°F for 30 min and allowed to cool. The coupon was weighed and general corrosion rates determined. Results Case history 1: offshore West Africa A major oil and gas producer in West Africa had been experiencing high levels of corrosion as solids loading from subsea and topside wells increased. Total fluids from the offshore platforms are brought on-shore via pipelines where they are processed and oil, water, and gas are separated.

The onshore separation facility consists of three oil production trains, currently processing about 110,000 barrels of oil per day (BOPD) and 77,000 barrels of water per day (BWPD) utilizing high pressure, low pressure and desalter separation vessels. This plant produces and exports both light oil and heavy oil. Corrosion control in these systems is critical to ensure safety as well as maximizing production as a shut-down in the system is very costly. With increasing solids production filling the vessels, corrosion rates were observed to increase above 5  mpy, which is the acceptable maximum for this system. Coupons are removed from the system on a monthly basis and analysed for corrosion rates. As shown in Fig. 2.1A and B, there was significant localized corrosion identified on the coupons over a period of approximately 14 months. Equipment reliability became a major concern as corrosion rates continued to rise and pinhole leaks developed. Corrosion rates were consistently in the range of 150–200 mpy, which is well above the desired range of less than 5 mpy. The facility was forced to redirect oil to clean out vessels and replace spool pieces and there were concerns that the high corrosion rates may result in certain wells being shut in, resulting in lost production. Subsequent ‘teardown reports’ revealed severe solids accumulation and what was suspected to be microbiologically influenced corrosion (MIC). Up to this point in time, no biocide treatment was being used in the system as culturebased monitoring methods (i.e. serial dilution for sulphate-reducing and acid producing bacteria) had never revealed significant microbial numbers. It was therefore assumed that microbial risks were low in the system and a biocide was not justified. However, the findings from the ‘teardown reports’ suggested that additional investigations into the microbial risk were required. To fully understand the potential microbial risks, fluid (data not shown) and solid samples were collected from the system and analysed using non-culture based methods including enumeration by qPCR and species identification via DGGE and single pass DNA sequencing. The critical findings from this system are the solid samples as this includes the biofilms which could be responsible for the accelerated corrosion rates.

26 | Keasler and Chatterjee

A

B

Figure 2.1 Corrosion coupons analysed from the production trains before the microbial mitigation program was implemented. (A) Close-up view of severe internal corrosion on the surface of a coupon. (B) View of one entire corrosion coupon showing significant corrosion.

Microbial enumeration by quantitative PCR (qPCR) confirmed extremely high microbial loads of both total bacteria and total Archaea (Table 2.1). In our experience, it is generally true that microbial numbers above 100,000 per gram are considered elevated and numbers greater than 1 million per gram are considered very elevated with immediate action to be considered. In this case the microbial numbers were 400–7200 times higher than what would normally be considered a ‘need for immediate action’. Species identification by DGGE and DNA sequencing confirmed that the predominant organism in the solid samples was Thermovirga lienii. This organism is an anaerobic, thermophilic sulphur-reducing bacterium that can thrive in

a temperature range between 37°C and 68°C (Dahle et al., 2006), which is consistent with the temperatures found in this system. As mentioned before, historical monitoring by culture based methods did not indicate the presence of any microorganisms in this system. The identification of Thermovirga lienii as the predominant species suggests that the reason the culture-based methods failed to identify microbes is because this microbe is difficult to culture, especially in the culture medium that is commonly used in the petroleum industry. This is consistent with the commonly reported phenomena demonstrating that only a fraction of the organisms present in any given system will actually be detectable by culture-based methods. Based on these findings, a microbial mitigation programme was immediately initiated using a weekly batch treatment of a biocide known to be effective in other systems in the same geographic area. This product was injected into all separation vessels. Owing to limited injection points for some vessels, this chemical was also applied to a closed drain header. During batch treatment into the closed drain headers, valve reconfigurations had to take place to ensure the biocide could reach the vessel. Although not ideal, this strategy proved very effective. The other critical change that was necessary for this system was to utilize a monitoring method that provided accurate results and was able to enumerate the organisms that had been undetected by culture-based methods. It is unrealistic to perform qPCR on a routine basis for this system, especially given its remote nature. Therefore, another non-culture based method was implemented for microbial enumeration. This method involved enumeration using a second-generation adenosine triphosphate (ATP) test method before and after biocide treatment. In brief, ATP is present only in actively growing organisms and can be captured from either a solid of fluid sample and

Table 2.1 Microbial enumeration in solid samples by qPCR Sample name Desalter water leg Hot first stage water leg

Total bacteria per gram

Total Archaea per gram

427,305,407

467,665,278

3,259,945,191

7,230,821,214

Using MMM in Oilfield Corrosion Management | 27

250 200 150 100 50 0

Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12 Month 13 Month 14 Month 15 Month 16 Month 17 Month 18 Month 19 Month 20 Month 21 Month 22 Month 23 Month 24

Mils per year (mpy)

A

B

measured by mixing with luciferase and measuring the light output with a handheld photometer. Field results using this technology have shown that, post biocide implementation, the microbial numbers have decreased significantly and the results correlate well with the reduced corrosion rates described below. The key performance indicator in this system was not simply microbial numbers, but corrosion rate and asset integrity. Importantly, the implementation of the microbial mitigation strategy (started in month 14; Fig. 2.2A) lead to an immediate improvement in equipment reliability, as corrosion rates were reduced from a running average of 200 mils per year (mpy) to less than 5 mpy (Fig. 2.2A). There was a single coupon that showed an elevated corrosion rate (in month 18) after the treatment was started, but the reason for single excursion is unclear. It is not entirely surprising as the oilfield is extremely dynamic and an asset may experience occasional excursions due to events such as well work overs, new production coming online, etc. Reduced corrosion can also be observed macroscopically by comparative visualization of the coupons before and after the biocide treatment was started (compare Fig. 2.1B and Fig. 2.2B). The value delivered

Figure 2.2 Corrosion rates before and after implementation of biocide. (A) Graph showing monthly corrosion rates (black line) before and after implementation of the biocide program. The biocide was implemented in month 14. The red line identifies an acceptable corrosion rate for this asset. (B) View of a coupon after implementation of the biocide program showing very little corrosion.

for this operator was the extension of the lifespan of the equipment. Furthermore, production and financial risks due to production downtime for maintenance and repair have been significantly reduced. Summary of key learning points from field example 1 1

2

3

Culture-depending monitoring can lead to a significant underestimation of the microbial population and the associated risks. In this system, the lack of microbial detection resulted in high corrosion rates and a historical treatment strategy that was not addressing the key challenge, which was microbial control. Utilizing qPCR and microbial speciation revealed a significant risk in the system and provided insight into the mechanism by which microbes could be influencing localized corrosion. Corrosion rates were significantly reduced after implementation of a biocide programme, suggesting that there was a direct link between the microbial population and the elevated corrosion rate.

28 | Keasler and Chatterjee

4

The potential risk of Thermovirga lienii with regards to corrosion was noted from this study. T. lienii was the predominant organism identified, although the author notes that it was not the only organism. This data would suggest, although indirectly, that T. lienii could be a high-risk organism with regards to corrosion risk and should be closely monitored for in oilfield systems.

Case history 2: offshore United States A major oil and gas producer in California has a mature offshore system with an increasing water cut. Increased water cuts often put a system at a higher risk for MIC as the growth environment for microbes usually improves. As with the system in West Africa, total fluids from the offshore platforms are brought on-shore where they are processed. Key corrosion measurements for this system are determined from coupons on the offshore platforms and not at the fluid separation terminal as was the case in West Africa. When the coupons are removed, they are analysed for weight loss, pitting corrosion, and culturable organisms are quantified by serial dilution for SRB and APBs. The coupons have historically shown low corrosion rates and the serial dilution results suggest that the microbial risk is low. However, this system experienced a pinhole leak in 2010 and the root cause failure analysis suggested that it was likely caused by MIC (Fig. 2.3). The evidence for MIC as identified in the failure analysis was an MIC-like pit morphology, significant sulphate levels in the fluid, and the identification of iron sulphide in the pit. It is important to note that the failure was located in a dead-leg section of the oil

and emulsion export line on the platform, which could represent a worst case scenario for localized corrosion based on the stagnant condition of the fluids and may not be representative of the type of corrosion being experienced by the rest of the system. In response to the failure that was identified and the potential for microbial corrosion, a study was undertaken to try and link microbial growth to corrosion rates in the system. For this system, a corrosion rate (both general and pitting) of less than 2 mpy is considered to meet specifications. A total of 13 coupons were removed from three different offshore platforms that are all part of the same operator asset. Each coupon was in the system for approximately 100–150 days. Of the 13 coupons analysed, data are presented below on five of the coupons that were removed from the same platform that experienced the failure described above. The five coupons described in Table 2.2 were located at the high-pressure (HP) production separator, at the low-pressure (LP) production separator (two coupons), upstream of the production surge tank, and at the oil/ emulsion export line leaving the platform. Each coupon was processed for microbial enumeration and species identification as well as general and pitting (localized) corrosion rate. As shown in Table 2.2, microbial numbers were extremely variable from one coupon to the next. Two of the coupons have very low microbial numbers while the other three have extremely high numbers. Further analysis to examine the species of organisms present on the three coupons with high numbers revealed that Thermovirga lienii was the predominant organism on all three coupons that exhibited the elevated microbial

Table 2.2 Microbial enumeration and corrosion rates from coupons removed from an offshore platform Coupon

General CR (mpy)

1

0.06

ND

1,061,637,894

720,364

550,664,772

2

0.33

ND

1,604

 95% ANI corresponds to ~70% DDH standard (Goris et al., 2007). Furthermore, at 95% ANI species can show as much as 35% gene content difference (or 20% gene content difference when hypothetical and mobile elements are excluded from the analysis; Konstantinidis et al., 2006). A predictive species definition should couple evolutionary and ecological relatedness. Species defined by the > 98% ANI criterion tend to share the same ecological niche and show smaller gene content differences in contrast to strains that share 90% of its supragenome contained within its core genome (Table 9.1). Bacillus anthracis also has a relatively small supragenome, which can be essentially described by only four genomes (Tettelin et al., 2005). However, in this case we argue it is because B. anthracis is not really its own species, but a conserved successful lineage within the highly diverse B. cereus species that has acquired a unique set of toxin genes via HGT. Our combining of these two ‘species’ within a single species is based on the observation that the inclusion of the B. anthracis genomes into the B. cereus supragenome does not substantively decrease the size of the B. cereus core genome, but rather it remains essentially unchanged in size (Fig. 9.1). The core and supragenome concepts have also been extended to higher taxonomic classifications including everything from the genus level (Donati et al., 2010) to the domain level (Lapierre et al., 2009). Lapierre and colleagues attempted to define a eubacterial core genome by analysing 573 eubacterial sequenced genomes, with the goal of defining essential bacterial genes and their functions. They reported: 1

2

An extended core genome composed of ~8% (n = 250) of eubacterial genes that are under high selective pressure and which are transmitted principally by vertical inheritance. The ‘character’ genes (~64%) that use gene duplication and domain shuffling as their

3

preferred mode of evolution and which enable the bacteria to quickly adapt to changing conditions and to inhabit new niches. These character genes are the most likely to be transferred between organisms via HGT mechanisms (e.g. type I polyketide synthases); and The accessory genes (~28%) which are genes with low levels of conservation that are scattered at low frequencies throughout the eubacterial domain.

The last group includes pseudogenes with previous functions in the genomes but that are now stripped of selective pressure. These fast evolving genes, some of which reside in phage genomes, represent an on-going gene creation pool, which only occasionally produces a new protein that may subsequently spread through large populations owing to it providing a new survival trait. Mathematical models to represent supragenomes Determining the best method for clustering orthologous genes is not a trivial manner, but has been addressed (Tettelin et al., 2005; Hogg et al., 2007; Donati et al., 2010; Boissy et al., 2011). However, calculating the gene cluster content of the core and distributed components of an empirically derived supragenome is a relatively trivial matter. Simply counting the gene clusters that have a member from each genome provides the core content, and all gene clusters, which do not have a member from every genome, make up the distributed clusters. While this simple calculation gives information about the current set of genomes, it does not necessarily reflect the reality of the entire population. The recent dramatic increase in the number of whole genome sequencing projects has provided an unprecedented amount of information available for analysis, yet there remain issues when trying to describe the true contents of a given species’ complete supragenome. The first and most obvious problem is simply that the number of genomes available for analysis from any one species does not begin to approach the actual number of strains within the species. Despite the increase in availability and affordability

114 | Nistico et al.

of sequencing, large sample sizes from any single species remain elusive and thus we need models which can use the data generated from small sample sizes to accurately predict what the outcome would be if we had actually sequenced more strains. Such data can be used to determine how many strains need to be sequenced to get various supragenome coverage levels for various gene frequency classes. For example within the species S. pneumoniae to get 99% coverage of non-rare genes (frequency ≥ 0.1) requires that far fewer genomes (n = 33) be sequenced than to get only 90% coverage of all gene frequency classes including rare genes (n = 142) (Table 9.2). Another significant problem is the choice of strains to sequence to obtain representative data for modelling the species. Generally, when the genome of a particular strain is sequenced it is done with some particular interest in mind, and usually the interest is not in selecting a completely random sample. For instance a researcher may be interested in some specific novel function or pathogenic trait that an organism exhibits. Or, perhaps the organism is a ‘model’ member of the species and its genetic profile fits all functions traditionally associated with the organism. No matter the reason, if an organism is being actively selected, then the bias for that organism is inherent in that sequencing project. Because of this most research is done on organisms that play some role in events that are perceived to be important to humans. An example of this can be seen looking at the supragenome data for Moraxella catarrhalis wherein the published work (Davie et al., 2011) demonstrated a somewhat limited supragenome among pathogenic strains. However, a subsequent analysis in which serosensitive strains were included in the

Table 9.2 Number of sequenced Sp strains needed to achieve various species-level coverages according to the finite supragenome model Population frequencies

Supragenome coverage

Strains sequenced

≥ 0.1

90%

11

≥ 0.1

95%

17

≥ 0.1

99%

33

all

90%

142

analyses has demonstrated much greater diversity within the species (Fig. 9.2). Thus, in the performance of species-level comparative genomic studies it is critical to include strains from as wide a phenotypic spectrum as possible, with additional consideration given to geographic distribution. The problem of bias is important to address early in comparative genomic studies, as appropriate sampling will provide for accurate supragenomic analyses at minimal costs. While an ideal dataset that is unbiased, large, and random would lessen the burden of finding the true distribution of core/distributed gene clusters, efforts have been made using mathematical modelling to make predictions about what these distributions would look like, given the data currently available. The goal of most of these models is to calculate the total number of core gene clusters in the population, the number of new gene clusters that will be added to the supragenome as additional genomes are included, and finally to estimate what percentage of the supragenome is covered, given the current number of sequenced genomes (Tettelin et al., 2005; Hogg et al., 2007; Snippen et al., 2009; Kislyuk et al., 2011). As sequencing becomes more affordable and available, new statistical procedures are required to glean information from the enormous amount of data available. Many of the above models have come from different areas that each added value in this relatively young field of comparative genomics. Going forward, a dialogue between research areas becomes more and more vital to develop robust analytical techniques as many of these techniques have been used in ecological modelling for some time. Environmental conditions that support HGT It is HGT that creates and maintains the distributed genome as a part of each species’ supragenome, and thus the mechanisms and conditions that support HGT are critical elements of diversity generation among bacteria. Multiple conditions are necessary for optimizing HGT among cocolonizing and co-infecting strains to promote genomic diversity and to ensure persistence in the face of active host defence mechanisms. These

Core and Supra Genomes to Determine Diversity and Natural Proclivities | 115

Figure 9.2 Dendrogram showing that the first group of M. catarrhalis genomes sequenced (shown in black), which all are phenotypically sero-resistant, cluster together (Davie et al., 2011) and are very different genomically from the sero-sensitive strains (shown in red), with a couple of exceptions. These data point out the affects of biased strain selection on supragenome studies.

include, biofilm formation, a ready supply of donor DNA; polyclonal and polymicrobial infections, as well as multiple molecular mechanisms for the uptake and/or transfer of DNA. Chronicity of colonization and infection are necessary to support HGT mechanisms, which in turn help to maintain chronicity through diversity generation; thus there is a positive feedback loop that underlies bacterial persistence. We hypothesized that microbial chronicity, in general, is closely tied to populations of microorganisms adopting the biofilm phenotype (Rayner et al., 1998). This postulate helped to energize the field of chronic infections and over the ensuing decade multiple chronic infectious and inflammatory conditions – arising in multiple organ systems – were all revealed to be biofilm-related diseases. Several studies demonstrated that biofilms were associated with chronic and recurrent otitis media (middle-ear disease) (Rayner et al., 1998; Ehrlich et al., 2002; Post et al., 2004;

Hall-Stoodley et al., 2006; Bakaletz et al., 2007; Kerschner et al., 2007; Post and Ehrlich, 2009; Apicella et al., 2009; Paluch-Oles et al., 2011); endodontitis (Carr et al., 2009); cholesteatoma (Chole and Faddis, 2002); tonsillitis (Chole and Faddis, 2003; Kriukov et al., 2008); adenoiditis (Zuliani et al., 2006; Hoa et al., 2009; Nistico et al., 2009, 2011); chronic rhino sinusitis (Palmer et al., 2006; Sanderson et al., 2006; Prince et al., 2008; Li et al., 2011; Singhal et al., 2011; Toth et al., 2011); osteomyelitis and periprosthetic joint infections (Marrie and Costerton, 1985; Ehrlich et al., 2004, 2005a,b; Stoodley et al., 2008, 2011; Costerton, 2005a,b; Tang, 2010); infection of the urogenital tracts in men and women (Swidsinski et al., 2008; Bartoletti, 2009; Mazzoli, 2010); surgical site infections (Kathju et al., 2009, 2010); and capsular contracture associated with breast implants (Costerton et al., 2005). HGT driven by natural competence and transformation (the most common mechanistic

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means of HGT) also requires that there must be DNA available in the environment to serve as donor genes. Again, biofilm communities provide this ingredient as it has been determined that the biofilm EPS matrix of all bacterial species examined contains DNA as one of the major polymeric constituents. This was first demonstrated for the biofilm model organism Pseudomonas aeruginosa by Whitchurch et al. (2002). Subsequently, Jursicek and Bakaletz (2007) used confocal imaging of H. influenzae biofilms to demonstrate not only the presence of large amounts of DNA in the EPS, but also high concentrations of type IV pili consistent with DNA transfer via conjugation. This latter data was soon supported, by Juhas et al.’s (2007) demonstration that approximately one third of NTHi strains carry a conjugal plasmid integrated within their genomes. Subsequently, Hall-Stoodley et al. (2008) and Ehrlich et al. (2010) showed that pneumococcal biofilms could be disrupted by treatment with DNAse I. Similar findings have been made regarding the biofilm EPS constituents of other bacterial species (Mann et al., 2009). Moreover, before the chemical nature of the EPS was worked out, Stoodley et al. (2002) demonstrated that the rheological properties of the biofilm matrix mimic those of DNA. The lack of hydrodynamic shear within a biofilm, coupled with the availability of high concentrations of DNA results in much higher transformation rates within biofilms that occur in planktonic envirovars of the same strains and species. Li and colleagues (2001) found that in 8- to 16-h-old S. mutans biofilms that the bacteria were transformed at a rate 10- to 600-fold higher than their planktonic counterparts, and that the donor DNA included both plasmids and S. mutans chromosomal DNA. Polyclonality, the simultaneous presence of multiple strains of an infecting species, is an absolute requirement for there to be meaningful HGT that results in the production of novel strains with unique gene possession profiles and allelic combinations. H. influenzae infections from OM patients (Smith-Vaughn et al., 1995, 1996, 1997), COPD patients (Murphy et al., 1999), as well as nasopharyngeal carriage in healthy and otitis-prone children and adults (Ecevit, 2004, 2005; Farjo et al., 2004) have been all found to be polyclonal in nature (reviewed in Ehrlich et al.,

2010). Similarly, characterization of S. pneumoniae strains isolated during carriage and disease have proven that both of these conditions are polyclonal in nature (Muller-Graf et al., 1999; Sa Leao et al., 2002); furthermore, recent evidence demonstrates that substantial strain diversity is generated in situ during the chronic infectious process by recombination occurring among polyclonally infecting strains (Hiller, 2010). Hansen et al. (2011) recently reported that independent adaptation and evolution of P. aeruginosa subpopulations in cystic fibrosis patients take place in the paranasal sinuses and which then serve to seed the lung. HGT requires that either the donor bacteria (or phage), or the recipient bacteria employ an active, energy-requiring system for DNA exchange or uptake, respectively. Most of the chronic bacterial pathogens that form biofilms contain energy requiring recombination mechanisms [conjugation (mating), competence and transformation]. These mechanisms provide the bacterial population with a means to support continuous/episodic exchanges of DNA between the polyclonally infecting strains of a single species in the population (and also between species in a polymicrobial biofilm), thus constantly generating new strains with a new combinations of genes (Ehrlich et al., 2010). To possess and maintain the genetic regulons that encode the machinery for uptake and transfer of DNA the bacteria must receive an evolutionary advantage that allows their persistence despite the presence of robust deletory mechanisms in most bacterial genomes that are designed to maintain a small genome size. DNA has a high viscoelasticity and its presence in the EPS confers stability to the biofilm also facilitating the conjugation process, in fact keeping the cells close to each other as it prevents the rupture of the conjugal bridge in presence of shear stress. Conclusion High levels of genomic diversity are nearly universal among bacterial species. The major component of this genomic diversity is gene possession differences that arise via HGT mechanisms. This means that a species’ genes are either core or distributed and collectively the core and distributed genomes

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make up the supragenome. The core genome can be used, along with recombination studies to determine natural proclivities among strains as a means to determine species boundaries. Acknowledgements The authors thank Ms Mary O’Toole for help in the preparation of this manuscript. This work was supported by Allegheny General Hospital, Allegheny-Singer Research Institute, grants from the Health Resources and Services Administration (HRSA); a system usage grant from the Pittsburgh Supercomputing Centre (G.D.E.); and NIH grants DC04173 (G.D.E.), DC02148 (G.D.E.), DC02148-16S1 (G.D.E) and AI080935 (G.D.E.). References

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Direct demonstration of viable Staphylococcus aureus biofilms in an infected total joint arthroplasty. A case report. J. Bone Joint Surg. Am. Vol. 98, 1751–1758. Stoodley, P., Conti, S.F., DeMeo, P.J., Nistico, L., Melton-Kreft, R., Johnson, S., Darabi, A., Ehrlich, G.D., Costerton, J.W., and Kathju, S. (2011). Characterization of a mixed MRSA/MRSE biofilm in an explanted total ankle arthroplasty. FEMS Immunol. Med. Microbiol. 62, 66–74. Swidsinski, A., Mendling, W., Loening-Baucke, V., Swidsinski, S., Dörffel, Y., Scholze, J., Lochs, H., and Verstraelen, H. (2008). An adherent Gardnerella vaginalis biofilm persists on the vaginal epithelium after standard therapy with oral metronidazole. Am. J. Obstetr. Gynecol. 198, 97. Tang, H., and Xu, Y., (2010). Bacterial biofilms and chronic osteomyelitis. Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi 24, 108–111. Review. Tettelin, H., Masignani, V., Cieslewicz, M.J., Medini, D., Ward, N.L., Angiuoli, S.V., Crabtree, J., Jones, A.L., Durkin, A.S., Deboy, R.T., et al. (2005). Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial ‘pan-genome’. Proc. Natl. Acad. Sci. U.S.A. 102, 13950–13955. Tettelin, H., Riley, D., Cattuto, C., and Medini, D. (2008). Comparative genomics: the bacterial pan-genome. Curr. Opin. Microbiol. 11, 472–477. Tóth, L., Csomor, P., Sziklai, I., and Karosi, T. (2011). Biofilm detection in chronic rhinosinusitis by combined application of hematoxylin–eosin and gram staining. Eur. Arch. Otorhinolaryngol. 268, 1455–1462. Wagner, M., Ivleva, N.P., Haisch, C., Niessner, R., and Horn, H. (2009). Combined use of confocal laser scanning microscopy (CLSM) and Raman microscopy (RM): investigations on EPS-Matrix. Water Res. 43, 63–76. Wertz, J.E., Goldstone, C., Gordon, D.M., and Riley, M.A. (2003). A molecular phylogeny of enteric bacteria and implications for a bacterial species concept. J. Evol. Biol. 16, 236–248. Whitchurch, C.B., Tolker-Nielsen, T., Ragas, P.C., and Mattick, J.S. (2002). Extracellular DNA required for bacterial biofilm formation. Science 295, 1487. Woese, C.R., and Fox, G.E. (1977). Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc. Natl. Acad. Sci. U.S.A. 74, 5088–5090. Zuliani, G., Carron, M., Gurrola, J., Coleman, C., Haupert, M., Berk, R., and Coticchia, J. (2006). Identification of adenoid biofilms in chronic rhinosinusitis. Int. J. Pediatr. Otorhinolaryngol. 70, 1613–1617.

Part III Environmental Microbiology

Quantitative PCR and Reverse Transcription Quantitative PCR Applied to Methane-cycling Archaea in the Marine Sediments of the White Oak River Estuary

10

Karen G. Lloyd

Abstract Quantitative PCR (qPCR) and reverse transcribed quantitative PCR (RT-qPCR) are powerful tools for quantifying the DNA and RNA, respectively, of specific groups of microorganisms in marine sediments. They can also be used to identify potential environmental functions of uncultured microorganisms by correlating microbial abundance and activity to geochemistry. An example of this usage is the present case study of the White Oak River estuary. Here uncultured anaerobic methaneoxidizing archaea (ANME) were shown to change in abundance (inferred from DNA quantified by qPCR) and activity (inferred from RNA quantified by RT-qPCR) in response to methane-oxidizing conditions, as well as methanogenic conditions. These non-culture-based methods therefore raises the hypothesis that ANME archaea are capable of reversing their metabolism to methanogenesis. qPCR and RT-qPCR in marine sediments require careful checking to ensure maximal DNA and RNA extraction efficiencies, and minimal coextraction of PCR inhibiting substances. If these pitfalls are avoided, qPCR and RT-qPCR can be used in other applications to develop hypotheses about the physiology of uncultured microorganisms in environmental samples. Introduction Many microorganisms that are important in environmental systems have never been grown in the lab (Rappé and Giovannoni, 2003); therefore

their metabolisms and contribution to ecosystem services are completely unknown. One way to discover these functions is to measure changes in the quantity of microorganisms relative to a spatial or temporal gradient in their natural environment (e.g. Lee et al., 1996; Hall et al., 2008). No universal standard methods have been established for quantifying uncultured microbes in the marine sedimentary environment. Currently, two main categories of methods are most commonly used for quantification of uncultured microbes in marine sediments: (1) Fluorescence in situ hybridization (FISH) of whole cells using oligonucleotide probes targeting ribosomal RNA molecules, with variations to try and improve signal to noise ratio (Pernthaler et al., 2002; Amann et al., 2001) and (2) Extraction of nucleic acids followed by quantitative PCR (qPCR), either with fluorescence-based real-time qPCR (Wittwer et al., 1997) or with competitive qPCR (Lee et al., 1996), using primers targeting specific phylogenetic groups or enzymes (Bustin et al., 2009; Inagaki et al., 2003; Nunoura et al., 2006; Schippers et al., 2012). Each method has advantages and disadvantages. For instance, FISH has the advantage that it allows visual confirmation of whole cells, but often suffers from imperfect efficiency in cell permeabilization and probe uptake (e.g. Knittel et al., 2003) and the inability to verify whether probes are bound only to the target population. qPCR has the advantage that amplified material can be sequenced to check the specificity of the quantifications (Lloyd et al., 2011; Kubo et al., 2012), but nucleic acids must

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be extracted completely from the sediment matrix and inhibiting substances such as minerals, salts, or humic acids can greatly obscure results (e.g. Lloyd et al., 2010). In the following case study, uncultured microorganisms were quantified to study methane cycling in the marine sediments of the White Oak Estuary, North Carolina (Lloyd et al., 2010, 2011). Many estuarine sediments on passive margins are perpetually submerged under saline water, and are not subject to advective pumping by fresh groundwater, ancient salt deposits, geothermal heating or tectonic activity. Concentrations of dissolved chemicals in pore water are therefore influenced only by diffusion from the overlying water, advection from benthic fauna, burial and compaction from sedimentation, and the metabolic reactions of the microbial population (Berner, 1980). Since many of those parameters can be numerically constrained, microbial metabolic activity can therefore be inferred from models (Iversen and Jørgensen, 1985; Alperin et al., 1988) and correlated with microbial quantifications. The White Oak River estuary is an example of such a passive margin environment and provides a chemical and depositional environment that is relatively stable over time, making it an ideal location for linking the abundance of uncultured microorganisms to geochemical functions (Martens et al., 1998). In particular, the White Oak River estuary has a temporally stable depth of sulphate depletion that coincides with the disappearance of methane diffusing upwards through the sediments. The relative locations of anaerobic methane oxidation and methane production can therefore calculated based on a model fit to the methane concentration changes with depth (Lloyd et al., 2011). This case study includes both DNA- and RNA-based quantifications because they provide distinct and complementary information. Since prokaryotes generally have a single copy of their genomes, quantification of genes that are in a low copy per genome can be a way to estimate the number of cells containing that particular gene in the environment. Primers can be made to target either broad groups or specific subgroups of microbes using small subunit 16S rRNA genes that are usually present in low copy numbers (usually 1000

Not applicable

Main advantages

Outperforms Ease of use, Analysis pipeline can be optimized for powerful comparison competing tools when analysing large tools individual datasets datasets of short reads

Powerful visualization of analysis results, only non-web-based software

Reference

Sun et al. (2011)

Huson et al. (2011)

>10,000

Markowitz et al. (2008)

Distantly related gene variants will either only be amplified poorly, which increases the amount of sequencing effort necessary to detect them, or not amplified at all. Consequently, in order to discover distant gene variants, there is little alternative to the metagenomic shotgun approach. Analysis of the obtained marker gene catalogues can be performed using one of several publicly available software suites. The RDP-II (Cole et al., 2009) and SILVA (Pruesse et al., 2007) projects focus only on the 16S rRNA marker gene and offer a web-based service for aligning and classifying hundreds of 16S rRNA sequences. Results of these projects can be downloaded and further processed for more thorough phylogenetic analysis with programs such as ARB (Ludwig et al., 2004) or MEGA (Tamura

Meyer et al. (2008)

et al., 2011). Since these platforms were mainly developed for full-length Sanger sequences, however, their use for analysing thousands of high-throughput sequences is limited. To overcome this limitation the RDP pyrosequencing pipeline was developed which offers a web-based toolset for high numbers of 16S rRNA amplicons generated by 454 pyrosequencing. Besides these web-based services, stand-alone software suites such as Mothur (Schloss et al., 2009) and QIIME (Caporaso et al., 2010) can be used for the analysis of high-throughput amplicon data. In contrast to the above-mentioned web-based services, those software suites can also easily be applied to the analysis of marker genes other then the 16S rRNA gene. They combine several third-party tools that range from an initial cleaning of reads, over

162 | Schreiber

phylogenetic analysis, to graphic visualization of the results. Some characteristics of the software suits optimized for analysing metagenomic amplicon data are summarized in Table 13.2. Alternatively, amplicon data can also be analysed by the previously introduced software suites CAMERA, IMG-M, MG-RAST, and MEGAN4 (see Table 13.1). Even though these suites were designed for the analysis of metagenomic shotgun

data, they can also be adapted to deal with amplicon data. The function-driven metagenomic approach Function-driven metagenomic studies use the heterologous expression of DNA fragments to screen metagenomic libraries. This approach

Table 13.2 Comparison of software suits for the analysis of metagenomic amplicon data Mothur

QIIME

VAMPS

RDP-II Pyro

SILVA

Website

www.mothur.org

qiime.org

vamps.mbl.edu

pyro.cme.msu. edu

www.arb-silva. de

Platform

Web-based Desktop, Desktop, command-line command-line (Linux, Win, Mac) (Linux, Win, Mac)

Web-based

Web-based and Desktop, command-line (Linux)

Graphical user interface

No

No

Yes

Yes

Yes

Focus

Analysis of all kinds of amplicons

Analysis of all kinds of amplicons

Comprehensive analysis of 16S rRNA amplicons

Alignment and phylogenetic classification of 16S rRNA amplicons

Alignment and phylogenetic classification of 16S rRNA amplicons

Processable data size (sequences)

Tens of thousands (depends on user’s hardware)

Millions (depends on user’s hardware)

Tens of thousands

Hundreds of thousands

500 (web), tens of thousands (Desktop)

Processing pipeline/tools for raw sequence data

Yes

Yes

Yes

Yes

No

Visualization tools Yes

Yes

Yes (limited)

No

No

Analysis overview Alignment, classification, diversity indices, rarefaction, library comparison, sequence clustering, multivariate statistics

Alignment, classification, diversity indices, rarefaction, library comparison, sequence clustering, multivariate statistics

Alignment, classification, diversity indices, rarefaction, library comparison, sequence clustering

Alignment, classification, diversity indices, rarefaction, library comparison

Alignment, classification

Main advantages

Ease of set-up and use, very comprehensive suit of integrated analysis tools

Comprehensive suit of analysis tools and powerful visualization features

Possibility to compare data to other archived datasets

Optimized for very large datasets

Optimized for compatibility with the ARB software suit, retrieval of related sequences possible

Reference

Schloss et al. (2009)

Caporaso et al. (2010)

–

Cole et al. (2009) Pruesse et al. (2007)

Metagenomic Analysis of Microbial Communities | 163

is especially interesting for biotechnological applications as it allows the discovery of novel enzymes and compounds with unusual properties. Early sequence-driven and function-driven metagenome studies once followed a very similar workflow, which involved: DNA extraction, cloning of the DNA fragments, clone separation, and finally clone screening. While clones of sequencedriven studies were screened by hybridization or PCR, clones of function-driven studies were screened by function-specific, expression-based assays. Although, NGS technologies have made the cloning step obsolete for sequence-driven studies, this step is still essential for most functiondriven metagenome studies. Because the main goal of function-driven studies is the discovery of novel biotechnologically relevant compounds, the choice of an appropriate sample becomes highly important. Choosing samples from extreme habitats such as highly acidic habitats, hypersaline habitats, or hyperthermal vents, for example, promises the potential for detecting unusually robust enzymes. Novel antibiotics, on the other hand, might best be discovered in samples where there is a high competition for resources, such as soil. The synthesis of most complex compounds requires a whole set of genes which can stretch for several thousands of base pairs. Therefore, a successful heterologous expression of such compounds heavily depends on having all genes of a particular function present and consequently depends on the size of the cloned DNA fragments. The size of the DNA fragment in turn dictates the cloning vector that can be used. Thus, the chance for a successful heterologues expression increases by employing vectors that allow for long insert sizes, e.g. cosmids and fosmids (insert sizes up to 45 kbp), or BACs (insert sizes of up to 200 kbp). Obtaining sufficiently high-molecular DNA from some environments or low biomass samples, however, can be challenging. Another factor that influences what cloning vector to use is the vector copy number in the host. Employing a high-copy plasmid vector can lead to a high expression of the target gene or function, and therefore make it easier to detect in expression assays. If the gene product, however, were toxic for the host, a high expression would simply kill the host, making that

fragment ‘non-clonable’ (cloning bias). This effect can be reduced by using low-copy vectors, such as low-copy plasmids, fosmids, or BACs. Nevertheless, low- as well as high-copy-number plasmid vectors, cosmids, fosmids, and BACs have all been successfully used for function-driven metagenomic studies (Daniel, 2005; Kakirde et al., 2010; Uchiyama and Miyazaki, 2009). Besides gene product toxicity, there can be several other reasons for the ‘non-expression’ of gene products (Ekkers et al., 2012): codon usage differences between the host and the DNA fragment, lack of proper initiation factors, lack of recognition of promoters, improper protein folding, absence of essential co-factors, accelerated breakdown of gene product in the host, or the inability of the host to excrete the gene product. As many of these problems are host-associated, they might be overcome by using hosts other then the standard Escherichia coli. Following this line of thought, several other host systems have been developed for the heterologues expression of genes, they include: Burkholderia, Bacillus, Sphingomonas, Streptomyces and Pseudomonas (Courtois et al., 2003; Martinez et al., 2004; Saito et al., 2006). The last step of a function-driven metagenomic study includes the detection of the expressed gene product by function-specific assays. These have classically been performed as agar- plate-based assays, such as (1) colorimetric detection assays in which either the colony or the agar substrate change colour if the target gene is present, (2) use of selective media that only allow the growth of hosts expressing the target gene (e.g. for detection of antibiotic resistance genes), or (3) halo-based assays where colonies with the target gene cause a halo around the colony (e.g. by degrading a turbid substrate or by lysing competitor microorganisms when screening for antibacterial activity). The spectrum of assays has lately been extended by methods that enable the detection of specific gene products at the single cell level (for a review see Link et al., 2007). Overall, function-driven studies have been highly successful for detecting novel enzymes and compounds, such as: antimicrobials (Brady and Clardy, 2000; Wang et al., 2000), proteases (Gupta et al., 2002), lipases (Morohoshi et al., 2011) or chitinases (LeCleir et al., 2007). Even

164 | Schreiber

though current function-driven studies mostly lead to the ‘re-discovery’ of functions (for a review see Ekkers et al., 2012), the application of novel single-cell-based screening methods promises a continuing discovery of new compounds and enzymes using this approach. Beyond metagenomics – metaanalysis of expression data One major drawback of all metagenomic analyses is that they can only tell us about the genetic potential of a microbial community. Which genes are expressed in a given environmental setting, however, remains unknown. This question can be answered by employing cultivation-independent expression-based techniques; more precisely metatranscriptomics to study gene expression on the RNA level and metaproteomics to study gene expression on the protein level. The typical workflow of a metatranscriptomic analysis involves RNA extraction and pre-filtering, transcription of the RNA into more stable cDNA, and a subsequent bioinformatics analysis of the cDNA. Extraction of RNA is hampered by its low stability compared to DNA as well as by a rapid turnover in cells as a reaction to environmental conditions (Belasco and Brawerman, 1993). Further, typical RNA extracts of a microbial community consist of only 1–5% messenger RNA (mRNA) while, the rest is made up of rRNA and tRNA (Karpinets et al., 2006; Neidhardt and Umbarger, 1996). This composition makes it possible to use the RNA-approach as a means to characterize the diversity of the microbial community, i.e. by using the highly conserved rRNA sequence as a phylogenetic marker. The results of such an rRNA-based analysis can also be used quantitatively for estimating microbial abundances. Sequence analysis of rRNA transcripts proceeds analogous with the analysis of 16S rRNA amplicons. If the expression of genes is the goal of the study, i.e. the analysis of mRNA, as much rRNA as possible should be removed prior to any downstream analysis. Methods that have been successfully used to this end include: (a) subtractive hybridization using rRNA specific probes (Pang et al., 2004; Su and Sordillo, 1998), (b) a reverse

transcription method where rRNA-specific primers are used to transcribe rRNA into cDNA and where the resultant rRNA/cDNA hybrids are digested with a duplex specific nuclease (Dunman et al., 2001), and (c) gel electrophoresis followed by the excision of non-rRNA bands (McGrath et al., 2008). Since abundances of individual mRNA transcripts reflect the level of gene expression in prokaryotic systems, mRNA-based data can be used quantitatively to infer the importance of individual genes in an environmental context. The varying expression of different mRNA transcripts, however, also requires an increased sequencing effort when comprehensive cataloguing of all gene expression of a community or the discovery of rare transcripts are desired. This problem can be minimized by a normalization of the cDNA transcripts prior to downstream analysis, e.g. by using a duplex-specific nuclease (Evrogen, 2012). If only low amounts of cDNA can be obtained from mRNA after RNA extraction and removal of rRNA, cDNA can be amplified by MDA prior to any downstream analysis. Owing to an inherent amplification bias of the MDA reaction (Bredel et al., 2005; Rodrigue et al., 2009); however, this comes at the cost of losing any quantitative information about mRNA expression. For the analysis of mRNA transcripts, the obtained sequences can be mapped onto a reference metagenome to identify expressed genes. For this mapping, reference metagenomes should ideally originate from the same or at least a very similar community to identify as many of the transcripts as possible. Metaproteomic analyses of microbial communities have become feasible by rapid advances in tandem mass spectrometry (MS/MS) proteomic technologies. Current MS/MS-based technologies allow high-throughput protein identification at costs comparable to those of metagenomic studies (Verberkmoes et al., 2008). When performing a metaproteomic study the goal of the study dictates to a high degree the protocol used for protein extraction. Membrane proteins are heavily involved in nutrient transport and energy transduction. Thus, if these processes are the focus of the study, protocols optimized for the extraction of membrane proteins should be used. Accordingly, for studying intracellular processes, protocols

Metagenomic Analysis of Microbial Communities | 165

focusing on the extraction of cellular proteins should be employed. The successful (and accurate) identification of individual proteins depends heavily, as in metatranscriptomic analyses, on the availability of relevant (i.e. environment-specific) reference metagenomes. The main advantage of a metaproteomic study compared to a metatranscriptomic one is the absence of (contaminating) rRNA background in the analysis, as well as the higher stability and longer turnover times of proteins. This, however, comes at the cost of not being able to amplify the protein fraction (compare: MDA for metatranscriptomic approach) prior to analysis. This makes the metaproteomic approach ill suited for environments that feature only a low abundance of microorganisms. Additionally, the resolution of metaproteomic analyses is currently limited to at most a couple of thousand proteins that can be identified from a given sample (Siggins et al., 2012). Concluding remarks Advances in sequencing technology and the progress in proteomic technologies enable environmental microbiologists to inexpensively generate immense numbers of data for a given habitat. This progress has shifted the bottleneck of the ‘meta-omic’ approaches introduced here, from sequencing costs to data analysis and storage. Handling and storing Gbps of data will, and already does, demand large capacities of computational power and infrastructure. Therefore, future meta-omic studies will most likely only focus on small fractions of the generated data while discarding the rest. Even though such an approach may sound strange to current microbiologists (who are still accustomed to high sequencing costs), limited data storage capacities will leave scientists no alternative. Finally, despite the existence of several software suites for the analysis of meta-omic data, many studies will require innovative approaches for data analysis that are difficult to realize without bioinformatic know-how. This will probably trigger a reformation of the field of environmental microbiology where future generations of microbiologists will increasingly focus on the computational analysis of large amounts of data.

Besides handling large numbers of data, current meta-omic studies face the problem that many of the detected genes, transcripts, and proteins cannot be assigned to a function. The main reason for this is the discrepancy between the ease and speed of meta-omic data generation and the laborious and time-consuming characterization of enzyme function. The whole procedure, starting from the isolation of a novel microbial species, over the identification of novel functions, to a final identification of the responsible genes can take years of dedicated work. Even though high-throughput cultivation techniques are a first step in the right direction, high-throughput approaches for function discovery and subsequent gene assignment are still rare. Thus, a considerable investment into the development of these techniques will be necessary to enable future environmental microbiologists to make full use of meta-omic approaches and to progress the field as a whole. References 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. Altug, G., Cardak, M., Ciftci, P.S., and Gurun, S. (2010). The application of viable count procedures for measuring viable cells in the various marine environments. J. Appl. Microbiol. 108, 88–95. Amann, R.I., Ludwig, W., and Schleifer, K.H. (1995). Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59, 143–169. Aparicio, S., Chapman, J., Stupka, E., Putnam, N., Chia, J.-m., Dehal, P., Christoffels, A., Rash, S., Hoon, S., Smit, A., et al. (2002). Whole-genome shotgun assembly and analysis of the genome of Fugu rubripes. Science 297, 1301–1310. Batzoglou, S., Jaffe, D.B., Stanley, K., Butler, J., Gnerre, S., Mauceli, E., Berger, B., Mesirov, J.P., and Lander, E.S. (2002). ARACHNE: a whole-genome shotgun assembler. Genome Res. 12, 177–189. Belasco, J., and Brawerman, G. (1993). Control of Messenger RNA Stability, 1st edn. (Academic Press, San Diego, CA, USA). Brady, S.F., and Clardy, J. (2000). Long-chain N-acyl amino acid antibiotics isolated from heterologously expressed environmental DNA. J. Am. Chem. Soc. 122, 12903–12904. Bredel, M., Bredel, C., Juric, D., Kim, Y., Vogel, H., Harsh, G.R., Recht, L.D., Pollack, J.R., and Sikic, B.I. (2005). Amplification of whole tumour genomes and gene-bygene mapping of genomic aberrations from limited sources of fresh-frozen and paraffin-embedded DNA. J. Mol. Diagn. 7, 171–182.

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Stable Isotope Probing in Environmental Microbiology Studies S. Jane Fowler and Lisa M. Gieg

Abstract Traditional microbiological methods involving the isolation of microbes from the environment in pure culture have been shown to be ineffective at accessing the majority of microbial diversity. Methods that allow the study of microbes in their native environment or in mixed cultures have been gaining in popularity in recent years. Stable isotope probing is a method that allows the identification of the taxonomic groups that are actively metabolizing a substrate, typically in enriched cultures but also in in situ communities. This technique involves incubations with an isotopically labelled substrate (e.g. 13C-labelled) during which time the isotope label is incorporated into the biomolecules of organisms actively degrading the substrate. This is followed by the extraction and analysis of these biomolecules in order to identify the organisms incorporating the isotope label. Stable isotope probing has been refined for the analysis of all classes of biomolecules, and has been used to identify the key microbes involved in a number of biological processes. Introduction A major goal of microbial ecology is to characterize and understand the roles of microbes in their natural environment. To achieve this, a traditional approach is to isolate microbes from their native environment and study their physiology in a laboratory setting; however, this has proven to be of limited success as traditional laboratory methods enable the purification of only a small fraction of microbes from the environment (Staley and Konopka, 1985). The development of cultivation

14

independent methods has transformed microbial ecology and helped to overcome many of the limitations of traditional methods by enabling the study of microbes in situ or in enrichment cultures under conditions that mimic more closely their native environment. Stable isotope probing (SIP) is a semi-cultivation independent to cultivation independent method, as it is frequently applied to enriched laboratory cultures, but can also be applied to unenriched environmental samples studied in the laboratory and directly in the environment for in situ studies. The method allows for the metabolism of a given substrate to be linked to specific microbes by using isotopically labelled substrates. The rationale behind this method is that members of a given microbial community that are actively metabolizing an isotopically labelled compound will incorporate this label into their biomolecules. Pioneered by Boschker et al. (1998), SIP was initially used to demonstrate the importance of type I methanotrophs in methane mineralization in lake sediments through the analysis of phospholipid fatty acids (PLFAs) that had incorporated 13C from 13CH4. Their method has subsequently been modified for the analysis of isotopically labelled DNA (Radajewski et al., 2000), RNA (Manefield et al., 2002) and proteins ( Jehmlich et al., 2008). The basic SIP methodology involves applying a pulse of an isotopically labelled substrate (typically 13C or 15N, though 18O has also been used successfully) for an incubation period long enough to allow for metabolism of the compound and incorporation of isotope into biomolecules, followed by extraction of the biomolecule(s) of interest and downstream phylogenetic analysis to associate the labelled

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biomolecules with specific organisms from the community. SIP has been applied to address a variety of research questions. Although SIP has predominantly been applied to enrichment cultures and unenriched samples collected from the environment, it is also attractive for use in field applications as stable isotopes are not detrimental to the environment, and close to in situ substrate concentrations can often be used. Methodologies PLFA-SIP The initial application of SIP involved the analysis of PLFAs from cell membranes of methanotrophs and sulphate-reducing bacteria (SRB) in sediment core samples that had been amended with 13 C-labelled methane and acetate respectively (Boschker et al., 1998). During the pulse, isotope label is incorporated by cells actively metabolizing a 13C-labelled compound, with the rate of PLFA turnover being related to cellular activity but independent of cell replication (Neufeld et al., 2007a). Following the pulse, PLFAs are extracted and are analysed by GC-C-IRMS (gas

chromatography combustion isotope-ratio mass spectrometry). The results of this analysis give the lipid composition of the sample as well as the degree of 13C labelling. Thus, unlike nucleic acid SIP (NA-SIP, described below), no prior separation of labelled and unlabelled lipids is required. Furthermore, this analysis is extremely sensitive and label incorporation as low as 0.1–0.2% can be detected (Boschker et al., 1998). This makes PLFA-SIP an attractive technique for field use as in situ substrate concentrations can be applied and very dilute labelling can be detected (Table 14.1). For example, PLFA-SIP has been used in the field to assess the secondary labelling of rhizosphereassociated microbes of plants that have received a 13 CO2 pulse (e.g. Evershed et al., 2006; Denef et al., 2007; Balasooriya et al., 2008; Jin and Evans, 2010). In order to make taxonomic assignments based on PLFA profiles, it is necessary to have some knowledge of the lipid content and specific lipid biomarkers of different phylogenetic groups within the sample. PLFA-SIP has been shown to be particularly effective in the analysis of the methanotrophic communities in soil, sediments, and the atmosphere as type I and II methanotrophs

Table 14.1 Advantages and drawbacks of different SIP methods Method

Pros

Cons

Useful for

PLFA-SIP

High sensitivity Can use in situ substrate concentrations Amenable to field studies No separation of labelled and unlabelled molecules required

Low phylogenetic resolution Poor quality and availability of databases of phylogenetic PLFA profiles Inability to identify novel organisms Limited to 13C substrates

Low complexity environments containing organisms with distinctive and well-characterized lipids Situations where label incorporation is expected to be very low

DNA-SIP

High phylogenetic resolution

Low sensitivity Requires highly labelled substrate and longer incubation times

Actively growing populations with relatively high activity

RNA-SIP

High phylogenetic resolution Higher sensitivity than DNASIP

Self-affinity of RNA can create technical difficulties in resolving heavy and light communities RNA recovery from environmental samples may be difficult

Environments with good activity but possibly slow growth rates

Analysis of uncharacterized microbial communities will likely require metagenomic sequencing to achieve desired phylogenetic resolution Protein extraction from environmental samples may be difficult

Cultures for which metagenomic or genomic sequencing of isolates of closely related organisms are available

Protein-SIP Relatively high sensitivity and phylogenetic resolution Can use in situ substrate concentrations

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have well described distinct membrane lipids that are characteristic of these taxonomic groups (McDonald et al., 2008). For example, PLFASIP has been used to differentiate the microbes responsible for methane oxidation in the organic horizon of forest soil at low and high methane concentrations (Bull et al., 2000). By incubating soil cores with low and high 13CH4 concentrations and analysing the enrichment of 13C in PLFAs, it was determined that at atmospheric (low) concentrations, methane oxidation was carried out primarily by a novel type II methanotroph related to Methylosinus genus, while at high methane concentrations, methane oxidation was carried out by both a type I methanotroph (Methylomonas sp.) as well as the type II methanotroph identified at low concentrations. This study demonstrates the benefit of the high sensitivity of PLFA-SIP as it was possible to differentiate high and low affinity methanotrophs by using low (in situ), and high methane concentrations respectively (Bull et al., 2000). The major drawback of PLFA-SIP is that the approach is limited to the identification of cultivated groups with characterized lipids and to environments with relatively low complexity (Table 14.1). The identification of novel groups of organisms with PLFA-SIP is unlikely as it is only possible if the group contains previously uncharacterized lipids. Moreover, the microbial diversity within the sample must be sufficiently low so that it is possible to distinguish individual lipid profiles (Manefield et al., 2002). Characterization of PLFAs of newly cultivated organisms and the improvement of PLFA databases should result in improved taxonomic resolution and more robust results from PLFA-SIP experiments (Bodelier et al., 2009). DNA-SIP The basic procedure used for DNA-SIP involves applying a pulse of a 13C, 15N or 18O-labelled substrate for an appropriate duration followed by DNA extraction. Light and heavy DNA molecules are then separated by caesium chloride (CsCl) density gradient ultracentrifugation. Resolved heavy and light DNA can be recovered from gradients visualized under UV light using a syringe if ethidium bromide is included in the

gradient solution, or alternatively gradients can be fractionated. Following this, DNA is precipitated and fingerprinting methods can be applied to identify the microbial taxa that have incorporated the label into newly synthesized DNA (Fig. 14.1). More methodological information is available in a detailed protocol (Neufeld et al., 2007b). The most common fingerprinting methods involve PCR amplification of the 16S rRNA gene (or a functional gene of interest) followed by clone library construction, a combination of clone library and T-RFLP (terminal restriction fragment length polymorphism), DGGE (denaturing gradient gel electrophoresis), or via pyrosequencing that allows for comparison and differentiation of organisms with labelled and unlabelled DNA. Alternatively, PCR-independent metagenomic shotgun sequencing of labelled DNA is gaining in popularity, although multiple displacement amplification (MDA) is often required due to the low yield of labelled DNA (Chen and Murrell, 2010). Despite this, amplification and metagenomic sequencing of labelled DNA has been shown to be a useful way to selectively target genomic information from the active members of a community (Neufeld et al., 2008). A minimum degree of labelling of 15–20 atom% is required to separate 13C-labelled and unlabelled nucleic acids (Radajewski et al., 2000; Whiteley et al., 2007). As label incorporation into DNA requires cell replication, DNA-SIP is less sensitive and requires longer incubation times than other SIP methods (Fig. 14.1 and Table 14.1). To improve labelling efficiency, a highly labelled substrate should be used, and it is often necessary to use higher than in situ substrate concentrations to obtain sufficiently labelled DNA for adequate separation. A drawback of this is that high concentrations of substrate may be inhibitory to some microbial community members and result in a culture bias. Another issue that may occur is cross feeding, which is the consumption of a by-product of metabolism of one group by another. If this byproduct is isotopically labelled, this results in the labelling of more than one group of organisms. Though cross feeding and culture bias can occur in any SIP experiment, it is more prevalent when longer incubation times or increased substrate concentrations are required, such as for DNA-SIP.

174 | Fowler and Gieg Sample processing

Pulse duration

PLFA-SIP

DNA-SIP

Incubate culture or environmental sample with labelled compound

Minutes - hours

PLFA extraction

GC-C-IRMS

Hours - weeks

DNA extraction

Density gradient centrifugation in CsCl

Fingerprinting of fractionated gradients

Fingerprinting of fractionated gradients

Minutes - days

RNA extraction

Density gradient centrifugation in CsTFA

Protein-SIP Minutes - days

Protein extraction

SDS-PAGE or 2-D gel electrophoresis

RNA-SIP

Taxonomic identification Comparison to lipid profiles of characterized organisms

MALDI-MS or LC-MS/MS

Figure 14.1 Sample treatment in different stable isotope probing methodologies. GC-C-IRMS (Gas combustion isotope ratio mass spectrometry), CsCl (caesium chloride), CsTFA (caesium trifluoroacetate), SDS-PAGE (sodium dodecyl sulphate polyacrylamide gel electrophoresis), 2-D (two-dimensional), MALDIMS (matrix-assisted laser desorption/ionization mass spectrometry), LC-MS/MS (liquid chromatography tandem mass spectrometry).

Performing time-resolved experiments can allow the identification of cross feeding, and actually enables the discovery of trophic networks by following the incorporation of isotope label into different groups of organisms over time. Methodological refinements to improve the recovery of low quantities of DNA from gradients include the addition of 13C-labelled carrier DNA to gradients (Gallagher et al., 2005) or the addition of glycogen during DNA precipitation (Neufeld et al., 2007b). Small differences in buoyant density exist between 13C-labelled and unlabelled nucleic acids (approximately 0.04 g/mL for DNA in CsCl) (Lueders et al., 2004). Variation in the nucleic acid buoyant density of different organisms due to their G+C contents further complicate the separation of labelled and unlabelled nucleic acid molecules. As a result, shallow gradients are required for adequate separation. These can be achieved using vertical or near vertical ultracentrifuge rotors, while large fixed angle and swinging

bucket ultracentrifuge rotors result in gradients that are insufficiently shallow for good separation (Lueders, 2010). While 13C-labelled substrates are most commonly used in DNA-SIP, 15N and 18O-labelled substrates have also been applied (Cadisch et al., 2005; Schwartz, 2007). Owing to the lower abundance of N and O atoms in DNA compared to carbon, separation of 15N and 18O-labelled and unlabelled DNA can be even more difficult. When using 15N-labelled substrates, a minimum degree of labelling of 40 atom% is required to achieve separation in environmental samples, and a longer, slower ultracentrifugation has been shown to improve separation of labelled and unlabelled DNA (Cadisch et al., 2005). In the case of 18O-labelled substrates, it is thought that the mass increase in 18O compared to 16O results in sufficient changes in DNA buoyant density to achieve effective separation without modifications to the protocol (Schwartz, 2007).

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Despite its low sensitivity and complications in separating labelled and unlabelled nucleic acids, NA-SIP has an extremely high phylogenetic resolution due to the ability to probe the 16S rRNA gene directly and capitalize on the wide abundance of 16S rRNA genes from different phyla in the reference databases (Table 14.1). The extremely high phylogenetic resolution and the relative ease of DNA extraction from environmental samples and enrichment cultures (compared to RNA and protein extraction) make DNA-SIP the most widely used of SIP methodologies at the present moment. In an effort to overcome the limitations in sensitivity and phylogenetic resolution of different SIP methods, it is possible to combine molecular methods with SIP techniques in order to gain additional insight into the environment of interest. For example, by using both PLFA-SIP and DNA-SIP, Webster et al. (2006) characterized SRB in unenriched marine sediments samples more completely than either method would have achieved alone. The analysis of 16S rRNA of the 13 C-labelled fractions of SRB grown on glucose revealed that all major community members incorporated carbon from glucose within 7 days. However, the high sensitivity of PLFA-SIP allowed the examination of glucose metabolism in a time resolved fashion, and revealed that specific uncultured SRB became labelled by days 1 and 4, while the remainder of the community became labelled by day 7. This allowed the authors to differentiate specific glucose oxidizers from organisms metabolizing glucose degradation products. In 13C-acetate incubated sediments, PLFA profiles implicated SRB including Desulfobacter spp. and Desulfococcus multivorans in acetate oxidation. While DNA-SIP analysis of 16S rRNA genes confirmed this, it also revealed the presence of 16S rRNA genes most closely related to uncultured Firmicutes and candidate division JS1 in 13C-labelled acetate-incubated fractions. Using PLFA- and DNA-SIP methods in combination allowed the authors to minimize the drawbacks of both methods, namely the low phylogenetic resolution and inability to identify uncultured organisms by PLFA-SIP and the low sensitivity of DNA-SIP. As a result, they provided a more complete picture of SRB in marine sediments

and described a metabolic role for the previously uncharacterized candidate division JS1 (Webster et al., 2006). RNA-SIP The method used for RNA-SIP is similar to that of DNA-SIP with some minor modifications. RNA turnover is a continuous process that is independent of cell replication and is higher in active compared to inactive cells (Manefield et al., 2002). For this reason, RNA-SIP is more sensitive than DNA-SIP, while maintaining the same high taxonomic resolution. Owing to the increased sensitivity of RNA-SIP, the duration of the incubation period is typically shorter (Fig. 14.1). Following the incubation, RNA is extracted and labelled and unlabelled RNA molecules are separated in a caesium trifluoroacetate (CsTFA) gradient solution rather than CsCl, as the buoyant density of RNA is higher than DNA (Whiteley et al., 2007). Labelled and unlabelled RNA are subsequently separated by fractionation, and RNA is precipitated (Whiteley et al., 2007; Fig. 14.1). Following reverse transcription of RNA to cDNA, 16S rRNA can be amplified and fingerprinting methods can be applied as described for DNA-SIP. Additionally, metatranscriptomic sequencing of mRNA is theoretically feasible, though this has not yet been demonstrated and considerable transcriptome amplification would be required. Methodological details can be found in a comprehensive RNA-SIP protocol (Whiteley et al., 2007). Equivalent problems in the separation of labelled and unlabelled nucleic acids in DNASIP exist for RNA-SIP. In addition, the tendency of RNA to self-associate further confounds the separation of labelled and unlabelled RNA. Interactions of labelled and unlabelled RNA molecules result in decreased gradient resolution and the use of high concentrations of RNA (>1  μg) results in aggregation, precipitation and incomplete separation (Lueders et al., 2004). To achieve efficient separation of heavy and light RNA, low quantities of RNA (~500  ng) must be used to minimize aggregation and prevent precipitation. At these concentrations, the visualization of RNA in ethidium bromide-containing gradients is not possible, and as a result, gradient fractionation

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was developed (Manefield et al., 2002). Gradient fractionation and RNA-SIP were first used to definitively identify the dominant phenol degrader in a bioreactor previously shown to be dominated by pseudomonads, γ-Proteobacteria and Cytophaga–Flavobacterium groups. In DGGE gels of the fractionated gradients, it became clear that a single band was dominant in the heavy (labelled) gradient fractions, and sequencing revealed that this phenol degrader was a Thauera sp. which had not previously been found to degrade phenol under aerobic conditions (Manefield et al., 2002). Fractionation has subsequently also been optimized for DNA-SIP as it allows a quantitative examination of the complete gradient, enabling the detection of incomplete separation due to partial label incorporation and diffusion. This is particularly relevant in slow growing communities wherein the incorporation of label is slow and may not be complete (Lueders et al., 2004). Owing to the diffusion that occurs in all gradients, variations in G+C content and the self-affinity of RNA in RNA-SIP, labelled and unlabelled nucleic acid molecules will be distributed over a range of buoyant densities in NA-SIP experiments (Table 14.1). Furthermore, particularly under anaerobic conditions, CO2 fixation may occur concurrently with substrate degradation, which results in dilution of the 13C label due to the concurrent incorporation of 12C from 12CO2 into nucleic acids (Lueders, 2010). As a result, care must be taken to differentiate the incorporation of isotope into certain templates from a background of unlabelled nucleic acids. To demonstrate true label incorporation, it is important to include control samples incubated with unlabelled substrate in parallel with labelled (Lueders, 2010). By using control samples incubated with unlabelled substrates, it becomes possible to demonstrate that the buoyant density of templates that incorporate the label are higher in samples incubated with labelled substrate than in control samples. In addition, to confirm that isotopic enrichment has occurred in a sample, nucleic acid samples can be subject to GC-C-IRMS to quantify the degree of labelling (Manefield et al., 2002).

Protein-SIP Protein-SIP is a recently developed method that provides a functional link between an environmental process and specific proteins in a microbial community by applying 13C and 15N-labelled substrates ( Jehmlich et al., 2008). Identified proteins can be assigned taxonomically by comparison to reference databases. This method has intermediate sensitivity, between that of PLFA-SIP and NA-SIP, with a minimum label incorporation of 2 and 5 atom% required for downstream analysis with 13C- and 15N-labelled substrates respectively ( Jehmlich et al., 2010) (Table 14.1). The phylogenetic resolution of this method is also at an intermediate level relative to PLFA and NA-SIP, as amino acid sequences can often provide more specific phylogenetic information than PLFA profiles, but are more highly conserved across taxonomic groups compared with nucleic acid sequences. The phylogenetic resolution of protein-SIP can be improved when accompanied by genomic information (e.g. metagenome or individual genomes) from the community of interest so that amino acid sequences can be associated with genes that are analysed phylogenetically at the genomic level ( Jehmlich et al., 2010). For poorly characterized samples this is likely required for good phylogenetic resolution as sequence information for many organisms is not available in reference databases. Protein-SIP is carried out by applying a pulse of appropriate duration with 13C or 15N-labelled substrates and unlabelled control substrate, followed by protein extraction and downstream analysis that involves either gel-based methods or gel-free shotgun proteomics (Fig. 14.1). Gelbased methods involve the separation of proteins by SDS-PAGE or 2D gel electrophoresis followed by protein digestion and analysis with MALDIMS (matrix assisted laser ionization/desorption mass spectrometry), LC-MS or LC-MS/MS to obtain peptide mass fingerprints (PMFs) for individual proteins ( Jehmlich et al., 2010). Gel-free methods include IPP (intact protein profiling), which involves the analysis of small undigested proteins by MALDI-MS to create a profile which is compared to profiles from reference strains, and SMM (shotgun mass mapping), which involves protein digestion followed by MALDI-MS analysis which provides peptide sequence information

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( Jehmlich et al., 2009). The degree of isotope incorporation into proteins is calculated based on a model of amino acid mass and the comparison of the respective masses of a protein in the labelled and unlabelled samples ( Jehmlich et al., 2009). Downstream data analysis and protein identification is carried out using search engines that assign peptide sequences from primary sequence reference databases to MS data. This requires MS data derived from control incubations with unlabelled substrate as the protein searches rely on the mass of unlabelled peptides ( Jehmlich et al., 2008). For further information on specific protein-SIP methods, a detailed protocol is available ( Jehmlich et al., 2010). The use of shotgun proteomics in protein-SIP could potentially reduce the intensive data analysis that is required for gel-based methods. IPP relies on protein profiles of reference strains and is likely not suitable for the analysis of microbial communities which consist largely of uncharacterized organisms ( Jehmlich et al., 2009). Although SMM has been shown to be more accurate than IPP and does not require reference strain profiles, adequate taxonomic resolution can only be achieved with the analysis of three peptides per protein, which creates large amounts of data ( Jehmlich et al., 2009). At present, few studies have applied protein-SIP to microbial communities derived from the environment, and these have relied on gel-based methods (Bastida et al., 2010, 2011; Bozinovski et al., 2012). Despite this, the potential for protein-SIP to be a useful method to analyse microbial communities has been demonstrated. Protein-SIP was recently used to identify the methyl tert-butyl ether (MTBE) degrader in an enrichment culture established from contaminated groundwater sediments (Bastida et al., 2010). The authors identified 27 different proteins, most of which originated from Methylibium petroleiphilum PM1 and members of the Comamonadaceae that were the dominant members of the community. Interestingly, MALDI-MS revealed that 13C enrichment was observed only in proteins belonging to M. petroleiphilum PM1 even after 5 days of MTBE degradation. This result suggested that M. petroleiphilum PM1 was the sole MTBE degrader in the culture, and that no cross feeding was occurring. It was proposed that

other community members were utilizing detritus from dead biomass as a carbon and energy source (Bastida et al., 2010). Currently, protein-SIP is still in its infancy, but it has the potential to be a useful technique for the study of microbial and even mixed microbial and eukaryotic communities (Bastida et al., 2011). As the degree of label incorporation into proteins corresponds to the metabolic activity of that organism, protein-SIP could be used to resolve trophic interactions in a variety of microbial communities ( Jehmlich et al., 2010). Further technical advancements such as developments in shotgun proteomics and improved protein extraction efficiency from environmental samples could make protein-SIP more user friendly and broaden the range of environments to which the method can be applied ( Jehmlich et al., 2010; Table 14.1). Based on the high sensitivity, relatively good phylogenetic resolution, and direct functional link between proteins and metabolism, protein-SIP could potentially be used in field applications and for the description of metabolic pathways and trophic interactions. Summary The development of molecular methods amenable to the study of mixed microbial communities in recent decades has led to a revolution in the field of microbial ecology. Much more is now known about the microbiota of a variety of natural environments such as marine and freshwater systems, terrestrial environments, the deep subsurface and the human microbiome. Stable isotope probing techniques are particularly intriguing due to their ability to link biological functions with specific microbes in a given environment (e.g. in enrichment cultures, in unenriched samples retrieved from an environment, or directly in situ). To date, SIP methods have provided new insight into biogeochemical processes such as sulphate reduction and methanotrophy as well as contaminant biodegradation. In the coming years, the combination of SIP methods with complementary molecular methods such as metagenomics and proteomics will further improve our understanding of the microbial world and the metabolic processes that are mediated by microbes in a variety of ecosystems.

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References

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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., 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 Sp. 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. Jin, V.L., and Evans, R.D. (2010). Microbial 13C utilization patterns via stable isotope probing of phospholipid biomarkers in Mojave Desert soils exposed to ambient and elevated atmospheric CO2. Glob. Change Biol. 16, 2334–2344. Lueders, T. (2010). Stable isotope probing of hydrocarbon-degraders. In Handbook of Hydrocarbon and Lipid Microbiology. Timmis, T.N. Ed. (Berlin, Springer-Verlag), pp. 4012–4024. Lueders, T., Manefield, M., and Friedrich, M.W. (2004). Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients. Environ. Microbiol. 6, 73–78. 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. Manefield, M., Whiteley, A.S., Griffiths, R.I., and Bailey, M.J. (2002). RNA stable isotope probing, a novel means of linking microbial community function to Phylogeny. Appl. Environ. Microbiol. 68, 5367–5373. Neufeld, J.D., Dumont, M.G., Vohra, J., and Murrell, J.C. (2007a). Methodological considerations for the use of stable isotope probing in microbial ecology. Microbial Ecol. 53, 435–442. Neufeld, J.D., Vohra, J., Dumont, M.G., Lueders, T., Manefield, M., Friedrich, M.W., and Murrell, J.C. (2007b). 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. 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. Schwartz, E. (2007). Characterization of growing microorganisms in soil by stable isotope probing with H2 18O. Appl. Environ. Microbiol. 73, 2541–2546. Staley, J.T., and Konopka, A. (1985). Measurement of in situ activities of nonphotosynthetic microorganisms in aquatic and terrestrial habitats. Ann. Rev. Microbiol. 39, 321–346.

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in sulphate-reducing marine sediment enrichment slurries. Environ. Microbiol. 8, 1575–1589. Whiteley, A.S., Thomson, B., Lueders, T., and Manefield, M. (2007). RNA stable-isotope probing. Nat. Protoc. 2, 838–844.

Fluorescence in situ Hybridization (FISH) for the Identification and Quantification of Microorganisms

15

Cristina Moraru and Elke Allers

Abstract Fluorescence in situ hybridization (FISH) targeting ribosomal RNA (rRNA) has become a standard method in molecular ecology. FISH allows identification and quantification of microorganisms. The main methodological variations are the use of fluorochrome-labelled probes (monolabelled FISH, DOPE-FISH) or horseradish peroxidase (HRP)-labelled probes, known as catalysed reporter deposition-FISH (or CARDFISH). The first section of this chapter provides an overview of (i) the two methodological variations, (ii) sample evaluation by microscopy and image analysis, and (iii) probe design and principles of specific hybridization. The second section discusses pros and cons of rRNA FISH, including a comparison of the monolabeled and CARDFISH variations. The final section describes different applications and new developments in rRNA FISH, emphasizing the wide range of questions and samples it can be applied to.

allows for the design of oligomeric probes specific for different phylogenetic groups, from domain to genus and even species level. In most applications, the small subunit rRNA is targeted (16S for Archaea and Bacteria, 18S for Eukarya), but probes can also be prepared for the large subunit rRNA as well (23S for Archaea and Bacteria, 28S for Eukarya). Depending on the type of probes used, there are two main methodological variations of rRNA FISH: (i) rRNA FISH with fluorochromelabelled probes, for example monolabeled FISH (Amann et al., 1990b; Fuchs et al., 2007) or DOPE-FISH (Stoecker et al., 2010); and (ii) catalysed reporter deposition-FISH (CARDFISH) (Pernthaler et al., 2002), with horseradish peroxidase (HRP)-labelled probes, which deposit fluorochrome-labelled tyramides in cells. Fig. 15.1 depicts the main steps of both approaches, underlining the additional steps required for CARD-FISH.

Method basics Fluorescence in situ hybridization (FISH) targeting ribosomal RNA (rRNA) is a standard technique in molecular microbial ecology, allowing identification and quantification of targeted microorganisms. Ribosomal RNA is an essential component of ribosomes, which, as indispensable components in protein synthesis, are found in all living cells. As such, rRNA encoding genes are among the most slowly evolving regions of a genome and carry useful sequence information for phylogenetic analyses. Being composed both of highly conserved and variable regions, rRNA

Sample preparation Several sample preparation steps are required prior to hybridization both to stabilize the cells and make them permeable for probe uptake. These are: fixation, sample immobilization on a solid support, agarose embedding, permeabilization and endogenous peroxidase inactivation. Fixation preserves the cellular structure and morphology. A common fixative for FISH is formaldehyde (or its variations – formalin and paraformaldehyde), in a final concentration of 1–4%. Formaldehyde is a cross-linking agent, as opposed to ethanol, an alternative fixative, which fixes the cells by dehydration and precipitation of

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A

B

C

Figure 15.1 rRNA-FISH with fluorochrome-labelled probes versus CARD-FISH. Panel A shows the main steps of the rRNA-FISH protocol; steps underlined in blue background are common to both procedures, while steps in white background are specific for CARD-FISH. Panel B schematically shows the rRNA detection step in the rRNA-FISH with fluorochrome-labelled probes: probes bind to intracellular ribosomes (in blue) and confer a fluorescent signal to the hybridized cell. Panel C schematically shows the rRNA detection steps in the CARD-FISH protocol. First, HRP-labelled probes bind to intracellular ribosomes during hybridization. Second, in the CARD step the HRP breaks down H2O2 to HO•, which in turn activates the fluorescently labelled tyramides by a radical reaction at their phenol ring; the activated tyramides then bind to protein residues with electron rich moieties, through a further radical reaction, delivering an amplified fluorescent signal to the hybridized cell.

the cellular components. In particular, ethanol is used for fixation of difficult to permeabilize Grampositive bacteria (Roller et al., 1994). Fixation also permeabilizes the cell membrane for probe penetration. FISH on unfixed cells has recently been reported (Yilmaz et al., 2010) but a systematic study on the extent of probe penetration or cell structure preservation in unfixed samples is lacking, therefore this protocol (FISH on unfixed cells) should not be relied upon for quantitative FISH studies. To prevent cell loss and to facilitate sample manipulation, the cells are immobilized either on membrane filters (e.g. 0.2  µm polycarbonate filters, usually for cells in liquid suspension

– cultures, water samples) or on glass slides. Uncoated glass slides can be used for samples with high cell density. However, for many types of samples (tissues, biofilms, activated sludge, etc.) coating is required to prevent cell loss (e.g. with poly-lysine, gelatine, agarose) and/or, to preserve the three dimensional structure of the sample (e.g. with polyacrylamide; Daims et al., 2006). For water samples, placement on a membrane filter ensures a more homogeneous cell distribution and enables quantification of absolute and relative cell numbers. Agarose embedding of filters (in 0.1–0.2% low gelling point agarose) is required to avoid cellular loss when extensive permeabilization is used, e.g. in CARD-FISH.

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Cell permeabilization is necessary to facilitate penetration of high molecular weight HRP-labelled probes during CARD-FISH or, in the case of thick cell walled Gram-positives, even of fluorochrome labelled probes for monolabeled FISH. Different permeabilizing agents are used depending on the composition of the cell walls, e.g. lysozyme for Gram-negative bacteria, achromopeptidase for Gram-positive bacteria, and proteinases, acids (e.g. HCl) or detergents (e.g. SDS) for archaea. The endogenous peroxidase inactivation step is mandatory when working with HRP-labelled probes, because intracellular enzymes with similar activity to HRP could otherwise cause false positive signals. For this step, HCl, H2O2 or methanol can be used. Detailed protocols covering different aspects of sample preparation can be found elsewhere (e.g. Manz et al., 1992; Pernthaler et al., 2002, 2004; Pernthaler and Amann, 2004; Pavlekovic et al., 2009). rRNA detection Hybridization During hybridization, oligonucleotide probes bind to specific target regions of the rRNA. The specificity of binding (that is, the ability of the probe to hybridize only to perfectly homologous regions of the rRNA molecule) is controlled by the hybridization conditions. The main components of the hybridization buffer are (i) NaCl, which stabilizes nucleic acid duplexes and promotes hybridization, (ii) formamide, which lowers the melting temperature, and thus allows hybridization at lower temperatures and avoidance of cell degradation, (iii) Tris-HCl, a pH buffer, and (iv) SDS, a detergent used to permeabilize cells and remove proteins, thereby facilitating probe access to rRNA. Additionally, especially in CARD-FISH, dextran sulphate is added to increase hybridization kinetics, while nucleic acid blocking reagent is used to prevent unspecific binding of probes to components other than nucleic acids. Most often hybridization proceeds at 46°C, for 3  h. Other temperatures can be used (e.g. 35°C, when the HRP used for probe labelling is sensitive to higher temperatures), as long as the combination

of buffer composition and hybridization temperature allows specific hybridization (e.g. as a rule of thumb, when the hybridization temperature is decreased from 46°C to 35°C, the formamide concentration in the hybridization buffer is increased by 20%; for more details, see section describing the determination of hybridization conditions). Cells with low ribosome content or probes targeting low accessibility rRNA regions (details below) may require longer hybridization times; on the other hand, longer incubation times can promote unspecific probe binding, high background, loss of cells and deterioration of cell morphology. After hybridization, a washing step is performed to remove unbound probe. The NON338 probe (Wallner et al., 1993) is used in a separate hybridization reaction as a negative control. This probe is the reverse complement of the EUB338 probe and, therefore, it has the same sequence and direction as the bacterial 16S rRNA (positions 338–355, Escherichia coli numbering) and will not bind to it. Although it has been designed based on bacterial sequences, it has no match in the archaeal domain either. Catalysed reporter deposition (CARD) When probes are HRP-labelled, the additional CARD step (for signal amplification) is necessary to enable visualization of the hybridized cells. An amplification buffer containing the substrates for CARD, i.e. H2O2 and fluorescently labelled tyramides, is used. The probe-bound HRPs will transform the H2O2 into HO· radicals, which in turn activate the tyramides to covalently bind to electron rich moieties present in cellular proteins (Speel et al., 1999). To prevent diffusion of activated tyramides outside the cells, dextran sulphate is added to the amplification buffer. Fluorescently labelled tyramides can be prepared from succinimidyl esters of the respective fluorochromes and tyramine, as described in Pernthaler et al. (2004). In the literature, the CARD reaction is also referenced as tyramide signal amplification (TSA). Sample evaluation Counterstaining and mounting At the end of the FISH protocol, when comparison of the hybridized cells with all cells is desired

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(for example, to enable calculation of the percentage of FISH identified cells), a counterstaining of the cells present in the sample is performed. This is often done using 4′,6-diamidino-2-phenylindole (DAPI), however other nucleic acid binding dyes can be used, e.g. acridine orange or Sybr Green, using appropriate fluorescence filter sets for microscopy for the respective dyes. In preparation for microscopy, the sample is embedded in mounting agents (e.g. Citifluor, VectaShield, ProLong Gold, SlowFade Gold) to protect the fluorochromes against rapid degradation and fading, or to ensure an appropriate pH for pH sensitive fluorochromes (e.g. fluorescein requires a pH higher than 9.0). Microscopy Evaluation of hybridized samples is performed most often by microscopy (epifluorescence or confocal laser scanning microscopy), although flow cytometry can also be used, especially when fluorescence enabled cell sorting is desired (Czechowska et al., 2008; Manti et al., 2011; Sekar et al., 2004). The purpose of microscopy is (i) to quantify the fraction of hybridized cells in relation to all cells detected by the general counterstain, and (ii) to localize the hybridized cells in relation to other components of the sample (e.g. in tissues, biofilms, activated sludge, marine aggregates, etc.). For a brief selection of examples please refer to the “Technology applicability” section below. In the case of quantification, two metrics can be obtained: (i) the relative abundance, that is the fraction of FISH positive cells expressed as percentage of all counterstained cells from the hybridized sample; and (ii) the absolute abundance, which represents the number of FISH-positive cells per unit (usually millilitres or grams) of sample. The absolute abundance is calculated from the relative abundance and total cell numbers. Total cell numbers can be acquired by flow cytometry or microscopy counts of DAPI, acridine orange, Sybr Green or Sybr Gold stained cells (Kepner and Pratt, 1994; Marie et al., 1997; Shibata et al., 2006). Owing to potential cell loss during the FISH procedure, it is best to count total cell numbers on unhybridized samples. Most often, microscopic counts are performed manually. However, automated counting

systems which greatly increase the throughput of FISH analyses have been developed (Zeder, 2009), consisting of hardware and software modules for image acquisition and image analysis (e.g. Automated Cell Measuring and Enumeration Tool – ACMe-Tool2, http://www.technobiology. ch/index.php?id=acmetool). When the sample consists of cell clusters (e.g. in activated sludge flocs, in marine snow aggregates, in biofilms or in anaerobic methane oxidizing consortia), quantification of cells can be challenging. Alternatively, a biovolume-based quantification can be performed, enabled by image acquisition by confocal laser microscopy and image analysis by software such as DAIME (http://131.130.66.201/daime/, (Daims et al., 2006)). Spatial localization and 3D visualization of hybridized samples is assisted by image analysis software associated to the microscopes used for image acquisition (e.g. Zen software, Carl Zeiss, Germany) or developed independently, e.g. DAIME, Imaris (http:// www.bitplane.com/go/products/imaris) and AutoQuant (http://www.mediacy.com/index. aspx?page=AutoQuant). Probe synthesis and labels rRNA probes are synthetic DNA oligomers (18–25  nt) that are mono-labelled (i.e. monoprobes) with any of the commercially available fluorochromes (e.g. Cy3, Cy5, Alexa dyes, Atto dyes) or with HRP, at the 5′ end. Double labelled probes (dopeprobes), with the fluorochrome both at the 5′ and 3′ ends, and tetra-labelled probes (tetraprobes) have recently become available (www.biomers.net). Helper and competitor probes are unlabelled oligonucleotides, similar to primers used in polymerase chain reaction (see below). Probes, probe design and determination of hybridization conditions rRNA FISH probes are designed to target phylogenetic groups of different ranks, from general Domain probes (e.g. the EUB338 – (Amann et al., 1990a), targeting most of Bacteria) down to genus- or even species-specific probes (e.g. DSC193, targeting Desulfosarcina variabilis – (Ravenschlag et al., 2000)). A large selection of

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probes for Archaea and Bacteria are now available and documented in the scientific literature or through online probe databases (ProbeBase, http://www.microbial-ecology.net/probebase/; Loy et al., 2007). Owing to the constantly and rapidly increasing number of 16S rRNA sequences deposited into publicly available databases, it is imperative to periodically check the specificity of existing probes by using probe match tools such as TestProbe (http://www.arb-silva.de/search/ testprobe/, which checks the probes against the SILVA rRNA databases) or probeCheck (http://131.130.66.200/cgi-bin/probecheck/ content.pl?id=home; Loy et al., 2008), which checks the probe sequence against several databases, e.g. Ribosomal Database Project II, GreenGenes and SILVA). The main questions to be answered during a probe check are (i) is the probe covering all sequences within the target group, or what fraction of the target group is covered, and (ii) how many sequences outside of the target group also match the probe sequence, i.e. how specifically does the probe target the group of interest? The above-mentioned websites provide convenient tools for testing existing and new probes. Further details on probe coverage and specificity, and on how these have evolved for group-specific probes since their design, are discussed in more detail elsewhere (Amann and Fuchs, 2008). The main requirements for designing new probes are (i) a comprehensive rRNA sequence database (e.g. SILVA, http://www.arb-silva.de/; Pruesse et al., 2007), to which the new sequences of interest can be added, and (ii) probe design software, (e.g. the Probe_Design tool from the ARB software package). Such software will search the database and return all oligonucleotides specific for the phylogenetic groups of interest, as well as a list of mismatched targets. Ideally, the probe is a perfect match only to the sequences of interest, and it has at least one (preferably more) mismatch in a central position to non-target sequences. To increase probe specificity against single-mismatch targets, competitor probes may be required; these are unlabelled oligonucleotides perfectly matching the non-target sequences (Pernthaler et al., 2001). A good example of such probe-competitor pairs are the GAM42a (5′-GCCTTCCCACATCGTTT-3′)

and BET42a (5′-GCCTTCCCACTTCGTTT-3′) probes, which differ by one central base and are used as competitor probes for one another, to discriminate between Gammaproteobacteria and Betaproteobacteria (Manz et al., 1992). Usually, probes are named with an abbreviation derived from the name of the targeted group, followed by numbers indicating the position of the probe along the rRNA gene sequence based on that of E. coli and commonly referred to as ‘E. coli numbering’ (Lane et al., 1985). Systematic studies of the probe binding efficiencies to different regions of the rRNA molecules have shown that, due to the tertiary structure of ribosomes, target sites can be differentially accessible for oligonucleotide probes (Behrens et al., 2003; Fuchs et al., 1998, 2001); this has led to classification of rRNA regions into brightness classes and the construction of accessibility maps (integrated into the Probe_Design tool from ARB). Further studies have shown that prolonged hybridization times (30–150 h, instead of 3 h) and/or design of probe–target pairs with high thermodynamic affinity, will render most regions and binding sites accessible (Okten et al., 2012; Yilmaz and Noguera, 2004; Yilmaz et al., 2006). Alternatively, especially to avoid prolonged hybridization times that damage the cells and increase background, helper probes (unlabelled oligonucleotides that bind to sites adjacent to the probe and increase its accessibility (Fuchs et al., 2000) or locked nucleic acid (LNA) probes (Kubota et al., 2006) can be used. All newly developed probes (including helpers and competitors, used in the same hybridization mixture as the probe) must be evaluated empirically in the laboratory in order to establish specific hybridization conditions. Usually, culture pairs, one having a perfectly matched and the other a mismatched rRNA sequence, are used to establish formamide dissociation curves. Often it is needed to design probes targeting uncultured microorganisms that are known only by existence of specific gene sequence information obtained through environmental genomics; in such instances probe evaluation must be performed on a pair of clones, one expressing the target and the other the mismatched rRNA – CloneFISH (Schramm et al., 2002). Dissociation curves (also known as

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melting curves) are performed by hybridizing at increasing concentrations of formamide and at fixed temperature (e.g. 46°C or 35°C), while the change in fluorescence signal is monitored (e.g. by acquiring photomicrographs and measuring the signal intensity with dedicated image analysis software, e.g. DAIME) to determine the formamide concentration at which the matched and mismatched organisms can be discriminated (the fluorescence signal of the matched organism is acceptable, while that of the mismatched organisms is negligible, see Pernthaler et al., 2001 for further details). Alternatively, when performing dissociation curves, the formamide concentration can be kept constant, and the temperature varied. Probe design and evaluation and determination of the stringency parameters can be assisted by MathFISH (http://mathfish.cee.wisc.edu/ index.html) (Yilmaz et al., 2011) – an online tool for evaluation of rRNA probes that integrates thermodynamic models for hybridization to predict, for given probe-target pairs, the following parameters: free energy, hybridization efficiency, dissociation curves, stability of mismatched pairs and competitor suitability. Pros and cons of rRNA FISH The power of rRNA FISH is its ability to identify morphologically undistinguishable microorganisms, in a cultivation-independent approach at the single-cell level in complex environmental samples. This allows not only the quantification of these microorganisms, but also the determination of their in situ localization in samples with complex spatial organization, e.g. activated sludge, marine snow, biofilms, plant and animal tissues, soil, and more. Rapid advances in sequencing technologies have led to a growing number of rRNA sequences and to the discovery of new, uncultured phylogenetic lineages. In the absence of cultivated isolates, the final (and sometimes the only) proof that these phylogenetic groups are real microorganisms and not just sequencing errors or ‘ghosts’ is most often offered by in situ cell visualization after hybridization with specific rRNA probes. Moreover, rRNA FISH can be coupled with measurements of cellular activity and assignment of metabolic functions

(further details are provided in the ‘Technology applicability’ section). In samples where single cell identification and quantification is possible (e.g. water column samples), FISH is usually more precise than other molecular methods, e.g. quantitative PCR or metagenomics, which estimate gene abundance (and certain genes may be present in multiple copies per cell, depending on genome content and/or on the replication state of the chromosomes), or slot-blot hybridizations that measure the amount of rRNA per unit of sample (again, cell numbers cannot be directly inferred since cellular rRNA content varies in different organisms and with a cell’s physiological state). Furthermore, recent improvements to rRNA FISH counting protocols enable detection and quantification of rare microbial populations, with relative abundances as low as 0.5% (or absolute abundances of only 100 cells per ml) (Gomez-Pereira et al., 2010). This detection limit is approaching that of qPCR (10–100 cells/ml) (Kurupati et al., 2004), although not yet that of the more sensitive rRNAtargeted reverse-transcription PCR (1 cell/ml) (Matsuda et al., 2007). Each of the two methodological variations described here – FISH with fluorochrome labelled probes and CARD-FISH – has advantages and disadvantages. Direct labelling of probes with fluorochromes requires a shorter protocol, with no need for a separate permeabilization step (cells are permeabilized during the ethanol dehydration series and by the SDS used in the hybridization buffer), except for some thick walled Gram-positives. Thus, it allows better probe penetration and preservation of cell morphology. CARD-FISH on the other hand is a longer protocol with increased requirements for optimization and sometimes extensive permeabilization potentially resulting in cell loss. CARD-FISH also uses more expensive reagents. Direct fluorochrome labelling gives lower signal intensities, whereas CARD-FISH increases the signal intensity by as much as 20–40 times due to the CARD step. In terms of rRNA numbers per cell, the detection limit, which also depends on the background fluorescence of the sample, has been shown to be (i) for FISH with monolabeled probes ~370 molecules per E. coli cell, when a pure culture was hybridized on

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glass slides (a sample with low background fluorescence), and 1400 molecules per E. coli cell in activated sludge (a sample with high background fluorescence); and (ii) for CARD-FISH ~8.9 to 14 molecules per E. coli cell in pure culture and ~36 to 54 molecules per E. coli cell in activated sludge (Hoshino et al., 2008). As a result, FISH with monolabeled probes is used mainly in nutrient rich samples (e.g. activated sludge, medical biofilms, enrichment cultures, etc.) containing cells with many ribosomes, while CARD-FISH has additional advantages in nutrient-poor or high fluorescence background environments (e.g. open ocean waters, sediments and soils). Recently developed dopeprobes and tetraprobes result in a 2- or 4-fold increase in sensitivity, extending the applicability of FISH with fluorochrome labelled probes. One of the main disadvantages of rRNA FISH is its relatively low throughput in terms of number of samples analysed, mostly due to the manual microscopic evaluation. However, advances in automated microscopic counting (Zeder, 2009) as well as protocol improvements which enable signal detection by flow cytometry (Sekar et al., 2004) have the potential to remove this bottleneck and to transform rRNA FISH into a higher throughput strategy for quantitative microbial ecology. Another disadvantage is that the detection efficiency depends on cellular ribosome content and therefore decreases, e.g. in environments with lower nutrient levels (e.g. open ocean gyres). Also, quantification of cell numbers is not always possible, especially in samples where cells form dense aggregates. However, in these cases a quantification of the target population based on biovolume can be performed, as detailed in the ‘Microscopy’ section. Furthermore, application of super-resolution microscopy in combination with FISH is promising for discrimination of single cells in such aggregates (Moraru and Amann, 2012). Technology applicability Applications of rRNA FISH are numerous. Like no other technique, 16S rRNA FISH allows simultaneous localization, identification and quantification of microbes. More specifically,

sample analysis across time or space, i.e. transects, depth profiles, or along environmental gradients of, e.g. light (Schattenhofer et al., 2009), oxygen (Allers et al., 2013a) and phytoplankton (Teeling et al., 2012) can provide a thorough description of microbial community dynamics in distinct environmental provinces. For example, rRNA FISH showed that in the nutrient-rich waters of the Benguela upwelling system the most abundant organisms are Bacteroidetes and Gammaproteobacteria in the photic zone and Crenarchaeota in the mesopelagic zone, while in the nutrient-poor Atlantic Gyres SAR11 dominate the photic zone and SAR202 the mesopelagic zone (Schattenhofer et al., 2009). Such comprehensive data on microbes combined with habitat descriptions enable some of these taxa to serve as biomarker proxies for certain environmental conditions and environmental changes, e.g. a bacterial succession following the phytoplankton succession of a spring bloom (Teeling et al., 2012). The same concept has been applied to industrial research questions, e.g. the description of the microbial community composition in ship ballast water (which additionally contributes to microbial biogeography on a global scale; Joachimsthal et al., 2004). rRNA FISH is also used in medical diagnostics, especially for studying the microbial communities of the gastro-intestinal tract, and has shown that the microflora of formula-fed newborns differs from that of breast-fed newborns (Bezirtzoglou et al., 2011), and that dynamics of bifidobacteria changed with raffinose addition to adult diet (Dinoto et al., 2006). Although methodologically more challenging, rRNA FISH has been applied also in environmental samples with high background fluorescence, such as sediments (Ishii et al., 2004) and soils (Kobabe et al., 2004), including hydrocarbon-contaminated aquifer samples (Tischer et al., 2012), and even in the highly iron-enriched acidic waters of Rio Tinto (Gonzalez-Toril et al., 2003). Furthermore, rRNA FISH allows highly resolved micro-spatial localization of organisms in complex environments, such as aggregates of tightly connected cells or tissues. For example, FISH enabled the visualization of the key players in anaerobic methane oxidation in microbial aggregates retrieved from marine sediments,

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which led to the description of several distinct types of methane oxidizing consortia (Knittel and Boetius, 2009). Similarly, FISH helped to localize anammox bacteria in marine snow particles in close proximity to O2 respiring heterotrophs, explaining their presence in waters where O2 concentration would otherwise be inhibitory (Woebken et al., 2007). Another example is the in situ taxonomic identification and localization of endosymbionts in gutless oligochaete worms by FISH whereas transmission electron microscopy could only demonstrate the endosymbionts’ existence (Ruehland et al., 2008). Further advances into resolving the structural organization of complex microbial communities have been recently made through CLASI-FISH (Valm et al., 2011, 2012) or through DOPE-FISH (Behnam et al., 2012), both rRNA-FISH methods which can simultaneously label and identify up to 28 and 6 different microbial populations, respectively. Eukaryotic microorganisms have also been evaluated by rRNA-FISH although to a lesser extent than prokaryotes. For example, rRNA FISH with fluorochrome labelled probes was used to analyse the distribution of heterotrophic flagellates in ocean waters (Massana et al., 2002; 2006). Not et al. (2002) and Biegala et al. (2003) developed TSA-FISH methods for quantifying eukaryotic picoplankton in combination with microscopy and flow cytometry, respectively. Furthermore, to link cell identity with morphology for eukaryotic microorganisms, Hirst et al. (2011) combined rRNA-FISH with staining of the cytoskeleton (microtubules, actin filaments) and organelles (mitochondria and hydrogenosomes). Whether alone or in combination with other techniques, rRNA FISH can yield insights into the function and metabolism of microbial cells. With fluorochrome-labelled probes, rRNA FISH signals not only enable quantification of cell numbers, but also estimation of the ribosome content of individual cells. To a certain extent, the ribosomal content reflects cellular metabolic activity. Early studies on E. coli showed that ribosome numbers per cell decrease with the growth rate (Maaloe and Kjeldgaard, 1966). Similarly, the shift from exponential growth to stationary phase or starvation conditions is accompanied by a decrease in ribosome numbers (Deutscher,

2003; Givskov et al., 1994; Kramer and Singleton, 1992). Ribosome content is not always an indication of metabolic activity, as the same number of ribosomes per cell can signify different physiological states for different species or even for the same species. For example, when cells go into starvation after experiencing high growth rates, they can have similar or even higher ribosome numbers than cells in exponential phase, but with low growth rates (Flärdh et al., 1992; Oda et al., 2000). Careful studies tracking changes in the ribosomal content in relation with environmental changes can provide valuable information about the physiological and metabolic status of microbes, and thus, improve our knowledge of microbial communities. Several studies calculate in situ growth rates using rRNA FISH signal intensities (Boye et al., 1995; DeLong et al., 1989; Leser et al., 1995; Møller et al., 1996; Poulsen et al., 1993). Recent advances that combine super-resolution microscopy and FISH allow subcellular localization of ribosomes in microorganisms and have the potential to achieve absolute quantification of ribosome numbers (Moraru and Amann, 2012). The precursor rRNA can reflect with higher accuracy changes in metabolic activity, because it has a short half life and is present only when new rRNA synthesis takes place (Cangelosi and Brabant, 1997). On the other hand, the mature rRNA can be maintained in cells long after cessation of metabolic activity, to enable revival when new nutrients are available (Segev et al., 2012). The precursor rRNA can be detected by using probes against the internal transcribed spacer region (ITS) of the rRNA operon and ITS FISH was used to assess the activity of microbial cells in activated sludge and in anaerobic ammoniumoxidizing bacteria in enrichments and biofilms (Oerther et al., 2000; Schmid et al., 2001). An added advantage of the ITS is its higher phylogenetic resolution, enabling discrimination at species and subspecies level (García-Martínez et al., 1999). When combined with gene or mRNA detection, i.e. in geneFISH (Moraru et al., 2010) or mRNA-FISH (Pernthaler and Amann, 2004), respectively, rRNA FISH can identify which microorganisms carry and express certain genes, even in complex environmental samples. The

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recently developed phageFISH (Allers et al., 2013b) allows simultaneous identification of intracellular bacteriophages and of their hosts, while discriminating between cells in early and late stages of infection. Furthermore, in samples incubated with radioactive or stable isotopes, it is possible not only to identify the microorganisms incorporating certain carbon sources (e.g. MARFISH (Alonso, 2012); Raman-FISH (Huang et al., 2007) but also to quantify substrate incorporation rates by specific microorganisms (FISH-SIMS (Orphan et al., 2001)); HISH-SIMS (Musat et al., 2008) or EL-FISH (Behrens et al., 2008). In conclusion, rRNA FISH is a powerful technique for identification, quantification and localization of microorganisms at the single-cell level in a wide variety of samples. Moreover, especially in combination with other in situ techniques, it can provide information about the metabolism of taxonomically identified microbial cells, in complex environments. References

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Quantitative Real-time Polymerase Chain Reaction (qPCR) Methods for Abundance and Activity Measures

16

Vibeke B. Rudkjøbing, Tine Y. Wolff and Torben Lund Skovhus

Abstract Quantitative real-time polymerase chain reaction (qPCR) is a method by which DNA and RNA target molecules can be quantified. The method has a wide range of applications, and its use has become increasingly popular in different scientific and commercial fields. The method is based on amplification of target molecules in the presence of fluorescent dyes that bind to DNA amplicons enabling detection. Despite this relatively simple principle there are many options for customizing and optimizing the reaction, both in terms of overall experimental setup and choice of dye and DNA/RNA target. Besides an introduction to the fundamentals of qPCR, this chapter contains a description of theory and strategy, and demonstrates how to use qPCR to determine microbial abundance and activity. Abundance measurements are generally performed by targeting DNA and typically include use of standard dilution series in order to obtain quantitative measurements. Conversely, the activity of microorganisms is determined by gene expression profiling, and the target is typically cDNA synthesized from RNA molecules. Such experiments generally do not provide absolute measurements, but instead are performed as a relative quantification, where the measurements of the target gene are related to a reference gene. The qPCR method has many advantages; chiefly it is a culture-independent method that offers great simplicity and flexibility along with a rapid turnaround time of a few hours. Additionally, the method has relatively low instrumentation demands. However, there are

some critical limitations and considerations to the method, which are described in the chapter. Introduction Quantification of microorganisms is not only of great value to scientific researchers, it is often mandated by federal organizations such as the U.S. Food and Drug Administration (FDA) for the protection of public health. Traditionally, serial dilution methods and plate counts have been used in food safety and clinical environments (Nolte and Caliendo, 2011; U.S. FDA, 1998). However, since the exact number of microorganisms initially added to the growth medium and their growth rates are usually not well defined, these culturebased methods have limited quantitative abilities. An alternative approach is to use culture-independent methods such as quantitative real-time polymerase chain reaction (qPCR) where quantification is based on the abundance of target DNA or RNA sequences. This is a sensitive and fast method, which also has the benefit of conceptual and practical simplicity (Bustin et al., 2009). The technique is based on the polymerase chain reaction (PCR), which allows for rapid in vitro amplification of DNA. PCR utilizes the DNA polymerase enzyme, which is responsible for the replication of DNA in all living cells. This ability is exploited in PCR, where short pieces of DNA (primers) are designed to attach to a specific sequence in the target DNA strand, and thereby direct the DNA synthesis carried out by the polymerase (Fig. 16.1). In this way, the primers dictate the overall sensitivity and specificity of PCR, and the design, choice and testing of primers is therefore of the utmost importance for successful

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Taq

Figure 16.1 The principle of PCR. The DNA polymerase (indicated as ‘Taq’) synthesizes strands of DNA complementary to the target strand. In order to initiate the synthesis, short pieces of DNA (primers) are required and these direct the DNA polymerase to copy the appropriate DNA target. For each PCR cycle the number of copies of the target strand is doubled, giving an exponential increase in the number of copies of the DNA target.

application of PCR (Whitby and Skovhus, 2011). The amplification of DNA is controlled by thermal cycling, where each cycle typically consists of three steps. The first is a heating step (approximately 95°C), where the original DNA strands are separated (denaturing). In the second step, the temperature is lowered (typically to 55–60°C) to encourage binding of primers (annealing). During the third step, the temperature is increased to the optimum temperature for the DNA polymerase (typically 72°C), to ensure high activity during DNA synthesis (elongation) (Saiki et al., 1988). Because of the high temperatures involved in denaturing, the DNA polymerase must be thermo­stable. The most frequently used polymerase for PCR is obtained from the thermophilic bacterium Thermus aquaticus and is referred to as Taq polymerase. In traditional PCR it is necessary to perform post-amplification analyses to obtain information about the pool of amplified DNA fragments. Gel electrophoresis is an example of such analyses where DNA fragments are separated according to size and visualized by staining with a DNAbinding dye (for instance SYBR Gold or ethidium bromide). These post-amplification analyses prolong the turnaround time and increase the risk of introducing errors during sample handling. The amount of product generated at the end of the PCR reaction not only depends on the initial amount of target sequence, but is also highly influenced by the overall amplification efficiency, which cannot be evaluated by traditional PCR. Here only the end product can be quantified and it is not possible to tell whether a low concentration

is obtained due to low initial concentration or inefficient amplification during PCR. For that reason, traditional PCR is not reliable as a real time method. The advantage of using qPCR is that the product accumulation is monitored at the end of each cycle (real-time analysis) and the efficiency with which the DNA is replicated can be evaluated. Thereby, qPCR offers the advantage that product formation and analysis are performed in the same tube, without further handling of the sample and the results can be used to quantify the original number of target sequences in the sample. Fluorescence measurements – the heart of qPCR The principle of qPCR is simple; the more target present at the beginning, the faster a large number of copies will be produced and detected. PCR product formation is monitored by a detector integrated in the qPCR machine, measuring fluorescence. As the PCR product accumulates with each cycle, the fluorescence intensity increases exponentially. Initially, the increase is not detectable since a certain fluorescence level has to be reached before it becomes measurable (lag phase). This is followed by an exponential increase in detected fluorescence (exponential phase). At some point, one or more of the reagents of the reaction will be exhausted and the formation of PCR product will stagnate (plateau phase) (Bustin, 2004). These conditions give rise to the characteristic S-shaped graphs illustrated in Fig. 16.2. In qPCR, the initial amount of target is

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Figure 16.2 In qPCR the fluorescence intensity increases exponentially with each cycle for samples containing the target DNA. On basis of the threshold line the threshold cycle (Ct) is determined, and the lower the Ct value the higher the initial amount of target DNA.

determined by establishing the threshold cycle (Ct), which is the number of cycles required for a reaction to reach a certain level of fluorescence (Fig. 16.2). In this way, a low Ct value indicates that large amounts of target were initially present in the sample. The threshold is set to be above the background fluorescence of the experiment, and can therefore vary between runs (Bustin, 2004; Skovhus et al., 2004; Smith and Osborn, 2009). There are two main types of fluorescence chemistries available: (1) non-specific chemistry (usually represented by intercalating dyes like SYBR Green) and (2) specific chemistry (a typical technique is hydrolysis or TaqMan® probes). In SYBR Green-based qPCR, a dye is added to the reaction mix that only emits light after interaction with double stranded DNA. A disadvantage to SYBR Green-based qPCR is that the dye binds to all double-stranded DNA, including primer-dimers, unspecific DNA fragments or any other secondary structures formed during PCR (Smith and Osborn, 2009). This increases the risk of obtaining a fluorescence signal from samples that do not contain the target sequence (a false positive signal). However, the application of a fluorescent dye such as SYBR Green makes it possible to evaluate the specificity of the PCR assay by determining the melting temperature

profile of the generated PCR products. That is achieved by gradually heating the PCR product to 95°C to obtain strand separation, whereby the dye detaches and the fluorescence ceases. The temperature at which the fluorescence drops represents the melting temperature of the PCR products. An unexpected value or several distinct fluorescence peaks are indicative of amplification of unspecific DNA fragments (Bustin, 2004). With probe-based qPCR it is possible to increase the specificity of the assay by including an additional small piece of DNA complementary to the target sequence (called a probe). Different types of probes exist but the so-called hydrolysis probe is the most widely applied. A hydrolysis probe typically consists of 25–30 bases designed to target an area of the sequence between the two primers. The probe has a fluorophore attached to the 5′-end and a quencher in the 3′-end – as long as the probe is intact, the fluorescence is quenched and no light is registered by the detector. In this type of qPCR, the endonuclease activity of the polymerase is utilized, which is the ability of the polymerase to degrade obstacles in its path (Fig. 16.3). As the polymerase amplifies the target molecule starting from the primer, it will encounter and degrade the probe resulting in the fluorophore and the quencher being separated.

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a) A

b) B

Taq

F

Taq

Q

F Taq

Q

Figure 16.3 Two of the most widely applied fluorescence chemistries in qPCR are (A) SYBR Green and (B) hydrolysis probes. In SYBR Green based qPCR, the DNA polymerase (illustrated as the yellow ‘Taq’) synthesizes a DNA strand complementary to the target sequence, and SYBR Green (illustrated as green rods) fluoresces upon binding to the newly created double strand. In hydrolysis probe based qPCR, the endonuclease activity of the Taq polymerase is utilized to degrade the probe (illustrated with orange bases), after which the fluorophore and the quencher of the probe (illustrated as ‘F’ and ‘Q’, respectively) are separated allowing the fluorophore to emit light.

The light emitted by the fluorophore is no longer quenched and the fluorescence intensity increases exponentially with each PCR cycle (Bustin, 2004). An advantage of using hydrolysis probes compared to SYBR Green chemistry is the possibility of performing multiplex analysis, where several sequences (e.g. different microorganisms) are detected within the same reaction tube. This can be achieved by applying probes with different fluorophores. Abundance measurements Determination of microbial abundance by qPCR can be valuable in many settings and typically begins with extraction of DNA from a sample (Fig. 16.4a). The extracted DNA is added to the qPCR reaction mixture containing primers, fluorescence reporters (dye or probe), nucleotides, DNA polymerase and suitable buffers and salts. If qPCR is performed directly from here, the result would only be a Ct value that would need to be related to other measurements performed in the same run in order to be biologically informative, since Ct values vary for each experiment

depending on the threshold setting. The initial concentration of target sequences in each sample can be determined by analysing serial dilutions of standards with known concentrations of the target DNA. The standard dilutions are included in the experimental run, and the Ct values of each template concentration are determined. Based on these concentrations a standard curve can be made, from which the Ct values of the samples can be translated into the concentration of the DNA target. If the number of copies of the target gene in an organism’s genome is known, the DNA concentration can be used to estimate the number of those cells in the sample (Bustin, 2004; Smith and Osborn, 2009). Activity measurements The activity of microorganisms can be determined according to gene expression profiling, which is measured in much the same way as abundance measurements. However, the methods differ in two important ways: (1) instead of using genomic DNA, the template for activity measurements is complementary DNA (cDNA) which has been

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A a)

B b) Step 1: RNA from all cells is extracted.

Step 1: DNA from all cells is extracted. RNA for gene of interest DNA from target organism Step 2: Target specific qPCR is performed. of DNA sequence q Amplification p from the target organism only. A fluorescent signal is generated. The qPCR output is proportional to the number of target organisms in the sample.

RNA for reference gene

Step 2: Synthesis of cDNA (reverse transcription).

cDNA for reference gene cDNA for target gene

Step 3: qPCR is performed targeting the reference and target genes.

Step 3: qPCR signal is analyzed. qPCR signal Number of the target organisms in the sample

Two qPCR assays are used to amplify the cDNA coding for the reference and target gene.

The assayy that g gives increase in signal fastest has the largest initial amount of template.

Signal

Step 4: qPCR signal is analyzed, and expression of the genes is compared.

Cycles

Figure 16.4 Overview of the steps involved in performing qPCR. (A) Abundance measurements on extracted DNA from samples, (B) Activity measurement by RT-qPCR, where the extracted RNA is transformed into cDNA by reverse transcription, which is then applied as target DNA in qPCR.

synthesized by reverse transcription (RT) of RNA molecules and (2) the fluorescence measurements are generally not related to standard dilution series, because of difficulties in obtaining a pure standard target where the efficiency of RT is comparable to the samples (Bustin, 2004; Bustin et al., 2009). In order to perform RT-qPCR, the RNA must first be extracted from the sample (Fig. 16.4b). The extraction can be targeted to either total RNA or pure mRNA molecules, and high-quality, DNA-free and un-degraded RNA is required to ensure the success of RT-qPCR. The RNA template is converted into cDNA by the reverse transcriptase enzyme (Bustin, 2004). Similar to the use of DNA polymerase in PCR, the use of reverse transcriptase is an example of molecular biology exploiting a reaction that occurs in nature. Reverse transcriptase is fundamental to the proliferation of retroviruses such as HIV. It is possible to perform RT-qPCR either as a one-step process (cDNA synthesis and qPCR reaction are performed in a single tube) or a two-step process (cDNA synthesis and qPCR occur in separate reaction tubes). Both have different advantages and disadvantages as reviewed elsewhere (e.g. Wong and Medrano, 2005). The qPCR on the cDNA is performed in the

same way as abundance measurements. However, since the data is not related to a standard curve, the Ct values must instead be related to a reference gene or internal control (referred to as relative quantification). Therefore, the results are a ratio of the Ct values for the gene of interest and the reference gene, and several mathematical models have been developed that determine the relative expression ratio of the genes from the investigated microorganisms or environment (Bustin, 2004). Technology applications Over the past decade qPCR and more recently RT-qPCR have gained ground as reliable and robust methods in a wide range of microbiological fields and the technology has proven to be particularly valuable in situations where the traditional culture-based methods fall short. In the medical area, cultivation has been the gold standard for identification and enumeration of pathogenic microorganisms for more than a century, and it continues to be the central technology in this field (Alain and Querellou, 2009; Mancini et al., 2010). In medical microbiology it is not sufficient to simply enumerate the microorganism; physicians need to determine how the pathogen in question can be eliminated to cure

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the patient. This is one reason why cultivation is so important within this field: It can provide the name of a species as well as information about important phenotypic characteristics such as antibiotic susceptibility. However, the ability to identify non-culturable or inactive micro-organisms makes culture-independent methods a valuable supplement to the traditional cultivationbased techniques. Furthermore, the reduced turnaround time that can be obtained is also often relevant in a medical setting. Application of the qPCR technique in medical microbiology includes monitoring of viral loads (for instance HIV), detection of intracellular bacterial species such as Chlamydia trachomatis, and cases where antibiotic treatment is initiated before specimen sampling (Espy et al., 2006; Scott et al., 2009; van der Pol et al., 2012). Another technological application of qPCR is measuring microbial growth in oilfield systems, which annually costs the oil industry a lot of money. The metabolites of sulphate-reducing and methane-producing microorganisms can cause biocorrosion of oilfield installations and in the worst-case result in failures (Whitby and Skovhus, 2011). Continuous monitoring of microbial numbers can help predict biocorrosion problems and direct application of appropriate biocides to prevent growth of certain groups of microorganisms. The majority of microorganisms obtained from the rather extreme conditions characteristic of oil reservoirs are difficult to cultivate using standard laboratory culture methods, and for this reason qPCR for specific groups of microorganisms such as sulphate-reducing prokaryotes and methanogenic archaea is highly relevant. Bioremediation refers to the use of microorganisms for degradation of xenobiotic compounds from polluted soil and water. An example is the ability of certain Dehalococcoides species to degrade the toxic compounds tetrachloroethene and trichloroethene to the nontoxic ethene (Ritalahti et al., 2006). Dehalococcoides species are anaerobic and very difficult to culture under standard laboratory conditions, and instead qPCR specific for Dehalococcoides species is widely applied for documenting the presence of these bacteria in environmental samples to investigate the efficiency of the bioremediation treatment.

The presence of fungal species in indoor environments constitutes a potential health risk for people living or working in a contaminated building. So far, most studies of fungi in these environments have been based on cultivation, but different studies indicate that many relevant fungal species require distinct growth conditions and are consequently overlooked by standard culture methods. Being culture-independent, the qPCR technique can be applied for rapid monitoring and quantification of selected fungal species (Pitkäranta et al., 2008). Technology considerations There are many advantages of using qPCR and RT-qPCR, importantly the culture-independent nature of the methods, the rapid turnaround time, the simplicity and flexibility since qPCR can be targeted to virtually any desired sequence. Despite the many advantages it is important to be aware of the limitations or problems associated with the techniques. Table 16.1 summarizes the advantages and disadvantages of qPCR and RT-qPCR described in this chapter. The qPCR technique quantifies the number of target DNA sequences present in a sample. Based on knowledge of how often this DNA sequence occurs in a microbial genome it is possible to calculate the abundance of the target organism. A point of criticism to qPCR is that it amplifies and detects all target DNA and it is unable to distinguish between living and dead organisms. To obtain knowledge of the living micro-organisms only, one may use selective DNA extraction protocols to minimize the presence of DNA from dead cells, or alternatively RT-qPCR can be performed. Bacterial mRNA has a very short half-life compared to DNA (usually only a few minutes) and therefore it is indicative of metabolically active cells (Hambraeus et al., 2003). However, the short half-life and sensitive nature of mRNA molecules also makes handling more difficult and requires a high degree of skill and practice. For activity measurements there are some considerations that must be taken into account, which further complicates the execution of such experiments. Firstly, a control of all samples consisting

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Table 16.1 Summary of advantages and disadvantages of qPCR Advantages

Disadvantages

• Results can be obtained much faster than with culture-based methods (a few hours).

• Increased price per sample compared to serial dilution methods and plate counts.

• Lack of post-amplification handling reduces the hands-on time compared to normal end-point PCR.

• Compared to normal end-point PCR the length of the target sequence is limited.

• Results are quantitative.

• Measured quantities may vary between laboratories and results are not portable.

• Highly sensitive analysis. • Assays can be designed for virtually any target of interest; both specific species or genes, total numbers of microorganisms and functional groups. • Various types of samples can be processed such as water, soil, tissue and biofilm/slime, and extracted DNA and RNA can be stored for extended periods of time. • Less vulnerable sampling – cells do not need to be alive and the sample can be fixed for long distance shipment. • Relatively low instrumentation demands (qPCR machine and general equipment for molecular work). • Analysis can be extended to measure activity by applying RT-qPCR. (101–102

• Can have low detection limit cells/ml) depending on assay and large linear detection range: 101–1010 cells/ml.

• Experienced personnel are required, since small variations are exaggerated through PCR amplification. • Choice of assay requires assumptions to be made about the microbial community under investigation, and target must have been sequenced. • Optimal DNA extraction method varies depending on type of sample and microorganism, but must remove inhibitors that are highly influential on qPCR outcome, and in some cases the concentration of DNA is insufficient. • Thorough optimization and verification of DNA extraction and PCR conditions are critical steps for good results. • Compared to standard culture methods some investments in equipment are required for setting up qPCR laboratory facilities. • Comparison to reference gene can be problematic.

• Some standardized qPCR assays are available in the market.

of naked RNA where RT has not been performed must be included in the qPCR runs, in order to test for the presence of contaminating genomic DNA (Bustin et al., 2009). Secondly, since the expression of the target gene should be related to the expression of a reference gene, the choice of this gene is of great importance. The reference gene must be constantly expressed by the organism, and additionally the assay targeting the reference gene must have the same efficiency as the assay for the target gene. Otherwise, it is possible that differences in expression will be either masked or exaggerated. Unfortunately, choice of suitable reference genes is especially problematic when studying prokaryotic genes (Smith and Osborn, 2009). As with any molecular method, the key to success or failure of qPCR is heavily influenced by the initial handling of the sample. DNA and RNA extraction will often introduce biases, and therefore the choice of extraction protocol is not straightforward. Issues such as extraction

efficiency and PCR inhibition must be addressed and tested for each new sample type investigated. Detection with the qPCR method is also heavily influenced by the primer and probe design. Not only the target sequence but also the structures of the nucleic acid targets have a substantial impact on the efficiency of amplification and thus the reliability of the obtained measurements. When developing a new qPCR assay, thorough optimization and validation must be undertaken in order to ensure that the assay will detect only the specific target (Bustin, 2004; Skovhus et al., 2004). Many resources are available on the Internet that can aid the design of primers and probes, ranging from checklists to free-ware programs that suggest primer and probe sequences (for instance Primer-BLAST at http://www.ncbi.nlm.nih.gov/ tools/primer-blast/). Likewise, sequence analysis software tools may be used in the design process, and programs such as ARB (http://arb-home. de/) have gained widespread use for this purpose. For SYBR Green-based quantification the

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possibility of determining melting temperature of PCR products can provide information about the specificity of the assay, which is not an option for probe based quantification. A prerequisite for using qPCR is that one must know what to look for, since qPCR will only give information about the presence and quantity of the DNA fragment targeted by the primers and probe. The method may be used to screen multiple samples for presence of certain species, but cannot give information about identity of all the members of a microbiological community, unless multiple assays are used that target all the species present. qPCR results are expressed as copy numbers. However, the user should be aware of the inherent variability of qPCR results, and that a minimal variation in initial template concentration may result in copy numbers that vary greatly when Ct values are translated into copy numbers. Therefore, high technical skills are required to produce reliable and reproducible results (Bustin, 2004). It is important to remember that any variation becomes exaggerated due to the exponential amplification of products, and thus the logarithmic nature of the results. Furthermore, the results from qPCR should always be related to other biological measurements, for instance total cell number, the amount of sample or tissue, total mass of RNA or DNA from the sample or similar. That way, the most biologically meaningful and robust conclusion can be drawn from the qPCR data. A downside to qPCR is the need for relatively expensive equipment and laboratory areas dedicated to sample preparation. Although the technology is a highly relevant tool at research institutions and centralized diagnostic facilities, it is currently not suitable for field work, point-of-care or on-site testing – at least not in its conventional form. Great effort has been invested in developing so-called ‘lab-on-a-chip’ devices in which sample lysis, purification and nucleic acid amplification are carried out in the same system. Devices of this type have great potential within the fields of medical and food microbiology as they can be applied on-site and provide rapid answers for instance in case of epidemic outbreaks or food contamination (Gubala et al., 2012; Verdoy

et al., 2012). Other advances include new highthroughput qPCR technologies that have been developed to enable analysis of more than 1000 reactions in a single run with individual reaction volumes as low as nano litre scale thus making the method more cost efficient and generally more accessible (Spurgeon et al., 2008). References Alain, K., and Querellou, J. (2009). Cultivating the uncultured: limits, advances and future challenges. Extremophiles 13, 583–594. Bustin, S.A. (2004). A-Z of Quantitative PCR (International University Line). Bustin, S.A., Benes, V., Garson, J.A., Hellemans, J., Huggett, J., Kubista, M., Mueller, R., Nolan, T., Pfaffl, M.W., Shipley, G.L., et al. (2009). The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622. Espy, M.J., Uhl, J.R., Sloan, L.M., Buckwalter, S.P., Jones, M.F., Vetter, E.A., Yao, J.D.C., Wengenack, N.L., Rosenblatt, J.E., Cockerill, F.R., et al. (2006). RealTime PCR in Clinical Microbiology: Applications for Routine Laboratory Testing. Clin. Microbiol. Rev. 19, 165–256. Gubala, V., Harris, L.F., Ricco, A.J., Tan, M.X., and Williams, D.E. (2012). Point of care diagnostics: status and future. Anal. Chem. 84, 487–515. Hambraeus, G., Von Wachenfeldt, C., and Hederstedt, L. (2003). Genome-wide survey of mRNA half-lives in Bacillus subtilis identifies extremely stable mRNAs. Mol. Genet. Genomics 269, 706–714. Mancini, N., Carletti, S., Ghidoli, N., Cichero, P., Burioni, R., and Clementi, M. (2010). The era of molecular and other non-culture-based methods in diagnosis of sepsis. Clin. Microbiol. Rev. 23, 235–251. Nolte, F.S., and Caliendo, A.M. (2011). Molecular Microbiology. In Manual of Clinical Microbiology, J. Versalovic, K.C. Carroll, G. Funke, J.H. Jorgensen, M.L. Landry, and D.W. Warnock, eds. (ASM Press), pp. 27–59. Pitkäranta, M., Meklin, T., Hyvärinen, A., Paulin, L., Auvinen, P., Nevalainen, A., and Rintala, H. (2008). Analysis of fungal flora in indoor dust by ribosomal DNA sequence analysis, quantitative PCR, and culture. Appl. Environ. Microbiol. 74, 233–244. Saiki, R.K., Gelfand, D.H., Stoffel, S., Scharf, S.J., Higuchi, R., Horn, G.T., Mullis, K.B., and Erlich, H.A. (1988). Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase. Science 239, 487–491. Scott, L.E., Noble, L.D., Moloi, J., Erasmus, L., Venter, W.D.F., and Stevens, W. (2009). Evaluation of the Abbott m2000 RealTime human immunodeficiency virus type 1 (HIV-1) assay for HIV load monitoring in South Africa compared to the Roche Cobas AmpliPrepCobas Amplicor, Roche Cobas AmpliPrep-Cobas TaqMan HIV-1, and BioMerieux NucliSENS EasyQ HIV-1 assays. J. Clin. Microbiol. 47, 2209–2217.

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Skovhus, T.L., Ramsing, N.B., Holmstrom, C., Kjelleberg, S., and Dahllof, I. (2004). Real-Time Quantitative PCR for Assessment of Abundance of Pseudoalteromonas Species in Marine Samples. Appl. Environ. Microbiol. 70, 2373–2382. Smith, C.J., and Osborn, A.M. (2009). Advantages and limitations of quantitative PCR (Q-PCR)-based approaches in microbial ecology. FEMS Microbiol. Ecol. 67, 6–20. Spurgeon, S.L., Jones, R.C., and Ramakrishnan, R. (2008). High throughput gene expression measurement with real time PCR in a microfluidic dynamic array. PLoS ONE 3, e1662. U.S. FDA (1998). Bacteriological Analytical Manual (U.S. Food and Drug Administration). Van Der Pol, B., Liesenfeld, O., Williams, J.A., Taylor, S.N., Lillis, R.A., Body, B.A., Nye, M., Eisenhut, C., and

Hook, E.W. (2012). Performance of the cobas CT/NG test compared to the Aptima AC2 and Viper CTQ/ GCQ assays for detection of Chlamydia trachomatis and Neisseria gonorrhoeae. J. Clin. Microbiol. 50, 2244–2249. Verdoy, D., Barrenetxea, Z., Berganzo, J., Agirregabiria, M., Ruano-López, J.M., Marimón, J.M., and Olabarría, G. (2012). A novel Real Time micro PCR based Point-ofCare device for Salmonella detection in human clinical samples. Biosensors and bioelectron. 32, 259–265. Whitby, C., and Skovhus, T.L. (2011). Applied Microbiology and Molecular Biology in Oil Field Systems (Springer). Wong, M.L., and Medrano, J.F. (2005). Real-time PCR for mRNA quantitation. BioTechniques 39, 75–85.

Investigation of Microorganisms at the Single-cell Level using Raman Microspectroscopy and Highresolution Secondary Ion Mass Spectrometry

17

Stephanie A. Eichorst and Dagmar Woebken

Abstract The field of microbial ecology has taken an exciting turn with the introduction of two powerful techniques, high-resolution secondary ion mass spectrometry (SIMS) and Raman microspectroscopy. This chapter describes the basic methodology of Raman microspectroscopy and high-resolution SIMS in conjunction with advantages and disadvantages of each method. Both methods have been applied across various scientific disciplines, including but not limited to, medicine, clinical diagnostics, industry, and microbial ecology. Raman microspectroscopy and high-resolution SIMS are of great value in the field of single-cell ecophysiology especially when combined with stable isotope tracer experiments and/or fluorescence in situ hybridization (FISH). These combined techniques have the potential to link the identity of uncultured microorganisms with their in situ activity and function. Introduction Raman microspectroscopy and high-resolution SIMS are techniques that permit the analysis of microbiological samples down to the single-cell level. These powerful techniques have recently helped define the field of single-cell ecophysiology especially when combined with stable isotope tracers and/or identification of the targeted cell using fluorescence in situ hybridization (FISH). However, they are distinct tools that differ in their basic principle and hence offer different

applications. Raman microspectroscopy detects the scattering of light due to interaction with chemical bonds of cell constituents thereby providing compound specific information, which can also be used for bacterial identification. Highresolution SIMS permits highly sensitive analysis of multiple elements or isotopes with sub-micrometre spatial resolution, allowing measurements of microbial activity when used in stable-isotope tracer experiments. In this chapter we present the principle for each technique, discuss their strengths and weaknesses, and document their applicability with particular emphasis on microbial ecology research. Raman microspectroscopy Method basics The principle of Raman spectroscopy is based on the findings of Sir C.V. Raman, who in 1928 observed that the wavelength of light can change when it transverses material (Raman and Krishnan, 1928). When light illuminates a sample, the incident photons can be transmitted, absorbed or scattered. Scattered photons harbouring the same energy as incident photons is referred to as elastic Rayleigh scattering, while scattered photons harbouring different energies than incident photons (only in a small fraction of photons, between 10–6 and 10–8) is described as Stokes or anti-Stokes scattering (Fig. 17.1A). The energy difference between incident and

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A

A

Energy

Virtual states Ei

ER = Ei

Es < Ei

Ei

EAS > Ei

Ei

Vibrational states Rayleigh scattering (elastic)

Stokes scattering

Anti-Stokes scattering

Raman (inelastic)

Raman intensity (a.u.)

B Terriglobus sp. strain TAA43

Flavobacterium johnsoniae

Paenibacillus kobensis

Escherichia coli 300

600

900

1200

1500

1800

Raman shift (cm-1) Figure 17.1 (A) Jablonski energy diagram illustrating the scattering of light photons (modified after Wagner 2009). (B) Raman spectra of four different bacterial species (spectra courtesy of Christoph Boehm).

scattered photons results in a shift in wavelength. Stokes scattering occurs when a molecule does not return to its vibrational ground state upon photon-induced excitation from the vibrational ground state. Anti-Stokes scattering results when a molecule’s vibrational ground state is its first excited vibrational state and upon photon excitement, returns to its original vibrational ground state (Fig. 17.1A). Stokes scattering occurs more frequently than anti-Stokes scattering owing to a higher occupancy of the ground state. Raman microspectroscopy (the combination of Raman spectroscopy and optical microscopy; Puppels et al., 1990) capitalizes on this principle and examines the scattering of light due to interaction with chemical bonds of various cell constituents. This provides compound specific information about the chemical composition of the sample. A monochromatic light source (such

as a laser in confocal Raman spectroscopy) generates scattered photons from the sample and a charge-coupled detector (CCD) camera detects shifts in the wavelength of Raman-scattered light and records them as a Raman spectrum (Fig. 17.1B). This approach enables an investigator to analyse samples at single-cell resolution (