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A metagenomic approach to study flavin mediated public good dynamics in hydrocarbon resource environments Ievdokymenko, Kateryna 2015

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A METAGENOMIC APPROACH TO STUDY FLAVIN MEDIATED PUBLIC GOOD DYNAMICS IN HYDROCARBON RESOURCE ENVIRONMENTS  by Kateryna Ievdokymenko   B.Sc. (Honours), Taras Shevchenko National University of Kyiv, 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Genome Science and Technology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2015  © Kateryna Ievdokymenko, 2015   ii   Abstract   Emerging lines of evidence indicate that microbes form distributed networks of metabolite exchange based in part on public goods. These networks have the potential to drive the evolution of microbial lineages, and contribute to essential functions and services in natural and engineered ecosystems. However, experimental systems in which to evolve and perturb public good dynamics remain poorly constrained. Here, a functional metagenomic screening approach was used to recover abundant biosynthetic gene clusters with the potential to mediate microbial interactions in the environment. Specifically, 29 gene clusters involved in the production of riboflavin sourced from diverse microbial donor genotypes were recovered by functional screening from two fosmid libraries constructed from methanogenic communities enriched on hydrocarbons. Active clones were sequenced and riboflavin encoding gene cassettes were verified using cluster subcloning. Focusing on observed relationships between mobile genetic elements, metabolite secretion patterns and gene frequency distributions, a role for riboflavin as a public good in hydrocarbon resource environments was posited. Overall, this work suggests that secreted riboflavin may have versatile and unrecognized roles in microbial hydrocarbon transformation with potential to modulate microbial community dynamics in hydrocarbon resource environments.     iii   Preface  The thesis represents original unpublished work by the author, Kateryna Ievdokymenko. A version of Chapter 2 and 3 are being prepared for submission to a peer-reviewed journal. The main text was written by Kateryna Ievdokymenko with input and feedback from Dr. Steven Hallam and Cameron Strachan.  Fosmid libraries analyzed in this study were constructed by Eugene Kuatsjah at the University of British Columbia from two enrichment cultures received from collaborators in the Dr. Gieg and Dr. Foght labs (University of Alberta). HPLC analysis was performed in the Dr. Yadav lab at University of British Columbia (UBC) with the help of Dr. Sandip Pawar. Keith Mewis at UBC assisted in the sequencing and assembly of several fosmids.  An undergraduate student under my supervision performed cloning experiments presented in Chapter 3. I performed all other work presented with the input and guidance from my supervisor, Dr. Hallam. Dr. Hallam and Cameron Strachan provided input in experimental design and interpretation of the results.     iv    Table of Contents   Abstract ....... .................................................................................................................................. ii Preface…….. ................................................................................................................................. iii Table of Contents ......................................................................................................................... iv List of Tables ................................................................................................................................ vi List of Figures .............................................................................................................................. vii List of Abbreviations ................................................................................................................. viii Acknowledgements ...................................................................................................................... ix Dedication…. ................................................................................................................................. x Chapter 1 : Introduction .............................................................................................................. 1 1.1 Microbial genomic diversity............................................................................................... 1 1.2 Evolutionary ecology ......................................................................................................... 2 1.3 Microbial cooperation ........................................................................................................ 5 1.4 Functional characterization of microbial traits ................................................................... 8 1.4.1 Environmental genomic library construction ............................................................... 8 1.4.2 Functional screening .................................................................................................... 9 1.5 Hydrocarbon resource environments ................................................................................ 10 1.6 Scope of the thesis ............................................................................................................ 12 Chapter 2 : Functional screening of large-insert fosmid libraries ......................................... 13 2.1 Materials and Methods ..................................................................................................... 13 2.1.1 Sample collection ....................................................................................................... 13 2.1.2 Fosmid library construction ....................................................................................... 14 2.1.3 High-throughput functional screening ....................................................................... 14 2.1.4 Characterization of the metabolite chemical structure ............................................... 14 2.1.5 Riboflavin-overexpression phenotype characterization ............................................. 15 2.2 Results .............................................................................................................................. 15 2.2.1 Functional screening .................................................................................................. 15 2.2.2 Metabolite characterization and quantification .......................................................... 17  v   2.2.3 Characterization of riboflavin-overproducing phenotype under different growth conditions............................................................................................................................... 18 2.3 Discussion ........................................................................................................................ 21 Chapter 3 : Genomic characterization of riboflavin-producing cassettes in hydrocarbon enriched environments ............................................................................................................... 23 3.1 Materials and Methods ..................................................................................................... 23 3.1.1 Full fosmid sequencing and end sequencing.............................................................. 23 3.1.2 Bioinformatic analysis of fosmid sequences .............................................................. 23 3.1.3 Riboflavin cassette subcloning .................................................................................. 24 3.2 Results .............................................................................................................................. 25 3.2.1 Genomic and taxonomic analysis of the fosmids....................................................... 25 3.2.2 Riboflavin cassette subcloning .................................................................................. 30 3.2.3 Metagenomes analysis ............................................................................................... 31 3.3 Discussion ........................................................................................................................ 36 Chapter 4 : Concluding chapter ................................................................................................ 40 Bibliography ................................................................................................................................ 42 Supplementary material ............................................................................................................. 55 Appendix A: Chapter 2 supplementary table ............................................................................ 55 Appendix B: Chapter 3 supplementary tables ........................................................................... 56      vi   List of Tables    Table 3.1 Taxonomic assignment of ORFs in riboflavin-producing fosmids. ............................. 29 Table A.1 Fluorescence intensities of riboflavin-producing clones in different growth conditions....................................................................................................................................................... 55 Table B.1 Characteristics of the NapDC and TolDC metagenomes generated by 454 and Illumina sequencing..................................................................................................................................... 56 Table B.2 Genomic characteristics of the riboflavin-producing clones ....................................... 56     vii   List of Figures    Figure 2.1 NapDC and TolDC fosmid libraries screening. .......................................................... 16 Figure 2.2  Functional analysis of metabolite secreted by NapDC43A06 fosmid........................ 17 Figure 2.3 Growth and fluorescence curves of riboflavin-producing clones................................ 19 Figure 2.4 Riboflavin secretion in aerobic and anaerobic conditions. .......................................... 20 Figure 2.5 Effect of plasmid copy number on riboflavin production by the fosmid clones. ........ 21 Figure 3.1 Genetic context maps for riboflavin-overproducing clones. ....................................... 26 Figure 3.2 Riboflavin biosynthesis pathway. ................................................................................ 27 Figure 3.3 Subcloning of the riboflavin biosynthetic cassette. ..................................................... 30 Figure 3.4 Microbial diversity in NapDC and TolDC enriched cultures. ..................................... 32 Figure 3.5 Genomic circos map of the riboflavin-secreting fosmids............................................ 34 Figure 3.6 Genomic context diagrams for fosmids of the viral origin.......................................... 35    viii   List of Abbreviations   EPCC1 – empty pCC1 fosmid FAD – flavin mononucleotide FMN – flavin adenine nucleotide  HGT – horizontal gene transfer HMM – hidden Markov model HPLC – high performance liquid chromatography HRE – hydrocarbon resource environment LCA – lowest common ancestor MGE – mobile genomic element MM – minimal media MS – mass spectrometry  NapDC – naphtha degrading culture ORF – open reading frame RF – riboflavin SDM – site-directed mutagenesis TolDC – toluene degrading culture tRNA – transfer RNA      ix   Acknowledgements    First, I would like to express my deepest gratitude to Dr. Steven Hallam for providing me with an opportunity to learn and grow as an independent scientist, for your mentorship and support, which have always motivated me to do better science. I would like to sincerely thank to my committee, Dr. Cullis, Dr. Tokuriki and Dr. Yadav, for your encouragement, helpful suggestions and feedback. I also thank Dr. Foster for agreeing to join my examination committee. To all Hallam lab members, thank your consistent help and support. Particularly to Cameron Strachan for all the great scientific discussions and your endless source of inspiration and support, especially during times of struggle. I would like to thank Genome Science and Technology program and NSERC-CREATE for financial support. I have been lucky to do research rotations in different fields before selecting a lab to conduct my MSc research. And lastly, I would like to thank my family, who has always supported me in all my endeavors and decisions. Special thanks to my best friends, Andrii and Inna, for being always supportive and encouraging. And many thanks to my partner, Ivan, for your endless love and support during my graduate studies and for making every day of my life more joyful.x  Dedication  To my parents!    1   Chapter 1 :  Introduction  1.1 Microbial genomic diversity  Microbes as the invisible majority of life on Earth represent an immense reservoir of genomic diversity and metabolic potential (Whitman et al. 1998; Keller & Zengler 2004; Venter et al. 2004). Prokaryotic life emerged 3.8 billion years ago and has managed to occupy almost every conceivable habitat on Earth (Torsvik et al. 2002), from hydrothermal vents in the deepest reaches of the ocean to particulate matter floating in the upper atmosphere (Dick et al. 2013; DeLeon-Rodriguez et al. 2013). The adaptive traits that arose through this process further drove the biogeochemical cycles that sustain all forms of multi-cellular life (Falkowski et al. 2008; Madsen 2011). However, understanding of the fundamental principles responsible for shaping the microbial world was previously limited to isolated organisms, largely ignoring their ecological context (Sniegowski et al. 1997; Thompson et al. 2001). The past study of microbes was additionally biased towards pathogens and industrially relevant organisms (Moxon et al. 1994; Demain 1981). While much work has been done to reconstruct and study microbial ecology using cultured microbes, our understanding of microbial community structure and function in the wild has been expedited by the development of DNA sequencing technologies and supporting analytic tools.  Over the past three decades cultivation-independent studies of the microbial world have expanded from tens to hundreds of reads generated using molecular cloning and Sanger sequencing (Pace 1997; Olsen et al. 1986) to tens of thousands to millions of reads generated on next generation platforms like 454 or Illumina transforming microbial ecology into a big data science (Sogin et al. 2011). Phylogenetic and functional gene anchor sequencing has enabled microbial community composition profiling and linked functional genes to biogeochemical processes (Tringe et al. 2005; Dinsdale et al. 2008). Expanding from the marker gene approach, shotgun metagenomic sequencing has enabled more quantitative taxonomic and metabolic pathway reconstruction directly from uncultivated genomes (Tyson et al. 2004; Venter et al. 2004; Hallam et al. 2004; Tringe et al. 2005). As environmental datasets have grown genome assembly has become and increasing analytic challenge. As a result, efforts have intensified to  2   sequence reference genomes to serve as fragment recruitment platforms for metagenomic sequencing data and to study genotypic diversity and adaptation within closely related microbial populations (Allen & Banfield 2005; Hunt et al. 2008; Thompson et al. 2005). More recently, rolling circle amplification sequencing methods have been developed enabling partial reconstruction of environmental genomes with single cell resolution (Ishoey et al. 2008; Lasken 2007; Stepanauskas 2012). To date, applying the above technologies has been divided into the fields of genomics and metagenomics depending on whether samples were cultured or taken directly from the environment, respectively (Handelsman 2004). However, with an increasing dependence on the integration of both genomic and metagenomic data in an environmental context, a more inclusive designation is environmental genomics, which will be used herein.  The transformation of microbial ecology into a big data science has created new opportunities for understanding microbial community structure and function from a systems biology perspective (Allen & Banfield 2005; Delong et al. 2006; Kunin et al. 2008). Such a holistic way of perceiving the microbial world provides insight in the ecoevolutionary forces shaping microbial interactions and defines recurring organizing principles that can be used to predict microbial community responses to natural and engineered perturbations. One consequence of this data-driven perspective is the need for more validation experiments that link environmental sequence information to real world processes. Linking microbial agents to biogeochemical process is a fruitful challenge, one that promises to shed new light on how microbial communities organize, adapt and cooperate to drive matter and energy transformations integral to ecosystem function.  1.2 Evolutionary ecology  To begin using environmental sequence information to resolve ecological relationships in natural and engineered ecosystems, there needs to be a framework for understanding how genomes are structured and what evolutionary forces are involved in shaping them. This is becoming increasingly important, as recent genomic studies, comparing multiple genomes from the same population, have observed that there is immense sequence diversity even within highly related strains (Hunt et al. 2008; Kashtan et al. 2014; Coleman & Chisholm 2010). More saliently, there appears to be rampant genetic exchange between microbial genomes and an  3   expansive exogenous gene pool that promotes rapid niche adaptation and ecotype diversification (Boucher et al. 2001). For example, comparative analysis of the genomes of marine bacteria, such as Proclorococcus, Synechococcus, Shewanella, has revealed a substantial amount of genomic diversity and might explain the observed physiological differences among closely related strains (Kettler et al. 2007; Dufresne et al. 2008; Konstantinidis et al. 2009). In the case of Proclorococcus, the genomic divergence found between two subpopulations was driven by the availability of phosphorus in the environment. The genes involved in phosphorus acquisition were significantly enriched in Atlantic versus Pacific population revealing differential selection pressure acting on these subpopulations (Coleman & Chisholm 2010). Several conceptual models have been proposed to codify patterns of microbial genome evolution that give rise to the dynamic genome architectures observed in extant microbial populations. For instance, when genes are conserved across all members of a group, the genes are said to be core genes. These are often associated with vital functions such as cell maintenance and replication. The remaining genes in the genomes are called flexible genes, which are often more specific to both the environments and communities that are being interacted with. Flexible genes are often more meaningful when attempting to differentiate specific ecological roles within a group of bacteria (Hacker & Carniel 2001). Finally, pan genome is a term used to capture the entire gene pool observed within a group of bacteria (Tettelin et al. 2008; Rasko et al. 2008).  The flexible genome of particular individuals is often a starting place for generating hypotheses about niche adaptation, especially under changing environmental conditions (Coleman & Chisholm 2010; Kettler et al. 2007; Cordero et al. 2012). However, this is often challenging with only sequence information, because the majority of well-characterized proteins are from the core genome. As an example, the flexible genome is often enriched with genes predicted to encode surface proteins, transporters and secondary metabolite biosynthesis with unknown specificity or function (Nakamura et al. 2004; Rankin et al. 2011). Moreover, it has been suggested that the repertoire of available genes within the flexible genome is enormous (Tettelin et al. 2008). This was very evident when models were created to estimate how many genomes need to be sequenced to recover a specific microbial pan genome. In the case of pathogenic Streptococcus, it was estimated that hundreds of genomes would not be enough to discover all the available genes in the flexible genome (Tettelin et al. 2005). Such estimates  4   become especially compelling when it is considered that bacteria can modify and enlarge the flexible gene pool by means of horizontal gene transfer (HGT) (Wiedenbeck & Cohan 2011; Cohan & Koeppel 2008). The flexible genome is largely shaped by HGT, which involves the transfer of genomic information between microorganisms (Boucher et al. 2003). Bacteria and Achaea are known to be capable of exogenous DNA uptake and integrate genomic information from diverse origins into highly variable regions on their chromosomes called genomic islands or islets (Juhas et al. 2009). These genomic intervals are usually enriched for genes associated with adaptive and auxiliary functions, mainly found within the flexible genome (Penn et al. 2009; Battle et al. 2009). The accumulation of sequenced bacterial genomes revealed that HGT plays a significant role in shaping microbial genomes (Nakamura et al. 2004; Popa et al. 2011). Further, evolutionary models of gene distributions across the three domains of life predict that most of gene birth occurred during the Archaean expansion approximately 3 billion years ago, suggesting that HGT has since then been the dominant mechanism of genomic diversification (David & Alm 2011). It should be noted that integration of foreign DNA into cellular genomes has the potential to cause deleterious effects. Furthermore, some genes are preserved in the flexible genome despite providing no obvious fitness benefit. This makes it complicated to infer ecological adaptations based on analysis of individual genome sequences. However, some acquired traits are advantageous to the host and might further sweep through the population based on the selective advantage they confer under different environmental conditions or response scenarios. It has been observed that recently transferred genes that perform metabolic functions are not immediately integrated into cellular metabolic networks but tend to be more involved in the evolution of peripheral metabolic networks more capable of responding to changing environmental conditions (Pál et al. 2005; Jain et al. 1999).  Our knowledge of gene transfer was originally extrapolated from laboratory studies without understanding the ecological parameters affecting the exchange of mobile genomic elements (MGE) in the environment (Ochman & Groisman 1996; Welch et al. 2002). Subsequently, direct isolation of genomic information from microbial communities expanded our view on the distribution of MGE in microbial genomes and their potential to diversify microbial populations in natural and engineered ecosystems. Further, exploration of HGT events and their  5   effects in natural microbial populations was facilitated by comparative genomics of multiple isolates, which revealed a substantial amount of interspecific diversity (Coleman et al. 2006; Coleman & Chisholm 2010; Konstantinidis et al. 2009; Cordero et al. 2012). Such variability is considered as one of the main mechanisms of differentiation, allowing species to exploit novel niches.  The ability of microorganisms to readily acquire novel genes has resulted in substantial ecological differences between closely related species. Adaptive genomic traits are able to spread through bacterial populations allowing hosts to exploit novel ecological niches. Therefore, gene transfer might uncouple environment-specific alleles from the rest of the genomic backbone triggering gradual differentiation of genomes. However, recent studies show that despite the rapid exchange of genomic information, microbial populations remain genotypically clustered (Polz et al. 2013). Such clusters may arise as a result of gradual specialization in the environment leading to the formation of ecologically cohesive units. At later stages, such units become increasingly distinct with specialized genome organization and ecological roles. Putatively, when the genotypic variance is much greater between than within communities, gene flow boundaries arise further separating emerged clusters. Within these ecological populations, the microorganisms are considered to form an interacting network acting as a population-level social organization. Interactions in bacterial community represent competitive or cooperative dynamics between members within the defined ecological populations. Such dynamics are modulated by social cooperation, which in turn drives the assembly of microbial communities and their collective metabolic actions.   1.3 Microbial cooperation Although often viewed as non-social organisms, microbes live in dynamic communities and can share metabolic activities  (Schink 2002; Morris et al. 2013). These include a number of stable or periodic associations, largely derived from products of metabolism (Faust & Raes 2012; Freilich et al. 2011; Zelezniak et al. 2015). Indeed, analysis of whole community metabolic capacity reveals that individual cells are usually specialized at metabolizing only a subset of the nutrients and intermediates available in the environment (Johnson et al. 2012). The resulting  6   ecological and social interactions between cells shape the assembly of the microbial community to consolidate the different metabolic specialties and ultimately use make more efficient use of nutrients and energy (Sieber et al. 2012; Katsuyama et al. 2009; Boetius et al. 2000). To balance these interactions, efficient communication between microbial cells is often mediated by metabolite exchange. The shared metabolites can form distributed metabolic networks able to modulate complex behaviors within mixed communities, such as biofilm formation or syntrophism (Phelan et al. 2011). Additionally, these networks have the potential to drive the evolution of microbial lineages, and contribute to essential functions and services in natural and engineered ecosystems (Brenner et al. 2008; Shong et al. 2012).  One prominent example of a social interaction is the secretion and exchange of public goods, which are extracellular metabolites that may provide a beneficial effect at the community level (West et al. 2007). These metabolites are produced and secreted by a specialized group of microorganisms, but can be used by neighboring cells regardless of their individual metabolic potential. A growing number of compounds have been demonstrated to act as a public good, which include siderophores, extracellular enzymes, components of biofilms, virulence or inhibitory secondary metabolites targeting competitors, as well as some beneficial “leaky” traits such as amino acids (Griffin et al. 2004; Diggle et al. 2007; Mee et al. 2014; Rainey & Rainey 2003; Pande et al. 2014; Nadell et al. 2009; Buckling et al. 2007).  Production of public goods is usually metabolically costly for producers, and a fraction of the secreted products might be lost to competitors resulting in a theoretical loss of fitness (West et al. 2007; Gore et al. 2009). Additionally, the population might be vulnerable to invasion of non-cooperating individuals or cheaters, which benefit from the cooperation without bearing the cost of the production. It is interesting to note that classic social evolutionary theory predicts that the benefits from cooperative behavior need to be directed towards other members that also carry the cooperative genes in order to keep the interaction stable (Hamilton 1964). Additionally, traits involved in cooperation often reside in fragments of bacterial genome that are shaped by horizontal transfer, which allows increase relatedness at mobile loci and therefore is one of the mechanisms driving cooperation (Mc Ginty et al. 2013; Mc Ginty et al. 2011; Nogueira et al. 2009). The cooperative traits can further “infect” non-producing cells through HGT.  Recent studies of social behavior have revealed that in addition to relatedness of cooperative genes, microbial community spatial structure also plays a significant role in  7   maintaining cooperation. In a well-structured microbial community, such as a biofilm, the spread of cheaters is limited, as cheaters and producers remain spatially segregated (Nadell et al. 2009; Xavier & Foster 2007). This spatial structure also favors the establishment of gene transfer networks by facilitating HGT in high dense microbial populations (Molin & Tolker-Nielsen 2003). However, it can remain difficult to reconcile public good dynamics in heterologous microbial communities with low levels of genetic relatedness, as the communities consist of multiple distantly related genotypes.  A more recent theory of reductive evolution, the Black Queen hypothesis, attempts to explain ecological interactions in an evolutionary context (Morris et al. 2012). The hypothesis states that natural selection might favor dependency through adaptive loss of genes encoding costly traits. Further, the primary concept is that many metabolites synthesized by a cell inevitably “leak” through cell membranes becoming public goods that can be utilized by surrounding community members. The ample presence of the metabolites in the environment then leads to the advantageous loss of biosynthetic genes by many genotypes while preserving the function in a subset of the community due to its vital function. This kind of interaction creates a division of labor separating the community into “beneficiaries” and “helpers” for a given metabolic function. This hypothesis expands from the earlier known mutualistic interactions established through by-products to more complex interdependencies in the microbial world. The examples of microbial cooperation supporting this hypothesis predict that microbial interactions largely affect the genomic repertoire of interacting microorganisms, such as Proclorococcus or Pelagibacter (Morris et al. 2011; Sachs & Hollowell 2012). The Black Queen therefore helps to explain genome reduction observed in some free-living microorganisms, as well as uncultivability of most microbial species due to metabolic interdependencies (D’Onofrio et al. 2010; Sachs & Hollowell 2012; Batut et al. 2014; Hallam & McCutcheon 2015). However, the spectrum of adaptive genes that can be subject to adaptive loss remains difficult to predict.   While microbial communities provide an exceptional opportunity to study social interactions and ecological functions of particular functional traits, the experimental systems in which to test and perturb social dynamics remain poorly constrained.     8   1.4 Functional characterization of microbial traits As microbial metabolic functions, including those involved in ecological interactions, are encoded in genomes, they can be described using environmental sequence information. While this is a powerful approach, it is limited to formulating hypothesis about specific functions. For example, based on genomic data we are unable to accurately describe how metabolites are exchanged in natural consortia or gain insight into the regulatory properties that drive the dynamic assembly of microbial communities (Levy & Borenstein 2013; Zelezniak et al. 2015; Paczia et al. 2012) . Therefore, we are in need of experimental systems that can be used to test hypotheses that are developed with the use of environmental sequence information. Ideally, these systems would allow us to perturb specific functions in the context of public good dynamics.  In order to study metabolic functions from uncultivable bacteria, new ways to harbor environmental DNA in surrogate or heterologous host systems have been developed (Béjà et al. 2000; Stein et al. 1996; Rondon et al. 2000). Further, we have made improvements in our ability to construct libraries of DNA in cultivable microorganisms and detect gene expression through functional screening (Lorenz et al. 2002; Schmitz et al. 2008). To date, these abilities have focused on the discovery of pharmaceuticals and biocatalysts with industrial applications, despite the enormous potential to probe functions related to microbial ecology (Lorenz & Eck 2005). Indeed, if the tools that have been developed and used for antibiotic and catalyst discovery were extended to the essential functions shaping microbial communities, they could provide compelling experimental evidence for specific interactions that have been largely left to speculation using sequence data. 1.4.1 Environmental genomic library construction  Environmental genomic libraries are created in order to sequence and screen uncultivated microbial DNA in tractable host systems, such as E.coli. The approach relies on the ability of the surrogate host to express exogenous genetic information (Ekkers et al. 2012). Library construction enables the possibility for high-throughput screening of thousands of clones for a desired metabolic activity (Lorenz et al. 2002). The likelihood of capturing target genes increases  9   dramatically if the selected environments are naturally enriched for the activity of interest (Taupp et al. 2011). The construction of such libraries begins with the isolation of high-molecular weight DNA from selected environments. The DNA is then purified, end-repaired, size selected, ligated into a vector, and transformed into the host organism. There are several vector systems that can be used, depending on the target size for the DNA insert and the number of genes that may be required for the activity. Currently, many functional screening studies utilize small-insert libraries for the discovery of a specific enzymatic activity that likely requires a single gene (Uchiyama et al. 2005; van Hellemond et al. 2007; Waschkowitz et al. 2009). This process can be inefficient if a complete pathway needs to be discovered. Large-insert libraries are more advantageous for the detection of pathways encoded by gene clusters and operons. Additionally, the large insert provides contextual sequence information enabling taxonomic assignment or the identification of islands or other mobile features associated with HGT.  1.4.2 Functional screening Environmental libraries can be interrogated using sequence- or functional-based approaches. Sequence-based approaches rely on previous knowledge about the nucleotide sequences underlying the activity of interest. This method is highly efficient when PCR can be applied to retrieve target genes based on homology with known genes. For example, this method has been used to detect polyketide, non-ribosomal proteins and other useful enzymes (Owen et al. 2013; Gontang et al. 2010) . Functional screening, on the other hand, is used to detect novel phenotypes promulgated by the host due to the expression of environmental genes. This approach provides an opportunity to detect the activity of genes whose functions cannot be easily predicted from sequence data. A broad spectrum of functional screening strategies has been developed to capture specific metabolic activities. The most popular strategies are observation of a defined phenotype (Craig et al. 2010; Liaw et al. 2010), heterologous complementation of host metabolism under a selective pressure (Wang et al. 2006; Simon et al. 2009), chemical based reporters and genetic screens  10   (including reporters or biosensors) (Uchiyama et al. 2005; Williamson et al. 2005; Strachan et al. 2014).   1.5 Hydrocarbon resource environments Certain ecosystems are repeatedly used to study fundamental principles in microbial ecology. In particular, these often include the water column, soil and the human microbiome (Delong et al. 2006; Fierer & Jackson 2006; Gill et al. 2006). However, the studies of environments that are highly influenced or engineered by humans are also important model ecosystems. This is especially true when attempting to develop strategies for energy recovery and bioremediation associated with hydrocarbon resource environments (HREs). The microbial communities inhabiting HREs may experience specific perturbations that trigger adaptive interactions in the microbial populations. For example, the perturbation may influence the availability of nutrients and the resulting microbial interactions necessary for efficient utilization that feed back on hydrocarbon conversion processes. Currently, we rely primarily on fossil fuels as primary sources of energy for the industrial and economic activities on the planet, and there are many environmental effects resulting from the exploitation of HREs. Energy resource exploration, recovery, transport, storage and consumption inevitably increases the environmental exposure to hydrocarbons and leads to contamination of surface and groundwater with toxic compounds (Kostka et al. 2011), as well as the production of climate changing greenhouse gases.  Hydrocarbons were mostly formed by the geological reactions that led to the decomposition of living organisms’ remains. In response to this, various microorganisms have evolved functional mechanisms by which to transform the available hydrocarbons in the environment to produce energy and materials for growth. Many hydrocarbon compounds are known to be transformed by microorganisms, including n–alkanes, unsaturated alkenes and alkynes, BTEX (benzene, toluene, ethylbenzene and xylenes) and polycyclic aromatics (Leahy & Colwell 1990). Due to the lack of oxygen containing functional groups, low reactivity of C-H bonds, and low solubility, these molecules are considered to be very inert and many bacteria are known to transform them in presence of oxygen as an electron acceptor. The most studied step in hydrocarbon degradation by microorganisms is the initial hydroxylation by mono- or  11   dioxygenases (Das & Chandran 2011), which has been long known in the context of microbial metabolism.  In the 1980s, microorganisms able to degrade hydrocarbons in strictly anaerobic conditions were identified (Schink 1985; Grbić-Galić & Vogel 1987). Further analysis of these anaerobic transformation processes has shown that the mechanisms of anaerobic degradation are completely different from those involved into oxygen–dependent hydrocarbon transformation (Widdel & Rabus 2001). Anaerobic biodegradation in many ecosystems requires microbial cooperation because substrate conversion often consists of multiple steps and further oxidation with limited energy yields. Most of the known anaerobic degraders carry out anaerobic respiration with sulfate, nitrate or metal ions, such as Fe(III) and Mn(IV) or degrade hydrocarbons, under methanogenic conditions (Boll et al. 2002; F Widdel & Rabus 2001; Rios-Hernandez et al. 2003; Jones et al. 2008). The methanogenesis occurs only in anaerobic environments with low concentration of sulphate, nitrate, Fe(III) and Mn(IV) , as in the presence of these electron acceptors, methanogenesis is thermodynamically outcompeted.  Methanogenic degradation involves synthrophic interactions, representing cooperation between hydrocarbon–degrading bacteria and methanogenic Archaea (Fowler et al. 2012; Stams 1994; Kouzuma et al. 2015; Jones et al. 2008). Such synthrophic cooperation benefits both organisms and allows bacterial members efficiently degrade the hydrocarbon substrates, while the inhibitory end products are maintained at low concentration by methanogenic Archaea. This type of “by-product” cooperation or syntrophy is one of the most well characterized and prominent examples of social interactions in microbial communities.  Our knowledge about microbial anaerobic hydrocarbon degradation is mostly limited to strains, whose pure cultures were obtained through growth on defined hydrocarbon substrates (Evans et al. 1991; Lovley et al. 1989; Rabus & Widdel 1995). However, isolation of pure syntrophic microorganisms is not easily realized in the laboratory due to the dependent metabolic activities involving other members of the community. A few methanogenic consortia, able to degrade particular hydrocarbon substrates, have been established and analyzed using genomic approaches (Tan et al. 2015; Fowler et al. 2012; Tan et al. 2013). However, the diversity of key metabolic processes and the organisms involved in hydrocarbon transformation is still poorly understood. Indeed, enrichment cultures often represent comparatively low complex  12   communities based on 16S rRNA sequencing data. Therefore, stable enrichment cultures represent a simplified syntrophic community that can provide some advantages when attempting to understand more complex interactions in microbial communities. With enrichment cultures, it is possible to sample the entire diversity of the system and capture all of the functional capacity, leading to a more complete picture of the processes that are occurring. Coupled with new approaches to recover and study microbial functions, as in the case of functional screening, syntrophic enrichment cultures could help us to zoom in on fine scale interactions in HREs. The information can then be extended into the design principles that will underlie our efforts to engineer efficient recovery and bioremediation processes.  1.6 Scope of the thesis The overall goal of this thesis work was to elucidate interactions in microbial communities associated with HREs. The work focuses on using functional metagenomic approaches to study flavin-mediated public goods in syntrophic enrichment cultures. By recovering various gene cassettes responsible for riboflavin production, I was able to generate an hypothesis about interactions involving the secreted metabolite. The hypothesis states that riboflavin is an important public good that drives microbial interactions in HREs.   The thesis consisted of the following objectives: 1. Perform functional screening of two large-insert fosmid libraries derived from anaerobic methanogenic communities enriched on hydrocarbons.  2. Conduct functional and genomic characterization of riboflavin overproducing clones recovered by functional screening.  3. Generate a hypothesis related to the role of riboflavin in HRE microbial communities.     13   Chapter 2 :  Functional screening of large-insert fosmid libraries  During a high-throughput screen for lignin transformation phenotypes using PemrR:GFP biosensor (Strachan et al. 2014), abnormally high fluorescence intensities produced by 29 clones from two metagenomic libraries sourced from hydrocarbon-degrading enrichment cultures was observed. Re-screening of these libraries in the absence of the biosensor and substrates revealed that the clones produce a semi-fluorescent metabolite emitting in the same spectra as GFP under standard growth condition in LB media.  2.1 Materials and Methods  2.1.1 Sample collection Two methanogenic enrichment cultures were used for the construction of large insert fosmid libraries analyzed in this study. Both libraries were constructed from anaerobic enrichment cultures mediating the transformation of naphtha or toluene into methane. The first enrichment represented a methanogenic naphtha-degrading community (NapDC) derived from mature fine tailings from Syncrude Mildred Lake Settling Basin (Alberta, Canada) that was enriched for naphtha-degrading consortia by growing the culture on 0.2% (v/v) hydrocarbon mixture naphtha as a sole carbon source (Tan et al. 2015; Widdel & Bak 1992). The second enrichment represented a methanogenic toluene-degrading culture (TolDC) derived from a shallow gas condensate-contaminated aquifer located beneath a natural gas production site in Weld County (Colorado, USA) that was enriched for toluene-degrading consortia by growing the culture on on 0.01% toluene (v/v) as a sole carbon source (Gieg et al. 1999, Fowler et al. 2012). The NapDC culture was routinely cultivated for 2 years, while TolDC was maintained in laboratory for more than 10 years before DNA extraction for metagenomic analysis. Total DNA for each culture was extracted using Phenol-Chloroform extraction protocol followed by cesium chloride density gradient purification (Wright et al. 2009) and was subjected to 454 pyrosequencing, Illumina sequencing and fosmid libraries construction.    14   2.1.2 Fosmid library construction  Fosmid libraries were constructed from both NapDC and TolDC enrichment cultures. Fosmid libraries were prepared using CopyControl Fosmid library Production Kit (Epicentre, Madison, WI) according to manufacture’s protocol. Briefly, the extracted DNA was sheared, blunt-end repaired and separated using pulse-field gel electrophoresis. The 35-45 kb fragments have been excised and gel purified. The DNA was ligated into copy-controlled pCC1fos fosmid vector, packaged into lambda phage and used to transfect EPI300 E. coli cells. Transfected cells were plated on LM-chloramphenicol plates and individual clones were picked into 384-well plates using Qpix2 robotic colony picker (Genetix).  2.1.3 High-throughput functional screening For the high-throughput screening, ~50000 metagenomic clones from two libraries were replicated into 384 well plates containing LB media supplemented with 100 μg/mL arabinose and 12.5 μg/mL chloramphenicol to maintain pCC1fos plasmid. After 24 hours of incubation at 37ºC, photometric (OD600) and fluorometric (excitation 481nm, emission 508nm) measurements have been taken on a Varioscan Flash Multimode Reader (Thermo Scientific). Selected hits were re-arrayed in triplicates into 96 deep well plates containing 2ml LB (supplemented with 100 μg/mL arabinose and 12.5 μg/mL chloramphenicol) and MM media (6.8 g/L Na2HPO4, 3g/L KH2PO4, 0.5g/L NaCl, 1 g/l NH4Cl, 4 g/L glucose, 2 mM MgSO4, 0.1 mM CaCl2, 100 μgL arabinose, 12.5 μg/mL chloramphenicol and 40 μg/mL leucine) for re-screening and validation experiments. In total, 23 clones sourced from NapDC and 6 clones from TolDC fosmid libraries were selected for downstream functional and genomic characterization.   2.1.4 Characterization of the metabolite chemical structure  NapDC43A06 and EPCC1fos (EPCC1) harboring an empty pCC1fos vector clones were grown in LB and MM at 37C for 24 and 48 hours respectively. Cells were pelleted by centrifugation (8000 rpm) and supernatant was collected. The supernatant was filter sterilized and applied to Perkin HPLC system operated with the Chromera software and separation was performed with the Atlantis dC18 (5μL, 4.6×250 mm) column (Waters). The injection volume  15   was 10 μL with a flow rate 1 ml/min. The compounds were separated with a mobile phase consisting of 60% methanol with 40% 10 mM ammonium acetate and monitored at 260 nm. The collected fraction of a yellow pigment was analyzed using AB Sciex QTrap® 5500 hybrid linear ion-trap triple quadrupole mass spectrometer in a positive mode (Faculty of Pharmaceutical Sciences, UBC). The obtained spectrum was compared to riboflavin standard (Sigma-Aldrich).   2.1.5 Riboflavin-overexpression phenotype characterization   All cultures were grown at 37oC in a 200-rpm rotary shaker unless stated otherwise. Certain riboflavin overproducing clones were selected for further riboflavin secretion phenotype characterization. Several riboflavin-producing fosmid clones as well as EPCC1 were grown in microplate in triplicates in 200 ul of LB and MM media for 20 hours. The growth (OD600) values and fluorescence values were read every 15 minutes using Tecan Infinite 200 PRO reader. For comparison of riboflavin secretion in aerobic and anaerobic conditions, EPCC1 clone, NapDC43A06, NapDC52I23 and TolDC25O24 were grown in triplicates in 5ml LB media and modified minimal media (6 g/L Na2HPO4, 3g/L KH2PO4, 0.5g/L NaCl, 1 g/l NH4Cl, 4 g/L glucose, 2 mM MgSO4, 0.1 mM CaCl2, 10 mM NaHCO3, 100 μgL arabinose, 12.5 μg/mL chloramphenicol and 40 μg/mL leucine) for 48 hours followed by fluorescence measurements of the supernatant. Anaerobic cultures were grown in Hungate tubes sealed with rubber stoppers. Oxygen was removed by flushing headspace with N2/CO2 gas. To determine the effect of plasmid copy number all clones were grown in a 96-deep well plate in triplicates in LB and MM media for 24 hours.  2.2 Results  2.2.1 Functional screening  Environmental DNA sourced from hydrocarbon-degrading cultures NapDC and TolDC was used to construct large-insert genomic DNA (fosmid) libraries. The fosmid libraries were constructed in pCC1FOS copy-control system to increase the chances of detecting heterologous expression in E.coli host (Martinez et al. 2007). The NapDC library contained 20000 clones and the TolDC contained 23000 clones arrayed into 384-well plates. For the functional screening, the libraries were replicated into 384-well plates and grown overnight before fluorescence  16   measurements were taken. All clones, which exhibited fluorescence value more than 4 standard deviations above the plates mean, were selected for downstream characterization (Figure 2.1A).   Figure 2.1 Screening results for NapDC and TolDC fosmid libraries. (A) High-throughput screening of the two fosmid libraries. Each dot represents normalized fluorescence for each clone in the library. Dotted lines represent 4 standard deviations above the plates mean. (B) Fluorescence intensity of the selected clones’ supernatants.  The selected clones were re-arrayed in triplicate into 96-deep well plates containing LB and glucose minimal media (MM) to confirm the signal and investigate the production of fluorescent compound in different growth media. Moreover, we noticed that a yellow pigment was accumulating in the culture supernatant of various clones, suggesting that the metabolite was secreted. Therefore, fluorescence measurements were taken directly from the culture supernatants after 24 hours of growth in LB and MM (Figure 2.1B) Indeed, we observed a range of metabolite secretion levels based on fluorescence intensities in both media. This result suggests that the metabolite synthesis by E.coli with environmental insertions is independent of rich media. A NapDC43A06 clone showing the highest level of metabolite secretion based on fluorescence intensity was selected for the further functional characterization.      17   2.2.2 Metabolite characterization and quantification  For the metabolite characterization the NapDC43A06 and EPCC1 (as control) clones were grown in LB and MM for 24 and 48 hours respectively followed by culture supernatant collection. High performance liquid chromatography (HPLC) analysis was then used to profile extracellular metabolites. The metabolite present in NapDC43A06 supernatants but absent in control was collected for chemical structure determination (Figure 2.2A). Subsequent tandem-MS/MS spectra of the metabolite matched that of riboflavin (Figure 2.2B).   Figure 2.2  Functional analysis of the metabolite secreted by the NapDC43A06 fosmid. The HPLC profile of the metabolite secreted by NapDC43A06 clone, but not by EPCC1 clone in MM and LB media.  (B) MS/MS spectra of the purified metabolite (upper plot) and riboflavin standard (lower plot).   It should be noted that E. coli, as well as most Bacteria and Archaea encode the complete biosynthetic pathway for producing riboflavin, but it is tightly regulated for intracellular  18   functions and typically not secreted. Thus the riboflavin overexpression phenotype observed in the screening is expected to result from the heterologous expression of environmental genes encoded on the fosmids. Riboflavin is a precursor of two cofactors - flavin adenine dinucleotide (FAD) and flavin mononucleotide (FMN), which are necessary for a wide range of redox reactions (Abbas & Sibirny 2011), including hydrocarbon transformation processes (Sah & Phale 2011; Wischgoll et al. 2005; Buder & Fuchs 1989; Van Beilen & Funhoff 2007). Flavin-binding proteins play an important role in cellular metabolism because of their versatility in catalyzing one- and two-electron transfer reactions based on the flavin’s ability to exist in oxidized, one-electron reduced and two-electron reduced states (De Colibus & Mattevi 2006) . The flavoenzymes can be classified into the groups based on the substrates and reactions catalyzed: transhydrogenases, dehydrogenase-oxidases, dehydrogenase-monooxygenases, dehydrogenase- electron transferases and electron transferases (Abbas & Sibirny 2011). Currently, it is estimated that 1-3% of microbial proteins belong to flavoenzymes.  As a range of fluorescent intensities was observed, we produced a riboflavin standard curve to quantify the amount of riboflavin produced by the fosmid clones using HPLC. The NapDC43A06, NapDC1K19, NapDC29K20, NapDC32E14, NapDC20E18, NapDC37L19, NapDC34A03, TolDC57M08, NapDC1B04 clones were grown in LB media for 24 hours. The supernatant was collected and analyzed by HPLC for riboflavin quantification. The range of riboflavin secreted by the selected clones was from 4.7 μg/ml to 40.7 μg/ml in LB media.  2.2.3 Characterization of riboflavin-overproducing phenotype under different growth conditions   To better understand the process of riboflavin over production by active clones we compared the relationship between cell growth and riboflavin secretion in EPI300 cells. Growth (OD) and fluorescence of the cultures were monitored for over 20 hours. In LB media, there is a direct correlation between growth and riboflavin production (Figure 2.3, left panel). Interestingly, in minimal media some riboflavin-producing clones stop growing after 10 hours, but keep producing the metabolite (Figure 2.3, right panel). The growth stop of these clones was probably caused by resource depletion. The fact that the control culture continued growing in  19   MM suggests that riboflavin production (which did not occur in the control culture) may be costly for the host. The requirement of 25 molecules of ATP per 1 molecule of synthesized riboflavin is consistent with this idea (Bacher et al. 2001; Marsili et al. 2008). The continuing growth of riboflavin concentration in the media, however, is puzzling, and could have been caused by cell death and lysis with a subsequent release of the intracellular riboflavin pool.  Figure 2.3 Growth and fluorescence curves of riboflavin over producing clones.  Seven riboflavin over producing clones were grown in triplicates in LB and MM media for 20 hours. The OD600 and fluorescence values were measured every 15 minutes (error bars represent standard deviations).   Second, as the active clones were derived from anaerobic cultures, we were curious to see if the riboflavin over production phenotype was oxygen-limited. Because the E. coli host is a facultative anaerobic bacterium, we were able to monitor simultaneous growth of the cultures in absence and presence of oxygen. The fluorescence measurements of the culture supernatant after 48 hours of growth revealed that riboflavin overproduction did not differ under anaerobic and aerobic condition for most clones, except NapDC52I23 (t-test, p-value ≤ 0.01) (Figure 2.4). In the case of NapDC52I23, secretion was observed in both conditions, but there were differences across media suggesting that it may be due to nutrients rather than the availability of oxygen.   20    Figure 2.4 Riboflavin secretion under aerobic and anaerobic conditions.  Fluorescence intensities were measured from cultures’ supernatant after 48 hours of growth. Fluorescence values were normalized to the clones’ growth (error bars represent standard deviations).    Additionally, as the original screen was conducted in the presence of the pentose sugar arabinose to induce multi-copy fosmid production, it is possible that riboflavin over production resulted from multiple biosynthetic gene copies rather than regulated production of the metabolite under single copy conditions. To reconcile these possibilities, we compared fluorescence intensities under aerobic single- and multi-copy conditions for all 29 fosmids. Curiously, all fosmids in the non-induced condition continued to overproduce and secrete riboflavin based on fluorescence intensities measurements compared to the EPPC1 control (t-test, p-value ≤ 0.01). Most of the fosmids (19 out of 29) produced riboflavin from single-copy fosmids at approximately multi-copy levels (t-test, p-value ≤ 0.01) (Figure 2.5).  LB MM0100200300400EmptyNapDC43A06NapDC52I23TolDC25O24EmptyNapDC43A06NapDC52I23TolDC25O24FluorescenceAirAerobicAnaerobicRiboflavin production 21    Figure 2.5 The effect of plasmid copy number on riboflavin production by the fosmid clones.  Fluorescence intensities were measured from cultures’ supernatants in the presence and absence of the  plasmid copy-number inducer arabinose (error bars represent standard deviations).   2.3 Discussion    Functional genomic approaches for high-throughput screening of large-insert environmental genomic libraries enable identification of new phenotypes conveyed to the host system through heterologous expression of environmental genes. Recovered genes can be further characterized to gain insight into the metabolic processes occurring in the environment. In this chapter, 29 active clones mediating riboflavin over production were recovered from two methanogenic enrichment culture sourced from HREs. These communities rely on syntrophic partnerships between hydrocarbon-degrading bacteria that perform the initial hydrocarbon transformation and methanogenic Archaea, which consume inhibitory end products of degradation to produce methane (Morris et al. 2013).  The observed overproduction phenotypes occurred under both aerobic and anaerobic conditions by E.coli host using both single and multi-copy conditions. The highest detected overproducer secretes 40 μg/ml of riboflavin after 24 hours growth in LB media. Riboflavin is  22   known to be essential for all living cells as a precursor of redox-active flavin cofactors for flavoenzymes. Cells, that require riboflavin only for production of co-factors for their own needs, are unlikely to allocate extra resource and overproduce the compound just to waste the excess by secretion to the environment. Then riboflavin might be an extracellular metabolite involved in certain links of interspecies cooperation and is released into the environment by some microorganisms as a public good. Indeed, riboflavin secretion has been documented for certain cultivated microorganism, such as Micrococcus luteus, Candida and Ashbya gossypii (Sims and O’Loughlin 1992; Goodwin and McEvoy 1959;Demain 1972; Stahmann et al. 2001). Recently, functional applications of secreted riboflavin and its relative co-factors have been started to be elucidated experimentally. It has been shown that flavins are involved in metals reduction and acquisition (Balasubramanian et al. 2010; Worst et al. 1998; Crossley et al. 2007), electron transfer (Marsili et al. 2008; Von Canstein et al. 2008) and quorum sensing (Rajamani et al. 2008).  To better determine the potential role of riboflavin overproduction in HREs we explore the genomic sequence and frequency distribution patterns of riboflavin gene cassettes on both the active clones and enrichment cultures in the following chapter.     23   Chapter 3 :  Genomic characterization of riboflavin-producing cassettes in hydrocarbon resource environments  This chapter covers the environmental genomics techniques used to elucidate the genomic basis of riboflavin overproduction in hydrocarbon-enriched environments. The twenty-nine clones recovered in the functional were completely sequenced. Each fosmid encoded and expressed a riboflavin biosynthetic cassette in various genomic contexts and resulted in overproduction and secretion of the metabolite. Subcloning of the biosynthetic cassette responsible for the highest level of riboflavin production revealed that a genomic interval containing four genes with a native promoter was sufficient for heterologous riboflavin overproduction by E.coli. Additionally, the analysis of the fosmid sequences elucidated a link between horizontal gene transfer, gene frequency and riboflavin secretion, suggesting that riboflavin may act as a public good in hydrocarbon resource environments.  3.1 Materials and Methods 3.1.1 Full fosmid sequencing and end sequencing Fosmid DNA from fosmid clones was extracted using the QIAprep Spin Miniprep Kit (Qiagen).  E.coli genomic DNA was removed using Plasmid-Safe DNase (Epicentre). Plasmid DNA concentrations were determined using Quant-iT PicoGreen (Invitrogen). DNA was sent for Illumina HiSeq sequencing at Michael Smith Genome Science Center and for MiSeq Illumina sequencing at UBC Sequencing Centre (Vancouver, Canada). 7680 clones from each fosmid library were Sanger end-sequenced at the Michael Smith Genome Science Center (Vancouver, Canada) with the pCC1-Forward (5’-GGATGTGCTGCAAGGCGATTAAGTTGG) and pCC1-Reverse (5’-CTCGTATGTTGTGTGGAATTGTGAGC) primers. 3.1.2 Bioinformatic analysis of fosmid sequences  Sequence analysis was performed using the Metapathway pipeline v2.0 ( Hanson et al. 2014). Briefly, ORFs from each fosmid were predicted using the Prodigal algorithm and genes were annotated by running protein BLAST (e-value ≤ 1e-05) against RefSeq, KEGG, COG and  24   MetaCyc databases. ORFs with no homologous in the databases were annotated as hypothetical proteins. Protein family searches using the online HMMER tool v1.8 , which implements Hidden Markov Models (HMM) (Finn et al. 2011), were performed to confirm BLAST hits and attempt to predict the functions of hypothetical proteins. Nucleotide similarity between fosmids was plotted using a custom Biopython script based on output from BLASTN (e-value ≤ 1e-05). A custom perl script was used to visualize protein relatedness of fosmids based on Circos ( (Krzywinski et al. 2009). The BWA algorithm v0.7 was used to map the raw Illumina metagenomic reads to the fosmids predicted ORFs (Li & Durbin 2010). All the mapped reads were normalized to 1kB. GC content of fosmid sequences was calculated for each 200 nucleotides with a shift +20 using a custom Python script. FMN riboswitches were predicted using the RiboSW online tool ( (Chang et al. 2012).  3.1.3 Riboflavin cassette subcloning  The fragment from position 17538 to 25761 on the fosmid NapDC43A06 fosmid was amplified using F_43_HH (5’-GCGGTTAACTGTATTGGCTCGGCTACCT) and R1_43_SH/HH (5’-GCGAAGCTTCCTGATTCCCGACAGGGTTA) primers. F2_N43A06 fragment was amplified using F2_43_SH (5’-GCGACTAGTTCGCTGAAAGTGGAGGAATCA) and R1_43_SH/HH primers. PCR products were run on agarose gel for size confirmation, followed by gel purification of the proper DNA fragments.  Purified PCR product was digested using HpaI (NEB) and HinDIII (NEB) restriction enzymes for the F_NapDC43A06 fragment and SpeI (NEB) and HinDIII restriction enzymes for theF2_NapDC43A06 fragment. The digestion product from the F_NapDC43A06 fragment was named F1_NapDC43A06. For the F2_NapDC43A06 clone pCC1FOS was modified to contain one single SpeI at position 7650bp. This was done, because the SpeI site present in pCC1FOS was within one of the genes necessary for the high fosmid copy number arabinose induction system. The new version of pCC1FOS called pCC1SFOS was obtained by site-directed mutagenesis (SDM) on the SpeI deletion and insertion site using primers SDM_SpeI_del (5’-TCAAGAACTAGCTTAAGCTCACG) and SDM_SpeI_ins (5’-ATCCGATACTAGTGTGTCGCTG) respectively. The introduced mutations were confirmed by Sanger sequencing using pCCI_seq_F (5’-ACGGTTATGTGGACAAAATACCTGGTTAC) and pCCIS_del_seq (5’-GTTATCACTCTTTTAACTTCTGTGC) primers. The purified digested  25   PCR products and purified vectors were ligated overnight with T4 ligase (NEB) at a 6:1 insert to vector ratio. The ligation products were transformed into EPI300 cells. Successful transformants were selected on chloramphenicol LB plates and confirmed by Sanger sequencing of the ends of the insertions using pCCI_seq_F and pCCI_seq_R (5’-GCTCACAATTCCACACAACATACGAG) primers.   3.2 Results   3.2.1 Genomic and taxonomic analysis of the fosmids  All 29 riboflavin producing clones, recovered in the functional screen, were completely sequenced on the Illumina platform. Genomic analysis of the resulting sequence information revealed that all fosmids harbored a gene cassette annotated to be involved in riboflavin biosynthesis (Figure 3.1). The genes involved in riboflavin biosynthesis have been studied extensively, across various organisms, and the series of enzymatic reactions, which start from GTP and ribose-5-phosphate, is well understood (Figure 3.2) (Bacher et al. 2001). Specifically, most clones carried bifunctional deaminase/reductase (ribD), the α-subunit of riboflavin synthase (ribE), GTP cyclohydrolase II/3,4-dihydroxy-2-butanone-4-phosphate synthase (ribBA) and the β-subunit of riboflavin synthase (ribH) genes (Figure 3.1), with some of the clones were missing the first (ribD) or last gene (ribE) in the riboflavin biosynthetic cassette (Table B.2). Moreover, two fosmids, NapDC6E18 and NapDC37L19, carried 3,4-dihydroxy-2-butanone-4-phosphate synthase (ribB) and riboflavin kinase (ribK) genes associated with riboflavin biosynthesis in Archaea.   26    Figure 3.1 Genetic context maps for riboflavin-overproducing clones.  Riboflavin biosynthetic genes, MGE, tRNAs, DNA helicase and FMN/FAD-binding enzymes are annotated. Connections represent nucleotide homologs with e-value ≤1E-05.   27    Figure 3.2 Riboflavin biosynthesis pathway.  Names of the genes encoding pathway enzymes are shown. The biosynthesis starts from GTP and ribose-5-phosphate.   Eight fosmids (NapDC51F12, NapDC45P18, NapDC49E22, NapDC21A06, NapDC11D17, NapDC26A07, NapDC42D14 and NapDC52H01) show a high level of nucleotide relatedness along the entire fosmid sequences. These fosmid clones demonstrate similar fluorescence intensities, except NapDC51F12, which exhibits a significantly higher level of riboflavin production in LB media as measured by fluorescence intensity (Figure 2.1B). Interestingly, NapDC51F12 fosmid clone contains a complete set of riboflavin biosynthetic genes, but lacks a specific RFN regulator that was removed in the process of library preparation. RFN is a riboswitch, a conserved RNA sequence, present in the 5’-untranslated region of mRNA  28   that acts as a negative regulator of riboflavin biosynthesis by binding a FMN molecule and terminating transcription of the downstream genes (Winkler et al. 2002; Vitreschak et al. 2002). It is further worth noting that 16 fosmids contain FAD- or FMN-binding enzymes predicted to be associated with oxidoreductase activities (Table B.2).  Open reading frames predicted on completely sequenced fosmids were queried against the RefSeq database and assigned taxonomy at the phylum level using the MEGAN LCA algorithm (Table 3.1). Based on the majority of assigned ORFs from each clone, all fosmids could be linked to three phylogenetic groups including Deltaproteobacteria, Firmicutes/Clostridia, and Methanomicrobia (Figure 3.1, Table A.1). Moreover, the highest riboflavin overproducer NapDC43A06 contains a 16S rRNA gene affiliated with Clostridia reinforcing taxonomy based on LCA. This same fosmid also harbored several mobile genetic elements and a complete set of riboflavin biosynthetic genes making it a good candidate for downstream expression studies related to both riboflavin biosynthesis and the role of horizontal gene transfer (HGT) in propagating this phenotype in the environment.     29 Table 3.1 Taxonomic assignment of ORFs in riboflavin-producing fosmids. Percentage of ORFs from each fosmid assigned to a particular taxonomic group based on LCA analysis.   Fosmid ID Archaea  Bacteria  Unassigned    Euryarchaeota Acidobacteria Bacteroidetes/ Chlorobi Chloroflexi Cyanobacteria Firmicutes Proteobacteria Spirochaetes Thermotogae Other                α β γ δ         NapDC1B04      17.07  7.32 9.76 51.22   2.44 12.19 NapDC1K19   3.03   6.06   12.12 39.39    39.40 NapDC3J15      16.28  6.98 9.30 53.49   2.33 11.62 NapDC6E18 93.02            2.33 4.65 NapDC11D17 2.86 5.71 5.71   37.14 2.86  2.85 5.71 11.53 2.86  22.77 NapDC14L22   2.33  6.98 18.60 2.33   34.88    34.88 NapDC20E18   2.38   73.81  2.38  2.38    19.05 NapDC21A06 3.33 3.33 6.67   36.66 3.33 3.33 3.33  26.67 3.33  10.02 NapDC26A07 2.78 5.56 5.56   36.11 2.78 2.78  5.56 11.11 2.78  24.98 NapDC29K20      5.00  2.50 7.50 35.00    50.00 NapDC31A03      77.27    4.54    18.19 NapDC31A09      39.47    2.63    57.90 NapDC32E14   2.70   16.21 5.41 2.70 13.51 35.14    24.33 NapDC34A03   2.44   68.29  2.44  4.87    21.96 NapDC37L19 82.93             17.07 NapDC42D14 2.70 5.41 8.11   37.84 2.70 2.70  5.41 5.41  2.70 27.02 NapDC43A06 3.45     55.17        41.38 NapDC43A20 2.38  2.38   14.29  4.76 19.05 42.86    14.28 NapDC45P18 2.78  5.56   36.11 2.78  2.78 2.78 16.67 2.78  27.76 NapDC49E22 2.78 2.78 5.56   33.33 2.78 2.78 2.78 2.78 16.67   27.76 NapDC51F12   3.13   31.25  9.38  3.13 12.50 3.13  37.48 NapDC52H01 3.23 6.45 6.45   35.48 3.23 3.23  6.45 6.45  3.23 25.80 NapDC52I23   2.70   16.22 5.41 2.70 13.51 35.14    24.32 TolDC14C29    2.56 2.56 33.33    7.69    53.86 TolDC25O24 2.70     56.77      2.70 2.70 35.13 TolDC44H20    5.88 2.94 38.24    11.76    41.18 TolDC46P08    4.76 2.38 33.33    4.76 2.38   52.39 TolDC48L18 2.86     57.14      2.86 2.86 34.28 TolDC57M08       12.82 2.56 33.33       7.69       43.60  30  3.2.2 Riboflavin cassette subcloning  To determine if the NapDC43A06 riboflavin gene cassette was sufficient for riboflavin overproduction we subcloned the cassette with different lengths of upstream sequence information into the PCC1fos vector. The first construct, F1_NapDC43A06, contained biosynthetic genes and an intact intragenic region including the FMN riboswitch and a native promoter. This construct demonstrated that the region of four genes with the complete intragenic region is sufficient for riboflavin production and secretion, and exhibited increased fluorescence relative to the full-length fosmid (t-test, p-value ≤ 0.01) when induced in rich media. The second construct, F2_NapDC43A06 contained only the gene cassette with no intragenic region or T7 promotor. Interestingly, fluorescence was still observed when induced in rich media indicating that the construct retained some residual expression possibly promoted from the vector sequence.   Figure 3.3 Subcloning of the riboflavin biosynthetic cassette.  (A) Genomic context map. NapDC43A06 represents the original fosmid identified in the functional screen, while F1_NapDC43A06 and F2_NapDC43A06 represent subcloned constructs. (B) The graph shows fluorescence  31  of the clones measured after 24 hours growth in LB and Minimal media.  It should be noted that several natural riboflavin overproducers have been described, such as flavinogenic yeast, fungi and several bacteria (Lim et al. 2001). However, the specific overproduction and secretion mechanisms used by these isolates have not been explicitly studied. The basic principles of the regulation of riboflavin expression are primarily understood in Bacillus subtilis and E. coli, which are known not to secrete riboflavin under standard growth conditions. In both bacteria, riboflavin biosynthesis and its transport is subjected to feedback regulation by the FMN riboswitch (Winkler et al. 2002). Additionally, it has been shown that iron starvation induces riboflavin secretion by flavinogenic yeast, suggesting that these processes are metabolically connected. For example, mutations preventing Pichia gulliermondii from overproducing riboflavin significantly retard growth under iron-limited conditions (Boretsky et al. 2007). Some bacteria are predicted to encode multiple copies of riboflavin producing genes per genome. For example, Shewanella oneidensis known for the utilization of flavins in extracellular electron transfer (Marsili et al. 2008), contains a complete riboflavin biosynthetic cassette (ribD-ribE-ribBA-ribH) with additional copies of specific riboflavin biosynthetic genes including separate ribB, ribA and ribE distributed separately throughout the genome. It has been suggested that multiple copies of biosynthetic genes might allow differential levels of riboflavin expression or secretion depending on environmental conditions (Brutinel 2013).   3.2.3 Metagenomes analysis  To develop a better understanding of the distribution of riboflavin biosynthesis genes in the original enrichment cultures used in fosmid library production and their potential ecological role in hydrocarbon resource environments we analyzed community composition profiles represented in the fosmid libraries and shotgun metagenomes. Open reading frames predicted in 7680 fosmid ends and 130 Mb of unassembled shotgun 454 pyrosequences from the NapDC library were queried against the RefSeq database and assigned taxonomy at the phylum level using the MEGAN LCA algorithm. The same analysis was performed on ORFs predicted on 7680 fosmid ends and 220 Mb of 454 pyrosequences from the TolDC enrichment. More than half of the ORFs in each dataset could not been assigned at phylum level. Based on assigned ORFs, the microbial  32  communities in both enriched cultures are dominated by Methanomicrobia with the Archaeal domain and Firmicutes and Proteobacteria within the Bacterial domain (Figure 3.4). The taxonomic distribution in all dataset showed a similar trend with all major taxa represented in the constructed fosmid libraries, with a lower relative abundance of Methanomicrobia in NapDC fosmid library (22%) and higher in TolDC (49%) compared to the shotgun metagenomes (36% and 36% respectively).   Figure 3.4 Microbial diversity in the NapDC and TolDC enrichment cultures.   33  The dot plot represents the relative abundance of microbial taxa in the NapDC and TolDC cultures, based on MEGAN LCA assignment of predicted ORFs. The fosmid end sequences and shotgun 454 pyrosequencing data were generated from the same enrichment cultures. Full fosmids represent riboflavin-secreting clones recovered in functional screening. (A) The plot shows microbial diversity at the domain level. (B) The plot shows microbial diversity at the  phylum level. Only taxa representing a relative abundance of > 0.1% are shown.  In order to determine the frequency distribution of genes encoded on the riboflavin overproducing fosmids in the original enrichment, we mapped unassembled metagenomic reads generated on the Illumina platform from NapDC and TolDC onto completed fosmid sequences. A range of distribution patterns was observed for predicted open reading frames (ORFs) encoded on each fosmid (Figure 3.5). Most fosmids manifested relatively even coverage, with some fosmids exhibiting differential coverage patterns.    34   Figure 3.5 A genomic circos map of the riboflavin-secreting fosmids.  Grey bars represent fosmid sequences with the size indicated in kilobases (kb). Coloured bars within fosmids denote the location of the riboflavin gene cluster (yellow) or transposase/integrase (red). G+C ratio for each 250 nucleotides with shift +20 nucleotides is represented on line plots for each fosmid sequence. The histogram shows the number of environmental raw reads mapped to each ORF (with blue histogram corresponding to NapDC and orange to TolDC cultures). Connections in the center represent protein homologs with minimum e-value of 1E-10. Yellow connections represent homology between riboflavin biosynthetic genes.    35  For example, the NapDC31A09 fosmid manifested high coverage (10,000-15,000 reads per kB) and low coverage (200-200 reads per kB) regions. Interestingly, ORFs predicted in the low coverage region were affiliated with Clostridia (Desulfomaculum sp.), while the ORFs predicted in the high coverage region were annotated as hypothetical and could not be assigned taxonomy at the phylum level. Open reading frames in the high coverage regions were compared to 7,680 fosmid end-sequences from the NapDC library, resulting in the identification of 3 fosmids with 100% identity across the entire sequenced region that were not detected in the functional screen. As none of these fosmids were fluorescent, we submitted them for full-length sequencing to compare with active clones. Interestingly, the 3 fosmids were predicted to contain genes annotated as hypothetical or encoding phage associated structural proteins using an HMM search consistent with a viral origin (Figure 3.6). Based on this comparison we surmise that the NapDC31A09 clone contains an insertion of phage-related genes proximal to the riboflavin biosynthetic gene cassette.   Figure 3.6 Genomic context diagrams for fosmids of the viral origin.  Phage structural proteins, bacterial genes, riboflavin biosynthetic genes and hypothetical proteins are indicated. Connections represent nucleotide homologs with e-value ≤1E-05.     36  Further, the HMM annotations for the predicted genes encoded on riboflavin-producing fosmids revealed that 13 fosmids contained mobile genomic elements, such as integrases and transposases (Table B.2), while the NapDC31A09 contained phage-related proteins. It is therefore possible that riboflavin-producing cassettes may be sweeping through microbial populations via HGT. Considering the range in riboflavin production levels observed in E.coli host, it would be of fundamental importance to understand how cassettes such as these are compatible across diverse microbial populations and expression hosts and the potential role of MGE in modulating expression levels.   3.3 Discussion In this chapter, the genomic analyses of the 29 riboflavin-producing fosmid clones recovered in the functional screening were performed. Active clones were completely sequenced and gene cassettes were shown to be sufficient in driving riboflavin overproduction by subcloning E.coli. The clones producing the highest level of riboflavin detected in the functional screen were associated with Clostridia and Deltaproteobacteria although Methanomicrobia donor genotypes were also recovered in the screen, suggesting that the observed riboflavin-overproducing phenotype is independent of phylogenetic distance from the EPI300 E.coli screening host. We determined that a gene cassette consisting of ribD-ribH-ribBA-ribE genes on fosmid NapDC43A06 including an upstream intragenic region encoding a predicted negative regulator is involved in riboflavin overproduction by E.coli. The secretion of riboflavin most likely occurs through non-specific transporters or membrane leakage, as E.coli does not encode known riboflavin exporters.  The original cultures, despite being enriched for hydrocarbon-degrading bacteria, represent microbial communities that consist of numerous taxonomic groups persisting over many passages (Tan et al. 2015). This diversity brings into question the role of each individual taxonomic group in sustaining the community’s growth on hydrocarbon substrates. Potentially, some community members are not directly involved in the transformation of hydrocarbon substrates, but instead or in addition, provide other essential products, such as vitamins or amino acids. Based on majority of assigned ORFs from each clone, fosmids responsible for the riboflavin overproduction phenotype were phylogenetically associated with Clostridia, Deltaproteobacteria (Bacteria) and Methanomicrobia (Archaea) in the NapDC culture. Only  37  fosmids affiliated with Clostridia were recovered from TolDC. This difference might be explained by the different geographical origins of these two cultures (Alberta and Colorado), their cultivation time (2 and 10 years) or substrates used for enrichment (naphtha versus toluene). Representatives from the two bacterial phyla have been shown to be primary hydrocarbon degraders (Winderl et al. 2010; Sun & Cupples 2012), while Methanoculleus within the Methanomicrobia is a hydrogenotrophic methanogen consuming inhibitory end products of hydrocarbon degradation (Demirel & Scherer 2008). Therefore the taxonomic groups detected by functional screening for riboflavin production could be primary players in hydrocarbon-degradation processes in both the enrichment cultures and source environments.  The detection of riboflavin overproduction in E.coli by conducting functional screens on two ecologically similar hydrocarbon-degrading enrichment cultures led to a hypothesis that extracellular riboflavin production may be linked to the degradation process. In addition to well-characterized roles as flavin co-factors, it has been shown recently that extracellular riboflavin along with FAD and FMN acts as electron shuttles and metal chelators (Von Canstein et al. 2008; Worst et al. 1998; Crossley et al. 2007). Also it has been noted that some microorganisms secrete riboflavin when grown on pyridine (Sims & O’Loughlin 1992) or hydrocarbons as sole carbon sources (Sabry et al. 1989), but no further experiments were performed to determine the molecular mechanisms of overproduction during growth on these substrates or to  link this trait to ecology.  There could be many ecological roles for extracellular riboflavin. Riboflavin and its relevant cofactors may be required for extracellular hydrocarbon transformation under methanogenic conditions as free redox compounds or as cofactors for degradation enzymes. Further, riboflavin may facilitate electron transfer to metals therefore assisting in their solubilization and further uptake. Flavins can exist in three redox states – fully oxidized, semi-reduced and fully reduced. This ability enables flavins or flavin-binding proteins to perform an enormous variety of redox reactions. Numerous known flavoenzymes that are involved in the degradation reactions are associated with the oxygenase family, catalyzing oxidation reactions with O2 as electron acceptor (Das & Chandran 2011). This raises the question as to whether there is a direct role for riboflavin in anaerobic hydrocarbon degradation. Interestingly, some strictly anaerobic methanogenic archaea utilize di-iron flavoproteins in oxygen detoxification by reducing O2 to H2O (Seedorf et al. 2004). The presence of these flavoproteins in archaeal  38  genomes promotes tolerance to low O2 concentrations. Consistent with this observation, a recent meta analysis of anaerobic hydrocarbon resource environment metagenomes revealed an unexpectedly high proportion of genes associated with aerobic degradation processes (An et al. 2013). Additionally, some anaerobic microorganisms have been shown to utilize a flavin-based electron bifurcation mechanism to couple endergonic redox reactions to exergonic redox reactions (Thauer et al. 2008). However, the multienzyme complexes involved in this type of reactions are cytoplasmic and therefore do not require flavin to be secreted.  Overall, flavins might possess versatile roles in microbial hydrocarbon degradation processes, potentially as free redox metabolites, co-factors of degrading enzymes or as oxygen detoxification mediators. Hypothetically, a subset of the hydrocarbon degrading community could be involved in flavin overproduction and secretion, making these compounds open to exploitation by other members. As a result, the overproducing microorganisms might be engaged into social interactions based on flavins secretion and acquisition. In this case, riboflavin would be secreted by some cells but benefit other members of the community independently of their investment in producing the metabolite and could lead to cooperative metabolism. One of the approaches that maintains cooperation in microbial communities is horizontal gene transfer. It has been shown that distribution of social traits is often shaped by HGT, as the mobility of public traits improves the stability of cooperative behavior in microbial consortia (Dimitriu et al. 2014; Mc Ginty et al. 2013; Rankin et al. 2011). The genomic analysis of the clones recovered in this study revealed that the riboflavin-producing phenotype has the potential to be mobilized via HGT in hydrocarbon resource environments. It was found that 14 fosmid sequences contained genetic signatures of viral/transposon origins. Therefore, it is possible that extracellular riboflavin production is involved in complex social interactions throughout the hydrocarbon-degrading process, but a direct link to any hydrocarbon degradation mechanism still remains to be identified. To gain insight into the specific effects of riboflavin secretion on the hydrocarbon transformation process, experimental systems appropriate to study microbial interactions among uncultivated microorganisms are required. Genomic analysis of single cells or metagenomes is not able to answer the question of which metabolites are secreted and exchanged between community members. Additionally, isolating individual strains from methanogenic consortia can be difficult when its metabolic activities are linked through syntrophy or co-metabolism,  39  especially when inhibitory metabolic products need to be removed by other microbial species in a community. Functional genomic approaches are required to experimentally elucidate a putative link between riboflavin secretion and hydrocarbon degradation. This is required to further understand how the ecological interactions based on metabolite exchange modulate the assembly and dynamics of microbial communities in hydrocarbon resource environments.      40  Chapter 4 :  Concluding chapter  Microbial communities drive matter and energy transformations integral to ecosystem functions through distributed metabolic networks (Falkowski et al. 2008; Mee et al. 2014). Our understanding of these networks is enabled in part by rapid advances in sequencing technologies that bypass the need for cultivation. The specific functional roles of individuals or populations are  predicted using computational methods from primary sequence data and used to reconstruct microbial community composition and metabolism (Tyson et al. 2004; Niels W Hanson et al. 2014). Taxonomic and metabolic composition of the community is often visualized as phylogenetic trees, patterns of abundance and co-occurrence networks, increasingly with the support from environmental parameters or process rate measurements. However, the genomic analysis of microbial populations is severely biased towards known annotated proteins and unable to predict the functional roles of novel genes. Also the genomic data provides limited information about ecological relationships, which are shaping microbial community structure. For example, by using only genomic data we are unable to accurately describe how metabolites are exchanged in natural consortia or gain insight into the regulatory properties that drive the dynamic assembly of microbial communities. Elucidating ecological interactions such as public goods is necessary to comprehend the complexity of particular environments. However, drawing meaningful ecological relationships from the environmental genomic data, especially with respect to metabolic interactions, is confounded by a dearth of tractable experimental systems.  This thesis outlines a functional metagenomic approach used to recover biosynthetic gene cassettes with the potential to mediate microbial interactions in the environment. Having observed several fosmid clones emitting abnormally high fluorescence in the functional screening of libraries from environments with similar biotic and abiotic conditions, I sought to determine the genetic basis of the phenotype and link it to the ecology. Both libraries were derived from microbial communities enriched on hydrocarbon substrates and shown to produce methane, thus sharing several ecological properties. The intrinsic fluorescence of the 29 clones was determined to be caused by the over-production and secretion of riboflavin, a molecule that is mainly known as a precursor of flavin co-factors for essential redox enzymes (flavoenzymes). The biosynthetic gene cassettes involved into riboflavin secretion were derived from distinctive donor genotypes and appeared to have undergone horizontal gene transfer and gene loss events.  41  Based on the obtained data a role for riboflavin as a secreted public good in hydrocarbon-enriched environments is suggested.   Riboflavin has been shown to be secreted by several different cultured organisms and, more recently, extracellular riboflavin along with its flavin cofactors has been proposed to be involved in iron acquisition and electron shuttling. Therefore, it is possible that extracellular riboflavin governs complex social interactions in microbial communities as in the case with siderophores (Cordero et al. 2012). The specific link with hydrocarbon degradation remains elusive, but it is interesting to note that there are several flavin-dependent enzymes involved in the initial degradation steps. The versatile ability of flavins to mediate one- and two-electron transfer reactions might be exploited by microorganism in hydrocarbon degradation processes to facilitate electron flow and efficiently transform the available substrates under methanogenic conditions. Interspecies electron transfer between members in methanogenic consortia is essential for efficient substrate degradation and community growth. Hydrogen and formate were shown to be the primary compounds for extracellular electron transfer (Stams & Plugge 2009), although other small molecules, such as sulfur compounds and humic substances, may also function as electron shuttles (Shrestha & Rotaru 2014). Additionally, it was demonstrated that extracellular flavins are involved in electron shuttling from cell membrane to solid electron acceptors in Shewanella biofilms (Marsili et al. 2008; Okamoto et al. 2014), but their specific role in interspecies electron transfer remains to be demonstrated. Due to their ability to transfer protons and electrons, flavins might mediate the dynamics of interspecies electron transfer by shuttling electrons from cell membrane of hydrocarbon-degrading bacteria to methanogenic archaea in hydrocarbon resource environments. The dynamics of flavins’ secretion and exchange in microbial communities remains to be experimentally confirmed. In summary, this thesis identifies new opportunities for functional genomic studies of the ecological interactions mediated by extracellular flavins in microbial hydrocarbon resource environments. 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Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proceedings of the National Academy of Sciences, 112(20), p.201421834.  55  Supplementary material   Appendix A: Chapter 2 supplementary table  Table A.1 Fluorescence intensities of riboflavin-producing clones in different growth conditions  Fosmid ID LB (Induced  plasmid)  LB (Non induced  plasmid) MM (Induced plasmid)  MM (Non induced  plasmid)  EPCC1 187±1.41 183.33±3.21 50.33±3.05 52.5±2.12 NapDC1B04 534±5.66 595.33±47.08 293±19.8 339±17.68 NapDC3J15 558.67±9.29 568.67±5.68 342.67±24.84 347.5±20.51 NapDC1K19 1061.5±3.53 1001.5±51.61 694±25.46 605.5±2.12 NapDC6E18 411.33±16.16 287.33±1.53 60.33±5.68 61.33±3.51 NapDC11D17 516.67±11.93 430±35.6 73±7.94 83.33±12.66 NapDC14L22 421.5±3.53 432±21.21 80±19.9 87±4.24 NapDC21A06 557±14.1 521±24.25 308.33±30.86 256.33±132.8 NapDC26A07 471±4.24 651.6±23.33 87.33±16.77 108±10.01 NapDC29K20 1265±57.98 1141±19.8 583.67±8.14 632.67±46.11 NapDC20E18 698±46.03 450.67±12.43 222±26.05 229±20.88 NapDC31A03 294.67±16.92 330±15.5 97.33±4.16 100.33±4.04 NapDC31A09 1180.5±9.19 471±46.67 217±15.55 208±18.38 NapDC32E14 966.67±69.06 1064±59.57 766.5±12.02 666.5±169.33 NapDC34A03 380.33±15.5 371.67±4.04 88.33±16.2 107±21.51 NapDc37L19 362.33±2.31 310±2 66±8.54 70.67±2.51 NapDC42D14 557.67±36.22 353.33±12.22 366.67±27.68 352.67±27.68 NapDC43A06 2255±196.9 2217±107.14 1547±197.99 935±46.67 NapDC43A20 1156±185.53 1087.33±37.63 930±22 815±13.31 NapDC45P18 580.67±150 449.67±4.72 66.33±3.21 103.33±3.05 NapDC49E22 546.67±19.22 435.33±35.08 99±1 124.33±7.02 NapDC51F12 1108±3.53 415.5±2.12 73.33±20.5 65.33±6.05 NapDC52H01 500±62 363.67±29.84 131±6.25 166.33±8.74 NapDC52I23 1074.67±92.09 969.67±15.53 605±26.21 575.33±59.43 TolDC14C19 355±7.81 319.67±23.39 67.67±3.2 66.33±4.93 TolDC25O24 889.67±65.68 818.5±10.6 398±0.5 316±15.58 TolDC44H20 631.5±65.7 260.67±6.51 63.33±1.5 63.33±2.51 TolDC46P08 657.33±26.31 269.33±4.62 68±6.25 68.5±6.36 TolDC48L18 542±40.33 468±41.32 311±25.32 299.67±58.7 TolDC57M08 351±5 309.67±19.65 67.67±6.43 61.67±6.35   56  Appendix B: Chapter 3 supplementary tables Table B.1 Characteristics of the NapDC and TolDC metagenomes generated by 454 pyrosequencing and Illumina sequencing   NapDC TolDC Number of reads (454 pyrosequencing) ~370000 ~550000 Number of reads (Illumina) ~440M ~380M Average length (bp)(454 pyrosequencing) 354 393 Average length (bp) (Illumina) 150 150   Table B.2 Genomic characteristics of the riboflavin-producing clones  Fosmid ID Ribo-switch RibD RibE RibBA RibE Riboflavin kinase MGE FMN/FAD binding genes TolDC14C09 +      2 0 TolDC25O24 + + + + + - 1 3 TolDC44H20 + + + + + - 2 0 TolDC46P08 + + + + + - 2 0 TolDC48L18 + + + + + - 1 3 TolDC57M08 + + + + + - 2 0 NapDC1B04 - + + + - - 0 0 NapDC1K19 - - + + + - 2 1 NapDC3J15 - + + + - - 0 0 NapDC6E18 - - - + - + 6 2 NapDC11D17 + + + + + - 0 1 NapDC14L22 - - + + + - 11 1 NapDC20E18 + + + + + - 0 0 NapDC21A06 + + + + + - 0 1 NapDC26A07 + + + + + - 0 1 NapDC29K20 - - + + + - 3 1 NapDC31A03 + + + + + - 1 0 NapDC31A09 + + + + + - 0 0 NapDC32E14 - - + + + - 2 3 NapDC34A03 + + + + + - 2 0 NapDC37L19 - - - + - + 6 2 NapDC42D14 + + + + + - 0 1 NapDC43A06 + + + + + - 4 0 NapDC43A20 - - + + + - 0 5 NapDC45P18 + + + + + - 0 0 NapDC49E22 + + + + + - 0 1 NapDC51F12 - + + + + - 0 0 NapDC52H01 + + + + + - 0 1 NapDC52I23 - - + + + - 2 3  


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