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A correlation of anaerobic methane oxidizing archaea with geochemical gradients in coastal Californian… Constan, Lea 2009

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A Correlation of Anaerobic Methane Oxidizing Archaea with Geochemical Gradients in Coastal Californian Marine Sediments by LEA CONSTAN A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Microbiology and Immunology) The University of British Columbia (Vancouver) March  2009  Lea Constan 2009 ii Abstract The anaerobic oxidation of methane (AOM) is a globally important process estimated to consume over 384.2 teragrams of the greenhouse gas methane in sediments per year. At least three anaerobic methane oxidizing archaeal groups, ANME-1, -2, -3, which further partition into subgroups, have been implicated in this process. However, none of these organisms have been isolated in pure culture and the ecological and functional dynamics of AOM remain poorly understood. The ANME express a homologue of methyl coenzyme M reductase (Mcr), known to catalyze methane formation in methane-producing archaea. These and other findings suggest that the ANME are capable of oxidizing methane through a reversal of methanogenesis. Although small subunit ribosomal DNA (SSU) is a good proxy for the identification of ANME groups, it is not sufficiently divergent to easily distinguish between ANME subgroups. The gene for the alpha subunit of Mcr (mcrA), on the other hand, is divergent enough to resolve fine scale ANME subgroup distributions and is a functional marker for AOM, as well. The objective of this study was to evaluate the role of geochemical parameters in the partitioning of ANME-1 and ANME-2 subgroups in coastal seep sediments. Using a set of quantitative polymerase chain reaction (qPCR) assays to target the mcrA subgroups, samples were quantified within and between three separate seep sediment sites off the coast of California and correlated to methane, sulfate, sulfide, ammonia and alkalinity measurements. The analysis indicates that methane porewater concentrations dictate the upper limit of total ANME abundance, suggesting it is the sole ANME energy source. ANME-2 population structure was strongly influenced by the defined environmental variables, with differential subgroup partitioning corresponding to sulfate and ammonia gradients. In contrast, the relationship of ANME-1 with chemistry was not well resolved, indicating that other factors, such as competition may be the prime determinants of ANME-1 population structure. The study establishes a defined set of parameters governing ANME subgroup partitioning, which provides a robust quantitative framework for inferring the ecological dynamics of methane oxidizing archaeal communities in a wide variety of settings. iii Table of contents Abstract.............................................................................................................................. ii Table of contents .............................................................................................................. iii List of tables....................................................................................................................... v List of figures.................................................................................................................... vi List of abbreviations ...................................................................................................... viii Acknowledgments ............................................................................................................ ix Co-authorship statement .................................................................................................. x Chapter I  Introduction.................................................................................................... 1 The discovery of anaerobic methane oxidizers.............................................................. 1 Phylogenetics of ANME groups .................................................................................... 3 ANME cluster morphology............................................................................................ 4 AOM sites ...................................................................................................................... 4 ANME ecology .............................................................................................................. 5 Linking ANME population structure to chemistry ........................................................ 6 AOM biochemical insights ............................................................................................ 7 Methyl coenzyme M reductase and AOM ..................................................................... 8 Syntrophic partners ...................................................................................................... 10 Transferred intermediates............................................................................................. 11 Summary ...................................................................................................................... 12 Hypothesis.................................................................................................................... 13 Thesis objectives .......................................................................................................... 13 References .................................................................................................................... 23 Chapter II  A study of subgroup partitioning trends along geochemical gradients in anaerobic methane oxidizing archaea........................................................................... 29 Abstract ........................................................................................................................ 29 Introduction.................................................................................................................. 29 Results .......................................................................................................................... 32 McrA assay performance ......................................................................................... 32 Sites studied............................................................................................................. 33 Subgroup partitioning trends within sites................................................................ 34 Santa Monica Basin............................................................................................ 34 La Goleta ............................................................................................................ 35 Tonya seep.......................................................................................................... 36 General subgroup partitioning trends between sites ............................................... 36 Distribution of mcrA populations between sites ..................................................... 37 Influence of chemistry on mcrA subgroup distribution .......................................... 38 ANME-2 associated subgroup partitioning............................................................. 39 ANME-1 associated subgroup partitioning............................................................. 40 Methane influence on ANME populations.............................................................. 40 Discussion .................................................................................................................... 41 ANME and chemistry distribution between sites.................................................... 41 iv Model for ANME-1/-2 subgroup dynamics ............................................................ 43 Summary ................................................................................................................. 43 Experimental procedures.............................................................................................. 44 Sampling.................................................................................................................. 44 DNA extraction ....................................................................................................... 44 Primer design........................................................................................................... 45 Standard isolation and quantification...................................................................... 45 Nonmetric multidimensional scaling....................................................................... 46 Comparison of chemistry to qPCR matrices. .......................................................... 46 Chemistry ................................................................................................................ 46 References .................................................................................................................... 63 Chapter III  Conclusion ................................................................................................. 70 ANME communities within and between sites............................................................ 70 ANME communities along chemical gradients ........................................................... 70 Influence of abundant subgroups on detecting trends in less abundant subgroups...... 72 Strengths and weaknesses of research methods ........................................................... 72 DNA extraction method .......................................................................................... 72 qPCR general methodology .................................................................................... 73 qPCR assays ............................................................................................................ 73 Chemistry ................................................................................................................ 73 Evaluation of current knowledge and proposals for new ideas.................................... 74 Status of relevant working hypothesis ......................................................................... 75 Conclusion ................................................................................................................... 75 References .................................................................................................................... 76 Appendix A qPCR assay development methodology................................................... 84 qPCR standard development ........................................................................................ 84 Subgroup assignments.................................................................................................. 84 Primer design ............................................................................................................... 85 McrA qPCR assay validation ....................................................................................... 86 Comparison to published ANME SSU assay results .............................................. 86 Addition of mcrA subgroups and comparison to group assay results..................... 86 Limits of detection .................................................................................................. 86 Appendix B. mcrA copy number in relation to chemical parameters measured in this study. ................................................................................................................................ 93 vList of tables Table 1.1. Anaerobic methane oxidizer (sub)group distribution separated according to relative abundance in various geographical locations…………………………………...19 Table 1.2. Environmental parameters proposed to play a role in ANME-1/ANME-2 subgroup partitioning with conditions leading to these hypotheses. ……………..……..21 Table 1.3. A functional overview of canonical methanogenic pathway components identified within methane oxidizing communities with special emphasis on ANME-1 and ANME-2 subgroups. ………………………………………………………………….....22 Table 2.1. Information on the sites examined in this study. ...…………………………..49 Table 2.2. McrA sequences, optimized annealing temperature and ANME SSU subgroup assignment for primer sets developed in this study. …………………………………….50 Table 2.3. Chi-square distances for mcrA species associations in ordination space. ......52 vi List of figures Figure 1.1. Typical a) sulfate methane transition zone (SMTZ) and b) succession of TEA in sea sediment layers with corresponding redox potentials (Eo’). .................................. 14 Figure 1.2. A phylogenetic comparison of genes encoding methyl coenzyme M reductase subunit alpha (mcrA) and small subunit ribosomal RNA (SSU rDNA) derived from environmental clones and primary methanogenic lineages. ............................................. 15 Figure 1.4. World map with sites where AOM activity and/or communities have been studied. .............................................................................................................................. 18 Figure 2.1. Phylogenetic tree derived from mcrA partial nucleotide sequences showing sequence coverage of ANME-1 primers developed in this study..................................... 53 Figure 2.2. Phylogenetic tree derived from mcrA partial nucleotide sequences showing sequence coverage of ANME-2 primers developed in this study..................................... 54 Figure 2.3. Quantification of ANME mcrA subgroups in three sediment cores from Santa Monica Basin. ................................................................................................................... 55 Figure 2.4. Quantification of ANME mcrA subgroups in three sediment cores from La Goleta. ............................................................................................................................... 57 Figure 2.5. Chemistry and quantification of ANME subgroups in sediment core from Tonya seep. ....................................................................................................................... 59 Figure 2.6. Ordination of sediment depth intervals in mcrA subgroup (a1, a2, b, c, d, e) space.................................................................................................................................. 60 Figure 2.7. Relative dissimilarity between overall mcrA subgroup (a1, a2, b, c, d, e) composition in 3 cm sediment depth intervals from Santa Monica Basin and La Goleta, and chemical gradients along which mcrA subgroups partition. ...................................... 61 Figure 2.8. ANME-1 associated mcrA (panel a) and ANME-2b and c associated mcrA (panel b) as a function of methane concentration. ............................................................ 62 Figure A2. mcrA qPCR temperature and specificity optimization gels. a) Temperature (0C) optimization for assay. The chosen temperature is highlighted in a red box; b) Non- target subgroup amplification. .......................................................................................... 87 Figure A2. mcrA qPCR temperature and specificity optimization gels. a) Temperature (0C) optimization for assay. The chosen temperature is highlighted in a red box; b) Non- target subgroup amplification. .......................................................................................... 88 Figure A3. Full length mcrA diagram with nucleotide positions, structural characteristics, position of conserved/posttranslationally modified amino acid residues, and position of mcrA primers designed in this study. ...................................................... 90 Figure A4. Comparison of mcrA qPCR assays designed in this study to published SSU assay results.  Values on x axis represent the cores analyzed........................................... 92 vii Figure B1. mcrA subgroups as a function of log methane concentration for Santa Monica Basin, La Goleta and Tonya seep. Slope , r2 and probability (p) values are displayed for the linear regression. ......................................................................................................... 93 Figure B2. mcrA subgroups as a function of sulfate concentration for Santa Monica Basin, La Goleta and Tonya seep. Slope , r2 and probability (p) values are displayed for the linear regression. ......................................................................................................... 94 Figure B3. mcrA subgroups as a function of sulfide concentration for Santa Monica Basin and La Goleta. Slope , r2 and probability (p) values are displayed for the linear regression. ......................................................................................................................... 95 Figure B4. mcrA subgroups as a function of ammonia concentration for Santa Monica Basin and La Goleta. Slope , r2 and probability (p) values are displayed for the linear regression. ......................................................................................................................... 96 viii List of abbreviations ANME Anaerobic methane oxidizers AOM Anaerobic oxidation of methane BES Bromoethanesulfonic acid CARD-FISH Catalyzed reporter deposition fluorescence in situ hybridization E0’ Redox potential EPS Extrapolymeric substances FISH Fluorescence in situ hybridization NMS Nonmetric multidimensional scaling OM Organic matter qPCR Quantitative polymerase chain reaction SRB Sulfate reducing bacteria SEEPS Studies on the Ecology and Evolution of Petroleum Seeps SSU Small subunit ribosomal DNA TEA Terminal electron acceptor ix Acknowledgments I wish to thank: Steven Hallam for providing opportunity and advice throughout this degree with his insightful guidance and imagination; my committee members Bill Mohn, Maria Maldonato and Michael Murphy for their time and useful advice during my postgraduate degree. I would also like to thank my external examiner, Philippe Tortell for his time; and our collaborator David Valentine of the University of California, at Santa Barbara, for inviting me on the Studies on the Ecology and Evolution of Petroleum Seeps (SEEPS 2007) cruise and for hosting me in his laboratory. I also acknowledge the crew members of the Atlantis and their assistance in collecting the samples used in this study; to our collaborator Frank Kinnaman of the David Valentine Laboratory for his chemistry measurements and helpful discussions; and for the technical support of the Hallam lab’s Jinshu Yang, David Walsh, Elena Zaikova and Lauren Weatherton, I am grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds Quebecois de Recherche sur la Nature et les Technologies (FQRNT) and the University of British Columbia Graduate fellowship for funding this project. xCo-authorship statement Chapter II is a manuscript in preparation written in the framework of the Studies on the Ecology and Evolution of Petroleum Seeps (SEEPS) program initiated by our collaborator David Valentine of the University of California at Santa Barbara. The methane, sulfate, sulfide, ammonia and alkalinity were all provided by our collaborator’s PhD student Frank Kinnaman. The qPCR assay development and application, data analysis, and writing were done by Lea Constan. 1Chapter I  Introduction The discovery of anaerobic methane oxidizers The anaerobic oxidation of methane is responsible for the removal of ninety percent of the methane generated anaerobically in ocean sediments (Reeburgh, 2007). Studies spanning three decades, including diffusion-advection modeling, and isotopic tracer and stable isotope distribution analyses, indicated that AOM was microbially mediated (Valentine and Reeburgh, 2000). However, the responsible organisms have just recently been identified (Hinrichs et al., 1999; Boetius et al., 2000; Orphan et al., 2001a), and no representatives have yet been cultured. In order to understand the rationale behind the studies leading to the discovery of anaerobic methane oxidizers (ANME), it is necessary to address the ecological determinants governing microbial partitioning in sea sediments. In sediments, microbial groups stratify in a predictable manner according to the redox potential (E0’) of their terminal electron acceptors (TEA). Figure 1.1 b represents the typical succession of TEA, their reduced counterparts, and their E0’ values. Oxidants fueling metabolism are in the form of organic matter (OM) sedimenting from the overlaying productive waters. Microbial groups able to reduce higher E0’compounds outcompete lower E0’ TEA users through the manipulation of surrounding hydrogen concentrations, thus rendering other reactions thermodynamically unfavorable (Lovley et al., 1982; Chapelle and Lovley, 1992). The resulting stratification occurs because high E0’ TEA concentrations decrease during the oxidation of OM, enabling lower E0’ TEA users to proliferate (Figure 1.1 b, reactions 1-6). Methanogenic euryarchaea, the only biological entities known to produce methane, are at the bottom of the energetic hierarchy, mediating reaction 6 in Figure 1.1 b. Since methanogenic enzymatic machinery has a significant predilection for C12 over C13 (isotopic depletion), methane of thermogenic and biogenic origin can usually effectively be separated, and the conversion products of methane of biogenic origin can easily be traced (Reeburgh, 2007). AOM typically occurs within a zone of concomitant sulfate and methane depletion, identified as the sulfate methane transition zone (SMTZ). Figure 1.1a depicts typical coastal sediment SMTZs. Sulfate diffuses into the sediments from the overlaying seawater, whereas 2methane tends to diffuse upward from deeper sediment layers and originates either from the reduction of CO2 by methanogens, or from thermogenic sources (Reeburgh, 2007). The occurrence of AOM between the methanogenic and sulfate reducing layers provided the first indications that a methanogen in consortium with a sulfate reducing bacteria (SRB) was responsible for the process (Martens and Berner, 1977). A key set of sediment incubation studies by Hoehler et al., 1994, lent credence to the methanogen-SRB consortium hypothesis. Sulfate- and methane-amended sediment incubations completely ceased to produce AOM activity when either molybdate, a specific sulfate reduction inhibitor, or bromoethanesulfonic acid (BES), a specific methanogenesis inhibitor was added (Martens and Berner, 1977; Hoehler et al., 1994). BES is a substrate analogue for coenzyme M, which is reduced to release methane in methanogens. The affected enzyme is methyl coenzyme M reductase (Mcr), the central enzyme in methanogenesis. The inhibitor studies showed that methane oxidation was directly coupled to sulfate reduction and directly implicated Mcr in AOM. Since Mcr was only known to exist in methanogens, this led to the hypothesis AOM occurs through a direct reversal of methanogenesis (Hoehler et al., 1994). The coupling of methane oxidation to sulfate reduction has been posited to occur through the following reaction under typical conditions: CH4 + SO4 2− + H+ = CO2 + HS − + 2H2O    ΔG°′ = −21 kJ/mol   (Shima and Thauer, 2005) The standard free energy change is very low, given the biological energy quantum is considered to be 20 kj/mol (Schink, 1997). The next key studies, done by two separate groups of researchers, identified the first two anaerobic methane oxidizer groups, followed by a third several years later. Armed with the knowledge that a methanogen relative is involved in AOM, identification of isotopically depleted lipids, combined with small ribosomal subunit (SSU) detection methods, resulted in the identification of ANME-1, -2 and -3. Since the signature of isotopically depleted methane can be traced, a lipid analysis, which can be used for broad taxonomic assignments, was undertaken from total sediment microbial population in an Eel River Basin hydrate dissociation seep core. The investigation 3revealed the presence of an organism with isotopically depleted lipids reminiscent of methanogenic archaea. An archaeal SSU assay done in conjunction with the lipid analyses showed that a previously unknown clade branching within methanogenic lineages was abundant at the site. This clade was named ANME-1 (Hinrichs et al., 1999). Shortly thereafter, ANME-2 was identified with a combination of fluorescence in situ hybridization (FISH) and lipid analyses at Hydrate Ridge, off the coast of Oregon (Boetius et al., 2000). However, the evidence linking the lipid analyses with the SSU data was circumstantial, and a combination of FISH and secondary ion mass spectrometry (FISH-SIMS), a method targeting single cell isotopic composition, confirmed that the lipids detected belonged to the suspected ANME-1 and -2 groups. A third group, ANME- 3, was identified more recently in the Haakon Mosby mud volcano (Thiel et al., 1999). The following section will explore the phylogeny of ANME groups in greater detail. Phylogenetics of ANME groups ANME groups, as predicted by Martens and Berner, 1977, are related to methanogenic archaea, and form three phylogenetically distinct clades. ANME-1 forms a cluster distinct from any other phylogenetic lineage (Figure 1.2) located between Methanomicrobiales and the Methanosarcinales. On the other hand, ANME-2 and ANME-3 both form clusters within methanosarcinal lineages, with ANME-2 closest to Methanosaeta and ANME-3 closest to Methanococcoides (Knittel et al., 2005). Figure 1.2 details the phylogenetic relationship of all three ANME groups, both with the SSU and the mcrA gene. The existence of three separately branching ANME lineages has raised interesting questions regarding the timing of the evolution of AOM and possible discrepancies in metabolism between the three lineages (Blumenberg et al., 2004). Based on the clustering of subgroups conserved between geographical locations, the ANME-1 can be further differentiated into ANME-1a, 1b and a third unnamed group, and the ANME-2 has been partitioned into an ANME-2a, 2b and 2c subgroup (Orphan et al., 2001b; Knittel et al., 2005). In the text herein below, ‘group’ will refer to ANME-1, -2 and -3 and ‘subgroup’ will refer to the divisions within groups. 4ANME cluster morphology The three ANME groups have distinct morphological traits, as identified by FISH (Boetius et al., 2000; Orphan et al., 2001b; Orphan et al., 2002; Losekann et al., 2007). Of the three, ANME-1 is the most distantly related, while ANME-2 and ANME-3 are morphologically similar (Figure 1.3). ANME-1 is generally rod shaped and either found in the form of long chains, short segmented rods or as single cells. The individual cells are 1-2 m in size and the chains range from 10-70 m (Orphan et al., 2002; Knittel et al., 2005) (Figure 1.3 a). ANME-2 is typically observed in tight clusters surrounded by SRB of the desulfococcus/desulfosarcina cluster. Recently, low levels of ANME-2c were found in association with desulfubulbus, alpha and betaproteobacterial partners (Orphan et al., 2002; Pernthaler et al., 2008), in consortia reaching diameters of up to 150 m (Figure 1.3 b,c,d) (Orphan et al., 2002). ANME-2c has also been observed in clusters without the presence of surrounding microbes and in loosely packed matrices with SRB (Orphan et al., 2002). FISH images also suggest that ANME-2a has a tighter association with SRB than ANME-2c (Figure 1.3 b,c,d). ANME-3 is morphologically similar to ANME-2 in that it forms tight clusters of cells in close association with SRB (Niemann et al., 2006). However, in contrast to the ANME-2, the SRB correspond to the order Desulfubulbus, and FISH images show the association is likely not specific to Desulfobulbus, as the use of a probe targeting the bacterial domain seems to reveal additional unidentified bacterial partners (Figure 1.3, e,f) (Niemann et al., 2006). Following the identification and morphological characterization of the ANME subgroups, a number of sites known to have AOM activity have been studied to examine community structure. AOM sites AOM occurs in a wide variety of settings around the world, broadly separated into discrete layers in coastal and open ocean sediments (Devol, 1983; Iversen and Jorgensen, 1985; Niewohner et al., 1998), hydrothermal systems (Teske et al., 2002; Brazelton et al., 2006), methane seep sites (Hinrichs et al., 1999; Michaelis et al., 2002), terrestrial and submarine mud volcanoes (Alain et al., 2006; Niemann et al., 2006), methane hydrate sites (Elvert et al., 1999; Hinrichs et al., 1999), and anoxic water columns (Wakeham et 5al., 2007). Figure 1.4 illustrates the globally distributed sites where AOM has been shown to occur and, in some instances, where ANME populations have been studied. The most thoroughly studied sites are Hydrate Ridge, Gulf of Mexico – both methane hydrate sites, and the Black Sea and Eel River Basin – both seep sites (Figure 1.4). Despite the large number of sites studied, the environmental variables underpinning ANME community structure remain poorly understood. ANME ecology Although the ecology of ANME groups has been addressed in a limited number of studies, the available data suggests that ANME dispersal does not seem to follow biogeographical patterns, but is instead suspected to follow yet undefined geochemical patterns (Orphan et al., 2002; Knittel et al., 2005; Kruger et al., 2008). Table 1.1 details the different sites examined with various methods and the respective ANME (sub)groups that were found. ANME (sub)group abundance patterns are not conserved within geographical locations. In a study of Hydrate Ridge sediments, Knittel et al, 2005 found sites where either ANME-2a or ANME-2c were most abundant in the superficial sediment layers. In addition, shifts in abundance of different ANME groups within a sediment core are often observed. For example, in studies at Eel River Basin, Orphan et al, 2002 found a clear overabundance of ANME-1 throughout one sediment core, but dominance shifted from ANME-2 in sediment surface layers to ANME-1 in deeper sediment layers in another core. Likewise, Orcutt et al, 2005, observed the same shift in abundance between ANME-2 and ANME-1 in Gulf of Mexico sediments. A comparison of results from different studies shows that almost all ANME-1 and ANME-2 subgroups occur in different proportions within and between sediment core and microbial mat samples taken from each of the well-studied Eel River Basin, Black Sea and Gulf of Mexico sites. Taken together, the heterogeneity that is consistently observed within sites suggests the ANME (sub)group distribution is not a result of random dispersal but likely follows geochemical trends.  Macroscopic surficial features between sites often contain similar ANME group distribution. In the Black Sea microbial mats wrapped around carbonate chimneys, ANME distribution patterns are conserved, with ANME-2 abundant near the mat-water 6interface and ANME-1 abundant in deeper mat layers. Also, cores sampled from around Calyptogena clam fields in Hydrate Ridge and in Kuroshima Knoll were both shown to contain ANME-2a populations. In addition, ANME-1 is often most abundant in seep sediments, suggesting a link between sites sharing geochemical features and ANME population structure. Linking ANME population structure to chemistry Based on empirical observation, ANME-1 and -2 partitioning has been proposed to be influenced by several ecological variables, as summarized in Table 1.2. ANME-2 normally proliferates in layers close to the water interface, and where there is a barrier to exchange such as a microbial mat or depth, ANME-1 is consistently numerically abundant. This distribution has led to the hypothesis that ANME-2 is either tolerant to oxygen, requires higher sulfate concentrations, or is inhibited by hydrogen sulfide, which accumulates with depth. ANME-1, with the exception of the Haakon Mosby mud volcano where ANME-3 was also detected, is numerically abundant in instances where hydrogen sulfide and sulfate concentrations are relatively higher and lower, respectively. This suggests that ANME-1 forms communities that are potentially more stable but adapted to less thermodynamically favorable conditions for survival (Orphan et al., 2002; Blumenberg et al., 2004; Knittel et al., 2005; Kruger et al., 2008). Methane concentrations greatly influence AOM rates and ANME-1 and ANME-2 populations respond differently to temperature fluctuations (Nauhaus et al., 2002; Kruger et al., 2005).  In a study conducted by Nauhaus et al, 2001 an ANME-1 population from Black Sea microbial mats and an ANME-2 population from Hydrate Ridge were subjected to a range of pH, temperature, methane and sulfate concentrations. Sediment enrichments amended with different methane concentrations via manipulation of partial pressure showed AOM rates heavily depended on methane concentrations in ANME-1 and ANME-2 populations. Furthermore, the ANME-2 community had significantly higher cell-specific AOM rates than the ANME-1 community (Nauhaus et al., 2005). Since the samples were taken from different sites, it is unclear if these differences are reflective of the environment in which the communities have evolved, or of actual physiological differences between the two groups. Repetition of the experiment with 7cores from different sites, or sectioning of the ANME-1 and ANME-2 layers within the same microbial mat could perhaps yield more definitive answers. Nauhaus et al., 2005 also showed that ANME-2 communities from Hydrate Ridge were more psychrophilic than an ANME-1 community from the Black Sea mats; optimal temperatures for the groups range from 0-15 oC and 15-30 oC, respectively (Nauhaus et al., 2005). The relevance of temperature in ANME population distribution in the environment is contentious, as relative abundance shifts within sediment cores over depth ranges while temperature remains presumably fairly constant (Table 1.1) (Orphan et al., 2002; Knittel et al., 2005). In summary, parameters governing ANME group partitioning have not yet been definitively resolved, although it does appear that the populations partition along chemical gradients. This chapter has so far focused on the taxonomic identification of ANME groups and subgroups in a variety of settings around the world and has superficially touched upon factors governing their differential partitioning in the environment. The remainder of the chapter will focus on functional aspects of ANME existence. The study of the genomes and proteomes of these organisms has yielded important insights into key physiological processes associated with AOM. AOM biochemical insights The ANME are thought to mediate AOM through a direct reversal of the methanogenic pathway. This hypothesis is supported by the phylogenetic proximity of the ANME to methanogens (figure 1.2) (Hinrichs et al., 1999; Orphan et al., 2001b), the inhibition of AOM activity by methanogen-specific inhibitors (Hoehler et al., 1994; Nauhaus et al., 2005), both of which were previously discussed in this chapter, and the presence and expression of genes from methanogenic pathways in AOM environments (Hallam et al., 2003; Kruger et al., 2003; Hallam et al., 2004; Meyerdierks et al., 2005a). An enzyme known to exist exclusively in methanogens is encoded and highly expressed in both ANME-1 and ANME-2. In a study by Hallam et al., mcrA genes were retrieved from multiple sites where AOM was known to occur and were shown to form at least 2 phylogenetically distinct clusters within methanogenic lineages. Since mcrA phylogeny mirrors SSU phylogeny, assignments to ANME-1 and ANME-2 were possible (Luton et al., 2002). Figure 1.2 shows the mcrA and SSU trees facing each other and the 8phylogenetic position of each ANME group in the context of methanogenic lineages. Since no physical linkage data exists between mcrA and SSU subgroup genomic fragments, assignments of mcrA subgroups to specific ANME SSU subgroups were not possible. Consequently separate subgroup assignments were devised based solely on mcrA (Hallam et al., 2003; Losekann et al., 2007). ANME-1 associated mcrA subgroups were designated a and b and ANME-2 associated subgroups were designated c, d, and e (Hallam et al., 2003). ANME-3 associated mcrA was designated mcrA f based on the cell sorting ANME-3 aggregates and mcrA amplification (Losekann et al., 2007). An AOM community containing exclusively ANME-1b SSU subgroup in a hypersaline brine in the Gulf of Mexico correspondingly only contained the mcrA a allelic variant, enabling the assignment of that mcrA subgroup to the ANME SSU subgroup (Lloyd et al., 2006). In addition, the Carpethian Mountains terrestrial mud volcano site ANME community was almost exclusively composed of ANME-2a, and mcrA e was the most abundant allele (Alain et al., 2006). However, since this evidence is based on SSU and mcrA libraries where primer biases are known to occur and are not quantitative, the assignment is not certain. In addition, the targeted immunocapture studies of ANME-2c cells followed by pyrosequencing and comparison to the fosmid GZfos35D7 (Hallam et al., 2003) directly linked the mcrA d allele with the ANME-2c subgroup (Pernthaler et al., 2008). The presence of the mcrA gene alone was compelling however not direct proof that the ANME were reversing methanogenesis. It is necessary to explore the topology of Mcr in methanogens in order to understand the differences between methanogen and ANME Mcr isoforms. Methyl coenzyme M reductase and AOM All known methanogens express Methyl coenzyme M reductase (Mcr), which catalyzes the release of methane, the final step in methanogenesis. It is a 300 kDa nickel- containing heterotrimeric α2β2γ2 subunit enzyme with two active sites. It contains a Ni(I) oxidation state porphinoid (F430) in its active conformation (Ermler et al., 1997). The active site periphery contains five modified amino acids: thioglycine445, N-methyl- histidine257, S-methyl-cysteine452, 5-(S)-methyl arginine271 and 2-(S)-methyl glutamine400. Methyl groups are biosynthetically derived from methyl group of 9methionine and from methyl groups of the methyl-coenzyme M (Ermler et al., 1997; Grabarse et al., 2000). While many of the features described above are conserved in ANME Mcr, ANME-specific studies have revealed important differences. Black Sea microbial mats highly enriched in ANME-1 and ANME-2 groups enabled the direct purification of Mcr, along with associated cofactors.  Kruger et al., 2003 isolated two Mcr homologues comprising a total of 10% of all proteins in the Black Sea microbial mats, where ANME-1 comprises 50-70% of microbial biomass (Kruger et al., 2003). The most abundant homologue, which is associated with ANME-1 (7% total protein), was shown to belong to McrA subgroup a by N-terminal sequencing. It had an associated modified F430 cofactor with a mass of 951 Da, in contrast to the 905 Da F430 found in all methanogenic archaea. The second homologue (3% total protein) was assigned to ANME-2 and had an associated F430 cofactor identical to that found in methanogens (Kruger et al., 2003; Mayr et al., 2008b). Methyl-coenzyme M reductases from methanogenic archaea catalyze methane formation with a maximal specific activity of approximately 100 U/mg, whereas microbial mats dominated by ANME-1 were estimated to have specific rates for AOM of only 0.1 mU/mg protein (Michaelis et al., 2002; Shima and Thauer, 2005). High concentrations of Mcr in the context of AOM may partially compensate for the slow kinetic yields of the reaction (Shima and Thauer, 2005). Further studies showed that the enzyme in ANME-1 also contained key differences that include a substitution of glutamine400 (Q) to valine400 (V) and a cysteine-rich motif (CCXXXXCXXXXXC) 403-415 adjacent to the active site. (Mayr et al., 2008b). The modified ANME-1 associated cofactor was recently elucidated (Mayr et al., 2008b). Based on this cofactor modification, it was hypothesized that the Mcr substitution in ANME-1 at 400 may alter the active site geometry to accommodate the modified F430 (Mayr et al., 2008b). Further studies on postrantionally modified sites with the Mcr enzyme associated with ANME-1 found only histidine257 to be methylated in the ANME-1 isoform. However, it is possible the glycine thioxylation was lost by spontaneous hydrolysis during handling (Kahnt et al., 2007). In summary, the Mcr enzyme of ANME-1, in contrast to ANME-2, contains key differences from the 10 methanogenic Mcr. Although most of the functional studies have focused on the Mcr enzyme, other components of the reverse methanogenic pathway have also been found. Metagenomics has enabled the identification of several enzymes from the methanogenic pathway in ANME-2 and all but one enzyme in ANME-1 (Kruger et al., 2003; Hallam et al., 2004; Meyerdierks et al., 2005a). Table 1.3 summarizes the results of these studies.  In addition, activity for three of these proteins has been measured in Black Sea microbial mats (Kruger et al., 2003). Many of the genes observed in Eel River Basin were also identified at the protein level by liquid chromatography coupled to tandem mass spectrometry analysis (Constan & Hallam, unpublished results).  The study of environmental samples has enabled the identification of many elements of the methanogenic pathway that are presumably used in reverse. Recent studies have also found that methanogenesis might also be occurring in AOM communities (Treude et al., 2007b). CO2 fixation rates corresponding to less than ten percent of AOM rates have recently been documented in Black Sea microbial mats. In these samples, ANME-1 and – 2 were the only detectable euryarchaeal phylotypes, raising the possibility that ANME are assimilating both CO2 and methane (Treude et al., 2007a). However, a long term incubation study with either C14 labeled methane or carbon dioxide indicated the SRB partners are assimilating the CO2, but that the assimilation is dependent on AOM activity (Wegener et al., 2008a). Syntrophic partners To date, sulfate has been the only TEA found to drive AOM in ANME-1 and ANME-2 communities. Still, a paucity of information is available on the SRB partners of ANME-2 and -3 and on the terminal electron accepting processes in ANME-1. In most environmental sites and sediment enrichment studies where AOM and sulfate reduction were measured, both processes occur at a 1:1 ratio, implying an absolute dependence of AOM on sulfate reduction (Nauhaus et al., 2002; Treude et al., 2003; Treude et al., 2005; Kruger et al., 2008; Orcutt et al., 2008; Wegener et al., 2008a). Wegener et al. 2008 studied AOM/Sulfate reduction rate ratios at Hydrate Ridge – an ANME-2a population, 11 Gullfaks oil field – an ANME-2c population, and Black Sea microbial mats – a mixed ANME-1 and ANME-2 population, and found them all to have a 1:1 AOM/sulfate reduction ratio (Wegener et al., 2008a). Conversely, sulfate reduction rates are sometimes higher than AOM rates, implying the SRB employ alternate metabolic strategies in these instances (Iversen and Blackburn, 1981; Iversen and Jorgensen, 1985; Joye et al., 2004). In a study of the Gulf of Mexico seeps, Joye et al. observed this decoupling and proposed hydrocarbons or oil as other possible SRB substrates (Joye et al., 2004). Interestingly, Gulf of Mexico sediments are often found to be ANME-1-dominated, raising the possibility that ANME-1 populations are not as tightly coupled with sulfate reduction (Table 1.1). Consistent with this hypothesis is the partial inhibition of AOM with molybdate in ANME-1 sediment enrichments. However, the possibility that the molybdate adsorbed to extra-polymeric substances cannot be discounted (Nauhaus et al., 2005). Sulfate reduction has traditionally been observed to be the process of choice linked to AOM. However, thermodynamic calculations indicate other TEA, such as Fe and Mn, could also be used (Valentine and Reeburgh, 2000; Shima and Thauer, 2005). There is one account of AOM coupled to denitrification in an enrichment culture from canal sediments. This consortium consisted of two microorganisms: a bacterium representing a phylum without any cultured species and an archaeon distantly related to ANME. However, the ratio of bacterial to archeal cells (~ 8:1) was different from the ratio reported for sulphate-dependent AOM (1:1). It has now been shown that the microbes accomplishing AOM are not ANME, but a novel bacterium of the NC10 candidate division (Raghoebarsing et al., 2006; Ettwig et al., 2008). Transferred intermediates The identity of the product shuttled between the SRB and the ANME cells is not known. Hydrogen (Nauhaus et al., 2002; Nauhaus et al., 2005), acetate (Nauhaus et al., 2002; Nauhaus et al., 2005), formate (Nauhaus et al., 2002; Nauhaus et al., 2005), methanol (Nauhaus et al., 2002), carbon monoxide (Nauhaus et al., 2005) and methylamines (Nauhaus et al., 2005) have been tested in the absence of methane in incubations and enrichments with no appreciable sulfate reduction rate increase. However, in Black Sea microbial mats dominated by ANME-1, but not in Hydrate Ridge 12 ANME-2 dominated sediments, sulfate reduction was stimulated to the same extent with formate and hydrogen, but never exceeded the rates measured with methane. Incubation of these compounds for several weeks did not result in the enrichment of the desulfucoccus/desulfosarcina syntrophic partners, casting doubt on the role of these compounds in shuttling electrons from ANME to SRB (Nauhaus et al., 2005). Humic acids, phenazine methanosulfate, phenazine ethosulfate, nitrate, Mn(IV) (MnO2), Fe (III) (ferrihydrite or citrate), sulfur and fumarate have all been added to an ANME-2 community from Hydrate Ridge in incubations without sulfate to test for methane oxidation. None of the compounds behaved as alternative TEAs, suggesting ANME-2 communities are restricted to sulfate-dependent methane oxidation (Nauhaus et al., 2005). Summary The AOM is a widespread phenomenon that plays a significant role in global methane cycling. Three groups of anaerobic methane oxidizers are currently known to exist, which could further be separated into subgroups. Current evidence indicates both ANME-1 and ANME-2 couple AOM to sulfate reduction, although the relationship is less obvious with ANME-1. Elements of the biochemical pathway for AOM through reverse methanogenesis have been validated for ANME-1 and ANME-2, both at the genomic, proteomic and functional level. In addition, the enzyme Mcr is thought to be the key enzyme governing this process, although the catalytic mechanism remains to be elucidated. There is also an increasing amount of evidence indicating subtle differences in physiology exist between subgroups, enabling partitioning within and between geographical locations. The exact parameters influencing subgroup abundance are not yet understood. Furthermore, the mcrA sequence, as in methanogens, is an excellent proxy for the phylogenetic classification of ANME. The use of this marker for the quantification of different subgroups linked to environmental variables could potentially yield some important insight into both the physiology and ecology of ANME subgroups. 13 Hypothesis We hypothesize that ANME subgroups partition along defined geochemical gradients. Thesis objectives Anaerobic methane oxidizing archaeal groups are stratified in sediment environments, as evidenced by lipid analyses (Blumenberg et al., 2004), FISH cell counts (Orphan et al., 2004; Knittel et al., 2005) and SSU libraries. The objectives of this project were to 1) design a qPCR assay to quantify different ANME subgroups based on mcrA, 2) to implement the assay within and between a variety of sites where AOM is known to occur and 3) to identify if correlations could be drawn between the qPCR data and methane, sulfate, sulfide, ammonia and alkalinity (concentration of acid necessary to neutralize a sample; bases usually include carbonate, bicarbonate, phosphates, and hydroxides) measurements. 14 Figure 1.1. Typical a) sulfate methane transition zone (SMTZ) and b) succession of TEA in sea sediment layers with corresponding redox potentials (Eo’). The sulfate methane transition zone (SMTZ) is delineated with a dashed line. Layer thickness is a rough approximation of relative importance of the process in sea sediments. All data in a) was taken from this site 4322-a7, microbial mat periphery of this study. Data in b) was taken from Thauer et al., 1977 except for * marked lines, which are from Thauer and Shima, 2008. 15 Figure 1.2. A phylogenetic comparison of genes encoding methyl coenzyme M reductase subunit alpha (mcrA) and small subunit ribosomal RNA (SSU rDNA) derived from environmental clones and primary methanogenic lineages. For purposes of tree construction and presentation, an HKY evolutionary model was used in maximum likelihood analysis and bootstrap values based on 100 replicates each are shown for branches with greater than 50% support. Methanopyrus kandleri was used as the out group reference. Scale bars represent 0.1 nucleotide substitutions per site. Accession numbers for corresponding mcrA sequences are as follows: Methanosarcina acetivorans (AE010299.1), Methanosarcina barkeri (CP000099.1), Methanococcoides burtoni (CP000300.1), Methanothermus fervidus (J03375.1), Methanospirillum hungatei (CP000254.1), Methanococcus jannaschii (L77117.1), Methanopyrus kandleri (AE009439.1), Methanocorpusculum labreanum (CP000559.1), Methanoculleus marisnigri (CP000562.1), Methanococcus maripaludis (BX950229.1), Methanosarcina mazei (AE008384.1), Methanosphaera stadtmanae (CP000102.1), Methanosaeta thermophila (CP000477.1), Methanococcus vannielii (CP000742.1), Methanococcus voltae (X07793.1), Methanothermus thermoautotrophicus (U10036.1), ANME-1 subgroup a/b representative (AY324369.1), ANME-2 subgroup c/d representative (AB362199.1), AOM-affiliated subgroup e representative (DQ521858.1), and ANME-3 subgroup f representative (AM407730.1). Accession numbers for corresponding SSU rDNA sequences are as follows: Methanosarcina acetivorans (M59137.2), Methanosarcina barkeri (NC_007355), Methanococcoides burtoni (NC_007955), Methanothermus fervidus (M59145.1), Methanospirillum hungatei (NC_007796), Methanococcus jannaschii (M59126.1), Methanopyrus kandleri (AB301476.1), Methanocorpusculum labreanum (NC_008942), Methanoculleus marisnigri (NC_009051), Methanococcus maripaludis (U38941.1), Methanosarcina mazei (AE008384), Methanosphaera stadtmanae (NC_007681), Methanosaeta thermophila (NC_008553), Methanococcus vannielii (NC_009634), Methanococcus voltae (NZ_ABHB01000001), Methanothermus thermoautotrophicus (AY196661.1), ANME- 1a representative (AY053468.1), ANME-1b representative (AJ578089.1), ANME-2a representative (AF354130.1), ANME-2c representative (AY323221.1), and ANME-3 representative (AJ578119.1) 16 17 Figure 1.3. Examples of FISH stained ANME cells. ANME-1 (Black Sea mat), ANME-2 (Hydrate Ridge) and ANME-3 (Haakon Mosby mud volcano) cells (Red) with probes specific for bacterial partner (green). (a) Color overlay of ANME-1 cells targeted with probe ANME-1-350 labeled with Cy3 (red) and SRB of the Desulfosarcina-Desulfococcus branch labeled with fluorescein (probe DSS658; green) in a Black Sea mat section. (b,c) ANME-2c/DSS aggregates (probe ANME-2c- 622 labeled with Cy3 [red] and probe DSS658 labeled with Cy5 [green]). (d) ANME- 2a/DSS aggregates (probe ANME-2a-647 labeled with Cy3 [red] and probe DSS658 labeled with Cy5) (e) Probe ANME3-1249, specific for ANME-3 archaea, is shown in red, and probe DBB660, specific for Desulfobulbus spp., is shown in green. (f) Color overlay of mixed-type consortia. Probe ANME3-1249, specific for ANME-3 archaea, is shown in red, and probe EUB338 I-III, specific for Bacteria, is shown in green. Scale bars represent 10 μm. Adapted from Knittel et al., 2005 (panels a-d) and Losekan et al., 2007 (panels e,f) with permission from the American Society for Microbiology. 18 Figure 1.4. World map with sites where AOM activity and/or communities have been studied. 19 Table 1.1. Anaerobic methane oxidizer (sub)group distribution separated according to relative abundance in various geographical locations. Site types are abbreviated as follows: M, microbial mat; W, water column; S, sediment; C, carbonates. Detection methods are abbreviated as follows: FISH, fluorescent in situ hybridization; SSU, small ribosomal subunit; lipids, lipid analysis; qPCR, quantitative polymerase chain reaction; CARD-FISH, catalyzed reporter deposition FISH; mcrA, amplification, cloning and sequencing of methyl coenzyme M reductase environmental alleles. 20 21 22 Table 1.3. A functional overview of canonical methanogenic pathway components identified within methane oxidizing communities with special emphasis on ANME-1 and ANME-2 subgroups. For purposes of gene identification, only loci linked to large insert genomic clones binned to ANME groups are included (Hallam et al., 2004). The term ND designates undetermined taxonomic affiliation. References cited include Eel River Basin (Hallam et al., 2004), Black Sea (Kruger et al., 2003; Meyerdierks et al., 2005b; Heller et al., 2008b; Mayr et al., 2008a), Hydrate Ridge (Kruger et al., 2003; Meyerdierks et al., 2005b) and Baltic Sea (Kruger et al., 2003). Structural characterization of the ANME-1 F430 cofactor variant is included in the cofactor identification label. 23 References Alain, K., Holler, T., Musat, F., Elvert, M., Treude, T., and Kruger, M. (2006) Microbiological investigation of methane- and hydrocarbon-discharging mud volcanoes in the Carpathian Mountains, Romania. Environ Microbiol 8: 574-590. 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(2007) Consumption of methane and CO2 by methanotrophic microbial mats from gas seeps of the anoxic black sea (vol 73, pg 2271, 2007). Applied and Environmental Microbiology 73: 3770-3770. Valentine, D.L., and Reeburgh, W.S. (2000) New perspectives on anaerobic methane oxidation. Environ Microbiol 2: 477-484. Wakeham, S.G., Amann, R., Freeman, K.H., Hopmans, E.C., Jorgensen, B.B., Putnam, I.F. et al. (2007) Microbial ecology of the stratified water column of the Black Sea as revealed by a comprehensive biomarker study. Organic Geochemistry 38: 2070-2097. 28 Wegener, G., Niemann, H., Elvert, M., Hinrichs, K.U., and Boetius, A. (2008) Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environmental Microbiology 10: 2287-2298. A version of this manuscript will be submitted for publication. Constan, L., Kinnaman, F., Valentine, D.L. and Hallam, S. J. A study of subgroup partitioning trends along geochemical gradients in anaerobic methane oxidizing archaea. 29 Chapter II  A study of subgroup partitioning trends along geochemical gradients in anaerobic methane oxidizing archaea Abstract The Anaerobic oxidation of methane is a globally significant process mediated by uncultured anaerobic methane oxidizers (ANME), and is responsible for the removal of up to ninety percent of the methane in sea sediments (Reeburgh, 1007). Three ANME groups, which further partition into subgroups, are known to exist. The object of this study was to identify parameters governing ANME subgroup partitioning in seep sediments. A suite of qPCR assays targeting the methyl coenzyme M reductase alpha subunit gene (mcrA) were developed to enable subgroup quantification and were applied to sediment cores from three seep sites off the Californian coast. The values were then correlated to methane, sulfate, sulfide, ammonia and alkalinity measurements. Results suggested methane was the sole energy source for all ANME populations. ANME-2 was abundant in superficial sediment layers, where sulfate concentrations are relatively elevated, indicating it is essential for its growth. The relationship of ANME-1 with sulfate was not as straightforward, implying their ability to reduce sulfate at greatly reduced concentrations, or a sulfate-independent metabolism. ANME-2 subgroup partitioning is explained by ammonia gradients.  ANME-1 subgroup partitioning was not entirely explained by chemical parameters examined in this study, and a competitive advantage of ANME-2 at higher sulfate concentrations is thought to be partially responsible for obscuring trends in ANME-1 partitioning. The development and application of these qPCR assays to compare environmental parameter measurements to ANME subgroup abundances has begun to reveal important trends underpinning the subgroup stratification of ANME in the environment. Introduction The identity of anaerobic methane oxidizers has recently been revealed through the clever combination of FISH and secondary ion mass spectrometry (FISH-SIMS) 30 (Orphan et al., 2001a). Almost 10 years later, 3 phylogenetically distinct groups of these methanogen-related euryarchaeotes are known, and consistently partition into subgroups as: ANME-1 (a and b), -2 (a,b and c), and -3, none of which has representatives in culture (Hinrichs et al., 1999; Boetius et al., 2000; Orphan et al., 2001b; Knittel et al., 2005; Niemann et al., 2006). ANME-1 forms a cluster of its own between the methanomicrobiales and methanosarcinales, whereas ANME-2 and ANME-3 both branch within methanosarcinal lineages. Interestingly, almost all geographical sites surveyed to date at the SSU level contain different combinations of ANME subgroups (Michaelis et al., 2002; Orphan et al., 2002; Teske et al., 2002; Inagaki et al., 2004; Knittel et al., 2005; Brazelton et al., 2006; Losekann et al., 2007). General discrimination at the ANME-1, ANME-2 and ANME-3 level has shown that spatial stratification exists and differs from site to site (Orphan et al., 2002; Orphan et al., 2004; Blumenberg et al., 2005; Knittel et al., 2005; Niemann et al., 2005; Orcutt et al., 2005; Nunoura et al., 2006; Kruger et al., 2008). The trends emerging in these studies show ANME-2 is generally numerically dominant at the sediment/mat-water interface, and ANME-1 becomes dominant deeper into the sample. It is proposed that higher oxygen tolerance, higher sulfate availability from overlying waters and improved sulfide evacuation favors ANME-2 proliferation (Kruger et al., 2008). Exceptions to this trend include microbial mats from carbonate mounds in the Black Sea (Treude et al., 2007a) and a core capped by a microbial mat from Eel River Basin, both of which are dominated by ANME-1 throughout (Orphan et al., 2002). So far, parameters governing finer-scale ecotype partitioning have only been addressed superficially because of the lack of practical tools to quantify large numbers of environments in a time sensitive manner. Quantitative methods include probe-based microscopic counts (FISH and CARD-FISH) and lipid analysis. Although probe-based methods for quantitation are exquisitely sensitive if properly designed and used, they are costly, time-consuming and are inherently subjective through their reliance on visual counts. Also, ANME cells often occur in tight clusters surrounded by a polysaccharide matrix and other syntrophic microbial partners, making it difficult to accurately assess actual cell abundance in a sample. In addition, since probe-based methods mainly target DNA transcripts, only metabolically active cells are counted. 31 ANME identification through isotopically depleted lipid analysis is used in many studies and certain lipids have even been assigned to specific ANME subgroups through the analysis of enrichment cultures and sites enriched in a specific ANME subdivision (Blumenberg et al., 2004; Nauhaus et al., 2007). As opposed to probe-based methods, this technique can be applied to a high number of samples. However, the relationship between lipid concentrations and cell growth status and abundance is not yet fully understood (Nauhaus et al., 2007). Many of the lipids are shared with closely-related methanogenic archaea, and the current technique currently only affords an ANME-1/ANME-2 and -3 level of resolution. Conversely, qPCR assays are a relatively modest investment and, once optimized, enable the simultaneous processing of large numbers of samples in short time intervals. In addition, they do not require a large amount of sampled material. It is important to note it does rely on the assumption each cell has one copy of the target gene and that dead and dormant cells do not make up a significant proportion of the community measured. For this reason, absolute measured values should not be taken and orders of magnitude should be favored (Smith et al., 2006). The development of primers targeting the SSU gene of ANME populations has been challenging, notably due to the difficulties associated with finding unique sites for each ANME subgroup. To our knowledge, only ANME-1 and ANME-2c SSU qPCR assays have been developed and optimized (Girguis et al., 2005). As an alternative, methyl coenzyme M reductase  subunit (mcrA) is an attractive target for quantitation of subgroups. The gene is only known to exist in methanogenic and ANME lineages, and it has been shown to yield better resolution than the SSU in the phylogenetic classification of methanogens (Luton et al., 2002). Another alluring aspect to the use of mcrA in quantitative assays is that, in addition to being a quantitative target, it is a functional target, as it is known to be essential to and possibly even directly mediate AOM in ANME (Hallam et al., 2003; Kruger et al., 2003; Hallam et al., 2004; Nauhaus et al., 2005) and has been co-localized with ANME cells (Heller et al., 2008a). For these reasons, amplification of the mcrA target could also apply to real time-PCR assays to correlate expression of mcrA levels with other environmental parameters. There are 32 currently over 250 ANME mcrA sequences from different geographical locations available in public databases. ANME-1 associated subgroups include mcrA a and b and another undefined group associated with hydrothermal vents, ANME-2 associated subgroups include mcrA c, d and e and ANME-3 associated subgroups include mcrA f. A Taqman assay exists to quantify ANME-1 and ANME-2 at the mcrA level (ab, cd, e) (Nunoura et al., 2006), but it does not allow discrimination within ANME-1 and ANME-2 subgroups. The aim of this study was to develop a suite of assays to quantify ANME subgroups in seep sediment environments and compare the measurements to defined environmental variables to identify parameters influencing subgroup partitioning. mcrA was chosen as a target for the reasons described above. Patterns of ANME subgroup partitioning were compared within and between 7 cores from 3 distinct methane seep sites off the coast of California with paired sulfate, sulfide, ammonia, and methane concentration measurements. Results McrA assay performance Preliminary results indicated ANME populations within sediment matrices, at a 3 cm resolution, were too heterogeneous to yield useful information on mcrA subgroup partitioning between sediment intervals (data not shown). Therefore, since subgroup partitioning by depth was the object of this study, homogenization of sediment intervals with liquid nitrogen was necessary. All primer sets were tested for amplification of methanosarcinal genomic DNA and all other non-target ANME-associated mcrA subunit clones, with the exception of the f subgroup. Clones and primer information are listed in table 2.2. As shown in the phylogenetic tree of figure 2.1 and 2.2, not all ANME-1 associated mcrA subgroups are targeted with the existing set of subgroup primers. The ANME-1 subgroups chosen (named a1 and a2) were the only ones found in clone libraries associated with the Northeast Pacific coastal sediments publicly available to date. The sum of all ANME-1 associated subgroup copy numbers always amounted to a 33 number equal to or lower than mcrA ab copy number. A lower value was interpreted as incomplete coverage of ANME-1 subgroups in the sample by the set of primers used in this study, either through small scale heterogeneity within targeted subgroups or outside a subgroup. Conversely, the ANME-2b and c associated subgroup counts added together amounted to the same copy number in all cores assayed in this study. To validate mcrA ab and mcrA cd primers, previously published and tested qPCR assays targeting the SSU DNA in ANME-1 and ANME-2c were used and the results were compared in all cores. In all cases, the results were similar for the complementary primer sets (Girguis et al., 2005). Sites studied The cores from the three sites were all from areas of active methane seeps. Table 2.1 provides details on their location, temperature and depth. Three different depth samples were taken from methane seeps that were expected to yield high ANME abundances. Since methane solubility is higher in deeper water (water pressure compresses gases, increasing partial pressure, and gas solubility increases with partial pressure), the effect of methane concentration on ANME populations, if any, could be measured. Both Tonya seep and 4322 cores were sampled in the geographical area named La Goleta, which contains sites of visible; hydrocarbon discharge. The cores from the 4322 all contained noticeable amounts of tar in them, with the upper 10 cm being black and viscous: whereas the Tonya seep core, sampled from an active methane seep, did not contain any noticeable tar. The cores 4322 A6, which was taken from the centre of a microbial mat that was approximately 50 cm in diameter, bubbled when ascended to the surface, indicating it contained large concentrations of methane. The core 4322 A7 was sampled from the periphery of this same microbial mat. It was suspected the microbial mat had grown on a methane seep. The Santa Monica Basin site, on the other hand, received its methane from dissociating hydrates within the sediments. The site contained large expanses of white and orange Beggiatoa microbial mats. With the aim of discerning whether or not ANME populations differed between sites with orange and white Beggiatoa, cores 4327 a2 and 4330 2 were 34 both sampled from orange mat areas and core 4330 7 was sampled from an area with white microbial mat. Subgroup partitioning trends within sites Santa Monica Basin In the two orange microbial mat capped cores from this site, mcrA ab (ANME-1) and cd (ANME-2) counts are similar, with mcrA ab (ANME-1) copies/g increasing with depth by an order of magnitude and mcrA cd (ANME-2) copies/g slightly decreasing with depth (Figure 2.3a,f). McrA ab (ANME-1) was more abundant at depth than mcrA cd (ANME-1) in both instances measured. The ANME-1 associated subgroup partitioning trends were different between the two orange cores, with the a1 subgroup copies/g steadily increasing with depth from 103 to 106, the most numerically abundant ANME subgroup at 12 cm in the 4330 a2, and the a2 subgroup counts staying in the 105 copies/g range in the 4327 2 core (Figure 2.3b, g). In core 4330 2,  the a1 subgroup at 104 copies/g throughout and the less abundant a2 subgroup (ANME-1) abundance was equally stable at 102 copies/g (Figure 2.3, f). In all three cores from this site mcrA subgroup b (ANME- 1) was not detected. In addition, the ANME-1 associated subgroups quantified did not amount to the counts obtained with the mcrA ab primers (101 - 102 copies/g), indicating the ANME-1 associated community in Santa Monica Basin was incompletely sampled with the subgroup primers designed in this study. The ANME-2 subgroup partitioning trends were similar between the two orange microbial mat cores and numbers fluctuated little with depth (Figure 2.3c, h). McrA c was consistently under 107 copies/g and mcrA e was an order of magnitude lower. McrA d, however, was present around 106 copies/g in core 4327 a2 but was in equal numbers to mcrA c in core 4330 2 at 107 copies/g (Figure 2.3c, h, m). In the white microbial mat core, there was a larger separation between mcrA ab (ANME-1) and mcrA cd (ANME-2) subgroups than in the orange mat cores, with both increasing in copies/g with depth from the 105 to 106 and 106 to 107 range, respectively (Figure 2.3i). Of the ANME-1 associated subtypes, mcrA a1 and a2 were similar in abundance at 104 copies/g throughout the core (Figure 2.3k). The distribution of ANME- 2 associated subtypes was similar to that of the orange microbial mat core 4330 2, with 35 subtypes c and d (ANME-2) dominating at 107 copies/g and the mcrA e (ANME-2) subgroup one order of magnitude lower (Figure 2.3, m). La Goleta In the cores capped with a microbial mat (4322 a6 and a7), ANME-1 associated mcrA subgroups clearly dominate at 105 to 106 copies/g (Figure 2.4f, k). In contrast, the McrA cd (ANME-2) subgroup copies/g fluctuate between 103 and 104.  Interestingly, the centre of the microbial mat contains all three mcrA subgroups quantified whereas the core taken in the proximity (20 cm away), only contains measurable amounts of the a2 (ANME-1) subgroup at 106 copies/g throughout (Figure 2.4g, l). In the center microbial mat core, subgroups a1 and b are both in the 104-105 copies/g range (Figure 2.4g). As opposed to the Santa Monica Basin cores, ANME-1 associated subgroups quantified separately add up to the mcrA ab (ANME-1) copy numbers in these two cores. There is also a clear separation between the ANME-2 associated subgroups, with the mcrA subgroup c composing the quasi-entirety of the ANME-2 population at 105 copies/g, followed by the mcrA subgroup d which is consistently 1.5-3 orders of magnitude lower in abundance. Subgroup e was not detected (Figure 2.4h, m). In the oily core sample (4322 a12), in contrast to the microbial mat cores from the same site (4322 a6, a7), mcrA cd (ANME-2) is most abundant in the surface intervals at 106 copies/g but its numbers decrease to 105 copies/g at depth and mcrA ab (ANME-1) dominates at depth, with 105 copies/g at surface intervals and 106 copies/g at depth (Figure 2.4a). The only ANME-1 associated subgroup consistently detected with depth is mcrA a2 (ANME-1), which fluctuates from 104 copies/g close to the surface to 102 copies/g at depth (Figure 2.4b). The other ANME-1 associated subgroups are not quantified reliably, with large error bars and no consistent presence throughout the core. The ANME-2 associated subgroups partition similarly to the other two cores from this site, with mcrA c (ANME-2) clearly dominating at 106 copies/g throughout, subgroup d at around 102-103 copies/g throughout and mcrA e not detected (Figure 2.4c). 36 Tonya seep McrA ab subgroups dominate throughout and numbers increase steadily from 104 copies/g at the surface to 106 copies/g at the 14-16 cm interval. McrA cd can only be detected from 8 to 16cm cm at 104 copies/g (Figure 2.5a). ANME-1 associated subgroups a2 and b fluctuate between 104 copies/g to 105 copies/g (Figure 2.5b). Subgroup a1 is not consistently detected in the core. The ANME-1 associated subgroups added together are equivalent to the mcrA ab quantification in the top half of the core but not in the bottom, indicating the incomplete quantification of ANME-1 subtypes at depth (101 copies/g). In addition, this is the only core analyzed where mcrA subgroup e dominates the ANME-2 associated subgroups at 104-105 copies/g (Figure 2.5c). Subgroup c (ANME-2) is only detected at 103-104 copies/g in the bottom half of the core and subgroup d (ANME-2) is only sporadically detected. General subgroup partitioning trends between sites ANME subgroup distribution was generally more consistent within a site than between sites, as confirmed with a nonmetric multidimensional scaling (NMS) ordination (Figure 2.6). Tonya seep is an exception to this trend, with the three highest depths clustering away from the rest of the core. This difference can be explained by the few subgroups detected in low abundance in the upper few centimeters of the core (Figure 2.6b, c). The samples collected from Santa Monica Basin, all covered by microbial mats, clustered more tightly together than the cores sampled from La Goleta. In contrast the samples from La Goleta all had different macroscopic characteristics: two cores had microbial mats on the surface, one of which had active methane bubbling activity, and the third had active oil seepage. ANME group numerical abundance shifted between sites studied (Figure 2.3, 2.4, 2.5 b, c, g, h, l, m). Both La Goleta and Tonya seep sites were dominated by ANME-1 associated subgroups, whereas in Santa Monica Basin, ANME-2 associated subgroups were abundant at shallow depths and ANME-1 associated subgroups dominated at deeper intervals. Total mcrA copies/g were consistent within a site and different between sites, with total mcrA copies/g consistently in the 107 range in the Santa Monica Basin site, 106 in the La Goleta site and 105-106 in the Tonya seep core. Furthermore, ANME-2 37 associated mcrA subgroup c is most abundant amongst ANME-2 associated mcrA subgroups in all sites but Tonya seep. In addition, the ANME-1 associated mcrA subgroup a2 (ANME-1) was present in varying concentrations in all cores assayed, whereas the other ANME-1 associated subgroups were not always detected. McrA  a2 (ANME-1) was most abundant in the sediments from La Goleta and in the deeper horizons of the 4327 a2 core in Santa Monica Basin and Tonya seep cores. McrA a1 (ANME-1), on the other hand, was more abundant in the Santa Monica Basin cores with the exception of the white microbial mat. Distribution of mcrA populations between sites McrA population structure correlates with surficial characteristics. In figure 2.6, the ordination represented the distribution of overall mcrA populations by sediment depth interval and contains data from all seven cores analyzed from Santa Monica Basin, La Goleta and Tonya seep (see table 2.1 for information on the cores sampled). Triangles clustering together have mcrA populations that are more similar than distantly spaced triangles (39 sites). The goal of this analysis was to determine whether the mcrA populations partitioned according to biogeographical patterns (i.e. populations are more similar within than between sites) or whether mcrA populations from environments sharing similar features tended to be similar. For this reason, all the mcrA data points, including those with no paired chemistry measurements, were included (39 samples, 6 subgroups). 92% of the variance observed in the original dataset was explained by the ordination. Significance of clustering by 1) site, 2) oily versus non-oily core, 3) microbial mat at the surface versus none, and 4) visible methane seepage versus none was assessed by multi-response permutation procedure (MRPP). This nonparametric statistical test examines distances of scores (coordinates of a site) within and between defined groups. By testing the hypothesis of no difference between groups, it is possible to determine if statistically significant clustering occurs within a group. The average within group score distance () is compared to the average  between scores for the whole dataset. The probability (p) value provided is the probability a smaller  occurs if the sample point distribution was randomly distributed. The Sorensen measure is the distance measure in 38 this case, since it was used in generating the ordination. The Chance-corrected within- group agreement, A, represents within-group agreement and a value of 1 indicates the scores are identical. Therefore, values closest to one show tighter clustering between sites (Mielke et al., 1984 describes the method in detail). The MRPP statistics indicate all the hypothesized reasons for mcrA subgroup clustering were statistically significant (p = <0.001). Chance-corrected within-group agreement, A, differed, and was highest for clustering according to site, followed by oily versus non oily cores, seep sites versus non seep sites and microbial mat capped cores. The exact A and p values for each MRPP are in figure 2.6. These results indicate all parameters may be involved, to some degree, in subgroups partitioning. Santa Monica Basin cores were all sampled from areas of either orange or white Beggiatoa microbial mats, and they tightly clustered together ( = 0.13). Conversely, La Goleta cores were sampled from sites with different macroscopic features, and the distances between the overall mcrA populations is much larger ( = 0.25). In addition, the seep cores, Tonya seep and La Goleta (a6), had a  of 0.24 if the top 3 cm of Tonya seep, which contained ANME abundances below the detection threshold, were removed from the analysis and cores capped by microbial mats had a  of 0.29 (fig. 2.6). In sum, clustering in cores from a single site with similar macroscopic features was tighter than for cores in a site containing different macroscopic features (mat versus no mat, visible gas or oil discharge). In addition, cores from separate sites containing similar macroscopic features (microbial mat cap and visible methane bubbling) clustered in a statistically significant manner. These data together suggest chemistry, and not biogeography, is likely the prime determinant in mcrA subgroup, and therefore ANME subgroup, partitioning. In the next section, measured chemical parameters were correlated to site partitioning according to mcrA subgroup composition to identify factors influencing partitioning in ANME subgroups. Influence of chemistry on mcrA subgroup distribution In figure 2.7, a subset of the sediment interval mcrA data for the cores used in the ordination in figure 2.6 from Santa Monica Basin and La Goleta that had paired methane, sulfate, sulfide, ammonia and alkalinity (24 of 39 sites). The positioning of the sediment 39 intervals in the ordination space was similar to that in figure 2.6. Therefore, mcrA subgroup distribution for La Goleta and Santa Monica Basin sediment intervals observed in figure 2.6b can be used to examine the subgroup partitioning behavior in relation to the chemical gradients represented in figure 2.7. The ordination cumulatively represented 96% of the original variance between points in the original high-dimensional space. The strength of the ordination axes was evaluated by comparison of dataset with a Monte Carlo (randomization) test (random reshuffling of the column contents). The probability of obtaining a similar reduction in stress in the randomized dataset compared to the real dataset is p = 0.008 and p = 0.004 for axis 1 and 2, respectively, indicating the observed stress reduction is significantly lower than stress reduction in the randomly shuffled dataset. The final cumulative stress of the run is 6.94, which is a good ordination with no real risk of drawing false inferences (Clarke, 1993). A stable solution was reached after 156 iterations. In this ordination, the relationships of mcrA populations with chemistry can be indirectly inferred through the correlation coefficients (r2) of mcrA populations and chemistry to the two axes. Exact r2 values are listed in figure 2.7 b. P values could not be assigned in NMS because the assumption of independence of variables does not hold. Axis 2 is weakly correlated (r2 = 0.109 – 0.206) with all 5 chemical parameters measured and all ANME-1 associated mcrA subgroups (a1, a2, b) correlate with this axis (r2 = 0.421-0.557). On the other hand, ammonia, sulfide, sulfate and alkalinity, in decreasing order, all correlated well with axis 1 (r2 = 0.322 – 0.811). ANME-2 associated mcrA subgroups (c, d, e), also correlated well with axis 1 (r2 =  0.731 – 0.884), indicating those chemical parameters play a role in ANME-2 subgroup partitioning. In sum, ANME-1 subgroup distribution was only partially explained by the chemical parameters measured whereas ANME-2 population distribution is explained by ammonia, sulfide, sulfate and alkalinity gradients. The next section develops the possible role of ammonia in the partitioning of ANME-2 subgroups. ANME-2 associated subgroup partitioning One distinction between ANME-2 associated mcrA subgroup (c, d, e) populations between La Goleta and Santa Monica Basin is the presence of all three mcrA subgroups in Santa Monica Basin and the presence of only mcrA c in high abundances in La Goleta, 40 followed by low levels of mcrA d and no mcrA e. The only measured parameters whose concentration changed appreciably between the two sites are ammonia and sulfate. The chemistry vectors in figure 2.7 indicate the concentration of the two parameters decrease in La Goleta. Since a positive correlation between mcrA abundance and the two parameters is observed, sulfide as a factor inhibiting ANME subgroup proliferation can be discarded. However, the positive correlation of mcrA d and e with ammonia indicates the compound may be a factor influencing the distribution of these subgroups. However, it does not appear to exert an influence the mcrA c subgroup population, enabling this subgroup to dominate over other ANME subgroups in conditions of low ammonia. ANME-1 associated subgroup partitioning ANME-1 associated subgroup partitioning was only partially explained by chemistry. Incidentally, a Chi-square (2) test for species association indicates that for subgroups a2 and, to a greater extent, subgroup b, a negative correlation occurs with ANME-2 subgroups (table 2.3). These data, together, suggest a combination of chemistry and competition with ANME-2 subgroups is responsible for the observed ANME-1 distribution in sediments. Methane influence on ANME populations ANME growth is absolutely dependent on methane. The distribution of ANME subgroup populations in the cores observed was not heavily influenced by methane concentration (Figure 2.7). Rather, overall mcrA copies/g sediment is dependent on methane until concentrations reach 0.5-2 mM (figure 2.8). This indicates methane is likely the only energy source for the ANME and parameters other than methane concentrations begin to influence overall ANME biomass beyond the methane threshold concentration mentioned above. Figure 2.8 also shows that in the cores studied, ANME-2 maximal cellular abundance exceeds that of ANME-1. It is not clear whether or not this represents a real trend or if this is only a bias introduced by the sites studied. ANME-1 cellular biomass also appears to increase at lower methane concentrations than ANME-2. This may indicate there exists a difference in catalytic efficiency of Mcr from different 41 ANME groups. This would be supported by observed differences in primary structure of Mcr (Hallam et al., 2003; Kruger et al., 2003). Discussion ANME and chemistry distribution between sites Our data suggests ANME subgroup population dynamics are not solely dependent on sulfate and methane concentrations. ANME-2 subgroup distribution was highly correlated with ammonia, sulfide and sulfate concentrations in this study and ANME-1 subgroup distribution is not as heavily correlated with these parameters. The possibilities for which ANME-1 correlations are not as high include 1) only partial dependence of ANME-1 on the parameters measured, 2) its capacity to persist in sediment strata where it is not metabolically active, 3) a competitive disadvantage to ANME-2 is obscuring direct relationships of ANME-1 to the chemistry, or 4) ANME-1 population densities are more weakly correlated to the chemical parameters because their substrate affinities are different than those of ANME-2. Our data does suggest total ANME biomass is dependent on methane concentrations (Fig B1). This dependence suggests methane is the only electron donor for both ANME-1 and ANME-2. This premise is supported by sediment incubation studies. In one instance, in a sediment core with no known methane activity, ANME-2c and ANME-1 population density increased when a methane atmosphere was added (Girguis et al., 2003; Girguis et al., 2005). In another, it was shown ANME populations density and methane oxidation rates increased with increasing methane partial pressures in sediment incubations (Nauhaus et al., 2002; Nauhaus et al., 2005; Nauhaus et al., 2007). Therefore, our results are consistent with observations made employing other techniques. The results of our study indicate that when ANME-2 is more abundant than ANME-1, it occurs in shallow sediment layers. Only limited data exists on the spatial stratification of ANME groups. However, ANME group quantification by FISH, CARD- FISH or lipid analyses with depth on Black Sea microbial mats, both resting on sediments and carbonate chimneys, Eel River Basin sediments and Gulf of Mexico Brine seep sediments identified similar trends (Orphan et al., 2004; Blumenberg et al., 2005; Orcutt 42 et al., 2005; Kruger et al., 2008). This pattern supports the higher requirement of ANME- 2 for sulfate than ANME-1, which diffuses from the overlaying seawater. Sulfide concentrations have previously been proposed as a parameter limiting ANME-2 proliferation. However, in this study, sulfide concentrations are positively correlated with copy number (Fig B3), indicating that it dodes not have a negative effect on ANME-2 biomass. Since sulfide is a product of sulfate reduction, correlation of ANME with sulfide is likely a consequence of sulfate as TEA. Chemistry profiles measured for a Black Sea microbial mat showed similar trends (Kruger et al., 2008). Orphan et al., 2004 discussed this same ANME-1 and -2 distribution, but noted that one core was dominated by ANME-1 throughout. It was proposed that the core was capped off with a microbial mat, not allowing free exchange of seawater into the sediments. We observed the same trends in the cores originating from La Goleta, but not the cores covered with Beggiatoa mats from Santa Monica Basin (Fig 2.4). An intriguing observation in ANME-2 associated mcrA subgroups is the apparent dependence of mcrA d and e subgroups on ammonia concentrations but not mcrA c. A causal relationship between ammonia concentrations and ANME subgroup dynamics has not yet been established in the literature. However, it has been shown, through targeted cell capture and secondary ion mass spectrometry, that the ANME-2c genome contains nitrogen fixation genes, and that they exhibit the incorporation of labeled N2 (Pernthaler et al., 2008). Since it is thought that mcrA c or d belong to ANME-2c (Losekann et al., 2007), the explanation for the observed ANME-2 subgroup partitioning may reside in the ability of some but not all ANME-2 subgroups to acquire nitrogen from sources other than ammonia. More measurements need to be made to establish whether or not this trend holds, as the range of ammonia concentrations between sites in this study was discontinuous. The weak correlation of ANME-1 associated mcrA with all the parameters measured (figure B1, B2, B3, B4) indicates other factors govern its distribution. Since the paradigm for AOM is based on AOM coupled to sulfate reduction, the lack of correlation with sulfate concentrations is the most surprising observation. In addition, sulfate reduction rates equal to AOM rates have been observed for both ANME-1 and ANME-2 dominated communities (Nauhaus et al., 2005). Since lower AOM rates were observed 43 for ANME-1 dominated communities, it is possible the lower requirement of sulfate of ANME-1 obscures the relationship between copy number and concentration in the environment. However, since ANME-1 cells are not consistently observed to be closely associated with SRB, the possibility of an alternate TEA for ANME-1 cannot be discarded (Orphan et al., 2002; Shima and Thauer, 2005). Model for ANME-1/-2 subgroup dynamics In light of the results from this study, ANME-2 proliferates when specific conditions are optimal and ANME-1 dominates when conditions for ANME-2 proliferation are not present. This includes but may not be limited to sufficient environmental sulfate concentrations. This agrees with FISH count observations in other sites (Orphan et al., 2004; Knittel et al., 2005; Orcutt et al., 2005; Wegener et al., 2008b), and similar conclusions have been proposed in other instances (Knittel et al., 2005; Wegener et al., 2008b). Subgroup abundance patterns in relation to chemistry suggest ammonia concentrations may play a role in subgroup partitioning for ANME-2. The basis for ANME-1 subgroup partitioning was not identified in this study. Summary In this study, a suite of qPCR assays targeting mcrA for different ANME-1 and ANME-2 subgroups were designed and applied to environmental samples. In the cores sampled, ANME-2 subgroup partitioning was dependent on chemical parameters (sulfate, sulfide, ammonia). Conversely, the basis for ANME-1 subgroup partitioning was more complicated, and was not solely explained by the chemical parameters measured. However, this study showes that the quantification of different subgroups and pairing of the information to measured chemistry allows to discern the different selection processes occurring ANME subgroups in sediments. Results in this study suggest that a broader set of parameters exist than previously thought that influence the distribution of ANME groups. 44 Experimental procedures Sampling Collection of sediment cores was performed by hand coring for the scuba accessible Tonya Seep and via the submersible DSV Alvin for the deeper La Goleta and SMB seeps during the SEEPS ‘07 (Studies on the Ecology and Evolution of Petroleum Seeps - 2007) cruise aboard the R/V Atlantis in July 2007. Samples from La Goleta and SMB seeps were collected with 5 cm ID push cores controlled by Alvin’s manipulator as previously described (Valentine, 2005).  Samples collected at La Goleta came from gas- charged sediments within and adjacent to a benthic microbial mat, a mat-free gas and oil seep with active bubbling. Table 2.1 provides details on the sites.  Samples from the Santa Monica Basin seep were collected from a sediment-laden area situated near the crest of the mound and about 10-20 m from an active gas vent.  The sediments were covered with thick microbial mats, distinctly colored white or orange.  Push cores from the Alvin were subsampled shortly after the submersible returned to the R/V Atlantis, typically within an hour.  Two smaller diameter core subsamples were taken from the same Alvin push core; one to quantify CH4 and conduct CH4 oxidation rate measurements and the other to collect sediment for mcrA quantification.  Entire push cores were dedicated to quantify chemicals dissolved in the pore fluids including sulfate, sulfide, and alkalinity. DNA extraction Cores separated into 2 or 3 cm intervals were homogenized with liquid nitrogen in a mortar and pestle. Approximately 0.5g of the resulting homogenate was extracted in triplicate with the Mobio PowerSoil DNA isolation kit (Mobio, Carlsbad, CA), Cesium Chloride gradients (Hallam et al., 2004) purified , and washed in 4 ml TE with an Amicon 10 000 kDa cutoff molecular weight filter (Millipore) 3 times. The resulting DNA was suspended to a fixed volume of 500 ul to reduce uncertainties for quantification. 45 Primer design Multiple alignments were done with all mcrA sequences associated with ANME- 1, ANME-2, ANME-3 and several representative sequences from each methanogenic lineage in public databases using the MUSCLE software (Edgar, 2004). The alignment was visually assessed and edited, and a phylogenetic tree was constructed using the software Phyml v.2.4.4 (Guindon and Gascuel, 2003). The primers were designed visually and, when possible, according to the subgroups proposed by Hallam et al. ,2003 and Losekann et al, 2007. When newly available sequence information invalidated the proposed groups, new groups were delineated based on distinct clade separation with good bootstrap support for convenience in this study. All primer sets were tested for specificity by BLAST analysis against the NCBI non-redundant database and by PCR against Methanosarcina mazei genomic DNA and at least 1 member of each ANME-1 and ANME-2 subgroup delineated. Table 2.2 provides the sequences and conditions for primer use, and details of qPCR assay optimization methods are in Appendix I. Unless otherwise noted in the table, the primers are specific for the subgroup they were designed to amplify under the conditions of use and with all the sequence information available to date. Samples were analyzed with an opticon II thermocycler (Biorad, Hercules, CA) and IQ SYBR green supermix (Biorad, Hercules, CA, U.S.A.) was used as a master mix for all samples analyzed in 25l final volume reactions. All assays had 15 seconds annealing (table 2.2) and extension (72oC) , and 30 seconds melting (95oC). Standard isolation and quantification Standards all represented clones with previously published sequences from (Hallam et al., 2003; Hallam et al., 2004) and are listed in table 2.2. Clones containing the plasmids were isolated with qiagen midi prep (Qiagen, Mississagua, ON, Canada). Residual genomic DNA was digested with Plasmid-safe DNAse (Madison, WI, U.S.A.) overnight according to the manufacturer’s instructions and the DNA was once again extracted with molecular biology grade phenol-Chloroform. The resulting plasmids were quantified by pico green (Molecular Probes, Invitrogen). 46 Nonmetric multidimensional scaling Since many subgroups were measured and the relationships between the chemical parameters and the subgroups were not straightforward, NMS ordination was done to reduce the complexity of the sample and to isolate factor(s) responsible for the distribution of the subgroups (figure 2.6 and 2.7). This method was chosen over the other ordination methods because of its ability to detect trends in species distribution from environmental samples which are not necessarily normally distributed (for clarifications on the method, see Mielke et al., 1984). The ordination was run with PC-ORD (Mather 1976 and Kruskal 1964). Sorensen distance measure was chosen because it has been found to perform the best with ecological data. Log transformation of cell copy numbers, a commonly used method for reducing the influence of outliers, was necessary to obtain a useful ordination. For both ordinations (figure 2.6 and 2.7), the optimal solution was 2- dimensional. A preliminary run with random seed was performed and the second run was seeded with the final configuration of run 1. Both runs consisted of 100 runs with real data, 250 runs with randomized data. The final stability of the solution was evaluated through a stress versus iteration plot. Comparison of chemistry to qPCR matrices. In figure 2.7, All mcrA subgroup measurements for depths for which all chemistry parameters were measured (22 total), from 2 of the three sites (Tonya seep missing), were input as a first matrix, and methane, sulfate, sulfide, ammonia and alkalinity measurements were in the second matrix. In certain instances, the depth at which the chemical parameters were measured did not match that for which the qPCR measurements were made. Linear interpolation was used to extract approximate values to match the depths for which mcrA was measured. In addition, subgroup mcrA measurements did not yield linear relationships with chemical parameters unless they were logarithmically transformed. Chemistry Cores designated for methane oxidation rate (MOR) measurements were treated as described previously (Kinnaman and Valentine, submitted) and injected with 47 radiotracer 14C-CH4 (1kBq 14CH4 dissolved in water, 20L injection volume) at 1 to 2 cm intervals and incubated at near in-situ temperature. The core was sub-sectioned after 18 hours and placed into vials with 1M NaOH and quickly sealed, ending the incubation and trapping the CO2. A small sample of headspace (0.2mL) was removed to determine CH4 concentration (which is not affected by the 14CH4 spike) by GC-FID (Shimadzu GC-4A, 6 ft length 80/100 mesh Molsieve 13X packed column run isothermally at 140oC with N2 carrier flow at 15 mL min-1). The remaining 14CH4 in the headspace of the vial was purged via a slow flow of air through a combustion tube filled with Cu(II)-oxide and maintained at 850oC, and the resulting 14CO2 was trapped using a mixture of phenethylamine and 2-methoxyethanol.  The remaining 14CO2, assumed to be microbially-produced, was measured by first transferring the sediment into a 100mL Erlenmeyer flask fitted with a small (7 mL) phenethylamine/NaOH-filled scintillation vial suspended beneath its rubber stopper.  6 mL of hydrochloric acid (6M) was injected through the rubber stopper to degas the CO2 from the sediment/NaOH slurry and the flask was placed in a shaker for ~8hrs to transfer the CO2 to the suspended scintillation vial. Radioactivity was quantified by scintillation counting (Beckman LSC 6500). The ex-situ CH4 oxidation rates (MOR) were calculated by the following equation: (1)  MOR = 14CO2 × CH4 / ( 14CH4 × v × t) Where 14CO2 is the activity of the microbially-produced CO2, CH4 is the amount of CH4 in the sample, 14CH4 is the activity of the injected CH4, v is the volume of the sediment and t is the incubation time.  The turnover or residence time of CH4 (CH4(i)) in an individual core interval is: (2) CH4(i) = (14CO2 + 14CH4) × t / 14CO2 A weighted average CH4 for the top 10cm of each core was calculated by weighting each CH4(i) according to its MOR. Porewaters were collected and analyzed as previously described (Li et al, submitted) using modified Reeburgh squeezers (Reeburgh, 1967) that were slowly pressurized with N2 up to ~20psi. Porewaters were collected via Teflon tubing into 15mL centrifuge tubes, which remained capped during porewater collection with the tubing 48 threaded through predrilled holes in the caps, but no other measures were taken to maintain anaerobic conditions. Sediment squeezing was conducted at room temperature over the course of ~15 minutes. Up to 8 squeezers were operated in parallel, allowing nearly all samples to be processed within 1 hour of the cores arriving onboard. Porewater aliquots were taken during squeezing and immediately analyzed for sulfate, sulfide and ammonia concentrations.  Porewater dissolved sulfate concentrations were measured shipboard by addition of excess BaCl2 to samples of known volume.  Absorbance of the resulting precipitate, BaSO4, was determined spectrophotometrically at 400 nm and compared to a calibration curve generated from varying dilutions of IAPSO standard seawater.  Porewater concentrations of dissolved sulfides were measured shipboard by addition of a copper sulfate reagent to samples of known volume (Cordruwisch, 1985). After the conversion of sulfide to colloidal copper sulfide, absorption was measured at 480nm and compared to a calibration curve.  Standards were prepared under anoxic conditions, and the standard error for replicate analyses was below 5%.  Ammonia was determined by the reaction of ammonia with salicylate and hypochlorite and measuring aborbance at 660nm (Bower and Holmhansen, 1980).  Standard error for this method was below 5%. 49 50 Table 2.2. McrA sequences, optimized annealing temperature and ANME SSU subgroup assignment for primer sets developed in this study. 51 52 Table 2.3. Chi-square distances for mcrA species associations in ordination space. A value of 1 represents a perfect positive correlation between species and a value of -1 represents a perfect negative correlation between species. Values were computed for an ordination with data from all core sections illustrated in figure 2.6. ANME-1 and ANME- 2 group assignments for mcrA subgroups were highlighted for ease of ANME group identification. mcrA a1 - mcrA a2 0.04 - mcrA b -0.01 0.07 - mcrA c 0.27 -0.26 -0.33 - mcrA d 0.11 -0.14 -0.63 0.24 - mcrA e 0.49 0.00 -0.13 0.20 -0.01 - mcrA a1 mcrA a2 mcrA b mcrA c mcrA d mcrA e ANME-1 ANME-2 53 Figure 2.1. Phylogenetic tree derived from mcrA partial nucleotide sequences showing sequence coverage of ANME-1 primers developed in this study. Groups outlined on the right side of the tree show ANME-1 (sub)group sequences targeted by the primers. HKY evolutionary model was used in maximum likelihood tree and bootstrap values based on replicates each are shown for branches with greater than 50% support. 54 Figure 2.2. Phylogenetic tree derived from mcrA partial nucleotide sequences showing sequence coverage of ANME-2 primers developed in this study. Groups outlined on the right side of the tree show ANME-1 (sub)group sequences targeted by the primers. HKY evolutionary model was used in maximum likelihood tree and bootstrap values based on replicates each are shown for branches with greater than 50% support. 55 Figure 2.3. Quantification of ANME mcrA subgroups in three sediment cores from Santa Monica Basin. Graphs in each row represent data from separate cores and graphs from each column represent mcrA and chemistry measurements for the same parameters. Legends at the bottom of the figure provide the subgroups and chemistry represented in the graphs. a,f,k, mcrA ab and cd; b,g,i, ANME-1 associated mcrA a1, a2, b; c,h,m, ANME-2 associated mcrA c,d,e;  d,I,n, sulfate, methane oxidation rate (MOR) shaded grey; e,j,o, sulfide and ammonia. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 56 57 Figure 2.4. Quantification of ANME mcrA subgroups in three sediment cores from La Goleta. Graphs in each row represent data from separate cores and graphs from each column represent mcrA and chemistry measurements for the same parameters. Legends at the bottom of the figure provide the subgroups and chemistry represented in the graphs. a,f,k, mcrA ab and cd; b,g,i, ANME-1 associated mcrA a1, a2, b; c,h,m, ANME-2 associated mcrA c,d,e;  d,I,n, sulfate, methane oxidation rate (MOR) shaded grey; e,j,o, sulfide and ammonia. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 58 59 Figure 2.5. Chemistry and quantification of ANME subgroups in sediment core from Tonya seep. See legend of Figure 2.4 for details on graph plots. Ammonia and sulfide measurements were not done for this core. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 60 Figure 2.6. Ordination of sediment depth intervals in mcrA subgroup (a1, a2, b, c, d, e) space. Axes are arbitrary and position of each triangle represents the relative difference in mcrA population distribution between depth intervals. The sediment cores included were from Santa Monica Basin, La Goleta and Tonya seep. In panel a, the legend on the top left represents the site from which the sample intervals were sampled. The labels on the individual triangles are the core name, followed by the depth interval (cm). Information on the cores could be found in table 2.1. Groupings represent conspicuous macroscopic features characterizing the site from which the cores were sampled.  A is chance- corrected within group agreement (1 means all points are identical, 0 means they are randomly distributed) and p is the probability of a smaller delta if partitioning was by chance. Circled groups were all computed with separate MRPP. All graphs in panel b illustrate the same ordination as panel a, but the relative abundance of each mcrA subgroup between sediment intervals is illustrated. Ordination was generated by NMS. Final stress of run 10.6, final instability 0.0000. 61 Figure 2.7. Relative dissimilarity between overall mcrA subgroup (a1, a2, b, c, d, e) composition in 3 cm sediment depth intervals from Santa Monica Basin and La Goleta, and chemical gradients along which mcrA subgroups partition. Refer to table 2.1 for core information and table 2.2 for assignments of mcrA subgroups to ANME subgroups. The ordination method used was NMS, with a final stress of 6.94 and final instability of 0.000005. Panel a illustrates the general relative distribution of the mcrA subgroups within the two sites with measured chemistry vectors overlaid. The vectors represent the hypotenuse of the correlation coefficient (r2) of the chemistry with the two axes. Correlation coefficients of the chemistry to the axes is included in panel b under the chemical parameter section. Labels on the colored triangles denote the core number followed by the depth of the sediment interval. Panel b illustrates the relative abundance of each mcrA subgroup in the same ordination as that illustrated in panel a, with the strength of the mcrA subgroup relationship (r2) to the two axes. A good correlation of both a chemical parameter and a mcrA subgroup to the same axis indicates a correlation between the distributions of the two. 62 Figure 2.8. 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(2003) Anaerobic oxidation of methane above gas hydrates at Hydrate Ridge, NE Pacific Ocean. Marine Ecology-Progress Series 264: 1-14. Treude, T., Orphan, V., Knittel, K., Gieseke, A., House, C.H., and Boetius, A. (2007) Consumption of methane and CO2 by methanotrophic microbial mats from gas seeps of the anoxic black sea (vol 73, pg 2271, 2007). Applied and Environmental Microbiology 73: 3770-3770. Valentine, D.L., and Reeburgh, W.S. (2000) New perspectives on anaerobic methane oxidation. Environ Microbiol 2: 477-484. Wakeham, S.G., Amann, R., Freeman, K.H., Hopmans, E.C., Jorgensen, B.B., Putnam, I.F. et al. (2007) Microbial ecology of the stratified water column of the Black Sea as revealed by a comprehensive biomarker study. Organic Geochemistry 38: 2070-2097. 69 Wegener, G., Niemann, H., Elvert, M., Hinrichs, K.U., and Boetius, A. (2008a) Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environmental Microbiology 10: 2287-2298. Wegener, G., Shovitri, M., Knittel, K., Niemann, H., Hovland, M., and Boetius, A. (2008b) Biogeochemical processes and microbial diversity of the Gullfaks and Tommeliten methane seeps (Northern North Sea). Biogeosciences 5: 1127-1144. 70 Chapter III  Conclusion Before this study, stratification of ANME groups was known to exist but was not systematically addressed. The objective of this study was to gain some understanding of subgroup partitioning along chemical gradients, by comparing ANME subgroup abundances and chemistry within and between cores from different sites. ANME communities within and between sites Total ANME cell abundances were found to scale with other cores from the same sites, and methane concentrations were correlated with total cellular abundance. Since methane solubility increases with water pressure, sediment samples from deeper sites contained higher ANME abundances. This dependency of ANME on methane indicates it is their sole energy source. These observations are consistent with predictions based on thermodynamic calculations (Valentine and Reeburgh, 2000; Shima and Thauer, 2005). On the other hand, large variations in subgroup partitioning were observed between cores, indicating that ANME subgroups are heavily influenced by the general chemical composition of their environments. Sediment floor macroscopic features are likely a reflection of the chemical state as well, so it is not surprising that core ANME community composition is correlated with surficial features. Similar trends have been observed for the Black Sea carbonate chimneys and microbial mats (Knittel et al., 2005; Kruger et al., 2008), in the Gulf of Mexico cold seeps (Orcutt et al., 2005), Eel River Basin and Hydrate Ridge (Orphan et al., 2004; Knittel et al., 2005). ANME communities along chemical gradients According to the results obtained in this study, methane is the sole energy source for ANME and dictates the upper limit of cellular abundances. Tonya seep, the shallow core that contains low concentrations of methane, has correspondingly lower concentrations of total ANME cells (105-106 copies/g), while the other two sites, which have higher concentrations of methane, and are correspondingly deeper, have higher cell abundances (106-107 copies/g). However, it does not appear that methane directly influences the subgroup partitioning behaviors that exist between sites. By contrast, sulfate, sulfide and ammonia likely play a role in subgroup partitioning, as the 71 concentration of these chemicals correlated with abundances of specific ANME subgroups. ANME-2 associated subgroups dominated in cores with high sulfate and ammonia concentrations. Strong correlations were found between mcrA c abundance and the concentrations of sulfate, but not of ammonia and sulfide (Figure B2). This indicates that methane and sulfate are the most significant of the surveyed factors governing the distribution of mcrA c. While the abundance of the mcrA d subgroup was likewise influenced by sulfate and methane, its distribution was also dependent on ammonia, suggesting it may serve as a sole nitrogen source for the mcrA d subgroup. Similarly, McrA e abundance was reliant on ammonia as indicated by the absence of the subgroup in low ammonia conditions. However, unlike mcrA c and d, mcrA e distribution was not explained by sulfate concentrations. This indicates that in addition to ammonia, other untested parameter(s) likely influence mcrA e population dynamics. ANME-1 dominates when chemical conditions become unfavorable for ANME-2. This is illustrated by the scatter plots of ANME-1 associated subgroups (mcrA a1, a2, b) (appendix B), which don’t properly correlate to the chemical parameters, with subpopulation abundances often falling below the trend line. Therefore, competition with ANME-2 has to be taken into account when interpreting the correlations of ANME-1 with chemical parameters. It is conceivable ANME-1 is not a sulfate reducer or it requires less TEA for survival. ANME-1 cells are most often observed alone or in filaments, with no obvious syntrophic partnership as with ANME-2 and ANME-3 (Orphan et al., 2001b; Michaelis et al., 2002; Knittel et al., 2005). It is therefore also possible that it is performing AOM in the absence of syntrophy (Orphan et al., 2002). This has been calculated to be more energetically demanding (Valentine and Reeburgh, 2000). Since subgroup partitioning has not yet been addressed in this way, there is no evidence in the literature in conflict with the findings of this study. Also, microbial mats in the Black Sea are consistently observed to have ANME-1 populations more deeply buried in the mats, where sulfate is not as accessible (Kruger et al., 2008). The consistency of this pattern between geographical locations suggests two possibilities: ANME-1 has the ability to grow in less thermodynamically favorable conditions, or ANME-1 is not a sulfate reducer (Orphan et 72 al., 2002; Knittel et al., 2005; Kruger et al., 2008). Nauhaus et al., 2005 incubated sediments dominated in ANME-1 and found a 1:1 AOM:Sulfate reduction rate. In addition, when the quantified mcrA ab (ANME-1) values are plotted as a function of sulfate, a correlation does exist, albeit with a slope not as pronounced as that of the ANME-2 subgroups (Figure B2). However, the range of cores samples assayed contained a relatively low range of ANME-1 concentrations compared to ANME-2 concentrations (Figure A4). Since ANME-1 populations proliferate at depth, higher fluctuations may be encountered in deeper sediment layers. However, if the trend holds with depth, it signifies ANME-1 forms more stable populations than ANME-2, and that its substrate requirements may be less high than ANME-2. Influence of abundant subgroups on detecting trends in less abundant subgroups In the cores assayed, mcrA c often dominated the sediment cores. The clear dominance of this subgroup may render relationships of less abundant groups with chemistry difficult to interpret. However, when comparing environments with marked differences in concentration for one chemistry parameter, a concomitant marked decrease in the abundance of a subgroup could yield meaningful information on the metabolism of the subgroup, in spite of the dominant subgroup. Strengths and weaknesses of research methods DNA extraction method As with all environmental microbiology studies, it is difficult to know if the DNA extraction method used was adequately sampling the target microbial population and not biasing the counts. For example, if one of the subgroup cell types is easier to lyse by bead beating, it could easily be seen how the groups could consistently be overrepresented in the qPCR counts. It is also impossible to know what the real DNA concentration of the sediments sampled is. This has an impact on the calculation of final copy number /g of the gene. Therefore, triplicate DNA extractions were performed to minimize the potential bias in DNA extraction and to determine the DNA concentration more accurately in the sediment horizons. The highest concentration obtained for a set of three extractions is 73 then used to calculate the copies/g ratio, with the assumption that DNA concentration represented 100% extraction efficiency. qPCR general methodology qPCR is an attractive method for the quantification of microbes in the environment, as it allows a large number of samples to be analyzed at once, but it suffers from biases that cannot easily be corrected. Due to the inherent allelic variation within closely related microbes in an environment. Therefore, it is impossible to predict the behavior of every variant in the qPCR reaction, and it is known alleles have different amplification efficiencies. A great level of allelic variation in a sample may result in erroneous quantification and therefore a false estimate of subgroup abundance. For this reason, numbers obtained in this study have been used as indicators of order of magnitude and not for absolute quantification. Another weakness of the methodology is that it only measures gene copy number. If nothing is known of the organisms measured, then one cannot determine how many copies of the target gene the organism has. In addition, we are measuring all gene copy numbers, including cells that are not metabolically active, dead, dormant or naked DNA. For these reasons, it is useful to perform FISH counts to compare the results and see if they commensurate. The Eel River Basin cores were extracted and analysed, because of previously documented corresponding FISH counts in the literature. qPCR assays The primer sets were made to encompass all the mcrA sequences available to date to our knowledge.  As more sequences from new environments are discovered, it is possible some of the primers no longer encompass whole subgroups. This is a likely occurrence in the ANME-1 associated subgroups, which are already known not to encompass whole groups. Chemistry The chemical measurements of the samples were performed by the David Valentine Laboratory.  One concern with this data is that there are no replicate values for the measurements. When analysis shows a smooth trend down a sediment core, it adds 74 credence to the measurements. However, when the values seem highly variable, caution must be had with using the absolute concentrations. Core compression and depth matching from cores with qPCR counts and chemistry were also considered. Samples were taken with a 4 inch diameter corer, and 1- inch diameter “subcores” were hammered into the larger core. Photographs were taken and correction factors were applied to match the data. One last concern is regarding the chemistry in the scatter plots and the NMS plots. The values were the result of linear interpolations of true values because the depths did not always match. If there were no good correlations, this may have become a concern. However, since solid correlations were drawn from the data, then it likely means the interpolated values represented realistic values. The chemistry measurements provided an advantage since they were taken from the same cores used for the qPCR assay. Such directly paired data allows for finer scale resolution for the effect of a parameter on microbial community partitioning. Evaluation of current knowledge and proposals for new ideas Since comparing ANME subgroup abundance measurements to chemistry has shown to be an effective technique at extracting ANME population dependencies, additional chemical parameters, such as iron and manganese, could be measured with with further experiments. Similar analyses should be performed on a larger set of samples from more sites, to improve the results and extend our knowledge of ANME community structure and dynamics. For additional research, I propose chemically biasing and incubating multiple cores samples from one site and monitoring temporal progression of ANME population structure. The samples should be from a site where ANME populations are sparse, so that any growth would be related to the chemicals added. This conforms to the observations of Girguis et al 2003 regarding the proliferation of ANME groups in a control core. Microbial populations would be monitored over time with the qPCR assays developed in this study. Data convergence would strengthen our current hypotheses. 75 Status of relevant working hypothesis In this study, the ANME partitioned along geochemical gradients and did not seem to follow biogeographical patterns. Therefore, the working hypothesis was supported. The parameters proposed to govern the distribution of ANME subgroups were among sulfate, sulfide, and methane. Our results indicated that sulfate concentrations do dictate ANME community partitioning. The positive correlation between sulfide and ANME cell abundances can be explained by the fact that sulfide is a by-product of ANME sulfate reduction. Methane was not found to have an effect on ANME population structure, but it is a critical parameter in the upper ANME abundance limit. Additionally, ammonia was observed to influence ANME-2 subgroup partitioning. Conclusion This study indicates ANME subgroups partition according to geochemical parameters. The qPCR assays developed in this study in combination with chemistry measurements have enabled the identification of several specific factors affecting ANME populations. This has demonstrated the effectiveness of the method at extracting meaningful information from ANME populations and, applied to a wider range of samples and compared to more chemistry measurements, may enable the discovery of new parameters. 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(2007) Microbial ecology of the stratified water column of the Black Sea as revealed by a comprehensive biomarker study. Organic Geochemistry 38: 2070-2097. Wegener, G., Niemann, H., Elvert, M., Hinrichs, K.U., and Boetius, A. (2008a) Assimilation of methane and inorganic carbon by microbial communities mediating the anaerobic oxidation of methane. Environmental Microbiology 10: 2287-2298. 83 Wegener, G., Shovitri, M., Knittel, K., Niemann, H., Hovland, M., and Boetius, A. (2008b) Biogeochemical processes and microbial diversity of the Gullfaks and Tommeliten methane seeps (Northern North Sea). Biogeosciences 5: 1127-1144. 84 Appendix A qPCR assay development methodology qPCR standard development The design of subgroup-specific qPCR assays requires the development of standards encoding the target sequence. The green-grey boxes in the flowchart in figure A1 outline the general procedure for standard development. In this study, standards were constructed by amplifying the target genes with the universal mcrA ME-1 and ME-3 primer sets from the environment (Hallam et al., 2003). The resulting sequences were purified, cloned, sequenced, and their subgroup affiliations were determined through sequence alignment and generation of phylogenetic trees with sequences of known subgroup membership (Hallam et al., 2003; Losekann et al., 2007). The standards were then isolated and purified according to the method described in the experimental methods section of Chapter II. For quantification of DNA, Pico green assays (Molecular Probes, Invitrogen) were used for their low imput DNA requirements according to the manufacturer’s instructions, and detection of fluorescence was done with a Typhoon scanner (GE healthcare, UK). Measurements of undiluted standards with the NanoDrop spectrophotometer (Thermo scientific, USA) yielded equivalent results. Subgroup assignments In addition to the mcrA sequences from environmental samples for standard construction, all mcrA sequences (for both methanogens and ANME) from the non- redundant database hosted on the National Center for Biotechnology Information webpage were downloaded. The grey boxes in figure A1 contain a general procedural outline for the collection and sorting of all relevant sequence. Sequences were first aligned with MUSCLE (Edgar, 2004) and a neighbor-joining tree was generated in Arb (Ludwig et al., 2004). Environmental sequences that did not cluster with mcr from ANME groups were removed. Sequences from methanogenic lineages with sequenced genomes and sequences clustering with known ANME sequences were retained and aligned. Manual amendments were done and a maximum likelihood tree was then constructed with Phyml (figures 2.1 and 2.2) (Guindon and Gascuel, 2003). Clusters formed in ANME groups with sequences of known affiliation were the basis for subgroup 85 assignments. The mcrA c and d subgroup separation, after the addition of all the sequences, did not yield useful bootstrap values (<50). However, the sequences between the two were distinct enough to design primers targeting each subgroup. Primer design Sequences not shared with other subgroups but were conserved within the (sub)group were selected. The resulting sequences were analyzed by BLAST adjusted for short queries (Altschul et al., 1990). Primers exclusively targeting the intended (sub)groups were kept. The primer sets were designed so their amplification product would not exceed 250 bp, with the exception of the mcrA e subgroup assay, which had an amplicon length of 500 bp. The general primer design logic is detailed in the purple-grey boxes of figure A1. The first step in primer optimization was the determination of the highest temperature under which the primer set still performed in a PCR reaction. This was done with a conventional thermocycler with a gradient function. Figure A2a contains the temperature gradient gels of the final mcrA assays. The second PCR reaction tested all non-target mcrA ANME subgroup sequences and Methanosarcina mazei genomic DNA (see figure A2b). In the ab, a1, b, cd, c, d, e assays, non target amplification was not observed. Conversely, in the a2 assay, low levels of the a1 and b target amplified. To assess the influence of the non-target amplicon in the reaction, the highest concentration of non-target standard dilutions was (107 copies) tested on the qPCR platform (Opticon II thermocycler, Biorad, CA). The amplification of the non-target was several orders of magnitude lower than that of the target gene, indicating the influence of the non-target subgroup in the results from environmental studies was negligible. The assays were then evaluated on the qPCR platform. Assays with logarithmic amplification curves and with a 90-110% amplification efficiency over the range of standards used were chosen. The sequences of each assay primers in the context of an alignment with representatives from each ANME subgroups and five methanogenic orders mapped onto the mcrA gene length are illustrated in figure A3. 86 McrA qPCR assay validation Comparison to published ANME SSU assay results To examine the validity of the values obtained with the mcrA qPCR assays developed, comparison of results for the mcrA ab and cd assays were made to the ANME-1 and ANME-2c SSU assays previously published in the literature, respectively (Girguis et al., 2005). The results of these assays for all environmental samples examined in this study are in figure A4. While mcrA ab and ANME-1 SSU assays yielded identical results (average = 1.14, Standard error = 0.18), ANME-2c SSU to mcrA cd ratios yielded a two-fold difference (average = 2.48, Standard error, 0.08). Since all standards were quantified according to the same method, results show that ANME-2 contains 2 SSU copies. Addition of mcrA subgroups and comparison to group assay results With the validity of the mcrA group primers confirmed, values obtained for subgroups were added and numbers were compared to total subgroup abundances. Figure A4 illustrates the comparison. As expected, the ANME-1 subgroups added together did not always add up to the mcrA ab quantity. Since the values were always equal to or lower than the total mcrA ab assay results, it is likely the ANME-1 mcrA populations were incompletely sampled for some sites with the primer sets designed in this study. On the other hand, ANME-2 subgroups c and d added together consistently resulted in the same trends for all sites, indicating the primer sets completely encompassed the mcrA cd groups. Limits of detection Since 0.5g of sediment DNA extracted was suspended into 500 l buffer, and 2.5 l was used per qPCR reaction, 400 copies/g would be the theoretical concentration at which 1 template copy would be in the qPCR reaction mixture. The opticon 2 specifications indicate single template copy per reaction mixture is possible. However, from the qPCR results plotted in Figure A4, it seems more reasonable to set the lower detection limit to the more conservative value of 103 copies/g. Concentrating the DNA should proportionally increase the detection limit. 87 Figure A1. Flowchart illustrating qPCR assay development 88 Figure A2. mcrA qPCR temperature and specificity optimization gels. a) Temperature (0C) optimization for assay. The chosen temperature is highlighted in a red box; b) Non- target subgroup amplification. 89 90 Figure A3. Full length mcrA diagram with nucleotide positions, structural characteristics, position of conserved/posttranslationally modified amino acid residues, and position of mcrA primers designed in this study. The mcrA sequence is represented by the central diagram, with structural characteristics colored by blocks which are detailed in the legend. The orange lines in the diagram represent the positions of conserved and posttranslationally modified catalytic residues. The residues are labeled orange. The black lines represent the position of mcrA primers developed in this study. Corresponding black labels identify the primer subgroup target and direction of primer: F = forward, R = reverse. Nucleotide positions according to Methanosarcina acetivorans full length mcrA sequence. Primer sequence location is linked to alignments spanning length of the primer sequences with representative sequences from each ANME mcrA subgroup and mcrA sequence from each methanogenic order. Alignment was generated with MUSCLE (Edgar, 2004). A1, mcrA subgroup a1 (AY324363.1); a2, mcrA subgroup a2 (EU496891.1); b, mcrA b subgroup (AY324369.1); c, mcrA c subgroup (AY324370.1); mcrA d subgroup (AY324371.1); e, mcrA e subgroup (DQ521857.1); f, mcrA f subgroup (AM407730.1); Metka, Methanopyrus kandleri, (AE009439.1); Methun, Methanospirillum hungatei (CP000254.1); Metac, Methanosarcina acetivorans (AE010299.1); Metfe, Methanothermus fervidus (J03375.1); Metsta, Methanosphaera stadtmanae (CP000102.1). Structural characteristics and conserved catalytic residues from Ermler et al., 1997. 91 92 Figure A4. Comparison of mcrA qPCR assays designed in this study to published SSU assay results.  Values on x axis represent the cores analyzed 93 Appendix B. mcrA copy number in relation to chemical parameters measured in this study. Figure B1. mcrA subgroups as a function of log methane concentration for Santa Monica Basin, La Goleta and Tonya seep. Slope , r2 and probability (p) values are displayed for the linear regression. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 94 Figure B2. mcrA subgroups as a function of sulfate concentration for Santa Monica Basin, La Goleta and Tonya seep. Slope , r2 and probability (p) values are displayed for the linear regression. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 95 Figure B3. mcrA subgroups as a function of sulfide concentration for Santa Monica Basin and La Goleta. Slope , r2 and probability (p) values are displayed for the linear regression. Error bars represent Standard error of triplicate DNA extractions from core section homogenate. 96 Figure B4. mcrA subgroups as a function of ammonia concentration for Santa Monica Basin and La Goleta. Slope , r2 and probability (p) values are displayed for the linear regression. Error bars represent Standard error of triplicate DNA extractions from core section homogenate.

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