SEARCH

SEARCH BY CITATION

Keywords:

  • mcrA ;
  • dsrAB ;
  • fhs ;
  • rdhA ;
  • deep biosphere;
  • contamination

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

The vast majority of microbes inhabiting the subseafloor remain uncultivated and their energy sources unknown. Thus, a focus of ocean drilling expeditions over the past decade has been to characterize the distribution of microbes associated with specific metabolic reactions. An important question has been whether microbes involved in key microbial processes, such as sulfate reduction and methanogenesis, differ fundamentally from their counterparts in surface environments. To this end, functional genes of anaerobic methane cycling (mcrA), sulfate reduction (dsrAB), acetogenesis (fhs), and dehalorespiration (rdhA) have been examined. A compilation of existing functional gene data suggests that subseafloor microbes involved in anaerobic methane cycling, sulfate reduction, acetogenesis, and dehalorespiration are not fundamentally different from their counterparts in the surface world. Moreover, quantifications of mcrA and dsrAB suggest that, unless the majority of subseafloor microbes involved in methane cycling and sulfate reduction are too genetically divergent to be detected with conventional methods, these processes only support a small fraction (< 1%) of total microbial biomass in the deep biosphere. Ecological explanations for the observed trends, target processes and methods for future investigations, and strategies for tackling the unresolved issue of microbial contamination in samples obtained by ocean drilling are discussed.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Protein-coding (functional) genes have been used as phylogenetic markers across a wide range of micro-organisms since the late 1980s. In 1988, Amann et al. demonstrated that bacterial phylogenetic trees based on gene sequences of the β-subunit of ATP-synthase were congruent with ones based on 16S ribosomal RNA (rRNA) genes. This finding confirmed 16S rRNA gene-based interpretations of evolution in Bacteria, showed that (certain) functional genes were suitable markers for the study of evolutionary relationships among organisms and genes, and offered a potentially powerful tool to link the genotype to the phenotype in unknown microbial populations.

Since then, the study of functional genes and gene transcripts of known metabolic and biosynthetic processes has enabled investigations on the distribution, diversity, abundance, and activity of (uncultured) micro-organisms. Nitrogen cycling processes, such as N fixation (nifH), ammonia oxidation/nitrification (amoA), nitrate reduction (nrfA), nitrite reduction (nirK, nirS, nrfA), nitrate reduction (narG, napA), and polyamine degradation (aphA), have been examined (e.g. Rotthauwe et al., 1997; Braker et al., 1998; Zehr et al., 2003; Mohan et al., 2004; Poretsky et al., 2005; Smith et al., 2007; Dang et al., 2009). Microbial sulfur cycling has been studied via functional genes of sulfate reduction (dsrAB, aprBA) and S oxidation (rdsrAB, soxB, aprBA; e.g. Klein et al., 2001; Meyer & Kuever, 2007; Loy et al., 2009; Hügler et al., 2010). Carbon cycling pathways have been analyzed through functional genes of methanogenesis and anaerobic methanotrophy (mcrA), acetogenesis (fhs), aerobic methylotrophy and methanotrophy (pmoA, mmoX, mxaF, fhcD), C fixation (aclB, cbbM, cbbL, oorA, acsB), fermentation (hydA), cellulose hydrolysis (cel5, cel48), aromatic hydrocarbon degradation (bssA, nah, pah), and aliphatic hydrocarbon compounds (alkB; e.g. Elsaied & Naganuma, 2001; Leaphart et al., 2003; Campbell & Cary, 2004; Friedrich, 2005; Heiss-Blanquet et al., 2005; Winderl et al., 2007; McDonald et al., 2008; Hügler et al., 2010; Pereyra et al., 2010; Liang et al., 2011; Mason et al., 2012). Further processes that have been targeted by the functional gene approach include reductive dehalogenation (pceA, tceA; e.g. Regeard et al., 2004), phosphorus (ppk, ppx) and metal cycling (cytc; e.g. Lu et al., 2011).

Ideally, functional genes used for process and phylogenetic studies will have the following characteristics: (1) the gene encodes an enzyme used for one specific reaction; (2) a large gene sequence database is available to infer the phylogenetic identity of environmental sequences; (3) nucleotide sequence conservation is sufficient, so genes can be comprehensively targeted by general or group-specific probes, such as PCR primers; and (4) the evolutionary history is well-constrained, so organisms which have obtained the gene through lateral transfer can be identified.

In practice, there may not be many functional genes that meet all these criteria. For instance, a common feature of even highly conserved genes is that different organismal groups may use it in opposite directions. An example is the gene encoding the alpha subunit of methyl coenzyme M reductase (mcrA), which catalyzes the reduction of the coenzyme M-bound methyl group to methane in methanogens and perhaps the reverse reaction in anaerobic methanotrophs (Hallam et al., 2004; Thauer, 2011). The similarly conserved dissimilatory sulfite reductase gene (dsrAB) operates in opposite directions, depending on whether it is used by sulfate (sulfite)-reducing or sulfide-oxidizing microbes (Loy et al., 2009). To complicate things further, the dsrAB gene has been transferred laterally multiple times throughout its evolutionary history. DsrAB is nonetheless a suitable marker for the study of S cycling due to a wealth of published information, which shows that sulfate reducers and sulfide oxidizers mostly fall into separate genetic clusters, and suggests that lateral gene transfer has only occurred in a few evolutionary events (Klein et al., 2001; Loy et al., 2008). Not all functional genes are as specific to processes as mcrA and dsrAB, however. The gene for formyl tetrahydrofolate synthetase (fhs), an enzyme that catalyzes the ATP-dependent activation of formate, and is used as a marker gene of acetogenesis, also occurs in aerobic heterotrophic, fermentative, and sulfate-reducing organisms (Leaphart et al., 2003; Lever et al., 2010). If multiple functions are known, it is thus useful to complement functional gene data with rate measurements, concentrations or isotopic compositions of metabolic educts and products, or thermodynamic calculations, all of which offer clues to whether the suspected metabolic function is indeed present (Lever et al., 2010).

Functional genes examined during ocean drilling expeditions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Ocean Drilling Program (ODP) and Integrated Ocean Drilling Program (IODP) sites from which there is published data on functional genes are shown in Fig. 1. So far, these studies have resulted in publications on four different functional genes: the aforementioned mcrA, dsrAB, and fhs, as well as the gene for reductive dehalogenase (rdhA), an enzyme which catalyzes the reductive removal of halogen groups in halogenated organic compounds (Futagami et al., 2009; Table 1). All publications have used PCR-based methods to obtain sufficient amounts of gene product for DNA sequencing and for quantitative analyses via real-time PCR. The phylogenetic and metabolic diversity of organisms detected so far, the sites where they have been found, and information on their closest cultured relatives are reviewed in the following sections.

Table 1. Overview of functional genes, representative sequences, GenBank accession numbers, region and site of origin, closest cultured relative based on nucleotide sequence similarity determined by megablast algorithm in basic local alignment search tool (blast), megablast blast nucleotide sequence similarity, energy substrates of closest cultured relative, and references of functional gene phylotypes published from ODP and IODP expeditions
GeneRepresentative cloneAccession no.OriginSiteClosest cultured relativeSequence similarity (%)Energy substrates used by closest cultured relativeReferences
  1. mrtA, isoenzyme of mcrA, so far only found in the Methanobacteriales and Methanococcales; ns, no significant sequence similarity to cultured strains; MG, methanogen; AOM, putatively involved in anaerobic oxidation of methane; SR, sulfate reducer; NR, nitrate reducer; Ac, acetate; MeOH, methanol; MA, methylamines; MS, methyl sulfides; FA, fatty acids; MeBut, methyl butyrate; EtOH, ethanol; lac, lactate; mal, malate; pyr, pyruvate; Corg, organic carbon compounds; CORK, circulation obviation retrofit kit; IODP, integrated ocean drilling program.

mcrA OD_mcrA2 AB598282 ShimokitaC9001 Methanosarcina mazei 94MG; H2/CO2, Ac, MeOH, MAImachi et al. (2011)
mcrA NANK-ME7326 AY436542 Nankai TroughODP 1173 Methanosarcina mazei 99MG; H2/CO2, Ac, MeOH, MANewberry et al. (2004)
mcrA OD_mcrA4 AB598284 ShimokitaC9001 Methanosarcina barkeri 90MG; H2/CO2, CO, Ac, MeOH, MA, MSImachi et al. (2011)
mcrA ME42-B3 AJ867765 Peru MarginODP 1229 Methanosarcina barkeri 92MG; H2/CO2, CO, Ac, MeOH, MA, MSWebster et al. (2006)
mcrA NANK-ME7311 AY436552 Nankai TroughODP 1173 Methanosarcina barkeri 94MG; H2/CO2, CO, Ac, MeOH, MA, MSNewberry et al. (2004)
mcrA OD_mcrA1 AB598281 ShimokitaC9001 Methanosarcina lacustris 90MG: H2/CO2, MeOH, MAImachi et al. (2011)
mcrA 1327C22-5 AB525713 Cascadia MarginIODP 1327 M. coccoides burtonii 95MG; MeOH, MAYoshioka et al. (2010)
mcrA 1230A4 EU161880 Peru TrenchODP 1230 M. saeta harundinacea 83MG: AcInagaki et al. (2006a)
mcrA 1327C22-16 AB525699 Cascadia MarginIODP 1327AOM; CH4Yoshioka et al. (2010)
mcrA 1327C31-7 AB525702 Cascadia MarginIODP 1327AOM; CH4Yoshioka et al. (2010)
mcrA OD_mcrA6 AB598286 ShimokitaC9001 Methanocella paludicola 87MG; H2/CO2, formateImachi et al. (2011)
mcrA Methanoculleus submarinus DQ229156 Nankai TroughODP 1173N/AN/AMG; H2/CO2, formateMikucki et al. (2003)
mcrA ME30-A5 AJ867760 Peru MarginODP 1229 M. brevibacter arboriphilus 99MG; H2/CO2, formateWebster et al. (2006)
mcrA Methanobrevibacter sp. MO-MVB AB598294 ShimokitaC9001 M. brevibacter arboriphilus 99MG; H2/CO2, formateImachi et al. (2011)
mcrA NANK-ME7331 AY436546 Nankai TroughODP 1173 M. brevibacter arboriphilus 99MG; H2/CO2, formateNewberry et al. (2004)
mcrA ME6-A9 AJ867759 Peru MarginODP 1229 M. brevibacter arboriphilus 99MG; H2/CO2, formateWebster et al. (2006)
mcrA 357_mcrA1 AB598287 ShimokitaC9001 Methanobacterium flexile 89MG; H2/CO2, formateImachi et al. (2011)
mcrA OD_mcrA5 AB598285 ShimokitaC9001Methanobacterium oryzae94MG; H2/CO2, formateImachi et al. (2011)
mcrA OD_mcrA3 AB598283 ShimokitaC9001Methanobacterium sp. AL-2190MG; H2/CO2, formateYoshioka et al. (2010)
mcrA 1327C1-1 AB525694 Cascadia MarginIODP 1327Methanobacterium sp. AL-2191MG; H2/CO2, formateYoshioka et al. (2010)
mrtA 560D_mcrA6 AB598290 ShimokitaC9001 Methanobacterium palustre 97MG; H2/CO2, formate, propanol/CO2Imachi et al. (2011)
mrtA 560D_mcrA5 AB598289 ShimokitaC9001 M. bacterium formicicum 92MG; H2/CO2, formateImachi et al. (2011)
mcrA Methanococcus aeolicus Nankai-3 CP000743 Nankai TroughODP 1173N/AN/AMG; H2/CO2, formateKendall et al. (2006)
mcrA 1327C21-8 AB525710 Cascadia MarginIODP 1327AOM; CH4Yoshioka et al. (2010)
mcrA 1327C20-3 AB525707 Cascadia MarginIODP 1327AOM; CH4Yoshioka et al. (2010)
mcrA 357D_mcrA4 AB598288 ShimokitaC9001 M.massiliicoccus luminyensis 92MG; substrate unpublishedImachi et al. (2011)
dsrAB PERU18-DSR AJ867773 Peru MarginODP 1228SR; unknownWebster et al. (2006)
dsrAB PERU14-DSR AK867770 Peru MarginODP 1228SR; unknownWebster et al. (2006)
dsrAB PERU12-DSR AJ867769 Peru MarginODP 1228SR; unknownWebster et al. (2006)
dsrAB PERU6-DSR AJ867767 Peru MarginODP 1228SR; unknownWebster et al. (2006)
dsrAB CORK dsr15* AB260074 JdF Ridge FlankODP 1026 Ammonifex degensii 76SR/NR; H2, formate, pyruvateNakagawa et al. (2006)
dsrAB CORK dsr18* AB260073 JdF Ridge FlankODP 1026 Ammonifex degensii 78SR/NR; H2, formate, pyruvateNakagawa et al. (2006)
dsrAB Desulfotomaculum sp. Srb55* AB260069 JdF Ridge FlankODP 1026 Desulfotomaculum geothermicum 89SR; H2, FA, EtOH, fructoseNakagawa et al. (2006)
dsrAB Desulfovibrionales bacterium* AB260070 JdF Ridge FlankODP 1026 Desulfovibrio salexigens 73SR; H2, formate, lac, mal, EtOHNakagawa et al. (2006)
dsrAB Desulfuromonadales bacterium Tc37* AB260071 JdF Ridge FlankODP 1026 Desulfocella halophila 87SR; FA, MeBut, alanine, pyrNakagawa et al. (2006)
fhs JdF_PC2D GU810099 JdF Ridge FlankIODP 1301 Desulfovibrio salexigens 72SR; H2, FA, EtOH, fructoseLever et al. (2010)
fhs JdF_ND1B GU810122 JdF Ridge FlankIODP 1301 Mesorhizobium loti 85Aerobic heterotrophLever et al. (2010)
fhs JdF_NC7A GU810143 JdF Ridge FlankIODP 1301 Mesorhizobium loti 92Ferments carbohydratesLever et al. (2010)
fhs JdF_NC4A GU810137 JdF Ridge FlankIODP 1301  Lever et al. (2010)
fhs JdF_NC1D GU810110 JdF Ridge FlankIODP 1301 Sphingomonas paucimobilis 85Aerobic chemoorganotrophLever et al. (2010)
fhs JdF_PC1F GU810092 JdF Ridge FlankIODP 1301 Lever et al. (2010)
fhs JdF_PC2F GU810098 JdF Ridge FlankIODP 1301 Lever et al. (2010)
fhs JdF_PC1H GU810084 JdF Ridge FlankIODP 1301 SR: oxidizes Corg to AcLever et al. (2010)
fhs JdF_NC1F GU810117 JdF Ridge FlankIODP 1301 Lever et al. (2010)
fhs JdF_PC1C GU810090 JdF Ridge FlankIODP 1301 SR; facultative acetogenLever et al. (2010)
fhs JdF_NC6C GU810141 JdF Ridge FlankIODP 1301 Methylobacterium extorquens 82Aerobic; facultative methylotrophLever et al. (2010)
fhs JdF_ND1H GU810116 JdF Ridge FlankIODP 1301 Methylibium petroleiphilum 83Aerobic methylotrophLever et al. (2010)
fhs JdF_NC2A GU810128 JdF Ridge FlankIODP 1301 Lever et al. (2010)
fhs JdF_PC2B GU810100 JdF Ridge FlankIODP 1301 SR and metal reducerLever et al. (2010)
fhs JdF_NC6D GU810142 JdF Ridge FlankIODP 1301Lever et al. (2010)
rdhA 30_Site C9001_216.8 mbsf AB499775 ShimokitaC9001 Reductive dehalogenationFutagami et al. (2009)
rdhA 18_Site 1230_0.3 mbsf AB499763 Peru TrenchODP 1230 Reductive dehalogenationFutagami et al. (2009)
rdhA 13_Site 1226_3.2 mbsf AB499758 E Eq PacificODP 1226 Reductive dehalogenationFutagami et al. (2009)
rdhA 12_Site 1226_3.2 mbsf AB499757 E Eq PacificODP 1226 Reductive dehalogenationFutagami et al. (2009)
rdhA 32_Site C9001_216.8 mbsf AB499777 ShimokitaC9001 Reductive dehalogenationFutagami et al. (2009)
rdhA 25_Site C9001_13.5 mbsf AB499770 ShimokitaC9001 Reductive dehalogenationFutagami et al. (2009)
rdhA 3_Site 1226_3.2 mbsf AB499748 E Eq PacificODP 1226 Reductive dehalogenationFutagami et al. (2009)
image

Figure 1. Map of ODP and IODP drilling locations from where functional gene data have been reported (map template adapted from http://en.wikipedia.org/wiki/File:BlankMap-World-162E-flat.svg).

Download figure to PowerPoint

mcrA

Methane-cycling archaeal communities have been phylogenetically characterized at five locations – all in the Pacific Ocean: the Nankai Trough (Newberry et al., 2004), the Peru Trench (Inagaki et al., 2006a), the Peru Margin (Webster et al., 2006), the Cascadia Margin (Yoshioka et al., 2010), and off Shimokita Peninsula (Imachi et al., 2011; Fig. 1, Table 1). In addition, mcrA genes have been quantified by real-time PCR in cores from Cascadia Margin (Colwell et al., 2008) and the Porcupine Seabight in the Atlantic Ocean (Webster et al., 2009), and two strains of hydrogenotrophic methanogens have been isolated from subseafloor sediment of the Nankai Trough (Mikucki et al., 2003; Kendall et al., 2006; Table 1). At most sites, mcrA genes were only detected and analyzed at few sampling depths. Therefore, detailed correlations between the community composition of methanogens and anaerobic methanotrophs and environmental variables, such as geochemical gradients or lithostratigraphy, are not possible, and it remains to be shown which variables drive the community composition of methane-cycling Archaea in subsurface habitats. The difficulty of detecting mcrA or 16S rRNA gene sequences of known methanogens or methanotrophs even at sites with high biogenic methane concentrations (Biddle et al., 2006) suggests that existing methods either underestimate the population size or that small populations of methanogens and methanotrophs produce and consume the vast biogenic methane reservoir found in subseafloor sediments (Inagaki et al., 2006a).

The published mcrA sequences show a considerable diversity of methane-cycling Archaea in subseafloor sediments, with genera belonging to the orders Methanosarcinales (Methanosarcina, Methanococcoides, Methanosaeta, ANME-2), Methanocellales (Methanocella), Methanomicrobiales (Methanoculleus), Methanobacteriales (Methanobrevibacter, Methanobacterium), Methanococcales (Methanococcus), ANME-1, and a novel order with one recent isolate (Methanomassiliicoccus; Fig. 2a, Table 1). The genera Methanosarcina, Methanobrevibacter, and Methanobacterium have been found in the greatest number of locations, with Methanosarcina and Methanobrevibacter in 3/5 (Newberry et al., 2004; Webster et al., 2006; Imachi et al., 2011) and Methanobacterium in 2/5 locations (Yoshioka et al., 2010; Imachi et al., 2011). In some cases, the degree of mcrA sequence similarity between sites is striking, with nearly identical phylotypes related to Methanosarcina barkeri detected in the Nankai Trough (Newberry et al., 2004) and Peru Margin (Webster et al., 2006) and equally similar mcrA sequences related to Methanobrevibacter arboriphilus detected in the Nankai Trough (Newberry et al., 2004), Peru Margin (Webster et al., 2006), and off Shimokita Peninsula (Imachi et al., 2011; Fig. 2a). All other genera have only been detected in single locations, suggesting that, despite overlaps, the community of methane-cycling Archaea varies widely between sites.

image

Figure 2. Phylogenetic distance trees of (a) mcrA, (b) dsrAB, and (c) fhs, including phylotypes obtained during ODP and IODP expeditions in bold. All trees are based on nucleotide sequences aligned in ARB (Ludwig et al., 2004) and were constructed using the neighbor-joining function in SeaView (http://pbil.univ-lyon1.fr/software/seaview.html). Bootstrap values (in %) were calculated from 1000 replicates each. Only values of ≥ 50% are shown.

Download figure to PowerPoint

Interestingly, none of the mcrA groups so far detected during drilling expeditions are unique to the deep biosphere. Several groups can be considered typical of marine environments, however. These include the putatively methanotrophic ANME-1 and ANME-2 Archaea, and the methanogenic Methanococcoides and Methanococcus genera. ANME-1 and ANME-2 Archaea have mainly been found in sulfate-rich habitats or within/near sulfate–methane transition zones (SMTZs), from estuarine and coastal to deep sea cold seep, CO2 lake, and hydrothermal sediments (e.g. Boetius et al., 2000; Thomsen et al., 2001; Michaelis et al., 2002; Teske et al., 2002; Inagaki et al., 2006b; Lloyd et al., 2011; Yanagawa et al., 2011; Biddle et al., 2012; for review see Knittel & Boetius, 2009). Similarly widespread in the marine environment are the Methanococcoides, a genus predominantly detected in and repeatedly isolated from marine sediment samples, that is comprised of obligately methylotrophic methanogens within the Methanosarcinales (Sowers & Ferry, 1983; Dhillon et al., 2005; Singh et al., 2005; Roussel et al., 2009a; Parkes et al., 2012; reviewed in Whitman et al., 2006). The H2/CO2 and formate-utilizing Methanococcus genus has mainly been detected in and isolated from salt marsh, estuarine and coastal sediment (Whitman et al., 1986; Franklin et al., 1998; Ward et al., 1989; reviewed in Whitman et al., 2006). Other genera, such as the Methanosarcina, Methanosaeta, Methanocella, and Methanoculleus, cannot be considered ‘typically marine’, as they are, apart from estuarine and marine sediment, frequent across a wide range of nonmarine anoxic habitats, including animal feces and sewage sludge, landfills, lake sediment and freshwater wetlands (e.g. Lanoil et al., 2001; Lueders et al., 2001; Luton et al., 2001; Castro et al., 2004; Dhillon et al., 2005; Liu & Whitman, 2008; Nunoura et al., 2008; Sakai et al., 2008; Zhang et al., 2008; Roussel et al., 2009a; Parkes et al., 2012; reviewed in Whitman et al., 2006 and Liu & Whitman, 2008). Perhaps the most surprising finding is the detection of the Methanobacterium and Methanobrevibacter genera. Traditionally, Methanobacterium species have been considered sensitive to salinity, with culture media (NaCl) in excess of 0.2 M (c. 40% of typical seawater salinity) inhibiting cell growth (Whitman et al., 2006). The recent isolation of several new species from marine or saline environments has changed this perception, however (Shlimon et al., 2004; Mori & Harayama, 2011). And the genus Methanobrevibacter is a typical inhabitant of animal intestines, sewage sludge, and decaying plant matter on land (Zeikus & Henning, 1975; Miller & Wolin, 1986; Ufnar et al., 2006; Whitman et al., 2006) – environments that differ strikingly from the energy-depleted deep subsurface biosphere!

According to thermodynamic competition theory, methanogenic Archaea using electron donors such as H2, formate and acetate are outcompeted by organisms performing energetically more favorable reactions, such as sulfate reduction, given the presence of electron acceptors required for the latter (e.g. Cappenberg, 1974; Cord-Ruwisch et al., 1988; Lovley & Goodwin, 1988). This principle has been shown to apply to coastal marine sediment (Hoehler et al., 1998). As a result, only minority populations of methanogens using so-called noncompetitive substrates, that is, C1 compounds such as methylamines, methyl sulfides, and methanol, not used by most sulfate reducers, can coexist with sulfate reducers in sulfate-rich sediment (e.g. Oremland & Polcin, 1982; King et al., 1983; Kiene et al., 1986). Based on closest cultured relatives, methanogens in the deep subsurface consume the full spectrum of known methanogenic substrates (H2/CO2, formate, acetate, C1 compounds; Table 1). The distribution only partially reflects the zonation observed elsewhere, however. While consistent with the notion of thermodynamics-driven competition, mcrA genes are below detection or in low numbers in sulfate-reducing sediment of the Peru Trench and Cascadia Margin, respectively, and increase within the methanogenesis zone (Inagaki et al., 2006a; Yoshioka et al., 2010); they are at peak abundance in sulfate-rich sediment above the SMTZ at IODP Sites 1244 and 1245 in the Cascadia Margin (Colwell et al., 2008). McrA with high sequence similarity to hydrogenotrophic M. arboriphilus are not only present but appear the most active in sulfate-rich surface sediments of ODP Site 1174 (Newberry et al., 2004). And the highest mcrA abundances detected so far in the deep subseafloor are from sulfate-rich sediments near the Porcupine Seabight Challenger Mound (IODP U1318), in depth layers with concomitantly high rates of hydrogenotrophic and aceticlastic methanogenesis (Webster et al., 2009).

dsrAB

So far, phylogenetic characterizations of sulfate (sulfite)-reducing microbes have been published from subseafloor sediment of the Peru Margin (ODP Site 1228; Webster et al., 2006) and rust deposits of a circulation obviation retrofit kit (CORK) at the seafloor on the Juan de Fuca Ridge Flank (ODP Site 1026; Nakagawa et al., 2006; Table 1). dsrA copy numbers have been quantified via real-time PCR in sediments of the Peru Margin (ODP Sites 1227, 1229, 1230; Schippers & Neretin, 2006), Porcupine Seabight (IODP Sites 1316–18; Webster et al., 2009), and the Gulf of Mexico continental slope (IODP Sites 1319–20, 1322, 1324; Nunoura et al., 2009).

Given that only one sampling depth from one subseafloor sediment column (48 mbsf, ODP Site 1228; Webster et al., 2006) has resulted in successful dsrA detection, general inferences regarding the ecology of sulfate-reducing microbes in this environment are yet to be made. The only phylotypes detected in this sample belong to a deeply branching, uncultivated dsrAB genetic cluster with unknown 16S rRNA gene identity that was first detected in hydrothermal sediment (Cluster IV; Dhillon et al., 2003; Fig. 2b). Under various monikers, this phylogenetically diverse group has since been reported from a wide range of estuarine to deep sea marine sediments and cold seeps (e.g. Bahr et al., 2005; Kaneko et al., 2007; Jiang et al., 2009; Leloup et al., 2009; Ye et al., 2009), as well as sulfidic springs, subsurface aquifers, and freshwater sediment on land (Elsahed et al., 2003; Bagwell et al., 2005; Pester et al., 2010). The repeated detection of Cluster IV well into sulfate-depleted marine sediment and freshwater sediment (Harrison et al., 2009; Leloup et al., 2009; Pester et al., 2010) suggests that it is capable of growth under low sulfate concentrations or even in the absence of sulfate.

The dsrAB community at ODP Site 1026 is from an artificial environment, a black rust deposit that was formed by the oxidation of a manmade steel structure during exposure to ridge flank crustal fluids and seawater (Nakagawa et al., 2006; Fig. 2b). Whether this community indeed mirrors sulfate-reducing communities in subseafloor basalt is thus to be determined. Two phylotypes share Ammonifex degensii, a sulfate and nitrate-reducing Chloroflexi species (Huber et al., 1996), as distant cultured relatives (76–78% sequence similarity; Table 1). Another phylotype, which was isolated and shown to reduce sulfate, is most closely related to Desulfotomaculum geothermicum, a Firmicute first isolated from geothermal groundwater (Daumas et al., 1988). The fact that relatives of A. degensii and a high diversity of Firmicutes were also detected in 16S rRNA gene-based clone libraries on fluids and colonization experiments from the same borehole (Cowen et al., 2003; Orcutt et al., 2010a) suggests that the rust deposit at ODP Site 1026 at least partially bears the signature of sulfate-reducing communities in basalt. The other two dsrAB phylotypes, strains Spi55 and Tc37, which were also isolated and shown to reduce sulfate, have Desulfovibrio salexigens and Desulfocella halophila as their closest cultured relatives, respectively (Table 1). The sequence similarity of Spi55 to D. salexigens is low (73%) thus leaving open whether Spi55 is indeed a member of the genus Desulfovibrio. No close relatives of Spi55 or Tc37 occurred in 16S rRNA gene clone libraries on crustal fluids or microcosms from the same borehole (Cowen et al., 2003; Orcutt et al., 2010a), leaving open the possibility of seawater or sedimentary origin.

All published real-time PCR quantifications of dsrA in subseafloor sediments have so far been obtained with the same primer pair (Kondo et al., 2004). Overall, dsrA copy numbers agree with sulfate profiles, showing an exponential decrease with depth on the Peru Margin (ODP Site 1227; Schippers & Neretin, 2006), and an overall decrease with depth across three sites in the Porcupine Seabight (IODP Sites U1316–18; Webster et al., 2009). At certain sites, that is, ODP Site 1229 on the Peru Margin, ODP Site 1230 in the Peru Trench, and IODP Sites 1319–20, 1322, and 1324 on the Gulf of Mexico continental slope, dsrA was detected at too few depths to identify clear depth-related trends (Schippers & Neretin, 2006; Nunoura et al., 2009). Perhaps surprisingly, considering the critical role of sulfate-reducing microbes in terminal organic matter remineralization in marine sediments (Jørgensen, 1982; D'Hondt et al., 2002, 2004) and high percentages of sulfate reducers reported from the coastal subsurface (Leloup et al., 2009), dsrA quantifications in sulfate-rich subseafloor sediments consistently suggest that sulfate reducers represent < 1% of total microbial populations (Schippers & Neretin, 2006; Nunoura et al., 2009; Webster et al., 2009).

fhs

Phylogenetic characterizations of fhs are so far limited to one site, IODP Site 1301 on the Juan de Fuca Ridge Flank, with no data reported on gene abundances (Lever et al., 2010; Fig. 2c, Table 1). Using a previously published degenerate primer pair (Leaphart et al., 2003) and a less degenerate primer pair that amplifies a smaller gene fragment (Lever et al., 2010), a high diversity of fhs was detected across eight depths spanning the upper sulfate reduction zone, the upper and lower SMTZ, and one depth in the methanogenesis zone. While no sequences of the classic acetogens (Cluster A) were found and many of the phylotypes detected have no close cultured relatives, the considerable similarity of several sequences to known Proteobacteria is noteworthy (Fig. 2c, Table 1). Proteobacterial relatives include sulfate reducers within the δ-Proteobacteria (D. salexigens), as well as several methylotrophic (Methylobacterium extorquens, Methylibium petroleiphilum) and fermentative (Mesorhizobium loti) α-Proteobacteria. No close relatives of micro-organisms with acetogenic capability were detected, leaving open which of the fhs phylotypes account for the 13C-depleted acetate that indicates in situ acetogenesis at this site (Lever et al., 2010). Given that fhs occurs widely in nature and is not unique to acetogens, reliable interpretations regarding the organisms involved in acetogenesis in subsurface sediments are currently not possible. This may change as complementary genetic information is gained from the sequencing of whole genomes of uncultivated subsurface Bacteria and Archaea, and as innovative isotope labeling experiments that combine measurements of process rates and isotopic incorporation into biomass (e.g. Morono et al., 2011) are complemented by detailed genetic and phylogenetic analyses.

rdhA

The widespread detection of Chloroflexi in subseafloor sediments (Inagaki et al., 2003; Kormas et al., 2003; Parkes et al., 2005; Inagaki et al., 2006a; Reed et al., 2006; also see review by Teske, 2006) has raised questions regarding potential energy sources utilized by this phylum. Considerable 16S rRNA gene sequence similarity to members of the Dehalococcoides, a genus comprised of obligate dehalorespiring bacteria, has led to the profiling and intercomparison of rdhA gene phylogenetic composition across six drilling sites within the Pacific Ocean (Futagami et al., 2009). These include the oligotrophic ODP Site 1226 in the eastern equatorial Pacific, as well as the more energy-rich Site 1227 on the Peru Margin, Site 1230 in the Peru Trench, IODP Site 1301 on the Juan de Fuca Ridge Flank, and sites C9001 off Shimokita and C0002 in the Nankai Trough Forearc Basin (Futagami et al., 2009). Several primer pairs targeting rdhA were tested, with one specifically designed to target Dehalococcoides (Krajmalnik-Brown et al., 2004), leading to successful detections. No significant sequence similarity to cultured strains was found using the megablast function in blast (Table 1). However, all rdhA sequences shared known reductive dehalogenators (Dehalococcoides and Dehalogenimonas spp.) as their closest, albeit phylogenetically distant, cultured relatives based on the discontiguous megablast algorithm in blast. Given that known reductive dehalogenators possess many (up to 32) nonidentical copies of rdhA genes, the extent to which the diversity of subseafloor rdhA genes detected reflects diversity on the organismal level remains to be shown (Hölscher et al., 2004; Wagner et al., 2009).

Outlook

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Given that only a small number of functional genes have been explored in subseafloor habitats from a limited number of depths and locations, our ability to link biogenic processes that are evident from geochemical gradients (e.g. D'Hondt et al., 2002, 2004; D'Hondt et al., 2009), isotopic compositions of metabolic educts and products (e.g. Böttcher et al., 2006; Heuer et al., 2009; Lever et al., 2010), and measured process rates (e.g. Parkes et al., 2005; Jørgensen et al., 2006) to the organisms responsible is still limited. Likewise, the importance of temperature, organic matter and mineral composition, and geologic provenance in regulating or determining the community composition of metabolic groups remains largely unknown. Nonetheless, a striking trend can be observed across functional gene data obtained so far: the majority of phylotypes fall into more or less well-characterized groups that are also found in the surface world and terrestrial deep biosphere. In some cases, phylotypes from elsewhere have high DNA sequence similarity (to 99%; Table 1) to ones found in ocean drilling cores. This suggests that the organisms performing well-studied metabolic processes in the deep biosphere are not genetically distinct from their counterparts in other ecosystems and is in marked contrast to the overwhelming majority of subseafloor bacterial and archaeal phyla of which the metabolism is unknown (Inagaki et al., 2006a; Teske & Sørensen, 2008).

Interestingly, processes such as sulfate reduction and methanogenesis/anaerobic methanotrophy, which support a large fraction of microbial populations in the deep biosphere nearshore (e.g. Leloup et al., 2009; Roussel et al., 2009b), appear to only sustain a small minority of microbes in subseafloor environments offshore (Schippers & Neretin, 2006; Nunoura et al., 2009; Webster et al., 2009). This is not only indicated by low to undetectable dsrA and mcrA copy numbers based on PCR assays, but also by the fact that known methanogens/anaerobic methanotrophs or sulfate reducers are typically absent or only account for a small fraction of sequences detected in 16S rRNA (gene) libraries (e.g. Biddle et al., 2006; Inagaki et al., 2006a; Webster et al., 2006; Nunoura et al., 2009). Unless the majority of subseafloor microbes involved in these processes harbor highly divergent or altogether different functional genes and metabolic pathways – a possibility that cannot be excluded with current knowledge – these results suggest that sulfate reduction and methane cycling consume only a small fraction of total energy flow in the deep biosphere.

Given the continuing uncertainty regarding the major energy sources and metabolisms of life in the deep biosphere and the limitations imposed by the methods so far used in studies of subseafloor functional genes, new research directions and approaches are necessary. The following sections cover possible future foci of functional gene research in the subseafloor relating to previously unstudied genes and processes, experimental and molecular methods, detection and contamination issues, and the integration of functional gene with genomic and experimental data.

Genes and processes

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Functional gene surveys on the deep subseafloor have so far focused mostly on genes indicative of sulfate reduction and methanogenesis – both anaerobic terminal remineralization pathways that appear to only support a small fraction of total microbial biomass. The energy sources and metabolic pathways of the vast majority of microbes are unknown, as are the pathways of biosynthesis and nutrient uptake. Our understanding of physiological and survival strategies of subseafloor microbes is thus rudimentary at best.

Given the large pool of organic matter in subseafloor sediments, it is conceivable that most microbes are supported by the breakdown of complex/refractory organic matter and microbial necromass. Not only fermentation but also acetogenesis and dehalorespiration of a wide range of (complex) organic compounds and their side groups could represent important energy sources. A recent study on bioreactors, in which glycoside hydrolase genes of cellulose degradation (cel48, cel5) and iron hydrogenase genes indicative of fermentation (hydA) were targeted using newly developed PCR primers, illustrates that organisms involved in the degradation of complex or intermediate organic matter can also be studied using PCR-based functional gene approaches (Pereyra et al., 2010). Similarly, functional gene assays have been used to study microbial communities involved in oil degradation (Winderl et al., 2007; Lu et al., 2011). And recently, PCR primers targeting the gene encoding the beta subunit of acetyl CoA synthase (acsB), a gene that is likely to provide a more reliable phylogenetic marker of acetogens than fhs, have been designed and successfully applied (Gagen et al., 2010).

As in the past, for example, with the discovery of AOM (e.g. Barnes & Goldberg, 1976; Martens & Berner, 1977), the study of functional genes will be in part driven by the discovery of new processes and pathways based on geochemical studies. Many energy-yielding reactions in the subseafloor have remained undiscovered and will perhaps remain so until chemical assays have been developed that can detect and quantify educt and product concentrations. An example of processes that might be targeted with specific functional gene probes in the future involves the production and fate of methylated compounds. A recent study on lake sediments revealed the direct formation of dimethylsulfide (DMS) from hydrogen sulfide and autotrophically reduced bicarbonate, showing that DMS is not only a product of organic matter breakdown (Lin et al., 2010). Methylated compound-cycling pathways may represent important energy sources in the subseafloor and are consistent with the significant role of acetogenesis (Heuer et al., 2009), because of the probably important role of methylated compounds as energy sources to acetogens (Lever et al., 2010; Lever, 2012). Similarly, geochemical gradients and thermodynamic calculations indicate that anaerobic oxidation of ammonia coupled to sulfate reduction might play a significant role in subseafloor sediments (Schrum et al., 2009). While the sulfate-reducing reaction might be carried out via the well-studied biochemical pathway of dissimilatory sulfate reduction, the organisms involved could be distinct from cultured strains. And the biochemical pathway and functional genes involved in the anaerobic oxidation of ammonia remain to be identified.

Given that large parts of the seafloor are located within oligotrophic ocean gyres, with oxygen penetration deep into sediments and even underlying basalt (D'Hondt et al., 2009; Expedition 329 Scientists, 2011), functional genes of aerobic pathways, such as aerobic ammonia oxidation or aerobic methylotrophy, could provide clues to important energy sources of microbes inhabiting these energy-starved environments. The recent documentation of actively methanogenic populations in fully oxygenated environments (Grossart et al., 2012) moreover questions traditional notions on redox requirements of methanogens and other ‘anaerobes’ and suggests that O2-independent energy production could be important even in O2-rich environments. Similarly, repeated isolations and widespread occurrence of aerobic microbes from permanently anoxic sediment (Süss et al., 2006, 2008) suggest that many aerobes can survive and prosper in the absence of O2. Based on geochemical gradients, processes such as methanogenesis in aerobic environments may be difficult to detect. The targeting of functional genes, for example, of anaerobic methane cycling (mcrA) in aerobic sediments, or aerobic methanotrophy (pmoA, mmoX, mxaF, fhcD) in anaerobic sediments, may, on the other hand, provide useful clues to the distributions of these potentially cryptic metabolisms.

Other potentially important life-sustaining processes could include the (cryptic) respiration of iron, manganese, and sulfur (Wang et al., 2008b; Holmkvist et al., 2011), transition metals (Amend & Shock, 2001), radiolysis (Blair et al., 2007), and reduced compounds released by mechanical stress and fracturing (Parkes et al., 2011). To date, no suitable phylogenetic marker genes that are diagnostic of dissimilatory iron, manganese, or sulfur-reducing microbes have been identified. Whether transition metals can serve as electron donors or electron acceptors remains unknown despite its thermodynamic feasibility. Production of electron donors, such as H2, by radiolysis or mechanical stress during crustal flexing and fracturing is highly likely. It remains unknown, however, which micro-organisms and biogenic reactions benefit from these presumably ephemeral bouts in high electron donor availability.

To date, the importance of chemoautotrophic C fixation in subseafloor environments is unknown. PCR primers targeting key genes of the Calvin–Benson–Bassham, reverse tricarboxylic acid, and reductive acetyl CoA pathway (e.g. Elsaied & Naganuma, 2001; Campbell & Cary, 2004; Gagen et al., 2010) have been developed and, in some cases, applied to deep sea sediment and hydrothermal vent samples. Data from the subseafloor is missing, however. Due to the large, photosynthetically derived organic carbon reservoir in subseafloor sediments, one might expect synthesis of new biomass to typically involve recycling of photosynthesis-derived organic building blocks that are already present. This notion is consistent with δ13C-isotopic compositions of archaeal cells, archaeal membrane lipids, and bulk organic carbon in subseafloor sediments of the Peru Margin and Peru Trench (Biddle et al., 2006). Yet, even based on the latter data, substantial chemoautotrophic contributions cannot be excluded due to (1) the considerable variability in measured δ13C-compositions of cells and lipids, (2) negative fractionations associated with the fixation of less negative dissolved inorganic carbon which could yield chemosynthetically derived δ13C-organic matter in the range of photosynthetic organic matter, (3) conceivable occurrence of organoautotrophic biomass synthesis with less pronounced isotopic fractionations than chemoautotrophy (Lever et al., 2010), and (4) in vitro experiments with subseafloor sediments that show high biomass incorporation rates of isotopically labeled bicarbonate (Morono et al., 2011). Compared to sediments, even less is known about subseafloor crustal environments. Due to the importance of chemoautotrophic biosynthesis and energy production on seafloor-exposed basalt (e.g. Edwards et al., 2003), one might expect chemoautotrophy to play a crucial role and subseafloor basalts to be net-autotrophic. Yet, the genetic (or geochemical) data to address this fundamental question is still lacking.

The extent to which microbes rely on different inorganic and organic nutrient sources in subseafloor environments is also unknown. Anoxic and (sub)oxic subseafloor sediments often have high concentrations of ammonia and nitrate (D'Hondt et al., 2004, 2009) and substantial amounts of N-containing cellular building blocks, such as amino acids (Mitterer, 2006; Lomstein et al., 2012). One might thus expect N2 fixation to play a subordinate role because of its high energetic cost compared to use of ammonia, nitrate or organic building blocks; but this has yet to be demonstrated, for example, by targeted studies of N-cycling genes and gene transcripts as in surface environments (e.g. Rotthauwe et al., 1997; Braker et al., 1998; Zehr et al., 2003; Mohan et al., 2004; Poretsky et al., 2005; Smith et al., 2007). Whether in situ microbes prefer inorganic over organic phosphorus species is also poorly understood. At IODP Site 1301, hydrolytic extracellular phosphatase activity increases in a subseafloor sediment interval overlying the basaltic basement and has been linked to the absence of high phosphate concentrations in this interval (Engelen et al., 2008). Thus, concentrations of gene transcripts linked to extracellular phosphatase activity might offer clues to microbial phosphorus cycling in the subseafloor.

Apart from Bacteria and Archaea, Eukaryotes and viruses may play important roles in subseafloor biogeochemical processes. Viruses are widespread and probably active as suggested by assays on prophage antirepressor genes (Engelhardt et al., 2012), yet little is known about the rates of viral-induced microbial mortality. Based on 16S rRNA gene and microfossil evidence, fungi are widespread and possibly dominate over other eukaryotic groups in subseafloor sediments (Edgcomb et al., 2010) and subseafloor basalts (Ivarsson, 2012; Ivarsson et al., 2012). The energy sources of fungi remain enigmatic, however, and may range from fermentation of complex organic matter to methylotrophy and even metal cycling (Lai et al., 2007; Connell et al., 2009; Raghukumar et al., 2010). The detection of 16S rRNA genes belonging to ciliates (Ciliophora) in subseafloor sediments, moreover, raises the question whether bacterial and archaeal grazing by protists might play an important role in the subseafloor. Expectedly, the energy sources of fungi and other eukaryotes will be identified in the future, as these organisms are isolated from subseafloor samples, their metabolic capabilities identified, and sensitive assays targeting metabolic marker genes developed.

Cultivation experiments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

To identify the metabolism of subseafloor microbes, cultivation experiments ranging from isolations to enrichments to in situ incubations under natural substrate conditions are necessary. Microbial isolations have the clear advantage over all other approaches that metabolic reactions and pathways can be unambiguously identified and microbial strains characterized in-depth, under a wide range of growth conditions. This enables the identification of new biochemical and thus genetic pathways that can be subsequently studied under natural conditions using pathway-specific gene probes. The obvious and well-known disadvantage besides high labor intensity is that, so far, the vast majority of microbes evades isolation (Rappé & Giovannoni, 2003).

Incubations in which microbes are incubated in their native sediment or rock matrix under elevated substrate concentrations but otherwise conditions mimicking those in their native environment are less invasive or time-consuming than isolations and enable the observation of growth responses across a wider range of microbes. Based on the production of intermediates and metabolic products, inferences regarding stoichiometries of important energy-producing reactions can be made (e.g. Liu & Suflita, 1993). If supplied substrates are isotopically labeled, stable isotope probing (SIP) followed by phylogenetic analyses on isotopically labeled genes and gene transcripts can be used to infer substrate use (Neufeld et al., 2007; McDonald et al., 2008). Using conventional SIP with low-biomass, low-activity subseafloor samples, sufficient isotopic labeling of bulk DNA or RNA may not be achievable without long-term incubations at high concentrations of labeled substrates. Here, the complementary use of labeled substrates and labeled water (D2O) may be preferable. Deuterated water is indiscriminately assimilated by all growing cells and thus provides a means to drastically increase isotope labeling, lower added substrate concentrations, and reduce incubation periods (Wegener et al., 2012).

Using nanometer-scale secondary ion mass spectrometry (Nano-SIMS), the sensitivity of detecting isotopic incorporation is even lower than with SIP analyses on bulk nucleic acids. Moreover, isotopic label incorporation is detected in single cells and can be directly linked to microbial identity, as has been shown by combining Nano-SIMS and FISH analyses on incubations of Pleistocene sediment from Site C9001 off Shimokita Peninsula, Japan (Morono et al., 2011). To further improve the efficacy of these assays, the use of radioisotope-labeled carbon substrates deserves consideration, as it would not only further lower the sensitivity of detecting isotope incorporation into DNA or RNA, but also improve the resolution with which label transfer across pools of metabolic intermediates and products can be monitored. Thus, a high-sensitivity, futuristic experimental setup to link microbial identity to metabolism in subseafloor samples under environmentally relevant conditions might combine the use of δ14C-labeled growth substrates, deuterated water, FISH with phylogenetic and functional gene probes, and Nano-SIMS – and would be performed in situ.

The field-based study of subseafloor microbial communities has been pioneered in cased boreholes equipped with CORKs, continuous fluid sampling devices, and flow-through chambers on the Juan de Fuca Ridge Flank (Cowen et al., 2003; Orcutt et al., 2010a, b). These studies provided the first insights to the microbial communities inhabiting subseafloor basaltic crust. Recently, a new form of observatory – the Microbial Methane Observatory for Seafloor Analysis (MIMOSA) – has been developed to monitor methane production and microbial communities associated with methane cycling in subseafloor sediments (Orcutt et al., 2011). In both CORK and MIMOSA observatories, environmental conditions are monitored using in situ fluid collection and preservation devices called osmosamplers (Jannasch et al., 2004) or GeoMICROBEs (Cowen et al., 2012) – with the bulk of geochemical and molecular analyses performed after retrieval in the laboratory. In addition to these sampling devices, recent advances have led to instruments that might be used for in situ measurements in the subseafloor in the future. One example is the In Situ Mass Spectrometer, with which low concentrations of gases, such as methane, H2, or O2, can be monitored (Wankel et al., 2010, 2011). Another is a robotic device, the environmental sample processor, which performs fully automated nucleic acid extractions and PCR quantifications in the field (Preston et al., 2011). These exciting new developments in sampling and measurement tools allude to the possibility of studying biogenic reactions, gene expression, and responses to experimental manipulations in situ, within the deep biosphere, within the next years and decades.

Molecular biological methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

The study of functional genes in the environment is rapidly transforming and includes the targeting of (novel) genes with PCR-based methods coupled to second generation sequencing (SGS) technologies, hybridization-based, and probe-independent methods. All methods bear advantages and pitfalls (e.g. Amann & Fuchs, 2008; Roh et al., 2010; Gilbert & Dupont, 2011). The best method for a given project thus requires careful a priori consideration.

PCR-based methods can have the advantage of high detection sensitivity, of being quantitative in the case of real-time PCR, and simplicity, as a result of which methodological adjustments, for example, use of new primers and PCR conditions, can be rapidly implemented. Combined with barcoding and high-throughput SGS platforms, for example, Illumina/Solexa sequencing, pyrosequencing, or ion semiconductor sequencing, large sample sizes involving one or multiple genes can be efficiently analyzed (e.g. Parameswaran et al., 2007; Pilloni et al., 2012) with, depending on sequence conservation, more or less accurate phylogenetic assignment (e.g. Liu et al., 2007; Wang et al., 2007). As phylogenetic biases as a result of the PCR primers used are a common problem (Lueders & Friedrich, 2003; Teske & Sørensen, 2008; Pinto & Raskin, 2012), existing PCR primers should be frequently re-evaluated based on both in silico analyses of updated gene databases and the output of PCR-based sequencing applications.

Hybridization-based methods with functional genes involve FISH of single cells, as well as microarrays with which DNA extracts from entire samples are analyzed. The application of FISH has the advantage over PCR-based approaches in that PCR-related biases are avoided. Nonetheless, phylogenetic biases related to differences in cell wall permeability, hybridization efficiency and specificity, and ribosomal copy number need to be considered (e.g. Teira et al., 2004; Kubota et al., 2008). So far, catalyzed reporter deposition FISH has been successfully applied to 16S rRNA gene of subseafloor microbes (Schippers et al., 2005, 2012). FISH of functional gene transcripts and functional genes have been achieved in samples from other environments, including marine surface sediments (Pernthaler & Amann, 2004; Hoshino & Schramm, 2010), and provide a powerful tool to link metabolic function to phylogenetic identity, especially when confirmed by group-specific 16S rRNA gene probes. Due to the presumably low copy numbers of functional gene transcripts and functional genes, further modifications, for example, inclusion of fluorescently labeled tyramides (Behrens et al., 2008), might be required until FISH of mRNA or DNA can be applied to cells in subseafloor environments.

Recent developments in microarray technologies indicate this technique to be a high-throughput method with potential for subseafloor applications. Using the GeoChip, a particularly sensitive and advanced model, over 400 different functional genes within the same DNA extract can be targeted using c. 70 000 different gene probes (Wu et al., 2006; Zhou et al., 2010). As with FISH, the phylogenetic coverage is limited by the design of the probes used, however. And for most subseafloor samples, sufficient amounts of DNA for GeoChip applications can only be obtained using whole-genome amplification, which produces additional phylogenetic biases (Wang et al., 2011). Yet, these methodological obstacles are likely to be overcome as detection sensitivity continues to improve. And data from hydrothermal environments, seafloor basalt, and hydrocarbon plumes already demonstrate the vast insights to the metabolism of uncultured microbes that can be gained from microarray technology (Wang et al., 2008a, b; Mason et al., 2009; Lu et al., 2011).

Metagenomic approaches are less phylogenetically biased than PCR- and hybridization-based approaches due to the absence of PCR primers or hybridization probes. The potential discovery of deviant known genes, novel genes, and even previously undetected organisms, moreover, opens the door to more focused studies on these genes and organisms using PCR- and hybridization-based approaches. The biggest challenges of metagenomic datasets lie in the analysis, due to the large numbers of genes with unknown function (Biddle et al., 2008) and the risk of incorrect gene assembly from short sequence reads of poorly known genes from diverse in situ communities (Pignatelli & Moya, 2011; Mende et al., 2012).

One solution to the problem of incorrect assembly in metagenomic analyses lies in the use of single-cell genomics. The latter offer a powerful tool to link metabolic potential based on functional genes to phylogenetic identity based on 16S rRNA genes on uncultivated cells from the environment (reviewed by Stepanauskas & Sieracki, 2007; Kalisky & Quake, 2011). Presently, single-cell genomics are not high-throughput as they rely on several preparation steps that bear their own challenges, for example, single cell isolation, DNA extraction, whole-genome amplification, DNA fragmentation, SGS, and sequence assembly. Due to the use and amplification of single cells, the risk of contamination is considerable. But this will change with increased automation, as is already evident from new, ‘all-in-one’ single-cell separation, extraction and amplification units involving microfluidics.

Further exciting developments that are likely to revolutionize the study of functional genes in the environment are third-generation sequencing (TGS) platforms involving single-strand sequencing of DNA (Schadt et al., 2010). Individual strands of DNA are thereby sequenced, for example, by real-time observation of DNA polymerase during DNA synthesis (Eid et al., 2009), changes in electrical potential as DNA passes through protein nanopores (Clarke et al., 2009), or improved microscopic imaging techniques (Bell et al., 2010). TGS will thus eliminate the phylogenetic biases and errors induced by PCR or whole-genome-amplification (WGA) and the limitations in read lengths and resulting gene/genome assembly problems of existing SGS technologies, while allowing high-throughput analyses of many samples. If any one of these TGS platforms holds what it promises, it will profoundly influence the future of functional gene studies in the deep subseafloor.

So far, the focus of this review has been on functional genes (DNA), rather than functional gene transcripts (mRNA). Clear disadvantages of studying genes rather than gene transcripts are that extracellular/fossil DNA belonging to (allochthonous) dead organisms or used in biofilm formation can be stable over geologic time periods (e.g. Willerslev et al., 2003; Pietramellara et al., 2009; Wu & Xi, 2009; Corinaldesi et al., 2011; Pawlowski et al., 2011), potentially leading to misinterpretation of the extant metabolic potential of microbial communities. This problem can be addressed using DNA extraction protocols involving the separate extraction of extra- and intracellular DNA pools (Ogram et al., 1988; Corinaldesi et al., 2005), but uncertainties due to possible cell lysis or incomplete recovery of extracellular DNA during initial extracellular DNA extraction steps remain. The typically short half-life of chemically unstable gene transcripts (Rauhut & Klug, 1999) thus makes mRNA-targeted approaches favorable over DNA-targeted approaches in assessing extant microbial metabolism (e.g. Frias-Lopez et al., 2008). Virtually all DNA-based methods outlined above can in theory be applied to mRNA. Interpretative caution is necessary with endospore-forming cells, which can maintain functional copies of mRNA during dormancy (e.g. Jeng & Doi, 1974; Segev et al., 2012). And there are currently no published accounts on mRNA composition in the low-activity subseafloor. But at least the latter may change with the new design or fine-tuning of existing mRNA extraction methods. Thus, it is hoped that the successful and routine assessment of metabolic activity in the subseafloor using mRNA-based approaches will be realized in the coming years.

Detection issues

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Until the vastly expanding and rapidly developing set of ‘high-tech’ molecular biological tools outlined in the previous section can be applied to the study of functional genes and gene transcripts in low-biomass, low-activity subseafloor environments, further developments on the ‘low-tech’-front will also be necessary. Existing nucleic acid extraction methods suffer from incomplete, phylogenetically biased nucleic acid recovery (e.g. Webster et al., 2003; Delmont et al., 2011) and have yet to succeed in extracting amplifiable DNA from ultra-oligotrophic environments. Even with successful extraction of DNA, successful amplification and accurate quantification by real-time PCR can be prevented by co-extracted compounds which inhibit DNA polymerase, necessitating novel PCR methods, such as digital PCR, that are less sensitive to inhibition (Hoshino & Inagaki, 2012). Thus, it is not surprising that to this day the detection of functional genes related to biogenic processes that are evident from chemical gradients has remained challenging even in organic-rich subseafloor sediments (Schippers & Neretin, 2006; Webster et al., 2006, 2009; Nunoura et al., 2009).

A further cause of detection problems lies in the high degree of degeneracy (e.g. Springer et al., 1995; Hales et al., 1996) or limited phylogenetic coverage of existing PCR primers. This is illustrated by the detection of fhs genes in the sediment column of IODP Site 1301. Here a degenerate published primer pair (Leaphart et al., 2003) allowed fhs detection at four depths. But using a new, less degenerate primer pair that targets a smaller gene fragment, fhs genes could be detected at four additional depths (Lever et al., 2010). Until PCR-independent, extraction-independent direct sequencing methods have been developed and can be applied to the low-biomass subseafloor, targeted studies on functional genes in the subseafloor will continue to depend on these ‘low-tech’ improvements to nucleic acid extraction methods, careful (re)design of PCR primers based on in silico and phylogenetic analyses, and/or complementary use of multiple low-degeneracy, group-specific PCR primers.

Sample contamination

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

Contamination of samples obtained by ocean drilling is a major concern in studies on the subseafloor biosphere, due to the use of drilling fluid (surface seawater, drilling mud) containing significant populations of microbes. While interiors of sediment cores obtained during riserless drilling on the R/V Joides Resolution typically have low contamination (Smith et al., 2000; Lever et al., 2006), this still needs to be demonstrated with riser drilling on the R/V Chikyu, on which contamination tests were pioneered during the Shimokita coalbed expedition, IODP Expedition 337, in summer 2012 (Inagaki et al., 2010). Contamination tests have also not been tested on mission-specific platforms, but are planned for the Baltic Sea Paleoenvironment expedition, IODP Expedition 347, in 2013 (B.B. Jørgensen, pers. commun.).

Even riserless drilling on the R/V Joides Resolution may bear higher risks of contamination than previously thought. During sampling of subseafloor basalt, contamination is evident from high concentrations of contamination tracer on the exterior of basalt rock (Lever et al., 2006) and 16S rRNA gene inventories on rock surfaces that are dominated by sequences associated with the human microbiome (Santelli et al., 2010). The occasional detection of DNA sequences belonging to Escherichia coli, chloroplasts (both in Kormas et al., 2003), and typical water column Archaea (Marine Group I Crenarchaeota; Inagaki et al., 2006a) in anoxic sediments that are hundred thousands of years old, moreover, suggests that even subseafloor sediments obtained by riserless drilling are at considerable risk of contamination.

With respect to functional genes, it awaits to be confirmed that mcrA with high sequence similarity (99%; Table 1) to Methanobrevibacter (Newberry et al., 2004; Webster et al., 2006; Imachi et al., 2011), a genus typically known from human/animal intestines and activated sludge (Whitman et al., 2006) and used as a tracer to identify sewage pollution (Ufnar et al., 2006), are indeed native to the subseafloor. The same is true of other methanogenic genera, for example, Methanobacterium, Methanoculleus, Methanosaeta, and Methanosarcina, which are widespread in anthropogenic wastewater (Liu & Whitman, 2008).

The risk of contamination is augmented if sewage dumping is performed during drilling operations. Wastewater released to surface waters may be directly entrained into drilling fluid or give rise to microbial blooms in water surrounding the ship (Santelli et al., 2010). Similarly, seawater used on the R/V Chikyu to prepare drilling mud should be carefully chosen, for example, from (pristine) offshore locations rather than polluted harbors. To address uncertainties regarding the sources of drilling-induced contamination, chemical tracer tests could be complemented by monitoring of microbial populations in wastewater onboard, drilling mud, surface seawater, and circulating fluids. Even the development of (functional) gene assays by which indicator organisms, for example, Bifidobacterium or Lachnospiraceae of sewage (Matsuki et al., 2004; Newton et al., 2011), SAR11 and/or Marine Group I Crenarchaea of seawater (Rappé & Giovannoni, 2003), or Xanthomonas of drilling mud (Masui et al., 2008), are quantitatively monitored in potential contamination sources deserves consideration.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

To obtain a better understanding of the energy sources, biosynthetic pathways, and survival strategies of microbes in the subseafloor, a highly interdisciplinary approach is necessary that combines traditional and new approaches from the fields of microbial ecology, molecular biology, microbiology, and geochemistry (Fig. 3).

image

Figure 3. Sketch illustrating how the functional gene approach can be combined with geochemical, microbiological, and molecular biological approaches to investigate the microbial ecology of subseafloor ecosystems.

Download figure to PowerPoint

For known biochemical pathways, functional gene assays involving SGS or microarrays combined with geochemical indicators offer an effective tool to correlate microbial identity with microbial activity across samples. Incubation experiments, for example, involving isotopically labeled substrates, can then be used to confirm suspected links between identity and activity and test hypotheses regarding the metabolisms of unknown micro-organisms. Successful enrichments lead to microbial characterizations under a range of growth and energy conditions and in some cases strain isolations. Combined with chemical analyses, new reaction stoichiometries and reaction-specific isotopic fractionations are identified, which open the door to the geochemical study of these reactions in the environment. Combined with genomics, well-known, deviant, and unknown genes involved in energy production and biosynthesis are linked to phylogenetic identity. With unknown organisms, these sequencing efforts spark the development of new functional gene and geochemical assays, which again lead to the study of encoded activities in the natural environment and experimental manipulations.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References

The author would like to thank Bo Barker Jørgensen and other members of the Center for Geomicrobiology and Department of Microbiology at Aarhus University (Denmark), Andreas Teske of the University of North Carolina at Chapel Hill, and Jennifer Biddle of the University of Delaware (both USA) for constructive discussions. This study was supported by the Danish National Research Foundation and the Max Planck Society (grants to Bo Barker Jørgensen), as well as a Marie Curie Intra-European Fellowship awarded to the author (# 255135). The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Functional genes examined during ocean drilling expeditions
  5. Outlook
  6. Genes and processes
  7. Cultivation experiments
  8. Molecular biological methods
  9. Detection issues
  10. Sample contamination
  11. Conclusion
  12. Acknowledgements
  13. References
  • Amann R & Fuchs BM (2008) Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat Rev Microbiol 6: 339348.
  • Amann R, Ludwig W & Schleifer KH (1988) β-Subunit of ATP-synthase: a useful marker for studying the phylogenetic relationship of Eubacteria. J Gen Microbiol 134: 28152821.
  • Amend JP & Shock EL (2001) Energetics of overall metabolic reactions of thermophilic and hyperthermophilic archaea and bacteria. FEMS Microbiol Rev 25: 175243.
  • Bagwell CE, Liu X, Wu L & Zhou J (2005) Effects of legacy nuclear waste on the compositional diversity and distributions of sulfate-reducing bacteria in a terrestrial subsurface aquifer. FEMS Microbiol Ecol 55: 424431.
  • Bahr M, Crump B, Klepac-Ceraj V, Teske A, Sogin M & Hobbie J (2005) Molecular characterization of sulfate-reducing bacteria in a New England salt marsh. Environ Microbiol 7: 11751185.
  • Barnes RO & Goldberg ED (1976) Methane production and consumption in anoxic sediments. Geology 4: 297300.
  • Behrens S, Lösekann T, Pett-Ridge J, Weber PK, Ng W-O, Stevenson BS, Hutcheon ID, Relman DA & Spormann AM (2008) Linking microbial phylogeny to metabolic activity at the single-cell level by using enhanced element labeling-catalyzed reporter deposition fluorescence in situ hybridization (EL-FISH) and NanoSIMS. Appl Environ Microbiol 72: 31433150.
  • Bell DC, Thomas WK, Murtagh K & Glover WR (2010) DNA sequencing with TEM. Microsc Microanal 16: 17681769.
  • Biddle JF, Lipp JS, Lever MA et al. (2006) Heterotrophic archaea dominate sedimentary subsurface ecosystems off Peru. Proc Natl Acad Sci USA 103: 38463851.
  • Biddle JF, Fitz-Gibbon S, Schuster SC, Brenchley JE & House CH (2008) Metagenomic signatures of the Peru Margin subseafloor biosphere show a genetically distinct environment. Proc Natl Acad Sci USA 105: 1058310588.
  • Biddle JF, Cardman Z, Mendlovitz H, Albert DB, Lloyd KG, Boetius A & Teske A (2012) Anaerobic oxidation of methane at different temperature regimes in Guaymas Basin hydrothermal sediments. ISME J 6: 10181031.
  • Blair CC, D'Hondt S, Spivack AJ & KiSGSley RH (2007) Radiolytic hydrogen and microbial respiration in subsurface sediments. Astrobiology 7: 951970.
  • Boetius A, Ravenschlag K, Schubert CJ, Rickert D, Widdel F, Gieseke A, Amann R, Jørgensen BB, Witte U & Pfannkuche O (2000) A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature 407: 623626.
  • Böttcher ME, Ferdelman TG, Jørgensen BB, Blake RE, Surkov AV & Claypool GE (2006) 6. Sulfur isotope fractionation by the deep biosphere within sediments of the Eastern Equatorial Pacific and Peru Margin. Proc ODP, Sci Res 201 (Jørgensen BB, D'Hondt SL & Miller DJ, eds), pp. 121. ODP, College Station, TX.
  • Braker G, Fesefeldt A & Witzel KP (1998) Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in environmental samples. Appl Environ Microbiol 64: 37693775.
  • Campbell BJ & Cary SC (2004) Abundance of reverse tricarboxylic acid cycle genes in free-living microorganisms at deep-sea hydrothermal vents. Appl Environ Microbiol 70: 62826289.
  • Cappenberg TE (1974) Inter-relations between sulfate-reducing and methane-producing bacteria in bottom deposits of a fresh-water lake. 2. Inhibition experiments. Antonie Van Leeuwenhoek 56: 12471258.
  • Castro H, Ogram A & Reddy KR (2004) Phylogenetic characterization of methanogenic assemblages in eutrophic and oligotrophic areas of the Florida Everglades. Appl Environ Microbiol 70: 65596568.
  • Clarke J, Wu H-C, Jayasinghe L, Patel A, Reid S & Bayley H (2009) Continuous base identification for single-molecule nanopore DNA sequencing. Nat Nanotechnol 4: 265270.
  • Colwell FS, Boyd S, Delwiche ME, Reed DW, Phelps TJ & Newby DT (2008) Estimates of biogenic methane production rates in deep marine sediments at Hydrate Ridge, Cascadia Margin. Appl Environ Microbiol 74: 34443452.
  • Connell L, Barrett A, Templeton A & Staudigel H (2009) Fungal diversity associated with an active deep sea volcano: Vailulu'u Seamount, Samoa. Geomicrobiol J 26: 597605.
  • Cord-Ruwisch R, Seitz H-J & Conrad R (1988) The capacity of hydrogenotrophic anaerobic bacteria to compete for traces of hydrogen depends on the redox potential of the terminal electron acceptor. Arch Microbiol 149: 350357.
  • Corinaldesi C, Danovaro R & Dell'Anno A (2005) Simultaneous recovery of extracellular and intracellular DNA suitable for molecular studies from marine sediments. Appl Environ Microbiol 71: 4650.
  • Corinaldesi C, Barucca M, Luna GM & Dell'Anno A (2011) Preservation, origin and genetic imprint of extracellular DNA in permanently anoxic deep-sea sediments. Mol Ecol 20: 642654.
  • Cowen JP, Giovannoni SJ, Kenig F, Johnson HP, Butterfield D, Rappé MS, Hutnak M & Lam P (2003) Fluids from aging ocean crust that support microbial life. Science 299: 120123.
  • Cowen JP, Copson DA, Jolly J, Hsieh C-C, Lin H-T, Glazer BT & Whest CG (2012) Advanced instrument system for real-time and time-series microbial geochemical sampling of the deep (basaltic) crustal biosphere. Deep Sea Res I 61: 4356.
  • Dang H, Luan X, Zhao J & Li J (2009) Diverse and novel nifH and nifH-like gene sequences in the deep-sea methane seep sediments of the Ohkotsk Sea. Appl Environ Microbiol 75: 22382245.
  • Daumas S, Cord-Ruwisch R & Garcia JL (1988) Desulfotomaculum geothermicum sp. nov., a thermophilic, fatty acid-degrading, sulfate-reducing bacterium isolated with H2 from geothermal water. Antonie Van Leeuwenhoek 54: 165178.
  • Delmont TO, Robe P, Cecillon S, Clark IM, Constancias F, Simonet P, Hirsch PR & Vogel TM (2011) Assessing the soil metagenome for studies of microbial diversity. Appl Environ Microbiol 77: 13151324.
  • Dhillon A, Teske A, Dillon J, Stahl DA & Sogin ML (2003) Molecular characterization of sulfate-reducing Bacteria in the Guaymas Basin. Appl Environ Microbiol 69: 27652772.
  • Dhillon A, Lever M, Lloyd KG, Albert DB, Sogin ML & Teske A (2005) Methanogen diversity evidenced by molecular characterization of methyl coenzyme M reductase A (mcrA) genes in hydrothermal sediments of the Guaymas Basin. Appl Environ Microbiol 71: 45924601.
  • D'Hondt S, Rutherford S & Spivack AJ (2002) Metabolic activity of subsurface life in deep-sea sediments. Science 295: 20672070.
  • D'Hondt S, Jørgensen BB, Miller DJ et al. (2004) Distribution of microbial activities in deep subseafloor sediments. Science 306: 22162221.
  • D'Hondt S, Spivack AJ, Pockalny R et al. (2009) Subseafloor sedimentary life in the South Pacific Gyre. Proc Natl Acad Sci USA 106: 1165111656.
  • Edgcomb VP, Beaudoin D, Gast R, Biddle JF & Teske A (2010) Marine subsurface eukaryotes: the fungal majority. Environ Microbiol 13: 172183.
  • Edwards KJ, McCollom TM, Konishi H & Buseck PR (2003) Seafloor bioalteration of sulfide minerals: results from in situ incubation studies. Geochim Cosmochim Acta 67: 28432856.
  • Eid J, Fehr A, Gray J et al. (2009) Real-time DNA sequencing from single polymerase molecules. Science 323: 133138.
  • Elsahed MS, Senko JM, Nazar FZ, Kenton SM, Roe BA, Dewers TA, Spear JR & Krumholz LR (2003) Bacterial diversity and sulfur cycling in a mesophilic sulfide-rich spring. Appl Environ Microbiol 69: 56095621.
  • Elsaied H & Naganuma T (2001) Phylogenetic diversity of ribulose-1,5-bisphosphate carboxylase/oxygenase large-subunit genes from deep-sea microorganisms. Appl Environ Microbiol 67: 17511765.
  • Engelen B, Ziegelmüller K, Wolf L et al. (2008) Fluids from the oceanic crust support microbial activity within the deep biosphere. Geomicrobiol J 25: 5666.
  • Engelhardt T, Sahlberg M, Cypionka H & Engelen B (2012) Biogeography of Rhizobium radiobacter and distribution of associated temperate phages in deep subseafloor sediments. ISME J 199209. doi: 10.1038/ismej.2012.92.
  • Expedition 329 Scientists (2011) South Pacific Gyre subseafloor life. IODP Prel Rept 329: 1108. doi: 10.2204/iodp.pr.329.2011.
  • Franklin MJ, Wiebe WJ & Whitman WB (1998) Populations of methanogenic bacteria in a Georgia salt marsh. Appl Environ Microbiol 54: 11511157.
  • Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, Schuster SC, Chisholm SW & DeLong EF (2008) Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci USA 105: 38053810.
  • Friedrich M (2005) Methyl-coenzyme M reductase genes: unique functional markers for methanogenic and anaerobic methane-oxidizing Archaea. Methods Enzymol 26: 428442.
  • Futagami T, Morono Y, Terada T, Kaksonen AH & Inagaki F (2009) Dehalogenation activities and distribution of reductive dehalogenase homologous genes in marine subsurface sediments. Appl Environ Microbiol 75: 69056909.
  • Gagen EM, Denman SE, Padmanabha J, Zadbuke S, Jassim RA, Morrison M & McSweeney CS (2010) Functional gene analysis suggests different acetogen populations in the bovine rumen and Tammar Wallaby forestomach. Appl Environ Microbiol 76: 77857795.
  • Gilbert JA & Dupont CL (2011) Microbial metagenomics: beyond the genome. Ann Rev Mar Sci 3: 347371.
  • Grossart H-P, Frindte K, Dziallas C, Eckert W & Tang KW (2012) Microbial methane production in oxygenated water column of an oligotrophic lake. Proc Natl Acad Sci USA 49: 1965719661.
  • Hales BA, Edwards C, Ritchie DA, Hall D, Pickup RW & Saunders JR (1996) Isolation and identification of methanogen-specific DNA from blanket bog peat by PCR amplification and sequence analysis. Appl Environ Microbiol 62: 668675.
  • Hallam SJ, Putnam N, Preston CM, Detter JC, Rokhsar D, Richardson PM & DeLong EF (2004) Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305: 14571462.
  • Harrison BK, Zhang H, Berelson W & Orphan VJ (2009) Variations in archaeal and bacterial diversity associated with the sulfate-methane transition zone in continental margin sediments (Santa Barbara Basin, California). Appl Environ Microbiol 75: 14871499.
  • Heiss-Blanquet S, Benoit Y, Maréchaux C & Monot F (2005) Assessing the role of alkane hydroxylase genotypes in environmental samples by competitive PCR. J Appl Microbiol 99: 13921403.
  • Heuer VB, Pohlman JW, Torres ME, Elvert M & Hinrichs K-U (2009) The stable carbon isotope biogeochemistry of acetate and other dissolved carbon species in deep subseafloor sediments at the northern Cascadia Margin. Geochim Cosmochim Acta 73: 33233336.
  • Hoehler TM, Alperin MJ, Albert DB & Martens CS (1998) Thermodynamic control on hydrogen concentrations in anoxic sediments. Geochim Cosmochim Acta 62: 17451756.
  • Holmkvist L, Ferdelman TG & Jørgensen BB (2011) A cryptic sulfur cycle driven by iron in the methane zone of marine sediment (Aarhus Bay, Denmark). Geochim Cosmochim Acta 75: 35813599.
  • Hölscher T, Krajmalnik-Brown R, Ritalahti KM, von Wintzingerode F, Görisch H, Löffler FE & Adrian L (2004) Multiple nonidentical reductive-dehalogenase-homologous genes are common in Dehalococcoides. Appl Environ Microbiol 70: 52905297.
  • Hoshino T & Inagaki F (2012) Molecular quantification of environmental DNA using microfluidics and digital PCR. Syst Appl Microbiol 35: 390395.
  • Hoshino T & Schramm A (2010) Detection of denitrification genes by in situ rolling circle amplification-fluorescence to link metabolic potential with identity inside bacterial cells. Environ Microbiol 12: 25082517.
  • Huber R, Rossnagel P, Woese CR, Rachel R, Langworth TA & Stetter KO (1996) Formation of ammonium from nitrate during chemolithoautotrophic growth of the extremely thermophilic bacterium Ammonifex degensii gen. nov. sp. nov. Syst Appl Microbiol 19: 4049.
  • Hügler M, Gärtner A & Imhoff JF (2010) Functional genes as markers of sulfur cycling and CO2 fixation in microbial communities of hydrothermal vents of the Logatchev field. FEMS Microbiol Ecol 73: 526537.
  • Imachi H, Aoi K, Tasumi E et al. (2011) Cultivation of methanogenic community from subseafloor sediments using a continuous-flow bioreactor. ISME J 5: 19131925.
  • Inagaki F, Suzuki M, Takai K, Oida H, Sakamoto T, Aoki K, Nealson KH & Horikoshi K (2003) Microbial communities associated with geological horizons in coastal subseafloor sediments from the Sea of Okhotsk. Appl Environ Microbiol 69: 72247235.
  • Inagaki F, Nunoura T, Nakagawa S et al. (2006a) Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean Margin. Proc Natl Acad Sci USA 103: 28152820.
  • Inagaki F, Kuypers MMM, Tsunogai U et al. (2006b) Microbial community in a sediment-hosted CO2 lake of the southern Okinawa Trough hydrothermal system. Proc Natl Acad Sci USA 103: 1416414169.
  • Inagaki F, Hinrichs K-U, Kubo Y & the Expedition 337 Project Team (2010) Deep coalbed biosphere off Shimokita: microbial processes and hydrocarbon system associated with deeply buried coalbed in the ocean. IODP Sci Prosp 337: 151. doi: 10.2204/iodp.sp.337.2010.
  • Ivarsson M (2012) Subseafloor basalts as fungal habitats. Biogeosciences 9: 36253635.
  • Ivarsson M, Bengtson S, Belivanova V, Stampanoni M, Marone F & Tehler A (2012) Fossilized fungi in subseafloor Eocene basalts. Geology 40: 163166.
  • Jannasch HW, Wheat CG, Plant J, Kastner M & Stakes D (2004) Continuous chemical monitoring with osmotically pumped water samplers: OsmoSampler design and applications. Limnol Oceanogr Meth 2: 102113.
  • Jeng Y-H & Doi RH (1974) Messenger ribonucleic acid of dormant spores of Bacillus subtilis. J Bacteriol 119: 514521.
  • Jiang L, Zheng Y, Peng X, Zhou H, Zhang C, Xiao X & Wang F (2009) Vertical distribution and diversity of sulfate-reducing prokaryotes in the Pearl River estuarine sediments, Southern China. FEMS Microbiol Ecol 70: 249262.
  • Jørgensen BB (1982) Mineralization of organic matter in the sea bed – the role of sulphate reduction. Nature 296: 643645.
  • Jørgensen BB, D'Hondt SL & Miller DJ (2006) 1. Leg 201 synthesis: controls on microbial communities in deeply buried sediments. Proc ODP, Sci Res 201 (Jørgensen BB, D'Hondt SL & Miller DJ, eds), pp. 181. ODP, College Station, TX.
  • Kalisky T & Quake SR (2011) Single-cell genomics. Nat Methods 8: 311314.
  • Kaneko R, Hayashi T, Tanahashi M & Naganuma T (2007) Phylogenetic diversity and distribution of dissimilatory sulfite reductase genes from deep-sea sediment cores. Mar Biotechnol 9: 429436.
  • Kendall MM, Liu Y, Sieprawska-Lupa M, Stetter KO, Whitman WB & Boone DR (2006) Methanococcus aeolicus sp. nov., a mesophilic, methanogenic archaeon from shallow and deep marine sediments. Int J Syst Evol Microbiol 56: 15251529.
  • Kiene RP, Oremland RS, Catena A, Miller LG & Capone DG (1986) Metabolism of reduced methylated sulfur compounds in anaerobic sediments and by a pure culture of an estuarine methanogen. Appl Environ Microbiol 52: 10371045.
  • King GM, Klug MJ & Lovley DR (1983) Metabolism of acetate, methanol, and methylated amines in intertidal sediments of Lowes Cove, Maine. Appl Environ Microbiol 45: 18481853.
  • Klein M, Friedrich M, Roger AJ, Hugenholtz P, Fishbain S, Abicht H, Blackall LL, Stahl DA & Wagner M (2001) Multiple lateral transfers of dissimilatory sulfite reductase genes between major lineages of sulfate-reducing prokaryotes. J Bacteriol 183: 60286035.
  • Knittel K & Boetius A (2009) Anaerobic oxidation of methane: progress with an unknown process. Ann Rev Microbiol 63: 311334.
  • Kondo R, Nedwell DB, Purdy KJ & de Queiroz Silva S (2004) Detection and enumeration of sulphate-reducing bacteria in estuarine sediments by competitive PCR. Geomicrobiol J 21: 145157.
  • Kormas KA, Smith DC, Edgcomb V & Teske A (2003) Molecular analyses of deep subsurface microbial communities in Nankai Trough sediments (ODP Leg 190, Site 1176). FEMS Microbiol Ecol 45: 115125.
  • Krajmalnik-Brown R, Hölscher T, Thomson IN, Saunders FM, Ritalahti KM & Löffler FE (2004) Genetic identification of a putative vinyl chloride reductase in Dehalococcoides sp. strain BAV1. Appl Environ Microbiol 70: 63476351.
  • Kubota K, Imachi H, Kawakami S, Nakamura K, Harada H & Ohashi A (2008) Evaluation of enzymatic cell treatments for application of CARD-FISH to methanogens. J Microbiol Meth 72: 5459.
  • Lai X, Cao L, Tan H, Fang S, Huang Y & Zhou S (2007) Fungal communities from methane hydrate-bearing deep-sea marine sediments in South China Sea. ISME J 1: 756762.
  • Lanoil BD, Sassen R, La Duc MT, Sweet ST & Nealson KH (2001) Bacteria and Archaea physically associated with Gulf of Mexico gas hydrates. Appl Environ Microbiol 67: 51435153.
  • Leaphart AB, Friez MJ & Lovell CR (2003) Formyltetrahydrofolate synthetase sequences from salt marsh plant roots reveal a diversity of acetogenic Bacteria and other bacterial functional groups. Appl Environ Microbiol 69: 693696.
  • Leloup J, Fossing H, Kohls K, Holmkvist L, Borowski C & Jørgensen BB (2009) Sulfate-reducing bacteria in marine sediment (Aarhus Bay, Denmark): abundance and diversity related to geochemical zonation. Environ Microbiol 11: 12781291.
  • Lever MA (2012) Acetogenesis in the energy-starved deep biosphere – a paradox? Front Microbiol 2: 284.
  • Lever MA, Alperin MJ, Engelen B, Inagaki F, Nakagawa S, Steinsbu BO & Teske A (2006) Trends in basalt and sediment core contamination during IODP Expedition 301. Geomicrobiol J 23: 517530.
  • Lever MA, Heuer VB, Morono Y, Masui N, Schmidt F, Alperin MJ, Inagaki F, Hinrichs K-U & Teske A (2010) Acetogenesis in deep subseafloor sediments of the Juan de Fuca Ridge Flank: a synthesis of geochemical, thermodynamic, and gene-based evidence. Geomicrobiol J 27: 183211.
  • Liang Y, van Nostrand JD, Deng Y, He Z, Wu L, Zhang X, Li G & Zhou J (2011) Functional gene diversity of soil microbial communities from five oil-contaminated fields in China. ISME J 5: 403413.
  • Lin YS, Heuer VB, Ferdelman TG & Hinrichs K-U (2010) Microbial conversion of inorganic carbon to dimethyl sulfide in anoxic lake sediment (Plußsee, Germany). Biogeosciences 7: 24332444.
  • Liu S & Suflita JM (1993) H2-CO2-dependent anaerobic Ο-demethylation activity in subsurface sediments and by an isolated bacterium. Appl Environ Microbiol 59: 13251331.
  • Liu Y & Whitman WB (2008) Metabolic, phylogenetic, and ecological diversity of the methanogenic Archaea. Ann NY Acad Sci 1125: 171189.
  • Liu Z, Lozupone C, Hamady M, Bushman FD & Knight R (2007) Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids Res 35: e120.
  • Lloyd KG, Alperin MJ & Teske A (2011) Environmental evidence for net methane production and oxidation in putative Anaerobic MEthanotrophic (ANME) archaea. Environ Microbiol 13: 25482564.
  • Lomstein BA, Langerhuus AT, D'Hondt S, Jørgensen BB & Spivack AJ (2012) Endospore abundance, microbial growth and necromass turnover in deep sub-seafloor sediment. Nature 484: 101104.
  • Lovley DR & Goodwin S (1988) Hydrogen concentrations as an indicator of the terminal electron-accepting reactions in aquatic sediments. Geochim Cosmochim Acta 52: 29933003.
  • Loy A, Duller S & Wagner M (2008) Evolution and ecology of microbes dissimilating sulfur compounds: insights from siroheme sulfite reductases. Microbial Sulfur Metabolism (Dahl C & Friedrich CG, eds), pp. 4659. Springer, Berlin, Germany.
  • Loy A, Duller S, Baranyi C, Mussmann M, Ott J, Sharon I, Béjà O, Le Paslier D, Dahl C & Wagner M (2009) Reverse dissimilatory sulfite reductase as phylogenetic marker for a subgroup of sulfur-oxidizing prokaryotes. Environ Microbiol 11: 289299.
  • Lu Z, Deng Y, Van Nostrand JD et al. (2011) Microbial gene functions enriched in the deepwater horizon deep-sea oil plume. ISME J 6: 451460.
  • Ludwig W, Strunk O, Westram R et al. (2004) ARB: a software environment for sequence data. Nucleic Acids Res 32: 13631371.
  • Lueders T & Friedrich M (2003) Evaluation of PCR amplification bias by terminal restriction fragment length polymorphism analysis of small-subunit rRNA and mcrA genes by using defined template mixtures of methanogenic pure cultures and soil DNA extracts. Appl Environ Microbiol 69: 320326.
  • Lueders T, Chin KJ, Conrad R & Friedrich M (2001) Molecular analyses of methyl-coenzyme M reductase α-subunit (mcrA) genes in rice field soil and enrichment cultures reveal the methanogenic phenotype of a novel archaeal lineage. Environ Microbiol 3: 194204.
  • Luton PE, Wayne JM, Sharp RJ & Riley PW (2001) The mcrA gene as an alternative to 16S rRNA in the phylogenetic analysis of methanogen populations in landfill. Microbiology 148: 35213530.
  • Martens CS & Berner RA (1977) Interstitial water chemistry of anoxic long island sound sediments. 1. Dissolved gases. Limnol Oceanogr 22: 1025.
  • Mason OU, Di Meo-Savoie CA, Van Nostrand JD, Zhou J, Fisk MR & Giovannoni SJ (2009) Prokaryotic diversity, distribution, and insights into their role in biogeochemical cycling in marine basalts. ISME J 3: 231242.
  • Mason OU, Hazen TC, Borglin S et al. (2012) Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to deepwater horizon oil spill. ISME J 6: 17151727.
  • Masui N, Morono Y & Inagaki F (2008) Microbiological assessment of circulation mud fluids during the first operation of riser drilling by the deep-earth research vessel Chikyu. Geomicrobiol J 25: 274282.
  • Matsuki T, Watanabe K, Fujimoto J, Takada T & Tanaka R (2004) Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analyses of predominant Bacteria in human feces. Appl Environ Microbiol 70: 72207228.
  • McDonald IR, Bodrossy L, Chen Y & Murrell JC (2008) Molecular ecology techniques for the study of aerobic methanotrophs. Appl Environ Microbiol 74: 13051315.
  • Mende DR, Waller AS, Sunagawa S, Järvelin AI, Chan MM, Arumugam M, Raes J & Bork P (2012) Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS ONE 7: e31386.
  • Meyer B & Kuever J (2007) Phylogeny of the alpha and beta subunits of the dissimilatory adenosine-5′-phosphosulfate (APS) reductase from sulfate-reducing prokaryotes – origin and evolution of the dissimilatory sulfate-reduction pathway. Microbiology 153: 20262044.
  • Michaelis W, Seifert R, Nauhaus K et al. (2002) Microbial reefs in the Black Sea fueled by anaerobic oxidation of methane. Science 297: 10131015.
  • Mikucki JA, Liu Y, Delwiche M, Colwell FS & Boone DR (2003) Isolation of a methanogen from deep marine sediments that contain methane hydrates, and description of Methanoculleus submarinus sp. nov. Appl Environ Microbiol 69: 33113316.
  • Miller TL & Wolin MJ (1986) Methanogens in human and animal intestinal tracts. Syst Appl Microbiol 7: 223229.
  • Mitterer RM (2006) 12. Data report: D/L ratios and concentrations of selected amino acids in interstitial waters, Equatorial Pacific and Peru Margin, ODP Leg 201, in Controls on microbial communities in deeply buried sediments, Eastern Equatorial Pacific and Peru Margin sites 1225–1231. Proc. ODP Scient. Res. 201 (Jørgensen BB, D'Hondt SL & Miller DJ, eds), pp. 17. ODP, College Station, TX.
  • Mohan SB, Schmid M, Jetten M & Cole J (2004) Detection and widespread distribution of the nrfA gene encoding nitrite reduction to ammonia, a short circuit in the biological nitrogen cycle that competes with denitrification. FEMS Microbiol Ecol 49: 433443.
  • Mori K & Harayama S (2011) Methanobacterium petrolearium sp. nov. and Methanobacterium ferruginis sp. nov., mesophilic methanogens isolated from salty environments. Int J Syst Evol Microbiol 61: 138143.
  • Morono Y, Terada T, Nishizawa M, Ito M, Hillion F, Takahata N, Sano Y & Inagaki F (2011) Carbon and nitrogen assimilation in deep subseafloor microbial cells. Proc Natl Acad Sci USA 108: 1829518300.
  • Nakagawa S, Inagaki F, Suzuki Y et al. (2006) Microbial community in black rust exposed to hot ridge flank crustal fluids. Appl Environ Microbiol 72: 67896799.
  • Neufeld JD, Vohra J, Dumont MG, Lueders T, Manefiled M, Friedrich MW & Murrell JC (2007) DNA stable-isotope probing. Nat Protoc 2: 860866.
  • Newberry CJ, Webster G, Cragg BA, Parkes RJ, Weightman AJ & Fry JC (2004) Diversity of prokaryotes and methanogenesis in deep subsurface sediments from the Nankai Trough, Ocean Drilling Program Leg 190. Environ Microbiol 6: 274287.
  • Newton RJ, VandeWalle JL, Borchardt MA, Gorelick MH & McLellan SL (2011) Lachnospiraceae and Bacteriodales alternative fecal indicators reveal chronic human sewage contamination in an urban harbor. Appl Environ Microbiol 77: 69726981.
  • Nunoura T, Inagaki F, Delwiche ME, Colwell FS & Takai K (2008) Subseafloor microbial communities in methane hydrate-bearing sediment at two distinct locations (ODP Leg 204) in the Cascadia Margin. Microbes Environ 23: 317325.
  • Nunoura T, Soffientino B, Blazejak A, Kakuta J, Oida H, Schippers A & Takai K (2009) Subseafloor microbial communities associated with rapid turbidite deposition in the Gulf of Mexico continental slope (IODP Expedition 308). FEMS Microbiol Ecol 69: 410424.
  • Ogram A, Sayler GS, Gustin D & Lewis RJ (1988) DNA adsorption to soils and sediments. Environ Sci Technol 22: 982984.
  • Orcutt BN, Bach W, Becker K, Fisher AT, Hentscher M, Toner BM, Wheat CG & Edwards KJ (2010a) Colonization of subsurface microbial observatories deployed in young ocean crust. ISME J 5: 692703.
  • Orcutt B, Wheat CG & Edwards KJ (2010b) Subseafloor ocean crust microbial observatories: development of FLOCS (FLow-through Osmo Colonization System) and evaluation of borehole construction materials. Geomicrobiol J 27: 143157.
  • Orcutt B, Lapham L & Girguis P (2011) Introducing MIMOSA – a microbial methane observatory for seafloor analysis. Geophys Res Abstr 13: 2368.
  • Oremland RS & Polcin S (1982) Methanogenesis and sulfate reduction: competitive and noncompetitive substrates in estuarine sediments. Appl Environ Microbiol 44: 12701276.
  • Parameswaran P, Jalili R, Tao L, Shokralla S, Gharizadeh B, Ronaghi M & Fire AZ (2007) A pyrosequencing-tailored nucleotide barcode design unveils opportunities for large-scale sample multiplexing. Nucleic Acids Res 35: 29.
  • Parkes RJ, Webster G, Cragg BA, Weightman AJ, Newberry CJ, Ferdelman TG, Kallmeyer J, Jørgensen BB, Aiello IW & Fry JC (2005) Deep sub-seafloor prokaryotes stimulated at interfaces over geological time. Nature 436: 390394.
  • Parkes RJ, Linnane CD, Webster G, Sass H, Weightman AJ, Hornibrook ERC & Horsfield B (2011) Prokaryotes stimulate mineral H2 formation for the deep biosphere and subsequent thermogenic activity. Geology 39: 219222.
  • Parkes RJ, Brock F, Banning N, Hornibrook ERC, Roussel EG, Weightman AJ & Fry JC (2012) Changes in methanogenic substrate utilization and communities with depth in a salt-marsh, creek sediment in southern England. Estuar Coast Shelf Sci 96: 170178.
  • Pawlowski J, Christen R, Lecroq B, Bachar D, Shahbazkia HR, Amaral-Zettler L & Guillou L (2011) Eukaryotic richness in the abyss: insights from pyrotag sequencing. PLoS ONE 6: e18169.
  • Pereyra LP, Hiibel SR, Prieto Riquelme MV, Reardon KF & Pruden A (2010) Detection and quantification of functional genes of cellulose-degrading, fermentative, and sulfate-reducing bacteria and methagenic archaea. Appl Environ Microbiol 76: 21922202.
  • Pernthaler A & Amann R (2004) Simultaneous fluorescence in situ hybridization of mRNA in environmental Bacteria. Appl Environ Microbiol 70: 54265433.
  • Pester M, Bittner N, Pinsurang D, Wagner M & Loy A (2010) A ‘rare biosphere’ microorganism contributes to sulfate reduction in a peatland. ISME J 4: 15911602.
  • Pietramellara G, Ascher J, Borgogni F, Ceccherini MT, Guerri G & Nannipieri P (2009) Extracellular DNA in soil and sediment: fate and ecological relevance. Biol Fertil Soils 45: 219235.
  • Pignatelli M & Moya A (2011) Evaluating the fidelity of de novo short read metagenomic assembly using simulated data. PLoS ONE 6: e19984.
  • Pilloni G, Granitsiotis MS, Engel M & Lueders T (2012) Testing the limits of 454 pyrotag sequencing: reproducibility, quantitative assessment and comparison to T-RFLP fingerprinting of aquifer microbes. PLoS ONE 7: e40467.
  • Pinto AJ & Raskin L (2012) PCR biases distort bacterial and archaeal community structure in pyrosequencing datasets. PLoS ONE 7: e43093.
  • Poretsky RS, Bano N, Buchan A, LeCleir G, Kleinkemper J, Pickering M, Pate WM, Moran MA & Hollibaugh JT (2005) Analysis of microbial gene transcripts in environmental samples. Environ Microbiol 72: 41214126.
  • Preston CM, Harris A, Ryan JP et al. (2011) Underwater application of quantitative PCR on an ocean mooring. PLoS ONE 6: e22522.
  • Raghukumar C, Damare SR & Singh P (2010) A review on deep-sea fungi: occurrence, diversity and adaptations. Bot Mar 53: 479492.
  • Rappé MS & Giovannoni SJ (2003) The uncultured microbial majority. Annu Rev Microbiol 57: 369394.
  • Rauhut R & Klug G (1999) mRNA degradation in bacteria. FEMS Microbiol Rev 23: 353370.
  • Reed AJ, Lutz RA & Vetriani C (2006) Vertical distribution and diversity of bacteria and archaea in sulfide and methane-rich cold seep sediments located at the base of the Florida Escarpment. Extremophiles 10: 199211.
  • Regeard C, Maillard J & Holliger C (2004) Development of degenerate and specific PCR primers for the detection and isolation of known and putative chloroethene reductive dehalogenase genes. J Microbiol Meth 56: 107118.
  • Roh SW, Abell GCJ, Kim K-H, Nam Y-D & Bae J-W (2010) Comparing microarrays and next-generation sequencing technologies for microbial ecology research. Trends Biotechnol 28: 291299.
  • Rotthauwe JH, Witzel KP & Liesack W (1997) The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl Environ Microbiol 63: 47044712.
  • Roussel EG, Sauvadet A-L, Chaduteau C, Fouquet Y, Charlou J-L, Prieur D & Cambon Bonavita M-A (2009a) Archaeal communities associated with shallow to deep subseafloor sediments of New Caledonia Basin. Environ Microbiol 11: 24462462.
  • Roussel EG, Sauvadet A-L, Allard J, Chaduteau C, Richard P, Cambon Bonavita M-A & Chaumillon E (2009b) Archaeal methane cycling communities associated with gassy subsurface sediments of Marennes-Oléron Bay (France). Geomicrobiol J 26: 3143.
  • Sakai S, Imachi H, Hanada S, Ohashi A, Harada H & Kamagata Y (2008) Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol 58: 929936.
  • Santelli CM, Banerjee N, Bach W & Edwards KJ (2010) Tapping the subsurface ocean crust biosphere: low biomass and drilling-related contamination calls for improved quality controls. Geomicrobiol J 27: 158169.
  • Schadt EE, Turner S & Kasarskis A (2010) A window into third-generation sequencing. Hum Mol Genet 19: R227R240.
  • Schippers A & Neretin LN (2006) Quantification of microbial communities in near-surface and deeply buried marine sediments on the Peru continental margin using real-time PCR. Environ Microbiol 8: 12511260.
  • Schippers A, Neretin LN, Kallmeyer J, Ferdelman TG, Cragg BA, Parkes RJ & Jørgensen BB (2005) Prokaryotic cells of the deep sub-seafloor biosphere identified as living bacteria. Nature 433: 861864.
  • Schippers A, Kock D, Höft C, Köweker G & Siegert M (2012) Quantification of microbial communities in subsurface marine sediments of the Black Sea and off Namibia. Front Microbiol 3: 16.
  • Schrum HN, Spivack AJ, Kastner M & D'Hondt S (2009) Sulfate-reducing ammonium oxidation: a thermodynamically feasible metabolic pathway in subseafloor sediment. Geology 37: 939942.
  • Segev E, Smith Y & Ben-Yehuda S (2012) RNA dynamics in aging bacterial spores. Cell 148: 139149.
  • Shlimon AG, Friedrich MW, Niemann H, Ramsing NB & Finster K (2004) Methanobacterium aarhusense sp. nov., a novel methanogen isolated from a marine sediment (Aarhus Bay, Denmark). Int J Syst Evol Microbiol 54: 759763.
  • Singh N, Kendall MM, Liu Y & Boone DR (2005) Isolation and characterization of methylotrophic methanogens from anoxic marine sediments in Skan Bay, Alaska: description of Methanococcoides alaskense sp. nov., and emended description of Methanosarcina baltica. Int J Syst Evol Microbiol 55: 25312538.
  • Smith DC, Spivack AJ, Fisk MR, Haveman SA & Staudigel H (2000) Tracer-based estimates of drilling-induced microbial contamination of deep sea crust. Geomicrobiol J 17: 207219.
  • Smith CJ, Nedwell DB, Dong LF & Osborn AM (2007) Diversity and abundance of nitrate reductase genes (narG and napA), nitrite reductase genes (nirS and nrfA), and their transcripts in estuarine sediments. Appl Environ Microbiol 73: 36123622.
  • Sowers KR & Ferry JG (1983) Isolation and characterization of a methylotrophic methanogen, Methanococcoides methylutens gen. nov., sp. nov. Appl Environ Microbiol 45: 684690.
  • Springer E, Sachs MS, Woese CR & Boone DR (1995) Partial gene sequences for the A subunit of methyl-coenzyme M reductase (mcrI) as a phylogenetic tool for the family Methanosarcinaceae. Int J Syst Bacteriol 45: 554559.
  • Stepanauskas R & Sieracki ME (2007) Matching phylogeny and metabolism in the uncultured marine bacteria, one cell at a time. P Natl Acad Sci USA 104: 90529057.
  • Süss J, Schubert K, Sass H, Cypionka H, Overmann J & Engelen B (2006) Widespread distribution and high abundance of Rhizobium radiobacter within Mediterranean subsurface sediments. Environ Microbiol 8: 17531763.
  • Süss J, Herrmann K, Seidel M, Cypionka H, Engelen B & Sass H (2008) Two distinct Photobacterium populations thrive in ancient Mediterranean sapropels. Microbial Ecol 55: 371383.
  • Teira E, Reinthaler T, Pernthaler A, Pernthaler J & Herndl GJ (2004) Combining catalyzed reporter deposition-fluorescence in situ hybridization and microautoradiography to detect substrate utilization by Bacteria and Archaea in the deep ocean. Appl Environ Microbiol 70: 44114414.
  • Teske AP (2006) Microbial communities of deep marine subsurface sediments: molecular and cultivation surveys. Geomicrobiol J 23: 357368.
  • Teske A & Sørensen KB (2008) Uncultured archaea in deep marine subsurface sediments: have we caught them all? ISME J 2: 318.
  • Teske A, Hinrichs K-U, Edgcomb V, Gomez AD, Kysela D, Sylva SP, Sogin ML & Jannasch HW (2002) Microbial diversity of hydrothermal sediments in the Guaymas Basin: evidence for anaerobic methanotrophic communities. Appl Environ Microbiol 68: 19942007.
  • Thauer RK (2011) Anaerobic oxidation of methane with sulfate: on the reversibility of the reactions that are catalyzed by enzymes also involved in methanogenesis from CO2. Curr Opin Microbiol 14: 292299.
  • Thomsen TR, Finster K & Ramsing NB (2001) Biogeochemical and molecular signatures of anaerobic methane oxidation in a marine sediment. Appl Environ Microbiol 67: 16461656.
  • Ufnar JA, Wang SY, Christiansen JM, Yampara-Iquise H, Carson CA & Ellender RD (2006) Detection of the nifH gene of Methanobrevibacter smithii: a potential tool to identify sewage pollution in recreational waters. J Appl Microbiol 101: 4452.
  • Wagner A, Adrian L, Kleinsteuber S, Andreesen JR & Lechner U (2009) Transcription analysis of genes encoding homologues of reductive dehalogenases in “Dehalococcoides” sp. Strain CBDB1 by using terminal restriction fragment length polymorphism and quantitative PCR. Appl Environ Microbiol 75: 18761884.
  • Wang Q, Garrity GM, Tiedje JM & Cole JR (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 52615267.
  • Wang F, Zhou H, Meng J et al. (2008a) GeoChip-based analysis of metabolic diversity of microbial communities at the Juan de Fuca Ridge hydrothermal vent. P Natl Acad Sci USA 106: 48404845.
  • Wang G, Spivack AJ, Rutherford S, Manor U & D'Hondt S (2008b) Quantification of co-occurring reaction rates in deep subseafloor sediments. Geochim Cosmochim Acta 72: 34793488.
  • Wang J, van Nostrand JD, Wu L, He Z, Li G & Zhou J (2011) Microarray-based evaluation of whole-community genome DNA amplification methods. Appl Environ Microbiol 77: 42414245.
  • Wankel SD, Joye SB, Samarkin VA, Shan SR, Friederich G, Melas-Kyriazi J & Girguis PR (2010) New constraints on methane fluxes and rates of anaerobic methane oxidation in a Gulf of Mexico brine pool via in situ mass spectrometry. Deep-Sea Res II 57: 2123.
  • Wankel SD, Germanovich LN, Lilley MD, Genc G, DiPerna CJ, Bradley AS, Olson EJ & Girguis PR (2011) Influence of subsurface biosphere on geochemical fluxes from diffuse hydrothermal fluids. Nat Geosci 4: 461468.
  • Ward JM, Smith PH & Boone DR (1989) Emended description of strain PS (=OGC 70 = ATCC 33273 = DSM 1537), the type strain of Methanococcus voltae. Int J Syst Bacteriol 39: 493494.
  • Webster G, Newberry CJ, Fry JC & Weightman AJ (2003) Assessment of bacterial community structure in the deep sub-seafloor biosphere by 16S rRNA-based techniques: a cautionary tale. J Microbiol Meth 55: 155164.
  • Webster G, Parkes RJ, Cragg BA, Newberry CJ, Weightman AJ & Fry JC (2006) Prokaryotic community composition and biogeochemical processes in deep subseafloor sediments from the Peru Margin. FEMS Microbiol Ecol 58: 6585.
  • Webster G, Blazejak A, Cragg BA et al. (2009) Subsurface microbiology and biogeochemistry of a deep, cold-water carbonate mound from the Porcupine Seabight (IODP Expedition 307). Environ Microbiol 11: 239257.
  • Wegener G, Bausch M, Holler T, Thang NM, Prieto-Mollar X, Kellermann MY, Hinrichs K-U & Boetius A (2012) Assessing sub-seafloor microbial activity by combined stable isotope probing with deuterated water and 13C-bicarbonate. Environ Microbiol 14: 15171527.
  • Whitman WB, Shieh J, Sohn S, Caras DS & Premachandran U (1986) Isolation and characterization of 22 mesophilic methanococci. Syst Appl Microbiol 7: 235240.
  • Whitman WB, Bowen TL & Boone DR (2006) The methanogenic bacteria. The Prokaryotes: An Evolving Electronic Resource for the Microbiological Community, Vol. 3 (Dworkin M, Falkow S, Rosenberg E, Schleifer K-H & Stackebrandt E, eds), pp. 165207. Springer, New York, NY.
  • Willerslev E, Hansen AJ, Binladen J, Brand TB, Gilbert MTP, Shapiro B, Bunce M, Wiuf C, Gilichinsky DA & Cooper A (2003) Diverse plant and animal genetic records from Holocene and Pleistocene sediments. Science 300: 791795.
  • Winderl C, Schaefer S & Lueders T (2007) Detection of anaerobic toluene and hydrocarbon degraders in contaminated aquifers using benzylsuccinate synthase (bssA) genes as a functional marker. Environ Microbiol 9: 10351046.
  • Wu J & Xi C (2009) Evaluation of different methods for extracting extracellular DNA from the biofilm matrix. Appl Environ Microbiol 75: 53905395.
  • Wu L, Liu X, Schadt CW & Zhou J (2006) Microarray-based analysis of subnanogram quantities of microbial community DNAs by using whole-community genome amplification. Appl Environ Microbiol 72: 49314941.
  • Yanagawa K, Sunamura M, Lever MA, Morono Y, Hiruta A, Ishizaki O, Matsumoto R, Urabe T & Inagaki F (2011) Niche separation of methanotrophic Archaea (ANME-1 and -2) in methane-seep sediments of the Eastern Japan Sea offshore Joetsu. Geomicrobiol J 28: 118129.
  • Ye G, Wang S, Jiang L, Xiao X, Wang F, Noakes J & Zhang C (2009) Distribution and diversity of bacteria and Archaea in marine sediments affected by gas hydrates at Mississippi Canyon in the Gulf of Mexico. Geomicrobiol J 26: 370381.
  • Yoshioka H, Maruyama A, Nakamura T, Higashi Y, Fuse H, Sakata S & Bartlett DH (2010) Activities and distribution of methanogenic and methane-oxidizing microbes in marine sediments from the Cascadia Margin. Geobiology 8: 223233.
  • Zehr PJ, Jenkins BD, Short SM & Steward GF (2003) Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ Microbiol 5: 539554.
  • Zeikus JG & Henning DL (1975) Methanobacterium arboriphilum sp. nov. An obligate anaerobe isolated from wetwood of living trees. Antonie Van Leeuwenhoek 41: 543552.
  • Zhang G, Tian J, Jiang N, Guo X, Wang Y & Dong X (2008) Methanogen community in Zoige wetland of Tibetan plateau and phenotypic characterization of a dominant uncultured methanogen cluster ZC-I. Environ Microbiol 10: 18501860.
  • Zhou J, He Z, Van Nostrand JD, Wu L & Deng Y (2010) Applying GeoChip analysis to disparate microbial communities. Microbe 5: 6065.