Open Access

Metagenomic analysis of microbial consortium from natural crude oil that seeps into the marine ecosystem offshore Southern California

  • Erik R. Hawley
  • , Hailan Piao
  • , Nicole M. Scott
  • , Stephanie Malfatti
  • , Ioanna Pagani
  • , Marcel Huntemann
  • , Amy Chen
  • , Tijana Glavina del Rio
  • , Brian Foster
  • , Alex Copeland
  • , Janet Jansson,
  • , Amrita Pati
  • , Susannah Tringe,
  • , Jack A. Gilbert,
  • , Thomas D. Lorenson
  • and Matthias Hess, , , ,
Corresponding author

DOI: 10.4056/sigs.5029016

Received: 02 January 2014

Accepted: 02 January 2014

Published: 15 June 2014

Abstract

Crude oils can be major contaminants of the marine ecosystem and microorganisms play a significant role in the degradation of its main constituents. To increase our understanding of the microbial hydrocarbon degradation process in the marine ecosystem, we collected crude oil from an active seep area located in the Santa Barbara Channel (SBC) and generated a total of about 52 Gb of raw metagenomic sequence data. The assembled data comprised ~500 Mb, representing ~1.1 million genes derived primarily from chemolithoautotrophic bacteria. Members of Oceanospirillales, a bacterial order belonging to the Deltaproteobacteria, recruited less than 2% of the assembled genes within the SBC metagenome. In contrast, the microbial community associated with the oil plume that developed in the aftermath of the Deepwater Horizon (DWH) blowout in 2010, was dominated by Oceanospirillales, which comprised more than 60% of the metagenomic data generated from the DWH oil plume. This suggests that Oceanospirillales might play a less significant role in the microbially mediated hydrocarbon conversion within the SBC seep oil compared to the DWH plume oil. We hypothesize that this difference results from the SBC oil seep being mostly anaerobic, while the DWH oil plume is aerobic. Within the Archaea, the phylum Euryarchaeota, recruited more than 95% of the assembled archaeal sequences from the SBC oil seep metagenome, with more than 50% of the sequences assigned to members of the orders Methanomicrobiales and Methanosarcinales. These orders contain organisms capable of anaerobic methanogenesis and methane oxidation (AOM) and we hypothesize that these orders – and their metabolic capabilities – may be fundamental to the ecology of the SBC oil seep.

Keywords:

Bioremediationhydrocarbon-degradationmarine ecosystemcrude oilnatural oil seepsanaerobic methane oxidationbacteriaarchaeametagenomics

Introduction

Oil-exposed marine microbial consortia are known to be capable of degrading hydrocarbons [1]. Hydrocarbon-degrading microbes have been used successfully in the remediation of oil that contaminated long stretches of shorelines [2,3]; and it was endorsed anew as a promising remediation strategy after the Deepwater Horizon (DWH) blowout [4]. Despite the significant resources that have been spent to study the microbial response to oil spills, most of the research data come from culture-based studies and relatively little is known about the dynamics and microbial processes that occur during the biological degradation of crude oil in uncontrolled and highly complex biological systems [5-8]. Advances in DNA sequencing technologies and computation provide insights into the metabolic blueprint of microbial cells and microbial communities directly from environmental samples. This has facilitated a better understanding of the genes and metabolic processes that underlie the phenotypes of individual cells and complex communities - without depending on axenic microbial cultures [9,10]. The potential of DNA sequencing to improve our understanding of microbial responses to large oil spills, was recognized immediately by the scientific community following the 4 million barrel DWH spill released into the Gulf of Mexico (GoM), resulting in a number of studies that employed metagenomics and metatranscriptomics to map the communities genetic response so as to eventually develop more sustainable remediation strategies [4,11-14]. The GoM has many natural oil seeps, which have primed the microbial community to be ready for larger spills. As the composition of the natural microbial community at a spill site could have a significant role in the bioremediation process following an oil spill [15], and considering that oil spills are not restricted to the GoM, it will be crucial to build an extended knowledgebase of native hydrocarbon degrading microbiomes from different geographical locations. Here we report on the first metagenome exceeding 50 Gb of raw DNA sequence data from a microbial community associated with natural crude oil seeps of the Santa Barbara Channel (SBC), one of the world’s largest natural hydrocarbon seep regions [16], which can be accessed publicly through IMG/M for further analysis by the scientific community.

Classification and features

A metagenome was generated from a hydrocarbon-adapted consortium collected using a remotely operated vehicle from a submarine oil seep located near Coal Oil Point at 34.39192° N, 119.84578° W, 79.4 m below sea level [Table 1]. The collected oil samples were transported immediately to the laboratory and stored at -20°C until DNA extraction was performed. Further details of sampling location and oil geochemistry have been described previously by Lorenson and colleagues [19].

Table 1

Classification and general features of the metagenome data set according to the Minimum Information about Genomes and Metagenomes (MIMS) standards [17].

MIMS ID

        Property

     Term

    Evidence codea

MIM 3

        Study Name

     Marine microbial communities from the Santa Barbara Channel oil seeps

        Sample Name

     Crude oil metagenome 2

        GOLD classification: Ecosystem

     Environmental

    NAS

        GOLD classification: Ecosystem Category

     Aquatic

        GOLD classification: Ecosystem Type

     Marine

        GOLD classification: Ecosystem Subtype

     Oil seeps

        GOLD classification: Specific Ecosystem

     unclassified

MIGS-22

        Carbon source

     Seep oil

    NAS

        Energy source

     Seep oil

    NAS

MIGS-6

        Habitat

     Aquatic, Marine, Oil seeps

    NAS

MIGS-14

        Pathogenicity

     none

    NAS

MIGS-4

        Geographic location

     Marine ecosystem, California, USA

    NAS

MIGS-5

        Sample collection time

     June, 2009

    NAS

MIGS-4.1

        Latitude

     34.39192

    NAS

MIGS-4.2

        Longitude

     −119.84578

    NAS

MIGS-4.3

        Depth

     79.4 m

    NAS

aEvidence codes - NAS: Non-traceable Author Statement (i.e. not directly observed for the living, isolated sample, but based on a generally accepted property for the species, or anecdotal evidence). These evidence codes are from the Gene Ontology project [18].

Metagenome sequencing information

Metagenome project history

This is the first metagenome associated with natural crude oils that seep into the SBC. The site was selected based on its geographical location near active offshore drilling and the distinct geochemical composition of SBC seep oils compared to those from the GoM. Sequence analysis of small subunit ribosomal RNA gene amplicons identified 1,045 taxa based on 97% sequence identity, and a fingerprint that is distinct from the community associated with the oil plume that formed after the DWH accident [20].

Growth conditions and DNA isolation

Environmental DNA (eDNA) was extracted from the seep oil sample using a FastDNA Spin Kit for Soil from MP Biomedicals according to the manufacturer’s protocol with 500mg of the seep oil as starting material. Bead-beating was conducted three times for 20 seconds using a Mini-Beadbeater-16 (Biospec Products, Bartlesville, OK, USA). Samples were kept on ice for 1 min between each round of bead-beating. Extracted eDNA was resuspended in a total of 100µL DNase/Pyrogen-Free H20. Concentration of obtained eDNA was measured using a Qubit 2.0 Fluorometer (Life Technologies, Grand Island, NY) according to the manufacturer's protocol. The quantity and quality of the extraction were checked by gel electrophoresis using standards for standard operational procedures of the Joint Genome Institute (JGI).

Metagenome sequencing and assembly

A total of 51.7 Gbp were generated from the oil-associated microbiome. Starting material (200ng of DNA) was sheared to 270 bp using the Covaris E210 (Covaris) and size selected using SPRI beads (Beckman Coulter). The fragments were treated with end-repair, A-tailing, and ligation of Illumina compatible adapters (IDT, Inc) using the KAPA-Illumina library creation kit (KAPA Biosystems). The prepared sample libraries were quantified by qPCR using KAPA Biosystem’s next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified sample libraries were then prepared for sequencing on the Illumina HiSeq2000 sequencing platform, utilizing a TruSeq paired-end cluster kit, v3, and Illumina’s cBot instrument to generate clustered flowcells for sequencing. Sequencing of the flowcells was performed on the Illumina HiSeq2000 platform using a TruSeq SBS sequencing kit 200 cycles, v3, following a 2x150 indexed run recipe. Raw metagenomic reads were trimmed using a minimum quality score cutoff of 10. Trimmed, paired-end Illumina reads were assembled using SOAPdenovo v1.05 [21] with a range of Kmers (81,85,89,93,97,101). Default settings for all SOAPdenovo assemblies were used (flags: –d 1 and –R). Contigs generated by each assembly (6 total contig sets) were sorted into two pools based on length. Contigs smaller than 1,800 bp were assembled using Newbler (Life Technologies, Carlsbad, CA) in an attempt to generate larger contigs (flags: -tr, -rip, -mi 98, -ml 80). All assembled contigs larger than 1,800 bp, as well as the contigs generated from the final Newbler run, were combined using minimus 2 (flags: -D MINID=98 -D OVERLAP=80) [AMOS [22]] Read depth estimations were based on mapping of the trimmed, screened, paired-end Illumina reads to assembled contigs with BWA [23]. The un-assembled, paired reads were merged with FLASH [24]. The assembled contigs along with the merged, un-assembled reads were submitted to IMG/M for functional annotation. Sequences are publicly available at IMG/M under the project ID 45292. Table 2 summarizes the project information and its association with MIGS version 2.0 compliance [17].

Table 2

Project information

MIGS ID

      Property

      Term

MIGS-31

      Finishing quality

      Standard Draft

MIGS-28

      Libraries used

      Illumina standard paired-end library (0.27 kb insert size)

MIGS-29

      Sequencing platforms

      Illumina HiSeq2000

MIGS-31.2

      Fold coverage

      NA

MIGS-30

      Assemblers

      SOAPdenovo v1.05, Newbler v2.5, minimus2

MIGS-32

      Gene calling method

      Genemark > Prodigal > Metagene > FragGeneScan

      GOLD ID

      Gm0045292

      GOLD sample ID

      Gs0002474

      IMG Project ID

      45292

      Project relevance

      biodegradation of pollutants, biotechnological

Metagenome annotation

Prior to annotation, all sequences were trimmed to remove low quality regions falling below a minimum quality of Q13, and stretches of undetermined sequences at the ends of contigs were removed. Each sequence was checked with the DUST algorithm [25] from the NCBI toolkit for low complexity regions and sequences with less than 80 unmasked nt were removed. Additionally very similar sequences (similarity > 95%) with identical 5’ pentanucleotides are replaced by one representative, typically the longest, using uclust [26]. The feature prediction pipeline included the detection of non-coding RNA genes (tRNA, and rRNA), followed by prediction of protein coding genes. Identification of tRNAs was performed using tRNAScan-SE-1.23 [27]. In case of conflicting predictions, the best scoring predictions were selected. Since the program cannot detect fragmented tRNAs at the end of the sequences, we also checked the last 150 nt of the sequences by comparing these to a database of nt sequences of tRNAs identified in the isolate genomes using blastn [28]. Hits with high similarity were kept; all other parameters are set to default values. Ribosomal RNA genes (tsu, ssu, lsu) were predicted using the hmmsearch [29] with internally developed models for the three types of RNAs for the domains of life.

Identification of protein-coding genes was performed using four different gene calling tools, GeneMark (v.2.6r) [30], Metagene (v. Aug08) [31], Prodigal (v2.50) [32] and FragGeneScan [33] all of which are ab initio gene prediction programs. We typically followed a majority rule based decision scheme to select the gene calls. When there was a tie, we selected genes based on an order of gene callers determined by runs on simulated metagenomic datasets (Genemark > Prodigal > Metagene > FragGeneScan). At the last step, CDS and other feature predictions were consolidated. The regions identified previously as RNA genes were preferred over protein-coding genes. Functional prediction followed and involved comparison of predicted protein sequences to the public IMG database (db) using the usearch algorithm [26], the COG db using the NCBI developed PSSMs [34], and the pfam db [35] using hmmsearch. Assignment to KEGG Ortholog protein families was performed using the algorithm described in [36].

Metagenome properties

The metagenome presented here contains 333,405,037 high-quality reads, totaling 50,010,755,550 bp. 38.80% of the reads were assembled into a total of 803,203 scaffolds, representing 495,862,225 bp, with 91,522 scaffolds ≥1 kb, 1,354 scaffolds ≥10 kb, 103 scaffolds ≥25 kb, 6 scaffolds ≥50 kb and 1 scaffold ≥250 kb. The GC content of the assembled metagenome was 44.95%, which is slightly higher compared to the 40.95% observed for the assembled metagenome from the oil plume (IMG ID 1892) that formed in the GoM after the DWH blowout in 2010 [14].

The assembled sequences included 1,143,552 predicted genes with 99.32% annotated as protein-coding genes. A total of 770,455 of the protein coding genes, corresponding to 67.37% of the total predicted protein-coding genes, were assigned to a putative family or function based on the presence of conserved Pfam domains with the remaining genes annotated as hypothetical proteins. The properties and the statistics of the metagenome are summarized in Table 3.

Table 3

Nucleotide content and gene count levels of the assembled SBC oil seep metagenome

Attribute

    Value

     % of Total

Total base pairs sequenced (Gb)

    51.7

     %100

Total number of sequences (scaffolds)

    803,203

     38.80%

DNA, total number of bases

    495,862,225

     0.99%

DNA G+C number of bases

    222,883,192

     44.95%*

Genes

RNA genes

    7,742

     0.68%

rRNA genes

    1,827

     0.16%

5S rRNA

    420

     0.04%

16S rRNA

    520

     0.05%

18S rRNA

    12

     0.00%

23S rRNA

    866

     0.08%

28S rRNA

    9

     0.00%

tRNA genes

    5,915

     0.52%

Protein coding genes

    1,135,810

     99.32%

with Product Name

    617,327

     53.98%

with COG

    620,853

     54.29%

with Pfam

    770,455

     67.37%

with KO

    461,840

     40.39%

with Enzyme

    265,509

     23.22%

with MetaCyc

    182,179

     15.93%

with KEGG

    266,160

     23.27%

COG Clusters

    4724

     96.94%

Pfam Clusters

    14,501

     97.77%

* GC percentage shown as count of G's and C's divided by a total number of G's, C's, A's, and T's. This is not necessarily synonymous with the total number of bases.

From the 1,135,810 genes predicted to encode proteins, 620,853 (54.66%) were assigned to one of the 25 general COG categories [Table 4]. Within genes for which a function could be assigned, most genes were assigned to COG categories (E) and (C), which are associated with amino acid transport and energy production and conversion respectively.

Table 4

Percentage of genes associated with the 25 general COG functional categories in two assembled metagenomes from hydrocarbon-enriched environments

Code

      %age

     Description

J

      5.71

     Translation, ribosomal structure and biogenesis

A

      0.06

     RNA processing and modification

K

      5.41

     Transcription

L

      6.3

     Replication, recombination and repair

B

      0.08

     Chromatin structure and dynamics

D

      1.1

     Cell cycle control, cell division, chromosome partitioning

Y

      <0.01

     Nuclear structure

V

      2.13

     Defense mechanisms

T

      5.54

     Signal transduction mechanisms

M

      6.28

     Cell wall/membrane/envelope biogenesis

N

      1.31

     Cell motility

Z

      0.02

     Cytoskeleton

W

      <0.01

     Extracellular structures

U

      2.34

     Intracellular trafficking, secretion, and vesicular transport

O

      4.12

     Posttranslational modification, protein turnover, chaperones

C

      8.16

     Energy production and conversion

G

      5.16

     Carbohydrate transport and metabolism

E

      8.82

     Amino acid transport and metabolism

F

      2.66

     Nucleotide transport and metabolism

H

      4.2

     Coenzyme transport and metabolism

I

      3.6

     Lipid transport and metabolism

P

      5.05

     Inorganic ion transport and metabolism

Q

      1.88

     Secondary metabolites biosynthesis, transport and catabolism

R

      12.12

     General function prediction only

S

      7.95

     Function unknown

Taxonomic gene diversity

The taxonomic diversity and phylogenetic structure of the oil metagenome were determined based on the assembled genes, classifying at a minimum 60% identity to members of the listed phyla. The phylogeny reported is the one used in IMG/M [37], which uses the phylogeny described as part of the Genomic Encyclopedia of Bacteria and Archaea (GEBA) project [38].

After removing sequences that could not be assigned phylogenetically, the assembled SBC oil seep metagenome was dominated by prokaryotic genes, with the Proteobacteria, Firmicutes, Bacteroidetes and Chloroflexi recruiting 12.9%, 6.5%, 2.3% and 2%, respectively, of the 1,135,810 protein encoding sequences with a phylogenetic classification. With 6,380 sequences, the archaeal phylum Euryarchaeota, recruited the fifth most sequences, suggesting that this phylum contributes to a large fraction of the microbial sequence data generated from the SBC seep oil. From the genes assigned to the Proteobacteria, genes assigned to Deltaproteobacteria, Epsilonproteobacteria, and Gammaproteobacteria were approximately equally frequent in the metagenome, recruiting about 15.8%, 15.2% and 12.4%, respectively, of the 294,783 genes classified as being of bacterial origin. Within the Deltaproteobacteria, 54% of the genes categorized at the family level were assigned to strains belonging to the sulfur-reducing Desulfobacteraceae (contributing 49%) and Desulfobulbaceae (contributing 15%) – bacterial families frequently found associated with hydrocarbon-rich sediments [39-42]. From the genes assigned to the Epsilonproteobacteria, only ~14% could be assigned at the family level within the Helicobacteraceae and Campylobacteraceae, phylogenetic groups that contain several well-characterized sulfur-oxidizers isolated from marine sediments and underground crude oil storage facilities [43-47], recruiting 68% and 32% of the genes, respectively. The Gammaproteobacteria was the most diverse class with the mostly anaerobic or micro-aerobic representatives from the Chromatiaceae, Ectothiorhodospiraceae, Methylococcaceae and Thiotrichaceae, recruiting 21%, 11%, 13%, and 12% of the genes that could be assigned at family level. In contrast, the metagenome from the aerobic DWH oil plume was dominated by reads derived the Oceanospirillales (~60%), an order of the Gammaproteobacteria [14]. Within the SBC metagenome only ~2% of the genes assigned at the family level were recruited by Oceanospirillales (i.e. Bermanella marisrubri, Marinomonas mediterranea, Marinomonas posidonica and Neptuniibacter caesariensis), suggesting that the metabolic capacities of these strict aerobes might contribute only little to the functionality of the indigenous microbiome associated with the SBC seep oils. There were very few sequences attributed to Eukaryota, with representatives from the Ascomycota, Streptophyta, Cnidaria, Chlorophyta and Porifera, accounting for <0.1% of the sequences. Plasmid population-associated genes were dominated by those associated with Firmicutes and Proteobacteria, outnumbering double-stranded DNA viruses by about two to one. The taxonomic diversity of the genes assembled from the consortium associated with SBC seep oil is summarized in Table 5. A more detailed analysis of the functional gene diversity of the SBC metagenome can be performed readily through IMG/M.

Table 5

Overview of taxonomic gene diversity in the assembled SBC oil seep metagenome.

Domain

      Phylum

      % Hits

Archaea

      Euryarchaeota

      0.56

      Crenarchaeota

      0.01

      Thaumarchaeota

      0.01

Bacteria

      Proteobacteria

      12.88

      Firmicutes

      6.48

      Bacteroidetes

      2.33

      Chloroflexi

      2.01

      Actinobacteria

      0.48

      Cyanobacteria

      0.34

      Ignavibacteria

      0.30

      unclassified

      0.20

      Acidobacteria

      0.13

      Verrucomicrobia

      0.12

      Planctomycetes

      0.10

      Deinococcus-Thermus

      0.10

      Chlorobi

      0.09

      Spirochaetes

      0.08

      Synergistetes

      0.04

      Thermotogae

      0.04

      Deferribacteres

      0.04

      Aquificae

      0.04

      Nitrospirae

      0.03

      Fusobacteria

      0.03

      Thermodesulfobacteria

      0.02

      Poribacteria

      0.02

      Lentisphaerae

      0.01

      Dictyoglomi

      0.01

      Gemmatimonadetes

      0.01

      Tenericutes

      0.01

      Chlamydiae

      0.01

Eukarya

      Ascomycota

      0.01

      Streptophyta

      0.01

      Cnidaria

      0.01

      Chlorophyta

      0.01

      Porifera

      00.1

      unclassified

      0.01

Unassigned

      73.38

Although gene counts of representative phyla and classes suggest phylogenetic differences, it can be assumed that the results are biased towards groups whose genomes and marker genes (e.g. 16S and 18S rRNA genes) are overrepresented in genomic reference databases. While the relative abundances of between-phyla comparisons may be questionable based on differential representation in the database, the relative abundances of taxa within a phylum is reflective of the distinct metabolic conditions within an analyzed metagenome [11].

Functional genes related to methane metabolism

Natural hydrocarbon seeps represent a habitat for microbial communities that might provide the molecular tool kit for sustainable strategies to reduce the negative impact of oil spills. They also are a persistent source of methane (CH4) [16], a greenhouse gas whose climate warming potential is 25 times greater than that of CO2 [48]. Biological CH4 oxidation in the marine ecosystem has been well documented and identified as a CH4 sink of global significance [49-51]. Anaerobic oxidation of methane (AOM), mediated by microbiomes associated with ocean sediments and deposits, has been proposed as the dominant biological process responsible for the removal of >300 Tg CH4 per year from the ocean [52,53]. Despite strong research efforts aimed at understanding AOM and its regulation, it remains poorly understood. Until recently, AOM in marine environments was thought to be mediated by consortia of anaerobic methanotrophic archaea (ANMEs) and sulfate reducing bacteria [54,55] or alternatively by microbial consortia that couple methane oxidation to the reduction of reactive metals [56]. It was not until 2010 that the first microorganism, Candidatus Methoxymirabilis oxyfera, capable of performing methane oxidation (coupled to nitrite reduction) in the absence of a metabolic partner was reported [57], followed by a second organism capable of performing single-organism AOM coupled sulfate reduction [58]. To explore if the indigenous microbial community in the SBC might have the genomic capacity to perform AOM and function as an efficient biofilter when large amounts of methane are released from the ocean subsurface, we generated a profile for genes involved in methane oxidation and methane generation. Pathway analysis based on the KEGG pathways map and the classification systems of the KEGG pathways database, was performed using the “Function Profile” tool implemented in IMG/M. Table 6 summarizes the results of the performed gene profile analysis. Key genes for AOM (and methanogenesis), including genes for the oxygen sensitive formylmethanofuran dehydrogenases (fmd; KEGG Orthology IDs K00200, K00201, K00202, K00203, K00205, K11261) and methyl coenzyme M reductases (mcr; KEGG Orthology IDs K00399, K00401, K00402) that catalyze the initial and terminal step of methane production, were identified within the metagenome (Table 6). The presence of the key enzymes for AOM would certainly facilitate reversed methanogenesis in an environment that is rich in non-biotic methane by members of the anaerobic methanotrophic Archaea (ANME) – as proposed previously by several groups [59,60]. ANME-mediated AOM would explain the dominance of genes from the Methanomicrobiales (containing ANME-1) and Methanosarcinaceae (containing ANME-2 and ANME-3) [61] within the archaeal genes of the SBC seep oil metagenome (totaling ~56% of the archaeal genes). Active aerobic methane oxidation is restricted to a thin surface layer of seep sediments due to a limited oxygen penetration of less than 2 cm [62]; genes encoding methane monooxygenase (pmo; KEGG Orthology IDs K10944, K10945, K10946), a key enzyme of the aerobic methane oxidation process, were identified within the SBC seep oil metagenome (Table 6), suggesting the potential for aerobic methane oxidation. This finding correlates with the fact that members of the Methylococcaceae, a group of microorganisms well known for the ability to perform aerobic methane oxidation, comprised ~0.31% of protein coding genes of the SBC seep oil metagenome. This is not the first time that simultaneous evidence of anaerobic and aerobic pathways for methane oxidation in SBC sediments has been reported based on metagenomic data. In 2011, Havelsrud [63] identified the complete suite of key enzymes for AOM in a metagenome from deep sediments (10 - 15 cm) offshore Coal Oil Point in the SBC, whereas sequencing of the shallower sediments (0 - 4 cm) failed to detect two of the key enzymes (methenyl-tetrahydromethanopterin cyclohydrolase and methylenetetrahydromethanopterin dehydrogenase) of AOM. Genes annotated as methane monooxygenase were identified within the shallow sediment metagenome [63], suggesting the possibility that the upper sediment layers of SBC sediments contain pockets of aerobic and anaerobic microhabitats.

Table 6

Counts of genes associated with methane metabolism in SBC seep oil metagenome

KEGG Orthology ID

       Description

      Gene count

K00192

       Acetyl-CoA pathway

      21

K00195

       Acetyl-CoA pathway

      6

K00440

       Coenzyme F420 hydrogenase

      1

K00441

       Coenzyme F420 hydrogenase

      62

K00443

       Coenzyme F420 hydrogenase

      3

K05884

       Coenzyme M biosynthesis

      11

K05979

       Coenzyme M biosynthesis

      20

K06034

       Coenzyme M biosynthesis

      2

K08097

       Coenzyme M biosynthesis

      13

K13039

       Coenzyme M biosynthesis

      5

K11212

       F420 biosynthesis

      63

K11780

       F420 biosynthesis

      7

K11781

       F420 biosynthesis

      6

K12234

       F420 biosynthesis

      66

K14941

       F420 biosynthesis

      40

K00018

       Formaldehyde assimilation

      77

K00024

       Formaldehyde assimilation

      277

K00600

       Formaldehyde assimilation

      463

K00830

       Formaldehyde assimilation

      116

K00850

       Formaldehyde assimilation

      558

K00863

       Formaldehyde assimilation

      2

K01595

       Formaldehyde assimilation

      133

K01624

       Formaldehyde assimilation

      276

K01689

       Formaldehyde assimilation

      380

K03841

       Formaldehyde assimilation

      122

K08093

       Formaldehyde assimilation

      20

K08094

       Formaldehyde assimilation

      32

K08691

       Formaldehyde assimilation

      35

K08692

       Formaldehyde assimilation

      13

K11529

       Formaldehyde assimilation

      6

K13812

       Formaldehyde assimilation

      14

K13831

       Formaldehyde assimilation

      26

K14067

       Formaldehyde assimilation

      14

K16370

       Formaldehyde assimilation

      10

K16158

       Methane oxidation

      2

K10944

       Methane oxidation; Nitrification

      3

K10945

       Methane oxidation; Nitrification

      3

K10946

       Methane oxidation; Nitrification

      19

K00200

       Methanogenesis

      20

K00201

       Methanogenesis

      27

K00202

       Methanogenesis

      26

K00203

       Methanogenesis

      8

K00204

       Methanogenesis

      0

K00205

       Methanogenesis

      10

K00319

       Methanogenesis

      5

K00320

       Methanogenesis

      111

K00399

       Methanogenesis

      10

K00401

       Methanogenesis

      7

K00402

       Methanogenesis

      3

K00577

       Methanogenesis

      12

K00578

       Methanogenesis

      3

K00579

       Methanogenesis

      7

K00580

       Methanogenesis

      7

K00581

       Methanogenesis

      9

K00582

       Methanogenesis

      2

K00583

       Methanogenesis

      5

K00584

       Methanogenesis

      18

K00625

       Methanogenesis

      77

K00672

       Methanogenesis

      14

K00925

       Methanogenesis

      144

K01499

       Methanogenesis

      21

K01895

       Methanogenesis

      671

K03388

       Methanogenesis

      1620

K03389

       Methanogenesis

      234

K03390

       Methanogenesis

      137

K04480

       Methanogenesis

      1

K11260

       Methanogenesis

      6

K11261

       Methanogenesis

      67

K13788

       Methanogenesis

      88

K14080

       Methanogenesis

      3

K14081

       Methanogenesis

      1

K14082

       Methanogenesis

      10

K14083

       Methanogenesis

      638

K14084

       Methanogenesis

      56

K16176

       Methanogenesis

      50

K16177

       Methanogenesis

      3

K16178

       Methanogenesis

      9

K16179

       Methanogenesis

      9

K00193

       Methanogenesis; Acetyl-CoA pathway

      16

K00194

       Methanogenesis; Acetyl-CoA pathway

      84

K00197

       Methanogenesis; Acetyl-CoA pathway

      149

To investigate the presence of genomic material from sulfur-reducing bacteria (SRB) – microbes mediating reverse methanogenesis – we analyzed the metagenomes for genes encoding dissimilatory sulfite reductase (dsr; KEGG Orthology IDs K11180, K11181). We identified a total of 204 reads annotated as dsr within the SBC seep oil metagenome (data not shown), suggesting that AOM via reverse methanogenesis – a process mediated primarily by consortia of archaeal methane oxidizers and bacterial sulfur reducers – may occur during the microbially mediated biofiltration of CH4 in the hydrocarbon rich sediments. The proposed CH4 biofiltration process under anaerobic conditions within the SBC sediments is summarized in Figure 1. Analysis of the metagenome data from the SBC revealed a total of 2,373 genes covering the complete suite of enzymes necessary for anaerobic methane oxidation/methanogenesis outlined in Figure 1. In contrast, the DWH oil plume metagenome (accessible through IMG/M) contained only a total of 9 genes (i.e. fwd, hdr and mer) that were assigned to this pathways that has been reported as a characteristic feature for microbiomes associated with anaerobic habitats rich in hydrocarbons [42,64,65].

Figure 1

Anaerobic methane oxidation/methanogenesis in sediments of the Santa Barbara Channel. Proposed pathway based on the genes involved in AOM and methanogenesis identified in the metagenome from Santa Barbara Channel seep oil.

Conclusion

Sequencing of eDNA extracted from crude oil that was collected from an active hydrocarbon seep in the Santa Barbara Channel (SBC) and subsequent taxonomic profiling of the protein coding genes suggests that the microbial processes associated with this particular microbiome are dominated by members of the Proteobacteria, Firmicutes, Bacteroidetes, Chloroflexi and Euryarchaeota. Members of the Oceanospirillales, a bacterial order that recruited more than 60% of the genes from the DWH oil plume metagenome [14], recruited only a small fraction (<2%) of the genes from the SBC metagenome, which suggests that Oceanospirillales might play a less significant role in the microbially mediated hydrocarbon conversion within the SBC seep oil compared to the DWH plume oil, which had an average oxygen saturation of 59% [4]. Oxygen depletion in SBC sediment has been reported previously [62] and we hypothesize that the distinct taxonomic fingerprint of the genes assembled from the SBC seep oil and DWH oil plume metagenome data is caused in part by the different concentrations of oxygen within these oils. This hypothesis is supported by recent findings by Kimes et al [66] that showed that Oceanospirillales contributed only a small fraction to the overall microbiome associated with cores collected from low oxygen sediments in the GoM. The hypothesis that the SBC seep oil contains low concentrations of oxygen and thus facilitates anaerobic processes is supported by the results from our functional gene analysis of the SBC seep oil metagenome, which revealed the presence of the genes essential for anaerobic methane oxidation, and the findings that members of the anaerobic methanotrophic archaea comprise the majority of the archaeal genes within the SBC seep oil metagenome. Taking these findings into consideration, it appears plausible that the taxonomic and functional make-up of the metagenome associated with the SBC seep oil and the DWH plume oil depends rather on the oxygen saturation of the oil then its geographical origin and that the metabolic capability of the associated microbiome might be dynamic. However, further studies are necessary to obtain a better understanding of the biological processes that are associated with these hydrocarbons and their microbially mediated degradation process.

The metagenome from natural oil that seeps into the SBC and the metagenome associated with the oil plume that formed in the aftermath of the DWH blowout are publicly accessible for further analysis at IMG/M. This provides a unique opportunity to study the metabolic profile of a hydrocarbon degrading community from the SBC and to infer the metabolic differences between microbial communities associated with natural hydrocarbons that enter the marine ecosystem at different geographical locations.

Declarations

Acknowledgements

MHess, ERH, HP and the work performed in the laboratory of MHess were funded by Washington State University. The work conducted by the U.S. Department of Energy Joint Genome Institute was supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work conducted by NMS and JAG was supported by the U.S. Deptartment of Energy under Contract DE-AC02-06CH11357. We are extremely thankful to our colleagues who provided letters of support for our CSP proposal. Additional thanks go to staff members of the Chemical and Biological Process Development Group – in particular David Culley, Jon Magnuson, Kenneth Bruno, Jim Collett, and Scott Baker – and the Microbial Community Initiative – in particular Allan Konopka, Jim Fredrickson and Steve Lindeman – at PNNL for scientific discussions throughout the project. Conception and design of the experiments: MHess, TDL, JAG, JJ; Performance of the experiments: MHess, ERH, TDL; Generation and processing of data: MHess, ERH, HP, SM, TGR, ST, BF, AC, IP, MHuntemann; Analysis of the data: MH, ERH, SM, AC, AP; Drafting of this article: MHess, NMS, TDL, JAG, ST, JJ


This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

  1. Vila J, Maria Nieto J, Mertens J, Springael D and Grifoll M. Microbial community structure of a heavy fuel oil-degrading marine consortium: linking microbial dynamics with polycyclic aromatic hydrocarbon utilization. FEMS Microbiol Ecol. 2010; 73:349-362PubMed
  2. Lindstrom JE, Prince RC, Clark JC, Grossman MJ, Yeager TR, Braddock JF and Brown EJ. Microbial populations and hydrocarbon biodegradation potentials in fertilized shoreline sediments affected by the T/V Exxon Valdez oil spill. Appl Environ Microbiol. 1991; 57:2514-2522PubMed
  3. Bragg JR, Prince RC, Harner EJ and Atlas RM. Effectiveness of bioremediation for the Exxon Valdez oil spill. Nature. 1994; 368:413-418 View Article
  4. Hazen TC, Dubinsky EA, DeSantis TZ, Andersen GL, Piceno YM, Singh N, Jansson JK, Probst A, Borglin SE and Fortney JL. Deep-sea oil plume enriches indigenous oil-degrading bacteria. Science. 2010; 330:204-208 View ArticlePubMed
  5. Mascarelli A. The mystery of the missing oil plume. Nature. 2010; 467:16 View ArticlePubMed
  6. Head IM, Jones DM and Roling WF. Marine microorganisms make a meal of oil. Nat Rev Microbiol. 2006; 4:173-182 View ArticlePubMed
  7. Redmond MC and Valentine DL. Natural gas and temperature structured a microbial community response to the Deepwater Horizon oil spill. Proc Natl Acad Sci USA. 2011; 109:20292-20297 View ArticlePubMed
  8. Alonso-Gutiérrez J, Figueras A, Albaiges J, Jimenez N, Vinas M, Solanas AM and Novoa B. Bacterial communities from shoreline environments (costa da morte, northwestern Spain) affected by the prestige oil spill. Appl Environ Microbiol. 2009; 75:3407-3418 View ArticlePubMed
  9. Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, Darling A, Malfatti S, Swan BK and Gies EA. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013; 499:431-437 View ArticlePubMed
  10. Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, Luo S, Clark DS, Chen F and Zhang T. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science. 2011; 331:463-467 View ArticlePubMed
  11. Kimes NE, Callaghan AV, Aktas DF, Smith WL, Sunner J, Golding B, Drozdowska M, Hazen TC, Suflita JM and Morris PJ. Metagenomic analysis and metabolite profiling of deep-sea sediments from the Gulf of Mexico following the Deepwater Horizon oil spill. Frontiers in Microbiology. 2013; 4:50 View ArticlePubMed
  12. Liu Z and Liu J. Evaluating bacterial community structures in oil collected from the sea surface and sediment in the northern Gulf of Mexico after the Deepwater Horizon oil spill. MicrobiologyOpen. 2013; 2:492-504 View ArticlePubMed
  13. Beazley MJ, Martinez RJ, Rajan S, Powell J, Piceno YM, Tom LM, Andersen GL, Hazen TC, Van Nostrand JD and Zhou J. Microbial community analysis of a coastal salt marsh affected by the Deepwater Horizon oil spill. PLoS ONE. 2012; 7:e41305 View ArticlePubMed
  14. Mason OU, Hazen TC, Borglin S, Chain PS, Dubinsky EA, Fortney JL, Han J, Holman HY, Hultman J and Lamendella R. Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J. 2012; 6:1715-1727 View ArticlePubMed
  15. Lu Z, Deng Y, Van Nostrand JD, He Z, Voordeckers J, Zhou A, Lee YJ, Mason OU, Dubinsky EA and Chavarria KL. Microbial gene functions enriched in the Deepwater Horizon deep-sea oil plume. ISME J. 2012; 6:451-460 View ArticlePubMed
  16. Hornafius JS, Quigley D and Luyendyk BP. The world's most spectacular marine hydrocarbon seeps (Coal Oil Point, Santa Barbara Channel, California): Quantification of emissions. Journal of Geophysical Research: Oceans. 1999; 104(C9):20703-20711 View Article
  17. Field D, Garrity G, Gray T, Morrison N, Selengut J, Sterk P, Tatusova T, Thomson N, Allen MJ and Angiuoli SV. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol. 2008; 26:541-547 View ArticlePubMed
  18. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS and Eppig JT. Gene Ontology: tool for the unification of biology. Nat Genet. 2000; 25:25-29 View ArticlePubMed
  19. Lorenson T, Leifer I, Wong F, Rosenbauer R, Campbell P, Hostetter L, Angela, Frances H, Greinert J, Finlayson D, Bradley E and others. Biomarker Chemistry and Flux Quantification Methods for Natural Petroleum Seeps and Produced Oils, Offshore Southern California. Geological Survey Scientific Investigations Report 2011:1-56.
  20. Hawley ER, Lorenson TD, Hess M. Analysis of microbial communities associated with natural oils that seep into the Santa Barbara Channel: Linking community dynamics with biological hydrocarbon degradation. 8th Annual DOE Joint Genome Institue User Meeting 2013.
  21. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, He G, Chen Y, Pan Q and Liu Y. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience. 2012; 1:18 View ArticlePubMed
  22. . Web Site
  23. Aligner BW. (BWA). Web Site
  24. Fast Length Adjustment of SHort reads (FLASH). Web Site
  25. Morgulis A, Gertz EM, Schaffer AA, Agarwala R. A fast and symmetric DUST implementation to mask low-complexity DNA sequences. Journal of Computational Biology: a journal of computational molecular cell biology 2006;13(5):1028-40.
  26. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010; 26:2460-2461 View ArticlePubMed
  27. Lowe TM and Eddy SR. tRNAscan-SE: A program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997; 25:955-964 View ArticlePubMed
  28. Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, Miller W and Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997; 25:3389-3402 View ArticlePubMed
  29. Eddy SR. Accelerated Profile HMM Searches. PLOS Comput Biol. 2011; 7:e1002195 View ArticlePubMed
  30. Besemer J and Borodovsky M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 2005; 33:W451-W454 View ArticlePubMed
  31. Noguchi H, Park J and Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 2006; 34:5623-5630 View ArticlePubMed
  32. Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW and Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010; 11:119 View ArticlePubMed
  33. Rho MN, Tang HX and Ye YZ. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010; 38:e191 View ArticlePubMed
  34. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, Krylov DM, Mazumder R, Mekhedov SL and Nikolskaya AN. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 2003; 4:41 View ArticlePubMed
  35. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G and Clements J. The Pfam protein families database. Nucleic Acids Res. 2012; 40(D1):D290-D301 View ArticlePubMed
  36. Mao XZ, Cai T, Olyarchuk JG and Wei LP. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005; 21:3787-3793 View ArticlePubMed
  37. Markowitz VM, Chen IM, Chu K, Szeto E, Palaniappan K, Grechkin Y, Ratner A, Jacob B, Pati A and Huntemann M. IMG/M: the integrated metagenome data management and comparative analysis system. Nucleic Acids Res. 2012; 40:D123-D129 View ArticlePubMed
  38. Wu D, Hugenholtz P, Mavromatis K, Pukall R, Dalin E, Ivanova NN, Kunin V, Goodwin L, Wu M and Tindall BJ. A phylogeny-driven Genomic Encyclopaedia of Bacteria and Archaea. Nature. 2009; 462:1056-1060 View ArticlePubMed
  39. Acosta-González A, Rosselló-Móra R and Marqués S. Characterization of the anaerobic microbial community in oil-polluted subtidal sediments: aromatic biodegradation potential after the Prestige oil spill. Environ Microbiol. 2013; 15:77-92 View ArticlePubMed
  40. Cravo-Laureau C, Labat C, Joulian C, Matheron R and Hirschler-Réa A. Desulfatiferula olefinivorans gen. nov., sp. nov., a long-chain n-alkene-degrading, sulfate-reducing bacterium. Int J Syst Evol Microbiol. 2007; 57:2699-2702 View ArticlePubMed
  41. Winderl C, Anneser B, Griebler C, Meckenstock RU and Lueders T. Depth-resolved quantification of anaerobic toluene degraders and aquifer microbial community patterns in distinct redox zones of a tar oil contaminant plume. Appl Environ Microbiol. 2008; 74:792-801 View ArticlePubMed
  42. Lloyd KG, Albert DB, Biddle JF, Chanton JP, Pizarro O and Teske A. Spatial structure and activity of sedimentary microbial communities underlying a Beggiatoa spp. mat in a Gulf of Mexico hydrocarbon seep. PLoS ONE. 2010; 5:e8738 View ArticlePubMed
  43. Sievert SM, Scott KM, Klotz MG, Chain PS, Hauser LJ, Hemp J, Hügler M, Land M, Lapidus A and Larimer FW. Genome of the epsilonproteobacterial chemolithoautotroph Sulfurimonas denitrificans. Appl Environ Microbiol. 2008; 74:1145-1156 View ArticlePubMed
  44. Sikorski J, Munk C, Lapidus A, Ngatchou Djao OD, Lucas S, Glavina Del Rio T, Nolan M, Tice H, Han C and Cheng JF. Complete genome sequence of Sulfurimonas autotrophica type strain (OK10). Stand Genomic Sci. 2010; 3:194-202PubMed
  45. Kodama Y and Watanabe K. Sulfuricurvum kujiense gen. nov., sp. nov., a facultatively anaerobic, chemolithoautotrophic, sulfur-oxidizing bacterium isolated from an underground crude-oil storage cavity. Int J Syst Evol Microbiol. 2004; 54:2297-2300 View ArticlePubMed
  46. Wirsen CO, Sievert SM, Cavanaugh CM, Molyneaux SJ, Ahmad A, Taylor LT, DeLong EF and Taylor CD. Characterization of an autotrophic sulfide-oxidizing marine Arcobacter sp. that produces filamentous sulfur. Appl Environ Microbiol. 2002; 68:316-325 View ArticlePubMed
  47. Kodama Y. Ha le T, Watanabe K. Sulfurospirillum cavolei sp. nov., a facultatively anaerobic sulfur-reducing bacterium isolated from an underground crude oil storage cavity. Int J Syst Evol Microbiol. 2007; 57:827-831 View ArticlePubMed
  48. IPCC. Climate Change 2007 - The Physical Science Basis. 2007. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.
  49. Kessler JD, Valentine DL, Redmond MC, Du M, Chan EW, Mendes SD, Quiroz EW, Villanueva CJ, Shusta SS and Werra LM. A persistent oxygen anomaly reveals the fate of spilled methane in the deep. Gulf Mex Sci. 2011; 331:312-315PubMed
  50. Reeburgh WS. Oceanic methane biogeochemistry. Chem Rev. 2007; 107:486-513 View ArticlePubMed
  51. Siegert M, Kruger M, Teichert B, Wiedicke M and Schippers A. Anaerobic Oxidation of Methane at a Marine Methane Seep in a Forearc Sediment Basin off Sumatra, Indian Ocean. Frontiers in Microbiology. 2011; 2:249 View ArticlePubMed
  52. Wankel SD, Adams MM, Johnston DT, Hansel CM, Joye SB and Girguis PR. Anaerobic methane oxidation in metalliferous hydrothermal sediments: influence on carbon flux and decoupling from sulfate reduction. Environ Microbiol. 2012; 14:2726-2740 View ArticlePubMed
  53. Callaghan AV. Enzymes involved in the anaerobic oxidation of n-alkanes: from methane to long-chain paraffins. Frontiers in Microbiology. 2013; 4:89 View ArticlePubMed
  54. Hinrichs KU, Hayes JM, Sylva SP, Brewer PG and DeLong EF. Methane-consuming archaebacteria in marine sediments. Nature. 1999; 398:802-805 View ArticlePubMed
  55. Boetius A, Ravenschlag K, Schubert CJ, Rickert D, Widdel F, Gieseke A, Amann R, Jorgensen BB, Witte U and Pfannkuche O. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature. 2000; 407:623-626 View ArticlePubMed
  56. Beal EJ, House CH and Orphan VJ. Manganese- and iron-dependent marine methane oxidation. Science. 2009; 325:184-187 View ArticlePubMed
  57. Ettwig KF, Butler MK, Le Paslier D, Pelletier E, Mangenot S, Kuypers MM, Schreiber F, Dutilh BE, Zedelius J and de Beer D. Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature. 2010; 464:543-548 View ArticlePubMed
  58. Milucka J, Ferdelman TG, Polerecky L, Franzke D, Wegener G, Schmid M, Lieberwirth I, Wagner M, Widdel F and Kuypers MMM. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature. 2012; 491:541-546 View ArticlePubMed
  59. Stokke R, Roalkvam I, Lanzen A, Haflidason H and Steen IH. Integrated metagenomic and metaproteomic analyses of an ANME-1-dominated community in marine cold seep sediments. Environ Microbiol. 2012; 14:1333-1346 View ArticlePubMed
  60. Hallam SJ, Putnam N, Preston CM, Detter JC, Rokhsar D, Richardson PM and DeLong EF. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science. 2004; 305:1457-1462 View ArticlePubMed
  61. Thauer RK. 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. 2011; 14:292-299 View ArticlePubMed
  62. Treude T and Ziebis W. Methane oxidation in permeable sediments at hydrocarbon seeps in the Santa Barbara Channel, California. Biogeosciences. 2010; 7:3095-3108 View Article
  63. Håvelsrud OE, Haverkamp TH, Kristensen T, Jakobsen KS and Rike AG. A metagenomic study of methanotrophic microorganisms in Coal Oil Point seep sediments. BMC Microbiol. 2011; 11:221 View ArticlePubMed
  64. Rubin-Blum M, Antler G, Turchyn AV, Tsadok R, Goodman-Tchernov BN, Shemesh E, Austin JA, Jr., Coleman DF, Makovsky Y, Sivan O, Tchernov D. Hydrocarbon-related microbial processes in the deep sediments of the Eastern Mediterranean Levantine Basin. [Epub ahead of print]. FEMS Microbiol Ecol 2013.
  65. Håvelsrud OE, Haverkamp TH, Kristensen T, Jakobsen KS and Rike AG. A metagenomic study of methanotrophic microorganisms in Coal Oil Point seep sediments. BMC Microbiol. 2011; 11:221 View ArticlePubMed
  66. Kimes NE, Callaghan AV, Aktas DF, Smith WL, Sunner J, Golding B, Drozdowska M, Hazen TC, Suflita JM and Morris PJ. Metagenomic analysis and metabolite profiling of deep-sea sediments from the Gulf of Mexico following the Deepwater Horizon oil spill. Front Microbiol.. 2013; 4:50 View ArticlePubMed