Gene expression in the deep biosphere

Journal name:
Nature
Volume:
499,
Pages:
205–208
Date published:
DOI:
doi:10.1038/nature12230
Received
Accepted
Published online

Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles1, 2, 3, but deep biosphere activities are not well understood1. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159metres below the sea floor, represented by over 1billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles1. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations1 and models4 of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

At a glance

Figures

  1. Biogeochemical and gene-expression profiles of the deep biosphere from Peru Margin sediment, Ocean Drilling Program Site 1229D.
    Figure 1: Biogeochemical and gene-expression profiles of the deep biosphere from Peru Margin sediment, Ocean Drilling Program Site 1229D.

    a, Cell abundance, sulphate concentrations and methane concentrations. Dotted lines indicate the SMTZs. Values were taken from the Ocean Drilling Program Janus Database (http://www-odp.tamu.edu/database/). b, Proportion of cell-division transcripts within the cluster of orthologous genes (COG) class D (cell cycle control/cell division/chromosome partitioning, n = 30.22million reads). See Supplementary Table 3 for a description of cell-division proteins. c, d, The proportion of dsr (c) and nar (d) transcripts relative to total transcripts involved in energy production (COG class C, n = 92.33million reads). See Supplementary Fig. 2 for number of sequences and ORFs used in each comparison, and E-values for hits in the COG database.

  2. Profiles of deep biosphere metabolic activities in Peru Margin sediment.
    Figure 2: Profiles of deep biosphere metabolic activities in Peru Margin sediment.

    The proportion of reads mapping to ORFs assigned to amino acid, lipid and carbohydrate metabolism (eleven most dominant taxa shown). Note the relative abundance of amino acid metabolism (both anabolic and catabolic) relative to lipid and carbohydrate metabolism across all depths. See Supplementary Fig. 2 for the number of sequences and ORFs used in each comparison, and E values for hits in the COG database.

  3. Transcripts involved in cell motility and DNA repair.
    Figure 3: Transcripts involved in cell motility and DNA repair.

    a, The percentage of reads mapping to ORFs coding for proteins involved in different modes of cellular motility. See Supplementary Table 3 for descriptions. b, A correlation of cell-motility transcripts versus sediment porosity (R2 = 0.8, P = 0.01) and 95% prediction interval (red dotted lines). c, The percentage of reads mapping to ORFs involved in DNA repair (only eleven most dominant taxa are shown). See Supplementary Table 3 for descriptions. d, A correlation of DNA-repair transcripts versus sediment depth (R2 = 0.9, P = 0.004) and 95% prediction interval (red dotted lines). See Supplementary Fig. 2 for the number of sequences and ORFs used in each comparison and E values for ORF hits in COG database.

  4. A comparison of gene-expression data to existing metagenomic studies from Ocean Drilling Program Site 1229.
    Figure 4: A comparison of gene-expression data to existing metagenomic studies13, 29 from Ocean Drilling Program Site 1229.

    Functional genes significantly (Kruskal–Wallis test, P<0.0005) overrepresented in the metatranscriptome samples relative to metagenomic data include DNA repair and replication transcripts, RNA polymerase and archaeal ATPase and DNA polymerase transcripts. The dendrogram represents an unweighted pair group method with arithmetic mean (UPGMA) hierarchical clustering analysis (Manhattan distance) of significantly overrepresented mRNA transcripts: note the complete separation of mRNA samples from DNA samples.

Accession codes

Referenced accessions

Sequence Read Archive

References

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Author information

Affiliations

  1. Department of Geology and Geophysics, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA

    • William D. Orsi &
    • Virginia P. Edgcomb
  2. College of Earth, Ocean, and Environment, University of Delaware, Lewes, Delaware 19958, USA

    • Glenn D. Christman &
    • Jennifer F. Biddle

Contributions

W.D.O. performed experiments, analysed data and wrote the paper; W.D.O., J.F.B. and V.P.E. designed experiments and developed ideas. W.D.O. and G.D.C. developed analytical tools. All authors participated in data interpretation and provided editorial comments on the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Data has been deposited in the NCBI Short Read Archive under accession number SRA058813 and in MG RAST (metagenomics.anl.gov) under accession numbers 4515478.3, 4515477.3, 4515476.3, 4510337.3, 4510336.3 and 4510335.3.

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  1. Supplementary Information (7.3 MB)

    This file contains Supplementary Figures 1-9 and Supplementary Tables 1-3.

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