Recycling and metabolic flexibility dictate life in the lower oceanic crust


The lithified lower oceanic crust is one of Earth’s last biological frontiers as it is difficult to access. It is challenging for microbiota that live in marine subsurface sediments or igneous basement to obtain sufficient carbon resources and energy to support growth1,2,3 or to meet basal power requirements4 during periods of resource scarcity. Here we show how limited and unpredictable sources of carbon and energy dictate survival strategies used by low-biomass microbial communities that live 10–750 m below the seafloor at Atlantis Bank, Indian Ocean, where Earth’s lower crust is exposed at the seafloor. Assays of enzyme activities, lipid biomarkers, marker genes and microscopy indicate heterogeneously distributed and viable biomass with ultralow cell densities (fewer than 2,000 cells per cm3). Expression of genes involved in unexpected heterotrophic processes includes those with a role in the degradation of polyaromatic hydrocarbons, use of polyhydroxyalkanoates as carbon-storage molecules and recycling of amino acids to produce compounds that can participate in redox reactions and energy production. Our study provides insights into how microorganisms in the plutonic crust are able to survive within fractures or porous substrates by coupling sources of energy to organic and inorganic carbon resources that are probably delivered through the circulation of subseafloor fluids or seawater.

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Fig. 1: Study site of the exploration of the intrusive oceanic crust by IODP Expedition 360.
Fig. 2: Summary of hole U1473A of IODP Expedition 360.
Fig. 3: Biological signatures at hole U1473A revealed by Raman spectroscopy and membrane lipid analyses.
Fig. 4: Schematic representation of metabolic processes inferred from observed transcripts in core samples from IODP Expedition 360.

Data availability

iTAG data for the 11 samples as well as 13 different negative controls (drilling muds and fluids, seawater and kit controls) are deposited in the NCBI BioProject under accession number PRJNA497074. Results are presented at approximately phylum level; however, taxonomic assignments at finer resolution are available from the corresponding author upon request. Raw reads for transcriptome data have also been deposited in BioProject under accession number PRJNA497074; iTAGs have been deposited in the NCBI SRA under accession numbers SRR8136794SRR8136814 and transcript raw reads can be found in the SRA under accession numbers SRR8141073SRR8141077. Assemblies for curated portions of the data presented are available upon request to the corresponding author. All relevant data are available from the corresponding author or are included with the manuscript as Supplementary Information, and the Source Data for Fig. 2 are available at (file name, 360_U1473A_macroscopic.xlsx). Source Data for Fig. 3 and Extended Data Figs. 1, 2, 6 are provided with the paper.


  1. 1.

    Shah Walter, S. R. et al. Microbial decomposition of marine dissolved organic matter in cool oceanic crust. Nat. Geosci. 11, 334–339 (2018).

  2. 2.

    D’Hondt, S., Rutherford, S. & Spivack, A. J. Metabolic activity of subsurface life in deep-sea sediments. Science 295, 2067–2070 (2002).

  3. 3.

    Jørgensen, B. B. Deep subseafloor microbial cells on physiological standby. Proc. Natl Acad. Sci. USA 108, 18193–18194 (2011).

  4. 4.

    Hoehler, T. M. & Jørgensen, B. B. Microbial life under extreme energy limitation. Nat. Rev. Microbiol. 11, 83–94 (2013).

  5. 5.

    Tully, B. J., Wheat, C. G., Glazer, B. T. & Huber, J. A. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 12, 1–16 (2018).

  6. 6.

    Santelli, C. M., Edgcomb, V. P., Bach, W. & Edwards, K. J. The diversity and abundance of bacteria inhabiting seafloor lavas positively correlate with rock alteration. Environ. Microbiol. 11, 86–98 (2009).

  7. 7.

    Jungbluth, S. P., Bowers, R. M., Lin, H. T., Cowen, J. P. & Rappé, M. S. Novel microbial assemblages inhabiting crustal fluids within mid-ocean ridge flank subsurface basalt. ISME J. 10, 2033–2047 (2016).

  8. 8.

    Shrenk, M. O., Huber, J. A. & Edwards, K. J. Microbial provinces in the subseafloor. Ann. Rev. Mar. Sci. 2, 279–304 (2010).

  9. 9.

    Mason, O. U. et al. First investigation of the microbiology of the deepest layer of ocean crust. PLoS ONE 5, e15399 (2010).

  10. 10.

    Zhang, X., Feng, X. & Wang, F. Diversity and metabolic potentials of subsurface crustal microorganisms from the western flank of the Mid-Atlantic Ridge. Front. Microbiol. 7, 363 (2016).

  11. 11.

    Früh-Green, G. L. et al. Magmatism, serpentinization and life: insights through drilling the Atlantis Massif (IODP Expedition 357). Lithos 323, 137–155 (2018).

  12. 12.

    Lipp, J. S. & Hinrichs, K.-U. Structural diversity and fate of intact polar lipids in marine sediments. Geochim. Cosmochim. Acta 73, 6816–6833 (2009).

  13. 13.

    Valentine, D. L. Adaptations to energy stress dictate the ecology and evolution of the Archaea. Nat. Rev. Microbiol. 5, 316–323 (2007).

  14. 14.

    Summons, R. E. & Lincoln, S. A. in Fundamentals of Geobiology (eds Knoll, A. H.) 269–296 (John Wiley and Sons, 2012).

  15. 15.

    Swan, B. K. et al. Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333, 1296–1300 (2011).

  16. 16.

    Sheik, C. S., Jain, S. & Dick, G. J. Metabolic flexibility of enigmatic SAR324 revealed through metagenomics and metatranscriptomics. Environ. Microbiol. 16, 304–317 (2014).

  17. 17.

    Grossi, V. et al. Mono- and dialkyl glycerol ether lipids in anaerobic bacteria: biosynthetic insights from the mesophilic sulfate reducer Desulfatibacillum alkenivorans PF2803T. Appl. Environ. Microbiol. 81, 3157–3168 (2015).

  18. 18.

    Hawley, A. K. et al. Diverse Marinimicrobia bacteria may mediate coupled biogeochemical cycles along eco-thermodynamic gradients. Nat. Commun. 8, 1507 (2017).

  19. 19.

    Kelley, D. S. et al. A serpentinite-hosted ecosystem: the Lost City hydrothermal field. Science 307, 1428–1434 (2005).

  20. 20.

    Puente-Sánchez, F. et al. Viable cyanobacteria in the deep continental subsurface. Proc. Natl Acad. Sci. USA 115, 10702–10707 (2018).

  21. 21.

    Klein, F., Grozeva, N. G. & Seewald, J. S. Abiotic methane synthesis and serpentinization in olivine-hosted fluid inclusions. Proc. Natl Acad. Sci. USA 116, 17666–17672 (2019).

  22. 22.

    Zolotov, M. & Shock, E. L. Abiotic synthesis of polycyclic aromatic hydrocarbons on Mars. J. Geophys. Res. Planets 104, 14033–14049 (1999).

  23. 23.

    Fonknechten, N. et al. Clostridium sticklandii, a specialist in amino acid degradation:revisiting its metabolism through its genome sequence. BMC Genomics 11, 555 (2010).

  24. 24.

    Cai, L. et al. Comparative genomics study of polyhydroxyalkanoates (PHA) and ectoine relevant genes from Halomonas sp. TD01 revealed extensive horizontal gene transfer events and co-evolutionary relationships. Microb. Cell Fact. 10, 88 (2011).

  25. 25.

    Jendrossek, D. & Handrick, R. Microbial degradation of polyhydroxyalkanoates. Annu. Rev. Microbiol. 56, 403–432 (2002).

  26. 26.

    Liu, G. et al. Enoyl-CoA hydratase mediates polyhydroxyalkanoate mobilization in Haloferax mediterranei. Sci. Rep. 6, 24015 (2016).

  27. 27.

    Han, J. et al. Complete genome sequence of the metabolically versatile halophilic archaeon Haloferax mediterranei, a poly(3-hydroxybutyrate-co-3-hydroxyvalerate) producer. J. Bacteriol. 194, 4463–4464 (2012).

  28. 28.

    Lin, H.-T. et al. Inorganic chemistry, gas compositions and dissolved organic carbon in fluids from sedimented young basaltic crust on the Juan de Fuca Ridge flanks. Geochim. Cosmochim. Acta 85, 213–227 (2012).

  29. 29.

    Santos-Beneit, F. The Pho regulon: a huge regulatory network in bacteria. Front. Microbiol. 6, 402 (2015).

  30. 30.

    Zinke, L. A. et al. Thriving or surviving? Evaluating active microbial guilds in Baltic Sea sediment. Environ. Microbiol. Rep. 9, 528–536 (2017).

  31. 31.

    Dick, H. J. B. et al. The Atlantis Bank gabbro massif, Southwest Indian Ridge. Prog. Earth Planet. Sci. 6, 64 (2019).

  32. 32.

    Fox, P. J. & Gallo, D. G. A tectonic model for ridge-transform-ridge plate boundaries: implications for the structure of oceanic lithosphere. Tectonophysics 104, 205–242 (1984).

  33. 33.

    Dick, H. J. B. et al. Dynamic accretion beneath a slow-spreading ridge segment: IODP hole 1473A and the Atlantis Bank oceanic core complex. J. Geophys. Res. Solid Earth 124, 12631–12659 (2019).

  34. 34.

    Baines, A. G. et al. Mechanism for generating the anomalous uplift of oceanic core complexes: Atlantis Bank, southwest Indian Ridge. Geology 31, 1105–1108 (2003).

  35. 35.

    Morono, Y., Terada, T., Kallmeyer, J. & Inagaki, F. An improved cell separation technique for marine subsurface sediments: applications for high-throughput analysis using flow cytometry and cell sorting. Environ. Microbiol. 15, 2841–2849 (2013).

  36. 36.

    Lundin, A., Hasenson, M., Persson, J. & Pousette, A. Estimation of biomass in growing cell lines by adenosine triphosphate assay. Methods Enzymol. 133, 27–42 (1986).

  37. 37.

    Coolen, M. J. & Overmann, J. Functional exoenzymes as indicators of metabolically active bacteria in 124,000-year-old sapropel layers of the eastern Mediterranean Sea. Appl. Environ. Microbiol. 66, 2589–2598 (2000).

  38. 38.

    Pella, E. Elemental organic analysis. Part 1, historical developments. Am. Lab. 22, 116–125 (1990).

  39. 39.

    Pella, E. Elemental organic analysis. Part 2: State of the art. Am. Lab. 22, 28–32 (1990).

  40. 40.

    Whiteside, J. H. et al. Pangean great lake paleoecology on the cusp of the end-Triassic extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 301, 1–17 (2011).

  41. 41.

    Sturt, H. F., Summons, R. E., Smith, K., Elvert, M. & Hinrichs, K. U. Intact polar membrane lipids in prokaryotes and sediments deciphered by high-performance liquid chromatography/electrospray ionization multistage mass spectrometry—new biomarkers for biogeochemistry and microbial ecology. Rapid Commun. Mass Spectrom. 18, 617–628 (2004).

  42. 42.

    Klein, A. T. et al. Investigation of the chemical interface in the soybean–aphid and rice–bacteria interactions using MALDI-mass spectrometry imaging. Anal. Chem. 87, 5294–5301 (2015).

  43. 43.

    Becker, K. W. et al. An improved method for the analysis of archaeal and bacterial ether core lipids. Org. Geochem. 61, 34–44 (2013).

  44. 44.

    Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).

  45. 45.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  46. 46.

    Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).

  47. 47.

    Sheik, C. S. et al. Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the Census of Deep Life. Front. Microbiol. 9, 840 (2018).

  48. 48.

    Cole, J. R. et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).

  49. 49.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  50. 50.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  51. 51.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

  52. 52.

    Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).

  53. 53.

    Petersen, T. N., Brunak, S., von Heijne, G. & Nielsen, H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat. Methods 8, 785–786 (2011).

  54. 54.

    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

  55. 55.

    Lagesen, K. et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 35, 3100–3108 (2007).

  56. 56.

    Glassing, A., Dowd, S. E., Galandiuk, S., Davis, B. & Chiodini, R. J. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 8, 24 (2016).

  57. 57.

    Le Calvez, T., Burgaud, G., Mahé, S., Barbier, G. & Vandenkoornhuyse, P. Fungal diversity in deep-sea hydrothermal ecosystems. Appl. Environ. Microbiol. 75, 6415–6421 (2009).

  58. 58.

    Burgaud, G., Arzur, D., Durand, L., Cambon-Bonavita, M.-A. & Barbier, G. Marine culturable yeasts in deep-sea hydrothermal vents: species richness and association with fauna. FEMS Microbiol. Ecol. 73, 121–133 (2010).

  59. 59.

    Valentine, D. L. et al. Propane respiration jump-starts microbial response to a deep oil spill. Science 330, 208–211 (2010).

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We thank the captain, crew and all who sailed on JOIDES Resolution for IODP Expedition 360, whose support was essential; T. Sehein and M. Torres-Beltran for assistance with statistical analyses; Q. Ma, E. S. Taylor (WHOI Creative) and Andrew Newman Design for assistance with figures; S. Yvon-Lewis for providing materials and assistance with methane measurements; and M. Sogin, J. Huber, B. Orcutt, K. Lloyd, J. Biddle and S. D’Hondt for helpful discussions on the relative merits of different molecular data-handling options for contamination controls. This study was funded by National Science Foundation grants OCE-1658031 to V.P.E. and F.K., OCE-1658118 to J.B.S., and OCE-1450528 and OCE-1637130 to H.J.B.D. F.S. acknowledges funding from the DFG under Germany’s Excellence Strategy (no. EXC-2077-390741603) and the Gottfried Wilhelm Leibniz Program (HI 616-14-1). Support to J.L. was provided by the National Science Foundation of China (no. 41772358) and the Ministry of Science and Technology of China (no. 2012CB417302).

Author information

V.P.E., J.B.S., F.K., F.S. and J.L. acquired funding. V.P.E. and J.B.S. collected samples, performed shipboard assays and established enrichment cultures. J.L. extracted RNA. P.M., J.L. and V.P.E. performed mRNA analyses. D.B., S.L. and R.C. extracted DNA, and D.B. and S.Y.W. analysed iTAG data. F.S. and L.A.E.M. performed lipid biomarker analyses. G.B. and M.Q. performed fungal isolations. F.K. performed Raman spectroscopy experiments and J.L. carried out SEM with F.K. J.B.S. and S.Y.W. performed cell counts and exoenzyme assays, and analysed methane-generation experiments. H.J.B.D. and F.K. provided the geological context, and D.K.B. compiled downhole-core description data. P.M. conceived the working hypotheses in Fig. 4. V.P.E. wrote the manuscript draft and all authors contributed to review and editing.

Correspondence to Virginia P. Edgcomb.

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The authors declare no competing interests.

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Peer review information Nature thanks Bo Barker Jørgensen, Jennifer Biddle and Steven D’Hondt for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Archaeal core lipid analyses.

a, b, Downhole changes in core lipid composition of archaeal IPLs (a) and archaeal core lipids (b). The relative abundances of the diether lipid archaeol and tetraether lipids with zero, one, two and three rings (GDGT-0, GDGT-1, GDGT-2 and GDGT-3, respectively) and crenarchaeol are shown. c, Structures of the most abundant archaeal lipids. 1G, monoglycosidic; 2G, diglycosidic; AR, archaeol; GDGT, glycerol dialkyl glycerol tetraether. Lipid data are from single measurements owing to sample constraints. Source Data are available online. Source data

Extended Data Fig. 2 Rate of methane production observed in long-term enrichment experiments.

The rate at 25 weeks was derived from the value measured at 25 weeks minus the initial value (0) divided by the time elapsed in days. The rate at 60 weeks was derived from the difference between methane measured at 60 weeks and methane measured at 25 weeks divided by the time elapsed since the 25-week measurement. Data are from single measurements owing to limited sample availability, except for the ASW blank (n = 3), for which the mean is displayed. Source Data are available online. Source data

Extended Data Fig. 3 16S rRNA iTAG composition and metabolic processes detected in metatranscriptomes.

Top, taxonomic composition at family level or deeper (where possible) of 16S rRNA iTAG sequences for taxa present as more than 1% of the total abundance in at least one sample. Taxa present at an abundance of less than 1% in every sample were grouped into the ‘Others’ category. Bottom, an overview of categories of expressed genes in each sample is given below the iTAG composition for each sample. The presence (black bars) or absence (white bars) indicates the detection or non-detection of genes associated with the processes and activities listed in that sample. A detailed discussion of metabolic pathways is provided in the Supplementary Information. Annotations at higher taxonomic resolution are available upon request from the corresponding author.

Extended Data Fig. 4 Non-metric multidimensional scaling and clustering analyses of detected prokaryotic OTUs and transcripts.

a, Non-metric multidimensional scaling analysis performed on the Jaccard distance matrix (Ward clustering using hclust; see Methods) of prokaryotic OTU presence or absence data for 11 biologically independent samples spanning the depth of hole U1473A showing only environmental vectors supported by P < 0.05. P values were generated as P = N + 1/n + 1 after a goodness-of-fit statistic, which is the squared correlation coefficient, and was calculated on 999 random permutations of the data using the vegan package of R. b, Clustering analysis of presence or absence prokaryotic OTU data for 11 samples spanning the depth of hole U1473A. Hierarchical clustering dendrogram based on a Jaccard distance matrix of the presence or absence data for 99% OTUs from 11 IODP Expedition 360 samples. Jaccard similarity values of >0.8, calculated with the clusterboot function in R, suggest a stable cluster. Shading highlights several samples that share certain OTUs. From left to right (1–4): 1, samples share SAR11 clade II and SAR406; 2, samples share Nitrosopumilaceae; 3, samples share SAR11 clade II; and 4, samples both contain the lowest OTU counts. c, Clustering analysis of curated transcripts for 11 samples within the functional categories presented. Clustering analysis was based on log + 1-transformed FPKM values and the Ward method, and a distance matrix was constructed using the Manhattan method and the pvclust function in R.

Extended Data Fig. 5 Lipid sample blanks.

a, Representative UHPLC–APCI-MS chromatogram, showing the detection of core GDGTs in sample 84R6 and the sample extraction blank. b, Representative multiple-reaction-monitoring HPLC–ESI-MS chromatograms for selected bacterial DEG lipids with summed chain lengths from C30 to C36 in sample 26R2 and the sample extraction blank. Lipid data are from single measurements owing to sample constraints.

Extended Data Fig. 6 Drilling mud contamination control during lipid analysis.

a, Depiction of outer, middle and inner sections subsampled from whole round core (WRC) sample 29R3 (259.03 mbsf) in order to test the influence of drilling mud (DM) on the lipid biomarker composition. bd, Composition of archaeal core lipids (b), IPLs (c) and bacterial DEGs (d) in the drilling mud, subsampled WRC sample 29R3 and selected rock samples analysed in this study. e, Extracted ion chromatograms of targeted DEG lipids from C28 to C39 summed carbon chain lengths in the drilling mud and subsampled WRC sample 29R3, showing the differences in relative abundance and isomer composition among these samples. Lipid data are from single measurements owing to sample constraints. 2R1, 10.7 mbsf; 31R1, 274.6 mbsf; 68R2, 619.6 mbsf; 81R2, 714.9 mbsf. Source Data are available online. Source data

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Li, J., Mara, P., Schubotz, F. et al. Recycling and metabolic flexibility dictate life in the lower oceanic crust. Nature 579, 250–255 (2020).

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