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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.


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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.

Corresponding author

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.

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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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