Soil microorganisms globally are thought to be sustained primarily by organic carbon sources. Certain bacteria also consume inorganic energy sources such as trace gases, but they are presumed to be rare community members, except within some oligotrophic soils. Here we combined metagenomic, biogeochemical and modelling approaches to determine how soil microbial communities meet energy and carbon needs. Analysis of 40 metagenomes and 757 derived genomes indicated that over 70% of soil bacterial taxa encode enzymes to consume inorganic energy sources. Bacteria from 19 phyla encoded enzymes to use the trace gases hydrogen and carbon monoxide as supplemental electron donors for aerobic respiration. In addition, we identified a fourth phylum (Gemmatimonadota) potentially capable of aerobic methanotrophy. Consistent with the metagenomic profiling, communities within soil profiles from diverse habitats rapidly oxidized hydrogen, carbon monoxide and to a lesser extent methane below atmospheric concentrations. Thermodynamic modelling indicated that the power generated by oxidation of these three gases is sufficient to meet the maintenance needs of the bacterial cells capable of consuming them. Diverse bacteria also encode enzymes to use trace gases as electron donors to support carbon fixation. Altogether, these findings indicate that trace gas oxidation confers a major selective advantage in soil ecosystems, where availability of preferred organic substrates limits microbial growth. The observation that inorganic energy sources may sustain most soil bacteria also has broad implications for understanding atmospheric chemistry and microbial biodiversity in a changing world.
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All data supporting the findings of the present study are available. All numerical data used to make figures are provided in Source Data 1 (XLSX format) and all raw phylogenetic trees are provided in Newick (NWK) format in Source Data 2 (ZIP format). All metagenomes sequenced for this project can be accessed at the Sequence Read Archive with accession number PRJNA656125 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA656125) and all MAGs are available at FigShare (https://figshare.com/articles/dataset/Metagenome_Assembled_Genomes/12782543). All previously sequenced metagenomes and metatranscriptomes analysed in this study are available at NCBI BioProject with the accession numbers listed in Supplementary Table 2. Source data are provided with this paper.
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This research was supported by an Australian Research Council DECRA Fellowship (DE170100310; C.G.), a Discovery Project grant (DP180101762; C.G. and P.L.M.C.), a Swiss National Foundation Early Mobility Postdoctoral Fellowship (P2EZP3_178421; E.C.), a NSFC grant (41906076; X.D.), a NERC grant (NE/T010967/1; J.B.), a Humboldt Foundation Fellowship (J.B.), Monash University PhD scholarships (S.K.B. and P.M.L.), a Holsworth Wildlife Research Endowment Grant (S.K.B) and an NHMRC EL2 Fellowship (APP1178715; salary for C.G.). We thank M. Chuvochina for etymological advice, J. Lloyd for field assistance and W. Whitman, S. Chown, S. Morales, M. Stott and G. Cook for formative discussions.
The authors declare no competing interests.
Peer review information Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Composition of the bacterial and archaeal communities sequenced in each soil metagenome.
Stacked barcharts depicting the relative abundance of different phyla in (a) Australian soils and (b) global soils based on reads for the single-copy ribosomal protein gene rplP. Alpha diversity is shown as observed and estimated richness (Chao1) in (c) Australian soils and (d) global soils. Boxplots show median, lower and upper quartile, and minimum and maximum values. This is based on four biological replicates for the four Australian soils (n = 16) at depths of 0-5, 5-10, 15-20, and 25-30 cm, and biological triplicates for the eight global soils (n = 24). For beta diversity, abundance-based distance matrix Bray-Curtis diversity is visualized on a multidimensional PcoA plot for both Australian and global soils. Testing for significant differences in community structure between depths and ecosystems was performed using a one-way PERMANOVA with 999 permutations. Source data
Extended Data Fig. 2 Metatranscriptomic analysis comparing metabolic marker gene expression across three global soils with paired metagenome and metatranscriptome datasets.
(a) Relative abundance of quality-filtered metatranscriptomic short reads expressed as the total reads per kilobase per million (RPKM). (b) Relative abundance of genes in each metatranscriptome mapped to the corresponding assemblies from each soil expressed as total transcripts per million (TPM). Source data
(a) Maximum-likelihood tree of amino acid sequences of particulate methane monooxygenase A subunit (PmoA), a marker for aerobic methane oxidation. The tree shows 2 sequences from soil metagenome-assembled genomes (blue) and 12 sequences from unbinned contigs (red) alongside 93 representative sequences from NCBI reference genomes (black). The tree shows the affiliation of the PmoA from Candidatus Methylotropicum kingii with those of amplicons of the tropical upland soil cluster (TUSC). Also shown are reference sequences of other members of the copper membrane monooxygenase superfamily, namely ammonia monooxygenase (AmoA), hydrocarbon monooxygenase (HmoA), and groups of unknown function (including PxmA). The tree was constructed using the JTT matrix-based model, used all sites, and was bootstrapped with 50 replicates and midpoint-rooted. Source data is provided in Newick (nwk) format. (b) Metabolic reconstruction of the putative novel methanotroph Candidatus Methylotropicum kingii. The core pathways associated with energy conservation and carbon acquisition are shown, with genes detected shown in italics. The bacterium is predicted to use methane, methanol, and acetate as energy and carbon sources. In addition, it can use molecular hydrogen as an electron donor via a group 1f [NiFe]-hydrogenase. The bacterium is predicted to use the electron acceptors oxygen via a cytochrome c oxidase and nitrous oxide via a nitrous oxide reductase. Its particulate methane monooxygenase forms a distinct phylogenetic lineage with amplicons from the Tropical Upland Soil Cluster (TUSC), whereas its methanol dehydrogenase is closely related to those in previously sequenced Gemmatimonadota MAGs inferred to be methylotrophic. The genome encodes key enzymes for the serine cycle for assimilation of one-carbon sources. Abbreviations: H4F = tetrahydrofolate; Hyd = group 1f [NiFe]-hydrogenase; pMMO = particulate methane monooxygenase; MDH = methanol dehydrogenase; PQQ = pyrroloquinoline quinone; I = NADH dehydrogenase (complex I); II = succinate dehydrogenase (complex II); IV = cytochrome aa3 oxidase (complex IV). Dashed black lines indicate diffusion. Dashed gray lines indicate unknown process or undetected genes. (c) Molecular model of the putative particulate methane monooxygenase (Pmo) from Candidatus Methylotropicum kingii and comparison with putative catalytic sites of other experimentally validated Pmos. (i) Molecular model of the functional homotrimer of the putative Pmo complex from Ca. M. kingii, shown as a cartoon representation. PmoA, PmoB, and PmoC subunits from one Pmo complex are colored in light blue, dark blue, and sky blue respectively. The other two Pmo complexes in the trimer are colored in transparent yellow. Cu ions bound in the putative active site of the colour-coded Pmo complex are shown as ochre spheres. TM = transmembrane. (ii) Zoomed view of the Pmo complex from panel A, with the three Pmo subunits labelled and the putative active sites CuB and CuC highlighted. (iii) Stick representation of residues in the CuB and CuC active sites of Pmo molecular models from Ca. M. kingii (Gemmatimonadota) and Methylacidiphilum infernorum (Verrucomicrobiota), and from the crystal structure (PDB ID: 3RGB) of Pmo from the Methylococcus capsulatus (Proteobacteria), showing that both sites are conserved between Pmo from Ca. M. kingii and M. capsulatus. Pmo from M. infernorum lacks the CuB site, suggesting this site is not responsible for methane oxidation. (iv) Sequence alignment of metal binding motifs from the CuB (left) and CuC (right) sites from Pmo from Ca. M. kingii (cMk.), M. capsulatus (Mc.), Candidatus Methylomirabilis limnetica (cMl.; Methylomirabilota), and M. infernorum (Mi.). Amino acids involved in Cu coordination are highlighted.
Extended Data Fig. 4 Biogeochemical measurements of in situ soil gas concentrations and validation of static chamber setup.
(a) Mean soil-gas profiles normalized to the respective ambient air concentration (dashed line) of four biological replicates collected at depths of 0, 2, 4, 6, 8, 10, and 16 cm across four Australian ecosystems (n = 112). Note that the different gases were sampled at identical depths, but points are plotted slightly offset on the y-axis for better visibility of error bars. (b) Laboratory static chamber incubations (n = 3) to control for abiotic release of trace gases from chamber’s plastic components. Chamber headspace mixing ratios of hydrogen (H2), carbon monoxide (CO), and methane (CH4) were measured at eight time points during ~25 minutes of incubation time of an inert stainless steel surface. Measurement setup, sampling procedure, and sampling frequency followed those applied during the in situ chamber incubations performed to measured soil-atmosphere flux of trace gases. Circles indicate mixing ratio averaged from three independent incubations, with the vertical lines denoting one standard deviation. Dashed lines indicate the mixing ratio of trace gas averaged across three air samples. Source data
Extended Data Fig. 5 Maximum-likelihood tree of amino acid sequences of group 3 [NiFe]-hydrogenase large subunits, a marker for hydrogen production during fermentation processes.
The tree shows 129 sequences from soil metagenome-assembled genomes (blue) alongside 172 representative sequences from NCBI reference genomes (black). The subgroup of each reference sequence is denoted according to the HydDB classification scheme40. The tree was constructed using the JTT matrix-based model, used all sites, and was bootstrapped with 50 replicates and midpoint-rooted. All sequences shorter than 350 amino acids were omitted. Source data is provided in Newick (nwk) format.
Shown are four biological replicates collected at depths of 0-5, 5-10, 15-20, and 25-30 cm across four Australian ecosystems (n = 64). Depicted are the oxidation rates recorded over time of (a) atmospheric H2, (b) atmospheric CO, and (c) atmospheric CH4 by soils at each depth compared to heat-killed control soils. Oxidation of the three gases was also measured in the dryland soils following hydration, that is Dryland (wet) samples. Points show average values and vertical lines the error bars representing one standard deviations of four biological replicates. Significance testing of differences in ex situ oxidation rates between ecosystem type and soil depth was carried out using a one-sided Kruskall Wallis H test followed by a pairwise Wilcoxon tests with adjusted p-values using a Benjamini Hochberg correction for multiple testing. Boxplots show median, lower and upper quartile, and minimum and maximum values comparing the ex situ rates of H2, CO, and CH4 oxidation between depths and ecosystems for (d) cell-specific rates and (e) bulk reaction rates per gram of dry soil. Source data
Extended Data Fig. 7 Copy number of the 16S rRNA gene per gram of soils of four biological replicates.
Soil samples were collected at depths of 0-5, 5-10, 15-20, and 25-30 cm across four Australian ecosystems, with four biological replicates for each depth (n = 64). Boxplots show median, lower and upper quartile, and minimum and maximum values. Source data
Extended Data Fig. 8 Comparison of the theoretical and measured population of trace gas oxidizers in soils.
Shown are four biological replicates collected at depths of 0-5, 5-10, 15-20, and 25-30 cm across four Australian ecosystems (n = 64). The theoretical populations, in line with the methods of Conrad68, were estimated from thermodynamic modeling and measured ex situ oxidation rates assuming a single maintenance energy for all cells. Two different values were used: 8.9 × 10-15 W cell-1 as assumed by Conrad68 and 1.9 × 10-15 W cell-1 as the median of metabolic rates reported by DeLong et al60. The measured populations of trace gas oxidizers were determined from 16S rRNA gene copy numbers and the relative abundance of the respective functional gene as determined from metagenomics. The ratio of theoretical vs. measured population is equivalent to dividing our calculated power per cell by the respective maintenance energy above. Boxplots show median, lower and upper quartile, and minimum and maximum values. Source data
Extended Data Fig. 9 Maximum-likelihood tree of amino acid sequences of ribulose 1,5-bisphosphate carboxylase / oxygenase (RbcL), a marker for carbon fixation through the Calvin-Benson cycle.
The tree shows 68 sequences from soil metagenome-assembled genomes (blue) alongside 126 representative sequences from NCBI reference genomes (black). The subtype of each reference sequence is denoted. The tree was constructed using the JTT matrix-based model, used all sites, and was bootstrapped with 50 replicates and midpoint-rooted. Source data is provided in Newick (nwk) format.
Extended Data Fig. 10 Summary of the processes and mediators of trace gas cycling at the soil-atmosphere interface.
The soil bacterial phyla capable of consuming the trace gases H2 (via group 1 and 2 [NiFe]-hydrogenases), CO (via form I carbon monoxide dehydrogenases), and CH4 (via particulate methane monooxygenases) are shown. They are listed in order of the number of metagenome-assembled genomes recovered (black for highly abundant and grey for less abundant trace gas oxidizers). The downward arrows show the net consumption of atmospheric H2, CO, and CH4 by soil bacteria. The upward arrows show processes that result in endogenous production and internal cycling of trace gases. The green boxes indicate biotic processes, whereas the brown boxes indicate abiotic processes.
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Bay, S.K., Dong, X., Bradley, J.A. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. Nat Microbiol (2021). https://doi.org/10.1038/s41564-020-00811-w