Abstract
Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1,2,3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.
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Data availability
All data analysed during the current study are publicly available. Supplementary Table 1 contains the accession numbers for all the metagenomes used. GMGCv1 is available for download at https://gmgc.embl.de. The full catalogue is available for download as are sub-catalogues specialized to individual habitats and the subset derived only from sequenced genomes (which can be further subset to obtain the pangenome of a species of interest). Both the full catalogue and a version containing only complete ORFs are available as they represent different tradeoffs: the complete catalogue achieves higher coverage, while the version with only complete ORFs may be more appropriate for analyses that require the whole gene. Similarly, protein families are available at different amino acid identity thresholds (see ‘Protein family cluster calculation’). In addition to being available for download, the catalogue can be queried with an amino acid sequence. We developed and use a novel k-mer based algorithm (see ‘k-mer based homology search’) to enable fast queries over the complete 303 million protein database and allow interactive use.
Code availability
The source code implementing the analyses in this manuscript is available on Github (https://github.com/luispedro/Coelho2021_GMGCv1) and is archived at Zenodo (https://doi.org/10.5281/zenodo.4769556).
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Acknowledgements
Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme (grant 686070: DD-DeCaF to P.B.) and Marie Skłodowska-Curie Actions (grant 713673 to A.R.d.R.), the European Research Council (ERC) MicrobioS (ERC-AdG-669830 to P.B.), JTC project jumpAR (01KI1706 to P.B.), a BMBF Grant (grant 031L0181A: LAMarCK to P.B.), the European Molecular Biology Laboratory (P.B.), the ETH and Helmut Horten Foundation (S.S.), the National Key R&D Program of China (grant 2020YFA0712403 to X.-M.Z.), National Natural Science Foundation of China (grant 61932008 to X.-M.Z.; grant 61772368 to X.-M.Z.; grant 31950410544 to L.P.C.), the Shanghai Municipal Science and Technology Major Project (grant 2018SHZDZX01 to X.-M.Z. and L.P.C.) and Zhangjiang Lab (X.-M.Z. and L.P.C.), the International Development Research Centre (grant 109304, EMBARK under the JPI AMR framework; to L.P.C.), la Caixa Foundation (grant 100010434, fellowship code LCF/BQ/DI18/11660009 to A.R.d.R.), the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain (grant SEV-2016-0672 (2017–2021) to C.P.C.), the Ministerio de Ciencia, Innovación y Universidades (grant PGC2018-098073-A-I00 MCIU/AEI/FEDER to J.H.-C. and J.G.-L.), the Innovation Fund Denmark (grant 4203-00005B, PNM), the Biotechnology and Biological Sciences research Council (BBSrC) Institute Strategic Programme Gut Microbes and Health BB/r012490/1 and its constituent project BBS/e/F/000Pr10355 (F.H.). R.A. is a member of the Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences. The authors thank the Bork group for helpful discussion, in particular A. Głazek for discussions of algorithm design, J. C. Somody (EMBL) for help with figure design, and A. Fullam (EMBL) for computational assistance in processing the MAGs.
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The study was conceived and supervised by P.B. and designed by L.P.C., S.S., J.H.-C. and P.B. L.P.C., R.A., A.R.d.R., P.N.M., T.S.S., A.O., F.H., T.V.R., S.K.F., S.K., O.M.M., P.F. and J.H.-C. analysed data. L.P.C., T.S.S., F.H., T.V.R., S.K.F., P.F., J.H.-C. and P.B. drafted the manuscript. L.P.C., R.A., A.R.d.R., C.P.C. and D.R.M. built the unigene, protein clusters and protein family catalogues. L.P.C., R.A., T.S.S., D.R.M., I.L., F.H., S.K.F., S.K. and J.H.-C. annotated the catalogue. A.R.d.R., C.P.C., J.G.-L., O.M.M. and J.H.-C. performed the selection pressure analyses. P.N.M. and H.B.N. built the MGSs. L.P.C., R.A., I.L., S.P., L.J., X.-M.Z., T.V.R. and J.H.-C. designed and implemented the web resource, including the search algorithms and the associated GMGC-mapper tool. L.P.C., T.S.S., F.H. and O.M.M. annotated metagenomes. T.S.S. and A.O. built the MAGs. All authors contributed to the review of the manuscript before submission for publication and approved the final version.
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Extended data figures and tables
Extended Data Fig. 1 Gene accumulation curves. Legend.
(a) For most (but not all) habitats, unigenes with high prevalence (≥ 5%) have been well-captured, while rare unigenes continue to be found in each new sample. (b-d) New unigenes continue to be found in each sample. Each grey line represents a random permutation of the samples, while the solid black line shows the mean over these random permutations. The dotted red line is least-squares fit of Heap’s Law (N = k · sample^alpha). In all cases, the parameter fit indicates that the number of has not reached saturation. (e) The number of assembled/detected genes per sample grows with sequencing depth without a plateau being reached. (f) Similarly, the number of detected ORFs per insert grows with sequencing depth.
Extended Data Fig. 2 Identity thresholds and their relationship to taxonomy and function in the GMGCv1.
Legend: (a) A 95% nucleotide identity threshold is a proxy for species. Shown is nucleotide identity of closest gene homolog within the same species or within the same genus (excluding within-species comparisons). The threshold used in this work (95%) is marked with a dashed red line. (b) Within well-conserved, universal, 40 single-copy orthologues (see Methods), the average pairwise amino acid identity is 49%, albeit with a wide range (27-75%) when considering within-orthologue averages. In dashed red, the thresholds used for building protein families are highlighted. Boxplots display quartiles and ranges (see Methods). (c) Proportion of genes annotated at each taxonomic level.
Extended Data Fig. 3 Short reads map to the GMGCv1 at higher rates (compared to a reference database of reference genomes).
Legend: (a) Mapping rates for short reads from metagenomes mapped against the GMGCv1 or the reference genomes in proGenomes2. (b) Fraction of short reads from human gut metagenomes mapping to a collection of sequenced genomes and the GMGCv1, per country, (c) Same data as (b), aggregated by the World Bank’s classification of countries into income groups. In all panels, boxplots show quartiles (including median) and range (except for outliers, see Methods). Blue boxes show mapping rates to proGenomes2, while orange boxes show mapping rates to GMGCv1.
Extended Data Fig. 4 MAGs only capture a small fraction of all genes in a sample.
Legend: Fraction of undetected genes when mapping to only the genes captured by metagenome-assembled genomes (MAGs) across the habitats compared to mapping to the full GMGCv1.
Extended Data Fig. 5 Species and protein cluster sharing between habitats is similar to unigene sharing, but sharing of protein families is more extensive.
Legend: (a) The sharing of metagenomic species between habitats mimics unigene sharing. Width of each ribbons represents the number of MGSs shared between the habitats (the largest number shared is between the human and the pig gut, which share 166 MGSs out of 1,908 MGSs in the human gut and 898 in pig gut, respectively). (b) Species-level unigene sharing between habitats by fraction of the number of unigenes from each habitat (cf. Fig. 1b, which uses abundance weighting). (c) Sharing of protein clusters (90% amino acid identity clusters) between habitats, abundance-weighted. (d) Sharing of protein families between habitats, abundance-weighted. When considering coarser clusterings of sequences, gene sharing between habitats increases, yet we still observed higher rates of sharing between similar habitats and significant fractions of habitat-specific families (e.g., in the marine environment, 31.3% of the genes, by abundance, are in marine-specific protein families).
Extended Data Fig. 6 Antibiotic resistance and mobile genes are more likely to be multi-habitat genes, while most species are found in a single habitat.
Legend: (a) Fraction of unigenes within each habitat which are multi-habitat genes (for all unigenes, or when considering only mobile elements or antibiotic resistance genes). (b) A total of 7,443 MGSs were built, across all the habitats as species proxies to reliably assess their habitats. Each circle shows the number of metagenomic species for each habitat, x-axis represents the number of genes in the catalogue specific to each habitat, the y-axis represents the number of samples. Note that differing sampling depth and habitat-specific biodiversity impact those numbers.
Extended Data Fig. 7 Determinants of functional community structure.
Legend: (a) principal coordinate analysis of all samples by protein family profile and the correlations with taxonomic and protein family richness (after rarefying to 1 million inserts to remove effects of sample depth). (b) Hierarchical clustering of the habitats using high-level functional profiles based.
Extended Data Fig. 8 Marine and soil richness patterns are a mixture of subpatterns.
Legend: Conspecific genes per species in marine (a) and (b) soil sub-habitats. The differences in the marine environment are particularly large when comparing the samples in the photic zones (the shallower, light-accessible, surface and deep-chlorophyll maximum samples) to the non-photic mesopelagic samples (deeper, beyond the reach of sunlight). The differences in the soil environment follow differences in acidity (with Podzol, Dystric Brunisol and Ultic soils being acidic, while Luvisols are usually neutral or alkaline) and differences in moisture (with Xeralfs being dry in the summer, while Glossudalfs are moist year round).
Extended Data Fig. 9 Most genes are detected only infrequently and rare genes are (on average) present at a lower abundance in metagenomes.
Legend: (a) Shown are the percentage of genes detected in at most 1,...,50 metagenomes (out of a total of 13,174). (b, c) Histograms of gene prevalence are roughly linear on a log-log scale, as predicted from neutral or nearly-neutral evolution models. Shown are histograms for 90% amino acid identity protein clusters (b) and 20% amino acid identity protein families (c), which behave similar to species-level unigenes (see Fig. 3). (d) Shown is the percentage of genes in each sample that is composed of rare genes (Count) and the total abundance represented by these (Abundance). Except for wastewater (likely due to under-sampling), rare genes represent a lower fraction of the abundance than of detection. Boxplots show quartiles (including median drawn as a line) and whiskers show the range of the data excluding outliers, which are shown as extra elements (see Methods).
Extended Data Fig. 10 More abundant and larger protein families are under more intense selection.
Legend: (a) dN/dS within each protein family, with protein families split into 5 abundance quintiles, showing a downward trend with abundance (higher negative selection). (b) dN/dS within each gene size category, similarly showing a downward trend with size. Categories are defined by increasing size, with each bin representing the same number of unigenes. Boxplots show quartiles and ranges (see Methods).
Supplementary information
Supplementary Table 1
List of samples included in the global gene catalogue including their habitat annotation. This table includes the accession ID of the metagenome, the GC fraction, and the size of the input (measured in total basepairs and number of inserts) both prior to (Raw) and post quality filtering (HQ, for high-quality). It also includes the number of ORFs predicted.
Supplementary Table 2
Mapping of our human-readable habitat terms to predefined ontology terms. List of habitat annotations used and their correspondence to EnvO/Uberon terms and NCBI taxonomic ID of the host (for host-associated habitat).
Supplementary Table 3
Statistics of (redundant) ORF predictions. The tables summarize the assembly statistics per habitat (including number of contigs, average contig size, N50) and the ORF stats per habitat, both complete ORFs and fragmentary ORFs.
Supplementary Table 4
Robustness checks for protein family definition and inclusion or exclusion of fragmentary ORFs. The major statements in the manuscript were tested using alternative protein family thresholds as well as including or excluding fragmentary ORFs to ensure robustness.
Supplementary Table 5
List of metagenome-assembled genomes and their statistics. All the assembled bins are included, including those marked as low-quality (following standard criteria, see Methods).
Supplementary Table 6
Pearson correlations between observed gene prevalence and theoretical prediction assuming neutral evolution.
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Coelho, L.P., Alves, R., del Río, Á.R. et al. Towards the biogeography of prokaryotic genes. Nature 601, 252–256 (2022). https://doi.org/10.1038/s41586-021-04233-4
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DOI: https://doi.org/10.1038/s41586-021-04233-4
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