Mobile genes in the human microbiome are structured from global to individual scales

  • A Corrigendum to this article was published on 22 March 2017

Abstract

Recent work has underscored the importance of the microbiome in human health, and has largely attributed differences in phenotype to differences in the species present among individuals1,2,3,4,5. However, mobile genes can confer profoundly different phenotypes on different strains of the same species. Little is known about the function and distribution of mobile genes in the human microbiome, and in particular whether the gene pool is globally homogenous or constrained by human population structure. Here, we investigate this question by comparing the mobile genes found in the microbiomes of 81 metropolitan North Americans with those of 172 agrarian Fiji islanders using a combination of single-cell genomics and metagenomics. We find large differences in mobile gene content between the Fijian and North American microbiomes, with functional variation that mirrors known dietary differences such as the excess of plant-based starch degradation genes found in Fijian individuals. Notably, we also observed differences between the mobile gene pools of neighbouring Fijian villages, even though microbiome composition across villages is similar. Finally, we observe high rates of recombination leading to individual-specific mobile elements, suggesting that the abundance of some genes may reflect environmental selection rather than dispersal limitation. Together, these data support the hypothesis that human activities and behaviours provide selective pressures that shape mobile gene pools, and that acquisition of mobile genes is important for colonizing specific human populations.

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Figure 1: Enrichment of functional mobile genes is locale-specific.
Figure 2: Microbiome composition across global and local populations.
Figure 3: Personal mobile genetic element architecture displays high variation due to recombination.
Figure 4: Genes are widespread across global populations, but specific mobile genetic element architecture is not.

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Acknowledgements

We thank our field collaborators in the Fiji Islands: the Wildlife Conservation Society, Fiji, Wetlands International-Oceania, K. Jenkins, S. Korovou, N. Litidamu, and K. Kishore. We thank T. Poon for sample, sequencing, and data coordination, and A. Materna (QIAGEN) for technical assistance. This work was supported by grants from the National Human Genome Research Institute (U54HG003067) to the Broad Institute, the Center for Environmental Health Sciences at MIT, the Center for Microbiome Informatics and Therapeutics at MIT, and the Fijian Ministry of Health. Additional support was provided by a Columbia University Earth Institute Fellowship (I.L.B.); a Broad Institute Lawrence Summers Fellowship (L.X.); a Burroughs Wellcome Fund Career Award at the Scientific Interface (P.C.B.); and an R01 DE020891 funded by the NIDCR and ENIGMA and a Lawrence Berkeley National Laboratory Scientific Focus Area Program supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (S.Y. and A.K.S.). Sandia is a multi-program laboratory operated by Sandia Corp., a Lockheed Martin Co., for the United States Department of Energy under Contract DE-AC04-94AL85000.

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I.L.B. and E.J.A. designed the study. I.L.B., S.D.J., A.P.J. and W.N. oversaw and performed the field collection of FijiCOMP data and samples. I.L.B., L.X., S.Y., and M.T. performed all experimental work. D.G., B.W.B., J.R.W., P.C.B., R.J.X. and A.K.S. oversaw the DNA sequencing production. I.L.B. and K.H. processed the shotgun data and performed alignments. I.L.B., K.H., and D.G. provided new analytical tools. I.L.B., K.H. and C.S.S. performed computational analysis. I.L.B. and E.J.A. wrote the manuscript.

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Correspondence to E. J. Alm.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks P. Bork, K. Forslund, P. Hugenholtz and C. Rinke for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Phylogeny of assemblies used in the study span the bacterial Tree of Life.

A phylogenetic tree constructed using a multiple sequence alignment of the full 16S rRNA gene or the V68 region of the 16S rRNA gene of all reference genomes and single-cell assemblies used in this analysis where available. 16S alignments were constructed using RDP. The tree was then assembled using FastTree. Support was low for all deep branches in the tree, so the archeal branch serves as the outgroup for illustrative purposes only. The outer colour bar displays taxonomic associations for archea and bacterial phyla. The inner colour bar displays the source of that operational taxonomic unit: HMP reference cells (n = 387 blue) and FijiCOMP single cell assemblies (n = 110, red). 16S rRNA gene sequences were not available for 70 FijiCOMP single-cell assembles, which are therefore not included in this tree.

Extended Data Figure 2 Methodology for identifying horizontally transferred genes and assessing their distribution within the metagenomic samples.

Horizontally transferred regions were first identified using pair-wise BLASTs between HMP reference genomes and FijiCOMP single cell assemblies. Open reading frames were annotated within the horizontally transferred regions. Genetic redundancy was removed in the mobile gene set to ensure accurate abundance estimates using a combination of UCLUST and BLAST. Metagenomic reads were then aligned to the data set of unique mobile genes. Alignments were filtered to retain only reads that aligned with 99% identity across over 50% of their read length. Abundances of genes in the metagenomic samples were determined for genes whose alignments had a minimum of 4 × alignment depth over 80% of the gene length.

Extended Data Figure 3 The abundance of mobile gene families is largely determine by cohort.

a, A heat map is plotted showing the abundances (FPKM) of mobile genes aggregated by functional gene family (COG assignment, KEGG, TIGRFAM or PFAM family) within each of the metagenomic samples (81 HMP samples and 172 FijiCOMP samples). Hierarchical clustering using complete linkage was performed on the Euclidean distances between profiles of functional gene families across individuals and on the distances between individuals’ mobile gene composition. Values are plotted on a logarithmic scale. b, A heat map is plotted showing the abundances (FPKM) of only those mobile gene families that were deemed of higher confidence within each of the metagenomic samples. These include mobile gene families from mobile genes that were annotated as horizontal transfer machinery or had additional support for their phylogenetic placement. The placements of gene families and individuals were maintained from a for comparative purposes. c, A heat map is plotted showing the abundances (FPKM) of only those mobile genes that were observed to be transferred between HMP reference genomes within each of the metagenomic samples. The placements of gene families and individuals were maintained from a for comparative purposes.

Extended Data Figure 4 Distributions of GH13 genes and glycoside hydrolase families within mobile genes of higher confidence display population-specific enrichment.

a, Prevalence and abundance (measured by FPKM) of mobile genes annotated as members of the GH13 family in the FijiCOMP (n = 172, red) and HMP (n = 81, blue) metagenomic stool samples. b, Prevalence and abundance of all glycoside hydrolase families within the higher confidence mobile gene subset present in the FijiCOMP (red) and HMP (blue) metagenomic stool samples. Only unique gene families from mobile genes that were annotated as horizontal transfer machinery or had additional support for their phylogenetic placement are included here. Abundances were measured by FPKM, aggregated according to glycoside hydrolase family, and plotted as a function of the density across samples. For each glycoside hydrolase family, the number of unique horizontally transferred genes observed is noted, as are the sources of their substrates.

Extended Data Figure 5 Composition of the gut microbiomes of HMP and FijiCOMP study participants.

a, Relative abundances of bacteria according to phylum plotted for metagenomic samples from individuals in the HMP (n =81, blue) and FijiCOMP (n =172, red) cohorts. Samples are sorted according to cohort and the abundance of the dominant phyla. b, Relative abundances of families within the order Bacteriodales plotted for metagenomic samples from individuals in the HMP (blue) and FijiCOMP (red) cohorts. Samples are sorted according to cohort and the abundance of the top Bacteroidales member.

Extended Data Figure 6 Mobile genes are observed in a wide variety of bacterial host backgrounds across the two cohorts.

a, b, A heat map is plotted showing the number of read-pairs per person that aligned to both a tRNA gene and two specific horizontally transferred genes. Colours within the heat map reflect the read abundance according to the species associated with the specific tRNA gene. The colour bar shows which meteganomic cohort the reads are from: FijiCOMP (red) and HMP (blue).

Extended Data Figure 7 The relative abundances of genes and contexts across populations is not sensitive to precise definitions.

Percentages of gene families, as determined by COG annotations (left), identical genes (middle) and gene contexts (right) between populations for a wide range of parameters. Bars are plotted in 5% increments. Bars shaded in black are the parameters that are plotted in Fig. 4.

Extended Data Figure 8 Horizontal transfer varies across cells at different phylogenetic distances.

a, Nucleotide identity cut-offs for full length 16S rRNA and the V68 16S rRNA region were compared to avoid comparisons between closely related cells. For each pair of HMP reference genomes, nucleotide identity for their full-length 16S rRNA is plotted against that of their V68 regions. 97% identity of full-length 16S (corresponding to approximately 75 million years of evolution) was used as a cut-off, whereas 95% was used as a cut-off when only sequences in the V68 region were available. Only those genomes above 90% similar at both the full-length and V68 region are shown. b, The number of cell–cell comparisons contributing to each of the lines. c, HGT frequency plotted as a function of the phylogenetic divergence between species between all cell-cell comparisons (black), between HMP reference genomes only (blue) and between the FijiCOMP single cell assemblies (red). This plot includes only cells for which full-length 16S rRNA genes could be identified.

Extended Data Figure 9 Representative genes chosen for the final mobile gene data set are highly similar to the genes that were filtered to reduce redundancy.

For each overlapping horizontally transferred region observed in cell–cell BLASTn comparisons between the reference genomes and single-cell assemblies, genes were clustered to identify unique genes and reduce the redundancy of the gene set. This step is essential for accurate abundance measurements of these genes in the metagenomic data sets after read alignment. All open reading frames from each overlapping horizontally transferred region were grouped using UCLUST. The nucleotide identities of each of the filtered genes and the gene chosen for read alignment (that is, the centroid) are plotted.

Extended Data Figure 10 Metagenomic reads align to mobile genes with high fidelity over their entire length.

Metagenomic reads were required to align with 99% identity to a mobile gene over at least 50% of the read length. Despite the seemingly low 50% cut-off, almost all reads align with near-perfect nucleotide identity over the entire length of the gene.

Supplementary information

Supplementary Tables

This zipped file contains Supplementary Tables 1-10. (ZIP 4704 kb)

Supplementary Data

This file contains the DNA sequences of all horizontally transferred genes observed in this study. A FASTA file containing 37,853 DNA sequences. Sequence identifiers correspond to the cell identifier followed by the contig number and the gene number on that contig. (TXT 31040 kb)

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Brito, I., Yilmaz, S., Huang, K. et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016). https://doi.org/10.1038/nature18927

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