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Metatranscriptome of human faecal microbial communities in a cohort of adult men


The gut microbiome is intimately related to human health, but it is not yet known which functional activities are driven by specific microorganisms' ecological configurations or transcription. We report a large-scale investigation of 372 human faecal metatranscriptomes and 929 metagenomes from a subset of 308 men in the Health Professionals Follow-Up Study. We identified a metatranscriptomic 'core' universally transcribed over time and across participants, often by different microorganisms. In contrast to the housekeeping functions enriched in this core, a 'variable' metatranscriptome included specialized pathways that were differentially expressed both across participants and among microorganisms. Finally, longitudinal metagenomic profiles allowed ecological interaction network reconstruction, which remained stable over the six-month timespan, as did strain tracking within and between participants. These results provide an initial characterization of human faecal microbial ecology into core, subject-specific, microorganism-specific and temporally variable transcription, and they differentiate metagenomically versus metatranscriptomically informative aspects of the human faecal microbiome.

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Fig. 1: Metatranscriptomic and metagenomic taxonomic and functional profile of a prospective human cohort.
Fig. 2: Core and variable metatranscriptomes of the stool microbiome.
Fig. 3: The gut metatranscriptome is personalized and broadly taxonomically distributed.
Fig. 4: Transcriptional landscape of the stool microbiome.
Fig. 5: Ecological interactions in the gut microbiome.
Fig. 6: Species-specific patterns of evolutionary divergence within species preserved across cohorts.


  1. 1.

    O’Doherty, K. C., Virani, A. & Wilcox, E. S. The human microbiome and public health: social and ethical considerations. Am. J. Public Health 106, 414–420 (2016).

    Article  Google Scholar 

  2. 2.

    Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroen. 31, 69–75 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  4. 4.

    Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).

    CAS  Article  Google Scholar 

  5. 5.

    Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Korpela, K. et al. Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun. 7, 10410 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Satinsky, B. M. et al. Microspatial gene expression patterns in the Amazon River plume. Proc. Natl Acad. Sci. USA 111, 11085–11090 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Turnbaugh, P. J. et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc. Natl Acad. Sci. USA 107, 7503–7508 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    CAS  Article  Google Scholar 

  10. 10.

    Segata, N. et al. Computational meta’omics for microbial community studies. Mol. Syst. Biol. 9, 666 (2013).

    Article  Google Scholar 

  11. 11.

    Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).

    CAS  Article  Google Scholar 

  12. 12.

    Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).

    CAS  Article  Google Scholar 

  13. 13.

    Chan, A. T. et al. Aspirin dose and duration of use and risk of colorectal cancer in men. Gastroenterology 134, 21–28 (2008).

    CAS  Article  Google Scholar 

  14. 14.

    Mehta, R. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. (in press).

  15. 15.

    Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).

    CAS  Article  Google Scholar 

  17. 17.

    Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44, D471–D480 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    CAS  Article  Google Scholar 

  19. 19.

    Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).

    CAS  Article  Google Scholar 

  20. 20.

    Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108, 4586–4591 (2011).

    CAS  Article  Google Scholar 

  21. 21.

    Virgin, H. W. The virome in mammalian physiology and disease. Cell 157, 142–150 (2014).

    CAS  Article  Google Scholar 

  22. 22.

    McCarty, R. M. & Bandarian, V. Biosynthesis of pyrrolopyrimidines. Bioorg. Chem. 43, 15–25 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Vinayak, M. & Pathak, C. Queuosine modification of tRNA: its divergent role in cellular machinery. Biosci. Rep. 30, 135–148 (2009).

    Article  Google Scholar 

  24. 24.

    Hauryliuk, V., Atkinson, G. C., Murakami, K. S., Tenson, T. & Gerdes, K. Recent functional insights into the role of (p)ppGpp in bacterial physiology. Nat. Rev. Microbiol. 13, 298–309 (2015).

    CAS  Article  Google Scholar 

  25. 25.

    Chistoserdova, L., Kalyuzhnaya, M. G. & Lidstrom, M. E. The expanding world of methylotrophic metabolism. Annu. Rev. Microbiol. 63, 477–499 (2009).

    CAS  Article  Google Scholar 

  26. 26.

    Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).

    CAS  Article  Google Scholar 

  27. 27.

    Levy, R. & Borenstein, E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc. Natl Acad. Sci. USA 110, 12804–12809 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).

    CAS  Article  Google Scholar 

  29. 29.

    Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    CAS  Article  Google Scholar 

  30. 30.

    Gosalbes, M. J. et al. Metatranscriptomic approach to analyze the functional human gut microbiota. PLoS ONE 6, e17447 (2011).

    CAS  Article  Google Scholar 

  31. 31.

    Sanchez, A. & Golding, I. Genetic determinants and cellular constraints in noisy gene expression. Science 342, 1188–1193 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Pande, S. et al. Fitness and stability of obligate cross-feeding interactions that emerge upon gene loss in bacteria. ISME J. 8, 953–962 (2014).

    CAS  Article  Google Scholar 

  33. 33.

    D’Souza, G. & Kost, C. Experimental evolution of metabolic dependency in bacteria. PLoS Genet. 12, e1006364 (2016).

    Article  Google Scholar 

  34. 34.

    Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    CAS  Article  Google Scholar 

  36. 36.

    Pimentel, D. Population regulation and genetic feedback. Science 159, 1432–1437 (1968).

    CAS  Article  Google Scholar 

  37. 37.

    O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).

    Article  Google Scholar 

  38. 38.

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

    CAS  Article  Google Scholar 

  39. 39.

    Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).

  40. 40.

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

    CAS  Article  Google Scholar 

  41. 41.

    Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).

    CAS  Article  Google Scholar 

  42. 42.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  Article  Google Scholar 

  43. 43.

    Ye, Y. & Doak, T. G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5, e1000465 (2009).

    Article  Google Scholar 

  44. 44.

    Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).

    CAS  Article  Google Scholar 

  45. 45

    Schwager, E., Mallick, H., Ventz, S. & Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput. Biol. 13, e1005852 (2017).

    Article  Google Scholar 

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We thank the participants in the MLVS and the HMP who graciously contributed to this research. This work was supported by funding from STARR Cancer Consortium Award no. I7-A714 to C.H., NCI R01CA202704 (A.T.C., C.H. and J.I.), NIDDK DK098311 (A.T.C.), and NIDDK U54DE023798 (C.H.). J.I. is further supported by Nebraska Tobacco Settlement Biomedical Research Development Funds. K.L.I. is supported by the National Health and Medical Research Council. Components of the Men’s Lifestyle Validation Study were supported by NCI U01CA152904 and UM1 CA167552. R.S.M. is supported by a Howard Hughes Medical Institute Fellowship Award. We are also grateful for initial pilot funding provided by B. Wu and E. Larsen. A.T.C. is a Stuart and Suzanne Steele MGH Research Scholar.

Author information




Study design and management were by J.I., A.T.C. and C.H. Sample collection and data generation were performed by K.L.I., D.A.D., C.D., E.R. and J.I. Data analysis was conducted by G.S.A.-A., R.S.M., J.L.-P., H.M. and T.B. Manuscript preparation and writing were conducted by G.S.A.-A., R.S.M., J.L.-P., H.M., D.A.D., J.I., A.T.C. and C.H.

Corresponding authors

Correspondence to Andrew T. Chan or Curtis Huttenhower.

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Competing interests

The authors declare no competing financial interests.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes, Supplementary Figures 1–5, Supplementary Figure Legends 1–10, Supplementary Table 1 and Supplementary References.

Life Sciences Reporting Summary

Supplementary Table 1

Metatranscriptomes and metagenomes.

Supplementary Table 2

Sample collection dates.

Supplementary Table 3

Taxonomic profiles.

Data set 1

Sequencing depth before and after quality filtering.

Data set 2

Community-wide and species-specific pathway transcript abundances.

Data set 3

Metagenomic pathway abundances.

Data set 4

Dispersion of pathway ECs.

Data set 5

HUMAnN2 mapping categories.

Supplementary Figure 6

Core and variable metatranscriptomes of the stool microbiome, with pathway definitions and distribution range of pathway transcript abundances.

Supplementary Figure 7

Per pathway species contributions to metagenomes and metatranscriptomes.

Supplementary Figure 8

Species-stratified distributions of metagenomic potential (DNA) and metatranscriptomic activity (RNA) for all pathways with non-zero abundance in at least 10% of samples.

Supplementary Figure 9

Ecological interactions in the gut microbiome for individual time points.

Supplementary Figure 10

Strain-level diversity is robust across cohorts.

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Abu-Ali, G.S., Mehta, R.S., Lloyd-Price, J. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat Microbiol 3, 356–366 (2018).

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