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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  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).

  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).

  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).

  5. 5.

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

  6. 6.

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

  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).

  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).

  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).

  10. 10.

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

  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).

  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).

  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).

  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).

  16. 16.

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

  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).

  18. 18.

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

  19. 19.

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

  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).

  21. 21.

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

  22. 22.

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

  23. 23.

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

  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).

  25. 25.

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

  26. 26.

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

  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).

  28. 28.

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

  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).

  30. 30.

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

  31. 31.

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

  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).

  33. 33.

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

  34. 34.

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

  35. 35.

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

  36. 36.

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

  37. 37.

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

  38. 38.

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

  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).

  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).

  42. 42.

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

  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).

  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).

  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).

Download references


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

Author notes

  1. Galeb S. Abu-Ali, Raaj S. Mehta, Andrew T. Chan and Curtis Huttenhower contributed equally to this work.


  1. Biostatistics Department, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    • Galeb S. Abu-Ali
    • , Jason Lloyd-Price
    • , Himel Mallick
    • , Tobyn Branck
    • , Casey DuLong
    •  & Curtis Huttenhower
  2. The Broad Institute, Cambridge, MA, USA

    • Galeb S. Abu-Ali
    • , Jason Lloyd-Price
    • , Himel Mallick
    • , Andrew T. Chan
    •  & Curtis Huttenhower
  3. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Raaj S. Mehta
    • , David A. Drew
    •  & Andrew T. Chan
  4. Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    • Raaj S. Mehta
    • , David A. Drew
    •  & Andrew T. Chan
  5. U.S. Army Natick Soldier Systems Center in Natick, Natick, MA, USA

    • Tobyn Branck
  6. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    • Kerry L. Ivey
    •  & Eric Rimm
  7. South Australian Health and Medical Research Institute, Infection and Immunity Theme, School of Medicine, Flinders University, Adelaide, South Australia, Australia

    • Kerry L. Ivey
  8. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    • Eric Rimm
    •  & Andrew T. Chan
  9. University of Nebraska, Lincoln, Lincoln, NE, USA

    • Jacques Izard


  1. Search for Galeb S. Abu-Ali in:

  2. Search for Raaj S. Mehta in:

  3. Search for Jason Lloyd-Price in:

  4. Search for Himel Mallick in:

  5. Search for Tobyn Branck in:

  6. Search for Kerry L. Ivey in:

  7. Search for David A. Drew in:

  8. Search for Casey DuLong in:

  9. Search for Eric Rimm in:

  10. Search for Jacques Izard in:

  11. Search for Andrew T. Chan in:

  12. Search for Curtis Huttenhower in:


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.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Andrew T. Chan or Curtis Huttenhower.

Supplementary information

  1. Supplementary Information

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

  2. Life Sciences Reporting Summary

  3. Supplementary Table 1

    Metatranscriptomes and metagenomes.

  4. Supplementary Table 2

    Sample collection dates.

  5. Supplementary Table 3

    Taxonomic profiles.

  6. Data set 1

    Sequencing depth before and after quality filtering.

  7. Data set 2

    Community-wide and species-specific pathway transcript abundances.

  8. Data set 3

    Metagenomic pathway abundances.

  9. Data set 4

    Dispersion of pathway ECs.

  10. Data set 5

    HUMAnN2 mapping categories.

  11. Supplementary Figure 6

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

  12. Supplementary Figure 7

    Per pathway species contributions to metagenomes and metatranscriptomes.

  13. 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.

  14. Supplementary Figure 9

    Ecological interactions in the gut microbiome for individual time points.

  15. Supplementary Figure 10

    Strain-level diversity is robust across cohorts.

About this article

Publication history




Issue Date



Further reading