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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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).
Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroen. 31, 69–75 (2015).
Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).
Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
Korpela, K. et al. Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children. Nat. Commun. 7, 10410 (2016).
Satinsky, B. M. et al. Microspatial gene expression patterns in the Amazon River plume. Proc. Natl Acad. Sci. USA 111, 11085–11090 (2014).
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).
Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).
Segata, N. et al. Computational meta’omics for microbial community studies. Mol. Syst. Biol. 9, 666 (2013).
Haiser, H. J. et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science 341, 295–298 (2013).
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).
Chan, A. T. et al. Aspirin dose and duration of use and risk of colorectal cancer in men. Gastroenterology 134, 21–28 (2008).
Mehta, R. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. (in press).
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).
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).
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).
Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012).
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).
Virgin, H. W. The virome in mammalian physiology and disease. Cell 157, 142–150 (2014).
McCarty, R. M. & Bandarian, V. Biosynthesis of pyrrolopyrimidines. Bioorg. Chem. 43, 15–25 (2012).
Vinayak, M. & Pathak, C. Queuosine modification of tRNA: its divergent role in cellular machinery. Biosci. Rep. 30, 135–148 (2009).
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).
Chistoserdova, L., Kalyuzhnaya, M. G. & Lidstrom, M. E. The expanding world of methylotrophic metabolism. Annu. Rev. Microbiol. 63, 477–499 (2009).
Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLoS Comput. Biol. 8, e1002606 (2012).
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).
Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).
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).
Gosalbes, M. J. et al. Metatranscriptomic approach to analyze the functional human gut microbiota. PLoS ONE 6, e17447 (2011).
Sanchez, A. & Golding, I. Genetic determinants and cellular constraints in noisy gene expression. Science 342, 1188–1193 (2013).
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).
D’Souza, G. & Kost, C. Experimental evolution of metabolic dependency in bacteria. PLoS Genet. 12, e1006364 (2016).
Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012).
Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).
Pimentel, D. Population regulation and genetic feedback. Science 159, 1432–1437 (1968).
O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
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).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Ye, Y. & Doak, T. G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5, e1000465 (2009).
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).
Schwager, E., Mallick, H., Ventz, S. & Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput. Biol. 13, e1005852 (2017).
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.
The authors declare no competing financial interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Notes, Supplementary Figures 1–5, Supplementary Figure Legends 1–10, Supplementary Table 1 and Supplementary References.
Metatranscriptomes and metagenomes.
Sample collection dates.
Sequencing depth before and after quality filtering.
Community-wide and species-specific pathway transcript abundances.
Metagenomic pathway abundances.
Dispersion of pathway ECs.
HUMAnN2 mapping categories.
Core and variable metatranscriptomes of the stool microbiome, with pathway definitions and distribution range of pathway transcript abundances.
Per pathway species contributions to metagenomes and metatranscriptomes.
Species-stratified distributions of metagenomic potential (DNA) and metatranscriptomic activity (RNA) for all pathways with non-zero abundance in at least 10% of samples.
Ecological interactions in the gut microbiome for individual time points.
Strain-level diversity is robust across cohorts.
About this article
Cite this article
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). https://doi.org/10.1038/s41564-017-0084-4
Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction
Frontiers in Microbiology (2021)
Nature Reviews Genetics (2021)
Microbial genetic and transcriptional contributions to oxalate degradation by the gut microbiota in health and disease
A catalog of tens of thousands of viruses from human metagenomes reveals hidden associations with chronic diseases
Proceedings of the National Academy of Sciences (2021)
Removable denture is a risk indicator for peri-implantitis and facilitates expansion of specific periodontopathogens: a cross-sectional study
BMC Oral Health (2021)