Abstract
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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
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).
Acknowledgements
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
Authors and Affiliations
Contributions
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
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
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.
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.
Rights and permissions
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41564-017-0084-4
This article is cited by
-
Functional and evolutionary significance of unknown genes from uncultivated taxa
Nature (2024)
-
Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes
BMC Microbiology (2023)
-
The gut microbiome modifies the associations of short- and long-term physical activity with body weight changes
Microbiome (2023)
-
Engraftment of essential functions through multiple fecal microbiota transplants in chronic antibiotic-resistant pouchitis—a case study using metatranscriptomics
Microbiome (2023)
-
H2 generated by fermentation in the human gut microbiome influences metabolism and competitive fitness of gut butyrate producers
Microbiome (2023)