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Cooking shapes the structure and function of the gut microbiome

Nature Microbiology volume 4, pages 2052–2063 (2019)Cite this article

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Abstract

Diet is a critical determinant of variation in gut microbial structure and function, outweighing even host genetics1,2,3. Numerous microbiome studies have compared diets with divergent ingredients1,2,3,4,5, but the everyday practice of cooking remains understudied. Here, we show that a plant diet served raw versus cooked reshapes the murine gut microbiome, with effects attributable to improvements in starch digestibility and degradation of plant-derived compounds. Shifts in the gut microbiota modulated host energy status, applied across multiple starch-rich plants, and were detectable in humans. Thus, diet-driven host–microbial interactions depend on the food as well as its form. Because cooking is human-specific, ubiquitous and ancient6,7, our results prompt the hypothesis that humans and our microbiomes co-evolved under unique cooking-related pressures.

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Fig. 1: Cooking impacts gut microbiota structure and function in tuber-fed mice.
Fig. 2: Starch digestibility drives cooking-related changes in gut microbial community structure.
Fig. 3: The raw tuber diet impairs gut microbial physiology.
Fig. 4: Cooking-induced changes in the gut microbiota are ecologically significant.

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Data availability

16S rDNA and RNA sequencing data have been deposited in the NCBI Sequence Read Archive under accession no. PRJNA504908. Metabolomics raw data are available for download at https://opengut.ucsf.edu/CookingData.tar.gz. Figure source data and additional study data are available on request from the corresponding authors.

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Acknowledgements

We are indebted to R. Dutton and R. Wrangham for pivotal discussions, to T. Herfel, B. Mickelson, N. Pai and C. Pelkman for help with diet development, to L. Bry, A. Bustion, M. Correa, C. Daly, M. Delaney, L. Deng, O. Erbilgin, A. Freedman, E. Groopman, S. Kosina, F. Pontiggia, C. Reardon, J. Thomas and V. Yeliseyev for technical assistance and to E. Balskus, L. David, R. Losick and R. Nayak for comments on the manuscript. This work was supported by the National Institutes of Health (P.J.T., R01HL122593; R.N.C., F32DK101154), Boston Nutrition Obesity Research Center, Leakey Foundation, G.W. Hooper Foundation, Harvard Dean’s Competitive Fund for Promising Scholarship, William F. Milton Fund, the Defense Advanced Research Projects Agency (T.R.N., HR0011516183) and the UCSF Department of Microbiology and Immunology. J.E.B. was the recipient of a Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship. P.J.T. is a Chan Zuckerberg Biohub investigator and a Nadia’s Gift Foundation Innovator supported, in part, by the Damon Runyon Cancer Research Foundation (DRR-42-16) and the Searle Scholars Program (SSP-2016-1352).

Author information

Authors and Affiliations

  1. Department of Microbiology & Immunology, University of California San Francisco, San Francisco, CA, USA

    Rachel N. Carmody, Jordan E. Bisanz, Elizabeth N. Bess, Peter Spanogiannopoulos, Qi Yan Ang & Peter J. Turnbaugh

  2. Center for Systems Biology, Harvard University, Cambridge, MA, USA

    Rachel N. Carmody, Corinne F. Maurice, Kylynda C. Bauer & Peter J. Turnbaugh

  3. Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Rachel N. Carmody & Katia S. Chadaideh

  4. Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Benjamin P. Bowen, Katherine B. Louie, Daniel Treen & Trent R. Northen

  5. DOE Joint Genome Institute, Walnut Creek, CA, USA

    Benjamin P. Bowen, Katherine B. Louie, Daniel Treen & Trent R. Northen

  6. Department of Microbiology & Immunology, Microbiome and Disease Tolerance Centre, McGill University, Montreal, Quebec, Canada

    Corinne F. Maurice

  7. Gladstone Institutes, University of California San Francisco, San Francisco, CA, USA

    Svetlana Lyalina & Katherine S. Pollard

  8. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Vayu Maini Rekdal

  9. Department of Medicine, Metabolic Phenotyping Core and In Vivo Imaging System Core, Boston University, Boston, MA, USA

    Thomas W. Balon

  10. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Peter J. Turnbaugh

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Contributions

R.N.C. and P.J.T. designed the study. R.N.C. and K.S.C. performed the animal experiments. R.N.C., K.S.C. and V.M.R. performed the human experiments. R.N.C., J.E.B., K.S.C. and P.J.T. performed 16S rDNA sequencing and/or associated data analysis. R.N.C. and J.E.B. performed qPCR and associated data analysis. R.N.C., J.E.B., S.L., K.S.P. and P.J.T. performed microbial RNA sequencing and/or associated data analysis. Q.Y.A. performed bomb calorimetry. J.E.B., B.P.B., K.B.L., D.T., E.N.B., T.R.N. and P.J.T. performed metabolomics assays and/or associated data analysis. C.F.M. and K.C.B. performed microbial physiology assays and associated data analysis. P.S. performed in vitro growth experiments and associated data analysis. T.W.B. validated and performed measurements of body composition in mice. R.N.C., J.E.B. and P.J.T. wrote the manuscript with input from all co-authors.

Corresponding authors

Correspondence to Rachel N. Carmody or Peter J. Turnbaugh.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Supplementary Notes, Supplementary References, Supplementary Table References and Supplementary Data Legends.

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Supplementary Tables

Supplementary Tables 1–13.

Supplementary Data 1–6

Supplementary Data 1 MZMine processing parameters for untargeted C18 data collected in negative ionization mode. Supplementary Data 2 MZMine processing parameters for untargeted C18 data collected in positive ionization mode. Supplementary Data 3 Peak heights of untargeted features collected in negative ionization mode with C18 chromatography. Supplementary Data 4 Peak heights of untargeted features collected in positive ionization mode with C18 chromatography. Supplementary Data 5 Peak heights for targeted analyses collected under C18 chromatography. Supplementary Data 6 Peak heights for targeted analyses collected under HILIC-Z chromatography.

Supplementary Data 7

Descriptions of metabolite identifications.

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Carmody, R.N., Bisanz, J.E., Bowen, B.P. et al. Cooking shapes the structure and function of the gut microbiome. Nat Microbiol 4, 2052–2063 (2019). https://doi.org/10.1038/s41564-019-0569-4

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