Cooking shapes the structure and function of the gut microbiome


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

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 Figure source data and additional study data are available on request from the corresponding authors.


  1. 1.

    Carmody, R. N. et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015).

    CAS  PubMed  Google Scholar 

  2. 2.

    Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  Google Scholar 

  6. 6.

    Carmody, R. N. & Wrangham, R. W. The energetic significance of cooking. J. Hum. Evol. 57, 379–391 (2009).

    PubMed  Google Scholar 

  7. 7.

    Carmody, R. N. et al. Genetic evidence of human adaptation to a cooked diet. Genome Biol. Evol. 8, 1091–1103 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Snow, P. & O’Dea, K. Factors affecting the rate of hydrolysis of starch in food. Am. J. Clin. Nutr. 34, 2721–2727 (1981).

    CAS  PubMed  Google Scholar 

  9. 9.

    Ze, X., Duncan, S. H., Louis, P. & Flint, H. J. Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J. 6, 1535 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cowan, M. M. Plant products as antimicrobial agents. Clin. Microbiol. Rev. 12, 564–582 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Witte, W. Medical consequences of antibiotic use in agriculture. Science 279, 996–997 (1998).

    CAS  PubMed  Google Scholar 

  12. 12.

    Carmody, R. N., Weintraub, G. S. & Wrangham, R. W. Energetic consequences of thermal and nonthermal food processing. Proc. Natl Acad. Sci. USA 108, 19199–19203 (2011).

    CAS  PubMed  Google Scholar 

  13. 13.

    Guan, Y., Wu, T., Lin, M. & Ye, J. Determination of pharmacologically active ingredients in sweet potato (Ipomoea batatas L.) by capillary electrophoresis with electrochemical detection. J. Agric. Food Chem. 54, 24–28 (2006).

    CAS  PubMed  Google Scholar 

  14. 14.

    Salyers, A. A., Vercellotti, J. R., West, S. E. & Wilkins, T. D. Fermentation of mucin and plant polysaccharides by strains of Bacteroides from the human colon. Appl. Environ. Microbiol. 33, 319–322 (1977).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Martens, E. C. et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 9, e1001221 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Sun, T., Laerke, H. N., Jorgenson, H. & Knudsen, K. E. B. The effect of extrusion cooking of different starch sources on the in vitro and in vivo digestibility in growing pigs. Anim. Feed Sci. Technol. 131, 66–85 (2006).

    CAS  Google Scholar 

  17. 17.

    Warren, F. J. et al. Food starch structure impacts gut microbiome composition. mSphere 3, e00086–00018 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Livesey, G. The impact of complex carbohydrates on energy balance. Eur. J. Clin. Nutr. 49, 89S–96S (1995).

    Google Scholar 

  19. 19.

    Maurice, C. F., Haiser, H. J. & Turnbaugh, P. J. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 152, 39–50 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Maurice, C. F. & Turnbaugh, P. J. Quantifying and identifying the active and damaged subsets of indigenous microbial communities. Methods Enzym. 531, 91–107 (2013).

    CAS  Google Scholar 

  21. 21.

    Borges, A., Ferreira, C., Saavedra, M. J. & Simoes, M. Antibacterial activity and mode of action of ferulic and gallic acids against pathogenic bacteria. Microb. Drug Resist. 19, 256–265 (2013).

    CAS  PubMed  Google Scholar 

  22. 22.

    Lou, Z. et al. p-Coumaric acid kills bacteria through dual damage mechanisms. Food Control 25, 550–554 (2012).

    CAS  Google Scholar 

  23. 23.

    Alves, M. J. et al. Antimicrobial activity of phenolic compounds identified in wild mushrooms, SAR analysis and docking studies. J. Appl. Microbiol. 115, 346–357 (2013).

    CAS  PubMed  Google Scholar 

  24. 24.

    Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Butaye, P., Devriese, L. A. & Haesebrouck, F. Antimicrobial growth promoters used in animal feed: effects of less well known antibiotics on Gram-positive bacteria. Clin. Microbiol. Rev. 16, 175–188 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Vijay-Kumar, M. et al. Metabolic syndrome and altered gut microbiota in mice lacking toll-like receptor 5. Science 328, 228–231 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Breton, J. et al. Gut commensal E. coli proteins activate host satiety pathways following nutrient-induced bacterial growth. Cell Metab. 23, 324–334 (2016).

    CAS  PubMed  Google Scholar 

  28. 28.

    Perez-Burillo, S. et al. Effect of food thermal processing on the composition of the gut microbiota. J. Agric. Food Chem. 66, 11500–11509 (2018).

    CAS  PubMed  Google Scholar 

  29. 29.

    Koppel, N., Maini Rekdal, V. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).

    PubMed  Google Scholar 

  30. 30.

    Moeller, A. H. et al. Rapid changes in the gut microbiome during human evolution. Proc. Natl Acad. Sci. USA 111, 16431–16435 (2014).

    CAS  PubMed  Google Scholar 

  31. 31.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, 1–18 (2011).

    Google Scholar 

  33. 33.

    Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).

    Google Scholar 

  34. 34.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, 1–17 (2014).

    Google Scholar 

  36. 36.

    Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Stein, S. E. & Scott, D. R. Optimization and testing of mass spectral library search algorithms for compound identification. J. Am. Soc. Mass Spectrom. 5, 859–866 (1994).

    CAS  PubMed  Google Scholar 

  38. 38.

    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    vegan: Community Ecology Package, R package v. 2.5-2 (cran.R-project, 2018);

  42. 42.

    Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    CAS  PubMed  Google Scholar 

  43. 43.

    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Silverman, J. D., Washburne, A. D., Mukherjee, S. & David, L. A. A phylogenetic transform enhances analysis of compositional microbiota data. eLife 6, e21887 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 7, 1–8 (2013).

    Google Scholar 

  46. 46.

    Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

    CAS  PubMed  Google Scholar 

  47. 47.

    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2008).

    CAS  PubMed  Google Scholar 

  49. 49.

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

    CAS  PubMed  Google Scholar 

  50. 50.

    Strauber, H. & Muller, S. Viability states of bacteria: specific mechanisms of selected probes. Cytometry A 77, 623–634 (2010).

    PubMed  Google Scholar 

  51. 51.

    Bouvier, T., Del Giorgio, P. A. & Gasol, J. M. A comparative study of the cytometric characteristics of high and low nucleic-acid bacterioplankton cells from different aquatic ecosystems. Environ. Microbiol. 9, 2050–2066 (2007).

    CAS  PubMed  Google Scholar 

  52. 52.

    Gasol, J. M., Zweifel, U. L., Peters, F., Fuhrman, J. A. & Hagström, Å. Significance of size and nucleic acid content heterogeneity as measured by flow cytometry in natural planktonic bacteria. Appl. Environ. Microbiol. 65, 4475–4483 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Lebaron, P., Servais, P., Agogue, H., Courties, C. & Joux, F. Does the high nucleic acid content of individual bacterial cells allow us to discriminate between active cells and inactive cells in aquatic systems? Appl. Environ. Microbiol. 67, 1775–1782 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Nayfach, S., Fischbach, M. A. & Pollard, K. S. MetaQuery: a web server for rapid annotation and quantitative analysis of specific genes in the human gut microbiome. Bioinformatics 31, 3368–3370 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    CLSI. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard 9th edn (Clinical and Laboratory Standards Institute, 2012).

  56. 56.

    Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).

    CAS  PubMed  Google Scholar 

  57. 57.

    Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).

    CAS  PubMed  Google Scholar 

  58. 58.

    Ivanisevic, J. et al. Toward ‘omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal. Chem. 85, 6876–6884 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Mahieu, N. G., Spalding, J. L., Gelman, S. J. & Patti, G. J. Defining and detecting complex peak relationships in mass spectral data: the mz.unity algorithm. Anal. Chem. 88, 9037–9046 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Bowen, B. P. & Northen, T. R. Dealing with the unknown: metabolomics and metabolite atlases. J. Am. Soc. Mass Spectrom. 21, 1471–1476 (2010).

    CAS  PubMed  Google Scholar 

  61. 61.

    Katajamaa, M., Miettinen, J. & Orešič, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).

    CAS  PubMed  Google Scholar 

  62. 62.

    Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Yao, Y. et al. Analysis of metabolomics datasets with high-performance computing and metabolite atlases. Metabolites 5, 431–442 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Thevenot, E. A., Roux, A., Xu, Y., Ezan, E. & Junot, C. Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J. Proteome Res. 14, 3322–3335 (2015).

    CAS  PubMed  Google Scholar 

  65. 65.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

Download references


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




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.

Ethics declarations

Competing interests

The authors declare no competing 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 Figs. 1–8, Supplementary Notes, Supplementary References, Supplementary Table References and Supplementary Data Legends.

Reporting Summary

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing