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.
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.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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.
Carmody, R. N. et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015).
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).
Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).
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).
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
Carmody, R. N. & Wrangham, R. W. The energetic significance of cooking. J. Hum. Evol. 57, 379–391 (2009).
Carmody, R. N. et al. Genetic evidence of human adaptation to a cooked diet. Genome Biol. Evol. 8, 1091–1103 (2016).
Snow, P. & O’Dea, K. Factors affecting the rate of hydrolysis of starch in food. Am. J. Clin. Nutr. 34, 2721–2727 (1981).
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).
Cowan, M. M. Plant products as antimicrobial agents. Clin. Microbiol. Rev. 12, 564–582 (1999).
Witte, W. Medical consequences of antibiotic use in agriculture. Science 279, 996–997 (1998).
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).
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).
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).
Martens, E. C. et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 9, e1001221 (2011).
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).
Warren, F. J. et al. Food starch structure impacts gut microbiome composition. mSphere 3, e00086–00018 (2018).
Livesey, G. The impact of complex carbohydrates on energy balance. Eur. J. Clin. Nutr. 49, 89S–96S (1995).
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).
Maurice, C. F. & Turnbaugh, P. J. Quantifying and identifying the active and damaged subsets of indigenous microbial communities. Methods Enzym. 531, 91–107 (2013).
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).
Lou, Z. et al. p-Coumaric acid kills bacteria through dual damage mechanisms. Food Control 25, 550–554 (2012).
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).
Cho, I. et al. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature 488, 621–626 (2012).
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).
Vijay-Kumar, M. et al. Metabolic syndrome and altered gut microbiota in mice lacking toll-like receptor 5. Science 328, 228–231 (2010).
Breton, J. et al. Gut commensal E. coli proteins activate host satiety pathways following nutrient-induced bacterial growth. Cell Metab. 23, 324–334 (2016).
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).
Koppel, N., Maini Rekdal, V. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).
Moeller, A. H. et al. Rapid changes in the gut microbiome during human evolution. Proc. Natl Acad. Sci. USA 111, 16431–16435 (2014).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, 1–18 (2011).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
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).
Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).
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).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
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).
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).
vegan: Community Ecology Package, R package v. 2.5-2 (cran.R-project, 2018); https://CRAN.R-project.org/package=vegan
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).
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).
Silverman, J. D., Washburne, A. D., Mukherjee, S. & David, L. A. A phylogenetic transform enhances analysis of compositional microbiota data. eLife 6, e21887 (2017).
Aronesty, E. Comparison of sequencing utility programs. Open Bioinforma. J. 7, 1–8 (2013).
Kopylova, E., Noé, L. & Touzet, H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2008).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Strauber, H. & Muller, S. Viability states of bacteria: specific mechanisms of selected probes. Cytometry A 77, 623–634 (2010).
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).
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).
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).
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).
CLSI. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard 9th edn (Clinical and Laboratory Standards Institute, 2012).
Want, E. J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).
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).
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).
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).
Bowen, B. P. & Northen, T. R. Dealing with the unknown: metabolomics and metabolite atlases. J. Am. Soc. Mass Spectrom. 21, 1471–1476 (2010).
Katajamaa, M., Miettinen, J. & Orešič, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).
Yao, Y. et al. Analysis of metabolomics datasets with high-performance computing and metabolite atlases. Metabolites 5, 431–442 (2015).
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).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
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).
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–8, Supplementary Notes, Supplementary References, Supplementary Table References and Supplementary Data Legends.
Supplementary Tables 1–13.
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.
Descriptions of metabolite identifications.
About this article
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). https://doi.org/10.1038/s41564-019-0569-4
Annual Review of Microbiology (2020)
Frontiers in Ecology and Evolution (2020)
Journal of Dairy Science (2020)
Frontiers in Nutrition (2020)
A nonenzymatic method for cleaving polysaccharides to yield oligosaccharides for structural analysis
Nature Communications (2020)