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Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut

Nature Microbiology volume 9, pages 1700–1712 (2024)Cite this article

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Abstract

Microbially derived short-chain fatty acids (SCFAs) in the human gut are tightly coupled to host metabolism, immune regulation and integrity of the intestinal epithelium. However, the production of SCFAs can vary widely between individuals consuming the same diet, with lower levels often associated with disease. A systems-scale mechanistic understanding of this heterogeneity is lacking. Here we use a microbial community-scale metabolic modelling (MCMM) approach to predict individual-specific SCFA production profiles to assess the impact of different dietary, prebiotic and probiotic inputs. We evaluate the quantitative accuracy of our MCMMs using in vitro and ex vivo data, plus published human cohort data. We find that MCMM SCFA predictions are significantly associated with blood-derived clinical chemistries, including cardiometabolic and immunological health markers, across a large human cohort. Finally, we demonstrate how MCMMs can be leveraged to design personalized dietary, prebiotic and probiotic interventions aimed at optimizing SCFA production in the gut. Our model represents an approach to direct gut microbiome engineering for precision health and nutrition.

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Fig. 1: MCMMs predict personalized SCFA production profiles.
Fig. 2: Relationship between predicted and measured butyrate production rates in in vitro and ex vivo co-cultures.
Fig. 3: Human stool ex vivo assays show quantitative agreement between measured and predicted SCFA production fluxes within and across fibre treatment groups.
Fig. 4: Predicted SCFA production profiles were associated with variable immune response groups following a high-fibre dietary intervention.
Fig. 5: SCFA flux predictions are significantly associated with blood-derived clinical markers.
Fig. 6: Microbial MCMMs can be used to design and select personalized prebiotic, probiotic, and dietary interventions aimed at optimizing SCFA production profiles.

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Data availability

Processed data for synthetically constructed cultures are available via GitHub at https://github.com/RyanLincolnClark/DesignSyntheticGutMicrobiomeAssemblyFunction. Raw sequencing data are available via Zenodo at https://doi.org/10.5281/zenodo.4642238 (ref. 61). Raw sequencing data for study A are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA937304. Processed data for ex vivo study B are available at https://github.com/ThaisaJungles/fiber_specificity. Raw sequencing data are available in the NCBI SRA under accession number PRJNA640404. Raw sequencing data for ex vivo study C are available in the NCBI SRA under accession number PRJNA939256. Raw sequencing data for ex vivo study D are available in the NCBI SRA under accession number PRJNA1033794. Processed data for the longitudinal high-fibre intervention study are available at https://github.com/SonnenburgLab/fiber-fermented-study/. Raw sequencing data are available in the NCBI SRA under accession number PRJNA743361. MCMM-predicted SCFA production, blood metabolomic data, clinical chemistries, taxonomic abundance and associated metadata for the Arivale cohort are available in Supplementary Data 1. Raw untargeted metabolomics data from Arivale blood plasma samples, generated by Metabolon (USA), are available at the MetaboLights database, under study number MTBLS2308. Qualified researchers can access the full Arivale de-identified dataset supporting the findings in this study for research purposes through signing a data use agreement. Inquiries to access the data can be made at data-access@isbscience.org and will be responded to within 7 business days. Media used for MCMM growth simulations are available at https://www.vmh.life/#nutrition. Source data are provided with this paper.

Code availability

Code used to run analysis and create figures for this manuscript are available at https://github.com/Gibbons-Lab/scfa_predictions.

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Acknowledgements

We thank members of the Gibbons Lab for helpful discussions and suggestions regarding this work. We thanks N. Price, A. Willis and L. Rajakovich for helpful input on this work. This research was funded by Washington Research Foundation Distinguished Investigator Award and by startup funds from the Institute for Systems Biology (to S.M.G.). The faecal sample collection at Fred Hutchinson Cancer Center was supported by P30 CA015704. Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under award number R01DK133468 (to S.M.G.), by the Global Grants for Gut Health from Yakult and Nature Portfolio (to S.M.G.) and by the National Institute on Aging of the National Institutes of Health under award number U19AG023122 (to N.R.). Illustrations were created with BioRender.com.

Author information

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Nick Quinn-Bohmann, Tomasz Wilmanski, Katherine Ramos Sarmiento, Christian Diener & Sean M. Gibbons

  2. Molecular Engineering Graduate Program, University of Washington, Seattle, WA, USA

    Nick Quinn-Bohmann & Sean M. Gibbons

  3. Fred Hutchinson Cancer Center, Seattle, WA, USA

    Lisa Levy & Johanna W. Lampe

  4. Pharmaceutical Biochemistry Group, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland

    Thomas Gurry

  5. Myota GmbH, Berlin, Germany

    Thomas Gurry

  6. Center for Phenomic Health, Buck Institute for Research on Aging, Novato, CA, USA

    Noa Rappaport

  7. Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA

    Erin M. Ostrem & Ophelia S. Venturelli

  8. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA

    Ophelia S. Venturelli

  9. Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA

    Ophelia S. Venturelli

  10. Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA

    Ophelia S. Venturelli

  11. Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University of Graz, Graz, Austria

    Christian Diener

  12. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

  13. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

  14. eScience Institute, University of Washington, Seattle, WA, USA

    Sean M. Gibbons

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Contributions

N.Q.-B., S.M.G. and C.D. conceptualized the study. N.Q.B. ran the analyses, interpreted results and authored the first draft of the manuscript. S.M.G. and C.D. provided funding, materials and resources for the work and supervised the work. S.M.G., C.D. and K.R.S. performed the ex vivo fermentation and sampling included in study A and study D. C.D. ran metagenomic analysis. J.W.L., L.L., O.S.V., E.M.O., K.R.S. and T.G. contributed data and resources. T.W. and N.R. provided support with analyses and statistical interpretation. All authors reviewed and edited the manuscript.

Corresponding authors

Correspondence to Christian Diener or Sean M. Gibbons.

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Competing interests

T.G. is an employee/shareholder of Myota, a company focused on developing microbiome-directed prebiotics.

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Nature Microbiology thanks Jens Nielsen, Rachel Carmody and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Predictions of SCFA production using 16S amplicon sequencing or shotgun metagenomic sequencing data show concordance.

Data from Study C included 16S amplicon sequencing as well as shotgun metagenomic sequencing. (a-b) Predictions for butyrate and propionate between models summarized to the genus level from 16S amplicon sequencing data and shotgun metagenome data (butyrate: Pearson’s Correlation r = 0.90, p = 9.9e-11; propionate: Pearson’s Correlation r = 0.52, p = .0058). Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line. (c-d) Predictions for butyrate and propionate from models built using shotgun metagenome data at the genus level and species level (butyrate: Pearson’s Correlation r = 0.72, p = 2.1-5; propionate: Pearson’s Correlation r = 0.33, p = .089). Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line.

Source data

Extended Data Fig. 2 Divergence in SCFA production between controls and fiber-treated samples is related to culture dilution.

Four independent ex vivo studies were used to validate predictions of MCMMs. Each study used a different dilution for the final culture, changing the scale of substrates available to the microbial communities. Illustrated here, the dilution factor, shown next to the study name, seems to show agreement with the divergence in SCFA production between control samples and fiber-treated samples. This was accounted for by diluting the residual fiber available to the microbial communities in the in silico medium. (a) Study A, N = 2 patient-derived fecal samples, (b) Study B, N = 10 patient-derived fecal samples, (c) Study C, N = 8 patient-derived fecal samples, (d) Study D, N = 9 patient-derived fecal samples. In each panel, the central line of the boxplot denotes the median value and the box contains the 25th to 75th percentile of data. The whiskers extend to the extreme points not more than 1.5*interquartile range from the median.

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Extended Data Fig. 3 MCMMs built from shotgun metagenomic sequencing data perform better when constructed at the species level, as compared to the genus level.

MCMMs from ex vivo studies A, C and D were constructed at the (a) genus (butyrate: Pearson’s Correlation r = 0.46, p =3.0e-4; propionate: Pearson’s Correlation r = 0.33, p = .011) and (b) species level (butyrate: Pearson’s Correlation r = 0.51, p = 7.1e-5; propionate: Pearson’s Correlation r = 0.45, p = 5.8e-4). Prediction production rate of butyrate and propionate more closely matched measured production rate in the species level model as compared to the genus level model. Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line. Color encoding indicates the specific study from which each point originates.

Source data

Extended Data Fig. 4 Alpha diversity of communities does not account for differences in SCFA production.

We compared Shannon index, a measure of alpha diversity, against SCFA production in ex vivo communities, as well as between immune response groups in a longitudinal high fiber study. (a) Propionate production in four ex vivo datasets was not consistently explained by alpha diversity (Study A: Pearson’s Correlation r = 0.15, p = .85; Study B, Pearson’s Correlation r = −0.15, p = .43; Study C, Pearson’s Correlation r = −0.021, p = .92; Study D, Pearson’s Correlation r = −0.44, p = .019). In study D, a significant relationship was observed, but this was not consistent between datasets. Pearson’s correlation r value and associated two-tailed p-value were calculated across all points The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line. (b) Butyrate production also showed no consistent correlation with alpha diversity, although a significant difference was again observed within Study D (Study A: Pearson’s Correlation r = 0.29, p = .71; Study B, Pearson’s Correlation r = −0.11, p = .55; Study C, Pearson’s Correlation r = −0.32, p = .12; Study D, Pearson’s Correlation r = −0.60, p = .8.8e-4) Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line. (c) No consistent pattern emerged with regard to alpha diversity between immune response groups throughout the course of the high fiber dietary intervention, as determined by Mann Whitney U test for significance. The central line of the boxplot denotes the median value and the box contains the 25th to 75th percentile of data. The whiskers extend to the extreme points not more than 1.5*interquartile range from the median. N = 18 individual study participants, * = p < 0.05.

Source data

Extended Data Fig. 5 Bacterial biomass shows no significant association with measured SCFA production.

Bacterial biomass, as estimated by shotgun metagenomic reads, showed no association with measured butyrate (a) or propionate (b) production from ex vivo cultures (butyrate: Pearson’s Correlation r = −0.20, p = .13, propionate: Pearson’s Correlation r = −0.007, p = .95). Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line.

Source data

Extended Data Fig. 6 Baseline SCFA measurements show some association with MCMM-predicted flux.

Baseline SCFA measurements from stool samples correlated with MCMM-predicted levels for butyrate (a) but not for propionate (b; butyrate: Pearson’s Correlation r = 0.56, p = 1.2e-5; propionate: Pearson’s Correlation r = 0.18, p = .17). Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line. Color encoding indicates the specific study from which each point originates.

Source data

Extended Data Fig. 7 Metabolomic measurement of butyrate in blood shows weak but significant correlation with MCMM-predicted butyrate production.

MCMM predicted values of butyrate production fluxes from the gut microbiome show weak, but significant association with blood metabolomic measures of circulating butyrate in the Arivale cohort (Pearson’s Correlation r = 0.053, p = .003). Each point represents one individual. Pearson’s correlation r value and associated two-tailed p-value were calculated across all points. The dashed black line denotes a linear regression line and the gray area denotes the 95% confidence interval of the regression line.

Source data

Extended Data Table 1 External Data Collection
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Extended Data Table 2 Within-group Pearson r and p for ex vivo fluxomic studies (Fig. 3)
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Extended Data Table 3 Beta coefficients and p values for significant associations between blood chemistries and predicted SCFA levels, Arivale Cohort. P-values based
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Quinn-Bohmann, N., Wilmanski, T., Sarmiento, K.R. et al. Microbial community-scale metabolic modelling predicts personalized short-chain fatty acid production profiles in the human gut. Nat Microbiol 9, 1700–1712 (2024). https://doi.org/10.1038/s41564-024-01728-4

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