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Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism

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Abstract

Nutrient handling is an essential function of the gastrointestinal tract. Hormonal responses of small intestinal enteroendocrine cells (EECs) have been extensively studied but much less is known about the role of colonic EECs in metabolic regulation. To address this core question, we investigated a mouse model deficient in colonic EECs. Here we show that colonic EEC deficiency leads to hyperphagia and obesity. Furthermore, colonic EEC deficiency results in altered microbiota composition and metabolism, which we found through antibiotic treatment, germ-free rederivation and transfer to germ-free recipients, to be both necessary and sufficient for the development of obesity. Moreover, studying stool and blood metabolomes, we show that differential glutamate production by intestinal microbiota corresponds to increased appetite and that colonic glutamate administration can directly increase food intake. These observations shed light on an unanticipated host–microbiota axis in the colon, part of a larger gut–brain axis, that regulates host metabolism and body weight.

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Fig. 1: Loss of colonic EECs results in hyperphagia and obesity.
Fig. 2: Colonic EECs regulate glucose homeostasis.
Fig. 3: Colonic EEC loss leads to intestinal dysbiosis and is associated with obesity.
Fig. 4: Colonic EECs control normal colonic motility and regulate the faecal metabolome.
Fig. 5: Dysbiosis in EECΔCol mice leads to hyperphagia and obesity.
Fig. 6: EEC loss is associated with a rise in faecal glutamate and is associated with hyperphagia.
Fig. 7: Bacterially derived colonic glutamate induces hyperphagia.

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Data availability

The published transcriptomics data for Extended Data Fig. 2 are available from the Gene Expression Omnibus under accession number GSE134202. The 16S rRNA data and metatranscriptomic data are deposited at the Sequence Read Archive (accession number PRJNA1063789, which includes 99 data files). Raw quantification data for metabolomics and l-glutamic acid measurements are deposited in the Science Data Bank (https://www.Scidb.cn/en/, entry identifier 31253.11.sciencedb.15521). To our knowledge, all information required to reanalyse the data reported here is publicly available. Source data are provided with this paper. Any additional data we inadvertently missed will be shared on reasonable request.

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Acknowledgements

We thank the following individuals and core facilities for their invaluable assistance with many aspects of this project: R. Gordillo at the Mouse Metabolic Phenoty** Core; J. Shelton and the Histo Pathology Core; the staff at Dallas Children’s Health Histology Laboratory; J. Sudderth at the Metabolomics Core at CRI/UTSW; and C. Behrendt. We also thank G. Gradwohl (Istitut de Genetique et de Biologie Moleculaire et Cellulaire, France) and A. Leiter (University of Massachusetts) for sharing with us the Neurog3fl/fl model. We also thank our colleagues J. Horton, S. Winter, J. Pfeiffer, J. Zigman, P. Scherer, M. Mitsche, L. Hooper and R. DeBerardinis for important comments and feedback. This work was supported by the following sources: UTSWNORC grant under NIDDK/NIH award number P30DK127984, UTSW GF facility under R01 DK070855 and funding from the HHMI; the Pollock Family Center for Research in Inflammatory Bowel Disease (to E.B.); NIH through R01DK130957 (to E.B.), R01DK107733 (to E.B.), K08DK127197 (to L.S.-D.), T32DK007745 (to J.L.S.), UL1TR003163 (to J.G.M.), P01HL160487 (to J.G.M.), P01DK119130 (to J.K.E.), R01DK127274 (to J.K.E.), R01DK100659 (to J.K.E.), R01HG011035 (to X.Z.) and U01AI169298 (to X.Z.); the MMK foundation (to L.S.-D.); the Nancy Cain Marcus and Jeffrey A Marcus Scholar in Medical Research, in honour of B. S. Vowell (to Y.O.); Pew Scholars Program in Biomedical Science (to Y.O.); the Scientific and Technological Research Program of Chongqing Municipal Education Commission through KJQN2021043 (to S.T.); Natural Science Foundation of Chongqing through CSTB2022NSCQ-MSX1027 (to S.T.); National Science Fund for Distinguished Young Scholars through 32125012 (to D.J.); and Natural Science Foundation of China 92254302 (to D.J.).

Author information

Author notes

  1. These authors jointly supervised this work: Shuai Tan, Luis Sifuentes-Dominguez, Ezra Burstein.

Authors and Affiliations

  1. Department of Endocrinology Children’s Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation base of Child development and Critical Disorders, Chongqing Key Laboratory of Child Rare Diseases in Infection and Immunity, Chongqing, P. R. China

    Shuai Tan, Li Li & Gaohui Zhu

  2. Division of Digestive and Liver Diseases, Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Shuai Tan, Qi Liu, Colin P. Bergstrom, Adam Lopez, Qing Chen & Ezra Burstein

  3. Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Jacobo L. Santolaya, Tiffany Freeney Wright & Luis Sifuentes-Dominguez

  4. Center for Hypothalamic Research, Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Teppei Fujikawa & Joel K. Elmquist

  5. Peter O’Donnell Jr. Brain Institute, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Teppei Fujikawa & Joel K. Elmquist

  6. Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China

    Sensen Chi & Aishun **

  7. Department of Molecular Genetics, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Goncalo Vale & Jeffrey G. McDonald

  8. Center for Human Nutrition, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Goncalo Vale & Jeffrey G. McDonald

  9. Department of Immunology, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Andrew Schmidt, Nguyen Vo & Yuuki Obata

  10. Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Jiwoong Kim & **aowei Zhan

  11. Department of Biochemistry, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Hamid Baniasadi

  12. Molecular Oncology Laboratory, Department of Orthopaedic Surgery and Rehabilitation Medicine, The University of Chicago Medical Center, Chicago, IL, USA

    Tong-Chuan He

  13. Key Laboratory of Birth Defects and Related Diseases of Women and Children, Department of Paediatrics, West China Second University Hospital, State Key Laboratory of Biotherapy and Collaborative Innovation Center of Biotherapy, Sichuan University, Chengdu, China

    Da Jia

  14. Department of Neuroscience, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Joel K. Elmquist

  15. Department of Pharmacology, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Joel K. Elmquist

  16. Department of Molecular Biology, The University of Texas Southwestern Medical Center, Dallas, TX, USA

    Ezra Burstein

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  1. Shuai Tan
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Contributions

S.T., J.L.S., T.F.W., Q.L., T.F., S.C., C.P.B., A.L., Q.C., G.V., J.G.M., A.S., N.V., Y.O., H.B., L.L., G.Z. and L.S.-D. carried out experiments. J.K. and X.Z. assisted in data analysis (microbiome and metatranscriptomics). L.S.-D., S.T., T.-C.H., A.J., D.J., J.K.E. and E.B. conceived the experimental approach, oversaw experiments and wrote the manuscript. S.T., J.L.S., T.F.W. and L.S.-D. performed statistical analysis.

Corresponding authors

Correspondence to Shuai Tan, Luis Sifuentes-Dominguez or Ezra Burstein.

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Nature Metabolism thanks Hervé Blottière, Scott E Kanoski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Yanina-Yasmin Pesch, in collaboration with the Nature Metabolism team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Generation and validation of EEC deficient mice.

(a) Schematic representation of the colonic EEC deficiency model. Created with BioRender.com. (b) qRT-PCR of Neurog3 gene along regions of the mouse bowel. Values are expressed as fold of the average of wild-type animals in the duodenum. Statistics derived from biological replicates (8 weeks of age, n = 3 per group). (c) Morphometric quantification of chromogranin A (CHGA) labeled cells along the intestinal epithelium. At least 10 confocal-acquired immunofluorescence images from each intestinal segment (duodenum, jejunum, ileum, cecum, proximal colon, distal colon) from 3 adult mice of either genotype were used for morphometric analysis (mice of 8 weeks of age). Chromogranin A (CHGA) positive cells were visually counted and normalized to the number of cells per 10 intestinal crypts. (d) Representative confocal images of immunofluorescent stained regions of the mouse intestine. (e) Representative light microscopy images of HE and Alcian blue stained regions of the mouse intestine (one representative animal in each group, n > 3 per group). Mean expression values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

Source data

Extended Data Fig. 2 Colonic EEC deletion is specific.

(a) Volcano plot of differentially expressed genes of bulk transcriptomics from colon of WT and EECΔCol mice and corresponding cellular enrichment in the Tabula muris database (only statistically significant cellular enrichments are shaded in blue, n = 3 mice). (b) qRT-PCR of Gcc along regions of the mouse bowel for the same samples shown in Extended Data Fig. 1b. Statistics derived from biological replicates. (c) qRT-PCR of Insl5 along regions of the mouse bowel for the same samples shown in Extended Data Fig. 1b. Statistics derived from biological replicates. (d) qRT-PCR of Pyy along regions of the mouse bowel for the same samples shown in Extended Data Fig. 1b. Statistics derived from biological replicates. (e) qRT-PCR of Muc2 (a goblet cell related marker) along regions of the mouse bowel for the same samples shown in Extended Data Fig. 1b. Statistics derived from biological replicates. Mean expression values are represented by bars and individual values by jitter plots. Values are expressed as fold of the average of wild-type animals in the duodenum. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

Source data

Extended Data Fig. 3 Metabolic cage analysis of EECΔCol and WT control.

(a) Heat production, (b) energy expenditure, (c) body weight and (d) total movement was examined in 10-week-old WT or EECΔCol mice (n = 6 mice). (e) Daily stool output and (f) stool bomb calorimetry from 11-week-old male WT and EECΔCol mice (n = 5 mice). (g) Concentration of individual fatty acids from stool of 16 and 23-week-old HFD-fed male EECΔCol or WT (aggregate of two repeats) mice (n = 7 WT or 8 EECΔCol mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

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Extended Data Fig. 4 An inducible model of colonic EEC loss recapitulates the hyperphagia induced obesity phenotype.

(a) Schematic representation of the inducible colonic EEC deficiency model. Created with BioRender.com. (b) Representative genoty** gels of novel Neurog3 floxed allele. (c) Targeting strategy for generation of novel Neurog3 conditional floxed allele. (d) qRT-PCR of Neurog3 along regions of the mouse bowel (10 weeks of age, n = 3 mice). Statistics derived from biological replicates. (e) Body weight curve and (f) food intake for vehicle (Veh) or tamoxifen (TMX) treated iEECΔCol male mice on a regular diet (n = 8 mice). (g) Body weight curve and (h) food intake for iEECΔCol or tamoxifen (TMX) treated non-Cre control mice on a regular diet (n = 7 No Cre or 8 iEECΔCol mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons. *P < 0.05, **P < 0.01, ***P < 0.001.

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Extended Data Fig. 5 Glucose intolerance is accentuated by HFD in EECΔCol mice.

(a) Body weight of experimental mice at time of IP glucose administration (n = 5 mice). (b) IPGTT, (c) serum insulin and (d) serum GLP-1 determination following IP glucose load on 8-week-old HFD-fed male mice (n = 5 mice). (e) ITT and (f) body weight of 8-week-old HFD-fed male mice (n = 6 mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

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Extended Data Fig. 6 Neurog3 heterozygous animals do not display metabolic phenotypes.

(a) Body weight curve of Neurog3fl/fl or Neurog3R/fl regular chow-fed male mice (n = 6 Neurog3fl/fl or 22 Neurog3R/fl mice). (b) Weight curve for 8-week-old Neurog3fl/fl or Neurog3R/fl HFD-fed (n = 5 Neurog3fl/fl or 16 Neurog3R/fl mice) and 16-week-old regular chow-fed male mice (n = 6 Neurog3fl/fl or 31 Neurog3R/fl mice). (c) IPGTT and body weight (n = 5 mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

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Extended Data Fig. 7 Pyy deficiency is not associated with hyperphagia.

(a) Targeting strategy for generation of a Pyy knockout allele. (b) Representative genoty** gels of novel Pyy knockout allele. (c) PYY plasma levels after overnight fast from serum of 8-week-old genotype-segregated male mice of either genotype on regular chow mice (n = 10 Pyy+/+ or 11 Pyy-/- mice). (d) Weight curve for Pyy-/- or Pyy+/+ HFD-fed and regular chow-fed male mice and (e) average daily food intake at 8 weeks of age (n = 8 mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test used for all pairwise comparisons.

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Extended Data Fig. 8 Colonic EEC deficiency is associated with dysbiosis.

(a) Relative frequency of bacterial phyla as determined by 16 s sequencing from co-housed and genotype segregated WT and EECΔCol animals (n = 5 mice). (b) Volcano plots of differential abundance testing of fecal microbiota species (sequence variants) from WT and EECΔCol male mice at 4 (top, n = 8 WT or 5 EECΔCol mice) and 6–8 (bottom, n = 14 mice) weeks of age. Only statistically significant features are shaded in color. (c) Alpha diversity by feature abundance, ß-diversity PCoA by unweighed UNIFRAC (PERMANOVA p = 0.004), and volcano plots of differential abundance testing of sequence variants from iEECΔCol or tamoxifen (TMX) treated non-Cre control mice (13 weeks of age, n = 6 mice). (d) Representative microphotographs of HE-stained liver (left panels) and adipose tissue (right panels) of male EECΔCol mice under vehicle or antibiotic treatment, scale bar 100 µm (one representative animal in each group, n > 3 per group). (e) PCoA plots of ß-diversity analysis of FMT from donor (WT or iEECΔCol) and GF WT recipient mice (n = 5 mice). Respective PERMANOVA calculated p values are noted in the panels.

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Extended Data Fig. 9 Female mice are protected from obesity.

(a) Weight curves of female WT and EECΔCol animals with HFD starting at 18 weeks of life (n = 6 mice). (b) PCoA of ß-diversity by unweighed UNIFRAC of 6–8 week old male (n = 14 mice) and female (n = 14 WT or 15 EECΔCol mice). PERMANOVA p = 0.001. (c) Volcano plot of differential abundance testing of fecal microbiota species (sequence variants) from WT and EECΔCol male and female mice at 6–8 weeks of age. (d) Volcano plot of differential abundance testing in different genders of EECΔCol mice. Only statistically significant features are shaded in color. Mean values and error bars representing ± s.e.m. are shown. Two-tailed unpaired t-test used for all pairwise comparisons.

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Extended Data Fig. 10 Fecal L-glutamic acid is diet independent and associated with obesity.

(a) Fecal L-glutamic acid concentration from 13-week-old WT or iEECΔCol mice in fasting (6 hours) and refed conditions (1 hour after refeeding, n = 6 mice). (b) L-glutamate KEGG metabolic pathway. Differentially abundant KOs found through metatranscriptomics are highlighted in blue and red. (c) Weight of 10-week-old WT or ob/ob male mice (n = 5 mice). (d) Fecal L-glutamic acid concentration (n = 5 mice) and (e) morphometric quantification of chromogranin A (CHGA) labeled cells along the intestinal epithelium (n = 6 mice). Mean values are represented by bars and individual values by jitter plots. Error bars are ± s.e.m. Two-tailed unpaired t-test were used for pairwise comparisons.

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Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–4.

Supplementary Video 1

The video for intestinal transit time, CMMC intestinal frequency and interval in WT and EECΔCol mice.

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Tan, S., Santolaya, J.L., Wright, T.F. et al. Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism. Nat Metab 6, 1076–1091 (2024). https://doi.org/10.1038/s42255-024-01044-5

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