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Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes

Nature Immunology volume 18, pages 552–562 (2017)Cite this article

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An Erratum to this article was published on 18 October 2017

An Erratum to this article was published on 19 July 2017

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Abstract

Gut dysbiosis might underlie the pathogenesis of type 1 diabetes. In mice of the non-obese diabetic (NOD) strain, we found that key features of disease correlated inversely with blood and fecal concentrations of the microbial metabolites acetate and butyrate. We therefore fed NOD mice specialized diets designed to release large amounts of acetate or butyrate after bacterial fermentation in the colon. Each diet provided a high degree of protection from diabetes, even when administered after breakdown of immunotolerance. Feeding mice a combined acetate- and butyrate-yielding diet provided complete protection, which suggested that acetate and butyrate might operate through distinct mechanisms. Acetate markedly decreased the frequency of autoreactive T cells in lymphoid tissues, through effects on B cells and their ability to expand populations of autoreactive T cells. A diet containing butyrate boosted the number and function of regulatory T cells, whereas acetate- and butyrate-yielding diets enhanced gut integrity and decreased serum concentration of diabetogenic cytokines such as IL-21. Medicinal foods or metabolites might represent an effective and natural approach for countering the numerous immunological defects that contribute to T cell–dependent autoimmune diseases.

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Figure 1: SCFA concentrations correlate with the incidence of T1D in NOD mice.
Figure 2: SCFAs delivered via diets protect against diabetes.
Figure 3: Acetate diminishes the frequency of autoimmune T cells.
Figure 4: An acetate-yielding diet affects B cell function and gene transcription.
Figure 5: Butyrate enhances Treg cell biology, which contributes to protection against diabetes.
Figure 6: Acetate- and butyrate-yielding diets improve parameters important to the pathogenesis of T1D, including LPS, IL-22 and IL-21.
Figure 7: Diets alter microbial ecology, which contributes to protection against diabetes.

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Change history

  • 05 April 2017

    In the version of this article initially published online, the callout for Figure 4g was incorrectly attributed, and Figure 5d was displayed incorrectly. The errors have been corrected in the print, PDF and HTML versions of the article.

  • 08 May 2017

    In the version of this article initially published, the graph curves in Figure 2b were displayed incorrectly. The error has been corrected in the HTML and PDF versions of the article.

  • 20 September 2017

    In the supplementary information originally posted online, Supplementary Tables 2 and 3 were missing titles and legends. The error has been corrected in this file as of 20 September 2017.

  • 18 October 2017

    Nat. Immunol. 18, 552–562 (2017); published online 27 March 2017; corrected online 5 April 2017; corrected after print 8 May 2017 In the version of this article initially published, the graph curves in Figure 2b were displayed incorrectly. The error has been corrected in the HTML and PDF versions ofthe article.

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Acknowledgements

We thank S. Akira (Osaka University) for Myd88−/− mice; D.R. Powell, S. Archer, Deltagen, B. Scherer, H. Thomas, G. Jhala, L. Mason, H.Y. Goh, M. Udugama, F. Lim, Y.A. Leong, C. Ang Kim Lian, C. McKenzie and Monash Animal Services for assistance; the NIH Tetramer Facility for the IGRP and BDC2.5 tetramers; and the Monash Histology Platform of Monash University. Supported by the Juvenile Diabetes Research Foundation (3-2013-94), the Diabetes Australia Research Trust (Y14M1-MARE), and the Australian National Health and Medical Research Council (program grant 1016953 and Australia Fellowship 606947).

Author information

Authors and Affiliations

  1. Department of Biochemistry, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia

    Eliana Mariño, James L Richards, Keiran H McLeod, Yu Anne Yap, Jacinta Knight, Craig McKenzie, Laurence Macia, Alison Thorburn, Lee H Wong & Charles R Mackay

  2. Central Queensland University, School of Medical and Applied Sciences, Rockhampton, Australia

    Dragana Stanley

  3. Institute for Immunology, Ludwig Maximilians University, Munich, Munich, Germany.,

    Jan Kranich

  4. Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Ana Carolina Oliveira

  5. Department of Anatomy and Developmental Biology, Monash University, Clayton, Australia

    Fernando J Rossello, Christian M Nefzger & Jose M Polo

  6. Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Clayton, Australia

    Fernando J Rossello, Christian M Nefzger & Jose M Polo

  7. Australian Regenerative Medicine Institute, Monash University, Clayton, Australia

    Fernando J Rossello, Christian M Nefzger & Jose M Polo

  8. Islet Biology Laboratory, St Vincent's Institute, Fitzroy, Australia

    Balasubramanian Krishnamurthy

  9. Nutritional Immunometabolism Node Laboratory, Charles Perkins Centre, University of Sydney, Sydney, Australia

    Laurence Macia

  10. School of Medical Sciences, University of Sydney, Sydney, Australia

    Laurence Macia

  11. Comparative Genomics Centre, Molecular Sciences, James Cook University, Townsville, Australia

    Alan G Baxter

  12. Harry Perkins Institute for Medical Research, Nedlands, Australia

    Grant Morahan

  13. Department of Microbiology, Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, Australia

    Robert J Moore

  14. School of Science, RMIT University, Bundoora West Campus, Bundoora, Australia

    Robert J Moore

  15. CSIRO Health and Biosecurity, North Ryde, Australia

    Trevor J Lockett

  16. CSIRO Health and Biosecurity, Adelaide, Australia

    Julie M Clarke & David L Topping

  17. Walter & Eliza Hall Institute of Medical Research, Parkville, Australia

    Leonard C Harrison

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  1. Eliana Mariño
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Contributions

E.M. and C.R.M. formulated different elements of the study and wrote the manuscript; E.M. supervised the experimental work and performed flow cytometry, adoptive-transfer experiments and sorting of cells; J.L.R. performed flow cytometry, disease-incidence studies, analysis of pancreata histology and assignment of insulitis scores; K.H.M. performed adoptive-transfer studies, single-sorting experiments, qPCR and GF disease-incidence studies and assisted D.S. in microbiota studies; Y.A.Y. performed flow cytometry of colon samples, in vitro experiments and extraction of fecal DNA; J. Knight. performed flow cytometry; J. Knight and J. Kranich performed disease-incidence studies; C.M. and L.H.W. performed chromatin-immunoprecipitation studies; A.C.O. performed ELISA; F.J.R. did bioinformatics; B.K. provided NOD.Foxp3-GFP mice; C.M.N. performed real-time PCR analysis and Treg cell single-cell expression analysis; L.M. and A.T. edited the manuscript; A.G.B. provided Myd88−/− mice; G.M. performed and assisted with genome-wide studies of single-nucleotide polymorphisms; J.M.P. did bioinformatics and gene-expression analysis; R.J.M. sequenced fecal bacteria DNA; T.J.L., J.M.C. and D.L.T. provided the diets and assisted with intellectual input; L.C.H. performed GF studies and manuscript editing; C.R.M. conceived of and initiated the overall project and provided the overarching hypothesis; and all authors provided specialized technical or intellectual support to the project.

Corresponding authors

Correspondence to Eliana Mariño or Charles R Mackay.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Effect of SCFAs on important parameters for diabetes development.

(a) T1D incidence in germ-free (GF) NOD.MyD88−/− mice (n=11) vs. specific pathogen-free (SPF) NOD.MyD88−/− mice (n=14); ****P<0.0001, Mantel-Cox log rank test. Concentrations of acetate, butyrate and propionate in feces of (b) 5-10 week-old female SPF and GF NOD and NOD.MyD88−/− mice, and (c) NP-fed age-matched female vs male NOD and C57BL/6 mice, n ≥ 4. (d) Body weights of 15 week-old female NOD mice (n=5 per group) fed with NP, HAMS, HAMSA or HAMSB diets. (e) Energy intake measured from NOD mice fed different diets. Panels (b,c,e) were analyzed by one-way or (d) two-way ANOVA with Bonferroni’s multiple comparisons test. ****P<0.0001, ***P<0.001, **P<0.01, *P<0.05. For all graphs, data represent mean ± SEM; each symbol represents an individual mouse. All data are representatives of three independent experiments. (f) T1D incidence in female NOD mice fed for 5 weeks with HAMSA diet starting at age 5 weeks (n=11) or 10 weeks (n=15). Blue arrows in the graph represent the start and end time point on HAMSA diet. NS= not significant (5 weeks vs 10 weeks). (g) T1D incidence in HAMSA- and HAMSB-fed female NOD mice starting from birth to 30 weeks of age (n=10 per group). Mantel-Cox log rank test.

Supplementary Figure 2 Effect of dietary SCFAs on autoreactive T cells.

(a) Gating strategy used for identification of autoreactive IGRP tetramer+ CD8+ T cells and BDC2.5 tetramer+ CD4+ T cells. TUM and hu CLIP were used as tetramer controls respectively. Data is representative from 10,000 events acquired from the total viable lymphocyte gate. Representative plots and cumulative data showing frequency and number of PLN and splenic (b) autoreactive IGRP tetramer+ CD8+ gated from total CD8+ T cells and (c) BDC2.5 tetramer+ CD4+ gated from total CD4+ T cells from 15 week-old female NOD mice fed NP, HAMS, HAMSA or HAMSB diet. Data from HAMS and HAMSA for spleen in b is the same as that is shown in Figure 3a. Numbers shown within the plots are representative of the frequency of tetramer+ T cells. (d) Representative plots and cumulative data showing frequency and number of IGRP tetramer+ CD8+ T cells in 6 week-old NOD8.3 mice. All data represent mean ± SEM. ****P<0.0001, **P<0.001, *P<0.05 (NP vs HAMS; HAMSA vs HAMS; HAMSB vs HAMS). Data analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test. Data is representative of at least two independent experiments, n ≥ 2.

Supplementary Figure 3 SCFAs modulate B cell subsets and surface marker expression.

(a) Gating strategy used for splenic B cell subsets identification. (b) Cumulative data showing frequency (upper panel) and numbers (lower panel) of TR B cells, T2-MZ B cells, FO B cells and MZ B cells. ****P<0.0001, **P<0.01, *P<0.05 (NP vs HAMS; HAMSA vs HAMS; HAMSB vs HAMS). Data shown is from three independent experiments, n ≥ 4. (c) Mean fluorescence index (MFI) +/- SD of surface protein expression for MHCI and CD86 on a per-cell basis on splenic IgM+B220+ B cells (NOD mice fed with diets), n ≥ 5. ***P<0.001, **P<0.01, *P<0.05, NS (HAMSA vs HAMS; HAMSB vs HAMS). (d) Real time PCR showing expression of CD86 and Il12 on sorted splenic CD21high CD23low (MZ B) and CD21mid CD23high (FO B) cells (gated from total IgM+B220+ B cells), n=3. **P<0.01 (HAMSA vs NP; HAMSB vs NP). (e) Real time PCR showing fold change in expression of C80/B7.1, C80/B7.2, B2M and PRDM1 relative to β-actin in splenic IgM+B220+ B cells from of 15 week-old HAMS-, HAMSA-, or HAMSB-fed female NOD mice, n ≥ 4. **P<0.01, *P<0.05, NS (HAMSA vs HAMS; HAMSB vs HAMS). Data in panels b-e are representative of at least three experiments analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test.

Supplementary Figure 4 SCFA effect on gene expression and APC function.

(a) Gene expression profile between purified splenic IgM+B220+ B cells from HAMSA (x-axis) against HAMSB (y-axis), relative to HAMS contrast. Circles in red represent log2 fold-change expressed genes with FDR < 0.05 for differential expression test between HAMSA and HAMSB diets. (b) Cumulative data showing frequency of CFSE-labeled NOD.8.3 CD8+ T as CFSE−Vβ8.1/8.3+CD8+ T cells in PLN. **P=0.0042 (HAMSA vs HAMS); NS (NP vs HAMS and HAMSB vs HAMS). (c) Cumulative data showing frequency of CFSE−Vβ8.1/8.3+CD8+ T cells in MLN. (d) 3[H]-thymidine uptake by CD4+CD25− T cells cultured with LPS stimulated B cells and DCs from NOD mice in the absence or presence of 100 μM acetate or butyrate pulsed or not with insulin peptide ± IL-2 in vitro. B cells ## P=0.0015 (No SCFAs, insulin vs no peptide), **P=0.021, *P=0.001, (Insulin, acetate vs PBS; butyrate vs PBS) and DCs *P=0.0318 (Insulin, butyrate vs PBS). Panels b and c analyzed by one-way ANOVA and d by two-way ANOVA with Bonferroni’s multiple comparisons test. Data is representative of three reproducible experiments.

Supplementary Figure 5 SCFAs affect Treg cells from colon and peripheral tissue.

(a) Cumulative data showing frequency and numbers of CD4+FoxP3+CD103+ T cells from total CD4+ T cells isolated from the colon of 15 week-old female NOD mice fed HAMS, HAMSA or HAMSB diet. Data represent mean ± SD, n=5 mice. **P<0.01 (HAMSA vs HAMS; HAMSB vs HAMS). (b) Facs plots showing frequency of splenic, PLN and MLN CD4+FoxP3+ T cells. Data represent mean ± SEM, n=5-6 mice. (c) Gating strategy used for identification of CD4+ Foxp3+ T cells gated from total splenic CD4+ T cells. Data is representative from 10,000 events acquired from the total viable lymphocyte gate. (d) Flow cytometric analysis of Foxp3 protein expression on a per-cell basis from splenic and PLN Foxp3+ Treg cells from NOD-SCID recipient mice after 3 weeks post-transfer of CD4+Foxp3− T cells from NOD.FoxP3-GFP mice, fed with different diets as indicated. The data are shown as mean fluorescence intensity (MFI) mean ± SEM. Spleen **P=0.0023 (HAMSA vs HAMS); ****P<0.0001 (HAMSB vs HAMS). (e) Gating strategy used for single sorted CD45RBlowCD25+CD4+ T cells from PLN showing expression of Foxp3. (f) Flow cytometric analysis of Foxp3 protein expression on a per-cell basis from PLN Foxp3+ Treg cells from 15 week-old NOD/Lt mice fed with HAMS, HAMSA and HAMSB diets as indicated. *P<0.048 (HAMSB vs HAMS). Data analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test. Data are representative of at least three independent experiments.

Supplementary Figure 6 The role of the metabolite-sensing receptor GPR43 in NOD mice.

(a) C57.Gpr43−/− mice were backcrossed 13 generations onto the NOD strain (NOD.Gpr43−/−). Once fully backcrossed, NOD.Gpr43−/− mice were genotyped at over 70,000 SNPs genome-wide. DNA from liver was purified and genotyped using the Mega-MUGA array (Geneseek, NB). Genotypes were compared to the reference (C57BL/6) alleles and to NOD alleles determined by the NOD genome sequence. The knockout was produced on the FVB background, so haplotypes are depicted as coming from the NOD genome (blue) or non-NOD genomes (red); grey indicates non-informative regions in which C57BL/6 and NOD have the same genotypes. This figure shows the strain of origin of haplotypes on each mouse chromosome, with the physical size of each chromosome shown on the X axis. This analysis demonstrated that all chromosomes were derived from the NOD strain, except for a region around the Gpr43 locus on chromosome 7. Thus, these mice harbor all NOD T1D susceptibility loci including the Idd7 and Idd27 loci mapped to ~19Mb and ~80-120 MB on chromosome 7, respectively. This enabled us to confirm that there have been no T1D susceptibility genes reported in the non-NOD interval. (b, c) T1D incidence in female NOD.Gpr43+/+ and NOD.Gpr43−/− littermates fed NP, HAMSA diet. *P=0.0392 (NOD.Gpr43+/+ mice vs NOD.Gpr43−/− fed HAMSA diet); *P=0.0067 (NOD.Gpr43−/− mice, HAMSB vs NP). Mantel-Cox log-rank test. Data shown is from two independent experiments (NOD.Gpr43−/− on HAMSA in b were from a different experiment to those shown in c). (d) Cumulative data showing absolute numbers of splenic and PLN CD4+FoxP3+ T cells and (e) IgM+B220+ B cells, n ≥ 5. Data were analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test. Data shown is from three independent experiments. (f) Frequency of PLN autoreactive IGRP tetramer+ CD8+ and IAg7/BDC2.5 tetramer+ CD4+ T cells from 15 week-old female NOD.Gpr43+/+ and NOD.Gpr43−/− mice fed NP or HAMSA diet. Data were analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test. IGRP+CD8+ T cells ***P<0.001 (NOD.Gpr43−/− vs NOD.Gpr43+/+ NP-fed; NOD.Gpr43−/− vs NOD.Gpr43+/+ HAMSA-fed; NS, NOD.Gpr43+/+ fed HAMSA vs NP); BDC2.5 CD4+ T cells (NOD.Gpr43+/+, HAMSA vs NP; NS, NOD.Gpr43−/− vs NOD.Gpr43+/+ NP-fed; NOD.Gpr43−/− vs NOD.Gpr43+/+ HAMSA-fed). Data represent mean ± SEM. Data shown is from three independent experiments.

Supplementary Figure 7 Acetate is associated with beneficial changes in gut microbial ecology.

(a) Microbial profile analysis of feces from NOD donors and GF re-colonized NOD mice fed HAMS, HAMSA and HAMSB diet by PCoA diagrams based on unweighted (left panel) and weighted (right panel) Unifrac distance metrics. The bacterial communities of different diets were significantly different based on both weighted and unweighted (P<1E−7) Unifrac, 1E7 permutations and Adonis permutational multivariate statistics. The legend represents the microbiota from donors NOD mice (circles) and GF NOD mice re-colonized by fecal transfer (FT) with HAMS, HAMSA and HAMSB modified microbiota in the same colour (squares). (b) Relative abundance of selected bacterial populations at (genus level) in donor NOD mice fed NP, HAMS, HAMSA or HAMSB diet and in GF NOD mice after fecal transfer. Data were analyzed by one-way ANOVA with Bonferroni’s multiple comparisons test. *P<0.05 (HAMSA vs HAMS; HAMSB vs HAMS; HAMSA.FT vs HAMS.FT); **P<0.01 (HAMS vs NP). Data represent mean ± SEM; each symbol represents individual mice. All data are representatives of two independent experiments. (c) Bar chart showing distribution of genera detected in feces from SPF donor NOD mice and GF NOD mice after fecal transfer (FT) for different diets, n=5-6 per group. Each genus is represented by a different colour and is proportional to the relative abundance in each sample. The legend shows the genera with relative abundance higher than 1%.

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Mariño, E., Richards, J., McLeod, K. et al. Gut microbial metabolites limit the frequency of autoimmune T cells and protect against type 1 diabetes. Nat Immunol 18, 552–562 (2017). https://doi.org/10.1038/ni.3713

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