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The microbiota programs DNA methylation to control intestinal homeostasis and inflammation

Nature Microbiology volume 5, pages 610–619 (2020)Cite this article

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Abstract

Although much research has been done on the diversity of the gut microbiome, little is known about how it influences intestinal homeostasis under normal and pathogenic conditions. Epigenetic mechanisms have recently been suggested to operate at the interface between the microbiota and the intestinal epithelium. We performed whole-genome bisulfite sequencing on conventionally raised and germ-free mice, and discovered that exposure to commensal microbiota induced localized DNA methylation changes at regulatory elements, which are TET2/3-dependent. This culminated in the activation of a set of ‘early sentinel’ response genes to maintain intestinal homeostasis. Furthermore, we demonstrated that exposure to the microbiota in dextran sodium sulfate-induced acute inflammation results in profound DNA methylation and chromatin accessibility changes at regulatory elements, leading to alterations in gene expression programs enriched in colitis- and colon-cancer-associated functions. Finally, by employing genetic interventions, we show that microbiota-induced epigenetic programming is necessary for proper intestinal homeostasis in vivo.

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Fig. 1: WGBS of colonic crypt cells isolated from GF versus CNV mice.
Fig. 2: Methylation changes in acute inflammation affect gene expression.
Fig. 3: ATAC-seq analysis of intestinal colitis.
Fig. 4: Expression and methylation changes of inflamed colon in the absence of microbiota.
Fig. 5: Molecular mechanism of microbiota-induced demethylation.

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Data availability

The data to support the findings of this study are available from the corresponding authors upon reasonable request. All sequencing data are available from the GEO database under accession number GSE137037. Source data for Figs. 2a–e, 4a,b,g and 5a–g and Extended Data Figs. 2e, 4b–g,i and 5a,b are included in this article.

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Acknowledgements

We thank all members of our groups for helpful discussions. This work was supported by research grants from the Israel Academy of Sciences (grant 734/13 Y.B.), the Israel Cancer Research Foundation (grant 211410 to Y.B.), The Emanuel Rubin Chair in Medical Sciences (Y.B.), the Israel Center of Excellence Program (grant 1796/12 to Y.B.), the Helmholtz-Israel-Cooperation in Personalized Medicine (to Y.B. and F.L.), the Helmholtz program ‘Aging and Metabolic Programming’ (AMPro, to F.L.) and the German-Israeli Foundation (grant 1424 to Y.B. and F.L.).

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Author notes

  1. These authors jointly supervised this work: Frank Lyko, Yehudit Bergman.

Authors and Affiliations

  1. Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, Hebrew University Medical School, Jerusalem, Israel

    Ihab Ansari, Meshi Ridnik, Daphne Cohen, Monther Abu-Remaileh & Yehudit Bergman

  2. Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany

    Günter Raddatz, Julian Gutekunst & Frank Lyko

  3. Department of Immunology, The Weizmann Institute of Science, Rehovot, Israel

    Timur Tuganbaev, Hagit Shapiro & Eran Elinav

  4. The Lautenberg Center for Immunology, Institute for Medical Research Israel-Canada, Hebrew University Medical School, Jerusalem, Israel

    Eli Pikarsky

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Contributions

I.A. conceived and carried out most of the experiments, and analysed and interpreted the results. M.R. prepared the samples, targeted bisulfite, qPCR and ATAC-seq analyses. D.C. performed targeted bisulfite analyses. M.A.-R. initiated the acute inflammation experiments. T.T performed and analysed the FACS experiments. H.S. conducted experiments with germ free mice. G.R., J.G. and I.A. analysed and interpreted the genome-wide data. E.P. evaluated all histological samples. E.E., E.P., F.L. and Y.B. designed and supervised this study. I.A, F.L. and Y.B. wrote the paper.

Corresponding author

Correspondence to Yehudit Bergman.

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Extended data

Extended Data Fig. 1 Microbiota induces transcriptional alterations.

a, Pie chart showing the number of significantly differentially expressed genes (1182) with a fold-change of ≥2, relative to germ free (GF). Conventional (CNV) upregulated genes are shown in red and downregulated genes are shown in green. b, Ingenuity pathway analysis of the 358 downregulated genes from (a). The highly enriched functions from the most highly enriched categories are shown. c, Gene Ontology (GO) analysis of the CNV 824 upregulated genes from (a). The highly enriched biological processes from the enriched categories are shown. P values (b,c) were calculated using two-tailed Fisher’s exact test. d, Expression levels of proliferation genes (Mki67 and Top2a) in GF (n = 3) and CNV (n = 3), data were extracted from RNA-seq analysis. e, Mki67 staining on distal colon specimens from GF (n = 3) and CNV (n = 3) mice. Scale bar 100 µm. Quantification of Mki67-positive cells is also shown. Significance (d,e) was determined using two-sided t-test and is expressed as the mean ± SEM.

Extended Data Fig. 2 Microbiota induces DNA methylation changes.

a, Average DNA methylation ratios of various intragenic sub-segments are shown for germ free (GF) (n = 2, yellow and green) and conventional (CNV) (n = 2, blue and red) mice. b, Average methylation profiles of all promoters in all 4 mice that were analyzed by whole-genome bisulfite sequencing. c, Average methylation profiles of all canyons in all 4 mice that were analyzed by whole-genome bisulfite sequencing. d, Number of unmethylated regions (UMRs) and low methylated regions (LMRs) in GF and CNV samples. e, Bisulfite sequencing results for LMRs defined by comparing CNV versus GF mice. The heatmap shows average methylation ratios of 4 LMR amplicons from GF (sorted intestinal epithelial cells (IECs) n = 4 and crypts n = 3) and CNV (sorted IECs n = 3 and crypts n = 4). P values were calculated using two-sided t-test for GF versus CNV IECs and for GF versus CNV crypts. The precise P values can be found in Source Data.

Source data

Extended Data Fig. 3 Acute inflammation in conventional (CNV) mice provokes DNA methylation changes.

a, A schematic diagram of the protocol used to induce acute inflammation. Briefly, acute inflammation was induced by administration of 2% DSS in the drinking water for 5 days followed by regular drinking water for an additional 16 days. b, Average global DNA methylation ratios are shown for CNV (n = 2, blue) and DSS-treated CNV (CNV/DSS) (n = 2, orange) mice, respectively. c, Average DNA methylation ratios of various intragenic sub-segments are shown for CNV (n = 2, yellow and green) and CNV/DSS (n = 2, blue and red) mice. d, Methylation and lamina-associated domain (LAD) tracks of mouse chromosome 4 (blue CNV, red CNV/DSS). e, Average methylation profiles of all canyons in all 4 mice that were analyzed by whole-genome bisulfite sequencing. f, Bar graph showing the number of up- and down- regulated genes in CNV/DSS versus CNV samples associated with hyper- and hypo- methylated promoters. g, Gene Ontology (GO) analysis of the upregulated genes (n = 185) associated with hypomethylated promoters in CNV/DSS compared to CNV. The highly enriched processes are shown (P values were calculated using two-tailed Fisher’s exact test). h, Diseases-related with the upregulated genes (n = 185) associated with hypomethylated promoter in CNV/DSS compared to CNV samples (P values were calculated using two-tailed Fisher’s exact test, adjusted p-value calculated using the Benjamini-Hochberg method for correction for multiple hypotheses testing).

Extended Data Fig. 4 Validation analyses of LMRs induced by acute inflammation.

a, Comparison of average LMR methylation levels in conventional (CNV) (n = 2) and DSS-treated CNV (CNV/DSS) (n = 2) mice. The upper (lower) line indicates the positions in the plot where CNV/DSS is exactly 0.1 hypermethylated (hypomethylated) compared to CNV. There are 20061 (14585) LMRs which are more than 0.1 hypermethylated (hypomethylated) in CNV/DSS versus CNV. b, Bisulfite sequencing results for LMRs defined in CNV versus CNV/DSS mice. The heatmap shows average methylation ratios of 5 LMR amplicons from CNV sorted intestinal epithelial cells (IECs) (n = 5) and CNV/DSS IECs (n = 5) mice. c, Changes in body weight of CNV (n = 5) and CNV/DSS (n = 7) mice and (d) disease activity index (DAI) were monitored daily. e,f, on day 21, mice weight and colon length (respectively) were measured. g, Histological score shows the combined score of inflammatory cell infiltration and tissue damage. h, Hematoxylin and eosin (H&E)-stained histologic images of the colon from CNV and CNV/DSS mice. Scale bar 100 µm. i, Bisulfite sequencing results for indicated LMRs defined in CNV versus CNV/DSS mice. The heatmap shows average methylation ratios of 5 LMR amplicons from CNV crypts (n = 5) and CNV/DSS crypts (n = 5) isolated from mice raised in a different animal facility than in (b). j, Average methylation profiles of hypomethylated LMRs-containing NF-κB and AP-1 binding sites, respectively. Significance (b-g and i) was determined using two-sided t-test and is expressed as the mean ± SEM. The exact P values (b-d and i) can be found in Source Data. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Source data

Extended Data Fig. 5 Validation analyses of LMR methylation changes in sterile inflammation.

a, Bisulfite sequencing results for indicated LMRs defined in germ free (GF) versus DSS-treated GF (GF/DSS) mice. The heatmap shows average methylation ratios of LMRs from GF crypt IECs (n = 5) and CNV/DSS crypt IECs (n = 5). b, Bisulfite sequencing results for indicated LMRs defined in GF versus GF/DSS mice. The heatmap shows average methylation ratios of LMRs from GF FACS-sorted intestinal epithelial cells (IECs) (n = 5) and CNV/DSS FACS-sorted IECs (n = 5). P values (a,b) were calculated using two-sided t-test. The exact P values can be found in Source Data. c, Pie chart indicating the number of up- and down- regulated genes that are associated with hypermethylated- and hypomethylated- LMRs in GF/DSS compared to GF mice.

Source data

Extended Data Fig. 6 TET2/3 play a key role in microbiota-induced DNA demethylation.

a, Expression levels of TET genes in conventional (CNV) (n = 3) and DSS-treated CNV (CNV/DSS) (n = 3) mice, data extracted from RNA-seq analysis. b, Normalized expression levels of TET2 and TET3 genes from colonic crypts isolated from germ free (GF) (n = 5), CNV (n = 5) and antibiotics (Abx)-treated (n = 5) mice. c, Normalized expression levels of TET2 and TET3 genes from WT (n = 4) and LPS-treated (n = 4) organoids. Significance (a-c) was determined using two-sided t-test and is expressed as the mean ± SEM. d, Average global DNA methylation ratios are shown for Tet2/3 fl/fl (WT, n = 3) and 3 Tet2/3 fl/fl VillinCre (KO, n = 3) mice, respectively. Significance (d) was determined using two-sided Welch two-sample t-test and is expressed as the mean ± SEM. e, Comparison of average LMR methylation levels in Tet2/3 fl/fl and Tet2/3 fl/fl VillinCre mice. The upper (lower) line indicates the positions in the plot where TET2/3 KO is exactly 0.1 hypermethylated (hypomethylated) compared to TET2/3 WT. f, Bar plot of all LMRs identified in TET2/3 WT and KO mice, emphasizing the hypermethylation in KO mice.

Supplementary information

Supplementary Information

Supplementary Fig. 1 and tables.

Reporting Summary

Supplementary Table 1

Supplementary Tables 3 and 8.

Source data

Source Data Fig. 2

Source data of the in vivo experiments and statistical analyses.

Source Data Fig. 4

Source data of the in vivo experiments and statistical analyses.

Source Data Fig. 5

Source data of the in vivo experiments and statistical analyses.

Source Data Extended Data Fig. 2

Source data of the in vivo experiments and statistical analyses.

Source Data Extended Data Fig. 4

Source data of the in vivo experiments and statistical analyses.

Source Data Extended Data Fig. 5

Source data of the in vivo experiments and statistical analyses.

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Ansari, I., Raddatz, G., Gutekunst, J. et al. The microbiota programs DNA methylation to control intestinal homeostasis and inflammation. Nat Microbiol 5, 610–619 (2020). https://doi.org/10.1038/s41564-019-0659-3

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