Abstract
Post-translational modifications (PTMs) on histones are a key source of regulation on chromatin through impacting cellular processes, including gene expression1. These PTMs often arise from metabolites and are thus impacted by metabolism and environmental cues2,3,4,5,6,7. One class of metabolically regulated PTMs are histone acylations, which include histone acetylation, butyrylation, crotonylation and propionylation3,8. As these PTMs can be derived from short-chain fatty acids, which are generated by the commensal microbiota in the intestinal lumen9,10,11, we aimed to define how microbes impact the host intestinal chromatin landscape, mainly in female mice. Here we show that in addition to acetylation, intestinal epithelial cells from the caecum and distal mouse intestine also harbour high levels of butyrylation and propionylation on lysines 9 and 27 of histone H3. We demonstrate that these acylations are regulated by the microbiota and that histone butyrylation is additionally regulated by the metabolite tributyrin. Tributyrin-regulated gene programmes are correlated with histone butyrylation, which is associated with active gene-regulatory elements and levels of gene expression. Together, our study uncovers a regulatory layer of how the microbiota and metabolites influence the intestinal epithelium through chromatin, demonstrating a physiological setting in which histone acylations are dynamically regulated and associated with gene regulation.
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Data availability
The RNA-seq and ChIP-seq data have been deposited to the NCBI Gene Expression Omnibus under accession code GSE216319. The histone MS data have been deposited to the MassIVE database under dataset MSV000090586. Metabolomics data have been deposited to the Metabolomics Workbench under study ID ST002969. Source data are available at Figshare (https://doi.org/10.6084/m9.figshare.24749973) and are provided with the paper. Any additional data that support the findings of this study or materials are available from the corresponding author upon request. Source data are provided with this paper.
Code availability
No custom code was used for this study.
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Acknowledgements
We thank all members of the Allis and Mucida laboratories. We thank S. Josefowicz (Weill Cornell Medical College), R. Niec (Memorial Sloan Kettering Cancer Centre), Y. Soto-Feliciano (Massachusetts Institute of Technology) and A. Soshnev (University of Texas San Antonio) for helpful discussions and support. C.D.A. acknowledges the encouragement of the above tri-institutional faculty and The Rockefeller University for financial support. Select schematics were generated using BioRender. L.A.G. was supported by the Shapiro-Silverberg Fund for the Advancement of Translational Research at The Rockefeller University, an NIH Ruth L. Kirschstein NRSA fellowship (F32GM134560) and an NIH MOSAIC Career Award (K99GM143550). P.J.L. was supported by the Crohn’s and Colitis Foundation (RFA 598467 to P.J.L.) and the NIH (T32CA009140). B.A.G. was supported by the NIH (R01AI118891 and R01HD106051 to B.A.G.). We thank MilliporeSigma for the generation of H3K27bu, H3K27pr and H3K9pr antibodies. We acknowledge the help and support of the following Resource Centres at The Rockefeller University: Bio-Imaging, Genomics, Bioinformatics, Proteomics, and Comparative Bioscience Center, and the Microbiome Core Laboratory at Weill Cornell Medicine. This article is dedicated to the memory of C. David Allis, who died on 8 January 2023.
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L.A.G. and C.D.A. conceptualized this project and wrote the paper with input from all authors. L.A.G., B.S.R., P.J.L., M.R.P., M. Leboeuf, A.M.D., Z.N., M. Lopes, F.N.V. and G.U. conducted experiments, contributed to sample preparation and provided conceptual advice. K.B., T.S.C., B.A.G., D.M. and C.D.A. participated in study supervision.
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Extended data
Extended Data Fig. 1 Chromatograms of histone acylations in the mouse intestine by mass spectrometry.
(A) Chromatograms of fragment ions from precursor histone peptides with acyl marks from female C57BL/6 J mouse caecum. Representative traces are shown out of n = 3 mice.
Extended Data Fig. 2 Fragmentation spectra and comparison of histone acylations.
(A) Fragmentation spectra of unmodified peptides and peptides with H3K27pr, H3K9bu, and H3K9pr. All representative spectra are from histones extracted from female C57BL/6 J mouse caecal samples. Representative traces are shown out of n = 3 mice. (B) Butyrylated peptides are not as readily detected as acetylated peptides at low relative abundances. Concomitant serial dilution of recombinant human nucleosomes with acetylated and butyrylated H3K9 and H3K27 nucleosomes into unmodified nucleosomes were analysed by mass spectrometry.
Extended Data Fig. 3 Targeted mass spectrometry for crotonylated histones in the mouse caecum.
Chromatograms of fragment ions from precursor histone peptides from recombinant standards with crotonylated residues (left) or female C57BL/6 J mouse caecum (right). Representative traces are shown out of n = 9 mice.
Extended Data Fig. 4 Characterization of site-specific butyryl antibodies.
Abbreviations are as follows: ac = acetyl, bu = butyryl, and cr = crotonyl. Testing of antibody specificity using recombinant nucleosomes. 30 or 100 ng recombinant human nucleosomes were run on an SDS-PAGE gel and subjected to immunoblotting. A representative blot is shown from two independent experiments.
Extended Data Fig. 5 Gut microbial composition is altered following antibiotic treatments.
C57BL/6 J female mice were treated either with vehicle (10 g/l Splenda, n = 4 and n = 5), an antibiotic cocktail of 1 g/l ampicillin, 1 g/l neomycin, 0.5 g/l vancomycin, and 0.5 g/l metronidazole (n = 4) or ampicillin alone (n = 5). (A) Caecal weights are increased upon antibiotic treatment. Mice and intact ceca were weighed and the ratio of caecal weight to bodyweight is displayed. Statistical significance was determined by Student’s t-test. Graphs display mean and standard error. (B) Treatment with antibiotics causes alterations in the gut microbiota. 16 S sequencing was performed on DNA extracted from mouse faeces and the top ten bacterial families detected are shown.
Extended Data Fig. 6 Supplemental RNA-seq data.
C57BL/6 J female mice were divided into three different groups (n = 3 biologically independent animals per group): vehicle treated and mock gavaged (Veh_Mock), Ampicillin treated and mock gavaged (Amp_Mock), and Ampicillin treated with tributyrin gavage (Amp_Tri). Caecal IECs were isolated for RNA sequencing. (A) Heatmap of correlation between samples. Pearson correlation was performed of gene expression measurements across samples using DEseq2. (B) Principal component analysis of RNA-seq samples. (C) Table of significant differential gene expression across groups. Gene expression changes were identified using DEseq2 and p-values less than 0.05 were considered significant. The Wald test was used as part of the DESeq2 package for differential gene analysis, along with multiple testing correction using Benjamini-Hochberg false discovery rate to get the padj value. (D) Visualization of expression of select genes in clusters 1 (blue) and 4 (red). Graphs display mean and standard error. * p-value = < 0.05, ** p-value = < 0.01, *** p-value = < 0.001 by one-way ANOVA and adjusted for multiple comparisons. (E) Gene ontology analysis of all clusters of differential gene expression. Over Representation Analysis was performed using a one-sided version of Fisher’s exact test in the clusterProfiler package.
Extended Data Fig. 7 Metabolic profiling of caecal tissues following treatments.
C57BL/6 J female mouse caecal tissue was processed for polar metabolomics analysis by mass spectrometry. (A) Metabolomics heatmap of all metabolites detected. Heatmap displays z-scores of normalized area under the curve for each metabolite across different samples (n = 5 biologically independent animals per group) and treatments. List of individual metabolites can be found in Supplementary Table 1. (B) Volcano plots displaying changes in metabolites comparing ampicillin treated mice to vehicle control. Red dots are metabolites that are significantly changed (p-value < 0.05 by unpaired two-tailed Student’s t-test) and grey line indicates significance threshold. (C) Metabolites related to butyrate metabolism are changing with ampicillin or tributyrin treatments in mice. Metabolites related to butyrate metabolism (3-hydroxybutyrate and butyryl-carnitine) are altered following mouse treatments, while carnitine serves as an example of a metabolite that is largely unchanging and does not follow the same pattern. Graphs display mean and standard error. Statistical significance was determined by one-way ANOVA and adjusted for multiple comparisons (n = 5 biologically independent animals per group).
Extended Data Fig. 8 Gene ontology of top H3K27bu ChIP-seq peaks.
The top 10% of H3K27bu peaks from female C57BL/6 J mouse caecal intestinal epithelial cells were analysed for enrichment of gene ontology categories. The peak counts of each of two replicate ChIP-seq experiments from biologically independent mice were averaged, and then the top 10% used for analysis. Over Representation Analysis was performed using a one-sided version of Fisher’s exact test in the clusterProfiler package. (A) Top 10 most significantly enriched GO categories are displayed. (B) All GO categories related to cellular metabolism, excluding any related to oxidative stress, are displayed.
Supplementary information
Supplementary Table 1
Supplementary Table 1. Sheet 1: gene list and cluster assignments, related to Fig. 3f. Significant genes changing in C57BL/6J female mouse caecal IECs between mice treated with ampicillin plus tributyrin versus ampicillin with mock gavage, Padj < 0.05 by pairwise test, are shown. Sheet 2: GO analysis of differential genes between C57BL/6J female mice treated with ampicillin plus tributyrin versus ampicillin with mock gavage, related to Fig. 3g and Extended Data Fig. 6e. Sheet 3: list of metabolites profiled in C57BL/6J female mouse caecal tissues by MS, related to Fig. 3b,c and Extended Data Fig. 7. Sheet 4: the top 10% of H3K27bu peaks from female C57BL/6J mouse caecal IECs were analysed for enrichment of GO categories, related to Extended Data Fig. 8.
Source data
Source Data Fig. 1
Western blots for Fig. 1b.
Source Data Fig. 2
Western blots for Fig. 2.
Source Data Fig. 3
Western blots for Fig. 3e.
Source Data Fig. 4
Source data for Fig. 4b,c (ChIP consensus peaks and RNA-seq quartiles).
Source Data Fig. 2
Quantitation of western blots for Fig. 2.
Source Data Fig. 3
Quantitation of western blots for Fig. 3e.
Source Data Extended Data Fig. 4
Western blots for Extended Data Fig. 4.
Source Data Extended Data Fig. 5
Source data for Extended Data Fig. 5 (caecal and body weights, 16S family counts).
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Gates, L.A., Reis, B.S., Lund, P.J. et al. Histone butyrylation in the mouse intestine is mediated by the microbiota and associated with regulation of gene expression. Nat Metab 6, 697–707 (2024). https://doi.org/10.1038/s42255-024-00992-2
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DOI: https://doi.org/10.1038/s42255-024-00992-2
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