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Dichotomous engagement of HDAC3 activity governs inflammatory responses

Abstract

The histone deacetylases (HDACs) are a superfamily of chromatin-modifying enzymes that silence transcription through the modification of histones. Among them, HDAC3 is unique in that interaction with nuclear receptor corepressors 1 and 2 (NCoR1/2) is required to engage its catalytic activity1,2,3. However, global loss of HDAC3 also results in the repression of transcription, the mechanism of which is currently unclear4,5,6,7,8. Here we report that, during the activation of macrophages by lipopolysaccharides, HDAC3 is recruited to activating transcription factor 2 (ATF2)-bound sites without NCoR1/2 and activates the expression of inflammatory genes through a non-canonical mechanism. By contrast, the deacetylase activity of HDAC3 is selectively engaged at ATF3-bound sites that suppress Toll-like receptor signalling. Loss of HDAC3 in macrophages safeguards mice from lethal exposure to lipopolysaccharides, but this protection is not conferred upon genetic or pharmacological abolition of the catalytic activity of HDAC3. Our findings show that HDAC3 is a dichotomous transcriptional activator and repressor, with a non-canonical deacetylase-independent function that is vital for the innate immune system.

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Fig. 1: HDAC3 activates LPS-stimulated inflammatory gene expression in a DA-independent manner.
Fig. 2: Differential recruitment and transcriptional functions of HDAC3 at DA-independent and DA-dependent LPS-responsive genes.
Fig. 3: Engagement of HDAC3 enzyme activity is determined by its differential recruitment by ATF2 and ATF3.
Fig. 4: Dichotomous functions of HDAC3 orchestrate the inflammatory response to endotoxin in vivo.

Data availability

All sequencing data from RNA-seq, ChIP–seq and GRO-seq analyses have been deposited to the Gene Expression Omnibus under accession number GSE140611. ChIP–seq data for Fos, JunB, JunD and CREB in LPS-stimulated BMDMs were obtained from GSE99895. All other data are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank D. Steger and H. Goodarzi for discussions, J. Marinis and J. DiSpirito for help with setting up the macrophage system, and the Functional Genomics Core of the Penn Diabetes Research Center (National Institutes of Health (NIH) P30 19525) for next-generation sequencing. This work was supported by NIH R01 DK43806 (M.A.L.), NIH T32 DK07314 (A.K.H.), American Diabetes Association 1-18-PDF-126 (M.A.) and the JPB Foundation.

Author information

Authors and Affiliations

Authors

Contributions

H.C.B.N. and M.A.L. conceived the project, designed experiments, analysed results and wrote the manuscript; H.C.B.N. performed animal experiments and LPS susceptibility assays on genetic mouse models, tissue culture, HDAC3/ATF2/ATF3/p65 immunoblots, RNA-seq, HDAC3/ATF2/NCor1/NCoR2 ChIP–seq, and GRO-seq experiments, as well as bioinformatic analyses. M.A. performed isolation of peritoneal macrophages from the LPS-injected genetic mouse model for RNA-seq/ChIP–seq analyses, and the LPS susceptibility assay on C57BL/6, control and MHD3KO mice treated with different doses of SAHA. A.K.H. performed the H3K27Ac immunoblot, and ChIP–seq analyses for H3K27Ac, ATF3 and p65.

Corresponding author

Correspondence to Mitchell A. Lazar.

Ethics declarations

Competing interests

M.A.L. receives research support from Pfizer for unrelated work, serves as an advisory board member for Pfizer, has consulted for Novartis, Madrigal, Calico and Third Rock, and holds equity in KDAC Therapeutics.

Additional information

Peer review information Nature thanks Gioacchino Natoli, Inez Rogatsky and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 DA-independent and DA-dependent functions of HDAC3 in the inflammatory response to LPS.

a, Scatter plots of RNA-seq experiments in control, MHD3KO, HDAC3(Y298F) and WT-rescue BMDM, with or without LPS, showing correlation between biological replicates using either log2 transformation (log2(x+1)), variance stabilizing transformation (vst), or regularized-logarithm transformation (rlog) of normalized read counts. b, Principal component analysis (PCA) plot using the vst values from RNA-seq experiments in control, MHD3KO, HDAC3(Y298F) and WT-rescue BMDM, with or without LPS (n = 4 biological replicates). c, Heat map of sample-to-sample distances using the vst values from RNA-seq experiments in control, MHD3KO, HDAC3(Y298F) and WT-rescue BMDM, with or without LPS (n = 4 biological replicates). d, Empirical cumulative distribution function (CDF) and associated two-sided Kolmogorov–Smirnov test D statistics of LPS-stimulated changes in statistically modelled DA-independent gene expression (385 genes) for control, MHD3KO, HDAC3(Y298F) and WT-rescue BMDM (n = 4 biological replicates). e, Empirical CDF and associated two-sided Kolmogorov–Smirnov test D statistics of LPS-stimulated changes in statistically modelled DA-dependent gene expression (377 genes) for control, MHD3KO, HDAC3(Y298F) and WT-rescue BMDM (n = 4 biological replicates). f, Heat map showing 142 LPS-downregulated genes that were rescued by wild-type HDAC3 but not by HDAC3(Y298F) (DA-dependent genes: n = 4 biological replicates, DE cutoff: |log2FC| > 1, two-sided Benjamini–Hochberg-adjusted FDR < 0.05 as determined by edgeR likelihood ratio test). g, Gene ontology analysis of 142 LPS-downregulated genes (n = 4 biological replicates). q-values represent Benjamini–Hochberg-adjusted one-sided hypergeometric P values for over-representation as determined by Enrichr. h, LPS-stimulated (762 genes) and IL4-stimulated (405 genes) transcriptomic correlation of MHD3KO and HDAC3(Y298F), compared with control BMDM (n = 4 biological replicates). Heat map showing Spearman’s correlation ρ values with corresponding two-sided P values calculated by t-tests.

Extended Data Fig. 2 Differential recruitment and enhancer activity of HDAC3 at LPS-responsive genes.

a, Scatter plots of ChIP–seq experiments for HDAC3 and H3K27Ac in vehicle or LPS-treated control BMDM (n = 3 biological replicates) showing correlation between biological replicates using tag counts per base-pair (TPB) at identified peak regions. Pearson’s P values were calculated with two-sided t-tests. b, Average density profiles in RPM of HDAC3 ChIP–seq showing mean ± s.e.m. (n = 3 biological replicates) at all identified HDAC3 peaks (10,966 total) in control and MHD3KO macrophages, with or without LPS. c, Average density profiles in RPM of GRO-seq showing mean ± s.e.m. (n = 3 biological replicates) at all identified eRNAs (12,192 total) in control and MHD3KO BMDM, with or without LPS. d, Average density profiles in RPM of H3K27Ac showing mean ± s.e.m. (n = 3 biological replicates) at all identified H3K27Ac peaks (50,247 total) in control and MHD3KO macrophages, with or without LPS. e, Western blot (performed independently twice) of HDAC3 and H3K27Ac protein levels in control and MHD3KO macrophages, with vinculin and histone H3 as loading controls. f, g, Genome-browser tracks showing three biologically replicated examples of enhancer and gene body activity as measured by GRO-seq relative to HDAC3 ChIP–seq peaks in control and MHD3KO macrophages, with or without LPS near DA-independent (f) or DA-dependent (g) genes. h, Top de novo enriched motifs at HDAC3-bound genomic regions (10,966 sites from n = 3 biological replicates). Statistics were determined by HOMER with one-sided hypergeometric P values for over-representation.

Extended Data Fig. 3 ATF2 and ATF3 differentially mediate HDAC3 transcriptional effects at DA-independent and DA-dependent sites, respectively.

a, Comparison of the performance of TBA classifiers modelled against DA-independent and DA-dependent 200-bp-DNA sequences as measured by the area under the receiver operating characteristic curve (auROC, n = 5 independent train-test iterations, data shown as mean ± s.d.). b, Heat map showing relative enrichment of several AP-1 family member DNA motifs as determined by TBA with likelihood ratio test against DA-independent and DA-dependent 200-bp-DNA sequences across 5 train-test iterations. The non-redundant, merged motifs from TBA include ATF1, 4, 5, 6, 7, Jun family, Fos family, and other related bZIP factors. c, Average density profiles in RPM of Fos, JunB, JunD and CREB ChIP–seqs obtained from Gene Expression Omnibus database at HDAC3-bound sites near either DA-independent or DA-dependent genes in LPS-treated BMDM. d, Average density profiles in RPM of bidirectional eRNA transcription measured by GRO-seq showing mean ± s.e.m. (n = 3 biological replicates) at HDAC3-bound enhancers with ATF2 motif (1,680 sites) or ATF3 motif (3,673 sites) (two-sided Wilcoxon’s P = 8.6 × 10−127) in LPS-treated BMDM. e, f, Genome-browser tracks showing HDAC3, ATF2 and ATF3 ChIP–seq peaks at enhancer elements upstream of Clec2d (e) or Gas6 (f), as well as gene body activity as measured by RNA-seq in control, MHD3KO, HDAC3(Y298F) and WT rescue macrophages, with or without LPS. g, h, Dual-luciferase assays of transcription driven by the Clec2d (g) or Gas6 (h) enhancers in control, MHD3KO, HDAC3(Y298F) rescue, WT-rescue (WT), ATF2-depleted and ATF3-depleted BMDM, with or without LPS stimulation. Data shown are mean ± s.d., n = 4 biological replicates. P values were calculated by one-way ANOVA. DA-independent sites = 172, DA-dependent sites = 141.

Extended Data Fig. 4 ATF2 and ATF3 recruit HDAC3 to sites near DA-independent and DA-dependent genes, respectively.

a, Scatter plots of ChIP–seq experiments for ATF2, ATF3, NCoR1, NCoR2 and p65 in LPS-treated BMDM (n = 3 biological replicates) showing correlation between biological replicates using average tag counts per base-pair (TPB) at identified peak regions. Pearson’s P values were calculated with two-sided t-tests. b, Relative gene expression levels of Atf2, Atf3 and p65 (also known as Rela) as measured by qPCR in control (siC), ATF2-depleted (siAtf2), ATF3-depleted (siAtf3), or p65-depleted (sip65) BMDM (n = 3 biological replicates). Data shown mean ± s.d., P values calculated by two-sided Student’s t-test. c, Western blots (performed once) of ATF2, ATF3 and p65 protein levels with vinculin as loading control for control (siControl), ATF2-depleted (siAtf2), ATF3-depleted (siAtf3) or p65-depleted (sip65) BMDM, each with 3 independent siRNAs. d, Heat map of sample-to-sample distances using the vst values from RNA-seq experiments in control (siC), ATF2-depleted (siAtf2), ATF3-depleted (siAtf3) or p65-depleted (sip65) BMDM, with or without LPS (n = 3). e, f, Genome-browser tracks showing biologically replicated examples of HDAC3 ChIP–seq peaks in control (siControl), ATF2-depleted (siAtf2), ATF3-depleted (siAtf3) or p65-depleted (sip65) LPS-stimulated BMDM near DA-independent (e) or DA-dependent (f) genes. g, h, Average density profiles in RPM of p65 ChIP–seq showing mean ± s.e.m. (n = 3 biological replicates) at HDAC3-bound sites near DA-independent genes (Control LPS versus MHD3KO LPS two-sided Wilcoxon’s P = 0.063) (g) or DA-dependent genes (Control LPS versus MHD3KO LPS two-sided Wilcoxon’s P = 2.4 × 10−16) in control and MHD3KO BMDM, with or without LPS (h). i, j, Box-and-whisker plot showing minimum, maximum, median, first quartile and third quartile from quantification of average ChIP–seq signal (n = 3 biological replicates) in RPM for input, HDAC3, NCoR1 and NCoR2 at HDAC3-bound sites near DA-independent (i) or DA-dependent (j) genes. P values were calculated with two-sided Mann–Whitney test. k, l, Average density profiles in RPM showing mean ± s.e.m. (n = 3 biological replicates) of NCoR1 (two-sided Wilcoxon’s P = 4.7 × 10−4) (k) and NCoR2 (two-sided Wilcoxon’s P = 1.2 × 10−5) (l) genomic colocalization with either ATF2 (19,594 peaks) or ATF3 (57,041 peaks). m, n, Genome-browser tracks showing three biologically replicated examples of ChIP–seq peaks for HDAC3, ATF2, ATF3, NCoR1, and NCoR2 in LPS-stimulated BMDM near DA-independent (k) or DA-dependent (l) genes. DA-independent sites = 172, DA-dependent sites = 141.

Extended Data Fig. 5 Loss of HDAC3 protein but not deacetylase activity protects mice from acute endotoxic shock.

a, Kaplan–Meier curves of wild-type C57BL/6 mice injected with increasing doses of LPS and observed for 120 h (n = 4 independent mice). b, Serum cytokine concentrations measured by ELISA for IL6 and TNF in 10 mg kg−1 LPS-injected control, MHD3KO, NSDAD, C57BL/6 mice (n = 5 independent mice) administered with increasing doses of SAHA (25, 100, 400 mg kg−1), and untreated control (n = 3 independent mice). Data shown mean ± s.d. P values calculated by one-way ANOVA. c, PCA plot using the vst values from RNA-seq experiments of in vivo peritoneal macrophages from control, MHD3KO and NSDAD mice injected with vehicle control (PBS) or 10 mg kg−1 LPS (n = 4 biological replicates, except for KO vehicle and NSDAD LPS with n = 3 biological replicates). d, Scatter plot showing correlation between in vitro (n = 4 biological replicates) and in vivo (n = 3 biological replicates) DA-independent differential gene expression (174 genes). Pearson’s P value was calculated with two-sided t-test. e, Empirical CDF and associated two-sided Kolmogorov–Smirnov test D statistics of LPS-stimulated changes in statistically modelled DA-independent in vivo gene expression (251 genes) for peritoneal macrophages from control (n = 4 biological replicates), MHD3KO (n = 4 biological replicates) and NSDAD (n = 3 biological replicates). f, Heat map showing 177 LPS-downregulated, HDAC3-dependent differentially expressed genes in vivo that were not rescued by NSDAD (DA-dependent genes: n = 4 biological replicates, except for KO vehicle and NSDAD LPS with n = 3 biological replicates; DE cutoff: |log2FC| > 1, two-sided Benjamini–Hochberg-adjusted FDR < 0.05 as determined by edgeR likelihood ratio test). g, Empirical CDF and associated two-sided Kolmogorov–Smirnov test D statistics of LPS-stimulated changes in statistically modelled DA-dependent in vivo gene expression (404 genes) for peritoneal macrophages from control (n = 4 biological replicates), MHD3KO (n = 4 biological replicates) and NSDAD (n = 3 biological replicates). h, Gene set enrichment analysis (GSEA) for the in vivo DA-dependent transcriptome (177 genes) showing enrichment of KEGG Toll-like receptor signalling pathway that was more upregulated in peritoneal macrophages from LPS-treated NSDAD mice (n = 3 biological replicates) than from LPS-treated control mice (n = 4 biological replicates). Bar graph showing Tlr4 gene expression level in normalized counts of peritoneal macrophages from control and NSDAD treated with LPS. Data shown mean ± s.d., two-sided P value was calculated by Student’s t-test. i, j, Average density profiles in RPM of HDAC3 ChIP–seq showing mean ± s.e.m. (n = 3 biological replicates) at HDAC3-bound sites near 167 in vivo DA-independent genes (two-sided Wilcoxon’s P = 1.7 × 10−23) (i) or 165 DA-dependent genes (two-sided Wilcoxon’s P = 0.153) (j) in peritoneal macrophages from vehicle- or LPS-treated control mice.

Extended Data Fig. 6 Dose-dependent effects of HDAC inhibitor SAHA on endotoxin susceptibility.

a, Kaplan–Meier curves of C57BL/6 mice subjected to 10 mg kg−1 of intraperitoneal injection of purified LPS, with increasing doses of SAHA (25, 100, 400 mg kg−1). SAHA vehicle (veh), 10% DMSO in PBS. *P = 0.036, **P = 0.001, ***P = 0.00065, n = 10 independent mice, calculated by two-sided Mantel–Cox test. b, Heat map showing LPS-induced transcriptional changes as a function of log2(fold change) (log2FC) of 179 in vivo DA-independent genes in LPS-exposed BMDM treated with increasing dose of SAHA (25, 100, 400 nM, n = 3 biological replicates) and peritoneal macrophages from LPS-exposed MHD3KO (n = 4 biological replicates) and NSDAD mice (n = 3 biological replicates). c, Average density profiles in RPM of HDAC3 ChIP–seq showing means of 3 biological replicates at HDAC3-bound sites near 172 DA-independent genes. d, Genome-browser tracks showing 3 biologically replicated examples of ChIP–seq peaks for HDAC3 in LPS-stimulated BMDM treated with increasing dose of SAHA (25, 100, 400 nM) near DA-independent genes. e, Average density profiles in RPM of HDAC3 ChIP–seq showing means of 3 biological replicates at HDAC3-bound sites near 141 DA-dependent genes. f, Genome-browser tracks showing 3 biologically replicated examples of ChIP–seq peaks for HDAC3 in LPS-stimulated BMDM treated with increasing dose of SAHA (25, 100, 400 nM) near DA-dependent genes.

Supplementary information

Supplementary Information

This file contains Supplementary Figure 1. Raw images of blots. Unprocessed images of scanned immunoblot membranes for data shown in Fig. 1b, Extended Data Fig. 2e, and Extended Data Fig. 4c. Supplementary Table 1. List of siRNAs, Ultramers, primers, and antibodies. The table include sequences for IDT siRNAs against Atf2, Atf3, and Rela, IDT ultramers for luciferase assays, primers used for qRT-PCR experiments, as well as antibodies used for immunoprecipitation and Western Blot experiments. Supplementary Table 2. ChIP-seq quality controls reporting the number of mapped reads, number of identified peaks, and corresponding FrIP scores. Supplementary Tables 3a-f. Raw output of de novo motif analyses as performed by HOMER for all performed ChIP-seqs: HDAC3, ATF2, ATF3, p65, NCoR1, and NCoR2. Statistics reporting one-sided hypergeometric unadjusted p-values calculated by HOMER for over-representation or under-representation of target DNA sequence over background DNA sequences.

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Nguyen, H.C.B., Adlanmerini, M., Hauck, A.K. et al. Dichotomous engagement of HDAC3 activity governs inflammatory responses. Nature 584, 286–290 (2020). https://doi.org/10.1038/s41586-020-2576-2

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