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Histone acetylome-wide associations in immune cells from individuals with active Mycobacterium tuberculosis infection

Abstract

Host cell chromatin changes are thought to play an important role in the pathogenesis of infectious diseases. Here we describe a histone acetylome-wide association study (HAWAS) of an infectious disease, on the basis of genome-wide H3K27 acetylation profiling of peripheral blood granulocytes and monocytes from persons with active Mycobacterium tuberculosis (Mtb) infection and healthy controls. We detected >2,000 differentially acetylated loci in either cell type in a Singapore Chinese discovery cohort (n = 46), which were validated in a subsequent multi-ethnic Singapore cohort (n = 29), as well as a longitudinal cohort from South Africa (n = 26), thus demonstrating that HAWAS can be independently corroborated. Acetylation changes were correlated with differential gene expression. Differential acetylation was enriched near potassium channel genes, including KCNJ15, which modulates apoptosis and promotes Mtb clearance in vitro. We performed histone acetylation quantitative trait locus (haQTL) analysis on the dataset and identified 69 candidate causal variants for immune phenotypes among granulocyte haQTLs and 83 among monocyte haQTLs. Our study provides proof-of-principle for HAWAS to infer mechanisms of host response to pathogens.

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Fig. 1: Histone acetylome-wide association study of TB.
Fig. 2: Differentially acetylated (DA) peaks between ATB and HC.
Fig. 3: Reproducibility of differential H3K27 acetylation in monocytes.
Fig. 4: Functional-enrichment analysis of DA peaks, KCNJ15 UCSC browser view and SE peak heights.
Fig. 5: Functional characterization of KCNJ15/Kir4.2.
Fig. 6: Landscape of haQTLs in granulocytes and monocytes.

Data availability

ChIP-seq data have been deposited at the European Genome-phenome Archive EGA (http://www.ebi.ac.uk/ega/), which is hosted by the EBI, under accession number EGAS00001003118. RNA-seq data have been deposited at NCBI’s Gene Expression Omnibus through GEO Series accession number GSE126614. Source data are provided with this paper.

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Acknowledgements

We thank the personnel of the Biological Resource Centre’s BSL3 laboratory and the Defence Science Organization’s BSL3 laboratory for facilitating this study. This project was supported by core funds from Singapore’s Agency for Science, Technology and Research (A*STAR); A*STAR translational programme in infectious disease no. IAF11003; A*STAR Joint Council Office Grant No. JCO-CDA15302FG151; BMRC-SERC grant no. 1121480006; SIgN Immunomonitoring platform grant no. IAF311006; SIgN and ID Labs core fund; BMRC transition fund grant no. H16/99/b0/011; Swiss National Foundation grant no. 310030-173240; the European Union’s Research and Innovation Program grant no. TBVAC2020 643381; and Nanyang Technological University Singapore’s Lee Kong Chian School of Medicine Start Up Grant. R.J.W. was supported by the Francis Crick Institute, which is funded by Cancer Research UK (FC0012018), MRC (UK) (FC0010218) and Wellcome (FC0010218). He also received support from Wellcome (104803, 203135) and NIH V01AI115940. For the purposes of open access, the authors have applied a CC-BY public copyright to any author-accepted manuscript arising from this submission.

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Authors and Affiliations

Authors

Contributions

S.P. and A.S. conceived the idea. R.C.H.d.R., J.P., G.D.L., A.S. and S.P. designed the study and analysed the data. R.C.H.d.R., J.P., P.K., C.Y.C., C.L., C.R., G.C.W., N.A.R., Z.Z., J.L., B.L., F.Z., M.P., S.T.O., H.S.H., M.M., X.L. and A.L. performed the experiments. S.G. performed heritability analyses. K.G.C. designed and analysed the electrophysiological and APG-4 data. D.K. and O.R. contributed to overexpression experiments. C.B.E.C., Y.T.W., E.D.B. and R.J.W. provided clinical samples and interpreted the results. C.C.K. designed and performed array genotyping. J.P., R.C.H.d.R., A.S. and S.P. wrote the manuscript. All authors discussed results and contributed to the manuscript.

Corresponding authors

Correspondence to Amit Singhal or Shyam Prabhakar.

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Nature Microbiology thanks Maziar Divangahi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Regression of confounding variables in H3K27ac ChIP-seq data.

(a) Method for selecting covariates to be regressed in discovery samples. The covariate that explained the most variance at each iteration of the method was plotted. (b) MA plot of peak heights for the discovery and validation cohorts. Only consistent samples were used.

Source data

Extended Data Fig. 2 ChIP-seq peak height z-scores, fold change correlation of ChIP-seq and RNA-seq data, and ethnicity-specific fold-change correlation for ChIP-seq data.

(a) DA peak height z-scores for the four ATB vs. HC Singapore (discovery and validation) datasets from samples from the core set. Each row represents a single individual, and each column a DA peak ascertained from the discovery cohort. The peaks in the top and bottom panels are arranged in the same order. Only consistent samples are displayed (granulocytes discovery: ATB = 16, HC = 20; monocytes discovery: ATB = 11, HC = 16; granulocytes validation: ATB = 12, HC = 14; monocytes validation: ATB = 8, HC = 21). (b,c) Correlation between differential acetylation and differential gene expression (related to Supplementary Table 18). DE and DA gene sets were defined using the default FDR threshold of 0.05 (b) as well as a more stringent FDR threshold of 0.01 (c). DA peaks were associated with DE genes using the GREAT tool and the Pearson correlation of log-fold change was calculated. P-values indicate concordance of the fold-change direction, calculated using a one-sided hypergeometric test. (d) Ethnicity-specific fold-change correlation between discovery and validation cohorts. Black dots are DA both in discovery and validation cohorts. Pink dots are DA only in the discovery cohort. R indicates Pearson correlation coefficient of log2(fold-change) between discovery and validation. P-value of concordance in fold-change direction for shared DA peaks was calculated using two-sided Fisher’s exact test.

Source data

Extended Data Fig. 3 Transcription factors and super enhancers in the KCNJ15 locus.

(a) Predicted gene regulatory interactions in granulocytes between differentially expressed TFs associated with DA peaks belonging to enriched GO terms in Fig. 5a. Blue boxes: genes. Orange ovals: proteins. Orange arrows: translation. Green arrows: autoregulatory loops. Red arrows: cross-regulation of paralogous TFs. Black arrows: cross-regulation of other TFs. (b) KCNJ15 locus: green and red ticks indicate DA peaks up-regulated in response to infection in granulocytes and monocytes respectively. Orange and blue boxes indicate the corresponding super-enhancer regions. Blue lines: predicted transcription factor binding sites for master transcription factors in DA peaks.

Source data

Extended Data Fig. 4 Role of KCNJ15/Kir4.2 during Mycobacterial infection.

(a) KCNJ15 mRNA was assessed by qRT-PCR in Mtb-infected primary monocytes. KCNJ15 expression normalized to GAPDH, relative to uninfected (UN) cells, is shown. P-values from two-sided, unpaired t-test. Data from 2 independent experiments. (b) Western blot analysis of Kir4.2 and GAPDH in Mtb-infected primary monocytes as in a. Data from 2 independent experiments. (c) Immunostaining (confocal microscopy) of Kir4.2 in THP-1 cells, which was increased upon M. bovis BCG infection. Green: Kir4.2; red: mCherry- BCG. Scale-bars: 2μm. Data from 3 independent experiments. (d) Uninfected or mcherry-BCG infected THP-1 monocytes stained for endo-lysosomes with Lysotracker (LTR) and KCNJ15/Kir4.2, 24 h post-infection. Images from BCG-infected cells are shown. Scale bar 2 µm. Data from 2 independent experiments. (e) Compiled data of Kir4.2 co-localization with LTR in uninfected (UN) and BCG-infected (IN) cells, as shown in d. n = 10-12 cells; P-value from two-sided, unpaired t-test. (f) Percentage of BCG co-localized with either endo-lysosomes only or Kir4.2 only, from d. n = 10-12 cells. (g) siRNA-mediated knockdown of KCNJ15 in THP-1 cells (KCNJ15KD), RT-PCR of KCNJ15 gene. Data from 5 independent experiments P-value from two-tailed, unpaired t-test. (h) Western blot for Kir4.2 protein in KCNJ15KD THP-1 cells. Data from 2 independent experiments. (i) CD14+ primary monocytes were transduced with either lentiviral plasmid carrying human KCNJ15 cDNA or the control plasmid. Transduction efficiency was determined by FACS using GFP which is constitutively expressed. Pink: un-transduced cells; orange: cells transduced with empty control vector; blue: cells transduced with KCNJ15 plasmid. Data from 3 independent experiments. (j) Transduction efficiency of primary monocytes in (i) was determined by Western blotting. Data from 2 independent experiments. (k) Growth curve of KCNJ15OE and Control cells (ControlOE) in vitro in RPMI medium, indicating no defect in the growth of KCNJ15OE cells. Cells were seeded at 105 cells/ml. Data from 2 independent experiments. Data in a and g: box and whiskers, minimum to maximum. Data in e,f,k: mean±SEM. Statistically significant P-values: * P < = 0.05, ** P < = 0.01, ***P < = 0.001, **** P < = 0.0001.

Source data

Extended Data Fig. 5 Comparison of haQTLs from different studies.

Comparison of granulocyte (a) and monocyte (b) haQTL effect size between the Singapore discovery cohort (Chinese) and the European cohort19. Each dot represents an haQTL in the Singapore discovery cohort (x-axis) and a corresponding European19 haQTL in LD with the former. Effect size is defined as the regression coefficient beta relating ChIP-seq peak height to genotype. R: Pearson correlation. P-value: two-sided Pearson correlation P-value. (c) Cumulative minor allele frequency distributions for granulocyte and monocyte haQTLs in the Singapore discovery cohort (Chinese) and the European dataset19. Blue curves: shared haQTLs. Red curves: cohort-specific haQTLs. The statistical significance (Kolmogorov-Smirnov test, two-sample, two-sided) of the shift in allele frequency between shared and non-shared haQTLs is indicated in each panel.

Source data

Extended Data Fig. 6 Comparison of haQTLs with eQTLs.

(a) – (d) Pie charts of granulocyte and monocyte eQTLs from previous studies that are in LD with corresponding haQTLs from this study. Enr: fold-enrichment; P-value: Z-score test (details are in Methods section on “Statistical significance of LD between haQTLs and eQTLs”). (e,f) Comparison of effect size between haQTLs in Singapore discovery cohort and eQTLs in the European dataset19 for (e) granulocytes and (f) monocytes. Each dot represents an haQTL in the Singapore discovery cohort (x-axis) and a corresponding eQTL in the European19 dataset that is in LD with the former. Effect size is defined as the regression coefficient beta relating ChIP-seq peak height to genotype. R: Pearson correlation. P-value: two-sided Pearson correlation P-value.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Tables 1,14,18,32,35–37 and Figs. 1–9.

Reporting Summary

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Supplementary Table

Supplementary Tables 2–13,15–17,19–31,33,34,38–53.

41564_2021_1049_MOESM5_ESM.mp4

Supplementary Video Localization of Kir4.2 to BCG-containing lysosomes. mcherry-BCG-infected THP-1 monocytes were stained for lysosomes and KCNJ15/Kir4.2 24 h post infection. Images were acquired using 3D-SIM. Scale bars, 2 µm.

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del Rosario, R.C.H., Poschmann, J., Lim, C. et al. Histone acetylome-wide associations in immune cells from individuals with active Mycobacterium tuberculosis infection. Nat Microbiol 7, 312–326 (2022). https://doi.org/10.1038/s41564-021-01049-w

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