Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence of a cancer-associated somatic mutation in white blood cells in the absence of overt hematological malignancy. It arises most commonly from loss-of-function mutations in the epigenetic regulators DNMT3A and TET2. CHIP predisposes to both hematological malignancies and atherosclerotic cardiovascular disease in humans. Here we demonstrate that loss of Dnmt3a in myeloid cells increased murine atherosclerosis to a similar degree as previously seen with loss of Tet2. Loss of Dnmt3a enhanced inflammation in macrophages in vitro and generated a distinct adventitial macrophage population in vivo which merges a resident macrophage profile with an inflammatory cytokine signature. These changes surprisingly phenocopy the effect of loss of Tet2. Our results identify a common pathway promoting heightened innate immune cell activation with loss of either gene, providing a biological basis for the excess atherosclerotic disease burden in carriers of these two most prevalent CHIP mutations.
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Data availability
The data supporting the findings of the present study are available within the paper and Supplementary information and Source data files. All sequencing data from the present study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus, accession nos. GSE225773 for scRNA-seq and GSE237599 for bulk RNA-seq. The UCSC mm10 reference genome was used for alignment of sequencing data. Multiplexed imaging data has been deposited in Mendeley (https://data.mendeley.com/datasets/dgyrt473vs/1).
Code availability
R code used to analyze scRNA-seq data has been deposited in GitHub (https://github.com/jkgopa/DNMT3A-single-cell).
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Acknowledgements
We thank O. Rozenblatt-Rosen (of Genentech, previously Broad Institute) and A. Rotem (of AstraZeneca, previously the Dana-Farber Cancer Institute) for expert advice regarding scRNA-seq and A. Sperling and M. Slabicki (both of the Dana-Farber Cancer Institute) for helpful discussions. We further thank the staff at the Klarman Cell Observatory of the Broad Institute, the Rodent Histopathology Core at Harvard Medical School, the Stanford Research Computing Center and M. Leventhal for technical assistance. Illustrations were created with BioRender. P.J.R. acknowledges funding support from the EvansMDS Foundation and the John R. Svenson Endowed Fellowship. T.N. is supported by the Japan Society for the Promotion of Science Overseas Fellowship. A.E.L. reports funding from the John S. LaDue Memorial Fellowship in Cardiology, the American Society of Hematology Research Training Grant for Fellows and the American College of Cardiology Merck Fellowship in Cardiovascular Disease and the Metabolic Syndrome. M.A. is supported by funding from the NIH (grant nos. 5U54CA20997105, 5DP5OD01982205, 1R01CA24063801A1, 5R01AG06827902, 5UH3CA24663303, 5R01CA22952904, 1U24CA22430901, 5R01AG05791504 and 5R01AG05628705), the Department of Defense (grant no. W81XWH2110143) and other funding from the Bill and Malinda Gates Foundation, the Cancer Research Institute, the Parker Center for Cancer Immunotherapy and the Breast Cancer Research Foundation. P.L. receives funding support from the National Heart, Lung, and Blood Institute (grant nos. 1R01HL134892 and 1R01HL163099-01), the RRM Charitable Fund and the Simard Fund. B.L.E. is an investigator of the Howard Hughes Medical Institute and received funding from the NIH (grant nos. P01CA066996, P50CA206963 and R01HL082945). S.J. is supported by the Burroughs Wellcome Fund, EvansMDS Foundation, Ludwig Center for Cancer Stem Cell Research, Leukemia and Lymphoma Society, Knight Initiative for Brain Resilience, the NIH (grant no. DP2-HL157540) and the Leducq Foundation.
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Contributions
P.J.R., P.L., B.L.E. and S.J. designed the study. P.J.R., J.G., A.J.S., H.A., M.M., T.N., T.R., A.E.L., M.F., D.N.C., A.V., E.S., G.S. and S.J. performed and/or analyzed in vivo experiments on atherosclerotic mice, aortic root imaging and flow cytometry. P.J.R., J.G., A.J.S. and S.J. performed and/or analyzed bulk RNA-seq experiments from BMDMs. P.J.R., J.G., D.N., K.B.R., E.S. and S.J. performed and/or analyzed scRNA-seq experiments. P.J.R., J.G., M.B., N.V.G., N.F.G, E.F.M., Z.K. and M.G. performed and/or analyzed MIBI-TOF experiments. A.J.S. and S.J. performed and/or analyzed ELISA experiments. S.B. and M.A. supervised MIBI-TOF experiments. P.L., B.L.E. and S.J. supervised the study. P.J.R. wrote the paper with contributions from all authors. S.J. revised the paper.
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Competing interests
M.B. is presently a consultant for the company IonPath Inc., which manufactured the MIBI-TOF instrument used in this paper. A.E.L. is a member of TenSixteen Bio, outside of the submitted work. E.M. previously consulted for IonPath Inc. M.A. is a board member and shareholder in IonPath, which develops and manufactures the commercial MIBI-TOF platform. P.L. is an unpaid consultant to, or involved in, clinical trials for Amgen, AstraZeneca, Baim Institute, Beren Therapeutics, Esperion Therapeutics, Genentech, Kancera, Kowa Pharmaceuticals, Medimmune, Merck, Moderna, Novo Nordisk, Novartis, Pfizer and Sanofi-Regeneron. P.L. is a member of the scientific advisory board for Amgen, Caristo Diagnostics, Cartesian Therapeutics, CSL Behring, DalCor Pharmaceuticals, Dewpoint Therapeutics, Eulicid Bioimaging, Kancera, Kowa Pharmaceuticals, Olatec Therapeutics, Medimmune, Novartis, PlaqueTec, TenSixteen Bio, Soley Therapeutics and XBiotech, Inc. P.L.’s laboratory has received research funding in the last 2 years from Novartis, Novo Nordisk and Genentech. P.L. is on the Board of Directors of XBiotech, Inc. and has a financial interest in XBiotech, a company developing therapeutic human antibodies; in TenSixteen Bio, a company targeting somatic mosaicism and CHIP to discover and develop new therapeutics to treat age-related diseases; and in Soley Therapeutics, a biotechnology company that is combining artificial intelligence with molecular and cellular response detection for discovering and developing new drugs, currently focusing on cancer therapeutics. P.L.’s interests were reviewed and are managed by Brigham and Women’s Hospital and Mass General Brigham in accordance with their conflict-of-interest policies. B.L.E. has received consulting fees from GRAIL. He is a member of the scientific advisory board and shareholder for Neomorph Therapeutics, Skyhawk Therapeutics and Exo Therapeutics. B.L.E.’s laboratory has received research funding in the last 2 years from Novartis and Calico. S.J. is a scientific advisor to AstraZeneca, Novartis, Genentech, AVRO Bio and Foresite Labs, reports speaking fees from GSK, is an equity holder and on the scientific advisory board for Bitterroot Bio, and is an equity holder, co-founder and on the scientific advisory board of TenSixteen Bio. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Extended phenotypic characterization of Dnmt3a loss impacting atherosclerosis.
a-b, Bi-allelic loss of Dnmt3a in hematopoietic cells accelerates atherosclerosis. (a) Oil red O (ORO) stained aortic root sections in female Ldlr−/− mice transplanted with either Dnmt3a+/+; Vav1-Cre (WT) or Dnmt3a−/−; Vav1-Cre (KO) marrow, in a 1:9 ratio with WT, after 9 weeks of feeding on high-fat, high-cholesterol diet. Atheromata are demarcated by dashed lines. Scale bars = 200 µm. (b) Quantification of lesion area in the aortic root. N = 10 animals for both groups. Unpaired two-tailed t-test with Welsh’s correction. Box plot shows min, 25th percentile, median, 75th percentile and max. c, Single-allele loss of Dnmt3a has minimal impact on atherosclerosis. Shown is lesion area after 9 weeks on diet in female Ldlr−/− mice that were transplanted with either Dnmt3a + /−; Vav1-Cre marrow (left) or Dnmt3a + /−; Lyz2-Cre marrow (right), compared to WT controls. N = 10 animals per group for Vav1-Cre experiment, N = 15 per group for Lyz2-cre experiment. Unpaired two-tailed t-test with Welsh’s correction. Box plots show min, 25th percentile, median, 75th percentile and max. d-e, Lesion size in advanced atherosclerosis does not differ between WT and Dnmt3a knock-out. (d) ORO-stained aortic root sections in female Ldlr−/− mice transplanted with either Dnmt3a+/+; Vav1-Cre (WT) or Dnmt3a−/−; Vav1-Cre (KO) marrow, after 20 weeks of feeding on high-fat, high-cholesterol diet. Atheromata are demarked by dashed lines. Scale bars = 200 µm. (e) Quantification of lesional area in the aortic root. N = 4 mice for WT and N = 5 for KO group. Unpaired two-tailed t-test with Welsh’s correction. Box plot shows min, 25th percentile, median, 75th percentile and max. f, Isotype control (rabbit IgG) IHC staining for CD68 on aortic root with atheroma (control for Fig. 1c-d) demonstrates specificity of staining. Scale bar = 100 µm.
Extended Data Fig. 2 Peripheral blood counts, chimerism and serum lipids in mice transplanted with Dnmt3a-deficient marrow.
a, Peripheral blood chimerism in mice transplanted with either CD45.2+ Vav1-Cre (wt) or CD45.2+ Dnmt3a−/−; Vav1-Cre (KO) marrow, in a 1:9 ratio with CD45.1+ WT, after 9 weeks on diet. N = 16 animals for WT group and N = 12 animals for KO group. Two-tailed unpaired t test was performed to compare the myeloid cell population between groups. b, Peripheral blood cell counts in transplanted mice. Indicated are transplanted genotypes and time on high-fat, high-cholesterol diet. Groups that received only wild-type marrow are marked in gray, and groups that received marrow with bi-allelic loss of Dnmt3a are marked in red. N = 9 for wt Vav 8 Wk, N = 11 for KO Vav 8 Wk, N = 12 for wt Vav 20 Wk and KO Vav 20 Wk, N = 13 for wt LysM 8 Wk, N = 15 for KO LysM 8 Wk. P values obtained by two-tailed unpaired t tests. c, Serum lipid concentrations in mice transplanted with either Vav1-Cre (wt) or CD45.2+ Dnmt3a−/−; Vav1-Cre (KO) marrow, after 14 weeks on diet. For total cholesterol, N = 9 for wt, N = 6 for KO. For HDL, N = 9 for wt, N = 7 for KO. For LDL, N = 9 for wt, N = 6 for KO. For triglycerides, N = 8 for wt, N = 7 for KO. Varying group sizes due to insufficient volume for all analyses in some samples. All box plots show min, 25th percentile, median, 75th percentile and max.
Extended Data Fig. 3 Comparative gene-set enrichment analysis.
a, Comparative GSEA using the merge_result function in clusterProfiler between Dnmt3a−/− and Tet2−/−; Vav1-Cre BMDM. Top enriched KEGG pathways in Dnmt3a−/− and Tet2−/− (showCategory = 10) are shown. P value estimation in the fgsea method used by clusterProfiler is based on an adaptive multi-level split Monte-Carlo scheme. Adjustment for multiple comparisons was performed using the Benjamini-Hochberg method. b, Venn diagram showing overlap in the identity of significantly enriched (FDR < 0.05, ES > 0) 2019 KEGG pathways obtained by gene-set enrichment analysis (GSEA) in Dnmt3a−/− vs. WT (red) or Tet2−/− vs. WT (blue) BMDM, see Supplementary Table for detailed listing of all enriched pathways. Statistical significance calculated by the hypergeometric test. c, Specificity test of enriched gene distribution analysis using Jak2VF as a comparator. Enriched (log2FC > 0.5 and p < 0.05, red dots) or depleted (log2FC < −0.5 and p < 0.05, green dots) transcripts in Dnmt3a−/− BMDM were tested for enrichment or depletion (p < 0.05) in Jak2VF BMDM. The resulting distribution for each permutation was statistically compared to an equipartition (boxes) by way of a two-sided Chi-square test.
Extended Data Fig. 4 Convergent changes in inflammatory pathways with Dnmt3a and Tet2 deficiency.
a, BMDM were cultured with LDL or vehicle (NT = non-treated) for 24 h, and mRNA was assessed by RNA-sequencing. Out of the top 200 genes that were affected by both LDL treatment and genotype (log2FC > 0.6, adjusted p-value < 0.05), 37 selected genes involved in inflammation are shown. Each column in the heatmap represent an individual biological replicate. b, Scatter plot comparing gene expression changes in Dnmt3a−/− vs. WT with the Tet2−/− vs. WT dataset from Jaiswal et al., 2017. Displayed are the most highly expressed genes significantly affected by 200 mg/dL LDL stimulation in BMDM. Red dots highlight genes involved in chemokine signaling. Shown are mean fold changes over 3 biological replicates per genotype and stimulation status for the WT vs Dnmt3a−/− comparison; for the WT vs. Tet2−/− comparison, 3 biological replicates per genotype for the non-treated condition and 2 replicates for the LDL condition. c, Intracellular flow cytometry validates increase in pro-IL-1β in stimulated Dnmt3a−/− and Tet2−/− BMDM vs. WT at the protein level. Flow cytometry plot depicts macrophage gating strategy (showing WT as a representative example). Histograms show unstimulated BMDM (gray) overlaid onto stimulated BMDM (orange). Bar graph shows quantification. N = 3 per group, mean ± SD. P values obtained by one-way ANOVA followed by Tukey’s post hoc test. d, ELISA for key secreted cytokines measured in supernatant of LDL-stimulated BMDM from WT or Dnmt3a−/−. N = 6 per group (biological replicates). Two-tailed unpaired t-tests. Box plots show min, 25th percentile, median, 75th percentile and max.
Extended Data Fig. 5 Single-cell RNA sequencing (scRNA-seq) from atheromata.
a, Flow cytometric analysis of chimerism in aortic single cell suspensions from mixed chimeric Ldlr−/− mice on 30 weeks of high fat, high cholesterol diet. Transplanted genotypes and ratios are indicated. Dot plots show live, doublet excluded, CD45 positive cells. b, Violin plots depict single-cell expression of key signature genes across the 5 classic mononuclear phagocyte populations. Color code follows Fig. 3b. c-e, Lesional cell distribution is shaped by genotype. (c) Major leukocyte lineages. Chi square test. (d) Monocyte subsets. Chi square test. (e) Proportions-of-clusters analysis within the lesional lymphocyte compartment. Displayed is log2 fold difference and 95% confidence interval. Permutation test with N = 1000 permutations. Full statistics are reported in Supplementary Table 3. f, Single-cell transcriptome-based cell cycle analysis in macrophages stratified by subset and genotype.
Extended Data Fig. 6 CHIP gives rise to a distinct macrophage population in atheromata.
a, UMAP plot of highly expressed genes that are differentially upregulated in the Folr2+ Mrc1+ Ccl8+ Cxcl1+ macrophage cluster separated by genotype (WT, Dnmt3a−/−; Vav1-Cre, and Tet2−/−; Vav1-Cre). b, Significantly enriched 2019 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in inflammation and aging in the CHIP-TR macrophage population, analogous to the analysis in BMDM depicted in Fig. 2. Tested genes were present in at least 25% of the CHIP-TR macrophage subpopulation with a positive log fold change of at least 0.25. Dotted blue line indicates P value of 0.05. P values were calculated by Fisher’s exact test using the R package enrichr. c, GSEA of a pathway comprising the top 25 upregulated genes in Tet2−/− vs. WT by log2FC within the Resident-like Mφ cluster (‘Tet2 KO sc-pathway’) within the Dnmt3a−/− vs. WT ranking in the same cluster, analogous to the BMDM analysis in Fig. 2d. P value estimation in the fgsea method is based on an adaptive multi-level split Monte-Carlo scheme. d, Volcano plot depicts differential gene expression analysis in Dnmt3a−/− vs. Tet2−/− within the resident-like macrophage (Mφ) cluster using the FindMarkers function in Seurat (logfc.threshold = 0.25) using a negative binomial generalized linear model. Labeled are genes with a |log2FC|>0.5 (vertical lines) and adjusted p-value < 10e-12 (horizontal line).
Extended Data Fig. 7 Expansion of Folr2+ Mrc1+ Ccl8+ Cxcl1+ macrophages in CHIP.
a, UMAP plots of aligned gene expression data in single CD45.2+ cells isolated from aortae from WT; Vav1-Cre (n = 5015), Dnmt3a−/−; Vav1-Cre (n = 4499), and Tet2−/−; Vav1-Cre (n = 6078) populations (24-week cohort). b, Folr2+ Mrc1+ Ccl8+ Cxcl1+ (CHIP-TR) macrophages highlighted on the UMAP plots from a following 24 weeks of high-fat, high-cholesterol diet. Gray dots correspond to all other cells. Quantification in Fig. 4d. c, Gating strategy for identification of donor-derived tissue resident-like (TR) macrophages in atheromata by flow cytometry. The population was defined as CD45.2+ CD11b+ F4/80+ CD206hi/+. Comparison to other macrophages (CD206lo) confirmed higher expression of LYVE1 and FOLR2, and lower expression of CD9, as expected from scRNA-seq.
Extended Data Fig. 8 Multiplex Ion Beam Imaging by Time-of-Flight (MIBI-TOF) of aortic roots in WT, Dnmt3a−/− and Tet2−/−.
a, MIBI-TOF of aortic roots in WT, Dnmt3a−/− and Tet2−/−. Shown are 3 exemplary antigens (in white) with relevance to atherosclerosis (von Willebrand factor [VWF], integrin subunit alpha X/CD11c and mannose receptor 1/CD206) out of 27 total markers recorded. DNA (blue) and SMA (magenta) channels are shown to provide anatomical reference. Scale bars = 200 μm. b, Biologically informed decision tree used to define cell identities (boxes) based on combinations of antigen markers. Black arrows signify condition fulfilled/marker positive. Red arrows signify that the preceding (combination of) condition(s) has not been fulfilled. & = AND. ∥ = OR. TR ΜΦ = tissue resident-like macrophage.
Extended Data Fig. 9 CHIP alters the spatial cellular composition of atheromata.
a, Quantification strategy for adventitial CD206+ TR macrophages in 5-week roots (N = 9). For each section (biological replicate), the average of 4 ROIs was reported (demarcated by yellow lines). Scale bars = 200 μm. b, Section of the arterial intima (white rectangles), high-power images below, depicting nascent atheromata (scale bars = 200 μm). c, IHC for CD206 on aortic roots in WT and Dnmt3a−/− mice. Graph shows quantification of 3,3′-Diaminobenzidine (DAB) density, normalized to (arc) length of the section measured. N = 7 mice per WT and N = 9 mice for KO group. Unpaired two-tailed t-test with Welsh’s correction. Box plots show min, 25th percentile, median, 75th percentile and max.
Extended Data Fig. 10 Summary.
Mutations in the epigenetic regulators Dnmt3a or Tet2 in hematopoietic cells converge in the emergence of a distinct macrophage subset in the arterial adventitia (denoted CHIP Resident-like Mφ) that combines surface markers associated with resident-like macrophages (depicted: mannose receptor/CD206) with a distinct inflammatory chemokine signature. These cells are surrounded by other myeloid cells and clusters of activated endothelium. Overall, lesional (inflammatory) macrophage content increases, while other immune cell subsets, in particular T lymphocytes, decrease. Collectively, these processes result in increased atherosclerosis.
Supplementary information
Supplementary Information
Supplementary Figs. 1–4.
Supplementary Tables
Supplementary Tables 1–6.
Supplementary Data File 1
LDL responsive genes.
Supplementary Data File 2
Cluster marker genes.
Supplementary Data File 3
Genes enriched in Dnmt3a−/− versus WT within the resident-like macrophage cluster (scRNA-seq).
Supplementary Data File 4
Genes enriched in Tet2−/− versus WT within the resident-like macrophage cluster (scRNA-seq).
Source data
Source Data Fig. 1
Statistical source data.
Source Data Fig. 4
Statistical source data.
Source Data Fig. 5
Statistical source data.
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Rauch, P.J., Gopakumar, J., Silver, A.J. et al. Loss-of-function mutations in Dnmt3a and Tet2 lead to accelerated atherosclerosis and concordant macrophage phenotypes. Nat Cardiovasc Res 2, 805–818 (2023). https://doi.org/10.1038/s44161-023-00326-7
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DOI: https://doi.org/10.1038/s44161-023-00326-7
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