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TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease

Nature Cardiovascular Research volume 2, pages 144–158 (2023)Cite this article

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Abstract

Somatic mutations in blood indicative of clonal hematopoiesis of indeterminate potential (CHIP) are associated with an increased risk of hematologic malignancy, coronary artery disease and all-cause mortality. Here we analyze the relation between CHIP status and incident peripheral artery disease (PAD) and atherosclerosis, using whole-exome sequencing and clinical data from the UK Biobank and the Mass General Brigham Biobank. CHIP associated with incident PAD and atherosclerotic disease across multiple beds, with increased risk among individuals with CHIP driven by mutation in DNA damage repair (DDR) genes, such as TP53 and PPM1D. To model the effects of DDR-induced CHIP on atherosclerosis, we used a competitive bone marrow transplantation strategy and generated atherosclerosis-prone Ldlr−/− chimeric mice carrying 20% p53-deficient hematopoietic cells. The chimeric mice were analyzed 13 weeks after grafting and showed increased aortic plaque size and accumulation of macrophages within the plaque, driven by increased proliferation of p53-deficient plaque macrophages. In summary, our findings highlight the role of CHIP as a broad driver of atherosclerosis across the entire arterial system beyond the coronary arteries and provide genetic and experimental support for a direct causal contribution of TP53-mutant CHIP to atherosclerosis.

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Fig. 1: CHIP and incident PAD risk.
Fig. 2: CHIP and incident pan-arterial atherosclerosis risk.
Fig. 3: Gene-specific association of CHIP with incident PAD.
Fig. 4: Accelerated atherosclerosis in a murine model of TP53 mutation-driven CHIP.
Fig. 5: Expansion of p53-deficient macrophages within atherosclerotic plaques.
Fig. 6: Increased proliferation of p53-deficient macrophages.

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Data availability

UKB individual-level data are available by request via application (https://www.ukbiobank.ac.uk). Individual-level MGBB data are available from https://personalizedmedicine.partners.org/Biobank/Default.aspx, only to Partners HealthCare investigators with appropriate approval from the Partners institutional review board. RNA-seq data are available at the Gene Expression Omnibus (GSE184420). The present article includes all other data generated or analyzed during this study.

Code availability

Our CHIP-calling Terra pipeline using Mutect2 (version 1) is available at https://app.terra.bio/#workspaces/terra-outreach/CHIP-Detection-Mutect2. R-scripts for the observational epidemiologic associations are available at https://github.com/mzekavat/CHIP_PAD.

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Acknowledgements

D.K. is supported by the Veterans Administration (award 1IK2BX005759-01). P.N. is supported by a Hassenfeld Scholar Award from Massachusetts General Hospital; grants from the National Institutes of Health (NIH) National Heart, Lung, and Blood Institute (NHLBI) (R01HL1427, R01HL148565 and R01HL148050); and a grant from Fondation Leducq (TNE-18CVD04). S.M.Z is supported by the NIH NHLBI (1F30HL149180-01) and the NIH Medical Scientist Training Program Training Grant (T32GM136651). J.P.P. is supported by a John S. LaDue Memorial Fellowship. K.P. is supported by NIH grant 5-T32HL007208-43. P.L. receives funding support from the NHLBI (1R01HL134892), the American Heart Association (18CSA34080399), the RRM Charitable Fund and the Simard Fund. J.J.F. is supported by grant RYC-2016-20026 from the Spanish ‘Ministerio de Ciencia e Innovación’ (MICIN/AEI /10.13039/501100011033’ and ‘ESF Investing in your future’); a 2019 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation; the European Research Area Network on Cardiovascular Diseases CHEMICAL (grant AC19/00133, funded by Instituto de Salud Carlos III and co-funded by the European Union, ERDF, ‘A way to make Europe’); and the Leducq Foundation (TNE-18CVD04). A.G.B. is supported by NIH grant DP5 OD029586, a Burroughs Wellcome Fund Career Award for Medical Scientists and a Pew-Stewart Scholar for Cancer Research award, supported by the Pew Charitable Trusts and the Alexander and Margaret Stewart Trust. C.G. is supported by the European Research Area Network on Cardiovascular Diseases CHEMICAL. A.H. is supported by the Leducq Foundation (TNE-18CVD04). C.M.B. is supported by NIH grant R01-HL148050. C.J.G. is supported by NIH/NCI K08CA263555. P.L. receives funding support from the NHLBI (1R01HL134892 and 1R01HL163099-01), the American Heart Association (18CSA34080399), the RRM Charitable Fund and the Simard Fund. M.A.-P. is supported by grant FPU18/02913 funded by MCIN/AEI /10.13039/501100011033 and by ‘ESF Investing in your future’. The project leading to these results also received funding from ‘la Caixa’ Foundation (ID 100010434), including fellowship code LCF/BQ/DR19/11740022 (to A.A.-C.) and agreement HR17-00267. Confocal microscopy was conducted at the Microscopy & Dynamic Imaging Unit at CNIC, ICTS-ReDib, co-funded by MCIN/AEI /10.13039/501100011033 and FEDER ‘Una manera de hacer Europa’ (ICTS-2018-04-CNIC-16). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the MICIN and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (grant CEX2020-001041-S funded by MICIN/AEI/10.13039/501100011033). We thank R. Moro for assistance with figure preparation and F. Sánchez-Cabo and M. Gómez for assistance with bioinformatics analysis of RNA-seq data. S.M.D. is supported by the Veterans Administration (award IK2-CX001780). We also thank participants and staff of the UK Biobank and the Mass General Brigham Biobank. UK Biobank analyses were conducted using application 7089.

Author information

Author notes

  1. These authors contributed equally: Seyedeh M. Zekavat, Vanesa Viana-Huete, Nuria Matesanz.

  2. These authors jointly supervised this work: Pradeep Natarajan, Alexander G. Bick, José J. Fuster, Derek Klarin.

Authors and Affiliations

  1. Department of Ophthalmology, Massachusetts Eye and Ear Institute, Boston, MA, USA

    Seyedeh M. Zekavat

  2. Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA

    Seyedeh M. Zekavat

  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Seyedeh M. Zekavat, Md Mesbah Uddin, Mark Trinder, Kaavya Paruchuri & Gabriel Griffin

  4. Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

    Vanesa Viana-Huete, Nuria Matesanz, María A. Zuriaga, Virginia Zorita, Alba Ferrer-Pérez, Marta Amorós-Pérez, Alejandra Aroca-Crevillen, Andrés Hidalgo, Valentín Fuster & José J. Fuster

  5. Vector Institute for Artificial Intelligence, Toronto, ON, Canada

    Saman Doroodgar Jorshery & Marzyeh Ghassemi

  6. Department of Electrical Engineering and Computer Science and Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Saman Doroodgar Jorshery

  7. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    Md Mesbah Uddin, Mark Trinder, Kaavya Paruchuri & Pradeep Natarajan

  8. Center for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada

    Mark Trinder

  9. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Kaavya Paruchuri & Pradeep Natarajan

  10. IRCCS Humanitas Research Hospital, Milan, Italy

    Paolo Kunderfranco, Roberta Carriero & Carolina M. Greco

  11. Department of Biomedical Sciences, Humanitas University, Milan, Italy

    Carolina M. Greco

  12. Vascular Biology and Therapeutics Program and Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA

    Andrés Hidalgo

  13. Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA

    Scott M. Damrauer

  14. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Scott M. Damrauer

  15. Center for Cardiometabolic Disease Prevention, Baylor College of Medicine, Houston, TX, USA

    Christie M. Ballantyne

  16. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Abhishek Niroula

  17. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Abhishek Niroula, Christopher J. Gibson & Benjamin L. Ebert

  18. Department of Laboratory Medicine, Lund University, Lund, Sweden

    Abhishek Niroula

  19. Cardiology Division, Massachusetts General Hospital, Boston, MA, USA

    James Pirruccello

  20. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    James Pirruccello

  21. Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA

    James Pirruccello

  22. Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA

    Gabriel Griffin

  23. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Gabriel Griffin

  24. Howard Hughes Medical Institute, Boston, MA, USA

    Benjamin L. Ebert

  25. Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Peter Libby

  26. Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Valentín Fuster

  27. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Hongyu Zhao

  28. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Marzyeh Ghassemi

  29. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Marzyeh Ghassemi

  30. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Pradeep Natarajan

  31. Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA

    Alexander G. Bick

  32. CIBER en Enfermedades Cardiovasculares (CIBER-CV), Madrid, Spain

    José J. Fuster

  33. VA Palo Alto Healthcare System, Palo Alto, CA, USA

    Derek Klarin

  34. Department of Surgery, Stanford University School of Medicine, Palo Alto, CA, USA

    Derek Klarin

Authors
  1. Seyedeh M. Zekavat
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  2. Vanesa Viana-Huete
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  3. Nuria Matesanz
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  4. Saman Doroodgar Jorshery
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  5. María A. Zuriaga
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  6. Md Mesbah Uddin
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  7. Mark Trinder
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  9. Virginia Zorita
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  11. Marta Amorós-Pérez
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  12. Paolo Kunderfranco
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  13. Roberta Carriero
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  14. Carolina M. Greco
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  15. Alejandra Aroca-Crevillen
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  16. Andrés Hidalgo
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  17. Scott M. Damrauer
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  18. Christie M. Ballantyne
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  19. Abhishek Niroula
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  20. Christopher J. Gibson
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  21. James Pirruccello
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  22. Gabriel Griffin
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  23. Benjamin L. Ebert
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  24. Peter Libby
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  26. Hongyu Zhao
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  27. Marzyeh Ghassemi
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  28. Pradeep Natarajan
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  29. Alexander G. Bick
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  30. José J. Fuster
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  31. Derek Klarin
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Contributions

S.M.Z., S.D.J., M.M.U., M.T., K.P., S.D., J.P., G.G. and A.N. designed, performed and analyzed population-level data from the UKB and the MGBB and interpreted data. S.M.Z. and D.K. interpreted data, created the figures and assisted in writing the respective parts of the paper. N.M. designed, performed and analyzed experiments; interpreted experimental data; created the figures; and assisted in writing the manuscript. V.V.-H. designed, performed and analyzed experiments; interpreted experimental data; and contributed to figure preparation. J.J.F., M.A.Z., M.A.-P. and A.A.-C. performed and analyzed experiments and interpreted experimental data. V.Z. and A.F.-P. provided technical support and performed experiments. P.K., R.C. and C.M.G. conducted RNA-seq studies and analyzed transcriptomics data. A.H., V.F., B.L.E., P.L., C.J.G., C.M.B., H.Z., M.G. and D.K. discussed the research strategy and the results and provided conceptual advice. P.N., A.G.B., J.J.F. and D.K. conceived the study, analyzed and interpreted data and created the figures.

Corresponding authors

Correspondence to José J. Fuster or Derek Klarin.

Ethics declarations

Competing interests

D.K. is a scientific advisor for and accepts consulting fees from Bitterroot Bio. P.N. reports grant support from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis; spousal employment and equity at Vertex; consulting income from Apple, AstraZeneca, Novartis, Genentech/Roche, Blackstone Life Sciences, Foresite Labs and TenSixteen Bio; and is a scientific advisory board member and shareholder of TenSixteen Bio and geneXwell, all unrelated to this work. 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, 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, Moderna, Novartis, PlaqueTec, TenSixteen Bio, Soley Therapeutics, and XBiotech. P.L.ʼs laboratory has received research funding in the last 2 years from Novartis. P.L. is on the board of directors of XBiotech. P.L. also 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 novel 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.ʼs 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 research funding from Celgene, Deerfield, Novartis and Calico and consulting fees from GRAIL. B.L.E. is a member of the scientific advisory board and a shareholder for Neomorph, TenSixteen Bio, Skyhawk Therapeutics and Exo Therapeutics. A.G.B. is a paid advisor and holds equity in TenSixteen Bio. S.M.D. has received research support via the University of Pennsylvania from RenalytixAI and Novo Nordisk. The other authors do not have any competing interests.

Peer review

Peer review information

Nature Cardiovascular Research thanks Alan Tall, Andrew Murphy, Gerard Pasterkamp and Steffen Massberg for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Association of CHIP with blood counts among individuals without prevalent hematologic malignancy in the UK Biobank (n = 37,657).

Blood counts were acquired at time of blood draw for whole exome sequencing. a) Association of CHIP and Large CHIP with normalized blood counts (SD). Associations are adjusted for age, age2, sex, smoking status, and the first ten principal components of genetic ancestry. b) Association of CHIP variant allele frequency (VAF) with blood counts (in units of 10^9 cells/L). The gray horizontal dotted lines reflect average counts across non-CHIP carriers. The vertical black dotted line reflects the cutoff VAF for Large CHIP (VAF > 0.1). Error bands reflect the standard error of a generalized additive model with integrated smoothness fit to the data. CHIP = clonal hematopoiesis of indeterminate potential; VAF = variant allele fraction.

Extended Data Fig. 2 Association of CHIP (a) and VAF (b) with incident hematologic malignancy among individuals without prevalent hematological malignancy in the UK Biobank (n = 37,657).

Associations are adjusted for age, age2, sex, smoking status, Townsend deprivation index, and the first ten principal components of genetic ancestry. (a) Error bars are centered at the HR and show the 95% CI for estimates. (b) Error bands reflect the standard error of a generalized binomial additive model with integrated smoothness fit to the data. CHIP = clonal hematopoiesis of indeterminate potential; VAF = variant allele fraction.

Extended Data Fig. 3 Epidemiological causal inference analysis for CHIP on incident peripheral artery disease in the UK Biobank.

a) Propensity scores by CHIP and Large CHIP status in the UKB (n = 37,657). b) Propensity score adjustment and stabilized inverse probability treatment weighting (IPTW) for the CHIP and Large CHIP association with incident PAD in the UKB. Error bars are centered at the HR and show the 95% CI for estimates. CHIP = clonal hematopoiesis of indeterminate potential; VAF = variant allele fraction; PAD = peripheral artery disease.

Extended Data Fig. 4 Association of Large CHIP (VAF > 10%) with incident pan-arterial atherosclerosis, combined across peripheral artery disease, coronary artery disease, aneurysms, chronic and acute mesenteric ischemia, cerebral atherosclerosis, and renal artery stenosis.

Error bars are centered at the HR and show the 95% CI for estimates. CHIP = clonal hematopoiesis of indeterminate potential; VAF = variant allele fraction.

Extended Data Fig. 5 Association of a) CHIP and b) Large CHIP genes with incident pan-arterial atherosclerosis, combined across peripheral artery disease, coronary artery disease, aneurysms, chronic and acute mesenteric ischemia, cerebral atherosclerosis, and renal artery stenosis.

Error bars are centered at the HR and show the 95% CI for estimates. CHIP = clonal hematopoiesis of indeterminate potential; VAF = variant allele fraction.

Extended Data Fig. 6 Effects of experimental p53-deficient CHIP on atherosclerosis development in Ldlr-/- mice.

a-e) 20% KO-BMT male mice (n = 19 mice) and 20% WT-BMT controls (n = 20 mice) were fed a high-fat/high-cholesterol (HF/HC) diet for 9 weeks, starting 4 weeks after BMT. a) Percentage of CD45.2+ cells in different white blood cell (WBC) lineages in peripheral blood, evaluated by flow cytometry. Two-tailed unpaired t-tests were used for statistical analysis (mean ± SEM, ****p < 0.0001). b) Absolute counts of main WBC sub-populations in peripheral blood, evaluated by flow cytometry (mean ± SEM). c) Body weight (mean ± SEM). d) Spleen weight (mean ± SEM). e) Total cholesterol level in serum, evaluated by enzymatic methods (mean ± SEM). f-k) 20% KO-BMT female mice and 20% WT-BMT controls were fed a high-fat/high-cholesterol (HF/HC) diet for 9 weeks, starting 4 weeks after BMT. f) Percentage of CD45.2+ cells in white blood cells at different timepoints, evaluated by flow cytometry. A two-way ANOVA with Sidak’s multiple comparison test was used for statistical analysis (mean ± SEM,****p < 0.0001). g) Percentage of CD45.2+ cells in different WBC lineages in peripheral blood after 9 weeks on HF/HC diet (13 weeks post-BMT), evaluated by flow cytometry. Two-tailed unpaired t-tests were used for statistical analysis (mean ± SEM, n = 16 20% WT-BMT mice, n = 13 20% KO-BMT mice, ****p < 0.0001). h) Body weight (mean ± SEM, n = 17 20% WT-BMT mice, n = 13 20% KO-BMT mice). i) Spleen weight (mean ± SEM, n = 17 20% WT-BMT mice, n = 13 20% KO-BMT mice). j) Total cholesterol level in serum, evaluated by enzymatic methods (mean ± SEM, n = 17 20% WT-BMT mice, n = 12 20% KO-BMT mice) k) Aortic root plaque size. A two-tailed unpaired t-test was used for statistical analysis (mean ± SEM, n = 17 20% WT-BMT mice, n = 13 20% KO-BMT mice). Representative images of hematoxylin and eosin-stained sections are shown; atherosclerotic plaques are delineated by dashed lines. Scale bars, 100 μm.

Extended Data Fig. 7 No effect of p53-deficient CHIP on plaque lipid content or aortic expression of the proinflammatory cytokines IL-1b and IL-6 or the NLRP3 inflammasome.

a) Aortic arch samples were obtained from HF/HC-fed 20% KO-BMT male mice and 20% WT-BMT controls, and gene expression was analyzed by qPCR (mean ± SEM, n = 11 20% WT-BMT mice, n = 10 20% KO-BMT mice). b) 20% KO-BMT male mice and 20% WT-BMT controls were fed a high-fat/high-cholesterol (HF/HC) diet for 9 weeks. Plaque lipid content was analyzed through Oil Red O (ORO) staining of cryostat sections (mean ± SEM, n = 12 20% WT-BMT mice, n = 15 20% KO-BMT mice). Representative images of ORO-stained sections are shown; atherosclerotic plaques are delineated by dashed lines. Scale bars, 200 μm.

Extended Data Fig. 8 Increased cell proliferation in conditions of p53-deficient CHIP.

a) 20% KO-BMT female mice and 20% WT-BMT controls were fed a high-fat/high-cholesterol (HF/HC) diet for 9 weeks, starting 4 weeks after BMT. Plaque cell proliferation was estimated based on immunohistochemical staining of the Ki-67 proliferation marker (mean ± SEM, n = 17 20% WT-BMT mice, n = 14 20% KO-BMT mice). A two-tailed unpaired t-test was used for statistical analysis. Representative images of Ki-67-stained sections are shown; color deconvolution was applied to show separately the staining of hematoxylin (nuclei) and Ki-67. Atherosclerotic plaques are delineated by dashed lines. Scale bars, 50 μm. b) Cell cycle phase distribution of cultured Trp53-/- and + /+ bone marrow-derived macrophages proliferating asynchronously in the presence of 100 ng/ml MCSF, evaluated by propidium iodide staining of cellular DNA content and flow cytometry (mean ± SEM, n = 6 Trp53 + /+ mice, n = 6 Trp53-/- mice). A two-way ANOVA with Sidak’s multiple comparison test was used for statistical analysis. c) Analysis of the proliferation of cultured Trp53-/- and + /+ bone marrow-derived macrophages through immunostaining of BrdU incorporation into the DNA. Quiescent G0-synchronized macrophages were treated with MCSF to induce proliferation (mean ± SEM, n = 3 Trp53 + /+ mice, n = 3 Trp53-/- mice). A two-tailed unpaired t-test was used for statistical analysis. A representative experiment is shown; three separate experiments were conducted. Scale bars, 100 μm.

Extended Data Fig. 9 Transcriptomic profiling of MCSF-stimulated p53-deficient macrophages.

An 18h-treatment with MCSF was used to induce cell cycle entry and progression in quiescent G0-synchronized Trp53-/- (KO) and + /+ (WT) murine macrophages in culture (n = 3 per genotype). mRNA sequencing was used for transcriptomic profiling. a) Heatmap of differentially expressed genes with fold change (FC) ≥ 1.5. b) Functional categories enriched in the set of genes differentially expressed between Trp53-/- and + /+ macrophages, based on Ingenuity Pathway Analysis (cancer-related pathways are not shown). c) Heatmap of the most upregulated genes within selected functional categories that are enriched in genes differentially expressed in Trp53-/- macrophages with fold change ≥ 1.5. d) GoPlot of selected functional categories (right hand-side) and the logFC values of the most differentially expressed genes included in these categories (left hand-side).

Extended Data Fig. 10 Association of CHIP and Large CHIP (variant allele fraction > 10%) with PAD in the UKB (N = 37,657).

under 1) unadjusted, 2) sparsely adjusted, and 3) fully adjusted models, where sparsely adjusted refers to the following covariates: age, age2, sex, smoking status, Townsend deprivation index, and the first ten principal components of genetic ancestry, and the fully adjusted model additionally includes normalized BMI, prevalent hypertension, hyperlipidemia, and type 2 diabetes as covariates. Given the minimal difference between the sparsely adjusted and fully adjusted model, the sparsely adjusted model was moved forward for use in analysis. Error bars are centered at the HR and show the 95% CI for estimates.

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Western blot for Fig. 6d

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Zekavat, S.M., Viana-Huete, V., Matesanz, N. et al. TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease. Nat Cardiovasc Res 2, 144–158 (2023). https://doi.org/10.1038/s44161-022-00206-6

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