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Suppression of IL-1β promotes beneficial accumulation of fibroblast-like cells in atherosclerotic plaques in clonal hematopoiesis

Nature Cardiovascular Research volume 3, pages 60–75 (2024)Cite this article

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Abstract

Clonal hematopoiesis (CH) is an independent risk factor for atherosclerotic cardiovascular disease. Murine models of CH suggest a central role of inflammasomes and IL-1β in accelerated atherosclerosis and plaque destabilization. Here we show using single-cell RNA sequencing in human carotid plaques that inflammasome components are enriched in macrophages, while the receptor for IL-1β is enriched in fibroblasts and smooth muscle cells (SMCs). To address the role of inflammatory crosstalk in features of plaque destabilization, we conducted SMC fate mapping in Ldlr−/− mice modeling Jak2VF or Tet2 CH treated with IL-1β antibodies. Unexpectedly, this treatment minimally affected SMC differentiation, leading instead to a prominent expansion of fibroblast-like cells. Depletion of fibroblasts from mice treated with IL-1β antibody resulted in thinner fibrous caps. Conversely, genetic inactivation of Jak2VF during plaque regression promoted fibroblast accumulation and fibrous cap thickening. Our studies suggest that suppression of inflammasomes promotes plaque stabilization by recruiting fibroblast-like cells to the fibrous cap.

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Fig. 1: Inflammasome components in human atheromas.
Fig. 2: IL-1β inhibition does not alter SMC differentiation.
Fig. 3: Cluster 6 fibroblast-like cells are enriched for matrix genes in mice modeling Jak2VF CH.
Fig. 4: IL-1β inhibition increases fibroblast-like cell accumulation in plaques.
Fig. 5: Cluster 0 fibroblast-like cells are enriched for matrix genes in mice modeling Tet2 CH.
Fig. 6: Ablation of Prg4+ cells blunts IL-1β antibody-mediated cap thickening.
Fig. 7: Genetic ablation of Jak2VF coupled to cholesterol lowering.
Fig. 8: Turning Jak2VF off promotes cap thickening when coupled to aggressive cholesterol lowering.

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Data availability

The datasets generated and/or analyzed during the current study are available in the Gene Expression Omnibus with SuperSeries no. GSE248395 and the following accession numbers: scRNA-seq of Myh11-Zsgreen1 mice modeling Jak2VF CH (Fig. 2), Tet2 CH (Fig. 4) and mice with WT bone marrow (Extended Data Fig. 4) (GSE248394); scRNA-seq of Dre-Jak2VF in atherosclerosis progression (Extended Data Fig. 8) (GSE248289) and Dre-Jak2VF in atherosclerosis regression (Fig. 8) (GSE248276); scRNA-seq data of human atheromas (Fig. 1) (GSE247238). Source data are provided with this manuscript.

Code availability

No custom code was used in these analyses. All software packages used for data analysis are indicated in the Methods section.

References

  1. Tsao, C. W. et al. Heart disease and stroke statistics—2022 update: a report from the American Heart Association. Circulation 145, e153–e639 (2022).

    Article  PubMed  Google Scholar 

  2. Virmani, R., Burke, A. P., Farb, A. & Kolodgie, F. D. Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 47, C13–C18 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Wirka, R. C. et al. Atheroprotective roles of smooth muscle cell phenotypic modulation and the TCF21 disease gene as revealed by single-cell analysis. Nat. Med. 25, 1280–1289 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Grootaert, M. O. J., Finigan, A., Figg, N. L., Uryga, A. K. & Bennett, M. R. SIRT6 protects smooth muscle cells from senescence and reduces atherosclerosis. Circ. Res. 128, 474–491 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Ridker, P. M. et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N. Engl. J. Med. 377, 1119–1131 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Svensson, E. C. et al. TET2-driven clonal hematopoiesis and response to canakinumab: an exploratory analysis of the CANTOS randomized clinical trial. JAMA Cardiol. 7, 521–528 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Tardif, J. C. et al. Efficacy and safety of low-dose colchicine after myocardial infarction. N. Engl. J. Med. 381, 2497–2505 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Nidorf, S. M. et al. Colchicine in patients with chronic coronary disease. N. Engl. J. Med. 383, 1838–1847 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Jones, A. V. et al. Widespread occurrence of the JAK2 V617F mutation in chronic myeloproliferative disorders. Blood 106, 2162–2168 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Bick, A. G. et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature 586, 763–768 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jaiswal, S. et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N. Engl. J. Med. 377, 111–121 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Fidler, T. P. et al. The AIM2 inflammasome exacerbates atherosclerosis in clonal haematopoiesis. Nature 592, 296–301 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Westerterp, M. et al. Cholesterol efflux pathways suppress inflammasome activation, NETosis, and atherogenesis. Circulation 138, 898–912 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yu, Z. et al. Genetic modification of inflammation and clonal hematopoiesis-associated cardiovascular risk. J. Clin. Invest. https://doi.org/10.1172/JCI168597 (2023).

  15. Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bennett, M. R., Sinha, S. & Owens, G. K. Vascular smooth muscle cells in atherosclerosis. Circ. Res. 118, 692–702 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pan, H. et al. Single-cell genomics reveals a novel cell state during smooth muscle cell phenotypic switching and potential therapeutic targets for atherosclerosis in mouse and human. Circulation 142, 2060–2075 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Choudhury, R. P. et al. Arterial effects of canakinumab in patients with atherosclerosis and type 2 diabetes or glucose intolerance. J. Am. Coll. Cardiol. 68, 1769–1780 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gomez, D. et al. Interleukin-1β has atheroprotective effects in advanced atherosclerotic lesions of mice. Nat. Med. 24, 1418–1429 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Alexanian, M. et al. Chromatin remodeling drives immune-fibroblast crosstalk in heart failure pathogenesis. Preprint at bioRxiv https://doi.org/10.1101/2023.01.06.522937 (2023).

  21. Amrute, J. M. et al. Targeting the immune-fibrosis axis in myocardial infarction and heart failure. Preprint at bioRxiv https://doi.org/10.1101/2022.10.17.512579 (2022).

  22. Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Kramann, R. et al. Adventitial MSC-like cells are progenitors of vascular smooth muscle cells and drive vascular calcification in chronic kidney disease. Cell Stem Cell 19, 628–642 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhang, X. et al. Thymosin beta 10 is a key regulator of tumorigenesis and metastasis and a novel serum marker in breast cancer. Breast Cancer Res. 19, 15 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kozhemyakina, E. et al. Identification of a Prg4-expressing articular cartilage progenitor cell population in mice. Arthritis Rheumatol. 67, 1261–1273 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ytterberg, S. R. et al. Cardiovascular and cancer risk with tofacitinib in rheumatoid arthritis. N. Engl. J. Med. 386, 316–326 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Dunbar, A. et al. Jak2V617F reversible activation shows an essential requirement for Jak2V617F in myeloproliferative neoplasms. Preprint at bioRxiv https://doi.org/10.1101/2022.05.18.492332 (2022).

  28. Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Schlegel, M. et al. Silencing myeloid netrin-1 induces inflammation resolution and plaque regression. Circ. Res. 129, 530–546 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zernecke, A. et al. Meta-analysis of leukocyte diversity in atherosclerotic mouse aortas. Circ. Res. 127, 402–426 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Raber, L. et al. Effect of alirocumab added to high-intensity statin therapy on coronary atherosclerosis in patients with acute myocardial infarction: the PACMAN-AMI randomized clinical trial. JAMA 327, 1771–1781 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Nicholls, S. J. et al. Effect of evolocumab on coronary plaque phenotype and burden in statin-treated patients following myocardial infarction. JACC Cardiovasc. Imaging 15, 1308–1321 (2022).

    Article  PubMed  Google Scholar 

  34. Redgrave, J. N., Gallagher, P., Lovett, J. K. & Rothwell, P. M. Critical cap thickness and rupture in symptomatic carotid plaques: the Oxford plaque study. Stroke 39, 1722–1729 (2008).

    Article  PubMed  Google Scholar 

  35. Tallquist, M. D. Cardiac fibroblast diversity. Annu. Rev. Physiol. 82, 63–78 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Komatsu, N. & Takayanagi, H. Mechanisms of joint destruction in rheumatoid arthritis—immune cell-fibroblast-bone interactions. Nat. Rev. Rheumatol. 18, 415–429 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Aghajanian, H. et al. Targeting cardiac fibrosis with engineered T cells. Nature 573, 430–433 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Abplanalp, W. T. et al. Cell-intrinsic effects of clonal hematopoiesis in heart failure. Nat. Cardiovasc. Res. https://doi.org/10.1038/s44161-023-00322-x (2023).

  39. Pelisek, J. et al. Biobanking: objectives, requirements, and future challenges—experiences from the Munich Vascular Biobank. J. Clin. Med. https://doi.org/10.3390/jcm8020251 (2019).

  40. Fasolo, F. et al. Long noncoding RNA MIAT controls advanced atherosclerotic lesion formation and plaque destabilization. Circulation 144, 1567–1583 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Winter, H. et al. Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression. Mol. Ther. 31, 1775–1790 (2023).

    Article  CAS  PubMed  Google Scholar 

  42. Mullally, A. et al. Physiological Jak2V617F expression causes a lethal myeloproliferative neoplasm with differential effects on hematopoietic stem and progenitor cells. Cancer Cell 17, 584–596 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Yurdagul, A. Jr. et al. Macrophage metabolism of apoptotic cell-derived arginine promotes continual efferocytosis and resolution of injury. Cell Metab. 31, 518–533.e10 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vromman, A. et al. Stage-dependent differential effects of interleukin-1 isoforms on experimental atherosclerosis. Eur. Heart J. 40, 2482–2491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Leducq Foundation (TNE-18CVD04) and National Institutes of Health (NIH) grants no. HL155431, no. HL170157 and no. HL107653 (to A.R.T.). T.P.F. was supported by grants no. F32HL151051-01 and no. K99HL157649, and grant no. R00HL157649 from the NIH, as well as a Mandl Connective Tissue Research Fellowship. R.L. was supported by National Cancer Institute grant no. R35 CA197594 and NIH/NCI Cancer Center Support Grant no. P30 CA008748. A.D. is a William Raveis Charitable Fund Physician-Scientist of the Damon Runyon Cancer Research Foundation (grant no. PST-24-19). He also has received funding from the American Association of Cancer Research (grant no. 17-40-11-DUNB) and the American Association of Clinical Oncology (grant no. 11520). We thank the staff of the Columbia Stem Cell Initiative Flow Cytometry Core Facility, under the leadership of M. Kissner, at Columbia University Irving Medical Center for their contributions to the work presented in this manuscript. Research reported in this publication was performed in the CCTI Flow Cytometry Core, supported in part by the Office of the Director, NIH, under awards no. S10RR027050 and no. S10OD020056. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Images were collected and/or image processing and analysis for this work was performed in the Confocal and Specialized Microscopy Shared Resource of the Herbert Irving Comprehensive Cancer Center at Columbia University, supported by NIH grant no. P30 CA013696.

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Authors and Affiliations

  1. Division of Molecular Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

    Trevor P. Fidler, Brian Hardaway, Sandra Abramowicz, Tong Xiao, Nan Wang & Alan R. Tall

  2. Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA

    Trevor P. Fidler

  3. Department of Physiology, University of San Francisco, San Francisco, CA, USA

    Trevor P. Fidler

  4. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Andrew Dunbar, Kavi O’Connor & Ross Levine

  5. Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Andrew Dunbar, Kavi O’Connor & Ross Levine

  6. Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA

    Eunyoung Kim & Muredach Reilly

  7. Department of Vascular and Endovascular Surgery, Technical University Munich, Munich, Germany

    Jessica Pauli, Chenyi Xue, Nadja Sachs & Lars Maegdefessel

  8. German Center for Cardiovascular Research (DZHK), Munich, Germany

    Jessica Pauli, Nadja Sachs & Lars Maegdefessel

  9. Department of Medicine, Karolinska Institute, Stockholm, Sweden

    Lars Maegdefessel

  10. Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, NY, USA

    Muredach Reilly

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Contributions

T.P.F. designed and performed experiments, analyzed data and wrote the manuscript. E.K. and C.X. aided in the design of scRNA-seq studies and conducted scRNA-seq analysis. B.H. designed experiments and aided in mouse studies, immunohistochemistry and analysis of data. A.D. and K.O. aided in studies related to Dre-Jak2VF. S.A. and T.X. aided in atherosclerosis studies, processed tissue, and conducted histology experiments and analysis. N.W. designed and performed experiments and analyzed data. L.M., N.S. and J.P. conducted studies of human carotid arteries. R.L. provided reagents and designed experiments. M.R. designed experiments and aided in analysis. A.R.T. designed experiments, analyzed data and wrote the manuscript.

Corresponding authors

Correspondence to Trevor P. Fidler or Alan R. Tall.

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Competing interests

R.L. is on the supervisory board of Qiagen and is a scientific advisor to Imago, Mission Bio, Zentalis, Ajax, Auron, Prelude, C4 Therapeutics and Isoplexis. He receives research support from Ajax and Zentalis and has consulted for Incyte, Janssen, Astra Zeneca and Novartis. He has received honoraria from Astra Zeneca, Roche, Lilly and Amgen for invited lectures and from Gilead for grant reviews. A.R.T. is on the SABs of Tensixteen Bio, and Beren Pharmaceuticals. A.D. has served on an advisory committee for Incyte. L.M. is a scientific consultant and medical advisor for Roche Diagnostics, Novo Nordisk and DrugFarm Inc., and is an advisory board member of Angiolutions. He further received research funding from Roche Diagnostics and Novo Nordisk. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 IL-1β inhibition does not alter smooth muscle cell differentiation.

Quantification of Jak2VF burden (CD45.1+ cells) in blood a. monocytes and b. neutrophils (n = 19 IgG, n = 20 IL-1β antibodies). c. Serum Cholesterol (n = 20 IgG, n = 19 IL-1β antibodies) d. Representative H&E image of aortic root lesions of mice modeling Jak2VF-CH treated with IgG or IL-1β antibodies. Quantification of e. lesion area and f. percent necrotic core area (n = 20 IgG, n = 19 IL-1β antibodies). g. Differential gene expression of the top 5 genes expressed per cluster from scRNA-Seq analysis of mice modeling Jak2-CH (related to Fig. 2j-l). Data are mean ± SEM. Two-Way ANOVA; a, b, & c. Two-tailed Students t-test e & f.

Source data

Extended Data Fig. 2 IL-1 signaling in mesenchymal stem cell derived cells does not regulate cap thickness.

Recipient mice with the indicated genotypes were transplanted with 20% Jak2VF bone marrow to model clonal hematopoiesis. Mice were fed a western type of diet for 12 weeks then lesions were analyzed. a. Representative picrosirius red image of aortic root lesions from mice modeling Jak2VF CH with the indicated genotypes, black bar indicates cap thickness. b. Quantification of cap thickness (Ldlr-/-Gli1-Cre+ n = 16, Ldlr-/-Gli1-Cre+ll1r1fl/fl n = 8). c. Immunofluorescent images of lesions from mice with the indicated genotypes, white arrows indicate Gli1-ZsGreen1+ cells in intima; ZsGreen1 Positive cells are Magenta. d. Quantification of Gli1-ZsGreen1+ in atheromas (Ldlr-/-Gli1-Cre+ZsGreen1+ n = 3, Ldlr-/-Gli1-Cre+ll1r1fl/fl ZsGreen1+ n = 6). Data are mean ± SEM. Two-tailed Students t-test b. Two-tailed Mann-Whitney test d.

Source data

Extended Data Fig. 3 IL-1β inhibition in mice with monocyte/macrophage restricted deletion of Tet2 Cx3cr1-Tet2fl/fl.

Percent of cells lacking Tet2 in blood (CD45.2+ cells) a. Monocytes and b. neutrophils (n = 12 IgG, n = 15 IL-1β antibodies). c. Representative H&E image of aortic root lesions from mice lacking Tet2 in monocytes and macrophages treated with IgG or IL-1β antibodies as outlined in Fig. 2a. Quantification of d. lesion area and e. Percent necrotic core area (n = 12 IgG, n = 15 IL-1β antibodies). f. Differential gene expression of the top 5 genes expressed per cluster from scRNA-seq analysis of mice with Tet2 deletion (related to Fig. 4e-g). Data are mean ± SEM. Two-Way ANOVA a & b. Two-tailed Students t-test d & e.

Source data

Extended Data Fig. 4 Fibroblast-like cells do not expand in lesions of mice with WT bone marrow.

Ldlr-/- mice were transplanted with WT bone marrow and subjected to a 10-week western type diet, then administered IgG or Il-1β antibodies for an addition 7 weeks with western type diet feeding. Aortas were then isolated for scRNA-Sequencing. a. UMAP visualization of cells isolated from aortas; Stem cell, endothelial cell, monocyte like cell (SEM). b. scRNA-Seq population distribution across. c. Differential gene expression of the top 5 genes expressed per cluster from scRNA-seq analysis of mice with WT bone marrow.

Extended Data Fig. 5 Prg4 is primarily expressed in fibroblast-like cells.

a. UMAP visualization of scRNA-Seq from aortas modeling Tet2-CH overlay with Prg4 expression in b. Zsgreen1+ and c. ZsGreen1Neg populations. d. Violin plot of Prg4 expression in all clusters from mice modeling d. Jak2VF-CH and e. Tet2-CH. f. Representative H&E image of aortic roots of mice with WT bone marrow (BM) and the indicated genotype. Quantification of g. Lesion area and h. Percent necrotic core area (n = 12 Ldlr-/-Prg4-Cre+, n = 13 Ldlr-/-Prg4-Cre+iDTA+). i. Representative picrosirius red staining of aortic root lesions, black bar indicates cap thickness. j. Quantification of cap thickness (n = 12 Ldlr-/-Prg4-Cre+, n = 13 Ldlr-/-Prg4-Cre+iDTA+). Data are mean ± SEM. Two-tailed Students t-test g, h, & j.

Source data

Extended Data Fig. 6 Hematopoietic cells in Dre-Jak2VF mice.

Percent Jak2VF (CD45.2+) a. monocytes, b. Neutrophils, c. Lymphocytes in blood (n = 15 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off), TAM indicates administration of tamoxifen diet to control and Jak2VF Off mice. Blood cells counts of d. White blood cells (WBC), e. monocytes, f. Lymphocytes, g. Neutrophils, h. RBCs, and i. Platelets (n = 15 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off). j. Spleen weight of mice related to Fig. 4b-d (n = 15 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off). Data are mean ± SEM. Two-Way ANOVA with Tukey post hoc analysis a-i. Kruskal-Wallis test with Dunn’s multiple comparisons j.

Source data

Extended Data Fig. 7 scRNA-seq analysis of Dre-Jak2VF plaques.

a. UMAP visualization of scRNA-seq analysis of CD45.1/2+ cells from aortas of mice modeling CH with Dre-Jak2VF. b. Differential gene expression of the top 5 genes expressed/cluster from scRNA-Seq analysis. c. Proportion of cells identified with scRNA-seq. d. UMAP visualization of scRNA-seq overlay with CD45.1 & CD45.2 barcoded antibodies to indicate Dre-Jak2VF status within e. Control, f. Jak2VF On and g. Jak2VF Off lesions, each sample are 5 pooled mice.

Extended Data Fig. 8 Features of plaque stability are unchanged in Dre-Jak2VF lesions.

a. Representative H&E images of aortic root lesions from mice with continuous Jak2VF expression (Jak2VF On) and mice that turn Jak2VF Off with tamoxifen (TAM) administration (Jak2VF Off), TAM chow was administered to control and Jak2VF Off groups. Quantification of b. lesion area and c. percent necrotic core area (n = 15 Control, n = 16 Jak2VF On, n = 16 Jak2VF Off). d. Representative picrosirius red stained aortic root lesions. Quantification of e. Cap thickness and f. collagen area marked by picrosirius red staining (n = 15 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off). g. Immunofluorescence (IF) staining of aortic root lesions. h. Quantification of Decorin mean fluorescence intensity (MFI) in lesions (n = 13 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off). i. IF staining of aortic root lesions. j. Quantification of macrophage density marked by percent MAC2+ cells in lesions (n = 14 Control, n = 17 Jak2VF On, n = 17 Jak2VF Off) k. Serum cholesterol levels (n = 15 Control, n = 17 Jak2VF On, n = 16 Jak2VF Off). mice. Data are mean ± SEM. One-Way ANOVA with Tukey’s post-hoc analysis b, c, e, f, h, & j). Two-Way ANOVA with Tukey’s multiple comparison test k.

Source data

Extended Data Fig. 9 Turning Jak2VF Off in conjunction with cholesterol lowering alters immune cell composition.

a. UMAP visualization of scRNA-Seq of aortic cells following cholesterol lowering. b. Population distribution of scRNA-Seq analysis. c. Differential gene expression of the top 5 genes expressed per cluster from scRNA-Seq analysis. d. Differential gene expression of the top 5 genes expressed per cluster from scRNA-Seq analysis of CD45+ myeloid cells.

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Fidler, T.P., Dunbar, A., Kim, E. et al. Suppression of IL-1β promotes beneficial accumulation of fibroblast-like cells in atherosclerotic plaques in clonal hematopoiesis. Nat Cardiovasc Res 3, 60–75 (2024). https://doi.org/10.1038/s44161-023-00405-9

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