Abstract
Chronic liver disease is a major public health burden worldwide1. Although different aetiologies and mechanisms of liver injury exist, progression of chronic liver disease follows a common pathway of liver inflammation, injury and fibrosis2. Here we examined the association between clonal haematopoiesis of indeterminate potential (CHIP) and chronic liver disease in 214,563 individuals from 4 independent cohorts with whole-exome sequencing data (Framingham Heart Study, Atherosclerosis Risk in Communities Study, UK Biobank and Mass General Brigham Biobank). CHIP was associated with an increased risk of prevalent and incident chronic liver disease (odds ratio = 2.01, 95% confidence interval (95% CI) [1.46, 2.79]; P < 0.001). Individuals with CHIP were more likely to demonstrate liver inflammation and fibrosis detectable by magnetic resonance imaging compared to those without CHIP (odds ratio = 1.74, 95% CI [1.16, 2.60]; P = 0.007). To assess potential causality, Mendelian randomization analyses showed that genetic predisposition to CHIP was associated with a greater risk of chronic liver disease (odds ratio = 2.37, 95% CI [1.57, 3.6]; P < 0.001). In a dietary model of non-alcoholic steatohepatitis, mice transplanted with Tet2-deficient haematopoietic cells demonstrated more severe liver inflammation and fibrosis. These effects were mediated by the NLRP3 inflammasome and increased levels of expression of downstream inflammatory cytokines in Tet2-deficient macrophages. In summary, clonal haematopoiesis is associated with an elevated risk of liver inflammation and chronic liver disease progression through an aberrant inflammatory response.
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Data availability
Source data used in this analysis are available to approved researchers through ARIC, TOPMed, the UK Biobank and MGB Biobank. CHIP variants identified in this study are listed in Supplementary Tables 3 and 12. Full summary statistics for the cirrhosis GWAS are available for download at https://cvd.hugeamp.org/downloads.html. RNA-sequencing datasets are available in the Gene Expression Omnibus repository under the accession code GSE223695. Source data are provided with this paper.
Change history
14 April 2023
In the version of this article initially published, Benjamin L. Ebert was not listed as a corresponding author, while Pradeep Natarajan was shown with an incorrect email address; the changes are made in the HTML and PDF versions of the article.
03 July 2023
A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06375-z
References
Anstee, Q. M., Reeves, H. L., Kotsiliti, E., Govaere, O. & Heikenwalder, M. From NASH to HCC: current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 16, 411–428 (2019).
Kisseleva, T. & Brenner, D. Molecular and cellular mechanisms of liver fibrosis and its regression. Nat. Rev. Gastroenterol. Hepatol. 18, 151–166 (2021).
Younossi, Z. M. et al. Epidemiology of chronic liver diseases in the USA in the past three decades. Gut 69, 564–568 (2020).
Scaglione, S. et al. The epidemiology of cirrhosis in the United States: a population-based study. J. Clin. Gastroenterol. 49, 690–696 (2015).
Brempelis, K. J. & Crispe, I. N. Infiltrating monocytes in liver injury and repair. Clin. Transl. Immunol. 5, e113 (2016).
Krenkel, O. & Tacke, F. Liver macrophages in tissue homeostasis and disease. Nat. Rev. Immunol. 17, 306–321 (2017).
Baeck, C. et al. Pharmacological inhibition of the chemokine CCL2 (MCP-1) diminishes liver macrophage infiltration and steatohepatitis in chronic hepatic injury. Gut 61, 416–426 (2012).
Krenkel, O. et al. Therapeutic inhibition of inflammatory monocyte recruitment reduces steatohepatitis and liver fibrosis. Hepatology 67, 1270–1283 (2018).
Krenkel, O. et al. Myeloid cells in liver and bone marrow acquire a functionally distinct inflammatory phenotype during obesity-related steatohepatitis. Gut 69, 551–563 (2020).
Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126, 9–16 (2015).
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).
Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).
Jaiswal, S. et al. Clonal hematopoiesis and risk of atherosclerotic cardiovascular disease. N. Engl. J. Med. 377, 111–121 (2017).
Bick, A. G. et al. Genetic interleukin 6 signaling deficiency attenuates cardiovascular risk in clonal hematopoiesis. Circulation 141, 124–131 (2020).
Fuster, J. J. et al. Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice. Science 355, 842–847 (2017).
Sano, S. et al. Tet2-mediated clonal hematopoiesis accelerates heart failure through a mechanism involving the IL-1β/NLRP3 inflammasome. J. Am. Coll. Cardiol. 71, 875–886 (2018).
Loh, P.-R. et al. Insights about clonal hematopoiesis from 8,342 mosaic chromosomal alterations. Nature 559, 350–355 (2018).
Bick, A. G. et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes. Nature 586, 763–768 (2020).
Emdin, C. A. et al. A missense variant in mitochondrial amidoxime reducing component 1 gene and protection against liver disease. PLoS Genet. 16, e1008629 (2020).
Bao, E. L. et al. Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells. Nature 586, 769–775 (2020).
Matsumoto, M. et al. An improved mouse model that rapidly develops fibrosis in non-alcoholic steatohepatitis. Int. J. Exp. Pathol. 94, 93–103 (2013).
Ikawa-Yoshida, A. et al. Hepatocellular carcinoma in a mouse model fed a choline-deficient, L-amino acid-defined, high-fat diet. Int. J. Exp. Pathol. 98, 221–233 (2017).
Buzzetti, E., Pinzani, M. & Tsochatzis, E. A. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism 65, 1038–1048 (2016).
Klein, I. et al. Kupffer cell heterogeneity: functional properties of bone marrow derived and sessile hepatic macrophages. Blood 110, 4077–4085 (2007).
Scott, C. L. et al. Bone marrow-derived monocytes give rise to self-renewing and fully differentiated Kupffer cells. Nat. Commun. 7, 10321 (2016).
Oo, Y. H. et al. Distinct roles for CCR4 and CXCR3 in the recruitment and positioning of regulatory T cells in the inflamed human liver. J. Immunol. 184, 2886–2898 (2010).
Fuster, J. J. et al. TET2-loss-of-function-driven clonal hematopoiesis exacerbates experimental insulin resistance in aging and obesity. Cell Rep. 33, 108326 (2020).
Zhao, Q., Wang, J., Hemani, G., Bowden, J. & Small, D. S. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann. Stat. 48, 1742–1769 (2020).
Wunderlich, F. T. et al. Interleukin-6 signaling in liver-parenchymal cells suppresses hepatic inflammation and improves systemic insulin action. Cell Metab. 12, 237–249 (2010).
Yamaguchi, K. et al. Blockade of interleukin-6 signaling enhances hepatic steatosis but improves liver injury in methionine choline-deficient diet-fed mice. Lab. Investig. J. Tech. Methods Pathol. 90, 1169–1178 (2010).
Mridha, A. R. et al. NLRP3 inflammasome blockade reduces liver inflammation and fibrosis in experimental NASH in mice. J. Hepatol. 66, 1037–1046 (2017).
Wree, A. et al. NLRP3 inflammasome activation results in hepatocyte pyroptosis, liver inflammation, and fibrosis in mice. Hepatology 59, 898–910 (2014).
Csak, T. et al. Fatty acid and endotoxin activate inflammasomes in mouse hepatocytes that release danger signals to stimulate immune cells. Hepatology 54, 133–144 (2011).
Chalasani, N. et al. The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American Association for the Study of Liver Diseases. Hepatology 67, 328–357 (2018).
Banerjee, R. et al. Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J. Hepatol. 60, 69–77 (2014).
Kleiner, D. E. et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41, 1313–1321 (2005).
Duewell, P. et al. NLRP3 inflamasomes are required for atherogenesis and activated by cholesterol crystals that form early in disease. Nature 464, 1357–1361 (2010).
Hoang, S. A. et al. Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease. Sci. Rep. 9, 12541 (2019).
Ryaboshapkina, M. & Hammar, M. Human hepatic gene expression signature of non-alcoholic fatty liver disease progression, a meta-analysis. Sci. Rep. 7, 12361 (2017).
Gerhard, G. S. et al. Transcriptomic profiling of obesity-related nonalcoholic steatohepatitis reveals a core set of fibrosis-specific genes. J. Endocr. Soc. 2, 710–726 (2018).
Verbanck, M., Chen, C.-Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).
Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32, 377–389 (2017).
Liu, M. et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 51, 237–244 (2019).
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).
Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).
Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).
Acknowledgements
The UK Biobank analyses were carried out under application numbers 7089 and 50834. The investigators thank the UK Biobank staff and participants. P.N. is supported by a Hassenfeld Scholar Award and the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine from the Massachusetts General Hospital, and grants from the National Heart, Lung, and Blood Institute (R01HL142711, R01HL148565 and R01HL148050) and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK125782). P.N. and B.L.E. are supported by a grant from the Fondation Leducq (TNE-18CVD04). B.L.E. is also supported by the NIH (R01HL082945, P01CA108631 and P50CA206963) and the Howard Hughes Medical Institute. W.J.W. is supported by a RUNX1 Research Program and Alex’s Lemonade Stand Foundation Early Career Investigator Grant. S.M.Z. was supported by the NIH National Heart, Lung, and Blood Institute (1F30HL149180-01) and the NIH Medical Scientist Training Program Training Grant (T32GM136651). A.N. was supported by funds from the Knut and Alice Wallenberg Foundation (KAW2017.0436). J.P.P. is supported by the NIH (K08HL159346). L.D. was supported by NIH grant K23 DK113220. C.J.G. is supported by the NIH (K08CA263555). R.S.S. is supported by a Kay Kendall Leukaemia Fund Intermediate Fellowship and by a CRUK Advanced Clinician Scientist Fellowship. A.V. received the Harold M. English Fellowship Fund from Harvard Medical School (Boston, USA). P.G.K. is supported by the Damon Runyon Physician-Scientist Award (PST-35-21) and the Edward P. Evans Foundation Evans Young Investigator Award. M.A. was supported by the Deutsche Forschungsgemeinschaft (DFG, AG252/1-1). P.-R.L. is supported by NIH grant DP2 ES030554 and a Burroughs Wellcome Fund Career Award at the Scientific Interfaces. R.T.C. is supported by NIH grants R01AI136715 and R01AI155140, and the MGH Research Scholars Program. Molecular data for the Trans-Omics in Precision Medicine (TOPMed) programme were supported by the National Heart, Lung, and Blood Institute. See Supplementary Table 14 for TOPMed-specific omics support information. Core support including centralized genomic read mapping and genotype calling, along with variant quality metrics and filtering, was provided by the TOPMed Informatics Research Center (3R01HL-117626-02S1; contract HHSN268201800002I). Core support including phenotype harmonization, data management, sample-identity quality control and general programme coordination were provided by the TOPMed Data Coordinating Center (R01HL-120393; U01HL-120393; contract HHSN268201800001I). We gratefully acknowledge the studies and participants who provided biological samples and data for TOPMed. We thank D. K. Li and P. G. Miller for experimental advice and critical reading of the manuscript. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the US Department of Health and Human Services.
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W.J.W., C. Emdin, B.L.E. and P.N. conceived of, designed and wrote the manuscript for this project. B.L.E. and P.N. provided supervision and project administration and secured financial support. C. Emdin led human genetics data curation and analyses, and W.J.W. led animal experiments and analyses. V.K., A.E.L., M. E. McConkey, A.V., R.S.S., P.G.K. and M.A. carried out experiments and revised the manuscript for intellectual content. A.G.B., S.M.Z., A.N., J.P.P., L.D., G.G., M.M.U. and C.J.G. contributed to human genetic data and revised the manuscript for intellectual content. R. Banerjee, R.C.N., A.D. and M. Kelly analysed the UK Biobank liver imaging data and revised the manuscript for intellectual content. All other authors (J. Weinstock, M.T.L., B.Y., P.-R.L., S. McCarroll, E. Boerwinkle, R.S.V., S.J., A.D.J., R.T.C., K.C., D. Levy and C. Ballantyne) revised the manuscript for intellectual content.
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W.J.W., C. Emdin, B.L.E. and P.N. are inventors on a US provisional patent application related to this work filed by Massachusetts General Hospital and Dana-Farber Cancer Institute (number 63/116,382, filed 20 November 2020). P.N. reports grant support from Amgen, Apple, AstraZeneca, Boston Scientific and Novartis, and personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Invitae, Novartis, Roche/Genetech and TenSixteen Bio, is a scientific advisory board member of Esperion Therapeutics, geneXwell and TenSixteen Bio, and reports spousal employment at and equity in Vertex, all distinct from the present work. C. Emdin reports personal fees from Acceleron Pharma, Korro Bio, Navitor Pharma, Nference, Novartis and Third Rock Ventures, all distinct from the present work. B.L.E. has received research financial support from Celgene, Deerfield, Novartis and Calico, and consulting fees from GRAIL, and serves on the scientific advisory boards for Neomorph Therapeutics, Skyhawk Therapeutics and Exo Therapeutics, all distinct from the present work. P.N. and B.L.E. are scientific co-founders of TenSixteen Bio, which focuses on somatic mosaicism and precision medicine. L.D. has received research support from Perspectum Ltd, Pfizer, Lumos Pharma and Recordati, is a MGB Innovation Fellow hosted by Third Rock Ventures (a venture capital firm) and remains full time at MGH during the period of this educational program (anticipated 1 October 2022–30 September 2024); the financial interests of L.D. were reviewed and are managed by MGH and MGB in accordance with their conflict-of-interest policies. M.A. received consulting fees from German Accelerator Life Sciences and is a co-founder of and holds equity in iuvando Health, all unrelated to the present work. R. Banerjee, R.C.N., A.D. and M. Kelly receive salaries from and have stock options in Perspectum and research interests in liver and cardiometabolic disease. S.J. is on advisory boards for Novartis, AVRO Bio and Roche Genentech, is a paid consultant for Foresite Labs, reports speaking fees and an honorarium from GSK, is an equity holder and a scientific advisory board member of Bitterroot Bio, and is a co-founder, equity holder, and scientific advisory board member of TenSixteen Bio. R.T.C. has received grant support to his institution from Abbvie, Boehringer, Gilead, Merck, BMS, Roche, Janssen and GSK all unrelated to the present work. All other authors declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 CHIP ascertainment.
a, Proportion of CHIP by mutated gene among 11,783 individuals with CHIP. b, Prevalence of CHIP by age. CHIP, clonal hematopoiesis of indeterminate potential.
Extended Data Fig. 2 Association of CHIP with chronic liver disease.
a, Association of CHIP with prevalent or incident chronic liver disease by variant allele fraction. b, Association of CHIP with chronic liver disease by mutated gene. c, Cumulative risk of chronic liver disease by clonal hematopoiesis status in the UK Biobank. d, Cumulative risk of chronic liver disease by clonal hematopoiesis status in the UK Biobank by age 80 years. Estimates derived using logistic regression with adjustment for age and sex in the UK Biobank, Framingham Heart Study and Atherosclerosis Risk in Communities study and pooled using inverse variance weighted fixed effects meta-analysis. Cumulative risk of chronic liver disease by age was modeled using Cox proportional hazards model with age as the underlying time variable and adjustment for sex. CHIP, clonal hematopoiesis of indeterminate potential; CI, 95% confidence interval; OR, odds ratio; VAF, variant allele fraction.
Extended Data Fig. 3 Mendelian randomization analysis of CHIP association with chronic liver disease.
a, Effect of genetic variants against exposure (CHIP) and outcome (cirrhosis). Effect estimates are oriented to CHIP-increasing alleles. b, Phenome-wide mendelian randomization analysis of CHIP with 22 phenotypes. MR analysis was performed using MR Base platform. Estimates were derived using inverse variance weighted meta-analysis using 90 independent variants associated with CHIP with p < 5 x 10−5.
Extended Data Fig. 4 Association of CHIP with serum biomarkers.
a, Association of CHIP with serum biomarkers in the UK Biobank. b, Association of CHIP with serum biomarkers in the UK Biobank excluding JAK2-mutant CHIP. c, Association of CHIP with serum biomarkers in the UK Biobank excluding TET2-mutant CHIP. ALP, alkaline phosphatase; ALT, alanine transaminase; AST, aspartate transaminase; CHIP, clonal hematopoiesis of indeterminate potential; CRP, C-reactive protein; GGT, gamma-glutamyl transferase; TBili, total bilirubin; WBC, white blood cell count. A p-value of 0.006 after Bonferroni adjustment (0.05/9 = 0.006) was considered significant.
Extended Data Fig. 5 Metabolic phenotype of Tet2−/− bone marrow transplanted mice fed CDAHFD.
Lethally irradiated C57BL/6J mice were transplanted with Tet2−/− (n = 30) or control vavCre+ (wt; n = 25) bone marrow cells. After hematopoietic reconstitution, mice were fed CDAHFD and body weight (a) and food intake (b) were measured over 30 days. Mice were sacrificed and terminal liver weight (c-d) and serum biomarkers (e) were measured. Control mice were transplanted with Tet2−/− (n = 6) or control vavCre+ (wt; n = 6) bone marrow cells and fed standard chow for the same duration. Data from one (a-d) or two independent experiments (e) are shown. AST, aspartate transaminase; ALT, alanine transaminase; TBILI, total bilirubin; ALB, albumin; TRIG, triglycerides; GLUC, glucose; CHOL, total cholesterol; HDL, high-density lipoprotein; LDL, low-density lipoprotein; NEFA, non-essential fatty acids.
Extended Data Fig. 6 Liver and hematological parameters of Tet2−/− bone marrow transplanted mice.
a, Ldlr−/− mice were transplanted with Tet2−/− (n = 25) or control vavCre+ (wt; n = 20) bone marrow cells and fed Western diet for 10 weeks. b, B6.SJL mice were transplanted with Tet2−/− (n = 19) or control vavCre+ (wt; n = 12) bone marrow cells and fed CDAHFD for 11 weeks. c, C57BL/6J mice were transplanted with Tet2−/− (n = 12) or control vavCre+ (wt; n = 13) bone marrow cells and fed CDAHFD for 19 weeks. After the prescribed dietary periods, mice were sacrificed and terminal liver weight and peripheral blood chimerism and hematological parameters were measured. Data from one (c) or two independent experiments (a, b) are shown. WBC, white blood cell count; Hgb, hemoglobin; Hct, hematocrit; RBC, red blood cell count; Plt, platelet count.
Extended Data Fig. 7 Steatohepatitis and liver fibrosis in Tet2−/− and Dnmt3a−/− transplanted mice.
a, Selected gene signatures enriched in bulk liver transcripts from Tet2−/− (n = 4) transplanted mice fed CDAHFD relative to control vavCre+ (wt; n = 4) transplanted mice. b, Histologic features of steatohepatitis in Ldlr−/− mice transplanted with Tet2−/− (n = 27) and control vavCre+ (wt; n = 30) bone marrow and fed Western diet for 10 weeks were graded on a semiquantitative scale and aggregated into a NASH activity score (NAS) using CRN histologic scoring criteria. c-f, Graded histologic features included steatosis (c), inflammatory foci (d), hepatocyte ballooning (e, arrowhead) and apoptosis (f, arrow). g, Masson’s trichrome staining demonstrates absence of perivenular fibrosis in control and Tet2−/− transplanted mice. h, B6.SJL mice were transplanted with Dnmt3a−/− (n = 24) or control vavCre+ (wt; n = 20) bone marrow cells and fed CDAHFD for 11 weeks. Steatohepatitis was assessed histologically for steatosis, inflammation, and hepatocyte ballooning. Collagen fibrosis was measured by Masson’s trichrome staining. i, B6.SJL mice were transplanted with Tet2−/− (n = 20) or control vavCre+ (wt; n = 15) bone marrow cells and fed CDAHFD for 11 weeks, then reverted to standard chow for 10 days. Compared to control animals, Tet2−/− transplanted mice show similar resolution of liver fat but show persistently greater inflammation and more hepatocyte ballooning. Collagen fibrosis, as measured by Masson’s trichrome staining, was not significantly different. Data from one (a) or two independent experiments (b, h, i) are shown. NES, normalized enrichment score; FDR, false discovery rate.
Extended Data Fig. 8 Fibrogenic response in hepatic stellate cells co-cultured with Tet2−/− hematopoietic cell populations.
Hepatic stellate cells were isolated from wild type livers (n = 5) and co-cultured with CD19+ B cells, CD3+ T cells, or CD11b+ hepatic macrophages isolated from Tet2−/− (n = 5) or control vavCre+ (wt; n = 5) mice for 2 days. Co-cultured hepatic stellate cells were harvested for RNA sequencing and selected differentially expressed genes (relative to hepatic stellate cell mono-culture) are shown (a). Gene expression profiles of co-cultured hepatic stellate cells were compared to published gene signatures of activated hepatic stellate cells from Zhang DY et al.48. (b) and Wang H et al.49. (c). Data from one experiment are shown. NES, normalized enrichment score; FWER, family-wise error rate.
Extended Data Fig. 9 Donor-derived Kupffer cells and hepatic macrophages after bone marrow transplantation.
a, C57BL/6J mice were transplanted with CD45.1+Tet2−/− (n = 2) bone marrow cells and fed CDAHFD or standard chow. After 4 weeks, mice were sacrificed and dissociated liver cells were subjected to flow cytometric analysis of CD45.1 (donor) and CD45.2 (recipient) expression in F4/80hiCD11bmod Kupffer cells and CD11bhiF4/80mod hepatic macrophages. b, B6.SJL mice were transplanted with CD45.2+vavCre+ (n = 2) bone marrow cells and fed CDAHFD or standard chow for 19 weeks. Immunohistochemical stains demonstrate the presence of CD45.2+CD45.1−F4/80+ macrophages (arrowheads) in livers of bone marrow transplanted mice fed CDAHFD. Representative data from one mouse per condition are shown. c, Selected gene signatures enriched in sorted liver macrophages from Tet2−/− (n = 4) transplanted mice fed CDAHFD relative to control vavCre+ (wt; n = 4) transplanted mice. Data shown are from one experiment. NES, normalized enrichment score; FWER, family-wise error rate.
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Wong, W.J., Emdin, C., Bick, A.G. et al. Clonal haematopoiesis and risk of chronic liver disease. Nature 616, 747–754 (2023). https://doi.org/10.1038/s41586-023-05857-4
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DOI: https://doi.org/10.1038/s41586-023-05857-4
