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Systematic Integrated Analysis of Genetic and Epigenetic Variation in Diabetic Kidney Disease

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Systematic Integrated Analysis of Genetic and Epigenetic Variation in Diabetic Kidney Disease Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease Xin Shenga,b, Chengxiang Qiua,b, Hongbo Liua,b, Caroline Glucka,b, Jesse Y. Hsuc,d, Jiang Hee, Chi-yuan Hsuf, Daohang Shad, Matthew R. Weirg, Tamara Isakovah,i, Dominic Rajj, Hernan Rincon-Cholesk, Harold I. Feldmana,c,d, Raymond Townsenda, Hongzhe Lic,d, and Katalin Susztaka,b,1 aDepartment of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA 19104; bDepartment of Genetics, University of Pennsylvania, Philadelphia, PA 19104; cDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104; dCenter for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104; eDepartment of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Tulane University Translational Science Institute, New Orleans, LA 70118; fDivision of Nephrology, Department of Medicine, University of California, San Francisco, CA 94143; gDivision of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201; hDivision of Nephrology and Hypertension, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611; iCenter for Translational Metabolism and Health, Institute for Public Health and Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611; jDivision of Kidney Disease and Hypertension, George Washington University School of Medicine, Washington, DC 20052; and kDepartment of Nephrology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH 44125 Edited by Rama Natarajan, City of Hope, Duarte, CA, and accepted by Editorial Board Member Christopher K. Glass September 26, 2020 (received for review March 31, 2020) Poor metabolic control and host genetic predisposition are critical phenomenon, and targeting such a pathway could be futile for diabetic kidney disease (DKD) development. The epigenome for DKD. integrates information from sequence variations and metabolic Metabolic factors—such as diabetes, obesity, aging, and in- alterations. Here, we performed a genome-wide methylome asso- trauterine nutritional environment—play critical roles in CKD ciation analysis in 500 subjects with DKD from the Chronic Renal development (9, 10). Intrauterine nutritional deprivation or pe- Insufficiency Cohort for DKD phenotypes, including glycemic con- riods of hyperglycemia will increase kidney disease risk, even trol, albuminuria, kidney function, and kidney function decline. after several decades of good metabolic control, a phenomenon We show distinct methylation patterns associated with each phe- called “metabolic memory or programming” (11–15). Epigenetic MEDICAL SCIENCES notype. We define methylation variations that are associated with changes have been proposed to mediate this long-lasting effect of underlying nucleotide variations (methylation quantitative trait nutritional environment, as epigenome editing enzymes require loci) and show that underlying genetic variations are important intermediates of cellular metabolism (such as acetyl and methyl drivers of methylation changes. We implemented Bayesian multi- trait colocalization analysis (moloc) and summary data-based Men- groups) for histone and DNA modifications; thus, nutrient delian randomization to systematically annotate genomic regions availability can directly influence the epigenome (16). Epigenetic that show association with kidney function, methylation, and modifications are maintained during cell division, therefore, the gene expression. We prioritized 40 loci, where methylation and epigenome can serve as a long-term environmental footprint. gene-expression changes likely mediate the genotype effect on kidney disease development. Functional annotation suggested Significance the role of inflammation, specifically, apoptotic cell clearance and complement activation in kidney disease development. Our Diabetic kidney disease (DKD) is the most common cause of study defines methylation changes associated with DKD pheno- chronic and end-stage renal failure in the world. In a geneti- types, the key role of underlying genetic variations driving meth- cally susceptible host, poor metabolic control contributes to ylation variations, and prioritizes methylome and gene-expression DKD development. The epigenome integrates signals from changes that likely mediate the genotype effect on kidney disease sequence variations and environmental alterations. We per- pathogenesis. formed genome-wide DNA methylation association analysis in one of the best-characterized kidney disease cohorts: The methylation quantitative trait loci (mQTL) | multitrait colocalization analysis Chronic Renal Insufficiency Cohort study. Complex computa- (moloc) | epigenetics | multiomics integration analysis | chronic kidney tional integration analysis indicated the key role of genetic disease variations in DNA methylation. Our analysis highlighted loci, where methylation and gene-expression changes likely medi- ore than 800 million people worldwide suffer from chronic ate the genotype effect on kidney disease development. Func- Mkidney disease (CKD) (1). Despite the important clinical tional annotation of high-confidence genes suggested the causal needs, there is no curative therapy for CKD. Current treatments role of inflammation, specifically, complement activation and mostly rely on improving blood pressure and blood glucose apoptotic cell clearance in kidney disease development. control. New therapies that target novel causal pathways are Author contributions: X.S., J.Y.H., H.I.F., H. Li, and K.S. designed research; X.S. performed desperately needed. research; X.S. analyzed data; X.S., J.Y.H., C.-y.H., D.S., M.R.W., T.I., D.R., H.R.-C., R.T., and The role of immune cells and inflammation in diabetic kidney K.S. wrote the paper.; C.Q., H. Liu, and C.G. helped with data analysis; and J.Y.H., J.H., C.- disease (DKD) development remains controversial (2, 3). DKD y.H., D.S., M.R.W., T.I., D.R., H.R.-C., H.I.F., R.T., H. Li, and K.S. helped X.S. with data anal- ysis and assisted with data generation and manuscript revision. is traditionally considered a nonimmune-mediated kidney dis- The authors declare no competing interest. ease (2). Genome-wide association analysis studies highlighted This article is a PNAS Direct Submission. R.N. is a guest editor invited by the the role of podocytes and proximal tubules in kidney disease Editorial Board. development (4, 5). On the other hand, human kidney gene- Published under the PNAS license. expression studies have reproducibly indicated a correlation 1To whom correspondence may be addressed. Email: [email protected]. between immune cells, certain cytokine levels, and disease se- This article contains supporting information online at https://www.pnas.org/lookup/suppl/ verity (6–8). The lack of genetic support for inflammation in doi:10.1073/pnas.2005905117/-/DCSupplemental. CKD led to the notion that inflammation might be a secondary First published November 3, 2020. www.pnas.org/cgi/doi/10.1073/pnas.2005905117 PNAS | November 17, 2020 | vol. 117 | no. 46 | 29013–29024 Downloaded by guest on September 25, 2021 A CRIC Study B 14 Hemoglobin A1c DKD 12 TXNIP 10 8 (P-value) 6 10 4 2 -log 0 1 2 3 4 5 6 7 8 9 10 11 1213141516 171819202122 Chromosome C ANKLE1 ~8×105 CpCs 8 Albuminuria UPK2 CIQTF1 Methylation 6 FAM110C PAK6 SRSF6 4 (P-value) 10 2 -log 0 1 2 3 4 5 6 7 8 9 10 11 1213141516 171819202122 Phenotype D Chromosome Glycemia (HgbA1c) 12 eGFR ZNF20 Albuminuria 10 Kidney function 8 eGFR) 6 Kidney function decline (P-value) (eGFR slope) 10 4 2 -log 0 12345678910111213141516171819202122 Chromosome MWAS E 8 eGFR Slope LYZ/YEATS4 RSPH1 IGFL2 6 4 (P-value) 10 2 Methylation -log Phenotype 0 1 2 3 4 5 6 7 8 9 10 11 1213141516 171819202122 Chromosome Fig. 1. Methylation changes associated with DKD phenotypes (glycemia, albuminuria, eGFR, and eGFR slope) in whole-blood samples of the CRIC study participants. (A) Study schematics. Methylation levels of ∼800,000 loci measured by Illumina Human MethylationEPIC arrays were used to analyze the as- sociations with DKD phenotypes (glycemia, albuminuria, eGFR, and eGFR slope) using a linear regression model. (B) Manhattan plot. The y axis is the −log10 of the association P value of hemoglobin A1c and methylation. The x axis represents the genomic location of the CpG probes. n = 473 samples. (C) Manhattan plot. The y axis is the −log10 of P value of albuminuria (24 h) and methylation. The x axis represents the genomic location of the CpG probes. n = 473 samples. (D) Manhattan plot. The y axis is the −log10 of P value of kidney function (baseline eGFR) and methylation. The x axis represents the genomic location of the CpG probes. n = 473 samples. (E) Manhattan plot. The y axis is the −log10 of P value of kidney function decline (eGFR slope) and methylation. The x axis represents the genomic location of the CpG probes. n = 410 samples. Methylome-wide associated studies (MWAS) have been per- Expression of quantitative trait (eQTL) studies have been pow- formed to characterize methylation changes in CKD (17, 18). erful to annotate disease-driving genetic variations to prioritize Studies from the Diabetes Control and Complications Trial (DCCT) disease-causing genes. Our initial integration of CKD GWAS and group identified changes around
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