title: Integrative biology identifies shared transcriptional networks in CKD.
abstract: A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR-associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic nephropathy. Taken together, the network integrates candidate genes from genome-wide association studies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases.
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;
authors.forename: Sebastian
authors.initials: S
authors.lastname: Martini
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;
authors.forename: Viji
authors.initials: V
authors.lastname: Nair
authors.affiliation: Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan;
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Keller
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;
authors.forename: Felix
authors.initials: F
authors.lastname: Eichinger
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;
authors.forename: Jennifer J
authors.initials: JJ
authors.lastname: Hawkins
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;
authors.forename: Ann
authors.initials: A
authors.lastname: Randolph
authors.affiliation: Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany;
authors.forename: Carsten A
authors.initials: CA
authors.lastname: Böger
authors.affiliation: Department of Medicine, Section of Nephrology and Kidney Transplantation, Temple University School of Medicine, Philadelphia, Pennsylvania;
authors.forename: Crystal A
authors.initials: CA
authors.lastname: Gadegbeku
authors.affiliation: Division of Intramural Research and Laboratory for Population and Metabolic Health, National Heart, Lung, and Blood Institute, Framingham, Massachusetts; Department of Endocrinology, Brigham and Women's Hospital, Boston, Massachusetts; and.
authors.forename: Caroline S
authors.initials: CS
authors.lastname: Fox
authors.affiliation: Institute of Physiology, University of Zürich, Zürich, Switzerland.
authors.forename: Clemens D
authors.initials: CD
authors.lastname: Cohen
authors.affiliation: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; kretzler@umich.edu.
authors.forename: Matthias
authors.initials: M
authors.lastname: Kretzler
mesh_terms: D000328:Adult; D000368:Aged; D030541:Databases, Genetic; D018450:Disease Progression; D005260:Female; D053263:Gene Regulatory Networks / Q000235:genetics*; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D008875:Middle Aged; D009656:North America; D020641:Polymorphism, Single Nucleotide; D051436:Renal Insufficiency, Chronic / Q000235:genetics* / Q000503:physiopathology*; D015398:Signal Transduction / Q000235:genetics; D014158:Transcription, Genetic / Q000235:genetics*; D059467:Transcriptome; D055815:Young Adult
keywords: CKD; chronic kidney failure; pathophysiology of renal disease and progression; renal progression; transcription regulation; transcriptional profiling