Integrative biology identifies shared transcriptional networks in CKD
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...
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Published in: | Journal of the American Society of Nephrology Vol. 25; no. 11; pp. 2559 - 2572 |
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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. |
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AbstractList | 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. 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. |
Author | Böger, Carsten A Nair, Viji Martini, Sebastian Keller, Benjamin J Randolph, Ann Eichinger, Felix Cohen, Clemens D Gadegbeku, Crystal A Kretzler, Matthias Hawkins, Jennifer J Fox, Caroline S |
Author_xml | – sequence: 1 givenname: Sebastian surname: Martini fullname: Martini, Sebastian organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan – sequence: 2 givenname: Viji surname: Nair fullname: Nair, Viji organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan – sequence: 3 givenname: Benjamin J surname: Keller fullname: Keller, Benjamin J organization: Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan – sequence: 4 givenname: Felix surname: Eichinger fullname: Eichinger, Felix organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan – sequence: 5 givenname: Jennifer J surname: Hawkins fullname: Hawkins, Jennifer J organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan – sequence: 6 givenname: Ann surname: Randolph fullname: Randolph, Ann organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan – sequence: 7 givenname: Carsten A surname: Böger fullname: Böger, Carsten A organization: Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany – sequence: 8 givenname: Crystal A surname: Gadegbeku fullname: Gadegbeku, Crystal A organization: Department of Medicine, Section of Nephrology and Kidney Transplantation, Temple University School of Medicine, Philadelphia, Pennsylvania – sequence: 9 givenname: Caroline S surname: Fox fullname: Fox, Caroline S organization: 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 – sequence: 10 givenname: Clemens D surname: Cohen fullname: Cohen, Clemens D organization: Institute of Physiology, University of Zürich, Zürich, Switzerland – sequence: 11 givenname: Matthias surname: Kretzler fullname: Kretzler, Matthias email: kretzler@umich.edu organization: Departments of Internal Medicine, Nephrology, and Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; kretzler@umich.edu |
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Copyright | Copyright © 2014 by the American Society of Nephrology. Copyright © 2014 by the American Society of Nephrology 2014 |
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Keywords | transcription regulation transcriptional profiling chronic kidney failure CKD renal progression pathophysiology of renal disease and progression |
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SubjectTerms | Adult Aged Basic Research Databases, Genetic Disease Progression Female Gene Regulatory Networks - genetics Genome-Wide Association Study Humans Male Middle Aged North America Polymorphism, Single Nucleotide Renal Insufficiency, Chronic - genetics Renal Insufficiency, Chronic - physiopathology Signal Transduction - genetics Transcription, Genetic - genetics Transcriptome Young Adult |
Title | Integrative biology identifies shared transcriptional networks in CKD |
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