4.7 Article

Integrative Biology Identifies Shared Transcriptional Networks in CKD

期刊

JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY
卷 25, 期 11, 页码 2559-2572

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AMER SOC NEPHROLOGY
DOI: 10.1681/ASN.2013080906

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资金

  1. O'Brien Renal Center National Institutes of Health [P30-DK081943-01]
  2. National Institutes of Health [R01-DK079912-01]
  3. National Center for Integrative Bioinformatics National Institutes of Health [U54-DA021519 01A1]
  4. Else Kroner-Fresenius-Stiftung [P48/08//A11/08, 2012_A147]

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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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