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Kidney Aging
Aging is a complex process defined by the gradual decline of a multitude of physiological functions leading to an increasing probability of death and thus best studied using a systems biology approach.
We are studying aging of the human kidney, which begins to show functional decline around age 40. Kidneys age at different rates, such that some people show little or no effects of aging whereas others show rapid functional decline of the kidney. We first performed whole-genome transcriptional profiling to find genes that change expression with age in the kidney (Rodwell et al., 2004). Such age-regulated genes include components of the mitochondrial electron transport chain, extracellular matrix, and ribosome. These pathways also were coordinately age-regulated in human muscle and brain tissue. Furthermore, the expression profile for kidney aging marks physiological age and not just chronological age. The gene expression profiles distinguish between individuals with rapid or slow kidney aging rates. Some elderly patients showed rapid renal aging, and these patients also exhibited gene expression profiles that were advanced for their age. Conversely, other kidney samples aged slowly and these kidney samples had gene expression profiles similar to those from much younger patients.
We are now using these age-regulated genes as candidates in a genetic association study for kidney aging (Wheeler et al., 2009). We sequentially used transcriptional profiling and expression quantitative trait loci (eQTL) mapping to narrow down which genes to test for association with kidney aging. We first performed whole-genome transcriptional profiling to find 630 genes that change expression with age in the kidney. Using two methods to detect expression-associated SNPs, we found eQTLs for 101 of these age-regulated genes. We tested the eQTLs for association with kidney aging, measured by glomerular filtration rate (GFR) using combined data from the Baltimore Longitudinal Study of Aging (BLSA) and the InCHIANTI study. We found a SNP association (rs1711437 in MMP20) with kidney aging (uncorrected p=3.6 x 10-5, empirical p=0.01) that explains 1-2% of the variance in GFR among individuals. The results of this sequential analysis may provide the first evidence for a gene association with kidney aging in humans.
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Kim, S.K. Genome-wide Views of Aging Gene Networks, Cold Spring Harbor (2008)