Clinical EpigeneticsBioinformaticsPeer-reviewed journal article
Kidney-specific methylation patterns correlate with kidney function and are lost upon kidney disease progression
Naor SagyiD, Noa Meyrom, Pazit BeckermaniD, Oren PleniceanuiD, Daniel Zvi BariD
- Citations
- 14
- FWCI
- 1.90
- Field percentile
- top 14%
Abstract
Chronological and biological age correlate with DNA methylation levels at specific sites in the genome. Linear combinations of multiple methylation sites, termed epigenetic clocks, can inform us of the chronological age and predict multiple health-related outcomes. However, why some sites correlate with lifespan, healthspan, or specific medical conditions remains poorly understood. Kidney fibrosis is the common pathway for Chronic Kidney Disease, which affects 10% of Europe and USA population. Here we identify epigenetic clocks and methylation sites that correlate with kidney function. Moreover, we identify methylation sites that have a unique methylation signature in the kidney. Methylation levels in the majority of these sites correlates with kidney state and function. When kidney function deteriorates, all of these sites regress towards the common methylation pattern observed in other tissues. Interestingly, while the majority of sites are less methylated in the kidney and become more methylated with loss of function, a fraction of the sites are highly methylated in the kidney and become less methylated when kidney function declines. These methylation sites are enriched for specific transcription-factor binding sites. In a large subset of sites, changes in methylation pattern are accompanied by changes in gene expression in kidneys of chronic kidney disease patients. These results support the information theory of aging, and the hypothesis that the unique tissue identity, as captured by methylation patterns, is lost as tissue function declines. However, this information loss is not random, but guided towards a baseline that is dependent on the genomic loci.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
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