Kidney systems biology: Building mechanisms for gene and environmental interactions

Integrating the massive data for renal physiology has gained importance with the emergence of artificial intelligence platforms. However, understanding publication bias and disease overlaps has been challenging for genes identified for renal traits. Therefore, we applied a system biology approach to 69 kidney traits extracted from Open Target Platforms, Open Target Genetics, ClinVar, and geneBurden to evaluate network interactions and potential variants that could link different biological pathways to disease mechanisms. From Open Target Platforms, information was gathered on 20,165 protein-coding genes, with 2,806 of those genes having at least one trait with a gene score in the range of 0.2 to 1. Open Target Genetics provided data on 2,004 genes, while geneBurden and ClinVar had data on 61 and 466 genes. The gene list was used to build a hierarchical cluster map of the 69 kidney traits using one minus Pearson correlation to identify overlapping and divergent gene signatures for disease. Kidney cancer-related traits cluster together with outliers in genetic and hereditary cancers. A separate cluster was present for CKD and AKI-related phenotypes with multiple rare kidney syndromes separately clustering outside these and cancer gene signatures. Rare pediatric cancer-related traits lack gene signatures compared to the Open Target Platform and Open Target Genetics data sets of common kidney traits. With our systems biology map, we have begun to enrich mechanistic insights by analyzing quantitative trait loci (QTLs), co-localization dynamics of QTLs, single-cell expression within the kidney, and toxicogenomic responses. These have shed light on genetic risks for exposures to corticosteroid derivatives and various biological chemicals within unique cell clusters of the kidney for traits such as cancer and CKD. These methods represent an exciting integration of systems biology with data integration to begin developing human-centric genetic models for environmental regulations. The work was funded by the American Heart Association grant (23SCISA1144887) and NIH Office of the Director (R01AI171984). This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.