Early biomarkers of renal damage due to prolonged exposure to embedded metal.

ABSTRACT: Background: Prolonged exposure to toxic heavy metals leads to deleterious health outcomes such as kidney injury and irreversible progression to chronic kidney disease. For example, veterans return from the battlefield with increasing amounts of retained metal fragments. Certain community water sources in the US are contaminated with varying levels of heavy metals, including uranium and lead. One of the key challenges is to detect damage to kidney tissue before glomerular filtration rate is affected. Methods: High-throughput transcriptomics (HTT) has recently been demonstrated have high sensitivity and specificity as a rapid and cost-effective assay for detecting tissue toxicity. To better understand the molecular signature of early kidney damage, we performed RNA-seq analysis on renal tissue using a rat model of soft tissue-embedded metal exposure. We them performed small RNA-seq analysis on serum samples from the same animals in an effort to identify miRNA biomarkers of kidney damage. Results: We found that metals, especially lead and depleted uranium, induces oxidative damage that mainly cause dysregulated mitochondrial gene expression. Utilizing publicly available single-cell RNA-seq datasets, we demonstrate that deep learning-based cell type decomposition effectively identified cells within the kidney that were affected by metal exposure. By combining random forest feature selection and statistical methods, we further identify miRNA-423 as a promising early systemic marker of kidney injury. Conclusion: Our data suggests that combining HTT and deep learning represents a promising approach for identifying cell injury in kidney tissue. We propose miRNA-423 as a potential serum biomarker for early detection of kidney injury.

ORGANISM(S): Rattus norvegicus

PROVIDER: GSE203624 | GEO | 2022/12/01

REPOSITORIES: GEO