Abstract
Background
Prolonged exposure to toxic heavy metals leads to deleterious health outcomes including kidney injury. Metal exposure occurs through both environmental pathways including contamination of drinking water sources and from occupational hazards, including the military-unique risks from battlefield injuries resulting in retained metal fragments from bullets and blast debris. One of the key challenges to mitigate health effects in these scenarios is to detect early insult to target organs, such as the kidney, before irreversible damage occurs.
Methods
High-throughput transcriptomics (HTT) has been recently demonstrated to 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 sequencing (RNA-seq) on renal tissue using a rat model of soft tissue-embedded metal exposure. We then performed small RNA-seq analysis on serum samples from the same animals to identify potential miRNA biomarkers of kidney damage.
Results
We found that metals, especially lead and depleted uranium, induce 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 suggest that combining HTT and deep learning is 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.
Graphical Abstract
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Data availability
All serum small RNA-seq and kidney tissue mRNA-seq data generated from this study have been deposited into the Gene Expression Omnibus (GEO) under the accession numbers GSE168757 and GSE203624, respectively.
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Acknowledgements
The animal surgeries and tissue collections were performed by John Kalinich and Jessica Hoffman, with histopathology support by W. Louis Wilkins, at the Armed Forces Radiobiology Research Institute of the Uniformed Services University.
Funding
The funding for this study was provided by the grant Health Effects of Blast Injuries and Embedded Metal Fragments (W81XWH-16–2-0058) from the Congressionally Directed Medical Research Program (CDMRP) Peer-Reviewed Medical Research Program.
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CAP, JJM, and YW conceptualized the studies and oversaw the progress. YW, IJV, TV, and APA performed experiments and collected and analyzed samples. YW, IJV, DL, and XDZ performed the computational and statistical analyses. YW prepared the figures and drafted the manuscript.
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Significance statement
Occupational and environmental exposures to nephrotoxic heavy metals are under-appreciated sources of health risk in the USA. Damage to the kidneys is typically detected at a late stage when irreversible progression to chronic kidney disease has already occurred. In our study, we utilized high-throughput transcriptomics and novel bioinformatics techniques to provide detailed molecular signatures of early kidney injury from metal exposure, preceding any detectable functional decline, and identified candidate systemic biomarker that would allow for early detection of nephrotoxicity.
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Wen, Y., Vechetti, I.J., Leng, D. et al. Early transcriptomic signatures and biomarkers of renal damage due to prolonged exposure to embedded metal. Cell Biol Toxicol 39, 2861–2880 (2023). https://doi.org/10.1007/s10565-023-09806-9
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DOI: https://doi.org/10.1007/s10565-023-09806-9