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

中文翻译：

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长期暴露于嵌入金属导致肾损伤的早期转录组学特征和生物标志物

背景

长期接触有毒重金属会导致有害的健康结果，包括肾损伤。金属暴露通过两种环境途径发生，包括饮用水源污染和职业危害，包括战场受伤导致子弹和爆炸碎片残留金属碎片的军事独特风险。在这些情况下减轻健康影响的关键挑战之一是在发生不可逆转的损害之前检测对目标器官（例如肾脏）的早期损伤。

方法

高通量转录组学 (HTT) 最近已被证明具有高灵敏度和特异性，可作为一种快速且经济高效的检测组织毒性的方法。为了更好地了解早期肾损伤的分子特征，我们使用软组织包埋金属暴露的大鼠模型对肾组织进行了 RNA 测序 (RNA-seq)。然后，我们对来自相同动物的血清样本进行了小分子 RNA-seq 分析，以确定肾脏损伤的潜在 miRNA 生物标志物。

结果

我们发现金属，尤其是铅和贫铀，会引起氧化损伤，这主要导致线粒体基因表达失调。利用公开可用的单细胞 RNA-seq 数据集，我们证明了基于深度学习的细胞类型分解有效地识别了肾脏内受金属暴露影响的细胞。通过结合随机森林特征选择和统计方法，我们进一步确定 miRNA-423 是一种很有前途的肾损伤早期系统性标志物。

结论

我们的数据表明，结合 HTT 和深度学习是识别肾组织细胞损伤的一种很有前途的方法。我们建议将 miRNA-423 作为早期检测肾损伤的潜在血清生物标志物。

图形概要