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et al. 2015; Song et al. 2016; Wang et al. 2009). In combination with the liver injury biomarkers cytokeratin-18 and glutamate dehydrogenase, miR-122 outperformed individual biomarkers and had a higher than 0.92 correlation with alanine aminotransferase (ALT) in a validation cohort of individuals with acetominophen overdose (Llewellyn et al. 2021). In pre-clinical settings, miR-122 did mildly improve the diagnosis of DILI (4% increase in predictive accuracy using a multiparameter approach) and was highly correlated with necrosis, vacuolization, and hepatocellular hypertrophy (Sharapova et al. 2016), which indicated its usefulness as a leakage biomarker. However, the use of miR-122 has been hampered by a notably high baseline variability in human samples (> 100-fold interval; Vogt et al. 2019). This was likely due to differences in ethnic backgrounds (Church et al. 2019), undiagnosed states such as milder pathologies of non-alcoholic fatty liver disease (Cermelli et al. 2011), and short circulatory half-life (Thulin et al. 2017).

A review by Harrill et al. (2016) comparing biofluid-based miRNAs (e.g., blood, urine) to traditional biomarkers of tissue toxicity found that miRNA often outperformed conventional biomarkers in terms of a better correlation with tissue injury and earlier detection. Recent studies in rats have identified miRNA biomarkers specific to different stages (early, middle, late) and types (hepatocellular injury, cholestasis, steatosis) of DILI (Kagawa et al. 2018) and to different lesion sites in the nephron due to nephrotoxicants (Chorley et al. 2021b). Further study is needed to determine whether these biomarkers are translatable for the detection of specific organ injuries in humans. Potential miRNA biomarker candidates for the early detection of DILI were identified by examining hepatic miRNA expression in ketoconazole-treated rats with miRNA-sequencing (Li et al. 2021). The investigators then looked for the candidates evolutionarily conserved between rats and humans in the culture medium of ketoconazole-treated HepaRG cells with quantitative polymerase chain reaction (PCR) and identified miR-34a-5p, miR-331-3p, and miR-15b-3p as translational biomarker candidates for the early detection of ketoconazole-induced liver injury.

      In addition to liver and kidney injury, circulating miRNAs are being investigated as mechanistic biomarkers of diseases such as cancer (Dutta et al. 2019; Jin et al. 2019; Lin et al. 2019; Pascut et al. 2019; Wang et al. 2018), cardiovascular disease (Zhou et al. 2018), metabolic syndrome (Huang et al. 2018), neurodegenerative disease (Juzwik et al. 2019; Sharma and Lu 2018), and chronic obstructive pulmonary disease (COPD) (Finicelli et al. 2020), all diseases whose etiology can include environmental toxicant exposure. Several recent reviews examine the dysregulation of miRNAs by environmental contaminants related to human health (Balasubramanian et al. 2020; Harrill et al. 2016; Qiao et al. 2019; Sollome et al. 2016; Tumolo et al. 2020; Wallace et al. 2020). Vrijens et al. (2015) summarized some of the miRNAs that respond to environmental exposure and their roles in human disease. The current knowledge linking cancer and neurodegenerative diseases to dysregulation of miRNAs after pesticide exposure was reviewed by Costa et al. (2020). A comprehensive literature review by Sima et al. 2011 (twenty-seven studies between 2012 and 2020) focused on miRNA expression in humans exposed to various air pollutants. Because air pollution is a factor in the development of lung cancer, they reported miRNAs commonly deregulated by both conditions, identifying twenty-five miRNAs that could serve as biomarkers of exposure to harmful pollutants that potentially contribute to lung cancer development. Several miRNAs were deregulated in multiple studies and may therefore be the most promising candidates: miRs-222, -21, -126-3p, -155 and -425.

Table 2.1 compiles the literature evidence from these and other studies in humans. The heterogeneity in sample types, methods of analysis, and study designs makes it difficult to compare the studies directly. Altered levels of some miRNAs are observed in several studies with the same pollutant category and sample type, but with different direction of expression (e.g., particulate matter (PM) exposure altered miRs-21-5p, -223-3p and -146; Bollati et al. 2010; Louwies et al. 2016; Motta et al. 2013; Rodosthenous et al. 2016). Additional miRNAs dysregulated by air pollution in more than one study included let-7g, miR-126 and miR-132. miR-146 was downregulated in both bronchial epithelial cells and induced sputum of smokers. miRs-21, -126 and -155 were dysregulated in both urine and blood–serum with arsenic exposure. Overall, miR-21, which is highly expressed and ubiquitous across most cell types, was most often reported as dysregulated across multiple categories of environmental contaminants. miR-21 plays a role in inflammation and is elevated in many different disease states, which suggests that it is commonly upregulated in a stress environment and is not specific to any one disease or exposure, as is probably the case for other ubiquitously expressed miRNA (Jenike and Halushka 2021).

Table 2.1 Human studies on environmental toxicant-induced changes in miRNA expression from accessible matrices.

Exposure	Sample type	miRNAs	Related Disease	References
Air Pollution	various biofluids	multiple, in particular miR-222, miR-21, miR-126-3p, miR-155 and miR-425	lung cancer	(Sima et al. 2021)
PM2.5, black carbon, organic carbon, sulfate	leukocytes	miR-126, miR-135a, miR-146a, miR-155, miR-9 ↓		(Fossati et al. 2014)
Coal fumes (miners)	blood lymphocytes	SNPs in pre-miRNA genes of miR-149	pneumoconiosus	(Wang et al. 2010b)
Black carbon and coal dust (urban traffic PM)	blood	let-7 g, miR-29, miR-146, miR-421 ↑		(Motta et al. 2013)
Air pollution PM10	plasma MV	miR-126 ↑		(Motta et al. 2013)
Metal rich fumes in steel industry (PM10)	blood leukocytes	miR-21 and miR-222 ↑		(Bollati et al. 2010)
Metal rich fumes in steel industry (PM10)	blood leukocytes	miR-146 ↓		(Bollati et al. 2010)
Metal rich PM	plasma	miR-128, miR-302c ↑		(Tumolo et al. 2020), (Bollati et al. 2015)
PM	blood	miR-21-5p, miR-223-3p ↓		(Tumolo et al. 2020), (Louwies et al. 2016)
PM	serum	miR-15a-5p, -19b-3p, -23a-3p, -93-5p, -126-3p, -130-3p, -142-3p, -146a-5p, -150-5p, -191-5p, -223-3p, let-7a-5p, let-7g-5p ↑		(Tumolo et al. 2020), (Rodosthenous et al. 2016)
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