Читать книгу Genomic and Epigenomic Biomarkers of Toxicology and Disease - Группа авторов - Страница 27
MicroRNAs as Bioindicators of Toxicity and Disease
ОглавлениеMuch miRNA biomarker research has been focused on the early detection of drug-induced liver injury (DILI), a major roadblock in drug development. Alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, and total bilirubin are the currently approved DILI biomarkers in clinical practice (Robles-Diaz et al. 2016). These biomarkers are not specific to hepatotoxicity, as they increase in almost all abnormal liver conditions. Furthermore, they lack sensitivity, as they appear once liver damage has developed, and consequently have limited use for predicting potential liver injury at an early stage. For this reason, circulating miRNAs are being investigated for the monitoring of drug-induced tissue injury (tissue degeneration and necrosis) in cases where highly abundant tissue-specific miRNAs are passively released after cell injury. In incidences of DILI, studies have demonstrated that miR-122 presence in blood correlates tightly with pathological indications and clinical chemistries (i.e., ALT, AST) of liver degeneration (Krauskopf 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) | |
| PM | placenta | miR-20a-5p, miR-21-5p (1st trimester) ↑ | (Tumolo et al. 2020), (Tsamou et al. 2018) | |
| PM | placenta | miR-21-5p, miR-146a-5p, miR-222-3p (2nd trimester) ↓ | (Tumolo et al. 2020), (Tsamou et al. 2018) | |
| DEP | bronchial epithelial cells | miR-375 ↑ | (Bleck et al. 2013) | |
| DEP | bronchial epithelial cells | miR-132-3p ↑ | (Tumolo et al. 2020), (Rider et al. 2016) | |
| DEP | bronchial epithelial cells | miR-183-5p ↓ | ||
| DEP | peripheral blood | miR-21-5p, miR-30e, miR-215, miR-144 ↑ | (Tumolo et al. 2020), (Yamamoto et al. 2013) | |
| TRAP | plasma | miR-1224-5p, miR-3127-5p ↑ | (Tumolo et al. 2020), (Krauskopf et al. 2018) | |
| TRAP | plasma | miR-27a-5p, miR-133a-3p, miR-145-5p, miR-193b-3p, miR-433-3p, miR-580-3p, miR-6716-3p ↓ | (Tumolo et al. 2020), (Krauskopf et al. 2018) | |
| O3 | induced sputum | miR-25-3p, miR-132-3p, miR-199a-3p, miR-222-3p, miR-434-5p, miR-582-5p ↑ | (Tumolo et al. 2020), (Fry et al. 2014) | |
| O3 | induced sputum | miR-143-3p, miR-223-3p, miR-145-5p, miR-199b-5p ↑ | (Tumolo et al. 2020), (Fry et al. 2014) | |
| Cigarette smoke | ||||
| cigarette smoke | bronchial epithelium | miR-181 ↑ | (Izzotti and Pulliero 2014), (Schembri et al. 2009) | |
| cigarette smoke | bronchial epithelium | miR-30a, miR-125b, miR-128, miR-146, miR-218, miR-223 and miR-500 ↓ | (Izzotti and Pulliero 2014), (Schembri et al. 2009) | |
| tobacco carcinogens | bronchial epithelial cells | miR-200b, miR-200c, miR-205↑ | (Tellez et al. 2011) | |
| smoking | induced sputum | let-7c, miR-146a, miR-150, miR-203, miR-340 ↓ | COPD | (Van Pottelberge et al. 2011) |
| smoking | placenta | miR-16, miR-21, miR-146a ↓ | (Maccani et al. 2010) | |
| smoking | maternal and cord blood | miR-223 ↑ | (Herberth et al. 2014) | |
| Organic chemicals | ||||
| PAHs | plasma | miR-142, miR-24, miR-27a, miR-28↓ | (Deng et al. 2014) | |
| PAHs | plasma | miR-150 ↑ | ||
| PCB-169 | peripheral blood | miR-191 ↑ | (Guida et al. 2013) | |
| PCBs | serum | miR-122-5p, miR-192-5p, 99a-5p, 320a ↑ | liver disease | In journal review |
| PCBs | serum | let-7d-5p, miR-17-5p, miR-24-3p, miR-197-3p, and miR-221-3p ↓ | liver disease | In journal review |
| POPs (PCBs, HCB and DDT) | peripheral blood | miR-152, miR-31-5p, miR-34a-5p, miR-21-5p, miR-29a ↑ | (Qiao et al. 2019), (Krauskopf et al. 2017) | |
| POPs (PCBs, HCB and DDT) | peripheral blood | miR-320d, miR-320e, miR-486-5p, miR-324-3p, miR-331-3p, miR-501-3p, miR-532-3p ↓ | (Qiao et al. 2019), (Krauskopf et al. 2017) | |
| Benzene and toluene | maternal and cord blood | miR-155, miR-223 ↑ | (Herberth et al. 2014) | |
| Toluene, xylene, ethylbenzene | blood | microRNA profiles (>100) | (Song and Ryu 2015) | |
| Toluene, xylene, ethylbenzene | blood | miR-6819-5p and miR-6778-5p ↑ | (Sisto et al. 2019) | |
| environmental pesticides | urine | miR-223, miR-518d-3p, miR-597, miR-133b, miR-517b, miR-28-5p ↑ | (Qiao et al. 2019), (Weldon et al. 2016) | |
| Methamidophos | peripheral blood | miR-133a, miR-214, miR-29b, miR-590 ↑ | (Qiao et al. 2019), (Yuan et al. 2018) | |
| Methamidophos | peripheral blood | miR-125b, miR-141-5p, miR-142a, miR-150 ↓ | ||
| Omethoate | peripheral blood lymphocytes | miR-145 | (Qiao et al. 2019), (Wang et al. 2019) | |
| PFOA | serum | miR-199a, miR-26b ↑ | (Wang et al. 2012) | |
| Organophosphates | serum | miR-155 ↓ | Guillain-Barre Syndrome | (Costa et al. 2020), (Yuan et al. 2018) |
| Heavy metals | ||||
| Arsenic | urine | miR-21, miR-126, miR-155, miR-221↑ | albuminuria (miR-21) | (Kong et al. 2012) |
| Arsenic | urine | miR-205↓ | (Gao et al. 2019) | |
| Arsenic | peripheral blood mononuclear cells | miR-21↑ | skin cancers | (Wallace et al. 2020), (Liu et al. 2018) |
| Arsenic | cord blood | has-let-7a, miR-107, miR-126, miR-16, miR-17, miR-195, miR-20a, miR-20b, miR-26b, miR-454, miR-96, miR-98 ↑ | (Rager et al. 2014) | |
| Arsenic | bronchial epithelial cells | miR-190 ↑ | (Beezhold et al. 2011) | |
| Arsenic | plasma | miR-423-5p, miR-423-5p, miR-142-5p -2, miR-454-5p | cardiometabolic disease | (Beck et al. 2018) |
| Arsenic | plasma | -320c-1, and -320c | ||
| Arsenic | serum | miR-126, miR-155 | (Wallace et al. 2020), (Ruiz-Vera et al. 2019) | |
| Lead | placenta | miR-146a, miR-10a, miR-190b, miR-431 ↑ | (Wallace et al. 2020), (Li et al. 2015) | |
| Lead | placenta | miR-651 ↓ | ||
| Lead | blood | miR-21-5p, miR-122-5p ↑ | (Wallace et al. 2020), (Guo et al. 2019) | |
| Lead (battery factory workers) | blood | miR-520c-3p, miR-148a, miR-141,and miR-211↑. | (Wallace et al. 2020), (Xu et al. 2017) | |
| Lead (battery factory workers) | blood | miR-572 and miR-130b↓ | (Wallace et al. 2020), (Xu et al. 2017) | |
| Mercury (female factory workers) | blood | miR-92a-3p, miR-122-5p, miR-451a, and miR-486-5p↑ | (Wallace et al. 2020), (Ding et al. 2016) | |
| Mercury (female factory workers) | blood | miR-16-5p, miR-30c-3p,miR-181a-5p, and let-7e-5p↓ | (Wallace et al. 2020), (Ding et al. 2016) | |
| Other | ||||
| 100 nm gold nanoparticles | cord blood | has-let-7a ↑ | (Balansky et al. 2013) | |
| Asbestos | peripheral blood | miR-103 ↑ | mesothelioma | (Weber et al. 2012) |
| Occupational grain dust | serum | miR-18a-5p, miR-124-3p and miR-574-3p ↑ | lung diseases | (Straumfors et al. 2020) |
| Occupational grain dust | serum | miR-19b-3p and miR-146a-5p ↓ | lung diseases | (Straumfors et al. 2020) |
Abbreviations: PM (particulate matter); SNPs (single nucleotide polymorphisms); MV (microvesicles); DEP (diethyl phthalate); TRAP (traffic-related air pollution); O3 (ozone); PAHs (polycyclic aromatic hydrocarbons); PCBs (polychlorinated biphenyls); POPs (persistent organic pollutants); HCB (hexachlorobenzene); DDT (dichloro-diphenyl-trichloroethane); PFOA (perfluorooctanoic acid)
The best miRNA biomarkers are likely to be those that indicate injury or perturbation to a specific cell type, such as the previously discussed hepatocyte-specific miR-122. Other cell-specific miRNAs that may be useful biomarkers of tissue injury include the myomiRs, miRs-133, -206, -208, and -499, which have shown promise through their ability to identify cardiac injury (Jenike and Halushka 2021). In addition, miRNA biomarkers that indicate the disruption of specific nuclear receptor or regulatory pathways as a result of chemical exposure would be desirable. Studies examining the changes in miRNA–mRNA interactions that are due to environmental exposures will be helpful in determining the mechanistic pathways involved. One such study applied a network approach, integrative Joint Random Forest, to investigate the effect of low-dose environmental chemical exposure on normal mammary gland development in rats (Petralia et al. 2017). The researchers detected miRNAs that regulated many mRNAs in the control group but not in the exposed groups, which suggested that the disruption of miRNA activity was due to chemical exposure. Messenger RNAs connecting with miR-200a and miR-375 in the control network only were enriched for the mammary gland development and gland morphogenesis pathways, and the effects of chemical exposure on these two miRNAs were confirmed in human breast cancer cell lines.
Song and Ryu (2015) identified characteristic miRNA profiles of human whole blood in workers exposed to volatile organic compounds (VOCs) and compared their effectiveness to the conventionally used metabolite markers of exposure. They were able to discern each VOC (toluene, xylene, and ethylbenzene) from the control group with higher accuracy, sensitivity, and specificity than the urinary biomarkers. In a recent study, serum miRNAs were associated with polychlorinated biphenyl (PCB) exposures and environmental liver disease in a residential cohort (Cave et al. 2022). This longitudinal cohort study enrolled residents of Anniston, Alabama that exhibited PCB levels twice and three times higher than found in the average population (Pavuk et al. 2014). A miRNA panel for liver toxicity and disease was assessed, and many of the miRNAs correlated with biomarkers of disease toxicity, metabolic changes, and inflammation. In addition, some of the miRNAs also correlated with levels of PCB measured in the serum. These miRNAs included miR-122-5p, -192-5p, and -99a-5p, which have been previously established to have important roles in liver disease and can serve as accessible biomarkers of disease progression (Brunetto et al. 2014; Gjorgjieva et al. 2019; Lopez-Sanchez et al. 2021). While more work needs to be done to establish the causative reasons why miRNAs in blood are correlating with both disease biomarkers and exposure, the indication is that they may serve as biomarkers of environmental pollutant health effects that include liver cell loss, change in function, and inflammatory response.
