4.3 depicts the candidate circulating miRNA biomarker expression status across different metals (or metal mixtures), stratified by body fluids (plasma, blood, serum and urine). Plasma and blood are by far the most heavily investigated biofluids; and they are followed by urine and serum. However, regardless of the body fluids assessed, very few miRNAs are represented as putative biomarkers across multiple metal exposures. Presently miR-21 seems to be the most promising biomarker candidate and the one with the strongest experimental evidence. Circulating miR-21 is higher in both the plasma and the blood of arsenic-exposed individuals from India and China. Additionally, plasma levels of miR-21 are suppressed in chromium-exposed individuals but induced in cadmium-exposed individuals. Circulating miR-21 has also been detected in the urine of individuals exposed to metal mixtures, although the nature of its expression varied depending on the population (Cardenas-Gonzalez et al. 2016; Kong et al. 2012).
Figure 4.3 Differential expression map of circulating miRNAs dysregulated after heavy metal or mixed metal exposure, stratified by body fluid. Circulating miRNAs dysregulated as a result of heavy metal exposure to arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), lead (Pb), and mixed metals (MM) across all studies examined in this chapter are presented within specific biofluids analysed: (A) plasma, (B) whole blood, (C) urine, and (D) serum. Induced circulating miRNAs are represented in blue, suppressed or lower circulating miRNAs are represented in green, miRNAs that were not examined in a particular biofluid for specific metals are represented in white, and miRNAs that showed conflicting results in multiple studies within a biofluid are shown in purple.
Figure 4.4 represents the circulating miRNA biomarker expression status across different body fluids for each metal (or metal mixture) exposure, individually. Even for the same metal exposure, the nature of dysregulated miRNA expression varies considerably within the different body fluids tested so far, with little or no overlap. Furthermore, even if the same miRNA is differentially expressed in multiple body fluids for a particular metal exposure, often the direction of expression change is not consistent across the different fluids. For example, miR-126 is dysregulated upon arsenic exposure in blood, plasma, and serum. However, while it is induced in blood, it is found to be suppressed in both plasma and serum (Figure 9.4), which suggests that the induction is in circulating cells. In summary, despite all the studies, there is still no circulating miRNA that is unequivocally representative of exposure to a specific heavy metal, either across all biofluids or across all heavy metals for any specific biofluid. This inconsistency is possibly due to the fact that the number of studies that are not on arsenic exposure is so limited. Such inconsistencies are further complicated by differences between study designs, some of which employ end points and statistical measures (e.g., point estimates versus interval estimates) that make it difficult to unify all the results and gain a holistic understanding.
Figure 4.4 Differential expression map of circulating miRNAs dysregulated after a specific heavy metal or mixed metal exposure across different body fluids. Circulating miRNAs dysregulated in different biofluids (blood, plasma, serum and urine) upon exposure to (A) arsenic (As), (B) cadmium (Cd), (C) chromium (Cr), (D) mercury (Hg), (E) lead (Pb), and (F) mixed metals (MM). Induced circulating miRNAs are represented in blue, suppressed or lower circulating miRNAs are represented in green, miRNAs that were not examined in a particular biofluid for specific metals are represented in white, and miRNAs that showed conflicting results in multiple studies within a biofluid are shown in purple.
Future Avenues of Research
Importantly, all circulating miRNAs associated with metal exposure are currently in stages of discovery and development and have not yet been validated; nor are they considered biomarkers. Biomarkers must be validated in accordance with well-established protocols that involve multiple independent qualitative and quantitative steps, before they can be employed for diagnosis or monitoring (Califf 2018). Consequently, there is a significant knowledge gap when it comes to identifying a circulating miRNA that can be used consistently or can be used as a unique biomarker for heavy metal exposure or for disease outcome(s) of such exposure. Therefore future studies should first find out whether there are differences between heavy metal exposures for the circulating miRNAs summarized in Figure 4.3. Furthermore, the assessment of these candidate biomarkers and of other circulating miRNAs should be validated in other fluids that do not rely on blood draw and contain high levels of miRNAs, for instance sweat, tears, saliva, semen, or breast milk (Barcelo et al. 2019; Karvinen et al. 2020; Rubio et al. 2018; Setti et al. 2020; Weber et al. 2010).
Other areas that need to be addressed are things such as limited sample sizes and the inclusion of geographically restricted populations. Both factors need to be taken into account when suggesting the use of a circulating miRNA as a putative biomarker. Ideally, future studies should be performed with these candidate circulating miRNAs across larger populations from numerous geographic regions. This approach will ultimately enable multiple stratifications across biological and socioeconomic parameters in order to investigate whether initial patterns discovered for circulating miRNAs are both consistent and associated with these additional factors.
Although causality is not a required criterion for a biomarker, it would be important and interesting to examine whether any of these dysregulated miRNAs plays a causal role in the etiology of any metal exposure-induced diseases. A few studies have been conducted in this direction. As consistent with human studies, miR-21 was increased in immortalized human keratinocytes (HaCaT) exposed to 500 nM arsenite for four weeks (Gonzalez et al. 2015) and in human umbilical vein endothelial cells (HUVEC) exposed to 20 μM arsenite for twenty-four hours (Li et al. 2012). A recent systematic review and meta-analysis suggests that arsenic-induced miR-21 expression suppresses phosphatase and tensin homolog (PTEN) and protein sprouty homolog 1 (Spry1) levels, leading to epithelial–mesenchymal transition (EMT) and malignant transformation (Liu et al. 2018). miR-21 is a well-conserved miRNA, frequently found upregulated in numerous types of cancer (Feng and Tsao 2016). Furthermore, in cadmium-exposed individuals, miR-21 was associated with renal dysfunction, characterized by increased excretion of the low molecular weight protein N-Acetyl-beta-(D)-glucosaminidase in urine (Lei et al. 2019). Thus it is important to consider circulating miR-21 as a potential biomarker for heavy metal exposure and to investigate its potential mechanistic role in heavy metal exposure-induced disease outcomes. Similarly, upregulation of miR-92a-3p and miR-486-5p after mercury exposure has been recapitulated in vitro by exposing HEK-293 and HUVEC human cell lines to mercuric chloride (Ding et al. 2017).
Techniques and Challenges for Identifying Circulating miRNAs
Although using circulating miRNA as biomarkers has its advantages, since this is a relatively non-invasive technique and also one that can in principle be used in the early diagnosis of diseases, miRNA profiling in biofluids is still in its infancy. Therefore it is necessary to highlight limitations that may lead to inconsistent findings. The identification of potential confounding factors will also help optimize the reproducibility of miRNA future biomarker studies used in metal toxicology.
Perhaps the biggest challenge to the successful identification of circulating biomarkers lies in the technical constraints on isolating and purifying the samples for analysis. Methods used by different groups to isolate miRNAs from blood vary greatly, which may contribute to reproducibility issues (Mariner et al. 2018). For instance, previous studies have shown that the differential expression analysis of circulating miRNA from plasma can be confounded by the presence of platelets, which require additional steps of centrifugation in order to accurately quantify differential circulating miRNA expression profiles (Cheng et al. 2013). However, one protocol to address this issue has already been developed
