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      Figure 4.1 Major routes of exposure for heavy metals. The most common sources, modes, and routes of exposure for arsenic (As), cadmium (Cd), chromium (Cr), mercury (Hg), and lead (Pb) are depicted. Large bold blue text indicates the predominant route of exposure for individual metals, while black small text indicates additional exposure routes. For Cd, ingestion is the predominant exposure route in the non-smoking population, while inhalation of cigarette smoke is the predominant exposure source in smokers.

      Biomarkers

The term “biomarker” refers to a characteristic that can be measured objectively, accurately, and reproducibly as an indicator of normal biological processes, pathogenic processes, or pharmacologic response to a therapeutic intervention (Califf 2018; Strimbu and Tavel 2010). Biomarkers should be separated from clinical outcome assessment (COA), which encompasses direct measures of how a patient feels, functions, or survives (Califf 2018). This distinction is important, because therapeutics must meet regulatory standards that rely on COAs for measuring the outcomes. Biomarkers, on the other hand, can serve various purposes, such as predicting a COA on the basis of a measurement. To be used in a clinical setting, biomarkers must undergo rigorous scrutiny and be validated for yielding reliable and reproducible results across multiple populations. Subtypes of biomarkers have been defined according to their applications and classified as diagnostic biomarkers, monitoring biomarkers, pharmacodynamic or response biomarkers, predictive biomarkers, prognostic biomarkers, and safety biomarkers (Califf 2018; see also Table 4.1). Importantly, a biomarker may fall into one or more biomarker subtypes.

Biomarkers of Heavy Metal Exposure

      Biomarkers that are typically used to assess heavy metal exposure are concentrations of heavy metals in blood, urine, bone, nail, hair, and other tissues and fall into both monitoring and safety biomarker subtypes. These traditional biomarkers are practical and reliable measures of exposure, can be vital when signs and symptoms of metal exposure are lacking, and can be useful for assessing the risk for disease development (Gil and Pla 2001). Typically, blood and urine are used to assess recent exposure, whereas bone, nail, and hair can be used to indicate chronic or past exposure. However, the type of biological tissue examined is highly dependent on the biological half-life of the metal, or the time it takes to excrete the metal from the body. For instance, lead can be retained in bone decades after exposure, whereas the half-life of lead in blood is only a few weeks. However, these traditional biomarkers of exposure are useful only in defining the severity and the duration of exposure (which can be either acute or chronic, depending on the biological sample and metal tested for) and do not have the capacity to determine disease susceptibility or whether adverse health effects can be mitigated. Additionally, metal toxicity can present non-descript and non-specific symptoms, which do not pinpoint a specific metal exposure if the type of exposure is unknown (Hackenmueller et al. 2019). Therefore an immediate need exists to find additional, specific, and reliable biomarkers that play dual roles in identifying exposures to types of heavy metals (i.e., in monitoring and safety) and may be used as early indicators of disease as a result of heavy metal exposure (i.e., for diagnostic purposes). In the last decade or so, ribonucleic acid (RNA) has emerged as a major target for diagnostics and therapeutics (Zampetaki et al. 2018), including in heavy metal exposure. This chapter focuses on microRNAs (miRNAs) as potential biomarkers in heavy metal toxicity and disease.

Table 4.4 Description of biomarker subtypes.

      This table has been created on the basis of information from Califf (2018).

miRNA Biogenesis

      RNAs can be broadly classified into two groups: coding RNAs, which code for proteins and are known as messenger RNAs (mRNAs); and non-coding RNAs (ncRNAs), which comprise the largest class of RNAs and include ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), long non-coding RNAs (lncRNAs, > 200 nucleotides) and small non-coding RNAs (< 200 nucleotides). miRNAs constitute the best studied class of small non-coding RNAs and act post-transcriptionally to regulate gene expression (Ebert and Sharp 2012). Mature miRNAs occur as single-stranded RNAs, are typically between 21 and 24 nucleotides in length, and are estimated to regulate about 50% of all mammalian protein-coding genes (Krol et al. 2010).

The biogenesis of miRNA has been comprehensively reviewed (Fabian and Sonenberg 2012; Ha and Kim 2014; Hammond 2015; Libri et al. 2013) and is shown diagrammatically in Figure. 4.2. The majority of miRNAs are encoded within introns or exons of non-coding RNAs or introns of pre-mRNA (Saliminejad et al. 2019). In the canonical pathway, miRNAs are transcribed by RNA polymerase II (pol II) into large primary miRNAs (pri-miRNAs); these contain embedded stem loop structures that are roughly 70 nucleotides long. Pri-miRNAs are cleaved within the nucleus by the microprocessor complex to form stem-loop structures called precursor miRNAs (pre-miRNAs). Pre-miRNAs are exported into the cytoplasm by Exportin 5 (XPO5) and are cleaved by Dicer to form a miRNA duplex (see Figure 4.2). miRNA duplexes are then loaded onto Argonaute protein (Ago), which stimulates the assembly of the RNA-induced silencing complex (RISC) (Ha and Kim 2014). The RISC facilitates the removal of the passenger strand RNA in order to generate mature miRNA (Michlewski and Caceres 2019).