Genomic and Epigenomic Biomarkers of Toxicology and Disease. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Химия
Год издания: 0
isbn: 9781119807698
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miRNAs as potential biomarkers for heavy metal(s) exposure and adverse health outcomes associated with types of heavy metal exposure. The discussion highlights current biofluids used for determining individual heavy metals (arsenic, lead, mercury, cadmium, chromium) and mixed metals exposure, miRNA biogenesis, and studies that have investigated circulating miRNAs associated with heavy metal exposure.

      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

      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.

Biomarker SubtypesDescription
DiagnosticConfirms the presence of a disease or condition with precision and reliability
MonitoringMeasured over a span of time and monitors the status of a disease or medical condition due to exposure of an individual to medicinal or environmental agents
Pharmacodynamic/ResponseChanges in response to exposure to a medicinal or environmental agent
PredictivePredicts whether favourable or unfavourable outcomes are more likely to be experienced by an individual or group of individuals
PrognosticIdentifies the likelihood of a clinical event, disease recurrence, or disease progression
SafetyMeasures before or after exposure to a medicinal or environmental agent to indicate likelihood, presence or extent of toxicity as an adverse event

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

      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).

      Figure 4.2 Simplified overview of miRNA biogenesis