Unraveling chromosomal and genotoxic damage in individuals occupationally exposed to coal from underground mining

Purpose

Coal mining is a vital sector in Colombia, contributing significantly to the nation's economy and the development of its regions. However, despite its importance, it has led to a gradual decline in the health of mine workers and nearby residents. While the adverse health effects of open-pit coal mining on exposed individuals have been well-documented in Colombia and globally, studies investigating genetic damage in underground coal miners are lacking.

Methods

The aim of our study was to evaluate chromosomal and genotoxic damage, in peripheral blood samples from a group of underground coal miners and residents of areas exposed to coal, in the town of Samacá, Boyacá-Colombia, and in a group of unexposed individuals by using banding and molecular cytogenetic techniques, as well as cytokinesis block micronucleus assays.

Results

Our results suggest that occupational exposure to coal induces chromosomal and genotoxic damage in somatic cells of underground coal miners. Chromosomal and genotoxic damage is an important step in carcinogenesis and the development of many other diseases. Our findings provide valuable insights into the effects of coal dust exposure on chromosomal integrity and genetic stability.

Conclusion

Our pilot study suggests that occupational exposure to coal induces chromosomal damage in underground coal miners, highlighting the importance of validating these findings with a larger sample size. Our results highlight the need to implement prevention and protection measures, as well as educational programs for underground coal miners. Characterizing and estimating exposure risks are extremely important for the safety of people exposed occupationally and environmentally to coal and its derivatives.

A New Strategy for Identification of Coal Miners With Abnormal Physical Signs Based on EN-mRMR

Coal miners’ occupational health is a key part of production safety in the coal mine. Accurate identification of abnormal physical signs is the key to preventing occupational diseases and improving miners’ working environment. There are many problems when evaluating the physical health status of miners manually, such as too many sign parameters, low diagnostic efficiency, missed diagnosis, and misdiagnosis. To solve these problems, the machine learning algorithm is used to identify miners with abnormal signs. We proposed a feature screening strategy of integrating elastic net (EN) and Max-Relevance and Min-Redundancy (mRMR) to establish the model to identify abnormal signs and obtain the key physical signs. First, the raw 21 physical signs were expanded to 25 by feature construction technology. Then, the EN was used to delete redundant physical signs. Finally, the mRMR combined with the support vector classification of intelligent optimization algorithm by Gravitational Search Algorithm (GSA-SVC) is applied to further simplify the rest of 12 relatively important physical signs and obtain the optimal model with data of six physical signs. At this time, the accuracy, precision, recall, specificity, G-mean, and MCC of the test set were 97.50%, 97.78%, 97.78%, 97.14%, 0.98, and 0.95. The experimental results show that the proposed strategy improves the model performance with the smallest features and realizes the accurate identification of abnormal coal miners. The conclusion could provide reference evidence for intelligent classification and assessment of occupational health in the early stage.

Sputum Microbiota in Coal Workers Diagnosed with Pneumoconiosis as Revealed by 16S rRNA Gene Sequencing

Coal worker's pneumoconiosis (CWP) is an occupationally induced progressive fibrotic lung disease. This irreversible but preventable disease currently affects millions across the world, mainly in countries with developed coal mining industries. Here, we report a pilot study that explores the sputum microbiome as a potential non-invasive bacterial biomarker of CWP status. Sputum samples were collected from 35 former and active coal miners diagnosed with CWP and 35 healthy controls. Sequencing of bacterial 16S rRNA genes was used to study the taxonomic composition of the respiratory microbiome. There was no difference in alpha diversity between CWP and controls. The structure of bacterial communities in sputum samples (β diversity) differed significantly between cases and controls (pseudo-F = 3.61; p = 0.004). A significant increase in the abundance of Streptococcus (25.12 ± 11.37 vs. 16.85 ± 11.35%; p = 0.0003) was detected in samples from CWP subjects as compared to controls. The increased representation of Streptococcus in sputum from CWP patients was associated only with the presence of occupational pulmonary fibrosis, but did not depend on age, and did not differ between former and current miners. The study shows, for the first time, that the sputum microbiota of CWP subjects differs from that of controls. The results of our present exploratory study warrant further investigations on a larger cohort.

Genetic damage in coal and uranium miners

Mining has a direct impact on the environment and on the health of miners and is considered one of the most hazardous occupations worldwide. Miners are exposed to several occupational health risks, including genotoxic substances, which may cause adverse health effects, such as cancer. This review summarizes the relation between DNA damage and mining activities, focusing on coal and uranium miners. The search was performed using electronic databases, including original surveys reporting genetic damage in miners. Additionally, a temporal bibliometric analysis was performed using an electronic database to create a map of cooccurrence terms. The majority of studies were performed with regard to occupational exposure to coal, whereas genetic damage was assessed mainly through chromosomal aberrations (CAs), micronuclei (MNs) and comet assays. The bibliometric analysis demonstrated associations of coal exposure with silicosis and pneumoconiosis, uranium miners with lung cancer and tumors and some associated factors, such as age, smoking, working time and exposure to radiation. Significantly higher DNA damage in miners compared to nonexposed groups was observed in most of the studies. The timeline reveals that classic biomarkers (comet assay, micronucleus test and chromosomal aberrations) are still important tools to assess genotoxic/mutagenic damage in occupationally exposed miners; however, newer studies concerning genetic polymorphisms and epigenetic changes in miners are being conducted. A major challenge is to investigate further associations between miners and DNA damage and to encourage further studies with miners of other types of ores.

Cytotoxicity and genotoxicity induced by coal and coal fly ash particles samples in V79 cells

Exposure to coal and coal ashes can cause harmful effects in in vitro and in vivo systems, mainly by the induction of oxidative damage. The aim of this work was to assess cytotoxic and genotoxic effects using the V79 cell line treated with coal and coal fly ash particles derived from a coal power plant located in Santa Catarina, Brazil. Two coal samples (COAL11 and COAL16) and two coal fly ash samples (CFA11 and CFA16) were included in this study. COAL16 was co-firing with a mixture of fuel oil and diesel oil. The comet assay data showed that exposure of V79 cells to coal and coal fly ash particles induced primary DNA lesions. Application of lesion-specific endonucleases (FPG and ENDO III) demonstrated increased DNA effects indicating the presence of high amounts of oxidative DNA lesions. The cytokinesis-block micronucleus cytome assay analysis showed that exposure of V79 cells to high concentrations of coal and coal fly ash particles induced cytotoxic effects (apoptosis and necrosis) and chromosomal instability (nucleoplasmic bridges, nuclear buds, and micronucleus (MN) formation). These results may be associated with compounds contained in the surface of the particles as hazardous elements, ultrafine/nanoparticles, and polycyclic aromatic hydrocarbons (PAHs) which were detected in the samples. Graphical abstract ᅟ.