Multiple factors influencing telomere length and DNA damage in individuals living near a coal-burning power plant

doi:10.21203/rs.3.rs-2873325/v1

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Multiple factors influencing telomere length and DNA damage in individuals living near a coal-burning power plant

https://doi.org/10.21203/rs.3.rs-2873325/v1

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Coal is a mixture of several chemicals, many of which have mutagenic and carcinogenic effects and a key contributor to the global burden of mortality and disease. Previous studies suggest that coal is related to telomeric shortening in individuals occupationally exposed, however little is known about the effects of mining and burning coal on the telomeres of individuals living nearby. Therefore, the aim of this study was to evaluate the influence of this exposure on genomic instability of individuals who live near coal activities, besides correlating results with individual characteristics, inflammatory responses, oxidative stress and inorganic elements. The study involved 80 men participants from three cities around a thermoelectric power plant and one city unexposed to coal and byproducts. DNA was isolated from peripheral blood samples from all individuals and telomeres (TL) were measured by quantitative real time polymerase chain reaction (qPCR). No significant difference was observed between exposed individuals (6.227 ± 2.884 pb) when compared to the unexposed group (5.638 ± 2.452 bp). Nevertheless, TL decrease was associated with age and risk for cardiovascular disease. Higher concentrations of Si and P in blood samples were associated with longer telomeres length. No correlations were observed between TL with comet assay, micronucleus test, oxidative stress, and inflammatory results. Further studies are needed to determine whether these alterations are associated with diseases and premature deaths.

coal burning

air pollution

genotoxicity

environmental exposure

telomere length

The burning of fossil fuels is a global problem that contributes to the development of several diseases and the increase of premature deaths (Vohra et al. 2021). Candiota city (Rio Grande do Sul, Brazil) holds about 90% of the national reserves of this mineral (ANEEL 2008) and the coal is processed by the largest coal-fired power plant of the state. Coal consists of several solid organic compounds, fossilized over millions of years. Its source is a very complex chemical structure, being one of the greatest natural sources of hydrocarbons (León-Mejía et al. 2014).

Exposure to coal mine dust and products of combustion may increase the risk for developing several diseases, through inhalation of complex mixtures (M R De Souza et al. 2019). Pneumoconiosis, silicosis, bronchitis, loss of lung function, emphysema, even stomach, liver or lung cancer and risk factor for non-melanoma skin carcinoma are some examples of coal-related disorders (Gasparotto and Martinello 2021). The evidence to date supports a strong link between the risk of cardiovascular events and all-cause mortality with PM 2.5 across a range of exposure levels, including to levels below current regulatory standards, with no 'safe' lower exposure levels at the population level (Al-Kindi et al. 2020) .There are some studies attempting to elucidate the relationship between coal and genotoxicity and diseases, including cancer; however, many variables are involved, leading to somewhat subjective results (Gasparotto and Martinello 2021). DNA damage and genomic instability in individuals occupationally exposed to coal mining waste has been repeatedly reported in the literature, suggesting a systemic exposure response in which the generation of damage can play an important role (Melissa Rosa de Souza et al. 2018). Although few studies on the population that lives around coal-related activities, they have also demonstrated a strong influence of this mineral on human health (Espitia-Pérez et al. 2018; Ishtiaq et al. 2018; M. Souza et al. 2021).

The main mechanism reported regarding DNA damage caused by coal exposition is oxidative damage (da Silva 2016). One important mechanism that can give rise to genomic instability is the functional status of telomeres. Human telomeres are composed of tandem repeats of DNA (5′- TTAGGG-3′) and a complex of associated proteins called shelterin (Zakian 2012), which protects the chromosome from nucleolytic degradation, chromosome end-to-end fusion and breakage-fusion-bridge-cycle (Blackburn 2000). Due to the semiconservative replication of the DNA, a small portion of DNA at the end of chromosomes is not replicated in each cell division (Zakian 2012). The high guanine content promotes a greater susceptibility to instability by oxidative stress in telomeric DNA, where free radicals and other reactive oxygen species (ROS) cause oxidative damage to DNA at a rate that can contribute to the development of age-related diseases (von Zglinicki et al. 2000). Epidemiological studies have shown that individuals with shorter telomeres have decreased life expectancy and increased mortality, risk of cancer and other diseases (Aviv and Shay 2018; Bojesen 2013; Hou et al. 2012).

Biomonitoring of genotoxicity in the human population exposed to coal is a useful tool to estimate the genetic risks of integrated exposure to complex mixtures. Exposure to coal and its genotoxic and mutagenic effects are being studied with different approaches and different organisms (M R De Souza et al. 2019). Regarding the health of environmental exposure to coal, there was an effect on the induction of DNA damage shown by comet assay, micronucleus test (MN), cell death and oxidative damage (León-Mejía et al. 2014; Rohr et al. 2013). There is growing evidence that telomere stability may be affected by environmental exposure. Therefore, the aim of this study was to evaluate genomic instability in coal workers by telomere length (TL) assessment. In addition, compare these results with our previous data, with the same individuals, which were evaluated using comet assay, MN test, inflammatory response, oxidative stress and inorganic elements (M. Souza et al. 2021).

2.1 Study population and sample collection

Considering previous results of coal mining and burning activities related to increased DNA damage parameters in occupationally exposed populations (Melissa Rosa de Souza et al. 2018; Rohr et al. 2013) and in order to differentiate substances possibly related to specific activities in the coal production chain, four sampling areas were defined. Three areas around the power plant and open pit mine: Candiota (where the power plant is located), Pinheiro Machado, and Aceguá, besides Bagé that was considered the control-city. The city of Bagé is cited in the literature as being the city least influenced by coal, probably because it is outside the route with the most predominance of winds (Menezes et al. 2013; Rohr et al. 2013). Data collection took place in the winter months of 2018–2019 resulting in 80 individuals, 18 being from Bagé, 22 from Candiota, 26 from Pinheiro Machado and 14 from Aceguá.

Blood was collected with a vacutainer in collection tubes containing anticoagulants required for each specific test. The collections were performed by a qualified professional, using sterile, disposable material. Everyone in the population was given an Informed Consent Form and a questionnaire on life habits, covering questions from the questionnaire of the Commission for the Protection against Environmental Mutagens and Carcinogens (Carrano and Natarajan 1988) and from the Alcohol Use Disorders Identification Test (AUDIT) (Babor et al. 2001). The purpose of the questionnaire was to characterize everyone regarding age, lifestyle, and body measurements. Only men aged at least 18 years old who did not perform activities related to exposure to mutagenic agents and who did not present any chronic pathology prior to the study were included in the research. This study was approved by the Ethics Committee on Human Research of the Lutheran University of Brazil (number 2.564.499).

2.2 Quantitative polymerase chain reaction (qPCR) for telomere length measurement

Genomic DNA was isolated from total blood through a salting-out method (Lahiri and Nurnberger 1991). Samples were quantified using a NanoDrop 1000 spectrophotometer and diluted for experimental requirements (5 ng/µL). qPCR was performed for TL measurement following the protocol described by Cawthon (2002), with slight modifications (Kahl et al. 2016). The primers used in the experiment were HPLC purified and diluted in appropriate volumes for PCR and stored at − 20°C until required (Cawthon 2002). Briefly, standard curves were established by the serial dilution of known quantities of a DNA pool sample. A single copy gene (SCG) was used for amplification control. The SCG used was 36B4, which encodes the acidic ribosomal phosphoprotein P0. The ratio of telomere (T) repeat copy number to SCG (S) was determined for each sample for final analysis. All samples were analyzed using the Step One Plus TM Real-Time PCR System (Applied Biosystems, Foster City, CA), in triplicate, with negative and positive controls, and standard curves. The reactions were performed using telomere and 36B4 specific primers in a 96-well plate containing a reference sample. Master mix solution were prepared according to the number of samples and contained Platinum SYBR Green supermix (Invitrogen by Life Technologies, Carlsbad, CA), 20 ng of DNA, injection water, 0.2 µmol of telomere primers (forward: 5′- GGT TTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT-3′; reverse: 5′-CCC GACTATCCCTATCCCTATCCCTATCCCTATCCCTA-3′) and 0.2 µmol of 36B4 primers (forward: 5′-CAGCAAGTGGGAAGGTGTAATCC-3′; reverse: 5′- CCCATTCTATCATCAACGGGTACAA − 3′). Cycling conditions (for both telomere and 36B4 amplicons) were: 10 min at 95°C, followed by 40 cycles of 15 s at 95°C, and 1 min at 60°C. In qPCR, the cycle at which the fluorescence of a sample is detectable above the background fluorescence is called the cycle threshold (Ct). The Ct point of each sample was used to calculate total telomere in kb per human diploid genome (Cawthon 2002). Values generated by qPCR software were exported to .csv format.

2.3 Comet assay (CA)

The alkaline CA was performed as described by (Tice et al. 2000) with minor modifications as previously described (M. Souza et al. 2021). The blood samples were included in low-melting point agarose, placed in lysis buffer and, after the DNA was subjected to electrophoresis, the DNA was finally stained with silver nitrate. Visual score of 100 cells from all were analyzed as described in detail by (Collins 2004). Cells were visually classified into classes, according to tail length, from 0 to 4. The visual score will be assigned to each comet according to its class, ranging from 0 (completely intact: 100 cells X 0) to 400 (with maximum damage: 100 cells X 4).

2.4 Cytokinesis blocking micronucleus assay (CBMN)

The CBMN assay was performed as previously described (M. Souza et al. 2021). For each blood sample, 2000 binucleated cells (BN) (1000 from each of the two slides prepared from duplicate cultures) were classified as suggested by (Michael Fenech 2007). The presence of micronuclei (MN), nuclear plasmatic bridges (NPB), and nuclear buds (NBUD) was evaluated. Individuals who had MN, NBUD or NPB above 10 were excluded from the study.

2.5 Thiobarbituric acid reactive substances (TBARS)

Lipid peroxidation was monitored by the formation of thiobarbituric acid reactive substances (TBARS) during an acid-heating reaction, according to (Wills 1966) with modifications. Specifically, 400 µL of supernatant from each plasma sample was combined with 600 µL of 15% trichloroacetic acid and 0.67% thiobarbituric acid. The mixture was heated at 100°C for 15 min. After cooling to room temperature, the samples were centrifuged at 5200 xg for 5 min. The supernatants were isolated, and their absorbance was measured at 530 nm. Hydrolyzed Malondialdehyde (MDA) was used as a standard, and the results were expressed as nmol MDA/mL.

2.6 Measurement of inflammatory markers

Levels of tumor necrosis factor-α (TNF-α), interleukin1β (IL-1β) and interleukin-10 (IL-10) were measured in serum using commercial ELISA kits. Results are needed in pg/mL. Serums were grouped according to city, age and smoking, generating 16 groups. ELISA kits for TNF-α, IL-1β and IL-10 were purchased from Invitrogen (Carlsbad, CA, USA).

2.7 Quantification of inorganic elements

The same pool used to study inflammatory levels was used to perform a metal analysis. The identification and quantification of inorganics were carried using the PIXE technique (Particle-Induced X-ray Emission) was performed as previously described (M. Souza et al. 2021). X-ray spectra were formed using the GUPIXWIN software developed at the University of Guelph (Campbell et al. 2010). The results were expressed as ppm.

2.8 Health index

A health index (HI) was calculated according to the study of Souza et al. (2021), using variables that are known to increase damage to genetic material or damage to health in general. All parameters were added up according to the values described in Table 1, which in the end resulted in what we now call the HI. With information on each parameter for each volunteer it was possible to generate an individual HI using the following equation: BMI (Body Mass Index) + WHtR (waist-to-height ratio) + Physical activity + Sleep + Hydration + Smoking + Alcohol consumption, ranging from 0 to 16.

Table 1

Parameters used to determine individual life habit indices (health index).
Parameters	Categories	References
BMI	0 = Obese	Body mass index: Below 18.5 = underweight; between 18.5 and 24.9 = ideal weight between 25.0 and 29.9 = overweight; above 30.0 = obesity (WHO, 2003)
	1 = Excess weight
	2 = Low weight
	3 = Eutrophic
WHtR	0 = High risk (> 0.5)	Person's waist-to-height ratio: Values above 0.5 indicate risk for cardiovascular disease and diabetes (Browning et al., 2010)
WHtR	1 = Low risk (< 0.5)
Physical activity	0 = Sedentary	Impact of less than 150 minutes per week of physical activity on health (Haskell et al., 2007)
	1 = Insufficient (< 150 min per week)
	2 = Active (> 150 min per week)
Sleep	0 = Bad (0 to 5h)	Impact of less than 6 hours of sleep per day on health (Grimaldi et al., 2016)
	1 = Reasonable (6h)
	2 = Good (above 6h)
Impact Hydration	0 = Bad (Less than 1.5 L)	Impact of less than 1.5 L daily water intake on health (Buyckx, 2007)
	1 = Reasonable (1.6L − 2.0L)
	2 = Good (Above 2.1 L)
Smoke	0 = Smoker (+ 10 cigarettes)	Impact of smoking related to the number of cigarettes per day on health (Bonassi et al., 2004)
	1 = Smoker (up to 10 cigarettes)
	2 = Ex-smoker
	3 = Never smoke
Alcohol	0 = Probable dependence (20–40)	Impact of alcohol on health: The Alcohol Use Disorders Identification Test (AUDIT: 0–40 according to alcohol consumption) (Babor et al., 2001)
	1 = Harmful (16–19)
	2 = Risk (8–15)
	3 = Low risk (0–7)

2.9 Statistical analysis

The statistical differences of TL between the cities were determined by 1-way ANOVA, Kruskal-Wallis test, while the comparison between exposed and unexposed were determined by Mann Whitney test (normal distribution). Correlations between these two parameters, as well as these parameters in relation to DNA damage, inflammatory response and inorganic elements data from our previous study that evaluated the same individuals (M. Souza et al. 2021), were assessed using Spearman’s test (for independent samples). Linear regression analysis was used to assess the influence of age on TL (dependent variable). P ≤ 0.05 was considered statistically significant. Statistical analyses were performed with the Graphpad PRISM software, version 5.01 (Graphpad Inc., San Diego, CA). Principal component analysis (PCA) was applied to the values of health parameters, biomarkers of mutagenicity, genotoxicity and inflammatory response obtained from all sites to evaluate the relationships between variables and the relative influence of different parameters on the overall results, using XLSTAT® 2020.3.1 (Addinsoft, 2021).

Healthy men, who did not report chronic disease, were selected for this study. Among individuals from the unexposed group, 18 were male, ranging in age from 18 to 62 years, with a mean and S.D (Standard Deviation) of 33.1 ± 9.4 years. Of the exposed group, 62 were male, ranging in age from 18 to 65 years, with a mean and S.D of 41.4 ± 13.6 years. This study found a mean and S.D for TL value 5.638 ± 2.452 bp in Unexposed and 6.227 ± 2.884 bp in Exposed groups. More demographic characteristics can be seen in Table 2. No difference related to mean age was observed between groups. There was no significant difference between the averages of the TL between the cities (Fig. 1A), as well as we did not observe any difference when comparing the telomeres of unexposed and exposed individuals (Fig. 1B).

Table 2

Main characteristics and parameters observed for the population studied: residents of Bagé, Candiota, Pinheiro Machado and Aceguá, (RS, Brazil).
Characteristics		Unexposed Group (Bagé)	Exposed Groups
Characteristics		Unexposed Group (Bagé)	Candiota	Pinheiro Machado	Aceguá	Total
Number of individuals (%)		18 (22)	22 (27)	26 (33)	14 (18)	62 (78)
Age (mean ± S.D)		33.1 ± 9.4	39.0 ± 15.2	43.4 ± 12.9	41.4 ± 12.1	41.4 ± 13.6
Health Index (mean ± S.D)		9.3 ± 2.4	9.0 ± 2.4	9.7 ± 2.3	9.4 ± 1.9	9.4 ± 2.2
Smoke Habit N (%)	Smoker > 10	3 (16.7)	4 (18.2)	3 (11.5)	0 (0)	7 (11.3)
	Smoker < 10	2 (11.1)	2 (9.1)	3 (11.5)	0 (0)	5 (8.1)
	Ex-smoker	1 (5.5)	5 (22.7)	6 (23.1)	4 (28.5)	15 (24.2)
	Non-Smoker	12 (66.7)	11 (50.0)	14 (53.8)	10 (71.5)	35 (56.4)
Alcohol Habit N (%)	Probable dependence	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
	Harmful	1 (5.6)	0 (0)	2 (7.7)	0 (0)	3 (4.6)
	Risk	2 (11.1)	1 (4.6)	5 (19.2)	1 (7.1)	9 (13.9)
	Low risk	15 (83.3)	21 (95.4)	19 (73.1)	13 (92.9)	53 (81.5)

The TL was related to the age of the unexposed and exposed individuals (Fig. 2). Exposed individuals have a relationship between telomere decreases with advanced age.

Previously, we have evaluated the same individuals using comet assay, MN test, inflammatory response, and inorganic elements (M. Souza et al. 2021). Data was compared with TL data from the current study. The inorganic elements Si, S, Cu, Zn and P were detected in all blood samples (Table 3). Exposed individuals presented the highest levels of Si and P, although not significant, the S also stands out in the exposed.

Table 3

Concentration means (± standard deviation; ppm) of inorganic elements in blood samples from unexposed and exposed groups.
Inorganic elements	Unexposed group	Exposed group
Si	305 ± 0.003	581 ± 456.8*
S	6610 ± 192.6	7015 ± 149.6
Cu	5.17 ± 0.83	4.23 ± 0.71*
Zn	28.7 ± 3.38	26.24 ± 2.45
P	1333 ± 28.64	2127 ± 20.75*
* Significant at P < 0.05 (Student t-test).

When the correlations were evaluated for the different parameters, Si, P, and alcohol were associated with higher TL length for the exposed group. Shorter telomere length was associated with a higher risk of obesity-related cardiovascular disease, according to the WHtR parameter in this same group (Table 4). The unexposed group showed no significant correlation between telomeric length, and the parameters observed in this study.

Table 4

Correlation of telomere length in relation to different biomarkers for individuals from unexposed and exposed groups (Spearman’s correlation analysis).
Biomarkers	Unexposed group		Exposed group
Biomarkers	r	p	r	p
DNA damage
Visual Score (CA)	0.0388	0.9047	0.2345	0.0942
MN	-0.03639	0.9106	-0.0186	0.9002
NPB	0.2689	0.3980	-0.1079	0.4655
NBUD	0.236	0.4602	0.0449	0.7829
Oxidative stress
TBARS	0.3056	0.2330	-0.08674	0.5026
Inflammatory response
TNF-α	-0.3858	0.1930	0.1151	0.4262
IL-1β	0.3858	0.1930	0.06704	0.6436
IL-10	0.3858	0.1930	-0.1771	0.2185
Inorganic elements
S	-0.3454	0.1603	0.2297	0.0725
Cu	0.04688	0.8534	0.1030	0.4257
Zn	0.08636	0.7333	0.1991	0.1208
Si	0.5000	0.1173	0.3889	0.0191
P	0.0913	0.7186	0.3448	0.0061
Health parameters
Health Index	0.0214	0.8509	-0.06574	0.6117
BMI	-0.1084	0.6687	-0.1553	0.2362
WHtR	-0.1642	0.5288	-0.4511	0.0051
Physical activity	0.2139	0.394	-0.03094	0.8114
Sleep	0.0749	0.7675	0.2337	0.0748
Impact Hydration	0.2621	0.2934	0.1493	0.2592
Smoke	-0.2943	0.2358	0.0896	0.4884
Alcohol	-0.1277	0.6137	0.3019	0.0171

The results of the factor analysis are presented graphically in Fig. 3 and highlight the importance of integrating data for better assessment and diagnosis of the most influential factors. The PCA was conducted to obtain an overview of the distribution of DNA damage, inflammatory process, and Inorganic elements to assess the relative implication of these data in the telomere length. The plot of scores shows the position of investigated cities in the ordination plane of the two significant main components (F1 and F2), accounting, respectively, for 21.9% and 18.1% of the total variation. Thus, factor analysis identified two factors as responsible for the data structure accounting for 40% of the total variance. F1 was positively loaded by ID, Si, S, P, Health Index, Cu, Il-10 and Zn. F1 discriminated the samples with the longest telomeric length strongly associated with the highest Si, S and P levels. According to the F2 axis, shorter telomeres were associated with inflammatory cytokines mutagenicity detected by the MN test and age.

Overall, findings suggest that the responses resulting from exposure to complex mixtures are varied and complex. Earlier studies have assessed the health of coal miners and their results demonstrate variance compared to non-exposed individuals, including telomere shortening (Melissa Rosa de Souza et al. 2018). Exposure to coal dust associated with coal burn products induced a significant increase in DNA damage by comet assay, MN frequency (Melissa Rosa de Souza et al. 2018; Espitia-Pérez et al. 2018; Rohr et al. 2013). NPB were also observed in these studies, which may occur due to misrepair of DNA breaks, telomere end fusions, resulting in a bridge covered by nuclear membrane (M. Fenech et al. 2011; Pavanello et al. 2010). However, our study did not observe a significant difference in the TL either between cities or between individuals considered exposed and unexposed. There are few studies showing the effect of environmental exposure to coal and its molecular effects linked to a shorter telomere (Matzenbacher et al. 2019), most studies only consider coal workers and are therefore exposed to a much higher load of this pollutant (Gao et al. 2020; Yu et al. 2020).

Commonly, telomeric region loss is related to aging and the same was observed in our study when we observed an exposed population (Liu et al. 2021; Zhu et al. 2019). Although telomere erosion may impair their function in protecting chromosome ends, resulting in genetic instability (Rampazzo et al. 2010) and short telomeres are likely to interfere with the efficient repair of double strand breaks, showing chromosome instability and increased MN frequency (Jacobus et al. 2008; Latre et al. 2003; Rampazzo et al. 2010).

Exposed individuals had significantly higher levels of Si and P than not exposed. Silica is a compound of coal and is associated with various lung diseases in workers (Pampena et al. 2019). As expected, exposed individuals showed higher levels of this element. However, phosphorus can be classified as a micronutrient and is present in the genetic code (DNA and RNA), being responsible for activating enzymes and maintaining the acid-base balance of the organism. The literature reports the presence of phosphorus (P) in coal (Ninomiya et al., 2004). In general, this element relates to other volatile metals form the ultrafine aerosols in particulate matter (PM) (Rattigan et al., 2002). Other studies with coal also highlight the presence of Si and its relationship with genomic instability (Espitia-Pérez et al. 2018; Msiska et al. 2010).

Despite this, a significant and positive relationship was observed between Si and TL, the same correlation with TL was observed for P and alcohol. Increase in TL has been described in different types of tumors, including gliomas, which have the highest rate of longer telomeres, demonstrating high telomerase activity (Barthel et al. 2017). However, it is also reported that the TL of prostate cancers is shorter compared with normal tissues (Meeker et al. 2002). We can conclude that as long or short telomeres can be indicators of genetic instability and can be used as a prognostic biomarker (Okamoto and Seimiya 2019).

Other result that deserves to be highlighted about correlations is the relation of the higher risk of obesity-related cardiovascular disease, according to the WHtR parameter, and shorter telomeric length. A person's WHtR is defined as their waist circumference divided by their height, both measured in the same units. Higher values of WHtR indicate higher risk of obesity-related cardiovascular diseases (Browning et al. 2010). Recently, growing evidence showed that DNA damage plays a role in the initiation and progression of atherosclerotic plaque and consequently it is related to coronary artery disease (CAD), the main cause of morbidity and mortality worldwide (Andreassi et al. 2020). Our study present associated with WHtR and shorter telomeres. Overweight and obese individuals have a reduced life expectancy due to cardiovascular disease, type 2 diabetes, stroke and cancer (Blüher 2019). The study by Mangge et al. (2019), investigated systemic inflammation and premature telomere shortening have been potential mechanisms linking these conditions and concluded that TL is negatively associated with parameters describing body fat composure.

PCA was conducted to obtain an overview of the spatial distribution of the chemical and biological data and to evaluate the relative implication of these data in differences among telomere length. F1 was positively loaded by visual score, Si, S, P, Health Index, Cu, Il-10, and Zn. F1 discriminated the samples with the longest telomeric length strongly associated with the highest Si, S and P levels. According to the F2 axis, shorter telomeres were associated with inflammatory cytokines mutagenicity detected by the MN test and age. The results obtained with the genotoxic evaluations, inflammatory response, quantification of inorganic elements and the correlations with health parameters, can contribute in large measure to the knowledge of the action mechanisms of coal. Chronic exposure to coal raises the levels of inflammatory cytokines in individuals exposed to mining pollution and coal burning. This process also contributes to the telomere shortening (Lustig et al. 2017). However, regarding these results, the factors related to a larger average of TL (visual score, Si, S, and P) and factors related to the lower average of the telomeric size (age, TBARS and NBUD) are noteworthy. We discussed previously about longer telomers and the relationship with cancer-related processes, as well as telomeric reduction in the aging process. What we highlight now is the relationship of TBARS in reducing the size of the telomere.

Previous data corroborate the results of our study when they show TBARS increasing the generation of free radicals because of stress conditions (Kahl et al. 2016). Oxidative stress induced by exposure can modify the pathways involved in the generation and repair of DNA damage. Oxidative stress also shortens telomeres in cell culture, but whether oxidative stress explains variation in telomere shortening in vivo at physiological oxidative stress levels is not well known (Boonekamp et al. 2017). Oxidative stress commonly leads to a disruption of metal ion homeostasis, and subsequently could induce cancer. Metals may change the conformation of biomolecules leading to direct damage (Jomova and Valko 2011). Previous studies with coal workers demonstrated a relationship between DNA damage and the presence of different inorganic elements, as well as with oxidative stress (Rohr et al. 2013).

Because of the complex nature of chemical exposure in coal mining, it is difficult to relate genotoxic effects to a specific agent or compound. The human organism is a metabolic system in which diet, physical activity and waste disposal orchestrate anabolic and catabolic reactions capable of determining development, maturation, and aging (López-Otín et al. 2016). All the factors that make up the lifestyle are important to achieve a satisfactory balance, therefore we must consider not only the environmental exposure factor but also the habits of these individuals. Having established a better understanding, it will be possible to provide new precautionary measures to reduce harmful effects, resulting in the identification of the best biomonitors and biomarkers for assessing the health of the population and which are the main risk factors. Based on our results, TL are good candidate biomarkers for environmental exposure to coal and its derivatives.

Funding

This work was supported by the Lutheran University of Brazil (ULBRA), and Brazilian agencies: National Council for Scientific and Technological Development (CNPq), Foundation for Research Support of Rio Grande do Sul (FAPERGS) and Committee for the Improvement of Higher Education Personnel (CAPES - Finance Code 001).

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Multiple factors influencing telomere length and DNA damage in individuals living near a coal-burning power plant

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Study population and sample collection

2.2 Quantitative polymerase chain reaction (qPCR) for telomere length measurement

2.3 Comet assay (CA)

2.4 Cytokinesis blocking micronucleus assay (CBMN)

2.5 Thiobarbituric acid reactive substances (TBARS)

2.6 Measurement of inflammatory markers

2.7 Quantification of inorganic elements

2.8 Health index

2.9 Statistical analysis

3 Results

4 Discussion

Declarations

Funding

References

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