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Benzene, toluene, ethylbenzene, and xylene (BTEX) are volatile compounds with proven toxic effects on human health[1,2]. These toxins are produced by the decomposition of fossil fuels in industries, are released into the air, and enter the body through inhalation and skin absorption[1-3]. Exposure to BTEX is linked to various medical conditions, particularly hematological disorders, owing to their effects as hematotoxins and leukomogens[4-7]. Abnormal hematological conditions induced by BTEXs often manifest as anemia, reduction of leukocytes, thrombocytopenia, pancytopenia, and leukemia[5,6,8,9]. Cumulative evidence suggests that the most important mechanism of BTEX hematotoxicity is the increase in the production of cellular reactive oxygen species, which induces various genomic and chromosomal abnormalities in hematopoietic cells through DNA double-strand breaks (DSBs)[10-13]. Hematopoietic cells, like other tissue cells, use key mutation repair systems, including non-homologous end joining (NHEJ) and homologous, to deal with DSB damage, with the NHEJ pathway being the most important one[14-16]. This repair system is controlled by various coding and non-coding genes, and its repair capacity is determined by the functional levels of regulatory loci[17-19]. Based on available evidence, changes in the capacity of repair systems can directly affect the ability of blood cells to eliminate the toxic effects of BTEX[13,19-22].
However, owing to genetic variations, including single nucleotide polymorphisms (SNPs) in the genes controlling repair systems, individuals may show different repair capacities to eliminate target mutations[22-27]. In this regard, some studies have shown that SNPs in genes regulating the DSB repair system are related to susceptibility to hematological complications caused by BTEX in individuals with similar exposure patterns[6,13,19,28]. For example, our study indicated that some SNPs in DSB repair system genes are associated with susceptibility to BTEX-related hematological abnormalities in exposed individuals[6]. Recently, it was revealed that microRNAs (miRNAs are one of the regulatory arms of the DNA DSB repair system[17,18,29]. miRNAs are a class of short non-coding RNAs with a length of 20–22 nucleotides, whose function inside the cell is to control the gene expression processes of protein-encoding genes[17,18]. The regulation of gene expression by miRNAs is done at the post-transcriptional level through binding to the 3′-untranslated region (UTR) of the corresponding mRNA molecules, leading to their inhibition or degradation[17,29]. It has been revealed that a part of the mechanism behind the hematotoxicity caused by BTEX is the induction of abnormal changes in the function and expression of miRNAs that regulate the hematopoietic system[30-33]. According to the available evidence, the presence of SNPs in the genes encoding miRNAs (miR-SNPs) regulating the DSB repair pathway is related to their functional change, which can be linked to the change in DSB repair capacity, particularly the interindividual variability in toxicant-induced adverse responses[23,34,35]. However, there is no evidence of a relationship between miR-SNPs and genetic susceptibility to hematological abnormalities caused by BTEX. Recent evidence has revealed that miR-100 is a hematopoiesis-associated miRNA which acts as an important regulator of DSB repair through binding to the 3′-UTR in mRNA of ataxia telangiectasia mutated (ATM), a key repair sensor during DNA DSB damage[18,36,37]. This miRNA plays a role in hematopoietic regulation by controlling blood cell proliferation and differentiation by targeting ATM and RBSP3 genes[37-39]. The enzyme product of the ATM locus plays a key role in the cell cycle, apoptosis, and DNA DSB mutation repair through serine threonine kinase activity, which sends a signal to its regulatory target substrates[40-42]. In addition, in the C57BL/6 mouse model exposed to benzene, it has been shown that a significant downregulation of miR-100 is linked to hematotoxicity and abnormal blood indices[30]. Previous evidence has shown that miR-100 carries a polymorphism termed rs543412 C>T in its upstream region, which confers functional potential, in which the T minor allele is linked to structural changes and reduces the efficiency of miR-100 processing[43]. Importantly, the minor TT homozygous genotype has been reported to be associated with a reduced risk of childhood acute lymphocytic leukemia[44]. MiR-506 is another regulator of the DSB repair signaling by suppressing the translation of RAD-51, a central mediator of homologous recombination in the DSB repair pathway[17,42,45,46]. RAD-51 executes a critical step in homologous recombination by strand invasion into duplex DNA and the pairing of homologous DNA strands, facilitating strand exchange[17,41,42]. MiR-506 plays an important role in controlling the proliferation and apoptosis of blood cells, and its expression is impaired in chronic myeloid leukemia and mantle cell lymphoma[47,48]. This miRNA also has a SNP including rs5905008 A>G at its 5′ end with a distance of 2 Kb. To our knowledge, this is the first study to investigate the relationship between this SNP and hematological abnormalities in individuals exposed to BTEX compounds.
Taken together, considering the importance of the above SNP variants in miR-100 and miR-506 as two important miRNA regulators of the DSB mutation repair system, which plays a key role in responding to DNA damage caused by BTEX, we conducted the present study to determine the association of miR-100-rs543412 C>T and miR-506-rs5905008 A>G with the risk of abnormal hematological indices in individuals exposed to BTEX in a sub-population of Iran.
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The subjects evaluated in this cross-sectional genetic study were 283 volunteers working in the chemical industry in southern Iran. All workers were randomly selected, and their exposure to BTEX compounds was continuous for at least two years. Exclusion criteria included any history of hematological and coagulation diseases, such as Favism (6-phosphate dehydrogenase deficiency), thalassemia minor, hemophilia, and any history of exposure to other toxins and chemical compounds with hematotoxic effects. Other individuals excluded from the study were those with chronic diseases such as diabetes, hypertension, dyslipidemia, cardiovascular diseases, and obesity. The clinical sample obtained from all participants was a peripheral whole-blood sample, which was done with two goals, one was to measure different blood indices, including complete blood count parameters, including white blood cell count (WBC), WBC differential, red blood cell count (RBC), hemoglobin (HB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution width (RDW), and platelet (PLT) counts, and the other was to extract DNA for genotyping the target SNPs of the study.
A total of 138 participants had one or more abnormal blood indices according to their medical records and complete blood count analyses, and 145 control subjects had normal hematological parameters. Demographic, medical, and occupational information of the participants was obtained through face-to-face interviews and questionnaires. Written informed consent was obtained from all the study participants in accordance with the principles of the 1964 Declaration of Helsinki. The different aspects of study design were approved by the ethics committee of the Fasa University of Medical Sciences (ethical code: 97526).
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The concentration of BTEX chemicals in the air of the exposed participants was determined using activated charcoal sorbent tubes and a sampling pump at a flow rate of 100–200 mL/min, according to the settings of the National Institute for Occupational Safety and Health protocol 1,501[49]. In this method, the collected exhaust air is passed through an adsorbent bed using a sampling pump, and contaminants are trapped in the spaces inside the adsorbent. The BTEX measurement protocol included the collection of three air samples from the personal breathing zone at different hours of each worker's shift. We also considered features such as pump type, sampling site, sampling time, temperature, humidity, wind speed and direction, and sampling duration for the sampling process. Additionally, samples were collected from the environmental air at the breathing zone height of each administrative office using the same sampling method. After air sampling, to prevent sample loss, both sides of the active tube were immediately sealed to ensure that no air passed through the tube and kept in a cool package at a temperature below −4 °C. They were then transferred to the laboratory for analysis and determination of the BTEX compounds. The amount of BTEX compounds was measured using gas chromatography with flame ionization detection based on the National Institute for Occupational Safety and Health procedure.
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Data extraction related to the target SNPs of this study, including miR-100-rs543412 C>T and miR-506-rs5905008 A>G, was performed using the 1,000 Genomes Project (https://www.internationalgenome.org/data) and dbSNP (https://www.ncbi.nlm.nih.gov/) databases. The SNPs had a MAF ≥ 30% whose allelic types lead to gain or loss of a restriction site. miR-100-rs543412 C>T is a variant of the upstream region of miRNA-100 on chromosome 11q24.1. The miR-506-rs5905008 A>G SNP is a sequence variant within 2 Kb 5′ of miRNA-506 on chromosome Xq27.3.
In the next step, the genomic DNA of samples obtained from the study subjects were extracted using a commercial kit (Qiagen, Hilden, Germany) and after determining their quality and quantity using a NanoDrop spectrophotometer, they were maintained at −20 °C until the PCR technique was performed. Genotyping was performed using the restriction fragment length polymorphism method, and specific enzymes were used to cut one allele from each SNP. Briefly, each of the PCR reactions was conducted in a final volume of 20 μL containing 10.5 μL of Taq DNA Polymerase 2x Master Mix (Ampliqon, Denmark), 2 μL of each primer (5 pmol/L), 3.5 μL sterile D.W, and 2 μL DNA template (≤ 100 ng). The primers used for PCR of two SNPs were as follows: miR-100-rs543412 C>T F: 5′-TGACAGCAGAGACCAGTTACA-3′, R: 5′-TGGTATTAGTCCGCACAAGC-3′, and miR-506-rs5905008 A>G F: 5′-CTCGTCATTACCAATGCCACC-3′, and R: 5′-CTCATCTGTAGTTTCCCCGTGT-3′, amplifying the fragments of 274 and 180 bp, respectively. Primers were designed using Primer BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/), PrimerQuest (https://www.idtdna.com/PrimerQuest/Home/Index), and Primer 3 (https://primer3.ut.ee/). The thermo cycler PCR program for both SNPs also included an initial denaturation at 94 °C for 5 min, followed by 36 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, extension at 72 °C for 35 s, and finally extension at 72 °C for 5 min.
After quality control of the size of the PCR products on 2% agarose gel, and to perform the genotyping using the restriction fragment length polymorphism method, two PCR products with sizes of 274 bp (for miR-100-rs543412 C>T) and 180 bp (miR-506-rs5905008 A>G) were digested for an overnight 16 h at a temperature of 37 °C by AvaII and HhaI (Thermo Scientific or Fermentas, Germany) restriction enzymes, respectively. After the end of enzymatic digestion, the restriction enzymes were inactivated at 65 °C for 20 min. Finally, the genotypic patterns of digested PCR products were determined using electrophoresis on 2.5% stained agarose gels and visualized using an UV transilluminator. For miR-100-rs543412 C>T, the uncut product of 274 bp represented the TT homozygous genotype, whereas the digestion pattern of the PCR product with sizes of 213 and 61 bp intended the CC homozygous genotype, and the size patterns of 274, 213, and 61bp fragments represented the CT heterozygous genotype. In relation to the miR-506-rs5905008 A>G polymorphism, the uncut product of 180 bp indicated a homozygous AA genotype, and the digested product with 140 and 40 bp fragments signified a homozygous GG genotype, while the simultaneous presence of fragments of 180, 140, and 40 bp specified a heterozygous AG genotype. Ten percent of DNA samples were selected randomly for repeat and the concordance was 100%.
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The normality of the data distributions was tested using the Kolmogorov–Smirnov test. Student's t-test and the chi-square test were used to compare the differences between continuous and categorical variables, respectively. A goodness-of-fit chi-squared test was also used to analyze whether the miR-100-rs543412 C>T and miR-506-rs5905008 A>G distributions conformed to the Hardy-Weinberg equilibrium (HWE) in the studied groups. The odds ratio (OR) and 95% confidence interval (95% CI) for estimating the associations between genotypes of two SNPs and the risk of hematotoxicity were obtained from both univariate and multivariate unconditional logistic regression models by adjusting for potential confounding factors, including age, body mass index (BMI), exposure duration, and smoking (adjusted OR and 95% CI). The wild-type genotype and allele acted as a reference, and the codominant heterozygous, codominant homozygous, recessive, dominant, and overdominant models were analyzed for both SNPs. Linear regression analysis was performed to evaluate the effect of different genotypes on hematological parameters in the exposure groups. Additionally, the combined genotype analysis of the two polymorphisms was achieved by genotype combination analysis, during which combined genotypes were compared between two groups with ANOVA test, and their association with the risk of hematological abnormalities was determined through regression analysis. Statistical calculations were performed using Microsoft Excel and the Statistical Package for the Social Sciences software (SPSS, version 22.0, IBM Corp.). All statistical assessments were two-sided, and a level of P < 0.05 was considered significant.
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The basic clinical and exposure data of the study participants (all male) are shown in Table 1. The statistical findings showed that there is no significant difference in age, BMI, daily exposure time, job history, and smoking history between the case and control groups. Regarding the case group, they had a mean age of 34.26 years (with a range of 21–58 years) and a mean exposure period of 11.8 hours (range of 8–16 h) over a job span of 7.38 years (range of 2–20 years). Moreover, their average BMI was 25.85 kg/m2 (range of 16.48–35.83 kg/m2) and 5.3 % of them had a history of smoking. The above information for the control group was as follows; a mean age of 34.47 years (with a range of 24–51 years), a mean exposure period of 11.66 hours (range of 4–15 h) over a job span of 7.84 years (range of 2–24), a mean BMI of 25.85 kg/m2 (range of 18.6–33.9 kg/m2), and 5.8% of them were smokers.
Table 1. Characteristics of the BTEX and other variables in two groups of the study participants.
Variables Case (n=138) Control (n=145) P-values Age (years), mean ± SD 34.26 ± 6.50 34.47 ± 5.36 0.67 BMI (kg/m2), mean ± SD 25.85 ± 3.38 25.40 ± 4.78 0.84 Length of exposure in a day (hours), mean ± SD 11.80 ± 1.44 11.66 ± 1.45 0.45 Job history (year), mean ± SD 7.38 ± 3.75 7.84 ± 4.16 0.47 Smoking, n (%) 0.87 Yes 7 (5.3) 8 (5.8) NO 125 (94.7) 131 (94.2) BTEX-exposure measurements (ppm), mean ± SD Benzene 0.57 ± 1.51 0.71 ± 1.59 0.99 Toluene 1.02 ± 2.03 1.11 ± 2.17 0.88 Ethylbenzene 1.44 ± 2.52 1.24 ± 2.70 0.27 O-xylene 0.437 ± 1.33 0.414 ± 1.64 0.27 P-xylene 2.47 ± 3.06 2.28 ± 3.04 0.49 M xylene 1.70 ± 2.46 1.79 ± 2.64 0.89 Note. Student’s t-test was used to determine the statistical significance of the differences in age, BMI, daily exposure time, and job history, and the chi-square test was used for smoking history; P-value < 0.05 was considered statistically significant (in bold). BMI, body mass index; BTEX, benzene toluene ethylbenzene xylene. *Some data were missing due to lack of information. -
As mentioned in the previously, in this study we measured the concentration of each of the BTEX components in the breathing air environment of the study participants to evaluate the exposure. The results of this assessment showed that the mean of arithmetic concentrations of benzene, toluene, and ethylbenzene in the air environment of the population was 0.70 (standard deviation = 1.67 ppm), 1.10 (standard deviation = 2.14 ppm), and 1.34 (standard deviation = 2.6 ppm), respectively. Moreover, the composition of xylene is considered as a set mixture of ortho, para, and meta isomers whose average concentrations were 0.44 (standard deviation = 1.48 ppm), 2.36 (standard deviation = 3.04), and 1.75 (standard deviation = 2.54 ppm), respectively. The findings also showed that there was no significant difference in the concentrations of various BTEX compounds in the breathing environments of both patients and healthy individuals (Table 1).
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As shown in Table 2, a significant difference was observed in the levels of some blood indices between the two study groups. The mean RDW and lymphocyte count were significantly higher in patients than in healthy controls (P = 0.018 and P = 0.001, respectively). The mean HB, MCV, MCH, MCHC, and granulocyte counts revealed a significant decrease in the case group compared to the control group (P = 0.001, P = 0.004, P = 0.001, P = 0.002, and P = 0.001, respectively). Other blood indices, including HCT, WBC, RBC, PLT count, and monocyte count, were not significantly different between the two groups. Additionally, regarding the RBC-related indices, including RBC count, HB, MCV, HCT, MCH, mean MCHC, and red RDW, it was found that their abnormalities in patients was 15.21%, 22.46%, 13.76%, 21.01%, 25.36%, 23.18%, and 9.42%, respectively. Moreover, 7.20% of the case group showed abnormal WBC index. The abnormality rates for lymphocytes, monocytes, and granulocytes was 62.31%, 10.86%, and 8.69%, respectively. For the platelet index, we observed a 10.14% aberration (Supplementary Tables, available in www.besjournal.com).
Table 2. Data on hematological indices in case and control subjects
Hematological indices Case (n = 138) Control (n=145) P-values WBC (×109/L) 6.97 ± 1.86 7.03 ± 1.51 0.80 RBC (×109/L) 5.04 ± 0.559 5. 0 ± 0.360 0.47 HB (g/dL) 14.77 ± 1.50 15.32 ± 0.840 0.001 HCT (%) 43.52 ± 3.65 44.22 ± 2.24 0.051 MCV (fL) 86.20 ± 9.30 88.67 ± 3.82 0.004 MCH (Pg) 29.64 ± 3.67 30.75 ± 1.71 0.001 MCHC (g/dL) 34.10 ± 1.89 34.66 ± 0.911 0.002 RDW (%) 12.41 ± 0.968 12.13 ± 0.562 0.018 Platelets (×109/L) 212 ± 46.64 210.8 ± 38.52 0.908 Lymphocytes (%) 44.72 ± 10.87 37.96 ± 5.72 0.001 Monocytes (%) 2.92 ± 1.59 3.08 ± 1.79 0.936 Granulocytes (%) 51.07 ± 10.68 57.97 ± 6.38 0.001 Note. Values are given as the mean ± standard deviation; Student t-test for comparing the difference of hematological indices in the case and control groups was used; P-value <0.05 was considered statistically significant (in bold). WBC, white blood cell count; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; RDW, red cell distribution width. -
As described in Table 3, in the current population, the genotype frequencies of miR-100-rs543412 C>T in the cases and controls were consistent with HWE (P > 0.05), whereas the genotypes of miR-506-rs5905008 A>G were not in equilibrium (P > 0.05). Regarding the comparison of allelic and genotype frequencies of miR-100-rs543412 C>T polymorphism between the two groups, we observed that patients differ from healthy individuals in both allelic (P = 0.005) and genotype (P = 0.013) frequencies, in which the patient group carried significantly fewer T minor allele (39.5% vs. 51.4 %, P = 0.005) and TT minor genotype (13.8% vs. 24.1%, P = 0.013) compared with the healthy group.
Table 3. Genotype and allele distribution of the selected polymorphisms in study population groups.
SNPs Cases (n=138),
N (%)Controls (n=145)
N (%)P-Value P-HWE miR-100-rs543412c>T Genotypes
CC
CT
TT
48(34.8)
71(51.4)
19(13.8)
31(21.4)
79(54.5)
35(24.1)
0.013
0.24Alleles
C
T
167(60.5)
109(39.5)
141(48.6)
149(51.4)0.005 miR-506-rs5905008a>G
Genotypes
AA
AG
GG
21(15.2)
17(12.3)
100(72.5)
23(15.9)
23(15.9)
99(68.3)
0.66
0.001Alleles
A
G
217(78.6)
59(21.4)
221(76.2)
69(23.8)
0.49Note. Person χ2 test used for difference in distributions between the case and control groups. A goodness-of-fit chi-squared test was used to evaluate the Hardy-Weinberg equilibrium in the study. population; P-value < 0.05 was considered statistically significant (in bold). SNPs, single nucleotide polymorphisms; P-HWE, P value for Hardy-Weinberg equilibrium. We also used multiple genetic models of inheritance by regression analysis to determine the risk or protective relationship of each allele and genotype of this SNP with abnormalities in blood indices in the exposed subjects (Table 4). It was observed that TT and CT genotypes of the rs543412 C > T was significantly associated with the decreased risk of abnormal blood indices under genetic models of codominant (CT vs. CC, OR: 0.546, 95% CI: 0.312–0.957, P = 0.034 & TT vs. CC, OR: 0.335, 95% CI: 0.158–0.711, P = 0.004), dominant (CT+TT vs. CC, OR: 0.486, 95% CI: 0.285–0.830, P = 0.008), and recessive (TT vs. CC+CT, OR: 0.507, 95% CI: 0.267–0.962, P = 0.038). It should be noted that this finding was maintained in both the adjusted (for variables of age, BMI, smoking, and length of exposure to BTEXs) and unadjusted states, except for the CT heterozygous genotype in the codominant model, which was not significant, with a borderline value (P = 0.054). Finally, we conducted further stratification analyses to evaluate the independent effect of each genotype on each hematological parameter in BTEX-exposed workers, and the findings were not significant for any subgroup (Supplementary Tables).
Table 4. Association results between miR-SNP genotypes and patterns of hematological parameters in multiple inheritance models.
SNPs Genotype models Genotypes Population groups Unadjusted OR (95%CI) P Adjusted OR (95%CI) P Case (n = 138) Control (n = 145) miR-100-RS543412C>T Codominant C/CC/TT/T 48 (34.8%) 31 (21.4%) 1 1 71 (51.4%) 79 (54.5%) 0.580 (0.334-1.010) 0.054 0.546 (0.312-0.957) 0.034 19 (13.8%) 35 (24.1%) 0.351 (0.171-0.719) 0.004 0.335 (0.158-0.711) 0.004 Dominant C/CC/T+T/T 48 (34.8%) 31 (21.4%) 1 1 90 (65.2%) 114 (78.6%) 0.510 (0.300-0.866) 0.013 0.486 (0.285-0.830) 0.008 Recessive C/C+C/TT/T 119 (86.2%) 110 (75. 9%) 1 1 19 (13.8%) 35 (24.1%) 0.502 (0.271-0.929) 0.028 0.507 (0.267-0.962) 0.038 Overdominant C/C+T/TC/T 67 (48.6%) 66 (45.5%) 1 1 71 (51.4%) 79 (54.4%) 0.885 (0.555-1.413) 0.609 0.822 (0.510 -1.325) 0.421 miR-506-RS5905008A>G Codominant A/AA/GG/G 21 (15.2%) 23 (15.9%) 1 1 17 (12.3%) 23 (15.9%) 0.732 (0.369 -1.453) 0.37 0.684 (0.339 -1.379) 0.28 100 (72.5%) 99 (68.3%) 0.904 (0.470 -1.738) 0.76 0.893 (0.457 -1.745) 0.74 Dominant A/AA/G+G/G 21 (15.2%) 23 (15.9%) 1 1 117 (84.8%) 122 (84.1%) 0.952 (0.500 -1.812) 0.88 0.933 (0.482 -1.804) 0. 83 Recessive A/A+A/GG/G 38 (27.5%) 46 (31.7%) 1 100 (72.5%) 99 (68.3%) 0.818 (0.490 -1.364) 0.44 0.780 (0.463 -1.315) 0. 35 Overdominant A/A+G/GA/G 121 (87.7%) 122 (84.1%) 1 1 17 (12.3%) 23 (15.9%) 0.745 (0.379 -1.464) 0.39 0.702 (0.352 -1.399) 0.31 Note. OR, Odds ratio; 95% CI, 95% confidence interval; P < 0.05, considered statistically significant; logistic regression model, OR adjusted for age, BMI, smoking and length of exposure to benzene. No significant differences were observed in the genotype and allele frequencies of the miR-506-rs5905008 A>G SNP between subjects with abnormal hematological indices and healthy controls. In addition, we did not find significant statistical values for the regression analysis data under different genetic models or for the results of its relationship with each subgroup of blood parameters. Nevertheless, when the combined genotypes of the miR-100-rs543412 C>T and miR-506-rs5905008 A>G polymorphisms were compared between the two groups using combined genotype analysis, it was revealed that the combined heterozygote genotype of the two SNPs was significantly different (P = 0.028) between the two groups and was associated with a reduced risk of blood index abnormalities (OR: 0.089, 95% CI: 0.009–0.857, P = 0.03) (Table 5).
Table 5. The results of the combined genotype analysis of the two polymorphisms under study.
Combined genotypes N (%) Cases Controls P-value OR (95%CI) P-value CCAA 17 (6.0) 9 (6.5) 8 (5.5)
0/028ref − CCAG 10 (3.5) 6 (4.3) 4 (2.8) 1.33 (0.274-6.49) 0.99 CCGG 52 (18.4) 33 (23.9) 19 (13.1) 1.544 (0.510- 4.670) 0.44 CTAA 16 (5.7) 11 (8.0) 5 (3.4) 1.956 (0.471- 8.11) 0.35 CTAG 25 (8.8) 9 (6.5) 16 (11.0) 0.089 (0.009- 0.857) 0.03 CTGG 109 (38.5) 51 (37.0) 58 (40.0) 0.500 (0.143- 1.753) 0.27 TTAA 11 (3.9) 1 (0.7) 10 (6.9) 0.782 (0.281- 2.176) 0.63 TTAG 5 (1.8) 2 (1.4) 3 (2.1) 0.593 (0.078- 4.498) 0.61 TTGG 38 (13.4) 16 (11.6) 22 (15.2) 0.646 (0.205- 2.041) 0.45 Note. Logistic regression model, OR, odds ratio, 95% CI 95% confidence interval P < 0.05 considered statistically significant. -
Previous studies have shown that individual genetic differences are responsible for variations in phenotypic responses of the individuals to the chemical toxicity effects of toxins[50-53]. BTEX components, the main toxic pollutants in various industries, can affect the bone marrow and blood cells, leading to various forms of hematotoxicity and related diseases in exposed individuals[4-6,54]. miRNAs play key roles in the regulation of genes involved in the implementation of the DBS break repair system against the harmful effects of BTEX, and evidence exist that their expression is disrupted during BTEX-induced damage[17,18,55-58]. Furthermore, it has been revealed that genes producing miRNAs may show genetic variation among individuals because they carry SNPs and thus have functional differences, and their relationship with various human diseases has also been reported[23,59,60].
In this study, we found that in the patient group, compared with controls, the hematological indices of RDW and lymphocyte counts increased, whereas the indices of HB, MCV, MCH, MCHC, and granulocyte counts decreased. These results are in line with the observation that exposure to BTEX compounds has adverse effects on the hematological system, which can be associated with abnormal changes in hematological parameters [5,61,62]. However, in different studies, the types of changes in hematological indices in individuals exposed to BTEX were not the same. For example, in line with the results of our study, Doherty et al. showed that the hemoglobin and MCH levels of people were reduced when exposed to BTEX. It has been consistently reported that the level of RDW increases upon exposure to BTEX[63]. In a study conducted by Zhang et al., contrary to our results, the number of lymphocytes decreased after exposure to BTEX, but their hemoglobin levels decreased, in line with our results[64]. In another study by Chen et al., increasing the amount of BTEX in the blood was associated with a decrease in hemoglobin and MCHC indices[5]. Kasemy et al. found that MCHC was elevated in individuals exposed to BTEX; however, in line with the results of our study, MCH and MCV decreased[65]. These observations show that exposure to BTEX compounds can be related to different changes in diverse hematological indices, which may depend on different doses of these compounds separately and in combination with other components, the type of BTEX production source, the genetic profile of the given population, the interaction of different environmental agents, and other factors.
In the present study, we demonstrated for the first time that the rs543412 C>T SNP, with a potential functional effect in the miR-100 gene, was significantly associated with a reduced risk of hematological abnormalities in BTEX-exposed individuals. According to this finding, the frequency of genotypes with minor T alleles (TT and CT) of this SNP was lower in BTEX-exposed subjects with abnormal blood indices than in exposed healthy subjects. Moreover, our results indicated that subjects carrying the minor homozygous TT genotype and the CT heterozygote genotype of the miR-100 rs543412 C>T had a significantly lower risk of abnormal blood indices in the codominant (CT vs. CC), dominant (CT+TT vs. CC), and recessive (TT vs. CC+CT) genotypes. Therefore, these observations may represent a protective effect of genotypes carrying the T minor allele of miR-100 rs543412 C>T against the hematotoxic effects of BTEX. miR-100 is a key hematopoiesis-regulatory miRNA by controlling DSB repair through interaction with the 3′-UTR of ATM mRNA, leading to the suppression of its expression[18,36,37,58]. ATM is necessary for the cell cycle checkpoint of DSBs, during which it transduces the DSB repair signal to the downstream pathway by phosphorylating protein substrates[40-42]. In addition, the role of ATM in the development of hematopoietic bone marrow-derived cells has been established, and its malfunction leads to serious abnormalities in these cells[66-68]. In addition, miR-100 controls the function of hematopoietic cells by regulating their proliferation and differentiation by targeting ATM and RBSP3 genes[37-39]. Previous studies demonstrated that miR-100 is dysregulated in hematological malignancies[37,39,44,69]. For example, miR-100 is overexpressed in AML cell lines and patients and promotes malignancy by increasing the survival of myelocytes and inhibiting their apoptosis and differentiation through suppression targeting of ATM and RBSP3 genes[37,38,70].
Evidence from a C57BL/6 mouse model exposed to benzene indicates that reduced expression of miR-100 is linked to hematotoxicity and abnormal blood indices[30]. The metabolism of BTEX compounds in human cells leads to the production of active reactants, including reactive oxygen species free radicals, which are direct sources of genomic damage, including DSB mutations that activate DSB repair responses[11,71-73]. Thus, miR-100, with its key actions in the blood system, may be one of the molecular targets of BTEX compounds that induce hematological damage. Mechanistically, SNPs within miRNA loci can affect miRNA function in different ways, including miRNA transcription, mature processing, secondary structure alteration, and interactions with target mRNAs[23,74]. The rs543412 C>T polymorphism is located in the upstream region of miR-100, and bioinformatics evaluations have indicated that its T minor allele confers functional potential by structural changes in miR-100 and reduced efficiency of its processing[43]. Therefore, this finding indicates that the significant association of the above SNP with abnormal alterations in hematological indices in individuals exposed to BTEX may have biological and functional reasons. In other words, subjects carrying genotypes with the T allele may have reduced levels of miR-100 owing to structural differences and altered processing, and this event increases the resistance of individuals to the effects of BTEX through different molecular pathways. The present study is the first report on the association of a non-coding polymorphism with the abnormal hematological effects of BTEX; therefore, it can provide a key clue to finding other non-coding loci associated with susceptibility to blood abnormalities related to these compounds. In addition, since the DNA DSB repair system that plays a major role in hematopoietic cells is the NHEJ pathway[14,16,75], in the context of hematopoietic cells and BTEX exposure, understanding the regulation and functionality of the NHEJ pathway, along with the involvement of miRNAs such as miR-100, is crucial for comprehending the molecular mechanisms underlying the observed hematological abnormalities.
In line with our research, previous studies have revealed that polymorphisms in several genes encoding proteins involved in BTEX metabolism and repair of BTEX-related DSB mutations are related to different levels of susceptibility to blood defects in exposed individuals[6,13,76,77]. For example, in a previous study, we found that genotypes carrying the minor allele of two DSB repair-related SNPs, ATM-rs228589 A>T and H2AX-rs7759 A>G, were associated with a risk of blood abnormalities in people exposed to BTEX[6].
The relationship between these miR-SNP variants and the susceptibility to various human diseases has been shown.
Xue et al. found that the mutant homozygous TT genotype of miR-100 rs543412 C>T had a significant protective effect on the risk of pediatric acute lymphocytic leukemia (Xue et al. 2019). They also observed that individuals carrying the mutant homozygous TT genotype of rs543412 had significantly lower level of miR‐100 in their plasma. This provides further evidence that the biological defect of this miRNA is linked to abnormal phenotypes of the hematopoietic system, whereas the minor T allele of this SNP confers a functional change in the form of its altered expression. Thus, it can be assumed that the T allele may have a negative effect on the expression and activity of mir-100, and a positive effect on important genes involved in the key functional pathways of DSB repair, apoptosis, and differentiation of blood cells, and in turn, protect them against the harmful effects of BTEXs by influencing the DSB repair capacity. Furthermore, these findings improve our understanding of the molecular mechanisms underlying hematotoxicity induced by BTEX defects in miRNAs. By exploiting these data, it is possible to identify people prone to hematological disorders during exposure to BTEX agents to prevent their occurrence. Although in this study there was no significant finding regarding the relationship of miR-506-rs5905008 A>G SNP with hematological abnormalities in exposed participants, when the relationship between the combined genotype of the two SNP was evaluated using genotypic combination analysis, we found a significant different in frequency of the combined heterozygote genotype of the two SNPs between two study groups, which was also associated with a reduction in the risk of abnormal blood indices. In other words, individuals who simultaneously carry the heterozygous genotype of both miR-100 rs543412 C>T and miR-506-rs5905008 A>G variants are less likely to develop blood defects. The miR-506 is also an important regulatory factor in the repair of BTEX-induced DNA damage[17,42,46]. Therefore, this observation suggests that the cumulative interaction between the two SNPs may create a specific biological event that, along with other factors, reduces the risk of harmful effects of BTEX on blood cells.
The current study has some limitations, including the limited sample size of the participants, which was due to the inclusion and exclusion criteria of the study, the reluctance of people to participate in the study, and the small number of workers in the desired workplace. Other limitations include the lack of knowledge about gene-environment interactions, the impossibility of investigating variants of other miRNAs, and the lack of functional studies, such as investigating the expression of target miRNAs. Therefore, it is imperative to replicate our findings in larger-scale studies to establish a robust understanding of the association between this SNP and BTEX-induced hematotoxicity.
doi: 10.3967/bes2024.110
A miR-100 Polymorphism Signature is Protectively Associated with Hematological Abnormalities in Individuals Exposed to Benzene, Toluene, Ethylbenzene, and Xylene
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Abstract:
Objective The DNA double-strand break (DSB) repair system plays a key role in eliminating DNA damage in hematopoietic cells caused by benzene, toluene, ethylbenzene, and xylene (BTEX) compounds, and the polymorphisms of genes controlling this DNA repair system are linked to the different genetic susceptibilities of individuals to respond to the effects of BTEXs. In addition, there is some evidence that BTEX can induce hematological abnormalities through changing the function of micro RNAs regulating the DSB system, but there is no report on the association of their polymorphisms with BTEX-related disorders. This study aimed to explore the relationship between important polymorphisms in miRNA-100 and miRNA-506, two key regulators of the hematopoietic DSB system, including miRNA-100 RS543412 C>T and miRNA-506 RS5905008 A>G, and hematological abnormalities in BTEX-exposed workers. Methods This study included 138 patients who were exposed to BTEX compounds and had one or more abnormalities in their hematological indices and 145 healthy individuals with the same exposure conditions but without any abnormal hematological defects. Polymorphism genotyping was performed using polymerase chain reaction restriction fragment length polymorphism. Results The results revealed that frequency of the T allele as well as the TT and CT genotypes of the miR-100-RS543412C>T single nucleotide polymorphism was significantly lower in patients than in healthy participants (allelic P = 0.005; genotype P = 0.013). Moreover, individuals with TT and CT genotypes in the codominant (CT vs. CC, OR: 0.546, P = 0.034; TT vs. CC, OR: 0.335, P = 0.004), dominant (CT+TT vs. CC, OR: 0.486, P = 0.008), and recessive (TT vs. CC+CT, OR: 0.507, P = 0.038) models had a lower risk of hematological abnormalities. We also observed that the combined heterozygous genotype of the two polymorphisms was significantly different (P = 0.028) between the two groups and was associated with a reduced risk of abnormalities in blood indices (OR: 0.089, P = 0.03). Conclusion These findings suggest that this miR-single nucleotide polymorphism may be a protective non-coding signature underlying the risk of BTEX exposure-related hematological abnormalities. However, this hypothesis requires further investigation. -
Key words:
- BTEX compounds /
- Genetic susceptibility /
- miR-100 SNP /
- rs543412C>T /
- Hematological abnormalities
&These authors contributed equally to this work.
注释:1) CONFLICTS OF INTEREST: -
Table 1. Characteristics of the BTEX and other variables in two groups of the study participants.
Variables Case (n=138) Control (n=145) P-values Age (years), mean ± SD 34.26 ± 6.50 34.47 ± 5.36 0.67 BMI (kg/m2), mean ± SD 25.85 ± 3.38 25.40 ± 4.78 0.84 Length of exposure in a day (hours), mean ± SD 11.80 ± 1.44 11.66 ± 1.45 0.45 Job history (year), mean ± SD 7.38 ± 3.75 7.84 ± 4.16 0.47 Smoking, n (%) 0.87 Yes 7 (5.3) 8 (5.8) NO 125 (94.7) 131 (94.2) BTEX-exposure measurements (ppm), mean ± SD Benzene 0.57 ± 1.51 0.71 ± 1.59 0.99 Toluene 1.02 ± 2.03 1.11 ± 2.17 0.88 Ethylbenzene 1.44 ± 2.52 1.24 ± 2.70 0.27 O-xylene 0.437 ± 1.33 0.414 ± 1.64 0.27 P-xylene 2.47 ± 3.06 2.28 ± 3.04 0.49 M xylene 1.70 ± 2.46 1.79 ± 2.64 0.89 Note. Student’s t-test was used to determine the statistical significance of the differences in age, BMI, daily exposure time, and job history, and the chi-square test was used for smoking history; P-value < 0.05 was considered statistically significant (in bold). BMI, body mass index; BTEX, benzene toluene ethylbenzene xylene. *Some data were missing due to lack of information. 
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Table 2. Data on hematological indices in case and control subjects
Hematological indices Case (n = 138) Control (n=145) P-values WBC (×109/L) 6.97 ± 1.86 7.03 ± 1.51 0.80 RBC (×109/L) 5.04 ± 0.559 5. 0 ± 0.360 0.47 HB (g/dL) 14.77 ± 1.50 15.32 ± 0.840 0.001 HCT (%) 43.52 ± 3.65 44.22 ± 2.24 0.051 MCV (fL) 86.20 ± 9.30 88.67 ± 3.82 0.004 MCH (Pg) 29.64 ± 3.67 30.75 ± 1.71 0.001 MCHC (g/dL) 34.10 ± 1.89 34.66 ± 0.911 0.002 RDW (%) 12.41 ± 0.968 12.13 ± 0.562 0.018 Platelets (×109/L) 212 ± 46.64 210.8 ± 38.52 0.908 Lymphocytes (%) 44.72 ± 10.87 37.96 ± 5.72 0.001 Monocytes (%) 2.92 ± 1.59 3.08 ± 1.79 0.936 Granulocytes (%) 51.07 ± 10.68 57.97 ± 6.38 0.001 Note. Values are given as the mean ± standard deviation; Student t-test for comparing the difference of hematological indices in the case and control groups was used; P-value <0.05 was considered statistically significant (in bold). WBC, white blood cell count; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; RDW, red cell distribution width.
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Table 3. Genotype and allele distribution of the selected polymorphisms in study population groups.
SNPs Cases (n=138),
N (%)Controls (n=145)
N (%)P-Value P-HWE miR-100-rs543412c>T Genotypes
CC
CT
TT
48(34.8)
71(51.4)
19(13.8)
31(21.4)
79(54.5)
35(24.1)
0.013
0.24Alleles
C
T
167(60.5)
109(39.5)
141(48.6)
149(51.4)0.005 miR-506-rs5905008a>G
Genotypes
AA
AG
GG
21(15.2)
17(12.3)
100(72.5)
23(15.9)
23(15.9)
99(68.3)
0.66
0.001Alleles
A
G
217(78.6)
59(21.4)
221(76.2)
69(23.8)
0.49Note. Person χ2 test used for difference in distributions between the case and control groups. A goodness-of-fit chi-squared test was used to evaluate the Hardy-Weinberg equilibrium in the study. population; P-value < 0.05 was considered statistically significant (in bold). SNPs, single nucleotide polymorphisms; P-HWE, P value for Hardy-Weinberg equilibrium.
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Table 4. Association results between miR-SNP genotypes and patterns of hematological parameters in multiple inheritance models.
SNPs Genotype models Genotypes Population groups Unadjusted OR (95%CI) P Adjusted OR (95%CI) P Case (n = 138) Control (n = 145) miR-100-RS543412C>T Codominant C/CC/TT/T 48 (34.8%) 31 (21.4%) 1 1 71 (51.4%) 79 (54.5%) 0.580 (0.334-1.010) 0.054 0.546 (0.312-0.957) 0.034 19 (13.8%) 35 (24.1%) 0.351 (0.171-0.719) 0.004 0.335 (0.158-0.711) 0.004 Dominant C/CC/T+T/T 48 (34.8%) 31 (21.4%) 1 1 90 (65.2%) 114 (78.6%) 0.510 (0.300-0.866) 0.013 0.486 (0.285-0.830) 0.008 Recessive C/C+C/TT/T 119 (86.2%) 110 (75. 9%) 1 1 19 (13.8%) 35 (24.1%) 0.502 (0.271-0.929) 0.028 0.507 (0.267-0.962) 0.038 Overdominant C/C+T/TC/T 67 (48.6%) 66 (45.5%) 1 1 71 (51.4%) 79 (54.4%) 0.885 (0.555-1.413) 0.609 0.822 (0.510 -1.325) 0.421 miR-506-RS5905008A>G Codominant A/AA/GG/G 21 (15.2%) 23 (15.9%) 1 1 17 (12.3%) 23 (15.9%) 0.732 (0.369 -1.453) 0.37 0.684 (0.339 -1.379) 0.28 100 (72.5%) 99 (68.3%) 0.904 (0.470 -1.738) 0.76 0.893 (0.457 -1.745) 0.74 Dominant A/AA/G+G/G 21 (15.2%) 23 (15.9%) 1 1 117 (84.8%) 122 (84.1%) 0.952 (0.500 -1.812) 0.88 0.933 (0.482 -1.804) 0. 83 Recessive A/A+A/GG/G 38 (27.5%) 46 (31.7%) 1 100 (72.5%) 99 (68.3%) 0.818 (0.490 -1.364) 0.44 0.780 (0.463 -1.315) 0. 35 Overdominant A/A+G/GA/G 121 (87.7%) 122 (84.1%) 1 1 17 (12.3%) 23 (15.9%) 0.745 (0.379 -1.464) 0.39 0.702 (0.352 -1.399) 0.31 Note. OR, Odds ratio; 95% CI, 95% confidence interval; P < 0.05, considered statistically significant; logistic regression model, OR adjusted for age, BMI, smoking and length of exposure to benzene.
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Table 5. The results of the combined genotype analysis of the two polymorphisms under study.
Combined genotypes N (%) Cases Controls P-value OR (95%CI) P-value CCAA 17 (6.0) 9 (6.5) 8 (5.5)
0/028ref − CCAG 10 (3.5) 6 (4.3) 4 (2.8) 1.33 (0.274-6.49) 0.99 CCGG 52 (18.4) 33 (23.9) 19 (13.1) 1.544 (0.510- 4.670) 0.44 CTAA 16 (5.7) 11 (8.0) 5 (3.4) 1.956 (0.471- 8.11) 0.35 CTAG 25 (8.8) 9 (6.5) 16 (11.0) 0.089 (0.009- 0.857) 0.03 CTGG 109 (38.5) 51 (37.0) 58 (40.0) 0.500 (0.143- 1.753) 0.27 TTAA 11 (3.9) 1 (0.7) 10 (6.9) 0.782 (0.281- 2.176) 0.63 TTAG 5 (1.8) 2 (1.4) 3 (2.1) 0.593 (0.078- 4.498) 0.61 TTGG 38 (13.4) 16 (11.6) 22 (15.2) 0.646 (0.205- 2.041) 0.45 Note. Logistic regression model, OR, odds ratio, 95% CI 95% confidence interval P < 0.05 considered statistically significant.
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