Abstract
Background and objective
Acute lymphoblastic leukemia (ALL) is the most frequent childhood malignancy, which is impacted by genetic, epigenetic, and environmental variables. Aberrant methylation of genes, such as O6-methylguanine-DNA-methyltransferase (MGMT), is one of the key mechanisms in carcinogenesis. The aim of the present study was to examine the association of exposure to diazinon with MGMT gene methylation and expression levels in children with ALL.
Methods
This case-control research was performed on 136 children with ALL and 136 healthy children as the control group. Demographic data were gathered using a questionnaire and blood sampling. Serum concentrations of diazinon were determined using gas chromatography (GC). DNA was extracted from nucleated cells, followed by bisulfite treatment and examination of MGMT gene promoter methylation using methylation-specific polymerase chain reaction (MSP). Gene expression levels were also determined using real-time Polymerase chain reaction (PCR). Acetylcholinesterase (AChE) activity and malondialdehyde (MDA) concentrations were evaluated as indicators of pesticide toxicity and oxidative stress.
Results
Diazinon levels were significantly increased in ALL patients compared to controls (P < .001) and were positively associated with elevated methylation levels of MGMT gene promoter. The odds ratio of ALL development was significantly higher in children with both increased diazinon concentrations and elevated MGMT methylation levels. Moreover, patients exhibited reduced AChE activity and higher MDA concentrations, suggesting the induction of neurotoxicity and oxidative stress triggered by diazinon.
Conclusion
Exposure to diazinon might contribute to the development and progression of ALL by triggering aberrant methylation of the MGMT gene, decreasing DNA repair capacity, and promoting oxidative damage. This study highlights the importance of minimizing pesticide exposure and suggests the use of MGMT methylation as a biomarker for the diagnosis and prognosis of ALL.
Keywords: diazinon, MGMT, methylation, leukemia
Introduction
Leukemia comprises a group of malignant hematopoietic disorders defined by the unusual proliferation of clonal leukocytes in the bone marrow, blood, and peripheral tissues, resulting in the impairment of normal hematopoiesis and organ dysfunction [1, 2]. The most prevalent type of this condition in childhood is acute lymphoblastic leukemia (ALL), marked by the excessive proliferation of immature lymphoblasts in the bone marrow and the suppression of normal hematopoietic cell function [3]. This complex hematological disease is impacted by the interplay of genetic, epigenetic, and environmental factors [4, 5]. Among environmental factors, pollutants and chemical toxins, particularly pesticides, are crucial in triggering epigenetic modifications, such as DNA methylation [6, 7].
Organophosphorus pesticides are one of the most extensively used chemical compounds in agriculture, which pose significant public health concerns due to their persistence in the environment, ease of absorption, and diverse effects on biological systems [8]. One of the most prominent of these compounds is the organophosphorus pesticide diazinon (O, O-diethyl O-[4-methyl-6-(propan-2-yl)pyrimidin-2-yl] phosphorothioate), which has garnered much attention owing to its neurotoxic characteristics and extensive effects on cellular biological processes [9]. Research suggests that diazinon leads to a slight elevation in the number of micronucleated cells in cultured human lymphocytes, demonstrating its genotoxic potential, and can induce anemia by causing chromosomal injury [10]. It has also been reported that exposure to diazinon can significantly lower red blood cell count, hemoglobin concentration, and hematocrit [11]. These effects are primarily mediated through the generation of reactive oxygen species induced by organophosphorus compounds, which impair hemoglobin synthesis and disrupt normal hematopoietic capacity [12]. Diazinon can play a role in the aberrant methylation of tumor suppressor gene promoters by triggering oxidative stress, interfering with metabolic pathways, and suppressing enzymes such as acetylcholinesterase (AChE), glutathione-S-transferase, and cytochrome P450 [13, 14]. The methylation of O6-methylguanine-DNA-methyltransferase (MGMT), by reducing its expression, results in genomic instability and a higher risk of hematological malignancies, such as ALL [15]. Diazinon is promptly absorbed through the skin, gastrointestinal tract, respiratory system, and mucosa and metabolized in the liver [16]. Its toxic metabolites, e.g. diazoxon, can lead to genetic damage by suppressing serine hydrolase enzymes and impairing DNA repair pathways [17, 18]. Pesticides such as diazinon, alpha-cypermethrin, gamma-hexachlorocyclohexane (HCH), dichlorodiphenyltrichloroethane (DDT), and dichlorodiphenyldichloroethylene (DDE) can contribute to a higher risk of leukemia by impacting DNA methylation and inducing oxidative stress [19].
DNA methylation, as an essential epigenetic modification, plays a crucial role in regulating gene expression, sustaining genomic stability, and managing the cell cycle [20]. In recent years, the importance of these epigenetic modifications, particularly DNA methylation, in the development and progression of ALL has been extensively examined [4]. Aberrant methylation in the promoter regions of certain vital genes can result in the silencing of tumor suppressor genes and render cells more susceptible to genetic harm [21]. One of the key genes in this context is MGMT, which plays a critical role in the DNA repair system, protecting cells against damage induced by alkylating compounds [22]. This protein serves as a direct DNA repair enzyme and, by eliminating alkylated compounds including O6-alkylguanine and O4-alkylthymine, contributes to preventing mutagenesis, genomic instability, and toxicity triggered by alkylating agents [23]. Additionally, MGMT facilitates the repair of toxic lesions induced by these agents by catalyzing the transfer of methyl groups from O6-alkylguanine and other methylated DNA bases to itself [24]. The precise regulation of MGMT expression is of great importance since as a direct DNA repair protein, this gene eliminates mutagenic alkylated compounds from O6-guanine and O4-thymine [25].
Considering that Kerman province is regarded as one of the agricultural hubs of Iran, and previous research has indicated that diazinon levels exceed the acceptable limit in agricultural products from this region, residents of Kerman are directly exposed to these toxins by consuming the products. The present study examines the effect of diazinon on MGMT gene promoter methylation and its association with an elevated risk of ALL in children. Understanding this association could facilitate the development of targeted preventive and therapeutic approaches to mitigate the carcinogenic effects of this chemical compound and improve its environmental surveillance.
Results
The results revealed that the levels of MGMT promoter methylation in patients with leukemia (79.1%) were significantly higher than in the control group (44%) (Fig. 1). Moreover, the level of MGMT expression was significantly lower in patients with ALL compared to the control group (P < .001) (Fig. 2).
Figure 1.
(A) Methylation statuses of MGMT promoter in ALL and control groups. (B) Electrophoretic image of MGMT promoter methylation status in the ALL and control. ALL: acute lymphoblastic leukemia; L: DNA ladder; M: methylated band; and U: unmethylated band.
Figure 2.
The level of MGMT expression was significantly lower in patients with ALL compared to the control group (P < .001).
The results presented in Table 1 indicated that there was no significant difference in age, sex, and body mass index (BMI) between the case and control groups (P ≥ .05 for all). There was a significant difference between the case and control groups in terms of residence, smoking, gene methylation, and farmer parents (P < .001 for all). The mean levels of AChE, platelets, and hemoglobin in the control group were significantly higher than in the case group (P < .001 for all). On the other hand, the mean levels of malondialdehyde (MDA) and white blood cells (WBCs) in the case group were significantly higher than in the control group (P < .001 for all). In addition, in the case group, the mean serum levels of all types of pesticides (diazinon, alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) were significantly higher compared to the control group (P < .001 for all) (Table 1).
Table 1.
The characteristics of the subjects in the ALL and control groups.
| Variables | ALL group (n = 136) | Control group (n = 136) | P-value |
|---|---|---|---|
| Age (years) | 16.13 ± 2.80 | 16.36 ± 2.12 | .46* |
| Sex (N) (%) | .38** | ||
| Male | 87 (64.0) | 80 (58.8) | |
| Female | 49 (36.0) | 56 (41.2) | |
| Residence (N) (%) | < .001** | ||
| North Kerman | 34 (25.0) | 77 (56.6) | |
| South Kerman | 102 (75.0) | 59 (43.4) | |
| Smoking (N) (%) | < .011** | ||
| Yes | 104 (76.5) | 65 (47.8) | |
| No | 32 (23.5) | 71 (52.2) | |
| Farmer-parents (N) (%) | < .001** | ||
| Yes | 108 (79.4) | 39 (28.7) | |
| No | 28 (20.6) | 97 (71.3) | |
| Gene methylation (N) (%) | < .001** | ||
| Yes | 106 (77.9) | 61 (44.9) | |
| No | 30 (22.1) | 75 (55.1) | |
| Stage of disease (N) (%) | – | – | |
| L1 | 34 (25.0) | ||
| L2 | 65 (47.8) | ||
| L3 | 37 (27.2) | ||
| BMI (kg/m2) | 19.68 ± 2.97 | 20.39 ± 3.71 | .08* |
| AChE (U/g Hb) | 4.90 ± 0.60 | 7.20 ± 1.43 | < .001* |
| Biochemical parameters | |||
| MDA (nmol/ml) | 2.12 ± 0.85 | 1.62 ± 0.53 | < .001* |
| Hemoglobin (g/dl) | 7.60 ± 1.86 | 12.94 ± 2.39 | < .001* |
| Platelets (× 106/mm3) | 72.16 ± 37.37 | 285.52 ± 61.10 | < .001* |
| WBC (× 106/mm3) | 40.85 ± 19.26 | 5.69 ± 1.20 | < .001* |
| Types of poisons | |||
| Diazinon (µg/ml) | 1.57 ± 0.38 | 0.39 ± 0.30 | < .001* |
| Alpha.HCH (ng/ml) | 2.09 ± 0.99 | 1.07 ± 0.63 | < .001* |
| Beta.HCH (ng/ml) | 1.14 ± 0.51 | 0.53 ± 0.35 | < .001* |
| Gama.HCH (ng/ml) | 0.54 ± 0.26 | 0.22 ± 0.17 | < .001* |
| For,for.DDE (ng/ml) | 2.25 ± 0.45 | 1.32 ± 0.25 | < .001* |
| Two,for.DDT (ng/ml) | 1.53 ± 0.41 | 1.21 ± 0.39 | < .001* |
Values are expressed as mean ± SD*. P < .05 was considered as significant using Independent t-test for comparison between the two groups. **. P < .05 was considered as significant using Chi-square test.
Abbreviation: ALL; acute lymphoblastic leukemia, BMI; body mass index, MDA; malondialdehyde, WBC; white blood cells, DDE; dichlorodiphenyldichloroethylene, DDT; dichlorodiphenyltrichloroethane, and HCH; hexachlorocyclohexane.
According to the results of the linear regression test, in the ALL group, there was a significant inverse relationship between the diazinon, alpha.HCH, and DDT pesticides and AChE in the crude model (P < .05 for all). Moreover, after adjustment for confounding factors (age, sex, BMI, smoking, residence, stage of disease, and farmer parents), a significant inverse relationship was observed between pesticides (diazinon, alpha.HCH, and DDT) and AChE (P < .05 for all) (Table 2). In addition, a significant positive association was found between the diazinon and alpha.HCH pesticides and MDA in both the crude and adjusted models (P < .05 for all) (Table 2).
Table 2.
The association between types of poisons (independent variables) with the AChE and MDA (dependent variables) in the ALL group.
| Variables | AChE | MDA | ||||
|---|---|---|---|---|---|---|
| B (Unstandardized) | R 2 | P-value | B (Unstandardized) | R 2 | P-value | |
| Diazinon | ||||||
| Model 1a | −0.66 | 0.17 | <.001 | 1.08 | 0.23 | <.001 |
| Model 2b | −0.65 | 0.20 | <.001 | 1.06 | 0.27 | <.001 |
| Alpha | ||||||
| Model 1a | −0.11 | 0.03 | .03 | 0.25 | 0.09 | <.001 |
| Model 2b | −0.13 | 0.08 | .03 | 0.20 | 0.11 | .02 |
| Beta | ||||||
| Model 1a | −0.14 | 0.01 | .14 | 0.12 | 0.006 | .39 |
| Model 2b | −0.14 | 0.06 | .16 | 0.10 | 0.07 | .47 |
| Gama | ||||||
| Model 1a | −0.15 | 0.005 | .43 | 0.12 | 0.002 | .65 |
| Model 2b | −0.06 | 0.05 | .74 | 0.12 | 0.07 | .66 |
| ForforDDE | ||||||
| Model 1a | −0.19 | 0.02 | .08 | 0.19 | 0.01 | .23 |
| Model 2b | −0.18 | 0.07 | .10 | 0.13 | 0.07 | .42 |
| TwoforDDT | ||||||
| Model 1a | −0.43 | 0.09 | <.001 | 0.21 | 0.01 | .21 |
| Model 2b | −0.40 | 0.12 | .001 | 0.23 | 0.08 | .19 |
P < .05 was considered as significant. a. Model 1: linear regression analysis without adjustment, b. Model 2: linear regression analysis with adjustment for age, sex, BMI, smoking, residence, stage of disease, and farmer-parents.
As reported in Table 3 (control group), an inverse significant relationship was found between DDT and AChE in the crude model (P = .02). However, after adjusting for the effects of confounding factors (age, sex, BMI, smoking, residence, and farmer parents), this relationship became nonsignificant (P = .19) (Table 3). Moreover, in the control group, there was a positive significant association between the diazinon and alpha.HCH pesticides and MDA in both the crude and adjusted models (P < .05 for all) (Table 3).
Table 3.
The association between types of poisons (independent variables) with the AChE and MDA (dependent variables) in the control group.
| Variables | AChE | MDA | ||||
|---|---|---|---|---|---|---|
| B (Unstandardized) | R 2 | P-value | B (Unstandardized) | R 2 | P-value | |
| Diazinon | ||||||
| Model 1a | 0.10 | 0.001 | .78 | 0.39 | 0.05 | .009 |
| Model 2b | −0.07 | 0.08 | .87 | 0.54 | 0.09 | .004 |
| Alpha | ||||||
| Model 1a | −0.07 | 0.001 | .71 | 0.14 | 0.08 | .05 |
| Model 2b | −0.18 | 0.08 | .42 | 0.19 | 0.06 | .02 |
| Beta | ||||||
| Model 1a | −0.49 | 0.01 | .15 | 0.02 | <0.001 | .15 |
| Model 2b | −0.43 | 0.09 | .21 | 0.02 | 0.03 | .19 |
| Gama | ||||||
| Model 1a | −0.36 | 0.002 | .60 | 0.33 | 0.01 | .19 |
| Model 2b | −0.15 | 0.08 | .82 | 0.24 | 0.04 | .37 |
| ForforDDE | ||||||
| Model 1a | −0.24 | 0.002 | .61 | 0.10 | 0.003 | .56 |
| Model 2b | −0.07 | 0.08 | .87 | 0.09 | 0.04 | .63 |
| TwoforDDT | ||||||
| Model 1a | −0.69 | 0.03 | .02 | 0.13 | 0.01 | .25 |
| Model 2b | −0.43 | 0.09 | .19 | 0.14 | 0.04 | .24 |
P < .05 was considered as significant. a. Model 1: linear regression analysis without adjustment, b. Model 2: linear regression analysis with adjustment for age, sex, BMI, smoking, residence, and farmer parents.
Odds ratios (95% CI) for ALL, with types of pesticides, gene methylation, and MDA as independent variables among individuals, are reported in Table 4. In the crude model, there was a significant increase in the risk of ALL with the increase of diazinon, alpha.HCH, beta.HCH, gamma.HCH, DDE, DDT, gene methylation, and MDA levels (OR; 4.94, 95% CI: 2.53–9.62, OR; 1.16, 95% CI: 1.11–1.21, OR; 1.32, 95% CI: 1.24–1.42, OR; 1.75, 95% CI: 1.53–2.00, OR; 1.70, 95% CI: 1.52–1.91, OR; 1.25, 95% CI: 1.16–1.35, OR; 4.34, 95% CI: 2.56–7.36, and OR; 2.18, 95% CI: 1.87–4.22, respectively). Moreover, after adjusting for confounding factors (age, sex, BMI, smoking, residence, and farmer parents), pesticides including diazinon, alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT, gene methylation, and MDA levels showed a significant association with an increased risk of ALL (P < .001 for all, except for gene methylation P = .03). In both crude and adjusted models, diazinon showed the highest odds ratios for ALL (Table 4).
Table 4.
Odds ratios (95% CI) for ALL according to the types of poisons, gene methylation, and MDA.
| Variables | B | OR (CI) | *P-value |
|---|---|---|---|
| Diazinon | |||
| Model 1a | 1.59 | 4.94 (2.53–9.62) | <.001 |
| Model 2b | 1.67 | 5.32 (2.49–11.38) | <.001 |
| Alpha | |||
| Model 1a | 0.15 | 1.16 (1.11–1.21) | <.001 |
| Model 2b | 0.11 | 1.11 (1.06–1.17) | <.001 |
| Beta | |||
| Model 1a | 0.28 | 1.32 (1.24–1.42) | <.001 |
| Model 2b | 0.30 | 1.35 (1.24–1.48) | <.001 |
| Gama | |||
| Model 1a | 0.56 | 1.75 (1.53–2.00) | <.001 |
| Model 2b | 0.55 | 1.73 (1.48–2.03) | <.001 |
| ForforDDE | |||
| Model 1a | 0.53 | 1.70 (1.52–1.91) | <.001 |
| Model 2b | 0.53 | 1.70 (1.49–1.95) | <.001 |
| TwoforDDT | |||
| Model 1a | 0.22 | 1.25 (1.16–1.35) | <.001 |
| Model 2b | 0.28 | 1.32 (1.19–1.46) | <.001 |
| Gene methylation | |||
| Model 1a | 1.46 | 4.34 (2.56–7.36) | <.001 |
| Model 2b | 0.72 | 2.06 (1.06–4.00) | .03 |
| MDA | |||
| Model 1a | 1.03 | 2.18 (1.87–4.22) | <.001 |
| Model 2b | 0.95 | 2.58 (1.57–4.24) | <.001 |
* P < .05 statistically significant by multivariable logistic regression. a. model 1: unadjusted. b. model 2: adjusted for age, sex, BMI, smoking, residence, and farmer-parents.
The risk of ALL development based on exposure to the combination of diazinon and other pesticides and gene methylation is reported in Table 5. In both crude and adjusted models, the combination of diazinon and alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT pesticides significantly increased the risk of ALL (P < .05 for all). Moreover, the combination of diazinon and gene methylation significantly increased the risk of ALL in both crude and adjusted models (OR; 8.89, 95% CI: 2.95–16.71, OR; 9.13, 95% CI: 2.55–12.60, respectively). Based on the results, exposure to a combination of diazinon and other pesticides (alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) and gene methylation increased the odds ratio of ALL development more than each of them alone. In both crude and adjusted models, the combination of diazinon and gene methylation presented the highest odds ratios for ALL (Table 5).
Table 5.
Odds ratios (95% CI) for ALL according to the combination of poisons and gene methylation.
| Variables | B | OR (CI) | *P-value |
|---|---|---|---|
| Diazinon and alpha | |||
| Model 1a | 1.61 | 5.00 (2.56–9.77) | <.001 |
| Model 2b | 1.68 | 5.39 (2.51–11.55) | <.001 |
| Diazinon and beta | |||
| Model 1a | 1.45 | 4.29 (2.22–8.30) | <.001 |
| Model 2b | 1.69 | 5.43 (2.25–13.12) | <.001 |
| Diazinon and gama | |||
| Model 1a | 1.52 | 4.60 (2.24–9.44) | <.001 |
| Model 2b | 1.86 | 6.48 (2.40–7.47) | <.001 |
| Diazinon and ForforDDE | |||
| Model 1a | 1.58 | 4.89 (1.83–10.01) | .001 |
| Model 2b | 2.86 | 17.49 (1.63–18.25) | .01 |
| Diazinon and TwoforDDT | |||
| Model 1a | 1.56 | 4.77 (2.48–9.19) | <.001 |
| Model 2b | 1.88 | 6.58 (2.63–16.46) | <.001 |
| Diazinon and gene methylation | |||
| Model 1a | 2.18 | 8.89 (2.95–16.71) | <.001 |
| Model 2b | 2.21 | 9.13 (2.55–12.60) | .001 |
* P < .05 statistically significant by multivariable logistic regression. a. model 1: unadjusted. b. model 2: adjusted for age, sex, BMI, smoking, residence, and farmer parents.
The findings related to the association of the combination of two pesticides and gene methylation with the risk of ALL are presented in Table 6. In the crude model, after adjusting for the effects of confounding factors (age, sex, BMI, smoking, residence, and farmer parents), the combination of diazinon and other pesticides (alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) and gene methylation significantly increased the risk of ALL (P < .05 for all). The inclusion of gene methylation to the combination of pesticides (diazinon with alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) increased the odds ratio of ALL development compared to the pesticide–pesticide combination. In addition, after adjusting for the effects of confounding factors, the combination of diazinon with alpha.HCH and gene methylation exhibited the highest odds ratio for ALL (OR; 22.46, 95% CI: 11.15–36.58) (Table 6).
Table 6.
Odds ratios (95% CI) for ALL according to the combination of poisons and gene methylation.
| Variables | B | OR (CI) | *P-value |
|---|---|---|---|
| Model 1a | |||
| Model 2b | 2.44 | 11.50 (5.55–11.79) | .001 |
| 3.11 | 22.46 (11.15–36.58) | .04 | |
| Diazinon and beta and gene methylation | |||
| Model 1a | 1.95 | 7.05 (2.50–9.89) | <.001 |
| Model 2b | 2.18 | 8.84 (2.42–10.99) | .01 |
| Diazinon and gama and gene methylation | |||
| Model 1a | 1.90 | 6.74 (2.40–10.92) | <.001 |
| Model 2b | 1.91 | 6.80 (2.18–11.21) | .001 |
| Diazinon and ForforDDE and gene methylation | |||
| Model 1a | 2.56 | 13.00 (1.26–13.86) | .03 |
| Model 2b | 2.70 | 14.94 (1.03–21.47) | .04 |
| Diazinon and TwoforDDT and gene methylation | |||
| Model 1a | 2.11 | 8.26 (2.84–13.96) | <.001 |
| Model 2b | 1.97 | 7.17 (2.36–11.76) | .001 |
* P < .05 statistically significant by multivariable logistic regression. a. model 1: unadjusted. b. model 2: adjusted for age, sex, BMI, smoking, residence, and farmer parents.
In Table 7, the odds ratios for gene methylation according to the types of pesticides (diazinon with alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) and MDA levels are reported. In the crude model, the risk of gene methylation significantly enhanced with an increase in the levels of all pesticides (diazinon with alpha.HCH, beta.HCH, gamma.HCH, DDE, and DDT) and MDA (P < .001 for all, except for DDT, P = .009). After adjustment for confounding factors, it was shown that diazinon, alpha.HCH, and MDA levels significantly increased the risk of gene methylation (OR; 1.12, 95% CI: 1.03–1.21, OR; 1.45, 95% CI: 1.29–1.65, OR; 2.71, 95% CI: 1.29–5.70, respectively) (Table 7).
Table 7.
Odds ratios (95% CI) for gene methylation according to the types of poisons and MDA.
| Variables | B | OR (CI) | *P-value |
|---|---|---|---|
| Diazinon | |||
| Model 1a | 0.21 | 1.24 (1.17–1.31) | <.001 |
| Model 2b | 0.11 | 1.12 (1.03–1.21) | .003 |
| Alpha | |||
| Model 1a | 0.40 | 1.49 (1.35–1.63) | <.001 |
| Model 2b | 0.37 | 1.45 (1.29–1.65) | <.001 |
| Beta | |||
| Model 1a | 0.09 | 1.10 (1.04–1.15) | <.001 |
| Model 2b | 0.008 | 1.00 (0.93–1.08) | .83 |
| Gama | |||
| Model 1a | 0.17 | 1.18 (1.07–1.30) | <.001 |
| Model 2b | −0.02 | 0.97 (0.84–1.13) | .77 |
| ForforDDE | |||
| Model 1a | 0.09 | 1.10 (1.05–1.15) | <.001 |
| Model 2b | −0.02 | 0.98 (0.92–1.04) | .52 |
| TwoforDDT | |||
| Model 1a | 0.08 | 1.08 (1.02–1.15) | .009 |
| Model 2b | 0.02 | 1.02 (0.93–1.13) | .56 |
| MDA | |||
| Model 1a | 1.04 | 2.83 (1.81–4.42) | <.001 |
| Model 2b | 1.00 | 2.71 (1.29–5.70) | .008 |
* P < .05 statistically significant by multivariable logistic regression. a. model 1: unadjusted. b. model 2: adjusted for age, sex, BMI, smoking, residence, and farmer parents.
Discussion
Methylation of promoter regions, especially in CpG islands, which are rich in cytosine–guanine sequences, is a major epigenetic mechanism in the steady silencing of tumor suppressor and DNA repair genes. Under physiological conditions, these regions remain unmethylated to allow normal gene expression. However, in many carcinogenesis processes, they undergo aberrant methylation, which changes the structure of chromatin and prevents the binding of transcription factors, resulting in the silencing of protective genes [26].
In this study, patients with ALL exhibited significantly higher MGMT promoter methylation levels than the controls, which was in agreement with the results of a study by Takeuchi et al. [21]. Their study indicated that aberrant MGMT gene methylation was more prevalent in ALL patients, particularly in more advanced stages of the disease. Moreover, a study by Zhang et al. [27] reported that MGMT methylation is linked to lower DNA repair capacity, which can cause genomic instability and accelerated carcinogenesis. Similarly, Sobieszkoda et al. [28] demonstrated that methylation of the MGMT gene promoter can be related to patient age and worse prognosis in acute leukemia. These findings emphasize the significance of studying the epigenetic status of the MGMT gene in understanding the molecular pathways implicated in genomic vulnerability and ALL progression. Given that aberrant methylation of this gene can serve as an efficient biomarker for early detection and targeted therapeutic approaches, identifying environmental factors impacting this process, namely diazinon exposure, is of great importance.
In this regard, the findings of this study revealed that diazinon concentration in the sera of ALL patients was significantly increased compared to that of the control group, and this increase was related to a higher methylation level in the MGMT gene promoter. This relationship remained even after adjusting for confounding factors such as age, sex, BMI, history of farming, and smoking. Notably, the combination of elevated diazinon concentrations with other pesticides alongside MGMT methylation had the highest odds ratio for ALL development, suggesting the synergistic effect of environmental and epigenetic factors in the pathophysiology of this disease.
Multiple investigations have reported that diazinon, which is one of the most commonly used organophosphate pesticides, can promote epigenetic alterations by triggering oxidative stress, interfering with the activity of methylation enzymes, and changing the expression of genes involved in DNA repair. A study by Mankame et al. [29] indicated that it lowered the DNA repair capacity and changed the expression of crucial cellular genes, which could predispose to mutagenesis and carcinogenesis. In addition, a study by Zhang et al. [14] using a K562 cell model showed widespread alterations in the methylation pattern of more than 1000 promoter regions of cancer-related genes as a result of diazinon exposure. These findings demonstrated that diazinon can contribute to the epigenetic silencing of protective genes, including MGMT, and affect the progression of hematological malignancies. Moreover, a study by Golestanian et al. [30] conducted on human gastric AGS cells reported that exposure to diazinon significantly enhanced the methylation of the CYP3A4 gene promoter and increased its expression, offering further evidence for the epigenetic role of diazinon in carcinogenesis mechanisms. However, it is worth noting that several other studies have reported conflicting results. For instance, Kovalchuk et al. [31] demonstrated that chronic low-dose exposure to radiation did not significantly impact MGMT gene methylation in muscle and liver tissues, which might suggest a tissue-specific response in epigenetic regulation. Furthermore, Zhang et al. [14] showed that despite the effect of diazinon on extensive gene methylation, it had no direct impact on the MGMT gene, which might be attributed to differences in target cell type or tissue sensitivity. On the other hand, a study conducted by Zhang et al. [32] in Chinese miners indicated that long-term exposure to radon gas was linked to a significant rise in promoter methylation of tumor suppressor genes, such as MGMT. Although this study was carried out in a different context than pesticides and in a specific population, it suggests that alterations in the methylation of protective genes might be a common reaction to various environmental factors. These disparities emphasize the importance of tissue-specific and population-based studies in epigenetic investigations.
In addition to diazinon, other compounds, including organochlorine pesticides such as DDT, DDE, alpha-HCH, and gamma-HCH, have been associated with elevated methylation levels of the MGMT gene promoter and the risk of hematological malignancies in different studies [33]. For example, the study by Rafeeinia et al. [15] reported that in children with ALL, higher serum levels of these pesticides were associated with a significant increase in MGMT methylation. Moreover, the study by Yousefi et al. [34] in glioma patients showed that high concentrations of gamma-HCH, DDE, and DDT were linked to higher MGMT gene promoter methylation. These results, along with the findings of the present study, which reported elevated levels of diazinon and its synergistic effect with MGMT methylation in raising the risk of ALL development, highlight the combined role of environmental and epigenetic factors in the pathology of this disease. The combination of diazinon with other pesticides and MGMT promoter methylation exhibited the highest odds ratio for ALL, which may indicate a complex interplay between environmental exposures and epigenetic modifications in carcinogenesis [35].
In addition to epigenetic evidence, biochemical results in this research also support the role of diazinon in the pathophysiology of ALL. The significant decline in AChE activity in patients, which is one of the indicators resulting from exposure to organophosphate pesticides, especially diazinon, suggests enzymatic inhibition by this compound. Zhao et al. [36] also confirmed the inhibitory effect of AChE by diazinon even at minimal doses using human kinetic modeling. Furthermore, the higher MDA levels in patients may indicates oxidative stress, which can cause DNA damage and induce aberrant methylation of tumor suppressor genes [37].
Several investigations have reported that occupational pesticide exposure, especially among farmers, can result in significant alterations in the methylation pattern of tumor suppressor genes, such as MGMT. An agricultural health study found that individuals over 59 years of age exposed to high-dose pesticides exhibited reduced MGMT promoter methylation in specific CpG island regions [38]. However, some other studies have demonstrated conflicting results. For example, the study by Yousefi et al. [34] reported elevated MGMT methylation in response to pesticide exposure. Overall, the findings of the present research indicated that diazinon might play a role in elevating the risk of ALL development through epigenetic pathways, including MGMT promoter methylation [39]. These effects can vary based on factors such as cell tissue type (blood, brain, or liver), pesticide dose and composition, exposure duration, or genetic and epigenetic differences among individuals. Therefore, it is apparent that the effect of pesticides on the methylation of protective genes is a multifactorial and complex process, which underscores the need for comprehensive research across diverse populations and tissue models.
Conclusion
The findings of the present study revealed that exposure to the organophosphate pesticide diazinon is associated with a significant increase in methylation levels of MGMT gene promoter in children with ALL. This epigenetic alteration, which is likely driven by the induction of oxidative stress, suppression of DNA repair enzymes, and impairment of methylation pathways, can reduce MGMT expression and ultimately enhance genomic instability. The results also indicated that the combination of diazinon with other pesticides and MGMT methylation synergistically raised the risk of ALL development, underscoring the crucial role of the interactions between environmental factors and epigenetic mechanisms in the pathophysiology of this disease.
These findings highlight the significance of monitoring pesticide exposure, particularly in at-risk groups such as children in agricultural areas. They also suggest that MGMT methylation could be used as a potential biomarker for screening, prognosis, and development of targeted therapeutic interventions for pediatric leukemia. It is suggested that given the complexity of epigenetic mechanisms and variations in cellular responses based on tissue type and genetic background, larger studies in diverse populations and biological models are necessary to more accurately understand the effects of pesticides on gene methylation. In addition, techniques such as sequencing, real-time PCR, High Resolution Melting (HRM), etc. can be used to accurately and quantitatively examine the promoter methylation levels of leukemia-related genes.
Materials and methods
Participants
This case-control study was performed on 136 children and adolescents with ALL as the patient group and 136 healthy individuals as the control group.
Nonrandom and accessible sampling was conducted from patients referred to the oncology departments of Bahonar and Afzalipour Hospitals affiliated with Kerman University of Medical Sciences. ALL diagnosis was based on the results of specialized blood and bone marrow tests and confirmation by a pediatric oncologist. The mean age of participants was 16.13 ± 2.80 years in the patient group and 16.36 ± 2.12 years in the control group. The ratio of boys to girls was 64% to 36% in the ALL group and 58.8% to 41.2% in the control group. After the confirmation of the diagnosis, whole blood samples were collected from the patients and transferred to the Clinical Biochemistry Department of Sirjan Medical School for testing. The control group comprised individuals who were referred to the general clinics at the same hospitals for medical concerns unrelated to cancer. These individuals were enrolled in the study after a physician’s health assessment and review of standard medical tests. The participants in the control group had no history of chronic or underlying conditions. During sample selection, an attempt was made to match cases and controls in terms of confounding variables such as age, gender, and BMI. This study was performed after obtaining written informed consent from all participants (or their parents/legal guardians) and after approval by the Research Ethics Committee of Sirjan University of Medical Sciences (ethics code IR.SIRUMS.REC.1403.006).
Sampling
Briefly, 5 ml of venous blood was obtained from each participant under sterile conditions and in accordance with aseptic principles. Then, 2 ml of the blood sample was aliquoted into tubes containing the anticoagulant Ethylenediaminetetraacetic acid (EDTA) and utilized to separate peripheral blood nucleated cells for molecular analyses, including DNA extraction, assessment of the methylation status of the MGMT gene promoter, and determination of gene expression levels. The samples were immediately stored at −80°C to prevent the degradation of genetic material. The remaining 3 ml of the blood sample was transferred into a tube without anticoagulant, left at room temperature for a few minutes, and subsequently centrifuged at 700 × g for 7 min. The isolated serum was aliquoted into sterile tubes and kept at −80°C until analysis. The resulting sera were employed to determine the concentration of the pesticide diazinon and to assess biochemical markers including the AChE enzyme activity and malondialdehyde (MDA) levels.
Measurement of pesticides
To measure pesticide concentrations in serum samples, the headspace solid-phase microextraction method was utilized along with gas chromatography–mass spectrometry (GC–MS). This approach was found to be suitable for measuring organophosphate pesticides as a result of its high sensitivity and the use of a small sample volume. In this method, polydimethylsiloxane SPME fiber was chosen and exposed to the headspace of the sample for 30 min at 60°C to extract diazinon from the sample. The extracted compounds were analyzed using an HP 5890 Series II Plus GC–MS. Chromatographic conditions included an initial column temperature of 70°C with a gradual temperature gradient, helium carrier gas, and a column with an inactive phase. Mass analysis was conducted based on indicator ions m/z = 137, 152, 179, and 199. The method’s detection limit was within the range of 0.01 to 0.3 µg/mL, and the total analysis time for each sample was about 44 min. Standard calibration solutions and control samples were utilized to maintain the accuracy and precision of the experiment [40].
DNA extraction and quality evaluation
In this study, genomic DNA was extracted from EDTA-containing peripheral blood samples using the salting-out method with modifications in the cell lysis step [41]. Since DNA only exists in nucleated cells, red blood cells were first eliminated from the samples using cold distilled water or lysis buffer, and WBCs were utilized for extraction. This step was performed by inducing an osmotic shock followed by centrifugation multiple times until the final precipitate became fully colorless. After that, nuclear lysis buffer with sodium dodecyl sulfate solution and proteinase K enzyme were added to the remaining cells at a temperature of 56°C–65°C for about 2 h for the degradation of proteins and disruption of cell membranes. Then by adding saturated NaCl solution and incubation on ice, the soluble proteins were precipitated and isolated via centrifugation. The clear liquid at the top of the tube, which contained DNA, was transferred to a new tube and DNA was precipitated by adding cold isopropanol or absolute ethanol. Afterward, the DNA precipitate was rinsed with 75% ethanol and dried at room temperature or 56°C to evaporate the alcohol residue. Finally, the DNA was slowly dissolved in deionized water or Tris–EDTA (TE) buffer and kept at −20°C. To verify sample quality, a Nanodrop device was employed to evaluate the optical absorption ratio (A260/A280). A ratio between 1.8 and 2.0 demonstrates the desired DNA purity. Moreover, the integrity of the DNA structure was assessed using 1.5% agarose gel electrophoresis, and clear bands confirmed the suitable quality of the extracted DNA.
Assessment of MGMT gene promoter methylation using methylation-specific polymerase chain reaction
To evaluate the methylation status in the MGMT gene promoter region, the extracted genomic DNA initially underwent sodium bisulfite (3 M) treatment. In this process, 1–2 μg of DNA was denatured with NaOH solution (3.5 µl) for 10 min at 40°C. Next, hydroquinone (10 mM) and sodium bisulfite were added, and the solution was incubated with mineral oils for 1 h at 95°C. After the treatment, DNA purification was conducted using the DNA Purification Kit (Fermentas). Then, it was neutralized by adding NaOH (3M) and ammonium acetate (5M), precipitated in ethanol, and finally reconstituted in TE buffer (pH 8.0). To perform methylation-specific polymerase chain reaction (MSP), two pairs of primers specific for methylated and unmethylated sequences of the MGMT gene were employed. PCR reactions were carried out in a final volume of 50 µl containing 2 µl of DNA, 5 µl of PCR buffer, 2.5 mM MgCl₂, 1 µl of dNTPs, 0.4 µl of SmartTek (2 U), and specific primers. To boost sensitivity, nested PCR was performed first, and then methylated and unmethylated PCR reactions were conducted separately. PCR products were loaded on 1.5% agarose gel containing DNA dye and visualized using a UV system. Commercial methylated DNA (InterGEN) was utilized as a positive control, and a reaction without DNA was used as a negative control.
Assessment of MGMT gene expression using real-time PCR
To examine the expression levels of the MGMT gene, total RNA was initially extracted from peripheral blood mononuclear cells and converted to complementary DNA using a cDNA synthesis kit. Gene expression was determined using the real-time PCR approach and the StepOne™ Real-Time PCR System (Applied Biosystems). The fluorescent dye SYBR Green I was used as a double-stranded DNA marker. To strengthen the reaction and improve the accuracy of the analysis, RealQ Plus 2X Master Mix Green, High ROX (Ampliqon, Denmark) was employed, which is specifically designed for SYBR Green-based assays. The β-actin gene was used as a reference gene (housekeeping gene) for data normalization. All reactions were carried out in a volume of 20 μl under standard thermal conditions, including initial denaturation at 95°C, followed by 40 cycles of 95°C for 15 s, and annealing/extension temperatures specific for each gene for 30 s. The data were analyzed using the 2^−ΔΔCt method, which is a validated approach for comparing the relative expression of genes under various biological conditions [42].
Measurement of AChE activity and MDA levels
To evaluate the effects of oxidative stress and possible neurotoxicity induced by exposure to diazinon, two biomarkers, including AChE activity and MDA concentrations, were measured in the serum samples of the participants.
AChE activity in red blood cells was measured using the Ellman colorimetric method, which is a conventional and extensively used approach for evaluating this enzyme [43]. In this method, acetylthiocholine iodide as a substrate is hydrolyzed by the AChE enzyme to thiocholine and acetyl iodide. The released thiocholine subsequently reacts with 5,5’-dithiobis-2-nitrobenzoic acid (DTNB) to produce a yellow compound known as 5-thio-2-nitrobenzoic acid with a maximum optical absorption at a wavelength of 410 nm. The elevation in optical absorption at this wavelength is regarded as proportional to the activity of the enzyme in the sample. The required solutions, namely phosphate buffer with adjusted pH (7.6), substrate solution, and DTNB, were prepared following standard guidelines, and the reactions were performed at room temperature. A ready-made solution of hyamine 1622 was utilized to halt the reaction. The results were expressed as changes in optical absorption per unit time.
MDA levels, as an indicator of lipid peroxidation, were determined using thiobarbituric acid (TBA) according to the method described by Ohkawa et al. In this method, MDA reacts with TBA and produces a pink complex that is absorbed at a wavelength of 532 nm. The MDA concentration was estimated based on optical absorption using a standard curve and expressed in nmol/ml.
Statistical analysis
Statistical analysis of the data was carried out using SPSS software version 23. In order to examine the normality of the distribution of quantitative variables, the Kolmogorov–Smirnov test was utilized. To compare variables with a normal distribution between the two groups (patients and controls), an independent t-test was used, and for non-normal variables, the Mann–Whitney U test was employed. The Chi-square test was used to analyze qualitative variables. For evaluating the association between pesticide levels and methylation status, logistic regression analysis was applied. The statistical significance level for all tests was considered less than 0.05 (P < .05).
Acknowledgements
We extend our sincere gratitude to all those who helped us in this research, especially the Student Research Committee of the Sirjan Faculty of Medical Sciences for their financial support of this project. This study received approval from the Ethics Committee of Sirjan University of Medical Sciences, Iran (ethics approval number IR.SIRUMS.REC.1403.006).
Contributor Information
Arash Rafeeinia, Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran; Department of Laboratory Sciences, Sirjan School of Medical Sciences, Sirjan, Iran.
Mehrnaz Karimi Darabi, Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran.
Reza Sadeghi, Department of Public Health, Sirjan School of Medical Sciences, Sirjan, Iran.
Hadi Bazyar, Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran.
Fatemeh Saeed, Student Research Committee, Sirjan School of Medical Sciences, Sirjan, Iran.
Mostafa Dianati, Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Malaysia; Department of Complex Genetics and Epidemiology, School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands.
Author contributions
Arash rafeeinia (Investigation [equal], Supervision [equal]), Mehrnaz Karimi Darabi (Data curation [equal]), Hadi Bazyar (Methodology [equal]), Reza Sadeghi (Visualization [equal]), Fatemeh Saeed (Writing – original draft [equal]), Mostafa Dianati (Methodology [equal]).
Conflicts of interest
None declared.
Funding
None declared.
Data availability
Data will be made available on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.