Interaction Between Genetic Variants and Serum Levels of Organochlorine Pesticides Contributes to Parkinson’s Disease

Background: There is evidence that genetic and environmental factors contribute to the onset and progression of Parkinson’s disease (PD). Pesticides are a class of environmental toxins that are linked to increased risk of PD. However, few studies have investigated the interaction between specic pesticides and genetic variants related to PD in the Chinese population. Methods: In this cross-sectional study, 19 serum levels of pesticides were measured. In addition, we also analyzed the interaction between specic pesticides and candidate genetic variants for PD. Finally, we investigated the mechanistic basis for the association between pesticides and increased risk of PD. Results: Serum levels of organochlorine pesticides including α-hexachlorocyclohexane (α-HCH), β-HCH, γ-HCH, δ-HCH, propanil, heptachlor, dieldrin, hexachlorobenzene, p,p’-dichlorodiphenyltrichloroethane (p,p’-DDE) and o,p’-dichloro-diphenyl-trichloroethane (o,p’-DDT) were higher in PD patients than in controls. α-HCH and propanil levels were associated with increased PD risk. Serum levels of dieldrin were associated with Hamilton Depression Scale and Montreal Cognitive Assessment scores in PD patients. The interaction between rs11931074 in α-synuclein (SNCA) and α-HCH or β-HCH, respectively, as well as rs16940758 in the microtubule-associated protein tau (MAPT) gene and δ-HCH were related to increased risk of PD. In addition, α-HCH and propanil enhanced the production of reactive oxygen species and decreased of mitochondrial membrane potential. Propanil but not α-HCH induced the aggregation of α-synuclein. Conclusions: Elevated serum levels of α-HCH and propanil are associated with increased risk of PD. Serum levels of dieldrin were associated and cognitive function in PD patients. The interaction between genetic variants and pesticides °C detection. The uorescence intensity of JC-10 was measured by ow cytometer with excitation and emission wavelengths of 490 and 530 nm to detect monomers of JC-10 and excitation and emission wavelengths of 525 and 590 nm to detect polymers of JC-10 respectively. ΔΨm was calculated by normalizing red uorescence to green uorescence signal intensity values. to estimate odds ratios (ORs) and their 95% condence intervals (CIs) for the association between serum pesticide levels and PD diagnosis. Multiplicative interactions between serum levels of pesticides and genotypes were tested using a logistic regression model with covariate adjustment. One-way ANOVA and post hoc corrections (Bonferroni) was used for comparisons of clinical characteristics in PD patients with different serum levels of pesticides and multiple PD, disease; Con,

In this study, we intended to recruit PD patients and their healthy spouses without a history of occupational pesticide exposure from urban and rural areas. We measured the serum levels of 19 commonly used pesticides including 16 OCPs and 3 OPs in the participants, and examined candidate gene loci in genes related to PD and pesticides transportation and metabolism to evaluate the contribution of the interaction between gene polymorphisms and pesticides to PD risk. Finally, we investigated the potential mechanisms of OCPs in neurons to determine how these agents may contribute to the development of PD.

Study population
A total of 90 PD patients from the Movement Disorder Clinic at the Department of Neurology of Ruijin Hospital (urban area) and the First People's Hospital of Tonglu (rural area) from September 2016 to September 2017. Healthy spouses with whom they had been living in the same household for at least 20 years were recruited for this hospital-based case-control study. All PD patients were examined by at least two movement disorder specialists at the Department of Neurology of Ruijin Hospital.
Inclusion criteria were as follows: (1) diagnosed as idiopathic PD according to 2015 MDS clinical diagnostic criteria for PD [18]; (2) no family history of PD extending to the rst-degree relatives; and (3)
The residue was resuspended in 100 µl of methylene chloride and detected with a Model TSQ8000 spectrometer (Thermo Fisher Scienti c, Waltham, MA, USA). The limit of detection (LOD) for the pesticides was appropriately 0.1 ng/ml. For quality control, ve blood samples in triplicate were spiked with a mixed standard of OCPs at 5 and 50 ng/ml. The average recoveries of samples exceeded 95%. PD patients and control samples were analyzed in the same batch. To maintain accuracy, a quality check sample was included in each set of samples. The results were adjusted for the total serum lipid content and were reported as nanogram per gram lipid. Total lipids in plasma were calculated as previously described [10].

Genetic analysis
Genomic DNA was prepared from peripheral blood leukocytes by the conventional phenol/chloroform extraction method. We used MassARRAY Assay Design 3.0 software (Sequenom, San Diego, CA, USA) to design Multiplexed single nucleotide polymorphism (SNP) MassEXTEND assay. Candidate SNPs in PD-related genes including rs11931074 and rs3775423 in SNCA gene, rs16940758 and rs2435211 located in microtubule-associated protein tau (MAPT) gene and rs733731 in peptidoglycan recognition protein 2 (PGLYRP2) gene were analyzed, along with SNPs located in genes related to pesticides transportation and metabolization, including rs1045642 in ABCB1, rs12829185 in NOS1, rs4646903 in cytochrome P450, family 1, subfamily A, polypeptide 1 (CYP1A1), rs1056836 in CYP1B1 and rs7260538 in CYP2B6 gene. SNP genotyping was performed by the chipbased matrix-assisted laser desorption ionization time-of-ight mass spectrometry method using the Mass-ARRAY RS1000 platform (Sequenom, San Diego, USA) [19]. The corresponding primers used for each SNP are listed in

Measurement of reactive oxidative stress
Reactive oxidative stress (ROS) was detected with chloromethyl derivative of dichlorodihydro uorescein diacetate (CM-H2DCFDA, Invitrogen, USA), which are readily taken up by cells. The probe was diluted to a nal concentration of 2 µM with dimethylsulfoxide (DMSO). After different treatments, cells were incubated with CM-H2DCFDA at 37 °C for 30 min before detection by ow cytometer at excitation and emission at wavelengths of 485 nm and 535 nm, respectively.

Measurement of mitochondrial membrane potential (ΔΨm)
Mitochondrial membrane potential (ΔΨm) was measured using uorescent probe JC-10 (Yeasen Technology, China). At a high potential, JC-10 polymers emit red uorescence, while at low potential, JC-10 exists as a monomer and is detectable based on green uorescence. After different treatments, cells were incubated with10 µM JC-10 probe at 37 °C for 30 min before detection. The uorescence intensity of JC-10 was measured by ow cytometer with excitation and emission wavelengths of 490 and 530 nm to detect monomers of JC-10 and excitation and emission wavelengths of 525 and 590 nm to detect polymers of JC-10 respectively. ΔΨm was calculated by normalizing red uorescence to green uorescence signal intensity values.

Western blot analysis
Cells with different treatments were lysed in radioimmunoprecipitation lysis buffer (50 mM Tris-HCl [pH 8.0], 1% NP-40, 0.5% sodium deoxycholate, 150 mM NaCl, 0.1% sodium-dodecyl sulfate containing protease inhibitor cocktails and 1 mM phenylmethylsulfonyl uoride). Proteins were separated by polyacrylamide gel electrophoresis and transferred onto polyvinylidene uoride membranes. The membranes were blocked with 5% bovine serum albumin for 2 h at room temperature and incubated overnight at 4 °C with primary antibodies (β-actin, Sigma; α-synuclein, BD). After three washes in Tris-buffered saline with 0.1% Tween-20 (TBST), the membrane was incubated with appropriate horseradish peroxidase-conjugated secondary antibodies (Jackson Laboratories) at 25 °C for 1 h. After being washed three times with TBST, protein bands were visualized using an enhanced chemiluminescence detection system.

Statistical analysis
All statistical analysis were conducted with SPSS software Version 21.0 (SPSS Inc., USA), STATA (Version 15) and GraphPad Prism 5.0 software. The Hardy-Weinberg equilibrium χ 2 test was used to assess the goodness of t for each SNP data. The χ 2 test was used to analyze categorical variables including sex, genotype, allele distribution and the detection rate of pesticides.
For samples with non-detectable pesticides levels, we used a value equal to half of the LOD (0.05 ng/mL) as input [20]. The Mann-Whitney U test was used to evaluate the differences in pesticide levels between PD patients and controls. Logistic regression analysis was performed to estimate odds ratios (ORs) and their 95% con dence intervals (CIs) for the association between serum pesticide levels and PD diagnosis. Multiplicative interactions between serum levels of pesticides and genotypes were tested using a logistic regression model with covariate adjustment. One-way ANOVA and post hoc corrections (Bonferroni) was used for comparisons of clinical characteristics in PD patients with different serum levels of pesticides and multiple Page 5/20 experimental conditions. Multiple stepwise linear regression was used to assess the precise association between the pesticides levels and clinical characteristics. All analysis were 2-tailed, and the level of statistical signi cance was set at P < 0.05.

Results
Demographic and clinical characteristics of the study population A total of 90 idiopathic PD patients and their healthy spouses from the same household were enrolled in this study.
Demographic and clinical characteristics of the study population are shown in Table 1. No signi cant difference in mean age was observed between the patients with PD and the control groups (65.76 ± 9.90 vs control: 64.23 ± 9.14, P = 0.285); however, there were more male patients in the PD group than in control group (53 male /37 female vs 37 male/53 female, P = 0.017).
Among the subjects, 44 patients with PD and their spouses were from the urban area in Shanghai City, whereas the others were recruited from the rural area of Tonglu County in Hangzhou City. There were no differences in the age and sex distribution between PD patients and controls in these two subgroups. UPDRS scores were obtained during the on-phase at the outpatient clinic. *: P < 0.05.

Correlation between pesticides and clinical features of PD patients
We compared the difference in clinical characteristics of PD patients with different serum levels of pesticides. Although some of the clinical characteristics of PD patients varied among Tertile 1, 2 and 3 groups for a-HCH, β-HCH, hexachlorobenzene and o,p'-DDT, there was no signi cant association between these characteristics and serum levels of four pesticides (data not shown A multiple comparison test revealed that the HAMD score was signi cantly higher in the Tertile 3 group compared to Tertile1 and 2 groups (P = 0.004 and 0.017, respectively), while the MoCA score was lower in the Tertile 3 group than Tertile 1 group (P = 0.015). Meanwhile, multiple comparison test showed no differences between groups in terms of HAMA score. After controlling for age and sex, serum level of dieldrin was positively associated with HAMD score and negatively associated with MoCA score in PD patients (P < 0.001, P = 0.042).

Genotype and allele distributions of candidate SNPs in PD patients and controls
We analyzed the genotype and allele distributions of candidate SNPs located in PD-related genes and genes related to pesticides transportation and metabolism. All the observed genotype or allele frequencies did not differ from the expected frequencies according to Hardy-Weinberg equilibrium. There were no signi cant differences in the genotype or allele distributions of the 10 candidate SNPs between PD patients and controls (Table e-2 in Additional le 1).

Effect of interaction between genotype and pesticides on the PD risk
The potential interaction between serum levels of pesticides and the genotype of the candidate SNPs on PD risk was evaluated with an unconditional logistic regression model. After controlling for confounding factors such as sex, age and region, statistically signi cant interactions were found between rs11931074 polymorphism in the SNCA gene and α-HCH (OR: 2.08; 95% CI: 1.131-3.826; P = 0.018), between rs11931074 in SNCA gene and β-HCH (OR: 2.08; 95% CI: 1.003-3.218; P = 0.049), and between rs16940758 in MAPT and δ-HCH (OR: 2.48; 95% CI: 1.049-5.880 P = 0.039), causing an increased risk for PD.

Effect of α-HCH and propanil on oxidative injury and α-synuclein aggregation in neuronal cells
Given that α-HCH and propanil levels were associated with increased risk of PD, we investigated the molecular basis for this observation using SH-SY5Y cells. We found that cell viability was decreased in a dose-dependent manner upon α-HCH or propanil stimulation. The effective concentration to induce about 25% and 50% decrease in cell viability was about 700 and 1000 µM for α-HCH (Fig. A) and 100 and 200 µM for propanil (Fig. B), respectively. We found here that α-HCH and propanil increased the production of ROS (Fig. C, D) while decreasing the ΔΨm in neuronal cells (Fig. E, F).
We further investigated whether α-HCH or propanil had an effect on α-synuclein levels. Results showed that α-synuclein aggregation was unaffected by α-HCH in SH-SY5Y cells, but was induced in a dose-dependent manner by propanil ( Figure G, H).

Discussion
Few studies to date have explored the potential contribution of speci c pesticides and their interactions between pesticides and genetic variants to PD. We demonstrated here that serum levels of α-HCH and propanil were associated with increased risk of PD in the Chinese population. Genetic variants related to PD or pesticides transportation and metabolization were not associated with genetic susceptibility of PD, but the interaction between certain polymorphisms and pesticides (rs11931074 and α-HCH, rs11931074 and β-HCH, and rs16940758 and δ-HCH) were associated with increased risk of PD. There results provide rst demonstration in the Chinese population that α-HCH and propanil, as well as the interaction between speci c OCPs and genetic variants, increased risk for PD.
Although the production and use of many pesticides tested in this study, with the exception of propanil, vinclozolin, quintozene and phosalone, have been reduced or eliminated in China (http://www.icama.org.cn/), these compounds can still be detected in the environment, animal and human tissues [22][23][24][25].
In this study, we found that all the 19 pesticides including 16 OCPs and 3 OPs examined were detectable in the serum of participants. Of all, α-HCH, β-HCH, aldrin, and p,p'-DDE were more easily detected in PD patients compared to controls. Two studies to date have compared the detection of pesticides between PD patients and normal subjects. Richardson reported that only β-HCH was more detectable in PD patients [9], while Chhillar found that β-HCH, dieldrin and p,p'-DDE were more easily detected in PD patients [26]. We found that α-HCH, β-HCH, γ-HCH, δ-HCH, propanil, heptachlor, dieldrin, hexachlorobenzene, p,p'-DDE and o,p'-DDT levels were higher in PD patients than controls, which is partly consistent with previous results. To date, only four studies have investigated the difference in serum levels of pesticides between PD patients and controls. Three of them reported that β-HCH was higher in PD patients [9,27,28], another study conducted in India demonstrated β-HCH, dieldrin and p,p'-DDE were elevated in PD patients [26].
After adjusting for sex, age and region, α-HCH, β-HCH, δ-HCH, propanil, heptachlor, dieldrin, p,p'-DDE and o,p'-DDT were associated with an increased risk for PD. Two studies in US and one study in Faroe Islands found that elevated levels of β-HCH were associated with PD after adjusting for sex and age or smoking [9,27,28]. However, when pesticides were added as a confounder, only α-HCH and propanil were associated with PD in our study. Others have reported that only dieldrin [10] or β-HCH and dieldrin were associated with PD when pesticides were included as confounders in the analysis [26]. There are several possible reasons for the discrepancies in the results, such as different types and amount of pesticides that were used, racial and ethnic differences, and the fact that healthy spouses were recruited as controls in our study. Studies with larger sample sizes and across multiple centers are needed to explore the relationship between OCPs concentrations and PD risk in greater detail.
We also analyzed the association between the serum OCPs levels and clinical features of PD patients. Results showed that dieldrin was positively associated with the depression score in patients with PD. A study conducted in Korea demonstrated exposure to pesticides was a risk factor for depression [29]. A longitudinal study also found a link between exposure to dieldrin and depression [30]. We also observed that dieldrin was negatively associated with cognitive function. Although there were no reports of an association between dieldrin levels and MoCA score in PD patients, this compound has been linked to increased risk of AD [31,32]. It remains to be determined whether speci c pesticides, and especially dieldrin are associated with depression symptoms and cognitive function in PD.
Gene-environment interactions play an important role in PD [11,12]. We analyzed the effect of the interaction between the speci c pesticides that were present at elevated in PD patients in this study and genetic variants of PD-related genes and genes related to pesticides transportation and metabolization. Although we did not nd an association between 10 SNPs and the genetic susceptibility of PD, which might have been due to the small sample size, interactions between rs11931074 in SNCA gene and α-HCH or β-HCH, and MAPT rs16940758 and δ-HCH increased the risk for PD in this study. This provides the rst evidence of an interaction between pesticide levels and rs11931074 of the SNCA gene on PD risk, although such a relationship has been demonstrated for the rs3775423 polymorphism and pesticide exposure [33]. The MAPT gene primarily encodes Tau protein, which is implicated in PD [34,35]. A study demonstrated that interaction between MAPT rs16940758 or rs2435211 and exposure to pesticides had no signi cant effect on PD either [33]. ABCB1, also known as multidrug resistance protein 1, encodes a highly expressed P-glycoprotein on the blood-brain barrier, which helps to eliminate multiple toxins from brains [36,37]. ABCB1 rs1045642 and NOS1 rs12829185 were reported to interact with OCPs exposure to increase PD risk [38,39], which were not substantiated in our results. CYP1A1, CYP1B1 and CYP2B6 are members of the cytochrome P450 family and are mainly involved in drugs and neurotoxins metabolism [40,41], although no association between their interactions and PD has been demonstrated, probably due to the limited sample size in this study.
We investigated the potential mechanisms of α-HCH and propanil which were found to be related to PD risk. Results showed that both agents enhanced ROS production. This is in accordance with the nding that α-HCH increased the ROS generation in keratinocytes [42] and that propanil induced lipid oxidation in rat models [43]. Mitochondria are the main source of intracellular ROS, and the ΔΨ was closely related to ROS production [44,45]. OCPs are known to decrease ΔΨ in neuronal cells [46], and this was con rmed for α-HCH and propanil in the present study. Additionally, we found that propanil induced the aggregation of αsynuclein in neuronal cells. Rotenone, another type of pesticide, was shown to have the same effect [47], and dieldrin was reported to accelerate the rate of a-synuclein brils formation in vitro [48]. However, we did not nd that α-HCH signi cantly altered the aggregation of α-synuclein. Our results suggested that α-HCH might be involved in PD via oxidative stress or other pathways, which needs to be further investigated.
There were some limitations to the present study, including the relatively small sample size, limited mechanisms of α-HCH and propanil and contribution of interactions between genetic variants and pesticides into PD development. On the other hand, we included the healthy spouses of PD patients as controls to minimize the differences in serum pesticides levels caused by dietary factors and recruited participants from both urban and rural areas to reduce the effects on the results. Finally, our ndings provide the rst demonstration of a correlation between speci c pesticides in serum and PD and the rst analysis of the interaction effect between speci c pesticides and gene variants on PD in the Chinese population.

Conclusion
In summary, our results indicate that elevated serum levels of α-HCH and propanil are associated with increased risk for PD. We also showed that serum levels of dieldrin are associated with depression and cognitive function in PD patients and that the interaction between genetic variants and pesticides increased PD risk. Based on these ndings, more stringent environmental regulations may need to be implemented to reduce PD risk in the population, especially in agricultural areas where communities may be exposed to unsafe pesticide levels. Figure 1 The effect of α-HCH and propanil on oxidative injury and α-synuclein aggregation in neuronal cells.