Relation of polymorphism of arsenic metabolism genes to arsenic methylation capacity and developmental delay in preschool children in Taiwan

Toxicology and Applied Pharmacology 321 (2017) 37–47

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Relation of polymorphism of arsenic metabolism genes to arsenic methylation capacity and developmental delay in preschool children in Taiwan Ru-Lan Hsieh a,b, Chien-Tien Su c,d, Horng-Sheng Shiue e, Wei-Jen Chen d, Shiau-Rung Huang d, Ying-Chin Lin f,g,h, Ming-I Lin i, Shu-Chi Mu i, Ray-Jade Chen j, Yu-Mei Hsueh c,k,⁎ a

Department of Physical Medicine and Rehabilitation, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan c Department of Family Medicine, Taipei Medical University Hospital, Taipei, Taiwan d School of Public Health, College of Public Health, Taipei Medical University, Taipei, Taiwan e Department of Chinese Medicine, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan f Department of Family Medicine, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan g Department of Health Examination, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan h Division of Family Medicine, School of Medicine, Taipei Medical University, Taipei, Taiwan i Department of Pediatrics, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan j Department of Digestive Surgery, Taipei Medical University Hospital, Taipei, Taiwan k Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan b

a r t i c l e

i n f o

Article history: Received 9 December 2016 Revised 17 February 2017 Accepted 20 February 2017 Available online 21 February 2017 Keywords: Arsenic methyltransferase Polymorphism Arsenic methylation capacity Developmental delays Arsenic

a b s t r a c t Inefficient arsenic methylation capacity has been associated with developmental delay in children. The present study was designed to explore whether polymorphisms and haplotypes of arsenic methyltransferase (AS3MT), glutathione-S-transferase omegas (GSTOs), and purine nucleoside phosphorylase (PNP) affect arsenic methylation capacity and developmental delay. A case-control study was conducted from August 2010 to March 2014. All participants were recruited from the Shin Kong Wu Ho-Su Memorial Teaching Hospital. In total, 179 children with developmental delay and 88 children without delay were recruited. Urinary arsenic species, including arsenite (AsIII), arsenate (AsV), monomethylarsonic acid (MMAV), and dimethylarsinic acid (DMAV) were measured using a high-performance liquid chromatography-linked hydride generator and atomic absorption spectrometry. The polymorphisms of AS3MT, GSTO, and PNP were performed using the Sequenom MassARRAY platform with iPLEX Gold chemistry. Polymorphisms of AS3MT genes were found to affect susceptibility to developmental delay in children, but GSTO and PNP polymorphisms were not. Participants with AS3MT rs3740392 A/G + G/G genotype, compared with AS3MT rs3740392 A/A genotype, had a significantly lower secondary methylation index. This may result in an increased OR for developmental delay. Participants with the AS3MT high-risk haplotype had a significantly higher OR than those with AS3MT low-risk haplotypes [OR and 95% CI, 1.59 (1.08–2.34)]. This is the first study to show a joint dose-response effect of this AS3MT high-risk haplotype and inefficient arsenic methylation capacity on developmental delay. Our data provide evidence that AS3MT genes are related to developmental delay and may partially influence arsenic methylation capacity. © 2017 Elsevier Inc. All rights reserved.

1. Introduction A review paper showed that many epidemiological studies have documented developmental neurotoxic effects in children with longterm arsenic exposure from contaminated milk powder (Dakeishi et

⁎ Corresponding author at: Department of Family Medicine, Taipei Medical University Hospital, and Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, No. 250 Wu-Hsing Street, Taipei 110, Taiwan. E-mail address: [email protected] (Y.-M. Hsueh).

http://dx.doi.org/10.1016/j.taap.2017.02.016 0041-008X/© 2017 Elsevier Inc. All rights reserved.

al., 2006). Subsequently, a study found that exposure to arsenic from drinking water was associated with reduced intellectual function in 301 6-year-old children in Araihazar, Bangladesh (Wasserman et al., 2007). A study of 591 Mexican schoolchildren living in an area contaminated with both arsenic and lead showed that arsenic contamination can affect children's cognitive development, as well as leading to disturbances in visual perception, psychomotor speed, attention, speech, and memory, independent of any effect of lead (Rosado et al., 2007). Another study reported that arsenic exposure indices from water, urinary arsenic levels, and toenail arsenic concentration were inversely associated with motor function scores among 303 children in Bangladesh (Parvez

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et al., 2011). Grandjean and Herz, in a recent review paper, showed that environmental chemicals such as lead, methylmercury, and arsenic each contributed to developmental neurotoxicity (Grandjean and Herz, 2015). Increased urine arsenic levels were associated with attention impairment in school children living in an industrialized area of southwestern Spain, even at urinary levels of arsenic considered safe (Rodriguez-Barranco et al., 2016). In addition, children living in two New Hampshire (USA) school districts who were exposed to water arsenic concentrations ≥ 5 μg/L showed significant reductions in full-scale intelligence quotient scores, resulting in losses of 5–6 points in most indices (Wasserman et al., 2014). However, a study in rural Matlab, Bangladesh assessed 1799 infants, and did not find any significant effect of arsenic exposure during pregnancy on infant development (Tofail et al., 2009). Another study did not find any association between prenatal exposure to arsenic and neuropsychological development in 385 four-year-old children (Forns et al., 2014). Therefore, the epidemiological evidence is not yet solid in regard to neurotoxic risks, and the developmental effects of arsenic exposure need further investigation. After exposure, biotransformation of arsenic takes place in the body and consists of a series of successive redox reactions and methylation. Arsenate (AsV) is reduced to arsenite (AsIII), and then methylated to monomethylarsonic acid (MMAV). MMAV is then reduced to methylarsonous acid (MMA III ), which is methylated to dimethylarsinic acid (DMA V ) (Thompson, 1993). In the past, this biotransformation process was thought to be the arsenic detoxification pathway, but trivalent methylated metabolites are considered to be more toxic than inorganic arsenic (Petrick et al., 2001). The relative proportion of urinary arsenic species is considered an index of an individual's arsenic methylation capacity. In general, the profile of urinary arsenic in humans is 10–30% inorganic arsenic, 10–20% MMAV, and 60–80% DMAV (Vahter and Concha, 2001). Our previous studies showed that changes in the arsenic excretion profile are associated with increased risk of skin cancer (Hsueh et al., 1997), urothelial carcinoma (Pu et al., 2007), and developmental delay (Hsieh et al., 2014). Epidemiological data show differences in individual susceptibility and response to arsenic exposure among the same population. Different susceptibility can be due to different arsenic metabolism capacity. Glutathione-S-transferase Omegas (GSTOs) (Zakharyan et al., 2001) and purine nucleoside phosphorylase (PNP) (Radabaugh et al., 2002) have been identified as candidate enzymes to catalyze the reduction of pentavalent inorganic arsenic. Methylation of trivalent arsenic is catalyzed by arsenic (+3 oxidation state) methyltransferase (AS3MT) in a reaction that uses S-adenosylmethionine (SAM) as the methyl group donor (Li et al., 2005). Genetic polymorphisms may alter enzyme function and possibly change the urinary metabolite profile. The Cross-Disorder Group of the Psychiatric Genomics Consortium analyzed genome-wide single-nucleotide polymorphism (SNP) data for attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia in individuals of European ancestry to identify arsenite methyltransferase (AS3MT) rs11191454, which may play a role in ADHD (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). Recently, a study also found overrepresentation of the A allele of the AS3MT rs11191454 polymorphism in children with ADHD (Park et al., 2015). These findings suggest polymorphism of arsenic methylation enzyme may be related to ADHD. However, the mechanisms underlying how arsenic methylation capacity affects developmental delay are still unclear. In this study, we aimed to evaluate the association between polymorphisms of AS3MT, GSTOs, and PNP genes, and developmental delay. In addition, we also explored the association between polymorphisms of AS3MT, GSTOs, and PNP genes and arsenic methylation capacity. Putting these results together, we sought to elucidate the joint effects of AS3MT, GSTOs, and PNP gene polymorphisms and arsenic methylation capacity on the risk of developmental delay.

2. Materials and methods 2.1. Study participants All participants were recruited from the Shin Kong Wu Ho-Su Memorial Teaching Hospital, a medical center located in northern Taiwan, between August 2010 and March 2014. Children with suspected developmental delays were referred to the medical center from local kindergartens, hospitals, and community centers near the hospital. All participants underwent developmental assessments to confirm developmental delays, including evaluations of gross motor, fine motor, speech-language, cognition, social, and emotional domains. The evaluations were performed by members of the early development intervention team at the medical center using the Peabody Developmental Motor Scales, Gross Motor Function Measure, Preschool Language Evaluation Tool, Child Expression Evaluation Tool, Chinese Wechsler Intelligence Scale for Children (3rd edition), and Bayley III Scales of Infant and Toddler Development. A developmental delay was defined as performance two standard deviations or greater below the mean on age-appropriate, standardized, norm-referenced tests. The evaluation team consisted of a physiatrist, a pediatrician, a psychiatrist, an otolaryngologist, an ophthalmologist, physical therapists, occupational therapists, speech therapists, a psychologist, and a social worker. A total of 179 preschool children who were diagnosed with developmental delays were included in the study. In addition, 88 children without developmental delays were recruited from the Department of Pediatrics of Shin Kong Wu Ho-Su Memorial Teaching Hospital to serve as controls. The Research Ethics Committee of the Shin Kong Wu Ho-Su Memorial Teaching Hospital approved the study. Parents or primary caregivers of all the children provided written informed consent before a questionnaire interview and biological specimen collection. This study was performed in accordance with the World Medical Association Declaration of Helsinki. 2.2. Questionnaire interview Well-trained personnel carried out standardized personal interviews based on a structured questionnaire. The information obtained by the questionnaire included demographics of children and their parents' socioeconomic characteristics. 2.3. Biological specimen collection Spot urine samples of children and their mothers were collected at the time of recruitment and immediately transferred to a −20 °C freezer and stored until the analysis of arsenic species. We used ethylene-diamine-tetraacetic acid (EDTA) syringes to collect peripheral blood samples from children and their mothers. The samples were centrifuged and the buffy coat from each sample was immediately transferred to a −80°C freezer and stored until DNA extraction for the identification of enzyme gene polymorphisms. 2.4. Urinary arsenic species assessment Urinary arsenic profiles of AsIII, DMAV, MMAV, and AsV were measured by high-performance liquid chromatography equipped with a hydride generator and atomic absorption spectrometer (HPLC-HG-AAS). The protocol for the determination of the presence of inorganic arsenic and its methylated species was described in a previous study (Hsueh et al., 1998). This method is not influenced by the presence of arsenobetaine and arsenocholine, from seafood, in urine. Our previous study found that frequencies of fish, shellfish, and seaweed dietary intake were not significantly correlated with inorganic arsenic and its methylated species (Hsueh et al., 2002). The recovery rates of AsIII, DMAV, MMAV, and AsV ranged from 93.8 to 102.2%, with detection limits of 0.02, 0.08, 0.05, and 0.07 μg/L, respectively. Freeze-dried SRM 2670

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urine was purchased from the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA), which contained 480 ± 100 μg/L arsenic to assess the validity of measurement. This control urine was analyzed along with the urine specimens of the study subjects. The detected concentration of arsenic in the SRM 2670 standard was 507 ± 17 μg/L (n = 4). The coefficient of variance (reproducibility) was maintained within 5%. To ensure the stability of urinary arsenic profiles, we performed the detection of arsenic species within 6 months after sample collection (Chen et al., 2002).

(Barrett et al., 2005). We tested for multiplicative interaction using the product terms, arsenic methylation capacity indices and AS3MT haplotype, in a logistic model. The additive interaction was assessed using the synergy index to evaluate the joint effects of arsenic methylation capacity indices and AS3MT haplotype on the OR of developmental delays (Hosmer and Lemeshow, 1992). All analyses were conducted using the Statistical Analysis Software (SAS) statistical package (SAS, version 9.4, Cary, NC, USA). A p value of b0.05 (two-sided) was considered significant.

2.5. Genotype determination

3. Results

Genomic DNA was extracted using proteinase K digestion after phenol and chloroform extraction. Genotyping for AS3MT rs3740393, rs3740392, rs11191438, rs3740391, rs11191439, rs11191453, rs11191454, rs10748835, rs1046778; PNP rs1049562, rs1049564, rs1130650; GSTO rs2282326, rs4925, rs11509438, rs2297235, rs156697 was performed using a Sequenom MassARRAY platform with iPLEX Gold chemistry (Sequenom, San Diego, CA, USA). According to the manufacturer's guidelines, the specific polymerase chain reaction (PCR) primer and extension primer sequences were designed using the Assay Designer software package (v.4.0). A 1-μL sample of genomic DNA (10 ng/μL) was used for the multiplex PCR reaction; the total reaction mixture had a volume of 5 μL and contained 0.2 units of Taq polymerase, 2.5 pmol of each PCR primer, and 25 mM of each deoxynucleotide (dNTP) (Sequenom, PCR Accessory and Enzyme Set). The thermocycler protocol was as follows: 94 °C for 4 min; 45 cycles of 94 °C for 20 s, 56 °C for 30 s, and 72 °C for 1 min; and a final step of 72 °C for 3 min. Unincorporated dNTPs were deactivated using 0.3 U of shrimp alkaline phosphatase. The single base extension reaction was conducted with the iPLEX enzyme, terminator mix, and extension primer mix. The thermocycler protocol was as follows: 94 °C for 30 s; 40 cycles of 94 °C for 5 s, 56 °C for 5 s and 80 °C for 5 s; and a final step of 72 °C for 3 min (Sequenom, iPLEX Gold kit). A cation exchange resin was added to remove residual salt from the reactions and 7 nL of the purified primer extension reaction was loaded onto a matrix pad of a SpectroCHIP (Sequenom). SpectroCHIPs were analyzed using the MassARRAY Analyzer 4 and the clustering analysis was completed with TYPER 4.0 software.

The sociodemographic characteristics of all children included in our study are shown in Table 1. Developmental delays included 7 cognitive dysfunctions, 28 speech-language delays, 19 gross and fine motor delays, 12 social/emotional delays, 98 global delays, and 15 delays for which information was unavailable. Children with developmental delays were younger and more often male than children without delays. The birth weights, birth height, and parity order were not different between children with developmental delays and children without delays. The mean gestation time for children with developmental delays was significantly shorter than the mean gestation time for children without delays (N = 159, 37.57 weeks vs. N = 86, 38.48 weeks; p = 0.01). The educational level of the mothers was not different between the two groups (data not shown). Table 2 shows the dose-response relationships between urinary total arsenic and arsenic methylation capacity indices and developmental delay. A trend analysis of exposure strata in tertiles revealed that urinary total arsenic, InAs%, MMAV%, and PMI, were significantly positively associated with developmental delay in a dose-response manner after age-gender adjustment. DMAV% and SMI were significantly negatively associated with developmental delay, also in a dose-response manner after age-gender adjustment. These findings were consistent with our previous study (Hsieh et al., 2014) even as the number of study subjects increased in this study, providing further evidence for the association between arsenic methylation capacity and developmental delays. On the other hand, we found no difference in arsenic methylation capacity between boys and girls (data not shown). We also analyzed the dose-response relationships between urinary total arsenic level indices and the risk of developmental delays in children stratified by gender, and found similar results (data not shown). Participants with the AS3MT rs3740393 G/C + C/C genotype had a higher OR for developmental delay, of 1.59 (95% CI 0.92–2.75), than those with G/G genotype but this difference was not significant. In addition, the AS3MT rs3740392 A/G + G/G genotype versus A/A genotype, AS3MT rs11191453 T/C + C/C versus T/T genotype, and AS3MT

2.6. Statistical analysis Inorganic arsenic (InAs) was defined as the sum of AsIII and AsV. Urinary total arsenic was defined as the sum of AsIII, AsV, MMAV, and DMAV. The relative proportions of each arsenic species (InAs%, MMAV% and DMAV%) were calculated by dividing the concentration of each species by the urinary total arsenic concentration. Primary methylation index (PMI) was MMAV divided by InAs, and secondary methylation index (SMI) was DMAV divided by MMAV. Two analyses were used to evaluate the differences in total urinary arsenic, InAs%, MMAV%, DMAV%, PMI, and SMI between different haplotypes: one-way ANOVA-least significant difference test with Bonferroni correction, and Kruskal-Wallis test. Multivariate logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) to determine the association between urinary arsenic profile, AS3MT, PNP, and GSTO polymorphisms, and the OR of developmental delay after adjustment for age, sex, and urinary creatinine. Dose-response relationships between urinary total arsenic concentration, InAs%, MMAV%, DMAV%, PMI, and SMI, and developmental delays were estimated by cut-off points of the respective tertile of the distribution of the controls. Significance tests for linear trends among ORs across exposure strata were calculated by categorizing exposure variables and treating scored variables as continuous. The order of the SNPs in the haplotypes was determined by the sorting position of each polymorphism on the chromosome. The strength of the linkage disequilibrium (LD), shown by Lewontin's D′, was calculated using the Haploview software package, version 4.1

Table 1 Sociodemographic characteristics of children with developmental delays and children without developmental delays. Variables

Children with developmental delays (n = 179)

Children without developmental delays (n = 88)

p value

Age (years) Body mass index (kg/m2) Birth height (cm) Birth weight (g) Gender Male Female Parity order One Two Three or above

4.93 ± 0.15 19.52 ± 3.47

6.33 ± 0.33 16.78 ± 0.39

b0.01 0.43

49.34 ± 0.47 2939.8 ± 55.33

50.27 ± 0.32 3079.6 ± 59.35

0.10 0.09

126 (70.39) 53 (29.61)

49 (55.68) 39 (44.32)

0.02

98 (55.68) 57 (32.39) 21 (11.93)

48 (55.17) 33 (37.93) 6 (6.90)

0.37

Thirty-four children were unavailable for body mass index measurement; seventy-five children were unavailable for birth height measurement; thirty-five children were unavailable for birth weight measurement; four children were unavailable for parity order.

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Table 2 Dose-response relationship between urinary total arsenic and arsenic methylation capacity indices and the risk of developmental delays in children. Variables

Children with developmental delays

Children without developmental delays

Urinary total arsenic (μg/L) ≤7.13 7.13–18.78 N18.78 Inorganic arsenic percentage (InAs%) ≤2.71 2.71–6.34 N6.34 Monomethylarsonic acid percentage (MMAV%) ≤0.53 0.53–2.14 N2.14 Dimethylarsinic acid percentage (DMAV%) ≤91.61 91.61–95.23 N95.23 Primary methylation index (PMI) ≤ 0.20 0.20–0.56 N 0.56 Secondary methylation index (SMI) ≤ 44.02 44.02–179.00 N 179.00

21.02 ± 1.47 38 (21.23) 66 (36.87) 75 (41.90) 6.19 ± 0.43 53 (29.61) 59 (32.96) 67 (37.43) 3.60 ± 0.32 44 (24.58) 46 (25.70) 89 (49.72) 90.21 ± 0.65 84 (46.93) 39 (21.79) 56 (31.28) 1.20 ± 0.21 49 (27.37) 56 (31.28) 74 (41.43) 176.1 ± 22.23 90 (50.28) 44 (24.58) 45 (25.14)

19.22 ± 2.49 30 (34.09) 29 (32.95) 29 (32.95) 6.06 ± 0.69 30 (34.09) 29 (32.95) 29 (32.95) 2.65 ± 0.41 30 (34.09) 29 (32.95) 29 (32.95) 91.29 ± 0.82 30 (34.09) 29 (32.95) 29 (32.95) 1.07 ± 0.27 30 (34.09) 29 (32.95) 29 (32.95) 208.4 ± 35.21 30 (34.09) 29 (32.95) 29 (32.95)

Age-sex adjusted ORs (95% CI) 1.00a,§ 1.71 (0.85–3.44) 2.42 (1.04–5.62)⁎ 1.00 1.53 (0.78–3.01) 1.71 (0.87–3.35) 1.00§ 1.25 (0.63–2.49) 2.60 (1.33–5.07)⁎⁎ 1.00§ 0.45 (0.23–0.88)⁎ 0.53 (0.27–1.02)+ 1.00 0.99 (0.52–1.90) 1.95 (0.99–3.86)+ 1.00§ 0.47 (0.24–0.91)⁎ 0.44 (0.22–0.84)⁎

a

Adjusted for age, sex and urine creatinine level. 0.05 ≤ P b 0.1. ⁎ P b 0.05. ⁎⁎ P b 0.01. § P b 0.05 for trend test. +

rs11191454 A/G + G/G versus A/A genotype also showed similar results (Table 3). Participants with AS3MT rs11191438 C/G + G/G genotype, AS3MT rs10748835 A/G + G/G genotype, and AS3MT rs1046778 C/ T + T/T genotype had significantly lower OR of developmental delays than those with the AS3MT rs11191438 C/C genotype, AS3MT rs10748835 A/A genotype, and AS3MT rs1046778 C/C genotypes. Their ORs and 95% CIs for developmental delays were 0.56 (0.31–1.00), 0.56 (0.31–1.00), and 0.34 (0.17–0.70), respectively (Table 3). The frequency of variant type (A allele) in GSTO rs11509438 was 0.0037 and (C allele) in AS3MT rs11191439 was 0.0150. These frequencies were very low; therefore, we did not further analyze these SNPs. None of the SNPs of the PNP and GSTO genes was associated with developmental delays. In the present study, the Lewontin's D′ between the PNP rs1049562, rs1049564, and rs1130650 polymorphisms ranged from 0.97 to 1.0, which indicated LD; all Lewontin's D′ between the GSTO rs2282326, rs4925, rs2297235, and rs156697 polymorphisms were 1.0, which indicated LD; all Lewontin's D′ between the AS3MT rs3740393, rs3740392, rs11191438, rs3740391, rs11191453, rs11191454, and rs10748835 polymorphisms were 1.0, which indicated LD (Fig. S1) and we also presented r2 between each pair of polymorphisms in Fig. S2. Table 4 reveals the distribution of PNP, GSTO, and AS3MT haplotypes and the risk of developmental delays in the children. The maximum number of samples of the C-A-C-T-C-G-A haplotype were used as a reference group to calculate the OR and 95% CIs for the AS3MT G-A-G-T-T-A-G, G-A-G-G-TA-G, and G-G-C-T-T-A-A haplotypes, which were 0.65 (0.41–1.04), 0.57 (0.34–0.95), and 0.71 (0.37–1.38), respectively. Therefore, we combined the AS3MT G-A-G-T-T-A-G, G-A-G-G-T-A-G, and G-G-C-T-TA-A haplotypes into a single group; this group was defined as the lowrisk haplotype group. The AS3MT C-A-C-T-C-G-A haplotype was defined as the high-risk haplotype. Participants with the AS3MT high-risk haplotype had a significantly higher OR than those with an AS3MT low-risk haplotype, the OR and 95% CI was 1.59 (1.08–2.34). We did not find that any haplotypes of PNP or GSTO were associated with the risk of developmental delays. Fig. 1 displays the effect of AS3MT haplotypes on urinary arsenic methylation indices. Different AS3MT haplotypes had significantly different inorganic arsenic percentages and DMAV percentages, as

determined by one-way ANOVA and post hoc test. Because of the dispersion of the observations, we further used a non-parametric test to analyze differences between InAs% and DMA% among different haplotypes, and we could not find any differences when we used a KruskalWallis test. These inconsistent results may be due to the fact that the two highest levels were observed for the G-A-G-T-T-A-G haplotype. Furthermore, we did not find that arsenic methylation capacity differed between the AS3MT high-risk haplotype and the AS3MT low-risk haplotypes (data not shown). Due to high collinearity between each arsenic methylation capacity index, we analyzed via a logistic regression model that included age, sex, InAs%, MMA%, DMA%, PMI, SMI, and AS3MT haplotypes using stepwise selection, and found that age, sex, MMA% and AS3MT haplotypes were observed in the final model, and high MMA% and AS3MT high risk haplotypes were significantly related to an increased OR of developmental delay (Table 5). Fig. 2 demonstrates the joint effects of AS3MT haplotypes and arsenic methylation capacity indices on developmental delays. Participants with low InAs% and AS3MT low-risk haplotype were used as the reference group. The ORs (95% CI) for developmental delays for the combinations of low InAs% and AS3MT high-risk haplotype, high InAs% and AS3MT low-risk haplotype, and high InAs% and AS3MT high-risk haplotype were 1.76 (1.01–3.07), 1.69 (1.01–2.82), and 2.63 (1.47–4.70), respectively. Similarly, the joint effects of AS3MT haplotype and MMAV% on the risk of developmental delays also showed a similar dose-response relationship. When participants with high DMAV% and AS3MT low-risk haplotype were used as the reference group, the ORs (95% CI) for developmental delays for the high DMAV% and AS3MT high-risk haplotype, low DMAV% and AS3MT low-risk haplotype, and low DMAV% and AS3MT high-risk haplotype were 1.90 (1.08–3.34), 2.03 (1.22–3.39), and 2.82 (1.61–4.94), respectively. The joint effects of AS3MT haplotype and SMI on the risk of developmental delays also showed a similar doseresponse relationship. We noted a joint effect for inefficient arsenic methylation capacity indices plus AS3MT high-risk haplotype, with a significant dose-response relationship observed for the risk of developmental delay (p b 0.05 for the trend test). However, multiplicative interaction was significant in the joint effect of AS3MT high risk haplotypes

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Table 3 Distribution of PNP, GSTO, and AS3MT genotypes in children with and without developmental delays; risk of developmental delays in children. SNP PNP rs1049562 G/G G/A A/A G/G G/A + A/A PNP rs1049564 C/C C/T T/T C/C C/T + T/T PNP rs1130650 C/C C/T T/T C/C C/T + T/T GSTO rs2282326 A/A A/C C/C A/A A/C + C/C GSTO rs4925 C/C C/A A/A C/C C/A + A/A GSTO rs11509438 G/G G/A GSTO rs2297235 A/A A/G G/G A/A A/G + G/G GSTO rs156697 T/T T/C C/C T/T T/C + C/C AS3MT rs3740393 G/G G/C C/C G/G G/C + C/C AS3MT rs3740392 A/A A/G G/G A/A A/G + G/G AS3MT rs11191438 C/C C/G G/G C/C C/G + G/G AS3MT rs3740391 T/T T/G G/G T/T T/G + G/G AS3MT rs11191439 T/T T/C AS3MT rs11191453 T/T

Children with developmental delays

Children without developmental delays

Age-sex adjusted ORs (95% C.I.)

139 (77.65) 37 (20.67) 3 (1.68) 139 (77.65) 40 (22.35)

68 (77.27) 18 (20.45) 2 (2.27) 68 (77.27) 20 (22.73)

1.00 0.92 (0.47–1.78) 0.51 (0.08–3.30) 1.00 0.87 (0.46–1.65)

116 (64.8) 56 (31.28) 7 (3.91) 116 (64.8) 63 (35.20)

54 (61.36) 32 (36.36) 2 (2.27) 54 (61.36) 34 (38.64)

1.00 0.67 (0.38–1.19) 1.00 (0.19–5.20) 1.00 0.69 (0.39–1.21)

117 (65.36) 55 (30.73) 7 (3.91) 117 (65.36) 62 (34.64)

54 (61.36) 32 (36.36) 2 (2.27) 54 (61.36) 34 (38.64)

1.00 0.64 (0.36–1.15) 0.98 (0.19–5.14) 1.00 0.67 (0.38–1.17)

107 (59.78) 61 (34.08) 11 (6.15) 107 (59.78) 72 (40.22)

51 (57.95) 35 (39.77) 2 (2.27) 51 (57.95) 37 (42.05)

1.00 0.85 (0.49–1.48) 2.53 (0.51–12.50) 1.00 0.95 (0.55–1.63)

130 (72.63) 43 (24.02) 6 (3.35) 130 (72.63) 49 (27.37)

68 (77.27) 19 (21.59) 1 (1.14) 68 (77.27) 20 (22.73)

1.00 1.14 (0.60–2.17) 3.86 (0.42–35.62) 1.00 1.26 (0.68–2.36)

177 (98.88) 2 (1.12)

88 (100.00) 0

130 (72.63) 43 (24.02) 6 (3.35) 130 (72.63) 49 (27.37)

68 (77.27) 19 (21.59) 1 (1.14) 68 (77.27) 20 (22.73)

1.00 1.14 (0.60–2.17) 3.86 (0.42–35.62) 1.00 1.26 (0.68–2.36)

105 (58.66) 63 (35.20) 11 (6.15) 105 (58.66) 74 (41.34)

51 (57.95) 35 (39.77) 2 (2.27) 51 (57.95) 37 (42.05)

1.00 0.89 (0.51–1.55) 2.57 (0.52–12.68) 1.00 0.98 (0.57–1.69)

91 (50.84) 75 (41.90) 13 (7.26) 91 (50.84) 88 (49.16)

55 (62.50) 28 (31.82) 5 (5.68) 55 (62.50) 33 (37.50)

1.00 1.58 (0.89–2.81) 1.64 (0.53–5.09) 1.00 1.59 (0.92–2.75)+

76 (42.46) 86 (48.04) 17 (9.50) 76 (42.46) 103 (57.54)

48 (54.55) 31 (35.23) 9 (10.23) 48 (54.55) 40 (45.45)

1.00 1.77 (0.99–3.16)+ 1.21 (0.48–3.06) 1.00 1.65 (0.96–2.84)+

70 (39.11) 81 (45.25) 28 (15.64) 70 (39.11) 109 (60.89)

24 (27.27) 39 (44.32) 25 (28.41) 24 (27.27) 64 (72.73)

1.00§ 0.66 (0.35–1.24) 0.39 (0.19–0.82)⁎ 1.00 0.56 (0.31–1.00)⁎

133 (74.30) 43 (24.02) 3 (1.68) 133 (74.30) 46 (25.70)

57 (64.77) 26 (29.55) 5 (5.68) 57 (64.77) 31 (35.23)

1.00 0.73 (0.40–1.34) 0.42 (0.09–1.92) 1.00 0.69 (0.39–1.23)

172 (96.09) 7 (3.91)

87 (98.86) 1 (1.14)

1.00 4.83 (0.57–41.07)

91 (50.84)

55 (62.50)

1.00 (continued on next page)

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Table 3 (continued) SNP

Children with developmental delays

Children without developmental delays

Age-sex adjusted ORs (95% C.I.)

T/C C/C T/T T/C + C/C AS3MT rs11191454 A/A A/G G/G A/A A/G + G/G AS3MT rs10748835 A/A A/G G/G A/A A/G + G/G AS3MT rs1046778 C/C C/T T/T C/C C/T + T/T

75 (41.90) 13 (7.26) 91 (50.84) 88 (49.16)

28 (31.82) 7 (5.68) 55 (62.50) 33 (37.50)

1.58 (0.89–2.81) 1.64 (0.53–5.09) 1.00 1.59 (0.92–2.75)+

91 (50.84) 75 (41.90) 13 (7.26) 91 (50.84) 88 (49.16)

55 (62.50) 28 (31.82) 5 (5.68) 55 (62.50) 33 (37.50)

1.00 1.58 (0.89–2.81) 1.64 (0.53–5.09) 1.00 1.59 (0.92–2.75)+

70 (39.11) 81 (45.25) 28 (15.64) 70 (39.11) 109 (60.89)

24 (27.27) 39 (44.32) 25 (28.41) 24 (27.27) 64 (72.73)

1.00§ 0.66 (0.35–1.24) 0.39 (0.19–0.82)⁎ 1.00 0.56 (0.31–1.00)⁎

54 (30.17) 87 (48.60) 38 (21.23) 54 (30.17) 125 (69.83)

12 (13.64) 47 (53.41) 29 (32.95) 12 (13.64) 76 (86.36)

1.00 0.37 (0.17–0.78)⁎⁎ 0.31 (0.13–0.69)⁎⁎ 1.00§ 0.34 (0.17–0.70)⁎⁎

All genotype distributions were in agreement with Hardy-Weinberg equilibrium in controls. + 0.05 ≤ p b 0.1. ⁎ p b 0.05. ⁎⁎ p b 0.01. § p b 0.05 for trend test.

and MMA%, and AS3MT high risk haplotypes and SMI, but not all synergy indices were statistically significant. 4. Discussion Our previous study reported that urinary total arsenic levels and inefficient arsenic methylation capacity (high MMAV%, and low DMAV%) were associated with the risk of developmental delays (Hsieh et al., 2014). The present study provides further evidence showing a relationship between polymorphisms of arsenic methylation-related genes and developmental delay, with arsenic methylation capacity taken into account. Therefore, this study simultaneously evaluated the associations

among the polymorphisms of PNP, GSTO, and AS3MT genes and arsenic methylation capacity indices on developmental delay in children in Taiwan exposed to low levels of arsenic. Participants with the variant allele of AS3MT rs3740393, rs3740392, rs11191453, and rs11191454 had approached significantly high ORs for developmental delay, and participants with the variant allele of AS3MT rs11191438, rs10748835, and rs1046778 had significantly lower ORs for developmental delay. All of the children lived in Taipei City and New Taipei City, an area without obvious arsenic exposure. They drank tap water provided by the Taipei Water Department of the Taipei City Government, a water source with arsenic levels less than the standard 10 μg/L. The major source of human exposure to arsenic is drinking water. Besides drinking

Table 4 Distribution of PNP, GSTO, and AS3MT haplotypes; risk of developmental delays in children. Haplotypes

Children with developmental delays

Children without developmental delays

Age-sex adjusted ORs (95% C.I.)

288 (80.45) 43 (12.01) 26 (7.26) 0 1 (0.28) 0

139 (78.98) 21 (11.93) 14 (7.95) 1 (0.57) 0 1 (0.57)

1.00 0.85 (0.47–1.52) 0.67 (0.33–1.37)

273 (76.26) 55 (15.36) 28 (7.82) 2 (0.56)

137 (77.84) 21 (11.93) 18 (10.23) 0

1.00 1.32 (0.75–2.33) 0.78 (0.40–1.51)

176 (49.16) 90 (25.14) 58 (16.20) 34 (9.50)

66 (37.50) 50 (28.41) 42 (23.86) 18 (10.23)

1.00 0.65 (0.41–1.04)+ 0.57 (0.34–0.95)⁎ 0.71 (0.37–1.38)

182 (50.84) 176 (49.16)

110 (62.50) 66 (37.50)

1.00 1.59 (1.08–2.34)⁎

a

PNP haplotypes G-C-C A-T-T G-T-T G-C-T G-T-C A-T-C GSTO haplotypesb A-C-A-T C-A-G-C C-C-A-C A-C-A-C AS3MT haplotypesc C-A-C-T-C-G-A G-A-G-T-T-A-G G-A-G-G-T-A-G G-G-C-T-T-A-A Combination of AS3MT haplotypesd Low-risk haplotypes High-risk haplotypes a

PNP haplotypes are listed in the following order: rs1049562, rs1049564, and rs1130650. GSTO haplotypes are listed in the following order: rs2282326, rs4925, rs2297235, and rs156697. AS3MT haplotypes are listed in the following order: rs3740393, rs3740392, rs11191438, rs3740391, rs11191453, rs11191454, and rs10748835. d Combination of AS3MT haplotypes: high-risk haplotypes defined as C-A-C-T-C-G-A, and low-risk haplotypes defined as G-A-G-T-T-A-G, G-A-G-G-T-A-G, and G-G-C-T-T-A-A. + 0.05 ≤ p b 0.1. ⁎ p b 0.05. b c

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Fig. 1. The comparison of InAs% and DMA% among four AS3MT haplotypes: AS3MT haplotypes are listed in the following order: rs3740393, rs3740392, rs11191438, rs3740391, rs11191453, rs11191454, and rs10748835. *p b 0.05, pairwise comparisons using least significant difference test.

water, several studies have reported that using arsenic-containing groundwater to irrigate can increase the arsenic content of various parts of rice plants grown in Taiwan (Hsu et al., 2012). Further, arsenic or arsenic species have been detected in cereals (Tsai and Jiang, 2011) and edible oils in Taiwan (Chu and Jiang, 2011). Therefore, the exact source of arsenic exposure for children in this study is unknown. One study found that infant development was not affected by arsenic exposure to the mother during pregnancy (Tofail et al., 2009), and other studies have found that prenatal or postnatal exposure to arsenic was not associated with child development at 18 months (Hamadani et al., 2010). On the other hand, arsenic exposure was found in one study to be significantly associated with intelligence in five-year-old girls, although not in boys (Hamadani et al., 2011). Another study detected a statistically significant nonlinear relationship between soil concentrations of arsenic near mothers' residences during pregnancy and mental retardation and developmental delays in their children (Liu et al., 2010). Unfortunately, we do not have information on whether children with developmental delay are affected by prenatal arsenic exposure or postnatal arsenic exposure in this study. To date, little is known about the association between AS3MT polymorphisms and adverse health effects caused by chronic inorganic arsenic exposure. Individuals carrying the AS3MT Met287Thr (T/C) TC + CC genotype showed a marginally increased risk of arsenic-related skin lesion [OR and 95% CI, 4.28 (1.0–18.5)] but AS3MT rs11191453 and AS3MT rs7085104 did not affect the risk of skin lesion in a Mexican population (Valenzuela et al., 2009). The AS3MT rs10748835 (SNP A35991G) genotype has been associated with low-level arsenic exposure-related cognitive functioning deficiencies (Edwards et al., 2014). Individuals with

one or more copies of the C allele in AS3MT rs11191439 (the AS3MT Met287Thr) and arsenic exposure from drinking water concurrently showed an elevated risk of bladder cancer, in terms of elevated risk per 1 μg/L increment [OR and 95% CI, 1.17 (1.04–1.32)] (BeebeDimmer et al., 2012). A Bangladesh study also showed that individuals carrying the AS3MT rs9527 A allele showed decreased DMA% and increased skin lesions [OR and 95% CI, 1.42 (1.16–1.72)] (Pierce et al., 2012). In contrast, another study found no SNPs of AS3MT to be associated with bladder cancer (Lesseur et al., 2012). Based on our findings, four SNPs of AS3MT were marginally significantly associated with increased ORs and three SNPs of AS3MT were significantly associated with decreased ORs of developmental delays. These findings are consistent with previous findings on specific SNPs associated with a range of psychiatric disorders, both childhood-onset and adult-onset (CrossDisorder Group of the Psychiatric Genomics Consortium, 2013). Clearly, more data are needed to better understand the role of AS3MT polymorphisms as a susceptibility marker in human populations. Heterologous expression of AS3MT or silencing of AS3MT expression in cultured cells produces a predicted increase or decrease in capacity for inorganic arsenic methylation (Drobna et al., 2006). The enzymatic regulation of arsenic metabolism is partially known. Genetic polymorphisms of the AS3MT enzyme have been related to differences in the distribution of arsenic metabolites in urine (Engstrom et al., 2011; Pierce et al., 2012; Schlawicke et al., 2007). According to previous studies, the C allele (coding for Thr) of AS3MT rs11191439 (Met287Thr) (T/C) results in higher InAs%, higher MMA%, and lower DMA% compared with TT carriers (Drobna et al., 2004; Engstrom et al., 2011; Hernandez et al., 2008; Lindberg et al., 2007). AS3MT rs3740400 (A/C) A-alleles (Meza et al.,

Table 5 Stepwise multiple logistic regression analyses of arsenic methylation capacity indices and AS3MT haplotypes for developmental delays. Variables

Age (years) Gender Male Female MMAV% Combination of AS3MT haplotypesa Low-risk haplotypes High-risk haplotypes R-square for model a

Model 1

Model 2

Model 3

ORs (95% C.I.)

ORs (95% C.I.)

ORs (95% C.I.)

0.80 (0.74–0.86)⁎⁎

0.79 (0.73–0.85)⁎⁎

0.79 (0.73–0.85)⁎⁎

1.00 0.55 (0.37–0.81)⁎⁎

1.00 0.53 (0.36–0.79)⁎⁎ 1.09 (1.03–1.17)⁎⁎

1.00 0.55 (0.37–0.81)⁎⁎ 1.09 (1.03–1.16)⁎⁎

0.1006

1.00 1.61 (1.09–2.37)⁎ 0.1103

0.0853

Combination of AS3MT haplotypes: high-risk haplotypes defined as C-A-C-T-C-G-A, and low-risk haplotypes defined as G-A-G-T-T-A-G, G-A-G-G-T-A-G, and G-G-C-T-T-A-A. ⁎ p b 0.05. ⁎⁎ p b 0.01.

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Fig. 2. The joint effects of AS3MT haplotype and arsenic methylation capacity indices on the risk of developmental delay. Definitions of AS3MT haplotypes: the C-A-C-T-C-G-A haplotype was defined as a high-risk haplotype, and the G-A-G-T-T-A-G, G-A-G-G-T-A-G, and G-G-C-T-T-A-A haplotypes were defined as low-risk haplotypes.

2005), rs3740393 (G/C) G-alleles (Agusa et al., 2009; Chung et al., 2009) and rs1046778 (T/C) T-alleles (Engstrom et al., 2011) show higher InAs%, higher MMA%, and lower DMA% compared with reference alleles. In addition, in occupational arsenic-exposed workers, AS3MT polymorphisms were also found to affect arsenic methylation, with the AS3MT rs3740400 GG genotype showing a significantly higher InAs% (21.8 ± 2.0) than the GC + CC genotype (16.0 ± 2.1) (Janasik et al., 2015). In our study, PMI was significantly higher in participants with the AS3MT rs3740391 T/T genotype than in those with the T/G + G/G genotype, and those with the AS3MT rs3740392 A/A genotype had a significantly higher SMI than those with the AS3MT rs3740392 A/G + G/G genotype (Table S3). However, the fact that the AS3MT rs3740392 A/G + G/G genotype showed a significantly lower SMI than the AS3MT rs3740392 A/A genotype may enhance suggesting that the AS3MT rs3740392 A/G + G/ G genotype decreases arsenic methylation capacity and increases the OR for developmental delay compared with the AS3MT rs3740392 A/A genotype. However, we do not have sufficient evidence that the AS3MT genotype causes developmental delay by affecting arsenic methylation. Perhaps the AS3MT genotype and arsenic methylation ability are independent effects on developmental delay in children. Polymorphisms of arsenic metabolism-related genes that affect disease through arsenic methylation capacity await further investigation. In contrast to AS3MT, we did not find that GSTO and PNP genotypes were associated with developmental delays in preschool children, and we only found that participants with GSTO rs2282326 A/C ± C/C and GSTO rs156697 T/C ± C/C genotypes had significantly higher InAs%

than those with GSTO rs2282326 A/A and GSTO rs156697 T/T genotypes. A recent review showed that the GSTO2 N142D polymorphism has been associated with multiple diseases including Alzheimer's disease, Parkinson's disease, familial amyotrophic lateral sclerosis, chronic obstructive pulmonary disease, age-related cataracts, and breast cancer (Board and Menon, 2016). Another study reported that AS3MT polymorphisms rs3740390, rs11191439, and rs11191453 were associated with statistically significant changes in urinary MMA%, but GSTO polymorphisms were not related to arsenic methylation, and there were insufficient data on PNP polymorphisms to evaluate their impact on arsenic metabolism (Antonelli et al., 2014). Our previous study found a significantly higher MMA% in people who carried the wild type GSTO1 (Ala140Asp) Ala/Ala, compared with those who carried other genotypes (p = 0.02). In this same study, we found that the GSTO2 (Asn142Asp) Asp/Asp genotype was significantly inversely associated with urothelial carcinoma risk (Chung et al., 2011). A Taiwanese study did not find a significant association between genetic polymorphisms of PNP, GSTO1, or GSTO2 and the risk for development of carotid atherosclerosis (Hsieh et al., 2011). A study also reported lack of association in Italian patients between essential hypertension and GSTO1, an uncommon genetic variant in Italians (Polimanti et al., 2012). Together, these studies illustrate the limited nature of the data on associations between GSTO and PNP polymorphisms and arsenic methylation capacity and/or human health effects. Further investigation is required on these associations. A study in Argentina reported that major AS3MT GCCATCAC haplotypes (AS3MT rs7085104, rs3740400, rs3740393, rs3740390,

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rs11191453, rs11191439, rs10748835, and rs1046778) were significantly associated with low MMA% and high DMA%, but the AS3MT AAGGTTGT haplotype had a significantly higher MMA% and lower DMA% (Engstrom et al., 2011). The same study group also reported that AS3MT haplotype strongly predicted gene expression for AS3MT and raised the possibility that several genes are important for arsenic metabolism (Engstrom et al., 2013). Another study reported that a frequency N5% of four AS3MT haplotypes (rs7085104, rs3740400, rs3740393, rs3740390, rs11191439, rs11191453, rs10748835, and rs1046778) significantly influenced the arsenic metabolite pattern of pregnant women in Bangladesh (Gardner et al., 2012). Recently, a study found that AS3MT haplotype status (rs3740400, rs3740393, rs11191439, and rs1046778) modified arsenic metabolism and increased arsenic-related basal cell carcinoma risk (Engstrom et al., 2015). Another study demonstrated a significant genotype-arsenic interaction: both subjects with AS3MT rs9527 A allele and subjects with AS3MT rs11191527 G allele had low DMA%, high MMA%, and high InAs%, related to a high risk of skin lesions among individuals with high arsenic exposure (Pierce et al., 2013). Another study also found a significant joint effect of a high level of well water arsenic (≥ 40.4 μg/L) and the GG genotype of AS3MT rs3740392; together, these two parameters were associated with a difference of 40.9 μm in carotid artery intima-media thickness (Wu et al., 2014). However, we found a significant dose response relationship between the joint effect of inefficient arsenic methylation indices and AS3MT high-risk haplotype on developmental delay in this study. Although the interaction was insignificant, the observed gene-environment joint effect, with its dose-response pattern, may support the hypothesized association between arsenic methylation capacity and developmental delay. Unfortunately, we did not find that the AS3MT high-risk haplotype was associated with a more inefficient arsenic methylation capacity than the AS3MT low-risk haplotype; we only found that the different haplotypes of AS3MT had different InAs% and DMAV% values. Importantly, the AS3MT high-risk haplotype may partially modify the association between inefficient arsenic methylation capacity and developmental delay. Therefore, arsenic methylation capacity seems to be a dominant risk factor for developmental delay, and the AS3MT gene may influence the susceptibility to developmental delay through other mechanisms not related to arsenic methylation. Our knowledge regarding this issue, however, is still very limited. There is little evidence regarding the mechanism by which arsenic can impair neurobehavioral development in children. However, a review focusing on current epidemiological evidence did find an association between arsenic exposure and neurological and cognitive dysfunction in children (Tyler and Allan, 2014). A recent study also reported that urine arsenic levels were associated with impaired attention and cognitive function in school children, even at levels considered safe (Rodriguez-Barranco et al., 2015). Animal studies have found that chronic exposure to low levels of arsenic causes alterations in locomotor activity and expression of antioxidant systems and dopaminergic elements (Rodriguez et al., 2010). Decreased locomotor activity and oxidative stress reactions due to arsenic toxicity may affect the central nervous system and impair intellectual function (von Ehrenstein et al., 2007). Arsenic reduces histone acetylation in the cortex and hippocampus (Cronican et al., 2013), and observed arsenic-induced hippocampaldependent behavioral deficits include impaired spatial, working, longterm, and short-term memory in rodent models (Cronican et al., 2013; Jing et al., 2012). On the other hand, arsenic exposure depleted SAM concentration and induced epigenetic modification of DNA (Reichard et al., 2007), which may result in aberrant gene expression in the brain; however, the association between DNA methylation and cognitive deficits is yet to be elucidated. Although the disposition of arsenicals was higher in liver, significant amounts of inorganic arsenic and arsenite metabolites MMA and DMA were seen in the brain tissue of exposed mice (Rodriguez et al., 2005). Another study also found that AS3MT is

45

ubiquitously expressed and methylated arsenic metabolites are present in all brain regions of mice (Sanchez-Pena et al., 2010), with the highest accumulation in the pituitary. This may help to explain the neuroendocrine effects associated with arsenic exposure. However, the mechanism by which arsenic crosses the blood brain barrier and the role of arsenic methylation in neurotoxicity merits further investigation. Since this study was designed as a case-control study, we cannot exclude the possibility that the association between arsenic methylation capacity and developmental delays is the result, and not the cause, of developmental delay. Most parents could not easily cooperate with study recruitment, so a large sample size was difficult to achieve, particularly for children without developmental delay. In this study, we did our best to increase the number of study subjects, but small sample size and low levels of arsenic exposure, along with uncertainties in lifetime arsenic exposure assessments, have likely hampered the evaluation of associations between AS3MT haplotypes, arsenic metabolism, and health effects. Due to small sample size, D′ may be very high when SNP frequencies are quite low, and statistical significance should be interpreted with caution with small sample sizes. Besides, while a shorter gestational period may be related to developmental delay, this variable was unavailable in some of the study subjects. The fact that it cannot be adjusted is another limitation of this study. In conclusion, to the best of our knowledge, this is the first study to show a dose-response relationship for the joint effects of inefficient arsenic methylation capacity indices and high-risk AS3MT haplotype on developmental delay. Our data provide evidence that, in children, AS3MT genes may partially explain the observed variation in susceptibility to arsenic methylation capacity. The AS3MT high-risk haplotype is related to developmental delay, but this may or may not be due to effects on arsenic methylation ability. Future studies should focus on developing a better understanding of the functional effects of variation related to AS3MT polymorphisms. This could help us better understand how variation in methylation efficiency affects health. Conflict of interest statement The authors declare that they have no conflicts of interest. Transparency document The Transparency document associated with this article can be found, in online version. Acknowledgement We appreciate the assistance provided by doctors Tseng-Chen Sung, Cheng Hui Lin, and Ling-Jen Wang of the Department of Pediatrics, Shin Kong Wu Ho-Su Memorial Hospital in the recruitment of study subjects. The study was supported by grants from Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan (SKH-TMU-102-10, SKH-TMU-100-06, and SKH-TMU-99-03) and from the National Science Council, Taipei, Taiwan (NSC 100-2314-B-038-026). The authors have indicated that they have no financial relationships relevant to this article to disclose. We disclosed all financial and interpersonal relationships that could be viewed as presenting a potential conflict of interest. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.taap.2017.02.016. References Agusa, T., Iwata, H., Fujihara, J., Kunito, T., Takeshita, H., Minh, T.B., Trang, P.T., Viet, P.H., Tanabe, S., 2009. Genetic polymorphisms in AS3MT and arsenic metabolism in residents of the Red River Delta, Vietnam. Toxicol. Appl. Pharmacol. 236, 131–141.

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Antonelli, R., Shao, K., Thomas, D.J., Sams, R., Cowden, J., 2014. AS3MT, GSTO, and PNP polymorphisms: impact on arsenic methylation and implications for disease susceptibility. Environ. Res. 132, 156–167. Barrett, J.C., Fry, B., Maller, J., Daly, M.J., 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265. Beebe-Dimmer, J.L., Iyer, P.T., Nriagu, J.O., Keele, G.R., Mehta, S., Meliker, J.R., Lange, E.M., Schwartz, A.G., Zuhlke, K.A., Schottenfeld, D., Cooney, K.A., 2012. Genetic variation in glutathione S-transferase omega-1, arsenic methyltransferase and methylene-tetrahydrofolate reductase, arsenic exposure and bladder cancer: a case-control study. Environ. Health 11, 43. Board, P.G., Menon, D., 2016. Structure, function and disease relevance of Omega-class glutathione transferases. Arch. Toxicol. 90, 1049–1067. Chen, Y.C., Amarasiriwardena, C.J., Hsueh, Y.M., Christiani, D.C., 2002. Stability of arsenic species and insoluble arsenic in human urine. Cancer Epidemiol. Biomark. Prev. 11, 1427–1433. Chu, Y.L., Jiang, S.J., 2011. Speciation analysis of arsenic compounds in edible oil by ion chromatography-inductively coupled plasma mass spectrometry. J. Chromatogr. A 1218, 5175–5179. Chung, C.J., Hsueh, Y.M., Bai, C.H., Huang, Y.K., Huang, Y.L., Yang, M.H., Chen, C.J., 2009. Polymorphisms in arsenic metabolism genes, urinary arsenic methylation profile and cancer. Cancer Causes Control 20, 1653–1661. Chung, C.J., Pu, Y.S., Su, C.T., Huang, C.Y., Hsueh, Y.M., 2011. Gene polymorphisms of glutathione S-transferase omega 1 and 2, urinary arsenic methylation profile and urothelial carcinoma. Sci. Total Environ. 409, 465–470. Cronican, A.A., Fitz, N.F., Carter, A., Saleem, M., Shiva, S., Barchowsky, A., Koldamova, R., Schug, J., Lefterov, I., 2013. Genome-wide alteration of histone H3K9 acetylation pattern in mouse offspring prenatally exposed to arsenic. PLoS One 8, e53478. Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013m. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381, 1371–1379. Dakeishi, M., Murata, K., Grandjean, P., 2006. Long-term consequences of arsenic poisoning during infancy due to contaminated milk powder. Environ. Health 5, 31. Drobna, Z., Waters, S.B., Walton, F.S., LeCluyse, E.L., Thomas, D.J., Styblo, M., 2004. Interindividual variation in the metabolism of arsenic in cultured primary human hepatocytes. Toxicol. Appl. Pharmacol. 201, 166–177. Drobna, Z., Xing, W., Thomas, D.J., Styblo, M., 2006. shRNA silencing of AS3MT expression minimizes arsenic methylation capacity of HepG2 cells. Chem. Res. Toxicol. 19, 894–898. Edwards, M., Hall, J., Gong, G., O'Bryant, S.E., 2014. Arsenic exposure, AS3MT polymorphism, and neuropsychological functioning among rural dwelling adults and elders: a cross-sectional study. Environ. Health 13, 15. von Ehrenstein, O.S., Poddar, S., Yuan, Y., Mazumder, D.G., Eskenazi, B., Basu, A., HiraSmith, M., Ghosh, N., Lahiri, S., Haque, R., Ghosh, A., Kalman, D., Das, S., Smith, A.H., 2007. Children's intellectual function in relation to arsenic exposure. Epidemiology 18, 44–51. Engstrom, K., Vahter, M., Mlakar, S.J., Concha, G., Nermell, B., Raqib, R., Cardozo, A., Broberg, K., 2011. Polymorphisms in arsenic(+III oxidation state) methyltransferase (AS3MT) predict gene expression of AS3MT as well as arsenic metabolism. Environ. Health Perspect. 119, 182–188. Engstrom, K.S., Hossain, M.B., Lauss, M., Ahmed, S., Raqib, R., Vahter, M., Broberg, K., 2013. Efficient arsenic metabolism - the AS3MT haplotype is associated with DNA methylation and expression of multiple genes around AS3MT. PLoS One 8, e53732. Engstrom, K.S., Vahter, M., Fletcher, T., Leonardi, G., Goessler, W., Gurzau, E., Koppova, K., Rudnai, P., Kumar, R., Broberg, K., 2015. Genetic variation in arsenic (+3 oxidation state) methyltransferase (AS3MT), arsenic metabolism and risk of basal cell carcinoma in a European population. Environ. Mol. Mutagen. 56, 60–69. Forns, J., Fort, M., Casas, M., Caceres, A., Guxens, M., Gascon, M., Garcia-Esteban, R., Julvez, J., Grimalt, J.O., Sunyer, J., 2014. Exposure to metals during pregnancy and neuropsychological development at the age of 4 years. Neurotoxicology 40, 16–22. Gardner, R.M., Engstrom, K., Bottai, M., Hoque, W.A., Raqib, R., Broberg, K., Vahter, M., 2012. Pregnancy and the methyltransferase genotype independently influence the arsenic methylation phenotype. Pharmacogenet. Genomics 22, 508–516. Grandjean, P., Herz, K.T., 2015. Trace elements as paradigms of developmental neurotoxicants: lead, methylmercury and arsenic. J. Trace Elem. Med. Biol. 31, 130–134. Hamadani, J.D., Grantham-McGregor, S.M., Tofail, F., Nermell, B., Fangstrom, B., Huda, S.N., Yesmin, S., Rahman, M., Vera-Hernandez, M., Arifeen, S.E., Vahter, M., 2010. Pre- and postnatal arsenic exposure and child development at 18 months of age: a cohort study in rural Bangladesh. Int. J. Epidemiol. 39, 1206–1216. Hamadani, J.D., Tofail, F., Nermell, B., Gardner, R., Shiraji, S., Bottai, M., Arifeen, S.E., Huda, S.N., Vahter, M., 2011. Critical windows of exposure for arsenic-associated impairment of cognitive function in pre-school girls and boys: a population-based cohort study. Int. J. Epidemiol. 40, 1593–1604. Hernandez, A., Xamena, N., Surralles, J., Sekaran, C., Tokunaga, H., Quinteros, D., Creus, A., Marcos, R., 2008. Role of the Met(287)Thr polymorphism in the AS3MT gene on the metabolic arsenic profile. Mutat. Res. 637, 80–92. Hosmer, D.W., Lemeshow, S., 1992. Confidence interval estimation of interaction. Epidemiology 3, 452–456. Hsieh, Y.C., Lien, L.M., Chung, W.T., Hsieh, F.I., Hsieh, P.F., Wu, M.M., Tseng, H.P., Chiou, H.Y., Chen, C.J., 2011. Significantly increased risk of carotid atherosclerosis with arsenic exposure and polymorphisms in arsenic metabolism genes. Environ. Res. 111, 804–810. Hsieh, R.L., Huang, Y.L., Shiue, H.S., Huang, S.R., Lin, M.I., Mu, S.C., Chung, C.J., Hsueh, Y.M., 2014. Arsenic methylation capacity and developmental delay in preschool children in Taiwan. Int. J. Hyg. Environ. Health 217, 678–686.

Hsu, W.M., Hsi, H.C., Huang, Y.T., Liao, C.S., Hseu, Z.Y., 2012. Partitioning of arsenic in soilcrop systems irrigated using groundwater: a case study of rice paddy soils in southwestern Taiwan. Chemosphere 86, 606–613. Hsueh, Y.M., Chiou, H.Y., Huang, Y.L., Wu, W.L., Huang, C.C., Yang, M.H., Lue, L.C., Chen, G.S., Chen, C.J., 1997. Serum beta-carotene level, arsenic methylation capability, and incidence of skin cancer. Cancer Epidemiol. Biomark. Prev. 6, 589–596. Hsueh, Y.M., Huang, Y.L., Huang, C.C., Wu, W.L., Chen, H.M., Yang, M.H., Lue, L.C., Chen, C.J., 1998. Urinary levels of inorganic and organic arsenic metabolites among residents in an arseniasis-hyperendemic area in Taiwan. J. Toxicol. Environ. Health A 54, 431–444. Hsueh, Y.M., Hsu, M.K., Chiou, H.Y., Yang, M.H., Huang, C.C., Chen, C.J., 2002. Urinary arsenic speciation in subjects with or without restriction from seafood dietary intake. Toxicol. Lett. 133, 83–91. Janasik, B., Reszka, E., Stanislawska, M., Wieczorek, E., Fendler, W., Wasowicz, W., 2015. Biological monitoring and the influence of genetic polymorphism of As3MT and GSTs on distribution of urinary arsenic species in occupational exposure workers. Int. Arch. Occup. Environ. Health 88, 807–818. Jing, J., Zheng, G., Liu, M., Shen, X., Zhao, F., Wang, J., Zhang, J., Huang, G., Dai, P., Chen, Y., Chen, J., Luo, W., 2012. Changes in the synaptic structure of hippocampal neurons and impairment of spatial memory in a rat model caused by chronic arsenite exposure. Neurotoxicology 33, 1230–1238. Lesseur, C., Gilbert-Diamond, D., Andrew, A.S., Ekstrom, R.M., Li, Z., Kelsey, K.T., Marsit, C.J., Karagas, M.R., 2012. A case-control study of polymorphisms in xenobiotic and arsenic metabolism genes and arsenic-related bladder cancer in New Hampshire. Toxicol. Lett. 210, 100–106. Li, J., Waters, S.B., Drobna, Z., Devesa, V., Styblo, M., Thomas, D.J., 2005. Arsenic (+3 oxidation state) methyltransferase and the inorganic arsenic methylation phenotype. Toxicol. Appl. Pharmacol. 204, 164–169. Lindberg, A.L., Kumar, R., Goessler, W., Thirumaran, R., Gurzau, E., Koppova, K., Rudnai, P., Leonardi, G., Fletcher, T., Vahter, M., 2007. Metabolism of low-dose inorganic arsenic in a central European population: influence of sex and genetic polymorphisms. Environ. Health Perspect. 115, 1081–1086. Liu, Y., McDermott, S., Lawson, A., Aelion, C.M., 2010. The relationship between mental retardation and developmental delays in children and the levels of arsenic, mercury and lead in soil samples taken near their mother's residence during pregnancy. Int. J. Hyg. Environ. Health 213, 116–123. Meza, M.M., Yu, L., Rodriguez, Y.Y., Guild, M., Thompson, D., Gandolfi, A.J., Klimecki, W.T., 2005. Developmentally restricted genetic determinants of human arsenic metabolism: association between urinary methylated arsenic and CYT19 polymorphisms in children. Environ. Health Perspect. 113, 775–781. Park, S., Park, J.E., Yoo, H.J., Kim, J.W., Cho, S.C., Shin, M.S., Cheong, J.H., Han, D.H., Kim, B.N., 2015. Family-based association study of the arsenite methyltransferase gene (AS3MT, rs11191454) in Korean children with attention-deficit hyperactivity disorder. Psychiatr. Genet. 25, 26–30. Parvez, F., Wasserman, G.A., Factor-Litvak, P., Liu, X., Slavkovich, V., Siddique, A.B., Sultana, R., Sultana, R., Islam, T., Levy, D., Mey, J.L., Van, G.A., Khan, K., Kline, J., Ahsan, H., Graziano, J.H., 2011. Arsenic exposure and motor function among children in Bangladesh. Environ. Health Perspect. 119, 1665–1670. Petrick, J.S., Jagadish, B., Mash, E.A., Aposhian, H.V., 2001. Monomethylarsonous acid (MMA(III)) and arsenite: LD(50) in hamsters and in vitro inhibition of pyruvate dehydrogenase. Chem. Res. Toxicol. 14, 651–656. Pierce, B.L., Kibriya, M.G., Tong, L., Jasmine, F., Argos, M., Roy, S., Paul-Brutus, R., Rahaman, R., Rakibuz-Zaman, M., Parvez, F., Ahmed, A., Quasem, I., Hore, S.K., Alam, S., Islam, T., Slavkovich, V., Gamble, M.V., Yunus, M., Rahman, M., Baron, J.A., Graziano, J.H., Ahsan, H., 2012. Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh. PLoS Genet. 8, e1002522. Pierce, B.L., Tong, L., Argos, M., Gao, J., Farzana, J., Roy, S., Paul-Brutus, R., Rahaman, R., Rakibuz-Zaman, M., Parvez, F., Ahmed, A., Quasem, I., Hore, S.K., Alam, S., Islam, T., Harjes, J., Sarwar, G., Slavkovich, V., Gamble, M.V., Chen, Y., Yunus, M., Rahman, M., Baron, J.A., Graziano, J.H., Ahsan, H., 2013. Arsenic metabolism efficiency has a causal role in arsenic toxicity: Mendelian randomization and gene-environment interaction. Int. J. Epidemiol. 42, 1862–1871. Polimanti, R., Piacentini, S., Lazzarin, N., Re, M.A., Manfellotto, D., Fuciarelli, M., 2012. Lack of association between essential hypertension and GSTO1 uncommon genetic variants in Italian patients. Genet. Test. Mol. Biomarkers 16, 615–620. Pu, Y.S., Yang, S.M., Huang, Y.K., Chung, C.J., Huang, S.K., Chiu, A.W., Yang, M.H., Chen, C.J., Hsueh, Y.M., 2007. Urinary arsenic profile affects the risk of urothelial carcinoma even at low arsenic exposure. Toxicol. Appl. Pharmacol. 218, 99–106. Radabaugh, T.R., Sampayo-Reyes, A., Zakharyan, R.A., Aposhian, H.V., 2002. Arsenate reductase II. Purine nucleoside phosphorylase in the presence of dihydrolipoic acid is a route for reduction of arsenate to arsenite in mammalian systems. Chem. Res. Toxicol. 15, 692–698. Reichard, J.F., Schnekenburger, M., Puga, A., 2007. Long term low-dose arsenic exposure induces loss of DNA methylation. Biochem. Biophys. Res. Commun. 352, 188–192. Rodriguez, V.M., Del Razo, L.M., Limon-Pacheco, J.H., Giordano, M., Sanchez-Pena, L.C., Uribe-Querol, E., Gutierrez-Ospina, G., Gonsebatt, M.E., 2005. Glutathione reductase inhibition and methylated arsenic distribution in Cd1 mice brain and liver. Toxicol. Sci. 84, 157–166. Rodriguez, V.M., Limon-Pacheco, J.H., Carrizales, L., Mendoza-Trejo, M.S., Giordano, M., 2010. Chronic exposure to low levels of inorganic arsenic causes alterations in locomotor activity and in the expression of dopaminergic and antioxidant systems in the albino rat. Neurotoxicol. Teratol. 32, 640–647. Rodriguez-Barranco, M., Gil, F., Hernandez, A.F., Alguacil, J., Lorca, A., Mendoza, R., Gomez, I., Molina-Villalba, I., Gonzalez-Alzaga, B., Aguilar-Garduno, C., Rohlman, D.S.,

R.-L. Hsieh et al. / Toxicology and Applied Pharmacology 321 (2017) 37–47 Lacasana, M., 2015. Postnatal arsenic exposure and attention impairment in school children. Cortex. Rodriguez-Barranco, M., Gil, F., Hernandez, A.F., Alguacil, J., Lorca, A., Mendoza, R., Gomez, I., Molina-Villalba, I., Gonzalez-Alzaga, B., Aguilar-Garduno, C., Rohlman, D.S., Lacasana, M., 2016. Postnatal arsenic exposure and attention impairment in school children. Cortex 74, 370–382. Rosado, J.L., Ronquillo, D., Kordas, K., Rojas, O., Alatorre, J., Lopez, P., Garcia-Vargas, G., Del Carmen, C.M., Cebrian, M.E., Stoltzfus, R.J., 2007. Arsenic exposure and cognitive performance in Mexican schoolchildren. Environ. Health Perspect. 115, 1371–1375. Sanchez-Pena, L.C., Petrosyan, P., Morales, M., Gonzalez, N.B., Gutierrez-Ospina, G., Del Razo, L.M., Gonsebatt, M.E., 2010. Arsenic species, AS3MT amount, and AS3MT gene expression in different brain regions of mouse exposed to arsenite. Environ. Res. 110, 428–434. Schlawicke, E.K., Broberg, K., Concha, G., Nermell, B., Warholm, M., Vahter, M., 2007. Genetic polymorphisms influencing arsenic metabolism: evidence from Argentina. Environ. Health Perspect. 115, 599–605. Thompson, D.J., 1993. A chemical hypothesis for arsenic methylation in mammals. Chem. Biol. Interact. 88, 89-14. Tofail, F., Vahter, M., Hamadani, J.D., Nermell, B., Huda, S.N., Yunus, M., Rahman, M., Grantham-McGregor, S.M., 2009. Effect of arsenic exposure during pregnancy on infant development at 7 months in rural Matlab, Bangladesh. Environ. Health Perspect. 117, 288–293. Tsai, C.Y., Jiang, S.J., 2011. Microwave-assisted extraction and ion chromatography dynamic reaction cell inductively coupled plasma mass spectrometry for the speciation analysis of arsenic and selenium in cereals. Anal. Sci. 27, 271–276.

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Tyler, C.R., Allan, A.M., 2014. The effects of arsenic exposure on neurological and cognitive dysfunction in human and rodent studies: a review. Curr. Environ. Health Rep. 1, 132–147. Vahter, M., Concha, G., 2001. Role of metabolism in arsenic toxicity. Pharmacol. Toxicol. 89, 1–5. Valenzuela, O.L., Drobna, Z., Hernandez-Castellanos, E., Sanchez-Pena, L.C., Garcia-Vargas, G.G., Borja-Aburto, V.H., Styblo, M., Del Razo, L.M., 2009. Association of AS3MT polymorphisms and the risk of premalignant arsenic skin lesions. Toxicol. Appl. Pharmacol. 239, 200–207. Wasserman, G.A., Liu, X., Parvez, F., Ahsan, H., Factor-Litvak, P., Kline, J., Van, G.A., Slavkovich, V., Lolacono, N.J., Levy, D., Cheng, Z., Graziano, J.H., 2007. Water arsenic exposure and intellectual function in 6-year-old children in Araihazar, Bangladesh. Environ. Health Perspect. 115, 285–289. Wasserman, G.A., Liu, X., Loiacono, N.J., Kline, J., Factor-Litvak, P., Van, G.A., Mey, J.L., Levy, D., Abramson, R., Schwartz, A., Graziano, J.H., 2014. A cross-sectional study of well water arsenic and child IQ in Maine schoolchildren. Environ. Health 13, 23. Wu, F., Jasmine, F., Kibriya, M.G., Liu, M., Cheng, X., Parvez, F., Paul-Brutus, R., Paul, R.R., Sarwar, G., Ahmed, A., Jiang, J., Islam, T., Slavkovich, V., Rundek, T., Demmer, R.T., Desvarieux, M., Ahsan, H., Chen, Y., 2014. Interaction between arsenic exposure from drinking water and genetic susceptibility in carotid intima-media thickness in Bangladesh. Toxicol. Appl. Pharmacol. 276, 195–203. Zakharyan, R.A., Sampayo-Reyes, A., Healy, S.M., Tsaprailis, G., Board, P.G., Liebler, D.C., Aposhian, H.V., 2001. Human monomethylarsonic acid (MMA(V)) reductase is a member of the glutathione-S-transferase superfamily. Chem. Res. Toxicol. 14, 1051–1057.