Environmental Health Laboratory, Department of Environmental Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
Grow School of Oncology and Developmental Biology, Department of Toxicogenomics, Maastricht University, the Netherlands.
Environ Int. 2020 Feb;135:105403. doi: 10.1016/j.envint.2019.105403. Epub 2019 Dec 18.
Groundwater Arsenic (As) contamination is a global public health concern responsible for various health implications and a neglected area of environmental health research in Pakistan. Because of interindividual differences in genetic predisposition, As-related health issues may not be equally distributed among the As-exposed population. However, till date, no studies have been conducted including multiple SNPs involved in As metabolism and disease risk using a linear mixed effect model approach to analyze peripheral blood transcriptomics results.
In order to detect early responses on the gene expression level and to evaluate the impact of selected SNPs inferring disease risks associated with As exposure, we designed a systematic study to investigate blood transcriptomics profiles of 57 differentially exposed rural subjects living in drinking water As-contaminated settings of Lahore and Kasur districts in Punjab Province in southeast Pakistan. Exposure among the subjects was correlated with individual transcriptome responses applying urinary As profiles as the main biomarker for risk stratification.
We performed whole genome gene expression analysis in blood of subjects using microarrays. Linear effect mixed models were applied for evaluating the combined impact of SNPs hypothetically increasing the risk for As exposure-induced health effects (GSTM1, GSTT1, As3MT, DNMT1, MTHFR, ERCC2 and EGFR).
Our findings confirmed important signaling, growth factor, cancer and other disease related pathways known to be associated with increased As exposure levels. In addition, upon implementing our integrative SNPs-based genetic risk factor, pathways associated with an increased risk of NAFLD and diabetes appeared significantly enhanced by down-regulation of genes NDUFV3, IKBKB, IL6R, ADIPOR1, PPARA, OGT and FOXO1.
We report the first comprehensive study applying state-of-the-art bioinformatics approaches to address multiple SNP-based inter-individual variability in adverse molecular responses among subjects exposed to drinking water As contamination in Pakistan thereby providing strong evidence of various gene expression targets associated with development of known As-related diseases.
地下水砷（As）污染是一个全球性的公共卫生问题，对健康有各种影响，也是巴基斯坦环境健康研究中被忽视的领域。由于个体遗传易感性的差异，与 As 相关的健康问题在暴露于 As 的人群中可能分布不均。然而，迄今为止，还没有研究使用线性混合效应模型方法分析外周血转录组学结果，包括涉及 As 代谢和疾病风险的多个单核苷酸多态性（SNP）。
为了检测基因表达水平的早期反应，并评估与 As 暴露相关的疾病风险推断的选定 SNP 的影响，我们设计了一项系统研究，以调查生活在巴基斯坦东南部旁遮普省拉合尔和卡苏尔地区饮用水砷污染地区的 57 名农村差异暴露者的血液转录组谱。应用尿砷谱作为风险分层的主要生物标志物，将个体暴露与个体转录组反应相关联。
我们使用微阵列对受试者的血液进行全基因组基因表达分析。应用线性效应混合模型评估假设增加 As 暴露诱导健康效应风险的 SNP 的综合影响（GSTM1、GSTT1、As3MT、DNMT1、MTHFR、ERCC2 和 EGFR）。
我们的研究结果证实了与增加的 As 暴露水平相关的重要信号、生长因子、癌症和其他疾病相关途径。此外，在实施我们基于整合 SNP 的遗传风险因素后，与非酒精性脂肪性肝病和糖尿病风险增加相关的途径由于 NDUFV3、IKBKB、IL6R、ADIPOR1、PPARA、OGT 和 FOXO1 等基因的下调而显著增强。
我们报告了第一项应用最先进的生物信息学方法解决巴基斯坦饮用水砷污染暴露人群中不良分子反应的个体间 SNP 差异的综合研究，从而提供了与已知 As 相关疾病发展相关的各种基因表达靶标的有力证据。
