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A Drosophila model for toxicogenomics: Genetic variation in susceptibility to heavy metal exposure

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Figshare2017-08-04 更新2026-04-29 收录
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https://figshare.com/articles/dataset/A_drosophila_model_for_toxicogenomics_Genetic_variation_in_susceptibility_to_heavy_metal_exposure/5231755
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The genetic factors that give rise to variation in susceptibility to environmental toxins remain largely unexplored. Studies on genetic variation in susceptibility to environmental toxins are challenging in human populations, due to the variety of clinical symptoms and difficulty in determining which symptoms causally result from toxic exposure; uncontrolled environments, often with exposure to multiple toxicants; and difficulty in relating phenotypic effect size to toxic dose, especially when symptoms become manifest with a substantial time lag. Drosophila melanogaster is a powerful model that enables genome-wide studies for the identification of allelic variants that contribute to variation in susceptibility to environmental toxins, since the genetic background, environmental rearing conditions and toxic exposure can be precisely controlled. Here, we used extreme QTL mapping in an outbred population derived from the D. melanogaster Genetic Reference Panel to identify alleles associated with resistance to lead and/or cadmium, two ubiquitous environmental toxins that present serious health risks. We identified single nucleotide polymorphisms (SNPs) associated with variation in resistance to both heavy metals as well as SNPs associated with resistance specific to each of them. The effects of these SNPs were largely sex-specific. We applied mutational and RNAi analyses to 33 candidate genes and functionally validated 28 of them. We constructed networks of candidate genes as blueprints for orthologous networks of human genes. The latter not only provided functional contexts for known human targets of heavy metal toxicity, but also implicated novel candidate susceptibility genes. These studies validate Drosophila as a translational toxicogenomics gene discovery system.
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2017-08-04
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