In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view.
In silico health effect prioritization of environmental chemicals through transcriptomics data exploration from a chemo-centric view.
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DOI：
10.1016/j.scitotenv.2020.143082
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Xian Liu;Huazhou Zhang;Q. Xue;Wenxiao Pan;A. Zhang
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文献类型：
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作者：
Xian Liu;Huazhou Zhang;Q. Xue;Wenxiao Pan;A. Zhang
随着合成化合物的爆炸性增长，外源性化学品暴露对人体健康的影响越来越引起人们的关注。对健康影响的预测是一个永无止境的故事。转录组学数据的集体资源为理解和识别小分子的多种健康影响提供了机会。受高健康风险的环境化学品经常共享相似的基因表达谱和某些药物的共同结构特征的事实的启发，我们在这里提出了一种新的计算效果优先级方法，通过转录组学数据探索从化学中心的观点。具体来说，采用非负矩阵因子分解（NMF）方法来获得将具有特定作用的药物的结构特征与转录组学特征联系起来的关联网络。该模型产生了13种关键类型的效应，即所谓的组分，它们代表具有共同化学和基因型特征的药物类别。此外，所建立的模型有效地优先考虑潜在的毒性作用的外部化学品从内分泌干扰物筛选计划（EDSP）的潜在雌激素和其他验证的风险。即使只对雌激素效应设置最高优先级，对这些化学物质的准确率和召回率也可以分别达到0.76和0.77。我们的努力提供了一个成功的奋进，以配置文件潜在的毒性作用，同时使用化学和组学数据的环境化学品。
With the explosive growth of synthetic compounds, the health effects caused by exogenous chemical exposure have attracted more and more public attention. The prediction of health effect is a never-ending story. Collective resource of transcriptomics data offers an opportunity to understand and identify the multiple health effects of small molecule. Inspired by the fact that environmental chemicals of high health risk frequently share both similar gene expression profile and common structural feature of certain drugs, we here propose a novel computational effect prioritization method for environmental chemicals through transcriptomics data exploration from a chemo-centric view. Specifically, non-negative matrix factorization (NMF) method has been adopted to get the association network linking structural features with transcriptomics characteristics of drugs with specific effects. The model yields 13 pivotal types of effects, so-called components, that represent drug categories with common chemo- and geno- type features. Moreover, the established model effectively prioritizes potential toxic effects for the external chemicals from the endocrine disruptor screening program (EDSP) for their potential estrogenicity and other verified risks. Even if only the highest priority is set for the estrogenic effect, the precision and recall can reach 0.76 and 0.77 respectively for these chemicals. Our effort provides a successful endeavor as to profile potential toxic effects simultaneously for environmental chemicals using both chemical and omics data.