毒理基因组学
暴露的
毒性
计算生物学
斑马鱼
相互作用体
生物
人类健康
药物开发
药品
药物毒性
化学毒性
保护
生物信息学
药物发现
风险评估
毒理
细胞毒性
慢性毒性
公共卫生
计算机科学
系统生物学
数据科学
生物信息学
作者
Xiao Gan,Geng Li,Weiyi Yang,Jun Xu,Réka Albert,Bei Gao
标识
DOI:10.1021/acs.est.5c08341
摘要
Humans are exposed to various chemicals, and understanding their toxicity is essential for safeguarding their health. However, current toxicological methods often focus on specific diseases or chemicals, missing broader patterns of toxicity. Here, we propose a general framework that conceptualizes toxicity as arising from protein-interaction-mediated relationships between chemicals and diseases. Using the Comparative Toxicogenomics Database, we show that the network proximity between chemical targets and disease-associated proteins in the protein interactome predicts chemical-disease associations. We validate this finding through zebrafish experiments for acute toxicity and analyses of human exposome data sets for chronic chemical-disease correlation. We highlight the applications of our framework in predicting a previously unknown toxic effect of a fungicide, explaining drug cytotoxicity in a COVID drug screening, and uncovering the overlooked ″indirect-hit″ chemical-disease toxicity pattern. Our approach establishes a new paradigm for toxicity research, offering insights into the health risks posed by chemicals and addressing public health challenges.
科研通智能强力驱动
Strongly Powered by AbleSci AI