Discovering Chemical Activity Networks-Predicting Bioactivity Based on Structure

项目总结 NIEHS已将预测性毒理学确立为推进环境健康科学的战略目标。 这项河水计划的首要目标是根据化学物质的结构预测其动物毒性。 我和我的团队将把数百万个斑马鱼胚胎暴露在一个由全球10,000种合成化学物质组成的图书馆中 浓度范围。如果一种化学物质显示出生物活性的迹象，我们将系统地分析整个动物的基因 在表型出现之前，表达会发生变化。我们将制定假设，说明哪些生物分子 以最初攻击的化学物质为目标，以及哪些路径通向观察到的终点。为了测试这些 假设，我们将通过CRISPR/Cas9编辑斑马鱼基因组，以敲除或过度表达关键基因，以 找出与化学表型有因果关系的基因。 这些研究将与人类健康高度相关。斑马鱼拥有完全整合的脊椎动物器官 系统执行与人类相同的功能，并表现出良好的保守性 生理学。参与人类疾病的基因中有84%也存在于斑马鱼中。斑马鱼 研究提供了一种快速、廉价的方法来筛选大量的化学物质，产生丰富的药物假说 开发，并优先考虑哺乳动物和人类细胞培养的毒性研究候选对象。我们会 将我们的结果与人类细胞培养研究的结果进行比较，以澄清各自的优势和劣势 方法，并减少与将斑马鱼结果应用于人类生物学相关的不确定性。 我们将把我们的实验结果发布在公共数据库中，该数据库解释了10,000种Tox21化学物质中 是生物活性的，它们攻击的初始靶点是什么，以及哪些途径通向胚胎和 幼年斑马鱼。这一信息将使绿色化学家能够通过将 生物活性不活跃的分子。它将帮助毒物学家和风险评估员优先考虑昂贵的化学品 用啮齿动物和人类细胞培养进行实验。它将为制药科学家提供成千上万的新数据 关于如何调节一个给定的基因靶点或激活一个给定的基因的假设 路径。 我们将使用基于机器学习的化学信息学方法来分析我们的斑马鱼数据，并推断 化学物质的结构与其生物活性之间的关系。我们关于化学物质的丰富数据 活动网络将促进科学界对化学品暴露之间的联系的了解 和表型。我们的工作将使科学家能够预测一种化学物质是否具有生物活性， 它将采取行动的目标，以及它将扰乱哪些网络，完全是基于其结构。它将使 科学家减少、改进和取代动物实验，包括斑马鱼，并预测化学物质 与计算机建立活动网络。

PROJECT SUMMARY NIEHS has established Predictive Toxicology as a strategic goal for advancing environmental health sciences. The overarching goal of this RIVER proposal is to predict animal toxicity of chemicals based on their structure. My team and I will expose millions of zebrafish embryos to a library of 10,000 synthetic chemicals across wide concentration ranges. If a chemical shows signs of bioactivity, we will systematically analyze whole animal gene expression changes before the phenotype appears. We will formulate hypotheses about which biomolecular targets the chemicals attacked initially and which pathways led to the observed endpoint. To test those hypotheses, we will edit the zebrafish genome via CRISPR/Cas9 to knock out or over-express critical genes, to discover the ones causally related to the chemical phenotypes. These studies will be highly relevant to human health. Zebrafish possess fully integrated vertebrate organ systems that perform the same functions as their human counterparts and demonstrate well-conserved physiology. Eighty-four percent of the genes that participate in human disease also exist in zebrafish. Zebrafish studies provide a fast, inexpensive way to screen a large volume of chemicals, generate rich hypotheses for drug development, and prioritize candidates for toxicity studies with mammals and human cell cultures. We will compare our results with those of human cell culture studies to clarify the strengths and weaknesses of each method and to reduce the uncertainty associated with applying zebrafish results to human biology. We will post our experimental results in a public database that explains which of the 10,000 Tox21 chemicals are bioactive, which initial targets they strike, and which pathways lead to which endpoints in embryonic and juvenile zebrafish. This information will enable green chemists to detoxify products by substituting a biologically inactive molecule. It will help toxicologists and risk assessors to prioritize chemicals for expensive experiments with rodents and human cell cultures. It will give pharmaceutical scientists thousands of new data points upon which to develop hypotheses about how to modulate a given gene target or activate a given pathway. We will use machine-learning-based chemoinformatic approaches to analyze our zebrafish data and infer the relationship between the structure of a chemical and its biological activity. Our rich data about chemical activity networks will advance the scientific community’s understanding of linkages between chemical exposure and phenotypes. Our work will enable scientists to predict whether a chemical will be biologically active, what target it will act upon, and what networks it will perturb, solely on the basis of its structure. It will enable scientists to reduce, refine, and replace experiments with animals, including zebrafish, and to predict chemical activity networks with computers.