Discovering Chemical Activity Networks-Predicting Bioactivity Based on Structure

项目摘要 NIEHS将预测毒理学作为推进环境健康科学的战略目标。 该RIVER提案的总体目标是根据化学品的结构预测其对动物的毒性。 我和我的团队将把数百万条斑马鱼胚胎暴露在一个包含10,000种合成化学物质的图书馆中， 浓度范围。如果一种化学物质显示出生物活性，我们将系统地分析整个动物基因， 在表型出现之前表达发生变化。我们将提出假设， 目标是最初被攻击的化学品以及导致观察到的终点的途径。测试这些 假设，我们将通过CRISPR/Cas9编辑斑马鱼基因组，敲除或过表达关键基因， 发现那些与化学表型有因果关系的。 这些研究将与人类健康密切相关。斑马鱼拥有完整的脊椎动物器官 系统执行与人类对应物相同的功能，并表现出良好的保守性， physiology.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物的毒性研究的候选人。我们将 将我们的结果与人类细胞培养研究的结果进行比较，以澄清各自的优点和缺点 方法，并减少与应用斑马鱼的结果，以人类生物学的不确定性。 我们将在公共数据库中公布我们的实验结果，解释10，000种Tox 21化学物质中 是生物活性的，它们攻击的最初目标，以及哪些途径导致胚胎和 幼年斑马鱼这一信息将使绿色化学家能够通过取代一种 无生物活性的分子。它将帮助毒理学家和风险评估人员优先考虑化学品， 啮齿动物和人类细胞培养物的实验。它将为制药科学家提供数千个新数据 关于如何调节给定的基因靶点或激活给定的 通路 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，并推断出 化学物质的结构与其生物活性之间的关系。我们丰富的化学数据 活动网络将促进科学界对化学品接触之间联系的理解， 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， 它将作用于什么样的网络，以及它将干扰什么样的网络，完全基于它的结构。它将使 科学家们减少，改进和取代动物实验，包括斑马鱼，并预测化学 活动网络与计算机。

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.