Discovering Chemical Activity Networks-Predicting Bioactivity Based on Structure

发现化学活性网络——根据结构预测生物活性

• 批准号: 10646393
• 负责人: Robyn L Tanguay
• 金额: $ 85.61万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-16 至 2029-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646393
• 关键词: Adolescent; Animals; Biological; CRISPR/Cas technology; Cell Culture Techniques; Chemical Exposure; Chemical Warfare; Chemicals; Communities; Computers; Data; Drug Metabolic Detoxication; Embryo; Environmental Health; Gene Expression; Gene Targeting; Genes; Goals; Health; Human; Human Biology; Informatics; Knock-out; Lead; Libraries; Machine Learning; Mammals; Methods; National Institute of Environmental Health Sciences; Pathway interactions; Pharmacologic Substance; Phenotype; Physiology; Research; Risk; Rodent; Scientific Advances and Accomplishments; Scientist; Structure; Testing; Toxic effect; Toxicology; Uncertainty; Work; Zebrafish; adverse outcome; body system; drug development; experimental study; human disease; overexpression; public database; zebrafish genome

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.

项目摘要 NIEHS已建立了预测毒理学，作为推进环境健康科学的战略目标。 该河流提案的总体目标是根据其结构预测动物的毒性。 我和我的团队将使数百万个斑马鱼胚胎在遍布宽的10,000个合成化学物质的库中 浓度范围。如果化学表现出生物活性的迹象，我们将系统地分析全动物基因 表达在出现表型之前发生变化。我们将提出关于哪种生物分子的假设 目标最初攻击的化学物质，并导致观察到的终点。测试这些 假设，我们将通过CRISPR/CAS9编辑斑马鱼基因组，以敲除或过表达关键基因 发现有时与化学表型有关的那些。 这些研究将与人类健康高度相关。斑马鱼电势完全整合脊椎动物器官 执行与人类同行相同功能并表现出良好的功能的系统 生理。斑马鱼中也存在84％参与人类疾病的基因。斑马鱼 研究提供了一种快速，廉价的方法来筛选大量化学物质，为药物产生丰富的假设 开发，并优先考虑使用哺乳动物和人类细胞培养的毒性研究的候选者。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，以阐明每个研究的优势和劣势 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。 我们将在公共数据库中发布我们的实验结果，该数据库解释了10,000个TOX21化学品中的哪些 是生物活性的，其最初的靶向靶向，以及哪种途径导致胚胎和 少年斑马鱼。该信息将使绿色化学家能够通过代替 生物学无活性分子。它将帮助毒理学家和风险评估者确定化学品优先考虑昂贵的化学品 实验啮齿动物和人类细胞培养物。它将为制药科学家提供数千个新数据 关于如何调节给定基因靶标的或激活给定的有关的点 路径。 我们将使用基于机器的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 化学结构与其生物学活性之间的关系。我们关于化学的丰富数据 活动网络将提高科学界对化学暴露之间联系的理解 和表型。我们的工作将使科学家能够预测化学物质是否会具有生物活性，什么 目标它将仅根据其结构来对付哪些网络以及它将扰动的网络。它将启用 科学家减少，完善和替代包括斑马鱼在内的动物的实验，并预测化学 带有计算机的活动网络。

项目成果

Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data.

• DOI: 10.1021/acs.est.2c01040
• 发表时间: 2022-05-03
• 期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
• 影响因子: 11.4
• 作者: Ciallella, Heather L.;Russo, Daniel P.;Sharma, Swati;Li, Yafan;Sloter, Eddie;Sweet, Len;Huang, Heng;Zhu, Hao
• 通讯作者: Zhu, Hao