Characterizing mechanisms of toxicological action for data-poor priority pollutants in Canada using novel model systems and transcriptomic methodologies

使用新颖的模型系统和转录组学方法来表征加拿大数据匮乏的优先污染物的毒理学作用机制

• 批准号: RGPIN-2021-02806
• 负责人: Yauk, Carole
• 金额: $ 3.64万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=745596
• 关键词: Characterizing; mechanisms; toxicological; action; data

There are thousands of chemicals in our environment that have never been subject to toxicological testing (referred to as `data-poor'). The root cause of this is reliance on traditional tests that are not high-throughput or economical. These tests also do not reveal toxicological mechanisms that inform species relevance and individual susceptibility. Transcriptomics (global gene expression profiling) provides a rich source of information to understand and predict toxicological effects. Transcriptomics has yet to be fully harnessed for use in regulatory decision-making and risk assessment. Objectives The overarching goal of my research and training program is to modernize regulatory evaluation of chemical toxicity testing. We aim to develop and apply pragmatic approaches to efficiently identify toxicological mechanisms and predict hazards from transcriptomic data. Specific objectives We will apply high-throughput transcriptomics and bioinformatics in human in vitro models to address knowledge gaps in data-poor chemicals of high concern. Projects will focus on two chemical families, per- and poly-fluorinated alkyl substances and bisphenols, because of their widespread use and the large number of data-poor chemicals within these families. The work will be broken into three phases: Phase 1a - Hypothesis generation. Deeply mine existing and new genomic data sets to decipher mechanisms of toxicity and identify physico-chemical drivers of toxicity; Phase 1b - Methods development. Apply/develop machine-learning tools to predict toxicological effects from transcriptomic profiles to increase the efficiency and reproducibility of data interpretation; Phase 2 - Hypothesis testing and methods validation. Conduct validation experiments to confirm molecular, biochemical and phenotypic predictions from Phase 1; Phase 3 - Discovery. Apply single-cell RNA-sequencing to identify cell type- and individual-specific mechanisms, potencies, and effects. The program supports 5 graduate students each year, for a total of 4 MSc, 3 PhD completed, and 2 MSc, 3 PhD initiated. The program provides a unique training ground focused on translation of research innovation into regulatory decision-making, policy and practice. Novelty and significance The work will provide critical information on the toxicity of data-poor chemicals and reveal how new models and artificial intelligence can be used to predict toxicological effects. The analytical approaches can be applied in green-chemistry development pipelines to reduce the probability of regrettable replacements. The work will impact policy related to priority chemical categories and use of genomic data in regulatory decision-making. The tools provide new approaches that increase analytical throughput and save cost/time for chemical evaluations, which impacts industry and regulatory agencies. Importantly, the training program will produce the next-generation of toxicologists and risk assessment specialists for Canada.

在我们的环境中，有成千上万的化学品从未经过毒理学测试（被称为“数据贫乏”）。其根本原因是依赖于不高通量或不经济的传统测试。这些测试也没有揭示告知物种相关性和个体易感性的毒理学机制。转录组学（全球基因表达谱分析）为理解和预测毒理学效应提供了丰富的信息来源。转录组学尚未完全用于监管决策和风险评估。 我的研究和培训计划的总体目标是现代化的化学毒性测试的监管评估。我们的目标是开发和应用务实的方法来有效地识别毒理学机制，并从转录组数据预测危害。 具体目标我们将在人类体外模型中应用高通量转录组学和生物信息学，以解决高度关注的数据匮乏化学品的知识缺口。项目将侧重于两个化学品族，即全氟烷基和多氟烷基物质和双酚，因为它们的广泛使用和这些族中大量缺乏数据的化学品。这项工作将分为三个阶段：阶段1a -假设生成。深入挖掘现有和新的基因组数据集，以破译毒性机制，并确定毒性的物理化学驱动因素;阶段1b -方法开发。应用/开发机器学习工具，从转录组学特征预测毒理学效应，以提高数据解释的效率和再现性;第2阶段-假设检验和方法验证。进行验证实验，以确认第1阶段和第3阶段-发现的分子、生物化学和表型预测。应用单细胞RNA测序来确定细胞类型和个体特异性机制，效力和影响。该计划每年支持5名研究生，共完成4名硕士，3名博士，2名硕士，3名博士。该计划提供了一个独特的培训基地，专注于将研究创新转化为监管决策，政策和实践。新奇和意义这项工作将提供关于数据贫乏的化学品毒性的关键信息，并揭示如何使用新模型和人工智能来预测毒理学效应。分析方法可以应用于绿色化学开发管道，以减少令人遗憾的更换的概率。这项工作将影响与优先化学品类别有关的政策以及在监管决策中使用基因组数据。这些工具提供了新的方法，可以提高分析吞吐量，节省化学品评估的成本/时间，从而影响行业和监管机构。重要的是，该培训计划将为加拿大培养下一代毒理学家和风险评估专家。