University of Colorado, Computer Science / Interdisciplinary Quantitative Biology, Boulder, CO 80309, USA.
University of Colorado Anschutz Medical Campus, Computational Bioscience, Denver, CO 80045, USA.
Toxicol In Vitro. 2020 Aug;66:104877. doi: 10.1016/j.tiv.2020.104877. Epub 2020 May 6.
When considering toxic chemicals in the environment, a mechanistic, causal explanation of toxicity may be preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This mechanistic inference framework is an advantageous tool for predictive toxicology, and the first of its kind to produce a mechanistic explanation for each prediction. MechSpy can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.
当考虑环境中的有毒化学物质时，与基于统计或机器学习的预测相比，人们可能更倾向于采用一种具有机制因果关系的毒性解释。然而，阐明毒性的机制是一个代价高昂且耗时的过程，需要来自多个领域的专家参与，通常还依赖于动物模型。我们提出了一种创新的机制推断框架（MechSpy），它可用作生成假说的辅助工具，以缩小机制毒理学分析的范围。MechSpy 通过将人类生物学、毒理学和生物化学的语义上相互关联的知识表示与人类组织的基因表达时间序列相结合，生成最有可能的毒性机制假说。通过使用生物实体的向量表示，MechSpy 在一个经过人工策展的毒性高级机制列表中寻求富集，该列表以生物化学和因果关系链接的本体概念表示。除了预测许多研究充分的化合物的典型毒性机制外，我们还通过实验验证了一些针对其他化学物质的预测，这些化学物质没有既定的毒性机制。这种机制推断框架是一种用于预测毒理学的有利工具，也是第一个为每个预测提供机制解释的工具。可以对 MechSpy 进行修改，以纳入其他毒性机制，并且可以推广到其他类型的人类生物学机制。
