不良结局途径
数量结构-活动关系
炎症
转录组
纤维化
毒理基因组学
通路分析
计算生物学
材料科学
生物信息学
基因
医学
化学
生物
免疫学
病理
基因表达
生物化学
作者
Karolina Jagiełło,Sabina Halappanavar,Anna Rybińska-Fryca,Andrew Willliams,Ulla Vogel,Tomasz Puzyn
出处
期刊:Small
[Wiley]
日期:2021-01-27
卷期号:17 (15)
被引量:31
标识
DOI:10.1002/smll.202003465
摘要
Abstract This study presents a novel strategy that employs quantitative structure–activity relationship models for nanomaterials (Nano‐QSAR) for predicting transcriptomic pathway level response using lung tissue inflammation, an essential key event (KEs) in the existing adverse outcome pathway (AOP) for lung fibrosis, as a model response. Transcriptomic profiles of mouse lungs exposed to ten different multiwalled carbon nanotubes (MWCNTs) are analyzed using statistical and bioinformatics tools. Three pathways “agranulocyte adhesion and diapedesis,” “granulocyte adhesion and diapedesis,” and “acute phase signaling,” that (1) are commonly perturbed across the MWCNTs panel, (2) show dose response (Benchmark dose, BMDs), and (3) are anchored to the KEs identified in the lung fibrosis AOP, are considered in modelling. The three pathways are associated with tissue inflammation. The results show that the aspect ratio (κ) of MWCNTs is directly correlated with the pathway BMDs. The study establishes a methodology for QSAR construction based on canonical pathways and proposes a MWCNTs grouping strategy based on the κ‐values of the specific pathway associated genes. Finally, the study shows how the AOP framework can help guide QSAR modelling efforts; conversely, the outcome of the QSAR modelling can aid in refining certain aspects of the AOP in question (here, lung fibrosis).
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