Canadian Nuclear Laboratories, Chalk River, Ontario, Canada.
School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, Canada.
Int J Radiat Biol. 2022;98(12):1789-1801. doi: 10.1080/09553002.2022.2110313. Epub 2022 Aug 22.
In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example.
Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered ( < .001, ≥3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs.
Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels.
This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.
在过去的三十年中，已经产生了大量关于暴露于电离辐射的影响和随之而来的基因表达变化的数据。这些数据使我们能够了解分子水平的事件，并显示出尽管在机构之间使用了各种格式和实验程序，但反应具有一致性。然而，需要探讨如何利用这些信息为健康风险评估提供策略。一种弥合这一差距的方法是不良结局途径（AOP）框架。AOP 代表了一个说明性框架，用于描述与一系列因果相关的关键事件（KEs）相关的应激源，这些 KEs 位于不同的生物组织水平，从分子起始事件（MIE）开始，最终导致不良结局（AO）。在这里，我们通过使用肺癌 AOP（AOP 272：https://www.aopwiki.org/aops/272）作为案例示例，展示了在电离辐射领域中如何在 AOP 框架内解释转录组数据集。
通过对文献的挖掘，确定了与 AOP 272 相关的肺癌、DNA 损伤反应和修复的辐射暴露相关转录组研究。对相关研究中的差异表达基因进行整理，并使用 Reactome 和 GeneMANIA 平台对途径和网络进行分析。确定的途径经过筛选（ < .001，≥3 个基因），并根据与 AOP 中的 KEs 的相关性进行分类。识别基因连接，并根据与基因表达相关的事件（AE）进一步分组。使用相关的定量剂量反应数据来告知网络中基因在 AE 中的表达方向。
Reactome 分析确定了 7 个具有多种途径和相关基因的高级生物学过程，这些基因映射到 AOP 272 中的潜在 KEs。基因连接进一步表示为具有相关表达谱的 AE 网络，突出了基因表达水平的模式。
本研究展示了转录组数据在 AOP 开发中的应用，并提供了有关潜在数据差距的信息。尽管该方法是新的，预计会不断发展，但它显示出了改善对疾病进展潜在机制的理解的前景，具有长期预测不良结局的潜力。
