Adverse outcome pathways and linkages to transcriptomic effects relevant to ionizing radiation injury

Abstract

Background

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.

Methods

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 (p < .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.

Results

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.

Conclusions

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.

Keywords:

• Gene expression
• radiation
• risk assessment
• AOPs
• adverse outcome pathways

Acknowledgments

The authors would like to acknowledge for insightful comments and edits to the manuscript. Special thanks to Dr. Edouard I. Azzam and Dr. Ruth C. Wilkins for their review, suggestions, and encouragement.

Disclosure statement

The authors declare they have no competing interests. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Additional information

Funding

This work was supported by Atomic Energy of Canada Limited’s Federal Nuclear Science & Technology Program, Genomics Research and Development Initiative, NSERC Discovery Grant, and Canada Research Chair Program.

Notes on contributors

Jihang Yu

Jihang Yu is a biologist at Canadian Nuclear Laboratories.

Wangshu Tu

Wangshu Tu Ph.D. is a post-doctoral fellow at Carleton University.

Andrea Payne

Andrea Payne is a 4th-year honor student at Carleton University.

Chris Rudyk

Chris Rudyk Ph.D. is a course instructor/lecturer at Carleton University and a scientific evaluator at Health Canada.

Sarita Cuadros Sanchez

Sarita Cuadros Sanchez is a research assistant at Health Canada.

Saadia Khilji

Saadia Khilji Ph.D. is a postdoctoral researcher at Health Canada.

Premkumari Kumarathasan

Premkumari Kumarathasan Ph.D. is a research scientist at Health Canada.

Sanjeena Subedi

Sanjeena Subedi Ph.D. is an assistant professor in the School of Mathematics and Statistics and a Canada Research Chair in Data Science and Analytics at Carleton University. Her research focuses on clustering and classification of high-dimensional data with application in bioinformatics.

Brittany Haley

Brittany Haley is a library assistant IV at Canadian Nuclear Laboratories.

Alicia Wong

Alicia Wong is a 4th-year honor student at McMaster University.

Catalina Anghel

Catalina Anghel Ph.D. is a computational research scientist at Canadian Nuclear Laboratories.

Yi Wang

Yi Wang Ph.D. is a research scientist and biologist at Canadian Nuclear Laboratories and an adjunct professor at the University of Ottawa.

Vinita Chauhan

Vinita Chauhan Ph.D. is a Senior Research Scientist at the Consumer and Clinical Radiation Protection Bureau of Health Canada. She is a Canadian delegate of the High-level group on low-dose research (HLG-LDR) and Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) of the OECD. She co-chairs the HLG-LDR Rad/Chem AOP Joint Topical Group and is the co-founder of the Canadian Organization of Health Effects from Radiation Exposure (COHERE) initiative.