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Chauhan V, Adam N, Kuo B, et al. Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment. Int J Radiat Biol. 2020;97(1):31-49doi: 10.1080/09553002.2020.1798543.
Chauhan, V., Adam, N., Kuo, B., Williams, A., Yauk, C. L., Wilkins, R., & Stainforth, R. (2021). Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment. International journal of radiation biology, 97(1), 31-49. https://doi.org/10.1080/09553002.2020.1798543
Chauhan, Vinita, et al. "Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment." International journal of radiation biology vol. 97,1 (2021): 31-49. doi: https://doi.org/10.1080/09553002.2020.1798543
Chauhan V, Adam N, Kuo B, Williams A, Yauk CL, Wilkins R, Stainforth R. Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment. Int J Radiat Biol. 2021;97(1):31-49. doi: 10.1080/09553002.2020.1798543. Epub 2020 Aug 18. PMID: 32687419.
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Int J Radiat Biol. 2021;97(1):31-49. doi: 10.1080/09553002.2020.1798543. Epub 2020 Aug 18.

Meta-analysis of transcriptomic datasets using benchmark dose modeling shows value in supporting radiation risk assessment.

International journal of radiation biology

Vinita Chauhan, Nadine Adam, Byron Kuo, Andrew Williams, Carole L Yauk, Ruth Wilkins, Robert Stainforth

Affiliations

  1. Consumer and Clinical Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada.
  2. Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada.
  3. Radiation Protection Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Canada.

PMID: 32687419 DOI: 10.1080/09553002.2020.1798543

Abstract

PURPOSE: Benchmark dose (BMD) modeling is used to determine the dose of a stressor at which a predefined increase in any biological effect above background occurs (e.g. 10% increase from control values). BMD analytical tools have the capacity to model transcriptional dose-response data to derive BMDs for genes, pathways and gene ontologies. We recently demonstrated the value of this approach to support various areas of radiation research using predominately 'in-house' generated datasets.

MATERIALS AND METHODS: As a continuation of this work, transcriptomic studies of relevance to ionizing radiation were retrieved through the Gene Expression Omnibus (GEO). The datasets were compiled and filtered, then analyzed using BMDExpress. The objective was to determine the reproducibility of BMD values in relation to pathways and genes across different exposure scenarios and compare to those derived using cytogenetic endpoints. A number of graphic visualization approaches were used to determine if BMD outputs could be correlated to parameters such as dose-rate, radiation quality and cell type.

RESULTS: Curated studies were diverse and derived from experiments with varied design and intent. Despite this, common genes and pathways were identified with low and high dose thresholds. The higher BMD values were associated with immune response and cell death, while transcripts with lower BMD values were generally related to the classic DNA damage response/repair processes, centered on

CONCLUSION: Overall, the results from this work highlight the value of the BMD methodology to derive meaningful outputs that are consistent across different models, provided the studies are conducted using a similar dose-range.

Keywords: Point of departure; benchmark dose; radiation; risk assessment

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