Interaction of fluoride exposure and CREB1 gene polymorphisms on thyroid function in school-age children

Xu, Kaihong; Feng, Zichen; Afrim, Francis Kojo; Ma, Jun; Yang, Shuo; Zhang, Xuanyin; Niu, Zeyuan; An, Ning; Du, Yuhui; Yu, Fangfang; Zhou, Guoyu; Ba, Yue

doi:10.1016/j.chemosphere.2022.135156

Cited by 2 publications

(1 citation statement)

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“…The first is obvious and has been frequently discussed: models may be selected to reflect optimally the human type of genetic background and the functional/regulatory networks encoded. For this reason, many human polymorphisms have been mapped to toxicant sensitivity differences (Axelrad et al 2019 ; Chao et al 2017 ; Gurol et al 2023 ; Monte et al 2022 ; Pham et al 2022 ; Reverte et al 2013 ; Salazar-Gonzalez et al 2023 ; Xu et al 2022a , b ). If hazard evaluation follows the AOP concept, it is especially important that there are no large taxonomic differences and that the fine-tuning of key event relationships by genetic factors is similar between the model system and the human population.…”

Section: Model Refinement By G × Ementioning

confidence: 99%

G × E interactions as a basis for toxicological uncertainty

et al. 2023

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To transfer toxicological findings from model systems, e.g. animals, to humans, standardized safety factors are applied to account for intra-species and inter-species variabilities. An alternative approach would be to measure and model the actual compound-specific uncertainties. This biological concept assumes that all observed toxicities depend not only on the exposure situation (environment = E), but also on the genetic (G) background of the model (G × E). As a quantitative discipline, toxicology needs to move beyond merely qualitative G × E concepts. Research programs are required that determine the major biological variabilities affecting toxicity and categorize their relative weights and contributions. In a complementary approach, detailed case studies need to explore the role of genetic backgrounds in the adverse effects of defined chemicals. In addition, current understanding of the selection and propagation of adverse outcome pathways (AOP) in different biological environments is very limited. To improve understanding, a particular focus is required on modulatory and counter-regulatory steps. For quantitative approaches to address uncertainties, the concept of “genetic” influence needs a more precise definition. What is usually meant by this term in the context of G × E are the protein functions encoded by the genes. Besides the gene sequence, the regulation of the gene expression and function should also be accounted for. The widened concept of past and present “gene expression” influences is summarized here as Ge. Also, the concept of “environment” needs some re-consideration in situations where exposure timing (Et) is pivotal: prolonged or repeated exposure to the insult (chemical, physical, life style) affects Ge. This implies that it changes the model system. The interaction of Ge with Et might be denoted as Ge × Et. We provide here general explanations and specific examples for this concept and show how it could be applied in the context of New Approach Methodologies (NAM).

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