ArticlePLoS computational biology2025
Computational modelling identifies primary mediators of crosstalk between DNA damage and oxidative stress responses.
Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Research progress on BTG2 in non‑tumor diseases (Review).International journal of molecular medicine · 2026Review
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7 authors.
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Abstract
Cells exposed to toxicants, such as drugs, activate a wide variety of stress pathways, often simultaneously. Two important pathways that can influence cell fate and consequently adverse reactions are the oxidative stress response (OSR) and the DNA damage response (DDR). Previous studies have presented evidence of crosstalk between the OSR and DDR. We aimed to develop computational models to describe experimentally observed dynamics of both OSR and DDR proteins in liver (HepG2) cells in vitro upon exposure to various concentrations of either diethyl maleate (DEM; an agent primarily invoking oxidative stress) or etoposide (an agent primarily causing DNA damage). With these models, we aimed to identify the key interactions that cause crosstalk and their importance in describing protein dynamics. We developed a new model for the OSR pathway, coupled it to a previously developed model for the DDR pathway, and extended the resulting combined model based on multiple potential modes of crosstalk described in the literature. The different models were applied to previously published data of HepG2 GFP-reporter cells with time-dynamic information on the relative amount of proteins important for the OSR (NRF2, SRXN1) or DDR (p53, p21, BTG2 and MDM2). The developed models properly described key OSR and DDR protein dynamics, and in silico knockdowns of key model components in most cases led to a moderate effect on the connected pathway. The largest effect occurred after knockdown of p21, which resulted in a substantial decrease in NRF2 and SRXN1. We expect these models could play a role in adversity predictions by coupling our models with other models that predict cell fate or adversity based on the expression of specific proteins.
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