Endocrine disrupting chemicals and COVID-19 relationships: A computational systems biology approach

Background: Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19. Objectives: To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied. Methods: As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis. Results: We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising. Conclusions: Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Automated summary

This study used an integrative systems biology approach to explore the potential links between EDCs and COVID-19 severity, identifying several signaling pathways that may be dysregulated by EDCs and contribute to COVID-19 severity. The findings highlight possible connections between exposure to environmental chemicals and disease development, emphasizing the relevance of computational systems biology methods in understanding molecular mechanisms.