Using transcriptomics data and Adverse Outcome Pathway networks to explore endocrine disrupting properties of Cadmium...

Omics-technologies such as transcriptomics offer valuable insights into toxicity mechanisms. However, integrating this type of data into regulatory frameworks remains challenging due to uncertainties regarding toxicological relevance and links to adverse outcomes. Furthermore, current assessments of endocrine disruptors (EDs) relevant to human health require substantial amounts of data, and primarily rely on standardized animal studies. Identifying EDs is a high priority in the EU, but so are efforts to replace and reduce animal testing. Alternative methods to investigate EDs are needed, and so are health risk assessment methods that support uptake of novel mechanistic information. This study aims to utilize Adverse Outcome Pathways (AOPs) to integrate transcriptomics data for identifying EDs, by establishing a link between molecular data and adverse outcomes. Cadmium (Cd) and 3,3',4,4',5-pentachlorobiphenyl (PCB126) were used as model compounds due to their observed effects on the endocrine system. An AOP network for the estrogen, androgen, thyroid, and steroidogenesis (EATS)-modalities was constructed. RNA sequencing (RNA-Seq) was conducted on zebrafish (Danio rerio) embryos exposed to Cd or PCB126 for 4 days. RNA-Seq data were then linked to the AOP network via Gene Ontology (GO) terms. Enrichment Maps in Cytoscape and the QIAGEN Ingenuity Pathway Analysis (IPA) software were also used to identify potential ED properties and to support the assessment. Potentially EATS-related GO Biological Process (BP) terms were identified for both compounds. A lack of accurate standardized terms in KEs of the AOP network hindered a data-driven mapping approach. Instead, manual mapping of GO BP terms onto the AOP network revealed more connections, underscoring the need for harmonizing AOP development for regulatory use. Both the Enrichment Maps and the IPA results further supported potentially EATS-related effects of both compounds. While AOP networks show promise in integrating RNA-Seq data, several challenges remain.