Exploring the toxicity mechanisms of acetyl tributyl citrate in premature ovarian insufficiency via network toxicology and molecular docking
收藏NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Exploring_the_toxicity_mechanisms_of_acetyl_tributyl_citrate_in_premature_ovarian_insufficiency_via_network_toxicology_and_molecular_docking/31135373
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Premature ovarian insufficiency (POI) is a complex disorder with diverse etiologies that profoundly impacts female fertility and overall health. Acetyl tributyl citrate (ATBC), a commonly used plasticizer in consumer products, has recently drawn attention for its potential role in disrupting ovarian function.
ATBC-associated targets were predicted using STITCH and Swiss Target Prediction tools. Genes implicated in POI were retrieved from the GeneCards and OMIM databases. Overlapping targets were identified and used to construct a protein–protein interaction (PPI) network through the STRING platform, with core targets visualized and analyzed using Cytoscape. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted to determine relevant biological processes and signaling pathways. Molecular docking was performed to evaluate the binding interactions between ATBC and the core target proteins.
A total of 84 overlapping targets were identified as potential mediators of ATBC-induced POI. PPI analysis highlighted five central hub proteins: STAT3, EGFR, PIK3CA, MMP9, and PRKCA. Enrichment analyses suggested involvement in oxidative stress, lysosomal activity, and serine/threonine kinase signaling. Key pathways included PI3K-AKT, MAPK, apoptosis, GnRH, and HIF-1 signaling cascades. Molecular docking results demonstrated favorable binding affinities between ATBC and the hub proteins.
This integrative study sheds light on the molecular mechanisms by which ATBC may contribute to POI. By identifying critical targets and pathways, our findings provide a foundation for further toxicological research and underscore the utility of combining computational prediction, network analysis to assess the reproductive risks of environmental contaminants.
创建时间:
2026-01-23



