Deciphering Adverse Outcome Pathway Network Linked to Bisphenol F Using Text Mining and Systems Toxicology Approaches

Abstract

Bisphenol F (BPF) is one of several Bisphenol A (BPA) substituents that is increasingly used in manufacturing industry leading to detectable human exposure. Whereas a large number of studies have been devoted to decipher BPA effects, much less is known about its substituents. To support decision making on BPF’s safety, we have developed a new computational approach to rapidly explore the available data on its toxicological effects, combining text mining and integrative systems biology, and aiming at connecting BPF to adverse outcome pathways (AOPs). We first extracted from different databases BPF-protein associations that were expanded to protein complexes using protein-protein interaction datasets. Over-representation analysis of the protein complexes allowed to identify the most relevant biological pathways putatively targeted by BPF. Then, automatic screening of scientific abstracts from literature using the text mining tool, AOP-helpFinder, combined with data integration from various sources (AOP-wiki, CompTox, etc.) and manual curation allowed us to link BPF to AOP events. Finally, we combined all the information gathered through those analyses and built a comprehensive complex framework linking BPF to an AOP network including, as adverse outcomes, various types of cancers such as breast and thyroid malignancies. These results which integrate different types of data can support regulatory assessment of the BPA substituent, BPF, and trigger new epidemiological and experimental studies.

Exposure to New Emerging Bisphenols Among Young Children in Switzerland

“…Adverse outcome pathways (AOP)-based thinking has been proposed as a driver for generating new experimental data for BPA [ 74 ]. AOPs have been proposed recently for BPS [ 78 ] and BPF [ 79 ]. Similarly, applying exposure-based, pathway-oriented approaches (e.g., aggregate exposure pathways, AEPs) [ 80 , 81 ] can support a better characterization of the source-to-outcome continuum by exploring the linkages between exposure sources and pathways with internal exposure information (i.e., biomonitoring data), which can be then linked to AOPs.…”

Quantitative adverse outcome pathway (qAOP) models for toxicity prediction

“…microplastics and radiation (Chauhan et al 2019;Jeong and Choi 2019;Jeong et al 2018). Furthermore, new ways of deriving AOPs have been proposed such as data mining, deep learning or a combination of machine learning techniques (Carvaillo et al 2019;Rugard et al 2020).…”

AOP4EUpest: mapping of pesticides in adverse outcome pathways using a text mining tool

“…Frequent itemset mining has also been used to decipher relevant information by integrating multiple data sources from HTS studies to establish predictive toxicological models ( Oki et al , 2016 ). A new informatics tool based on artificial intelligence, AOP-helpFinder, has been developed to identify reliable associations between events and a chemical of interest ( Carvaillo et al , 2019 ; Rugard et al , 2020 ). Yet, additional automated tools are required to screen even larger number of texts and datasets with a higher efficiency and relevance.…”