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

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.

Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach.

BACKGROUND: Available toxicity data can be optimally interpreted if they are integrated using computational approaches such as systems biology modeling. Such approaches are particularly warranted in cases where regulatory decisions have to be made rapidly. OBJECTIVES: The study aims at developing and applying a new integrative computational strategy to identify associations between bisphenol S (BPS), a substitute for bisphenol A (BPA), and components of adverse outcome pathways (AOPs). METHODS: The proposed approach combines a text mining (TM) procedure and integrative systems biology to comprehensively analyze the scientific literature to enrich AOPs related to environmental stressors. First, to identify relevant associations between BPS and different AOP components, a list of abstracts was screened using the developed text-mining tool AOP-helpFinder, which calculates scores based on the graph theory to prioritize the findings. Then, to fill gaps between BPS, biological events, and adverse outcomes (AOs), a systems biology approach was used to integrate information from the AOP-Wiki and ToxCast databases, followed by manual curation of the relevant publications. RESULTS: Links between BPS and 48 AOP key events (KEs) were identified and scored via 31 references. The main outcomes were related to reproductive health, endocrine disruption, impairments of metabolism, and obesity. We then explicitly analyzed co-mention of the terms BPS and obesity by data integration and manual curation of the full text of the publications. Several molecular and cellular pathways were identified, which allowed the proposal of a biological explanation for the association between BPS and obesity. CONCLUSIONS: By analyzing dispersed information from the literature and databases, our novel approach can identify links between stressors and AOP KEs. The findings associating BPS and obesity illustrate the use of computational tools in predictive toxicology and highlight the relevance of the approach to decision makers assessing substituents to toxic chemicals. https://doi.org/10.1289/EHP4200.

TTClust: A Versatile Molecular Simulation Trajectory Clustering Program with Graphical Summaries.

It is extremely helpful to be able to partition the thousands of frames produced in molecular dynamics simulations into a limited number of most dissimilar conformations. While robust clustering algorithms are already available to do so, there is a distinct need for an easy-to-use clustering program with complete user control, taking as input a trajectory from any molecular dynamics (MD) package and outputting an intuitive display of results with plots allowing at-a-glance analysis. We present TTClust (for Trusty Trajectory Clustering), a python program that uses the MDTraj package to fill this need.