Interpretable predictive models of genome-wide aryl hydrocarbon receptor-DNA binding reveal tissue-specific binding determinants

• Published in: Toxicological sciences 2023-11, Vol.196 (2), p.170-186
• Main Authors: Filipovic, David, Qi, Wenjie, Kana, Omar, Marri, Daniel, LeCluyse, Edward L, Andersen, Melvin E, Cuddapah, Suresh, Bhattacharya, Sudin
• Format: Article
• Language: English
• Subjects: Basic Helix-Loop-Helix Transcription Factors - metabolism; Computational Toxicology and Databases; Genome, Human; Humans; Machine Learning; Organ Specificity; Receptors, Aryl Hydrocarbon - metabolism
• ISSN: 1096-6080; 1096-0929
• DOI: 10.1093/toxsci/kfad094

Abstract The aryl hydrocarbon receptor (AhR) is an inducible transcription factor whose ligands include the potent environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Ligand-activated AhR binds to DNA at dioxin response elements (DREs) containing the core motif 5′-GCGTG-3′. However, AhR binding is highly tissue specific. Most DREs in accessible chromatin are not bound by TCDD-activated AhR, and DREs accessible in multiple tissues can be bound in some and unbound in others. As such, AhR functions similarly to many nuclear receptors. Given that AhR possesses a strong core motif, it is suited for a motif-centered analysis of its binding. We developed interpretable machine learning models predicting the AhR binding status of DREs in MCF-7, GM17212, and HepG2 cells, as well as primary human hepatocytes. Cross-tissue models predicting transcription factor (TF)-DNA binding generally perform poorly. However, reasons for the low performance remain unexplored. By interpreting the results of individual within-tissue models and by examining the features leading to low cross-tissue performance, we identified sequence and chromatin context patterns correlated with AhR binding. We conclude that AhR binding is driven by a complex interplay of tissue-agnostic DRE flanking DNA sequence and tissue-specific local chromatin context. Additionally, we demonstrate that interpretable machine learning models can provide novel and experimentally testable mechanistic insights into DNA binding by inducible TFs.