TXG-MAPr

TXG-MAPr maps weighted gene co-expression modules using WGCNA on Primary Human Hepatocytes (PHH) TG-GATEs data to annotate mechanistic responses for mechanism-based toxicogenomic risk assessment.

Key Features:

• WGCNA: TXG-MAPr applies weighted gene co-expression network analysis (WGCNA) to derive 398 gene co-expression modules from PHH TG-GATEs data.
• Module annotation: Modules are annotated with pathway enrichment results and transcription factor activity information to enable mechanistic interpretation.
• Stress-response capture: Annotations capture stress-response pathways including endoplasmic reticulum (ER) stress and oxidative stress that are perturbed by specific compounds.
• Cross-species and cross-platform evaluation: The framework enables evaluation of module responses across human and rat systems (primary hepatocytes and in vivo liver), with noted exceptions for DNA damage and oxidative stress responses.
• Compound-level mechanism assessment: Module eigengene scores (EGS) are calculated to evaluate perturbations induced by compounds such as cyclosporine A, tunicamycin, and acetaminophen.
• External dataset integration: Module responses can be computed from external datasets including different hepatocyte cell types and targeted RNA-seq, allowing imputation of biological responses from limited gene sets.
• Donor sensitivity analysis: The approach supports analysis of donor-specific sensitivity, identifying variations in basal intrinsic immune response among donors with pre-existing liver pathology, exemplified by tunicamycin exposure.

Scientific Applications:

• Mechanism-based risk assessment: Linking co-expression modules to perturbations to support mechanism-based toxicogenomic risk assessment.
• Cross-species extrapolation: Comparing module responses between human PHH and rat primary hepatocytes and in vivo liver for cross-species evaluation.
• Compound mechanism elucidation: Assessing mechanisms of toxicity for compounds such as cyclosporine A, tunicamycin, and acetaminophen via module eigengene perturbations.
• Donor stratification: Investigating donor-specific differences in basal immune-response and sensitivity related to pre-existing liver pathology.

Methodology:

Computational methods explicitly include WGCNA to derive 398 modules from PHH TG-GATEs, annotation of modules with pathway enrichment and transcription factor activities, calculation of module eigengene scores (EGS), and computation of module responses from external datasets including targeted RNA-seq with imputation from limited gene sets.