Mapping DEHP-induced metabolic dysregulation through multi-omics and systems biology: an adverse outcome pathway network approach.

Di(2-ethylhexyl) phthalate (DEHP) is a widespread endocrine-disrupting plasticizer associated with metabolic disorders. To elucidate their mechanistic basis, we developed an Adverse Outcome Pathway Network (AOPN) informed by DEHP-induced perturbations, by integrating multi-omics data, systems biology modeling, and targeted literature evidence. Multi-omics data were generated in 3D human hepatic cells, pancreatic β-cells, and zebrafish larvae exposed to DEHP across biologically relevant concentration ranges. Transcriptomic and metabolomic analyses revealed perturbations in lipid metabolism, mitochondrial function and oxidative stress pathways, including altered sphingolipid signaling and dysregulation of sphingosine-1-phosphate (S1P). Systems biology modeling contextualized these perturbations within metabolic pathways and generated dose-dependent predictions for key intermediates, highlighting potential reinforcing feedback mechanisms such as ATP-ROS interactions that may further destabilize metabolic homeostasis. Integrating experimental and literature evidence identified key event relationships proposed to link early molecular perturbations to downstream adverse metabolic outcomes, including hepatic steatosis, insulin resistance, non-alcoholic fatty liver disease (NAFLD), and obesity. Central mechanistic features of the network converge on lipid remodeling, mitochondrial dysfunction, oxidative stress, serine/S1P pathway disruption, and impaired insulin signaling. Because the individual omics contrasts are based on three replicates per condition, statistical support in this work is evaluated at pathway rather than single-feature level: under Benjamini-Hochberg control almost no individual transcript is significant, whereas the pathways carrying the mitochondrial and lipid-handling key events remain significant in competitive gene-set tests. Cross-system comparison showed that suppression of oxidative-energy metabolism is the only module conserved across all three systems, whereas PPAR signalling and fatty-acid handling are liver-specific and non-monotonic. Together, these data support a coherent, evidence-integrating mechanistic hypothesis linking early molecular events to adverse metabolic outcomes across multiple levels of biological organization, rather than a quantitatively validated causal pathway. To our knowledge, this study provides a novel integrative AOPN framework combining multi-omics data across liver and pancreatic models and reveals previously underappreciated metabolic cross-talk, including involvement of serine and S1P pathways. The proposed framework is explicitly hypothesis-generating and evidence-integrating: it provides a transparent, FDR-audited basis for future quantitative AOP development and supports mechanistically informed chemical risk assessment by identifying early key events, potential biomarkers, and intervention points relevant to human metabolic health.