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Background <p>The causal bridge from environmental exposure to endometriosis (Ems) biology remains incompletely defined. Di(2-ethylhexyl) phthalate (DEHP) is repeatedly implicated in elevated Ems risk, yet actionable molecular anchors linking exposure to phenotype are scarce.</p> Methods <p>We established a multi-layered pipeline centered on DEHP. Comprehensive in silico target prediction across ChEMBL, PharmMapper, and SwissTargetPrediction yielded 1364 de-duplicated candidate proteins. Three transcriptomic cohorts (GSE51981, GSE6364, GSE7305) were integrated and analyzed using differential expression and Weighted Gene Co-expression Network Analysis (WGCNA) to derive a 229-gene, high-confidence Ems set. The intersection identified 17 overlapping genes, which were contextualized by protein–protein interaction (PPI) networks and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment. Interpretable machine learning with SHapley Additive exPlanations (SHAP) prioritized a core signature, followed by molecular docking and 100-ns molecular dynamics (MD) simulations to validate binding feasibility and temporal stability.</p> Results <p>The 17-gene overlap formed a compact functional subnetwork aligned with a “membrane-lipid homeostasis to vesicular transport to detoxification/de-esterification” axis. Classifiers showed robust discrimination across training and external cohorts (most area under the receiver operating characteristic curve [AUC] &gt; 0.75), while single-gene receiver operating characteristic (ROC) analyses highlighted UGT8 (AUC = 0.869) and EPHX1 (0.853) as highly transferable. SHAP prioritized a seven-gene signature—ELOVL6, LYPLA1, UGT8, SLC1A5, HMGCR, EPHX1, and VAMP2—and revealed non-linear relationships, including ELOVL6–UGT8 synergy, HMGCR–LYPLA1 antagonism, and EPHX1–SLC1A5 context dependence. Docking supported pocket complementarity with ~ 2.2–3.3 Å hydrogen bonds plus extensive hydrophobic/π contacts; MD confirmed stable, compact, and persistent binding for UGT8–DEHP, ELOVL6–DEHP, and HMGCR–DEHP over 100 ns.</p> Conclusions <p>This study establishes a comprehensive workflow spanning from chemical exposure identification to target discovery, disease network mapping, interpretable computational modeling, and structural/dynamical validation. We propose a DEHP–Ems regulatory framework underpinned by lipid metabolism, vesicular trafficking, and detoxification pathways. The resulting seven-gene signature provides a clinically applicable panel for diagnostic stratification and highlights potential therapeutic entry points, particularly along the HMGCR axis and via SLC1A5-mediated glutamine uptake. These findings lay the groundwork for future mechanistic studies in primary cell systems, organoid models, in vivo experiments, and prospective clinical validation.</p>

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Closing the evidence loop—membrane-lipid homeostasis and vesicular transport link DEHP exposure to endometriosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Yonggang Dai,
  • Lu Zhang,
  • Tian Wang,
  • Hao Liu,
  • Wenyi Yang,
  • Hongya Wang

摘要

Background

The causal bridge from environmental exposure to endometriosis (Ems) biology remains incompletely defined. Di(2-ethylhexyl) phthalate (DEHP) is repeatedly implicated in elevated Ems risk, yet actionable molecular anchors linking exposure to phenotype are scarce.

Methods

We established a multi-layered pipeline centered on DEHP. Comprehensive in silico target prediction across ChEMBL, PharmMapper, and SwissTargetPrediction yielded 1364 de-duplicated candidate proteins. Three transcriptomic cohorts (GSE51981, GSE6364, GSE7305) were integrated and analyzed using differential expression and Weighted Gene Co-expression Network Analysis (WGCNA) to derive a 229-gene, high-confidence Ems set. The intersection identified 17 overlapping genes, which were contextualized by protein–protein interaction (PPI) networks and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment. Interpretable machine learning with SHapley Additive exPlanations (SHAP) prioritized a core signature, followed by molecular docking and 100-ns molecular dynamics (MD) simulations to validate binding feasibility and temporal stability.

Results

The 17-gene overlap formed a compact functional subnetwork aligned with a “membrane-lipid homeostasis to vesicular transport to detoxification/de-esterification” axis. Classifiers showed robust discrimination across training and external cohorts (most area under the receiver operating characteristic curve [AUC] > 0.75), while single-gene receiver operating characteristic (ROC) analyses highlighted UGT8 (AUC = 0.869) and EPHX1 (0.853) as highly transferable. SHAP prioritized a seven-gene signature—ELOVL6, LYPLA1, UGT8, SLC1A5, HMGCR, EPHX1, and VAMP2—and revealed non-linear relationships, including ELOVL6–UGT8 synergy, HMGCR–LYPLA1 antagonism, and EPHX1–SLC1A5 context dependence. Docking supported pocket complementarity with ~ 2.2–3.3 Å hydrogen bonds plus extensive hydrophobic/π contacts; MD confirmed stable, compact, and persistent binding for UGT8–DEHP, ELOVL6–DEHP, and HMGCR–DEHP over 100 ns.

Conclusions

This study establishes a comprehensive workflow spanning from chemical exposure identification to target discovery, disease network mapping, interpretable computational modeling, and structural/dynamical validation. We propose a DEHP–Ems regulatory framework underpinned by lipid metabolism, vesicular trafficking, and detoxification pathways. The resulting seven-gene signature provides a clinically applicable panel for diagnostic stratification and highlights potential therapeutic entry points, particularly along the HMGCR axis and via SLC1A5-mediated glutamine uptake. These findings lay the groundwork for future mechanistic studies in primary cell systems, organoid models, in vivo experiments, and prospective clinical validation.