Distinct molecular targets of ProEGCG from EGCG and superior inhibition of angiogenesis signaling pathways for treatment of endometriosis
Bibliographic record
Abstract
Endometriosis is a common chronic gynecological disease with endometrial cell implantation outside the uterus. Angiogenesis is a major pathophysiology in endometriosis. Our previous studies have demonstrated that the prodrug of epigallocatechin gallate (ProEGCG) exhibits superior anti-endometriotic and anti-angiogenic effects compared to epigallocatechin gallate (EGCG). However, their direct binding targets and underlying mechanisms for the differential effects remain unknown. In this study, we demonstrated that oral ProEGCG can be effective in preventing and treating endometriosis. Additionally, 1D and 2D Proteome Integral Solubility Alteration assay-based chemical proteomics identified metadherin (MTDH) and PX domain containing serine/threonine kinase-like (PXK) as novel binding targets of EGCG and ProEGCG, respectively. Computational simulation and BioLayer interferometry were used to confirm their binding affinity. Our results showed that MTDH-EGCG inhibited protein kinase B (Akt)-mediated angiogenesis, while PXK-ProEGCG inhibited epidermal growth factor (EGF)-mediated angiogenesis via the EGF/hypoxia-inducible factor (HIF-1a)/vascular endothelial growth factor (VEGF) pathway. In vitro and in vivo knockdown assays and microvascular network imaging further confirmed the involvement of these signaling pathways. Moreover, our study demonstrated that ProEGCG has superior therapeutic effects than EGCG by targeting distinct signal transduction pathways and may act as a novel antiangiogenic therapy for endometriosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".