Loss and Recovery of Mgat3 and GnT-III Mediated E-cadherin N-glycosylation Is a Mechanism Involved in Epithelial-Mesenchymal-Epithelial Transitions
Bibliographic record
Abstract
BACKGROUND: N-acetylglucosaminyltransferase-III (GnT-III) is a glycosyltransferase encoded by Mgat3 that catalyzes the addition of β1,4-bisecting-N-acetylglucosamine on N-glycans. GnT-III has been pointed as a metastases suppressor having varying effects on cell adhesion and migration. We have previously described the existence of a functional feedback loop between E-cadherin expression and GnT-III-mediated glycosylation. The effects of GnT-III-mediated glycosylation on E-cadherin expression and cellular phenotype lead us to evaluate Mgat3 and GnT-III-glycosylation role during Epithelial-Mesenchymal-Transition (EMT) and the reverted process, Mesenchymal-Epithelial-Transition (MET). METHODOLOGY/PRINCIPAL FINDINGS: We analyzed the expression profile and genetic mechanism controlling Mgat3 expression as well as GnT-III-mediated glycosylation, in general and specifically on E-cadherin, during EMT/MET. We found that during EMT, Mgat3 expression was dramatically decreased and later recovered when cells returned to an epithelial-like phenotype. We further identified that Mgat3 promoter methylation/demethylation is involved in this expression regulation. The impact of Mgat3 expression variation, along EMT/MET, leads to a variation in the expression levels of the enzymatic product of GnT-III (bisecting GlcNAc structures), and more importantly, to the specific modification of E-cadherin glycosylation with bisecting GlcNAc structures. CONCLUSIONS/SIGNIFICANCE: Altogether, this work identifies for the first time Mgat3 glycogene expression and GnT-III-mediated glycosylation, specifically on E-cadherin, as a novel and major component of the EMT/MET mechanism signature, supporting its role during EMT/MET.
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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".