Mgat4b-mediated selective <i>N</i> -glycosylation regulates melanocyte development and melanoma progression
Bibliographic record
Abstract
Melanocyte development involves key pathways that are often recapitulated during melanoma initiation, highlighting the importance of understanding these regulatory processes. Our study identifies mgat4b , a glycosyl transferase involved in selective N -glycan branching enriched in pigment progenitors, as a regulator of directional cell migration and establishment of melanocyte stem cell (McSC) pool during early development. Single cell RNA (scRNA) sequencing analysis in zebrafish upon targeted disruption of mgat4b reveals, that migratory melanocyte progenitors marked by galectin expression fail to persist. Lectin affinity proteomic analysis reveals the glycosylation of key melanocyte proteins GPNMB, KIT, and TYRP1 to be under the control of MGAT4B in melanocytic cells. Additionally, mislocalization of Junctional plakoglobin (JUP) explains the observed defects in cell adhesion and migration to be regulated by MGAT4B but not its isozyme MGAT4A. Our meta-analysis further reveals that melanoma patients with both the BRAF V600E mutation and elevated MGAT4B levels have significantly worse survival outcomes compared to those with only the BRAF V600E mutation. By leveraging the zebrafish MAZERATI platform to model BRAF V600E driver mutation in vivo, we show that mgat4b mutant cells fail to aggregate and initiate tumors. RNA profiling of the transformed melanocytes revealed cell–cell junction, adhesion, and ECM binding to be probable contributing factors that resulted in the failure of tumor onset. Using a small-molecule inhibitor we demonstrate that complex N -glycosylation inhibits early-stage melanoma progression. Our study underscores the importance of selective N -glycan branching in both melanocyte development and melanoma initiation, suggesting MGAT4B as a promising therapeutic target for melanoma treatment.
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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".