The acetylglucosaminyltransferase GnT-Ⅲ regulates erythroid differentiation through ERK/MAPK signaling
Bibliographic record
Abstract
Differentiation therapy is an alternative strategy used in treating chronic myelogenous leukemia to induce the differentiation of immature or cancerous cells toward mature cells and inhibit tumor cell proliferation. We aimed to explore N-glycans' roles in erythroid differentiation using the sodium butyrate (NaBu)-induced model of K562 cells (WT/NaBu cells). Here, using lectin blot, flow cytometry, real-time PCR, and mass spectrometry analyses, we demonstrated that the mRNA levels of N-acetylglucosaminyltransferase Ⅲ ((encoded by the MGAT3 gene) and its product (bisected N-glycans) were significantly increased during erythroid differentiation. To address the importance of GnTN-acetylglucosaminyltransferase-Ⅲ in this progress, we established a stable MGAT3 KO K562 cell line using the CRISPR/Cas9 technology. Compared to WT/NaBu cells, MGAT3 KO significantly impeded the progression of erythroid differentiation, as shown in decreased cell color and levels of erythroid markers, glycophorin A (CD235a), and β-globin. Consistently, MGAT3 KO mitigated the inhibitory impact of NaBu on cell proliferation. During induction, MGAT3 KO suppressed the cellular phosphorylated tyrosine and phospho-extracellular signal-regulated kinase (ERK)1/2 levels. Inhibition of the ERK/mitogen-activated protein kinase signaling pathway using U0126 blocked erythroid differentiation while concurrently suppressing the expression levels of MGAT3 and bisected N-glycans. Furthermore, the lack of bisecting GlcNAc modification on c-Kit and transferrin receptor 1 (CD71) suppressed cellular signaling and accelerated the degradation of the CD71 protein, respectively. Our study highlights the critical role of MGAT3 in regulating erythroid differentiation associated with the ERK/mitogen-activated protein kinase signaling pathway, which may shed light on identifying new differentiation therapy in chronic myelogenous leukemia.
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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 | 0.001 |
| 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".