Focal-adhesion kinase regulates the sialylation of N-glycans via the PI4KIIα-PI4P pathway
Bibliographic record
Abstract
Sialylation is a terminal glycosylated modification of glycoproteins that regulates critical biological events such as cell adhesion and immune response. Our previous study showed that integrin α3β1 plays a crucial role in regulating the sialylation of N-glycans. However, the underlying mechanism for the regulation remains unclear. This study investigated how sialylation is affected by focal adhesion kinase (FAK), which is a critical downstream signal molecule of integrin β1. We established a stable FAK knockout (KO) cell line using the CRISPR/Cas9 system in HeLa cells. The results obtained from lectin blot, flow cytometric analysis, and MS showed that the sialylation levels were significantly decreased in the KO cells compared with that in wild-type (WT) cells. Moreover, phosphatidylinositol 4-phosphate (PI4P) expression levels were also reduced in the KO cells due to a decrease in the stability of phosphatidylinositol 4-kinase-IIα (PI4KIIα). Notably, the decreased levels of sialylation, PI4P, and the complex formation between GOLPH3 and ST3GAL4 or ST6GAL1, which are the main sialyltransferases for modification of N-glycans, were significantly restored by the re-expression of FAK. Furthermore, the decreased sialylation and phosphorylation of Akt and cell migration caused by FAK deficiency all were restored by overexpressing PI4KIIα, which suggests that PI4KIIα is one of the downstream molecules of FAK. These findings indicate that FAK regulates sialylation via the PI4P synthesis pathway and a novel mechanism is suggested for the integrin-FAK-PI4KIIα-GOLPH3-ST axis modulation of sialylation in N-glycans.
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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) | 0.001 | 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 | 0.001 |
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".