Glucosamine Treatment-mediated O-GlcNAc Modification of Paxillin Depends on Adhesion State of Rat Insulinoma INS-1 Cells
Bibliographic record
Abstract
Protein-protein interactions and/or signaling activities at focal adhesions, where integrin-mediated adhesion to extracellular matrix occurs, are critical for the regulation of adhesion-dependent cellular functions. Although the phosphorylation and activities of focal adhesion molecules have been intensively studied, the effects of the O-GlcNAc modification of their Ser/Thr residues on cellular functions have been largely unexplored. We investigated the effects of O-GlcNAc modification on actin reorganization and morphology of rat insulinoma INS-1 cells after glucosamine (GlcN) treatment. We found that paxillin, a key adaptor molecule in focal adhesions, could be modified by O-GlcNAc in INS-1 cells treated with GlcN and in pancreatic islets from mice treated with streptozotocin. Ser-84/85 in human paxillin appeared to be modified by O-GlcNAc, which was inversely correlated to Ser-85 phosphorylation (Ser-83 in rat paxillin). Integrin-mediated adhesion signaling inhibited the GlcN treatment-enhanced O-GlcNAc modification of paxillin. Adherent INS-1 cells treated with GlcN showed restricted protrusions, whereas untreated cells showed active protrusions for multiple-elongated morphologies. Upon GlcN treatment, expression of a triple mutation (S83A/S84A/S85A) resulted in no further restriction of protrusions. Together these observations suggest that murine pancreatic β cells may have restricted actin organization upon GlcN treatment by virtue of the O-GlcNAc modification of paxillin, which can be antagonized by a persistent cell adhesion process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".