O‐GlcNAcylation inhibits NFκB activation in rat aortic smooth muscle cells through proteasome inhibition
Bibliographic record
Abstract
O‐GlcNAcylation is a dynamic protein posttranslational modification that adds the monosaccharide N‐acetylglucosamine (GlcNAc) to specific serine or threonine residues on target proteins. We have found that O‐GlcNAc modification is an endogenous inhibitor of the proteasome, the primary cellular complex to degrade proteins. A crucial element of the vascular injury response is the activation of NFκB dependent inflammatory genes. NFκB interacts with IκBα in the cytosol and is activated by IκBα degradation, allowing NFκB to translocate into the nucleus to induce gene expression. In this study, we tested if O‐GlcNAc can inhibit NFκB mediated inflammation by blocking IκBα degradation through proteasome inhibition. Rat aortic smooth muscle cells (RASMCs) were treated with 5 mM glucosamine (GlcN) for 1, 2, 4, 6, 12 hrs. At 12 hrs of GlcN treatment, O‐GlcNAc was increased 2‐fold and IκBα level by 21% compared to vehicle control. GlcN pretreatment significantly inhibited TNF‐α induced degradation of IκBα. RASMCs were pretreated with 5 mM GlcN and then incubated with TNF‐α (10 ng/ml) for an additional 60 min, resulting in a 60% increase in IκBα levels after 12 hrs of GlcN treatment. A 32% decrease in proteasome activity was detected after 12 hrs GlcN treatment (Figure). This study indicated that the O‐GlcNAc might inhibit TNF‐α induced inflammatory response in RASMCs by blocking NFκB activation through proteasome inhibition.
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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) | 0.002 | 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".