Association between TLR4 and PTEN Involved in LPS-TLR4 Signaling Response
Bibliographic record
Abstract
In this study, we explored the potential mechanisms of how PTEN regulating LPS induced TLR4 signaling pathway. The initial findings from ELISA demonstrate that PTEN influences TNF-α secretion by its lipid phosphatase activity. Subsequently, western blot, immunoprecipitation assay, and immunofluorescence were performed to explore the activation process of PTEN by stimulation with LPS. As early as 20 minutes after LPS stimulation, reduced phosphorylation of PTEN was found obviously. Accordingly, the whole cell-scattered PTEN translocated towards the cell membrane 20 minutes after stimulating with LPS. Moreover, the weak physical association between PTEN and TLR4 in resting RAW264.7 cells increased gradually after the stimulation of LPS. Furthermore, our study showed PTEN decreased LPS-induced Akt activity and upregulated NF-κB-dependent gene transcription, identifying indirectly that the PTEN could regulate the activation of NF-κB by its downstream Akt kinase. In summary, our study illustrates the potential signal transduction process of PTEN while stimulated by LPS: by increasing the association of TLR4, PTEN recruits to its phosphoinositide substrate PI(3,4,5)P3 located on the cell membrane and exerts its dephosphorylated function and subsequently depresses the activity of downstream molecule Akt and results in activation of NF-κB, followed by the secretion of inflammatory mediators TNF-α.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".