Deletion of Tac1 gene impact kinase phosphorylation involved in signaling pathways associated with pain
Bibliographic record
Abstract
Abstract Pain in elderly persons is often not adequately treated, and current treatments may lead to poor outcomes. Therefore, new treatment strategies need to be developed based on a better understanding of the mechanisms underlying the development of chronic pain. Recent studies have shown that Tac1 -/- mice display a significant decrease in nociceptive pain responses to moderate or intense stimuli but present no phenotypic changes following light or nonpainful stimuli. Moreover, the deletion of the Tac1 gene led to a deficit of opioid peptides, which are essential to endogenous pain control mechanisms. Thus, we investigated whether Tac1 -/- mice show defective pain modulatory pathways by specifically profiling protein kinases in mice spinal cord using phosphoproteomics and bioinformatics. Protein phosphorylation is a key feature of the cellular regulatory mechanism, and phosphorylation status is related to the regulation and modulation of protein–protein binding. Bioinformatics analysis revealed that MAPK, tyrosine kinase, senescence, interleukin signaling, and TCR signaling are modulated in Tac1 -/- mice. Interestingly, these processes are intimately linked with inflammatory responses leading to the release of cytokines and chemokines implicated in the interactions and communications between cells. They are key players involved in the initiation and persistence of pathologic pain. The absence of the Tac1 gene products may trigger a much wider cell response to compensate for the lack of important components of the nociceptive pain transmission system.
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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) | 0.004 | 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".