Characterization of neuropeptide K processing in rat spinal cord S9 fractions using high‐resolution quadrupole–Orbitrap mass spectrometry
Bibliographic record
Abstract
Abstract Tachykinins are a family of pronociceptive neuropeptides with a specific role in pain and inflammation. Several mechanisms regulate endogenous tachykinins levels, including the differential expression of protachykinin mRNA and the controlled secretion of tachykinin peptides from neurons. We suspect that proteolysis regulates extracellular neuropeptide K (NPK) and neurokinin A (NKA) concentrations and NPK is a precursor of NKA. Here, we provide evidence that proteolysis controls NPK and NKA levels in the spinal cord, leading to the formation of active C‐terminal peptide fragments. Using high‐resolution mass spectrometry, specific tachykinin fragments were identified and characterized. The metabolic stability in rat spinal cord fractions of NPK and NKA was very short, resulting in half‐lives of 1.9 and 2.2 min respectively. Following the degradation of NPK, several C‐terminal fragments were identified, including NPK 1–26 , NKA, NKA 2–10 , NKA 3–10 , NKA 5–10 and NKA 6–10 , which conserve affinity for the neurokinin 2 receptor but also for the neurokinin 1 receptor. Interestingly, the same fragments were identified following the degradation of NKA. A specific proprotein convertases inhibitor was used and showed a significant reduction in the rate of formation of NKA, providing strong evidence that proprotein convertase is involved in C‐terminal processing of NPK in the spinal cord, leading to the formation of NKA.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".