A pilot study on nitration/dysfunction of NK1 segment of myogenic stem cell activator HGF
Bibliographic record
Abstract
Protein tyrosine residue (Y) nitration, a post-translational chemical-modification mode, has been associated with changes in protein activity and function; hence the accumulation of specific nitrated proteins in tissues may be used to monitor the onset and progression of pathological disorders. To verify the possible impact of nitration on postnatal muscle growth and regeneration, a pilot study was designed to examine the nitration/dysfunction of hepatocyte growth factor (HGF), a key ligand that is released from the extracellular tethering and activates myogenic stem satellite cells to enter the cell cycle upon muscle stretch and injury. Exposure of recombinant HGF (a hetero-dimer of α- and β-chains) to peroxynitrite induces Y nitration in HGF α-chain under physiological conditions. Physiological significance of this finding was emphasized by Western blotting that showed the NK1 segment of HGF (including a K1 domain critical for signaling-receptor c-met binding) undergoes nitration with a primary target of Y198. Peroxynitrite treatment abolished HGF-agonistic activity of the NK1 segment, as revealed by in vitro c-met binding and bromodeoxyuridine-incorporation assays. Importantly, direct-immunofluorescence microscopy of rat lower hind-limb muscles from two aged-groups (2-month-old “young” and 12-month-old “retired/adult”) provided in vivo evidence for age-related nitration of extracellular HGF (Y198). Overall, findings provide the insight that HGF/NK1 nitration/dysfunction perturbs myogenic stem cell dynamics and homeostasis; hence NK1 nitration may stimulate progression of muscular disorders and diseases including sarcopenia.
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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 | 0.000 |
| 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".