Covalent Binding of 4-Hydroxynonenal to Matrix Metalloproteinase 13 Studied by Liquid Chromatography–Mass Spectrometry
Bibliographic record
Abstract
Osteoarthritis (OA) is caused by the degradation of articular cartilage and affects approximately 80% of people over the age of 65. Matrix metalloproteinases (MMPs) belong to a group of zinc endopeptidases that degrade extracellular matrix (ECM) proteins in cartilage. MMP-13, also known as collagenase 3, cleaves type II collagen more rapidly than other MMPs and therefore is an important target for the treatment of OA. The lipid peroxidation product 4-hydroxy-2-(E)-nonenal (HNE), generated under oxidative stress, is known to play a crucial role in cartilage degradation; however, the mechanism is not yet fully understood. An approach has been developed to monitor HNE modification sites by incubating rhMMP-13 ± HNE in vitro followed by analysis of tryptic digests by UHPLC coupled to high resolution (HR) quadrupole-time-of-flight (QqTOF) tandem mass spectrometry (MS/MS). The analysis elucidated several covalently modified histidine and cysteine residues. The reaction was monitored using different HNE concentrations and incubation times. A targeted assay, using multiple-reaction monitoring (MRM), was then optimized to increase the sensitivity of detecting these modification sites in biological samples. HNE-related covalent modifications of MMP-13 were confirmed in enriched extracts from interleukin 1β-activated chondrocytes from OA patients using HR-MS/MS and MRM analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".