Moonlighting matrix metalloproteinase substrates: Enhancement of proinflammatory functions of extracellular tyrosyl-tRNA synthetase upon cleavage
Bibliographic record
Abstract
Tyrosyl-tRNA synthetase ligates tyrosine to its cognate tRNA in the cytoplasm, but it can also be secreted through a noncanonical pathway. We found that extracellular tyrosyl-tRNA synthetase (YRS) exhibited proinflammatory activities. In addition to acting as a monocyte/macrophage chemoattractant, YRS initiated signaling through Toll-like receptor 2 (TLR2) resulting in NF-κB activation and release of tumor necrosis factor α (TNFα) and multiple chemokines, including MIP-1α/β, CXCL8 (IL8), and CXCL1 (KC) from THP1 monocyte and peripheral blood mononuclear cell–derived macrophages. Furthermore, YRS up-regulated matrix metalloproteinase (MMP) activity in a TNFα-dependent manner in M0 macrophages. Because MMPs process a variety of intracellular proteins that also exhibit extracellular moonlighting functions, we profiled 10 MMPs for YRS cleavage and identified 55 cleavage sites by amino-terminal oriented mass spectrometry of substrates (ATOMS) positional proteomics and Edman degradation. Stable proteoforms resulted from cleavages near the start of the YRS C-terminal EMAPII domain. All of the MMPs tested cleaved at ADS 386 ↓ 387 LYV and VSG 405 ↓ 406 LVQ, generating 43- and 45-kDa fragments. The highest catalytic efficiency for YRS was demonstrated by MMP7, which is highly expressed by monocytes and macrophages, and by neutrophil-specific MMP8. MMP-cleaved YRS enhanced TLR2 signaling, increased TNFα secretion from macrophages, and amplified monocyte/macrophage chemotaxis compared with unprocessed YRS. The cleavage of YRS by MMP8, but not MMP7, was inhibited by tyrosine, a substrate of the YRS aminoacylation reaction. Overall, the proinflammatory activity of YRS is enhanced by MMP cleavage, which we suggest forms a feed-forward mechanism to promote inflammation.
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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".