Neu1 sialidase and matrix metalloproteinase-9 cross-talk regulates nucleic acid-induced endosomal TOLL-like receptor-7 and -9 activation, cellular signaling and pro-inflammatory responses
Bibliographic record
Abstract
The precise mechanism(s) by which intracellular TOLL-like receptors (TLRs) become activated by their ligands remains unclear. Here, we report a molecular organizational G-protein coupled receptor (GPCR) signaling platform to potentiate a novel mammalian neuraminidase-1 (Neu1) and matrix metalloproteinase-9 (MMP-9) cross-talk in alliance with neuromedin B GPCR, all of which form a tripartite complex with TLR-7 and -9. siRNA silencing Neu1, MMP-9 and neuromedin-B GPCR in RAW-blue macrophage cells significantly reduced TLR7 imiquimod- and TLR9 ODN1826-induced NF-κB (NF-κB-pSer(536)) activity. Tamiflu, specific MMP-9 inhibitor, neuromedin B receptor specific antagonist BIM23127, and the selective inhibitor of whole heterotrimeric G-protein complex BIM-46174 significantly block nucleic acid-induced TLR-7 and -9 MyD88 recruitment, NF-κB activation and proinflammatory TNFα and MCP-1 cytokine responses. For the first time, Neu1 clearly plays a central role in mediating nucleic acid-induced intracellular TLR activation, and the interactions involving NMBR-MMP9-Neu1 cross-talk constitute a novel intracellular TLR signaling platform that is essential for NF-κB activation and pro-inflammatory responses.
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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) | 0.001 | 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".