Neu1 Sialidase and Matrix Metalloproteinase-9 Cross-talk Is Essential for Toll-like Receptor Activation and Cellular Signaling
Bibliographic record
Abstract
The signaling pathways of mammalian Toll-like receptors (TLRs) are well characterized, but the precise mechanism(s) by which TLRs are activated upon ligand binding remains poorly defined. Recently, we reported a novel membrane sialidase-controlling mechanism that depends on ligand binding to its TLR to induce mammalian neuraminidase-1 (Neu1) activity, to influence receptor desialylation, and subsequently to induce TLR receptor activation and the production of nitric oxide and proinflammatory cytokines in dendritic and macrophage cells. The α-2,3-sialyl residue of TLR was identified as the specific target for hydrolysis by Neu1. Here, we report a membrane signaling paradigm initiated by endotoxin lipopolysaccharide (LPS) binding to TLR4 to potentiate G protein-coupled receptor (GPCR) signaling via membrane Gα(i) subunit proteins and matrix metalloproteinase-9 (MMP9) activation to induce Neu1. Central to this process is that a Neu1-MMP9 complex is bound to TLR4 on the cell surface of naive macrophage cells. Specific inhibition of MMP9 and GPCR Gα(i)-signaling proteins blocks LPS-induced Neu1 activity and NFκB activation. Silencing MMP9 mRNA using lentivirus MMP9 shRNA transduction or siRNA transfection of macrophage cells and MMP9 knock-out primary macrophage cells significantly reduced Neu1 activity and NFκB activation associated with LPS-treated cells. These findings uncover a molecular organizational signaling platform of a novel Neu1 and MMP9 cross-talk in alliance with TLR4 on the cell surface that is essential for ligand activation of TLRs and subsequent cellular signaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".