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Record W1978536616 · doi:10.3791/2142

Detection of Neu1 Sialidase Activity in Regulating TOLL-like Receptor Activation

2010· article· en· W1978536616 on OpenAlex
Schammim Ray Amith, Preethi Jayanth, Trisha M. Finlay, Susan Franchuk, Alanna Gilmour, Samar Abdulkhalek, Myron R. Szewczuk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Visualized Experiments · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsSialidaseToll-like receptorReceptorChemistryCell biologyBiologyBiochemistryEnzymeInnate immune systemNeuraminidase

Abstract

fetched live from OpenAlex

Mammalian Toll-like receptors (TLRs) are a family of receptors that recognize pathogen-associated molecular patterns. Not only are TLRs crucial sensors of microbial (e.g., viruses, bacteria and parasite) infections, they also play an important role in the pathophysiology of infectious diseases, inflammatory diseases, and possibly in autoimmune diseases. Thus, the intensity and duration of TLR responses against infectious diseases must be tightly controlled. It follows that understanding the structural integrity of sensor receptors, their ligand interactions and signaling components is essential for subsequent immunological protection. It would also provide important opportunities for disease modification through sensor manipulation. Although the signaling pathways of TLR sensors are well characterized, the parameters controlling interactions between the sensors and their ligands still remain poorly defined. We have recently identified a novel mechanism of TLR activation by its natural ligand, which has not been previously observed. It suggests that ligand-induced TLR activation is tightly controlled by Neu1 sialidase activation. We have also reported that Neu1 tightly regulates neurotrophin receptors like TrkA and TrkB, which involve Neu1 and matrix metalloproteinase-9 (MMP-9) cross-talk in complex with the receptors. The sialidase assay has been initially use to find a novel ligand, thymoquinone, in the activation of Neu4 sialidase on the cell surface of macrophages, dendritic cells and fibroblast cells via GPCR Gαi proteins and MMP-9. For TLR receptors, our data indicate that Neu1 sialidase is already in complex with TLR-2, -3 and -4 receptors, and is induced upon ligand binding to either receptor. Activated Neu1 sialidase hydrolyzes sialyl α-2,3-linked β-galactosyl residues distant from ligand binding to remove steric hinderance to TLR-4 dimerization, MyD88/TLR4 complex recruitment, NFkB activation and pro-inflammatory cell responses. In a collaborative report, Neu1 sialidase has been shown to regulate phagocytosis in macrophage cells. Taken together, the sialidase assay has provided us with powerful insights to the molecular mechanisms of ligand-induced receptor activation. Although the precise relationship between Neu1 sialidase and the activation of TLR, Trk receptors has yet to be fully elucidated, it would represent a new or pioneering approach to cell regulation pathways.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.376
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it