MétaCan
Menu
Back to cohort
Record W4206224480 · doi:10.3390/v14010088

Influenza A Virus Infection Activates NLRP3 Inflammasome through Trans-Golgi Network Dispersion

2022· article· en· W4206224480 on OpenAlexafffund
Kannu Priya Pandey, Yan Zhou

Bibliographic record

VenueViruses · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInflammasomeInnate immune systemCell biologyInfluenza A virusPyroptosisAIM2NigericinImmune systemVirusBiologyVirologyChemistryInflammationImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The NLRP3 inflammasome consists of NLRP3, ASC, and pro-caspase-1 and is an important arm of the innate immune response against influenza A virus (IAV) infection. Upon infection, the inflammasome is activated, resulting in the production of IL-1β and IL-18, which recruits other immune cells to the site of infection. It has been suggested that in the presence of stress molecules such as nigericin, the trans-Golgi network (TGN) disperses into small puncta-like structures where NLRP3 is recruited and activated. Here, we investigated whether IAV infection could lead to TGN dispersion, whether dispersed TGN (dTGN) is responsible for NLRP3 inflammasome activation, and which viral protein is involved in this process. We showed that the IAV causes dTGN formation, which serves as one of the mechanisms of NLRP3 inflammasome activation in response to IAV infection. Furthermore, we generated a series of mutant IAVs that carry mutations in the M2 protein. We demonstrated the M2 proton channel activity, specifically His37 and Trp41 are pivotal for the dispersion of TGN, NLRP3 conformational change, and IL-1β induction. The results revealed a novel mechanism behind the activation and regulation of the NLRP3 inflammasome in IAV infection.

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.

How this classification was reachedexpand

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.959

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 studies0.0010.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueVirusesSame topicInflammasome and immune disordersFrench-language works237,207