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Record W2594523706 · doi:10.1084/jem.20160880

Inflammatory monocytes require type I interferon receptor signaling to activate NK cells via IL-18 during a mucosal viral infection

2017· article· en· W2594523706 on OpenAlexafffund
Amanda J. Lee, Branson Chen, Marianne V. Chew, Nicole G. Barra, Mira M. Shenouda, Tina Nham, Nico van Rooijen, Manel Jordana, Karen Mossman, Matthias Mack, Ali A. Ashkar

Bibliographic record

VenueThe Journal of Experimental Medicine · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMcMaster University Medical Centre
FundersNational Cancer InstituteCanada Research ChairsCanadian Institutes of Health ResearchJounce TherapeuticsAgios Pharmaceuticals
KeywordsInterferonViral infectionImmunologyVirologyChemistryBiologyVirus

Abstract

fetched live from OpenAlex

The requirement of type I interferon (IFN) for natural killer (NK) cell activation in response to viral infection is known, but the underlying mechanism remains unclear. Here, we demonstrate that type I IFN signaling in inflammatory monocytes, but not in dendritic cells (DCs) or NK cells, is essential for NK cell function in response to a mucosal herpes simplex virus type 2 (HSV-2) infection. Mice deficient in type I IFN signaling, Ifnar −/− and Irf9 −/− mice, had significantly lower levels of inflammatory monocytes, were deficient in IL-18 production, and lacked NK cell–derived IFN-γ. Depletion of inflammatory monocytes, but not DCs or other myeloid cells, resulted in lower levels of IL-18 and a complete abrogation of NK cell function in HSV-2 infection. Moreover, this resulted in higher susceptibility to HSV-2 infection. Although Il18 −/− mice had normal levels of inflammatory monocytes, their NK cells were unresponsive to HSV-2 challenge. This study highlights the importance of type I IFN signaling in inflammatory monocytes and the induction of the early innate antiviral response.

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 categoriesInsufficient payload (model declined to judge)
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 score0.999

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)0.0020.000

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 head0.024
GPT teacher head0.294
Teacher spread0.270 · 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.

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

Citations114
Published2017
Admission routes2
Has abstractyes

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