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Record W2901703782 · doi:10.1126/scisignal.aap9921

IFN-γ–inducible antiviral responses require ULK1-mediated activation of MLK3 and ERK5

2018· article· en· W2901703782 on OpenAlexaff
Diana Saleiro, Gavin T. Blyth, Ewa M. Kościuczuk, Patrick A. Ozark, Beata Majchrzak-Kita, Ahmet Arslan, Mariafausta Fischietti, Neha K. Reddy, Curt M. Horvath, Roger J. Davis, Eleanor N. Fish, Leonidas C. Platanias

Bibliographic record

VenueScience Signaling · 2018
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsProtein kinase RASK1Cyclin-dependent kinase 9STAT1MAP kinase kinase kinaseCell biologyCyclin-dependent kinase 2Mitogen-activated protein kinase kinaseBiologyKinaseULK1TANK-binding kinase 1STAT proteinMAP2K7Activator (genetics)Protein kinase ACancer researchSignal transductionBiochemistryReceptorSTAT3AMPK

Abstract

fetched live from OpenAlex

It is well established that activation of the transcription factor signal transducer and activator of transcription 1 (STAT1) is required for the interferon-γ (IFN-γ)-mediated antiviral response. Here, we found that IFN-γ receptor stimulation also activated Unc-51-like kinase 1 (ULK1), an initiator of Beclin-1-mediated autophagy. Furthermore, the interaction between ULK1 and the mitogen-activated protein kinase kinase kinase MLK3 (mixed lineage kinase 3) was necessary for MLK3 phosphorylation and downstream activation of the kinase ERK5. This autophagy-independent activity of ULK1 promoted the transcription of key antiviral IFN-stimulated genes (ISGs) and was essential for IFN-γ-dependent antiviral effects. These findings define a previously unknown IFN-γ pathway that appears to be a key element of the 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.334
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2018
Admission routes1
Has abstractyes

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Same venueScience SignalingSame topicAutophagy in Disease and TherapyFrench-language works237,207