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Record W2917771798 · doi:10.1016/j.celrep.2019.01.062

AMPK Promotes Xenophagy through Priming of Autophagic Kinases upon Detection of Bacterial Outer Membrane Vesicles

2019· article· en· W2917771798 on OpenAlexafffund
Truc T. Losier, Mercy Akuma, Olivia C. McKee-Muir, Nicholas D. LeBlond, Yujin Suk, Reham M. Alsaadi, Zhihao Guo, Ryan Reshke, Subash Sad, François‐Xavier Campbell‐Valois, Derrick Gibbings, Morgan D. Fullerton, Ryan C. Russell

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsInstitute of Infection and ImmunityUniversity of Ottawa
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsAutophagyAMPKVesicleCell biologyKinasePriming (agriculture)MembraneChemistryAMP-activated protein kinaseProtein kinase ABiologyBiochemistryApoptosisBotany

Abstract

fetched live from OpenAlex

The autophagy pathway is an essential facet of the innate immune response, capable of rapidly targeting intracellular bacteria. However, the initial signaling regulating autophagy induction in response to pathogens remains largely unclear. Here, we report that AMPK, an upstream activator of the autophagy pathway, is stimulated upon detection of pathogenic bacteria, before bacterial invasion. Bacterial recognition occurs through the detection of outer membrane vesicles. We found that AMPK signaling relieves mTORC1-mediated repression of the autophagy pathway in response to infection, positioning the cell for a rapid induction of autophagy. Moreover, activation of AMPK and inhibition of mTORC1 in response to bacteria is not accompanied by an induction of bulk autophagy. However, AMPK signaling is required for the selective targeting of bacteria-containing vesicles by the autophagy pathway through the activation of pro-autophagic kinase complexes. These results demonstrate a key role for AMPK signaling in coordinating the rapid autophagic response to bacteria.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.633

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)0.0010.000

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 head0.013
GPT teacher head0.249
Teacher spread0.236 · 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

Citations61
Published2019
Admission routes2
Has abstractyes

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