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Record W4390191687 · doi:10.1002/alz.082840

mTOR signaling regulates microglia metabolism and survival via 4E‐BP‐controlled mRNA translation

2023· article· en· W4390191687 on OpenAlexaff
Sara Bermudez, Jung‐Hyun Choi, Sung‐Hoon Kim, Niaz Mahmood, Luke M. Healy, Nahum Sonenberg

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayMicrogliaEIF4EBiologyCell biologyTREM2Translation (biology)RPTORMechanistic target of rapamycinEukaryotic initiation factorPhosphorylationSignal transductionMessenger RNABiochemistryImmunologyInflammationGene

Abstract

fetched live from OpenAlex

Abstract Background Rare loss‐of‐function variants in TREM2 (Triggering Receptor Expressed in Myeloid cells 2) are associated with an increased risk of AD. There are ongoing efforts to therapeutically boost microglial neuroprotective functions by targeting TREM2. However, the intracellular mechanisms underlying these functions in AD are not fully understood. TREM2 signaling maintains metabolic fitness through activation of the mechanistic target of rapamycin (mTOR) allowing microglia to sustain their metabolism during amyloidosis (Ulland et al., 2017). Detrimental decreases in microglial mTOR signalling during amyloidosis independent of TREM2 loss‐of‐function has also been reported (Baik et al., 2019). The mTOR pathway acts upstream of the eukaryotic mRNA translation by suppressing the translation repressor, eukaryotic translation initiation factor 4E (eIF4E)‐binding proteins (4E‐BPs). mTOR inhibits the 4E‐BPs via phosphorylation, thereby releasing eIF4E and allowing mRNA translation to initiate (Hay and Sonenberg, 2004). Work from our group demonstrated that mTOR boosts mitochondrial metabolism through 4E‐BP‐controlled mRNA translation (Morita et al., 2013). Therefore, we aimed to understand the dysregulated mechanisms at the intersection of mTOR‐controlled mRNA translation and metabolism in microglia response to Amyloid‐β (Aß). Method We used immunoblotting to investigate the effect on 4E‐BP phosphorylation following chronic exposure to Aß aggregates and TREM2 signaling inhibition in a microglia cell line. We manipulated the mTOR pathway by knocking out the downstream effectors, 4E‐BPs, to promote the translation of 4E‐BP‐controlled mRNAs in microglia in vitro and in vivo. We crossed the microglia‐specific 4E‐BPs knock out mouse with a RiboTag mouse to pull‐down ribosome‐bound mRNAs, providing a genome‐wide pool of actively translating mRNAs (the translatome) in the absence of 4E‐BPs in microglia. Result We showed that 4E‐BP‐controlled mRNA translation is inhibited upon chronic exposure to Aß or TREM2 signaling inhibition. The deletion of 4E‐BPs in microglia in‐vitro leads to a decrease in expression of pro‐inflammatory mediators and microglia cell death, and an increase in reliance on oxidative phosphorylation upon exposure to Aß. We also observed that 4E‐BP deletion changes the microglia translatome and decreases expression of inflammatory mediators in vivo after an intracranial Aß infusion. Conclusion We conclude that mTOR signaling exerts key effects on microglia function and metabolism through 4E‐BP‐controlled mRNA translation.

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 score0.008

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 head0.047
GPT teacher head0.268
Teacher spread0.221 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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