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The Roles of mTOR Complexes in Lipid Metabolism

2015· review· en· W2156469236 on OpenAlexafffund
Alexandre Caron, Denis Richard, Mathieu Laplante

Bibliographic record

VenueAnnual Review of Nutrition · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health Research
KeywordsmTORC2mTORC1AnabolismPI3K/AKT/mTOR pathwayLipogenesisLipid metabolismCatabolismLipolysisMechanistic target of rapamycinMetabolismBiologyBiochemistryRPTORCell biologyChemistrySignal transductionAdipose tissue

Abstract

fetched live from OpenAlex

The synthesis of lipids in response to food intake represents a key advantage that allows organisms to survive when energy availability is limited. In mammals, circulating levels of insulin and nutrients, which fluctuate between fasting and feeding, dictate whether lipids are synthesized or catabolized by tissues. The mechanistic target of rapamycin (mTOR), a kinase that is activated by anabolic signals, plays fundamental roles in regulating lipid biosynthesis and metabolism in response to nutrition. The mTOR kinase nucleates two large protein complexes named mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2). Following their activation, these complexes facilitate the accumulation of triglycerides by promoting adipogenesis and lipogenesis and by shutting down catabolic processes such as lipolysis and β-oxidation. Here, we review and discuss the roles of mTOR complexes in various aspects of lipid metabolism in mammals. We also use this opportunity to discuss the implication of these relations to the maintenance of systemic lipid homeostasis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations317
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueAnnual Review of NutritionSame topicPI3K/AKT/mTOR signaling in cancerFrench-language works237,207