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Record W2897235195 · doi:10.18632/oncotarget.26188

Mechanisms through which lithocholic acid delays yeast chronological aging under caloric restriction conditions

2018· article· en· W2897235195 on OpenAlexaff
Anthony Arlia‐Ciommo, Anna Leonov, Karamat Mohammad, Adam Beach, Vincent R. Richard, Simon D. Bourque, Michelle T. Burstein, Alexander A. Goldberg, Pavlo Kyryakov, Alejandra Gomez-Perez, Olivia Koupaki, Vladimir I. Titorenko

Bibliographic record

VenueOncotarget · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsConcordia University
Fundersnot available
KeywordsLithocholic acidCalorie restrictionYeastCaloric theorySaccharomyces cerevisiaeBiologyBiochemistryCell biologyLongevityChemistryGeneticsBile acidEndocrinology

Abstract

fetched live from OpenAlex

under caloric restriction conditions. Here, we investigated mechanisms through which lithocholic acid can delay chronological aging of yeast limited in calorie supply. We provide evidence that lithocholic acid causes a stepwise development and maintenance of an aging-delaying cellular pattern throughout the entire chronological lifespan of yeast cultured under caloric restriction conditions. We show that lithocholic acid stimulates the aging-delaying cellular pattern and preserves such pattern because it specifically modulates the spatiotemporal dynamics of a complex cellular network. We demonstrate that this cellular network integrates certain pathways of lipid and carbohydrate metabolism, some intercompartmental communications, mitochondrial morphology and functionality, and liponecrotic and apoptotic modes of aging-associated cell death. Our findings indicate that lithocholic acid prolongs longevity of chronologically aging yeast because it decreases the risk of aging-associated cell death, thus increasing the chance of elderly cells to survive.

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 score0.265
Threshold uncertainty score0.976

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)0.0000.000

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 head0.018
GPT teacher head0.276
Teacher spread0.258 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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