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Record W2891835498 · doi:10.1093/gerona/gly213

Resistance to Stress Can Be Experimentally Dissociated From Longevity

2018· article· en· W2891835498 on OpenAlexaff
Dylan J. Dues, Emily K. Andrews, Megan M. Senchuk, Jeremy M. Van Raamsdonk

Bibliographic record

VenueThe Journals of Gerontology Series A · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Institute of General Medical SciencesNational Institutes of HealthVan Andel Research Institute
KeywordsLongevityBiologyGeneGeneticsMutantTranscription factorCell biology

Abstract

fetched live from OpenAlex

On the basis of multiple experiments demonstrating that high resistance to stress is associated with long lifespan, it has been proposed that stress resistance is a key determinant of longevity. However, the extent to which high resistance to stress is necessary or sufficient for long life is currently unclear. In this work, we use a genetic approach to disrupt different stress response pathways and examine the resulting effect on the longevity of the long-lived insulin-like growth factor 1 (IGF1) receptor mutant daf-2. Although mutation of the heat shock factor gene hsf-1, deletion of sod genes, deletion of the p38 MAPK kinase gene pmk-1, or deletion of the transcription factor gene egl-27 all resulted in decreased resistance to at least one form of stress and decreased lifespan, the magnitude of change in stress resistance did not correspond to the magnitude of change in lifespan. In addition, we found that deletion of the glycerol-3-phosphate dehydrogenase genes gpdh-1 and gpdh-2 or deletion of the DAF-16 cofactor gene nhl-1 also results in decreased resistance to at least one form of stress but increases lifespan. Overall, our results suggest that while increased stress resistance is associated with longevity, stress resistance, and lifespan can be experimentally dissociated.

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 score0.026
Threshold uncertainty score0.995

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 science0.0010.000
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 head0.029
GPT teacher head0.298
Teacher spread0.270 · 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

Citations53
Published2018
Admission routes1
Has abstractyes

Explore more

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