An accelerated assay for the identification of lifespan-extending interventions in <i>Drosophila</i> melanogaster
Bibliographic record
Abstract
Recent advances in aging research have uncovered genes and genetic pathways that influence lifespan in such diverse organisms as yeast, nematodes, flies, and mice. The discovery of genes and drugs that affect lifespan has been delayed by the absence of a phenotype other than survivorship, which depends on the measurement of age at death of individuals in a population. The use of survivorship to identify genetic and pharmacological interventions that prolong life is time-consuming and requires a large number of homogeneous animals. Here, we report the development of an assay in Drosophila melanogaster using the expression of molecular biomarkers that accelerates the ability to evaluate potential lifespan-altering interventions. Coupling the expression of an age-dependent molecular biomarker to a lethal toxin reduces the time needed to perform lifespan studies by 80%. The assay recapitulates the effect of the three best known environmental life-span-extending interventions in the fly: ambient temperature, reproductive status, and calorie reduction. Single gene mutations known to extend lifespan in the fly such as Indy and rpd3 also extend lifespan in this assay. We used this assay as a screen to identify drugs that extend lifespan in flies. Lipoic acid and resveratrol were identified as being beneficial in our assay and shown to extend lifespan under normal laboratory conditions. We propose that this assay can be used to screen pharmacological as well as genetic interventions more rapidly for positive effects on lifespan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".