Aging as an evolvable plastic response to extrinsic mortality cues

The disposable soma, mutation accumulation and antagonistic pleiotropy frameworks explain why selection weakens after reproduction, but they do not address how organisms adjust senescence rates in real time to match ecological conditions. I hypothesize that conserved nutrient‑sensing and stress‑response pathways act as a rheostat that evolution has tuned to modulate investment in somatic maintenance versus reproduction based on perceived extrinsic mortality. When cues indicate high risk (e.g., predator signals, crowding), the rheostat shifts toward rapid reproduction and accelerated senescence; when cues indicate low risk, it shifts toward enhanced maintenance and extended lifespan. This model predicts that manipulating the rheostat will shift aging trajectories without violating individual‑level selection, and that the direction of change will be predictable from the cue.

Core mechanistic proposal

1. Sensory integration – Organisms detect extrinsic mortality through chemosensory or mechanosensory pathways (e.g., insulin‑like peptides, octopamine, serotonin) that feed into conserved regulators such as FOXO, mTOR, and AMPK.
2. Resource allocation switch – These regulators transcriptionally reprogram metabolic flux, diverting ATP and NAD+ from repair processes (DNA repair, proteostasis) to gonadal growth and gamete production when mortality cues are high.
3. Epigenetic memory – Repeated exposure to cues establishes a stable chromatin state (e.g., H3K27ac at promoters of somatic‑maintenance genes) that persists for several generations, providing a transgenerational component to the rheostat.

Testable predictions

• Prediction 1: In Caenorhabditis elegans, exposure to predator‑derived metabolites (e.g., fusarium‑derived ascarosides) will increase intestinal lipophagy and decrease DAF‑16/FOXO nuclear localization, shortening lifespan; genetic ablation of the chemosensory receptor srg‑36 will block this effect.
• Prediction 2: In Drosophila melanogaster, raising larval density will elevate circulating Drosophila insulin‑like peptide 2 (Dilp2), increase S6K activity, and reduce lifespan; pharmacological inhibition of TOR with rapamycin will rescue the lifespan shift only when density cues are present.
• Prediction 3: In the African turquoise killifish (Nothobranchius furzeri), populations from high‑predation habitats will show higher baseline mTORC1 activity and shorter intrinsic lifespan than low‑predation counterparts; cross‑fostering embryos will reverse the phenotype, indicating a cue‑driven rather than fixed genetic difference.
• Prediction 4: Human epidemiologic data will reveal that individuals reporting chronic perceived danger (e.g., high crime neighborhoods) exhibit accelerated epigenetic aging clocks independent of socioeconomic status, and that interventions reducing perceived threat (community policing, green spaces) correlate with deceleration of those clocks.

Experimental approach

• Use mutant or RNAi lines targeting sensory receptors (e.g., srg‑36 in worms, Orco in flies) to uncouple cue detection from downstream signaling.
• Measure hallmarks of aging (senescence‑associated β‑galactosidase, γH2AX foci, proteasome activity) and reproductive output across cue gradients.
• Apply phosphoproteomics to quantify real‑time changes in mTOR, AMPK, and FOXO signaling pathways.
• Perform reciprocal transplant or cross‑fostering experiments to separate genetic from environmental effects.
• In humans, leverage existing cohorts (e.g., MOVE‑Agri, MIDUS) with geocoded crime statistics and longitudinal DNA‑methylation data.

Falsifiability

If manipulating extrinsic‑mortality cues fails to produce consistent, directional changes in lifespan and senescence markers across taxa, or if the changes are not mediated by the predicted nutrient‑sensing nodes, the rheostat model would be refuted. Conversely, confirmation would support the view that aging is a flexible, evolvable trait shaped by real‑time ecological feedback rather than a fixed, non‑adaptive byproduct of declining selection.

Key references: Mutation accumulation 1; Antagonistic pleiotropy 2; Disposable soma 3; Environmental modulation of aging in worms 4; Density‑dependent aging in flies 5; Predation‑shaped lifespan in killifish 6; Perceived danger and epigenetic aging in humans 7.