Reduction of Reactive Oxygen Species reduces the Acetate-Dependent Aging of Chlamydomonas reinhardtii
Bibliographic record
Abstract
Factors regulating aging and longevity in microalgae remain largely underexplored. The unicellular alga Chlamydomonas reinhardtii serves as an ideal model for aging studies due to its mixotrophic capabilities and ease of culture. While microalgae are immortal under favourable conditions, nutrient limitation induces conditional senescence and ultimately death of the culture. Acetate is a key carbon source for mixotrophic growth in Chlamydomonas and higher acetate concentrations accelerate senescence and reduce longevity in batch culture. We hypothesized that elevated acetate enhances metabolic activity and reactive oxygen species (ROS) production, causing cellular damage and reducing lifespan. We confirmed that high acetate cultures produce more ROS as the culture ages, specifically hydrogen peroxide as measured with DCFH-DA, correlating with reduced longevity. Interestingly, cells in high-acetate conditions exhibited increased resistance to external ROS-inducing agents such as rose bengal and hydrogen peroxide, suggesting the induction of antioxidant defense pathways in these cells, likely related to the higher endogenous production of ROS during growth in high acetate conditions. Treatment with ROS quenchers catalase (hydrogen peroxide), 2,2-dipyridyl (hydroxyl radical), and diphenylamine (singlet oxygen) partially reversed the high-acetate senescence, supporting the role of ROS as a trigger for the acetate-dependent aging. In addition, inhibition of the TOR regulatory kinase with rapamycin also increased longevity in stationary phase cultures growing in high acetate. These findings support a role of ROS and the nutrient-sensing TOR pathway in the regulation of lifespan under high-acetate conditions, providing insights into conserved mechanisms of aging across biological systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".