Are reactive oxygen species always bad? Lessons from hypoxic ectotherms
Bibliographic record
Abstract
Oxygen (O2) is required for aerobic energy metabolism but can produce reactive oxygen species (ROS), which are a wide variety of oxidant molecules with a range of biological functions from causing cell damage (oxidative distress) to cell signalling (oxidative eustress). The balance between the rate and amount of ROS generated and the capacity for scavenging systems to remove them is affected by several biological and environmental factors, including oxygen availability. Ectotherms, and in particular hypoxia-tolerant ectotherms, are hypothesized to avoid oxidative damage caused by hypoxia, although it is unclear whether this translates to an increase in ecological fitness. In this Review, we highlight the differences between oxidative distress and eustress, the current mechanistic understanding of the two and how they may affect ectothermic physiology. We discuss the evidence of occurrence of oxidative damage with hypoxia in ectotherms, and that ectotherms may avoid oxidative damage through (1) high levels of antioxidant and scavenging systems and/or (2) low(ering) levels of ROS generation. We argue that the disagreements in the literature as to how hypoxia affects antioxidant enzyme activity and the variable metabolism of ectotherms makes the latter strategy more amenable to ectotherm physiology. Finally, we argue that observed changes in ROS production and oxidative status with hypoxia may be a signalling mechanism and an adaptive strategy for ectotherms encountering hypoxia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".