Metabolic defence against oxidative stress: the road less travelled so far
Bibliographic record
Abstract
). They are known to evoke intricate strategies to defend themselves from the reactive by-products of oxygen-reactive oxygen species (ROS). Many of these detoxifying mechanisms have been extensively characterized; superoxide dismutase, catalases, alkyl hydroperoxide reductase and the glutathione (GSH)-cycling system are responsible for neutralizing specific ROS. Meanwhile, a pool of NADPH-the reductive engine of many ROS-combating enzymes-is maintained by metabolic enzymes including, but not exclusively, glucose-6 phosphate dehydrogenase (G6PDH) and NADP-dependent isocitrate dehydrogenase (ICDH-NADP). So, it is not surprising that evidence continues to emerge demonstrating the pivotal role metabolism plays in mitigating ROS toxicity. Stemming from its ability to concurrently decrease the production of the pro-oxidative metabolite, NADH, while augmenting the antioxidative metabolite, NADPH, metabolism is the fulcrum of cellular redox potential. In this review, we will discuss the mounting evidence positioning metabolism and metabolic shifts observed during oxidative stress, as critical strategies microbes utilize to thrive in environments that are rife with ROS. The contribution of ketoacids-moieties capable of non-enzymatic decarboxylation in the presence of oxidants-as ROS scavengers will be elaborated alongside the metabolic pathways responsible for their homeostases. Further, the signalling role of the carboxylic acids generated following the ketoacid-mediated detoxification of the ROS will be commented on within the context of oxidative stress.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".