Reactive nitrogen species (RNS)-resistant microbes: adaptation and medical implications
Bibliographic record
Abstract
Nitrosative stress results from an increase in reactive nitrogen species (RNS) within the cell. Though the RNS - nitric oxide (·NO) and peroxynitrite (ONOO-) - play pivotal physiological roles, at elevated concentrations, these moieties can be poisonous to both prokaryotic and eukaryotic cells alike due to their capacity to disrupt a variety of essential biological processes. Numerous microbes are known to adapt to nitrosative stress by elaborating intricate strategies aimed at neutralizing RNS. In this review, we will discuss both the enzymatic systems dedicated to the elimination of RNS as well as the metabolic networks that are tailored to generate RNS-detoxifying metabolites - α-keto-acids. The latter has been demonstrated to nullify RNS via non-enzymatic decarboxylation resulting in the production of a carboxylic acid, many of which are potent signaling molecules. Furthermore, as aerobic energy production is severely impeded during nitrosative stress, alternative ATP-generating modules will be explored. To that end, a holistic understanding of the molecular adaptation to nitrosative stress, reinforces the notion that neutralization of toxicants necessitates significant metabolic reconfiguration to facilitate cell survival. As the alarming rise in antimicrobial resistant pathogens continues unabated, this review will also discuss the potential for developing therapies that target the alternative ATP-generating machinery of bacteria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".