Mechanisms to reduce the cytotoxicity of pharmacological nicotinamide concentrations in the pathogenic fungus <i>Candida albicans</i>
Bibliographic record
Abstract
Candida albicans is a pathogenic fungus that causes systemic infections and mortality in immunosuppressed individuals. We previously showed that deacetylation of histone H3 lysine 56 by Hst3 is essential for C. albicans viability. Hst3 is a fungal‐specific NAD+‐dependent protein deacetylase of the sirtuin family. In vivo, supraphysiological concentrations of nicotinamide (NAM) are required for Hst3 inhibition and cytotoxicity. This underscores the importance of identifying mechanisms by which C. albicans can modulate intracellular NAM concentrations. For the first time in a pathogenic fungus, we combine genetics, heavy isotope labeling, and targeted quantitative metabolomics to identify genes, pathways, and mechanisms by which C. albicans can reduce the cytotoxicity of high NAM concentrations. We discovered three distinct fates for supraphysiological NAM concentrations. First, upon transient exposure to NAM, high intracellular NAM concentrations rapidly return near the physiological levels observed in cells that are not exposed to NAM. Second, during the first step of a fungal‐specific NAM salvage pathway, NAM is converted into nicotinic acid, a metabolite that cannot inhibit the sirtuin Hst3. Third, we provide evidence that NAM enters the NAD+ metabolome through a NAM exchange reaction that contributes to NAM‐mediated inhibition of sirtuins. However, in contrast to the other fates of NAM, the NAM exchange reaction cannot cause a net decrease in the intracellular concentration of NAM. Therefore, this reaction cannot enhance resistance to NAM. In summary, we demonstrate that C. albicans possesses at least two mechanisms to attenuate the cytotoxicity of pharmacological NAM concentrations. It seems likely that those two mechanisms of resistance to cytotoxic NAM concentrations are conserved in many other pathogenic fungi.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".