Alcohol Dehydrogenase Restricts the Ability of the Pathogen <i>Candida albicans</i> To Form a Biofilm on Catheter Surfaces through an Ethanol-Based Mechanism
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Bibliographic record
Abstract
Candida biofilms formed on indwelling medical devices are increasingly associated with severe infections. In this study, we used proteomics and Western and Northern blotting analyses to demonstrate that alcohol dehydrogenase (ADH) is downregulated in Candida biofilms. Disruption of ADH1 significantly (P = 0.0046) enhanced the ability of Candida albicans to form biofilm. Confocal scanning laser microscopy showed that the adh1 mutant formed thicker biofilm than the parent strain (210 microm and 140 microm, respectively). These observations were extended to an engineered human oral mucosa and an in vivo rat model of catheter-associated biofilm. Inhibition of Candida ADH enzyme using disulfiram and 4-methylpyrazole resulted in thicker biofilm (P < 0.05). Moreover, biofilms formed by the adh1 mutant strain produced significantly smaller amounts of ethanol, but larger amounts of acetaldehyde, than biofilms formed by the parent and revertant strains (P < 0.0001), demonstrating that the effect of Adh1p on biofilm formation is mediated by its enzymatic activity. Furthermore, we found that 10% ethanol significantly inhibited biofilm formation in vitro, with complete inhibition of biofilm formation at ethanol concentrations of >/=20%. Similarly, using a clinically relevant rabbit model of catheter-associated biofilm, we found that ethanol treatment inhibited biofilm formation by C. albicans in vivo (P < 0.05) but not by Staphylococcus spp. (P > 0.05), indicating that ethanol specifically inhibits Candida biofilm formation. Taken together, our studies revealed that Adh1p contributes to the ability of C. albicans to form biofilms in vitro and in vivo and that the protein restricts biofilm formation through an ethanol-dependent mechanism. These results are clinically relevant and may suggest novel antibiofilm treatment strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it