Transcriptomic and metabolomic analyses of the antimicrobial activity of phenoxyethanol against phylotype IA1 and II <i>Cutibacterium acnes</i>
Bibliographic record
Abstract
AIMS: Phenoxyethanol is a broad-spectrum antimicrobial agent widely used in cosmetic formulations. However, its antibacterial effects on different skin bacteria, particularly the predominant Cutibacterium acnes and its various phylotypes, remain unclear. The objective of this study was to examine the antimicrobial effects of phenoxyethanol on C. acnes and explore the mechanism. METHODS AND RESULTS: Phenoxyethanol exhibited strong antimicrobial effects against both C. acnes ATCC6919 (phylotype IA1) and CCSM0331 (phylotype II), achieving a minimum inhibitory concentration (MIC) of 0.5% (v/v). Sub-MIC concentrations showed a stronger inhibitory effect on CCSM0331. RNA-seq and metabolomic analyses revealed that phenoxyethanol disrupted cell membrane integrity and influenced essential metabolic pathways, such as energy metabolism, amino acid metabolism, and pyrimidine metabolism. Additionally, glycolysis and the Wood-Werkman cycle were inhibited in CCSM0331 but enhanced in ATCC6919. The expression of genes involved in porphyrin metabolism, associated with inflammation, was significantly reduced. CONCLUSIONS: Phenoxyethanol exhibits the antimicrobial activity against C. acnes, with differential effects on phylotypes, targeting critical metabolic pathways and cellular processes. These findings indicate its potential for acne treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".