Ethanol at Subinhibitory Concentrations Enhances Biofilm Formation in Salmonella Enteritidis
Bibliographic record
Abstract
The survival of Salmonella Enteritidis in the food chain is relevant to its biofilm formation capacity, which is influenced by suboptimal environmental conditions. Here, biofilm formation pattern of this bacterium was assessed in the presence of ethanol at sub-minimal inhibitory concentrations (sub-MICs) by microtiter plate assays, cell characteristic analyses, and gene expression tests. It was observed that ethanol at subinhibitory concentrations (1/4 MIC, 2.5%; 1/2 MIC, 5.0%) was able to stimulate biofilm formation in S. Enteritidis. The OD595 value (optical density at 595 nm) used to quantify biofilm production was increased from 0.14 in control groups to 0.36 and 0.63 under 2.5% and 5.0% ethanol stresses, respectively. Ethanol was also shown to reduce bacterial swimming motility and enhance cell auto-aggregation ability. However, other cell characteristics such as swarming activity, initial attachment and cell surface hydrophobicity were not remarkedly impacted by ethanol. Reverse transcription quantitative real-time PCR (RT-qPCR) analysis further revealed that the luxS gene belonging to a quorum-sensing system was upregulated by 2.49- and 10.08-fold in the presence of 2.5% and 5.0% ethanol, respectively. The relative expression level of other biofilm-related genes (adrA, csgB, csgD, and sdiA) and sRNAs (ArcZ, CsrB, OxyS, and SroC) did not obviously change. Taken together, these findings suggest that decrease in swimming motility and increase in cell auto-aggregation and quorum sensing may result in the enhancement of biofilm formation by S. Enteritidis under sublethal ethanol stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".