Microarray-based transcriptome of<i>Listeria monocytogenes</i>adapted to sublethal concentrations of acetic acid, lactic acid, and hydrochloric acid
Bibliographic record
Abstract
Listeria monocytogenes , an important foodborne pathogen, commonly encounters organic acids in food-related environments. The transcriptome of L. monocytogenes L502 was analyzed after adaptation to pH 5 in the presence of acetic acid, lactic acid, or hydrochloric acid (HCl) at 25 °C, representing a condition encountered in mildly acidic ready-to-eat food kept at room temperature. The acid-treated cells were compared with a reference culture with a pH of 6.7 at the time of RNA harvesting. The number of genes and magnitude of transcriptional responses were higher for the organic acids than for HCl. Protein coding genes described for low pH stress, energy transport and metabolism, virulence determinates, and acid tolerance response were commonly regulated in the 3 acid-stressed cultures. Interestingly, the transcriptional levels of histidine and cell wall biosynthetic operons were upregulated, indicating possible universal response against low pH stress in L. monocytogenes. The opuCABCD operon, coding proteins for compatible solutes transport, and the transcriptional regulator sigL were significantly induced in the organic acids, strongly suggesting key roles during organic acid stress. The present study revealed the complex transcriptional responses of L. monocytogenes towards food-related acidulants and opens the roadmap for more specific and in-depth future studies.
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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) | 0.000 | 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) | 0.001 | 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".