Toxin-antitoxin systems in <i>Listeria monocytogenes</i> : Regulation, stress adaptation, and pathogenesis
Bibliographic record
Abstract
Background and Aim Toxin-antitoxin (TA) systems contribute to bacterial persistence, stress adaptation, and antibiotic tolerance. While extensively studied in Escherichia coli and Staphylococcus aureus, their functions in Listeria monocytogenes, a major foodborne pathogen, remain poorly defined. This review synthesizes current knowledge of TA systems in L. monocytogenes.Experimental Approach We conducted a comprehensive literature review of TA loci in L. monocytogenes, with comparative analyses to well-characterized systems in other pathogens, and evaluated available methodologies for TA investigation.Key Findings and Conclusions Type II TA modules, notably MazEF, likely regulate stress responses through mRNA cleavage and integration with regulators such as σB and two-component systems. Bioinformatics and comparative data suggest roles in persistence, biofilm formation, and adaptation, though experimental validation is limited. We highlight critical gaps and propose TA systems as potential targets for controlling L. monocytogenes in food safety and clinical settings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".