Phosphotransferase System-Dependent Extracellular Growth of Listeria monocytogenes Is Regulated by Alternative Sigma Factors σ<sup>L</sup>and σ<sup>H</sup>
Bibliographic record
Abstract
Alternative sigma (σ) factors and phosphotransferase systems (PTSs) play pivotal roles in the environmental adaptation and virulence of Listeria monocytogenes. The growth of the L. monocytogenes parent strain 10403S and 15 isogenic alternative σ factor mutants was assessed in defined minimal medium (DM) with PTS-dependent or non-PTS-dependent carbon sources at 25°C or 37°C. Overall, our results suggested that the regulatory effect of alternative σ factors on the growth of L. monocytogenes is dependent on the temperature and the carbon source. One-way analysis of variance (one-way ANOVA) showed that the factor "strain" had a significant effect on the maximum growth rate (μmax), lag phase duration (λ), and maximum optical density (ODmax) in PTS-dependent carbon sources (P < 0.05) but not in a non-PTS-dependent carbon source. Also, the ODmax was not affected by strain for any of the three PTS-dependent carbon sources at 25°C but was affected by strain at 37°C. Monitoring by quantitative real-time PCR (qRT-PCR) showed that transcript levels for lmo0027, a glucose-glucoside PTS permease (PTS(Glc)-1)-encoding gene, were higher in the absence of σ(L), and lower in the absence of σ(H), than in the parent strain. Our data thus indicate that σ(L) negatively regulates lmo0027 and that the increased μmax observed for the ΔsigL strain in DM with glucose may be associated with increased expression of PTS(Glc)-1 encoded by lmo0027. Our findings suggest that σ(H) and σ(L) mediate the PTS-dependent growth of L. monocytogenes through complex transcriptional regulations and fine-tuning of the expression of specific pts genes, including lmo0027. Our findings also reveal a more important and complex role of alternative σ factors in the regulation of growth in different sugar sources than previously assumed.
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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) | 0.000 | 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".