ClpP is required for proteolytic regulation of type II toxin–antitoxin systems and persister cell formation in Streptococcus mutans
Bibliographic record
Abstract
The type II toxin–antitoxin (TA) modules, mazEF and relBE, in Streptococcus mutans have been implicated in stress response, antibiotic tolerance and persister cell formation. However, how S. mutans regulates these systems to prevent unwanted toxin activation and persister cell formation is unclear. In this study, we provide evidence that ClpP is required for the proteolytic regulation of these TA systems and persister cell formation in S. mutans following antibiotic challenge. A persister viability assay showed that S. mutans UA159 (WT) formed a larger quantity of persister cells than its isogenic mutant ΔclpP following antibiotic challenge. However, the lux reporter assay revealed that clpP deletion did not affect the transcriptional levels of mazEF and relBE, since no significant differences (P>0.05) in the reporter activities were detected between the wild-type and ΔclpP background. Instead, all antibiotics tested at a sub-minimum inhibitory concentration (sub-MIC) induced transcriptional levels of mazEF and relBE operons. We then examined the protein profiles of His-tagged MazE and RelB proteins in the UA159 and ΔclpP backgrounds by Western blotting analysis. The results showed that S. mutans strains grown under non-stress conditions expressed very low but detectable levels of MazE and RelB antitoxin proteins. Antibiotics at sub-MICs induced the levels of the MazE and RelB proteins, but the protein levels decreased rapidly in the wild-type background. In contrast, a stable level of MazE and RelB proteins could be detected in the ΔclpP mutant background, suggesting that both proteins accumulated in the ΔclpP mutant. We conclude that ClpP is required for the proteolytic regulation of cellular levels of the MazE and RelB antitoxins in S. mutans , which may play a critical role in modulating the TA activities and persister cell formation of this organism following antibiotic challenge.
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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".