Global transcriptional analysis of acid-inducible genes in Streptococcus mutans: multiple two-component systems involved in acid adaptation
Bibliographic record
Abstract
Streptococcus mutans in dental biofilms is regularly exposed to cycles of acidic pH during the ingestion of fermentable dietary carbohydrates. The ability of S. mutans to tolerate low pH is crucial for its virulence and pathogenesis in dental caries. To better understand its acid tolerance mechanisms, we performed genome-wide transcriptional analysis of S. mutans in response to an acidic pH signal. The preliminary results showed that adaptation of S. mutans to pH 5.5 induced differential expression of nearly 14 % of the genes in the genome, including 169 upregulated genes and 108 downregulated genes, largely categorized into nine functional groups. One of the most interesting findings was that the genes encoding multiple two-component systems (TCSs), including CiaHR, LevSR, LiaSR, ScnKR, Hk/Rr1037/1038 and ComDE, were upregulated during acid adaptation. Real-time qRT-PCR confirmed the same trend in the expression profiles of these genes at pH 5.5. To determine the roles of these transduction systems in acid adaptation, mutants with a deletion of the histidine-kinase-encoding genes were constructed and assayed for the acid tolerance response (ATR). The results revealed that inactivation of each of these systems resulted in a mutant that was impaired in ATR, since pre-exposure of these mutants to pH 5.5 did not induce the same level of protection against lethal pH levels as the parent did. A competitive fitness assay showed that all the mutants were unable to compete with the parent strain for persistence in dual-strain mixed cultures at acidic pH, although, with the exception of the mutant in liaS, little effect was observed at neutral pH. The evidence from this study suggests that the multiple TCSs are required for S. mutans to orchestrate its signal transduction networks for optimal adaptation to acidic pH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".