Characterization of a Glutamate Transporter Operon, <i>glnQHMP</i> , in <i>Streptococcus mutans</i> and Its Role in Acid Tolerance
Bibliographic record
Abstract
Glutamate contributes to the acid tolerance response (ATR) of many Gram-negative and Gram-positive bacteria, but its role in the ATR of the oral bacterium Streptococcus mutans is unknown. This study describes the discovery and characterization of a glutamate transporter operon designated glnQHMP (Smu.1519 to Smu.1522) and investigates its potential role in acid tolerance. Deletion of glnQHMP resulted in a 95% reduction in transport of radiolabeled glutamate compared to the wild-type UA159 strain. The addition of glutamate to metabolizing UA159 cells resulted in an increased production of acidic end products, whereas the glnQHMP mutant produced less lactic acid than UA159, suggesting a link between glutamate metabolism and acid production and possible acid tolerance. To investigate this possibility, we conducted a microarray analysis with glutamate and under pH 5.5 and pH 7.5 conditions which showed that expression of the glnQHMP operon was downregulated by both glutamate and mild acid. We also measured the growth kinetics of UA159 and its glnQHMP-negative derivative at pH 5.5 and found that the mutant doubled at a much slower rate than the parent strain but survived at pH 3.5 significantly better than the wild type. Taken together, these findings support the involvement of the glutamate transporter operon glnQHMP in the acid tolerance response in S. mutans.
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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".