Involvement of the Detoxifying Enzyme Lactoylglutathione Lyase in <i>Streptococcus mutans</i> Aciduricity
Bibliographic record
Abstract
Streptococcus mutans, a normal inhabitant of dental plaque, is considered a primary etiological agent of dental caries. Its main virulence factors are acidogenicity and aciduricity, the abilities to produce acid and to survive and grow at low pH, respectively. Metabolic processes are finely regulated following acid exposure in S. mutans. Proteome analysis of S. mutans demonstrated that lactoylglutathione lyase (LGL) was up-regulated during acid challenge. The LGL enzyme catalyzes the conversion of toxic methylglyoxal, derived from glycolysis, to S-D-lactoylglutathione. Methylglyoxal inhibits the growth of cells in all types of organisms. The current study aimed to investigate the relationship between LGL and aciduricity and acidogenicity in S. mutans. An S. mutans isogenic mutant defective in lgl (LGLKO) was created, and its growth kinetics were characterized. Insertional inactivation of lgl resulted in an acid-sensitive phenotype. However, the glycolytic rate at pH 5.0 was greater for LGLKO than for S. mutans UA159 wild-type cells. LGL was involved in the detoxification of methylglyoxal, illustrated by the absence of enzyme activity in LGLKO and the hypersensitivity of LGLKO to methylglyoxal, compared with UA159 (MIC of 3.9 and 15.6 mM, respectively). Transcriptional analysis of lgl conducted by quantitative real-time PCR revealed that lgl was up-regulated (approximately sevenfold) during the exponential growth phase compared with that in the stationary growth phase. Gene expression studies conducted at low pH demonstrated that lgl was induced during acidic growth (approximately 3.5-fold) and following acid adaptation (approximately 2-fold). This study demonstrates that in S. mutans, LGL functions in the detoxification of methylglyoxal, resulting in increased aciduricity.
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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".