Involvement of an amino acid transporter in the acid tolerance of streptococcus mutans
Bibliographic record
Abstract
Streptococcus mutans, an etiological agent of dental caries, can tolerate and dominate the acidic environment of carious sites (as low as pH 3.0). For this, S. mutans has acid protective mechanisms. To study these mechanisms, acid-sensitive mutants, GTX-B and KLGLUP2 were created by inactivation of a gene designated gluP. Computer analysis suggested that the substrate of the system was a polar amino acid. C14 amino acid transport suggested that the substrate was L-aspartate and competing substrates including glutamate and glutamine. Potassium stimulated the transport of aspartate and glutamate. Significant differences between the parent and mutants were observed in the unadapted and adapted acid tolerance response. Complete operon disruption resulted in a mutant with increased doubling time at neutral and mildly acidic pH. Addition of KPO4 to the growth medium resulted in increased growth yields in all strains. This work demonstrates that amino acid transport via this system is linked to K+ utilization and that inactivation of the system results in diminished growth rates and acid tolerance. Supported by Grant MT-15431 from The Canadian Institute of Health Research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".