Induced tigecycline resistance in<i>Streptococcus pneumoniae</i>mutants reveals mutations in ribosomal proteins and rRNA
Bibliographic record
Abstract
OBJECTIVES: Tigecycline is a broad-spectrum antibiotic acting at the level of the 30S ribosomal subunit to inhibit translation. While Streptococcus pneumoniae remains susceptible to tigecycline, resistance is beginning to emerge in some species and mainly involves efflux or mutations in ribosome constituents. We describe here the characterization of S. pneumoniae mutants selected for resistance to tigecycline. METHODS: Molecular determinants of resistance to tigecycline in S. pneumoniae were studied through WGS of two series of mutants made resistant to tigecycline in vitro in a stepwise fashion and by reconstructing tigecycline resistance using DNA transformation. RESULTS: The tigecycline-resistant S. pneumoniae M1TGC-6 and M2TGC-6 mutants were cross-resistant to tetracycline and minocycline. A role in tigecycline resistance could be attributed to 4 of the 12 genes that were mutated in both mutants. Mutations in ribosomal proteins S10 and S3, acquired early and late during selection, respectively, were implicated in resistance in both mutants. Similarly, mutations were detected in the four alleles of the 16S ribosomal RNA at sites involved in tigecycline binding and the number of mutated alleles correlated with the level of resistance. Finally, the gene spr1784 encodes an RsmD-like 16S rRNA methyltransferase for which inactivating mutations selected in the S. pneumoniae tigecycline-resistant mutants were found to decrease susceptibility to tigecycline. CONCLUSIONS: This first report about tigecycline resistance mechanisms in S. pneumoniae revealed that, in contrast to Gram-negative species, for which efflux appears central for tigecycline resistance, resistance in the pneumococcus occurs through mutations related to ribosomes.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".