Multiple mutations and increased RNA expression in tetracycline-resistant <i>Streptococcus pneumoniae</i> as determined by genome-wide DNA and mRNA sequencing
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to characterize chromosomal mutations associated with resistance to tetracycline in Streptococcus pneumoniae. METHODS: Chronological appearance of mutations in two S. pneumoniae R6 mutants (R6M1TC-5 and R6M2TC-4) selected for resistance to tetracycline was determined by next-generation sequencing. A role for the mutations identified was confirmed by reconstructing resistance to tetracycline in a S. pneumoniae R6 WT background. RNA sequencing was performed on R6M1TC-5 and R6M2TC-4 and the relative expression of genes was reported according to R6. Differentially expressed genes were classified according to their ontology. RESULTS: WGS of R6M1TC-5 and R6M2TC-4 revealed mutations in the gene rpsJ coding for the ribosomal protein S10 and in the promoter region and coding sequences of the ABC genes patA and patB. These cells were cross-resistant to ciprofloxacin. Resistance reconstruction confirmed a role in resistance for the mutations in rpsJ and patA. Overexpression of the ABC transporter PatA/PatB or mutations in the coding sequence of patA contributed to resistance to tetracycline, ciprofloxacin and ethidium bromide, and was associated with a decreased accumulation of [(3)H]tetracycline. Comparative transcriptome profiling of the resistant mutants further revealed that, in addition to the overexpression of patA and patB, several genes of the thiamine biosynthesis and salvage pathway were increased in the two mutants, but also in clinical isolates resistant to tetracycline. This overexpression most likely contributes to the tetracycline resistance phenotype. CONCLUSIONS: The combination of genomic and transcriptomic analysis coupled to functional studies has allowed the discovery of novel tetracycline resistance mutations in S. pneumoniae.
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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) | 0.001 | 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".