Genomic Analyses of DNA Transformation and Penicillin Resistance in Streptococcus pneumoniae Clinical Isolates
Bibliographic record
Abstract
Alterations in penicillin-binding proteins, the target enzymes for β-lactam antibiotics, are recognized as primary penicillin resistance mechanisms in Streptococcus pneumoniae. Few studies have analyzed penicillin resistance at the genome scale, however, and we report the sequencing of S. pneumoniae R6 transformants generated while reconstructing the penicillin resistance phenotypes from three penicillin-resistant clinical isolates by serial genome transformation. The genome sequences of the three last-level transformants T2-18209, T5-1983, and T3-55938 revealed that 16.2 kb, 82.7 kb, and 137.2 kb of their genomes had been replaced with 5, 20, and 37 recombinant sequence segments derived from their respective parental clinical isolates, documenting the extent of DNA transformation between strains. A role in penicillin resistance was confirmed for some of the mutations identified in the transformants. Several multiple recombination events were also found to have happened at single loci coding for penicillin-binding proteins (PBPs) that increase resistance. Sequencing of the transformants with MICs for penicillin similar to those of the parent clinical strains confirmed the importance of mosaic PBP2x, -2b, and -1a as a driving force in penicillin resistance. A role in resistance for mosaic PBP2a was also observed for two of the resistant clinical isolates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".