Point mutations in functionally diverse genes are associated with increased natural DNA transformation in multidrug resistant <i>Streptococcus pneumoniae</i>
Bibliographic record
Abstract
DNA transformation is key for phenotypic diversity and adaptation of Streptococcus pneumoniae including in the emergence of multidrug resistance (MDR). Under laboratory conditions, DNA transformation is facilitated by the artificial triggering of competence by the competence stimulating peptide (CSP). In ongoing DNA transformation work, we observed that exogenous CSP was dispensable depending on the combination of strains and culture media. Here, we carried out a chemogenomic screen to select for S. pneumoniae mutants capable of natural transformation in medium that normally would not sustain natural transformation. Our chemogenomic screen relied on chemical mutagenesis followed by selection of mutants with increased DNA transformation capacities. Sequencing the genome of these mutants revealed an abundance and diversity of mutated genes proven experimentally to increase natural transformation. A genome wide association study between MDR and sensitive clinical isolates revealed gene mutations associated with MDR, many of which intersected with those pinpointed by our chemogenomic screens and that were proven to increase natural transformation. S. pneumoniae has adopted DNA transformation as its lifestyle and can select for mutations facilitating DNA transformation.
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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 | 0.001 | 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) | 0.002 | 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".