Mutations compensating for the fitness cost of rifampicin resistance in<i>Escherichia coli</i>exert pleiotropic effect on RNA polymerase catalysis
Bibliographic record
Abstract
The spread of drug-resistant bacteria represents one of the most significant medical problems of our time. Bacterial fitness loss associated with drug resistance can be counteracted by acquisition of secondary mutations, thereby enhancing the virulence of such bacteria. Antibiotic rifampicin (Rif) targets cellular RNA polymerase (RNAP). It is potent broad spectrum drug used for treatment of bacterial infections. We have investigated the compensatory mechanism of the secondary mutations alleviating Rif resistance (Rifr) on biochemical, structural and fitness indices. We find that substitutions in RNAP genes compensating for the growth defect caused by βQ513P and βT563P Rifr mutations significantly enhanced bacterial relative growth rate. By assaying RNAP purified from these strains, we show that compensatory mutations directly stimulated basal transcriptional machinery (2-9-fold) significantly improving promoter clearance step of the transcription pathway as well as elongation rate. Molecular modeling suggests that compensatory mutations affect transcript retention, substrate loading, and nucleotidyl transfer catalysis. Strikingly, one of the identified compensatory substitutions represents mutation conferring rifampicin resistance on its own. This finding reveals an evolutionary process that creates more virulent species by simultaneously improving the fitness and augmenting bacterial drug resistance.
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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".