Efflux Pump-Mediated Intrinsic Drug Resistance in <i>Mycobacterium smegmatis</i>
Bibliographic record
Abstract
The Mycobacterium smegmatis genome contains many genes encoding putative drug efflux pumps. Yet with the exception of lfrA, it is not clear whether these genes contribute to the intrinsic drug resistance of this organism. We showed first by reverse transcription (RT)-PCR that several of these genes, including lfrA as well as the homologues of Mycobacterium tuberculosis Rv1145, Rv1146, Rv1877, Rv2846c (efpA), and Rv3065 (mmr and emrE), were expressed at detectable levels in the strain mc(2)155. Null mutants each carrying an in-frame deletion of these genes were then constructed in M. smegmatis. The deletions of the lfrA gene or mmr homologue rendered the mutant more susceptible to multiple drugs such as fluoroquinolones, ethidium bromide, and acriflavine (two- to eightfold decrease in MICs). The deletion of the efpA homologue also produced increased susceptibility to these agents but unexpectedly also resulted in decreased susceptibility to rifamycins, isoniazid, and chloramphenicol (two- to fourfold increase in MICs). Deletion of the Rv1877 homologue produced some increased susceptibility to ethidium bromide, acriflavine, and erythromycin. The upstream region of lfrA contained a gene encoding a putative TetR family transcriptional repressor, dubbed LfrR. The deletion of lfrR elevated the expression of lfrA and produced higher resistance to multiple drugs. Multidrug-resistant single-step mutants, independent of LfrA and attributed to a yet-unidentified drug efflux pump (here called LfrX), were selected in vitro and showed decreased accumulation of norfloxacin, ethidium bromide, and acriflavine in intact cells. Finally, use of isogenic beta-lactamase-deficient strains showed the contribution of LfrA and LfrX to resistance to certain beta-lactams in M. smegmatis.
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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".