Harnessing the power of transposon mutagenesis for antibacterial target identification and evaluation
Bibliographic record
Abstract
Determining the mechanism of action of bacterial growth inhibitors can be a formidable challenge in the progression of small molecules into antibacterial therapies. To help address this bottleneck, we have developed a robust transposon mutagenesis system using a suite of outward facing promoters in order to generate a comprehensive range of expression genotypes in Staphylococcus aureus from which to select defined compound-resistant transposon insertion mutants. Resistance stemming from either gene or operon over/under-expression, in addition to deletion, provides insight into multiple factors that contribute to a compound's observed activity, including means of cell envelope penetration and susceptibility to efflux. By profiling the entire resistome, the suitability of an antibacterial target itself is also evaluated, sometimes with unanticipated results. We herein show that for the staphylococcal signal peptidase (SpsB) inhibitors, modulating expression of lipoteichoic acid synthase (LtaS) confers up to a 100-fold increase in the minimal inhibitory concentration. As similarly efficient transposition systems are or will become established in other bacteria and cell types, we discuss the utility, limitations and future promise of Tnp mutagenesis for determining both a compound's mechanism of action and in the evaluation of novel targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".