Construction of a Multiplex Promoter Reporter Platform to Monitor Staphylococcus aureus Virulence Gene Expression and the Identification of Usnic Acid as a Potent Suppressor of psm Gene Expression
Bibliographic record
Abstract
As antibiotic resistance becomes phenomenal, alternative therapeutic strategies for bacterial infections such as anti-virulence treatments have been advocated. We have constructed a total of 20 gfp-luxABCDE dual-reporter plasmids with selected promoters from S. aureus virulence-associated genes. The plasmids were introduced into various S. aureus strains to establish a gfp-lux based multiplex promoter reporter platform for monitoring S. aureus virulence gene expressions in real time to identify factors or compounds that may perturb virulence of S. aureus. The gene expression profiles monitored by luminescence correlated well with qRT-PCR results and extrinsic factors including carbon dioxide and some antibiotics were shown to suppress or induce the expression of virulence factors in this platform. Using this platform, sub-inhibitory ampicillin was shown to be a potent inducer for the expression of many virulence factors in S. aureus. Bacterial adherence and invasion assays using mammalian cells were employed to measure S. aureus virulence induced by ampicillin. The platform was used for screening of natural extracts that perturb the virulence of S. aureus and usnic acid was identified to be a potent repressor for the expression of psm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".