Raloxifene attenuates Pseudomonas aeruginosa pyocyanin production and virulence
Bibliographic record
Abstract
There has been growing interest in disrupting bacterial virulence mechanisms as a form of infectious disease control through the use of 'anti-infective' drugs. Pseudomonas aeruginosa is an opportunistic pathogen noted for its intrinsic antibiotic resistance that causes serious infections requiring new therapeutic options. In this study, an analysis of the P. aeruginosa PAO1 deduced proteome was performed to identify pathogen-associated proteins. A computational screening approach was then used to discover drug repurposing opportunities, i.e. identifying approved drugs that bind and potentially disrupt the pathogen-associated protein targets. The selective oestrogen receptor modulator raloxifene, a drug currently used in the prevention of osteoporosis and/or invasive breast cancer in post-menopausal women, was predicted from this screen to bind P. aeruginosa PhzB2. PhzB2 is involved in production of the blue pigment pyocyanin produced via the phenazine biosynthesis pathway. Pyocyanin is toxic to eukaryotic cells and has been shown to play a role in infection in a mouse model, making it an attractive target for anti-infective drug discovery. Raloxifene was found to strongly attenuate P. aeruginosa virulence in a Caenorhabditis elegans model of infection. Treatment of P. aeruginosa wild-type strains PAO1 and PA14 with raloxifene resulted in a dose-dependent reduction in pyocyanin production in vitro; pyocyanin production and virulence were also reduced for a phzB2 insertion mutant. These results suggest that raloxifene may be suitable for further development as a therapeutic for P. aeruginosa infection and that such already approved drugs may be computationally screened and potentially repurposed as novel anti-infective/anti-virulence agents.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".