Membrane damage-responsive biosensors for the discovery of antimicrobials from Lenzites betulina and Haploporus odorus
Bibliographic record
Abstract
Antibiotic resistance of pathogens to antibiotics is a growing concern in medical treatment of infections. Conventional methods for antimicrobial screening relies on using disc diffusion to observe zones of inhibition. However, this method is not sensitive enough to possibly detect sub-lethal activities. We have developed biosensors in Pseudomonas aeruginosa that produce light from transcriptional lux fusions in response to compounds that damage the outer membrane. Sub-lethal exposure to antimicrobial peptides, the antibiotic cycloserine, and cation chelators induces the expression of arnC::lux (PA3553) and speE2::lux (PA4774). These genes encode outer surface modifications that ultimately protect the P. aeruginosa outer membrane from exposure to sub-lethal concentrations of membrane-active compounds. We used high throughput screening in 96 well micro plate format to test the culture supernatants from a panel of fungal species isolated in Alberta. Preliminary results showed a significant induction of both strains of lux biosensors in 23 of the 29 fungal species screened. Due to the significant amount of biosensor induction induced by the supernatants from Lenzites betulina and Haploporus odorus, efforts have been directed to the purification, isolation and classification of the active component of the supernatant. Whole genome sequencing has become necessary in order to properly analyze the mass spectrometry results that have already been obtained. In a short period of time, it has been possible to quickly and more accurately detect sub-lethal concentrations of antimicrobial compounds that might have been over looked if tradition methods, such as disc diffusion, had been employed. * Indicates faculty mentor
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".