Exopolysaccharide biosynthetic glycoside hydrolases can be utilized to disrupt and prevent <i>Pseudomonas aeruginosa</i> biofilms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bacterial biofilms present a significant medical challenge because they are recalcitrant to current therapeutic regimes. A key component of biofilm formation in the opportunistic human pathogen Pseudomonas aeruginosa is the biosynthesis of the exopolysaccharides Pel and Psl, which are involved in the formation and maintenance of the structural biofilm scaffold and protection against antimicrobials and host defenses. Given that the glycoside hydrolases PelAh and PslGh encoded in the pel and psl biosynthetic operons, respectively, are utilized for in vivo exopolysaccharide processing, we reasoned that these would provide specificity to target P. aeruginosa biofilms. Evaluating these enzymes as potential therapeutics, we demonstrate that these glycoside hydrolases selectively target and degrade the exopolysaccharide component of the biofilm matrix. PelAh and PslGh inhibit biofilm formation over a 24-hour period with a half maximal effective concentration (EC50) of 69.3 ± 1.2 and 4.1 ± 1.1 nM, respectively, and are capable of disrupting preexisting biofilms in 1 hour with EC50 of 35.7 ± 1.1 and 12.9 ± 1.1 nM, respectively. This treatment was effective against clinical and environmental P. aeruginosa isolates and reduced biofilm biomass by 58 to 94%. These noncytotoxic enzymes potentiated antibiotics because the addition of either enzyme to a sublethal concentration of colistin reduced viable bacterial counts by 2.5 orders of magnitude when used either prophylactically or on established 24-hour biofilms. In addition, PelAh was able to increase neutrophil killing by ~50%. This work illustrates the feasibility and benefits of using bacterial exopolysaccharide biosynthetic glycoside hydrolases to develop novel antibiofilm therapeutics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it