Effects of the Quinone Oxidoreductase WrbA on Escherichia coli Biofilm Formation and Oxidative Stress
Bibliographic record
Abstract
The effects of natural compounds on biofilm formation have been extensively studied, with the goal of identifying biofilm formation antagonists at sub-lethal concentrations. Salicylic and cinnamic acids are some examples of these compounds that interact with the quinone oxidoreductase WrbA, a potential biofilm modulator and an antibiofilm compound biomarker. However, WrbA’s role in biofilm development is still poorly understood. To investigate the key roles of WrbA in biofilm maturation and oxidative stress, Escherichia coli wild-type and ∆wrbA mutant strains were used. Furthermore, we reported the functional validation of WrbA as a molecular target of salicylic and cinnamic acids. The lack of WrbA did not impair planktonic growth, but rather affected the biofilm formation through a mechanism that depends on reactive oxygen species (ROS). The loss of WrbA function resulted in an ROS-sensitive phenotype that showed reductions in biofilm-dwelling cells, biofilm thickness, matrix polysaccharide content, and H2O2 tolerance. Endogenous oxidative events in the mutant strain generated a stressful condition to which the bacterium responded by increasing the catalase activity to compensate for the lack of WrbA. Cinnamic and salicylic acids inhibited the quinone oxidoreductase activity of purified recombinant WrbA. The effects of these antibiofilm molecules on WrbA function was proven for the first time.
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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) | 0.001 | 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".