Critical Assessment of Methods to Quantify Biofilm Growth and Evaluate Antibiofilm Activity of Host Defence Peptides
Bibliographic record
Abstract
Biofilms are multicellular communities of bacteria that can adhere to virtually any surface. Bacterial biofilms are clinically relevant, as they are responsible for up to two-thirds of hospital acquired infections and contribute to chronic infections. Troublingly, the bacteria within a biofilm are adaptively resistant to antibiotic treatment and it can take up to 1000 times more antibiotic to kill cells within a biofilm when compared to planktonic bacterial cells. Identifying and optimizing compounds that specifically target bacteria growing in biofilms is required to address this growing concern and the reported antibiofilm activity of natural and synthetic host defence peptides has garnered significant interest. However, a standardized assay to assess the activity of antibiofilm agents has not been established. In the present work, we describe two simple assays that can assess the inhibitory and eradication capacities of peptides towards biofilms that are formed by both Gram-positive and negative bacteria. These assays are suitable for high-throughput workflows in 96-well microplates and they use crystal violet staining to quantify adhered biofilm biomass as well as tetrazolium chloride dye to evaluate the metabolic activity of the biofilms. The effect of media composition on the readouts of these biofilm detection methods was assessed against two strains of Pseudomonas aeruginosa (PAO1 and PA14), as well as a methicillin resistant strain of Staphylococcus aureus. Our results demonstrate that media composition dramatically alters the staining patterns that were obtained with these dye-based methods, highlighting the importance of establishing appropriate biofilm growth conditions for each bacterial species to be evaluated. Confocal microscopy imaging of P. aeruginosa biofilms grown in flow cells revealed that this is likely due to altered biofilm architecture under specific growth conditions. The antibiofilm activity of several antibiotics and synthetic peptides were then evaluated under both inhibition and eradication conditions to illustrate the type of data that can be obtained using this experimental setup.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".