Histamine Promotes Pseudomonas aeruginosa Biofilm Formation and Renders P. aeruginosa Biofilms More Resistant to Gentamicin and Azithromycin
Bibliographic record
Abstract
INTRODUCTION: Pseudomonas aeruginosa biofilms contribute to the persistent presence of this bacterium in the cystic fibrosis airways. P. aeruginosa produces histamine in vitro and expresses histamine receptors. We investigated whether histamine regulated P. aeruginosa biofilm formation in vitro and contributed to bacterial virulence in Galleria mellonella. METHODS: P. aeruginosa biofilms were measured by staining bacteria adhered on polystyrene with crystal violet. Histamine concentrations were measured by ELISA. G. mellonella survival upon inoculation with P. aeruginosa was measured in the absence or presence of histamine. RESULTS: The concentration of histamine in the BHI broth was 140 ng/mL (1.3 μm). Addition to the broth of diamine oxidase (DAO), an enzyme that catabolizes histamine, reduced by ∼3-fold the concentration of histamine and by 2-fold PAO1 strain biofilms. Addition of histamine (10-9m-10-4m) to the LB medium augmented P. aeruginosa biofilms. Maximum effects were observed with concentrations of 10-5m and 10-8m for the mucoid NH57388A strain and the PAO1 strain, respectively. DAO reduced mucoid NH57388A biofilms induced by histamine (10-4m) added to the LB medium. Addition of histamine to 48 h formed biofilms reduced anti-biofilm activities of gentamicin and azithromycin. Inoculation of G. mellonella with the PAO1 strain led to augmented histamine concentration in the haemolymph. Inoculation of histamine (10-8m) reduced the survival rate of G. mellonella infected with the PAO1 strain. CONCLUSION: Histamine produced during periods of infection may augment P. aeruginosa virulence by promoting the biofilm mode of life of this bacterium.
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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) | 0.002 | 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".