Identification of natural compounds to quench quorum sensing in Campylobacter jejuni for the reduction of biofilm formation
Bibliographic record
Abstract
Quorum sensing is a process of bacterial intercellular communication that relies on the production and perception of chemical signals called autoinducers. Via quorum sensing, bacteria can modulate a range of behaviours in response to the change of population density for better fitness. Quorum sensing mediated biofilm formation is a vital survival strategy for many bacteria. At a high population density, bacteria can aggregate and attach to a surface by producing and secreting extracellular polymeric substances (EPS). The surrounding EPS protects the encased cells from the unfavourable conditions. Campylobacter jejuni is a major bacterial cause of human diarrhoeal diseases worldwide. Despite its sensitivity to environmental stresses, C. jejuni ubiquitously distributes throughout the poultry production chain. Autoinducer-2 (AI-2) mediated biofilm formation is known to be critical to the environmental survival of C. jejuni. C. jejuni possesses LuxS, the enzyme involved in the production of AI-2. In this dissertation, two natural fatty acids, namely decanoic acid and lauric acid, were identified as AI-2 inhibitors of C. jejuni. Decanoic acid and lauric acid at 100 ppm inhibited ~90% AI-2 activity of C. jejuni without inactivation of the bacterial cells. Anti-biofilm effect of decanoic acid and lauric acid was investigated. C. jejuni culture was incubated with or without the treatment by fatty acids for 72 h. Biofilm biomass was determined by crystal violet assay, and the numbers of viable planktonic and biofilm-associated cells were separately determined by the conventional plating assay. Except one C. jejuni strain increased biofilm biomass at the treatment groups, the fatty acids reduced 10-50% of the biofilm biomass in the other strains. Lauric acid at 100 ppm achieved ~3-log and ~2-log reduction of C. jejuni biofilm-associated and planktonic cells, respectively. In addition, both fatty acids effectively reduced C. jejuni motility. This study identified the AI-2 inhibitors of C. jejuni and investigated their inhibitory effect on biofilm formation and motility. The findings of this study can aid in the development of alternative C. jejuni control strategies.
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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 | 0.001 |
| 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".