Enhanced Biofilm Formation by Ferrous and Ferric Iron Through Oxidative Stress in Campylobacter jejuni
Bibliographic record
Abstract
Campylobacter is a leading foodborne pathogen worldwide. Biofilm formation is an important survival mechanism that sustains the viability of Campylobacter under harsh stress conditions. Iron affects biofilm formation in some other bacteria; however, the effect of iron on biofilm formation has not been investigated in Campylobacter. In this study, we discovered that ferrous (Fe2+) and ferric (Fe3+) iron stimulated biofilm formation in Campylobacter jejuni. The sequestration of iron with an iron chelator prevented the iron-mediated biofilm stimulation. The level of total reactive oxygen species (ROS) in biofilms was increased by iron. However, the supplementation with an antioxidant prevented the total ROS level from being increased in biofilms by iron and also inhibited iron-mediated biofilm stimulation in C. jejuni. This suggests that iron promotes biofilm formation through oxidative stress. Based on the results of fluorescence microscopic analysis, Fe2+ and Fe3+ enhanced both microcolony formation and biofilm maturation. The levels of extracellular DNA and polysaccharides in biofilms were increased by iron supplementation. The effect of iron on biofilm formation was also investigated with 70 C. jejuni isolates from raw chicken. Regardless of the inherent levels of biofilm formation, iron stimulated biofilm formation in all tested strains; however, there were strain variations in iron concentrations affecting biofilm formation. The biofilm formation of 92.9% (65 of 70) strains was enhanced by either 40 µM Fe2+ or 20 µM Fe3+ or both (the iron concentrations that enhanced biofilm formation in C. jejuni NCTC 11168), whereas different iron concentrations were required to promote biofilms in the rest of the strains. The findings in this study showed that Fe2+ and Fe3+ contributed to the stimulation of biofilm formation in C. jejuni through oxidative stress.
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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 | 0.000 |
| 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".