The influence of short-chain fatty acids on the survival and virulence of <i>Arcobacter butzleri</i>
Bibliographic record
Abstract
AIMS: Arcobacter butzleri, a widespread bacterium linked to gastrointestinal disease, can bypass host colonization resistance mechanisms; however, its response to short-chain fatty acids (SCFAs) remains poorly understood. This study investigated the impact of SCFAs on A. butzleri's survival and virulence. METHODS AND RESULTS: Eight A. butzleri isolates were assessed under varying concentrations of individual SCFAs and mixtures (m-SCFAs). Higher SCFAs concentrations inhibited bacterial growth in a strain-dependent manner. Transcript analysis of putative virulence genes revealed upregulation of ciaB and flaA across most m-SCFAs concentrations, while luxS expression increased at 90 mM. SCFAs generally reduced bacterial motility, with sodium propionate reducing motility but enhancing biofilm-forming ability in the model strain. Additionally, SCFAs exposure decreased the ability of A. butzleri to adhere to and invade the Caco-2 intestinal epithelial cell line. Whole-genome sequencing of the eight A. butzleri isolates revealed extensive genetic diversity, particularly in virulence- and stress-associated genes, although consistent genotype/phenotype correlations were not observed. CONCLUSIONS: Altogether, these findings demonstrate that SCFAs modulate A. butzleri survival and virulence, providing novel insights into their significance in shaping pathogen behaviour and host-pathogen interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".