Expression and characterization of cell-signalling molecules in Campylobacter jejuni
Bibliographic record
Abstract
AIMS: This study investigated the production and effects of cell-signalling compounds on selected survival and virulence mechanisms of Campylobacter jejuni. METHODS AND RESULTS: The production of Autoinducer 1 (AI-1) compounds by Camp. jejuni was investigated in-vitro using a variety of available AI-1 bioassays. We further examined the role of a range of commercially available homoserine lactones (HSL) and a novel compound (cjA) isolated from Camp. jejuni. The selected attributes included the transformation to a viable but nonculturable (VBNC) state, biofilm formation, interleukin 8 (IL-8) stimulation in INT-407 cells and virulence gene expression as determined by qRT-PCR. This study is the first to report an HSL or HSL mimic produced by Camp. jejuni. Short chained HSLs and the novel compound cjA prolonged the delay to a VBNC state as well as inhibiting biofilm formation and the majority of HSLs examined and the HSL mimic cjA significantly affected virulence gene expression as well as increasing the production of IL-8 in challenged INT-407 cells. CONCLUSIONS: Despite the lack of a homologous HSL kinase or sensor, Camp. jejuni appears to produce, as well as detect, exogenous signalling molecules and respond accordingly to aid in the survival and virulence capabilities of this micro-organism. SIGNIFICANCE AND IMPACT OF THE STUDY: This study suggests that Camp. jejuni is able to detect and utilize as well as possibly produce cell-signalling molecules that enhance both survival and virulence attributes. This possibility opens a new field in the search for Camp. jejuni reduction and elimination 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 | 0.000 |
| 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".