Detection of Genetic Diversity in Campylobacter jejuni Isolated from a Commercial Turkey Flock Using flaA Typing, MLST Analysis and Microarray Assay
Bibliographic record
Abstract
Campylobacter is genetically highly diverse and undergoes frequent intraspecific recombination. Turkeys have been identified as an important reservoir for Campylobacter jejuni which is of public health significance. The assessment of the genetic diversity among Campylobacter population is critical for our understanding of the epidemiology of this bacterium. The genetic profiles were different according to the molecular typing methods used. The performance of established flaA genotyping, multilocus sequencing typing (MLST) and DNA microarray assay based on the ArrayTube™ technology was evaluated using 14 Campylobacter jejuni isolated from a commercial turkey flock. The flaA typing was performed using PCR-RFLP with restriction enzymes Sau3AI, AluI, a 'composite' flaA analysis of AluI and Sau3AI and DdeI. The 14 isolates were differentiated into 3, 5, 7 and 9 genotypes, respectively. Entire flaA gene and short variable region (SVR) sequences were analysed. Sequencing of the entire flaA provided 11 different genotypes. flaA-SVR sequence analysis detected 8 flaA alleles and 4 flaA peptides. One new flaA allele type (528) was identified. MLST analysis represented 10 different sequence types (STs) and 5 clonal complexes (CCs). The microarray assay recognised 14 different genotypes. The discriminatory indices were 0.560, 0.802, 0.857, and 0.912 for flaA-RFLP depending on the used enzymes, 0.890 for flaA-SVR, 0.967 for entire flaA sequencing, 0.945 for MLST and 1.00 for the DNA microarray assay. The flaA gene was genetically stable over 20 passages on blood agar. In conclusion, the different typing tools demonstrated a high level of genetic heterogeneity of Campylobacter jejuni in a turkey flock, indicating that a single flock can be infected by multiple genotypes within one rearing cycle. DNA microarray-based assays had the highest discriminatory power when compared with other genotyping tools.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".