High-Resolution Genetic Fingerprinting of European Strains of <i>Anaplasma phagocytophilum</i> by Use of Multilocus Variable-Number Tandem-Repeat Analysis
Bibliographic record
Abstract
Anaplasma phagocytophilum is a widely distributed tick-borne pathogen of humans, livestock, and companion animals. We used in silico methods to identify 10 variable-number tandem-repeat (VNTR) loci within the genome sequence of the A. phagocytophilum HZ strain and used these data to develop a multilocus VNTR-based typing scheme for the species. Having confirmed the stability of four of the loci in replicates of the A. phagocytophilum strain that had been subjected to different numbers of passages through cell cocultures in vitro, we then used this typing scheme to discriminate between 20 A. phagocytophilum strains of diverse geographical and host provenances. Extensive diversity was found at each of the four loci studied, with total allele numbers ranging from 13 to 18 and Hunter-Gaston discriminatory index values ranging from 0.93 to 0.99. Only 2 of the 20 strains examined shared alleles at all four loci. The discriminatory power of VNTR analysis was found to be greater than that of either partial msp4 or 16S rRNA gene sequence comparison. The extremely high sensitivity of this novel approach to the genetic fingerprinting of A. phagocytophilum strains should serve well in molecular epidemiological studies of infection transmission, particularly when fine-scale strain delineation is required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".