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The published genome sequence of Campylobacter jejuni strain NCTC 11168 was used to model an accurate and highly reproducible fluorescent amplified fragment length polymorphism (FAFLP) analysis. Predicted and experimentally observed amplified fragments (AFs) generated with the primer pair HindIII+A and HhaI+A were compared. All but one of the 61 predicted AFs were reproducibly detected, and no unpredicted fragments were amplified. This FAFLP analysis was used to genotype 74 C. jejuni strains belonging to the nine heat-stable (HS) serotypes most prevalent in human disease in England and Wales. The 74 C. jejuni strains exhibited 60 FAFLP profiles, and cluster analysis of them yielded a radial tree showing genetic relationships between and within 13 major clusters. Some clusters were related, and others were unrelated, to a single HS serotype. For example, all strains belonging to serotypes HS6 and HS19 grouped into corresponding single genotypic clusters, while strains of serotypes HS11 and HS18 each grouped into two genotypic clusters. Strains of HS50, the most prevalent serotype infecting humans, were found both in one large (multiserotype) cluster complex and dispersed throughout the tree. The strain genotypes within each FAFLP cluster were characterized by a particular combination of AFs, and among the cluster there were additional differential AFs. Identification of such AFs could act as a search tool to look for potential associations with disease or animal hosts, when applied to large number of human isolates. Genome-sequence based FAFLP, thus, has the potential to establish a genetic database for epidemiological investigations of Campylobacter.

journal of clinical microbiology

Genome Sequence-Based Fluorescent Amplified Fragment Length Polymorphism of <i>Campylobacter jejuni</i> , Its Relationship to Serotyping, and Its Implications for Epidemiological Analysis

Genome Sequence-Based Fluorescent Amplified Fragment Length Polymorphism of Campylobacter jejuni , Its Relationship to Serotyping, and Its Implications for Epidemiological Analysis