title: Optimization of analytical parameters for inferring relationships among Escherichia coli isolates from repetitive-element PCR by maximizing correspondence with multilocus sequence typing data.
abstract: Repetitive-element PCR (rep-PCR) is a method for genotyping bacteria based on the selective amplification of repetitive genetic elements dispersed throughout bacterial chromosomes. The method has great potential for large-scale epidemiological studies because of its speed and simplicity; however, objective guidelines for inferring relationships among bacterial isolates from rep-PCR data are lacking. We used multilocus sequence typing (MLST) as a "gold standard" to optimize the analytical parameters for inferring relationships among Escherichia coli isolates from rep-PCR data. We chose 12 isolates from a large database to represent a wide range of pairwise genetic distances, based on the initial evaluation of their rep-PCR fingerprints. We conducted MLST with these same isolates and systematically varied the analytical parameters to maximize the correspondence between the relationships inferred from rep-PCR and those inferred from MLST. Methods that compared the shapes of densitometric profiles ("curve-based" methods) yielded consistently higher correspondence values between data types than did methods that calculated indices of similarity based on shared and different bands (maximum correspondences of 84.5% and 80.3%, respectively). Curve-based methods were also markedly more robust in accommodating variations in user-specified analytical parameter values than were "band-sharing coefficient" methods, and they enhanced the reproducibility of rep-PCR. Phylogenetic analyses of rep-PCR data yielded trees with high topological correspondence to trees based on MLST and high statistical support for major clades. These results indicate that rep-PCR yields accurate information for inferring relationships among E. coli isolates and that accuracy can be enhanced with the use of analytical methods that consider the shapes of densitometric profiles.
authors.affiliation: Department of Pathobiology, College of Veterinary Medicine, 2001 South Lincoln Avenue, Urbana, IL 61801, USA. tlgoldbe@uiuc.edu
authors.forename: Tony L
authors.initials: TL
authors.lastname: Goldberg
authors.forename: Thomas R
authors.initials: TR
authors.lastname: Gillespie
authors.forename: Randall S
authors.initials: RS
authors.lastname: Singer
mesh_terms: D000483:Alleles; D001483:Base Sequence; D004269:DNA, Bacterial / Q000235:genetics; D004926:Escherichia coli / Q000145:classification / Q000235:genetics* / Q000302:isolation & purification*; D014644:Genetic Variation; D005838:Genotype; D010802:Phylogeny; D016133:Polymerase Chain Reaction / Q000379:methods*; D012091:Repetitive Sequences, Nucleic Acid
chemical_list: D004269:DNA, Bacterial