Ribotype diversity of Listeria monocytogenes strains associated with outbreaks of listeriosis in ruminants.

Ribotyping is a molecular method for the characterization, identification, and typing of bacterial isolates that has value in epidemiological studies. To demonstrate the utility of this technique for typing of Listeria monocytogenes, four outbreaks of epizootic listeriosis in ruminants were investigated through coordinated detection and characterization methods utilizing classical microbiology and nucleic acid-based techniques. L. monocytogenes strains isolated from clinical samples and the silage consumed by the affected animals were ribotyped to establish the causal relationship between feed and the disease outbreak. For all but one outbreak, we were able to isolate L. monocytogenes strains represented by the same ribotype from both clinical and silage samples. Additional L. monocytogenes strains with ribotypes different from those of the respective clinical samples were isolated from all silage samples. This indicates that a diverse population of L. monocytogenes strains exists in farm environments, of which some may be more likely than others to cause disease.

Sets of EcoRI fragments containing ribosomal RNA sequences are conserved among different strains of Listeria monocytogenes.

To classify Listeria monocytogenes using taxonomic characters derived from the rRNA operons and their flanking sequences, we studied a sample of 1346 strains within the taxon. DNA from each strain was digested with a restriction endonuclease, EcoRI. The fragments were separated by gel electrophoresis, immobilized on a membrane, and hybridized with a labeled rRNA operon from Escherichia coli. The pattern of bands, positions, and intensities of hybridized fragments were electronically captured. Software was used to normalize the band positions relative to standards, scale the signal intensity, and reduce the background so that each strain was reproducibly represented in a data base as a pattern. With these methods, L. monocytogenes was resolved into 50 pattern types differing in the length of at least one polymorphic fragment. Pattern types representing multiple strains were recorded as the mathematical average of the strain patterns. Pattern types were arranged by size polymorphisms of assigned rRNA regions into subsets, which revealed the branching genetic structure of the species. Subtracting the polymorphic variants of a specific assigned region from the pattern types and averaging the types within each subset resulted in reduced sets of conserved fragments that could be used to recognize strains of the species. Pattern types and reduced sets of conserved fragments were conserved among different strains of L. monocytogenes but were not observed in total among strains of other species.

Types of Listeria monocytogenes predicted by the positions of EcoRI cleavage sites relative to ribosomal RNA sequences.

By using taxonomic characters derived from EcoRI restriction endonuclease digestion of genomic DNA and hybridization with a labeled rRNA operon from Escherichia coli, a polymorphic structure of Listeria monocytogenes, characterized by fragments with different frequencies of occurrence, was observed. This structure was expanded by creating predicted patterns through a recursive process of observation, expectation, prediction, and assessment of completeness. This process was applied, in turn, to normalized strain patterns, fragment bands, and positions of EcoRI recognition sites relative to rRNA regions. Analysis of 1346 strains provided observed patterns, fragment sizes, and their frequencies of occurrence in the patterns. Fragment size statistics led to the creation of unobserved combinations of bands, predicted pattern types. The observed fragment bands revealed positions of EcoRI sites relative to rRNA sequences. Each EcoRI site had a frequency of occurrence, and unobserved fragment sizes were postulated on the basis of knowing the restriction site locations. The result of the recursion process applied to the components of the strain data was an extended classification with observed and predicted members.