The Listeria monocytogenes Core-Genome Sequence Typer (LmCGST): a bioinformatic pipeline for molecular characterization with next-generation sequence data

• Published in: BMC microbiology Vol. 15; no. 1; p. 224
• Main Authors: Pightling, Arthur W., Petronella, Nicholas, Pagotto, Franco
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 22.10.2015; BioMed Central
• Subjects: Analysis; Chromosomes; Computational Biology - methods; Electrophoresis; Genetic aspects; Genomes; High-Throughput Nucleotide Sequencing; Listeria; Listeria monocytogenes - classification; Listeria monocytogenes - genetics; Methods; Molecular Typing - methods; Organisms; Phylogenetics; Phylogeny; Proteins; Sequence Analysis, DNA; Single nucleotide polymorphisms
• ISSN: 1471-2180; 1471-2180
• DOI: 10.1186/s12866-015-0526-1

Next-generation sequencing provides a powerful means of molecular characterization. However, methods such as single-nucleotide polymorphism detection or whole-chromosome sequence analysis are computationally expensive, prone to errors, and are still less accessible than traditional typing methods. Here, we present the Listeria monocytogenes core-genome sequence typing method for molecular characterization. This method uses a high-confidence core (HCC) genome, calculated to ensure accurate identification of orthologs. We also developed an evolutionarily relevant nomenclature based upon phylogenetic analysis of HCC genomes. Finally, we created a pipeline (LmCGST; https://sourceforge.net/projects/lmcgst/files/) that takes in raw next-generation sequencing reads, calculates a subject HCC profile, compares it to an expandable database, assigns a sequence type, and performs a phylogenetic analysis. We analyzed 29 high-quality, closed Listeria monocytogenes chromosome sequences and identified loci that are reliable targets for automated molecular characterization methods. We identified 1013 open-reading frames that comprise our high-confidence core (HCC) genome. We then populated a database with HCC profiles from 114 taxa. We sequenced 84 randomly selected isolates from the Listeriosis Reference Service for Canada's collection and analysed them with the LmCGST pipeline. In addition, we generated pulsed-field gel electrophoresis, ribotyping, and in silico multi-locus sequence typing (MLST) data for the 84 isolates and compared the results to those obtained using the CGST method. We found that all of the methods yielded results that are generally congruent. However, due to the increased numbers of categories, the CGST method provides much greater discriminatory power than the other methods tested here. We show that the CGST method provides increased discriminatory power relative to typing methods such as pulsed-field gel electrophoresis, ribotyping, and multi-locus sequence typing while it addresses several shortcomings of other methods of molecular characterization with next-generation sequence data. It uses discrete, well-defined groupings (types) of organisms that are phylogenetically relevant and easily interpreted. In addition, the CGST scheme can be expanded to include additional loci and HCC profiles in the future. In total, the CGST method provides an approach to the molecular characterization of Listeria monocytogenes with next-generation sequence data that is highly reproducible, easily standardized, portable, and accessible.