The Cano-eMLST Program: An Approach for the Calculation of Canonical Extended Multi-Locus Sequence Typing, Making Comparison of Genetic Differences Among Bunches of Bacterial Strains

A machine learning-based typing scheme refinement for Listeria monocytogenes core genome multilocus sequence typing with high discriminatory power for common source outbreak tracking

Background

As whole-genome sequencing for pathogen genomes becomes increasingly popular, the typing methods of gene-by-gene comparison, such as core genome multilocus sequence typing (cgMLST) and whole-genome multilocus sequence typing (wgMLST), are being routinely implemented in molecular epidemiology. However, some intrinsic problems remain. For example, genomic sequences with varying read depths, read lengths, and assemblers influence the genome assemblies, introducing error or missing alleles into the generated allelic profiles. These errors and missing alleles might create “specious discrepancy” among closely related isolates, thus making accurate epidemiological interpretation challenging. In addition, the rapid growth of the cgMLST allelic profile database can cause problems related to storage and maintenance as well as long query search times.

Methods

We attempted to resolve these issues by decreasing the scheme size to reduce the occurrence of error and missing alleles, alleviate the storage burden, and improve the query search time. The challenge in this approach is maintaining the typing resolution when using fewer loci. We achieved this by using a popular artificial intelligence technique, XGBoost, coupled with Shapley additive explanations for feature selection. Finally, 370 loci from the original 1701 cgMLST loci of Listeria monocytogenes were selected.

Results

Although the size of the final scheme (LmScheme_370) was approximately 80% lower than that of the original cgMLST scheme, its discriminatory power, tested for 35 outbreaks, was concordant with that of the original cgMLST scheme. Although we used L. monocytogenes as a demonstration in this study, the approach can be applied to other schemes and pathogens. Our findings might help elucidate gene-by-gene–based epidemiology.

Genome-based reclassification of Lactobacillus casei: emended classification and description of the species Lactobacillus zeae

Taxonomic relationships between Lactobacillus casei , Lactobacillus paracasei and Lactobacillus zeae have long been debated. Results of previous analyses have shown that overall genome relatedness indices (such as average nucleotide identity and core nucleotide identity) between the type strains L. casei ATCC 393T and L. zeae ATCC 15820T were 94.6 and 95.3 %, respectively, which are borderline for species definition. However, the digital DNA‒DNA hybridization value was 57.3 %, which was clearly lower than the species delineation threshold of 70 %, and hence raised the possibility that L. casei could be reclassified into two species. To re-evaluate the taxonomic relationship of these taxa, multilocus sequence analysis (MLSA) based on the concatenated five housekeeping gene (dnaJ, dnaK, mutL, pheS and yycH) sequences, phylogenomic and core genome multilocus sequence typing analyses, gene presence and absence profiles using pan-genome analysis, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling analysis, cellular fatty acid compositions, and phenotype analysis were carried out. The results of phenotypic characterization, MLSA, whole-genome sequence-based analyses and MALDI-TOF MS profiling justified an independent species designation for the L. zeae strains, and supported an emended the description of the name of Lactobacillus zeae (ex Kuznetsov 1956) Dicks et al. 1996, with ATCC 15820T (=DSM 20178T=BCRC 17942T) as the type strain.