Genomic characterization and phylogenetic analysis of Salmonella enterica serovar Javiana

Identification of virulence factors and antibiotic resistance in Salmonella enterica subsp. enterica serovar Javiana (FARPER-220) isolated from broiler chickens

Genome sequencing of highly virulent Salmonella enterica subsp. enterica serovar Javiana strain FARPER-220 (ST-1674) isolated from broiler chickens in Peru revealed multiple virulence factors, antibiotic resistance genes, and invasion-related subcategories. The results provide insights into the potential importance of this strain in causing infections in various animals.

Phylogeny and Genomic Characterization of Clinical Salmonella enterica Serovar Newport Collected in Tennessee

Salmonella enterica subsp. enterica serovar Newport (S. Newport) is a clinically and epidemiologically significant serovar in the United States. It is the second most prevalent clinically isolated Salmonella serovar in the United States, and it can contaminate a wide variety of food products. In this study, we evaluated the population structure of S. Newport clinical isolates obtained by the Tennessee Department of Health during routine surveillance (n = 346), along with a diverse set of other global clinical isolates obtained from EnteroBase (n = 271). Most of these clinical isolates belonged to established lineages II and III. Additionally, we performed lineage-specific phylogenetic analyses and were able to identify 18 potential epidemiological clusters among the isolates from Tennessee, which represented a greater proportion of Tennessee isolates belonging to putative epidemiological clusters than the proportion of isolates of this serovar that are outbreak related. IMPORTANCE This study provides insight on the genomic diversity of one of the Salmonella serovars that most frequently cause human illness. Specifically, we explored the diversity of human clinical isolates from a localized region (Tennessee) and compared this level of diversity with the global context. Additionally, we showed that a greater proportion of isolates were associated with potential epidemiological clusters (based on genomic relatedness) than historical estimates. We also identified that one potential cluster was predicted to be multidrug resistant. Taken together, these findings provide insight on Salmonella enterica serovar Newport that can impact public health surveillance and responses and serve as a foundational context for the Salmonella research community.

Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure

The White-Kauffmann-Le Minor (WKL) scheme is the most widely used Salmonella typing scheme for reporting the disease prevalence of the enteric pathogen. With the advent of whole-genome sequencing (WGS), in silico methods have increasingly replaced traditional serotyping due to reproducibility, speed and coverage. However, despite integrating genomic-based typing by in silico serotyping tools such as SISTR, in silico serotyping in certain contexts remains ambiguous and insufficiently informative. Specifically, in silico serotyping does not attempt to resolve polyphyly. Furthermore, in spite of the widespread acknowledgement of polyphyly from genomic studies, the prevalence of polyphyletic serovars is not well characterized. Here, we applied a genomics approach to acquire the necessary resolution to classify genetically discordant serovars and propose an alternative typing scheme that consistently reflect natural Salmonella populations. By accessing the unprecedented volume of bacterial genomic data publicly available in GenomeTrakr and PubMLST databases (>180 000 genomes representing 723 serovars), we characterized the global Salmonella population structure and systematically identified putative non-monophyletic serovars. The proportion of putative non-monophyletic serovars was estimated higher than previous reports, reinforcing the inability of antigenic determinants to depict the complexity of Salmonella evolutionary history. We explored the extent of genetic diversity masked by serotyping labels and found significant intra-serovar molecular differences across many clinically important serovars. To avoid false discovery due to incorrect in silico serotyping calls, we cross-referenced reported serovar labels and concluded a low error rate in in silico serotyping. The combined application of clustering statistics and genome-wide association methods demonstrated effective characterization of stable bacterial populations and explained functional differences. The collective methods adopted in our study have practical values in establishing genomic-based typing nomenclatures for an entire microbial species or closely related subpopulations. Ultimately, we foresee an improved typing scheme to be a hybrid that integrates both genomic and antigenic information such that the resolution from WGS is leveraged to improve the precision of subpopulation classification while preserving the common names defined by the WKL scheme.