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38 records · Page 3

Genomic and phenotypic comparison of two variants of multidrug-resistant Salmonella enterica serovar Heidelberg isolated during the 2015–2017 multi-state outbreak in cattle

Salmonella enterica subspecies enterica serovar Heidelberg (Salmonella Heidelberg) has caused several multistate foodborne outbreaks in the United States, largely associated with the consumption of poultry. However, a 2015–2017 multidrug-resistant (MDR) Salmonella Heidelberg outbreak was linked to contact with dairy beef calves. Traceback investigations revealed calves infected with outbreak strains of Salmonella Heidelberg exhibited symptoms of disease frequently followed by death from septicemia. To investigate virulence characteristics of Salmonella Heidelberg as a pathogen in bovine, two variants with distinct pulse-field gel electrophoresis (PFGE) patterns that differed in morbidity and mortality during the multistate outbreak were genotypically and phenotypically characterized and compared. Strain SX 245 with PFGE pattern JF6X01.0523 was identified as a dominant and highly pathogenic variant causing high morbidity and mortality in affected calves, whereas strain SX 244 with PFGE pattern JF6X01.0590 was classified as a low pathogenic variant causing less morbidity and mortality. Comparison of whole-genome sequences determined that SX 245 lacked ~200 genes present in SX 244, including genes associated with the IncI1 plasmid and phages; SX 244 lacked eight genes present in SX 245 including a second YdiV Anti-FlhC(2)FlhD(4) factor, a lysin motif domain containing protein, and a pentapeptide repeat protein. RNA-sequencing revealed fimbriae-related, flagella-related, and chemotaxis genes had increased expression in SX 245 compared to SX 244. Furthermore, SX 245 displayed higher invasion of human and bovine epithelial cells than SX 244. These data suggest that the presence and up-regulation of genes involved in type 1 fimbriae production, flagellar regulation and biogenesis, and chemotaxis may play a role in the increased pathogenicity and host range expansion of the Salmonella Heidelberg isolates involved in the bovine-related outbreak.

59 BASIC BIOLOGICAL SCIENCES↗

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.

59 BASIC BIOLOGICAL SCIENCES↗