Microbial GenomicsGenomicsPeer-reviewed journal article
Genomic diversity of non-typhoidal Salmonella found in patients suffering from gastroenteritis in Norfolk, UK
Steven J. RudderiD, Bilal DjeghoutiD, Ngozi ElumogoiD, Nicol JaneckoiD, Gemma C. LangridgeiD
Abstract
Salmonella is a significant public health pathogen responsible for a wide spectrum of diseases, ranging from gastroenteritis to invasive non-typhoidal salmonellosis (iNTS) and enteric fever. Although advancements in whole genome sequencing (WGS) have improved surveillance and outbreak investigations, traditional single-colony sequencing methods overlook within-host diversity, potentially underestimating the complexity of infections. This study explores the genome-wide diversity of Salmonella strains recovered from stool samples of eight patients, with up to 20 isolates analysed per sample. A total of 156 Salmonella enterica isolates were recovered. All isolates from individual patients displayed consistent serovars and sequence types. Despite the serotype consistency, microevolution was observed in S. Java ST149 and S. Java ST43, with SNP analyses revealing higher diversity (13 and 5 SNP differences, respectively) compared to the clonal populations of other serovars. Phylogenetic analysis of S. Java ST149 isolates from Patient 2 revealed distinct branching, driven by mutations in genes such as secY and cnoX, while S. Java ST43 isolates from Patient 1 displayed multiple clades with notable SNPs affecting transcriptional regulators. Genome structure (GS) analyses using hybrid assemblies identified uniform GS1.0 across all isolates. Antimicrobial resistance (AMR) profiling revealed the presence of multidrug efflux pump genes (mdsA and mdsB) in all isolates. However, S. Typhimurium isolates from Patient 4 exhibited additional AMR genes, including sul2, aph(3)-Ib, and blaTEM-1, associated with an 8.7 kb resistance region. A single isolate from Patient 4 lacked these additional genes due to the deletion of a [~]19 kb genomic region, highlighting structural variation as a driver of phenotypic differences. These findings emphasise the genetic diversity of Salmonella within hosts, particularly in serovars such as S. Java, and underscore the limitations of single-colony sequencing in capturing this complexity. The study highlights the utility of hybrid sequencing strategies for comprehensive analysis of genome variation, offering valuable insights into transmission dynamics, antimicrobial resistance, and evolutionary processes in Salmonella. Impact statementThis study provides a transformative perspective on Salmonella genomics, uncovering the significant within-host diversity of Salmonella populations through the analysis of multiple single-colony isolates. By leveraging hybrid sequencing technologies, we reveal critical insights into the microevolution of Salmonella during infection, capturing genome-wide variation at both single nucleotide and chromosomal scales. Our findings highlight the inherent limitations of traditional single-colony sequencing in detecting within-host diversity, particularly in genetically diverse serovars such as S. Java ST149. The identification of antimicrobial resistance (AMR) determinants and structural genomic variation demonstrates the dynamic nature of Salmonella genomes, offering crucial implications for clinical management and public health surveillance. Importantly, we show that within-host diversity may influence epidemiological interpretations, particularly in outbreak investigations and source attribution, where genetic differences between isolates can mask deeper transmission links. Data SummaryAll relevant supporting data are available in the accompanying supplementary data files. The online version of this article contains four supplementary table. All Salmonella isolate genome sequences are available in the National Centre for Biotechnology Information (NCBI), Sequence Read Archive (SRA) under the Bioproject accession numbers PRJNA1230128. SRA accession numbers and associated metadata for isolate genomes are included in the supplementary table S2-S4.
- organism: bacteria (other)
- assay: whole-genome sequencing
- research focus: variant calling
- approach: statistical model
- Citations
- 3
- FWCI
- 1.41
- Field percentile
- top 20%
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