Last data update: Aug 15, 2025. (Total: 49733 publications since 2009)
| Records 1-4 (of 4 Records) |
| Query Trace: Smith PA[original query] |
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| Validation of Core and Whole-Genome Multi-Locus Sequence Typing Schemes for Shiga-Toxin-Producing E. coli (STEC) Outbreak Detection in a National Surveillance Network, PulseNet 2.0, USA
Leeper MM , Schroeder MN , Griswold T , Thakur M , Krishnan K , Katz LS , Hise KB , Williams GM , Stroika SG , Im SB , Lindsey RL , Smith PA , Huffman J , Kelley A , Cleland S , Collins AJ , Gautam S , Tyagi E , Park S , Carriço JA , Machado MP , Pouseele H , Michielsen D , Carleton HA . Microorganisms 2025 13 (6)
Shiga-toxin-producing E. coli (STEC) is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Whole-genome sequencing (WGS) is a powerful tool used in public health and microbiology for the detection, surveillance, and outbreak investigation of STEC. In this study, we applied three WGS-based subtyping methods, high quality single-nucleotide polymorphism (hqSNP) analysis, whole genome multi-locus sequence typing using chromosome-associated loci [wgMLST (chrom)], and core genome multi-locus sequence typing (cgMLST), to isolate sequences from 11 STEC outbreaks. For each outbreak, we evaluated the concordance between subtyping methods using pairwise genomic differences (number of SNPs or alleles), linear regression models, and tanglegrams. Pairwise genomic differences were highly concordant between methods for all but one outbreak, which was associated with international travel. The slopes of the regressions for hqSNP vs. allele differences were 0.432 (cgMLST) and 0.966 wgMLST (chrom); the slope was 1.914 for cgMLST vs. wgMLST (chrom) differences. Tanglegrams comprised of outbreak and sporadic sequences showed moderate clustering concordance between methods, where Baker's Gamma Indices (BGIs) ranged between 0.35 and 0.99 and Cophenetic Correlation Coefficients (CCCs) were ≥0.88 across all outbreaks. The K-means analysis using the Silhouette method showed the clear separation of outbreak groups with average silhouette widths ≥0.87 across all methods. This study validates the use of cgMLST for the national surveillance of STEC illness clusters using the PulseNet 2.0 system and demonstrates that hqSNP or wgMLST can be used for further resolution. |
| Genomic Characterization of Escherichia coli O157:H7 Associated with Multiple Sources, United States
Wirth JS , Leeper MM , Smith PA , Vasser M , Katz LS , Vidyaprakash E , Carleton HA , Chen JC . Emerg Infect Dis 2025 31 (13) 109-116
In the United States, Shiga toxin-producing Escherichia coli (STEC) outbreaks cause >265,000 infections and cost $280 million annually. We investigated REPEXH01, a persistent strain of STEC O157:H7 associated with multiple sources, including romaine lettuce and recreational water, that has caused multiple outbreaks since emerging in late 2015. By comparing the genomes of 729 REPEXH01 isolates with those of 2,027 other STEC O157:H7 isolates, we identified a highly conserved, single base pair deletion in espW that was strongly linked to REPEXH01 membership. The biological consequence of that deletion remains unclear; further studies are needed to elucidate its role in REPEXH01. Additional analyses revealed that REPEXH01 isolates belonged to Manning clade 8; possessed the toxins stx2a, stx2c, or both; were predicted to be resistant to several antimicrobial compounds; and possessed a diverse set of plasmids. Those factors underscore the need to continue monitoring REPEXH01 and clarify aspects contributing to its emergence and persistence. |
| Rapid identification of enteric bacteria from whole genome sequences using average nucleotide identity metrics
Lindsey RL , Gladney LM , Huang AD , Griswold T , Katz LS , Dinsmore BA , Im MS , Kucerova Z , Smith PA , Lane C , Carleton HA . Front Microbiol 2023 14 1225207
Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well as rare and closely related species. The RGDv2 contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp., while the TGDv1 contains 454 enteric bacterial genomes representing 42 different species. The analysis showed that, when a standard minimum of 70% genome bases alignment existed, the ANI threshold values determined for these species were ≥95 for Escherichia/Shigella and Vibrio species, ≥93% for Salmonella species, and ≥92% for Campylobacter and Listeria species. Using these metrics, the RGDv2 accurately classified all validation strains in TGDv1 at the species level, which is consistent with the classification based on previous gold standard methods. |
| Reoccurring Escherichia coli O157:H7 strain linked to leafy greens-associated outbreaks, 2016-2019
Chen JC , Patel K , Smith PA , Vidyaprakash E , Snyder C , Tagg KA , Webb HE , Schroeder MN , Katz LS , Rowe LA , Howard D , Griswold T , Lindsey RL , Carleton HA . Emerg Infect Dis 2023 29 (9) 1895-1899
Genomic characterization of an Escherichia coli O157:H7 strain linked to leafy greens-associated outbreaks dates its emergence to late 2015. One clade has notable accessory genomic content and a previously described mutation putatively associated with increased arsenic tolerance. This strain is a reoccurring, emerging, or persistent strain causing illness over an extended period. |
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