Campylobacter is a leading causing of bacterial foodborne and zoonotic illnesses in the USA. Pulsed-field gene electrophoresis (PFGE) and 7-gene multilocus sequence typing (MLST) have been historically used to differentiate sporadic from outbreak Campylobacter isolates. Whole genome sequencing (WGS) has been shown to provide superior resolution and concordance with epidemiological data when compared with PFGE and 7-gene MLST during outbreak investigations. In this study, we evaluated epidemiological concordance for high-quality SNP (hqSNP), core genome (cg)MLST and whole genome (wg)MLST to cluster or differentiate outbreak-associated and sporadic Campylobacter jejuni and Campylobacter coli isolates. Phylogenetic hqSNP, cgMLST and wgMLST analyses were also compared using Baker’s gamma index (BGI) and cophenetic correlation coefficients. Pairwise distances comparing all three analysis methods were compared using linear regression models. Our results showed that 68/73 sporadic C. jejuni and C. coli isolates were differentiated from outbreak-associated isolates using all three methods. There was a high correlation between cgMLST and wgMLST analyses of the isolates; the BGI, cophenetic correlation coefficient, linear regression model R ² and Pearson correlation coefficients were >0.90. The correlation was sometimes lower comparing hqSNP analysis to the MLST-based methods; the linear regression model R ² and Pearson correlation coefficients were between 0.60 and 0.86, and the BGI and cophenetic correlation coefficient were between 0.63 and 0.86 for some outbreak isolates. We demonstrated that C. jejuni and C. coli isolates clustered in concordance with epidemiological data using WGS-based analysis methods. Discrepancies between allele and SNP-based approaches may reflect the differences between how genomic variation (SNPs and indels) are captured between the two methods. Since cgMLST examines allele differences in genes that are common in most isolates being compared, it is well suited to surveillance: searching large genomic databases for similar isolates is easily and efficiently done using allelic profiles. On the other hand, use of an hqSNP approach is much more computer intensive and not scalable to large sets of genomes. If further resolution between potential outbreak isolates is needed, wgMLST or hqSNP analysis can be used.

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.

Cronobacter species, especially C. sakazakii and C. malonaticus, are opportunistic pathogens that are linked to severe infections in infants with high case fatality rates. In this study, we investigated whole genome sequencing (WGS) analysis approaches, specifically 7-gene multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST) to subtype Cronobacter isolates. We analyzed a comprehensive set of 743 Cronobacter isolates derived from clinical, food, and environmental sources. We also evaluated high-quality single nucleotide polymorphism (hqSNP), cgMLST, and wgMLST to cluster epidemiologically related and differentiate sporadic C. sakazakii isolates. Our results indicate that both cgMLST and wgMLST accurately identify closely related isolates and are consistent with epidemiological findings. The allele-based analyses were also comparable with hqSNP analyses, the current gold standard. Our workflow also outputs 7-gene MLST allele calls, Cronobacter sequence types, and clonal complexes, which may be useful for historic comparisons during outbreak investigations. Following the recent classification of Cronobacter infections as nationally notifiable in the United States, our findings demonstrate the efficacy of WGS-based approaches within the PulseNet framework to improve outbreak detection and response strategies for Cronobacter . IMPORTANCE: Cronobacter species, specifically C. sakazakii and C. malonaticus , are opportunistic pathogens linked to severe infections in infants with high case fatality rates. This study highlights the critical importance of advanced molecular techniques in public health surveillance, using whole genome sequencing (WGS) methodologies such as multi-locus sequence typing (7-gene MLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST). The validation of these WGS-based approaches within the PulseNet framework is timely, especially following the recent classification of Cronobacter infections as nationally notifiable in the United States. WGS methods not only enhance outbreak detection but can also inform public health guidance aimed at preventing infections and reducing mortality in vulnerable populations, especially infants. Our research supports implementation of cgMLST as a standardized approach for routine PulseNet surveillance of Cronobacter , with wgMLST and hqSNP analyses providing additional discriminatory power for outbreak investigations and high resolution phylogenetic analysis.

Monitoring antimicrobial resistance (AR) in surface water has been proposed to identify sources, drivers, and transmission of AR in the environment and signal emerging AR threats. We conducted a year-long surface water AR assessment to inform the design of environmental AR monitoring approaches for an urbanized watershed in Atlanta, Georgia, USA. Escherichia coli with extended-spectrum β-lactamase (ESBL-Ec) production, colistin resistance (CST-Ec), or meropenem nonsusceptibility (MEM-Ec) were quantified by culture from 11 sites (6 river mainstem, 5 tributary) and characterized by whole-genome sequencing. ESBL-Ec was detected across all sites in 77% (109/142) of samples tested, while CST-Ec was detected in 35% (48/138) of samples, primarily from two sites near combined sewer overflows and a large hospital. We detected MEM-Ec at 5 sites (5/85 samples; 6%), including an ST167 E. coli isolate carrying the blaNDM-5 gene in January 2019; statewide, Georgia reported only 10 clinical, blaNDM-harboring E. coli isolates in 2019. Spatial stream network models showed strong clustering of resistant E. coli in time, but not space, and weak associations with environmental factors like land cover and wastewater infrastructure. This suggests that public health data can be gleaned from monitoring AR in this watershed at sentinel sites representing the major population center.