Whole genome sequencing analysis of multiple Salmonella serovars provides insights into phylogenetic relatedness, antimicrobial resistance, and virulence markers across humans, food animals and agriculture environmental sources

Pornsukarom, Suchawan; Vliet, Arnoud H. M. van; Thakur, Siddhartha

doi:10.1186/s12864-018-5137-4

Cited by 105 publications

(96 citation statements)

References 65 publications

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“…Similar to the results of Tyson et al (2016) and Pornsukarom et al (2018), discrepancies were observed for genotypic prediction of STR resistance even after broth and agar microdilution testing. A study by Koskiniemi et al (2011) found activation of a chromosomally encoded adenyl transferase (aadA) combined with mutations affecting the electron transport chain (ETC) resulted in increased STR resistance.…”

Section: Activation Of Str Resistance In Salmonella Spp Occurs In MIsupporting

confidence: 71%

“…Specific serotypes and AMR profiles can be linked to food commodities which can vary depending on antimicrobial usage for food production in different countries (Butaye et al, 2006;Zhao et al, 2007;Hong et al, 2016;Yoon et al, 2017). As such, the resistance profile and serotype of a Salmonella isolate can provide clues as to the epidemiology of an infection (Pornsukarom et al, 2018). Some examples include MDR S. enterica ser.…”

Section: Discussionmentioning

confidence: 99%

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Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

et al. 2020

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Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database-Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene-and coverage-dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicinand two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.

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Section: Activation Of Str Resistance In Salmonella Spp Occurs In MIsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

et al. 2020

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“…Our results (Figures 1, 2, 4) demonstrated that the core genome analysis as performed by Hindermann et al (2017) is not discriminative enough to determine whether or not the S. Infantis strains from Switzerland are really clustered together with, and could be epidemiologically related to strains from Hungary, Japan, Italy, United States, and Israel. Our results also indicated that the analysis of core genomes provides less detailed information about the different genetic lineages as discussed by Pornsukarom et al (2018). In this respect, the phylogenetic reconstructions based on cloud genes (Figures 3, 4) remarkably differ also from that based on the whole genomes (Figure 1).…”

Section: Discussionmentioning

confidence: 53%

Comparative Genome Analysis of Hungarian and Global Strains of Salmonella Infantis

et al. 2020

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Introduction: The emergence and spread of new strains of zoonotic bacteria, such as multidrug resistant (MDR) Salmonella Infantis, represent a growing health risk for humans in and outside Europe due to foodborne infections of poultry meat origin. Objectives: In order to understand genome relations of S. Infantis strains from Hungary and from different geographic regions, we performed a comprehensive genome analysis of nine Hungarian and 67 globally selected strains of S. Infantis and 26 Salmonella strains representing 13 non-Infantis serovars. Results: Analyses of whole-, and accessory genomes, showed that almost all S. Infantis strains were separated from the non-Infantis serovars. S. Infantis strains from Hungary formed subclusters based on their time of isolation. In whole genome sequence analysis, the Swiss strains of S. Infantis were closely related to each other and clustered together with subclusters of strains from Hungary, Japan, Italy, United States, and Israel. The accessory genome analysis revealed that the Swiss strains were distinct from most of the strains investigated, including the Hungarian ones. Analysis of the cloud genes offered the most detailed insight into the genetic distance and relationship of S. Infantis strains confirming that the Swiss and Hungarian strains belonged to different lineages. As expected, core genome analysis provided the least discriminatory power for analysis of S. Infantis. Genomic sequences of nine strains from Brazil, Israel, Mexico, Nigeria, and Senegal (deposited as S. Infantis) proved to be outliers from the S. Infantis clade. They were predicted to be Salmonella Rissen, Salmonella Ouakarm, Salmonella Kentucky, Salmonella Thompson, and Salmonella enterica subsp. diarizonae. Conclusion: Accessory genome of S. Infantis showed the highest diversity suggesting a faster evolution than that of the whole genomes contributing to the emergence of multiple genetic variants of S. Infantis worldwide. Accordingly, in spite of the comprehensive analysis of several genomic characteristics, no epidemiologic links between these S. Infantis strains from different countries could be established. It is also concluded that several strains originally designated as S. Infantis need in databanks reclassification.

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“…Few studies have been conducted to identify genetic factors associated with Salmonella serovars isolated from swine using WGS [19,20]. In order to understand the genetic variations among different Salmonella serovars, the WGS of fourteen Salmonella serovars isolated from swine and swine swab were analyzed.…”

Section: Introductionmentioning

confidence: 99%

Genomic comparison of diverse Salmonella serovars isolated from swine

et al. 2019

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Food animals act as a reservoir for many foodborne pathogens. Salmonella enterica is one of the leading pathogens that cause food borne illness in a broad host range including animals and humans. They can also be associated with a single host species or a subset of hosts, due to genetic factors associated with colonization and infection. Adult swine are often asymptomatic carriers of a broad range of Salmonella servoars and can act as an important reservoir of infections for humans. In order to understand the genetic variations among different Salmonella serovars, Whole Genome Sequences (WGS) of fourteen Salmonella serovars from swine products were analyzed. More than 75% of the genes were part of the core genome in each isolate and the higher fraction of gene assign to different functional categories in dispensable genes indicated that these genes acquired for better adaptability and diversity. High concordance (97%) was detected between phenotypically confirmed antibiotic resistances and identified antibiotic resistance genes from WGS. The resistance determinants were mainly located on mobile genetic elements (MGE) on plasmids or integrated into the chromosome. Most of known and putative virulence genes were part of the core genome, but a small fraction were detected on MGE. Predicted integrated phage were highly diverse and many harbored virulence, metal resistance, or antibiotic resistance genes. CRISPR (Clustered regularly interspaced short palindromic repeats) patterns revealed the common ancestry or infection history among Salmonella serovars. Overall genomic analysis revealed a great deal of diversity among Salmonella serovars due to acquired genes that enable them to thrive and survive during infection.

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Whole genome sequencing analysis of multiple Salmonella serovars provides insights into phylogenetic relatedness, antimicrobial resistance, and virulence markers across humans, food animals and agriculture environmental sources

Cited by 105 publications

References 65 publications

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

Comparative Genome Analysis of Hungarian and Global Strains of Salmonella Infantis

Genomic comparison of diverse Salmonella serovars isolated from swine

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