Comparing serotyping with whole-genome sequencing for subtyping of non-typhoidal Salmonella enterica: a large-scale analysis of 37 serotypes with a public health impact in the USA

Abstract

Serotyping has traditionally been used for subtyping of non-typhoidal Salmonella (NTS) isolates. However, its discriminatory power is limited, which impairs its use for epidemiological investigations of source attribution. Whole-genome sequencing (WGS) analysis allows more accurate subtyping of strains. However, because of the relative newness and cost of routine WGS, large-scale studies involving NTS WGS are still rare. We aimed to revisit the big picture of subtyping NTS with a public health impact by using traditional serotyping (i.e. reaction between antisera and surface antigens) and comparing the results with those obtained using WGS. For this purpose, we analysed 18 282 sequences of isolates belonging to 37 serotypes with a public health impact that were recovered in the USA between 2006 and 2017 from multiple sources, and were available at the National Center for Biotechnology Information (NCBI). Phylogenetic trees were reconstructed for each serotype using the core genome for the identification of genetic subpopulations. We demonstrated that WGS-based subtyping allows better identification of sources potentially linked with human infection and emerging subpopulations, along with providing information on the risk of dissemination of plasmids and acquired antimicrobial resistance genes (AARGs). In addition, by reconstructing a phylogenetic tree with representative isolates from all serotypes (n=370), we demonstrated genetic variability within and between serotypes, which formed monophyletic, polyphyletic and paraphyletic clades. Moreover, we found (in the entire data set) an increased detection rate for AARGs linked to key antimicrobials (such as quinolones and extended-spectrum cephalosporins) over time. The outputs of this large-scale analysis reveal new insights into the genetic diversity within and between serotypes; the polyphyly and paraphyly of certain serotypes may suggest that the subtyping of NTS to serotypes may not be sufficient. Moreover, the results and the methods presented here, leading to differentiation between genetic subpopulations based on their potential risk to public health, as well as narrowing down the possible sources of these infections, may be used as a baseline for subtyping of future NTS infections and help efforts to mitigate and prevent infections in the USA and globally.

Keywords: Salmonella subtyping; antimicrobial resistance; foodborne infections; non-typhoidal Salmonella; source attribution.

Fig. 1.

Approximate maximum-likelihood phylogenetic trees were reconstructed with FastTree using SNPs found in the core genomes of the 37 Salmonella serotypes. For each serotype, a core genome alignment was created including two S. Paratyphi type A outgroup strains (SRR3033248, SRR3277289; not included in the figure). Bootstrap replicates (n=5000) were used for branch support. Tree tips were coloured according to the identified genetic subpopulations. The scale bar indicates SNP difference.

Fig. 2.

Venn diagrams demonstrating the degree of overlap between sources when data were subtyped by serotypes (upper inset) or genetic subpopulations (lower inset). The number of serotypes/subpopulations is indicated within each category (values higher than zero are highlighted in blue). Sources were coloured as follows: human (purple), bovine (blue), poultry (brown), porcine (orange) and other (grey).

Fig. 3.

An ML phylogenetic tree was reconstructed with RAxML using SNPs found in the core genome of representative sequences from all 37 serotypes (n=370). Ten sequences were selected from each serotype phylogeny to represent the diversity of the genetic subpopulations. The tree was rooted using S. Paratyphi type A outgroup strains (SRR3033248, SRR3277289; not included in the figure). Bootstrap replicates (n=5000) were used for branch support. Full and empty circles indicate ≥70 and <70 % bootstrap support for major branches, respectively. Tree tips were coloured according to the serotype (Fig. S3 includes the same tree, annotated by the serotype and the genetic subpopulations).

Fig. 4.

General trends found in the data during the period between 2006 and 2017. (i) The presence of different AARGs sets conferring resistance to ampicillin, streptomycin, sulfonamides, tetracycline and chloramphenicol (i.e. ACSSuT; purple) or without chloramphenicol (i.e. ASSuT; yellow) (see text for additional details) (upper inset). The number of genes detected is indicated on the right. (ii) The presence of selected acquired antimicrobial resistance genes (AARGs) conferring resistance to ESCs (brown) or quinolones (pink) (middle inset). The number of genes detected is indicated on the right. (iii) The number of available whole-genome sequences (as raw reads) of NTS isolates in the NCBI SRA (lower inset).