Whole-genome sequencing to investigate two concurrent outbreaks of Salmonella Enteritidis in South Africa, 2018

Enteric fever cluster identification in South Africa using genomic surveillance of Salmonella enterica serovar Typhi

The National Institute for Communicable Diseases in South Africa participates in national laboratory-based surveillance for human isolates of Salmonella species. Laboratory analysis includes whole-genome sequencing (WGS) of isolates. We report on WGS-based surveillance of Salmonella enterica serovar Typhi (Salmonella Typhi) in South Africa from 2020 through 2021. We describe how WGS analysis identified clusters of enteric fever in the Western Cape Province of South Africa and describe the epidemiological investigations associated with these clusters. A total of 206 Salmonella Typhi isolates were received for analysis. Genomic DNA was isolated from bacteria and WGS was performed using Illumina NextSeq technology. WGS data were investigated using multiple bioinformatics tools, including those available at the Centre for Genomic Epidemiology, EnteroBase and Pathogenwatch. Core-genome multilocus sequence typing was used to investigate the phylogeny of isolates and identify clusters. Three major clusters of enteric fever were identified in the Western Cape Province; cluster one (n=11 isolates), cluster two (n=13 isolates), and cluster three (n=14 isolates). To date, no likely source has been identified for any of the clusters. All isolates associated with the clusters, showed the same genotype (4.3.1.1.EA1) and resistome (antimicrobial resistance genes: bla _TEM-1B, catA1, sul1, sul2, dfrA7). The implementation of genomic surveillance of Salmonella Typhi in South Africa has enabled rapid detection of clusters indicative of possible outbreaks. Cluster identification allows for targeted epidemiological investigations and a timely, coordinated public health response.

Unlocking the Potential of Genomic Data to Inform Typhoid Fever Control Policy: Supportive Resources for Genomic Data Generation, Analysis, and Visualization

The global response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic demonstrated the value of timely and open sharing of genomic data with standardized metadata to facilitate monitoring of the emergence and spread of new variants. Here, we make the case for the value of Salmonella Typhi (S. Typhi) genomic data and demonstrate the utility of freely available platforms and services that support the generation, analysis, and visualization of S. Typhi genomic data on the African continent and more broadly by introducing the Africa Centres for Disease Control and Prevention's Pathogen Genomics Initiative, SEQAFRICA, Typhi Pathogenwatch, TyphiNET, and the Global Typhoid Genomics Consortium.

Virulence and Antimicrobial Resistance Profiling of Salmonella Serovars Recovered from Retail Poultry Offal in KwaZulu-Natal Province, South Africa

As poultry organ meat is widely consumed, especially in low- and middle-income countries, there is reason to investigate it as a source of Salmonella infections in humans. Consequently, the aim of this study was to determine the prevalence, serotypes, virulence factors and antimicrobial resistance of Salmonella isolated from chicken offal from retail outlets in KwaZulu-Natal, South Africa. Samples (n = 446) were cultured for the detection of Salmonella using ISO 6579-1:2017. Presumptive Salmonella were confirmed using matrix-assisted laser desorption ionisation time-of-flight mass spectrometry. Salmonella isolates were serotyped using the Kauffmann-White-Le Minor scheme and antimicrobial susceptibility was determined by the Kirby-Bauer disk diffusion technique. A conventional PCR was used for the detection of Salmonella invA, agfA, lpfA and sivH virulence genes. Of the 446 offal samples, 13 tested positive for Salmonella (2.91%; CI = 1.6-5). The serovars included S. Enteritidis (n = 3/13), S. Mbandaka (n = 1/13), S. Infantis (n = 3/13), S. Heidelberg (n = 5/13) and S. Typhimurium (n = 1/13). Antimicrobial resistance against amoxicillin, kanamycin, chloramphenicol and oxytetracycline was found only in S. Typhimurium and S. Mbandaka. All 13 Salmonella isolates harboured invA, agfA, lpfA and sivH virulence genes. The results show low Salmonella prevalence from chicken offal. However, most serovars are known zoonotic pathogens, and multi-drug resistance was observed in some isolates. Consequently, chicken offal products need to be treated with caution to avoid zoonotic Salmonella infections.

Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning

Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by the consumption of imported food products or contracted during foreign travel, therefore, making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2313 S. Enteritidis genomes, collected by the UKHSA between 2014-2019, were used to train a 'local classifier per node' hierarchical classifier to attribute isolates to four continents, 11 sub-regions, and 38 countries (53 classes). The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661, respectively). A number of countries commonly visited by UK travelers were predicted with high accuracy (hF1: >0.9). Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in <4 min per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted.

Rapid Identification and Source Tracing of a Salmonella Typhimurium Outbreak in China by Metagenomic and Whole-Genome Sequencing

Salmonella spp. are among the most prevalent foodborne pathogens. Rapid identification of etiologic agents during foodborne outbreaks is of great importance. In this study, we report a traceback investigation of a Salmonella outbreak in China. Metagenomic sequencing of suspected food samples was performed on MinION and MiSeq platforms. Real-time nanopore sequencing analysis identified reads belonging to the Enterobacteriaceae family. MiSeq sequencing identified 63 reads specifically mapped to Salmonella. Conventional methods including quantitative-PCR and culture-based isolation confirmed as Salmonella enterica serovar Typhimurium. The foodborne outbreak of Salmonella Typhimurium was further recognized by whole-genome sequencing and pulsed-field gel electrophoresis analysis. Our study demonstrates the ability of metagenomic sequencing to rapidly identify enteric pathogens directly from food samples. These results highlight the capacity of metagenomic sequencing to deliver actionable information rapidly and to expedite the tracing and identification of etiologic agents during foodborne outbreaks.

Genomic Characterization of Endemic and Ecdemic Non-typhoidal Salmonella enterica Lineages Circulating Among Animals and Animal Products in South Africa

In Africa, the burden of illness caused by non-typhoidal Salmonella enterica is disproportionally high; however, whole-genome sequencing (WGS) efforts are overwhelmingly concentrated in world regions with lower burdens. While WGS is being increasingly employed in South Africa to characterize Salmonella enterica, the bulk of these efforts have centered on characterizing human clinical strains. Thus, very little is known about lineages circulating among animals in the country on a genomic scale. Here, we used WGS to characterize 63 Salmonella enterica strains isolated from livestock, companion animals, wildlife, and animal products in South Africa over a 60-year period. Genomes were assigned to serotypes Dublin, Hadar, Enteritidis, and Typhimurium (n = 18, 8, 13, and 24 strains, respectively) and sequence types (STs) ST10 (all S. Dublin), ST33 (all S. Hadar), ST11/ST366 (n = 12 and 1 S. Enteritidis, respectively), and ST19/ST34 (n = 23 and 1 S. Typhimurium, respectively; via seven-gene multi-locus sequence typing). Within-ST phylogenies were constructed using genomes sequenced in this study, plus publicly available genomes representative of each ST's (i) global (n = 2,802 and 1,569 S. Dublin and Hadar genomes, respectively) and (ii) African (n = 716 and 343 S. Enteritidis and Typhimurium genomes, respectively) population. For S. Dublin ST10, a largely antimicrobial-susceptible, endemic lineage circulating among humans, animals, and food in South Africa was identified, as well as a lineage that was likely recently introduced from the United States. For S. Hadar ST33, multiple South African lineages harboring streptomycin and tetracycline resistance-conferring genes were identified. African S. Enteritidis ST11 could be primarily partitioned into one largely antimicrobial-susceptible and one largely multidrug-resistant (MDR) clade, with South African isolates confined to the largely antimicrobial-susceptible clade. S. Typhimurium ST19/ST34 strains sequenced here were distributed across the African S. Typhimurium ST19/ST34 phylogeny, representing a diverse range of lineages, including numerous MDR lineages. Overall, this study provides critical insights into endemic and ecdemic non-typhoidal Salmonella enterica lineages circulating among animals, foods, and humans in South Africa and showcases the utility of WGS in characterizing animal-associated strains from a world region with a high salmonellosis burden.

Infection Heterogeneity and Microbiota Differences in Chicks Infected by Salmonella enteritidis

This study was conducted to compare the infection heterogeneity and cecal microbiota in chicks infected by S. enteritidis. Forty-eight 8-d-old female Arbor Acres chicks were challenged with S. enteritidis and euthanized 24 h later. The eight chicks with the highest Salmonella tissue loads were assigned to group S (S. enteritidis-susceptible), and the eight chicks with the lowest Salmonella tissue loads were assigned to group R (S. enteritidis-resistant). Chicks in group S showed a higher liver index (p < 0.05), obvious liver lesions, and an decreasing trend for the villus height-to-crypt depth ratio (p < 0.10), compared with those in group R. Gene expression of occludin, MUC2, and IL10 was higher, whereas that of iNOS and IL6 was lower (p < 0.05), in chicks of group R relative to those in group S. Separation of the cecal microbial community structure has been found between the two groups. The S. enteritidis-susceptible chicks showed higher abundance of pathogenic bacteria (Fusobacterium and Helicobacter) in their cecal, while Desulfovibrio_piger was enriched in the cecal of S. enteritidis-resistant chicks. In summary, chicks showed heterogeneous responses to S. enteritidis infection. Enhanced intestinal barrier function and cecal microbiota structure, especially a higher abundance of Desulfovibrio_piger, may help chicks resist S. enteritidis invasion.

Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, Shigella sonnei

Shigella sonnei is the most common agent of shigellosis in high-income countries, and causes a significant disease burden in low- and middle-income countries. Antimicrobial resistance is increasingly common in all settings. Whole genome sequencing (WGS) is increasingly utilised for S. sonnei outbreak investigation and surveillance, but comparison of data between studies and labs is challenging. Here, we present a genomic framework and genotyping scheme for S. sonnei to efficiently identify genotype and resistance determinants from WGS data. The scheme is implemented in the software package Mykrobe and tested on thousands of genomes. Applying this approach to analyse >4,000 S. sonnei isolates sequenced in public health labs in three countries identified several common genotypes associated with increased rates of ciprofloxacin resistance and azithromycin resistance, confirming intercontinental spread of highly-resistant S. sonnei clones and demonstrating the genomic framework can facilitate monitoring the spread of resistant clones, including those that have recently emerged, at local and global scales.