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. 2023 Jun 16;17(6):e0011285.
doi: 10.1371/journal.pntd.0011285. eCollection 2023 Jun.

Salmonella Typhi whole genome sequencing in Rwanda shows a diverse historical population with recent introduction of haplotype H58

Jean Pierre Rutanga  1   2   3 , Tessa de Block  2 , Wim L Cuypers  2   4 , Josephine Cafmeyer  2 , Marjan Peeters  2 , Esperance Umumararungu  5 , Jean Claude S Ngabonziza  5   6 , Aniceth Rucogoza  5 , Olivier Vandenberg  7 , Delphine Martiny  8   9   10 , Angélique Dusabe  11 , Théoneste Nkubana  11 , Gordon Dougan  12 , Claude Mambo Muvunyi  13 , Ivan Emil Mwikarago  5 , Jan Jacobs  2   3 , Stijn Deborggraeve  2 , Sandra Van Puyvelde  2   12   14   15
Affiliations

Salmonella Typhi whole genome sequencing in Rwanda shows a diverse historical population with recent introduction of haplotype H58

Jean Pierre Rutanga et al. PLoS Negl Trop Dis. .

Abstract

Salmonella enterica serovar Typhi (S. Typhi) is the cause of typhoid fever, presenting high rates of morbidity and mortality in low- and middle-income countries. The H58 haplotype shows high levels of antimicrobial resistance (AMR) and is the dominant S. Typhi haplotype in endemic areas of Asia and East sub-Saharan Africa. The situation in Rwanda is currently unknown and therefore to reveal the genetic diversity and AMR of S. Typhi in Rwanda, 25 historical (1984-1985) and 26 recent (2010-2018) isolates from Rwanda were analysed using whole genome sequencing (WGS). WGS was locally implemented using Illumina MiniSeq and web-based analysis tools, thereafter complemented with bioinformatic approaches for more in-depth analyses. Whereas historical S. Typhi isolates were found to be fully susceptible to antimicrobials and show a diversity of genotypes, i.e 2.2.2, 2.5, 3.3.1 and 4.1; the recent isolates showed high AMR rates and were predominantly associated with genotype 4.3.1.2 (H58, 22/26; 84,6%), possibly resulting from a single introduction in Rwanda from South Asia before 2010. We identified practical challenges for the use of WGS in endemic regions, including a high cost for shipment of molecular reagents and lack of high-end computational infrastructure for the analyses, but also identified WGS to be feasible in the studied setting and giving opportunity for synergy with other programs.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Workflow for implementation of WGS in typhoid surveillance.
Microbiological work-up of bacterial isolates, which includes bacterial identification (Gram staining, pure subculture and serotyping) and antimicrobial susceptibility testing (AST) (a); A WGS workflow, starting with cryopreservation of pure S. Typhi isolates, including determination of genotypes and genetic determinants of AMR, may result in sharing data in the global AMR monitoring for control and prevention (GLASS) programme (b).
Fig 2
Fig 2. Distribution of S. Typhi genotypes among historical (a) and recent (b) S. Typhi isolates per year of isolation.
Fig 3
Fig 3. Phylogenetic tree of the S. Typhi isolates as generated in Pathogenwatch and visualized in Microreact (https://microreact.org/project/syLwGwmBRvbSbVeq1sET2x).
Metadata defined in the legend indicate, from left to right (i) the type of isolate collection (recent or historical), (ii) district, (iii) genotype, (iv) AMR [multidrug resistant (MDR), decreased ciprofloxacin susceptibility (DCS), or MDR + DCS), (v) presence or absence of the IncHI1 plasmid replicon per isolate.
Fig 4
Fig 4. Maximum likelihood phylogenetic tree of 22 genotype 4.3.1.2 isolates from Rwanda in the context of global genotype 4.3.1 S.
Typhi (H58) isolates (a); and a detailed phylogenetic tree in the context of neighbor isolates (b).
See this image and copyright information in PMC

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