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[Preprint]. 2025 Jul 31:2024.09.30.613582.
doi: 10.1101/2024.09.30.613582.

Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi

Danielle J Ingle  1 , Jane Hawkey  2   3 , Martin Hunt  4   5   6   7 , Zamin Iqbal  4   8 , Jacqueline A Keane  9 , Ayorinde O Afolayan  10   11 , Niyaz Ahmed  12 , Saadia Andleeb  13 , Philip M Ashton  14   15 , Isaac I Bogoch  16 , Megan E Carey  17   18 , Marie Anne Chattaway  19 , John A Crump  20 , Paula Diaz Guevara  21 , Benjamin P Howden  1   22   23 , Hidemasa Izumiya  24 , Jobin John Jacob  25 , Louise M Judd  1   23 , Arti Kapil  26 , Karen H Keddy  27 , Justin Y Kim  28 , Myron M Levine  29 , Masatomo Morita  24 , Satheesh Nair  19 , Sophie Octavia  30 , Iruka N Okeke  10 , Precious E Osadebamwen  10 , Sadia Isfat Ara Rahman  31 , Assaf Rokney  32 , David A Rasko  29   33   34 , Varun Shamanna  35 , Michael J Sikorski  29   33 , Anthony M Smith  36   37 , Gabriel T Sunmonu  10 , Kaitlin A Tagg  28 , Ryan R Wick  1 , Zoe A Dyson  17   38 , Kathryn E Holt  2   17 ; Global Typhoid Genomics Consortium
Affiliations

Typhi Mykrobe: fast and accurate lineage identification and antimicrobial resistance genotyping directly from sequence reads for the typhoid fever agent Salmonella Typhi

Danielle J Ingle et al. bioRxiv. .

Update in

Abstract

Background: Typhoid fever results from systemic infection with Salmonella enterica serovar Typhi (Typhi) and causes 10 million illnesses annually. Disease control relies on prevention (water, sanitation, and hygiene interventions or vaccination) and effective antimicrobial treatment. Antimicrobial resistant (AMR) Typhi lineages have emerged and become established in many parts of the world. Knowledge of local pathogen populations informed by genomic surveillance, including of lineages (defined by the GenoTyphi scheme) and AMR determinants, is increasingly used to inform local treatment guidelines and to inform vaccination strategy. Current tools for genotyping Typhi require multiple read alignment or assembly steps and have not been validated for analysis of data generated with Oxford Nanopore Technologies (ONT) long-read sequencing devices. Here, we introduce Typhi Mykrobe, a command line software tool for rapid genotyping of Typhi lineages, AMR determinants, and plasmid replicons direct from sequencing reads.

Results: We validated Typhi Mykrobe lineage genotyping by comparison with the current standard read mapping-based approach and demonstrated 99.8% concordance across nearly 13,000 genomes sequenced with Illumina platforms. For the few isolates with discordant calls, we show that Typhi Mykrobe results are better supported by the evidence from raw sequence read data than the results generated using the mapping-based approach. We also demonstrate 99.9% concordance for detection of AMR determinants compared with the current standard assembly-based approach, with similar results for plasmid marker detection. Typhi Mykrobe predicts clinical resistance categorisation (S/I/R) for eight drug classes, and we show strong agreement with phenotypic categorisations generated from reference laboratory minimum inhibitory concentration (MIC) data for n=1,572 Illumina-sequenced isolates (>99% agreement within one doubling dilution). We show strong concordance (>96% for genotype and >98% for AMR and plasmid) between calls made from ONT reads and those made from Illumina reads for isolates sequenced on both platforms (n =93 genomes). Typhi Mykrobe takes less than a minute per sample and is available at https://github.com/typhoidgenomics/genotyphi.

Conclusions: Typhi Mykrobe provides rapid and sensitive genotyping of Typhi genomes direct from Illumina and ONT reads, although lower accuracy was observed for R9 ONT data. It demonstrated accurate assignment of GenoTyphi lineage, detection of AMR determinants and prediction of corresponding AMR phenotypes, and identification of plasmid replicons.

Keywords: Antimicrobial Resistance (AMR); Salmonella Typhi; Typhoid fever; Whole Genome Sequencing (WGS); genotyping; lineage; pathogen sequencing.

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Conflict of interest statement

MML was a co-developer of a Trivalent Salmonella (Enteritidis/Typhimurium/Typhi Vi) conjugate vaccine with Bharat Biotech International and the Wellcome Trust. MML has also received payments from Pfizer for consultancy work. MML holds US patents for “Compositions and Methods for Producing Bacterial Conjugate Vaccines”. MML was a member of a NIH DSMB that oversaw US government-funded efficacy trials of COVID-19 vaccines. DSMB was disbanded after several vaccines were given Emergency Use Authorization. MML was a member of the Vaccines and Related Biological Products Advisory Committee of the FDA. IIB was a consultant for the Weapons Threat Reduction Program, Global Affairs Canada. AJP has been involved an Oxford University partnership with AZ for development of COVID-19 vaccines. AJP has received payments for consultancy work from Shionogi. AJP is chair of DHSC’s Joint Committee on Vaccination and Immunisation, is a chair of WHOs Salmonella TAG, and was a member of WHOs SAGE. AJP received support from MRNA – Moderna. KLC has received payments from Pfizer for presentations and travel support from BD. INO has received payments from the Wellcome Trust for consultancy work and receives royalties for books or book chapters published via Springer, Cornell University Press, and Oxford University Press. INO has received travel support from BMGF, ESCMID, and the American ASM and has held leadership or advisory roles for Wellcome SEDRIC, the BMGF surveillance advisory group, the Thomas Bassir Biomedical Foundation, and International Centre for Antimicrobial Resistance Solutions (ICARS) Technical Advisory Forum. ZI has received travel support from ETH Zurich.

Figures

Figure 1:
Figure 1:. Typhi Mykrobe functionality
A) A schematic overview of the Typhi Mykrobe pipeline. B) Table of epidemiologically important AMR determinants targeted for detection/typing by Typhi Mykrobe, grouped by drug. C) Table of epidemiologically important plasmid replicons targeted for detection by Typhi Mykrobe.
Figure 2:
Figure 2:. Validation of Typhi Mykrobe AMR predictions vs AMR phenotypes
Summary of Typhi Mykrobe’s genotype-based clinical resistance categorisations compared with susceptibility phenotypes, for 4,018 isolates with publicly available matched genome and phenotype data. Comparisons are summarised as categorical agreement, and calculated separately for the three different source datasets, which used different phenotyping methods and interpretive standards (US CDC: MIC, CLSI; UKHSA: MIC, EUCAST; SEAP: disk diffusion, EUCAST; see Methods). Major errors were defined as susceptible isolates with AMR determinants detected (reported as R by Mykrobe). Very major errors were defined as resistant isolates with no AMR determinants detected (reported as S by Mykrobe). Error bars are shown that represent 95% confidence interval of proportion. Note UKHSA tested amoxicillin rather than ampicillin, the result is reported in the ampicillin row.
Figure 3:
Figure 3:. Typhi Mykrobe genotypes vs minimum inhibitory concentration from two reference laboratories, for ciprofloxacin.
a) UKHSA dataset, using EUCAST. b) US CDC dataset, using CLSI. Each column represents the set of isolates in which Typhi Mykrobe identified a unique combination of genetic determinants, indicated in the panel at the bottom. For each column, the violin plots show the distribution of ciprofloxacin MIC values, and stacked barplots show the proportion of genomes called as S, I, or R (coloured as per inset legend, and labelled with counts). The solid horizontal lines on the violin plots mark the R breakpoint, the dashed line marks the CLSI I breakpoint used for the US CDC dataset.
Figure 4:
Figure 4:. Typhi Mykrobe genotypes vs disk diffusion data from the SEAP study, for three drug classes
Data for three drugs are shown in panels a) Azithromycin, b) Ceftriaxone, and c) Ciprofloxacin. Each column represents the set of isolates in which Typhi Mykrobe identified a unique combination of genetic determinants, indicated in the panel at the bottom. For each column, the violin plots show the distribution of disk diffusion values for azithromycin, ceftriaxone or ciprofloxacin. The stacked barplots show the proportion of genomes called as S, I, or R (coloured as per inset legend, and labelled with counts). The solid horizontal lines on the violin plots mark the R EUCAST breakpoint used for the SEAP disk diffusion data. The dashed line marks the EUCAST breakpoint for I.
See this image and copyright information in PMC

References

    1. Meiring JE, Khanam F, Basnyat B, Charles RC, Crump JA, Debellut F, et al. Typhoid fever. Nat Rev Dis Prim. 2023;9: 71. doi: 10.1038/s41572-023-00480-z - DOI - PubMed
    1. Collaborators G 2017 T and P, Stanaway JD, Reiner RC, Blacker BF, Goldberg EM, Khalil IA, et al. The global burden of typhoid and paratyphoid fevers: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Infect Dis. 2019;19: 369–381. doi: 10.1016/s1473-3099(18)30685-6 - DOI - PMC - PubMed
    1. The WHO AWaRe (Access, Watch, Reserve) antibiotic book. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO.
    1. Nabarro LE, McCann N, Herdman MT, Dugan C, Ladhani S, Patel D, et al. British infection association guidelines for the diagnosis and management of enteric fever in England. J Infect. 2022;84: 469–489. doi: 10.1016/j.jinf.2022.01.014 - DOI - PubMed
    1. Collaborators G 2021 F, Vollset SE, Ababneh HS, Abate YH, Abbafati C, Abbasgholizadeh R, et al. Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403: 2204–2256. doi: 10.1016/s0140-6736(24)00685-8 - DOI - PMC - PubMed

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