Salmonella Typhi (Typhi) is a type of bacteria that causes typhoid fever. More than 110,000 people die from this disease each year, predominantly in areas of sub-Saharan Africa and South Asia with limited access to safe water and sanitation. Clinicians use antibiotics to treat typhoid fever, but scientists worry that the spread of antimicrobial-resistant Typhi could render the drugs ineffective, leading to increased typhoid fever mortality.

The World Health Organization has prequalified two vaccines that are highly effective in preventing typhoid fever and may also help limit the emergence and spread of resistant Typhi. In low resource settings, public health officials must make difficult trade-off decisions about which new vaccines to introduce into already crowded immunization schedules. Understanding the local burden of antimicrobial-resistant Typhi and how it is spreading could help inform their actions.

The Global Typhoid Genomics Consortium analyzed 13,000 Typhi genomes from 110 countries to provide a global overview of genetic diversity and antimicrobial-resistant patterns. The analysis showed great genetic diversity of the different strains between countries and regions. For example, the H58 Typhi variant, which is often drug-resistant, has spread rapidly through Asia and Eastern and Southern Africa, but is less common in other regions. However, distinct strains of other drug-resistant Typhi have emerged in other parts of the world.

Resistance to the antibiotic ciprofloxacin was widespread and accounted for over 85% of cases in South Africa. Around 70% of Typhi from Pakistan were extensively drug-resistant in 2020, but these hard-to-treat variants have not yet become established elsewhere. Variants that are resistant to both ciprofloxacin and ceftriaxone have been identified, and azithromycin resistance has also appeared in several different variants across South Asia.

The Consortium’s analyses provide valuable insights into the global distribution and transmission patterns of drug-resistant Typhi. Limited genetic data were available fromseveral regions, but data from travel-associated cases helped fill some regional gaps. These findings may help serve as a starting point for collective sharing and analyses of genetic data to inform local public health action. Funders need to provide ongoing supportto help fill global surveillance data gaps.

Salmonella enterica serovar Typhi (Typhi) causes typhoid fever, a predominantly acute bloodstream infection associated with fever, headache, malaise, and other constitutional symptoms. If not treated appropriately, typhoid fever can be fatal; mortality ratios are estimated <1% today, but in the pre-antibiotic era ranged from 10% to 20% (Andrews et al., 2018; Stuart and Pullen, 1946). Historically, the disease was responsible for large-scale epidemics, triggered by the unsanitary conditions created during rapid urbanisation. Typhoid fever has since been largely controlled in many parts of the world due to large-scale improvements in water, sanitation, and hygiene (WASH) (Cutler and Miller, 2005), but was still responsible for an estimated 10.9 million illnesses and 116,800 deaths worldwide in 2017, largely in parts of the world where WASH is suboptimal (GBD 2017 Typhoid and Paratyphoid Collaborators, 2019). Antimicrobial therapy has been the mainstay of typhoid control, but multidrug resistance (MDR, defined as combined resistance to ampicillin, chloramphenicol, and co-trimoxazole) emerged in the 1970s, and resistance to newer drugs including fluoroquinolones, third-generation cephalosporins, and azithromycin has been accumulating over the last few decades (Marchello et al., 2020).

In 2001, the first completed whole genome sequence of Typhi was published (Parkhill et al., 2001). The sequenced isolate was CT18, an MDR isolate cultured from a typhoid fever patient in the Mekong Delta region of Vietnam in 1993. The genome was the result of 2 years of work piecing together plasmid-cloned paired-end sequence reads generated by Sanger capillary sequencing. Together with other early bacterial pathogen genomes, including a second Typhi genome (Ty2) published 2 years later in 2003 (Deng et al., 2003), the CT18 genome was heralded as a major turning point in the potential for disease control, treatment, and diagnostics, providing new tools for epidemiology, molecular microbiology, and bioinformatics. It formed the basis for new insights into comparative and functional genomics (Boyd et al., 2003; Faucher et al., 2006), and facilitated early genotyping efforts (Baker et al., 2008; Roumagnac et al., 2006). When high-throughput sequencing technologies such as 454 and Solexa (subsequently Illumina) emerged, Typhi was an obvious first target for in-depth characterisation of a single pathogen population (Holt et al., 2008), and genomics has been increasingly exploited to describe the true population structure and global expansion of this highly clonal pathogen (Wong et al., 2015). Now, whole genome sequencing (WGS) is becoming a more routine component of typhoid surveillance. Salmonella were among the first pathogens to transition to routine sequencing by public health laboratories in high-income countries (Chattaway et al., 2019; Stevens et al., 2022), and these systems often capture Typhi isolated from travel-associated typhoid infections, providing an informal mechanism for sentinel genomic surveillance of pathogen populations in typhoid endemic countries (Ingle et al., 2019). More recently, WGS has been adopted for typhoid surveillance by national reference laboratories in endemic countries including the Philippines, Nigeria (Okeke et al., 2022), and South Africa (Lagrada et al., 2022), and PulseNet International is gradually transitioning to WGS (Davedow et al., 2022; Nadon et al., 2017). Following the first global genomic snapshot study, which included nearly 2000 genomes of Typhi isolated from numerous typhoid prevalence and incidence studies conducted across Asia and Africa (Wong et al., 2015), WGS has become the standard tool for characterising clinical isolates. Given the very high concordance between antimicrobial susceptibility to clinically relevant drugs and known genetic determinants of antimicrobial resistance (AMR) in Typhi (Argimón et al., 2021a; Chattaway et al., 2021; da Silva et al., 2022), WGS is also increasingly used to infer resistance patterns.

The adoption of WGS for surveillance relies on the definition of a genetic framework with linked standardised nomenclature, often supplied by multilocus sequence typing (MLST) and core genome multilocus sequence typing (cgMLST) for clonal pathogens. Typhi evolves on the order of 0.5 substitutions per year, much more slowly than host-generalist Salmonella, such as S. enterica serovars Kentucky and Agona (five substitutions per year) (Achtman et al., 2021; Duchêne et al., 2016). As a result, the cgMLST approach, which utilises 3002 core genes (Zhou et al., 2020) (two-thirds of the genome) and is popular with public health laboratories for analysis of non-typhoidal S. enterica, has limited utility for Typhi. Instead, most analyses rely on identifying single nucleotide variants (SNVs) and using these to generate phylogenies. This approach allows for fine-scale analysis of transmission dynamics (although not resolving individual transmission events, due to the slow mutation rate; Campbell et al., 2018) and tracking the emergence and dissemination of AMR lineages (Klemm et al., 2018; da Silva et al., 2022; Wong et al., 2015). In the absence of a nomenclature system such as that provided by cgMLST, an alternative strategy was needed for identifying and naming lineages. To address this challenge, a genotyping framework (‘GenoTyphi’) was developed that uses marker SNVs to assign Typhi genomes to phylogenetic clades and subclades (Wong et al., 2016a), similar to the strategy that has been widely adopted for Mycobacterium tuberculosis (Coll et al., 2014). The GenoTyphi scheme was initially developed based on an analysis of almost 2000 Typhi isolates from 63 countries (Wong et al., 2016a). This dataset was used to define a global population framework based on 68 marker SNVs, which were used to define 4 primary clades, 15 clades, and 49 subclades organised into a pseudo-hierarchical framework. This analysis demonstrated that most of the global Typhi population was highly structured and included many subclades that were geographically restricted, with the exception of Haplotype 58, or H58 (so named by Roumagnac et al., 2006, and designated as genotype 4.3.1 under the GenoTyphi scheme). H58 (genotype 4.3.1) was strongly associated with AMR and was found throughout Asia as well as Eastern and Southern Africa (Wong et al., 2016a). The GenoTyphi framework has evolved and expanded to reflect changes in global population structure and the emergence of additional AMR-associated lineages (Dyson and Holt, 2021), and has been widely adopted by the research and public health communities for the reporting of Typhi WGS data (Chattaway et al., 2021; Ingle et al., 2021; da Silva et al., 2022). The genotyping framework, together with functionality for identifying AMR determinants and plasmid replicons, and generating clustering-based trees, is available within the online genomic epidemiology platform Typhi Pathogenwatch (Argimón et al., 2021b). This system is designed to facilitate genomic surveillance and outbreak analysis for Typhi, including contextualisation with global public data, by public health and research laboratories (Argimón et al., 2021a; Ikhimiukor et al., 2022a; Lagrada et al., 2022) without requiring major investment in computational infrastructure or specialist bioinformatics training.

The increasing prevalence of AMR poses a major threat to effective typhoid fever control. The introduction of new antimicrobials to treat typhoid fever has been closely followed by the development of resistance, beginning with widespread chloramphenicol resistance in the early 1970s (Anderson, 1975; Andrews et al., 2018). By the late 1980s, MDR typhoid had become common. The genetic basis for MDR was a conjugative (i.e. self-transmissible) plasmid of incompatibility type IncHI1 (Anderson, 1975), which was first sequenced as part of the Typhi str. CT18 genome in 2001 (Parkhill et al., 2001). This plasmid accumulated genes (bla_TEM-1, cat, dfr, and sul) encoding resistance to all three first-line drugs, mobilised by nested transposons (Tn6029 in Tn21, in Tn9) (Holt et al., 2011b; Wong et al., 2015). The earliest known H58 isolates were MDR, and it has been proposed that selection for MDR drove the emergence and dissemination of H58 (Holt et al., 2011b), which is estimated to have originated in South Asia in the mid-1980s (Carey et al., 2022; da Silva et al., 2022; Wong et al., 2015) before spreading throughout South East Asia (Holt et al., 2011a; Pham Thanh et al., 2016b) and into Eastern and Southern Africa (Feasey et al., 2015; Kariuki et al., 2010; Wong et al., 2015). The MDR transposon has subsequently migrated to the Typhi chromosome on several independent occasions (Ashton et al., 2015; Wong et al., 2015), allowing for loss of the plasmid and fixation of the MDR phenotype in various lineages. Other MDR plasmids do occur in Typhi but are comparatively rare (Argimón et al., 2021b; Ingle et al., 2019; Rahman et al., 2020; Tanmoy et al., 2018; Wong et al., 2015).

The emergence of MDR Typhi led to widespread use of fluoroquinolones (mainly ciprofloxacin) as first-line therapy in typhoid fever treatment. Ciprofloxacin non-susceptibility (CipNS, defined by minimum inhibitory concentration [MIC]≥0.06 mg/L) soon emerged and became common, particularly in South and South East Asia (Chau et al., 2007; Dyson et al., 2019). The genetic basis for this is mainly substitutions in the quinolone resistance determining region (QRDR) of core chromosomal genes gyrA and parC, which directly impact fluoroquinolone binding. These substitutions have arisen in diverse Typhi strain backgrounds (estimated >80 independent emergences) (da Silva et al., 2022) but appear to be particularly common in H58 (4.3.1) subtypes (Roumagnac et al., 2006; da Silva et al., 2022; Wong et al., 2015). The most common genetic pattern is a single QRDR mutation (typically at gyrA codon 83 or 87), which results in a moderate increase in ciprofloxacin MIC to 0.06–0.25 mg/L (Day et al., 2018) and is associated with prolonged fever clearance times and increased chance of clinical failure when treating with fluoroquinolones (Pham Thanh et al., 2016a; Wain et al., 1997). An accumulation of three QRDR mutations raises ciprofloxacin MIC to 8–32 mg/L and is associated with higher occurrence of clinical failure (Pham Thanh et al., 2016a). Triple mutants appear to be rare, with the exception of a subclade of 4.3.1.2 bearing GyrA-S83F, GyrA-D87N, and ParC-S80I (designated genotype 4.3.1.2.1; Ingle et al., 2022), which emerged in India in the mid-1990s and has since been introduced into Pakistan, Nepal, Bangladesh, and Chile (Britto et al., 2020; Maes et al., 2020; da Silva et al., 2022; Pham Thanh et al., 2016a).

The challenge of fluoroquinolone non-susceptible typhoid was met with increased therapeutic use of third-generation cephalosporins (such as ceftriaxone and cefixime) or azithromycin (for non-severe disease) (Balasegaram et al., 2012; Basnyat et al., 2021; Rai et al., 2012). Reports of ceftriaxone treatment failure in late 2016 in Hyderabad, Pakistan, led to the discovery of an extensively drug-resistant (XDR, defined as MDR plus resistance to fluoroquinolones and third-generation cephalosporins) clone of Typhi (genotype 4.3.1.1.P1, a subtype of H58), which subsequently spread throughout Pakistan (Klemm et al., 2018; Rasheed et al., 2020; Yousafzai et al., 2019). This XDR clone harbours a common combination of chromosomal AMR determinants (integrated MDR transposon plus single QRDR mutation, GyrA-83) but has also acquired an IncY-type plasmid carrying resistance genes, including qnrS (which, combined with GyrA-83 results in a ciprofloxacin-resistant [CipR] phenotype with MIC >1 mg/L) and the extended spectrum beta-lactamase (ESBL) encoded by bla_CTX-M-15 (Klemm et al., 2018). The ESBL gene has subsequently migrated from plasmid to chromosome in some 4.3.1.1.P1 isolates (Nair et al., 2021). Other ESBL-producing, ceftriaxone-resistant (CefR) Typhi strains have been identified in India (Argimón et al., 2021a; Jacob et al., 2021; Nair et al., 2021; Rodrigues et al., 2017; Sah et al., 2019), via both local ‘in-country’ surveillance and travel-associated infections. The only oral therapy available to treat non-severe XDR Typhi infection is azithromycin (Levine and Simon, 2018), which, although effective, shows prolonged bacteremia and fever clearance times in the human challenge model and is not recommended for treatment of complicated typhoid fever (Jin et al., 2019). Azithromycin-resistant (AziR) Typhi, which is associated with mutations in the chromosomal gene acrB, has now been reported across South Asia (Carey et al., 2021; Duy et al., 2020; Iqbal et al., 2020; Sajib et al., 2021) and has been linked to treatment failure in Nepal Duy et al., 2020; however, the prevalence so far remains low (Hooda et al., 2019; da Silva et al., 2022). Imported infections caused by XDR Typhi 4.3.1.1.P1 have been identified in Australia (Ingle et al., 2021), Europe (Herdman et al., 2021; Nair et al., 2021), and North America Eshaghi et al., 2020; François Watkins et al., 2020; imported AziR Typhi infections are rarer but have been reported in Singapore (Octavia et al., 2021).

The accumulation of resistance to almost all therapeutic options means that there is an urgent need to track the emergence and spread of AMR Typhi, both to guide empiric therapy to prevent treatment failure (Nabarro et al., 2022), and to direct the deployment of preventative interventions like typhoid conjugate vaccines (TCVs) and WASH infrastructure. Given the wealth of existing and emerging WGS data for Typhi, we aimed to create a system to enhance visibility and accessibility of genomic data to inform current and future disease control strategies, including identifying where empiric therapy may need review, and monitoring the impact of TCVs on AMR and vaccine escape. In forming the Global Typhoid Genomics Consortium (GTGC), we aim to engage with the wider typhoid research community to aggregate Typhi genomic data and standardised metadata to facilitate the extraction of relevant insights to inform public health policy through inclusive, reproducible analysis using freely available and accessible pipelines and intuitive data visualisation. Here, we present a large, geographically representative dataset of 13,000 Typhi genomes, and provide a contemporary snapshot of the global genetic diversity in Typhi and its spectrum of AMR determinants. The establishment of the GTGC, which marked 21 years of typhoid genomics, provides a platform for future typhoid genomics activities, which we hope will inform more sophisticated disease control.

All data analysed during this study are publicly accessible. Raw Illumina sequence reads have been submitted to the European Nucleotide Archive (ENA), and individual sequence accession numbers are listed in Supplementary file 2. The full set of n=13,000 genome assemblies generated for this study are available for download from FigShare: https://doi.org/10.26180/21431883. All assemblies of suitable quality (n=12,849) are included as public data in the online platform Pathogenwatch (https://pathogen.watch). The data are organised into collections, which each comprise a neighbour-joining phylogeny annotated with metadata, genotype, AMR determinants, and a linked map. Each contributing study has its own collection, browsable at https://pathogen.watch/collections/all?organismId=90370. In addition, we have provided three large collections, each representing roughly a third of the total dataset presented in this study: Typhi 4.3.1.1 (https://pathogen.watch/collection/2b7mp173dd57-clade-4311), Typhi lineage 4 (excluding 4.3.1.1) (https://pathogen.watch/collection/wgn6bp1c8bh6-clade-4-excluding-4311), and Typhi lineages 0-3 (https://pathogen.watch/collection/9o4bpn0418n3-clades-0-1-2-and-3). In addition, users can browse the full set of Typhi genomes in Pathogenwatch and select subsets of interest (e.g. by country, genotype, and/or resistance) to generate a collection including neighbour-joining tree for interactive exploration.

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