Genomic Epidemiology of 3,076 Vibrio cholerae Isolates Reveals ST69 Clonal Expansion and Multidrug Resistance across Africa

Genomic Epidemiology of 3,076 Vibrio cholerae Isolates Reveals ST69 Clonal Expansion and Multidrug Resistance across Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic Epidemiology of 3,076 Vibrio cholerae Isolates Reveals ST69 Clonal Expansion and Multidrug Resistance across Africa Ahmed Olowo-okere, Mohammed Yahaya, Abdourahmane Yacouba, Abubakar Jibril, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9358362/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Cholera remains a major public health threat across Africa, driven by complex interactions between environmental, socioeconomic, and microbial factors. However, the continental genomic epidemiology of Vibrio cholerae remains incompletely characterised. We aimed to conduct a comprehensive continental genomic analysis of V. cholerae to determine its spatiotemporal dynamics, virulence, and antimicrobial resistance (AMR) profiles. Methods We conducted a large-scale genomic analysis of 3,076 V. cholerae isolates collected from 29 African countries between 1949 and 2025. Genomic data were retrieved from publicly available databases and subjected to standardized quality control, assembly, and annotation pipelines. Phylogenomic reconstruction was performed using core genome single-nucleotide polymorphism (SNP) analysis with recombination filtering. We conducted multilocus sequence typing (MLST), virulence profiling, AMR gene detection, and mobile genetic element characterization using established bioinformatics tools to assess associations between lineage distribution, geographic regions, temporal trends, and resistance patterns. Results The population is highly clonal and dominated by sequence type 69 (ST69) (2,682 [87.2%] of 3,076 genomes). ST69 almost exclusively drove the 2022–2024 continent-wide outbreaks, representing 98.9% (463/468) of genomes in 2022, 99.3% (605/609) in 2023, and 97.3% (252/259) in 2024. Toxigenic O1 El Tor markers and ctxA/ctxB co-carriage exceeded 91% in clinical genomes but were significantly attenuated in environmental strains. We identified 101 distinct AMR genes, with 94.1% of isolates classified as multidrug-resistant. Ubiquitous chromosomal mutations, primarily gyrA_S83I (92.88%) and parC_S85L (84.82%), drove universal fluoroquinolone resistance. An SXT conjugative element conferring multidrug resistance was present in roughly 64% of the population. Clinical isolates exclusively harboured high-risk resistance determinants absent environmentally, including macrolide resistance genes (6.6%–6.8%) and extended-spectrum beta-lactamases such as blaPER-7 (6.4%). Conclusion Cholera transmission across Africa is persistently driven by the clonal expansion of the ST69 lineage. Universal fluoroquinolone resistance and clinically restricted emergence of macrolide and beta-lactamase resistance highlight a critical therapeutic challenge. Sustained genomic surveillance is essential to monitor this multidrug-resistant clone and inform regional outbreak control. Vibrio cholera Genomic Epidemiology Antimicrobial resistance Cholera Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Vibrio cholerae is a Gram-negative bacterium, responsible for the acute secretory diarrheal disease known as cholera [1]. The disease remains a formidable and persistent threat to global public health, with the World Health Organization (WHO) estimating between 1.3 and 4.0 million cases and 21,000–143,000 deaths annually worldwide [2,3]. Sub-Saharan Africa bears a disproportionate fraction of this burden, accounting for over 30% of global cases and more than 80% of cholera deaths worldwide, with the region suffering 83% of global cholera mortality between 2000 and 2015 [4]. The scale of this crisis escalated dramatically in recent years: in 2023, Africa experienced a 125% increase in reported cholera cases and a 62% rise in deaths compared with 2022, with 21 countries collectively reporting 225,857 cases and 3,167 deaths, yielding a case fatality ratio (CFR) of 1.4% [5]. By July 2024, a cumulative total of 399,508 cases and 7,023 deaths (CFR: 1.8%) had been reported across the continent since January 2022, with the Democratic Republic of the Congo (DRC), Ethiopia, Malawi, Mozambique, and Zimbabwe collectively accounting for 72.2% of all cases and 62.3% of all deaths [6]. These explosions of transmission are driven by complex intersections of inadequate water, sanitation, and hygiene (WASH) infrastructure, the exacerbating effects of climate change — including cyclones and El Niño-driven flooding — and persistent armed conflicts that fracture healthcare systems and displace populations [7–9]. Geospatial analyses further reveal that the annual incidence of cholera in Africa has dynamically shifted from western to central and eastern regions between 2011 and 2020, with an estimated 296 million people residing in high-incidence administrative units by 2020 [10]. The evolutionary success of V. cholerae , particularly the El Tor biotype responsible for the ongoing seventh pandemic (7PET), is intrinsically linked to its high genomic plasticity [11,12]. The pathogen possesses a bipartite genome consisting of a larger Chromosome I (~2.9 Mb), which encodes the majority of essential housekeeping genes and primary virulence determinants — including the toxin-coregulated pilus (TCP), encoded by the VPI-1 pathogenicity island, and the core cholera toxin (CTX) prophage — and a smaller Chromosome II (~1.1 Mb) that houses accessory metabolic genes and the chromosomal superintegron [13]. The acquisition and exchange of mobile genetic elements (MGEs) play a significant role in driving the emergence of novel, highly virulent lineages and disseminating antimicrobial resistance genes [14]. Phylogenomic reconstructions have resolved the African 7PET population into a series of distinct sublineages, designated AFR10 through AFR15. Recent genomic analyses of 763 V. cholerae O1 isolates collected across sub-Saharan Africa between 2019 and 2024 identified the rapid continental circulation of the AFR15 lineage, which has been associated with unusually large outbreaks in Southern Africa and has expanded into new African Union member states, while cross-border transmission was found to be prevalent across both West and East Africa [15]. Multiple co-circulating lineages have been identified within individual countries, and what were previously classified as sporadic outbreak strains have, upon genomic re-evaluation, proven to be representatives of larger undersampled outbreaks, underscoring the persistent gaps in continental surveillance [16]. Compounding the threat of hypervirulent sublineages is the accelerating emergence of AMR within V. cholerae populations. The primary vehicle for multidrug resistance (MDR) in African 7PET isolates is the SXT/R391 family of integrating conjugative elements (ICEs), which are ~99–100 kb self-transmissible elements that integrate site-specifically into the 5′ end of the chromosomal prfC gene [17]. These ICEs encode resistance to sulfamethoxazole, trimethoprim, streptomycin, chloramphenicol, nalidixic acid, and tetracycline, and carry well-characterised antimicrobial resistance genes (ARGs)[18]. Fluoroquinolone resistance, however, represents the most clinically alarming contemporary trend. Reduced ciprofloxacin susceptibility in 7PET strains arises primarily through sequential accumulation of point mutations in the quinolone resistance-determining regions (QRDRs) of chromosomal topoisomerase genes, most notably the Ser83Ile substitution in gyrA (encoding DNA gyrase subunit A) and a corresponding Ser85Leu substitution in parC (encoding topoisomerase IV subunit C), which together confer high-level fluoroquinolone resistance [19]. A landmark example of the consequences of unchecked AMR accumulation was the 2018 cholera outbreak in Zimbabwe, in which the causative AFR13 sublineage strain acquired an approximately 160-kb IncA/C2 plasmid bearing 14 additional AMR genes; the isolates were intermediately resistant or resistant to tetracycline ( tetA ) and ciprofloxacin ( gyrA and parC mutations and aac(6′)-Ib-cr ) and produced the extended-spectrum beta-lactamase CTX-M-15, rendering the outbreak strain resistant to two of the three WHO-recommended cholera antibiotics [20]. More recent PulseNet Africa genomic surveillance of isolates from Côte d'Ivoire, Ghana, Zambia, and South Africa (2010–2024) has confirmed the persistence of resistance genes associated with quinolones and trimethoprim, raising serious concerns about the continued efficacy of current treatment protocols [21]. Despite the severe and escalating impact of cholera across Africa, comprehensive genomic epidemiology of the pathogen remains fragmented. Between 2010 and 2019, sub-Saharan Africa documented 999 suspected cholera outbreaks with over 480,000 probable patients across 744 sub-national regions in 25 countries, yet molecular characterisation has largely been limited to localized, outbreak-specific investigations [22]. Consequently, the continental genomic diversity, pan-genome dynamics, AMR gene reservoir, and broad evolutionary relationships of African V. cholerae isolates remain incompletely characterised. The difficulty in containing cholera is compounded by poor understanding of how the pathogen circulates throughout the region; within-country variation in transmission dynamics and the finding that several formerly classified "sporadic" outbreaks represent undersampled continuous transmission events highlight the critical limitations of non-genomic surveillance [23]. Without a unified, large-scale genomic framework, tracking cross-border transmission events and identifying the emergence of high-risk MDR lineages in real time remains extremely challenging. To address this critical knowledge gap, this study presents a comprehensive, large-scale comparative genomic analysis of 2,726 V. cholerae genomes sourced from across the African continent. Ultimately, this analysis provides a robust genomic framework to elucidate the evolutionary trajectory of the pathogen, characterise the continental distribution of AMR determinants, and inform highly targeted, real-time public health interventions consistent with the WHO Global Roadmap to End Cholera by 2030. Materials and Methods 2.1 Data Identification and Genome Acquisition Genomic data for V. cholerae were identified via the NCBI Pathogen Detection Isolates Browser (https://www.ncbi.nlm.nih.gov/pathogens/isolates%23/search/%23/search/), accessed on 19 th March 2026. An initial global query yielded 19,040 isolate records, which were subsequently filtered to retain only 3185 originating from 29 African countries. These isolates were stratified into two cohorts: those with pre-assembled genomes deposited in the NCBI Assembly database ( n =2733), and those available exclusively as raw paired-end sequencing reads in the NCBI Sequence Read Archive (SRA/ENA) ( n =452). These were subsequently retrieved using NCBI Datasets v18.9.0 [24] and fasterq-dump v3.2.1 [25], respectively. We verified the mutual exclusivity of the two cohorts by cross-referencing their BioSample identifiers, which confirmed that no single isolate was represented in both the assembly and raw read datasets. The genomes metadata, including isolation country, collection year, and sample source were programmatically retrieved via the NCBI E-utilities API [26]. 2.2 Raw Read Processing, De Novo Genome Assembly, and Annotation The downloaded raw paired-end reads were assembled de novo into contigs using the Shovill pipeline v1.1.0 (https://github.com/tseemann/shovill), which employs SPAdes v4.2.0 as the underlying assembler, with –trim option [27]. All genome assemblies were annotated in a standardised, reproducible manner using Prokka v1.13 [28]. 2.4 Genome Quality Control and Taxonomic Verification To ensure the integrity of downstream analyses, all assembled and downloaded genomes were subjected to rigorous quality control. Assembly contiguity was evaluated using QUAST v5.2.0, while genome completeness and contamination were assessed utilizing CheckM2 v1.0.2 [29,30]. Assemblies were systematically excluded from the dataset if they exhibited a completeness of less than 90% ( n = 6) or a contamination level exceeding 10% ( n = 17). Furthermore, genomes were discarded if their total assembly length fell outside the biologically expected range for V. cholerae (approximately 3.5 to 4.5 Mbp), or if they displayed high fragmentation, contig N50 lower than 10,000 bp or an L90 metric exceeding 100 contigs ( n = 86). To preclude the inclusion of misclassified taxa, pairwise Average Nucleotide Identity (ANI) of all retained assemblies was calculated against the V. cholerae O1 biovar El Tor str. N16961 (GCF_900205735.1) using FastANI v1.3.4 [31]. Only assemblies exhibiting an ANI ≥ 95% were definitively classified as V. cholerae and retained for downstream analyses [33]. All genomes passed this threshold, with pairwise ANI against the reference ranging from 96.01 to 100% (mean = 99.88%; median = 99.98%; SD = 0.45%), confirming the species-level identity of all retained assemblies. Following the application of these exclusion filters, a final high-quality dataset comprising 3,076 genomes was retained (Table S1) . Within this refined cohort, genome completeness ranged from 97.67% to 100% (mean = 100%, median = 100%), and contamination varied between 0% and 9.82% (mean = 2.25%, median = 0.05%) (Table S2) . The retained assemblies contained between 2 and 281 contigs (mean = 75, median = 72), with N50 values spanning 39.0 to 3,141.1 Kb (mean = 278.8 Kb, median = 235.7 Kb). The overall GC content ranged from 46.93% to 48.01% (mean = 47.51%, median = 47.50%), and total genome sizes spanned 3.78 to 4.48 Mbp (mean = 4.10 Mbp, median = 4.05 Mbp) (Table S3; Table S4) . These assembly metrics are highly consistent with the known genomic architecture and bipartite chromosomal structure of V. cholerae [32]. 2.6 Core Genome SNP Alignment and Recombination Masking Per-sample read mapping and variant calling were performed using Snippy v4.6.0 (https://github.com/tseemann/snippy) against the reference genome. To ensure high-quality variant detection, minimum thresholds were set for read depth (4×; --mincov 4) and variant allele frequency (0.75; --minfrac 0.75) [33]. A core genome alignment of all 3,076 isolates was subsequently generated using snippy-core, retaining only sites present across the entire dataset [33]. This strict core alignment spanned 4,033,501 bp of the reference genome, with a mean per-sample reference coverage of 96.14%, and yielded 245,973 variant sites. To mitigate the confounding effects of homologous recombination and mobile genetic elements (MGEs) on phylogenetic inference, recombinant regions were identified and masked prior to tree reconstruction using Gubbins v3.4.3 [34]. Gubbins detects recombination under an iterative maximum-likelihood framework by identifying genomic windows with significantly elevated SNP densities relative to the background. The pipeline was configured to construct an initial guide tree using FastTree (--first-tree-builder fasttree), followed by iterative refinement using IQ-TREE (--tree-builder iqtree) under the GTRGAMMA substitution model. The process was capped at five iterations (--iterations 5), with convergence assessed via the weighted Robinson-Foulds metric. Recombinant blocks were defined by a minimum of three SNPs (--min-snps 3), and sequences containing more than 25% missing data were excluded from the analysis (--filter-percentage 25). The resulting recombination-free polymorphic site alignment produced by Gubbins was utilized for all downstream phylogenetic analyses. Finally, to characterize within-dataset genomic diversity and delineate putative transmission clusters, pairwise SNP distances were calculated from this filtered alignment using snp-dists v0.8.2 [35]. 2.7 Maximum Likelihood Phylogenetic Reconstruction and Temporal Analysis A maximum-likelihood (ML) phylogeny was reconstructed from the Gubbins recombination-free core SNP alignment using IQ-TREE 2 v2.2.6 [36]. The optimal nucleotide substitution model was selected automatically using the integrated ModelFinder algorithm under the Bayesian Information Criterion (BIC) to avoid model misspecification [37]. Topological robustness was assessed by 1,000 ultrafast bootstrap (UFBoot2) replicates, with branch support values ≥95% considered indicative of well-supported nodes [38]. The N16961 reference genome was included as an outgroup to root the phylogeny. Phylogenetic visualization and metadata annotation were performed using ggtree v3.16.3 and ggtreeExtra v1.18.1 [39,40] Multilocus sequence typing Multilocus sequence typing (MLST) was performed on all genomic assemblies using the command-line MLST tool v2.23.0 (https://github.com/tseemann/mlst), which performs automated allele matching against curated PubMLST databases. The Heidelberg seven-locus V. cholerae MLST scheme (vcholerae) was applied, interrogating the following housekeeping gene loci: adenylate kinase (adk), DNA gyrase subunit B (gyrB), malate dehydrogenase (mdh), homocysteine synthase (metE), pyridine nucleotide transhydrogenase (pntA), phosphoribosylformylglycinamidine synthetase (purM), and dihydroorotase (pyrC). Sequence types (STs) were assigned based on exact allele matches against the PubMLST V. cholerae reference database (https://pubmlst.org/vcholerae). Only assemblies yielding a PERFECT or GOOD call status, defined as 100% identity and full-length allele matches across all seven loci, were retained for downstream ST-based analyses. 2.8 In Silico Profiling of Virulence Factors, AMR Determinants, and Mobile Genetic Elements Virulence, Serogrouping, and Biotyping. Genomic assemblies were screened using CholeraeFinder v2.1 (Center for Genomic Epidemiology) to predict serogroup and biotype, and to identify major V. cholerae-specific virulence genes, resistance determinants, and mobile genetic elements [41]. Species identity was confirmed by detection of ompW, a highly conserved outer membrane protein gene specific to V. cholerae. Serogroup was assigned based on detection of the O-antigen biosynthesis genes rfbV_O1 (O1 serogroup) and wbfZ_O139 (O139 serogroup), with a minimum nucleotide identity threshold of ≥98% applied to the wbfZ_O139 marker to exclude cross-reactive hits; isolates below this threshold were classified as Non-O1/Non-O139. Biotype was differentiated into El Tor, El Tor Variant, or Classical lineages using allele-specific markers for the transcriptional regulator (rstR_et, rstR_cc) and the toxin-coregulated pilus subunit (tcpA_et3, tcpA_cc), with El Tor Variant designation requiring co-detection of the ctxB_7 allele. The cholera toxin B subunit (ctxB) was further genotyped into allelic variants (ctxB_1, ctxB_3, ctxB_7) to resolve lineage-level diversity. To contextualise isolates within the ongoing seventh pandemic, the seventh pandemic El Tor (7PET) lineage-specific marker gene (VC2346) was assessed. Isolates were classified as toxigenic upon detection of the cholera enterotoxin A subunit gene (ctxA), the catalytic determinant of cholera toxin production; ctxB genotyping was used independently for lineage discrimination. The presence of V. Pathogenicity Islands (VPI-1, VPI-2) and Seventh Pandemic Islands (VSP-I, VSP-II) was assessed using the CholeraeFinder reference database, which employs embedded reference loci derived from the V. cholerae O1 El Tor N16961 reference genome (GenBank: AE003852). Mobile genetic elements were characterised by detection of the SXT/R391 integrating conjugative element (ICE) integrase gene (intSXT) and the Class 1 integron integrase (intI1). Antimicrobial Resistance (AMR) Profiling and MDR Classification The resistome of 3,076 V. cholerae isolates was characterized by screening genomic assemblies with AMRFinder plus v3.10.1 [42]. The abundance of AMR genes within the population was evaluated by calculating the Average Copy Number (ACN) per isolate, both overall and stratified by isolation source (clinical versus environmental) as previously described [43]. Gene prevalence was determined as the percentage of genomes harboring a specific resistance determinant. Isolates were classified as multidrug-resistant (MDR) if they harbored acquired resistance genes spanning three or more distinct antibiotic classes as previously described [44]. To assess spatial and source-specific distributions, the diversity of unique AMR genes and their specific ACNs were compared between the clinical and environmental subsets. Identification of Mobile Genetic Elements and Genomic Localization of Genes Plasmid-derived contigs were identified using a consensus approach combining PlasForest and geNomad v1.8.0 [45,46]. PlasForest was run using the plasforest.sav model against the RefSeq plasmid database, while geNomad was executed in end-to-end mode with the --cleanup and --splits 8 flags to simultaneously detect plasmid and prophage sequences. As previously reported, contigs were classified as prophage where geNomad assigned a virus score exceeding both the chromosome and plasmid scores [43]. To determine the genomic context of the resistome, the coordinates of the identified AMR genes were intersected with the MGE predictions, allowing each gene occurrence to be localized to the chromosome, a plasmid, or a prophage region. 2.8 Statistical Analysis and Data Visualization Descriptive statistics, including frequencies and percentages, were used to summarize categorical variables across isolates. All statistical analysis and visualizations were performed in R v4.5.1 and RStudio v2025.9.0.387 (R Core Team, 2023). Results A total of 3,076 sequenced V. cholerae genomes from the African continent were analyzed in this study. Geographically, the isolates originated from 29 countries, with a pronounced concentration in Central Africa ( n = 1,305; 42.4%) and East Africa ( n = 1,174; 38.2%), followed by West Africa ( n = 358; 11.6%), Southern Africa ( n = 221; 7.2%), and North Africa (n = 18; 0.6%) (Fig. 1A; Table S1). The Democratic Republic of the Congo (DRC) contributed the largest proportion of genomes ( n = 1,039; 33.8%), followed by Cameroon ( n = 259; 8.4%), Zambia ( n = 255; 8.3%), Kenya ( n = 254; 8.3%), and Mozambique ( n = 245; 8.0%). The majority of sequenced genomes were derived from clinical human samples ( n = 1,988), while environmental sources accounted for a minor fraction of the dataset ( n = 96; 3.1%), including water ( n = 101), food/aquatic animal ( n = 17), and other/unknown sources ( n = 12). Temporal analysis of collection dates revealed isolates spanning from 1949 to 2025, with 150 genomes lacking collection year metadata. The mean number of isolates per year was 84, ranging from 1 to 609 across years with available data (Table S1; Fig 1C). A marked escalation in genomic surveillance was observed in recent years, with the highest isolate counts recorded in 2023 ( n = 609; 19.8%) and 2022 ( n = 468; 15.2%), coinciding with intensified large-scale cholera outbreaks across the continent. Historical isolates from earlier decades (≤2010) were characterised by lower sequencing volumes, with the earliest isolate in the dataset dating to 1949. ST69 and its related lineage ST515 are the most dominant sequence types in Africa Multi-locus sequence typing (MLST) of the 3,076 genome dataset revealed a highly clonal population structure characterised by the near-complete dominance of a single lineage across the continent (Fig. 3B, 3D). All isolates were typed using the V. cholerae MLST scheme, yielding 33 unique sequence types, with MLST assignments achieving perfect allelic concordance in 2,980 genomes (96.9%), while 35 genomes carried novel allelic profiles, 32 were partially assigned (OK), 27 were mixed, and 2 were missing data. MLST scores ranged from 74 to 100 (median = 100; mean = 99.6%), reflecting high-quality typing across the dataset (Figure 1; Table S5). Sequence Type 69 (ST69) was the overwhelmingly prevalent sequence type, accounting for 2,682 genomes (87.2% of the entire dataset). Spatially, ST69 exhibited a pervasive pan-African distribution, driving epidemics across all five African regions — Central Africa (n = 1,117), East Africa (n = 1,113), West Africa (n = 314), Southern Africa (n = 137), and North Africa (n = 1). It was the dominant lineage in all heavily burdened nations, most notably the Democratic Republic of the Congo (DRC) (n = 869), Cameroon (n = 243), Mozambique (n = 243), Kenya (n = 250), Zambia (n = 247), Tanzania (n = 180), and Nigeria (n = 156). Longitudinally, ST69 has circulated persistently since at least 1970 and was almost exclusively responsible for the massive surge in clinical genomes sequenced during the recent 2022–2024 continent-wide outbreak waves, accounting for 463 of 468 genomes in 2022 (98.9%), 605 of 609 in 2023 (99.3%), and 252 of 259 in 2024 (97.3%). The secondary lineage ST515 (n = 196; 6.4%) emerged as the only other numerically substantial sequence type and demonstrated restricted geographic mobility heavily localised to Central and East Africa. ST515 drove distinct temporal transmission peaks in 2015 (n = 76) and 2018 (n = 33), with the vast majority of isolates originating from the DRC (n = 150) and Tanzania (n = 18), alongside minor representation in Zambia (n = 4). Notably, ST515 was detected almost exclusively in clinical samples (n = 103) and was virtually absent from environmental sources. The remaining sequence types collectively constituted a minor proportion of the dataset (n = 198; 6.4%), with 31 additional STs each accounting for ≤0.8% of isolates. ST75 (n = 24; 0.8%) was predominantly observed in Southern Africa, particularly South Africa (n = 21), and showed a recent temporal resurgence between 2018 and 2025. ST1251 (n = 15; 0.5%) was geographically restricted to West Africa, almost entirely from Ghana (n = 15), suggesting a localised lineage. ST555 (n = 6; 0.2%) was exclusively detected in South Africa, while ST68 (n = 6; 0.2%) was confined to North Africa. Historically older isolates from 1949 were typed as ST73, representing one of the earliest sequenced African V. cholerae genomes in the dataset. Ninety-six genomes (3.1%) could not be assigned a definitive ST, disproportionately represented among environmental isolates (n = 34 of 96 environmental genomes), suggesting greater allelic diversity in non-clinical reservoir strains. Collectively, these findings underscore the profound geographic entrenchment and epidemiological dominance of the ST69 clone across contemporary African cholera epidemics, with secondary lineages exhibiting spatially and temporally restricted circulation patterns. Cross-tabulation of MLST sequence types with ctxB genotypes revealed distinct lineage-level patterns within the dataset. ST69, the dominant sequence type (n = 2,746), exhibited a near-equal distribution of ctxB_1 (n = 1,276; 46.47%) and ctxB_7 (n = 1,329; 48.40%) alleles, alongside minor proportions of ctxB_3 (n = 31; 1.13%) and undetected ctxB (n = 62; 2.26%). The co-occurrence of both ctxB alleles within a single dominant sequence type is consistent with CTX prophage-mediated ctxB allele replacement within the 7PET genomic backbone, a well-documented mechanism of intra-lineage diversity in circulating V. cholerae [REF]. ST515, the second most prevalent sequence type (n = 197), was almost exclusively associated with ctxB_1 (n = 193; 97.97%), indicating this lineage predates the post-2010 Haitian variant expansion. Notably, ctxB_7 carriage was almost entirely confined to ST69 (n = 1,329; 98.7% of all ctxB_7 isolates), confirming that the Haitian variant allele was acquired within the ST69 backbone rather than introduced via a distinct sequence type. Minor sequence types, collectively accounting for 314 isolates, were predominantly ctxB -negative, consistent with non-toxigenic environmental or pre-pandemic strains. These findings indicate that ctxB allele diversity in African V. cholerae reflects dynamic CTX prophage remodelling within a clonally dominant ST69 population rather than the co-circulation of genetically distinct pandemic lineages (Table S6). Genomic Characterization and Reservoir-Specific Distribution Genomic characterization of the V. cholerae isolates revealed a stark dichotomy in serogroup and biotype prevalence between clinical and environmental reservoirs (Table 1). The O1 serogroup marker ( rfbV ) was overwhelmingly dominant among clinical isolates across all African regions, ranging from 91.8% in Southern Africa to 99.2% in Central Africa. This trend was mirrored by biotype analysis, confirming the near-universal predominance of the El Tor biotype ( rstR_et ) in clinical settings (82.3% to 99.2%). The classical biotype marker ( rstR_cc ) was rare, detected only in minor clinical subsets (e.g., 8.9% in Southern Africa and 2.8% in East Africa). In sharp contrast, environmental isolates exhibited marked genomic heterogeneity and a distinct lack of canonical epidemic markers. The O1 serogroup and El Tor markers were entirely absent (0.0%) in the Southern African environmental cohort and severely reduced in Central Africa (31.6%) and West Africa (36.4% to 43.6%). Regional Shifts in CTX Prophage Carriage and ctxB Allelic Variants Intact CTX prophage carriage was a defining hallmark of clinical isolates. Co-carriage of the cholera toxin genes ctxA and ctxB exceeded 91% in clinical genomes from all regions, reaching 98.4% in Central Africa and 100% in North Africa. Conversely, environmental isolates demonstrated significant attenuation or complete absence of the CTX element, with carriage dropping to 36.4% in West Africa, 31.6% in Central Africa, and 0.0% in Southern Africa. Analysis of ctxB allelic variants uncovered distinct geographic partitioning across the continent. In Central Africa, ctxB variant 1 (classical) dominated the clinical landscape, accounting for 92.0% of isolates. However, a major allelic shift was observed elsewhere: ctxB variant 7 (El Tor) was the dominant allele in clinical isolates from West Africa (83.4%), Southern Africa (80.4%), and East Africa (70.2%). This geographic divergence in ctxB alleles suggests established, region-specific clonal endemicity rather than a homogenous continental population. Conservation of Pandemic Markers and Pathogenicity Islands The 7th pandemic El Tor (7PET) lineage-specific marker (VC2346) was highly conserved within the clinical subset (82.3% to 99.3%), confirming their pandemic lineage. In environmental subsets, the presence of VC2346 was substantially degraded, ranging from 43.6% in West Africa down to 0.0% in Southern Africa. The four major V. cholerae pathogenicity islands (VPI-1, VPI-2, VSP-1, VSP-2) and the Type VI Secretion System (T6SS) core effector vasX followed identical trajectories of reservoir-specific conservation. Key anchoring loci for these islands were highly stable in clinical isolates, consistently detected in the vast majority of genomes across all regions (generally >91%). Environmental isolates, however, displayed systemic degradation of these crucial virulence loci. Notably, VPI-2 ( VC1758 ) and the T6SS ( vasX ) appeared slightly more resilient in environmental subsets (e.g., VPI-2 was retained in 68.4% of Central African and 49.1% of West African environmental isolates) compared to the VSP elements, though all remained significantly diminished compared to their clinical counterparts. Antimicrobial Resistance Extensive AMR Burden and Ubiquitous Chromosomal Resistance Genomic profiling of the 3,076 V. cholerae isolates revealed an extensive antimicrobial resistance repertoire, identifying 101 distinct AMR genes spanning 9 categories. The population exhibited a mean burden of 10.81 AMR genes per isolate (range: 3–24), with a staggering 94.1% of the collection classified as multidrug-resistant (MDR) (Figure 2). Chromosomal resistance was ubiquitous across the dataset. Specifically, polymyxin resistance-associated lipid A modification genes ( almE , almF , and almG ) were detected in 100% of the isolates. Furthermore, the entire collection exhibited chromosomal mutations in the fluoroquinolone resistance-determining regions, driven predominantly by the highly prevalent gyrA_S83I (92.88%) and parC_S85L (84.82%) substitutions. This universal baseline resistance to fluoroquinolones—particularly ciprofloxacin, a WHO-recommended first-line therapy for severe cholera—highlights a critical and widespread therapeutic challenge across the continent. Horizontally Acquired Determinants and the SXT/ICE Module Beyond intrinsic chromosomal mutations, horizontally acquired AMR determinants were deeply entrenched in the population. A dominant SXT integrating conjugative element (ICE)-associated multidrug resistance module—comprising sul2 (64.37%), aph(3'')-Ib (64.34%), aph(6)-Id (64.24%), and floR (64.04%)—was identified in approximately 64% of the population, closely mirroring the anticipated carriage rates of the mobile element. Other highly prevalent acquired determinants included the chloramphenicol resistance gene catB9 (95.61%) and the trimethoprim resistance gene dfrA1 (93.21%). The near-universal co-occurrence of chromosomal fluoroquinolone mutations with these acquired MDR determinants signals deeply consolidated multidrug resistance within the African V. cholerae population. Diversity and Abundance Across Antibiotic Classes Significant variation was observed in the diversity and abundance of genes within specific antibiotic categories. beta-lactams represented the most highly diverse resistance category, encompassing 30 unique AMR genes. Despite this striking diversity, the metallo-beta-lactamase varG emerged as the overwhelmingly dominant determinant (97.46%). Similar structural diversity was observed among aminoglycosides and fluoroquinolones, which featured 15 unique determinants each. In terms of genomic burden, the highest Average Copy Number (ACN) was observed in the MLS category, yielding 3.94 ± 0.37 copies per strain among those carrying them, significantly higher than the overall ACNs for aminoglycosides (1.88) or sulfonamides (1.00). Ecological Discrepancy: Clinical Isolates Harbor Emerging Therapeutic Threats A comparative analysis revealed a sharp discrepancy between isolation sources, with clinical isolates harboring a significantly broader and more concerning diversity of AMR determinants. Specifically, 38 resistance genes were completely restricted to the clinical population and absent from environmental counterparts, whereas only 7 genes (including blaCARB-2 and tet(G) ) were uniquely environmental (Figure 3). This clinically restricted pool acts as a reservoir for emerging, high-risk resistance mechanisms. Of particular public health concern, macrolide resistance genes—including mph(A) , mrx(A) , msr(E) , and mph(E) —were restricted entirely to a clinical subset, present in 6.6% to 6.8% of the collection. Furthermore, a wide array of extended-spectrum $\beta$-lactamases (ESBLs) were clinically exclusive, including blaCTX-M-202 , blaTEM-1 , and notably blaPER-7 (detected in 6.4% of isolates). Finally, clinical isolates exhibited an elevated resistance gene burden within shared categories, demonstrating higher ACNs for fluoroquinolones (1.92 ± 0.28 vs. 1.50 ± 0.59 in environmental strains) and phenicols (1.70 ± 0.47 vs. 1.33 ± 0.48). Together, the elevated genomic burden and the presence of clinically restricted MLS and ESBL determinants signal emerging resistance to azithromycin and broad-spectrum beta-lactams, threatening alternative and critically important therapeutic options for severe cholera cases (Figure 4). Discussion This study provides the most comprehensive genomic characterization to date of V. cholerae across the African continent. The population structure of V. cholerae in Africa is defined by the overwhelming dominance of ST69, reflecting a pattern of extreme clonal expansion within the 7th pandemic El Tor (7PET) lineage. This aligns with the global paradigm of 7PET-driven cholera [47]. Recent studies in Asia, particularly in historically endemic regions such as Bangladesh, also reported that the majority of the seventh pandemic El Tor (7PET) V. cholerae O1 strains belong to ST69 [48]. In Europe, cholera is no longer endemic, and most cases are imported or travel-associated, with limited or no sustained local transmission. Genomic studies of European isolates typically reflect high diversity linked to multiple international sources, rather than local clonal expansion [49]. The secondary lineage ST515 in Africa further illustrates these continental contrasts. While Asia often harbors multiple co-circulating lineages with varying degrees of epidemic potential, ST515 remains geographically restricted and temporally episodic [50], largely confined to Central [51] and East Africa [52,53]. Its limited spread suggests that, unlike in Asia, where lineage competition and replacement are common, the African cholera ecosystem is dominated by competitive exclusion, with ST69 maintaining a strong selective advantage. The genomic analysis of V. cholerae isolates across African regions reveals a pronounced divergence between clinical and environmental reservoirs, underscoring the ecological and epidemiological complexity of cholera transmission on the continent. A key finding is the overwhelming dominance of the O1 serogroup (rfbV) among clinical isolates, consistently exceeding 90% across all regions and reaching near fixation in Central Africa (99.2%). These genomic patterns observed in African V. cholerae isolates reveal strong similarities with, but also important distinctions from, other global regions, including Asia, Europe, and the Americas. Across all settings, a common theme emerges: epidemic cholera is driven by a highly conserved subset of pathogenic lineages, while environmental reservoirs harbor substantial genetic diversity. Asia differs in one critical aspect: the frequent detection of atypical El Tor variants and hybrid strains combining classical and El Tor traits [54,55]. These hybrid genotypes, often associated with increased virulence or altered toxin production, are more commonly reported in Asia than in Africa, where classical markers (rstR_cc) remain rare and geographically limited [54]. This suggests that Africa may experience less frequent recombination events or may have different selective pressures shaping strain evolution. In the Americas, particularly following the introduction of cholera in Haiti in 2010, genomic analyses have demonstrated a strong link to South Asian El Tor strains [56]. Much like in Africa, clinical isolates in the Americas are overwhelmingly dominated by O1 El Tor lineages, reflecting the global spread of a relatively uniform pandemic clone [57]. The present analysis highlights pronounced regional heterogeneity in CTX prophage carriage and ctxB allelic distribution among V. cholerae isolates across Africa, reinforcing the dynamic nature of cholera epidemiology on the continent. A central finding is the near-universal presence of intact CTX prophage elements in clinical isolates. The high co-carriage rates of ctxA and ctxB genes—exceeding 91% across all regions and reaching complete prevalence in North Africa—underscore the essential role of the CTXΦ prophage in pathogenicity. This observation aligns with established evidence that cholera toxin production, encoded by these genes, remains the primary virulence determinant driving epidemic disease [51,58]. The particularly high prevalence in Central and North Africa suggests strong selective pressure favoring toxigenic strains [59]. The geographic partitioning of ctxB allelic variants provides further insight into cholera evolution and transmission dynamics. The dominance of ctxB variant 1 (classical biotype) in Central Africa contrasts sharply with the predominance of variant 7 (El Tor biotype) in West, East, and Southern Africa. This pattern is consistent with the global replacement of classical strains by El Tor variants, particularly the emergence of altered El Tor strains carrying classical toxin genes [60]. However, the persistence of classical ctxB alleles in Central Africa suggests either historical lineage retention or localized evolutionary trajectories. The differential distribution of CTX prophage carriage and ctxB variants across Africa underscores the need for regionally tailored cholera control measures. Continued genomic surveillance will be essential to monitor lineage dynamics, detect emerging variants, and inform targeted interventions. The high prevalence of intact CTX prophage in African clinical isolates mirrors observations from cholera-endemic regions in Asia, such as Bangladesh and India, and Russia, where toxigenic V. cholerae strains consistently dominate clinical cases [61–63]. In Asia, the epicenter of cholera evolution, a transition from classical to El Tor and subsequently to altered El Tor strains has been well documented [60]. In the Americas, cholera outbreaks have largely been attributed to imported El Tor strains, predominantly carrying ctxB7 [56]. The geographic structuring of ctxB alleles observed in Africa reflects broader global evolutionary trends [51,55,64,65]. The major finding of this study is the extensive and deeply entrenched antimicrobial resistance across African V. cholerae populations. The genomic analysis of 3,076 V. cholerae isolates from Africa reveals an alarming antimicrobial resistance (AMR) landscape, characterized by both high gene diversity and widespread chromosomal resistance. With 101 distinct AMR genes across 9 categories and an average of 10.81 genes per isolate, the dataset reflects one of the most heavily burdened regional resistomes reported to date. The finding that 94.1% of isolates are multidrug-resistant (MDR) underscores a critical public health challenge for cholera management across the African continent. The near-universal presence of chromosomal fluoroquinolone resistance mutations, combined with widespread carriage of SXT/R391 integrative conjugative elements, indicates that multidrug resistance is now a defining feature of circulating epidemic strains. This has serious implications for treatment, particularly given the reliance on ciprofloxacin and azithromycin for severe cholera cases [66]. Fluoroquinolones, particularly ciprofloxacin, are recommended by the World Health Organization as first-line treatment for severe cholera [67]. The widespread baseline resistance observed here suggests that standard treatment protocols may be increasingly ineffective in African settings, raising concerns about treatment failure and prolonged transmission. Asia—particularly regions like Bangladesh and India—has historically been a hotspot for cholera and AMR [50,54]. The extensive AMR burden observed in African V. cholerae isolates signals a critical turning point in cholera management. Unlike other regions where resistance is still evolving, Africa shows a deeply entrenched resistance landscape, driven by both chromosomal mutations and mobile genetic elements. Addressing this challenge will require coordinated global action, integrating antimicrobial stewardship, genomic surveillance, and strengthened public health infrastructure. The dataset used in this study was derived from publicly available genomic sequences, which may be subject to sampling bias, with overrepresentation of certain countries and outbreak periods. There is also limited environmental sampling that restricts the ability to fully assess environmental reservoirs and transmission pathways. Moreover, phenotypic antimicrobial susceptibility testing was not performed, and resistance was inferred solely from genomic data. Finally, incomplete metadata for some isolates, including missing collection dates and clinical details, may limit the precision of temporal and epidemiological inferences. Conclusion This study demonstrates that cholera transmission across Africa is driven by the clonal expansion of a dominant multidrug-resistant V. cholerae lineage with sustained cross-border spread. The high prevalence of antimicrobial resistance and conserved virulence determinants underscores the growing challenge of managing cholera outbreaks in the region. Strengthening genomic surveillance and integrating real-time sequencing into public health systems will be critical for tracking transmission, guiding treatment strategies, and achieving the WHO target of cholera elimination by 2030. Declarations Funding This study received no specific external funding. Availability of data and materials The dataset supporting the conclusions of this article is included within the article and its supplementary files. Genome sequence data of Vibrio cholerae isolates are available in NCBI Sequence Read Archive and NCBI GenBank. All accession numbers are listed in supplementary Table S1. Acknowledgement The authors acknowledge the use of publicly available genomic datasets and computational resources that supported this work. Ethics approval and consent to participate This study involve analysis of publicly available datasets. No patient specimens were used and patient‐protected health information was not collected. Therefore, informed consent was not required. Consent for publication Not applicable. Conflict of Interest The authors declare no conflicts of interest. Authors Contribution AO and YM contributed equally to this work. AO: Conceptualization, Methodology, Formal analysis, Data curation, Writing - Original draft preparation. YM: Conceptualization, Methodology, Formal analysis, Data curation, Writing - Original draft preparation. AY: Software, Validation, Visualization, Writing - Review & editing. AJ: Software, Validation, Investigation, Writing - Review & editing. RFA: Investigation, Resources, Writing - Review & editing. 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Supplementary Files SupplMat.xlsx Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9358362","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635779628,"identity":"5ea96300-0167-422c-98b7-6a66bf3e3ab0","order_by":0,"name":"Ahmed Olowo-okere","email":"data:image/png;base64,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","orcid":"","institution":"University of Abuja","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Olowo-okere","suffix":""},{"id":635779629,"identity":"b2c5e1aa-afdf-4ff0-9fa7-679d7f90bba9","order_by":1,"name":"Mohammed Yahaya","email":"","orcid":"","institution":"Usmanu Danfodiyo University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Yahaya","suffix":""},{"id":635779631,"identity":"6cfd6752-30a2-4ea7-b203-f951f97fb726","order_by":2,"name":"Abdourahmane Yacouba","email":"","orcid":"","institution":"Abdou Moumouni University","correspondingAuthor":false,"prefix":"","firstName":"Abdourahmane","middleName":"","lastName":"Yacouba","suffix":""},{"id":635779633,"identity":"cd480e01-cb2d-41f2-9e60-c2bc6f6c03de","order_by":3,"name":"Abubakar Jibril","email":"","orcid":"","institution":"Federal University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abubakar","middleName":"","lastName":"Jibril","suffix":""},{"id":635779635,"identity":"feb1faa1-720f-4ef3-bd9d-d9fc153ee79b","order_by":4,"name":"Razaq Funsho Atata","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Razaq","middleName":"Funsho","lastName":"Atata","suffix":""},{"id":635779637,"identity":"b096a15b-7c6f-449a-8722-3fc5aebde9ae","order_by":5,"name":"Busayo Olalekan Olayinka","email":"","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":false,"prefix":"","firstName":"Busayo","middleName":"Olalekan","lastName":"Olayinka","suffix":""}],"badges":[],"createdAt":"2026-04-08 14:38:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9358362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9358362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108947604,"identity":"91a4dd55-3a52-4659-8862-f7792dfb88ce","added_by":"auto","created_at":"2026-05-11 06:30:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution, temporal trends, and sequence type dynamics of \u003cem\u003eV. cholerae \u003c/em\u003eisolates across Africa, 1949–2025. \u003cstrong\u003e(A)\u003c/strong\u003e Geographic distribution of \u003cem\u003eV. cholerae\u003c/em\u003eisolates across Africa. The choropleth map displays the total number of genomes per country, with colour intensity proportional to isolate count. Overlaid pie charts represent the proportion of isolates by source category (clinical, environmental, and unknown). \u003cstrong\u003e(B)\u003c/strong\u003e Geographic distribution of isolates stratified by multilocus sequence types (MLST). Pie charts illustrate the relative contribution of dominant sequence types across African regions, highlighting the widespread distribution of ST69. \u003cstrong\u003e(C)\u003c/strong\u003e Temporal distribution of sequenced isolates from 1949 to 2025. Bars represent the annual number of isolates, stacked by source category (clinical, environmental, and unknown), demonstrating increased sequencing activity in recent outbreak years. \u003cstrong\u003e(D)\u003c/strong\u003e Temporal distribution of MLST sequence types. Stacked bar plots show the yearly distribution of major sequence types, illustrating the sustained dominance of ST69 over time, particularly during the 2022–2024 outbreak period.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/fe010433aa4a8d3aae1ac732.png"},{"id":108947606,"identity":"6f2f11d7-56a9-4069-8fb8-a7b09cf53b61","added_by":"auto","created_at":"2026-05-11 06:30:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic location, profile, and prevalence of antimicrobial resistance (AMR) genes in African \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. cholerae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e isolates. \u003c/strong\u003e\u0026nbsp;(A) Ranked prevalence of detected AMR genes across the analyzed isolate population. Bars are colored according to their respective resistance class, corresponding to the legend in Panel B. (B) Circular representation of the identified AMR gene repertoire. The inner ring represents the overarching antimicrobial resistance class, while the outer ring visually differentiates individual AMR genes and point mutations. (C) Genomic location of AMR genes across chromosomal, plasmid, and prophage contexts.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/b828f9ae809e7b3331330ff9.png"},{"id":108947607,"identity":"c3ecae03-36ed-45ca-831e-198706d87abd","added_by":"auto","created_at":"2026-05-11 06:30:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1175791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructure and geographic distribution of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eV. cholerae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e resistome.\u003c/strong\u003e (A) Proportional representation of antibiotic resistance gene (ARG) classes across clinical and environmental reservoirs. Ribbon width represents the percentage contribution of each ARG class to the total reservoir resistome. (B) Absolute ARG counts distributed across five African sub-regions. Ribbon width is proportional to raw gene counts. Sector colours denote antibiotic class (see legend), reservoir origin (Panel A), or geographic region (Panel B). Directionality (height difference at chord ends) indicates asymmetric ARG sharing. Colour palette validated for colour-vision deficiency.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/2c503910e82f0690fc00a27e.png"},{"id":108947608,"identity":"ac451a79-f473-4974-96bf-c89708dbdf8a","added_by":"auto","created_at":"2026-05-11 06:30:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":718360,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic epidemiology of \u003cem\u003eV. cholerae \u003c/em\u003eacross the African continent. Midpoint-rooted maximum-likelihood phylogenetic tree based on core single nucleotide polymorphisms. Metadata rings (innermost to outermost) represent: Africa Region, Country, Host, and Collection Year. Missing or unavailable metadata is designated in light gray.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/5023c935d0d8190e6d0a5f3e.png"},{"id":108979819,"identity":"d63adf8e-bb4f-4bca-af4b-c997a713aac7","added_by":"auto","created_at":"2026-05-11 12:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2609111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/fbdc561e-b9ea-4708-8840-99c1a51dee74.pdf"},{"id":108947603,"identity":"74585967-d95f-4e9b-be94-cf40c6d115d4","added_by":"auto","created_at":"2026-05-11 06:30:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3268280,"visible":true,"origin":"","legend":"","description":"","filename":"SupplMat.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/d176659cf72d03ea8a8306ca.xlsx"},{"id":108977394,"identity":"5e4e2e4b-070a-40fd-945f-e4229cb4ca43","added_by":"auto","created_at":"2026-05-11 11:31:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18306,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9358362/v1/835161a9fff5f5c6a5cf118e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic Epidemiology of 3,076 Vibrio cholerae Isolates Reveals ST69 Clonal Expansion and Multidrug Resistance across Africa","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eVibrio cholerae\u003c/em\u003e is a Gram-negative bacterium, responsible for the acute secretory diarrheal disease known as cholera [1]. The disease remains a formidable and persistent threat to global public health, with the World Health Organization (WHO) estimating between 1.3 and 4.0 million cases and 21,000\u0026ndash;143,000 deaths annually worldwide [2,3]. Sub-Saharan Africa bears a disproportionate fraction of this burden, accounting for over 30% of global cases and more than 80% of cholera deaths worldwide, with the region suffering 83% of global cholera mortality between 2000 and 2015 [4]. The scale of this crisis escalated dramatically in recent years: in 2023, Africa experienced a 125% increase in reported cholera cases and a 62% rise in deaths compared with 2022, with 21 countries collectively reporting 225,857 cases and 3,167 deaths, yielding a case fatality ratio (CFR) of 1.4% [5]. By July 2024, a cumulative total of 399,508 cases and 7,023 deaths (CFR: 1.8%) had been reported across the continent since January 2022, with the Democratic Republic of the Congo (DRC), Ethiopia, Malawi, Mozambique, and Zimbabwe collectively accounting for 72.2% of all cases and 62.3% of all deaths [6]. These explosions of transmission are driven by complex intersections of inadequate water, sanitation, and hygiene (WASH) infrastructure, the exacerbating effects of climate change \u0026mdash; including cyclones and El Ni\u0026ntilde;o-driven flooding \u0026mdash; and persistent armed conflicts that fracture healthcare systems and displace populations [7\u0026ndash;9]. Geospatial analyses further reveal that the annual incidence of cholera in Africa has dynamically shifted from western to central and eastern regions between 2011 and 2020, with an estimated 296 million people residing in high-incidence administrative units by 2020 [10].\u003c/p\u003e\n\u003cp\u003eThe evolutionary success of \u003cem\u003eV. cholerae\u003c/em\u003e, particularly the El Tor biotype responsible for the ongoing seventh pandemic (7PET), is intrinsically linked to its high genomic plasticity [11,12]. The pathogen possesses a bipartite genome consisting of a larger Chromosome I (~2.9 Mb), which encodes the majority of essential housekeeping genes and primary virulence determinants \u0026mdash; including the toxin-coregulated pilus (TCP), encoded by the \u003cem\u003eVPI-1\u003c/em\u003e pathogenicity island, and the core cholera toxin (CTX) prophage \u0026mdash; and a smaller Chromosome II (~1.1 Mb) that houses accessory metabolic genes and the chromosomal superintegron [13]. The acquisition and exchange of mobile genetic elements (MGEs) play a significant role in driving the emergence of novel, highly virulent lineages and disseminating antimicrobial resistance genes [14]. Phylogenomic reconstructions have resolved the African 7PET population into a series of distinct sublineages, designated AFR10 through AFR15. Recent genomic analyses of 763 \u003cem\u003eV. cholerae\u003c/em\u003e O1 isolates collected across sub-Saharan Africa between 2019 and 2024 identified the rapid continental circulation of the AFR15 lineage, which has been associated with unusually large outbreaks in Southern Africa and has expanded into new African Union member states, while cross-border transmission was found to be prevalent across both West and East Africa [15]. Multiple co-circulating lineages have been identified within individual countries, and what were previously classified as sporadic outbreak strains have, upon genomic re-evaluation, proven to be representatives of larger undersampled outbreaks, underscoring the persistent gaps in continental surveillance [16].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompounding the threat of hypervirulent sublineages is the accelerating emergence of AMR within \u003cem\u003eV. cholerae\u003c/em\u003e populations. The primary vehicle for multidrug resistance (MDR) in African 7PET isolates is the SXT/R391 family of integrating conjugative elements (ICEs), which are ~99\u0026ndash;100 kb self-transmissible elements that integrate site-specifically into the 5\u0026prime; end of the chromosomal \u003cem\u003eprfC\u003c/em\u003e gene [17]. These ICEs encode resistance to sulfamethoxazole, trimethoprim, streptomycin, chloramphenicol, nalidixic acid, and tetracycline, and carry well-characterised antimicrobial resistance genes (ARGs)[18].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFluoroquinolone resistance, however, represents the most clinically alarming contemporary trend. Reduced ciprofloxacin susceptibility in 7PET strains arises primarily through sequential accumulation of point mutations in the quinolone resistance-determining regions (QRDRs) of chromosomal topoisomerase genes, most notably the Ser83Ile substitution in \u003cem\u003egyrA\u003c/em\u003e (encoding DNA gyrase subunit A) and a corresponding Ser85Leu substitution in \u003cem\u003eparC\u003c/em\u003e (encoding topoisomerase IV subunit C), which together confer high-level fluoroquinolone resistance [19]. A landmark example of the consequences of unchecked AMR accumulation was the 2018 cholera outbreak in Zimbabwe, in which the causative AFR13 sublineage strain acquired an approximately 160-kb IncA/C2 plasmid bearing 14 additional AMR genes; the isolates were intermediately resistant or resistant to tetracycline (\u003cem\u003etetA\u003c/em\u003e) and ciprofloxacin (\u003cem\u003egyrA\u003c/em\u003e and \u003cem\u003eparC\u003c/em\u003e mutations and \u003cem\u003eaac(6\u0026prime;)-Ib-cr\u003c/em\u003e) and produced the extended-spectrum beta-lactamase CTX-M-15, rendering the outbreak strain resistant to two of the three WHO-recommended cholera antibiotics [20]. More recent PulseNet Africa genomic surveillance of isolates from C\u0026ocirc;te d\u0026apos;Ivoire, Ghana, Zambia, and South Africa (2010\u0026ndash;2024) has confirmed the persistence of resistance genes associated with quinolones and trimethoprim, raising serious concerns about the continued efficacy of current treatment protocols [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the severe and escalating impact of cholera across Africa, comprehensive genomic epidemiology of the pathogen remains fragmented. Between 2010 and 2019, sub-Saharan Africa documented 999 suspected cholera outbreaks with over 480,000 probable patients across 744 sub-national regions in 25 countries, yet molecular characterisation has largely been limited to localized, outbreak-specific investigations [22]. Consequently, the continental genomic diversity, pan-genome dynamics, AMR gene reservoir, and broad evolutionary relationships of African \u003cem\u003eV. cholerae\u003c/em\u003e isolates remain incompletely characterised. The difficulty in containing cholera is compounded by poor understanding of how the pathogen circulates throughout the region; within-country variation in transmission dynamics and the finding that several formerly classified \u0026quot;sporadic\u0026quot; outbreaks represent undersampled continuous transmission events highlight the critical limitations of non-genomic surveillance [23]. Without a unified, large-scale genomic framework, tracking cross-border transmission events and identifying the emergence of high-risk MDR lineages in real time remains extremely challenging.\u003c/p\u003e\n\u003cp\u003eTo address this critical knowledge gap, this study presents a comprehensive, large-scale comparative genomic analysis of 2,726 \u003cem\u003eV. cholerae\u003c/em\u003e genomes sourced from across the African continent. Ultimately, this analysis provides a robust genomic framework to elucidate the evolutionary trajectory of the pathogen, characterise the continental distribution of AMR determinants, and inform highly targeted, real-time public health interventions consistent with the WHO Global Roadmap to End Cholera by 2030.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data Identification and Genome Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic data for \u003cem\u003eV. cholerae\u003c/em\u003e were identified via the NCBI Pathogen Detection Isolates Browser (https://www.ncbi.nlm.nih.gov/pathogens/isolates%23/search/%23/search/), accessed on 19\u003csup\u003eth\u003c/sup\u003e March 2026. An initial global query yielded 19,040 isolate records, which were subsequently filtered to retain only 3185 originating from 29 African countries. These isolates were stratified into two cohorts: those with pre-assembled genomes deposited in the NCBI Assembly database (\u003cem\u003en\u003c/em\u003e=2733), and those available exclusively as raw paired-end sequencing reads in the NCBI Sequence Read Archive (SRA/ENA) (\u003cem\u003en\u003c/em\u003e=452). These were subsequently retrieved using NCBI Datasets v18.9.0 [24] and \u003cem\u003efasterq-dump\u003c/em\u003e v3.2.1 [25], respectively. We verified the mutual exclusivity of the two cohorts by cross-referencing their BioSample identifiers, which confirmed that no single isolate was represented in both the assembly and raw read datasets. The genomes metadata, including isolation country, collection year, and sample source were programmatically retrieved via the NCBI E-utilities API [26]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Raw Read Processing, \u003cem\u003eDe Novo\u003c/em\u003e Genome Assembly, and Annotation \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe downloaded raw paired-end reads were assembled \u003cem\u003ede novo\u003c/em\u003e into contigs using the Shovill pipeline v1.1.0 (https://github.com/tseemann/shovill), which employs SPAdes v4.2.0 as the underlying assembler, with \u0026ndash;trim option [27]. All genome assemblies were annotated in a standardised, reproducible manner using Prokka v1.13 [28]. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Genome Quality Control and Taxonomic Verification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure the integrity of downstream analyses, all assembled and downloaded genomes were subjected to rigorous quality control. Assembly contiguity was evaluated using QUAST v5.2.0, while genome completeness and contamination were assessed utilizing CheckM2 v1.0.2 [29,30]. Assemblies were systematically excluded from the dataset if they exhibited a completeness of less than 90% (\u003cem\u003en\u003c/em\u003e = 6) or a contamination level exceeding 10% (\u003cem\u003en\u003c/em\u003e = 17). Furthermore, genomes were discarded if their total assembly length fell outside the biologically expected range for \u003cem\u003eV. cholerae\u003c/em\u003e (approximately 3.5 to 4.5 Mbp), or if they displayed high fragmentation, contig N50 lower than 10,000 bp or an L90 metric exceeding 100 contigs (\u003cem\u003en\u003c/em\u003e = 86). To preclude the inclusion of misclassified taxa, pairwise Average Nucleotide Identity (ANI) of all retained assemblies was calculated against the \u003cbr\u003e\u003cem\u003eV. cholerae\u003c/em\u003e O1 biovar El Tor str. N16961\u003cu\u003e \u003c/u\u003e(GCF_900205735.1) using FastANI v1.3.4 [31]. Only assemblies exhibiting an ANI \u0026ge; 95% were definitively classified as V. cholerae and retained for downstream analyses [33]. All genomes passed this threshold, with pairwise ANI against the reference ranging from 96.01 to 100% (mean = 99.88%; median = 99.98%; SD = 0.45%), confirming the species-level identity of all retained assemblies. \u003c/p\u003e\n\u003cp\u003eFollowing the application of these exclusion filters, a final high-quality dataset comprising 3,076 genomes was retained \u003cstrong\u003e(Table S1)\u003c/strong\u003e. Within this refined cohort, genome completeness ranged from 97.67% to 100% (mean = 100%, median = 100%), and contamination varied between 0% and 9.82% (mean = 2.25%, median = 0.05%) \u003cstrong\u003e(Table S2)\u003c/strong\u003e. The retained assemblies contained between 2 and 281 contigs (mean = 75, median = 72), with N50 values spanning 39.0 to 3,141.1 Kb (mean = 278.8 Kb, median = 235.7 Kb). The overall GC content ranged from 46.93% to 48.01% (mean = 47.51%, median = 47.50%), and total genome sizes spanned 3.78 to 4.48 Mbp (mean = 4.10 Mbp, median = 4.05 Mbp) \u003cstrong\u003e(Table S3; Table S4)\u003c/strong\u003e. These assembly metrics are highly consistent with the known genomic architecture and bipartite chromosomal structure of \u003cem\u003eV. cholerae \u003c/em\u003e[32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Core Genome SNP Alignment and Recombination Masking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePer-sample read mapping and variant calling were performed using Snippy v4.6.0 (https://github.com/tseemann/snippy) against the reference genome. To ensure high-quality variant detection, minimum thresholds were set for read depth (4\u0026times;; --mincov 4) and variant allele frequency (0.75; --minfrac 0.75) [33]. A core genome alignment of all 3,076 isolates was subsequently generated using snippy-core, retaining only sites present across the entire dataset [33]. This strict core alignment spanned 4,033,501 bp of the reference genome, with a mean per-sample reference coverage of 96.14%, and yielded 245,973 variant sites.\u003c/p\u003e\n\u003cp\u003eTo mitigate the confounding effects of homologous recombination and mobile genetic elements (MGEs) on phylogenetic inference, recombinant regions were identified and masked prior to tree reconstruction using Gubbins v3.4.3 [34]. Gubbins detects recombination under an iterative maximum-likelihood framework by identifying genomic windows with significantly elevated SNP densities relative to the background. The pipeline was configured to construct an initial guide tree using FastTree (--first-tree-builder fasttree), followed by iterative refinement using IQ-TREE (--tree-builder iqtree) under the GTRGAMMA substitution model. The process was capped at five iterations (--iterations 5), with convergence assessed via the weighted Robinson-Foulds metric. Recombinant blocks were defined by a minimum of three SNPs (--min-snps 3), and sequences containing more than 25% missing data were excluded from the analysis (--filter-percentage 25).\u003c/p\u003e\n\u003cp\u003eThe resulting recombination-free polymorphic site alignment produced by Gubbins was utilized for all downstream phylogenetic analyses. Finally, to characterize within-dataset genomic diversity and delineate putative transmission clusters, pairwise SNP distances were calculated from this filtered alignment using snp-dists v0.8.2 [35].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Maximum Likelihood Phylogenetic Reconstruction and Temporal Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA maximum-likelihood (ML) phylogeny was reconstructed from the Gubbins recombination-free core SNP alignment using IQ-TREE 2 v2.2.6 [36]. The optimal nucleotide substitution model was selected automatically using the integrated ModelFinder algorithm under the Bayesian Information Criterion (BIC) to avoid model misspecification [37]. Topological robustness was assessed by 1,000 ultrafast bootstrap (UFBoot2) replicates, with branch support values \u0026ge;95% considered indicative of well-supported nodes [38]. The N16961 reference genome was included as an outgroup to root the phylogeny. Phylogenetic visualization and metadata annotation were performed using ggtree v3.16.3 and ggtreeExtra v1.18.1 [39,40]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultilocus sequence typing \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultilocus sequence typing (MLST) was performed on all genomic assemblies using the command-line MLST tool v2.23.0 (https://github.com/tseemann/mlst), which performs automated allele matching against curated PubMLST databases. The Heidelberg seven-locus V. cholerae MLST scheme (vcholerae) was applied, interrogating the following housekeeping gene loci: adenylate kinase (adk), DNA gyrase subunit B (gyrB), malate dehydrogenase (mdh), homocysteine synthase (metE), pyridine nucleotide transhydrogenase (pntA), phosphoribosylformylglycinamidine synthetase (purM), and dihydroorotase (pyrC). Sequence types (STs) were assigned based on exact allele matches against the PubMLST V. cholerae reference database (https://pubmlst.org/vcholerae). Only assemblies yielding a PERFECT or GOOD call status, defined as 100% identity and full-length allele matches across all seven loci, were retained for downstream ST-based analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 \u003cem\u003eIn Silico\u003c/em\u003e Profiling of Virulence Factors, AMR Determinants, and Mobile Genetic Elements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVirulence, Serogrouping, and Biotyping.\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eGenomic assemblies were screened using CholeraeFinder v2.1 (Center for Genomic Epidemiology) to predict serogroup and biotype, and to identify major V. cholerae-specific virulence genes, resistance determinants, and mobile genetic elements [41]. Species identity was confirmed by detection of ompW, a highly conserved outer membrane protein gene specific to V. cholerae. Serogroup was assigned based on detection of the O-antigen biosynthesis genes rfbV_O1 (O1 serogroup) and wbfZ_O139 (O139 serogroup), with a minimum nucleotide identity threshold of \u0026ge;98% applied to the wbfZ_O139 marker to exclude cross-reactive hits; isolates below this threshold were classified as Non-O1/Non-O139. Biotype was differentiated into El Tor, El Tor Variant, or Classical lineages using allele-specific markers for the transcriptional regulator (rstR_et, rstR_cc) and the toxin-coregulated pilus subunit (tcpA_et3, tcpA_cc), with El Tor Variant designation requiring co-detection of the ctxB_7 allele. The cholera toxin B subunit (ctxB) was further genotyped into allelic variants (ctxB_1, ctxB_3, ctxB_7) to resolve lineage-level diversity.\u003c/p\u003e\n\u003cp\u003eTo contextualise isolates within the ongoing seventh pandemic, the seventh pandemic El Tor (7PET) lineage-specific marker gene (VC2346) was assessed. Isolates were classified as toxigenic upon detection of the cholera enterotoxin A subunit gene (ctxA), the catalytic determinant of cholera toxin production; ctxB genotyping was used independently for lineage discrimination. The presence of \u003cem\u003eV.\u003c/em\u003e Pathogenicity Islands (VPI-1, VPI-2) and Seventh Pandemic Islands (VSP-I, VSP-II) was assessed using the CholeraeFinder reference database, which employs embedded reference loci derived from the V. cholerae O1 El Tor N16961 reference genome (GenBank: AE003852). Mobile genetic elements were characterised by detection of the SXT/R391 integrating conjugative element (ICE) integrase gene (intSXT) and the Class 1 integron integrase (intI1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntimicrobial Resistance (AMR) Profiling and MDR Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resistome of 3,076 \u003cem\u003eV. cholerae\u003c/em\u003e isolates was characterized by screening genomic assemblies with AMRFinder plus v3.10.1 [42]. The abundance of AMR genes within the population was evaluated by calculating the Average Copy Number (ACN) per isolate, both overall and stratified by isolation source (clinical versus environmental) as previously described [43]. Gene prevalence was determined as the percentage of genomes harboring a specific resistance determinant. Isolates were classified as multidrug-resistant (MDR) if they harbored acquired resistance genes spanning three or more distinct antibiotic classes as previously described [44]. To assess spatial and source-specific distributions, the diversity of unique AMR genes and their specific ACNs were compared between the clinical and environmental subsets.\u003c/p\u003e\n\u003cp\u003eIdentification of Mobile Genetic Elements and Genomic Localization of Genes\u003c/p\u003e\n\u003cp\u003ePlasmid-derived contigs were identified using a consensus approach combining PlasForest and geNomad v1.8.0 [45,46]. PlasForest was run using the plasforest.sav model against the RefSeq plasmid database, while geNomad was executed in end-to-end mode with the --cleanup and --splits 8 flags to simultaneously detect plasmid and prophage sequences. As previously reported, contigs were classified as prophage where geNomad assigned a virus score exceeding both the chromosome and plasmid scores [43]. To determine the genomic context of the resistome, the coordinates of the identified AMR genes were intersected with the MGE predictions, allowing each gene occurrence to be localized to the chromosome, a plasmid, or a prophage region.\u003c/p\u003e\n\u003cp\u003e2.8 Statistical Analysis and Data Visualization\u003c/p\u003e\n\u003cp\u003eDescriptive statistics, including frequencies and percentages, were used to summarize categorical variables across isolates. All statistical analysis and visualizations were performed in R v4.5.1 and RStudio v2025.9.0.387 (R Core Team, 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 3,076 sequenced \u003cem\u003eV.\u0026nbsp;\u003c/em\u003e\u003cem\u003echolerae\u003c/em\u003e genomes from the African continent were analyzed in this study. Geographically, the isolates originated from 29 countries, with a pronounced concentration in Central Africa (\u003cem\u003en\u003c/em\u003e = 1,305; 42.4%) and East Africa (\u003cem\u003en\u003c/em\u003e = 1,174; 38.2%), followed by West Africa (\u003cem\u003en\u003c/em\u003e = 358; 11.6%), Southern Africa (\u003cem\u003en\u003c/em\u003e = 221; 7.2%), and North Africa (n = 18; 0.6%) (Fig. 1A; Table S1). The Democratic Republic of the Congo (DRC) contributed the largest proportion of genomes (\u003cem\u003en\u003c/em\u003e = 1,039; 33.8%), followed by Cameroon (\u003cem\u003en\u003c/em\u003e = 259; 8.4%), Zambia (\u003cem\u003en\u003c/em\u003e = 255; 8.3%), Kenya (\u003cem\u003en\u003c/em\u003e = 254; 8.3%), and Mozambique (\u003cem\u003en\u003c/em\u003e = 245; 8.0%).\u003c/p\u003e\n\u003cp\u003eThe majority of sequenced genomes were derived from clinical human samples (\u003cem\u003en\u003c/em\u003e = 1,988), while environmental sources accounted for a minor fraction of the dataset (\u003cem\u003en\u003c/em\u003e = 96; 3.1%), including water (\u003cem\u003en\u003c/em\u003e = 101), food/aquatic animal (\u003cem\u003en\u003c/em\u003e = 17), and other/unknown sources (\u003cem\u003en\u003c/em\u003e = 12).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTemporal analysis of collection dates revealed isolates spanning from 1949 to 2025, with 150 genomes lacking collection year metadata. The mean number of isolates per year was 84, ranging from 1 to 609 across years with available data (Table S1; Fig 1C). A marked escalation in genomic surveillance was observed in recent years, with the highest isolate counts recorded in 2023 (\u003cem\u003en\u003c/em\u003e = 609; 19.8%) and 2022 (\u003cem\u003en\u003c/em\u003e = 468; 15.2%), coinciding with intensified large-scale cholera outbreaks across the continent. Historical isolates from earlier decades (\u0026le;2010) were characterised by lower sequencing volumes, with the earliest isolate in the dataset dating to 1949.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eST69 and its related lineage ST515 are the most dominant sequence types in Africa\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMulti-locus sequence typing (MLST) of the 3,076 genome dataset revealed a highly clonal population structure characterised by the near-complete dominance of a single lineage across the continent (Fig. 3B, 3D). All isolates were typed using the V. cholerae MLST scheme, yielding 33 unique sequence types, with MLST assignments achieving perfect allelic concordance in 2,980 genomes (96.9%), while 35 genomes carried novel allelic profiles, 32 were partially assigned (OK), 27 were mixed, and 2 were missing data. MLST scores ranged from 74 to 100 (median = 100; mean = 99.6%), reflecting high-quality typing across the dataset (Figure 1; Table S5).\u003c/p\u003e\n\u003cp\u003eSequence Type 69 (ST69) was the overwhelmingly prevalent sequence type, accounting for 2,682 genomes (87.2% of the entire dataset). Spatially, ST69 exhibited a pervasive pan-African distribution, driving epidemics across all five African regions \u0026mdash; Central Africa (n = 1,117), East Africa (n = 1,113), West Africa (n = 314), Southern Africa (n = 137), and North Africa (n = 1). It was the dominant lineage in all heavily burdened nations, most notably the Democratic Republic of the Congo (DRC) (n = 869), Cameroon (n = 243), Mozambique (n = 243), Kenya (n = 250), Zambia (n = 247), Tanzania (n = 180), and Nigeria (n = 156). Longitudinally, ST69 has circulated persistently since at least 1970 and was almost exclusively responsible for the massive surge in clinical genomes sequenced during the recent 2022\u0026ndash;2024 continent-wide outbreak waves, accounting for 463 of 468 genomes in 2022 (98.9%), 605 of 609 in 2023 (99.3%), and 252 of 259 in 2024 (97.3%).\u003c/p\u003e\n\u003cp\u003eThe secondary lineage ST515 (n = 196; 6.4%) emerged as the only other numerically substantial sequence type and demonstrated restricted geographic mobility heavily localised to Central and East Africa. ST515 drove distinct temporal transmission peaks in 2015 (n = 76) and 2018 (n = 33), with the vast majority of isolates originating from the DRC (n = 150) and Tanzania (n = 18), alongside minor representation in Zambia (n = 4). Notably, ST515 was detected almost exclusively in clinical samples (n = 103) and was virtually absent from environmental sources.\u003c/p\u003e\n\u003cp\u003eThe remaining sequence types collectively constituted a minor proportion of the dataset (n = 198; 6.4%), with 31 additional STs each accounting for \u0026le;0.8% of isolates. ST75 (n = 24; 0.8%) was predominantly observed in Southern Africa, particularly South Africa (n = 21), and showed a recent temporal resurgence between 2018 and 2025. ST1251 (n = 15; 0.5%) was geographically restricted to West Africa, almost entirely from Ghana (n = 15), suggesting a localised lineage. ST555 (n = 6; 0.2%) was exclusively detected in South Africa, while ST68 (n = 6; 0.2%) was confined to North Africa. Historically older isolates from 1949 were typed as ST73, representing one of the earliest sequenced African V. cholerae genomes in the dataset. Ninety-six genomes (3.1%) could not be assigned a definitive ST, disproportionately represented among environmental isolates (n = 34 of 96 environmental genomes), suggesting greater allelic diversity in non-clinical reservoir strains. Collectively, these findings underscore the profound geographic entrenchment and epidemiological dominance of the ST69 clone across contemporary African cholera epidemics, with secondary lineages exhibiting spatially and temporally restricted circulation patterns.\u003c/p\u003e\n\u003cp\u003eCross-tabulation of MLST sequence types with \u003cem\u003ectxB\u003c/em\u003e genotypes revealed distinct lineage-level patterns within the dataset. ST69, the dominant sequence type (n = 2,746), exhibited a near-equal distribution of \u003cem\u003ectxB_1\u003c/em\u003e (n = 1,276; 46.47%) and \u003cem\u003ectxB_7\u003c/em\u003e (n = 1,329; 48.40%) alleles, alongside minor proportions of \u003cem\u003ectxB_3\u003c/em\u003e (n = 31; 1.13%) and undetected \u003cem\u003ectxB\u003c/em\u003e (n = 62; 2.26%). The co-occurrence of both \u003cem\u003ectxB\u003c/em\u003e alleles within a single dominant sequence type is consistent with CTX prophage-mediated \u003cem\u003ectxB\u003c/em\u003e allele replacement within the 7PET genomic backbone, a well-documented mechanism of intra-lineage diversity in circulating \u003cem\u003eV. cholerae\u003c/em\u003e [REF]. ST515, the second most prevalent sequence type (n = 197), was almost exclusively associated with \u003cem\u003ectxB_1\u003c/em\u003e (n = 193; 97.97%), indicating this lineage predates the post-2010 Haitian variant expansion. Notably, \u003cem\u003ectxB_7\u003c/em\u003e carriage was almost entirely confined to ST69 (n = 1,329; 98.7% of all ctxB_7 isolates), confirming that the Haitian variant allele was acquired within the ST69 backbone rather than introduced via a distinct sequence type. Minor sequence types, collectively accounting for 314 isolates, were predominantly \u003cem\u003ectxB\u003c/em\u003e-negative, consistent with non-toxigenic environmental or pre-pandemic strains. These findings indicate that \u003cem\u003ectxB\u003c/em\u003e allele diversity in African \u003cem\u003eV. cholerae\u003c/em\u003e reflects dynamic CTX prophage remodelling within a clonally dominant ST69 population rather than the co-circulation of genetically distinct pandemic lineages (Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic Characterization and Reservoir-Specific Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic characterization of the \u003cem\u003eV. cholerae\u003c/em\u003e isolates revealed a stark dichotomy in serogroup and biotype prevalence between clinical and environmental reservoirs (Table 1). The O1 serogroup marker (\u003cem\u003erfbV\u003c/em\u003e) was overwhelmingly dominant among clinical isolates across all African regions, ranging from 91.8% in Southern Africa to 99.2% in Central Africa. This trend was mirrored by biotype analysis, confirming the near-universal predominance of the El Tor biotype (\u003cem\u003erstR_et\u003c/em\u003e) in clinical settings (82.3% to 99.2%). The classical biotype marker (\u003cem\u003erstR_cc\u003c/em\u003e) was rare, detected only in minor clinical subsets (e.g., 8.9% in Southern Africa and 2.8% in East Africa). In sharp contrast, environmental isolates exhibited marked genomic heterogeneity and a distinct lack of canonical epidemic markers. The O1 serogroup and El Tor markers were entirely absent (0.0%) in the Southern African environmental cohort and severely reduced in Central Africa (31.6%) and West Africa (36.4% to 43.6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Shifts in CTX Prophage Carriage and \u003cem\u003ectxB\u003c/em\u003e Allelic Variants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntact CTX prophage carriage was a defining hallmark of clinical isolates. Co-carriage of the cholera toxin genes \u003cem\u003ectxA\u003c/em\u003e and \u003cem\u003ectxB\u003c/em\u003e exceeded 91% in clinical genomes from all regions, reaching 98.4% in Central Africa and 100% in North Africa. Conversely, environmental isolates demonstrated significant attenuation or complete absence of the CTX element, with carriage dropping to 36.4% in West Africa, 31.6% in Central Africa, and 0.0% in Southern Africa.\u003c/p\u003e\n\u003cp\u003eAnalysis of \u003cem\u003ectxB\u003c/em\u003e allelic variants uncovered distinct geographic partitioning across the continent. In Central Africa, \u003cem\u003ectxB\u003c/em\u003e variant 1 (classical) dominated the clinical landscape, accounting for 92.0% of isolates. However, a major allelic shift was observed elsewhere: \u003cem\u003ectxB\u003c/em\u003e variant 7 (El Tor) was the dominant allele in clinical isolates from West Africa (83.4%), Southern Africa (80.4%), and East Africa (70.2%). This geographic divergence in \u003cem\u003ectxB\u003c/em\u003e alleles suggests established, region-specific clonal endemicity rather than a homogenous continental population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConservation of Pandemic Markers and Pathogenicity Islands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 7th pandemic El Tor (7PET) lineage-specific marker (VC2346) was highly conserved within the clinical subset (82.3% to 99.3%), confirming their pandemic lineage. In environmental subsets, the presence of VC2346 was substantially degraded, ranging from 43.6% in West Africa down to 0.0% in Southern Africa.\u003c/p\u003e\n\u003cp\u003eThe four major \u003cem\u003eV. cholerae\u003c/em\u003e pathogenicity islands (VPI-1, VPI-2, VSP-1, VSP-2) and the Type VI Secretion System (T6SS) core effector \u003cem\u003evasX\u003c/em\u003e followed identical trajectories of reservoir-specific conservation. Key anchoring loci for these islands were highly stable in clinical isolates, consistently detected in the vast majority of genomes across all regions (generally \u0026gt;91%). Environmental isolates, however, displayed systemic degradation of these crucial virulence loci. Notably, VPI-2 (\u003cem\u003eVC1758\u003c/em\u003e) and the T6SS (\u003cem\u003evasX\u003c/em\u003e) appeared slightly more resilient in environmental subsets (e.g., VPI-2 was retained in 68.4% of Central African and 49.1% of West African environmental isolates) compared to the VSP elements, though all remained significantly diminished compared to their clinical counterparts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAntimicrobial Resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtensive AMR Burden and Ubiquitous Chromosomal Resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic profiling of the 3,076 \u003cem\u003eV. cholerae\u003c/em\u003e isolates revealed an extensive antimicrobial resistance repertoire, identifying 101 distinct AMR genes spanning 9 categories. The population exhibited a mean burden of 10.81 AMR genes per isolate (range: 3\u0026ndash;24), with a staggering 94.1% of the collection classified as multidrug-resistant (MDR) (Figure 2).\u003c/p\u003e\n\u003cp\u003eChromosomal resistance was ubiquitous across the dataset. Specifically, polymyxin resistance-associated lipid A modification genes (\u003cem\u003ealmE\u003c/em\u003e, \u003cem\u003ealmF\u003c/em\u003e, and \u003cem\u003ealmG\u003c/em\u003e) were detected in 100% of the isolates. Furthermore, the entire collection exhibited chromosomal mutations in the fluoroquinolone resistance-determining regions, driven predominantly by the highly prevalent \u003cem\u003egyrA_S83I\u003c/em\u003e (92.88%) and \u003cem\u003eparC_S85L\u003c/em\u003e (84.82%) substitutions. This universal baseline resistance to fluoroquinolones\u0026mdash;particularly ciprofloxacin, a WHO-recommended first-line therapy for severe cholera\u0026mdash;highlights a critical and widespread therapeutic challenge across the continent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHorizontally Acquired Determinants and the SXT/ICE Module\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBeyond intrinsic chromosomal mutations, horizontally acquired AMR determinants were deeply entrenched in the population. A dominant SXT integrating conjugative element (ICE)-associated multidrug resistance module\u0026mdash;comprising \u003cem\u003esul2\u003c/em\u003e (64.37%), \u003cem\u003eaph(3\u0026apos;\u0026apos;)-Ib\u003c/em\u003e (64.34%), \u003cem\u003eaph(6)-Id\u003c/em\u003e (64.24%), and \u003cem\u003efloR\u003c/em\u003e (64.04%)\u0026mdash;was identified in approximately 64% of the population, closely mirroring the anticipated carriage rates of the mobile element. Other highly prevalent acquired determinants included the chloramphenicol resistance gene \u003cem\u003ecatB9\u003c/em\u003e (95.61%) and the trimethoprim resistance gene \u003cem\u003edfrA1\u003c/em\u003e (93.21%). The near-universal co-occurrence of chromosomal fluoroquinolone mutations with these acquired MDR determinants signals deeply consolidated multidrug resistance within the African \u003cem\u003eV. cholerae\u003c/em\u003e population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiversity and Abundance Across Antibiotic Classes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant variation was observed in the diversity and abundance of genes within specific antibiotic categories. beta-lactams represented the most highly diverse resistance category, encompassing 30 unique AMR genes. Despite this striking diversity, the metallo-beta-lactamase \u003cem\u003evarG\u003c/em\u003e emerged as the overwhelmingly dominant determinant (97.46%). Similar structural diversity was observed among aminoglycosides and fluoroquinolones, which featured 15 unique determinants each. In terms of genomic burden, the highest Average Copy Number (ACN) was observed in the MLS category, yielding 3.94 \u0026plusmn; 0.37 copies per strain among those carrying them, significantly higher than the overall ACNs for aminoglycosides (1.88) or sulfonamides (1.00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEcological Discrepancy: Clinical Isolates Harbor Emerging Therapeutic Threats\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparative analysis revealed a sharp discrepancy between isolation sources, with clinical isolates harboring a significantly broader and more concerning diversity of AMR determinants. Specifically, 38 resistance genes were completely restricted to the clinical population and absent from environmental counterparts, whereas only 7 genes (including \u003cem\u003eblaCARB-2\u003c/em\u003e and \u003cem\u003etet(G)\u003c/em\u003e) were uniquely environmental (Figure 3).\u003c/p\u003e\n\u003cp\u003eThis clinically restricted pool acts as a reservoir for emerging, high-risk resistance mechanisms. Of particular public health concern, macrolide resistance genes\u0026mdash;including \u003cem\u003emph(A)\u003c/em\u003e, \u003cem\u003emrx(A)\u003c/em\u003e, \u003cem\u003emsr(E)\u003c/em\u003e, and \u003cem\u003emph(E)\u003c/em\u003e\u0026mdash;were restricted entirely to a clinical subset, present in 6.6% to 6.8% of the collection. Furthermore, a wide array of extended-spectrum $\\beta$-lactamases (ESBLs) were clinically exclusive, including \u003cem\u003eblaCTX-M-202\u003c/em\u003e, \u003cem\u003eblaTEM-1\u003c/em\u003e, and notably \u003cem\u003eblaPER-7\u003c/em\u003e (detected in 6.4% of isolates).\u003c/p\u003e\n\u003cp\u003eFinally, clinical isolates exhibited an elevated resistance gene burden within shared categories, demonstrating higher ACNs for fluoroquinolones (1.92 \u0026plusmn; 0.28 vs. 1.50 \u0026plusmn; 0.59 in environmental strains) and phenicols (1.70 \u0026plusmn; 0.47 vs. 1.33 \u0026plusmn; 0.48). Together, the elevated genomic burden and the presence of clinically restricted MLS and ESBL determinants signal emerging resistance to azithromycin and broad-spectrum beta-lactams, threatening alternative and critically important therapeutic options for severe cholera cases (Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the most comprehensive genomic characterization to date of \u003cem\u003eV. cholerae\u003c/em\u003e across the African continent. The population structure of \u003cem\u003eV. cholerae\u003c/em\u003e in Africa is defined by the overwhelming dominance of ST69, reflecting a pattern of extreme clonal expansion within the 7th pandemic El Tor (7PET) lineage. This aligns with the global paradigm of 7PET-driven cholera [47]. Recent studies in Asia, particularly in historically endemic regions such as Bangladesh, also reported that the majority of the seventh pandemic El Tor (7PET) \u003cem\u003eV. cholerae\u003c/em\u003e O1 strains belong to ST69 [48]. In Europe, cholera is no longer endemic, and most cases are imported or travel-associated, with limited or no sustained local transmission. Genomic studies of European isolates typically reflect high diversity linked to multiple international sources, rather than local clonal expansion [49].\u003c/p\u003e\n\u003cp\u003eThe secondary lineage ST515 in Africa further illustrates these continental contrasts. While Asia often harbors multiple co-circulating lineages with varying degrees of epidemic potential, ST515 remains geographically restricted and temporally episodic [50], largely confined to Central [51] and East Africa [52,53]. Its limited spread suggests that, unlike in Asia, where lineage competition and replacement are common, the African cholera ecosystem is dominated by competitive exclusion, with ST69 maintaining a strong selective advantage.\u003c/p\u003e\n\u003cp\u003eThe genomic analysis of \u003cem\u003eV. cholerae\u003c/em\u003e isolates across African regions reveals a pronounced divergence between clinical and environmental reservoirs, underscoring the ecological and epidemiological complexity of cholera transmission on the continent. A key finding is the overwhelming dominance of the O1 serogroup (rfbV) among clinical isolates, consistently exceeding 90% across all regions and reaching near fixation in Central Africa (99.2%). These genomic patterns observed in African \u003cem\u003eV. cholerae\u003c/em\u003e isolates reveal strong similarities with, but also important distinctions from, other global regions, including Asia, Europe, and the Americas. Across all settings, a common theme emerges: epidemic cholera is driven by a highly conserved subset of pathogenic lineages, while environmental reservoirs harbor substantial genetic diversity. Asia differs in one critical aspect: the frequent detection of atypical El Tor variants and hybrid strains combining classical and El Tor traits [54,55]. These hybrid genotypes, often associated with increased virulence or altered toxin production, are more commonly reported in Asia than in Africa, where classical markers (rstR_cc) remain rare and geographically limited [54]. This suggests that Africa may experience less frequent recombination events or may have different selective pressures shaping strain evolution. In the Americas, particularly following the introduction of cholera in Haiti in 2010, genomic analyses have demonstrated a strong link to South Asian El Tor strains [56]. Much like in Africa, clinical isolates in the Americas are overwhelmingly dominated by O1 El Tor lineages, reflecting the global spread of a relatively uniform pandemic clone [57].\u003c/p\u003e\n\u003cp\u003eThe present analysis highlights pronounced regional heterogeneity in CTX prophage carriage and ctxB allelic distribution among \u003cem\u003eV. cholerae\u003c/em\u003e isolates across Africa, reinforcing the dynamic nature of cholera epidemiology on the continent. A central finding is the near-universal presence of intact CTX prophage elements in clinical isolates. The high co-carriage rates of ctxA and ctxB genes\u0026mdash;exceeding 91% across all regions and reaching complete prevalence in North Africa\u0026mdash;underscore the essential role of the CTX\u0026Phi; prophage in pathogenicity. This observation aligns with established evidence that cholera toxin production, encoded by these genes, remains the primary virulence determinant driving epidemic disease [51,58]. The particularly high prevalence in Central and North Africa suggests strong selective pressure favoring toxigenic strains [59].\u003c/p\u003e\n\u003cp\u003eThe geographic partitioning of ctxB allelic variants provides further insight into cholera evolution and transmission dynamics. The dominance of ctxB variant 1 (classical biotype) in Central Africa contrasts sharply with the predominance of variant 7 (El Tor biotype) in West, East, and Southern Africa. This pattern is consistent with the global replacement of classical strains by El Tor variants, particularly the emergence of altered El Tor strains carrying classical toxin genes [60]. However, the persistence of classical ctxB alleles in Central Africa suggests either historical lineage retention or localized evolutionary trajectories.\u003c/p\u003e\n\u003cp\u003eThe differential distribution of CTX prophage carriage and ctxB variants across Africa underscores the need for regionally tailored cholera control measures. Continued genomic surveillance will be essential to monitor lineage dynamics, detect emerging variants, and inform targeted interventions.\u003c/p\u003e\n\u003cp\u003eThe high prevalence of intact CTX prophage in African clinical isolates mirrors observations from cholera-endemic regions in Asia, such as Bangladesh and India, and Russia, where toxigenic \u003cem\u003eV. cholerae\u003c/em\u003e strains consistently dominate clinical cases [61\u0026ndash;63]. In Asia, the epicenter of cholera evolution, a transition from classical to El Tor and subsequently to altered El Tor strains has been well documented [60]. In the Americas, cholera outbreaks have largely been attributed to imported El Tor strains, predominantly carrying ctxB7 [56]. The geographic structuring of ctxB alleles observed in Africa reflects broader global evolutionary trends [51,55,64,65].\u003c/p\u003e\n\u003cp\u003eThe major finding of this study is the extensive and deeply entrenched antimicrobial resistance across African \u003cem\u003eV. cholerae\u003c/em\u003e populations. The genomic analysis of 3,076 \u003cem\u003eV. cholerae\u003c/em\u003e isolates from Africa reveals an alarming antimicrobial resistance (AMR) landscape, characterized by both high gene diversity and widespread chromosomal resistance. With 101 distinct AMR genes across 9 categories and an average of 10.81 genes per isolate, the dataset reflects one of the most heavily burdened regional resistomes reported to date. The finding that 94.1% of isolates are multidrug-resistant (MDR) underscores a critical public health challenge for cholera management across the African continent. The near-universal presence of chromosomal fluoroquinolone resistance mutations, combined with widespread carriage of SXT/R391 integrative conjugative elements, indicates that multidrug resistance is now a defining feature of circulating epidemic strains. This has serious implications for treatment, particularly given the reliance on ciprofloxacin and azithromycin for severe cholera cases [66]. Fluoroquinolones, particularly ciprofloxacin, are recommended by the World Health Organization as first-line treatment for severe cholera [67]. The widespread baseline resistance observed here suggests that standard treatment protocols may be increasingly ineffective in African settings, raising concerns about treatment failure and prolonged transmission. Asia\u0026mdash;particularly regions like Bangladesh and India\u0026mdash;has historically been a hotspot for cholera and AMR [50,54]. The extensive AMR burden observed in African \u003cem\u003eV. cholerae\u003c/em\u003e isolates signals a critical turning point in cholera management. Unlike other regions where resistance is still evolving, Africa shows a deeply entrenched resistance landscape, driven by both chromosomal mutations and mobile genetic elements. Addressing this challenge will require coordinated global action, integrating antimicrobial stewardship, genomic surveillance, and strengthened public health infrastructure.\u003c/p\u003e\n\u003cp\u003eThe dataset used in this study was derived from publicly available genomic sequences, which may be subject to sampling bias, with overrepresentation of certain countries and outbreak periods. There is also limited environmental sampling that restricts the ability to fully assess environmental reservoirs and transmission pathways. Moreover, phenotypic antimicrobial susceptibility testing was not performed, and resistance was inferred solely from genomic data. Finally, incomplete metadata for some isolates, including missing collection dates and clinical details, may limit the precision of temporal and epidemiological inferences.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that cholera transmission across Africa is driven by the clonal expansion of a dominant multidrug-resistant \u003cem\u003eV. cholerae\u003c/em\u003e lineage with sustained cross-border spread. The high prevalence of antimicrobial resistance and conserved virulence determinants underscores the growing challenge of managing cholera outbreaks in the region. Strengthening genomic surveillance and integrating real-time sequencing into public health systems will be critical for tracking transmission, guiding treatment strategies, and achieving the WHO target of cholera elimination by 2030.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific external funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article and its supplementary files. Genome sequence data of \u003cem\u003eVibrio cholerae\u003c/em\u003e isolates are available in NCBI Sequence Read Archive and NCBI GenBank. All accession numbers are listed in supplementary Table S1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of publicly available genomic datasets and computational resources that supported this work.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study involve analysis of publicly available datasets. No patient specimens were used and patient‐protected health information was not collected. Therefore, informed consent was not required.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Consent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;AO and YM contributed equally to this work. AO: Conceptualization, Methodology, Formal analysis, Data curation, Writing - Original draft preparation. YM: Conceptualization, Methodology, Formal analysis, Data curation, Writing - Original draft preparation. AY: Software, Validation, Visualization, Writing - Review \u0026amp; editing. AJ: Software, Validation, Investigation, Writing - Review \u0026amp; editing. RFA: Investigation, Resources, Writing - Review \u0026amp; editing. BOO: Supervision, Project administration, Conceptualization, Writing - Review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChowdhury F, Ross AG, Islam MT, McMillan NAJ, Qadri F. Diagnosis, management, and future control of cholera. Clin Microbiol Rev 2022; 35: e00211-21.\u003c/li\u003e\n\u003cli\u003eAli M, Nelson AR, Lopez AL, Sack DA. Updated global burden of cholera in endemic countries. PLoS Negl Trop Dis 2015; 9: e0003832.\u003c/li\u003e\n\u003cli\u003eKapaya F, et al. An assessment of the progress made in the implementation of the regional framework for cholera prevention and control in the WHO African region. BMJ Glob Health 2025; 10. doi:10.1136/bmjgh-2024-017xxx.\u003c/li\u003e\n\u003cli\u003eEnserink M, Ochoa TJ, Pham NTK, et al. Global patterns of trends in cholera mortality. 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UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol 2018; 35: 518\u0026ndash;22.\u003c/li\u003e\n\u003cli\u003eXu S, et al. ggtreeExtra: compact visualization of richly annotated phylogenetic data. Mol Biol Evol 2021; 38: 4039\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eYu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 2017; 8: 28\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eSiriphap A, et al. Characterization and genetic variation of \u003cem\u003eV. cholerae\u003c/em\u003e isolated from clinical and environmental sources in Thailand. PLoS One 2017; 12: e0169324.\u003c/li\u003e\n\u003cli\u003eFeldgarden M, et al. Validating the AMRFinder tool and resistance gene database by using antimicrobial resistance genotype-phenotype correlations in a collection of isolates. 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Microorganisms 2024; 12: 1935.\u003c/li\u003e\n\u003cli\u003eFoster-Nyarko E, et al. Genomic diversity and antimicrobial resistance of \u003cem\u003eV. cholerae\u003c/em\u003e isolates from Africa: a PulseNet Africa initiative using nanopore sequencing to enhance genomic surveillance. Microb Genomics 2025; 11: 001586.\u003c/li\u003e\n\u003cli\u003eSmirnova NI, et al. Genomic diversity of toxigenic strains of \u003cem\u003eV. cholerae\u003c/em\u003e O1 biovar El Tor isolated during three waves of the 7th cholera pandemic. Dokl Biochem Biophys 2025; 525: 593\u0026ndash;604.\u003c/li\u003e\n\u003cli\u003eIslam MT, et al. \u003cem\u003eV. cholerae\u003c/em\u003e O47 associated with a cholera-like diarrheal outbreak concurrent with seasonal cholera in Bangladesh. mSphere 2025; 10: e00831-24.\u003c/li\u003e\n\u003cli\u003eHavelikar U, et al. Recent epidemiological and management approaches for cholera in India. Proc (Bayl Univ Med Cent) 2026; published online ahead of print. doi:10.1080/08998280.2026.xxxxxxx.\u003c/li\u003e\n\u003cli\u003eSmirnova NI, et al. New genetic variants of the cholera agent and their distribution in endemic countries and Russia. Mol Genet Microbiol Virol 2023; 38: 8\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eRouard C, et al. Genomic analysis of \u003cem\u003eV. cholerae\u003c/em\u003e O1 isolates from cholera cases, Europe, 2022. Euro Surveill 2024; 29: 2400069.\u003c/li\u003e\n\u003cli\u003eSmirnova NI, Rybal\u0026apos;chenko DA, Lozovsky YuV, Kutyrev VV. Genome instability of the cholera agent: role in the evolution of pathogenicity, drug resistance, and adaptation to a changing environment. Mol Genet Microbiol Virol 2025; 40: 91\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eChowdhury F, Ross AG, Islam MT, McMillan NAJ, Qadri F. Diagnosis, management, and future control of cholera. Clin Microbiol Rev 2022; 35: e00211-21.\u003c/li\u003e\n\u003cli\u003eGlobal Task Force on Cholera Control. Technical note on the use of antibiotics for the treatment of cholera. Geneva: World Health Organization, 2018.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vibrio cholera, Genomic Epidemiology, Antimicrobial resistance, Cholera, Africa","lastPublishedDoi":"10.21203/rs.3.rs-9358362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9358362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground Cholera remains a major public health threat across Africa, driven by complex interactions between environmental, socioeconomic, and microbial factors. However, the continental genomic epidemiology of Vibrio cholerae remains incompletely characterised. We aimed to conduct a comprehensive continental genomic analysis of V. cholerae to determine its spatiotemporal dynamics, virulence, and antimicrobial resistance (AMR) profiles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods We conducted a large-scale genomic analysis of 3,076 V. cholerae isolates collected from 29 African countries between 1949 and 2025. Genomic data were retrieved from publicly available databases and subjected to standardized quality control, assembly, and annotation pipelines. Phylogenomic reconstruction was performed using core genome single-nucleotide polymorphism (SNP) analysis with recombination filtering. We conducted multilocus sequence typing (MLST), virulence profiling, AMR gene detection, and mobile genetic element characterization using established bioinformatics tools to assess associations between lineage distribution, geographic regions, temporal trends, and resistance patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults The population is highly clonal and dominated by sequence type 69 (ST69) (2,682 [87.2%] of 3,076 genomes). ST69 almost exclusively drove the 2022–2024 continent-wide outbreaks, representing 98.9% (463/468) of genomes in 2022, 99.3% (605/609) in 2023, and 97.3% (252/259) in 2024. Toxigenic O1 El Tor markers and ctxA/ctxB co-carriage exceeded 91% in clinical genomes but were significantly attenuated in environmental strains. We identified 101 distinct AMR genes, with 94.1% of isolates classified as multidrug-resistant. Ubiquitous chromosomal mutations, primarily gyrA_S83I (92.88%) and parC_S85L (84.82%), drove universal fluoroquinolone resistance. An SXT conjugative element conferring multidrug resistance was present in roughly 64% of the population. Clinical isolates exclusively harboured high-risk resistance determinants absent environmentally, including macrolide resistance genes (6.6%–6.8%) and extended-spectrum beta-lactamases such as blaPER-7 (6.4%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion Cholera transmission across Africa is persistently driven by the clonal expansion of the ST69 lineage. Universal fluoroquinolone resistance and clinically restricted emergence of macrolide and beta-lactamase resistance highlight a critical therapeutic challenge. Sustained genomic surveillance is essential to monitor this multidrug-resistant clone and inform regional outbreak control.\u003c/p\u003e","manuscriptTitle":"Genomic Epidemiology of 3,076 Vibrio cholerae Isolates Reveals ST69 Clonal Expansion and Multidrug Resistance across Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:30:17","doi":"10.21203/rs.3.rs-9358362/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"106616480120969059053322095284161016484","date":"2026-05-05T08:23:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176602809105239916288657291679074857574","date":"2026-05-04T12:47:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216028686103751011181108352873365098346","date":"2026-05-04T12:18:56+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-01T00:34:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T05:18:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T10:16:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genome Medicine","date":"2026-04-08T14:22:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genome-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Genome Medicine](https://genomemedicine.biomedcentral.com/)","snPcode":"13073","submissionUrl":"https://submission.springernature.com/new-submission/13073/3","title":"Genome Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f8680fc-9e3d-4d09-b29d-65fa340deb16","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"106616480120969059053322095284161016484","date":"2026-05-05T08:23:25+00:00","index":15,"fulltext":""},{"type":"reviewerAgreed","content":"176602809105239916288657291679074857574","date":"2026-05-04T12:47:05+00:00","index":14,"fulltext":""},{"type":"reviewerAgreed","content":"216028686103751011181108352873365098346","date":"2026-05-04T12:18:56+00:00","index":13,"fulltext":""},{"type":"reviewersInvited","content":"5","date":"2026-05-01T00:34:07+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:30:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:30:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9358362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9358362","identity":"rs-9358362","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}