https://orcid.org/0000-0001-5045-3065
Figures
Abstract
Seventh pandemic Vibrio cholerae was first identified in Cameroon in 1971, causing several sporadic disease clusters with few cases. More recent years have seen larger cholera outbreaks, but the mechanism behind these periodic outbreaks is poorly understood, and it is unclear the degree to which antibiotic resistant strains contribute to disease burden and spread. We used whole genome sequencing to characterize 13 V. cholerae isolates from the 2018–2019, 2020, and 2021–2023 cholera epidemics in Cameroon. All these isolates belonged to the T12 lineage, and most showed the same antimicrobial resistance (AMR) pattern regardless of year. This suggests that cholera outbreaks in Cameroon are, at least in part, a continuation of the outbreaks previously reported in 2018 and as far back as 2012. This finding has important implications for cholera management since it suggests the ongoing presence of pathogenic cholera even in years with few reported cases. Similarly, the AMR results suggest the need for new treatment approaches, as resistance to many common antibiotics was found even within our limited sample set. As such, whole genome sequencing should be implemented in low-income countries such as Cameroon to improve disease surveillance and to detect and predict pathogen antibiotic resistance profiles.
Citation: Ngomtcho SCH, Akenji BM, Ndip R, Azman AS, Tayimetha YC, Guenou E, et al. (2025) Continued T12 transmission and shared antibiotic resistance during 2018–2023 Vibrio cholerae outbreaks in Cameroon. PLOS Glob Public Health 5(2): e0003763. https://doi.org/10.1371/journal.pgph.0003763
Editor: Hui-min Neoh, Universiti Kebangsaan Malaysia, MALAYSIA
Received: August 21, 2024; Accepted: December 26, 2024; Published: February 24, 2025
Copyright: © 2025 Ngomtcho et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Raw data for all sequenced isolates is available under NCBI BioProject accession: PRJNA954566. Source data files have been provided for all figures as S1 to S5 Data. Laboratory protocol and bioinformatics pipeline used for analysis are publicly available at https://github.com/HopkinsIDD/minion-vc.
Funding: This work was supported by the Bill and Melinda Gates Foundation (OPP1195157 to JL; INV-025321 to SW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Since the first documented cholera pandemic in 1817, seven Vibrio cholerae pandemics have been recorded [1]. The seventh pandemic originated in Southeast Asia in 1961 and is still ongoing. It reached the African continent in 1970, first affecting West African countries (Guinea-Bissau and Guinea Conakry), and then reaching Central Africa, and specifically Cameroon, in 1971 [2,3]. Despite having been cholera-free before 1970, Africa now bears the major burden of the global cholera cases, with over 4 million cholera cases and 143,000 deaths reported in Africa per year [2].
Cameroon reported its first cholera cases in February 1971. This was followed by a 20-year period characterized by sporadic disease clusters with few cases. Following this period, larger outbreaks were reported in 1991, 1996, 1998, 2004, 2010, 2011, and over 46,000 suspected cholera cases and 1,800 deaths were reported between 2004 and 2013 [4]. Minimal cases were reported in intervening years, and the mechanism behind these periodic cholera outbreaks remains poorly understood. Specifically, it is unclear if successive outbreaks are caused by sustained circulation of existing strains, or if each is caused by a new introduction.
More recent cholera outbreaks in Cameroon have followed a similar trend, despite increased efforts to improve surveillance nationwide. On May 18, 2018, two suspected cases of cholera were reported in the North Region of Cameroon and ultimately confirmed as Vibrio cholerae by bacterial culture. Cases associated with this outbreak spread into the Far North Region and continued until December 2019, resulting in a total of 1212 suspected cases and a 5.1% case fatality rate (CFR) in the North Region and 350 cases (CFR: 4.6%) in the Far-North [5].
After a brief period of few cases, the Ministry of Health in Cameroon declared another outbreak following laboratory confirmation of Vibrio cholerae in late January 2020. 18 health districts were affected in four neighboring administrative regions (South-West, Littoral, South and Center) and the outbreak lasted throughout the final weeks of 2020, causing 1892 cases (CFR: 4.2%) [6]. After several months of minimal cases (<200 in total), a new outbreak was declared in Cameroon on October 29, 2021. As of October 2023, over 21,000 suspected cases have been reported (CFR: 2.4%) (S1 Data) [7]. This 2023 epidemic has spread to the six main regions of the country, with the central Littoral Region becoming the epicenter of the epidemic. Understanding how each of these outbreaks spread and the relationship between them may inform approaches to cholera containment in Cameroon and have implications for outbreak management elsewhere.
Molecular characterization of seventh pandemic V. cholerae may be one important tool for understanding cholera outbreak emergence and spread. Already, genomic data has led to new insights about global cholera transmission and has highlighted the important role of transmission within and between Asia and Africa [8,9]. Combining genomic and epidemiological data has allowed scientists to better understand the dynamics of ongoing outbreaks, and advances in sequencing technology have recently made whole genome sequencing more feasible even in low-resource settings. Previous studies initially identified three waves of seventh pandemic V. cholerae [10] that were later broken down into at least 14 plausible introduction events from Asia into Africa, termed T1–T14 [8,9,11]. These lineages can be used to identify the approximate source of case clusters; for example, cases caused by the same lineage as a previous outbreak might suggest undetected sustained transmission, while identification of a new lineage could suggest that new cases were caused by a new international introduction.
Even so, identification of V. cholerae strains responsible for cholera in Cameroon epidemics has been largely limited to bacteriological culture, without more advanced molecular analyses like whole genome sequencing [12]. To better understand the cholera dynamics in Cameroon and Sub-Saharan Africa as a whole, we characterized V. cholerae O1 isolates from the 2018-2019, 2020, and 2021–2023 (still ongoing) epidemics, with the goal of identifying and comparing the V. cholerae lineages in circulation in Cameroon and ultimately using this information to better understand cholera transmission dynamics in order to prioritize public health action. As antimicrobial resistance is becoming an increasingly serious threat to public health [13], we also determined the antimicrobial resistance profile of these samples, in the hopes of providing information that can be used to limit the spread of pathogenic drug-resistant V. cholerae.
Results
V. cholerae sequencing in low-resource settings
To better understand V. cholerae transmission patterns in Cameroon, we sequenced 55 isolates from epidemiological and laboratory-confirmed cases from the 2018–2019, 2020 and 2021–2023 epidemics (Table 1). We selected these isolates to ensure representation in as many regions of Cameroon as possible (samples were sequenced from eight of the ten regions of Cameroon, see Fig 1). We sequenced these isolates at the National Public Health Laboratory in Cameroon using the Oxford Nanopore platform and generated whole genome sequences (>85% reference genome coverage with >100 median depth) from 13 of the 55 isolates (24% success rate) (S2 Data). Although we successfully obtained sequences from six included regions, none of the isolates from the 2018-2019 epidemic achieved our inclusion threshold and were therefore excluded from phylogenetic analysis. We observed higher sequencing success rate from samples from the most recent epidemics, perhaps due to contamination issues associated with poor long-term storage conditions of older samples.
Blue: high quality genomes included in downstream analyses; white: sequenced isolates that did not produce high quality genomes (see also S2 Data). The number of cholera cases reported between 18 May 2018 – 17 October 2023 are shown in shades of red (S1 Data). Map created with R raster package. Shapefile source: https://gadm.org/maps/CMR.html.
To more specifically understand sequenced isolates that did not generate a high-quality V. cholerae O1 genome, we looked at samples that generated a large number of reads that did not map to the N16961 V. cholerae O1 reference sequence. We identified 24 samples that generated at least 39,000 high-quality reads (the minimum number of filtered reads used to assemble a high-quality V. cholerae O1 genome in samples sequenced as part of this study) that were ultimately excluded from our analysis. We performed a de novo assembly and taxon identification on these samples and grouped them into three categories based on the results: samples containing O1 V. cholerae and no other organisms (n = 4), samples containing V. cholerae (n = 5) and other organisms, and samples without detected V. cholerae (n = 16) (S3 Data). These results suggest that, although these isolates likely represented cholera cases at the time of diagnostic testing and laboratory confirmation, sample storage or laboratory processes may be facilitating the decline of viable V. cholerae and growth of other organisms. That said, while many of the other organisms detected are common gut bacteria (e.g., Morganella morganii), several are clinically significant, suggesting possible co-infections that should be investigated more closely.
Continuous circulation of the T12 lineage
We generated a maximum likelihood tree using the 13 successful sequences to better understand how V. cholerae O1 was circulating in Cameroon over time and space. Combining these sequences with previously published global V. cholerae genomes demonstrated that they belonged to the T12 lineage [9], which was shown to be circulating in Cameroon as recently as 2018 [12] and is the only lineage to have been observed in Cameroon since 2009 (S1 Fig, see also S4 Data. Sequences from both 2020 and 2022 are found in this lineage, with no evidence for sequences belonging to other lineages (Fig 2).
Sequences generated in this study form two distinct T12 sub-clusters and are shown in bright blue. Previously generated sequences from Cameroon are shown in white, and other previously generated sequences from the T12 lineage are colored by the country in which the isolate was collected. Groups of newly generated sequences are labeled by the regions in Cameroon the corresponding isolates were collected in, and sequences with some degree of resistance to doxycycline (see S5 Data) are indicated by a red asterisk. Scale bar: nucleotide substitutions per site. Full V. cholerae O1 tree including other lineages: S1 Fig.
Combined with the results presented in Ekeng et al., our observation of T12 sequences in both the 2020 and 2021 outbreaks provides strong evidence for continuous circulation of this lineage within Cameroon. From our data, it is clear that the T12 lineage is even more widespread than previously thought, as we observed this lineage in three new epidemiological areas: the North West, West, and South Regions (previously published sequences from the 2018–2019 outbreak were exclusively from the North, Littoral and Center Regions of the country) (Fig 2). The new sequences generated in this study form two distinct Cameroon-specific sub-clusters that both contain 2020 and 2022 sequences—as well as sequences from multiple regions of the country—suggesting circulation and evolution within Cameroon during the 2020 and 2021 outbreaks. These sub-clusters appear to be descendants of the West Africa T12 sub-cluster from 2018, supporting prior evidence for transmission between the neighboring countries of Nigeria, Niger, and Cameroon.
Year-to-year consistency in antibiotic resistance screening
We also explored the results of antimicrobial resistance (AMR) testing to better understand the relationship between isolates. We performed AMR testing on 54 isolates (including 8 of the 13 that produced V. cholerae O1 whole genome sequences) using disk-diffusion methods (S5 Data). We found that resistance to several of the antibiotics tested was mixed, and that these results did not cluster by time or place of sample collection. This finding highlights why doxycycline, azithromycin, and ciprofloxacin are still some of the primary antibiotics (with doxycycline used routinely as the first-line antibiotic) used to combat cholera disease in Cameroon, though continued monitoring for resistance will be important given their widespread use—especially given that some resistance has already been observed.
We also attempted to validate these AMR results using genotypic information. First, we found that phenotypic doxycycline resistance occurs in only one of the two T12 subclusters on the phylogenetic tree (Fig 2). This observation should be validated with a larger sample size (especially since it is the only tested antibiotic that shows such clustering), but a close relationship between whole genome sequence and AMR resistance could suggest local spread of resistance during an outbreak, further supporting a need for regular AMR monitoring. When we looked more closely at the AMR-associated genes in our samples, we found that most of the isolates that ultimately generated high quality genomes contained the following AMR genes on the V. cholerae core genome: varG, almE, almF, almG, catB9, and dfrA1, which have been associated with resistance to beta-lactam, colistin, phenicol, and trimethoprim antibiotic classes, respectively (Fig 3).
Blue = gene presence; gray = gene absence. Isolates to the left of the dotted line also underwent antibiotic resistance testing in the laboratory, and the results of these assays are presented in S5 Data. The top six genes shown were also observed in Cameroonian samples from a similar timeframe [14].
These results are consistent with previous studies [14], with missing genes likely due to genome coverage—though the possibility of gene loss should be investigated more thoroughly. In some samples, we also observed parC_S85L and gyrA_S83I, which are both known to confer resistance to nalidixic acid [15]. This is consistent with the phenotypic AMR testing and recent findings [16,17] (S5 Data), though other comparisons of genotype and phenotype will require additional follow-up, ideally with larger sample sizes. We also noted the presence of plasmids in two samples (NPHL-VcNS16 and NPHL-VcNS17); in the case of NPHL-VcNS16, presence of this plasmid was associated with the presence of additional AMR-related genes (ant(6)-la, arm(B), sat(4), tet(M)). Further testing will be required to determine if these plasmids are part of the V. cholerae accessory genome, or—given the results detailed in S3 Data—if these plasmids are a result of contamination with other bacterial organisms. Of note, none of our sequenced isolates contained the IncA/C plasmid previously associated with AMR in V. cholerae.
Discussion
Our finding of the T12 lineage in both 2020 and 2022 in Cameroon supports previous evidence suggesting continuous circulation of the lineage in the country (or at least in the region) since it was first observed in 2009. This suggests that cholera containment efforts should, at least in part, focus on reducing community transmission, either by reducing transmission routes between linked regions, or focusing on prevention measures such as drinking and using clean water, washing hands with soap more often, and using adequate sanitation in regions with high case numbers. We also show that there were several different sub-clusters of the T12 lineage circulating during this period, suggesting distinct transmission patterns that can be tracked and monitored using whole genome sequencing.
One challenge to cholera containment may be the fact that V. cholerae strains circulating in Africa have reportedly been expanding the number of antibiotic classes to which they confer resistance [8]. This is cause for concern, as these antibiotic drugs have long been used for treating severe cholera cases. Our results show the presence of at least some resistant isolates in all drugs tested, highlighting the need for continued monitoring of antimicrobial resistance, even when cases seem to be derived from the same persistent lineage.
Despite the clear implications of our genomic findings for containment of the T12 lineage, genomic surveillance in low-resource public health settings continues to be challenging, in part because samples are not always collected and preserved with sequencing in mind. For example, we found that less than 30% of V. cholerae isolates sequenced in this study produced a high-quality genome. De novo assembly showed that most failed sequences contained bacterial species other than V. cholerae, which can result from laboratory processes that decrease the viability of V. cholerae and allow other organisms to grow. This could also stem from inappropriate transport conditions from the field to NPHL, or lack of resources (i.e., transport media, technology support) needed for storage, transport, or sequencing. Notably, almost all samples from 2023 failed because of lack of sequencing platform support. This highlights some of the challenges of conducting genomics-based surveillance in low-resource settings, and we hope that presenting these data can inform sequencing efforts in the future, as well as investment in surveillance systems that further develop local capacity and decrease the need for long-term storage between sequencing efforts.
Since the introduction of V. cholerae into Africa from Asia in 1961, a limited number of isolates from central and western sub-region of Africa have been sequenced, with fewer than 100 (as of time of writing) sequences available from Cameroon, most of which were sequenced abroad. In the present study, we greatly increase the fraction of sequences generated in-country, which may ultimately decrease the time between sample collection and generation of actionable genomic results. This highlights the necessity for public health authorities in low-income countries to further develop genomic surveillance strategies of pathogens with epidemic potential such as cholera.
Materials and methods
Ethics statement
Samples used in this study were collected as part of ongoing outbreak investigations for the monitoring of cholera in Cameroon. Only leftover clinical diagnostic samples (already stored as bacterial isolates) were used for sequencing. No identifying patient information was included in this study, and only bacterial isolates were sequenced (i.e., no chance of sequencing human data). Ethical approval was granted by the University of Buea (Cameroon) Faculty of Health Sciences Institutional Review Board (2023/1974-02/UB/SG/IRB/FHS).
Sample selection, collection, and cholera confirmation
In this study, we selected randomly from all V. cholerae isolates stored at NPHL from the 2018-2023 outbreaks (see Table 1). We excluded samples for which we did not obtain V. cholerae growth after replating the isolate on TCBS and Muller Hinton media.
These isolates were originally collected and cultured as follows: isolates were generated from fresh (within 24–48 hours of symptom onset) stool samples from suspected cholera cases during the three most recent cholera outbreaks in Cameroon (starting in 2018, 2020, and 2021). These samples were initially collected in sterile screw-capped stool recipients by laboratory technicians and trained community healthcare workers in communities and health facilities in districts that notified suspected cases. Specifically, two sterile cotton swabs were immersed into the stool and aseptically inserted into a screw tube containing 10 ml Cary-Blair transport medium. For children who could not produce stools on demand, samples came from rectal swabs collected using moist cotton swabs and dipped into Cary-Blair transport media. Samples were placed in a triple package insulated box and transported to the National Public Health Laboratory (NPHL) in Cameroon within a maximum of 48 hours.
At NPHL, each stool sample collected during the outbreaks was first directly inoculated on thiosulfate citrate bile salt sucrose (TCBS) and incubated at 37°C for 24 hours. Immediately after culturing the first TCBS dish, sample was introduced into alkaline peptone water (APW) and incubated at 37°C for 8 hours. A second TCBS dish was then inoculated with the stool sample enriched on APW and incubated at 37°C. After 24 hours of incubation, characteristic V. cholerae colonies were selected and finally subcultured on Muller Hinton agar and incubated at 37°C for 24 hours. Colonies resulting from this culture on Muller Hinton medium were resuspended in 5ml of sterile saline solution to obtain a 0.5 McFarland inoculum and used for the phenotypic identification of V. cholerae based on biochemical characteristics and serotyping. Biochemical identification was done using API 20E biochemical galleries (BioMerieux, USA). Each well was inoculated using a sterile pipette and then incubated for 16–24 hours at 37°C and the reactions were noted as either positive or negative. Serotyping was done by agglutination tests using commercial V. cholerae Ogawa and Inaba antisera kits (Deben Diagnostics, India) according to the manufacturer instructions.
Antibiotic susceptibility testing
Antibiotic susceptibility testing was performed by disk-diffusion method [18] for the following antimicrobial drugs: amoxicillin (20 µg), amoxicillin/clavulanic acid (10–20µg), cephalotin (20 µg), cefotaxim (30 µg), chloramphenicol (30 µg), gentamicin (15 µg), nalidixic acid (20 µg), doxyciclin (30 µg), tetracycline (30 µg), polymixin B, azythromicin (15 µg) and ciprofloxacin (30 µg). The tested antibiotics were chosen based on the European Committee on Antimicrobial Susceptibility Testing (EUCAST, http://www.eucast.org), and susceptibility was determined by zones of inhibition for Enterobacteriaceae when established breakpoints for V. cholerae were not available [16] (indicated in S5 Data). These antibiotics and the agar swabbing inoculation technique described below underwent quality control using Escherichia coli ATCC 25922 reference strains before use for V. cholerae. In brief, after 5 min of incubation of V. cholerae inoculum on Mueller Hinton agar at room temperature, antibiotic discs were applied on the medium. Plates were incubated at 37°C overnight. Interpretation was done by measuring inhibition diameters around the disks using a caliper. Sensitivity, resistance, or the intermediate phenotypes of the strain to antibiotics was interpreted applying the appropriate EUCAST guidelines. Abricate software (www.github.com/tseemann/abricate) and the Comprehensive Antibiotic Resistance Database (CARD) (www.card.mcmaster.ca/analyze/rgi) online tool were used to identify genes and single-nucleotide polymorphisms (SNPs) associated with antibiotic resistance.
DNA extraction and quantification
DNA extraction was performed at NPHL on V. cholerae isolates from the 2018–2019 and 2020 outbreaks. Samples stored at −80°C in glycerol-brain heart infusion medium were allowed to thaw at room temperature, and then were re-plated on Mueller Hinton agar (MH) and TCBS. Samples from the 2021 outbreak were directly plated on TCBS culture medium. All plates were incubated at 37°C overnight. 2–4 colonies were then inoculated into 4ml of sterile BactoTMPeptone (alkaline peptone water) and incubated at 37°C overnight. A shaker was used during this incubation. Afterwards 1ml of cloudy culture media was used for DNA extraction using the Qiagen QIAmp DNA Mini Kit, with a final elution volume of 150 µL. The extracted DNA concentration was directly measured using a Quantus Fluorometer, following the instructions of the manufacturer.
Oxford nanopore library construction and sequencing
Library preparation and sequencing was performed at NPHL in Yaoundé, Cameroon. We used modified version of the Oxford Nanopore 1D Native barcoding genomic DNA (with EXP-NBD104, EXP-NBD114, and SQK-LSK109) as described in Ekeng et al. [12]. The extracted DNA was diluted to 1 µg in 49 µL with nuclease-free water and 48µL of this solution was used for library preparation. For samples with a DNA concentration below 20 ng/µL, 48µl of extracted DNA was used regardless of the final amount (<1µg). Sequences were prepared in 6 different batches (S2 Data).
After library preparation following the Oxford Nanopore protocol, we diluted the final library to a final volume of 12 µL in Elution Buffer and combined with 37.5 µL Sequencing Buffer and 25.5 µL Loading Beads. This mixture was loaded onto a primed R9.4.1 FLO-MIN106 Oxford Nanopore MinION flow cell. For each sequencing library, the MinION was run for 48 hours.
Reference-based genome assembly
As described in Ekeng et al. [12], sequencing runs were basecalled using Guppy version 4.0.4 with the flip-flop model (dna_r9.4.1_450bps_fast.cfg). Porechop (https://github.com/rrwick/Porechop) was used for demultiplexing and adapter removal, and we filtered out low quality sequencing reads using Filtlong (https://github.com/rrwick/Filtlong) with the following options: ‘filtlong --keep_percent 90 --target_bases 800000000’.
Using the resulting filtered FASTQ files, we performed reference-based assembly following the bioinformatics pipeline available here: https://github.com/HopkinsIDD/minion-vc. We used the N16961 strain (Genbank accession: AE003852/AE003853) as a reference for these seventh pandemic O1 sequences and required coverage of at least 100x to call a base at each site across the genome. Assemblies with less than 85% coverage of the reference genome and a median read depth (across the complete N16961 reference genome) below 100 were excluded from subsequent phylogenetic analysis.
De novo genome assembly
De novo assembly was performed on the demultiplexed, unfiltered FASTQ files described above using the TheiaProk ONT workflow publicly available on DockStore [19]. As part of this pipeline, we used gambit version 1.0.0 to identify taxa present in contigs, AMRFinderPlus v3.12.8 [20] with database version 2024-07-22.1 to identify genes associated with antibiotic resistance, and PlasmidFinder version 2.1.6 [21] to identify the presence of plasmids in our data.
Vibrio cholerae typing
Typing was performed by mapping reads from sequenced isolate to reference genes ctxA, tcpA_classical, tcpA_eltor, toxR, wbeO1, and wbfO139, with Genbank accession numbers AF463401, M33514.1, KP187623.1, KF498634.1, KC152957 and AB012956, respectively [22]. We mapped filtered reads to each reference using minimap2 version 2.17 [23] using scripts included as part of the publicly available bioinformatics pipeline noted above. Using samtools, we counted the number of reads mapping to each gene reference, the fraction of the gene covered by mapped reads, and the depth of coverage of each site across the reference gene of interest.
Maximum likelihood estimation
We concatenated reference-based assemblies generated during this study to 1383 previously published V. cholerae O1 whole genome sequences [8–10,12,24,25] also assembled to the N16961 reference. These background sequences were selected to represent most of the publicly available whole genome sequences (S4 Data). We masked recombinant sites in these sequences as previously described, first with a selection of fixed masked sites from Weill et al. 2017 [8] (available at: https://figshare.com/s/d6c1c6f02eac0c9c871e) and then using Gubbins version 3.2.1 [26]. To generate maximum likelihood trees, we ran IQ-TREE version 1.4.4 [27] using the ModelFinder [28] option, which identified TVM+F+ASC+G4 as the best nucleotide substitution model, and 1000 ultrafast bootstrap replicates [29]. The final tree was rooted on the taxa labeled 5174_7_5 (accession: ERR025382), as described in Weill et al. 2019 [9] and Ekeng et al. 2021 [12]. We visualized the resulting tree in FigTree version 1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/).
Supporting information
S1 Data. Reported cases in Cameroon by region.
https://doi.org/10.1371/journal.pgph.0003763.s001
(XLSX)
S2 Data. Sample metadata and sequencing metrics.
https://doi.org/10.1371/journal.pgph.0003763.s002
(XLSX)
S3 Data. Taxon identification from de novo assembly results.
https://doi.org/10.1371/journal.pgph.0003763.s003
(XLSX)
S4 Data. Background sequences included in phylogenetic analysis.
https://doi.org/10.1371/journal.pgph.0003763.s004
(XLSX)
S5 Data. Antimicrobial resistance testing results.
https://doi.org/10.1371/journal.pgph.0003763.s005
(XLSX)
S1 Fig. V. cholerae O1 maximum likelihood tree.
Maximum likelihood reconstruction of 13 newly generated sequences from Cameroon plus 1383 previously published background sequences. Tips are colored by introduction event and newly generated sequences are indicated in blue.
https://doi.org/10.1371/journal.pgph.0003763.s006
(TIF)
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