Whole genome sequencing unravels cryptic circulation of divergent dengue virus lineages in the rainforest region of Nigeria

ABSTRACT

Dengue is often misclassified and underreported in Africa due to inaccurate differential diagnoses of nonspecific febrile illnesses such as malaria, sparsity of diagnostic testing and poor clinical and genomic surveillance. There are limited reports on the seroprevalence and genetic diversity of dengue virus (DENV) in humans and vectors in Nigeria. In this study, we investigated the epidemiology and genetic diversity of dengue in the rainforest region of Nigeria. We screened 515 febrile patients who tested negative for malaria and typhoid fever in three hospitals in Oyo and Ekiti States in southern Nigeria with a combination of anti-dengue IgG/IgM/NS1 rapid test kits and metagenomic sequencing. We found that approximately 28% of screened patients had previous DENV exposure, with the highest prevalence in persons over sixty. Approximately 8% of the patients showed evidence of recent or current infection, and 2.7% had acute infection. Following sequencing of sixty samples, we assembled twenty DENV-1 genomes (3 complete and 17 partial). We found that all assembled genomes belonged to DENV-1 genotype III. Our phylogenetic analyses showed evidence of prolonged cryptic circulation of divergent DENV lineages in Oyo state. We were unable to resolve the source of DENV in Nigeria owing to limited sequencing data from the region. However, our sequences clustered closely with sequences in Tanzania and sequences reported in Chinese with travel history to Tanzania in 2019. This may reflect the wider unsampled bidirectional transmission of DENV-1 in Africa, which strongly emphasizes the importance of genomic surveillance in monitoring ongoing DENV transmission in Africa.

KEYWORDS:

• Dengue
• IgG
• IgM
• NS1
• genomic sequencing
• NGS
• rain forest region
• Nigeria

Introduction

Dengue virus (DENV) is synchronously expanding alongside Aedes species worldwide due to climate change [1]. DENV is a member of the Flaviviridae family in the Flavivirus genus and has four antigenically distinct serotypes designated DENV 1-4 [2]. Dengue is a significant public health challenge in tropical and subtropical regions as four billion people, approximately fifty percent of the world's population, reside in areas at risk for dengue [3]. Approximately 400 million people worldwide contract dengue each year, of which one in four cases develop symptomatic disease and one in twenty progress to severe disease [4].

Dengue symptoms are often nonspecific and indistinguishable from other causes of febrile illnesses, such as malaria and typhoid fever, making diagnosis challenging [5,6]. The burden of dengue across Africa and Nigeria, in particular, is underestimated as routine screening and surveillance are not performed. Nigeria has six ecological zones: the Sahel, Sudan, Guinea Savannah, forest mosaic, rain forest, and mangrove zones. They represent a Sahelian hot and semi-arid climate in the North, a tropical savanna climate for most of the central regions, and a tropical monsoon climate in the south. As a result, there are lower precipitation levels in the North relative to the South [7,8]. Different mosquito species predominate across these regions depending on microclimatic factors, human activities such as deforestation, urbanization, agricultural practices, irrigation, and sanitary conditions [9]. Select studies have reported serological evidence of DENV infection in humans in Nigeria. Previous studies have reported seroprevalences of 17.2% and 51.6% in the cross-section of the general population in the Guinea Savannah (GS) in 2014 and 2016, respectively [10,11] and 30.8% in the pediatric population of the GS [12]. Only a few studies have investigated DENV seroprevalence in Nigeria's rainforest region. Onoja et al. reported a seroprevalence of 23.4% in a cross-section of the general population in Oyo State [13], with a seroprevalence of 77% reported in children under five years old in the forested region of Southeastern Nigeria [14].

The vector capacity of Aedes species is poorly understood in Nigeria [15]. However, DENV has been detected in Aedes species in the rainforest region [16] and Sudan Savanna of northern Nigeria [17]. The rainforest region of Nigeria is an ecological zone with a high risk for DENV infection owing to several known drivers of Aedes breeding and DENV transmission: The region experiences heavy rainfall during its extended rainy season (>6 months), and the dry season is marked by water shortages which result in households storing water in open containers with limited sanitation infrastructure in the region largely [17].

Although the first identification of DENV-1 and -2 serotypes in Africa was in Nigeria in 1964 [18], genomic data on current and historic circulating serotypes on the continent is sparse. Dengue has been reported in 34 of the 54 African countries, with DENV-1 and DENV-2 predominating [19]. Recently, Onoja et al. reported a shift in the ecotypes of DENV-2 serotypes in Nigeria [20], reinforcing the need to understand the genomic epidemiology of DENV in the country.

In this study, we investigated the seroepidemiology of previous and current DENV infections among non-malarial/typhoid febrile patients in Nigeria's rainforest region to understand better the true burden of dengue in the high-risk region. Additionally, we characterized the genomic epidemiology of DENV in Nigeria by generating the first full-length DENV-1 genomes from Nigeria.

Materials and methods

Study area, ethics statement and study population

We enrolled febrile participants from tertiary hospitals in Oyo State (Adeoyo Hospital Yemetu and Oni Memorial Children Hospital) and Ekiti State [University Teaching Hospital Ado-Ekiti (EKSUTH)] following ethical approval obtained from the University of Ibadan/University College Hospital Research Ethics Committee (UI/EC/13/0412). We also obtained informed consent from each participant and parental assent for minors.

Samples were collected from the end of 2020 to September 2021. All participants tested negative for malaria parasites by microscopy blood film test and typhoid fever by the Widal test. The inclusion criteria for participation were patients with a fever ≥38°C of unknown etiology within the last seven days with at least one of the following symptoms: headache, joint pain, backache, abdominal pain, vomiting, fatigue, anorexia or diarrhea. Patients with confirmed infectious diseases such as HIV, HBV and those with fever lasting more than 7 days were excluded from the study. Demographic data were obtained by questionnaire.

Serological assay for IgG, IgM and NS1 antigen

We collected two millilitres of whole blood via venipuncture from each participant in an EDTA tube and spun for five minutes at 3000 rpm to obtain plasma stored at −20°C. Following the manufacturer's instructions, we detected anti-dengue IgG, IgM and NS1 antigens from plasma using the Neo Nostics® Combo kit (China).

RNA extraction and metagenomic sequencing

We extracted viral RNA from the representative plasma samples positive by the Anti-dengue IgG, IgM and NS1 antigen kit using a QIAamp® Viral RNA extraction kit (Qiagen®, Hilden, Germany) with an elution volume of 60 μL according to the manufacturer's instructions. As previously described, we constructed sequencing libraries using an unbiased next-generation RNA sequencing method [21,22].

In brief, we subjected viral RNA to turbo DNAse treatment to remove contaminating DNA. We synthesized cDNA using random primers and used the Illumina Nextera XT kit for making sequencing libraries. We performed paired-end sequencing using the NextSeq 1000/2000 P1 Reagents (300 Cycles) on the Illumina Nextseq 2000 platform. We filtered the raw reads for quality and removed sequencing adapters via trimmomatic [23]. We used kraken2 for taxonomic identification and to detect DENV reads in samples and confirmed via Microsoft premonition pipeline (https://innovation.microsoft.com/en-us/premonition) [24]. Initially, we performed de novo assembly on samples with DENV reads using MetaSPAdes [25]. Furthermore, we also attempted reference-based assembly via viral-ngs (https://zenodo.org/records/3509008) pipeline [26] to confirm DENV genome assemblies. We identified assembled contigs using BLASTn and annotated contigs using ORFfinder (https://www.ncbi.nlm.nih. gov/orffinder/) and Bacterial and Viral Bioinformatics Resource Center (BV-BRC) [27,28]. We used nuclease-free water as a negative control for each sample preparation step. We checked that the negative control was clean and not contaminated as part of the quality control.

Phylogenetic and phylogeographic analysis

We downloaded all publicly available DENV sequences from Genbank for the phylogeographic analyses. We genotyped all sequences with Genome Detective [29] and filtered for DENV-1 genotype 3 based on our sequences’ genotyping. We curated two datasets: one alignment of the E gene (the most frequently sequenced subgenomic region as it is used in genotyping) and a smaller dataset of whole genomes. We filtered both datasets for high-quality genomes based on 80% alignment length and <10% ambiguous nucleotides. We screened for recombination using RDP and GARD and, filtered out mosaic sequences, and partitioned the whole genome alignment into two non-recombinant regions (NRRs) based on the single inferred breakpoint at 8030nt [30,31]. For the E gene and NRRs, we aligned the dataset with MAFFTv7.505 [32] and reconstructed phylogenies with IQTREE2 under ModelFinder [33,34]. We investigated temporal structure in our datasets across rooting strategies using temporal regression in TreeTime 0.9.3, excluding outlying sequences of more than three interquartile ranges using the clock filter [35]. We reconstructed time-scaled phylogenies with BEAST 1.10.5 under the HKY substitution model, with a gamma-distributed rate variation among sites for both the E gene and the NRRs [36]. We used a strict clock with a CTMC prior. We performed respective analyses under a constant and exponential coalescent model for the E gene and a constant coalescent model for the NRRs. For the constant coalescent model, we combined two independent MCMC chains of 100 million states ran with the BEAGLE computational library [37]. For the exponential E gene and NRRs analyses, we used a single chain of 100 million states. Parameters and trees were sampled every 10000 steps, with the first 10% of steps discarded as burn-in. Convergence and mixing of the MCMC chains were assessed in Tracer v1.7, to ensure the effective sample size of all estimated parameters was >100 [38]. We performed an asymmetric discrete trait analysis with geographic states on a country level to reconstruct the location-transition history across an empirical distribution of 10000 time-calibrated trees sampled from each of the posterior tree distributions estimated above. We restricted our primary phylogeographic analyses to the E gene as it is the best sampled genomic region for genotyping and, therefore, represents the most spatiotemporally representative sample.

Amino acid divergence analysis

To assess the diversity of DENV amino acid sequences between the Nigerian strains and previously described human DENV lineages, the genomes were aligned and observed for synonymous and non-synonymous mutations. Analysis was also focused on the immuno-protective pre-membrane (prM) and the envelope (E) protein region.

Statistical analysis

We performed Statistical analyses using Python software version 3.11 and Matplotlib, Scipy and Sklearn libraries. We tested the association between study location (categorical variables) and the age (in its continuous format) using Fisher’s Exact Test and the seromarkers (IgG, IgM and NS1) using Logistic Regression. P value <0.05 was considered statistically significant.

Results

Sample demographic

We screened 515 patients, including 215 (41.7%) men and 300 (58.3%) women. There were 387 (75.1%) participants from Oyo State and 128 (24.9%) from Ekiti State. In Oyo State, 140 participants were recruited from Oni Memorial Children Hospital (27.2%), 247 participants from Adeoyo Hospital (48%), and 128 participants (24.9%) from Ekiti State University Teaching Hospital (EKSUTH).

Serological results

We used an IgG seromarker to determine evidence of past dengue infections, an IgM seromarker to detect recent infections and the NS1 antigen for acute DENV infection. In our cohort, 27.8% of screened participants tested positive for IgG, suggesting that more than a quarter of patients had evidence of previous DENV infection. IgG to DENV starts increasing 5–7 days after primary infection [39], with the IgG titers peaking during the third week of a primary infection. In secondary infections, IgG titres rise to extremely high levels (much higher than in primary infections) from day 7 of fever to the next two weeks [40]. Dengue IgG antibodies can persist for years [41].

The seroprevalence for IgM and NS1 was 8.0% and 2.7%, respectively, suggesting that 3-8% of patients had recent or current DENV infections. Prevalence based on gender, age and location for both IgG and IgM antibodies and NSI is shown in Tables S1-4. We observed high IgM and NS1 antigenemia among infants and children analyzed in this study, suggesting many children admitted to the hospitals during our study likely had acute dengue. (Figure 1B and Table S1). Dengue IgM levels increase by the third day of primary infection and peak two weeks after the onset of fever. NS1 is an early-phase protein that depicts acute infection and can be used as a point-of-care test for patient management instead of a PCR assay [42]. A reactive NS1 Antigen occurs in dengue patients during days 1–9 of fever occurrence [43].

Figure 1. Seropositivity of IgG, IgM and NS1 by A) Date and B) Age category.

However, it may be undetectable in secondary dengue cases, as seen during antibody-dependent enhancement (ADE). This phenomenon occurs when antibodies from past infections form a complex with antigens in the current infection to activate the complement cascade, leading to hemorrhagic forms of dengue [44]. Some participants were positive for multiple DENV seromarkers (Table S1). Individuals who are concurrently positive for IgG and IgM are at risk of dengue hemorrhagic fever (DHF), as it indicates the patient has experienced a secondary DENV infection. Secondary, tertiary or quaternary infections can result in the formation of antibody–antigen complex, which activates the complement cascade, resulting in increased permeability of the vascular endothelium, thereby leading to the hemorrhagic form of dengue. DHF has been previously described in parts of Nigeria [45].

Our result showed that age was significantly correlated to IgG (coefficient 0.035, p value = 0.0000) but not IgM or NS1. This suggests that the probability of past dengue infection increases with age, as expected. We found that only IgG is significantly associated with study locations in the rainforest region (Table 1). Although high rates of previous DENV infection are consistent with the rainforest ecology, climatic conditions and vegetation that influence vector abundance and behaviour [46], the observable difference in both locations in the region results from more sampling in Oyo State compared to Ekiti State. Ekiti has a population of less than 2.3 million inhabitants, while Oyo has over 5.5 million residents in densely populated areas [47]. The odds ratio (OR) in Table 1 shows that residents in Oyo are three times more likely to be infected with dengue.

Table 1. Correlation of geographical location in rain forest region with IgM, IgG and NS1.

		Oyo State	Ekiti State	P value	OR
IgG	Positive	126	16	0.0004	3.38
	Negative	261	112
IgM	Positive	32	9	8.8505	1.19
	Negative	355	119
NS1	Positive	8	6	0.1233	0.43
	Negative	379	122

We observed a decline in IgG positivity from April to November, excepting June to July. We observed peak IgG positivity in April, which declined towards November (Figure 1A). This seasonality can be ascribed to the region’s climatic conditions: in the latter parts of the year, the amount of rainfall decline and resultantly reduce the vector’s breeding potential, which again intensifies from March to September shortly after the August break of rainfall [48].

Metagenomics and phylogeographic analysis

We performed metagenomic sequencing of sixty DENV-positive patients’ plasma to characterize the circulating DENV diversity. We found DENV reads in 35/60 (58.33%) samples and successfully assembled three full and 17 partial genomes (Table 2 and Table S5). All of our assembled genomes belonged to DENV-1 genotype III. We found that the three complete genomes formed a monophyletic clade with high support (bootstrap = 99) in the E gene, as well as in the non-recombined regions (NRRs) partitioned by the single breakpoint inferred by GARD at 8030 nt. We performed phylogeographic reconstruction for the three full genomes with the background global DENV-1 genotype III dataset to determine the timing and origin of the Nigerian lineage's emergence. We performed phylogeographic reconstruction for the largest of the NRRs but predominantly focussed our analyses on the E gene as it is the most sampled genomic region for genotyping (N = 774 vs N = 135 whole genome sequences) and has more geographic representation (Figure 2A, Figure S2).

Figure 2. A) Time-calibrated phylogeny of the E gene under the constant coalescent model. Branches are coloured by country-level geographic state reconstruction. Internal nodes annotated with black points represent posterior support >0.75. B) Subtree with the Nigeria lineage in Figure 2A. Text annotation reflects the posterior support for the geographic state reconstruction at key internal nodes. C) Time to the most recent common ancestor for key nodes annotated in Figure 2B under the coalescent (cons) and exponential (expo) model for the E gene and the whole genome (WG), reflected by the largest NRR (1-8030nt). The Nigeria lineages tMRCA (Node A in Figure 2B) is in red, with its ancestor (Node B) in light blue and the full subtree’s tMRCA in dark blue (Node C).

Table 2. Summary of DENV reads detected in the three genomes included in the phylogenetic tree.

Sequence ID	Accession Number	Biosample Accession	Number of Reads	Mapped reads	Mean Coverage	GC %	Coverage in relation to NC_001477	Pairwise identity in relation to NC_001477 (%)
DV36	OR259173	SAMN35565128	24,963,042	11,990	134.7	47.75	98.3	91.9
DV40	OR259174	SAMN35565129	15,155,000	2,557,677	30,217.7	47.31	99.7	92.0
DV44	OR259175	SAMN35565130	25,630,416	30,064	477.0	46.85	99.2	91.9

In our phylogeographic reconstructions, the three Nigerian sequences cluster as monophyly consistently (posterior 0.76 for the E gene, 1 for whole genome NRR), suggesting the sampled lineage was established by a single introduction or represents a single undersampled endemic lineage (Figure 2B). We estimated that the sampled Nigerian lineage most likely emerged in mid-2018 to early 2019 (Figure 2B, C). We found that the time to the most recent common ancestor (tMRCA) of the Nigerian lineage was modestly different under the exponential and constant coalescent models for the E gene (Figure 2C). Notably, the tMRCA was significantly earlier for the larger NRR1 (8030nt), attributable to a lower clock estimate in the NRR compared to the shorter, primary immune target E glycoprotein (median 26 June 2018 [95% highest posterior density (HPD) 18 July 2017 to 25 April 2019] for NRR1 vs 25 May 2019 [28 May 2018, 8 June 2020] for the E gene] (Figure 2C, Figure S1) [49]. However, all of our estimates consistently suggest that DENV-1 was circulating cryptically in Oyo state for two to three years before detection in mid-to-late 2021.

This supported prolonged cryptic circulation is consistent with the significant observed divergence within the Nigerian clade. We found that sequences DV36 and DV44 were identical across the whole genome, representing samples isolated from patients at Adeoyo Hospital in Oyo state in June and September of 2021, respectively. We estimated their common ancestor circulated 2–4 months before sampling, with no additional metadata to suggest direct epidemiological linkage apart from the sampling location in Ibadan. We found that DV40 clustered with DV36/44 consistently (posterior 0.76 for E gene, posterior = 1 for NRRs, Figure 2B). However, we found that DV40 diverged from DV36 and DV44 by 32 synonymous and four non-synonymous SNPS, with no recombination signal detected in the Nigeria sequences. We sampled DV40 from a patient at the same hospital as DV36/44 in Ibadan in July 2022, within months of DV36/44. The co-circulation of divergent lineages suggests significant cryptic transmission of DENV-1 in Oyo state.

We found that under both models for the E gene and NRR, the Nigerian sequences formed a sister lineage (posterior = 1) to a group of sequences isolated in China from August to October 2019, India from October 2019 and two sequences isolated in China with travel histories to Tanzania isolated in April-May 2019 (Figure 2B). We estimated the common ancestor for the Nigerian lineage and its sister lineage circulated approximately 12–15 months prior to the Nigerian lineage’s estimated emergence. This suggests a possible introduction into Nigeria happened between March 2017 and early 2019 (Figure 2C). In our discrete phylogeographic reconstructions, we estimated that the introduction into Nigeria most probably occurred from China (posterior support = 0.69) (Figure 2B). However, the posterior support is low across both models owing to phylogenetic uncertainty in the internal structure of Nigeria’s sister lineage, with 0.18 posterior support that the Nigerian lineage was imported from Tanzania. Without additional sampling, we cannot infer the source country of the Nigerian lineage’s introduction. We also cannot investigate the possibility of endemic circulation in Nigeria without significant additional sampling. We estimate that the common ancestor of the full clade originated in India under both models with high support (posterior = 0.98, Figure 2B), although the tMRCA of the full subtree is substantially older in estimates based on the largest NRR. The close relationship between sequences from Nigeria and Tanzania suggests that there is a wider unsampled bidirectional transmission of DENV-1 in Africa. However, the possibility cannot be investigated without significant additional sampling, as there are currently only n = 25 E-gene and 10 high quality genomes from Africa (Figure S2).

We observed a high frequency of non-synonymous mutations in both the structural and non-structural proteins of the Nigerian strains (Figure S3 and Table S6). All the Nigerian strains had similar mutations except for mutations in the E gene (P169S which was observed in DV36/DV44 but absent in DV40 while K295R was seen only in DV40) and NS5 (K513R was seen only in DV40). The observed amino acid divergence in the three Nigerian strains suggests that these amino acid sites may be highly prone to evolutionary drift.

Discussion

Dengue prevalence is severely underestimated in Nigeria due to underreporting associated with limited diagnostic testing and the nonspecific symptoms of dengue against a background of other tropical diseases such as malaria and a lack of awareness by clinicians [19]. There are sporadic reports of dengue, primarily in urban and semi-urban areas in Nigeria [50]. However, the reported cases have been relatively low compared to other mosquito-borne diseases.

In this study, we found that 28% of patients in our cohort in the rainforest region of Nigeria showed evidence of prior DENV infection. Identifying dengue in febrile patients who tested negative for malaria and typhoid reinforces the role of DENV as an aetiological agent of febrile illnesses in southern Nigeria. Our estimates are higher than the seroprevalence previously reported in Lagos State at 18% [51] but lower than a recent report in the heavily forested area in Southeastern Nigeria at 77% [14].

A lower prevalence of current dengue infection was reported in the GS [52], while there was a previous report of a higher prevalence of current infection in the GS [10]. The differences in seroprevalence across regions are driven by varying climate and ecological factors that influence the distribution and activity of Aedes mosquitoes and human behaviour [53]. The intense rainfall and flooding activities in the coastal state of Lagos may disrupt the stability of the mosquito eggs and larvae [54], whereas the forested regions provide more vegetation cover and high relative humidity to support mosquito breeding. Aedes aegypti, the primary vector for DENV, thrives well in urban and semi-urban environments, breeding in stagnant water and hollow containers [55]. The presence and distribution of Aedes spp. varies across the different regions of Nigeria [56]. The patients infected in this study are from urban and rural areas where Aedes species, which are the competent vectors for DENV, are abundant [57]. Apart from favourable environmental and climatic factors, poor drainages, stagnant water around neighbourhoods, and indiscriminate disposal of used tyres and hollow containers facilitate the breeding of these vectors, posing significant risk factors [55].

Our study also found dengue-infected infants, indicating the endemic nature of dengue in the rainforest region (Figure 1). Our estimates for current and acute infections are lower than previously observed in children under five [12,14]. This is attributable to different sampling strategies, as previous studies focused on children within that age group, whereas the present study was in a cross-section of the general population. The unprecedented levels of neonatal infection reported in our study emphasize that additional attention should be paid to younger as well as geriatric patients. Particularly, attention should be paid to patients with dual IgG and IgM positivity owing to the incidence of DHF, which has been highlighted as an emerging phenomenon in Nigeria [45].

In this study, we used serological assays as a screening method as it is easy to perform and can detect NS1 antigen and anti-dengue IgM/IgG antibodies simultaneously. Such simple diagnostic tests are desirable as healthcare facilities in many dengue-endemic countries lack laboratory support for routine testing. NS1 is an early-phase protein that depicts acute infection and can be used as a point-of-care test for patient management instead of a PCR assay for dengue [58]. We deployed a lateral-flow diagnostic assay, which technicians can use at the Primary healthcare level [59]. Such quick and routine dengue screening will ensure proper surveillance and control activities in high-prevalence areas.

Prior to this study, DENV sequences available from Nigeria were subgenomic regions generated by Sanger sequencing. This study reports the first whole genome of DENV-1 assembled from Nigeria since the first local report of the virus in 1968. Previous studies reported DENV-2 serotype predominated in Nigeria [18,60]. However, in our study, we only observed DENV-1 genotype III across two States. Notably, we found genomic evidence of prolonged cryptic circulation and significant diversification of DENV lineages within a relatively limited ecological zone in Nigeria. Increased genomic surveillance will likely uncover ongoing transmission of divergent lineages. We could not resolve the source-sink dynamics of dengue transmission owing to sampling limitations in both the current study and the global dataset. Notably, undersampling in the entire African region will expectedly obscure the importance of regional connectedness in sustaining epidemics.

However, the close relationship between these sequences from Nigeria and those with travel histories to Tanzania supports the assumption that there may be unsampled regional bidirectional transmission of cryptic DENV-1 lineages in Africa.

This study also showed the importance of metagenomic deep sequencing in obtaining the DENV genome and facilitating a thorough analysis and assessment of the DENV surveillance in Nigeria. However, DENV RNA degradation may be the cause of the absence, and low DENV reads in some samples that tested positive for NS1 and IgM. Metagenomics has a number of drawbacks, including the need for high viral load and the capacity to amplify any DNA or RNA genome randomly.

Our findings emphasize the need for sustained and systematic dengue clinical and genomic surveillance in humans and vectors as an early warning system for monitoring and providing actionable public health information. Precise methods for distinguishing recent and acute dengue infections from other undifferentiated febrile illnesses are vital for proper clinical management.

Author contributions

BAO designed the study and collected the samples; BAO, KEA, PA, TFO, and IO performed the serological assay and the data analysis; JUO, UEG, and PEE performed the molecular assays and whole genome sequencing; JUO, UEG, and EP conducted the bioinformatics; BAO, JUO, UEG, KEA, PA, TFO, IOI and EP wrote the initial draft manuscript; PA and SH performed statistical analysis; AJA, CTH supervised the work and provided mentorship. BAO funded the serological assay; CTH funded the molecular analyses, genomics sequencing and Bioinformatics aspects of the work. All the authors read and approved the final manuscript before submission.

Acknowledgement

This work received support from ACEGID laboratory and TED’s Audacious Project, including the ELMA Foundation, MacKenzie Scott, and the Skoll Foundation. This work was supported by grants from the National Institute of Allergy and Infectious Diseases (https://www.niaid.nih.gov), award numbers U01HG007480 to C.T.H and U01AI151812 to E.P), NIH-H3Africa (https://h3africa.org) award number U54HG007480. The World Bank grants projects ACE-019 and ACE-IMPACT. This work was also supported by the Rockefeller Foundation (Grant #2021 HTH), the Africa CDC through the African Society of Laboratory Medicine [ASLM] (Grant #INV018978), and the Science for Africa Foundation. The authors thank Mr. Agbaje for facilitating the Ekiti University Teaching Hospital sample collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The sequenced raw reads for this study and the generated genomes have been deposited in NCBI under BioProject PRJNA979106 with accession numbers OR259173-OR259175 for the dengue genomes.

Additional information

Funding

This work was supported by The Rockefeller Foundation: [Grant Number #2021 HTH]; TED's Audacious project [including the ELMA Foundation, MacKenzie Scott, and the Skoll Foundation]; The World Bank grants projects ACE-019 and ACE-IMPACT; National Institute of Allergy and Infectious Diseases: [Grant Number U01HG007480 to C.T.H and U01AI151812 to E.P]; The Africa CDC through the African Society of Laboratory Medicine [ASLM]: [Grant Number #INV018978] and NIHH3Africa: [Grant Number U54HG007480].