No association of a Vascular endothelial growth factor A (VEGFA) gene polymorphism with pre-eclampsia among pregnant women in Uganda.

BACKGROUND: Vascular endothelial growth factor A (VEGFA) is a major angiogenic factor that plays an important role in the formation of blood vessels during embryonic development. VEGFA has been implicated in the pathophysiology of pre-eclampsia (PE), since pre-eclamptic women present with reduced levels of free circulating VEGFA. The 3' untranslated region (3'-UTR) of the VEGFA gene consists of elements that regulate the transcription and hence expression of the VEGFA protein in circulation. Hence it is suggested that variations thereof could underlie the reduced VEGFA levels observed in pre-eclamptic women. The purpose of this study was to investigate presence of the + 936C/T polymorphism, a common single nucleotide polymorphism (SNP) in the 3'-UTR of the VEGFA gene, and determine its association with PE among pregnant women in Uganda. RESULTS: There was no significant difference observed in the allele and genotype frequencies of the + 936C/T 3' UTR-VEGFA polymorphism between pre-eclamptic and normotensive pregnant women (P > 0.05). Additionally, there was no significant difference in the median plasma levels of free VEGFA among women with the wild type, CT and TT genotypes of the + 936C/T VEGFA polymorphism (median = 0.84 pg/mL (IQR = 0.39-1.41) Vs 1.05 (0.61-1.18) Vs 1.05 (1.05-1.05) respectively, p-value = 0.7161). CONCLUSIONS: These study findings indicate that the + 936C/T 3' UTR-VEGFA polymorphism had no significant association with increased susceptibility to PE among women in Uganda. Further studies with a larger sample size are recommended.

Rapid Replacement of SARS-CoV-2 Variants by Delta and Subsequent Arrival of Omicron, Uganda, 2021.

Genomic surveillance in Uganda showed rapid replacement of severe acute respiratory syndrome coronavirus 2 over time by variants, dominated by Delta. However, detection of the more transmissible Omicron variant among travelers and increasing community transmission highlight the need for near-real-time genomic surveillance and adherence to infection control measures to prevent future pandemic waves.

Employing phylogenetic tree shape statistics to resolve the underlying host population structure.

BACKGROUND: Host population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure. RESULTS: In this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number ([Formula: see text]) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models. CONCLUSIONS: Our classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of [Formula: see text] using SVM-polynomial classifier.

Mosquito-borne arboviruses in Uganda: history, transmission and burden.

Mosquito-transmitted arboviruses constitute a large proportion of emerging infectious diseases that are both a public health problem and a threat to animal populations. Many such viruses were identified in East Africa, a region where they remain important and from where new arboviruses may emerge. We set out to describe and review the relevant mosquito-borne viruses that have been identified specifically in Uganda. We focused on the discovery, burden, mode of transmission, animal hosts and clinical manifestation of those previously involved in disease outbreaks. A search for mosquito-borne arboviruses detected in Uganda was conducted using search terms 'Arboviruses in Uganda' and 'Mosquitoes and Viruses in Uganda' in PubMed and Google Scholar in 2020. Twenty-four mosquito-borne viruses from different animal hosts, humans and mosquitoes were documented. The majority of these were from family Peribunyaviridae, followed by Flaviviridae, Togaviridae, Phenuiviridae and only one each from family Rhabdoviridae and Reoviridae. Sixteen (66.7 %) of the viruses were associated with febrile illnesses. Ten (41.7 %) of them were first described locally in Uganda. Six of these are a public threat as they have been previously associated with disease outbreaks either within or outside Uganda. Historically, there is a high burden and endemicity of arboviruses in Uganda. Given the many diverse mosquito species known in the country, there is also a likelihood of many undescribed mosquito-borne viruses. New generation diagnostic platforms have great potential to identify new viruses. Indeed, four novel viruses, two of which were from humans (Ntwetwe and Nyangole viruses) and two from mosquitoes (Kibale and Mburo viruses) including the 2010 yellow fever virus (YFV) outbreak were identified in the last decade using next generation sequencing. Given the unbiased approach of detection of viruses by this technology, its use will undoubtedly be critically important in the characterization of mosquito viromes which in turn will inform other diagnostic efforts.

Mosquito-borne arboviruses in Uganda: history, transmission and burden.

Mosquito-transmitted arboviruses constitute a large proportion of emerging infectious diseases that are both a public health problem and a threat to animal populations. Many such viruses were identified in East Africa, a region where they remain important and from where new arboviruses may emerge. We set out to describe and review the relevant mosquito-borne viruses that have been identified specifically in Uganda. We focused on the discovery, burden, mode of transmission, animal hosts and clinical manifestation of those previously involved in disease outbreaks. A search for mosquito-borne arboviruses detected in Uganda was conducted using search terms 'Arboviruses in Uganda' and 'Mosquitoes and Viruses in Uganda' in PubMed and Google Scholar in 2020. Twenty-four mosquito-borne viruses from different animal hosts, humans and mosquitoes were documented. The majority of these were from family Peribunyaviridae, followed by Flaviviridae, Togaviridae, Phenuiviridae and only one each from family Rhabdoviridae and Reoviridae. Sixteen (66.7%) of the viruses were associated with febrile illnesses. Ten (41.7%) of them were first described locally in Uganda. Six of these are a public threat as they have been previously associated with disease outbreaks either within or outside Uganda. Historically, there is a high burden and endemicity of arboviruses in Uganda. Given the many diverse mosquito species known in the country, there is also a likelihood of many undescribed mosquito-borne viruses. Next generation diagnostic platforms have great potential to identify new viruses. Indeed, four novel viruses, two of which were from humans (Ntwetwe and Nyangole viruses) and two from mosquitoes (Kibale and Mburo viruses) were identified in the last decade using next generation sequencing. Given the unbiased approach of detection of viruses by this technology, its use will undoubtedly be critically important in the characterization of mosquito viromes which in turn will inform other diagnostic efforts.

HIVseqDB: a portable resource for NGS and sample metadata integration for HIV-1 drug resistance analysis.

Summary: Human immunodeficiency virus (HIV) remains a public health threat, with drug resistance being a major concern in HIV treatment. Next-generation sequencing (NGS) is a powerful tool for identifying low-abundance drug resistance mutations (LA-DRMs) that conventional Sanger sequencing cannot reliably detect. To fully understand the significance of LA-DRMs, it is necessary to integrate NGS data with clinical and demographic data. However, freely available tools for NGS-based HIV-1 drug resistance analysis do not integrate these data. This poses a challenge in interpretation of the impact of LA-DRMs, mainly for resource-limited settings due to the shortage of bioinformatics expertise. To address this challenge, we present HIVseqDB, a portable, secure, and user-friendly resource for integrating NGS data with associated clinical and demographic data for analysis of HIV drug resistance. HIVseqDB currently supports uploading of NGS data and associated sample data, HIV-1 drug resistance data analysis, browsing of uploaded data, and browsing and visualizing of analysis results. Each function of HIVseqDB corresponds to an individual Django application. This ensures efficient incorporation of additional features with minimal effort. HIVseqDB can be deployed on various computing environments, such as on-premises high-performance computing facilities and cloud-based platforms. Availability and implementation: HIVseqDB is available at https://github.com/AlfredUg/HIVseqDB. A deployed instance of HIVseqDB is available at https://hivseqdb.org.

Viruses associated with measles-like illnesses in Uganda.

OBJECTIVES: In this study, we investigated the causes of measles-like illnesses (MLI) in the Uganda national surveillance program in order to inform diagnostic assay selection and vaccination strategies. METHODS: We used metagenomic next-generation sequencing (M-NGS) on the Illumina platform to identify viruses associated with MLI (defined as fever and rash in the presence of either cough, coryza or conjunctivitis) in patient samples that had tested IgM negative for measles between 2010 and 2019. RESULTS: Viral genomes were identified in 87/271 (32%) of samples, of which 44/271 (16%) contained 12 known viral pathogens. Expected viruses included rubella, human parvovirus B19, Epstein Barr virus, human herpesvirus 6B, human cytomegalovirus, varicella zoster virus and measles virus (detected within the seronegative window-period of infection) and the blood-borne hepatitis B virus. We also detected Saffold virus, human parvovirus type 4, the human adenovirus C2 and vaccine-associated poliovirus type 1. CONCLUSIONS: The study highlights the presence of undiagnosed viruses causing MLI in Uganda, including vaccine-preventable illnesses. NGS can be used to monitor common viral infections at a population level, especially in regions where such infections are prevalent, including low and middle income countries to guide vaccination policy and optimize diagnostic assays.

QuasiFlow: a Nextflow pipeline for analysis of NGS-based HIV-1 drug resistance data.

Summary: Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. Availability and implementation: QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

African Genomic Medicine Portal: A Web Portal for Biomedical Applications.

Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

Circulating levels of angiogenic factors and their association with preeclampsia among pregnant women at Mulago National Referral Hospital in Uganda.

Preeclampsia (PE) is a major cause of maternal and new-born morbidity and mortality. Angiogenic factors contribute a major role in the vascular dysfunction associated with PE. We investigated the circulating levels of vascular endothelial growth factor (VEGF), placental growth factor (PlGF) and soluble Feline McDonough Sarcoma (fms)-like tyrosine kinase-1 (sFlt1), their association with PE and diagnostic performance of disease among pregnant women in Uganda. Using a case-control study design, 106 women with PE and 106 with normal pregnancy were enrolled. Demographic and clinical characteristics, and anticoagulated blood samples were collected from participants. Plasma VEGF, PlGF and sFlt1 levels were measured using Luminex and enzyme linked immunosorbent assays (ELISA). Conditional logistic regression was used to explore association of angiogenic factors with PE and receiver operating characteristic analysis was performed to investigate PE diagnostic performance. Levels of VEGF and PIGF were significantly lower in cases compared to controls (VEGF: median = 0.71 pg/ml (IQR = 0.38-1.11) Vs 1.20 pg/ml (0.64-1.91), p-value<0.001 and PlGF: 2.20 pg/ml (1.08-5.86) Vs 84.62 pg/ml (34.00-154.45), p-value<0.001). Plasma levels of sFlt1 were significantly higher in cases than controls (median = 141.13 (71.76-227.10) x103 pg/ml Vs 19.86 (14.20-29.37) x103 pg/ml). Increasing sFlt1 levels were associated with increased likelihood of PE (aOR = 4.73; 95% CI, 1.18-19.01; p-value = 0.0287). The sFlt1/PlGF ratio and sFlt1 had a better performance for diagnosis of PE, with AUC = 0.95 (95% CI, 0.93-0.98) followed by PlGF with AUC = 0.94 (95% CI, 0.91-0.97). Therefore, sFlt1, sFlt1/PlGF ratio and PlGF are potential candidates for incorporation into algorithms for PE diagnosis in the Ugandan population.

Phylogenetic Networks and Parameters Inferred from HIV Nucleotide Sequences of High-Risk and General Population Groups in Uganda: Implications for Epidemic Control.

Phylogenetic inference is useful in characterising HIV transmission networks and assessing where prevention is likely to have the greatest impact. However, estimating parameters that influence the network structure is still scarce, but important in evaluating determinants of HIV spread. We analyzed 2017 HIV pol sequences (728 Lake Victoria fisherfolk communities (FFCs), 592 female sex workers (FSWs) and 697 general population (GP)) to identify transmission networks on Maximum Likelihood (ML) phylogenetic trees and refined them using time-resolved phylogenies. Network generative models were fitted to the observed degree distributions and network parameters, and corrected Akaike Information Criteria and Bayesian Information Criteria values were estimated. 347 (17.2%) HIV sequences were linked on ML trees (maximum genetic distance ≤4.5%, ≥95% bootstrap support) and, of these, 303 (86.7%) that consisted of pure A1 (n = 168) and D (n = 135) subtypes were analyzed in BEAST v1.8.4. The majority of networks (at least 40%) were found at a time depth of ≤5 years. The waring and yule models fitted best networks of FFCs and FSWs respectively while the negative binomial model fitted best networks in the GP. The network structure in the HIV-hyperendemic FFCs is likely to be scale-free and shaped by preferential attachment, in contrast to the GP. The findings support the targeting of interventions for FFCs in a timely manner for effective epidemic control. Interventions ought to be tailored according to the dynamics of the HIV epidemic in the target population and understanding the network structure is critical in ensuring the success of HIV prevention programs.

H3ABioNet genomic medicine and microbiome data portals hackathon proceedings.

African genomic medicine and microbiome datasets are usually not well characterized in terms of their origin, making it difficult to find and extract data for specific African ethnic groups or even countries. The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for developing data portals for African genomic medicine and African microbiomes to address this and ran a hackathon to initiate their development. The two portals were designed and significant progress was made in their development during the hackathon. All the participants worked in a very synergistic and collaborative atmosphere in order to achieve the hackathon's goals. The participants were divided into content and technical teams and worked over a period of 6 days. In response to one of the survey questions of what the participants liked the most during the hackathon, 55% of the hackathon participants highlighted the familial and friendly atmosphere, the team work and the diversity of team members and their expertise. This paper describes the preparations for the portals hackathon and the interaction between the participants and reflects upon the lessons learned about its impact on successfully developing the two data portals as well as building scientific expertise of younger African researchers. Database URL: The code for developing the two portals was made publicly available in GitHub repositories: [https://github.com/codemeleon/Database; https://github.com/codemeleon/AfricanMicrobiomePortal].

The Molecular Epidemiology and Transmission Dynamics of HIV Type 1 in a General Population Cohort in Uganda.

The General Population Cohort (GPC) in south-western Uganda has a low HIV-1 incidence rate (<1%). However, new infections continue to emerge. In this research, 3796 HIV-1 pol sequences (GPC: n = 1418, non-GPC sites: n = 1223, Central Uganda: n = 1010 and Eastern Uganda: n = 145) generated between 2003-2015 were analysed using phylogenetic methods with demographic data to understand HIV-1 transmission in this cohort and inform the epidemic response. HIV-1 subtype A1 was the most prevalent strain in the GPC area (GPC and non-GPC sites) (39.8%), central (45.9%) and eastern (52.4%) Uganda. However, in the GPC alone, subtype D was the predominant subtype (39.1%). Of the 524 transmission clusters identified by Cluster Picker, all large clusters (≥5 individuals, n = 8) involved individuals from the GPC. In a multivariate analysis, clustering was strongly associated with being female (adjusted Odds Ratio, aOR = 1.28; 95% CI, 1.06-1.54), being >25 years (aOR = 1.52; 95% CI, 1.16-2.0) and being a resident in the GPC (aOR = 6.90; 95% CI, 5.22-9.21). Phylogeographic analysis showed significant viral dissemination (Bayes Factor test, BF > 3) from the GPC without significant viral introductions (BF < 3) into the GPC. The findings suggest localized HIV-1 transmission in the GPC. Intensifying geographically focused combination interventions in the GPC would contribute towards controlling HIV-1 infections.

Phylogenetic and Demographic Characterization of Directed HIV-1 Transmission Using Deep Sequences from High-Risk and General Population Cohorts/Groups in Uganda.

Across sub-Saharan Africa, key populations with elevated HIV-1 incidence and/or prevalence have been identified, but their contribution to disease spread remains unclear. We performed viral deep-sequence phylogenetic analyses to quantify transmission dynamics between the general population (GP), fisherfolk communities (FF), and women at high risk of infection and their clients (WHR) in central and southwestern Uganda. Between August 2014 and August 2017, 6185 HIV-1 positive individuals were enrolled in 3 GP and 10 FF communities, 3 WHR enrollment sites. A total of 2531 antiretroviral therapy (ART) naïve participants with plasma viral load >1000 copies/mL were deep-sequenced. One hundred and twenty-three transmission networks were reconstructed, including 105 phylogenetically highly supported source-recipient pairs. Only one pair involved a WHR and male participant, suggesting that improved population sampling is needed to assess empirically the role of WHR to the transmission dynamics. More transmissions were observed from the GP communities to FF communities than vice versa, with an estimated flow ratio of 1.56 (95% CrI 0.68-3.72), indicating that fishing communities on Lake Victoria are not a net source of transmission flow to neighboring communities further inland. Men contributed disproportionally to HIV-1 transmission flow regardless of age, suggesting that prevention efforts need to better aid men to engage with and stay in care.