Phylogenomic analysis uncovers a 9-year variation of Uganda influenza type-A strains from the WHO-recommended vaccines and other Africa strains.

Genetic characterisation of circulating influenza viruses directs annual vaccine strain selection and mitigation of infection spread. We used next-generation sequencing to locally generate whole genomes from 116 A(H1N1)pdm09 and 118 A(H3N2) positive patient swabs collected across Uganda between 2010 and 2018. We recovered sequences from 92% (215/234) of the swabs, 90% (193/215) of which were whole genomes. The newly-generated sequences were genetically and phylogenetically compared to the WHO-recommended vaccines and other Africa strains sampled since 1994. Uganda strain hemagglutinin (n = 206), neuraminidase (n = 207), and matrix protein (MP, n = 213) sequences had 95.23-99.65%, 95.31-99.79%, and 95.46-100% amino acid similarity to the 2010-2020 season vaccines, respectively, with several mutated hemagglutinin antigenic, receptor binding, and N-linked glycosylation sites. Uganda influenza type-A virus strains sequenced before 2016 clustered uniquely while later strains mixed with other Africa and global strains. We are the first to report novel A(H1N1)pdm09 subclades 6B.1A.3, 6B.1A.5(a,b), and 6B.1A.6 (± T120A) that circulated in Eastern, Western, and Southern Africa in 2017-2019. Africa forms part of the global influenza ecology with high viral genetic diversity, progressive antigenic drift, and local transmissions. For a continent with inadequate health resources and where social distancing is unsustainable, vaccination is the best option. Hence, African stakeholders should prioritise routine genome sequencing and analysis to direct vaccine selection and virus control.

Molecular Epidemiology and Evolutionary Dynamics of Human Influenza Type-A Viruses in Africa: A Systematic Review.

Genomic characterization of circulating influenza type-A viruses (IAVs) directs the selection of appropriate vaccine formulations and early detection of potentially pandemic virus strains. However, longitudinal data on the genomic evolution and transmission of IAVs in Africa are scarce, limiting Africa's benefits from potential influenza control strategies. We searched seven databases: African Journals Online, Embase, Global Health, Google Scholar, PubMed, Scopus, and Web of Science according to the PRISMA guidelines for studies that sequenced and/or genomically characterized Africa IAVs. Our review highlights the emergence and diversification of IAVs in Africa since 1993. Circulating strains continuously acquired new amino acid substitutions at the major antigenic and potential N-linked glycosylation sites in their hemagglutinin proteins, which dramatically affected vaccine protectiveness. Africa IAVs phylogenetically mixed with global strains forming strong temporal and geographical evolution structures. Phylogeographic analyses confirmed that viral migration into Africa from abroad, especially South Asia, Europe, and North America, and extensive local viral mixing sustained the genomic diversity, antigenic drift, and persistence of IAVs in Africa. However, the role of reassortment and zoonosis remains unknown. Interestingly, we observed substitutions and clades and persistent viral lineages unique to Africa. Therefore, Africa's contribution to the global influenza ecology may be understated. Our results were geographically biased, with data from 63% (34/54) of African countries. Thus, there is a need to expand influenza surveillance across Africa and prioritize routine whole-genome sequencing and genomic analysis to detect new strains early for effective viral control.

Whole-genome analysis to determine the rate and patterns of intra-subtype reassortment among influenza type-A viruses in Africa.

Influenza type-A viruses (IAVs) present a global burden of human respiratory infections and mortality. Genome reassortment is an important mechanism through which epidemiologically novel influenza viruses emerge and a core step in the safe reassortment-incompetent live-attenuated influenza vaccine development. Currently, there are no data on the rate, spatial and temporal distribution, and role of reassortment in the evolution and diversification of IAVs circulating in Africa. We aimed to detect intra-subtype reassortment among Africa pandemic H1N1pdm09 (2009-10), seasonal H1N1pdm09 (2011-20), and seasonal H3N2 viruses and characterize the genomic architecture and temporal and spatial distribution patterns of the resulting reassortants. Our study was nested within the Uganda National Influenza Surveillance Programme. Next-generation sequencing was used to generate whole genomes (WGs) from 234 H1N1pdm09 (n = 116) and H3N2 (n = 118) viruses sampled between 2010 and 2018 from seven districts in Uganda. We combined our newly generated WGs with 658 H1N1pdm09 and 1131 H3N2 WGs sampled between 1994 and 2020 across Africa and identified reassortants using an automated Graph Incompatibility Based Reassortment Finder software. Viral reassortment rates were estimated using a coalescent reassortant constant population model. Phylogenetic analysis was used to assess the effect of reassortment on viral genetic evolution. We observed a high frequency of intra-subtype reassortment events, 12 · 4 per cent (94/758) and 20 · 9 per cent (256/1,224), and reassortants, 13 · 3 per cent (101/758) and 38 · 6 per cent (472/1,224), among Africa H1N1pdm09 and H3N2 viruses, respectively. H1N1pdm09 reassorted at higher rates (0.1237-0.4255) than H3N2 viruses (0 · 00912-0.0355 events/lineage/year), a case unique to Uganda. Viral reassortants were sampled in 2009 through 2020, except in 2012. 78 · 2 per cent (79/101) of H1N1pdm09 reassortants acquired new non-structural, while 57 · 8 per cent (273/472) of the H3N2 reassortants had new hemagglutinin (H3) genes. Africa H3N2 viruses underwent more reassortment events involving larger reassortant sets than H1N1pdm09 viruses. Viruses with a specific reassortment architecture circulated for up to five consecutive years in specific countries and regions. The Eastern (Uganda and Kenya) and Western Africa harboured 84 · 2 per cent (85/101) and 55 · 9 per cent (264/472) of the continent's H1N1pdm09 and H3N2 reassortants, respectively. The frequent reassortment involving multi-genes observed among Africa IAVs showed the intracontinental viral evolution and diversification possibly sustained by viral importation from outside Africa and/or local viral genomic mixing and transmission. Novel reassortant viruses emerged every year, and some persisted in different countries and regions, thereby presenting a risk of influenza outbreaks in Africa. Our findings highlight Africa as part of the global influenza ecology and the advantage of implementing routine whole-over partial genome sequencing and analyses to monitor circulating and detect emerging viruses. Furthermore, this study provides evidence and heightens our knowledge on IAV evolution, which is integral in directing vaccine strain selection and the update of master donor viruses used in recombinant vaccine development.

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.

Immune Responses Following BCG Immunization of Infants in Uganda and United Kingdom Are Similar for Purified Protein Derivative but Differ for Secretory Proteins of Mycobacterium tuberculosis.

Introduction: The immunogenicity of BCG vaccination in infants differs between populations. We hypothesized that prenatal exposure to mycobacterial antigens might explain the differences in immune responses to BCG seen in other studies of infants in Africa and the United Kingdom (UK) and we explored this in birth cohorts in Uganda and the UK. Materials and Methods: Blood samples were obtained from BCG-immunized infants of mothers with (n = 110) and without (n = 121) latent Mycobacterium tuberculosis infection (LTBI) in Uganda and BCG-immunized infants of mothers without LTBI (n = 25) in the UK at 10 and 52 weeks after birth. Cytokine and chemokine responses to PPD were measured to assess responses to BCG immunization, and to ESAT6/CFP10 to assess exposure to or infection with M. tuberculosis or non-tuberculous mycobacteria (NTM) in 6-day whole blood culture supernatants by a 17-plex Luminex assay. Median responses were compared between Ugandan infants (together, and separated by maternal LTBI status) and UK infants. Results: The IFN-γ response to BCG vaccination was similar between Ugandan and UK infants at 10 and 52 weeks. At week 52, TNF production was marginally higher in Ugandan infants, but after adjusting for multiple comparisons this difference was not significant. At weeks 10 and 52, stimulation of blood with ESAT6/CFP10 produced significantly higher IFN-γ, TNF, IL-12p40, IL-1α, IL-1ß, IL-1Ra, IP-10, MIP-1α, MIP-1ß, and GM-CSF in Ugandan compared to UK infants. Stimulation of blood with ESAT6/CFP10 produced significantly higher amounts of IL-8 (p = 0.0001), IL-10 (p = 0.0022), and IL-13 (p = 0.0020) in the UK than in Ugandan infants of mothers without LTBI at week 10, but not at week 52. Conclusions: Immune responses to mycobacterial antigens following BCG immunization are similar for PPD, but differ for ESAT6/CFP10, between infants in Uganda and the UK. Neither maternal LTBI nor infant exposure to or infection with mycobacteria impacts the response to BCG. The observed global differences in immune response to BCG immunization are likely to be due to other causes.

Maternal Latent Mycobacterium tuberculosis Does Not Affect the Infant Immune Response Following BCG at Birth: An Observational Longitudinal Study in Uganda.

Background: BCG has low efficacy in tropical countries. We hypothesized that maternal latent Mycobacterium tuberculosis (M.tb) infection (LTBI) results in fetal tolerance to mycobacterial antigens and impaired responses to BCG immunization. Methods: We enrolled 132 LTBI-positive and 150 LTBI-negative mothers and their babies in Entebbe, Uganda. Infants were BCG-immunized at birth. Cord blood and samples at weeks 1, 4, 6, 10, 14, 24, and 52 were analyzed for cytokine/chemokine responses to M.tb antigens by Luminex 17-plex assay in 6-day whole blood cultures and antibody responses by ELISA. Of the 17 Luminex analytes, seven (IL-2, IL-5, IL-10, IL-13, IL-17A, TNF, and IFN-γ) were included in the main analysis as they were considered most likely to represent T cell responses. Immune sensitization was defined as a detectable cord blood cytokine response to PPD for any of the seven cytokines. Patterns of cytokine and antibody responses were compared between infants of mothers with and without LTBI using linear mixed models adjusting for confounders. Results: Most infants (73%) were sensitized in utero to M.tb antigens, with no overall difference seen between infants born to mothers with or without LTBI. Patterns of post-BCG cytokine and antibody responses to mycobacterial antigens were similar between the two infant groups. Conclusions: Our data do not support the hypothesis that maternal LTBI results in an impaired response to BCG immunization, in Ugandan infants. BCG vaccination at or shortly after birth is likely to be beneficial to all infants, irrespective of maternal LTBI status.

Continuous research monitoring improves the quality of research conduct and compliance among research trainees: internal evaluation of a monitoring programme.

Background: Research site monitoring (RSM) is an effective way to ensure compliance with Good Clinical Practice (GCP). However, RSM is not offered to trainees (investigators) at African Institutions routinely. The Makerere University/Uganda Virus Research Institute Centre of Excellence in Infection and Immunity Research and Training (MUII-Plus) introduced internal monitoring to promote the quality of trainees' research projects. Here, we share our monitoring model, experiences and achievements, and challenges encountered. Methods: We analysed investigators' project reports from monitoring visits undertaken from April 2017 to December 2019. Monitors followed a standard checklist to review investigator site files and record forms, and toured site facilities. We planned four monitoring visits for each trainee: one at site initiation, two interim, and a closeout monitoring visit. A team of two monitors conducted the visits. Results: We monitored 25 out of the 26 research projects in progress between April 2017 and December 2019. Compliance with protocols, standard operating procedures, GCP, and GCLP improved with each monitoring visit. Median (IQR) compliance rate was 43% (31%, 44%) at site initiation visit for different monitoring items, 70% (54%, 90%) at the 1st interim monitoring visit, 100% (92%, 100%) at 2nd interim monitoring visit and all projects achieved 100% compliance at site closeout.  All investigators had good work ethics and practice, and appropriate facilities. Initially, some investigators' files lacked essential documents, and informed consent processes needed to be improved. We realized that non-compliant investigators had not received prior training in GCP/GCLP, so we offered them this training. Conclusions: Routine monitoring helps identify non-compliance early and improves the quality of research. We recommend continuous internal monitoring for all research studies. Investigators conducting research involving human subjects should receive GCP/GCLP training before commencing their projects. Institutional higher degrees and research ethics committees should enforce this as a requirement for project approvals.