Everlyn Kamau

Spread and Evolution of Respiratory Syncytial Virus A Genotype ON1, Coastal Kenya, 2010–2015

In February 2012, the novel respiratory syncytial virus (RSV) group A, genotype ON1, was detected in Kilifi County, coastal Kenya. ON1 is characterized by a 72-nt duplication within the highly variable G gene (encoding the immunogenic attachment surface protein). Cases were diagnosed through surveillance of pneumonia in children at the county hospital. Analysis of epidemiologic, clinical, and sequence data of RSV-A viruses detected over 5 RSV seasons (2010/2011 to 2014/2015) indicated the following: 1) replacement of previously circulating genotype GA2 ON1, 2) an abrupt expansion in the number of ON1 variants detected in the 2014/2015 epidemic, 3) recently accumulation of amino acid substitutions within the ON1 duplicated sequence, and 4) no clear evidence of altered pathogenicity relative to GA2. The study demonstrates the public health importance of molecular surveillance in defining the spread, clinical effects, and evolution of novel respiratory virus variants.

Recent sequence variation in probe binding site affected detection of respiratory syncytial virus group B by real-time RT-PCR

Highlights • Sequence variation at probe target site inhibited detection of a new RSV-B variant.• RSV-B virus diversity was consistent with real-time RT-PCR sensitivity.• Reduced PCR insensitivity could underestimate disease prevalence in clinical settings.• Regular check of primer and probe target sites for rapidly evolving viruses is key.

Surveillance of respiratory viruses in the outpatient setting in rural coastal Kenya : baseline epidemiological observations

Background: Endemic and seasonally recurring respiratory viruses are a major cause of disease and death globally. The burden is particularly severe in developing countries. Improved understanding of the source of infection, pathways of spread and persistence in communities would be of benefit in devising intervention strategies. Methods: We report epidemiological data obtained through surveillance of respiratory viruses at nine outpatient health facilities within the Kilifi Health and Demographic Surveillance System, Kilifi County, coastal Kenya, between January and December 2016. Nasopharyngeal swabs were collected from individuals of all ages presenting with acute respiratory infection (ARI) symptoms (up to 15 swabs per week per facility) and screened for 15 respiratory viruses using real-time PCR. Paediatric inpatient surveillance at Kilifi County Hospital for respiratory viruses provided comparative data. Results: Over the year, 5,647 participants were sampled, of which 3,029 (53.7%) were aged <5 years. At least one target respiratory virus was detected in 2,380 (42.2%) of the samples; the most common being rhinovirus 18.6% (1,050), influenza virus 6.9% (390), coronavirus 6.8% (387), parainfluenza virus 6.6% (371), respiratory syncytial virus (RSV) 3.9% (219) and adenovirus 2.7% (155). Virus detections were higher among <5-year-olds compared to older children and adults (50.3% vs 32.7%, respectively; χ 2(1) =177.3, P=0.0001). Frequency of viruses did not differ significantly by facility (χ 2(8) =13.38, P=0.072). However, prevalence was significantly higher among inpatients than outpatients in <5-year-olds for RSV (22.1% vs 6.0%; χ 2(1) = 159.4, P=0.0001), and adenovirus (12.4% vs 4.4%, χ 2(1) =56.6, P=0.0001). Conclusions: Respiratory virus infections are common amongst ARI outpatients in this coastal Kenya setting, particularly in young children. Rhinovirus predominance warrants further studies on the health and socio-economic implications. RSV and adenovirus were more commonly associated with severe disease. Further analysis will explore epidemiological transmission patterns with the addition of virus sequence data.

Transmission patterns and evolution of respiratory syncytial virus in a community outbreak identified by genomic analysis.

Abstract Detailed information on the source, spread and evolution of respiratory syncytial virus (RSV) during seasonal community outbreaks remains sparse. Molecular analyses of attachment (G) gene sequences from hospitalized cases suggest that multiple genotypes and variants co-circulate during epidemics and that RSV persistence over successive seasons is characterized by replacement and multiple new introductions of variants. No studies have defined the patterns of introduction, spread and evolution of RSV at the local community and household level. We present a whole genome sequence analysis of 131 RSV group A viruses collected during 6-month household-based RSV infection surveillance in Coastal Kenya, 2010 within an area of 12 km2. RSV infections were identified by regular symptom-independent screening of all household members twice weekly. Phylogenetic analysis revealed that the RSV A viruses in nine households were closely related to genotype GA2 and fell within a single branch of the global phylogeny. Genomic analysis allowed the detection of household-specific variation in seven households. For comparison, using only G gene analysis, household-specific variation was found only in one of the nine households. Nucleotide changes were observed both intra-host (viruses identified from same individual in follow-up sampling) and inter-host (viruses identified from different household members) and these coupled with sampling dates enabled a partial reconstruction of the within household transmission chains. The genomic evolutionary rate for the household dataset was estimated as 2.307 × 10 − 3 (95% highest posterior density: 0.935–4.165× 10 − 3) substitutions/site/year. We conclude that (i) at the household level, most RSV infections arise from the introduction of a single virus variant followed by accumulation of household specific variation and (ii) analysis of complete virus genomes is crucial to better understand viral transmission in the community. A key question arising is whether prevention of RSV introduction or spread within the household by vaccinating key transmitting household members would lead to a reduced onward community-wide transmission.

Human rhinovirus spatial-temporal epidemiology in rural coastal Kenya, 2015-2016, observed through outpatient surveillance

Background: Human rhinovirus (HRV) is the predominant cause of upper respiratory tract infections, resulting in a significant public health burden. The virus circulates as many different types (~160), each generating strong homologous, but weak heterotypic, immunity. The influence of these features on transmission patterns of HRV in the community is understudied. Methods: Nasopharyngeal swabs were collected from patients with symptoms of acute respiratory infection (ARI) at nine out-patient facilities across a Health and Demographic Surveillance System between December 2015 and November 2016. HRV was diagnosed by real-time RT-PCR, and the VP4/VP2 genomic region of the positive samples sequenced. Phylogenetic analysis was used to determine the HRV types. Classification models and G-test statistic were used to investigate HRV type spatial distribution. Demographic characteristics and clinical features of ARI were also compared. Results: Of 5,744 NPS samples collected, HRV was detected in 1057 (18.4%), of which 817 (77.3%) were successfully sequenced. HRV species A, B and C were identified in 360 (44.1%), 67 (8.2%) and 390 (47.7%) samples, respectively. In total, 87 types were determined: 39, 10 and 38 occurred within species A, B and C, respectively. HRV types presented heterogeneous temporal patterns of persistence. Spatially, identical types occurred over a wide distance at similar times, but there was statistically significant evidence for clustering of types between health facilities in close proximity or linked by major road networks. Conclusion: This study records a high prevalence of HRV in out-patient presentations exhibiting high type diversity. Patterns of occurrence suggest frequent and independent community invasion of different types. Temporal differences of persistence between types may reflect variation in type-specific population immunity. Spatial patterns suggest either rapid spread or multiple invasions of the same type, but evidence of similar types amongst close health facilities, or along road systems, indicate type partitioning structured by local spread.

Whole genome analysis of local Kenyan and global sequences unravels the epidemiological and molecular evolutionary dynamics of RSV genotype ON1 strains

Abstract The respiratory syncytial virus (RSV) group A variant with the 72-nucleotide duplication in the G gene, genotype ON1, was first detected in Kilifi in 2012 and has almost completely replaced circulating genotype GA2 strains. This replacement suggests some fitness advantage of ON1 over the GA2 viruses in Kilifi, and might be accompanied by important genomic substitutions in ON1 viruses. Close observation of such a new virus genotype introduction over time provides an opportunity to better understand the transmission and evolutionary dynamics of the pathogen. We have generated and analysed 184 RSV-A whole-genome sequences (WGSs) from Kilifi (Kenya) collected between 2011 and 2016, the first ON1 genomes from Africa and the largest collection globally from a single location. Phylogenetic analysis indicates that RSV-A circulation in this coastal Kenya location is characterized by multiple introductions of viral lineages from diverse origins but with varied success in local transmission. We identified signature amino acid substitutions between ON1 and GA2 viruses’ surface proteins (G and F), polymerase (L), and matrix M2-1 proteins, some of which were positively selected, and thereby provide an enhanced picture of RSV-A diversity. Furthermore, five of the eleven RSV open reading frames (ORFs) (G, F, L, N, and P) formed distinct phylogenetic clusters for the two genotypes. This might suggest that coding regions outside of the most frequently studied G ORF also play a role in the adaptation of RSV to host populations, with the alternative possibility that some of the substitutions are neutral and provide no selective advantage. Our analysis provides insight into the epidemiological processes that define RSV spread, highlights the genetic substitutions that characterize emerging strains, and demonstrates the utility of large-scale WGS in molecular epidemiological studies.

A34 Spread and evolution of respiratory syncytial virus A genotype ON1, coastal Kenya, 2010–2015

A33 Respiratory syncytial virus group B evolutionary trends in the attachment (G) glycoprotein in Kilifi, Kenya, 2003–2015 Everlyn Kamau, Clement Lewa, Graham F. Medley, Patricia A. Cane, D. James Nokes, and Charles N. Agoti Wellcome Trust Research Programme, Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI), Kilifi, Kenya, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK, Public Health England, Salisbury, UK, School of Life Sciences and SBIDER, University of Warwick, Coventry, UK and Department of Biomedical Sciences, Pwani University, Kilifi, Kenya

A33 Respiratory syncytial virus group B evolutionary trends in the attachment (G) glycoprotein in Kilifi, Kenya, 2003–2015

A33 Respiratory syncytial virus group B evolutionary trends in the attachment (G) glycoprotein in Kilifi, Kenya, 2003–2015 Everlyn Kamau, Clement Lewa, Graham F. Medley, Patricia A. Cane, D. James Nokes, and Charles N. Agoti Wellcome Trust Research Programme, Epidemiology and Demography Department, Kenya Medical Research Institute (KEMRI), Kilifi, Kenya, Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK, Public Health England, Salisbury, UK, School of Life Sciences and SBIDER, University of Warwick, Coventry, UK and Department of Biomedical Sciences, Pwani University, Kilifi, Kenya

Human Coronavirus NL63 Molecular Epidemiology and Evolutionary Patterns in Rural Coastal Kenya

Abstract Background Human coronavirus NL63 (HCoV-NL63) is a globally endemic pathogen causing mild and severe respiratory tract infections with reinfections occurring repeatedly throughout a lifetime. Methods Nasal samples were collected in coastal Kenya through community-based and hospital-based surveillance. HCoV-NL63 was detected with multiplex real-time reverse transcription PCR, and positive samples were targeted for nucleotide sequencing of the spike (S) protein. Additionally, paired samples from 25 individuals with evidence of repeat HCoV-NL63 infection were selected for whole-genome virus sequencing. Results HCoV-NL63 was detected in 1.3% (75/5573) of child pneumonia admissions. Two HCoV-NL63 genotypes circulated in Kilifi between 2008 and 2014. Full genome sequences formed a monophyletic clade closely related to contemporary HCoV-NL63 from other global locations. An unexpected pattern of repeat infections was observed with some individuals showing higher viral titers during their second infection. Similar patterns for 2 other endemic coronaviruses, HCoV-229E and HCoV-OC43, were observed. Repeat infections by HCoV-NL63 were not accompanied by detectable genotype switching. Conclusions In this coastal Kenya setting, HCoV-NL63 exhibited low prevalence in hospital pediatric pneumonia admissions. Clade persistence with low genetic diversity suggest limited immune selection, and absence of detectable clade switching in reinfections indicates initial exposure was insufficient to elicit a protective immune response.

A26 Transmission patterns and evolution of RSV in a community outbreak identified by genomic analysis.

well as host/virus interaction. In our study, using 454/Illumina sequencing, we have obtained large amount of whole genome sequences. We designed a preliminary bioinformatics analysis pipeline to classify these NGS reads. First we mapped our nucleotide reads to GenBank reference sequences using BLAST, and classified them by their taxonomic family, such as host, virus and unclassified. Then, for a specific type of virus (e.g. influenza virus, MERS coronavirus), we conducted de novo and reference based assembly of the reads to obtain the full genome sequences for further phylogenetic study. In the future, through advanced bioinformatics tools, we hope to get more detailed information from our large amount of NGS sequences of field/clinical samples, experimental data, especially in the following areas: (i) finding novel pathogens in unclassified sequences; (ii) virus/virus interactions; (iii) pathogen/host interaction.