Genomic epidemiology and evolutionary dynamics of respiratory syncytial virus group B in Kilifi, Kenya, 2015-17.
Everlyn KamauJames Richard OtienoNickson MurungaJohn W OketchJoyce M NgoiZaydah R de LaurentAnthony MwemaJoyce U NyiroCharles N AgotiDavid James NokesPublished in: Virus evolution (2020)
Respiratory syncytial virus (RSV) circulates worldwide, occurring seasonally in communities, and is a leading cause of acute respiratory illness in young children. There is paucity of genomic data from purposively sampled populations by which to investigate evolutionary dynamics and transmission patterns of RSV. Here we present an analysis of 295 RSV group B (RSVB) genomes from Kilifi, coastal Kenya, sampled from individuals seeking outpatient care in nine health facilities across a defined geographical area (∼890 km2), over two RSV epidemics between 2015 and 2017. RSVB diversity was characterized by multiple virus introductions into the area and co-circulation of distinct genetic clusters, which transmitted and diversified locally with varying frequency. Increase in relative genetic diversity paralleled seasonal virus incidence. Importantly, we identified a cluster of viruses that emerged in the 2016/17 epidemic, carrying distinct amino-acid signatures including a novel nonsynonymous change (K68Q) in antigenic site ∅ in the Fusion protein. RSVB diversity was additionally marked by signature nonsynonymous substitutions that were unique to particular genomic clusters, some under diversifying selection. Our findings provide insights into recent evolutionary and epidemiological behaviors of RSVB, and highlight possible emergence of a novel antigenic variant, which has implications on current prophylactic strategies in development.
Keyphrases
- respiratory syncytial virus
- genetic diversity
- genome wide
- copy number
- healthcare
- dna methylation
- amino acid
- risk factors
- mental health
- public health
- palliative care
- liver failure
- human health
- climate change
- heavy metals
- risk assessment
- drug induced
- gene expression
- machine learning
- water quality
- aortic dissection
- health insurance