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

Otieno JR, Kamau EM, Oketch JW, Ngoi JM, Gichuki AM, Binter S, Otieno GP, Ngama M, Agoti CN, Cane PA, Kellam P, Cotten M, Lemey P, Nokes DJ
Virus Evol. 2018;4

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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.