Molecular Evolutionary Analysis of pH1N1 2009 Influenza Virus in Reunion Island, South West Indian Ocean Region: A Cohort Study

Background/Objectives

Molecular epidemiology is a powerful tool to decipher the dynamics of viral transmission, quasispecies temporal evolution and origins. Little is known about the pH1N1 molecular dynamics in general population. A prospective study (CoPanFlu-RUN) was carried out in Reunion Island to characterize pH1N1 genetic variability and molecular evolution occurring in population during the pH1N1 Influenza pandemic in 2009.

Methodology

We directly amplified pH1N1 genomes from 28 different nasal swabs (26 individuals from 21 households). Fifteen strains were fully sequenced and 13 partially. This includes pairs of sequences from different members of 5 separate households; and two pairs from individuals, collected at different times. We assessed the molecular evolution of pH1N1 by genetic variability and phylogenetic analyses.

Principal Findings

We found that i) Reunion pH1N1 sequences stemmed from global “clade 7” but shaped two phylogenetic sub-clades; ii) D239E mutation was identified in the hemagglutinin protein of all Reunion sequences, a mutation which has been associated elsewhere with mild-, upper-respiratory tract pH1N1 infecting strains; iii) Date estimates from molecular phylogenies predicted clade emergence some time before the first detection of pH1N1 by the epidemiological surveillance system; iv) Phylogenetic relatedness was observed between Reunion pH1N1 viruses and those from other countries in South-western Indian Ocean area; v) Quasispecies populations were observed within households and individuals of the cohort-study.

Conclusions

Surveillance and/or prevention systems presently based on Influenza virus sequence variation should take into account that the majority of studies of pH1N1 Influenza generate genetic data for the HA/NA viral segments obtained from hospitalized-patients, which is potentially non-representative of the overall viral diversity within whole populations. Our observations highlight the importance of collecting unbiased data at the community level and conducting whole genome analysis to accurately understand viral dynamics.