Archives of Virology

, Volume 163, Issue 9, pp 2465–2469 | Cite as

Evolutionary analyses of Sindbis virus strains isolated from mosquitoes in Kenya

  • Faith SigeiEmail author
  • Fredrick Nindo
  • Silvanos Mukunzi
  • Zipporah Ng’ang’a
  • Rosemary Sang
Brief Report


Sindbis virus (SINV) is a mosquito borne virus maintained in nature in a mosquito-bird cycle, with human outbreaks known to occur in Northern Europe and parts of Africa. We analyzed five SINV strains isolated in Kenya from five different mosquito species and geographic locations between 2007 and 2013. Phylogenetic relationships and evolutionary inferences were performed using maximum likelihood and Bayesian phylogenetic inference approaches. Selection analyses were carried out based on the virus envelope glycoproteins (E1, E2) and non-structural protein (nsP4) genes. Phylogenetic analysis revealed that all the Kenyan SINV isolates belonged to genotype 1 with selection analyses suggesting that SINV E1, E2 and nsP4 protein encoding genes were predominantly evolving under negative selection.



The authors wish to acknowledge Caroline Tigoi, Edith Chepkorir, Samuel Arum and the entire KEMRI-VHF Nairobi team for their technical assistance and support during the study.


This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

Ethical clearance to conduct this study was sought and granted by the Kenya Medical Research Institute (KEMRI) Ethics Review Committee (ERC), under protocol approval scientific steering committee (SSC) 2786.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Faith Sigei
    • 1
    Email author
  • Fredrick Nindo
    • 2
  • Silvanos Mukunzi
    • 3
  • Zipporah Ng’ang’a
    • 1
  • Rosemary Sang
    • 3
  1. 1.Jomo Kenyatta University of Agriculture and TechnologyNairobiKenya
  2. 2.Division of Computational Biology, Department of Integrative Biomedical Sciences, Faculty of Health SciencesUniversity of Cape TownCape TownSouth Africa
  3. 3.Centre for Virus ResearchKenya Medical Research InstituteNairobiKenya

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