PAGES: DOI: 10.1590/0074- 02760180053 Short communication
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: Another brick in the wall.

Eduardo Fukutani1, Moreno Rodrigues1, José Irahe Kasprzykowski1,2, Cintia Figueiredo de Araujo3, Alexandre Rossi Paschoal4, Pablo Ivan Pereira Ramos1, Kiyoshi Ferreira Fukutani5,+, Artur Trancoso Lopo de Queiroz1,2

1Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (FIOCRUZ),Salvador, Brazil
2Post-Graduation Program in Biotechnology in Health and Investigative Medicine, Fundação Oswaldo Cruz (FIOCRUZ), Salvador, Brazil
3Serviço de Imunologia, Federal University of Bahia, Salvador, Brazil
4Federal University of Technology — Paraná, UTFPR, Campus Cornélio Procópio, Cornélio Procópio, Brazil
5Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.


The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases with global impact. In a previous meta-analysis, our group has identified a vector gene set comprised by 110 genes strongly associated to infection by Dengue, West Nile and Yellow Fever viruses, of which four genes allowed highly accurate classification of infected status. More recently, a new study of A. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this "infection" gene set is also altered during ZIKV infection. Our hypothesis is that the infection-associated signature may serve as a proxy also to classify zika virus infection in the vector. Raw data associated to the NCBI/BioProject was downloaded and reanalysed. A total of 18 paired-end replicates corresponding to 3 ZIKV-infected and 3 controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls, with good predictive power to classificate the Zika-infected mosquitoes.

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