Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall

Autor(es)

Eduardo Fukutani ^[1] ; Moreno Rodrigues ^[1] ; José Irahe Kasprzykowski ; Cintia Figueiredo de Araujo ^[3] ; Alexandre Rossi Paschoal ^[4] ; Pablo Ivan Pereira Ramos ^[1] ; Kiyoshi Ferreira Fukutani ^{[5, 6]} ; Artur Trancoso Lopo de Queiroz ^{[1, 7]}

Afiliação do(s) autor(es)

^[1] Fundacao Oswaldo Cruz Fiocruz, Inst Goncalo Moniz, Salvador, BA - Brazil

^[3] Univ Fed Bahia, Serv Imunol, Salvador, BA - Brazil

^[4] Univ Tecnol Fed Parana, Cornelio Procopio, PR - Brazil

^[5] Univ Salvador, Salvador, BA - Brazil

^[6] Univ Sao Paulo, Fac Med Ribeirao Preto, Ribeirao Preto - Brazil

^[7] Fundacao Oswaldo Cruz Fiocruz, Programa Posgrad Biotecnol Saude Invest, Salvador, BA - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Memórias do Instituto Oswaldo Cruz; v. 113, n. 6 2018-05-28.

Resumo

The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this “infection” gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three 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 classify the Zika-infected mosquitoes. (AU)

• Processo FAPESP: 17/03491-6 - CEPID: Centros de Pesquisa, Inovação e Difusão
• Beneficiário: Kiyoshi Ferreira Fukutani
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado