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(Referência obtida automaticamente do SciELO, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Fukutani, Eduardo [1] ; Rodrigues, Moreno [1] ; Kasprzykowski, Jose Irahe [1, 2] ; de Araujo, Cintia Figueiredo [3] ; Paschoal, Alexandre Rossi [4] ; Pereira Ramos, Pablo Ivan [1] ; Fukutani, Kiyoshi Ferreira [5, 6] ; Lopo de Queiroz, Artur Trancoso [1, 2]
Número total de Autores: 8
Afiliação do(s) autor(es):
[1] Fundacao Oswaldo Cruz Fiocruz, Inst Goncalo Moniz, Salvador, BA - Brazil
[2] Fundacao Oswaldo Cruz Fiocruz, Programa Posgrad Biotecnol Saude Invest, 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
Número total de Afiliações: 6
Tipo de documento: Artigo Científico
Fonte: Memórias do Instituto Oswaldo Cruz; v. 113, n. 6, p. -, 2018.
Citações Web of Science: 0
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
Linha de fomento: Bolsas no Brasil - Pós-Doutorado