Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall
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.
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Instituto Oswaldo Cruz, Ministério da Saúde
2018
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oai:scielo:S0074-027620180006004022018-05-23Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wallFukutani,EduardoRodrigues,MorenoKasprzykowski,José IraheAraujo,Cintia Figueiredo dePaschoal,Alexandre RossiRamos,Pablo Ivan PereiraFukutani,Kiyoshi FerreiraQueiroz,Artur Trancoso Lopo de RNA-seq signature transcriptome Zika virus 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.info:eu-repo/semantics/openAccessInstituto Oswaldo Cruz, Ministério da SaúdeMemórias do Instituto Oswaldo Cruz v.113 n.6 20182018-01-01info:eu-repo/semantics/othertext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762018000600402en10.1590/0074-02760180053 |
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Fukutani,Eduardo Rodrigues,Moreno Kasprzykowski,José Irahe Araujo,Cintia Figueiredo de Paschoal,Alexandre Rossi Ramos,Pablo Ivan Pereira Fukutani,Kiyoshi Ferreira Queiroz,Artur Trancoso Lopo de |
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Fukutani,Eduardo Rodrigues,Moreno Kasprzykowski,José Irahe Araujo,Cintia Figueiredo de Paschoal,Alexandre Rossi Ramos,Pablo Ivan Pereira Fukutani,Kiyoshi Ferreira Queiroz,Artur Trancoso Lopo de Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
author_facet |
Fukutani,Eduardo Rodrigues,Moreno Kasprzykowski,José Irahe Araujo,Cintia Figueiredo de Paschoal,Alexandre Rossi Ramos,Pablo Ivan Pereira Fukutani,Kiyoshi Ferreira Queiroz,Artur Trancoso Lopo de |
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Fukutani,Eduardo |
title |
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
title_short |
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
title_full |
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
title_fullStr |
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
title_full_unstemmed |
Follow up of a robust meta-signature to identify Zika virus infection in Aedes aegypti: another brick in the wall |
title_sort |
follow up of a robust meta-signature to identify zika virus infection in aedes aegypti: another brick in the wall |
description |
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. |
publisher |
Instituto Oswaldo Cruz, Ministério da Saúde |
publishDate |
2018 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0074-02762018000600402 |
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