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

Abstract

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.

In vitro shared transcriptomic responses of Aedes aegypti to arboviral infections: example of dengue and Rift Valley fever viruses

“…Applicability of other assays such as the Lawrence Livermore Microbial Detection Array (LLMDA) revealed the presence of mosquito-borne viruses and insect-specific viruses in field-collected mosquitoes with similar limitations [24]. Recently, in 2018, Fukutani et al [25,26] proposed an innovative approach, based on the transcriptomic response of the vectors to various arboviruses. In short, they listed a group of co-regulated genes whose expression levels significantly changed in vectors infected by different viruses.…”

The sound of silent RNA in tuberculosis and the lncRNA role on infection

“…Datasets from other regions were maintained as test datasets, as they were single-end RNA-seq data (Romania and South Africa) and were sequenced in more than one batch, demanding the application of a batch effect correction algorithm (India). This approach of using machine learning algorithms to identify biomarkers has been used before with HTLV-1, 29 mosquitoes with dengue, Zika, Chikungunya, and Yellow Fever 30 , 31 and to identify a predictive model in cardiovascular diseases. 32 The model composed of five lncRNAs ( ADM-DT , LINC02009 , LINC02471 , SOX2-OT, and GK-AS1 ) achieved AUCs >0.85 when discriminating patients with TB from HC and >0.9 with TB/DM from HC, even in samples from other populations, exhibiting an outstandingly consistent accuracy.…”

The impact of ZIKV infection on gene expression in neural cells over time

“…A similar approach has been widely employed to identify important genes and molecular pathways with the aim of discovering potential biomarkers in several pathologies, such as endometrial cancer [ 19 ], Sickle cell disease [ 20 ], and HTLV-1 [ 21 ]. Related studies have analyzed Aedes aegypti expression datasets to identify important genes and pathways related to mosquito infection by Dengue, Yellow fever, West Nile, and Zika viruses [ 22 , 23 ]. The present investigation analyzed neuronal stem cell gene expression data among 39 samples of which 30 were ZIKV-infected samples collected at different times post-infection and 9 non infected samples.…”