Meta-Analysis of Expression Datasets: Comparing Virus Infection and Blood-Fed Transcriptomes to Identify Markers of Virus Presence.

The mosquito (L.) is vector of several arboviruses including dengue, yellow fever, chikungunya, and more recently zika. Previous transcriptomic studies have been performed to elucidate altered pathways in response to viral infection. However, the intrinsic coupling between alimentation and infection were unappreciated in these studies. Feeding is required for the initial mosquito contact with the virus and these events are highly dependent. Addressing this relationship, we reinterrogated datasets of virus-infected mosquitoes with two different diet schemes (fed and unfed mosquitoes), evaluating the metabolic cross-talk during both processes. We constructed coexpression networks with the differentially expressed genes of these comparison: virus-infected versus blood-fed mosquitoes and virus-infected versus unfed mosquitoes. Our analysis identified one module with 110 genes that correlated with infection status (representing ~0.7% of the genome). Furthermore, we performed a machine-learning approach and summarized the infection status using only four genes (AAEL012128, AAEL014210, AAEL002477, and AAEL005350). While three of the four genes were annotated as hypothetical proteins, AAEL012128 gene is a membrane amino acid transporter correlated with viral envelope binding. This gene alone is able to discriminate all infected samples and thus should have a key role to discriminate viral infection in the mosquito. Moreover, validation using external datasets found this gene as differentially expressed in four transcriptomic experiments. Therefore, these genes may serve as a proxy of viral infection in the mosquito and the others 106 identified genes provides a framework to future studies.