An in silico integrative protocol for identifying key genes and pathways useful to understand emerging virus disease pathogenesis.

The pathogenesis of an emerging virus disease is a difficult task due to lack of scientific data about the emerging virus during outbreak threats. Several biological aspects should be studied faster, such as virus replication and dissemination, immune responses to this emerging virus on susceptible host and specially the virus pathogenesis. Integrative in silico transcriptome analysis is a promising approach for understanding biological events in complex diseases. In this study, we propose an in silico protocol for identifying key genes and pathways useful to understand emerging virus disease pathogenesis. To validate our protocol, the emerging arbovirus Zika virus (ZIKV) was chosen as a target micro-organism. First, an integrative transcriptome data from neural cells infected with ZIKV was used to identify shared differentially expressed genes (DEGs). The DEGs were used to identify the potential candidate genes and pathways in ZIKV pathogenesis through gene enrichment analysis and protein‑protein interaction network construction. Thirty DEGs (24 upregulated and 6 downregulated) were identified in all ZIKV-infected cells, primarily associated with endoplasmic reticulum stress and DNA replication pathways. Some of these genes and pathways had biological functions linked to neurogenesis and/or apoptosis, confirming the potential of this protocol to find key genes and pathways involved on disease pathogenesis. Moreover, the proposed in silico protocol performed anintegrated analysis that is able to predict and identify putative biomarkers from different transcriptome data. These biomarkers could be useful to understand virus disease pathogenesis and also help the identification of candidate antiviral drugs.