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.

中文翻译：

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一种用于识别关键基因和通路的计算机集成方案，有助于了解新出现的病毒病发病机制。

由于在爆发威胁期间缺乏有关新出现病毒的科学数据，因此新出现病毒病的发病机制是一项艰巨的任务。应该更快地研究几个生物学方面，例如病毒的复制和传播，对易感宿主的这种新兴病毒的免疫反应，特别是病毒的发病机制。集成计算机转录组分析是了解复杂疾病中生物事件的一种很有前景的方法。在这项研究中，我们提出了一种用于识别关键基因和途径的计算机方案，有助于了解新出现的病毒病发病机制。为了验证我们的协议，新出现的虫媒病毒寨卡病毒 (ZIKV) 被选为目标微生物。第一的，来自感染 ZIKV 的神经细胞的综合转录组数据用于识别共享的差异表达基因 (DEG)。通过基因富集分析和蛋白质-蛋白质相互作用网络构建，DEGs用于鉴定ZIKV发病机制中的潜在候选基因和途径。在所有 ZIKV 感染的细胞中鉴定出 30 个 DEG（24 个上调和 6 个下调），主要与内质网应激和 DNA 复制途径相关。其中一些基因和通路具有与神经发生和/或细胞凋亡相关的生物学功能，证实了该协议在寻找与疾病发病机制相关的关键基因和通路方面的潜力。而且，提议的计算机协议进行了综合分析，能够从不同的转录组数据中预测和识别推定的生物标志物。这些生物标志物可能有助于了解病毒疾病的发病机制，也有助于识别候选抗病毒药物。