de Almeida Rita M C, Thomas Gilberto L, Glazier James A
Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
Biocomplexity Institute and Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
NAR Genom Bioinform. 2022 Mar 15;4(1):lqac020. doi: 10.1093/nargab/lqac020. eCollection 2022 Mar.
To understand the difference between benign and severe outcomes after Coronavirus infection, we urgently need ways to clarify and quantify the time course of tissue and immune responses. Here we re-analyze 72-hour time-series microarrays generated in 2013 by Sims and collaborators for SARS-CoV-1 infection of a human lung epithelial cell line. Transcriptograms, a Bioinformatics tool to analyze genome-wide gene expression data, allow us to define an appropriate context-dependent threshold for mechanistic relevance of gene differential expression. Without knowing in advance which genes are relevant, classical analyses detect every gene with statistically-significant differential expression, leaving us with too many genes and hypotheses to be useful. Using a Transcriptogram-based top-down approach, we identified three major, differentially-expressed gene sets comprising 219 mainly immune-response-related genes. We identified timescales for alterations in mitochondrial activity, signaling and transcription regulation of the innate and adaptive immune systems and their relationship to viral titer. The methods can be applied to RNA data sets for SARS-CoV-2 to investigate the origin of differential responses in different tissue types, or due to immune or preexisting conditions or to compare cell culture, organoid culture, animal models and human-derived samples.
为了了解冠状病毒感染后良性和严重后果之间的差异，我们迫切需要方法来阐明和量化组织及免疫反应的时间进程。在此，我们重新分析了2013年西姆斯及其合作者针对人肺上皮细胞系感染SARS-CoV-1所生成的72小时时间序列微阵列。转录图谱是一种用于分析全基因组基因表达数据的生物信息学工具，它使我们能够为基因差异表达的机制相关性定义一个合适的、依赖于上下文的阈值。在事先不知道哪些基因相关的情况下，传统分析会检测到每个具有统计学显著差异表达的基因，这就给我们留下了太多的基因和假设，以至于无法发挥作用。使用基于转录图谱的自上而下的方法，我们确定了三个主要的、差异表达的基因集，其中包括219个主要与免疫反应相关的基因。我们确定了线粒体活性、先天性和适应性免疫系统的信号传导及转录调控变化的时间尺度，以及它们与病毒滴度的关系。这些方法可应用于SARS-CoV-2的RNA数据集，以研究不同组织类型中差异反应的起源，或由于免疫或既往存在的状况导致的差异反应，或者用于比较细胞培养、类器官培养、动物模型和人类来源的样本。
