PDF(Pubmed)
Abstract：
The two-stage molecular profile of the progression of SARS-CoV-2 (SCOV2) infection is explored in terms of five key biological/clinical questions: (a) does SCOV2 exhibits a two-stage infection profile? (b) SARS-CoV-1 (SCOV1) vs. SCOV2: do they differ? (c) does and how SCOV2 differs from Influenza/INFL infection? (d) does low viral-load and (e) does COVID-19 early host response relate to the two-stage SCOV2 infection profile? We provide positive answers to the above questions by analyzing the time-series gene-expression profiles of preserved cell-lines infected with SCOV1/2 or, the gene-expression profiles of infected individuals with different viral-loads levels and different host-response phenotypes.
Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI\'s gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.
The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.
The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.
Our analytical methodology follows an in-silico quest organized around an elaborate multi-step analysis pipeline including: (a) utilization of fifteen gene-expression datasets from NCBI\'s gene expression omnibus/GEO repository; (b) thorough designation of SCOV1/2 and INFL progression stages and COVID-19 phenotypes; (c) identification of differentially expressed genes (DEGs) and enriched biological processes and pathways that contrast and differentiate between different infection stages and phenotypes; (d) employment of a graph-based clustering process for the induction of coherent groups of networked genes as the representative core molecular fingerprints that characterize the different SCOV2 progression stages and the different COVID-19 phenotypes. In addition, relying on a sensibly selected set of induced fingerprint genes and following a Machine Learning approach, we devised and assessed the performance of different classifier models for the differentiation of acute respiratory illness/ARI caused by SCOV2 or other infections (diagnostic classifiers), as well as for the prediction of COVID-19 disease severity (prognostic classifiers), with quite encouraging results.
The central finding of our experiments demonstrates the down-regulation of type-I interferon genes (IFN-1), interferon induced genes (ISGs) and fundamental innate immune and defense biological processes and molecular pathways during the early SCOV2 infection stages, with the inverse to hold during the later ones. It is highlighted that upregulation of these genes and pathways early after infection may prove beneficial in preventing subsequent uncontrolled hyperinflammatory and potentially lethal events.
The basic aim of our study was to utilize in an intuitive, efficient and productive way the most relevant and state-of-the-art bioinformatics methods to reveal the core molecular mechanisms which govern the progression of SCOV2 infection and the different COVID-19 phenotypes.
摘要：
■根据五个关键的生物学/临床问题，探索了SARS-CoV-2（SCOV2）感染进展的两阶段分子谱：（a）SCOV2是否表现出两阶段感染谱？（b）SARS-CoV-1（SCOV1）与SCOV2：它们是否不同？（c）SCOV2与流感/INFL感染是否以及如何？（d）低病毒载量和（e）COVID-19早期宿主反应是否与两阶段SCOV2感染谱有关？我们通过分析感染SCOV1/2或，具有不同病毒载量水平和不同宿主反应表型的感染个体的基因表达谱。
■我们的分析方法遵循围绕精心的多步骤分析流程组织的计算机内搜索，包括：（a）利用来自NCBI基因表达综合/GEO存储库的15个基因表达数据集；（b）彻底指定SCOV1/2和INFL进展阶段以及COVID-19表型的差异表达基因的识别，以区分基于不同COID的分子诱导过程（DED）和生物学途径的此外,依靠一组合理选择的诱导指纹基因，并遵循机器学习方法，我们设计并评估了不同分类器模型的性能，以区分由SCOV2或其他感染引起的急性呼吸道疾病/ARI（诊断分类器），以及预测COVID-19疾病严重程度(预后分类器)，结果令人鼓舞。
■我们实验的中心发现证明了I型干扰素基因（IFN-1）的下调，在SCOV2感染早期阶段，干扰素诱导基因（ISGs）和基本的先天免疫和防御生物过程以及分子途径，在后面的过程中保持反向。强调的是，感染后早期这些基因和途径的上调可能有助于预防随后的不受控制的高炎症和潜在的致命事件。
■我们研究的基本目的是利用直觉，高效和高效的方式，最相关和最先进的生物信息学方法，以揭示控制SCOV2感染进展和不同COVID-19表型的核心分子机制。
■我们的分析方法遵循围绕精心的多步骤分析流程组织的计算机内搜索，包括：（a）利用来自NCBI基因表达综合/GEO存储库的15个基因表达数据集；（b）彻底指定SCOV1/2和INFL进展阶段以及COVID-19表型的差异表达基因的识别，以区分基于不同COID的分子诱导过程（DED）和生物学途径的此外,依靠一组合理选择的诱导指纹基因，并遵循机器学习方法，我们设计并评估了不同分类器模型的性能，以区分由SCOV2或其他感染引起的急性呼吸道疾病/ARI（诊断分类器），以及预测COVID-19疾病严重程度(预后分类器)，结果令人鼓舞。
■我们实验的中心发现证明了I型干扰素基因（IFN-1）的下调，在SCOV2感染早期阶段，干扰素诱导基因（ISGs）和基本的先天免疫和防御生物过程以及分子途径，在后面的过程中保持反向。强调的是，感染后早期这些基因和途径的上调可能有助于预防随后的不受控制的高炎症和潜在的致命事件。
■我们研究的基本目的是利用直觉，高效和高效的方式，最相关和最先进的生物信息学方法，以揭示控制SCOV2感染进展和不同COVID-19表型的核心分子机制。
