PDF(Pubmed)
Abstract：
BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic occurred due to the dispersion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Severe symptoms can be observed in COVID-19 patients with lipid-related comorbidities such as obesity and diabetes. Yet, the extensive molecular mechanisms of how SARS-CoV-2 causes dysregulation of lipid metabolism remain unknown.
METHODS: Here, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.
RESULTS: The analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.
CONCLUSIONS: Taken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.
METHODS: Here, an advanced search of articles was conducted using PubMed, Scopus, EBSCOhost, and Web of Science databases using terms from Medical Subject Heading (MeSH) like SARS-CoV-2, lipid metabolism and transcriptomic as the keywords. From 428 retrieved studies, only clinical studies using next-generation sequencing as a gene expression method in COVID-19 patients were accepted. Study design, study population, sample type, the method for gene expression and differentially expressed genes (DEGs) were extracted from the five included studies. The DEGs obtained from the studies were pooled and analyzed using the bioinformatics software package, DAVID, to determine the enriched pathways. The DEGs involved in lipid metabolic pathways were selected and further analyzed using STRING and Cytoscape through visualization by protein-protein interaction (PPI) network complex.
RESULTS: The analysis identified nine remarkable clusters from the PPI complex, where cluster 1 showed the highest molecular interaction score. Three potential candidate genes (PPARG, IFITM3 and APOBEC3G) were pointed out from the integrated bioinformatics analysis in this systematic review and were chosen due to their significant role in regulating lipid metabolism. These candidate genes were significantly involved in enriched lipid metabolic pathways, mainly in regulating lipid homeostasis affecting the pathogenicity of SARS-CoV-2, specifically in mechanisms of viral entry and viral replication in COVID-19 patients.
CONCLUSIONS: Taken together, our findings in this systematic review highlight the affected lipid-metabolic pathways along with the affected genes upon SARS-CoV-2 invasion, which could be a potential target for new therapeutic strategies study in the future.
摘要：
背景：2019年冠状病毒病（COVID-19）大流行是由于严重急性呼吸道综合征冠状病毒2（SARS-CoV-2）的分散而发生的。在患有肥胖和糖尿病等脂质相关合并症的COVID-19患者中可以观察到严重的症状。然而，SARS-CoV-2如何引起脂质代谢失调的广泛分子机制仍然未知.
方法：这里，使用PubMed对文章进行了高级搜索，Scopus,EBSCOhost,和WebofScience数据库使用来自医学主题标题（MeSH）的术语，例如SARS-CoV-2，脂质代谢和转录组学作为关键字。从检索到的428项研究中，仅接受在COVID-19患者中使用下一代测序作为基因表达方法的临床研究.研究设计,研究人群,样品类型,从五项纳入的研究中提取了基因表达方法和差异表达基因（DEGs）。从研究中获得的DEGs进行汇总和分析，使用生物信息学软件包，大卫,确定富集的途径。通过蛋白质-蛋白质相互作用（PPI）网络复合物的可视化，使用STRING和Cytoscape选择并进一步分析了脂质代谢途径中涉及的DEGs。
结果：分析确定了PPI复合物中的9个显着簇，其中簇1显示最高的分子相互作用得分。三个潜在的候选基因（PPARG，IFITM3和APOBEC3G）是从本系统综述的综合生物信息学分析中指出的，并且由于它们在调节脂质代谢中的重要作用而被选择。这些候选基因显著参与丰富的脂质代谢途径，主要在调节脂质稳态影响SARS-CoV-2的致病性，特别是在COVID-19患者的病毒进入和病毒复制机制中。
结论：综合来看，我们在这篇系统综述中的发现强调了SARS-CoV-2入侵时受影响的脂质代谢途径以及受影响的基因，这可能是未来新的治疗策略研究的潜在目标。
方法：这里，使用PubMed对文章进行了高级搜索，Scopus,EBSCOhost,和WebofScience数据库使用来自医学主题标题（MeSH）的术语，例如SARS-CoV-2，脂质代谢和转录组学作为关键字。从检索到的428项研究中，仅接受在COVID-19患者中使用下一代测序作为基因表达方法的临床研究.研究设计,研究人群,样品类型,从五项纳入的研究中提取了基因表达方法和差异表达基因（DEGs）。从研究中获得的DEGs进行汇总和分析，使用生物信息学软件包，大卫,确定富集的途径。通过蛋白质-蛋白质相互作用（PPI）网络复合物的可视化，使用STRING和Cytoscape选择并进一步分析了脂质代谢途径中涉及的DEGs。
结果：分析确定了PPI复合物中的9个显着簇，其中簇1显示最高的分子相互作用得分。三个潜在的候选基因（PPARG，IFITM3和APOBEC3G）是从本系统综述的综合生物信息学分析中指出的，并且由于它们在调节脂质代谢中的重要作用而被选择。这些候选基因显著参与丰富的脂质代谢途径，主要在调节脂质稳态影响SARS-CoV-2的致病性，特别是在COVID-19患者的病毒进入和病毒复制机制中。
结论：综合来看，我们在这篇系统综述中的发现强调了SARS-CoV-2入侵时受影响的脂质代谢途径以及受影响的基因，这可能是未来新的治疗策略研究的潜在目标。
