ZHOU Yan and JIANG Jingzhou,Association studies between COVID-19 and SSc-ILD[J].Journal of Shenzhen University Science and Engineering,2023,40(2):171-178.[doi:10.3724/SP.J.1249.2023.02171]





Association studies between COVID-19 and SSc-ILD
周彦1 江竞舟12
1)深圳大学数学与统计学院,广东深圳 518060
2)南方科技大学理学院,广东深圳 518055
ZHOU Yan1 and JIANG Jingzhou1 2
1) School of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
2) School of Science, Southern University of Science and Technology, Shenzhen 518055, Guangdong Province, P.R.China
bioinformatics multivariate statistics differentially expressed genes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interstitial lung disease associated with systemic sclerosis (SSc-ILD) Beta-Poisson model enrichment analysis protein-protein interactions (PPI)
与系统性硬化症相关间质性肺病(interstitial lung disease associated with systemic sclerosis, SSc-ILD)类似,重症新型冠状肺炎疾病(corona virus disease 2019, COVID-19)患者可能会出现肺部纤维化的临床症状.为研究这2种疾病的病理标志物,利用Beta-Poisson模型鉴定差异表达基因捕捉核糖核酸测序(ribonucleic acid sequencing, RNA-Seq)数据的双峰特征.通过引入Beta-Poisson模型,即用Beta分布替换Gamma-Poisson分布的Gamma分布,构建新的混合分布刻画RNA-Seq数据中双峰特征.分析肺上皮细胞数据集中的新型冠状病毒感染和SSc-ILD疾病的转录组,确定新型冠状病毒和SSc-ILD的共同差异表达基因,使用基因功能与信号通路富集分析和蛋白质相互作用(protein-protein interaction, PPI)网络为患有新冠肺炎感染的SSc-ILD疾病找到共同的途径和药物靶点.结果表明,COVID-19和SSc-ILD有50个共同的差异表达基因,这些基因功能主要富集在免疫系统应答和干扰素信号通路等相关信号通路,并且富集在细胞对病毒防御反应和干扰素调节等生物学过程.基于PPI网络检测出hub基因,预测STAT1、ISG15、IRF7、MX1、EIF2AK2、DDX58、OAS1、OAS2、IFIT1和IFIT3是涉及两种疾病的病理表型关键基因.基于关键基因又鉴别了转录因子(transcription factor, TF)、小分子核糖核酸(micro ribonucleic acid, miRNA)与常见差异表达基因的相互作用.研究结果揭示了COVID-19和SSc-ILD两种疾病的病理标志物,以及相关的疾病治疗分子调控机制,可为治疗两种疾病提供理论研究依据.
Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases.


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Received: 2022- 06-20; Accepted: 2022-09-05; Online (CNKI): 2022-11-24
Foundation: National Natural Science Foundation of China (12071305, 11871390, 11871411); Natural Science Foundation of Guangdong Province (2023A1515011399, 2020B1515310008)
Corresponding author: Associate professor ZHOU Yan. E-mail: zhouy1016@szu.edu.cn
Citation: ZHOU Yan, JIANG Jingzhou. Association studies between COVID-19 and SSc-ILD [J]. Journal of Shenzhen University Science and Engineering, 2023, 40(2): 171-178.(in Chinese)
作者简介:周彦(1982—),深圳大学副教授、博士生导师.研究方向:生物统计.E-mail: zhouy1016@szu.edu.cn
中文作者简介:江竞舟(1999—),南方科技大学硕士研究生.研究方向:医学统计.E-mail: 201819303@email.szu.edu.cn
更新日期/Last Update: 2023-03-30