Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Department of Electrical and Electronics Engineering, Islamic University, Kushtia, Bangladesh.
Comput Biol Med. 2023 Jun;159:106885. doi: 10.1016/j.compbiomed.2023.106885. Epub 2023 Mar 31.
Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.
新型冠状病毒疾病（COVID-19）已成为大流行传染病。最近的流行病学数据表明，吸烟者更容易感染 COVID-19；然而，吸烟（SMK）对 COVID-19 感染患者和死亡率的影响尚不清楚。在这项研究中，我们旨在利用 COVID-19 感染肺上皮细胞的转录组学数据和与肺上皮细胞对照匹配的 SMK 转录组学数据，利用生物信息学分析来探讨 SMK 对 COVID-19 感染患者的影响。基于生物信息学的分析揭示了对转录变化水平和途径的分子见解，这些水平和途径对于确定吸烟对 COVID-19 感染和流行的影响非常重要。我们比较了 COVID-19 与 SMK 之间差异表达基因（DEGs），并在转录组水平上鉴定了 59 个一致失调的 DEGs。使用 WGCNA R 包为这些常见基因构建了关联网络分析，以观察这些基因之间的关系。使用网络分析（蛋白质-蛋白质相互作用）对这些 DEGs 进行了整合，结果显示在 COVID-19 患者和 SMK 之间存在 9 个关键基因作为关键的所谓“候选关键基因”。基因本体论和途径分析表明，炎症途径如 IL-17 信号通路、白细胞介素-6 信号通路、TNF 信号通路和 MAPK1/MAPK3 信号通路等都存在富集，这些途径可能是吸烟人群 COVID-19 的治疗靶点。鉴定的基因、途径、枢纽基因及其调节剂可能被认为是 SMK 和 COVID-19 的关键基因和药物靶点。
