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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

ABSTRACT: Background: The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. Methods: We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) from clinical sample data sets. By taking the intersection, 42 co-morbid host factors for COVID-19 and COPD were finally obtained. On the basis of shared host factors, we conducted a series of bioinformatics analysis, including protein-protein interaction analysis, gene ontology and pathway enrichment analysis, transcription factor-gene interaction network analysis, gene-microRNA co-regulatory network analysis, tissue-specific enrichment analysis and candidate drug prediction. Results: We revealed the comorbidity mechanism of COVID-19 and COPD from the perspective of host factor interaction, obtained the top ten gene and 3 modules with different biological functions. Furthermore, we have obtained the signaling pathways and concluded that dexamethasone, estradiol, progesterone, and nitric oxide shows effective interventions. Conclusion: This study revealed host factor interaction networks for COVID-19 and COPD, which could confirm the potential drugs for treating the comorbidity, ultimately, enhancing the management of the respiratory disease.

SUBMITTER: Zheng W

PROVIDER: S-EPMC8733735 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

Zheng Wenjiang W Wang Ting T Wu Peng P Yan Qian Q Liu Chengxin C Wu Hui H Zhan Shaofeng S Liu Xiaohong X Jiang Yong Y Zhuang Hongfa H

Frontiers in pharmacology 20211223

<b>Background:</b> The COVID-19 pandemic poses an imminent threat to humanity, especially for those who have comorbidities. Evidence of COVID-19 and COPD comorbidities is accumulating. However, data revealing the molecular mechanism of COVID-19 and COPD comorbid diseases is limited. <b>Methods:</b> We got COVID-19/COPD -related genes from different databases by restricted screening conditions (top500), respectively, and then supplemented with COVID-19/COPD-associated genes (FDR<0.05, |LogFC|≥1) ...[more]

PMID: 35002688

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Project description:Purpose:To explore the potential mechanism underpinning the development of chronic obstructive pulmonary disease (COPD) and to investigate the role of the Roundabout signaling pathway in COPD. Methods:Three microarray datasets (GSE1650, GSE38974 and GSE76925) including 139 cases of severe COPD and 52 cases of normal smokers without carcinoma, were integrated to screen differentially expressed genes (DEGs) using bioinformatics methods. Gene ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of the DEGs were performed by a DAVID online tool. Finally, a cigarette smoke (CS)- induced emphysema mice model was established, the lung mRNA expression levels of genes associated with Slit guidance ligand 2 (SLIT2) -Roundabout (ROBO) signaling pathway were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the protein level of SLIT2 was examined by immunohistochemistry staining. Results:A total of 315 DEGs were identified in three databases. GO and KEGG pathway analyses suggested that the inflammatory response, extracellular matrix disassembly, immune response, the apoptotic signaling pathway, ubiquitination and the Roundabout signaling pathway all together were involved in the development of COPD. The genes SLIT2 and ROBO2 were decreased in patients with COPD and these decreases were significantly negatively correlated with the disease stages of COPD. Consistently, the mRNA expression levels of SLIT2, ROBO1 and ROBO2, and the protein level of SLIT2 were revealed to be lower in the lungs of CS-induced emphysema mice compared with the air-exposed control mice. In addition, the SLIT2 protein level was negatively associated with alveolar mean linear intercept. Conclusion:Integrated bioinformatics analysis may provide novel insights into the complicated pathogenesis of COPD, and to the best of our knowledge, this study is the first to provide evidence to suggest that the Roundabout signaling pathway may be involved in the pathogenesis of COPD.

Project description:Integrative Bioinformatics analysis helps to explore various mechanisms of Nitroglycerin activity in different types of cancers and help predict target genes through which Nitroglycerin affect cancers. Many publicly available databases and tools were used for our study. First step in this study is identification of Interconnected Genes. Using Pubchem and SwissTargetPrediction Direct Target Genes (activator, inhibitor, agonist and suppressor) of Nitroglycerin were identified. PPI network was constructed to identify different types of cancers that the 12 direct target genes affected and the Closeness Coefficient of the direct target genes so identified. Pathway analysis was performed to ascertain biomolecules functions for the direct target genes using CluePedia App. Mutation Analysis revealed Mutated Genes and types of cancers that are affected by the mutated genes. While the PPI network construction revealed the types of cancer that are affected by 12 target genes this step reveals the types of cancers affected by mutated cancers only. Only mutated genes were chosen for further study. These mutated genes were input into STRING to perform NW Analysis. NW Analysis revealed Interconnected Genes within the mutated genes as identified above. Second Step in this study is to predict and identify Upregulated and Downregulated genes. Data Sets for the identified cancers from the above procedure were obtained from GEO Database. DEG Analysis on the above Data sets was performed to predict Upregulated and Downregulated genes. A comparison of interconnected genes identified in step 1 with Upregulated and Downregulated genes obtained in step 2 revealed Co-Expressed Genes among Interconnected Genes. NW Analysis using STRING was performed on Co-Expressed Genes to ascertain Closeness Coefficient of Co-Expressed genes. Gene Ontology was performed on Co-Expressed Genes to ascertain their Functions. Pathway Analysis was performed on Co-Expressed Genes to identify the Types of Cancers that are influenced by co-expressed genes. The four types of cancers identified in Mutation analysis in step 1 were the same as the ones that were identified in this pathway analysis. This further corroborates the 4 types of cancers identified in Mutation analysis. Survival Analysis was done on the co-expressed genes as identified above using Survexpress. BIOMARKERS for Nitroglycerin were identified for four types of cancers through Survival Analysis. The four types of cancers are Bladder cancer, Endometrial cancer, Melanoma and Non-small cell lung cancer.

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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

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Host Factor Interaction Networks Identified by Integrative Bioinformatics Analysis Reveals Therapeutic Implications in COPD Patients With COVID-19.

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