Network-Based Analysis of The Genetic Effects of SARS-CoV-2 Infection To Patients With Exacerbation of Virus-Induced Asthma (VAE)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel RNA virus that emerged in late 2019 and was responsible for coronavirus disease (COVID-19). The WHO has declared the COVID-19 in the world pandemic. The most exacerbations of asthma are triggered by viral infections. However, the genetic effects of COVID-19 on asthma need to be further studied. Eighty-eight common differentially expressed genes (cDEGs) were identied in datasets GSE147507 and GSE30326. Function analysis showed that cDEGs has antiviral activity, histone kinase activity, chemokine activity and viral protein interaction with cytokine activity. protein–protein interactions (PPIs) network revealed that the proteins encoded by CDEGs interact with each other at a high frequency. Hub genes and essential modules were detected based on the PPIs network. Transcription factors (TF) and miRNA interaction with cDEGs are identied. Drug molecules such as suloctidil HL60 UP and Yu Ping Feng San were recommended for the treatment of novel coronavirus-induced exacerbation of asthma.

In this study, bioinformatics methods were used to screen for common differentially expressed genes (cDEGs) between COVID-19 and VAE. Enrichment analysis understands the biological process (BP), cellular component (CC), molecular functions (MF) and metabolic pathways of cDEGs via Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The hub genes and essential modules were identi ed from protein-protein interactions (PPIs) network. Transfer factor (TF)-genes and miRNA interaction with cDEGs are also identi ed. The present study aims to provide a reference for the management of patients with asthma and the development of new therapeutic agents in the context of the COVID-19 pandemic.

Retrieval of datasets
Two datasets were involved in this study, GSE147507 and GSE30326, which both were from GEO database (https://www.ncbi.nlm.nih.gov/geo/). GSE147507 performed a comprehensive analysis of gene expression in terms of human lung epithelial cells responding to SARS-CoV-2 and airway of COVID-19 patients [36]. GSE30326 illustrated global patterns of gene expression was pro led in nasal lavage samples obtained from asthmatic children during an acute picornavirus-induced exacerbation [11]. Since SARS-CoV-2 is RNA virus, we chose the dataset GSE30326 to match with GSE147507.
Identi cation of COVID-19 and VAE common differentially expressed genes (cDEGs) To identify DEGs for GSE147507, the limma package of R programming language is implemented. Data that is produced from microarray analysis is retrieved through DESeq2 and limma package [37,38]. DEGs for the GSE30326 dataset were analyzed through GEO2Rweb tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) and limma package. Cut-off criteria was obtained for GSE147507 using adjusted P value < 0.05 and log2-fold change (absolute) > 1.0. The cDEGs of GSE147507 and GSE35145 were identi ed by the R programming language. Enrichment analysis of the Gene ontology and KEGG signaling pathway The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the module gene. ClusterPro ler package (v.3.14.3) was used for enrichment analysis while Org.Hs.eg.db package (v.3.10.0) was for ID conversion.

Module genes network construction
The edge le, including source node and target node genes, was generated by WGCNA for protein-protein interaction (PPI) analysis in STRING (v11.5, https://www.string-db.org/). Parameter setting: organism selects Homo sapiens, required score selects medium con dence (0.400), and false discovery rate (FDR) stringency chooses medium (5 percent).
Identi cation of hub genes and module analysis PPIs were analyzed via Cytoscape software, and the hub genes for the current research were revealed by the degree topological algorithm. The nodes that have the most interactions were considered to be a hub gene. Molecular Complex Detection (MCODE) plugin of Cytoscape software was used to detect the most profound modules from the PPIs network.
Evaluation of interaction between transcription factors (TF) and hub genes and construction of TF-miRNA co-regulatory network NetworkAnalyst database (https://www.networkanalyst.ca/) was used to identify TF-gene interaction with identi ed cDEGs and TF-miRNA co-regulatory network. The common network topology measures were also computed based on well-established the igraph R package. TF and gene target data derived from the ENCODE ChIP-seq data. Only peak intensity signal < 500 and the predicted regulatory potential score < 1 were used.

Screening of potential therapeutic drugs
Drugs that modulate 88 cDEGs were screened using Enrichr platform. The access of the DSigDB database was acquired through Enrichr (https://amp.pharm.mssm.edu/Enrichr/). Potential therapeutic agents were determined by the adj. P values and the abundance of acting on cDEGs. Target genes of traditional Chinese medicine (TCM) were retrieved in Pubmed database (https://pubmed.ncbi.nlm.nih.gov/). If the target genes were consistent with hub genes in this study, they would be identi ed as potential therapeutic TCM formula.

Results
Identi cation of common differentially expressed genes (cDEGs) between COVID-19 and VAE As regards the COVID-19, 814 differentially expressed genes were identi ed from GSE147507 dataset, including 419 down-regulated genes and 395 upregulated genes. As for the VAE, 477 differentially expressed genes were identi ed from GSE30326, including 474 down-regulated genes and 3 up-regulated genes. Eighty-eight cDEGs were identi ed in both datasets (Fig. 1).

Enrichment analysis of the GO and KEGG
The current study analyzes GO terms including biological process (BP), cellular component (CC) and molecular functions (MF) for 88 cDEGs, as well as KEGG pathway. Gene ontology revealed that in terms of BP, these cDEGs were mainly involved in mitotic nuclear division, defense response to virus and organelle ssion. From the perspective of CC, it mainly involves in spindle and condensed chromosome. As for MF, cDEGs were involved in binding and motor activity, histone kinase activity and chemokine activity. KEGG Enrichment analysis on the 88 cDEGs showed that the top 5 signal pathways which were signi cantly enriched were in In uenza A, NOD-like receptor signaling pathway, Cell cycle, Viral protein interaction with cytokine and cytokine receptor and Oocyte meiosis (Tab. 1). Visualization of GO and KEGG enrichment analysis see Fig The analyzed network holds 87 nodes and 638 edges (Fig. 3). Average node degree: 14.7. Expected number of edges: 31. Avg. local clustering coe cient: 0.632. It is suggested that these proteins have more interactions among themselves than what would be expected for a random set of proteins of similar size, drawn from the genome.

Detection of hub genes and module analysis
According to topology analysis, 10 cDEGs degree values are the highest, and these genes are considered as Hub genes (Tab. 3). The proteins encoded by the hub genes have rich interactions with other proteins, including 52 nodes and 618 edges (Fig. 4). Module analysis showed that TRIM38, GBP3, STAT1, CASP5, C19orf66 and other genes were high-density modules, including 41 spots and 126 edges. In addition, except for STAT1, which is both hub gene and highdensity module, other hub genes are closely related to high-density module (Fig. 5 (Fig. 6).

Discussion
As a respiratory disease, COVID-19 has a subtle relationship with asthma. On the one hand, WHO guidelines emphasize that people with asthma are at high risk for COVID-19 [12]. On the other hand, viral infection is an important cause of asthma deterioration, manifested by increased airway in ammation, increased mucus secretion and lower respiratory symptoms [13,14]. Analysis of the possible causes of virus-induced exacerbation of asthma: (1) Asthma shows imbalance of Th1\Th2 cells in cytology, and Th2 cells increase [15], which reduces the generation of Th1 cytokine interferon, causing asthma patients to be de cient in antiviral and increase viral load [16]; (2) IgE antibodies eliminate the biological activity of IFN-α and reduce its concentration and antiviral ability [17]; (3) The dephosphorylation and deubiquitination ability of SARS-CoV is able to inhibit IFN signaling [18]. Worse, it is controversial in treatment between virus-induced asthma aggravation and COVID-19, particularly with uncertainty regarding the object and timing of glucocorticoid therapy [12,19]. Therefore, in the context of the COVID-19 pandemic and the lack of effective drugs, understanding the genetic effects of the two diseases is not only conducive to the research of vaccines for COVID-19 and development of gene-targeted therapeutics, but also important for the treatment of severe asthma.
In this study, bioinformatics methods were used to analyze datasets GSE147507 and GSE30326, and 88 cDEGs were identi ed. The biological process, cellular components, molecular functions and KEGG enrichment of these cDEGs were comprehensively analyzed. Through analysis, we speculated the molecular activity, cellular role, executive function location and metabolic pathway of cDEGs. Tasnimul et al. analyzed data sets GSE147507 and GSE35145, identi ed 11 common differentially expressed genes, and analyzed them for GO and KEGG enrichment [20]. More cDEGs were identi ed in this study than the above studies, and in both GO and KEGG enrichment analysis, mainly suggesting BP enrichment of cDEGs in antiviral and nuclear division; and MF, cDEGs in protein kinase activity and chemokine activity; coincidentally, KEGG signaling suggested signi cant enrichment of viral protein-cytokines. It is speculated that antiviral and chemokine management are the key to the treatment of these two diseases, which is consistent with the current treatment strategy advocated [21,22].
Protein-protein interaction (PPI) is necessary for most of the biological processes and requisite for host-pathogen communication. Mapping protein interactions is to understand the interaction of proteins highly associated with the disease during development, and raising awareness of its function [23]. As can be seen from Figure 3, there are highly dense regions of interaction between proteins encoded by cDEGs, suggesting that these genes are involved in important disease processes. Ten hub genes were identi ed according to the frequency and degree of action of cDEGs (such as STAT1, MX1, IFIH1, et al.).
Studies have shown that ISG15/ IRF7/ IFIT1 is considered as a potential drug target for the treatment of COVID-19 [24]; STAT1 is considered to be an important gene in mediating COVID-19 and asthma [25,26]; CXCL10 encodes viral infection-related proteins and is considered to be an important signaling pathway that induces asthma exacerbation [27]. The results of this study support the above reports. Module analysis found that TRIM38, GBP3, STAT1, CASP5, et al. are high-density modules. Besides STAT1, other hub genes interact with high-density modules at a high frequency. Therefore, it is speculated that the above hub genes are potential therapeutic target for asthma exacerbation induced by SARS-CoV-2.
Transcription factors are the regulatory factors of gene expression and are closely related to the occurrence and development of human diseases. In this study, NF-κB1, SP1 and RALA were identi ed as the most widely active transcription factors. NF-κB1, a member of the Rel protein family, plays a key role in the in ammation of lung tissues by mediating asthma and COVID-19, which is activated by many stimulating factors and mediates the production of many in ammatory cytokines [28]. Sp1 belongs to Sp/KLF family of transcription factors. It is a paradigm for a ubiquitously expressed transcription factor and is involved in regulating the expression of genes associated with cancer, Huntington's disease, and Alzheimer's disease [29]. RALA participates in the regulation of NLRP3 in ammasome and has anti-in ammatory effects [30]. TF-miRNA co-regulation revealed that miR-186, miR-135a, miR-362-5p and miR-409-3p had the highest frequency, and miR-409-3p and miR-496 were highly expressed in patients with COVID-19 [31]. Therefore, we believe that targeting transcription factors and Mir-as therapeutic targets can provide a novel therapeutic strategy for SARS-CoV-2-induced asthma exacerbations.
Common differentially expressed genes were used to predict potential therapeutic agents in DSigDB database. Drugs such as Suloctidil, 3'-Azido-3 deoxythymidine et al. were screened for pharmacological effects such as anti-in ammatory, antiviral, and inhibition of in ammatory cytokine storms. This result was consistent with the results of GO-KEGG analysis. It is worth noting that TCM has unique advantages in the treatment of COVID-19 and asthma [32,33]. Search the pubmed for TCMs that can act on hub genes. It was found that Yu Ping Feng San could act on ISG15 [34], Qing Fei Pai Du decoction could act on IFIT1/3,IFIH1 [35]. Both genes such as ISG15 and IFIT1/3 affect interferon expression, which are key drugs for treatment.

Conclusions
In conclusion, cDEGs from two datasets of GSE147507 and GSE30326 were identi ed. On this basis, we constructed the differentially expressed gene PPIs, screened hub genes, and used module analysis to con rm the high-frequency interaction between hub genes and other cDEGs in the course of these two diseases. We further con rmed the critical role of hub genes in diseases through TFs and its co-regulatory network with miRNA. Finally, we screened out potential therapeutic agents (including TCMs). However, due to the sample size limitation, we also need to further experimentally con rm the expression and function of the identi ed hub genes in disease progression, aiming to provide new strategies for the prevention of COVID-19 and its induction of aggravated asthma.

Declarations Authors' contributions
Wei Guo and Yilong Xi conceived the idea and wrote the manuscript. Yi CAO, Ziyun JIANG and Hui LUO depicted gures and analyzed data. Hui LIU contributed for revision. Yilong Xi contributed for overall editing and supervision. All authors approved its submission.

Funding
This study was supported by National Natural Science Foundation of China, Grant Number 31971562.
Ethics approval and consent to participate Not applicable Consent to publish Protein-protein interactions (PPIs) network for identi ed cDEGs that are shared by COVID-19 and VAE. Node indicate cDEGs and edge represents their interactions of two genes. PPI enrichment P-value < 1.0e-16.

Figure 6
Network for TFs interaction with hub genes. The highlighted red color nodes represent the hub genes, and the blue color nodes represent TFs.