On 12 March 2020, the World Health Organization declared that COVID-19 had the characteristics of a global pandemic.SARS-COV-2 pneumonia infection early period performance similar to acute respiratory infections, some patients developed rapidly for acute respiratory distress syndrome (acute respiratory distress syndrome, ARDS), and in a short period of time, lead to multiple organ failure (Wu et al., 2020). Imaging data showed that most of the patients had spots in both lungs with ground-glass shadows, indicating that the lungs were the main target organs for SARS-COV-2 invasion. After infecting lung cells, the virus triggers inflammatory cell factor storm, which leads to acute lung injury and severely damages the ventilation function of the lung. The lung CT shows a large expanse of white, namely "white lung". Patients will suffer from respiratory failure until death from hypoxia (Chen et al., 2020). At present, the mechanism of acute lung injury caused by SARS-COV-2 infection is unknown, and targeted therapeutic drugs are missing. In this study, bioinformatics analysis was carried out on the genome-wide expression profile data of SARS-COV-2 infected cells to explore the mechanism of acute lung injury after SARS-COV-2 infection and to search for potential therapeutic drugs, so as to provide theoretical basis for the treatment of clinical patients and the optimization of diagnosis and treatment schemes.

1 Results and Analysis

1.1 Differential expression gene recognition

In the primary lung epithelial (NHBE) cells infected with SARS-COV-2, 154 differential genes were screened out, including 114 up-regulated genes and 40 down-regulated genes (Figure 1); In the transformed alveolus (A549) cells infected with SARS-COV-2, 68 differential genes were screened out, including 58 up-regulated genes and 10 down-regulated genes (Figure 2).

1.2 Enrichment analysis

The GO enrichment analysis results showed (Figure 3) that 113 items were enriched in the biological process, which were mainly related to the regulation of innate immune response, regulation of viral life cycle and the regulation of inflammatory response. 4 items were enriched in the cellular component, which were mainly related to blood particles and lumen.Molecular function was enriched in 8 bars, of which were mainly related to cytokine activity, double-stranded RNA binding and chemokine receptor binding.

The results of KEGG analysis showed (Figure 4) that 196 genes were enriched, and these significant genes were mainly enriched in apoptosis, cell infection, acute inflammation, signal transduction, immune factor secretion, binding, signal transduction and other related signaling pathways. Subsequently, 47 signaling pathways at level of acute lung injury phase were screened through literature review, and 73 key genes were extracted from them. Select part of the signal pathway for display (Figure 5).

1.3 PPI network analysis and core gene screening

The PPI network of 73 key genes in 47 pathways of acute lung injury was constructed by using STRING online software, and a total of 200 interrelationships were obtained, with an average point of 5.48. The results were visualized (Figure 6), and node scores were calculated for genes in PPI network. The top 10 core genes (Figure 7) were screened out: IFITM1, hla-f, C3, IRF9, STAT1, IL6, MX1, ISG15, IRF7 and IFIT1.

1.4 Disease association analysis

Based on the Epigenomic Precision Medicine Prediction Platform (EpiMed) the prediction and analysis of 73 key genes in 47 pathways of acute lung injury were carried out, and the drugs with positive correlation and negative correlation in the results were selected to obtain the diseases, drugs and matching targets (Table 1).

2 Discussion

The results of this study showed that after SARS-COV-2 infected beings, on the one hand, it affected the expression patterns of IFITM1, HLA-F, C3, IRF9, STAT1, IL6, MX1, ISG15, IRF7, IFIT1 and other genes, and the above-mentioned genes were confirmed to be related to inflaatory response in literatures (Huang et al., 2018; Wang et al., 2020; Wu et al., 2020). On the other hand, signaling pathways such as IL17, MAPK and NF-KB were highly activated, and viral infection, acute inflaation, signal transduction, leukocyte chemoattractor migration, iune response and other related signaling pathways were also highly expressed. At the same time, related drugs and diseases were predicted by Epigenomic Precision Medicine Prediction Platform (EpiMed) to search for potential theutic drugs, which provided theoretical basis for the treatment of clinical patients and the optimization of cases of diagnosis and treatment. To sum up, SARS-COV-2 infection in body will cause changes in groups of all genes including various molecules, signaling pathways and transcriptional regulation, which is an event of omics network.

From the signaling pathways and affected genes found in this study, lung injury after SARS-COV-2 infection may be mainly involved in inflaatory initiation and epigenomic disorders. In the JAK/STAT signaling pathway, the up-regulated genes include PLA2G4E, TNF, IL1B, IL1A, MAP3K8, PDGFB, and the down-regulated genes include PLA2G4C. In the IL17 signaling pathway, up-regulated genes include CXCL2, IL1B, MMP13, S100A9, CXCL1, TNFAIP3, etc. In the NF-κB signaling pathway, up-regulated genes include CXCL14, IL3RA, CCL5, CXCL2, IL1B, IL1A, etc. In the PI3K-Akt signaling pathway, up-regulated genes include PDGFB, TLR2, ITGB3, IL6 and CSF3, and down-regulated genes include IL3RA. In the tumor necrosis factor (TNF) signaling pathway, up-regulated genes include CCL5, CXCL1, CXCL6, MMP9, IL6, CSF2, etc. Activated neutrophils were found to promote the development and progression of acute lung injury. PI3K and downstream Akt play a central role in regulating neutrophil function, including respiratory burst, chemotaxis, and apoptosis. Exposure of neutrophils to endotoxin resulted in Akt phosphorylation, NF-κB activation, and expression of pro-inflaatory cytokines IL-1B and TNF-α through the PI3K dependent pathway (Lang et al., 2017). Another study found that the abnormal activation of interleukins (ILS) on JAK/STAT was critical for persistent inflaation. LLL12, a small molecule STAT3 inhibitor, inhibited LPS-induced acute lung injury in mice. These results suggested that the signaling pathways and genes mentioned above play an important role in causing acute lung injury (Zhao et al., 2016).

Based on the gene and the change of the channel, using our self-developed Epigenomic Precision Medicine Prediction Platform (EpiMed), predict potential drug treatment of SARS-COV-2 infection related lung injury, the result includes atractylodes, poria cocos, asarum, radix paeoniae rubra, cordate, houttuynia, resveratrol, cidofovir, famciclovir, chloroquine, interferon-alpha, glucocorticoid. According to China News on March 15, 1261 COVID-19 patients in 10 provinces were treated with qingfei detoxification soup, of which 1 102 were cured, 29 disappeared and 71 improved. Of the 40 patients with severe diseases, 28 have been discharged from hospital, and another 12 have been treated in hospital. Among them, 10 have improved to different degrees, with a total effective rate of reaching 97.78%, including atractylodis atractylodis, poria cocos, asarum and other drugs. Radix paeoniae rubra in research on the effect of endotoxin induced acute lung injury rats found that neutrophils in the lung tissue of rats and infiltration of inflaatory cells such as macrophages, pulmonary edema, pulmonary vascular endothelial cell damage can be big, in the doses of radix paeoniae rubra is reduced, thus play a role of the protection of the lung tissue (Han et al., 2017). It has also been found that houttuynia houttuynia, a traditional herb that has been used to treat severe acute respiratory syndrome (SARS), can significantly reduce acute lung injury by reducing pulmonary edema and protein exudation in bronchoalveolar lavage fluid in rats by in lps-induced acute lung injury studies. In addition, by reducing the deposition of complement stimulation live products in the lung, the imbalance of antioxidant antioxidants was improved, which inhibited the rat fever, reduced the number of white blood cells, and restored the serum complement level was further used to prevent the occurrence of acute lung injury to a certain extent (Lu et al., 2018). In addition, studies have found that chloroquine can block viral infection by increasing the pH value in vivo required for the fusion of virus and cell, and can interfere with the glycosylation of SARS-COV cell receptor (Wang et al., 2020). Cidofovir, famciclovir and other drugs need to be further confirmed by clinical trials. Among the positively correlated diseases, severe acute respiratory syndrome (SARS), inflaatory response of pneumonia and other diseases, further verifying the accuracy of the prediction.

3 Materials and Methods

3.1 Data sources and experimental design

The research data were from gene expression database: Gene Expression Omnibus (GEO), which was searched in the GEO database with the keyword "SARS-COV-2" as the key word, and screened according to the criteria of chip data of whole gene expression group or transcription sequencing data, at least 2 biological replicates, species of or mouse, and clear design idea, etc. The genome-wide expression profile of GSE147507 SARS-COV-2 infected cells was selected for subsequent analysis. Experiment is divided into two groups: (1) people infected SARS-CoV-2 primary lung epithelial (primary human lung epithelium, NHBE) cells and uninfected NHBE cells(2) the SARS-CoV-2 infection of alveolar transformed lung alveolar, A549 cells and uninfected A549 cells.

3.2 Screening of differentially expressed genes

By Bioconductor web site (http://www.bioconductor.org) download the R prograing language of biological information analysis (Gentleman et al., 2004). Because the open database data collation method is not unified, the result is not standard, and cannot be directly used in the later analysis. The Impute package of in Bioconductor was used to normalize the obtained expression profile (Hastie et al., 2019). Use the GEO query package to download the platform annotation file that matches the test data set and write code in the R language to map the probe to the gene. Finally, two sets of expression profiles were obtained which could be used for subsequent analysis. Then the Lia package was used to screen the differentially expressed genes between the treatment group and the control group. Differential expression genes were screened with |logFC|>1 and FDR<0.05 as thresholds. The RRA algorithm in R language was used to screen the significant genes in the difference table obtained from the above analysis (Kolde et al., 2012).

3.3 Enrichment analysis

The cluster analysis database coonly used in bioinformatics is: Coents, visualization and integration found database (the database for annotation, visualization and integrated discovery, DAVID) (Huang et al., 2009)(including the Kyoto encyclopedia of gene and genome the kyoto encyclopedia of genes and genomes, KEGG) pathway analysis and gene ontology (gene ontology, GO) (Ashburner et al., 2000; Kanehisa et al., 2000). In this study, Fisher exact probability method was used to obtain the biological processes (BP), cell components (CC), molecular functions (MF) and KEGG pathways for differentially expressed genes enrichment by using FDR <0.05 and p< 0.05 as the screening conditions.

3.4 Construct a protein interaction network of significant genes

Genetic variations will be uploaded to the STRING source database to build the significant genes protein interactions (protein - protein interaction, ppi) network (Franceschini et al., 2013). Take the reliability threshold of >0.4 as the truncation value and download the data. Then, data were imported into Cytoscape visualization analysis software to visualize PPI network and remove free nodes. Degree algorithm was used to evaluate the importance of gene node in the network, and the top ten genes of Degree value were selected as the core genes (Shannon et al., 2003).

3.5 Disease association analysis

This study the early stage of the research is based on "systems biology" and "functional genomics" theory to design the algorithm "integration of multiple omics analysis", and on the basis of the set up to cover all disease, more than 9 000 kinds of coonly used clinical drugs, more than 1 000 kinds of traditional Chinese medicine (TCM) and the human body work nearly 100 000 kinds of compounds can be big data of clinical genomics bioinformatics apparent accurate treatment prediction platform (Epigenomic Precision Medicine Prediction Platform, EpiMed) (Lu et al., 2009; Lu et al., 2012; Lu et al., 2012; Chen et al., 2020; Zhang et al., 2020). The application platform for the differentially expressed genes significantly more set of association analysis, the known compounds, clinical coonly used drugs and traditional Chinese medicine (TCM) for effect can regulate the SARS-CoV-2 virus infection of lung injury related drugs or compounds, first select the results in negative correlation results as a theutic effect of drugs, then on the basis of correlation coefficient>|0.1|, p<0.05 for the threshold as a threshold value.

By analyzing the clinical biological information of cells infected with SARS-COV-2, this study explored the mechanism of acute lung injury after novel coronavirus infection and found drugs that can be used for clinical treatment accurately, which will help provide theoretical basis for the treatment of clinical patients and the optimization of diagnosis and treatment scheme.

Authors’ contributions

Chen Hongfei was directly involved in the preparation and design of the experiment. Conducting research；Analyze, interpret data, and draft articles；Authors Zhi Peng, Zhang Haomin, Wang Yixing, Wang Haiying, Chen Haoran, Chen Ximeng, Zhang Jundong, Li Zhuoyang, Liu Geliang, Yang Bo participated in the statistical analysis of data and provided supportive help. Song Yunlong and Lu Xuechun were the initiators and principals of the project, guiding the experimental design, data analysis, paper writing and modification. All authors read and approved the final manuscript.

Acknowledgments

The project is subject of 2017 national geriatric clinical medicine research center bidding (approval number: NCRCG-PLAGH-2017011), transformation medicine project of PLA General Hospital (approval number: 2017TM-020), 2019 special health research project of PLA General Hospital (approval number: 19BJZ28), discipline leader plan of Pudong New Area Health Committee (approval number: PWRd2019-04) and Pudong New Area TCM treatment and prevention peak discipline (approval No.: PDZY-2018-0603).

References

Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., Harris M.A., Hill D.P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J.C., Richardson J.E., Ringwald M., Rubin G.M., and Sherlock G., 2000, Gene ontology: tool for the unification of biology, Nat. Genet., 25(1): 25-29
https://doi.org/10.1038/75556
PMid:10802651 PMCid:PMC3037419

Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y., Xia J., Yu T., Zhang X., Zhang L., 2020, Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, The Lancet, 395(10223): 507-513
https://doi.org/10.1016/S0140-6736(20)30211-7

Chen X.M., Cao F., Zhang H.M., Chen H.R., Zhang J.D., Zhi P., Li Z.Y., Wang Y.X. and Lu X.C., 2020, Exploration of omics mechanism and drug prediction of coronavirus-induced heart failure based on clinical bioinformatics, Zhonghua Xinxueguanbing Zazhi (Chinese Journal of Cardiology), 48(2020-03-31)

Franceschini A., Szklarczyk D., Frankild S., Kuhn M., Simonovic M., Roth A., Lin J., Minguez P., Bork P., von Mering C., and Jensen L.J., 2013, STRING v9.1: protein-protein interaction networks, with increased coverage and integration, Nucleic Acids Res., 41(Database issue): D808-D815
https://doi.org/10.1093/nar/gks1094
PMid:23203871 PMCid:PMC3531103

Gentleman R.C., Carey V.J., Bates D.M., Bolstad B., Dettling M., Dudoit S., Ellis B., Gautier L., Ge Y., Gentry J., Hornik K., Hothorn T., Huber W., Iacus S., Irizarry R., Leisch F., Li C., Maechler M., Rossini A.J., Sawitzki G., Smith C., Smyth G., Tierney L., Yang J.Y., and Zhang J., 2004, Bioconductor: open software development for computational biology and bioinformatics, Genome Biol., 5(10): R80
https://doi.org/10.1186/gb-2004-5-10-r80
PMid:15461798 PMCid:PMC545600

Han W.J., Wang Q., Zhang Q., Ding N., Gao J., Chen D.Y., and Li Q., 2017, Preventive effect of radix paeoniae rubra on lipopolysaccharide－induced acute lung onjury in rats, Zhongyiyao Xinxi (Information on Traditional Chinese Medicine), 34(2): 14-18

Hastie T., and Tibshirani R., 2019, Narasimhan B.Impute: imputation for microarray data. http:/ /master.bioconductor.org /packages /release /bioc /manuals /impute /man /impute．pdf．

Huang C., Lewis C., Borg N.A., Canals M., Diep H., Drummond G.R., Goode R.J., Schittenhelm R.B., Vinh A., Zhu M., Kemp-Harper B., Kleifeld O., and Stone M.J., 2018, Proteomic identification of interferon-induced proteins with tetratricopeptide repeats as markers of m1 macrophage polarization, Journal of Proteome Research, 17(4)
https://doi.org/10.1021/acs.jproteome.7b00828
PMid:29508616

Huang D.W., Sherman B.T., and Lempicki R.A., 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc., 4(1): 44-57
https://doi.org/10.1038/nprot.2008.211
PMid:19131956

Kanehisa M., and Goto S., 2000, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28(1): 27-30
https://doi.org/10.1093/nar/28.1.27
PMid:10592173 PMCid:PMC102409

Kolde R., Laur S., Adler P., and Vilo J., 2012, Robust rank aggregation for gene list integration and meta-analysis, Bioinformatics, 28(4): 573-580
https://doi.org/10.1093/bioinformatics/btr709
PMid:22247279 PMCid:PMC3278763

Lang S., Li L., Wang X., Wang X., Sun J., Xue X., Xiao Y., Zhang M., Ao T., and Wang J., 2017, CXCL10/IP-10 neutralization can ameliorate lipopolysaccharide-induced acute respiratory distress syndrome in rats, PLoS One, 12(1): e0169100
https://doi.org/10.1371/journal.pone.0169100
PMid:28046003 PMCid:PMC5207674

Lu X.C., Yang B., Chi X.H, and Yu R.L, 2014, A novel etiology of aplastic anemia: the uncontrolled adipogenic differentiation of mesenchymal stem cells in bone marrow induced by an abnormal immunological reaction, Medical Journal of Chinese People's Liberation Army, 39(3): 173-179

Lu X.C.,Yang B., Chi X.H., Cai L.L., Yu R.L, Liu Y., Liu L.H., Li B.j., Wu X.X., Li S.W., Tuo S., Zhang F., Tuo C.W., Yao S.Q., and Fan L., 2012, Observations on the short-term therapeutic effects of combined therapy with metformin hydrochloride for aplastic anemia, Jiefangjun Yixue Zazhi (PLA Medical School), 37(3): 229-233

Lu X.C.,Yang B., Zhu H.L., Fan H., Li S.X., Liu Y., and Yao S.Q., 2009, Application of bioinformatics analysis to optimize mnifosfine combination therapeutic regimen of myelodyspla-stic syndrome, Zhonghua Yixue Zazhi (National Medical Journal of China) 89(26): 1834-1837

Lu Y., Jiang Y., Ling L., Zhang Y., Li H., and Chen D., 2018, Beneficial effects of Houttuynia cordata polysaccharides on "two-hit" acute lung injury and endotoxic fever in rats associated with anti-complementary activities, Acta Pharm. Sin. B., 8(2): 218-227
https://doi.org/10.1016/j.apsb.2017.11.003
PMid:29719782 PMCid:PMC5925397

Shannon P., Markiel A., Ozier O., Baliga N.S., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., and Ideker T., 2003, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13(11): 2498-2504
https://doi.org/10.1101/gr.1239303
PMid:14597658 PMCid:PMC403769

Wang C.M., Li Y., Wu X.Y., Tian H., Jiang S., Xu T., Liu Z., Sun J.L., and Qi X., 2020, IRF3 and IRF7 contribute to diesel exhaust particles-induced pulmonary inflammation by mediating mTORC1 activation and restraining autophagy in mice, European Journal of Immunology, in press
https://doi.org/10.1002/eji.201948415
PMid:32135578

Wang M., Cao R., Zhang L., Yang X., Liu J., Xu M., Shi Z., Hu Z., Zhong W., and Xiao G., 2020, Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro, Cell Res., in press
https://doi.org/10.1038/s41422-020-0282-0

Wu R.D., Yin M.M., and Zhang L.J., 2020, Research progresses of the innate immune mechanisms of acute lung injury by coronavirus infection (Journal of Xiamen University (Natural Science)), 1-6

Wu Z.H., Zheng L.Y., Jiang J.J., and Chen C., 2020, Correlation study of different pathogenic infections with serum PCT, IL-6 and TNF in the respiratory medicine intensive care unit, International Infections Diseases (Electronic Edition), 9(2): 5-7

Zhang H.M., Chen H.R., Yang Y.K., Chen X.M., Zhang J.D., Guo B., Zhi P., Li Z.Y., Liu G.L., Yang B., Chi X.H., Wang Y.X., and Lu X.C., 2020, Bioinformatics prediction of molecular mechanism and intervention drugs of SARS-related immune injury and their significance for COVID-19 treatment, Zhonghua Weishengwuxue He Mianyixue Zazhi (Chinese Journal of Microbiology and Immunology), 40(3): 165-173

Zhao J., Yu H., Liu Y., Gibson S.A., Gibson S.A., Yan Z., Xu X., Gaggar A., Li P.K., Li C., Wei S., Benveniste E.N., and Qin H., 2016, Protective effect of suppressing STAT3 activity in LPS-induced acute lung injury, Am. J. Physiol. Lung Cell Mol. Physiol., 311(5): 868-880
https://doi.org/10.1152/ajplung.00281.2016
PMid:27638904 PMCid:PMC5130536