Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis.

INTRODUCTION: The coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19.
MATERIALS AND METHODS: Based on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs.
RESULTS: A total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01).
CONCLUSION: Identifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel enveloped RNA beta coronavirus, is accountable for an ongoing outbreak of coronavirus disease 2019 (COVID-19) [1,2], which constitutes an enormous global burden on society. COVID-19 has resulted in over 224 million confirmed cases and over 4.68 million deaths globally. The research and development of anti-COVID-19 vaccine is currently ongoing; moreover, controlling disease transmission requires the development of effective drugs to cure it.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease with an irreversible advanced lung failure. IPF patients suffer from lung function decline, respiratory failure, and ultimately death [3]. The risk factors for IPF and COVID-19 are reported to be common [4]. However, the molecular mechanism underlying a crosstalk between COVID-19 and IPF was poorly defined. Identification of novel molecular targets has thus become imperative for the advancement of targeted therapy for COVID-19 with antifibrotic strategies.

The goal of the current study was to investigate the potential role of differentially expressed genes (DEGs) in the association between COVID-19 and IPF. We performed an overlap of DEGs between two the diseases on a basis of 5 datasets, followed by distinguishing the upregulated and downregulated genes. Based on a series of enrichment analysis, we interpreted the function of upregulated and downregulated DEGs. Furthermore, we carried out a protein-protein interaction (PPI) network analysis in which 22 upregulated hub genes and 11 downregulated hub genes were identified. Then, we analyzed the prominent function of 22 hub genes, and it was revealed that these hub genes upregulated in COVID-19 and IPF were involved in cytokine mediation, such as cell response to interferon. Finally, we performed a drug-target analysis and 155 candidate drugs targeting upregulated hub genes were identified. The workflow of the current study is shown in Fig 1. Herein, our findings demonstrated that hub gene and the candidate drug will be beneficial to the COVID-19 treatment. We also provide an insight that we can design and develop a candidate drug against virus variant such as Delta SARS-CoV-2, when there are common risk factors between a different disease and that caused by Delta.

Fig 1

The workflow of the current study.

The high-throughput data of SARS-CoV-2 infection was obtained from biopsy of a COVID-19 patient in GSE147507. The data of IPF was obtained from biopsy of IPF patient in five datasets, including GSE13065, GSE110147, GSE1i01286, GSE53845, and GSE24206. Venn diagram was used to reveal overlapped DEGs. The magenta circle represents DEGs in GSE147507 and yellow one represents DEGs in IPF datasets. Subsequently, common DEGs were subjected to a series of enrichment analysis and PPI network investigation. Based on an identification of highly expressed hub genes, a candidate drug was predicted to be available for a crosstalk between COVID-19 and IPF during the COVID-19 therapy.

Materials and methods

The collection of databases and the identification of DEGs

DEGs were obtained from six datasets in Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database [5,6] The DEGs related to SARS-CoV-2 were obtained from GSE147507 including SARS-CoV-2 infection in lung epithelium and lung alveolar cells of humans in Apr 07, 2021 [7,8]. Five GEO datasets were collected to obtain the DEGs related to IPF, including GSE13065 with 3 IPF samples and 3 normal samples lastly updated in May 02, 2019 [9], GSE110147 with 22 IPF samples and 11 normal samples lastly updated in Aug 19, 2018 [10], GSE101286 with 12 IPF samples and 3 normal samples lastly updated in Jul 25, 2021 [11], GSE53845 with 40 IPF samples and 8 normal samples lastly updated in Jan 23, 2019 [12], and GSE24206 with 17 IPF samples and 6 normal samples lastly updated in Mar 25, 2019 [13]. DEGs for the datasets were analyzed through GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool which uses limma package for identifying DEGs and visualized by ggplot 2 in R package. Benjamini-Hochberg method was applied to both the datasets for controlling of false discovery rate (FDR). Cut-off criteria was obtained for GSE147507 using adjusted P-value < 0.05 and log2-fold change (absolute) > 1.0. All data generated or analyzed during this study are included in this published article and its supplementary information files.

Identification of common genes between COVID-19 and IPF

To determine detailed information of DEGs, these genes were further divided by aberrant expression level in distinct database. The adjusted P-value < 0.05 and log2-fold change > 1.0 is used as cut-off criteria for high expression DEGs, and adjusted P-value < 0.05 and log2-fold change < −1.0 for low expression DEGs in distinct dataset.

The upregulated as well as downregulated DEGs in GSE147507 were overlapped with other five datasets related to IPF.

Enrichment analysis for common DEGs

To understand a functional characteristic of DEGs in COVID-19 and IPF, a series of enrichment analysis were adopted to gain a detailed information of biological function and pathways. Gene Ontology (GO) was performed to provide three terms, including biological process, molecular function, and cellular component [14]. Kyoto Encyclopedia of Genes and Genomes (KEGG) was used to identify metabolic pathway [15]. An online tool Enrichr (https://amp.pharm.mssm.edu/Enrichr/) was carried out to enrich the significant pathways, including WikiPathways, Reactome, and BioCarta database [16,17]. Based on the enrichment analysis, we concentrated on biological function of DEGs in both COVID-19 and IPF.

PPI network analysis for the identification of hub genes

For assessing an association between DEGs, we established a PPI network on the Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db.org/) [18], which was utilized to predict physical and functional associations between proteins. Subsequently, we determined hub genes via an analysis of Cytohubba and MCODE on Cytoscape (3.8.2). Cytohubba (http://apps.cytoscape.org/apps/cytohubba) is a plugin of Cytoscape to explore protein associations according to topological algorithms. Top 10 hubba node was set to obtain the hub genes from DEGs. Molecular Complex Detection (MCODE) (http://apps.cytoscape.org/apps/mcode) is another plugin to provide clusters of subnetworks. The parameter of MCODE is Degree Cutoff = 2, Node score cutoff = 2, and K-score = 2.

Prediction of candidate drugs for hub genes

The final stage of the study was designed to determine candidate drug for highly expressed hub genes. The access of the Drug Signatures database (DSigDB) is acquired through Enrichr (https://amp.pharm.mssm.edu/Enrichr/) platform, which contains the largest number of drugs/compound-related gene sets to date, were extracted and compiled from quantitative inhibition data of drugs/compounds from a variety of databases and publications [19]. Enrichr is mostly used as an enrichment analysis platform that represents numerous visualization details on collective functions for the genes that are provided as input. We predicted candidate drug targeted hub gene. The adj.P.value < 0.01 was considered statistically significant. The candidate drugs can be sorted by adj.P.value and combined score ranking.

Results

Identification of common DEGs between COVID-19 and IPF

From GSE147507 dataset, we identified 812 DEGs including 396 upregulated genes and 417 downregulated genes (Fig 2). Out of 5977 DEGs identified from five IPF GEO datasets, 2369 were upregulated and 3608 were downregulated. We then overlapped DEGs from one SARS-CoV-2-infected sample dataset and five IPF datasets. A total of 117 and 71 genes were identified as common upregulated (Fig 3A) and downregulated DEGs (Fig 3B), respectively. Next, we tried to identify the function of common DEGs involved during the progression of COVID-19 and IPF.

Fig 2

Volcano plot of differentially expressed gene from COVID-19 patient samples and IPF patient samples.

A: Volcano plot of differentially expressed genes between uninfected human lung biopsies and that of deceased COVID-19 patient. B-F: Volcano plot of differentially expressed genes between lung samples from IPF patients and healthy control. The red plots with |log FC| > 1 and P value < 0.05 represent upregulated genes and blue represents downregulated genes.

Fig 3

Venn diagram showing differentially expressed genes between COVID-19 and IPF.

A: Upregulated DEGs between COVID-19 and IPF. B: Downregulated DEGs between COVID-19 and IPF. The blue circle in Venn diagram represents DEGs in COVID-19 dataset, and yellow circle represents five DEGs in IPF datasets.

GO and pathway identification by gene set enrichment analysis

To further understand the function and pathways of common DEGs, enrichment analysis was preformed to show that common upregulated DEGs in COVID-19 and IPF were involved in cytokine mediation, such as cell response to interferon (Fig 4, Table 1). The DEGs downregulated in both the diseases, however, were associated in the disassembly of cellular components and extracellular matrix (Fig 5, Table 2). The upregulated DEGs were mainly located in cellular component, and main molecular function of these genes was found to bind to small molecules and metabolites. Among upregulated DEGs, 25 genes were involved in cytokine-mediated signaling pathway and 11 genes were involved in cellular response to type I interferon and 11 genes in type I interferon signaling pathway in GO terms. The downregulated DEGs located at the intracellular membrane organelle and nucleus, appeared to bind with RNA and catalytic enzymes, and mediate channel activity. The GO results were consistent with those of a serial pathway analysis, including KEGG, Reactome, wikipathway, and Biocarta. For instance, Reactome and wikipathway revealed that upregulated DEGs were related to interferon signaling and cytokine signaling pathway (S1 Fig), and downregulated DEGs were involved in MMP activation and collagen degradation (S2 Fig). Overall, these results indicated that upregulated and downregulated DEGs influenced entirely different biological functions.

Fig 4

GO terms of upregulated DEGs between COVID-19 and IPF.

A: GO analysis of upregulated DEGs related to biological process. B: GO analysis of upregulated DEGs related to molecular function. C: GO analysis of upregulated DEGs related to cellular component.

Table 1

Go enrichment analysis of upregulated DEGs between COVID-19 and IPF.

Category	GO ID	GO Pathways	P-values	Genes
GObiological process	GO:0045087	innate immune response	9.387041929923436E-15	IFITM3;IFITM1;CR1;FCER1G;GCH1;MX1;IFI6;ISG15;RNASE2;TREM1;SIRPB1;CXCL16;CXCL10;CLEC4D;TYROBP;CLEC7A;IRF7;S100A12;CLEC4E
	GO:0019221	cytokine-mediated signaling pathway	1.1506069546074619E-14	IFITM3;IFITM1;CSF3R;SPI1;FPR1;IFI6;IL27;IL2RG;OASL;CA1;CCL2;HLA-DRB5;FCER1G;STAT1;IL1R2;MX1;ISG15;CXCL10;IL1A;OAS2;IL1B;IRF7;PELI1;XAF1;IRF9
	GO:0043312	neutrophil degranulation	4.286470567853142E-14	MGAM;CR1;FCER1G;GCA;GMFG;FPR1;GPR84;FPR2;RNASE2;MMP8;SIRPB1;PLAC8;MMP25;CLEC4D;TYROBP;SELL;S100A12;OLR1;CYSTM1;S100A9;S100A8;SIGLEC5
	GO:0002283	neutrophil activation involved in immune response	5.071176119881218E-14	MGAM;CR1;FCER1G;GCA;GMFG;FPR1;GPR84;FPR2;RNASE2;MMP8;SIRPB1;PLAC8;MMP25;CLEC4D;TYROBP;SELL;S100A12;OLR1;CYSTM1;S100A9;S100A8;SIGLEC5
	GO:0002446	neutrophil mediated immunity	5.746693830188601E-14	MGAM;CR1;FCER1G;GCA;GMFG;FPR1;GPR84;FPR2;RNASE2;MMP8;SIRPB1;PLAC8;MMP25;CLEC4D;TYROBP;SELL;S100A12;OLR1;CYSTM1;S100A9;S100A8;SIGLEC5
	GO:0071357	cellular response to type I interferon	9.61002949577372E-14	IFITM3;IFITM1;OAS2;STAT1;MX1;IRF7;IFI6;ISG15;XAF1;IRF9;OASL
	GO:0060337	type I interferon signaling pathway	9.61002949577372E-14	IFITM3;IFITM1;OAS2;STAT1;MX1;IRF7;IFI6;ISG15;XAF1;IRF9;OASL
	GO:0051607	defense response to virus	1.5573135651590148E-11	IFITM3;CXCL10;IFITM1;OAS2;STAT1;MX1;IRF7;IFI6;ISG15;RNASE2;IFI44L;OASL
	GO:0140546	defense response to symbiont	1.3693191748252762E-10	IFITM3;IFITM1;OAS2;STAT1;MX1;IRF7;IFI6;ISG15;RNASE2;IFI44L;OASL
	GO:0050832	defense response to fungus	3.9184743402451796E-9	CLEC4D;CLEC7A;S100A12;CLEC4E;S100A9;S100A8
GO Molecular Function	GO:0030667	secretory granule membrane	6.097577975166167E-10	MGAM;CR1;FCER1G;FPR1;GPR84;FPR2;SIRPB1;MMP25;CLEC4D;TYROBP;SELL;OLR1;CYSTM1;SIGLEC5
	GO:0070820	tertiary granule	2.7444531601972747E-9	MGAM;CLEC4D;CR1;FCER1G;FPR1;OLR1;CYSTM1;GPR84;FPR2;MMP8;SIGLEC5
	GO:0070821	tertiary granule membrane	9.197680245668367E-9	MGAM;FCER1G;CLEC4D;OLR1;CYSTM1;FPR2;GPR84;SIGLEC5
	GO:0101003	ficolin-1-rich granule membrane	5.8340251207518634E-8	MGAM;CR1;FCER1G;CLEC4D;FPR1;FPR2;SIGLEC5
	GO:0101002	ficolin-1-rich granule	1.1269234942705415E-5	MGAM;CLEC4D;CR1;FCER1G;GMFG;FPR1;FPR2;SIGLEC5
	GO:0030659	cytoplasmic vesicle membrane	1.226772047106552E-5	PSENEN;HLADRB5;TYROBP;CR1;SELL;NCF4;FPR1;IRF7;LY96;SIRPB1;SIGLEC5
	GO:0034774	secretory granule lumen	8.887911522711858E-5	PLAC8;SRGN;GCA;GMFG;S100A12;RNASE2;MMP8;S100A9;S100A8
	GO:0035579	specific granule membrane	1.8064502767407338E-4	MMP25;CLEC4D;OLR1;FPR2;GPR84
	GO:0042581	specific granule	3.2742843178608567E-4	MMP25;CLEC4D;OLR1;GPR84;FPR2;MMP8
	GO:0045335	phagocytic vesicle	0.002661302293846886	GNLY;NCF4;RAC2;CLEC4E
GOCellular Component	GO:0030667	secretory granule membrane	6.097577975166167E-10	MGAM;CR1;FCER1G;FPR1;GPR84;FPR2;SIRPB1;MMP25;CLEC4D;TYROBP;SELL;OLR1;CYSTM1;SIGLEC5
	GO:0070820	tertiary granule	2.7444531601972747E-9	MGAM;CLEC4D;CR1;FCER1G;FPR1;OLR1;CYSTM1;GPR84;FPR2;MMP8;SIGLEC5
	GO:0070821	tertiary granule membrane	9.197680245668367E-9	MGAM;FCER1G;CLEC4D;OLR1;CYSTM1;FPR2;GPR84;SIGLEC5
	GO:0101003	ficolin-1-rich granule membrane	5.8340251207518634E-8	MGAM;CR1;FCER1G;CLEC4D;FPR1;FPR2;SIGLEC5
	GO:0101002	ficolin-1-rich granule	1.1269234942705415E-5	MGAM;CLEC4D;CR1;FCER1G;GMFG;FPR1;FPR2;SIGLEC5
	GO:0030659	cytoplasmic vesicle membrane	1.226772047106552E-5	PSENEN;HLADRB5;TYROBP;CR1;SELL;NCF4;FPR1;IRF7;LY96;SIRPB1;SIGLEC5
	GO:0034774	secretory granule lumen	8.887911522711858E-5	PLAC8;SRGN;GCA;GMFG;S100A12;RNASE2;MMP8;S100A9;S100A8
	GO:0035579	specific granule membrane	1.8064502767407338E-4	MMP25;CLEC4D;OLR1;FPR2;GPR84
	GO:0042581	specific granule	3.2742843178608567E-4	MMP25;CLEC4D;OLR1;GPR84;FPR2;MMP8
	GO:0045335	phagocytic vesicle	0.002661302293846886	GNLY;NCF4;RAC2;CLEC4E

Fig 5

GO terms of downregulated DEGs between COVID-19 and IPF.

A: GO analysis of downregulated DEGs according to biological process. B: GO analysis of downregulated DEGs according to molecular function. C: GO analysis of downregulated DEGs according to cellular component.

Table 2

Go enrichment analysis of downregulated DEGs between COVID-19 and IPF.

Category	GO ID	GO Pathways	P-values	Genes
GObiological process	GO:0022411GO:0022617GO:0034644	cellular component disassemblyextracellular matrix disassemblycellular response to UV	3.6908055963859072E-63.6908055963859072E-61.6226306755965077E-4	MMP14;MMP1;SH3PXD2B;A2M;MMP10MMP14;MMP1;SH3PXD2B;A2M;MMP10MMP1;XPC;CRIP1;TAF1
	GO:0000122	negative regulation of transcription by RNA polymerase II	6.80695281487198E-4	ZNF451;CCND1;GADD45A;ATRX;CTR9;TCF21;NR1D2;TBX2;TAF1
	GO:0034724	DNA replication-independent nucleosome organization	9.449631056544972E-4	NASP;ATRX
	GO:0070141	response to UV-A	0.0010999291367171904	CCND1;MMP1
	GO:0046173	polyol biosynthetic process	0.001443814289271468	ITPKB;ISYNA1
	GO:0031571	mitotic G1 DNA damage checkpoint signaling	0.001599460082312502	CCND1;PRMT1;GADD45A
	GO:0048566	embryonic digestive tract development	0.002265165629135075	RARRES2;TCF21
	GO:0030330	DNA damage response, signal transduction by p53 class mediator	0.0023202515386528087	PRMT1;GADD45A;ATRX
GO Molecular Function	GO:0070679	inositol 1,4,5 trisphosphate binding	6.693872237504711E-4	ITPR1;ITPR3
	GO:0005217	intracellular ligand-gated ion channel activity	6.693872237504711E-4	ITPR1;ITPR3
	GO:0003723	RNA binding	0.0012169581083683082	PTCD3;UTP6;PRMT1;RNMT;DDX42;CIRBP;URB1;GNL2;MFAP1;MPHOSPH10;TSR1;PPIG;SREK1
	GO:0015278	calcium-release channel activity	0.001632573414944103	ITPR1;ITPR3
	GO:0099604	ligand-gated calcium channel activity	0.0020433278549486355	ITPR1;ITPR3
	GO:0017025	TBP-class protein binding	0.002741371475892866	PSMC5;TAF1
	GO:0008170	N-methyltransferase activity	0.0029955844322773523	PRMT1;RNMT
	GO:0004222	metalloendopeptidase activity	0.003106381363248335	MMP14;MMP1;MMP10
	GO:0080025	phosphatidylinositol-3,5-bisphosphate binding	0.003260434667143527	SH3PXD2B;WIPI1
	GO:0140296	general transcription initiation factor binding	0.003821741127686192	PSMC5;TAF1
GOCellular Component	GO:0005634	nucleus	0.000147678458404248	ZNF451;ATF6B;RNMT;DDX42;TCF21;DLST;STC1;XPC;TRAK1;CCND1;HMOX1;RALGDS;EGR1;PRMT1;GADD45A;ZNF160;ATRX;CIRBP;NR1D2;GNL2;TBX2;ITPKB;GCHFR;MMP14;PSMC5;NASP;MFAP1;HBP1;PPIG;TAF1
	GO:0043231	intracellular membrane-bounded organelle	0.000151288413440493	ZNF451;ATF6B;RNMT;DDX42;ITPR1;TCF21;DLST;STC1;XPC;TRAK1;CCND1;PODXL;HMOX1;RALGDS;EGR1;PRMT1;GADD45A;ZNF160;ATRX;CIRBP;NR1D2;GNL2;TBX2;ITPKB;GCHFR;MMP14;PSMC5;NASP;MFAP1;RAPGEF1;HBP1;PPIG;TAF1
	GO:0031095	platelet dense tubular network membrane	0.000440161889353639	ITPR1;ITPR3
	GO:0031094	platelet dense tubular network	0.000669387223750471	ITPR1;ITPR3
	GO:0005730	nucleolus	0.00110914228877878	SELENBP1;UTP6;PODXL;MPHOSPH10;TSR1;XPC;URB1;GNL2;TAF1
	GO:0031981	nuclear lumen	0.00124200795340088	SELENBP1;UTP6;PODXL;MPHOSPH10;TSR1;XPC;URB1;GNL2;TAF1
	GO:0042827	platelet dense granule	0.00249787275567805	RARRES2;ITPR1
	GO:0043232	intracellular non-membrane-bounded organelle	0.00752249102119212	SELENBP1;UTP6;PODXL;SH3PXD2B;MPHOSPH10;TSR1;XPC;URB1;GNL2;TAF1
	GO:0032040	small-subunit processome	0.0076371805235121	UTP6;MPHOSPH10
	GO:0016529	sarcoplasmic reticulum	0.0111473609254622	ITPR1;ITPR3

Identification of hub genes via PPI network analysis

The PPI network analysis revealed an association of DEGs between COVID-19 and IPF. Among 117 upregulated DEGs, 22 hub genes were identified on the basis of Cytohubba and MCODE analysis, including MX1, CCL2, CXCL10, TYROBP, STAT1, S100A12, IRF7, IL1B, TREM1, SPI1, UBE2L6, IFI44L, XAF1, IRF9, EPSTI1, ISG15, OASL, IFITM1, CMPK2, IFI6, OAS2, IFITM3. Among 71 downregulated DEGs, 11 hub genes were identified, including HMOX1, PPIG, MPHOSPH10, GNL2, MMP1, GADD45A, UTP6, TSR1, CCND1, PRMT1, URB1 (Fig 6).

Fig 6

Identification of hub genes from PPI network using Cytoscape plugins Cytohubba and MCODE.

A: Hub genes identified among upregulated genes using Cytohubba plugin in Cytoscape software. B: Hub genes identified among upregulated genes using MCODE plugin in Cytoscape software. C: Hub genes identified among downregulated genes using Cytohubba plugin in Cytoscape software. D: Hub genes identified among downregulated genes using MCODE plugin in Cytoscape software.

Enrichment analysis of hub genes

The GO enrichment analysis revealed that the 22 hub genes were upregulated in cellular response to type I interferon and type I interferon signaling pathway. The analysis also exhibited significant involvement of mitochondrial envelope and adenylyl transferase activity in the upregulated group (S3 Fig). In the downregulated group, 11 hub genes mostly enriched in nucleolus and nuclear lumen, were appeared to be evolved in RNA binding and mitotic G1 DNA damage checkpoint signaling (S4 Fig).

Candidate drug prediction for targeting hub genes between COVID-19 and IPF

For further investigating the significant role of common hub genes, candidate drugs targeting the 22 upregulated hub genes were predicted (Table 3). A total of 155 candidate drugs were identified with adj.P.value < 0.01 (S3 Table). These drugs were further examined to affect molecular activity of 22 hub genes and their downstream molecules, which are displayed as a list (S4 Table). Among these drugs, 11 were predicted to target more than 10 hub molecules, while 69 drugs targeted less than 3 hub molecules.

Table 3

Prediction of TOP 10 candidate drugs for high expressed hub genes.

Name of drugs	P-value	Adjusted P-value	Genes
suloctidil HL60 UP	1.17603903224093E-25	1.27835442804589E-22	IFITM1;STAT1;MX1;IFI6;UBE2L6;ISG15;IFI44L;OASL;CXCL10;OAS2;IRF7;CCL2;XAF1;IRF9
prenylamine HL60 UP	1.06795174564932E-22	5.80431773760409E-20	CXCL10;STAT1;MX1;IFI6;IRF7;ISG15;XAF1;IFI44L;IRF9;OASL
acetohexamide PC3 UP	2.44772613152278E-19	8.86892768321756E-17	IFITM1;STAT1;OAS2;MX1;IFI6;IFI44L;IRF9;OASL
chlorophyllin CTD 00000324	1.34723424808812E-15	3.66110906917947E-13	CXCL10;IFITM1;STAT1;OAS2;MX1;IFI6;ISG15
3’-Azido-3’-deoxythymidine CTD 00007047	4.95534561362891E-14	1.07729213640292E-11	IFITM1;STAT1;OAS2;IL1B;MX1;IFI6;IRF7;EPSTI1;CCL2;ISG15;IFI44L
prochlorperazine MCF7 UP	1.95377160519666E-13	3.53958289141462E-11	IFITM1;STAT1;IFI6;IRF7;ISG15;IRF9;OASL
terfenadine HL60 UP	2.50627914129244E-13	3.89189346654984E-11	STAT1;MX1;IFI6;IRF7;ISG15;XAF1;IRF9
etoposide HL60 UP	2.05940731534332E-12	2.79821968972273E-10	STAT1;IL1B;MX1;IFI6;IRF7;CCL2;ISG15;IRF9
Arsenenous acidCTD 00000922	8.47115116155285E-11	1.02312681251199E-08	IFITM3;IFITM1;STAT1;MX1;IFI6;UBE2L6;ISG15;IFI44L;CXCL10;OAS2;IL1B;CCL2;XAF1
propofol MCF7 UP	3.01735334589948E-09	3.27986308699273E-07	IFITM1;IFI6;ISG15;IRF9;OASL

Discussion

A strong association between COVID-19 and IPF has been previously reported [4,20,21], and IPF was reported as risk factor for COVID-19 [4]. On the contrary, anti-fibrosis therapies are available for inhibiting severe COVID-19 progression [4]. Moreover, COVID-19 has changed the approach to treat IPF patients, since SARS-CoV-2 infection is reported to impact the prognosis of IPF patients [22]. The relevance between COVID-19 and IPF is supposed to be through the association between up- and downregulated genes. One COVID-19 dataset and five IPF datasets were analyzed, the latter are designed to analyze only the lung samples. These datasets were published from 2011 to 2019, ranging from America to East Asia to ensure that our study is broadly representative. Our finding of aberrant expressed genes from 6 GEO datasets suggested that these DEGs influenced the crosstalk between COVID-19 and IPF. In addition, the present study was designed for the identification of hub genes and the prediction of their potential drug, which may enable novel molecular targets as new COVID-19 strategies with antifibrotic treatment.

Given that common DEGs can drive the development of drugs against COVID-19 and IPF, we concentrated on the DEG-related function after dividing upregulated and downregulated genes. Except for immune response and defense response to virus, it is somewhat surprising that upregulated DEGs are enriched in inflammatory molecules, especially cytokine-related function. Type I interferon signaling pathway and cytokine-mediated signaling pathway were mainly related to upregulated DEGs. An association between type I interferon and IPF has been reported to show that type I interferon pathway may drive chronic inflammation and fibrosis [23]. Type I interferon response was amplified based on ex vivo evidence of IPF [24]. It has been reported that there were similar cytokine profiles in IPF and COVID-19 [22], which is consistent with an observation that the level of profibrotic mediators in COVID-19 patients was increased at the serum level. Our finding was an important evidence to support an antifibrotic therapy for COVID-19 patients by mediating cytokine signaling.

In case of downregulated genes between COVID-19 and IPF, the biological function was enriched in disassembly of cellular components and extracellular matrix. The pathological changes in IPF developed from an alteration of extracellular matrix, which can replace the healthy lung tissue, contributing to the deterioration of lung compliance [25]. The lung architecture is destructed due to the secretion of excessive amounts of extracellular matrix from fibroblast and myofibroblast foci [26]. Our findings were in accordance with the previous research. In our study, matrix metalloproteinases (MMPs) which accounted for disassembly of extracellular matrix were downregulated in both COVID-19 and IPF. These findings may help us to understand that absence of these genes in COVID-19 patients might induce the progression to fibrosis.

The common hub genes between COVID-19 and IPF were the most strongly associated among all DEGs. The hub genes were indeed relevant with IPF progression. For example, our study revealed that several hub genes were related to interferon signal pathway, which was demonstrated to influence IPF treatment. Besides, 19 hub genes are involved in the enrichment of chemokines. Previous research showed that chemokine CCL2 and its downstream pathways were the key to the development of IPF [27]. Our findings from PPI network analysis were consistent with our above functional enrichment, suggesting that these hub genes could be novel therapeutic targets between COVID-19 and IPF.

Considering that the hub genes played a vital role in a crosstalk between COVID-19 and IPF, we used hub genes to identify potential candidate drugs. We found several potential candidate drugs which probably contributed to the treatment of COVID-19 and IPF. Among all candidate drugs, the current study highlights the top 10 significant drugs. Among them, candidate drugs targeting exogenous invasion enabled to be an important approach along with suloctidil, which has been suggested as potential antifungal agent [28]. 3’-Azido-3’-deoxythymidine CTD 00007047 was used as an anti-viral agent and a reverse transcriptase inhibitor active against HIV-1, and thioridazine was proved to exhibit anti-viral activity [29]. Moreover, a previous study has revealed that myofibroblast activation and uncontrolled proliferation associated IPF with cancer [30]. Several candidate drugs exhibit anticancer activities. Chlorophyllin CTD 00000324 was determined to deactivate ERKs and inhibit breast cancer cell proliferation [31]. Prochlorperazine has been proved to exhibit anticancer activity towards different types of human cancer [32]. Terfenadine, demonstrated to be effective against PC-3 and DU-145 cells (two prostate cancer cell lines) by inducing cell apoptosis [33], and etoposide were identified as anticancer drugs as they induced cancer cell apoptosis [34]. It can be assumed that candidate drugs which possess anticancer activity with the inhibition of cell proliferation and fibroblast activation might contribute to the treatment of IPF and COVID-19. In summary, the present study raised the possibility that existing drug and compounds may be available for the development of COVID-19 therapy.

Although the risk factors for IPF and COVID-19 are common, our study provides insufficient evidence to support the clinical practice of candidate drug for COVID-19 and IPF treatment. Furthermore, due to this limitation, the downstream molecules of hub genes should be determined in the future, and the role of the hub genes in crosstalk between COVID-19 and IPF should be confirmed using clinical samples and experimental models. Although the current research against COVID-19 has been conducted and data on COVID-19 in GEO are rapidly enriched, GSE147507 dataset has been verified to be reliable with solid evidence. Our conclusions were based on the responses of 5 GSEs in GEO database and so might not reflect processes via the in vivo and in vitro experiments.

Conclusion

In summary, our results provide the common DEGs between COVID-19 and IPF, which add to the accumulating evidence that suggests a treatment for COVID-19 patients in the pulmonology ward administered antifibrotic therapy. With a series of enrichment analysis, herein, we offer new insights into the development of COVID-19 treatment on the basis of biological function. The current study unveiled a potential role of hub genes in COVID-19 and IPF, contributing to a combined COVID-19 treatment. Moreover, our findings offer some suggestions on therapeutic target identification in diseases caused by the Delta SARS-CoV-2 variant, when the common risk factor of the Delta associated with a distinct disease will be uncovered.

Pathway-based enrichment analysis of upregulated DEGs between COVID-19 and IPF.

Biological entity of upregulated DEGs between COVID-19 and IPF in Wikipathways (A), KEGG (B), Reactome (C), and Biocarta (D).

(TIF)

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Pathway-based enrichment analysis of downregulated DEGs between COVID-19 and IPF.

Biological entity of downregulated DEGs between COVID-19 and IPF in Wikipathways (A), KEGG (B), Reactome (C), and Biocarta (D).

(TIF)

Click here for additional data file.

GO and KEGG functional enrichment analysis of upregulated hub genes.

GO analysis of upregulated hub genes according to biological process (A), molecular function (B) and cellular component (C). The results of pathway terms through KEGG analysis of upregulated hub genes(D).

(TIF)

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GO and KEGG functional enrichment analysis of downregulated hub genes.

GO analysis of downregulated hub genes according to biological process (A), molecular function (B) and cellular component (C). The results of pathway terms through KEGG analysis of downregulated hub genes(D).

(TIF)

Click here for additional data file.

Pathway enrichment analysis of upregulated DEGs between COVID-19 and IPF.

(DOCX)

Click here for additional data file.

Pathway enrichment analysis of downregulated DEGs between COVID-19 and IPF.

(DOCX)

Click here for additional data file.

Prediction of candidate drugs for upregulated hub genes.

(DOCX)

Click here for additional data file.

The downstream molecules of 22 hub genes.

(DOCX)

Click here for additional data file.

8 Nov 2021

PONE-D-21-32287Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis.PLOS ONE

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Reviewer #1: Summary of the research:

The authors have aimed at identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis (IPF) by bioinformatics analysis using publicly available databases.

Main Research Question: The authors designed their experiment to find out the hub genes upregulated in COVID-19 and IPF and subsequently predict candidate molecules against COVID-19 and IPF.

Claims: A total of 22 hub genes were reported to be upregulated in COVID-19 and IPF, for which 155 candidate molecules were predicted as potential therapeutic agents.

Conclusion of the study: The current study unveiled a potential role of hub genes

in COVID-19 and IPF, contributing to a combined treatment for COVID-19. Based on their current reports, the authors discussed on a therapeutic target identification in diseases caused by the Delta SARS-CoV-2 variant, when the common risk factor of the Delta associated with distinct disease was uncovered.

Strengths:

1. The authors have acknowledged that a strong relationship between COVID-19 and IPF has been reported in the literature and IPF was reported as risk factor for COVID-19. The antifibrosis therapies were available for inhibiting severe COVID-19 progression. The strength of the current work is in the in-depth and to a good extent exhaustive study of the upregulated hub genes in COVID-19 and IPF through database mining and computational analysis of the datasets using opensource computational tools.

2. As reported by the authors, the current study may be the first to use multiple databases to conduct study of the upregulated hub genes in COVID-19 and IPF through database mining, such as gene expression, co-expression, gene pathway enrichment analysis, etc to explore the potential molecular mechanisms of these respiratory symptoms.

Weakness: ( A few comments have been put up in the specific Major/ Minor comments for the authors along with the following points to consider. These however may not directly limit the merit of the current work. )

1. In case of all computational and public database mining and analysis the limitations of the uniformity of various datasets may pose as a limitation which have been observed here as well. Attempts by the authors to remove these are also noted.

2. The authors self-critical approach is appreciated in mentioning the perceived weakness of the study. “Although the risk factor for COVID-19 is shared with IPF, there is insufficient evidence in our analysis to support clinical practice of candidate drug for COVID-19 and IPF

treatment. Furthermore, due to the limitation, the downstream molecules of hub genes

should be determined in the future, and the role of the hub gene in crosstalk between

COVID-19 and IPF should be confirmed using clinical samples and experimental

models. Our conclusions were based on the responses of 5 GSEs in GEO database and

so might not reflect processes via the in vivo and in vitro experiment.”

Overall Recommendation: Revision recommended.

Examples and evidence:

Major issues:

1. The authors have worked in a domain of COVID-19 which still needs a lot of research and understanding. The availability of data and the fast-evolving research on the therapeutic interventions against COVID-19 makes the current work very well timed and at the same time prone to a lot of questions still unanswered. The computational analysis is always as good as the datasets. As I understand, these data on COVID-19 in GEO and other databases are rapidly enriched within very small timeframe these days. Authors comment on these lines are missing in the discussion. This may enhance the impact of the manuscript.

2. The authors self-critical approach is appreciated in mentioning the perceived weakness of the study. “Although the risk factor for COVID-19 is shared with IPF, there is insufficient evidence in our analysis to support clinical practice of candidate drug for COVID-19 and IPF

treatment.” The authors may consider, if deemed suitable, to include alternative strategies to further establish the claim for the candidate drug through support of relevant literature or relevant computational studies like DNA binding sites, binding affinity, molecular docking, MD simulations, etc.

3. The authors have also included in their discussion that “Furthermore, due to the limitation, the downstream molecules of hub genes should be determined in the future, and the role of the hub gene in crosstalk between COVID-19 and IPF should be confirmed using clinical samples and experimental models.” If the authors find it appropriate, the inclusion of the list of the downstream molecules of the 22 hub genes reported may enhance the scientific coverage of the topic aimed at in the manuscript. This will open up new avenues for researches to further enhance the knowledge gained in the current study and it will also compliment the line cited here.

4. The authors mentioned that “Our conclusions were based on the responses of 5 GSEs in GEO database and so might not reflect processes via the in vivo and in vitro experiment.” The authors may consider to add their criteria for selection/exclusion of the GSEs and establish that whether the selections were exhaustive, at least till the last date of updating of the study. If the data is exhaustive, relevant literatures in support of the claims may enhance the discussion of the manuscript.

Minor issues:

1. Page 11: The details of methods for prediction of candidate drug for hub gene is only limited to the name of the database used and the adjusted P value. The detailed criteria used for prediction, the algorithm used by the program, any adjustments applied by the authors to the default running parameters, cut-offs (apart from adjusted P value) used if any, rationale behind the choice of the specific database and its search tool, etc. may be quite useful to establish the method as reproducible as well as for clear understanding of the reader.

2. The databases, especially those on COVID-19, are rapidly getting enriched. In this context, mentioning the dates of last accession of the databases like GEO, DSigDB database, etc. This also helps the reader to understand the timeframe of the study.

3. The overall structure of the manuscript is observed to have slightly overlapping and sometimes repetitive lines or comments in methodology, results and discussion sections. The authors may consider including exclusively the points in the various sections without recurrence, for a more concise write-up.

4. Very minor but significant typographical / grammar and formatting issues were observed in the manuscript. It is assumed that a thorough proof reading by the authors during further processing will take care of these issues.

Reviewer #2: Chen et al. report an interesting analysis on common differentially expressed genes (DEGs) between individuals suffering from idiopathic pulmonary fibrosis (IPF) and COVID-19 using bioinformatics tools. In my opinion, the study is timely and deserve a publication in Plos one journal after the following comments are addressed:

1. The manuscript needs a thorough grammar check from a native English speaker as there’s random use of comma in several sentences and some sentences need to be re-framed for clarity and better understanding. For e.g., first sentence of the Abstract. There are many instances like that throughout the manuscript.

2. The last paragraph of the introduction seems redundant to me and that information is already described in the Methods section. I would suggest rewriting the last paragraph of the introduction to provide a brief overview of the study along with its findings.

3. In Fig. 1, what does magenta and yellow circles signify?

4. I think it would be better to name some of the prominent downregulated and upregulated genes in Figure 2.

4. I feel that figures 4 and 5 can be included in supplementary data.

5. Can the authors elaborate more on the role of hub genes that are common between IPF and COVID?

6. In general, the legends for the figures are less informative and should be rewritten to provide more information to the readers.

7. The details on therapeutic target identification on SARS-CoV-2 delta variant need more explanation particularly when the author highlighted that in the introduction. Could the same be applied to other VOCs?

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16 Dec 2021

Dear Academic Editor Chandrabose Selvaraj,

Thank you for your letter and for the comments concerning our manuscript entitled Bioinformatic “Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis”. Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our research. We have submitted the tracked version of the manuscript, in which the revised part was colored in red in the manuscript. The authors focus on each one of comment separately and give the answers as follows:

Reviewer#1:

Major issues:

1. The authors have worked in a domain of COVID-19 which still needs a lot of research and understanding. The availability of data and the fast-evolving research on the therapeutic interventions against COVID-19 makes the current work very well timed and at the same time prone to a lot of questions still unanswered. The computational analysis is always as good as the datasets. As I understand, these data on COVID-19 in GEO and other databases are rapidly enriched within very small timeframe these days. Authors comment on these lines are missing in the discussion. This may enhance the impact of the manuscript.

Response: Thank you for the valuable comments. It is really true as Reviewer suggested that the current research against COVID-19 is recently conducted, leading to a rapid enrichment of data on COVID-19 in GEO database. In our study, a dataset of GSE147507, published on Mar 25,2020, has been analyzed by a series of studies and has been verified to be reliable with this evidence. This dataset illustrates infections of SARS-CoV-2 in transcriptional responses and provides solid samples of human lung epithelium and lung alveolar cells not blood sample, which make this dataset more representative with a great reference value. Thank you again for this comment, and this point has been written in the paragraph six in Discussion.

2. The authors self-critical approach is appreciated in mentioning the perceived weakness of the study. “Although the risk factor for COVID-19 is shared with IPF, there is insufficient evidence in our analysis to support clinical practice of candidate drug for COVID-19 and IPF treatment.” The authors may consider, if deemed suitable, to include alternative strategies to further establish the claim for the candidate drug through support of relevant literature or relevant computational studies like DNA binding sites, binding affinity, molecular docking, MD simulations, etc.

Response: We gratefully appreciate for your valuable suggestion, which are helpful to establish the claim for the candidate drug. In our study, the candidate drug is predicted from DSigDB database on Enrichr platform. Enrichr database is currently contains a large collection of diverse gene set libraries available for analysis and download and DSigDB gene sets were extracted and compiled from quantitative inhibition data of drugs/compounds from a variety of databases and publications, representing the direct targets of the drugs/compounds. We can provide credible candidates drug via an analysis of DSigDB as a support for clinical practice of COVID-19 and IPF.

Seven relevant literatures from Ref.28 to 34 in manuscript establish a claim for the candidate drug, providing an applicative prospect of candidate drugs in treating COVID-19 and IPF. The application of other computational studies is a great challenge for us. At present, our study is under a limitation of deeper analysis. Therefore, the online tools mentioned above provide reference for the series of prediction and offer more options for future pharmacodynamic study. Thanks again for your suggestion and this is a valuable comment for a future study to search deeper with computational methods.

3. The authors have also included in their discussion that “Furthermore, due to the limitation, the downstream molecules of hub genes should be determined in the future, and the role of the hub gene in crosstalk between COVID-19 and IPF should be confirmed using clinical samples and experimental models.” If the authors find it appropriate, the inclusion of the list of the downstream molecules of the 22 hub genes reported may enhance the scientific coverage of the topic aimed at in the manuscript. This will open up new avenues for researches to further enhance the knowledge gained in the current study and it will also compliment the line cited here.

Response: Thank you for your valuable comments. As Reviewer’s professional suggestions, we analyzed 22 hub genes based on WikiPathways platform. The downstream molecules of 22 hub genes are listed in Supplementary Table 4.

Among 22 hub genes, the downstream molecules of three hub genes were unavailable including MX1, IFI6, and IFITM3. Hence, we illustrated their roles as follows. MX1 targeting viruses include negative-stranded RNA viruses and HBV, IFI6 negatively regulating the intrinsic apoptotic signaling pathway and TNFSF10-induced apoptosis. IFITM3 inhibits the entry of viruses to the host cell cytoplasm by preventing viral fusion with cholesterol depleted endosomes.

We have supplied the analysis in paragraph “Candidate drug prediction for targeting hub gene between COVID-19 and IPF” in Result and the list of 22 hub gene downstream molecules are presented in Supplementary Table 4.

4.The authors mentioned that “Our conclusions were based on the responses of 5 GSEs in GEO database and so might not reflect processes via the in vivo and in vitro experiment.” The authors may consider to add their criteria for selection/exclusion of the GSEs and establish that whether the selections were exhaustive, at least till the last date of updating of the study. If the data is exhaustive, relevant literatures in support of the claims may enhance the discussion of the manuscript.

Response: We are sorry about that this part has not been fully proposed in manuscript. The Five datasets of idiopathic pulmonary fibrosis are selected to analyze the lung samples obtained from IPF patients. The datasets with blood sample, for example, are excluded because we focus mainly on lung solid tissue. And all five datasets are supported by literatures. Among them, the literature related to GSE135065 is published on Plos One, for instance.

As the reviewer concerning that if our selections are exhaustive, the five datasets are selected considering the difference of time and region. We selected GSEs published from 2011 to 2019, ranging from America to East Asia to ensure that our study is broadly representative. According to the comment, we have added some content of our criteria for selection of GSEs in the first paragraph in Discussion.

Minor issues:

1. Page 11: The details of methods for prediction of candidate drug for hub gene is only limited to the name of the database used and the adjusted P value. The detailed criteria used for prediction, the algorithm used by the program, any adjustments applied by the authors to the default running parameters, cut-offs (apart from adjusted P value) used if any, rationale behind the choice of the specific database and its search tool, etc. may be quite useful to establish the method as reproducible as well as for clear understanding of the reader.

Response: We appreciate your valuable comments. In this study, DSigDB as an online tool was used to predicted candidate drug. However, there is less parameter on this tool. For a clear understanding of the reader, detailed information of DSigDB was provided in Ref.19 in our manuscript. Readers can follow this reference to obtain the data of drugs/compound-related gene sets. DSigDB were extracted and compiled from quantitative inhibition data of drugs/compounds from a variety of databases and publications. These genes represent the direct targets of the drugs/compounds. Apart from P-value, candidate drugs are also sorted by combined score ranking and we highlighted top ten in Table 3. The combined score is performed by the Enrichr web tool, which depends on the log of the P-value and z-score. We have rewritten the paragraph “Prediction of candidate drug for hub gene” in Methods and Material.

2. The databases, especially those on COVID-19, are rapidly getting enriched. In this context, mentioning the dates of last accession of the databases like GEO, DSigDB database, etc. This also helps the reader to understand the timeframe of the study.

Response: Thank you for pointing out this problem in manuscript. We analyzed the datasets from GEO database and predicted potential drug targets in August 2021. We have added the dates of last accession of databases in paragraph “The collection of databases and the identification of DEGs” in Methods and Material and we appreciate your valuable comments.

3. The overall structure of the manuscript is observed to have slightly overlapping and sometimes repetitive lines or comments in methodology, results and discussion sections. The authors may consider including exclusively the points in the various sections without recurrence, for a more concise write-up.

Response: We are sorry for some redundant content in manuscript. We have rewritten the parts according to Reviewer’s suggestion.

4. Very minor but significant typographical / grammar and formatting issues were observed in the manuscript. It is assumed that a thorough proof reading by the authors during further processing will take care of these issues.

Response: We are very sorry for our construction and spelling, and we have rewritten some parts of this manuscript to improve the reading fluency. The manuscript has been edited to ensure language and grammar accuracy. The Editing Certification is submitted as “Other” document.

Reviewer #2:

1. The manuscript needs a thorough grammar check from a native English speaker as there’s random use of comma in several sentences and some sentences need to be re-framed for clarity and better understanding. For e.g., first sentence of the Abstract. There are many instances like that throughout the manuscript.

Response: Thank you for this comment. The manuscript has been edited to ensure language and grammar accuracy. The Editing Certification is submitted as “Other” document.

2. The last paragraph of the introduction seems redundant to me and that information is already described in the Methods section. I would suggest rewriting the last paragraph of the introduction to provide a brief overview of the study along with its findings.

Response: Thank you for pointing out this problem in manuscript. It is true that our last paragraph of the Introduction in manuscript is overlapping with Methods. We have made correction according to the Reviewer’s comments.

3. In Fig. 1, what does magenta and yellow circles signify?

Response: We are sorry we didn’t make it clear. The magenta and yellow circles in Figure.1 are based on an analysis of Venn diagram with a data overlap between COVID-19 gene sets and five IPF gene sets. The high expression group and low expression group are depicted separately. We have rewritten the figure legend of Figure. 1 for better understanding.

4. I think it would be better to name some of the prominent downregulated and upregulated genes in Figure 2.

Response: Thank you so much for your careful check and your constructive comments. In PPI analysis, hub genes were identified from the common DEGs via two plugins including Cytohubba and MCODE. These hub genes are regarded as the prominent genes in our study, which were still identified when the volcano plots were displayed. To be frankly, we have tried several approaches to mark the differential expressed genes. At present, this study is unable to encompass this exhibition, while this is a valuable comment.

5. I feel that figures 4 and 5 can be included in supplementary data.

Response：Thank you for your valuable comments. In our manuscript, figures 4 and 5 present the result of GO functional enrichment analysis of upregulated and downregulated common DEGs between COVID-19 and IPF. It is indicated that upregulated common DEGs were mainly involved in cytokine mediation, such as cell response to interferon. In our research, we found that type I interferon pathway may drive chronic inflammation and fibrosis in IPF, and type I interferon plays an important role in lung pathology of COVID-19, which we have elaborated in our Discussion in manuscript. Hence, we presented these two figures in our manuscript for a better understanding of our results. Hopefully, our findings may help to investigate potential targets of COVID-19 and provide ideas for future research.

6. Can the authors elaborate more on the role of hub genes that are common between IPF and COVID?

Response: Thank you for the valuable comments. We agree that the role of hub genes in our manuscript need to be elaborated. The distinct role of single hub gene is to cross talk with other genes in biological network, thus we elaborated the role of hub genes by performing functional enrichment analysis. We performed the GO/KEGG enrichment analysis of hub genes, and the results were presented as Supplementary Figure 3 and 4. We have illustrated it in the paragraph “Enrichment analysis of hub gene” in Results.

7. In general, the legends for the figures are less informative and should be rewritten to provide more information to the readers.

Response: We are sorry we didn’t make the legends for the figures detailed and we have recorrected this part in our manuscript. Thanks for your valuable comments.

8. The details on therapeutic target identification on SARS-CoV-2 delta variant need more explanation particularly when the author highlighted that in the introduction. Could the same be applied to other VOCs?

Response: Thanks for your valuable comment. As mentioned in manuscript, our study provides an insight that we can design and develop candidate drug for virus variant, such as Delta SARS-CoV-2. We aim to provide a referable approach for candidate drug prediction for other VOCs. Give that the approach was confined to sufficient data of VOCs, this research was not yet conducted. Actually, it was a valuable direction for us. Thank you again for this comment.

Once again, thank you very much for your comments and suggestions. Look forward to your reply. Please contact me if there is any question.

Your sincerely,

Shilin Xia (corresponding author) xiashilin@dmu.edu.cn

On behalf of all the authors:

Qianyi Chen, Hua Sui, Xueying Shi, Bingqian Huang, Tingxin Wang.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

5 Jan 2022

Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis.

PONE-D-21-32287R1

Dear Dr. Xia,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Chandrabose Selvaraj, Ph.D.

Academic Editor

PLOS ONE

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Reviewer #3: Authors have revised the whole manuscript as per reviewer comments and in present form, it can be accepted

10 Jan 2022

PONE-D-21-32287R1

Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis

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