key: cord-0867730-fdf3dpkn
authors: Li, Xinyu; Peng, Cheng; Zhu, Ziqing; Cai, Haozheng; Zhuang, Quan
title: The networks of m(6)A-SARS-CoV-2 related genes and immune infiltration patterns in idiopathic pulmonary fibrosis
date: 2021-03-01
journal: Aging (Albany NY)
DOI: 10.18632/aging.202725
sha: dac8547c2ca02973e26157bc549e2be3744bdfa8
doc_id: 867730
cord_uid: fdf3dpkn

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis. The current coronavirus disease 2019 (COVID-19) shares some similarities with IPF. SARS-CoV-2 related genes have been reported to be broadly regulated by N(6)-methyladenosine (m(6)A) RNA modification. Here, we identified the association between m(6)A methylation regulators, COVID-19 infection pathways, and immune responses in IPF. The characteristic gene expression networks and immune infiltration patterns of m(6)A-SARS-CoV-2 related genes in different tissues of IPF were revealed. We subsequently evaluated the influence of these related gene expression patterns and immune infiltration patterns on the prognosis/lung function of IPF patients. The IPF cohort was obtained from the Gene Expression Omnibus dataset. Pearson correlation analysis was performed to identify the correlations among genes or cells. The CIBERSORT algorithm was used to assess the infiltration of 22 types of immune cells. The least absolute shrinkage and selection operator (LASSO) and proportional hazards model (Cox model) were used to develop the prognosis prediction model. Our research is pivotal for further understanding of the cellular and genetic links between IPF and SARS-CoV-2 infection in the context of the COVID-19 pandemic, which may contribute to providing new ideas for prognosis assessment and treatment of both diseases.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease characterized by progressive pulmonary interstitial fibrosis and lung dysfunction. IPF patients usually show a poor prognosis with an average survival time of 3-5 years [1] , and the specific etiology and pathogenesis of IPF remain unclear. N 6 -methyladenosine (m 6 A) is the most abundant mRNA modification in mammalian cells. There are three types AGING respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged, causing a global pandemic of acute respiratory disease, named "coronavirus disease 2019" . To date, no effective treatment has been available. Recently, researchers identified 332 high-confidence SARS-CoV-2-human protein-protein interactions, revealing targets for drug repurposing [9] . Because COVID-19 is an acute pulmonary infection with a certain potential correlation with IPF, the association between COVID-19 and IPF at the cellular and genetic levels is worth exploring.

In this study, we innovatively explored the association between m 6 A methylation regulators, COVID-19 infection pathways, and immune responses. The characteristic gene expression networks and immune infiltration patterns of m 6 A-SARS-CoV-2 related genes in different tissues of IPF were revealed. Moreover, we evaluated the influence of these related gene expression and immune infiltration patterns on the prognosis/lung function of the IPF patients. During the COVID-19 pandemic, these results would help to further understand the link between IPF and SARS-CoV-2 infection and provide new insights for prognosis assessment and treatment of both diseases.

The discovery cohort included 176 IPF patients from the GEO database (GSE70867). The batch effect was eliminated using the sva package ( Figure 1A , 1B). We extracted the expression matrices of 19 m 6 A-related genes and 305 SARS-CoV-2 related genes from the GEO datasets. A total of 110 SARS-CoV-2 related genes were found to be significantly correlated with m 6 A-related genes, which were defined as m 6 A-related-CoV genes. Combined with the survival information, univariate Cox regression was then applied to screen prognostic genes, and 9 m 6 A-related-CoV genes (p < 0.01) were retained ( Figure 1C ). Then, we performed a LASSO regression, and eight optimal variables were obtained from the above 9 m 6 A-related-CoV genes. The correlations between the m 6 A-related genes and the 8 m 6 A-related-CoV genes are shown in Figure 1D .

Additionally, we used the expression levels of 8 m 6 Arelated-CoV genes and the corresponding coefficients derived from the multivariate Cox regression model to estimate the risk score for each patient: Score 1 = 0.3869 × expression of BRD4 + 0.6027 × expression of ATP13A3 + 0.3401 × expression of MARK3 + 0.3019 × expression of ACAD9 -0.8837 × expression of REEP5 -0.3489 × expression of REEP6 -0.4576 × expression of PPIL3 -0.2066 × expression of POR. Based on the Score 1 of each patient, we divided the patients into two groups using the maximally selected rank method: high-risk group and low-risk group ( Figure 1E, 1F) .

Due to the different prognostic outcomes, we sought to investigate the possible differences between the high-risk and low-risk groups using the gene set enrichment analysis (GSEA). The results suggested that high-risk patients had high levels of response to oxygen-containing compound signaling pathway, reactive oxygen species (ROS) metabolic process signaling pathway, and response to ROS signaling pathway ( Figure 1G ). In gene ontology (GO) functional annotation, biological process analysis suggested that overexpressed genes in the high-risk group were significantly enriched in the regulation of inflammatory response, cell adhesion, immune response, cell-cell signaling, and extracellular matrix organization. For cellular component analysis, these genes were significantly enriched in the integral components of the membrane, plasma membrane, and extracellular exosome. Molecular function analysis revealed that these proteins were significantly enriched in protein binding and receptor binding ( Figure 1H ).

To further explore the gene regulatory network, we identified eight lncRNAs that were significantly correlated with both SARS-CoV-2 related genes and m 6 A related genes, which were defined as m 6 A-CoV related lncRNAs. Figure 2A , 2B shows the correlations between the lncRNAs and SARS-CoV-2/m 6 A related genes, respectively (SARS-CoV-2/m 6 A related genes that are not significantly related are not shown in the figure). After univariate Cox and LASSO screening, 2 m 6 A-CoV related lncRNAs were screened for a multivariate Cox regression model to estimate the risk score: Score 2 = -0.3251 × expression of MGC4859 -0.1319 × expression of HYMAI. The IPF patients were also divided into high-risk and low-risk groups based on Score 2 ( Figure 2C, 2D) .

The ESTIMATE algorithm was used to obtain stromal and immune scores. The LASSO-Cox analysis demonstrated that the stromal and immune scores were positively correlated with the risk of poor prognosis, suggesting that high immune infiltration might be a risk factor for prognosis. 

To further identify the immune infiltration patterns in IPF, the composition of 22 immune cells was assessed using CIBERSORT. Univariate Cox analysis showed that activated natural killer (NK) cells, activated mast cells, and resting dendritic cells (DCs) were significantly correlated with the survival of IPF patients (p < 0.01). LASSO-Cox analysis showed that activated NK cells and activated mast cells were positively correlated with the risk of poor prognosis. In addition, resting DCs negatively correlated with the risk score. Based on the risk score calculated by these three types of immune cells, we divided the patients into two groups, and four differentially expressed SARS-CoV2related genes were identified as DEGs ( Figure 2E, 2F) . Univariate Cox regression demonstrated that four DGEs had significant associations with survival prognosis (p < 0.01) ( Figure 2G ). The correlations between the four DGEs and three immune cells are shown in Figure 2H , which suggests a co-expression pattern of SARS-CoV2related genes and immune cell infiltration.

Furthermore, we included 3 immune cells and 4 DGEs in the multivariate Cox regression model to estimate the risk score: Score 3 = 5.57 × composition of activated NK cells + 2.33 × composition of activated mast cells -19.4 × composition of resting DCs -0.0269 × expression of NINL + 0.0497 × expression of SLC27A2 + 0.156 × expression of F2RL1 + 0.22 × expression of FBLN5. We also identified differences in the activation/resting state of NK cells, mast cells, and DCs in the two groups, which showed that the activation ratio of the three immune cells was higher in the high-risk group, while the resting ratio was higher in the low-risk group ( Figure 2I ).

Based on the risk models of m 6 A-related-CoV genes (Score 1), m 6 A-CoV related lncRNAs (Score 2), and SARS-CoV-2 related immune infiltration patterns (Score 3), multivariate Cox regression was used to establish the combined model: Score-combined = 0.7902 × Score 1 + 0.576 × Score 2 + 0.4434 × Score 3.

Finally, we performed a prognostic validation using three independent models and a combined model. The results demonstrated that the group survival verifications of the three independent models and the combined model were significant ( Figure 3A -3F). The receiver operating characteristic (ROC) curve showed that the area under the curve (AUC) values of the combined model within 1-5 years were all greater than 0.75 in the discovery cohort, which were also greater than the AUC of the independent models ( Figure 3G -3J). This suggests that the combined model has a better predictive value for the prognosis of the IPF patients.

We further developed a nomogram for 1-5 years of overall survival prediction based on the combined Cox model ( Figure 3K ).

We further verified the above prediction method in external data sets (GSE93606), which included the peripheral whole blood from 57 patients with IPF. Similarly, we screened nine m 6 A-related-CoV genes ( Figure 4A ). Multivariate Cox regression model to estimate the risk score was used as follows: Score 1 = 0.8919 × expression of ZC3H7A -0.1496 × expression of YIF1A -1.4191 × expression of BAG5 + 3.9659 × expression of RHOA + 2.3642 × expression of NUP214 + 0.4134 × expression of INTS4 + 0.7913 × expression of GLA + 1.4476 × expression of AAR2 + 1.4582 × expression of ATP1B1.

Univariate Cox regression showed that γδT cells and M2 macrophages were significantly correlated with the survival of patients with IPF (p < 0.01). LASSO-Cox analysis showed that γδT cells and M2 macrophages were positively correlated with the risk of poor prognosis. We included two immune cells in the multivariate Cox regression model to estimate the risk score: Score 2 = 14.4 × composition of γδT cells + 24.7 × composition of M2 macrophages. Based on Score 2, we divided the patients into two groups, and three differentially expressed SARS-CoV2-related DEGs were obtained ( Figure 4B ). The co-expression patterns of SARS-CoV2-related genes and immune cell infiltration are shown in Figure 4C .

Based on the risk models of m 6 A-related-CoV genes (Score 1), the SARS-CoV-2 related immune infiltration patterns (Score 2), and the multivariate Cox regression model was used to establish the combined model: Score-combined = 1.0217 × Score 1 + 1.0217 × Score 2. Prognostic validations of the two independent models and the combined model were performed. The results demonstrated that the group survival verifications of the two models and the combined model were significant ( Figure 4D-4F) . The AUC of the combined model within 1-3 years was greater than that of the two independent models ( Figure 4H, 4I) .

We verified the above prediction method in the external dataset "GSE28221," which included 120 IPF patients' peripheral blood mononuclear cells. Similarly, we screened seven m 6 A-related-CoV genes ( Figure 5A ). Meanwhile, there were 5 m 6 A-CoV related lncRNAs extract from the datasets. The alluvial diagram showed a correlation between m 6 A-CoV related lncRNAs and m6A related genes and SARS-CoV-2 related genes ( Figure 5B ). LASSO-Cox analysis suggested that the composition of naïve CD4 + T cells was significantly correlated with the survival of the IPF patients, which could be seen as a protective factor (p < 0.01). HOOK1 expression correlated with the infiltration of naïve CD4 + T cells, showing a co-expression pattern. Therefore, the expression of HOOK1 and infiltration of naïve CD4 + T cells were combined to build a model. After the multivariate Cox regression analysis, Figure 5C , 5D plots the discrepancy between HOOK1 expression and naïve CD4 + T cell infiltration in the high-risk and lowrisk groups.

Therefore, based on the risk models of m 6 A-related-CoV genes (Score 1), m 6 A-CoV related lncRNAs (Score 2), and SARS-CoV-2 related immune infiltration patterns (Score 3), a multivariate Cox regression model was used to establish the combined model: Scorecombined = 0.73795 × Score 1 + 0.09324 × Score 2 + 0.09324 × Score 3. The group survivorship curve and ROC curve of the three models and the combined model are shown in Figure 5E -5L. The group survival verifications of these three independent models and the combined model were remarkable. The AUC of the combined model within 1-3 years was greater than that of the three independent models.

Lastly, we verified the correlation between our prediction method and pulmonary function in patients with IPF. We screened five m 6 A-CoV related genes that were significantly related to forced vital capacity (FVC) and carbon monoxide diffusing capacity (DLCO). The correlations between the 5 m 6 A-CoV related genes and FVC/DLCO are plotted in Figure 6A . LARP7 and CHPF levels were significantly correlated with DLCO. LARP7 was identified as a protective gene, and CHPF was considered a dangerous gene. GRPEL1, DNAJC11, and SEPSECS were significantly correlated with FVC, which were both identified as protective genes. Based on the expression of LARP7 and CHPF, patients were divided into high and low DLCO clusters. Meanwhile, based on the expression of GRPEL1, DNAJC11, and SEPSECS, patients were divided into high FVC and low FVC groups. The discrepancies in DLCO and FVC in different clusters are plotted in Figure 6B , 6D, and the different expressions of the above-described genes in different groups are shown in Figure 6C , 6E. In addition, Table 1 summarizes the networks of m 6 A-SARS-CoV-2 related genes and the immune infiltration patterns of patients with IPF.

There is some overlap in the progress between COVID-19 and pulmonary fibrosis [10] [11] [12] . In addition, IPF is a risk factor for COVID-19 as IPF patients have a higher expression of some SARS-CoV-2 related genes, which may play a crucial role in SARS-CoV-2 entry, processing, and attachment [13, 14] . Considering the connections between these two diseases, researchers have identified common differentially expressed genes, pathogenic pathways, and candidate drug targets for IPF and COVID-19 [14] . Here, we identified the association between m 6 A methylation regulators, COVID-19 infection pathways, and immune responses in IPF.

Our results suggest that some SARS-CoV-2 related genes in IPF are broadly regulated by m 6 A. Increasing evidence has shown a relationship between m 6 A RNA modification and antiviral responses [15] [16] [17] . Among the eight m 6 A-SARS-CoV-2 related genes in BAL cells, BRD4 appears to be the most promising drug target for pulmonary fibrosis [18] . Emerging evidence has revealed the role of BRD4 in fibrosis and airway remodeling. It was found that BRD4 was involved in pulmonary fibrosis by downregulating signals after growth factor stimulation [19] , driving TGF-β-induced NOX4 expression in human lung fibroblasts [20] , and mediating NF-kappaB-dependent epithelialmesenchymal transition of airway epithelium [21] . In COVID-19, BRD4, ubiquitous hubs commonly found in multiple tissues, is also expected to be drug targets for rescuing multiple organ injuries and dealing with inflammation [22] . As another risk gene, RHOA encodes a member of the Rho family of small GTPases, which cycles between inactive GDP-bound and active GTP-bound states and functions as a molecular switch in signal transduction cascades [23] . RHOA signaling can regulate cyclin D1 expression and activate proliferation in IPF lung fibroblasts [24] . In summary, patients with high expression levels of these risk genes should be considered. For IPF patients, more attention In addition, the role of immune cells in the pathogenesis of IPF has attracted increasing attention. Our study found six types of immune cells and their co-expression patterns with SARS-CoV-2 related genes that significantly affected the prognosis of IPF. High infiltration of activated NK cells, activated mast cells, γδT cells, and M2 macrophages were risk factors, while naive CD4+T cells and resting DCs were protective factors. This suggests that the high activation state of immune cells in IPF is detrimental to patient prognosis.

NK cells play a role in blocking fibrotic liver diseases [25] ; however, their role in the lung is still under debate [26] . Our study showed that activated NK cells had high infiltration in the high-risk group, which might be associated with infection and inflammation in patients. Compared with other pulmonary fibrosis diseases, more mast cells and a high level of TGF-β were found in patients with IPF [27] . Mast cells not only secrete TGFβ, a profibrotic mediator that contributes to fibroblast proliferation, but also enhances their response [28] . Moreover, mast cells and fibroblasts are closely related and may promote the development of pulmonary fibrosis through synergistic action [29] [30] [31] . Our study also suggests that activated CD4+ T cells may be a risk factor. T-helper (Th) 1 secretes IFN-γ to reduce pulmonary fibrosis, whereas Th2 cytokines IL-4, IL-5, and IL-13 stimulate fibroblast proliferation, collagen production, and fibroblast activation [32] . Th17 has a pro-fibrotic effect by promoting fibroblast proliferation and cytokine secretion in a bleomycin-induced systemic sclerosis mouse model [33] . Th9 cells, which produce IL-9, also play dual roles in pulmonary fibrosis. Overexpression of IL-9 in vivo leads to the accumulation of collagen and laminin in bronchial tubes, resulting in a pro-fibrotic effect [34] . M2 macrophages had recognized pro-fibrotic effects. The M2-type macrophage marker CCL18 was significantly increased in the serum and BAL of IPF patients, involved in the formation of fibrosis [35] ; M2 macrophages can also produce TGF-β and PDGF to continuously activate fibroblasts and promote myofibroblast proliferation [36] .The role of γδT cells in pulmonary fibrosis remains controversial. A recent study showed that pulmonary inflammation and fibrosis were promoted by PM2.5-induced secretion of IL-17A, which inhibits autophagy in bronchial epithelial cells [37] . In contrast, a previous study also suggested that pulmonary γδT cells seemed to play a regulatory role in suppressing fibrosis via the suppression of IL-17A production and IL-17A(+) CD4(+) T cells. The results of our study support this former viewpoint.

Therefore, high immune infiltration in the lung microenvironment is a risk factor for poor prognosis. At the same time, these immune cells may be widely associated with SARS-CoV-2 and fibrosis-related genes. The control of pulmonary inflammation has clinical significance in relieving the symptoms of IPF or COVID-19 patients.

In conclusion, we identified SARS-CoV-2 related genes associated with IPF prognosis and lung function and demonstrated that their expression was widely regulated by m 6 A regulators. In addition, we found some characteristic co-expression networks of immune cells and SARS-CoV-2 related genes that were thought to mediate special immune response patterns in IPF. Our research is important for further understanding the genetic and cellular links between IPF and SARS-CoV-2 infection in the context of the COVID-19 pandemic. These associations could also be beneficial for the management of IPF and COVID-19.

The general ideas and methodologies used in this study are drawn as a flow chart (Figure 7 ).

The discovery cohort of the study contained 176 IPF patients from the Gene Expression Omnibus (GEO, available at: https://www.ncbi.nlm.nih.gov/geo/) database (GSE70867), including 176 IPF patients' BAL cells. Three validation cohorts were used for the external validation (GSE93606, GSE28221, and GSE38958) to examine the predictive effect of the prediction method. The microarray data of GSE93606 included 57 patients with IPF and peripheral whole blood samples. The microarray data of GSE28221 included 120 peripheral blood mononuclear cells from patients with IPF. The microarray data of GSE38958 included 60 IPF patients' peripheral whole blood samples. All procedures in this study complied with the protocol. For analyses of data from a public database, approval and informed consent from the local ethics committee were not required.

Human genes that may be relevant for SARS-CoV-2 infection were defined as SARS-CoV-2 related genes. Three hundred thirty-two SARS-CoV-2 related proteins were identified to be associated with 26 SARS-CoV-2 proteins in human cells [9] , which can also be downloaded from "http://geneontology.org/covid-19.html." Human genes related to m6A modification were defined as m6A-related genes, and 22 m6A-related genes were identified from the literature [38] .

The expression matrices of available m 6 A-related and SARS-CoV-2 related genes were extracted. Pearson correlation analysis was performed to identify correlations between SARS-CoV-2 related genes and m 6 A-related genes. A SARS-CoV-2 related gene whose expression value was related to (with the | Pearson R | > 0.5 and p < 0.05) one or more of the m 6 A-related genes was defined as an m 6 A-related-CoV gene.

Based on the long non-coding RNA (lncRNA) annotation file of the Genome Reference Consortium Human Build 38 (GRCh38) acquired from GENCODE (https://www.gencodegenes.org/), expression matrixes of lncRNAs were identified in the GEO dataset. lncRNAs that were significantly correlated (with the | Pearson R | > 0.5, and p < 0.05) with both m 6 A-related genes and SARS-CoV2-related genes were defined as m 6 A-CoV related lncRNAs.

The ESTIMATE algorithm was applied to identify the degree of immune cell infiltration and to predict the immune status [39] . Furthermore, the CIBERSORT algorithm was utilized to assess the infiltration of 22 types of immune cells in IPF [40] . Only those samples with a CIBERSORT output of p < 0.05 were deemed worthy of further analysis.

Potential prognostic factors (such as genes, lncRNAs, and cells) were screened using univariate Cox analysis, and factors with p < 0.01 were retained. The least absolute shrinkage and selection operator (LASSO) was applied to select the optimal variables, which is a type AGING of linear regression using shrinkage [41] . Then, we created a prognosis prediction model of IPF using the selected prognostic factors.

For each patient, the formula for calculating the risk score was as follows: Score=∑i=1nCoefi*xi, where Coefi indicates that the coefficient is derived from the multivariate Cox regression, and xi is the value of each factor. According to the risk scores, the optimal cutting point was identified using the maximally selected rank method, and a prognosis prediction model of IPF was developed.

The Kaplan-Meier method was employed to examine the survival curves and compare the differences in survival across different scoring subgroups. The ROC curve of the risk score model was constructed to evaluate the impact of different factors in patients with IPF.

The limma algorithm was used to identify differentially expressed genes (DEGs) between the two groups. Genes with an FDR adjusted p-value < 0.0001 and an absolute value of log2 (fold change) > 1 were considered immune-related DEGs.

GO analysis was used to evaluate the degree of enrichment of DEGs in biological processes, cellular components, and molecular functions. Those with p < 0.05 and count (the number of enriched genes) ≥3 were considered as the cutoff criterion. GSEA was used to determine the functional or pathway enrichment under the proportion of genes with a log2FC greater than 1 (or lower than −1) within a given gene set.

All analyses were performed with R version 4.0.2 (https://www.r-project.org/) and the corresponding packages.

Data analyzed in this manuscript is already publicly available from the following GEO (https://www.ncbi.nlm.nih.gov/geo/) accession numbers: GSE70867, GSE93606, GSE28221, and GSE38958.

Study was conceived and designed by Q.Z. Statistical analyses were performed by X.L. and C.P. Software package was prepared by Z.Z. and H.C. Manuscript was written by X.L. and C.P. X.L. and C.P. are joint first authors.

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The authors declare that they have no conflicts of interest.

This study was supported by grants of the National Natural Science Foundation of China (81700658) and the Hunan Provincial Natural Science Foundation-Outstanding Youth Foundation (2020JJ3058).