
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261509
70659
10.1038/s41598-024-70659-1
Article
Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals ubiquitin promotes pulmonary fibrosis in chronic pulmonary diseases
Wen Zhuman 12
Ablimit Abduxukur 806613359@qq.com

1
1 https://ror.org/01p455v08 grid.13394.3c 0000 0004 1799 3993 Department of Histology and Embryology, Basic Medical College, Xinjiang Medical University, Ürümqi, China
2 College of Nursing and Health, Xinjiang Career Technical College, Kuitun, China
11 9 2024
11 9 2024
2024
14 2119516 11 2023
20 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
It is estimated that there are 544.9 million people suffering from chronic respiratory diseases in the world, which is the third largest chronic disease. Although there are various clinical treatment methods, there is no specific drug for chronic pulmonary diseases, including chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and idiopathic pulmonary fibrosis (IPF). Therefore, it is urgent to clarify the pathological mechanism and medication development. Single-cell transcriptome data of human and mouse from GEO database were integrated by “Harmony” algorithm. The data was standardized and normalized by using “Seurat” package, and “SingleR” algorithm was used for cell grouping annotation. The “Findmarker” function is used to find differentially expressed genes (DEGs), which were enriched and analyzed by using "clusterProfiler", and a protein interaction network was constructed for DEGs, and four algorithms are used to find the hub genes. The expression of hub genes were analyzed in independent human and mouse single-cell transcriptome data. Bulk RNA data were used to integrate by the "SVA" function, verify the expression levels of hub genes and build a diagnostic model. The L1000FWD platform was used to screen potential drugs. Through exploring the similarities and differences by integrated single-cell atlas, we found that the lung parenchymal cells showed abnormal oxidative stress, cell matrix adhesion and ubiquitination in COPD, corona virus disease 2019 (COVID-19), ILD and IPF. Meanwhile, the lung resident immune cells showed abnormal Toll-like receptor signals, interferon signals and ubiquitination. However, unlike acute pneumonia (COVID-19), chronic pulmonary disease shows enhanced ubiquitination. This phenomenon was confirmed in independent external human single-cell atlas, but unfortunately, it was not confirmed in mouse single-cell atlas of bleomycin-induced pulmonary fibrosis model and influenza virus-infected mouse model, which means that the model needs to be optimized. In addition, the bulk RNA-Seq data of COVID-19, ILD and IPF was integrated, and we found that the immune infiltration of lung tissue was enhanced, consistent with the single-cell level, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD, and had a strong correlation with the expression of cell matrix adhesion genes. UBA52 and UBB have good diagnostic efficacy, and salermide and SSR-69071 can be used as their candidate drugs. Our study found that the disorder of protein ubiquitination in chronic pulmonary diseases is an important cause of pathological phenotype of pulmonary fibrosis by integrating scRNA-Seq and bulk RNA-Seq, which provides a new horizons for clinicopathology, diagnosis and treatment.

Keywords

Chronic pulmonary diseases
Single-cell RNA sequencing
Bulk RNA sequencing
Ubiquitination
Immune infiltration
Subject terms

Computational biology and bioinformatics
Data integration
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Up to the year 2017, a total of 544.9 million individuals globally were afflicted by chronic respiratory system diseases1. These conditions persist as a primary cause of mortality and morbidity on a global scale. Chronic pulmonary conditions predominantly encompass chronic airway diseases, chronic ILDs, and chronic pneumonia2. As a typical type of chronic airway disease, Chronic obstructive pulmonary disease (COPD) is a form of chronic bronchitis characterized by airflow obstruction, which is associated with an aberrant inflammatory response triggered by the inhalation of noxious particles3. This condition is characterized by atypical airway epithelial cells and immune cell involvement. Chronic ILD4 represents a form of chronic pulmonary condition characterized by parenchymal cell injury mediated by varying degrees of inflammation and fibrosis. IPF, denotes an ILD with unknown etiology, and both IPF and ILD exhibit a progressive pattern of pulmonary fibrosis. Prior investigations have demonstrated that COVID-19 can rapidly trigger immune cells, inciting a systemic cytokine storm, which may promote pulmonary fibrosis and, in severe cases, culminate in acute pneumonia5,6. While pulmonary fibrosis plays a pivotal role in exacerbating the progression of various pulmonary diseases, the clinical management of this condition lacks precise pharmacological interventions. Therefore, it is imperative to comprehensively comprehend the underlying mechanisms responsible for pulmonary fibrosis and develop efficacious treatment strategies. Given that COPD, ILD, IPF, and viral infections exhibit analogous pathological characteristics, such as immune activation and pulmonary fibrosis, it is of paramount importance to investigate the shared underlying pathological mechanisms among these conditions.

A comprehensive understanding of the origins of pulmonary fibrosis in the context of various pulmonary diseases holds the potential to facilitate early diagnosis and precise therapeutic interventions, thereby greatly benefiting patients. Tissue fibrosis plays a crucial role in the initiation and progression of pulmonary diseases. Prior research has underscored immune dysfunction, oxidative stress, extracellular matrix formation, and collagen deposition as significant factors contributing to fibrosis7–10. Of particular interest, macrophages have garnered substantial attention as regulatory agents in the pathogenic fibrotic response. Macrophages located in proximity to myofibroblasts play a pivotal role in promoting extracellular matrix production in pulmonary fibrosis, primarily through heightened expression of fibroblast stimulating factors11. Furthermore, recent years have witnessed growing recognition of the involvement of protein ubiquitination regulation in the onset and progression of numerous diseases. Some studies have indicated that E3 ubiquitin ligases regulate epithelial-mesenchymal transition mediated by the TGF-β-Smad pathway through the ubiquitin–proteasome pathway12, thereby facilitating the development of idiopathic fibrosis.

As the widespread adoption of single-cell technology continues, there is a gradual reevaluation of the pathological mechanisms underlying diseases13. However, sequencing data from a single disease often falls short in identifying and elucidating the key pathological phenotypes and essential molecular features. Consequently, bioinformatics, founded on data integration, serves as a critical tool for exploring and unveiling the pathological underpinnings shared between diseases. This study capitalizes on single-cell transcriptome data from four distinct pulmonary diseases—namely, COPD, COVID-19, ILD, and IPF. It delves into an investigation of both the commonalities and distinctions within lung parenchymal and immune cells, corroborating these findings with another human single-cell integration atlas encompassing COVID-19, ILD, and IPF. Furthermore, the analysis expands to include a single-cell atlas involving a bleomycin-induced pulmonary fibrosis model and an influenza virus-infected mouse model. Additionally, the study explores immune cell infiltration, key gene expression patterns, diagnostic potential, and drug screening applicability across these various diseases by employing integrated human lung tissue transcriptome data, encompassing COVID-19, ILD, and IPF. The overarching aim of this study is to reassess the commonalities and distinctions inherent in different pulmonary diseases from the novel vantage point of integrated genomics. This research endeavor seeks to offer fresh insights into the understanding of pathological mechanisms, clinical diagnostics, and precision treatments for these diseases.

Materials and methods

Sample collection and preprocessing

Thirteen scRNA-seq data sets including Normal, COPD, ILD and IPF were retrieved and collected in GEO database, and single-cell atlas were integrated by using "Seurat" package14, and "Harmony" algorithm was used to remove the batch effect of different data sets, and UMAP dimensionality reduction clustering was used to judge its effect. When the RNA count is greater than 300 and the proportion of mitochondrial genes is less than 25%, the single-cell data are included, and the expression profiles of the included genes are logarithmically standardized, looking for high variable genes, normalized, dimensionality reduced by PCA and TSNE. Finally, "SingleR" package15 is used for cell classification annotation. In addition, seven bulk RNA data sets including Normal, COVID-19, ILD and IPF were collected, and the "sva" algorithm was used to remove the batch effect of different data sets, and PCA and TSNE algorithms were used for dimensionality reduction and drawing to judge the removal of batch effect. See Table 1 for detailed data set information. Table 1 Sample Source and Grouping Information.

Source	Species	Sequencing type	Position	Group	Sample size	
GSE136831	Human	scRNA	Lung	Normal:COPD:IPF	17:18:11	
GSE159354	Human	scRNA	Lung	Normal:ILD	15:13	
GSE159585	Human	scRNA	Lung	Normal:COVID-19	5:7	
GSE171524	Human	scRNA	Lung	Normal:COVID-19	7:20	
GSE227136	Human	scRNA	Lung	Normal:ILD:IPF	20:21:51	
GSE121611	Mouse	scRNA	Lung	PF	2	
GSE132771	Mouse	scRNA	Lung	Control:PF	4:4	
GSE149857	Mouse	scRNA	Lung	Influenza	2	
GSE183545	Mouse	scRNA	Lung	Control:PF	1:1	
GSE201698	Mouse	scRNA	Lung	Control:PF	1:5	
GSE202325	Mouse	scRNA	Lung	Influenza	12	
GSE213016	Mouse	scRNA	Lung	Control:PF	1:1	
GSE228594	Mouse	scRNA	Lung	Control:Influenza	1:8	
GSE153131	Human	Bulk RNA	Lung	Normal:COVID-19	3:6	
GSE183533	Human	Bulk RNA	Lung	Normal:COVID-19	10:31	
GSE205099	Human	Bulk RNA	Lung	Normal:COVID-19	16:48	
GSE208076	Human	Bulk RNA	Lung	Normal:COVID-19	3:7	
GSE21369	Human	Bulk RNA	Lung	Normal:ILD	6:23	
GSE150910	Human	Bulk RNA	Lung	Normal:IPF	103:103	
GSE173355	Human	Bulk RNA	Lung	Normal:IPF	14:23	

DEGs analysis and gene set enrichment analysis

So as to clarify the gene expression changes of different cells in different disease types, The DEGs between disease group and normal group were screened out by using the "FindMarkers" function. The screening criteria of DEGs are that the adjusted p value is less than 0.05 and the Log|FC| is greater than 0.25. Volcanic groups are drawn by using the function to characterize the gene distribution. To explore the biological functions of these differential genes, DEGs were used in gene set enrichment analysis (GSEA) by using hallmark gene sets(v2023)16.

GO and KEGG analysis

For explore the biological processes and pathways involved in different diseases and different cell types, DEGs was analyzed by using "clusterProfiler" package17, and the p value was less than 0.05 as the truncation value. For the enrichment results of the same cells in different disease states, the enrichment term were intersected and displayed by "GOplot" function18.

Hub genes screening and enrichment analysis of gene co-expression network

"STRING" database is used to analyze the relationship between DGEs. At the same time, four algorithms (Stress, BottleNeck, Betweenness and Radioactivity) from "cytohubba"19 are used to screen hub genes, and their intersections are shown by Upset diagram. The co-expression network of common hub genes was constructed and enriched by online database geneminia.

Analysis of gene expression and immune cell infiltration

The expression ratio and abundance of hub genes were calculated by "Dotplot" package. The immune cell infiltration of the integrated bulk RNAseq was analyzed by “CIBERSORT” function20, and the results were analyzed and visualized by variance analysis.

Receiver operating characteristic analysis and correlation analysis between genes

In order to determine whether the hub gene can be used for diagnosis, the ROC analysis of the integrated bulk RNAseq data was carried out using the "bioinformatics" platform (https://www.bioinformatics.com.cn/). Pearson correlation analysis was used to analyze the correlation between hub genes and genes related to cell matrix adhesion.

Drug screening based on hub genes

For obtain potential therapeutic drugs based on target genes, the drug screening platform L1000FWD21 based on gene expression was used to predict potential drugs, and online tool CB-DOCK222 was used to carry out molecular docking between hub genes and drugs, and the results were displayed with docking score less than 0 as the threshold.

Results

Pulmonary disease with imbalance of cell ratio

Single-cell transcriptome data of 86 samples were gained from three data sets including Normal, COPD, COVID-19 and IPF, and a total of 358,217 cell expression profiles were obtained by using the "Harmony" function of Seurat package to remove the batch effect (Fig. 1A–C). After comparing the Acquired single-cell atlas with the human cell atlas database, nine cell types were identified, including endothelial cells, epithelial cells, smooth muscle cells, B cells, T cells, macrophages and monocytes (Fig. 1D). However, the proportion of these cells is seriously out of balance in different disease types (Fig. 1E and F). For example, the proportion of lung macrophages in COVID-19 patients decreased and the proportion of smooth muscle cells increased, while macrophages in IPF patients, T cells in COPD patients and epithelial cells in ILD patients proliferated. These results seem to indicate that four different diseases have different pathological mechanisms.Fig. 1 Integrated single cell atlas, cell type and cell ratio of four diseases. UMAP diagram before (A) and after (B) batch removal by integrating the single cell atlas; UMAP diagram of cell grouping (C) and annotation (D); The proportion of all cell types (E) and the proportion of immune cells (F).

Abnormal cell matrix adhesion and ubiquitination of endothelial cells

Visualization and differential gene analysis of 42,821 endothelial cells from four kinds of patients showed that the endothelial cells showed abnormal gene expression (Fig. 2A and B), but this abnormality did not show the same functional abnormality, such as oxidative phosphorylation activation of endothelial cells in COPD, ILD and IPF, while COVID-19 showed inhibition (Fig. 2C–F). Notably, endothelial cells in COPD and ILD showed similar inflammatory signal activation and increased collagen formation. GO enrichment analysis showed that protein ubiquitination, oxidative stress, integrin and abnormal cell matrix adhesion (Supplementary Fig. S1A) occurred in lung disease, but ubiquitination hydrolysis was up-regulated in COVID-19 and down-regulated in others (Supplementary Fig. S1B). For explore the key genes that drive these changes, hub gene were screened for DEGs (Supplementary Fig. S1C–F), and found that Ubiquitin-ribosomal protein eL40 fusion protein (UBA52), Polyubiquitin-C (UBC), Polyubiquitin-B (UBB) and other genes participated in the ubiquitination protein ligand binding (Fig. 2G,H and Supplementary Fig. S1G and H) by constructing a gene co-expression network. In terms of oxidative stress of endothelial cells, IPF and ILD showed abnormal expression of Epidermal growth factor receptor (EGFR), Hypoxia-inducible factor 1-alpha (HIF1A) and RACK1, while COVID-19 showed abnormal expression of Cytochrome c (CYCS), JUN and IL-6. Briefly, the endothelial cells in four diseases have abnormal processes of ubiquitination, cell matrix formation and oxidative stress.Fig. 2 DEGs in endothelial cells and functional analysis. UMAP diagram of endothelial cell grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Epithelial oxidative stress and abnormal protein ubiquitination

DEGs analysis of 52,572 epithelial cells showed that chemokine family and ribosome family genes were down-regulated in four diseases (Fig. 3A and B). Interestingly, epithelial cells in COPD, COVID-19 and IPF showed multi-signal down-regulation, while ILD showed interferon activation and increased collagen synthesis (Fig. 3C–F). In addition, epithelial cells also show increased apoptosis and oxidative stress, and abnormal cell matrix adhesion and ubiquitination (Supplementary Fig. S2A and B). Through the screening of hub genes (Supplementary Fig. S2C–F), we found that epithelial cells showed obvious ubiquitination abnormalities (UBA52, UBC, UBB). In addition, EGFR, JUN, RACK1 participated in the oxidative stress process (Fig. 3G,H and Supplementary Fig. S2G and H) of COVID-19 epithelial cells.Fig. 3 DEGs in epithelial cells and functional analysis. UMAP diagram of epithelial cell grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Protein ubiquitination and oxidative stress disorder of smooth muscle cells

By analyzing the gene expression of 55,783 smooth muscle cells from four diseases, it was found that the expression of various chemokines was down-regulated and collagen-related molecules were up-regulated (Fig. 4A and B). GSEA showed that only ILD showed extensive inflammatory signal activation (Fig. 4C–F), which was consistent with KEGG results (Supplementary Fig. S3B). At the same time, smooth muscle cells also showed the disorder of protein ubiquitination, oxidative stress, cell matrix adhesion and collagen synthesis (Supplementary Fig. S3A) in different disease types. Through the screening and enrichment analysis of hub genes, it was found that UBA52, heat shock protein family A member 8 (HSPA8), UBC, UBB, heat shock protein 90 alpha family class A member 1 (HSP90AA1) and JUN participated in the protein ubiquitination process of different diseases (Fig. 4G,H and Supplementary Fig. S3G and H).Fig. 4 DEGs and functional analysis in smooth muscle cells. UMAP diagram of smooth muscle cell grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Abnormal interferon signal activation and ubiquitination signals of B cells and T cells

Comparative analysis of 10,083 B cells (Fig. 5A and B) showed that hypoxia of COVID-19 and IPF was inhibited, while ILD was activated (Fig. 5C–F). In addition, COVID-19, ILD and IPF showed abnormal ubiquitination signals, but only COVID-19 upregulated endoplasmic reticulum protein synthesis (Supplementary Fig. S4A and B). The enrichment analysis of hub genes (Supplementary Fig. S4C–F) showed that UBA52, UBB, UBC and HSPA8 were involved in Toll-like receptor signal, interferon signal and ubiquitination abnormality of B cells in various diseases (Fig. 5G and H and Supplementary Fig. S4G and H). DEGs of 31,507 T cells showed that the ribosome gene was abnormally expressed in COVID-19, which seemed to be related to the decrease of oxidative phosphorylation of T cells. (Fig. 6A–F). Ubiquitin of protein and abnormal T cells seem to be the common features of T cells in four diseases, but only COVID-19 is in the activated state (Supplementary Fig. S5A and B). The hub gene screening (Supplementary Fig. S5C–F) and functional analysis showed similar phenomena to those of B cells (Fig. 6G and H and Supplementary Fig. S5G and H).Fig. 5 DEGs in B cells and functional analysis. UMAP diagram of B cell grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Fig. 6 DEGs in T cells and functional analysis. UMAP diagram of T cell grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Increased chemotaxis in monocyte and macrophage

In COVID-19 and ILD, 130,273 macrophages showed a large number of DEGs, but their signals activation and inhibition were very difference (Fig. 7A–F). GO enrichment analysis showed abnormal differentiation and chemotaxis of macrophages, which may be related to the activation of HIF-1 signal and chemotaxis signal (Supplementary Fig. S6A and B). Different from others, there is no abnormality of protein ubiquitination in macrophages of IPF. The hub genes (Supplementary Fig. S6C–F), such as UBB, UBC, HSPA8 and JUN, are involved in the Toll-like receptor signal and ubiquitination process of macrophages in COPD, COVID-19 and ILD (Fig. 7G and H and Supplementary Fig. S6G and H). The 25,194 monocytes showed that the cells were abnormally active when the disease occurred, which was related to the abnormal activation of cellular interferon signal (Fig. 8A–F). Different from others, there was no abnormal protein ubiquitination in ILD monocytes (Supplementary Fig. S7A), but COVID-19 showed the activation of multiple immune pathways (Supplementary Fig. S7B). The hub gene screening (Supplementary Fig. S7C–F) showed that UBA52, UBC and HSP90AA1 were involved in the process of interferon and protein ubiquitination (Fig. 8G and H and Supplementary Fig. S7G and H) of COPD and COVID-19.Fig. 7 DEGs and functional analysis of macrophages. UMAP diagram of macrophage grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Fig. 8 DEGs and functional analysis of monocytes. UMAP diagram of monocyte grouping (A). Compared with the normal group, the volcano map of DEGs in different disease groups (B). GSEA analysis (C–F) of DEGs in COPD, COVID-19, ILD and IPF, co-expression network and enrichment analysis of hub genes of COPD (G) and IPF (H).

Analysis of expression proportion and abundance of hub genes

To validate the expression stability of hub genes, the data sets of human and mice were integrated. Firstly, the human single-cell atlas (Supplementary Fig. S8A–D) with 400,555 cells including COVID-19, ILD and IPF from external data set was integrated, and then the mouse single-cell atlas (Supplementary Fig. S8E–H) with 281,880 cells from influenza virus mouse model and pulmonary fibrosis model was integrated. Analysis of the expression ratio and abundance of hub genes in the single-cell atlas shows that UBA52 and ribosomal protein S27a (RPS27A) are highly expressed in the endothelial cells of IPF, while UBB, UBC, HSP90AB1 and RACK1 are highly expressed in the endothelial cells of ILD (Fig. 9A). In the another human single atlas, UBC, UBA52, UBB and HSP90AB1 are highly expressed in IPF and ILD (Fig. 9D). However, in the mouse single-cell atlas, the traditional pulmonary fibrosis model cannot simulate the abnormal protein ubiquitination of endothelial cells (Supplementary Fig. S9A). In the two human single-cell atlases, HSP90AB1, JUN, UBA52, UBB, HSP90AA1 and other hub genes are highly expressed in the epithelial cells of IPF and ILD (Fig. 9B–E). Different from COVID-19, UBB, UBC and HSP90AB1 are highly expressed in the influenza animal model and the proportion of cells increases (Supplementary Fig. S9B). The high expression of hub gene and the increased cell proportion can be observed in human smooth muscle cells with two lung diseases (Fig. 9C–F). In addition, the hub genes were significantly up-regulated in B cells, T cells, macrophages and monocytes of IPF and ILD patients in the two single-cell atlases, but the opposite was found in COVID-19 (Fig. 10). In the pulmonary fibrosis model, the high expression of genes and the increased cell proportion can only be observed in T cells, which indicates that the pulmonary fibrosis model induced by bleomycin needs to be further optimized (Supplementary Fig. S9C–F).Fig. 9 Expression proportion and abundance of hub genes in two atlases. The expression of hub genes in endothelial cells, epithelial cells and smooth muscle cells in first atlas (A–C) and second atlas (D–F). The circle represents the proportion of genes in cell type, and the color represents the average expression of genes.

Fig. 10 Expression proportion and abundance of hub genes in two atlases. The expression of hub genes of B cells, T cells, macrophages and monocytes in first atlas (A–D) and second atlas (E–H). The circle represents the proportion of genes in cell type, and the color represents the average expression of genes.

Bulk-RNAseq data integration and validation

To further observe the differences of hub genes, we try to verify them on bulk RNA-seq. Firstly, 396 samples from 7 data sets were collected and integrated by "sva" algorithm. Sample distribution boxplot and TSNE analysis before and after integration showed the removal of batch effect (Supplementary Fig. S10). In view of the difference in DEGs of immune cells displayed by single-cell atlas, we found that the infiltration of immune cells in lung tissue was generally out of balance. For example, in plasma cells differentiated from B cells, the infiltration of COVID, ILD and IPF patients increased, while that of CD4 T cells and monocytes decreased (Fig. 11A). On the other hand, the differences of hub genes such as UBA52, UBB, UBC, CD74 and CDC42 between groups can be clearly observed (Fig. 11B). When using hub genes to construct diagnosis models respectively for different diseases, the performance of the hub gene is poor (Fig. S11), while the hub genes shows good diagnostic performance when distinguish normal and diseases (Fig. 11C). In COVID-19, ILD and IPF lung tissues, UBA52, UBB and UBC had a good expression correlation with cell matrix related genes annexin A2 (ANXA2), S100A10 and COL family genes (Fig. 11D), which indicated that protein ubiquitination modification might be related to tissue fibrosis.Fig. 11 Bulk RNA-seq data integration analysis of COVID-19, ILD and IPF. Immune infiltration analysis (A), hub gene expression analysis (B), gene diagnosis model (C) and ECM-related genes correlation analysis (D) of three diseases.

Drug screening based on hub genes

L1000FWD database was used to predict the potential regulatory drugs of hub genes, and we screened out 10 small molecular compounds that are expected to be used for treatment (Table 2). Due to the good binding scores of these compounds to hub genes, molecular docking was performed to explore the possibility of these drugs interacting with ubiquitination-related proteins UBA52, UBB and UBC. It was found that molecules, such as salermide and SSR-69071, have low binding energy with hub genes and have good therapeutic potential. Table 2 Drug Screening Based on Hub Genes.

Drug	Similarity score	P value	Z score	Combined score	UBA52 (kCal·mol−1)	UBB (kCal·mol−1)	UBC (kCal·mol−1)	
BNTX	 − 0.4	0.000013	1.87	 − 9.14	 − 6.6	 − 7	 − 7.4	
SSR-69071	 − 0.4	0.0000175	1.8	 − 8.54	 − 6.5	 − 7.3	 − 7	
tyrphostin-AG-556	 − 0.4	0.0000229	1.68	 − 7.81	 − 6.3	 − 6.8	 − 6.1	
FCCP	 − 0.4	0.0000223	1.68	 − 7.82	 − 5.4	 − 5.8	 − 5.4	
amoxapine	 − 0.4	0.0000146	1.87	 − 9.06	 − 5.5	 − 6.1	 − 6	
TER-14687	 − 0.4	0.0000177	1.71	 − 8.11	 − 4.8	 − 4.9	 − 4.8	
salermide	 − 0.4	0.0000191	1.78	 − 8.41	 − 7	 − 7.5	 − 7.5	
ryuvidine	 − 0.4	0.000016	1.84	 − 8.83	 − 5.6	 − 6.3	 − 6.5	
cycloheximide	 − 0.4	0.0000229	1.6	 − 7.41	 − 5.4	 − 5.9	 − 6.1	
WZ-3105	 − 0.4	0.0000156	1.64	 − 7.86	 − 6.3	 − 7	 − 6.8	

Discussion

Although a large number of studies have shown that COPD, COVID-19, ILD and IPF may have similar pathogenesis, their similarities and differences in gene expression and function remain to be clarified. Fatigue and discomfort after exertion are post-COVID syndrome, which is due to endothelial dysfunction caused by COVID-19 infection with endothelial cells23. The difference is that angiogenesis is a typical manifestation of IPF in the early stage24. Similarly, we found that endothelial cells function have great differences between COVID-19 and IPF. Unlike COPD, ILD and IPF, MAPK and PI3K-Akt pathways were activated in COVID-19, which may be an independent feature that drives endothelial dysfunction25. At the same time, in COVID-19, expression of UBA52, UBB, UBC and other ubiquitination-related proteins in endothelial cells was decreased. Unfortunately, only UBA52 can be simulated in the animal model of influenza virus, which suggests that the similarity between animal model and human infection needs to be further improved. Previous studies have shown that ubiquitination modification is secondary to inflammatory reaction26, which can affect the barrier function of alveolar epithelial cells by targeting sodium channels in lung injury. Our study found that, unlike the epithelial cells of ILD, COPD, COVID-19 and IPF showed DNA damage repair and mTOR signal inhibition, which is an important manifestation of epithelial cell injury27,28. Studies have shown that oxidative stress in epithelial cells leads to mitochondrial dysfunction and fibrosis, thus activating toll-like receptors and stimulating TGF-β29. It is not difficult to find that epithelial cells of COPD, COVID-19 and IPF release more cell matrix, and involved in toll-like receptors, TGF-β and oxidative stress, which may be related to the ubiquitination activation. Therefore, ubiquitination of targeted regulatory proteins may be an important way to effectively prevent epithelial cell damage. Different from COVID-19, the myogenesis signal of smooth muscle cells in COPD, ILD and IPF are activated, and the epithelial-mesenchymal transition in ILD and IPF is activated, which can promote pulmonary fibrosis30. To sum up, we found that lung parenchymal cells are in a state of extensive oxidative stress injury and fibrosis promotion, which is closely related to abnormal protein ubiquitination, especially in ILD and IPF, which will provide a new strategy for the diagnosis and treatment of pulmonary fibrosis.

A large number of studies31–33 show that immune cells are the key inducement to promote pathological injury and fibrosis of parenchymal cells. Our research shows the differences and common mechanisms of B cells, T cells, macrophages and monocytes in different disease types. Studies have shown that the protein ubiquitination34,35 can inhibit the proliferation of B cells and thus inhibit fibrosis. Our study found that B cells have great heterogeneity in the four disease types. TNF-α, mTOR and apoptosis signals are activated in ILD, which is accompanied by the increase of the expression ratio and abundance of UBC and HSP90AA1 genes. These genes may be used as new targets in the treatment of ILD related fibrosis. RACK1 is highly expressed in IPF, and apoptosis is inhibited, which may be related to RACK1 antagonism to TNF-α-induced cell death36. Ubiquitin of T cell receptors is involved in controlling T cell activation and inducing immune tolerance37,38. In ILD and IPF, the high expression of ubiquitination genes such as UBB, UBC and HSP90AA1 may be related to T cell activation, which means that regulating ubiquitination is beneficial to alleviate T cell-mediated fibrosis. In recent years, monocytes and macrophages promote fibrosis39,40, which is mainly caused by local inflammation caused by chemokines and cytokines. Our study found that the chemotaxis and migration ability of macrophages and monocytes increased in many diseases, accompanied by the high expression of UBB, UBC and RPS27A in pathological state. However, an interesting but puzzling problem is that for immune cells, hub genes have nothing to do with immune process, but are related to protein ubiquitination. A reasonable explanation is that immune-related molecules may be regulated by ubiquitination-related genes widely involved in protein degradation, DNA repair and transcriptional activation41. In addition, the lack of research on protein ubiquitination and the inaccuracy of animal models greatly limited the discovery of this phenomenon, which benefited from the rapid development of single cell sequencing technology. In short, Immune cell function is obvious abnormal in COPD, COVID-19, ILD and IPF. However, different from COVID-19, there are high expressions of ubiquitination genes in others, which indicates that acute lung disease and chronic lung disease have diametrically opposite fibrosis mechanisms.

When trying to verify the heterogeneity of immune cells in different diseases in bulk RNA-seq, we obviously observed the abnormal immune infiltration in COVID-19, ILD and IPF, such as B cells, monocytes. For macrophage infiltration, the increased M1 macrophages appeared in COVID and M2 macrophages appeared in ILD, which was consistent with the single cell level. Consistent with single-cell atlas, UBA52, UBB and UBC were low expressed in COVID-19 and high expressed in ILD in bulk RNA-seq. In addition, the gene diagnosis performance of a single disease is not ideal, which seems to be related to the fact that the change of hub gene expression is not easy to be captured at the tissue level. At the same time, it is found that the diagnostic efficiency of hub genes for three diseases is higher than that of single disease, which indicates that there are similar expression patterns of hub genes in three diseases to increase the diagnostic performance, which also confirms that at the single cell level, hub genes are highly expressed in many cells of different diseases. Salermide42 had found to have the potential to treat non-small cell lung cancer by activating various transcription factors, and SSR-6907143 had selected as an oral preparation for treating COPD and cystic fibrosis. We found that salermide and SSR-69071 have the potential to regulate multi-disease fibrosis by inhibiting protein ubiquitination. This confirms that ubiquitination-related proteins may be a new target of drug therapy.

Conclusion

The study reveals the commonness and differences of four pulmonary diseases by using three single-cell atlases and bulk RNA-seq, and clarifies the close relationship between ubiquitination gene and lung tissue fibrosis, providing a new perspective for disease pathology, prediction and treatment.

Supplementary Information

Supplementary Figures.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-70659-1.

Acknowledgements

Te research is supported by the National Natural Science Foundation of China (No. 32060158). We thank the Gene Expression Omnibus (GEO) for sharing a large amount of data.

Author contributions

Zhuman Wen performed the study and wrote the manuscript. Abduxukur Ablimit was responsible for supervising the research progress.

Data availability

The datasets analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/browse/), including GSE136831, GSE159354, GSE159585, GSE171524, GSE227136, GSE121611, GSE132771, GSE149857, GSE183545, GSE201698, GSE202325, GSE213016, GSE228594, GSE153131, GSE183533, GSE205099, GSE208076, GSE21369, GSE150910, and GSE173355.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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