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Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension
BMC Genomics volume 24, Article number: 788 (2023)
Abstract
Background
Pulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.
Methods
Single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.
Results
Nineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.
Conclusion
WDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.
Introduction
Pulmonary artery hypertension (PAH) is a chronic severe progressive cardiopulmonary disease characterized by pulmonary arterial pressure elevation and right ventricular hypertrophy [1]. The prevalence of PAH is 10.6 per million adults in America nowadays [2]. Despite the benefits of treatments targeting nitric oxide, prostacyclin, and endothelin pathways to delayPAH progression and improve survival, only lung transplantation is considered a curative approach [3]. PAH remains an incurable chronic disease with a poor prognosis [4]. Vasoconstriction, obstructive pulmonary vasculopathy characterized by hyperproliferation and anti-apoptosis phenotype of PASMCs, excessive fibrosis, inflammation, thrombosis, and altered mitochondrial metabolic all participated in the mechanisms implicated in PAH [5]. However, there remains largely unexplored on the pathogenesis of PAH. Therefore, systematic analysis of the function of different cell types in the pulmonary tissue of PAH patients might help deepen understanding of the pathological mechanism of PAH.
Microarray transcriptome has been increasingly and widely used to examine gene expression in PAH [6, 7]. However, data of microarray transcriptome represents the average gene expression amounts of various cells at the whole level of tissue [8]. Lung tissues contain various cell types, including smooth muscle cells, endothelial cells, fibroblasts, immune cells, inflammatory cells, etc. They play different roles throughout the development of PAH. Currently, a novel single-cell RNA sequencing (scRNA-seq) technology is emerging to investigate cell heterogeneity, characterize each cell subpopulation, and putative intracellular communication [9, 10]. This innovative technology has advanced our understanding of PAH at the cell subpopulation level. scRNA-seq has been carried out in lung samples of both PAH rodent models and PAH patients. Previous research reported NF-κB signaling activation in immune cells of monocrotaline and hypoxia-induced PH rat model [11]. Based on the scRNA-seq data of lung ECs from hypoxic pulmonary hypertension mice, Julie and his colleagues indicated CD74 was involved in the regulation of endothelial cell proliferation and barrier integrity [12]. However, scRNA-seq data on PAH is relatively small and still in its infancy currently.
In the present study, integrated bioinformatics analysis of scRNA-seq and microarray transcriptome data from the GEO dataset was analyzed to identify the hub genes in PAH. Differentially expressed genes (DEGs) from GSE210248 and GSE113439 were identified and common DEGs were selected. Protein-protein interaction network (PPI) network was constructed using the aforementioned DEGs, followed by hub gene selection through Cytoscape software. Finally, GNL2 and WDR43 were identified as hub genes, which might provide new insight into the pathogenesis of PAH and act as novel candidates and therapeutic targets for PAH.
Materials and methods
Data acquisition
Data were all processed and analyzed by R software (Version 4.3.0). Both scRNA-seq (GSE210248) and microarray transcriptome (GSE113439) data were obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database [13] and downloaded through the GEO query package (Version 2.68.0). GSE210248 and GSE113439 were selected in the current research because the samples in the two datasets were obtained from the lung/pulmonary arteries of participants with pulmonary hypertension, rather than the PAH rodent model. Additionally, GSE11339 has a relatively large The details of the two datasets enrolled in this study were listed in Table 1. The GSE210248 scRNA-seq data and GSE113439 array data were generated on GPL20301 Illumina Hiseq 4000 and GPL6244 Affymetrix Human Gene 1.0 ST Array platform, respectively. GSE210248 data included pulmonary arteries from 3 PAH patients and 3 healthy donor control. The dataset contains 19,576 cells from the control group and 22,704 cells from the PAH group. The data of GSE113439 included fresh frozen lung samples from the recipients’ organs of 15 PAH patients and 11 normal lung samples obtained from tissue flanking lung cancer resections.
Processing of scRNA-seq data
Seurat package (Version 4.3.0) was used for quality control. Cells with 200–2500 genes and < 5% mitochondrial genes were selected for consequent analysis. A total of the 19,576 cells in control group and 21,896 cells in the PAH group were screened out for analysis. Data of genes was further normalized using the “LogNormalize” method and further scaled. Then, the top 2000 highly variable genes (HVGs) were identified by the FindVariableFetures function with the “vst” method. Subsequently, principal component analysis (PCA) was applied to identify significant principal components (PCs), and the p-value was visualized using the JackStraw and ScoreJackStraw functions. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction with 20 PCs and cluster identification across these cells. “Harmony” R package was used for batch correction to avoid the batch effect of sample identity which might disrupt the downstream analysis [14]. SingleR package (Version 2.2.0) [15] was utilized for cell annotation according to the reference datasets HumanPrimaryCellAtlasData [16] and BlueprintEncodeData [17]. FindAllMarkers analysis with |log2 fold change (FC)|> 1 and adjusted p value < 0.05 were performed to screen out the differentially expressed genes (DEGs) for each cell cluster. scRNAtoolVis package (Version 0.0.5) was performed to display the top DEGs and visualized by jjvolcano.
Processing of microarray transcriptome data
DEGs between the control and PAH groups with an adjusted p value < 0.05 were screened out using the limma package (Version 3.56.2) [18]. All DEGs were visualized using the volcano plot and the top 50 DEGs were visualized through the heatmap plot in the “ggplot2” package.
Functional enrichment analysis
Gene Ontology (GO) [19] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [20] analysis were carried out by the clusterprofiler package [21]. GO enrichment included 3 subontologies: biological process (BP), molecular function (MF), and cellular component (CC) [19]. P < 0.05 is considered statistically significant.
Microenvironment Cell Populations counter (MCP counter)
The infiltration of microenvironment immune cells including B lineage, CD8 T cells, cytotoxic lymphocytes, endothelial cells, monocytic lineage, myeloid dendritic cells, neutrophils, NK cells, and T cells was quantified through the MCP counter (Version 1.2.0) based on scRNA-seq data [22].
PPI network construction and identification of hub genes
DEGs in scRNA-seq and microarray transcriptome data were screened by FindAllMarkers analysis in the Seurat package and the limma package. Subsequently, protein–protein interaction (PPI) networks were constructed for the prediction of internal connection among the picked DEGs using the STRING database (Version 11.5, https://string-db.org/) with an interaction conference score set to 0.4 [23]. Then, hub genes were screened out and network visualization was performed using Cytoscape software (Version 3.10.0) [24]. The Molecular Complex Detection (MCODE) plug-in was used to build clustering function modules in the PPI network. Then, the CentiScaPe plug-in was used to calculate the degree, betweenness, and centroid value of each gene within the network. CytoHubba plug-in was used for ranking nodes in the target network using Maximal Clique Centrality (MCC) methods. The Venn diagram was produced by the jvenn website (https://jvenn.toulouse.inrae.fr/app/example.html) for gene overlapping and common gene selection.
Results
ScRNA profiling in PAH
The scRNA-seq data of GSE210248 was downloaded from the GEO database and analyzed through R software. In general, 42,280 cells comprising 19,576 cells from donors (control) and 22,704 cells from PAH patients were included. After filtrating improper gene amounts or mitochondrial genes ≥ 5%, 19,576 cells from donors and 21,896 cells from PAH patients remained. Figure 1A presented the expression characteristics of each sample. As shown in Fig. 1B, nCount_RNA (the number of unique molecular identifiers) was positively correlated with nFeature_RNA (the number of genes) with a correlation coefficient of 0.93. Figure 1C displayed and labeled the top 10 HVGs: SFTPC, CCL21, SFTPA1, IGKC, STFPA2, STFPB, PGC, TPSB2, TPSAB1, S100A12. The top 20 PCs identified by PCA were visualized by JackStrawPlot (Fig. 1D). In addition, the top 10 DEGs in each cluster were presented by heatmap and labeled in yellow (Fig. 1E).
Cell clusters identification in scRNA-seq
Forty-one thousand four hundred seventy-two cells were divided into 42 cell clusters and visualized through UMAP, and cell annotation was performed by SingleR package (Fig. 2A). The number of cells in some clusters was too small, therefore, 10 clusters (Adipocytes, CD4+ T cells, CD8+ T cells, chondrocytes, endothelial cells, epithelial cells, fibroblasts, macrophages, monocytes, and NK cells) with the top 10 cell amounts were selected for subsequent analysis. Cell numbers of each cluster were shown in Table 2. The distribution of each cluster in the selected 10 clusters was presented in Fig. 2B, and the results of cell cluster distribution grouped by control and PAH were displayed in Fig. 2C. Additionally, the number and proportion of cells in each sample were exhibited in Fig. 2D. In comparison with the control group, the proportion of adipocytes (39.4% vs. 5.6%) and fibroblasts (18.1% vs. 2.5%) was significantly reduced in the PAH group, while CD8+ T cell (3.3% vs. 50.0%) and macrophages (4.3% vs. 14.8%) were notably increased in PAH lung tissues compared with donors.
DEGs of each cluster in GSE210248
The DEGs of each cluster between the control and PAH groups were identified using the FindMarkers function. The top 10 DEGs in each cluster were listed in Table 3. For instance, GZMB, ATP5E, GNB2L17, GKN2, ATP5E6, SLPI5, SELM2, SFTPC3, SFTPC2, and GNB2L11 were the most significant DEG in NK cells, macrophages, fibroblasts, monocytes, epithelial_cells, endothelial_cells, chondrocytes, CD8+ T-cells, CD4+ T-cells, and adipocytes based on adjusted p-value. scRNAtoolVis package was further performed to intuitively illustrate the top 5 upregulated and the top 5 downregulated genes in the PAH group compared with the control group and visualized by jjvolcano (Fig. 3).
DEGs of pulmonary tissue in GSE113439
The limma package was utilized to explore the DEGs in lung samples of 11 control and 15 PAH patients. DEGs with |logFC|> 0.856 and adjusted p-value < 0.05 were presented in Fig. 4A. Compared with the control group, 828 genes were found upregulated, and 169 genes were downregulated in the lung tissue of PAH patients. A Heatmap of the top 50 DEGs was shown in Fig. 4B. The majority of DEGs were upregulated, only the gene GPR146 was found downregulated among these top 50 DEGs. The results of KEGG functional enrichment analysis were shown in Fig. 4C. These upregulated DGEs were enriched in ribosome biogenesis in eukaryotes, herpes simplex virus 1 infection, RNA transport, homologous recombination, cell cycle, proteoglycans in cancer, aminoacyl-tRNA biosynthesis, spliceosome, fatty acid metabolism, small cell lung cancer, etc. The downregulated DEGs were enriched in systemic lupus erythematosus, Notch signaling pathway, hypertrophic cardiomyopathy, alcoholism, asthma, vascular smooth muscle constriction, cAMP signaling pathway, cardiac muscle contraction, breast cancer, and adrenergic signaling in cardiomyocytes. The cell component (CC), biological process (BP), and molecular function (MF) of GO enrichment analysis were presented in Fig. 4D-F. The top 10 enriched pathways in CC included chromosomal region, nuclear speck, spindle, microtubule, condensed chromosome, chromosome, centromeric region, spindle pole, mitotic spindle, midbody, and centriole; The top 10 enriched pathways in BP included chromosome segregation, organelle fission, nuclear division, ribonucleoprotein complex biogenesis, nuclear chromosome segregation, mitotic nuclear division, sister chromatid segregation, mitotic sister chromatid segregation, regulation of chromosome organization, protein localization to chromosome; The top 10 enriched pathways in MF included ATPase activity, tubulin binding, microtubule binding, catalytic activity, acting on DNA, GTPase binding, helicase activity, DNA-dependent ATPase activity, protein folding chaperone, RNA helicase activity, and RNA-dependent ATPase activity.
Different immune cell infiltration of pulmonary tissue in GSE113439
Using microarray transcriptome data from GSE113439, the MCP counter was utilized to evaluate the immune cell infiltration in control and PAH lung samples. As shown in Fig. 5A, statistically decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells were found in lung tissues of PAH patients compared with control subjects. However, increased infiltration of monocytic lineage was found in PAH lung tissue. The Heatmap further displayed the abundance of each cell type with normalization value ranging from 0–1 in each sample between the control and PAH group (Fig. 5B) (PAH: GSM310626-GSM3106340; Control: GSM3106341-GSM3106351).
Protein-protein interaction network (PPI) network and common DEGs identification in GSE210248 and GSE113439
The PPI network of DEGs from GSE113439 was generated by STRING (Fig. 6). The PPI network consisted of 945 nodes and 7266 edges in 997 DEGs. Then, the PPI network of the DEGs in each cluster of GSE210248 was constructed through the STRING online website. Figures 7A and 8H presented the PPI network of 914 DEGs in adipocytes, 411 DEGs in CD8+ T cells, 572 DEGs in chondrocytes, 377 DEGs in endothelial cells, 93 DEGs in epithelial cells, 1139 DEGs in fibroblasts, 822 DEGs in macrophages, and 1013 DEGs in monocytes with the adjusted p-value < 0.05. A Venn diagram was drawn to screen out the common hub genes from GSE210248 and GSE113439. As shown in Fig. 8, a series of genes were identified through overlapping DEGs from each cluster in GSE210248 and DEGs from GSE113439. The details of overlapped genes were listed in Table 4.
Hub genes identification in PAH
Considering the amounts of selected common DEGs were relatively large. MCODE was utilized for the selection of candidate hub genes from the PPI network of 997 DEGs in GSE113439. Module 1 with the highest score (68 nodes and 1142 edges) was screened out (Fig. 9A). The centralities of the candidate genes in module 1 were evaluated by the CentiScaPe plug-in and the details were shown in Table 5. Additionally, the CytoHubba plug-in was used for ranking nodes in module 1 using MCC methods. The MCODE score of each gene is also summarized in Table 5. Cytohubba plug-in was performed to further simplify these hub genes and pick out the most critical genes using the MCC method. No overlapped genes were found between DEGs from adipocytes, CD4+ T cells, and DEGs from GSE113409. Therefore, the top 20 genes were filtered out in the remaining 8 clusters of GSE210248 and presented in Fig. 9B-I. Less than 20 DEGs in chondrocytes and epithelial cells were presented because there are only 15 DEGs in chondrocytes and 14 DEGs in epithelial cells were included in the network of Cytoscape software. We further screened out the common hub genes using data from module 1 and the top 20 genes in these clusters. As shown in Fig. 10, WDR43 in chondrocytes and GNL2 in CD8+ T cells were finally identified as the most significant genes in PAH. Furthermore, we detected the expression of WDR43 and GNL2 in the lung samples of 15 PAH patients and 11 control subjects in GSE113439 and found significantly increased WDR43 and GNL2 expression (Fig. 11).
Discussion
The present study for the first time indicated WDR43 and GNL2 might act as key genes involved in the pathogenesis of PAH, providing a novel potential underlying mechanism of PAH. In the current study, common DEGs were screened out using integrated analysis of scRNA-seq and microarray transcriptome through the limma package and Seurat package in R software. Subsequently, the PPI network of DEGs was constructed using the STRING website. Then, Cytoscape software was utilized to screen out the hub genes in the cluster of GSE210248 and GSE113439. Ultimately, we identified two hub genes (WDR43 and GNL2) in PAH through a series of bioinformatics analyses.
MCP counter illustrated dysregulated landscape of immune cells in lung tissues of PAH patients, which is consistent with previous reports. Marlene reviewed immune dysregulation in PAH and how immune-mediated vascular injury promoted PAH development [25]. For instance, circulating autoantibodies against endothelial cells might enhance the apoptosis of endothelial cells in PAH [26]. T cells and NK cells were considered as beneficial factors during the pathogenesis of PAH [27, 28]. Additionally, the role of perivascular macrophages has received extensive attention from researchers. Widespread Cd68+ macrophages were detected in occlusive plexiform lesions in clinical and experimental PAH models [29]. Inactivation or deletion of macrophages could prevent the development of PAH [30]. More researches need to be carried out to further explore the role of various immune cells in PAH and the underlying mechanisms.
The WD40 repeat (WDR) domain is the most abundant protein interaction domain in the human proteome. The WDR43 gene is located on chromosome 2 and encodes the WDR43 protein containing 677 amino [31]. Of note, WDR43 is an essential subunit of multiprotein complexes and is involved in a series of signaling pathways including ubiquitin-proteasome pathway, epigenetic regulation, DNA damage repair, and immune-related pathways [32]. For instance, the NOL11-WDR43-Cirhin protein complex is necessary for mitotic chromosome segregation [33]. Intriguingly, several bioinformatics analysis identified WDR43 as a crucial oncogene contributing to the development of colorectal/lung cancer via promoting the migration and proliferation of cancer cells through GEO and The Cancer Genome Atlas (TCGA) database. Mechanistically, c-MYC/WDR43/MDM2 mediated p53 degradation, and cyclin-dependent kinase 2 were involved in the underlyng mechanism [34,35,36]. However, the role of WDR43 in PAH remains uninvestigated.. Similarly, the imbalance of proliferation and apoptosis in pulmonary artery smooth muscle cells (PASMCs) was also the key characteristic in pulmonary hypertension [37]. Therefore, we speculate WDR43 might contribute to PASMCs proliferation and migration, then leading to the pulmonary artery remodeling.
GNL2, the G protein nucleolar 2, was found essential for cell growth and development through participating in the cell-cycle regulation pathway [38]. GNL2 acts as a checkpoint for ribosome export, and it plays a vital role in facilitating ribosomal biogenesis and protein synthesis [39]. GNL2 was found to play a critical role in the RNA metabolic network and was associated with proliferation [40]. Increased expression of GNL2 was correlated with poor prognosis in ovarian cancer patients with 1p34.3 amplifications [41]. Results from another scRNA-seq data of periodontitis revealed GNL2 was upregulated in T cells [42]. While the role of GNL2 in PAH and its potential underlying mechanisms needs further exploration. In combination with the KEGG analysis in the current study, GNL2 might participate in the underlying mechanism of PH through the influence on the biosome biogenesis and cell cycle.
Nowadays, high-throughput RNA sequencing has been widely used to explore novel mechanisms of PAH [6, 43]. Especially, with the rapid development of single-cell sequencing, integrated bioinformatics analysis of microarray transcriptome and scRNA-seq, a newly-rising research method, has attracted researchers’ attention lately [44]. A recent study indicated hpgd was a key gene in pulmonary artery endothelial cells (PAECs) using scRNA-seq data from PAECs of control and PAH rodents [45]. There still remains largely unknown on the mechanism of PAH through integrated bioinformatics analysis. The current research might provide a novel insight into the pathogenesis of PAH.
Conclusion
In summary, we performed an integrated bioinformatics analysis of single-cell sequencing andmicroarray transcriptome. Multi-step analysis suggested that WDR43 and GNL2 were increased in PAH lung tissues and they were identified as hub genes in the pathogenesis of PAH. Our results highlight WDR43 and GNL2 as potential biomarkers and pharmacological therapeutic targets for PAH.
Availability of data and materials
Publicly available GEO datasets were analyzed in this study (https://www.ncbi.nlm.nih.gov/geo/) under the accession numbers GSE210248 and GSE113439.
References
Runo JR, Loyd JE. Primary pulmonary hypertension. Lancet. 2003;361(9368):1533–44.
Ruopp NF, Cockrill BA. Diagnosis and treatment of pulmonary arterial hypertension: a review. JAMA. 2022;327(14):1379–91.
Galiè N, Channick RN, Frantz RP, Grünig E, Jing ZC, Moiseeva O, Preston IR, Pulido T, Safdar Z, Tamura Y, et al. Risk stratification and medical therapy of pulmonary arterial hypertension. Eur Respir J. 2019;53(1):1801889.
Lau EMT, Giannoulatou E, Celermajer DS, Humbert M. Epidemiology and treatment of pulmonary arterial hypertension. Nat Rev Cardiol. 2017;14(10):603–14.
Thenappan T, Ormiston ML, Ryan JJ, Archer SL. Pulmonary arterial hypertension: pathogenesis and clinical management. BMJ (Clinical research ed). 2018;360:j5492.
Ruffenach G, Medzikovic L, Aryan L, Li M, Eghbali M. HNRNPA2B1: RNA-binding protein that orchestrates smooth muscle cell phenotype in pulmonary arterial hypertension. Circulation. 2022;146(16):1243–58.
Li D, Shao NY, Moonen JR, Zhao Z, Shi M, Otsuki S, Wang L, Nguyen T, Yan E, Marciano DP, et al. ALDH1A3 coordinates metabolism with gene regulation in pulmonary arterial hypertension. Circulation. 2021;143(21):2074–90.
Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, Krzak M, Ranson M, Ashford B. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Brief Bioinform. 2021;22(6):bbab259.
Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018;18(1):35–45.
Clark IC, Gutiérrez-Vázquez C, Wheeler MA, Li Z, Rothhammer V, Linnerbauer M, Sanmarco LM, Guo L, Blain M, Zandee SEJ, et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science. 2021;372(6540):eabf1230.
Hong J, Arneson D, Umar S, Ruffenach G, Cunningham CM, Ahn IS, Diamante G, Bhetraratana M, Park JF, Said E, et al. Single-cell study of two rat models of pulmonary arterial hypertension reveals connections to human pathobiology and drug repositioning. Am J Respir Crit Care Med. 2021;203(8):1006–22.
Rodor J, Chen SH, Scanlon JP, Monteiro JP, Caudrillier A, Sweta S, Stewart KR, Shmakova A, Dobie R, Henderson BEP, et al. Single-cell RNA sequencing profiling of mouse endothelial cells in response to pulmonary arterial hypertension. Cardiovasc Res. 2022;118(11):2519–34.
Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R. NCBI GEO: mining millions of expression profiles–database and tools. Nucleic Acids Res. 2005;33(Database issue):D562-566.
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289–96.
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163–72.
Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013;14:632.
Martens JH, Stunnenberg HG. BLUEPRINT: mapping human blood cell epigenomes. Haematologica. 2013;98(10):1487–9.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9.
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.
Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, Selves J, Laurent-Puig P, Sautès-Fridman C, Fridman WH, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362-d368.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.
Rabinovitch M, Guignabert C, Humbert M, Nicolls MR. Inflammation and immunity in the pathogenesis of pulmonary arterial hypertension. Circ Res. 2014;115(1):165–75.
Arends SJ, Damoiseaux JG, Duijvestijn AM, Debrus-Palmans L, Vroomen M, Boomars KA, Brunner-La Rocca HP, Reutelingsperger CP, Cohen Tervaert JW, van Paassen P. Immunoglobulin G anti-endothelial cell antibodies: inducers of endothelial cell apoptosis in pulmonary arterial hypertension? Clin Exp Immunol. 2013;174(3):433–40.
Tamosiuniene R, Tian W, Dhillon G, Wang L, Sung YK, Gera L, Patterson AJ, Agrawal R, Rabinovitch M, Ambler K, et al. Regulatory T cells limit vascular endothelial injury and prevent pulmonary hypertension. Circ Res. 2011;109(8):867–79.
Ormiston ML, Chang C, Long LL, Soon E, Jones D, Machado R, Treacy C, Toshner MR, Campbell K, Riding A, et al. Impaired natural killer cell phenotype and function in idiopathic and heritable pulmonary arterial hypertension. Circulation. 2012;126(9):1099–109.
Vergadi E, Chang MS, Lee C, Liang OD, Liu X, Fernandez-Gonzalez A, Mitsialis SA, Kourembanas S. Early macrophage recruitment and alternative activation are critical for the later development of hypoxia-induced pulmonary hypertension. Circulation. 2011;123(18):1986–95.
Thenappan T, Goel A, Marsboom G, Fang YH, Toth PT, Zhang HJ, Kajimoto H, Hong Z, Paul J, Wietholt C, et al. A central role for CD68(+) macrophages in hepatopulmonary syndrome. Reversal by macrophage depletion. Am J Respir Crit Care Med. 2011;183(8):1080–91.
Bi X, Xu Y, Li T, Li X, Li W, Shao W, Wang K, Zhan G, Wu Z, Liu W, et al. RNA targets ribogenesis factor WDR43 to chromatin for transcription and pluripotency control. Mol Cell. 2019;75(1):102-116.e109.
Schapira M, Tyers M, Torrent M, Arrowsmith CH. WD40 repeat domain proteins: a novel target class? Nat Rev Drug Discov. 2017;16(11):773–86.
Fujimura A, Hayashi Y, Kato K, Kogure Y, Kameyama M, Shimamoto H, Daitoku H, Fukamizu A, Hirota T, Kimura K. Identification of a novel nucleolar protein complex required for mitotic chromosome segregation through centromeric accumulation of Aurora B. Nucleic Acids Res. 2020;48(12):6583–96.
Di Y, Jing X, Hu K, Wen X, Ye L, Zhang X, Qin J, Ye J, Lin R, Wang Z, et al. The c-MYC-WDR43 signalling axis promotes chemoresistance and tumour growth in colorectal cancer by inhibiting p53 activity. Drug Resist Updat. 2023;66:100909.
Sun H, Sun Q, Qiu X, Zhang G, Chen G, Li A, Dai J. WD repeat domain 43 promotes malignant progression of non-small cell lung cancer by regulating CDK2. Int J Biochem Cell Biol. 2022;151:106293.
Li Z, Feng M, Zhang J, Wang X, Xu E, Wang C, Lin F, Yang Z, Yu H, Guan W, et al. WD40 repeat 43 mediates cell survival, proliferation, migration and invasion via vimentin in colorectal cancer. Cancer Cell Int. 2021;21(1):418.
Qin Y, Qiao Y, Li L, Luo E, Wang D, Yao Y, Tang C, Yan G. The m(6)A methyltransferase METTL3 promotes hypoxic pulmonary arterial hypertension. Life Sci. 2021;274:119366.
Essers PB, Pereboom TC, Goos YJ, Paridaen JT, Macinnes AW. A comparative study of nucleostemin family members in zebrafish reveals specific roles in ribosome biogenesis. Dev Biol. 2014;385(2):304–15.
Matsuo Y, Granneman S, Thoms M, Manikas RG, Tollervey D, Hurt E. Coupled GTPase and remodelling ATPase activities form a checkpoint for ribosome export. Nature. 2014;505(7481):112–6.
Iuchi S, Paulo JA. RNAmetasome network for macromolecule biogenesis in human cells. Commun Biol. 2021;4(1):1399.
Nakamura K, Reid BM, Chen A, Chen Z, Goode EL, Permuth JB, Teer JK, Tyrer J, Yu X, Kanetsky PA, et al. Functional analysis of the 1p34.3 risk locus implicates GNL2 in high-grade serous ovarian cancer. Am J Hum Genet. 2022;109(1):116–35.
Wang Z, Chen H, Peng L, He Y, Wei J, Zhang X. DNER and GNL2 are differentially m6A methylated in periodontitis in comparison with periodontal health revealed by m6A microarray of human gingival tissue and transcriptomic analysis. J Periodontal Res. 2023;58(3):529–43.
Wang J, Niu Y, Luo L, Lu Z, Chen Q, Zhang S, Guo Q, Li L, Gou D. Decoding ceRNA regulatory network in the pulmonary artery of hypoxia-induced pulmonary hypertension (HPH) rat model. Cell Biosci. 2022;12(1):27.
Kuksin M, Morel D, Aglave M, Danlos FX, Marabelle A, Zinovyev A, Gautheret D, Verlingue L. Applications of single-cell and bulk RNA sequencing in onco-immunology. Eur J Cancer. 2021;149:193–210.
He M, Tao K, Xiang M, Sun J. Hpgd affects the progression of hypoxic pulmonary hypertension by regulating vascular remodeling. BMC Pulm Med. 2023;23(1):116.
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This study was supported by grants from the National Natural Science Foundation of China (No. 82170433 and No. 81970237).
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Yuhan Qin and Gaoliang Yan designed the study. Yuhan Qin, Gaoliang Yan, Yong Qiao, and Dong Wang collected the data and performed bioinformatics analysis. Yuhan Qin wrote the manuscript. Gaoliang Yan and Chengchun Tang: reviewed and edited the manuscript. All authors read and approved the manuscript.
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Qin, Y., Yan, G., Qiao, Y. et al. Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension. BMC Genomics 24, 788 (2023). https://doi.org/10.1186/s12864-023-09892-3
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DOI: https://doi.org/10.1186/s12864-023-09892-3