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Identification of novel candidate genes involved in the progression of emphysema by bioinformatic methods

Purpose By reanalyzing the gene expression profile GSE76925 in the Gene Expression Omnibus database using bioinformatic methods, we attempted to identify novel candidate genes promoting the development of emphysema in patients with COPD. Patients and methods According to the Quantitative CT data in GSE76925, patients were divided into mild emphysema group (%LAA-950<20%, n=12) and severe emphysema group (%LAA-950>50%, n=11). Differentially expressed genes (DEGs) were identified using Agilent GeneSpring GX v11.5 (corrected P-value <0.05 and |Fold Change|>1.3). Known driver genes of COPD were acquired by mining literatures and retrieving databases. Direct protein–protein interaction network (PPi) of DEGs and known driver genes was constructed by STRING.org to screen the DEGs directly interacting with driver genes. In addition, we used STRING.org to obtain the first-layer proteins interacting with DEGs’ products and constructed the indirect PPi of these interaction proteins. By merging the indirect PPi with driver genes’ PPi using Cytoscape v3.6.1, we attempted to discover potential pathways promoting emphysema’s development. Results All the patients had COPD with severe airflow limitation (age=62±8, FEV1%=28±12). A total of 57 DEGs (including 12 pseudogenes) and 135 known driving genes were identified. Direct PPi suggested that GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, and IMPDH2 were potential pathogenic genes. GPR65 could regulate the response of immune cells to the acidic microenvironment, and NPSR1’s expression on eosinophils was associated with asthma’s severity and IgE level. Indirect merging PPi demonstrated that the interacting network of TP53, IL8, CCR2, HSPA1A, ELANE, PIK3CA was associated with the development of emphysema. IL8, ELANE, and PIK3CA were molecules involved in the pathological mechanisms of emphysema, which also in return proved the role of TP53 in emphysema. Conclusion Candidate genes such as GPR65, NPSR1, and TP53 may be involved in the progression of emphysema.


Introduction
COPD, characterized by persistent respiratory symptoms and airflow limitation, is the third leading cause of mortality worldwide. 1 Airflow limitation is mainly due to small airway obstruction and emphysema, which have distinct physiopathologic mechanisms. 2,3 Most patients with COPD have pathological alterations of both emphysema and small airway obstruction, while some have only one or no obvious change. 4 Therefore, the two pathological phenotypes are regarded as potential subtypes of COPD. 5 Contrary to the feature of small airway remodeling, emphysema is due to decreased deposition and excessive destruction of extracellular matrix, leading to loss of alveolar septum and attachment. 6,7 However, many studies show that the pathogenesis and progression mechanism of emphysema are complex and heterogeneous, which need to be further elucidated. 6,8,9 As a noninvasive tool to measure morphological indices, quantitative computed tomography (QCT) is an effective approach to determine the severity of COPD and distinguishing the above subtypes. 10 Its assessment of emphysema has been demonstrated to be reliable, correlating well with indices of lung function, microscopic manifestations of emphysema, and clinical status of COPD patients. In addition, its assessment of small airway obstruction is also well associated with FEV 1 %. 11,12 After searching the Gene Expression Omnibus (GEO) database, which is one of the largest gene expression databases in the world, we found the original gene expression profile GSE76925 with records of QCT index. 13 To investigate the inherent molecular mechanisms in emphysema subtype of COPD, by using several bioinformatics methods, [14][15][16] we constructed the interacting network of differentially expressed genes (DEGs) in this profile and known COPD driver genes to identify novel candidate genes promoting progression of emphysema.

Acquisition of microarray data
The GEO database (http://www.ncbi.nlm.nih.gov/geo, May 18, 2017) was retrieved to obtain gene expression profiles of lung tissues of COPD patients. The dataset GSE76925, the only one with QCT indices, was downloaded. 13 Tests for these surgically resected lung tissue samples in GSE76925 dataset were performed using the GPL10558 platform, Illumina HumanHT-12 V4.0 expression beadchip.
All the continuous variables were expressed as mean ± standard deviation, and t-tests were applied to make comparison between the two groups. The categorical variables were described by constituent ratio and analyzed by Pearson chisquared test. All statistical analyses were performed using GraphPad Prism 7 (GraphPad Software Inc, La Jolla, CA, USA). A two-side P,0.05 was considered to be statistically significant.

Identification of DEGs between severe and mild emphysema groups
To explore the underlying genes, we filtered DEGs between severe and mild emphysema groups, using GeneSpring GX software v11.5 (Agilent technologies, Santa Clara, CA, USA) at the cutoff value of corrected P-value ,0.05 and |Fold Change|.1.3. We annotated them with Gene Oncology by manually retrieving Gene database (http://www.ncbi.nlm.nih. gov/gene, July 16, 2017) and roughly classified them according to the section of biological process in Gene Oncology 17 by retrieving the Database for Annotation, Visualization and Integrated Discovery (DAVID) 18 v6.8 (https://david.ncifcrf. gov/, October 9, 2018).

Retrieval of COPD driver genes
There has been a variety of known COPD-related genes in Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD) guideline, 3 peer-reviewed literatures, [19][20][21][22][23] Online Mendelian Inheritance (OMIM) database 24 (https:// www.ncbi.nlm.nih.gov/omim/, July 4, 2017), and Genetic Association Database 25 (GAD, http://geneticassociationdb. nih.gov/). The GOLD guideline illustrated some mainstream mecha nisms of COPD and emphysema, such as proteaseantiprotease imbalance, which guided us to further search for specific genes in some canonical reviews. In addition, OMIM and GAD are open access databases, providing a comprehensive and authoritative compendium of genetic alterations associated with disease phenotypes. Based on the keywords of COPD or emphysema, we retrieved the above literatures and databases and identified driver genes of COPD.

Direct protein-protein interaction network of DEGs and known driver genes
The topological and functional analysis of protein interaction network is helpful in the identification of key genes and functional modules that participate in disease onset and progression. 16 In network pharmacology, merging the interaction networks of drug predicted targets and driver genes of disease is an effective and original method to identify the concrete genes or pathways by which drug affects the

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Potential candidate genes in emphysema disease. 15,16 Enlightened by this analytical method, we tried to analyze the interacted relationship between DEGs and accepted mechanism of COPD in order to identify more credible DEGs participating in emphysema development. STRING v10.5 26 (https://www.string-db.org/, July 20, 2017), a web database recording physical and functional protein-protein interaction (PPi) information, was used to predict the interacted relationship between driver genes and DEGs. A variety of active interaction sources in STRING were included into our search strategy, such as text mining, experiment record, database record, coexpression, neighborhood, gene fusion, and co-occurrence. The interaction network was further visualized by Cytoscape 27 v3.6.1 which is an open access software aimed at annotating and visualizing biological pathways and molecular interaction networks.

Indirect PPi of Degs and known driver genes
Weighed protein-protein interaction network analysis has been regarded as a novel approach to highlight key functional genes of complex disorders like frontotemporal dementia. 14 It indicates that analyzing disease-spectrum genes, also known as first-layer interacting proteins of key genes, is a greatly potential approach to validate previous findings and explore novel disease-related mechanisms.
Thus, we retrieved STRING database v10.5 to obtain the first-layer proteins associated with DEGs products and constructed an indirect PPi of these proteins. The first-layer interacting proteins were roughly classified according to the clustering annotation of Gene Oncology and Kyoto Encyclopedia of Genes and Genomes 28 by the Functional Annotation tool in the DAVID database. Then, the merge tool of Cytoscape software v3.6.1 27 was applied to merge the indirect PPi with driver genes' PPi to discover the interconnected and intersected functional modules and target the core genes.
In addition, highly connected nodes with a great number of edges in the network are likely to be significantly functional in the disease context and defined as hub genes. 29 The number of each gene node's edges in the indirect PPi network was ranked to identify hub genes with functional significance in emphysema by Cytoscape software.

Identification of candidate transcription factors
TRANSFAC ® Professional database 30 is an authoritative and paid database, recording comprehensive information of transcription factor (TF), their regulated genes and binding sites prediction profiles. We performed the TF prediction of core genes by using Gene Radar tool on the GCBI website (Genminix Informatics Ltd., Shanghai, China). Based on all transcripts of each gene (Ensembl database GRCh38 version), the Gene Radar tool could acquire comprehensive TF prediction results from the TRANSFAC Professional database. In addition, Gene Radar tool could screen out high-recommended TFs by integrating the scores from the TRANSFAC database, the existence of single-nucleotide polymorphism (SNP) loci and methylation modification in TF binding sites. Therefore, we identified the candidate TFs of core genes with high recommendation grade.

Results
Baseline characteristic between severe and mild emphysema groups As Table 1 shows, all patients were former smokers and presented with severe to very severe airflow limitation according to the GOLD guideline. 3 Despite relatively small sample size, a significant difference of many characteristics between two groups was observed, like the ratio of FEV 1 /FVC and body mass index, proving the credibility of %LAA-950-dependent grouping method.  Table 2). The Gene Oncology annotations of 45 genes were shown in Table S1. According to involved pathways, 135 retrieved COPD driver genes were separately placed in extracellular

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Potential candidate genes in emphysema matrix-associated column, oxidative stress column, inflammation column, and others column (shown in Table 3).

Candidate genes directly interacted with driver genes
A total of 180 genes (45 DEGs +135 driver genes) were recruited to construct the network, 7 were withdrawn for failed identification of gene symbol and 147 were found to have interaction with others. Eight of the 45 DEGs were found to have interaction relationship with driver genes: G-protein coupling receptor 65 (GPR65), Neuropeptide S receptor 1 (NPSR1), purinergic receptor P2RY13, RhoGEF and GTPase activating protein (BCR), G protein subunit β4 (GNB4), BCL2-associated athanogene 4 (BAG4), inosine monophosphate dehydrogenase 2 (IMDPH2), and Hsp40 member 14 (DNAJB14; shown in Figure 1). A relatively separate interacting set was composed of GPR65, NPSR1, P2RY13, and GNB4. In addition, BCR and BAG4, IMDPH2 and DNAJB14 had separately bilateral relationship. When the cutoff value of the combined interaction score was set at 0.9, we found that GNB4 and P2RY13 mostly interacted with chemokines and chemokine receptors, such as CXCR1, CCR2, CXCR2, IL8, CXCR3, CCR5, CCL5, and CCR6. BAG4 interacted with TNFα and its receptor as well as heat shock protein (HSP) family. In addition, PIK3CA and PIK3R1 may play an important role by interacting with GPR65, GNB4, BCR, NPSR1, and BAG4.

Common key genes and their TFs filtered by merging of indirect PPi and driver PPi
A total of 422 first-layer interacting proteins were attained by retrieving STRING database v10.5 (shown in Table S2). Among them, 375 proteins were recruited to construct the indirect PPi and the remaining proteins were withdrawn due to failed identification or isolation from interaction network. According to the number of each node's edges in the topological network, 375 proteins in indirect PPi were ranked and the top 20 are shown in Table S3. PIK3CA, TP53, and MAPK1, the top three genes in the rank of topological nodes of indirect PPi, had separately 86, 74, and 72 interacting nodes, which showed their potentially predominant and interconnected roles in the mechanism of emphysema progression.

Discussion
We identified eight novel candidate genes (GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, IMPDH2, and TP53) promoting the progression of emphysema by means of network analysis of DEGs and COPD driver genes. This is the first study that QCT index was applied to classify emphysema for analyzing DEGs, and known COPD driver genes were retrieved to construct interacting networks with DEGs. Our method of direct and indirect network analysis has some merit. For analysis of DEGs, it is a difficult problem to interpret the biological role of the identified single gene in the pathogenic mechanism. Performing external and experimental validation for all DEGs is cumbersome and inefficient. Incorporating driver genes into direct network analysis with DEGs is helpful in quickly highlighting causative DEGs and excluding random DEGs caused by covariates, making the role of identified DEGs more credible. In addition, protein function is regulated not only at transcriptional level but also at posttranscription level which DNA microarray could not detect. A previous study of breast cancer demonstrated that known driver genes, with their expression profiles not changed, were still capable of interconnecting many transcriptionally dysregulated genes in the protein interacting network. 31 Therefore, we innovatively used the method of first-layer protein interacting network to further explore the indirect effects of DEGs and seek potential ignored genes. Furthermore, for the polygenic complex disease, a single gene is incapable of comprehensively illustrating the molecular mechanism of phenotypes. By merging the indirect PPi and driver PPi, we could efficiently extract the candidate protein networks involved in a specific disease phenotype. This method, 14 of which the efficacy has been confirmed in a study of frontotemporal dementia, could be applied in exploring other complex disorders or extended to other phenotypes of COPD, such as airway remodeling. By comparing difference of the critical protein interactome in different phenotypes, we could unveil the different molecular mechanisms promoting complex pathological processes, which was crucial to promote biomarker and drug discovery.
As a proton-sensing receptor, GPR65 could regulate the immune response of T cells and macrophages and induce the production of MMP3 in the acidic microenvironment. [32][33][34] Asthma, another chronic airway disease with obstructive airflow limitation, was demonstrated to have local acidic

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Potential candidate genes in emphysema microenvironment, 35 where eosinophil showed decreased apoptosis and increased viability in a GPR65-depedent manner. 36 The SNP of NPSR1 was associated with the decline of FEV 1 after adjusting for covariates in normal aging population. 37 Moreover, DNA methylation status of NPSR1 in adult severe asthma population and childhood allergic asthma population was distinct from that of control population. 38 NPSR1's expression on peripheral blood eosinophils was positively correlated with asthma's severity and serum IgE level. 39 Asthma and COPD have many common traits in terms of risk factors, inflammatory responses, clinical features, and therapeutic methods. 3,40 Furthermore, the role of eosinophils in the pathogenesis and treatment of COPD is gradually recognized. 3,41 Therefore, we speculated that the above genes related to asthma were highly likely to be involved in the pathogenesis of COPD and emphysema.
As an extracellular ADP receptor, P2RY13 participated in purinergic signaling pathway, resulting in the apoptosis of pancreatic β-cells 42 and differentiation of marrow stem cells into osteoblasts. 43 Since the roles of extracellular adenosine ATP and its receptor P2RX in COPD have been confirmed, 44,45 ADP, the intermediate in purinergic metabolic pathways, may also have pathogenic effects on COPD.
In addition, common key genes identified by the indirect method matched well with the two-hit hypothesis of COPD, 46 especially the part of senescence and senescence-associated secretary phenotype (SASP). 47 Senescence is an irreversible cell state, at which a cell is deprived of its replicative capacity with cell cycle arrest. 48 The p53 (encoded by TP53)/p21 pathway participated in all types of senescence mechanisms, arresting cell cycle at the G1/S and G2/M check points. 49 SASP refers to the alteration of aging cell's secretome toward more production of proinflammatory cytokines, including IL-8 and monocyte chemotactic protein 1 (MCP-1). 49 IL-8 and its receptor CXCR2, with neutrophil chemotactic ability, are just one of the most important chemokine-receptor pairs in COPD pathogenesis, as well as MCP-1 encoded by CCR2. 6 Moreover, phosphoinositide 3 kinase (PI3K), the product of PIK3CA, was also known as a pro-senescent kinase by inactivating HDAC-2 which is an antiaging molecule, because knockdown of HDAC-2 could induce cellular senescence by enhancing p53-dependent transcriptional responses. 50 Based on these evidence, we hypothesized that TP53 might play a central role in promoting progression of emphysema. Firstly, beside IL-8, CXCR2 and CCR2, elastase, the products of ELANE, and PI3K are also well-recognized COPD driver genes playing an important role in proteaseantiprotease imbalance and chronic inflammation of COPD. 6 The involvement of these genes supports our results and in return proves the role of TP53.
Secondly, TP53 can induce cell cycle arrest, apoptosis, senescence, DNA repair, or metabolic alterations, in response to oxidative stress and DNA damage. 51 Some studies confirmed that TP53 was overexpressed in the emphysematous lung tissue. 52 A Genome-Wide Association Study for 365 patients with emphysema proved the association of TP53's SNP with apoptotic signaling and smoking-related emphysematous changes in smoker's lungs. 53 Furthermore, a RNA-sequencing study of COPD patients' lung tissues identified the enrichment of p53/hypoxia pathway and the phenomenon of much frequent molecule's alternative splicing in this pathway. 54 Thirdly, the role of TP53 in senescence might reveal its effects in COPD. Many evidences have shown the association between senescence and pathogenesis of COPD. Cellular experiments proved that alveolar epithelial and endothelial cell as well as fibroblast underwent accelerated senescence in emphysematous lung. 55,56 Epidemiological surveys indicated that the incidence of COPD and the decline of FEV 1 increased with growth of age. 3 Moreover, airway and parenchyma of the patients with COPD and healthy senior citizens had similar structural changes. 50,57 There are many studies searching for key genes associated with emphysema. In a research on seeking differently expressed miRNAs of emphysema, the miR-638 was identified as an effector molecule and it could regulate accelerated senescence, which was partially consistent with our hypothesis. 58 However, we did not reproduce the DEGs of other studies for emphysema. On one hand, it was due to different grouping methods; 59 on the other hand, their samples mainly came from patients with moderate COPD (FEV 1 % was about 60%), 60,61 so their results mainly explained the early mechanisms of emphysema progression.
Our study has some limitations. Firstly, the sample size is relatively small, 62 which is due to limited numbers of accessible datasets in GEO database. Secondly, the selection of COPD driver genes is potentially biased and incomplete so that some meaningful DEGs may be ignored. Thirdly, PPi prediction has false positives and false negatives. The web tool STRING v10.5 defines PPi by the standard of text mining, experiment record, database record, coexpression, neighborhood, gene fusion, and co-occurrence, which may have a bit of controversy. In addition, interactions proved by experiments in vitro may also have differences compared with those in vivo.
Despite these limitations, our study has put forward some novel candidate genes, and following experiments or larger databases are needed to testify the role of the above candidate genes in the mechanism of emphysema progression.

Conclusion
We have identified several novel candidate genes promoting emphysema, like GPR65, NPSR1, and TP53, which may be helpful in filling in the gap of knowledge in the field of COPD.  Table S1 GO annotation of DEGs between severe and mild emphysema groups

Functional category Gene Symbol GO annotation
Transcriptional regulation

KanK1
Positive regulation of Wnt signaling pathway, negative regulation of actin filament polymerization and so on PhF1 Involved in regulation of histone H3-K27 methylation and cellular response to DNA damage stimulus PhF6 Negative regulation of transcription from RNA polymerase II promoter, an oncogene TaDa2a A transcriptional activator adaptor; acetylating and destabilizing nucleosomes TrIM34 Involved in interferon signaling pathways ZFHX3 Transcription factor activity, RNA polymerase II distal enhancer sequence-specific binding ZNF322 Regulate transcriptional activation in MAPK signaling pathways ZNF451 Negative regulation of transcription initiation from RNA polymerase II promoter, histone H3-K9 acetylation and TGF-β signaling pathway Membrane receptor and signal pathway

Bag4
Negative regulation of apoptotic process, response to TnFα BCr GTPase activator activity and Rho guanyl-nucleotide exchange factor activity FYB1 Involved in TCR signaling pathways, the expression of IL-2 and process of NLS-bearing protein importing into nucleus gnB4 A subunit of heterotrimeric guanine nucleotide-binding proteins involved in cellular response to glucagon stimulus GPR65 Involved in G-protein coupled receptor signaling pathway, actin cytoskeleton reorganization, and apoptotic process nPsr1 Neuropeptide and vasopressin receptor activity, increased expression in lung for asthma nPhP4 Involved in actin cytoskeleton organization, hippo signaling, and negative regulation of canonical Wnt signaling pathway P2rY13 G-protein coupled purinergic nucleotide receptor and negative regulation of adenylate cyclase activity signaling pathway rnF213 ATPase activity, ubiquitin-protein transferase activity, and negative regulation of noncanonical Wnt signaling pathway ZFP106 Insulin receptor signaling pathway ZC3HAV1 Defense response to virus Metabolism DPM3 GPI anchor biosynthetic process and protein mannosylation elOVl3 Fatty acid elongase activity providing precursors for synthesis of sphingolipids and ceramides eTnK2 A member of choline/ethanolamine kinase family that catalyses phosphatidylethanolamine biosynthetic process IMPDh2 Purine ribonucleoside monophosphate biosynthetic process, neutrophil degranulation, and oxidation-reduced process Cilium IFT140 Intraciliary transport involved in cilium assembly TMeM80 Integral component of membrane

Protein modification UsP33
Protein deubiquitination and involved in slit-dependent cell migration and beta-2 adrenergic receptor signaling NUP58 A component of the nuclear pore complex playing a role of nucleocytoplasmic transporter activity ParP16 naD+ ADP-ribosyltransferase activity and protein serine/threonine kinase activator activity

Others DnaJB14
Hsp70 protein binding and chaperone cofactor-dependent protein refolding ZBTB8OS tRNA splicing via endonucleolytic cleavage and ligation ehBP1 Endocytosis and its mutation associated with prostate cancer aTP1B2 ATP hydrolysis coupled transmembrane transport and cell adhesion  Serine-type endopeptidase inhibitor activity and association with central and peripheral nervous system development sVeP1 A ligand for integrin α9β1 and involved in cell adhesion

VPs28
Endosomal transport, macroautophagy, negative regulation of protein ubiquitination and viral budding OgFOD3 Oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen Note: Some COPD-associated genes are highlighted in bold. Abbreviations: DEGs, differentially expressed genes; GO annotation, Gene Oncology annotation.    (Continued)

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