
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39276358
10.1080/07853890.2024.2403729
2403729
Version of Record
Research Article
Pulmonary Medicine
Mechanism underlying the therapeutic effects of effective component compatibility of Bufei Yishen formula III combined with exercise rehabilitation on chronic obstructive pulmonary disease
L. Huang et al.
Exploring the Therapeutic Effects of ECC-BYF III + ER on COPD And HUB Genes
Huang Lidong ab#
https://orcid.org/0000-0001-7741-6440
Guan Qingzhou ab#
Lu Ruilong ab
Zhang Zhenzhen ab
Liu Chunlei ab
Tian Yange ab
https://orcid.org/0000-0002-6485-2371
Li Jiansheng bc
a Academy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou, China
b Henan Key Laboratory of Chinese Medicine for Respiratory Disease, Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China
c The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China
# These authors contributed equally to this work.

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2403729.

CONTACT Jiansheng Li li_js8@163.com Henan Key Laboratory of Chinese Medicine for Respiratory Disease, Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan & Education Ministry of P.R. China, Henan University of Chinese Medicine, Zhengzhou, China
Yange Tian yange0910@126.com Academy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou, China
14 9 2024
2024
14 9 2024
56 1 24037298 7 2023
4 9 2024
5 9 2024
KnowledgeWorks Global Ltd.14 9 2024
published online in a building issue14 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Objective

To explore the mechanism underlying the therapeutic effect of Bufei Yishen Formula III combined with exercise rehabilitation (ECC-BYF III + ER) on chronic obstructive pulmonary disease (COPD) and further identify hub genes.

Materials and Methods

Gene Set Enrichment Analysis was used to identify the COPD-associated pathways and reversal pathways after ECC-BYF III + ER treatment. Protein-protein interaction network analysis and cytoHubba were used to identify the hub genes. These genes were verified using independent datasets, molecular docking and quantitative real-time polymerase chain reaction experiment.

Results

Using the high-throughput sequencing data of COPD rats from our laboratory, 49 significantly disturbed pathways were identified in COPD model compared with control group via gene set enrichment analysis (false discovery rate < 0.05). The 34 pathways were reversed after ECC-BYF III + ER treatment. In the 2306 genes of these 34 pathways, 121 of them were differentially expressed in COPD rats compared with control samples. A protein–protein interaction network comprising 111 nodes and 274 edges was created, and 34 candidate genes were identified. Finally, seven COPD hub genes (Il1b, Ccl2, Cxcl1, Apoe, Ccl7, Ccl12, and Ccl4) were well identified and verified in independent COPD rat data from our laboratory and the public dataset GSE178513. The area under the receiver operating characteristic curve values ranged from 0.86 to 1 and from 0.67 to 1, respectively. The reliability of the mentioned genes, which can bind to the active ingredients of ECC-BYF III through molecular docking, were further verified through qRT-PCR experiments.

Conclusion

Thirty-four COPD-related pathways and seven hub genes that may be regulated by ECC-BYF III + ER were identified and well verified. The findings of this study may provide insights into the treatment and mechanism underlying COPD.

KEY MESSAGES

GSEA method can circumvent the limitations of the preacquisition of DEGs for ORA and is suitable for small sample data.

34 COPD-related pathways that can be regulated by ECC-BYF III + ER were identified.

Seven COPD hub genes were identified and well verified in independent RNA-seq data and PCR experiment, and they may play a crucial role in TCM treatment.

Keywords

Chronic obstructive pulmonary disease
pathway
ECC-BYF III
exercise rehabilitation
hub genes
National Postdoctoral Program for Innovative Talents 10.13039/501100012152 BX20200115 National Natural Science Foundation of China 10.13039/501100001809 82074406 82305018 The plan for the Key Scientific Research Foundation of the Higher Education Institutions of Henan Province 21A360019 This research was funded by China National Postdoctoral Program for Innovative Talents (Grant number: BX20200115); National Natural Science Foundation of China (Grant number: 82074406, 82305018); The plan for the Key Scientific Research Foundation of the Higher Education Institutions of Henan Province (Grant number: 21A360019).
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pmc1. Introduction

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease with airflow limitation, which is partially reversible and can seriously affect the quality of life of individuals, and threaten human health. The 2017 Global Burden of Chronic Respiratory Diseases study reported 3.9% COPD prevalence [1]. In addition, the World Health Organization reported that by 2030, COPD will be the third leading cause of death globally and impose considerable economic burden on patients [2, 3]. Genes play an important role in the development of COPD [4, 5]. Molloy et al. [6] observed the association of the heterozygous deficiency of the SERPINA1Z allele on a chromosome with susceptibility to COPD. Gong et al. [7] reported an associated FAM13A polymorphism with susceptibility to COPD. Genes function through complex biological pathways [8]. Disease-related research based on pathway levels has received interest [9]. FAM13A deficiency can promote Wnt-mediated ATII cell repair and regeneration, and may attenuate cigarette smoke–induced alveolar damage [10]. The Notch signaling pathway may positively regulate the secretion of Muc5ac induced by particulate matter 2.5 through Hes1 [11, 12]. Moreover, the pathogenesis of COPD has not been established. Thus, studies should explore the pathogenesis of COPD related to pathways and identify COPD hub genes that may guide the prevention and treatment of COPD.

Current conventional treatment drugs for COPD include corticosteroids, bronchodilators, antibiotics, and expectorants. Although these drugs can alleviate the symptoms of COPD, they have adverse side effects that lead to osteoporosis, hyperglycemia, and pneumonia [13]. In addition, some non-drug treatments, such as home oxygen therapy, noninvasive mechanical ventilation, and lung volume reduction surgery [14], can effectively improve the ventilation insufficiency of patients with COPD, relieve hypoxemia, and reduce the frequency of COPD exacerbation [15, 16]. However, these approaches are expensive and show strong mechanical dependence [15, 17].

Traditional Chinese medicine (TCM) has unique advantages, such as high efficacy and weak side effects. Bufei Yishen formula (BYF) can effectively alleviate the symptoms of COPD and reduce the frequency of acute exacerbation [18]. However, the composition of BYF compound is complex, and its mechanism is difficult to fully elucidate. To circumvent these issues, Li et al. [19] confirmed ECC-BYF I, which comprises the 10 main pharmacodynamic components of BYF and has efficacy comparable to that of BYF according to systematic pharmacology and in vitro experiments. To ensure the efficacy and safety, we optimized ECC-BYF I into ECC-BYF II, which was composed of five medicinal components, through COPD rat experiment [20]. Finally, through in vitro and in vivo experiments, the effective components and ratios were optimized. ECC-BYF III, which was composed of five effective components, was obtained. It exhibited the same efficacy as ECC-BYF II [21] and effectively improved lung function and reduced mucus secretion in COPD rats [22]. Pulmonary rehabilitation for COPD treatment attracted considerable interest in recent years, and exercise rehabilitation (ER) is one of its important component [23]. ER can effectively increase the exercise endurance of patients with COPD and improve their dyspnea and psychosocial status [24–26]. ECC-BYF III + ER can improve lung function and regulate the inflammatory response of COPD rats [27]. However, its underlying mechanism has not been fully established. Therefore, exploring the intervention mechanism of BYF III + ER on COPD is of great importance.

Common pathway analyses include over-representation analysis (ORA) and functional class scoring (FCS). ORA requires the preacquisition of differentially expressed genes (DEGs), which may result in gene loss and minimal but important changes in gene expression level and is unsuitable for small sample data [28]. FCS does not require the predefinition of DEGs. It sorts all genes according to a scoring rule and analyzes the distribution of the genes of a selected pathway. FCS analysis has a unique advantage for genes with minimal changes in expression and an important biological function. This method can circumvent the low statistical power associated with small sample size. The most representative method of FCS is gene set enrichment analysis (GSEA) [28, 29]. Path topology (PT) can also be applied to pathway analysis, which comprehensively assesses the contribution of each gene to a pathway and includes information, such as gene location, function, and synergy with other genes; however, PT has an evident deficiency attributed to imperfect knowledge network about genes [30].

Considering the distinct advantages of GSEA, the efficacy of ECC-BYF III + ER treatment, and the urgent need to explore the mechanism underlying COPD, we identified COPD-related pathways through GSEA. Then, potential pathways that can be regulated by ECC-BYF III + ER were further identified. Finally, COPD hub genes, which can be regulated by ECC-BYF III + ER, were identified and verified in independent datasets, molecular docking, and qRT-PCR experiment, etc. This study may guide studies on the COPD’s mechanism, TCM prevention, and COPD treatment, providing novel insights into other diseases.

2. Materials and methods

2.1. COPD rat data and preprocessing

15 SPF male rats were obtained from the laboratory animal center of Henan (No. SCXK 2017-0001; weight: 200 ± 20 g; animal certification number: 41003100005995). The rats were randomly divided into control, model, ECC-BYF III, ER, and ECC-BYF III + ER groups, with three replicates for each group, as shown in Table 1. The study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (No. YFYDW2019031). The control group rats were treated with a nasal drip of saline once every 5 days for 8 weeks. The other groups received a nasal drip of 0.1 ml Klebsiella pneumoniae (6 × 108 CFU/ml) once every 5 days and exposed to cigarette smoke twice a day [31]. From week 9 to week 16, the control and model groups received 0.5% CMC-Na (5 ml/kg) [31] through gastric irrigation once a day; the ECC-BYF III + ER and ECC-BYF III groups received ECC-BYF III (5.5 mg/kg) [21] in the same manner once a day. The rats in the ECC-BYF III + ER and ER groups were trained using a ZH-PT animal treadmill (Anhui Zhenghua Biology & Equipment Co., Ltd.) daily for 8 weeks at a speed of 8 m/min for 20 min.

Table 1. High-throughput gene sequencing data of COPD rats.

Group	Gene number	Sample size	
Control	14083	3	
Model	14083	3	
ECC-BYF III	14083	3	
ER	14083	3	
ECC-BYF III + ER	14083	3	

On week 24, all rats were sacrificed (intraperitoneal injection of 2% pentobarbital sodium, 40 mg/kg) for lung tissues collection and high-throughput sequencing. An RNA library was constructed in accordance with the specification of the manufacturer. Each sample was sequenced using Illumina. Image analysis and base calling to obtain original data were performed using HiSeq control software + OLB + GAPipeline and stored in FASTQ format. FastQC software was used in evaluating the quality of origin data. Cutadapt software was used in cleaning origin data, and clean data were justified using the reference genome by Hisat2 (v2.0.1). Count data for the corresponding genes were obtained. The high-throughput gene sequencing data of the COPD rat model were obtained from one of our previous studies (the access date of the RNA-seq data is September 17, 2019) [27]. Finally, the edgeR package for R software was used for the analysis of DEGs [32].

2.2. Identification of differentially expressed genes and pathway enrichment analysis

DEGs were identified using the edgeR package for R software (v3.6.2). A total of 343 rat Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway files containing 8940 genes were downloaded from the KEGG database on April 7, 2022 (https://www.genome.jp/kegg/). Based on the identified DEGs, a hypergeometric distribution test was applied for pathway enrichment analysis, and significance was calculated as follows: P=1−∑i=0k−1(mi)(N−mn−i)(Nn)

Notes: m represents the number of annotated genes in a given pathway, n indicates the number of DEGs, N denotes the number of background genes detected via high-throughput sequencing, and k refers to the number of DEGs in a specific pathway.

2.3. Gene set enrichment analysis

The pathway files obtained from the KEGG database were converted to the format required for GSEA (.gmt). Similarly, gene expression data and sample labels were formatted. Pathway analysis was performed using GSEA software, and the permutation type parameter was set as the gene set. Default values were used for the remaining parameters. A pathway was considered reversed when its NES was transformed from >0 to <0 or from <0 to >0.

2.4. Protein-protein interaction network construction and identification of hub genes

A protein-protein interaction (PPI) network was developed based on genes of interest using the STRING database. The species was Rattus norvegicus, and the interaction score was set at 0.40. The ‘TVS’ files of the PPI were downloaded and imported into Cytoscape software on May 22, 2022. Seven algorithms (MCC, MNC, Degree, EPC, Closeness, Stress, and Radiality) of the cytoHubba plugin in Cytoscape software (3.9.1 version) were respectively used to identify the hub genes. The top 50 genes identified by each algorithm were defined as its hub genes. The genes that were reproducible for at least six algorithms were selected as candidate genes for subsequent analysis.

2.5. Independent dataset validation

The GSE178513 dataset, composed of three normal lung and three COPD rat samples, was downloaded from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/gds/) database on June 21, 2021. In addition, a COPD rat data, including six normal and six COPD rats (the access date of the RNA-seq data is October 11, 2020), from our own laboratory was constructed as described in Section ‘2.1 COPD rat data and preprocessing’. Then, the lung tissues of rats were sent for high-throughput sequencing and their gene expression data were obtained. This study has been approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine. The reliability of hub genes was verified using the GEO independent dataset from public database and other independent COPD rat data from our own laboratory. One-way analysis of variance (ANOVA) and edgeR algorithm were applied to identify the DEGs for microarray and RNA-seq data, respectively. The reliability of the COPD hub genes was verified according to their direction and p-values. Furthermore, the accuracy of the identified hub genes were evaluated through receiver operating characteristic (ROC) curve analysis.

2.6. Molecular docking

ECC-BYF III contained five active ingredients: 20-S-ginsenoside Rh1, astragaloside IV, nobiletin, icariin, and paeonol. The two-dimensional files of the active ingredients were downloaded from PubChem database on July 29, 2022, and their energy was minimized. The protein crystal structures of candidate hub genes were downloaded from UniProt database on August 1, 2022, and PyMOL software was used to remove their water, ligand, and hydrogenation. The binding sites of the target proteins and active ingredients were determined using AutoDockTools-1.5.6 software. Molecular docking was performed using AutoDock Vina software. The affinity of the target protein and active ingredient was determined according to their binding energy. Low binding energy was associated with stable binding and desirable binding effects. Visualization of molecular docking results was achieved using PyMOL software.

2.7. Quantitative real-time polymerase chain reaction assay

The primers of rat genes of interest were designed by Zhengzhou Dianjing Technology Co., Ltd., and their primers sequences are shown in Table 2. Glyceraldehyde-3-phosphate dehydrogenase was designated as an internal reference gene for subsequent gene expression analysis. Total RNA was extracted from a rat lung tissue homogenate using QIAzol Lysis reagent in accordance with the manufacturer’s instructions. The extracted RNA was reverse transcribed to cDNA using HiScript® II Q RT SuperMix and SYBR Green Master Mix. QuantStudio TM Real-Time PCR software was used to analyze the qRT-PCR reaction of reverse cDNA. Finally, the relative mRNA expression levels were analyzed using the 2-△△CT method. Each expression level was presented as mean ± standard deviation. ANOVA was used for quantitative data analysis by SPSS 22.0. When the variance was uniform, least-significant difference (LSD) was used, otherwise the Dunnett’s T3 test was used.

Table 2. Primers for qRT-PCR analysis of genes.

Gene name	Sequence (5’->3’)	bp	
Ccl2	Forward-TGCAGGTCTCTGTCACGCTT	21	
 	Reverse-GGCATTAACTGCATCTGGCTGA	23	
Cxcl1	Forward-ACCGAAGTCATAGCCACACTC	21	
 	Reverse-TTACTTGGGGACACCCTTTAGC	22	
Ccl4	Forward-CAGCCAGCTGTGGTATTTCTGA	22	
 	Reverse-AGTCATTCACATACTCATTGACCCA	25	
Il1b	Forward-CAGCTTTCGACAGTGAGGAGA	21	
 	Reverse-TCTGGACAGCCCAAGTCAAG	20	
Ccl7	Forward-AGATCTCTGCCGCGCTT	17	
 	Reverse-GATGAATTGGTCCCATCTGGT	21	
Ccl12	Forward-CACATTCGGAGGCTAAAGAGC	21	
 	Reverse-AGTACAGTTCTGAAGATCACAGC	23	
Ccr3	Forward-ACTACCTGACTGGTGTATGTGA	22	
 	Reverse-GATGCCATTTTACTTGTCTCTGATG	25	
Apoe	Forward-AAACTAAGATCGTGAGACTGGC	22	
 	Reverse-ATCCTGTCAGCAATGGGACC	20	
Gapdh	Forward-ACAGCAACAGGGTGGTGGAC	20	
 	Reverse-TTTGAGGGTGCAGCGAACTT	20	

3. Result

3.1. Identification of differentially expressed genes based on over-representation analysis

For the high-throughput gene sequencing data, with false discovery rate (FDR) < 0.05, only three DEGs were identified in the COPD rat model compared with the control sample (3 vs 3), which may be attributed to the low statistical efficiency caused by the small sample size. As a compromise, the relatively loose threshold condition (p-values < 0.05), which is also the common threshold for data analysis, was performed for the identification of DEGs. With p-values < 0.05, 871 DEGs, of which 267 were upregulated and 604 were downregulated, were identified, as shown in the Figure 1a. Then, a total of 25 pathways were obtained at statistical significance set at p < 0.05, as shown in Figure 1b and Supplementary Table S1. We found that most of these pathways are related to COPD [33–36]. This further indicated that the identified DEGs had certain reliability under the condition of p < 0.05.

Figure 1. Differentially expressed gene and KEGG pathway enrichment analyses. (a) Volcano plot showing the results on 871 DEGs based on edgeR algorithm. (b) Bar chart showing the pathway enrichment results for 871 DGEs.

3.2. KEGG pathway enrichment analysis based on GSEA

Using the high-throughput gene sequencing data of the COPD rat and GSEA method, 49 significantly disturbed pathways were identified in the COPD rat model compared with the control samples (3 vs 3, FDR < 0.05). The finding indicated the high statistical efficiency of GSEA for small-sample data. Moreover, some of these pathways were correlated with COPD. For example, oxidative phosphorylation could promote the production of endogenous reactive oxygen species, affect the expression of inflammatory factors, and aggravate the inflammatory response of COPD [37]. DNA replication may be correlated with COPD [38]. Wnt signaling pathway is closely related to stem cell differentiation and homeostasis, and wnt3a can promote GSK-3β phosphorylation and β-catenin accumulation, affecting COPD airway remodeling [39]. IL-17 signaling pathway is associated with lung inflammation caused by COPD cigarette smoke by mediating macrophage damage [33]. The above results demonstrated the reliability of the GSEA-based KEGG pathway enrichment results.

Nine pathways identified by ORA were then identified through GSEA, as shown in Figure 2. Among the 49 COPD-associated pathways, the treatment of COPD rats with ECC-BYF III, ER, and ECC-BYF III + ER reversed the disturbed directions of 39, 45, and 48 pathways, respectively. In addition, 13, 20, and 34 pathways had statistically significant reversals (p < 0.05), as shown in Table 3 and Supplementary Tables S2–S5. This finding suggests that ECC-BYF III + ER may have exert therapeutic effects than ECC-BYF III and ER, by regulating the dysregulated direction of COPD-related pathways.

Figure 2. Pathway enrichment number and overlap condition based on ORA and GSEA method.

Table 3. Reversal pathways observed after ECC-BYF III + ER treatment.

Pathway name	Size	P-value	Reversal	
Ribosome	120	1.00E-16	Y	
Oxidative phosphorylation	102	1.00E-16	Y	
Proteasome	41	1.00E-16	Y	
Coronavirus disease - COVID-19	191	1.00E-16	Y	
Parkinson disease	220	1.00E-16	Y	
Prion disease	216	1.00E-16	Y	
Lysine degradation	50	1.00E-16	N	
Protein export	21	1.00E-03	Y	
Viral protein interaction with cytokine and cytokine receptor	70	1.00E-03	Y	
Antigen processing and presentation	55	1.00E-03	Y	
DNA replication	32	1.00E-03	Y	
ECM-receptor interaction	70	1.00E-03	N	
Non-alcoholic fatty liver disease	127	3.00E-03	Y	
Hedgehog signaling pathway	45	3.00E-03	Y	
Huntington disease	247	4.00E-03	Y	
Wnt signaling pathway	128	7.00E-03	Y	
Axon guidance	153	9.00E-03	N	
Focal adhesion	176	9.00E-03	N	
Thermogenesis	178	1.00E-02	Y	
Legionellosis	50	1.10E-02	Y	
Glutathione metabolism	49	1.10E-02	Y	
Phosphatidylinositol signaling system	86	1.10E-02	Y	
Folate biosynthesis	16	1.20E-02	N	
Diabetic cardiomyopathy	172	1.20E-02	Y	
IL-17 signaling pathway	79	1.50E-02	Y	
Chemical carcinogenesis - reactive oxygen species	190	1.50E-02	Y	
Glutamatergic synapse	76	1.50E-02	Y	
RNA polymerase	28	1.70E-02	N	
Cardiac muscle contraction	60	1.80E-02	N	
Alzheimer disease	310	1.80E-02	Y	
Mismatch repair	21	2.00E-02	Y	
Drug metabolism - other enzymes	50	2.20E-02	Y	
Signaling pathways regulating pluripotency of stem cells	102	2.40E-02	N	
Spliceosome	121	2.80E-02	Y	
Primary immunodeficiency	28	2.90E-02	Y	
Systemic lupus erythematosus	48	3.20E-02	Y	
Arachidonic acid metabolism	42	3.30E-02	N	
Pyruvate metabolism	36	3.40E-02	Y	
Inositol phosphate metabolism	69	3.40E-02	N	
Rheumatoid arthritis	69	3.50E-02	Y	
Staphylococcus aureus infection	47	3.60E-02	Y	
Adherens junction	62	3.70E-02	N	
Insulin resistance	89	3.80E-02	N	
Cysteine and methionine metabolism	43	4.10E-02	Y	
Biosynthesis of nucleotide sugars	35	4.20E-02	Y	
Protein digestion and absorption	73	4.20E-02	N	
Arrhythmogenic right ventricular cardiomyopathy	58	4.20E-02	N	
Parathyroid hormone synthesis, secretion and action	84	4.50E-02	N	
Nucleotide excision repair	42	5.00E-02	Y	
Note: Reversal (Y) denotes that the direction of the pathway was reversed when the COPD rat was treated with ECC-BYF III + ER; N means that pathway direction was not reversed.

3.3. Protein-protein interaction network analysis and hub gene identification

Among the 2306 genes within the 34 identified reversed pathways, 121 were differentially expressed in the COPD rats compared with the control samples. The 121 genes were mapped to the STRING database, and a PPI network comprising 111 nodes and 274 edges was obtained, as shown in Figure 3. Based on the PPI network, seven algorithms (MCC, MNC, Degree, EPC, Closeness, Stress, and Radiality) in the cytoHubba plugin for Cytoscape were used in the identification of hub genes, as shown in Figure 4 and Supplementary Tables S6 and S7. A total of 34 genes were identified by at least six algorithms, and 28 of these were reversed after the ECC-BYF III + ER treatment. Il1b, Ccl2, Ccl12, Ccl7, Ccr3, H2az1, Cxcl1, Apoe, Ccl4, and Snrpf showed statistical significance (p < 0.05), as shown in Table 4. Il1b, Ccl2, Ccl4, Ccr3, Cxcl1, Apoe, Ccl7, and Ccl12 are related to COPD or inflammation [40–43]. Thus, these eight genes were considered candidate hub genes in the subsequent analysis.

Figure 3. Protein-protein interaction network based on genes of interest.

Figure 4. Identification results for the candidate hub genes obtained by each of the seven algorithms. Dot represent this gene was identified as candidate hub gene by the corresponding algorithm, ‘×’ means that the gene was not identified as a hub gene.

Table 4. Differential information on COPD hub genes in the ECC-BYF III + ER and model groups.

Gene name	LogFC (mx_kb)	LogFC (yy_mx)	P-value (yy_mx)	Reversal	
Ccl2	1.303	−1.557	2.25E-04	Y	
Ccl12	2.108	−1.781	2.84E-04	Y	
Ccl7	1.479	−1.820	2.71E-03	Y	
Ccr3	1.514	−1.256	4.57E-03	Y	
H2az1	0.653	−0.610	5.80E-03	Y	
Cxcl1	1.795	−0.888	1.10E-02	Y	
Apoe	1.046	−0.819	1.18E-02	Y	
Ccl4	1.179	−0.694	1.87E-02	Y	
Snrpf	0.702	−0.524	2.95E-02	Y	
Il1b	0.908	−0.626	4.14E-02	Y	
Mrpl22	0.781	−0.453	6.31E-02	Y	
Tap2	1.763	−1.406	6.50E-02	Y	
Mapk10	−2.101	1.688	7.66E-02	Y	
Hspa5	1.038	−0.398	1.68E-01	Y	
Calr	0.690	−0.289	2.03E-01	Y	
Ccl3	1.249	−0.404	2.41E-01	Y	
Cebpb	0.995	−0.280	2.42E-01	Y	
Cd14	0.747	−0.248	2.94E-01	Y	
Hsp90b1	0.768	−0.293	2.94E-01	Y	
Ccl5	0.823	−0.257	3.37E-01	Y	
Map3k5	−0.821	0.294	3.77E-01	Y	
Hspd1	0.584	−0.168	4.48E-01	Y	
Il1a	−2.498	−0.257	5.53E-01	N	
Cd28	−2.237	0.460	6.96E-01	Y	
Pf4	1.042	0.116	7.07E-01	N	
Hsp90ab1	0.752	−0.099	7.29E-01	Y	
RT1-S3	0.596	−0.068	7.82E-01	Y	
Ppargc1a	−1.611	−0.253	7.86E-01	N	
Gnai1	−1.411	0.134	8.05E-01	Y	
Gli1	−1.365	0.154	8.57E-01	Y	
Pik3r3	−1.810	−0.108	8.85E-01	N	
Pik3r1	−0.610	−0.025	9.21E-01	N	
Foxo3	−1.095	0.025	9.60E-01	Y	
Gli3	−2.675	−0.016	1.00E + 00	N	
Note: logFC (yy_mx) denotes the logFC value for gene measurement in the ECC-BYF III + ER group compared with the model group. logFC (mx_kb) indicates the logFC value for gene measurement in the model group compared with the control group; P-value (yy_mx) refers to the statistical significance of the gene in the ECC-BYF III + ER group compared with the model group using edgeR algorithm. Reversal denotes the direction of the gene with respect to whether it is reversed or not when a COPD rat was treated with ECC-BYF III + ER..

3.4. Validation of hub genes using independent datasets, molecular docking, and qRT-PCR experiment

Independent high-throughput gene sequencing data, including six control and six COPD rat model samples, from our laboratory revealed that the identified eight genes were also significantly upregulated (edgeR, p < 0.01), as shown in the Figure 5 and Table 5. In addition, these genes presented a direction consistent with the above data analysis results. Moreover, the ROC analysis showed that the eight candidate hub genes had excellent classification efficiency, and the area under the curve (AUC) ranging from 0.86 to 1, as shown in Figure 6.

Table 5. Validation of hub genes in independent data.

Gene name	Our_data	GSE178513	
P-value(edgeR)	Direction	P-value(ANOVA)	Direction	
Ccl4	1.17E-08	up	1.25E-02	up	
Ccl2	2.00E-08	up	1.41E-01	up	
Cxcl1	1.69E-06	up	8.81E-03	up	
Ccr3	2.11E-06	up	1.15E-01	up	
Il1b	7.50E-04	up	1.42E-04	up	
Apoe	2.44E-05	up	3.99E-02	up	
Ccl7	1.05E-08	up	6.90E-02	up	
Ccl12	2.27E-03	up	3.30E-01	up	
Note: Our_data denotes the COPD rat RNA-seq data (6 normal vs 6 model) from our laboratory; Direction denotes the dysregulated direction of gene measurement in the model group compared with the control group. P-value indicates the statistical significance of the corresponding gene in the model group compared with the control group using edgeR or ANOVA algorithm.

Figure 5. Validation of hub genes in independent COPD rat data from our laboratory (6 vs 6 samples). ** indicates p < 0.001, * indicates p < 0.01.

Figure 6. ROC curves of eight hub genes in COPD rat data from our laboratory.

For the independent dataset GSE178513 (including three control and three COPD rat samples), the ANOVA algorithm showed that all the eight genes were upregulated, proving the reliability of the identified genes. Four genes (Cxcl1, Ccl4, Apoe, and Il1b) were significantly upregulated (ANOVA, p < 0.05), Ccr3 (p = 0.11), Ccl7 (p = 0.069), Ccl2 (p = 0.14) and Ccl12 (p = 0.33) showed weak statistical significance. The findings may be ascribed to insufficient statistical power due to small sample size. The results are shown in Figure 7 and Table 5. Moreover, the result of ROC analysis showed the better classification of the eight genes in the GSE178513 dataset, with AUC values ranging from 0.67 to 1, as shown in Figure 8.

Figure 7. Validation of eight hub genes in the independent dataset GSE178513 (3 vs 3 samples). ** indicates p < 0.001, * indicates p < 0.05.

Figure 8. ROC curves of the eight hub genes in the dataset GSE178513.

Lower binding energy is associated with more stable structures. Binding energy less than −5 kJ/mol−1 indicates that the gene and active ingredient have binding activities [44]. Molecular docking results revealed that the eight genes (Il1b, Ccl2, Cxcl1, Ccr3, Ccl4, Apoe, Ccl7, and Ccl12) can effectively be combined with the active ingredients of ECC-BYF III. Astragaloside IV, 20-S-ginsenoside Rh1, nobiletin, and icariin can be combined effectively with the eight genes, with binding energy ranging from −5.1 kJ/mol−1 to −9.4 kJ/mol−1. Paeonol bonded strongly to Ccr3 but weakly to other genes, as shown in Table 6 and Figure 9.

Table 6. Molecular docking results of ECC-BYF III active ingredients and hub genes.

Gene name	20-S-ginsenoside Rh1(kJ/mol−1)	Astragaloside IV(kJ/mol−1)	Nobiletin (kJ/mol−1)	Icariin (kJ/mol−1)	Paeonol (kJ/mol−1)	
Ccl2	−5.9	−6.6	−5.5	−6.4	−4.5	
Ccl4	−6.7	−7	−5.6	−6.4	−4.4	
Ccr3	−9.2	−8.9	−5.5	−6.2	−6.2	
Cxcl1	−5.6	−6.2	−5.3	−6.2	−4.3	
Il1b	−8.7	−8.9	−7	−9.4	−5.1	
Apoe	−6.8	−6.6	−5.5	−7	−4.5	
Ccl7	−6.4	−6.7	−5.7	−6.3	−4.3	
Ccl12	−7.1	−7	−6	−6.9	−4.3	

Figure 9. Visual results of molecular docking of hub genes and the active components of ECC-BYF III. Yellow represents the target protein of the hub gene, and blue denotes the active component of ECC-BYF III.

The eight genes were also verified through qRT-PCR experiments. The expression levels of Ccl2, Ccl7, Cxcl1, Ccl12, Il1b, Apoe, and Ccl4 genes were upregulated (model group vs. control group), and they were then downregulated after the treatment with ECC-BYF III + ER (ECC-BYF III + ER group vs. model group), consistent with the results of data analysis, as shown in Figure 10. Ccl2, Ccl7, Cxcl1 and Ccl12 showed statistical significance in both groups (model group vs. control group and ECC-BYF III + ER group vs. model group). Il1b showed significantly different expression levels in the model and control groups, and those in the ECC-BYF III + ER and model groups exhibited a weak statistically significant difference (pyy_mx = 0.052). Similarly, difference in the expressions level of Ccl4 between the model and control groups showed weak statistical significance (pmx_kb = 0.06); the difference of its expressions in the ECC-BYF III + ER and model groups tended to be significant (pyy_mx = 0.274). Apoe tended to be significant difference in the model and control groups (pmx_kb = 0.48), and significantly different expressions was noted in the ECC-BYF III + ER and model groups. Hence, Ccl2, Ccl7, Cxcl1, Ccl12, Il1b, Apoe, and Ccl4 were considered hub genes, which should be further explored in the subsequent project.

Figure 10. qRT-PCR experiment of COPD hub genes. (a) Results of the qRT-PCR experiment for Ccl2, (b) Ccl7, (c) Cxcl1, (d) Il1b, (e) Ccl12, (f) Ccl4, (g) and represents Apoe; * or # indicates p < 0.2; ** or # # indicates p < 0.05; the crossbar indicates the degree of data discretization; YY denotes ECC-BYF III + ER group.

4. Discussion

COPD has high morbidity and mortality and causes serious damage to the physical and mental health of patients [45]. Lung transplantation is an effective treatment strategy for patients with end-stage COPD, who are suitable for surgical indications, which can improve quality of life and survival rate. However, the treatment it also has the disadvantages of high cost, insufficient lung source, and chronic lung allograft dysfunction [46, 47]. Currently, inhaled corticosteroids, antibiotics, bronchodilators, etc. are the conventional therapeutic drugs for COPD patients. Although these drugs can effectively reduce the frequency of acute exacerbations and alleviate lung inflammation [48–50], their side effects cannot be neglected. For example, inhaled cortisol hormones can affect microbial changes of the lung tissues in COPD patients with stable phase and increase the probability of severe pneumonia and pulmonary bacterial infection [51]. A combination therapy involving long-acting muscarinic antagonists or long-acting beta-agonists may increase the incidence of cardiovascular complications [52]. Inhaled corticosteroids are associated with cardiovascular adverse events and increased the incidence of pneumonia [52, 53]. In addition, some non-drug therapies, including home oxygen therapy, and lung volume reduction surgery have been administered to patients with COPD [14], but they are expensive.

Alternatively, TCM has a good effect on alleviating the symptoms of COPD, delaying the disease process, improving the quality of life of patients, and having few side effects [54, 55]. BYF is a compound found in 12 kinds of Chinese herbs. It provides beneficial effects on the improvement of lung function, inhibition of inflammatory response, and reduction of the number of acute episodes in patients with COPD [56]. However, the complex composition of BYF compound hinders the in-depth study of its mechanism. Based on systematic pharmacology, and through in vitro and in vivo experiments, the main active components of BYF were optimized to obtain ECC-BYF I, ECC-BYF II, and finally ECC-BYF III was obtained. ECC-BYF III is composed of five active components (astragaloside IV, 20-S-ginsenoside Rh1, paeonol, nobiletin, and icariin), whose efficacy was equivalent to that of BYF [20, 21]. It presented a good effect on improving lung function, reducing airway mucus secretion and inflammatory response in COPD rats [31, 57].

Moreover, psychological problems, such as depression and anxiety caused by COPD, aggravates dysfunction and increase the risk of death from COPD [58]. ER has a certain regulatory effect on the psychological state of COPD [59]. In addition, ER can effectively improve lung function, reduce respiratory rate, and increase exercise endurance in patients with COPD [24–26]. Langer et al. [60] revealed that inspiratory muscle training can considerably improve dyspnea, functional exercise capacity and inspiratory muscle endurance in COPD patients. According to Wada et al. [26], combining aerobic training with inspiratory muscle training can reduce the symptoms of dyspnea and improve lung function in COPD patients. Given the distinct advantages of ECC-BYF III and ER, exploring the efficacy of their combination therapy is of great significance. ECC-BYF III + ER may be beneficial and may improve lung function, pathology and mucus secretion in COPD rats, but its interventional mechanism has not been clearly elucidated [22]. Thus, the therapeutic effects of ECC-BYF III + ER on COPD rat and its interventional mechanism was further explored in this study.

In view of the unique advantages of GSEA method for small sample data, this study demonstrated the superior performance of GSEA method to those of ORA method. GSEA identified 49 COPD-related pathways, of which 34 were significantly reversed after the treatment of COPD rats with ECC-BYF III + ER. Among the genes in the above 34 reversal pathways, seven COPD hub genes were identified and verified using the PPI network, molecular docking, independent datasets, and ROC analysis. These seven genes were also verified via the qRT-PCR experiment.

Some of the identified 34 pathways that can be regulated by ECC-BYF III + ER, were reported to be related to COPD or inflammation and immune function. For example, ROS plays an important role in inducing oxidative stress and promoting chronic inflammation in COPD, and its sources include endogenous and exogenous ROS [61]. Endogenous ROS is a by-product of ATP produced by the oxidization of phosphate in mitochondria [62, 63]. Smoke exposure causes insufficient oxidative phosphorylation, and defects in oxidative phosphorylation lead to ROS accumulation, which further promotes lung inflammation in COPD [37]. Sergio et al. [38] found that oxidative stress in COPD can induce DNA damage, and DNA repair may play a role in reducing the mortality and morbidity of COPD. Some genes in the peripheral blood of patients with COPD after exercise were related to DNA replication and oxidative phosphorylation biological processes [64]. Wnt signaling pathway is related to lung development and pulmonary fibrosis. It is stimulated by smoke, regulates the expression of inflammatory genes, and affects lung inflammation and airway remodeling in COPD. Interventions altering Wnt signaling pathway regulatory genes can play a positive role in the alleviation of COPD symptoms [39]. Cigarette smoke enhances the expression of IL-17, IL-17, and TNF-α, and induces epithelial cell injury [33]. MiR-937 can reduce smoke-induced lung inflammation through the IL-17 signaling pathway [33]. Pei et al. [36] noted that exosomes in the plasma of COPD patients are related to immune mechanisms underlying COPD and substantially enriched in viral protein interactions with cytokines and the cytokine receptor pathway. Cigarette smoke extract induces RANKL/RANK expression in C2C12 myotubes in mice and participates in COPD-associated skeletal muscle dysfunction through the activation of the RANK-induced ubiquitin-proteasome pathway [65]. Ambrocio-Ortiz et al. [66] reported the association of heat shock family with environmental pollutants that stimulate antigen presentation and the association of its gene polymorphism with chronic respiratory diseases, such as COPD. These results indicated the possible therapeutic effects of ECC-BYF III + ER through regulation of these reversal pathways. Among the genes in the reversal pathways, hub genes were further identified in this research.

Some studies have reported that the hub genes were closely related to the occurrence and development of COPD. Compared with airway epithelium, lung tissue, sputum, and blood, Il1b gene is highly expressed in the small airway epithelium in the patient with COPD [40, 67, 68]. The polymorphism of the Il1b gene is associated with susceptibility to COPD, and some of the alleles (−511 C/T, −31 T/C) are racially specific to COPD susceptibility [40, 67, 68]. Ccl2, Ccl4, Cxcl1, Ccl7, and Ccl12 can collectively act as chemokines to mediate airway inflammation in COPD. Ccl2 can mediate negative feedback and the cascade reaction of airway inflammation in COPD [40, 69, 70]. Stimulated by cigarette smoke, CD4+ T cells can produce a large number of Ccl2 and Ccl3. The increased Ccl2 in the sputum and serum of COPD can recruit monocytes that can be activated into macrophages, which participate and aggravated in the inflammatory responses [71, 72]. The expression of Ccl4 in the COPD serum increases after stimulation by environmental ultrafine particles [41]. Ccl4 stimulates the expression and secretion of T cells and monocytes, and these processes are positively correlated with the severity of airflow limitation in COPD [73]. Airway smooth muscle cells and alveolar epithelial cells of COPD are stimulated by cigarette smoke, which promotes the expression of Cxcl1, and Cxcl1 acts as an inflammatory chemokine to recruit neutrophils to promote COPD inflammation [74, 75]. Patients with COPD show increased expression of Ccl7, which can be induced by macrophages [42]. Libby amphibole inhibits the ERK signal transduction upstream of NALP3 inflammasome, promotes the expression of CCL7, CCL12, CXCL3 and COX2, and promotes the production of fibrosis [76]. Ccr3 is a receptor of inflammatory chemokines, and mediates the chronic airway inflammation of COPD by recruiting immune cells, such as eosinophils, macrophages, and dendritic cells [43]. An interaction between Apoe allele rs429358 and smoking factors was found in COPD patients [77]. Apoe may indirectly induce inflammation by activating the TLR2/4 signaling pathway and participate in the occurrence and development of COPD [78]. Therefore, the function of these genes should be further explored.

ORA identified three DEGs in the model group compared with the control group (edgeR, FDR < 0.05). No significantly disturbed pathway was identified at the same threshold. At a relatively loose threshold (p < 0.05), 871 DEGs and 25 significantly disturbed pathways were identified. The high-throughput data analysis, used p-value as a relatively loose threshold, which possibly resulted in high false positives. To overcome the limitation of ORA, which requires obtaining DEGs in advance and is unsuitable for small samples, we used GSEA for pathway analysis in this study. When FDR < 0.05, 49 significant disturbed pathways were identified in the model group compared with the control group; of these pathways, nine were identified by ORA, and such result is unlikely to be coincidental (hypergeometric test, p < 0.05). The above results demonstrated the reliability of ORA and GSEA, GSEA showed a higher statistical efficiency and was more suitable for small sample data.

In summary, GSEA offset the limitation of the preacquisition of DEGs for ORA and is suitable for small sample data, resulting in reliable results. Among the genes of reversal pathways, seven COPD hub genes were identified and verified via molecular docking, independent data analysis, ROC analysis, and qRT-PCR experiments after treatment with ECC-BYF III + ER. The functions of these pathways and genes in COPD deserved further studies.

5. Conclusions

Given the distinct merits of GSEA and the efficacy of TCM (ECC-BYF III + ER), we first demonstrated that GSEA has a higher statistical efficiency than ORA and is suitable for small sample data. Then, a total of 49 COPD-related pathways were identified, with 34 of these pathways exhibited reversal after ECC-BYF III + ER treatment. Based on the genes involved in the 34 reversal pathways, our COPD rat RNA-seq data and PPI network, Il1b, Ccl2, Cxcl1, Apoe, Ccl7, Ccl12, and Ccl4 were identified as COPD hub genes, and they were verified in other independent COPD rat data from our laboratory and another public dataset (GSE178513). The reliability of the seven genes were further confirmed through qRT-PCR experiments. The findings of this study may provide insights into the treatment and mechanism underlying COPD.

ARRIVE guidelines statement

We have adhered to ARRIVE guidelines.

Supplementary Material

Supplemental Material

Authors contributions

All authors contributed to the study conception and design. LDH analyzed the data, created Figures 1–6 and 10, performed data analysis, and wrote the manuscript. QZG performed data analysis, wrote the manuscript, and checked and revised the work. ZZZ performed the data analysis and created Figures 7–9. CLL performed data analysis. YGT and RLL developed the COPD rat model and performed corresponding animal experiments. JSL and YGT helped write the manuscript and checked the work. All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

The animal study protocol was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (YFYDW2019031).

Data availability statement

The data supporting the findings of this study are available upon request from the corresponding author.
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