Mechanistic Insights into the Effect of Yiqi Zishen Formula on Chronic Obstructive Pulmonary Disease: A Multiomics Integration Study

Abstract

Objective

This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula (YZF) in chronic obstructive pulmonary disease (COPD) management.

Methods

A COPD rat model was established through exposure to tobacco smoke and Klebsiella pneumoniae infections from weeks 1 to 8, followed by treatment with YZF from weeks 9 to 20. No treatment was administered from weeks 21 to 31. At week 32, all rats were euthanized, and lung tissue samples and blood specimens were collected for subsequent analyses. Then, comprehensive multiomics profiling—encompassing transcriptomics, proteomics, and metabolomics—was conducted to identify differentially expressed molecules in lung tissues and elucidate the underlying molecular mechanisms.

Results

By week 32, sustained therapeutic efficacy became apparent, characterized by diminished inflammatory cytokine expression, mitigation of protease–antiprotease dysregulation, and reduced collagen deposition. These differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity, antioxidant homeostasis, focal adhesion, tight junction formation, adherens junction dynamics, and lipid metabolism regulation. Integrative analysis of predicted targets, transcriptomic, proteomic, and metabolomic datasets revealed that differentially expressed molecules in YZF-treated rats and YZF-targeted proteins collectively participated in lipid metabolism, inflammatory responses, oxidative stress, and focal adhesion pathways.

Conclusion

YZF provides sustained therapeutic benefits in COPD rat models, potentially through systemic regulation of lipid metabolism, inflammatory responses, oxidative stress, and focal adhesion pathways.

 

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Keywords

chronic obstructive pulmonary disease - Yiqi Zishen Formula - multiomics profiling - system mechanism

Introduction

Chronic obstructive pulmonary disease (COPD) poses a significant societal challenge, marked by its high prevalence, increasing incidence, and profound socioeconomic impact. Despite advancements in current therapeutic modalities, their clinical efficacy remains suboptimal.[1] [2] Traditional Chinese medicine (TCM) formulations have been extensively utilized in China for millennia to manage COPD. A defining feature of these formulas is their diverse bioactive components, which can modulate multiple molecular pathways, resulting in synergistic and sustained therapeutic effects. COPD is referred to as lung distention (Feizhang disease) in TCM. Lung–kidney qi and yin deficiency is one of the important syndrome types of COPD, which included panting and shortness of breath, lassitude, being prone to catching a cold, weakness in the lower back and knees, tinnitus and dizziness, a dry cough or scanty sputum and difficult in spitting, spontaneous sweating or night sweating, feverishness in the palms and soles, a pale or red tongue with thin and little fur, and a thready pulse, thready weak pulse, or thready rapid pulse. Yiqi Zishen Formula (YZF, patent: ZL 201210277150.8), a TCM prescription comprising 13 medicinal herbs, has demonstrated clinical efficacy in COPD patients with lung–kidney qi and yin deficiency.[3] In our previous systems pharmacology investigation, we identified 158 bioactive constituents in YZF and their 192 potential targets, primarily associated with inflammatory response activation, immune modulation, and matrix metalloproteinase regulation.[4] Additionally, we administered YZF to COPD rat models during weeks 9 to 20 and observed short-term therapeutic efficacy by week 20. Nevertheless, the sustained therapeutic profile and mechanistic basis of YZF in COPD management remain incompletely understood. To address this gap, we employed systematic approaches to investigate the long-term therapeutic effects and underlying mechanisms of YZF in COPD treatment.

Fortunately, integrative systems technologies—encompassing transcriptomics, proteomics, metabolomics, and systems pharmacology—capture the complexity of biological systems to offer a holistic framework for investigating TCM formulas through systems biology.[5] Transcriptomics, often termed genome-wide expression profiling, systematically characterizes the complete set of mRNA transcripts produced within cells.[5] [6] In parallel, proteomics enables large-scale profiling of protein species, allowing concurrent analysis of all proteins expressed in a cell or tissue. This field derives its name by analogy to transcriptomics, serving as a comprehensive tool to study protein functions, particularly their roles in biological processes.[7] Metabolomics, as a technique for detecting small-molecule diversity in cellular environments, provides metabolic insights that mirror the functional outputs of the transcriptome and proteome.[8] Significantly, systems pharmacology effectively elucidates the dynamic interplays between pharmaceutical agents and biological systems via the integration of systems biology with pharmacokinetic and pharmacodynamic principles.[5] [9] [10] Collectively, the integration of these approaches holds significant promise for elucidating the intricate therapeutic mechanisms of TCM formulas.[11]

In this study, COPD rats were treated with YZF from weeks 9 to 20, and its long-term therapeutic effects were evaluated at week 32. We executed extensive multiomics characterizations—including transcriptomics, proteomics, and metabolomics—on lung tissues derived from both COPD rats and YZF-treated controls. Subsequently, we integrated these multiomics datasets with systems pharmacology data to comprehensively decipher the systemic mechanisms underlying YZF's long-term therapeutic effects in COPD rat models.

 

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Materials

Animals and Microbial Strain

Klebsiella pneumoniae (strain ID: 46114) was obtained from the National Center for Medical Culture Collection (Beijing, China). A total of 42 Sprague-Dawley rats (21 males and 21 females), body weight (200 ± 20)g, were procured from the Experimental Animal Center of Henan Province (Zhengzhou, China, animal certification number: SCXK (YU) 20050001). These animals were housed in a controlled environment with a constant temperature of (25 ± 2)°C, a 12-hour light–dark cycle, and ad libitum access to food and water. All animal care protocols and housing conditions were approved by the Experimental Animal Care and Ethics Committee of the First Affiliated Hospital, Henan University of Chinese Medicine.

 

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Chemicals

The tobacco product (Hongqi Canal filtered cigarette; tar content: 10 mg; nicotine: 1.0 mg; carbon monoxide: 12 mg, Henan tobacco industry, Zhengzhou, China). Aminophylline (Shandong Xinhua Pharmaceutical Co., Ltd., Zibo, China, Lot No: 100810). Antibodies specific to interleukin-1 β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), soluble tumor necrosis factor receptor 2 (sTNFR2), collagen type I (Col I), collagen type III (Col III), collagen type IV (Col IV), matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), and tissue inhibitor of metalloproteinase-1 (TIMP-1) (Boster biological technology, Inc., Wuhan, China, Catalog number: BA14789, BA4339, BA0131, BA1438, BA0325, M00788, A01411, MA00286, BA2202, A00561). Mayer's hematoxylin solution, 1% eosin–alcohol solution (MUTO Pure Chemicals, Tokyo, Japan, Catalog number: 30002, 32002). Agilent Quick Amp Labeling Kit (Agilent Technologies, Santa Clara, California, United States, Catalog number: 5190-2305).

 

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Instrument

Unrestrained whole-body plethysmography system (Buxco Inc., Wilmington, North Carolina, United States, instrument model: PLY3211); Olympus light microscope (Olympus, Tokyo, Japan, instrument model: BX51); Agilent DNA Microarray Scanner (Agilent Technologies, Santa Clara, California, United States, instrument model: G2505B); Electronic balance (Sartorius, Germany, instrument model: BS210S); Automatic slicer (Leica, Germany, instrument model: LEICARM 2145); Animal breeding enclosures (Suzhou Fengshi, China, instrument model: IVC-II).

 

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Methods

Chronic Obstructive Pulmonary Disease Model and Drug Administration

The YZF formula comprised the following herbal components: Ginseng Radix et Rhizoma (9 g), Polygonati Rhizoma (15 g), Schisandrae Chinensis Fructus (9 g), Lycii Fructus (12 g), Rehmanniae Radix (15 g), Fritillariae Thunbergii Bulbus (9 g), Moutan Cortex (12 g), Perillae Fructus (9 g), Stemonae Radix (9 g), Citri Reticulatae Pericarpium (9 g), Ophiopogonis Radix (15 g), Cinnamomi Cortex (3 g), and Pheretima (12 g). These herbal ingredients were authenticated and formulated into a fluid extract following standardized protocols established by the Department of Pharmaceutics, First Affiliated Hospital of Henan University of Traditional Chinese Medicine (Zhengzhou, China).[4]

The COPD rat model was established as previously described.[12] Briefly, 32 rats were maintained in a sealed chamber and subjected to concurrent tobacco smoke exposure and repeated Klebsiella pneumoniae infections. By the ninth week, the COPD rats exhibited a significant reduction in tidal volume (TV), peak expiratory flow (PEF), and expiratory flow at 50% of TV (EF50), confirming the successful establishment of the COPD model. Furthermore, two rats were sacrificed to examine the pathological changes in their lung tissues to verify the success of the model establishment. Thirty COPD rats were randomly allocated into three experimental groups. From week 9 to 20, rats in each group received daily oral gavage with either normal saline (2 mL), YZF (4.44 g/kg, 0.5 g/mL), or aminophylline (2.3 mg/kg). Control animals underwent identical treatment with normal saline (2 mL) over the same period. No treatment was administered from weeks 21 to 31. At week 32, all rats were euthanized, and lung tissue samples and blood specimens were collected for subsequent analyses.

 

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Pulmonary Function Analysis

Pulmonary function assessments were performed using an unrestrained whole-body plethysmography system, with measurements conducted at 4-week intervals from week 0 to week 32. For each test, rats were positioned within a sealed, unrestrained plethysmograph chamber. Respiratory function parameters were transduced into electrical signals via a pressure transducer and amplifier, then digitized and processed using specialized software. Key metrics—including TV, PEF, and EF50—were subsequently extracted and analyzed to evaluate lung function dynamics over the study period.

 

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Histological Analyses

Lung tissue specimens were collected for histological and immunohistochemical analyses. Specimens were fixed in 10% neutral-buffered formalin, embedded in paraffin, and sectioned into 4-μm-thick slices using a microtome. Tissue sections were stained with hematoxylin–eosin (H&E), and morphological alterations were assessed using an Olympus light microscope, with specific focus on bronchial architecture, lung parenchymal injury, and bronchiolar stenosis under light microscopy. All morphometric analyses were conducted by an investigator blinded to the experimental groups to ensure objectivity.

 

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Immunohistochemical Staining

Antigen retrieval was conducted using heat-induced epitope retrieval in citrate buffer (pH 6.0), followed by quenching of endogenous peroxidase activity with 3% hydrogen peroxide. Sections were then incubated with primary antibodies, including IL-1β (1:200), IL-6 (1:200), TNF-α (1:200), sTNFR2 (1:100), Col I (1:150), Col III (1:150), Col IV (1:150), MMP-2 (1:100), MMP-9 (1:100), TIMP-1 (1:150) overnight at 4°C in a humidified chamber. After thorough washing, sections were treated with species-specific secondary antibodies (1:500) conjugated to horseradish peroxidase (HRP) for 1 hour at room temperature. Antigen–antibody complexes were visualized using a DAB chromogenic substrate kit, which produced a distinct brown precipitate at the binding sites of HRP. Finally, sections were counterstained with hematoxylin to enhance nuclear contrast. Image analysis of immunohistochemical staining intensity was performed using Image-Pro Plus (IPP) 6.0 software to quantify protein expression levels.

 

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IL-1β, IL-6, TNF-α, and sTNFR2 Level Determination

Blood was harvested from rats and processed via centrifugation at 3000 r/min for 20 minutes to isolate serum. The resultant sera were flash-frozen and maintained at −80°C until analysis. Concentrations of IL-1β, IL-6, TNF-α, and sTNFR2 in serum were measured using enzyme-linked immunosorbent assay (ELISA) kits, with procedures strictly following the manufacturer's instructions.

 

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Gene Expression Analysis

Total RNA was isolated and purified from lung tissue samples. Following reverse transcription and amplification, RNA was tagged with the Agilent Quick Amp Labeling Kit and hybridized onto Agilent Whole Rat Genome Oligo Microarrays (4 × 44K format). Posthybridization, microarray slides underwent washing and scanning using an Agilent DNA Microarray Scanner. Raw data were analyzed using Agilent GeneSpring GX software v.11.0, differentially expressed gene analysis between two sample groups was performed using Student's t-test and fold change (FC). In this study, these genes were defined as significantly differentially expressed genes when p < 0.05 and |log₂FC| > 1.

 

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Protein Expression Analysis

Proteomic profiling was performed using a previously validated protocol. Briefly, lung tissue samples were homogenized in a lysis buffer using a mechanical homogenizer. The resulting homogenates were clarified by centrifugation to remove cellular debris, followed by trypsin digestion to generate peptide mixtures. Tryptic peptides were then labeled with 8-plex isobaric tags for relative and absolute quantitation reagents, and were separated by liquid chromatography–tandem mass spectrometry (LC–MS/MS). Proteomic data processing included normalization via loess and global median methods, followed by log₂ transformation. Similar for gene expression analysis, differentially expressed protein analysis was also conducted via Student's t-test (p < 0.05) and fold change (|log₂FC| > 1).

 

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Metabolites Analysis

Lung tissue samples were homogenized in ice–cold methanol/water (4:1, v/v) using a mechanical homogenizer, followed by sonication to facilitate lysis. The homogenates were clarified via centrifugation to remove cellular debris, and the resulting supernatants were lyophilized and resuspended in methanol/water (4:1, v/v) for subsequent analysis. Metabolic profiling of tissue lysates was conducted, with raw datasets exported using Agilent Mass Hunter Qualitative Analysis Software (Agilent Technologies, Palo Alto, California, United States). Statistical significance between groups was assessed via Student's t-test and one-way analysis of variance (ANOVA) across three biological replicates, with p < 0.05 considered statistically significant.

 

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Integrative Bioinformatics Analysis of Multiomics Data

Functional annotations of differentially expressed genes and proteins were conducted using Cytoscape v3.1.1 plugins, including BINGO and CluGO.[13] [14] Pathway enrichment analysis was performed with the DAVID and KEGG databases to identify significantly regulated pathways (p < 0.05). Integrated multiomics pathway analysis, combining gene, protein, and metabolite datasets, was executed via Metscape.[12] Key metabolite-associated pathways were further explored using MetaboAnalyst 3.0 to prioritize biologically relevant metabolic signatures.[15]

 

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Statistical Analysis

Statistical differences between groups were analyzed using one-way ANOVA with the SPSS 19.0 software package (IBM Corp., Armonk, New York, United States). Data are expressed as mean ± standard error of the mean. A two-tailed p-value of less than 0.05 was considered statistically significant for all tests.

 

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Results

Long-Term Effect of Yiqi Zishen Formula on Chronic Obstructive Pulmonary Disease Rats

Previous studies have confirmed the therapeutic efficacy of YZF in COPD patients and its short-term effects in COPD rodent models. This study aimed to investigate the sustained therapeutic effects of YZF in COPD rat models. YZF was administered to COPD rats from weeks 9 to 20, with pulmonary function evaluations conducted from weeks 0 to 32 and histological analyses performed at week 32. As shown in [Fig. 1], YZF administration significantly improved pulmonary function parameters—including TV, PEF, and forced expiratory flow at 50% vital capacity (EF50). Additionally, YZF treatment significantly alleviated pathological changes, as depicted in [Fig. 2]. Key histological indices, such as lung injury scores, bronchiole wall thickness, small pulmonary vessel wall thickness, bronchiole stenosis, and alveolar diameter—parameters elevated in COPD rats—were notably reduced by YZF treatment (p < 0.01, [Fig. 2A–F]). Notably, YZF treatment also mitigated the reduction in alveolar count (p < 0.05, [Fig. 2G]). Furthermore, amlodipine (APL), a well-established bronchodilator, showed comparable therapeutic outcomes to YZF. Collectively, these findings indicate that YZF exerts a sustained therapeutic effect in COPD rat models.

Fig. 1 Prolonged therapeutic efficacy of Yiqi Zishen Formula (YZF) on respiratory function in chronic obstructive pulmonary disease (COPD) rat models. (A) tidal volume (TV); (B) peak expiratory flow (PEF); (C) expiratory flow at 50% of tidal volume (EF50). Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01 indicating significance compared with the COPD model group. SEM, standard error of mean.

Fig. 2 Long-term impact of Yiqi Zishen Formula (YZF) on histopathological changes in chronic obstructive pulmonary disease (COPD) rat lung tissues. (A) Lung tissue morphology was assessed via hematoxylin–eosin (H&E) staining (×100). (B) Quantitative analyses of lung injury score. (C) Bronchiole stenosis. (D) Bronchial wall thickness. (E) Small pulmonary vessel wall thickness. (F) Alveolar count. (G) Alveolar diameter. Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01, indicating significance compared with the COPD model group. SEM, standard error of mean.

 

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Long-Term Effect on the Inflammatory Responses of Yiqi Zishen Formula on Chronic Obstructive Pulmonary Disease Rats

Inflammatory cytokines serve as central pathogenic drivers in the pathogenesis of immune-mediated pulmonary disorders, including COPD, and orchestrate diverse biological processes.[16] [17] To explore the sustained impact of YZF interventions, we examined the expression profiles of key inflammatory cytokines at the 32-week time point. As shown in [Fig. 3], both YZF and APL treatments markedly downregulated the expression of IL-1β, IL-6, TNF-α, and sTNFR2 in lung tissues by week 32(p < 0.01).

Fig. 3 Long-term effects of Yiqi Zishen Formula (YZF) on cytokine expression in chronic obstructive pulmonary disease (COPD) rat lung tissues. (A) Expression of IL-1β, IL-6, TNF-α, and sTNFR2 in lung tissues (×200) was detected via immunohistochemistry (IHC). (B) Quantification of IL-6, IL-1β, TNF-α, and sTNFR2 expression levels in lung tissues. Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01 indicating significance compared with the COPD model group. SEM, standard error of mean.

Systemic inflammation is increasingly recognized as a defining feature of COPD, where uncontrolled local pulmonary inflammation may trigger a “spillover” effect into the systemic circulation.[18] [19] To further assess this, we analyzed serum levels of these cytokines at week 32 and observed that YZF treatment potently suppressed their expression(p < 0.01),As shown in [Fig. 4]. These results collectively demonstrate that YZF exerts sustained inhibitory effects on both local (pulmonary) and systemic inflammatory responses in COPD rat models by week 32.

Fig. 4 Serum levels of inflammatory cytokines in chronic obstructive pulmonary disease (COPD) rats at week 32. (A) IL-1β. (B) IL-6. (C) TNF-α. (D) sTNFR2. Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01 indicating significance compared with the COPD model group. SEM, standard error of mean.

 

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Long-Term Effect on the Protease–Antiprotease Imbalance and Collagen Degradation of Yiqi Zishen Formula on Chronic Obstructive Pulmonary Disease Rats

Secreted proteases and antiproteases derived from respiratory epithelial cells are pivotal in preserving respiratory system homeostasis. Imbalances between these proteolytic enzymes and their inhibitors are increasingly recognized as key contributors to the pathogenesis of chronic respiratory disorders, including COPD.[20] In COPD, dysregulated collagen degradation—driven by persistent tissue injury—serves as a hallmark feature underlying the development of characteristic airflow limitation.[21] In this study, we evaluated the sustained impact of YZF on the expression dynamics of matrix metalloproteinases (MMP-2, MMP-9), tissue inhibitors of metalloproteinases (TIMP-1), and collagen subtypes (I, III, IV) within lung tissues. As illustrated in [Fig. 5], YZF treatment (alongside APL) significantly reduced protein levels of MMP-2 and MMP-9 (p < 0.05), while concurrently attenuating the decline in TIMP-1 expression (p < 0.01). Furthermore, YZF administration markedly downregulated the expression of collagen types I, III, and IV (p < 0.01, [Fig. 6]). Collectively, these results demonstrate that by modulating the expression of MMP-2, MMP-9, TIMP-1, and collagen subtypes I/III/IV, YZF effectively mitigates protease–antiprotease disequilibrium and collagen accumulation.

Fig. 5 The levels of matrix metalloproteinases (MMPs) and tissue inhibitor of metalloproteinases (TIMP-1) in chronic obstructive pulmonary disease (COPD) rat lungs. (A) Expression of MMP-2, MMP-9, and TIMP-1 in lung tissues (×200) was evaluated by immunohistochemistry (IHC). (B) Quantitative analysis. Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01 indicating significance compared with the COPD model group. SEM, standard error of mean.

Fig. 6 Long-term effects of Yiqi Zishen Formula (YZF) on collagen expression in chronic obstructive pulmonary disease (COPD) rat lungs. (A) Collagen I, III, and IV levels in lung tissues (×100) was analysis by immunohistochemistry (IHC). (B) Quantification of collagen I, III, and IV expression levels. Data are shown as mean ± SEM, with *p < 0.05 and **p < 0.01 indicating significance compared with the COPD model group. SEM, standard error of mean.

 

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Molecular Regulation Profiles in the Transcriptome, Proteome, and Metabolome of Lung Tissues

YZF therapy has demonstrated prolonged therapeutic efficacy in COPD rat models, potentially attributed to its persistent inhibitory actions on inflammatory cytokine expression, protease–antiprotease dysregulation, and collagen accumulation. To further understand the systemic long-term mechanisms of YZF, we employed multiomics approaches—including transcriptomics, proteomics, and metabolomics—to investigate molecular perturbations in lung tissues.

Using microarray-based RNA expression profiling, we identified approximately 41,000 expressed genes in lung tissue samples. Among these, 844 genes displayed significant differential expression in COPD model rats compared with controls, and 2,026 genes were differentially expressed in YZF-treated rats compared with COPD models ([Supplementary Tables S1], [S2], available in online version only). Functional annotation linked these differentially expressed transcripts to diverse molecular processes, such as oxidoreductase activity, metalloendopeptidase activity, intracellular ligand-gated ion channel function, and NF-κB binding ([Fig. 7A]). As shown in [Tables 1], [2], KEGG pathway enrichment analysis further classified these transcripts into critical biological pathways, including oxidative phosphorylation, focal adhesion, and the citrate cycle.

Fig. 7 Functional annotation of differentially expressed genes in the lung. (A) Enriched molecular functions of differentially expressed genes between the chronic obstructive pulmonary disease (COPD) group and the Control group. (B) Enriched molecular functions of differentially expressed genes between the Yiqi Zishen Formula (YZF)-treated group and the COPD group. Analyses were performed using ClueGO software, with nodes representing functional terms and node size reflecting enrichment significance.

Table 1 Analyzed pathways of transcriptomics data regulated in lung tissue of chronic obstructive pulmonary disease rats

Term	Count	%	p-Value
Ribosome	16	0.253	0
Ubiquitin-mediated proteolysis	12	0.1898	0.0008
Neurotrophin signaling pathway	11	0.174	0.0027
Spliceosome	10	0.1582	0.0067
Long-term potentiation	7	0.1107	0.0108
Oxidative phosphorylation	10	0.1582	0.0145
Nonsmall cell lung cancer	6	0.0949	0.0147
Renal cell carcinoma	6	0.0949	0.0437
Parkinson's disease	9	0.1423	0.0457
Focal adhesion	11	0.174	0.0469
GnRH signaling pathway	7	0.1107	0.048
Alzheimer's disease	11	0.174	0.0497

Table 2 Analyzed pathways of transcriptomics data regulated in lung tissue of Yiqi Zishen-treated chronic obstructive pulmonary disease rats

Term	Count	%	p-Value
Citrate cycle (TCA cycle)	9	0.068493151	4.74E − 04
Ribosome	15	0.114155251	7.74E − 04
Spliceosome	18	0.136986301	0.001867617
Prostate cancer	14	0.106544901	0.004894534
Protein export	4	0.0304414	0.016872311
N-Glycan biosynthesis	8	0.060882801	0.021595877
Fc epsilon RI signaling pathway	11	0.083713851	0.023722648
Oxidative phosphorylation	16	0.121765601	0.03068478
Huntington's disease	20	0.152207002	0.037587079

To characterize the proteomic landscape, we utilized liquid chromatography–mass spectrometry (LC-MS)-based proteomics to analyze lung tissue protein expression. This analytical approach identified 161 differential proteins in COPD model rats relative to control groups and 74 differential proteins in YZF-treated rats compared with COPD models (detailed in [Supplementary Tables S3], [S4], available in online version only). As illustrated in [Fig. 8], these proteins were predominantly linked to molecular functions including oxidoreductase activity, peroxiredoxin activity, and NAD binding. Pathway enrichment analysis further highlighted their participation in key biological networks such as ECM–receptor interaction, focal adhesion, tight junction, and leukocyte transendothelial migration (summarized in [Tables 3], [4]). Notably, 61 overlapping proteins were shared between the COPD model and YZF-treated groups. Among these, YZF modulated the expression of 31 proteins that were altered in COPD rats ([Supplementary Table S5], available in online version only). The 61 overlapping proteins were enriched in functions like oxidoreductase activity, antioxidant activity, lipase inhibitor activity, ATPase activity, and nucleoside-triphosphatase activity ([Fig. 8C]), which may underlie YZF's therapeutic effects.

Fig. 8 Functional analysis of differentially expressed proteins in the lung. (A) Enriched molecular functions of differentially expressed proteins between the chronic obstructive pulmonary disease (COPD) group and the Control group. (B) Enriched molecular functions of differentially expressed proteins between the Yiqi Zishen Formula (YZF)-treated group and the COPD group (generated via ClueGO). (C) Analysis of 61 overlap proteins between COPD (vs. Control) and YZF-treated (vs. COPD) groups was conducted using BiNGO.

Table 3 Analyzed pathways of proteomics data regulated in lung tissue of chronic obstructive pulmonary disease rats

Term	Count	%	p-Value
ECM–receptor interaction	8	0.3666	0.0002
Focal adhesion	11	0.5041	0.0006
Leukocyte transendothelial migration	7	0.3208	0.0069
Glycolysis/gluconeogenesis	6	0.275	0.0079
Propanoate metabolism	4	0.1833	0.0125
Pyruvate metabolism	4	0.1833	0.0196
Tryptophan metabolism	4	0.1833	0.0254
Valine, leucine, and isoleucine degradation	4	0.1833	0.0303
Small cell lung cancer	5	0.2291	0.0343
Regulation of actin cytoskeleton	8	0.3666	0.0348
Tight junction	6	0.275	0.0433
Nitrogen metabolism	3	0.1375	0.045
Pentose phosphate pathway	3	0.1375	0.0487

Table 4 Analyzed pathways of proteomics data regulated in lung tissue of Yiqi Zishen-treated chronic obstructive pulmonary disease rats

Term	Count	%	p-Value
ECM–receptor interaction	5	7.246376812	0.001080671
Focal adhesion	6	8.695652174	0.00458628
Prion diseases	3	4.347826087	0.016794242
Oocyte meiosis	4	5.797101449	0.024885963
Neurotrophin signaling pathway	4	5.797101449	0.034475688

Finally, we used LC-MS-based metabolomics to profile metabolic changes in lung tissues from COPD and YZF-treated rats. Compared with the control group, 41 different metabolites were identified in the COPD group. Compared with the COPD group ([Supplementary Table S6], available in online version only), 72 different metabolites were identified in the YZF-treated group ([Supplementary Table S7], available in online version only). Pathway enrichment and topological analyses using MetaboAnalyst indicated predominant involvement of lipid metabolism pathways, including arachidonic acid, linoleic acid, and glutathione metabolism ([Fig. 9], [Tables 5], [6]).

Fig. 9 Pathway analysis of the metabolites. Pathway maps were generated using MetaboAnalyst with an interactive Google Maps-style visualization platform. (A) Enriched pathway of differential metabolites between the chronic obstructive pulmonary disease (COPD) group and the Control group. (B) Enriched pathway of differential metabolites between the Yiqi Zishen Formula (YZF)-treated group and the COPD group.

Table 5 Analyzed pathways of metabolomics data regulated in lung tissue of chronic obstructive pulmonary disease rats

Term	Total	Expected	Hits	Raw p
Biosynthesis of unsaturated fatty acids	42	0.65906	6	2.62E − 05
Arachidonic acid metabolism	36	0.56491	4	0.0019285
Linoleic acid metabolism	5	0.078459	2	0.0022856
Sphingolipid metabolism	21	0.32953	3	0.0037176
Glycerophospholipid metabolism	30	0.47076	3	0.010353
Cyanoamino acid metabolism	6	0.094151	1	0.09069
α-Linolenic acid metabolism	9	0.14123	1	0.13304
Methane metabolism	9	0.14123	1	0.13304
Fatty acid biosynthesis	43	0.67475	2	0.1443
Steroid hormone biosynthesis	70	1.0984	2	0.3014
Cysteine and methionine metabolism	28	0.43937	1	0.36059
Glycine, serine, and threonine metabolism	32	0.50214	1	0.40061
Steroid biosynthesis	35	0.54922	1	0.42905
Primary bile acid biosynthesis	46	0.72183	1	0.52269
Aminoacyl-tRNA biosynthesis	67	1.0514	1	0.66232

Table 6 Analyzed pathways of metabolomics data regulated in lung tissue of Yiqi Zishen-treated chronic obstructive pulmonary disease rats

Term	Total	Expected	Hits	Raw p
Arachidonic acid metabolism	36	0.84736	7	1.06E − 05
Biosynthesis of unsaturated fatty acids	42	0.98859	5	0.0024129
Linoleic acid metabolism	5	0.11769	2	0.0051419
Glycerophospholipid metabolism	30	0.70613	3	0.0313
Steroid hormone biosynthesis	70	1.6476	4	0.078135
α-Linolenic acid metabolism	9	0.21184	1	0.19345
Vitamin B6 metabolism	9	0.21184	1	0.19345
Steroid biosynthesis	35	0.82382	2	0.19806
Purine metabolism	68	1.6006	3	0.2127
Fatty acid biosynthesis	43	1.0121	2	0.26864
Primary bile acid biosynthesis	46	1.0827	2	0.29533
Selenoamino acid metabolism	15	0.35307	1	0.3017
Pantothenate and CoA biosynthesis	15	0.35307	1	0.3017
Sphingolipid metabolism	21	0.49429	1	0.3958
Alanine, aspartate, and glutamate metabolism	24	0.56491	1	0.43812
Glutathione metabolism	26	0.61198	1	0.46472
Fatty acid elongation in mitochondria	27	0.63552	1	0.47755
Fatty acid metabolism	39	0.91797	1	0.61013
Arginine and proline metabolism	44	1.0357	1	0.65516
Aminoacyl-tRNA biosynthesis	67	1.577	1	0.80508

 

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Holistic Views on Gene, Protein, Metabolites, and System Pharmacology Data

System pharmacology has previously been employed to characterize the active compounds and potential targets of YZF ([Supplementary Table S8], available in online version only). In the current study, we identified a panel of differentially expressed genes, proteins, and metabolites among the control, COPD, and YZF-treated rats. To gain a holistic understanding of YZF's long-term therapeutic mechanisms in COPD, we integrated multiomics data encompassing system pharmacology, transcriptomics, proteomics, and metabolomics.

First, we utilized Metscape software to analyze the underlying correlations among metabolite, gene, and protein profiles. As illustrated in [Fig. 10A, B], two gene–metabolite networks were constructed based on transcriptomic and metabolomic data from COPD and YZF-treated rats, predominantly composed of lipid metabolism and purine metabolism pathways. In [Fig. 10C, D], protein–metabolite networks were developed using metabolomic and proteomic data from the same groups, primarily enriching lipid metabolism and glutathione metabolism pathways. Additionally, by integrating YZF's target proteins with differential metabolomics between YZF-treated and COPD rats, we constructed a target protein–metabolite network ([Fig. 10E]), mainly associated with lipid metabolism. Notably, over half of the identified genes/proteins and nearly all metabolites were implicated in lipid metabolism processes.

Fig. 10 Network correlations between regulated metabolites, genes, proteins, and Yiqi Zishen Formula (YZF) targets. (A) The interaction network of differential metabolites and differently expressed genes between the chronic obstructive pulmonary disease (COPD) group and Control group. (B) The interaction network of differential metabolites and differentially expressed genes between the YZF-treated group and COPD group; (C) The interaction network of differential metabolites and differential proteins between the COPD group and control group. (D) The interaction network of differential metabolites and differential proteins between the YZF-treated group and control COPD group; (E) The interaction network of differential metabolites between the YZF-treated group and control COPD group and the YZF's target proteins. Network interactions were visualized using MetScape, with nodes representing metabolites (circles), and enzymes (hexagons). Input gene/protein nodes are depicted in red, whereas input metabolite nodes are represented in blue.

Next, we observed direct molecular correlations between YZF's potential targets and differentially expressed transcripts/proteins. For example, 10 overlapping proteins—SOD1, COL1A2, CDK2, HSP90AA1, MAPK3, MAPK14, ADRA2B, ACHE, and OPRD1—were identified at the intersection of YZF target proteins and the differently expressed genes between the YZF-treated and COPD group. These proteins were functionally linked to oxidoreductase activity, antioxidant activity, MAP kinase signaling, adrenoceptor modulation, opioid receptor activity, and extracellular matrix (ECM) binding ([Fig. 11A]). Furthermore, eight overlapping proteins—CALM1, HSPA5, COL1A2, and SOD1—were shared between YZF targets and the differently expressed proteins between the YZF-treated and COPD group, participating in oxidoreductase activity, antioxidant defense, ECM structural organization, and caspase regulation ([Fig. 11B]).

Fig. 11 Functional characterization of overlapping proteins between Yiqi Zishen Formula (YZF) targets and omics data. (A) Overlap between YZF targets and lung transcriptomic data; (B) overlap between YZF targets and lung proteomic data. Analyses were performed using BiNGO, with node area reflecting the number of proteins in the test set.

 

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Discussion

TCM encompasses the clinical practices and theoretical frameworks for disease prevention and treatment developed in China. It conceptualizes the human body as a complex system, with a primary focus on holistic treatment methods. Chinese herbal formulas are the main modality for disease prevention and treatment within TCM. These formulas are characterized by their holistic and intricate nature, which poses significant challenges in elucidating their pharmacological mechanisms. First, Chinese herbal formulas consist of complex chemical systems that typically include a large number of compounds. Second, unlike most Western pharmaceuticals that are designed to selectively target a single biological entity, the majority of bioactive components in herbs may exert weak to moderate effects on multiple biological molecules. Consequently, conventional pharmacological analysis methods are insufficient for systematically elucidating the mechanisms of action of Chinese herbal formulas. In this study, we developed a systems-level framework to elucidate the systemic mechanisms and long-term therapeutic effects of YZF in COPD rat models through the integrated application of system pharmacology, transcriptomics, proteomics, and metabolomics. We administered YZF to COPD rats from weeks 9 to 20 and observed sustained therapeutic efficacy, characterized by the suppression of proinflammatory cytokine expression, resolution of protease–antiprotease imbalance, and reduction of collagen deposition. To systematically explore YZF's long-term mechanisms, we profiled the transcriptomic, proteomic, and metabolomic signatures of lung tissues from both COPD and YZF-treated rats and constructed a comprehensive schema of YZF's therapeutic mechanisms in COPD rat models. As shown in [Fig. 12], this systems-level framework is organized into four core modules: lipid metabolism, inflammatory response, oxidative stress, and cell junction pathways. Multiomics analyses (transcriptomics, proteomics, and metabolomics) revealed extensive involvement of lipid metabolism-related molecules. Key dysregulated pathways included arachidonic acid and linoleic acid metabolism, with YZF treatment significantly reducing levels of downstream metabolites such as linoleate, dihomo-γ-linoleate, arachidonate, LTA4, LTB4, 15-keto-PGF2α, and 15(s)-HETE. Notably, LTA4, LTB4, and arachidonate are known to drive airway inflammation in COPD. Concurrently, YZF was predicted to target rate-limiting enzymes in these pathways—including ALOX5, PTGS1/2, LTA4H, and AKR1C3—suggesting its anti-inflammatory effects may stem from modulating arachidonic acid metabolism. Beyond lipid metabolism, YZF demonstrated sustained inhibition of proinflammatory cytokines (IL-1β, IL-6, TNF-α) and their soluble receptor (sTNFR2) in both lung tissues and serum of COPD rats. Systems pharmacology analysis further highlighted ERK, p38, JNK, and NF-κB as putative YZF targets, implying that YZF may suppress cytokine expression by modulating the activation of these signaling pathways. COPD pathogenesis is linked to heightened oxidative stress or diminished antioxidant capacity, which perpetuates inflammatory cascades.[22] [23] Key antioxidants include glutathione, superoxide dismutase (SOD), catalase, glutathione peroxidase, and glutathione-S-transferase.[24] Our data showed YZF modulated glutathione metabolism, as evidenced by altered levels of L-ornithine (a metabolic intermediate), along with differential expression of related transcripts, proteins, and targets (GSR, GSTT2, GSTA4, GSTM1/2).[25] Notably, YZF upregulated SOD1, a critical antioxidant enzyme ([Supplementary Table S2], available in online version only), suggesting oxidative stress regulation contributes to its anti-inflammatory effects. Additionally, focal adhesion—a pathway overlapping with proteomic findings and YZF targets ([Tables 4], [7])—was significantly regulated by YZF. This involved modulation of MAPK1/3, MAPK8, and JUN (putative YZF targets), which are known to drive proinflammatory cytokine transcription.[26]

Fig. 12 Integrated multiomics network of Yiqi Zishen Formula (YZF) targets and regulatory changes in chronic obstructive pulmonary disease (COPD) rats. Potential YZF targets, along with transcriptomic, proteomic, and metabolomic data, are represented as color-coded rectangles. Regulation direction is indicated by arrow color: red (upregulated), blue (downregulated), and gray (unregulated).

Table 7 Analyzed pathways of the potential target of Yiqi Zishen formula

Term	Count	%	p-Value
Neuroactive ligand–receptor interaction	34	0.852343946	1.44E-13
Calcium signaling pathway	23	0.57658561	4.98E − 09
Pathways in cancer	29	0.726999248	1.80E − 07
hsa05215:prostate cancer	14	0.350965154	1.43E − 06
Amyotrophic lateral sclerosis (ALS)	11	0.275758335	2.28E − 06
Bladder cancer	10	0.250689396	2.44E − 06
Nonsmall cell lung cancer	10	0.250689396	2.16E − 05
Progesterone-mediated oocyte maturation	12	0.300827275	3.49E − 05
Gap junction	12	0.300827275	4.83E − 05
Metabolism of xenobiotics by cytochrome P450	10	0.250689396	5.16E − 05
Drug metabolism	10	0.250689396	6.73E − 05
Glioma	10	0.250689396	7.66E − 05
Colorectal cancer	11	0.275758335	1.48E − 04
Small cell lung cancer	11	0.275758335	1.48E − 04
Pancreatic cancer	10	0.250689396	2.20E − 04
VEGF-signaling pathway	10	0.250689396	3.01E − 04
NOD-like receptor signaling pathway	9	0.225620456	3.98E − 04
Prion diseases	7	0.175482577	4.60E − 04
GnRH signaling pathway	11	0.275758335	5.30E − 04
Insulin signaling pathway	13	0.325896215	5.33E − 04
Toll-like receptor signaling pathway	11	0.275758335	6.74E − 04
Apoptosis	10	0.250689396	9.13E − 04
Melanoma	9	0.225620456	0.001002876
T cell receptor signaling pathway	11	0.275758335	0.001140305
Thyroid cancer	6	0.150413638	0.001358222
Focal adhesion	15	0.376034094	0.002100847
Type II diabetes mellitus	7	0.175482577	0.002278586
Alzheimer's disease	13	0.325896215	0.002764619
ErbB signaling pathway	9	0.225620456	0.003713533
Complement and coagulation cascades	8	0.200551517	0.003796633
Endometrial cancer	7	0.175482577	0.003831741
Arginine and proline metabolism	7	0.175482577	0.004218593
Vascular smooth muscle contraction	10	0.250689396	0.005258359
Arachidonic acid metabolism	7	0.175482577	0.005552863
Tryptophan metabolism	6	0.150413638	0.005811579
Fatty acid metabolism	6	0.150413638	0.005811579
Neurotrophin signaling pathway	10	0.250689396	0.010094951
Adipocytokine signaling pathway	7	0.175482577	0.013128958
Propanoate metabolism	5	0.125344698	0.013450751
Oocyte meiosis	9	0.225620456	0.014822027
PPAR signaling pathway	7	0.175482577	0.015038812
Renal cell carcinoma	7	0.175482577	0.016062719
MAPK signaling pathway	15	0.376034094	0.023799282
Phenylalanine metabolism	4	0.100275758	0.02524371
β-Alanine metabolism	4	0.100275758	0.02524371
Fc epsilon RI signaling pathway	7	0.175482577	0.026035167
Graft-versus-host disease	5	0.125344698	0.026292672
Aldosterone-regulated sodium reabsorption	5	0.125344698	0.030957832
Tyrosine metabolism	5	0.125344698	0.038815492
Cytokine–cytokine receptor interaction	14	0.350965154	0.042450876
Long-term potentiation	6	0.150413638	0.048120417
p53 signaling pathway	6	0.150413638	0.048120417

Analyses of these profiles revealed that differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity, antioxidant defense, and lipid metabolism. Integrative analysis of multiomics data suggested that YZF mediates its sustained therapeutic effects in COPD rats through the coordinated regulation of lipid metabolism, inflammatory responses, oxidative stress, and focal adhesion pathways. However, the primary limitation of this study is the necessity for additional experimental validation, such as the significant modulation of genes, proteins, and metabolites in rats with chronic obstructive pulmonary disease treated with a TCM formula; this aspect will be addressed in future research.

 

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Conclusion

This work integrated use of transcriptomics, proteomics, metabolomics, and system pharmacology to demonstrate that YZF's long-term therapeutic efficacy in COPD arises from its multifaceted regulation of lipid metabolism, inflammatory responses, oxidative stress, and focal adhesion pathways.

 

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Conflict of Interest

The authors declare no conflict of interest.

CRediT Authorship Contribution Statement

Jiansheng Li: Conceptualization, funding acquisition, data curation, supervision, and writing-review & editing. Peng Zhao: Project administration, data curation, formal analysis, visualization and writing original draft. Yange Tian: Investigation, data curation, project administration, and writing-review & editing. Ya Li: Investigation, formal analysis, and software. Xuefang Liu: Investigation, data curation, and software.

• References

• 1 James SL, Lucchesi LR, Bisignano C. et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the Global Burden of Disease Study 2017. Inj Prev 2020; 26 (Suppl. 01) i3-i11
• 2 Zhou M, Wang H, Zeng X. et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019; 394 (10204): 1145-1158
• 3 Li SY, Li JS, Wang MH. et al. Effects of comprehensive therapy based on traditional Chinese medicine patterns in stable chronic obstructive pulmonary disease: a four-center, open-label, randomized, controlled study. BMC Complement Altern Med 2012; 12: 197
• 4 Li J, Zhao P, Tian Y. et al. Systems pharmacology-based dissection of the active ingredients and targets of Yiqi Zishen formula for application to COPD. Int J Clin Exp Med 2017; 10 (08) 20
• 5 Li X, Liu Z, Liao J, Chen Q, Lu X, Fan X. Network pharmacology approaches for research of Traditional Chinese Medicines. Chin J Nat Med 2023; 21 (05) 323-332
• 6 Rao A, Barkley D, França GS, Yanai I. Exploring tissue architecture using spatial transcriptomics. Nature 2021; 596 (7871): 211-220
• 7 Ding Z, Wang N, Ji N, Chen ZS. Proteomics technologies for cancer liquid biopsies. Mol Cancer 2022; 21 (01) 53
• 8 Petrova B, Guler AT. Recent developments in single-cell metabolomics by mass spectrometry—a perspective. J Proteome Res 2025; 24 (04) 1493-1518
• 9 Zhang P, Zhang D, Zhou W. et al. Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. Brief Bioinform 2023; 25 (01) bbad518
• 10 Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin J Nat Med 2013; 11 (02) 110-120
• 11 Wilmes A, Limonciel A, Aschauer L. et al. Application of integrated transcriptomic, proteomic and metabolomic profiling for the delineation of mechanisms of drug induced cell stress. J Proteomics 2013; 79: 180-194
• 12 Karnovsky A, Weymouth T, Hull T. et al. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 2012; 28 (03) 373-380
• 13 Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005; 21 (16) 3448-3449
• 14 Bindea G, Mlecnik B, Hackl H. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009; 25 (08) 1091-1093
• 15 Xia J, Sinelnikov IV, Han B, Wishart DS. MetaboAnalyst 3.0–making metabolomics more meaningful. Nucleic Acids Res 2015; 43 (W1): W251-7
• 16 Bruzzaniti S, Bocchino M, Santopaolo M. et al. An immunometabolic pathomechanism for chronic obstructive pulmonary disease. Proc Natl Acad Sci U S A 2019; 116 (31) 15625-15634
• 17 Mark NM, Kargl J, Busch SE. et al. Chronic obstructive pulmonary disease alters immune cell composition and immune checkpoint inhibitor efficacy in non-small cell lung cancer. Am J Respir Crit Care Med 2018; 197 (03) 325-336
• 18 Ye C, Yuan L, Wu K, Shen B, Zhu C. Association between systemic immune-inflammation index and chronic obstructive pulmonary disease: a population-based study. BMC Pulm Med 2023; 23 (01) 295
• 19 Kapellos TS, Baßler K, Fujii W. et al. Systemic alterations in neutrophils and their precursors in early-stage chronic obstructive pulmonary disease. Cell Rep 2023; 42 (06) 112525
• 20 McKelvey MC, Brown R, Ryan S, Mall MA, Weldon S, Taggart CC. Proteases, mucus, and mucosal immunity in chronic lung disease. Int J Mol Sci 2021; 22 (09) 5018
• 21 Christopoulou ME, Papakonstantinou E, Stolz D. Matrix metalloproteinases in chronic obstructive pulmonary disease. Int J Mol Sci 2023; 24 (04) 3786
• 22 Arezina R, Chen T, Wang D. Conventional, complementary and alternative medicines: mechanistic insights into therapeutic landscape of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis 2023; 18: 447-457
• 23 Barnes PJ. Oxidative stress-based therapeutics in COPD. Redox Biol 2020; 33: 101544
• 24 Barnes PJ. Cellular and molecular mechanisms of chronic obstructive pulmonary disease. Clin Chest Med 2014; 35 (01) 71-86
• 25 Oit-Wiscombe I, Virág L, Kilk K, Soomets U, Altraja A. Pattern of expression of genes involved in systemic inflammation and glutathione metabolism reveals exacerbation of COPD. Antioxidants 2024; 13 (08) 953
• 26 Wiegman CH, Li F, Ryffel B, Togbe D, Chung KF. Oxidative stress in ozone-induced chronic lung inflammation and emphysema: a facet of chronic obstructive pulmonary disease. Front Immunol 2020; 11: 1957

Publikationsverlauf

Eingereicht: 12. März 2025

Angenommen: 17. Juni 2025

Artikel online veröffentlicht:
30. September 2025