Therapeutic substance basis of Bu-Ti-Hua-Tan-Tang for chronic obstructive pulmonary disease: A targeted protein extraction approach

Therapeutic substance basis of Bu-Ti-Hua-Tan-Tang for chronic obstructive pulmonary disease: A targeted protein extraction approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Therapeutic substance basis of Bu-Ti-Hua-Tan-Tang for chronic obstructive pulmonary disease: A targeted protein extraction approach Xin Zha, Wen-wei Gong, Xue-ying Li, Qing Xia, Jing-hua Ruan, Zhu-sheng Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7026355/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, and there are some limitations in the therapeutic effect of COPD triple therapy. Bu-Ti-Hua-Tan-Tang (BTHTT), a classic Traditional Chinese Medicine formula, has demonstrated significant clinical efficacy, yet its therapeutic substance basis and mechanisms remain unclear. This study employed a targeted protein extraction strategy to broadly screen for BTHTT components that migrate into the bloodstream, focusing on core protein targets associated with key pathological targets. We achieved intelligent identification of the therapeutic substance basis and mechanisms of BTHTT. Based on the significant therapeutic effects observed in the high-dose group, we identified key metabolic small molecules such as glutathione (oxidized form), phosphatidylcholine, and L-palmitoylcarnitine, and determined critical metabolic regulatory pathways of BTHTT, including phenylalanine, tyrosine, and tryptophan biosynthesis. Furthermore, we discovered IL1β as a core protein target within its protein regulatory interaction network and elucidated the primary mechanism of BTHTT, which involves competitive reversal of the functional binding of IL1β to IL1R1 via IL1R2. We identified seven components, including tanshinone IIA and cryptotanshinone, with high binding affinity (binding energy ≤−8 kcal/mol) to the IL1R2-IL1β and IL1β-IL1R1 interactions. This study is the first to reveal the potential therapeutic substance basis of BTHTT and explore its mechanism through competitive inhibition of IL1β binding to the functional receptor IL1R1 via IL1R2. It provides a scientific basis for further investigation into the mechanisms of BTHTT and offers a novel approach for the integrated multi-omics study of Traditional Chinese Medicine formulas. Biological sciences/Biochemistry Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Bu-Ti-Hua-Tan-Tang chronic obstructive pulmonary disease multiomics multi-algorithm mining technology therapeutic substance basis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Chronic obstructive pulmonary disease (COPD), one of the most common respiratory diseases in clinical practice, has become the third leading cause of death globally due to its extremely high disability rate and recurrent acute exacerbations [1] . COPD is a significant factor leading to the loss of labor capacity in the population and a sharp increase in family care costs, with social and economic losses far exceeding those of most chronic diseases [2] . In the “Healthy China Initiative 2030”, COPD is listed as a key disease for prevention and treatment. Due to the incomplete understanding of the mechanisms underlying COPD, the combined use of bronchodilators and corticosteroids remains the primary treatment for COPD. While this approach provides relatively obvious symptom relief, it cannot prevent the progressive decline in lung function. Moreover, the long-term use of corticosteroids inevitably increases the incidence of adverse reactions, further affecting the long-term efficacy of the drugs and the patients' tolerance [3] . Studies have shown that even with triple therapy, 28%–31% of patients still experience acute exacerbations or worsening of symptoms. Therefore, the development of effective drugs for COPD remains a key and primary issue in current COPD research [4,5] . Traditional Chinese Medicine (TCM) has a long history in the diagnosis and treatment of COPD [6] . It considers the pathogenesis of COPD to be primarily characterized by “qi and yin deficiency”, which is often manifested as the depletion of lung qi and insufficiency of yin fluids, resulting in the failure of the lung to disperse and descend qi, and the occurrence of dyspnea due to the reversal of qi. This is a classic type of TCM dyspnea syndrome. TCM believes that the onset of COPD is closely related to factors such as external pathogenic invasion, dysfunction of the viscera, and the interconnection of phlegm and blood stasis. Therefore, the classic “root deficiency with superficial excess” therapeutic theory has been proposed, that is, through the compound combination idea of “tonify, moisten, and astringe”, to tonify lung qi, nourish lung yin, and address the pathological characteristics of qi and yin deficiency in COPD [7] . At the same time, on this basis, the action of tonifying qi is strengthened to nourish yin and consolidate the foundation, and to improve the main pathogenesis of lung function decline [8] . To address these needs, classic TCM formulas such as Bu-Ti-Hua-Tan-Tang (BTHTT), Shengmai Yin, and Bu Fei Tang have been developed. Among them, BTHTT is particularly notable for its significant effects in tonifying the lung, boosting energy, and resolving phlegm to relieve cough [9,10] . BTHTT is widely used in the clinical treatment of COPD, not only for its remarkable efficacy and minimal side effects, but also for its potential to reverse the progressive decline in lung function. It holds great promise in clinical and social contexts and has the potential for “secondary development” as a major drug, with significant scientific and economic value. However, due to the lack of systematic scientific research over the long term, studies on BTHTT have generally suffered from unclear understanding of its component basis and unidentified core targets of action. Fundamentally, this is due to the significant deficiencies in research on the therapeutic substance basis of BTHTT. Disease is a pathological generalization, representing a comprehensive expression of the location, cause, nature, and trend of the disease at a certain stage. It is an overall functional state of the body in response to various external environmental changes and pathogenic factors. Essentially, it is the imbalance of key pathological targets in the body, leading to changes in proteins or protein networks. As treatment progresses, these characteristic changes are reflected through alterations in target proteins, which in turn cause further changes in the protein network. These changes are objectively manifested through the expression profiles of endogenous metabolic components, highlighting the body's response to treatment [11,13] . To address this, integrating multi-omics and multi-algorithm techniques such as metabolomics, transcriptomics, and multi-algorithm mining, focused on characteristic core protein targets within the body's internal environment, can help explore the essence of treatment. This approach also characterizes the metabolic profiles and biomarkers of diseases, and assesses the overall effects of herbal formulas based on these metabolic profiles and biomarkers [14,16] . Moreover, for TCM formulas like BTHTT, which are primarily taken orally, the entry of the drug into the bloodstream is the primary prerequisite for exerting therapeutic effects. The concentration of various pharmacologically active components in the body at steady-state determines the body's overall biological response. Therefore, the active components that truly exert therapeutic effects may differ from the inherent marker components of the formula. Thus, studying the blood-borne components under effective conditions is fundamental and essential for research on the therapeutic substance basis [17,19] . In summary, this study adopted a targeted protein extraction strategy, based on the premise of syndrome and formula correspondence. On one hand, it utilized TCM serum pharmacochemistry techniques to identify the blood-borne components of BTHTT under effective conditions. On the other hand, focusing on the key pathological targets of COPD, it comprehensively used tissue metabolomics technology to discover disease biomarkers from endogenous metabolic small molecules, and constructed a precise evaluation of the overall effects of the formula. Meanwhile, combined with multi-algorithm mining and transcriptomics identification analysis, it determined the core protein targets regulated by BTHTT under effective conditions. Through high-speed screening of components under molecular docking and molecular dynamics analysis, it resolved the intrinsic biological relationship between exogenous components and endogenous substances, and established a “dose–effect” correlation between components and the body. Ultimately, focusing on the core protein targets, it intelligently identified the therapeutic substance basis of BTHTT, determined its main mechanisms, and provided a new perspective and technical approach for the study of therapeutic substance basis and mechanisms. 2 Results 2.1 The epigenetic pharmacological study of BTHTT in treating COPD The phenotype, physiological and biochemical parameters, and histopathological analysis of the COPD rat model constructed by intratracheal instillation of LPS combined with cigarette smoke exposure revealed the following: Compared with the control group, the model group rats exhibited a significant decrease in body weight ( ** p < 0.01) (Figure 1A) , and the spontaneous activity analysis showed a highly significant decline ( ** p < 0.01) (Figure 1B) . Serum and bronchoalveolar lavage fluid (BALF) tests indicated that the levels of pro-inflammatory factors (TNF-α, IL-6, IL-8, MCP-1, and OPN) were significantly elevated in the model group ( * p < 0.05, ** p < 0.01) (Figure 1C) . Histopathological observations revealed extensive inflammatory infiltration in the lung tissue of the model group, characterized by thickening of the alveolar walls with granulocyte infiltration (Figure 1D) , lymphocyte aggregation around the bronchioles, and focal macrophage infiltration. Typical pathological changes included hydropic degeneration of the bronchial epithelium (pale cytoplasmic swelling), abnormal mucus secretion in the lumen, and proliferation of epithelioid cells forming cystic structures containing necrotic debris. No significant pathological changes were observed in the lung tissue of the blank control group. The animal model with typical pathological features of COPD was successfully established in this study. After two weeks of intervention with BTHTT (Figure 2) , the body weight of rats in the model group increased slowly, while the weight gain in the treatment groups, especially the high-dose group, was significant ( p < 0.01), and the overall weight had returned to the level of the control group. Spontaneous activity analysis showed a significant improvement in the number of movements in the treatment groups ( p < 0.01). Serum and BALF tests indicated that BTHTT intervention significantly reduced the elevated levels of TNF-α, IL-6, IL-8, MCP-1, and OPN in the model group ( p < 0.05, p < 0.01), and the levels of inflammatory factors in the high-dose group were no longer significantly different from those in the control group. Histopathological examination revealed persistent lymphocyte infiltration around the bronchioles and thickening of the alveolar walls with granulocyte infiltration in the model group; the low-dose group showed reduced inflammatory infiltration (local lymphocyte/neutrophil infiltration, slight alveolar thickening); and the high-dose group exhibited near-complete repair of pathological damage (no significant inflammatory cell infiltration or alveolar structural abnormalities). The results demonstrated that BTHTT has a dose-dependent therapeutic effect on COPD, with the high-dose group (twice the clinical dose) showing the best pathological repair capability. 3.2 Metabolomics study of BTHTT against COPD The total ion current (TIC) chromatograms of the QC samples are shown in Figure S1A and S1B . The response intensity and retention times of the chromatographic peaks largely overlapped, with correlation coefficients between QC samples exceeding 0.9, indicating good experimental reproducibility (Figure S1C and S1D) . The proportion of peaks with relative standard deviation (RSD) ≤ 30% in the QC samples accounted for over 70% of the total number of peaks in the QC samples, confirming the stability of the analytical system and the suitability of the data for further analysis (Figure S1E and S1F) . PCA analysis revealed distinct clustering within groups and clear separation between the blank and model groups, indicating successful induction of metabolic disturbances by the COPD model (Figure 3A and 3B) . The OPLS-DA model effectively distinguished the model group from the blank group (Figure 3C and 3D) , with Q2 values of 0.659 and 0.757 in the positive and negative ion modes, respectively (Q2 > 0.5), demonstrating the model's stability and reliability. Permutation tests (Figure 3E and 3F) showed that the R2 and Q2 values of the random models decreased with increasing permutation retention, confirming the absence of overfitting and the robustness of the original model. A total of 29 differential metabolites (positive/negative ion modes) were identified based on VIP > 1 and p < 0.05 (Table S1) , as shown in Figure 3G and 3H . Pearson correlation analysis was performed on the identified small molecule metabolites to explore the metabolic relationships and regulatory interactions during the biological state changes. Metabolites with correlation coefficients greater than 0.8 were identified as key small molecules involved in synthesis and transformation. As shown in Figures 4A and 4B , glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine were identified as key endogenous metabolites according to the average correlation coefficient greater than 0.8. MetPA and MSEA enrichment analyses were conducted to identify the critical pathways associated with these metabolites and to uncover metabolically significant pathways with lower abundance changes. MetPA analysis (Figure 4C) identified 16 major metabolic pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, tyrosine metabolism, pyrimidine metabolism, and glycine, serine, and threonine metabolism, with phenylalanine, tyrosine, and tryptophan biosynthesis (impact > 0.5) being the key metabolic pathways in the COPD rat model. MSEA analysis (Figure 4D) identified seven major metabolic pathways, including pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and melanogenesis, with pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism being the most critical ( p < 0.01). In summary, the five most critical metabolic pathways in the COPD rat model were identified as phenylalanine, tyrosine, and tryptophan biosynthesis; ketone acid metabolism; propionate metabolism; glycolysis/gluconeogenesis; and glycerolipid metabolism. During BTHTT treatment, the metabolic markers in the treatment groups showed a trend towards normalization. In the high-dose group, 16 metabolic markers were significantly reversed ( p < 0.05, p < 0.01), including the key endogenous metabolites glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine ( p < 0.05, p < 0.01), as shown in Figure 4E . In contrast, only seven metabolic markers were significantly reversed in the low-dose group, with no significant reversal observed for the key endogenous metabolites (Table S1) . These results further confirmed the superior therapeutic effects of the high-dose BTHTT treatment group. MetPA analysis revealed that the high-dose group significantly regulated 12 metabolic pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis (impact > 0.5) (Figure 4F) . Compared with the control group, the MSEA analysis of the high-dose group showed no significant regulation of key metabolic pathways such as pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism (Figure 4G) . In conclusion, BTHTT demonstrated significant therapeutic effects on COPD, with the high-dose group (twice the clinical dose) showing comprehensive reversal of the core metabolic disturbances in the COPD model. 2.2 T ranscriptomics study of BTHTT against COPD Based on the aforementioned results, further transcriptomics research was conducted on high-dose BTHTT for the treatment of COPD to identify key regulatory genes. The sequencing data and quality control met the experimental requirements, as shown in Table S2 and Figure S2 . The analysis results are presented in Figure 5A , with 173 regulatory genes identified using the criteria of p -ad j 1, including 134 downregulated and 39 upregulated genes (Table S3) . Subsequently, functional annotation analysis was performed on the differentially expressed genes. The KEGG pathway annotation results of the differentially expressed genes are shown in Figure 5B , revealing a strong association with the IL-17 signaling pathway, TNF signaling pathway, and cytokine-cytokine receptor interaction ( p -adj < 0.05). As illustrated in Figure 5C , protein-protein interaction network analysis of the differentially expressed genes indicated that, based on the centrality measure with a degree ≥ 20, IL1β was identified as the central node of the protein interaction network. This suggests that IL1β is a core protein target for high-dose BTHTT in the treatment of COPD. 2.3 Characteristic study of IL1β in COPD via multi-algorithm integrated analysis Bioinformatics methods were employed to conduct a characteristic study of COPD genes, particularly focusing on the key regulatory gene (IL1β) of BTHTT, through integrated multi-algorithm analysis. Based on the GEO database, after batch correction analysis, the clinical patient experimental data from different chips were randomly ordered, indicating that the batch effect had been eliminated (Figure S3) . Following data correction, differential gene analysis was conducted with the criteria of |logFC|≥1 and p ≤ 0.05. The results are shown in Figure 6A , identifying 32 upregulated and 2 downregulated differential genes, including SRPX2, CLDN10, TFF3, MUC1, TMEM45A, and LTF. Firstly, Lasso regression model was applied to screen the features of the 34 COPD differential genes. With the λ value corresponding to the minimum cross-validation error as the threshold, 16 core feature genes were identified (Figures 6B and 6C) , namely SRPX2, CLDN10, TFF3, HRASLS2, ALDH3A1, MUC1, MUC4, SERPINF1, TMEM45A, CYP1A1, IL1R2, MUC16, FOSB, SERPINB4, LTF, and CEACAM6. Secondly, based on SVM machine learning screening analysis, 30 characteristic genes of COPD disease were determined, as shown in Figure 6E . The results were verified by Figure 6D , with the point of minimum error identified as the screening outcome. These genes included SERPINB4, ALDH3A1, FOSB, SRPX2, TFF3, CEACAM6, IL1R2, and HRASLS2. Lastly, using the random forest machine learning method (Figures 6F and 6G) , disease characteristic genes were screened based on their importance, including SRPX2, ATP6V0A4, TCN1, MUC1, IL1R2, CLDN10, TFF3, SERPINF1, FOSB, MUC4, CEACAM5, WFDC2, MSMB, ALDH1A3, TSPAN1, HRASLS2, S100P, ALDH3A1, GDF15, ADH7, and AGR2. Combining the above results, as shown in Figure 6H , 10 core characteristic genes of COPD were identified: SRPX2, CLDN10, TFF3, HRASLS2, ALDH3A1, MUC1, MUC4, SERPINF1, IL1R2, and FOSB. Differential expression analysis revealed significant differences in the expression of these genes between the COPD and control groups ( p < 0.05, p 0.7) (Figure 6J) . Validation set analysis further demonstrated significant differential expression of IL1R2, SRPX2, and TFF3 in the validation set ( p < 0.05, p 0.7, and an AUC of 0.816 for IL1R2 (Figure 6L) . In summary, the characteristic nature of IL1R2 in COPD detection was established. Although the characteristic results of IL1β were not directly found, combined with the literature, it can be revealed that IL1R2 is closely related to IL1β. Interleukin-1 receptor (IL-1R) is a cytokine receptor that binds to interleukin-1 (IL-1 α or IL-1 β). It exists in two forms: type I receptor il-1r1 (IL1R1) and type II receptor il-1r2 (IL1R2). IL1R2 competes with IL1R1 for binding to IL1β. 2.4 Validation analysis of IL1R2 competitive inhibition in IL1 β - IL1R1 Based on the aforementioned results, we further verified the specific competitive role of IL1R2 in IL1 β - IL1R1. Firstly, we conducted detection studies of IL1β levels in blood and BALF during both the modeling and therapeutic intervention periods. The study revealed that during the modeling period, the levels of IL1β in the model group rats from BALF to blood were significantly elevated ( p < 0.01) (Figure 7A) . After therapeutic intervention (Figure 7B) , the levels of IL1β in all treatment groups significantly decreased ( p < 0.05, p < 0.01), with the most pronounced reduction observed in the high-dose group, which showed no significant difference compared to the blank control group. In the first part, the results showed that the occurrence and development of IL1 β were closely related to COPD in both key pathological tissues and systemic pathological changes. On the basis of treatment effectiveness, both could significantly reverse the content of IL1 β in all cases, and was in direct proportion to the treatment effect. Secondly, based on the analysis results of IL1β in key pathological tissues, further transcriptome studies were carried out to observe the changes of IL1R2 and IL1R1 when the changes of IL1β happened with before and after treatment. In the transcriptomics study, during the modeling period (Figure 7C) , when IL1β levels in the model group rats were significantly elevated, IL1R2 levels were no significantly changing, while IL1R1 levels were significantly increased. However, after therapeutic intervention (Figure 7D) , the best therapeutic effect group（high group），the levels of IL1R2 has significant increased，IL1R1 was significantly reduced. This indicates that BTHTT have significantly increased IL1R2 and decreased IL1R1. The decrease of IL1R1 makes the binding of IL1 β - IL1R1 difficult, and the sudden increase of IL1R2 makes the binding of IL1 β - IL1R2 more possible. As a natural inhibitor, IL1R2 can bind and neutralize IL1β, further blocking its interaction with IL1R1, thereby terminating the related reactions of the downstream inflammatory cascade (see the IL-6, IL-8, TNF - α and other results for details). This performance reached a related state similar to competitive inhibition. Finally, based on the biochemical indicators and transcriptomic analysis results, immunohistochemical analysis of IL1R2 in lung tissue was conducted to observe the expression changes of IL1R2 protein in each group. Compared with the expression level of the best treatment group (high group), the expression level of IL1R2 positive cells in the lung tissue of rats in the high group was higher (Figure 7E) . The results further indicate that the complement phlegm resolving decoction can significantly increase the protein expression level of IL1R2 in the lungs of COPD rats. 2.5 In vivo and in vitro component analysis of BTHTT a gainst COPD Ultra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) was employed for the analysis. QC analysis demonstrated good experimental reproducibility with Pearson correlation coefficients greater than 0.9, indicating stable and reliable data (Figure S4). The base peak chromatograms (BPC) in both positive and negative ion modes are shown in Figure 8A and 8B . The serum samples from the high-dose group and the in vitro test samples exhibited significant differences in the chromatograms. Moreover, clear distinctions were observed between these groups and the blank group as well as the blank serum plus in vitro test samples. The acquired data, including mass, isotope distribution, and MS/MS fragmentation information, were compared with the commercial standard Traditional Chinese Medicine database from Shanghai Applied Protein Technology CO., Ltd. (Shanghai, China). The results were further matched with public databases such as GNPS [20] , ReSpect [21] , and Massbank [22] for compound identification and annotation. A total of 2547 in vitro components of BTHTT were identified (1649 in positive ion mode and 968 in negative ion mode) using a mass error threshold of 0.7 for MS2. NPClassifier analysis indicated that the predominant in vitro components were alkaloids (24%) and shikimate/phenylpropanoid derivatives (24%) (Figure 8C) . Further analysis of the base peak chromatograms led to the selection of 38 high-abundance in vitro components of BTHTT (Figures 8D and 8E, Table S4) , with shikimate/phenylpropanoid derivatives accounting for 51% (Figure 8F) . Based on the analysis and identification results of blank control serum and blank serum plus in vitro test samples of BTHTT, combined with the in vitro full component analysis results, a background subtraction algorithm in the chemometric module was applied to the high-dose group to ultimately determine the in vivo components of BTHTT. A total of 283 in vivo components were identified (153 in positive ion mode and 132 in negative ion mode). NPClassifier chemical classification revealed that the predominant in vivo components were shikimate/phenylpropanoid derivatives (39%), alkaloids (20%), and terpenoids (16%) (Figure 8G) . The results of the in vivo components were further cross-referenced with public databases such as PubChem and ChemSpider. Using a mass error threshold of ppm ≤ ±5 and a matching score > 0.9 for MS2, 71 in vivo components of BTHTT were ultimately identified (Table S5) . Among them, seven high-abundance in vitro components that were also detected in vivo were identified: Vitamin B1, magnoflorine, cryptotanshinone, tanshinone IIA, 3,4-dihydroxyphenylacetic acid, salvianolic acid A, and gibberellin A4. 2.6 Identification of the therapeutic substance basis of BTHTT a gainst COPD Focusing on the core protein targets associated with the critical pathological targets of COPD, molecular docking analysis was performed on the in vivo components of BTHTT using the SMINA software to establish a “dose-effect” relationship between the components and the body. Through the AI supercomputing platform, the therapeutic substance basis of BTHTT against COPD was intelligently identified. As shown in the Table S6 , with a binding energy threshold of ≤ −8 kcal/mol, seven important compounds in BTHTT were ultimately identified to potentially interact well with the core protein target (IL1β) via IL1R2, and it should be pointed out that tanshinone IIA and cryptotanshinone are not only the two components with the lowest molecular docking potential, but also the only two components with high content in vitro . Based on the results of molecular docking, molecular dynamics simulations were conducted to further confirm the binding affinity of tanshinone IIA and cryptotanshinone with IL1R2, providing a detailed description of the dynamic stability of their interactions. As shown in Figure 9A and B, after 300 ps, the system energies of tanshinone IIA and cryptotanshinone converge, reaching the minimum energy. After NVT equilibration, the temperature of both systems stabilized around 300 K, indicating proper temperature control. Following NPT equilibration, the pressure of both systems remained stable around 1 bar. Although fluctuations were observed, the system density remained relatively stable, suggesting proper pressure control. After 17 ns, the RMSD of both systems reached stability, fluctuating within a range of 0.2 nm. During the 20 ns simulation, the energy of both systems exhibited minimal fluctuation, indicating good stability of the simulated systems. In conclusion, tanshinone IIA and cryptotanshinone exhibit good stability upon binding with IL1R2. 3 Discussion This study, focusing on the therapeutic substance basis of BTHTT, for the first time adopted a “targeted protein extraction strategy”. Using the core protein targets associated with the critical pathological targets of COPD as the entry point, and integrating metabolomics, transcriptomics, multi-algorithm joint mining, and TCM serum pharmacochemistry techniques, a systematic research method was constructed that links “endogenous substance regulation” with “exogenous component action”. This strategy breaks through the traditional single-direction screening model of “component-target-disease” in the study of TCM formulas. By characteristically screening core targets (IL1β) in a reverse manner to lock in the therapeutic substances, the precision of the research was significantly enhanced. The study results reveal that IL1β, as a core protein target in the regulatory network of BTHTT, is closely associated with the pathological development of COPD. As a key pro-inflammatory cytokine of the IL-1 family, IL1β activates the IL-1 receptor (IL-1R1) and recruits accessory proteins (IL-1RAcP), directly triggering the activation of the downstream NF-κB pathway [2324] . This activation induces the efficient expression of pro-inflammatory cytokines such as TNF-α and IL-6. Subsequently, IL1β and IL-6 work together to promote the differentiation of Th17 cells and induce the secretion of IL-17. TNF-α and IL-17 further collaborate to enhance the differentiation capacity of Th17 cells, thereby amplifying the inflammatory response [25] . The escalating inflammatory response induces the expression of MUC proteins (mucin family), which is successively confirmed by MUC1, MUC4, and MUC13 in the transcriptomics analysis. As transmembrane mucins, MUC proteins are crucial for the protection of the mucosal barrier. They can alter mucus viscosity by binding to extracellular matrix components, exacerbating airway obstruction. Airway obstruction is a core pathological feature of COPD and a direct driver of lung function decline [26,27] . The metabolomics study further complements the aforementioned mechanistic hypothesis. Inflammation, by activating the NF-κB pathway, inhibits the activity of phenylalanine hydroxylase (PAH), leading to the accumulation of phenylalanine and a reduction in tyrosine synthesis [28] . The decrease in phenylalanine results in an increase in intermediate products such as phenylpyruvate, with significant changes observed in the biosynthesis of phenylalanine, tyrosine, and tryptophan. Concurrently, the enhanced inflammatory response is inevitably accompanied by the generation of reactive oxygen species (ROS). The accumulation of ROS leads to the oxidation of reduced glutathione (GSH) to oxidized glutathione (GSSG), disrupting the intracellular redox balance and exacerbating tissue damage. The elevation of GSSG may further activate inflammasomes, promoting the cleavage of pro-IL1β and the release of mature IL1β, thus creating a vicious cycle [29] . The significance of GSSG is also highlighted in the metabolomics analysis. IL1β, by activating the inflammatory response, inhibits mitochondrial fatty acid β-oxidation, leading to the accumulation of long-chain acylcarnitines (e.g., L-palmitoylcarnitine) within cells. This accumulation exacerbates energy metabolism disorders and subsequently leads to disturbances in ketone acid metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism. However, it is important to note that the activation of IL-1 receptors by IL1β involves not only IL-1R1 but also IL1R2, which can compete with IL-1R1. In fact, the relationship between IL1R2 and IL1β is not limited to simple receptor-ligand binding but is tightly linked through multiple mechanisms, including competitive inhibition, signaling pathway regulation, and immune microenvironment remodeling. Their interactions play a central role in inflammatory diseases, tumor progression, and immunotherapy, and targeted interventions against this axis (neutralizing antibodies and combination immunotherapy) have shown significant potential. This close functional dependence and pathological relevance make it a highly promising key target for the treatment of COPD. As a decoy receptor for IL1β, IL1R2 competitively binds to IL1β with high affinity through its extracellular domain, blocking the binding of IL1β to the functional receptor IL1R1. Since IL1R2 lacks intracellular signaling domains (TIR domains), this binding does not activate downstream pro-inflammatory signaling pathways (e.g., NF-κB), thereby inhibiting a series of pro-inflammatory responses induced by IL1β [30,33] . We have comprehensively demonstrated through physiological and biochemical indicators, transcriptomics, and immunohistochemistry that BTHTT can significantly activate the increase of IL1R2 and the decrease of IL1R1. Moreover, the sudden increase of IL1R2 makes the binding of IL1 β - IL1R2 more likely. This competitive inhibitory state may be one of the main mechanisms of BTHTT. Therefore, molecular docking and molecular dynamics analysis were further employed to screen the in vivo components of BTHTT around IL1R2 and IL1R1. The results suggest that these components could serve as the therapeutic substance basis, they were efficiently binding to IL1R2 to competitively inhibit the functional binding of IL1R1 and IL1β in large probability. In summary, this study for the first time reveals the therapeutic substance basis of BTHTT and explores its main therapeutic effects against COPD through the competitive binding of IL1R2, which reverses the functional interaction between IL1R1 and IL1β. as shown in Figure 10.This provides a scientific basis for further investigation into its mechanisms and offers a new approach for the integrated multi-omics study of TCM formulas. 4 Materials and Methods 4.1 Instruments and r eagents Vanquish UHPLC-Q-Exactive HFX MS and Vanquish Neo UHPLC-Orbitrap Exploris 480 MS systems (Thermo Scientific, USA), Nanodrop ND-2000 spectrophotometer, Qubit 4.0 fluorometer, and Agilent 4150 bioanalyzer (Agilent Technologies, USA), Illumina Novaseq 6000 sequencing platform (Illumina, USA), and Eppendorf Centrifuge 5430 R (Eppendorf, Germany) were used in this study. The small animal spontaneous activity recorder KW-ZF was purchased from Nanjing Karlvin Biotechnology (China). BTHTT (Astragalus membranaceus, Pseudostellaria heterophylla, Cinnamomum cassia, Angelica sinensis, Salvia miltiorrhiza, Perilla frutescens, Lepidium sativum, Lepidium apetalum, Mahonia fortunei) was obtained from Guizhou Shanhaitongjia Pharmaceutical Co., Ltd. and authenticated to meet the pharmacopoeia standards by the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine. Materials for animal model construction, including LPS (Shandong Huino Pharmaceutical), experimental cigarettes (Harbin Taihua), and sodium pentobarbital (Shanghai Experimental Reagent Procurement Company), were purchased through regular channels. ELISA kits (TNF-α and IL-1β) were purchased from Shanghai Enzyme-Linked Biotechnology. Molecular biology reagents, such as Trizol (Magen, China) and RNA library construction kits (ABclonal, USA), were selected from commercial brands. Chromatographic-grade acetonitrile and methanol (Fisher, USA) and ammonium acetate (SIGMA, Germany) were used for LC-MS analysis. 4.2 Animals A total of 72 male Sprague-Dawley (SD) rats of SPF grade (230±20 g) were purchased from Tianqin Biotechnology (Changsha, China; SYXK2018-007). After 1 week of adaptive housing under standard conditions (25±1℃, 50±5% humidity, 12 h light-dark cycle), the rats were randomly divided into the control group (n = 24) and the model group (n = 48). The experimental protocol was approved by the Animal Ethics Committee of Guizhou Medical University (2303411) and followed the ARRIVE guidelines and animal welfare regulations. The model group was subjected to dual-factor modeling: Intermittent cigarette smoke exposure (9 weeks, 6 days per week, 3 Huangshan cigarettes per day divided into 2 sessions, 30 min per session). Intratracheal instillation of LPS (200 μg per instillation, once every other week) [34,35] . The control group received an equivalent volume of normal saline. After successful modeling, the model group was randomly divided into the model group (n = 12), the high-dose BTHTT group (High, 2× clinical dose, n = 12), and the low-dose group (Low, 1× clinical dose, n = 12), with continuous gavage intervention for 2 weeks. The control and model groups were given distilled water synchronously. Body weight and spontaneous activity parameters (number of movements/time) were recorded weekly. At the end of modeling and treatment, lung tissue, serum, and bronchoalveolar lavage fluid (BALF) were collected. Levels of inflammatory markers (i.e., TNF-α, IL-1β, IL-6, IL-8, OPN, and MCP-1) were measured using ELISA according to the kit instructions. Tissue pathology and multi-omics analyses were also performed. 4.3 Determination of BTHTT d osing COPD case records revealed that, based on previous clinical dosing, the standard dose of BTHTT for adults is approximately 1.57 g/kg/day. Therefore, the corresponding dose for rats should be 9.9 g/kg/day. Considering the final concentrated volume of BTHTT as 50 mL, the clinical equivalent dose for rats was calculated to be 1.6 mL, administered via gavage once daily. 4.4 Sample preparation and collection 4.4.1 Tissue sample collection At the experimental endpoints (Week 9 and Week 11), rats were anesthetized via intraperitoneal injection of 5% sodium pentobarbital (1 mL/100 g). Blood was collected from the portal vein, allowed to stand for 30 minutes at room temperature, and then centrifuged at 13000×g for 15 minutes at 4℃. The supernatant was stored at -80℃. Lung tissues were rapidly frozen in liquid nitrogen and stored at -80℃. BALF was collected by three consecutive lavages with cold PBS. 4.4.2 Sample preparation for metabolomics analysis Lung tissue (20-50 mg) was homogenized in pre-chilled methanol-acetonitrile-water (2:2:1, v/v) and sonicated in an ice bath. The homogenate was centrifuged at 13000 rpm for 20 minutes at 4℃. The supernatant was vacuum-concentrated, re-dissolved in acetonitrile-water (1:1, v/v), and filtered through a 0.22 μm membrane for LC-MS analysis [36] . 4.4.3 Sample preparation for transcriptomics analysis Total RNA was extracted from lung tissue using TRIzol reagent and quality-controlled using Nanodrop ND-2000 (A260/A280 > 1.8) and Agilent 4150 (RIN ≥ 7.0). RNA sequencing libraries were constructed using the ABclonal mRNA-seq Kit: mRNA was enriched with oligo(dT), fragmented, and used to synthesize double-stranded cDNA, which was then ligated with Illumina adapters and amplified by PCR. Libraries were quantified using Qubit and quality-checked with Agilent 4150 before sequencing on the Illumina Novaseq 6000 platform [37] . 4.4.4 Preparation of BTHTT samples for in vitro testing BTHTT was extracted by decoction in water (twice, 30 minutes each), concentrated to 1.1-1.2 g/mL, and then freeze-dried. Before analysis, 600 μL of the freeze-dried powder solution was mixed with 400 μL of methanol, re-dissolved in 40% methanol, and centrifuged at 16000 rpm to collect the supernatant. 4.4.5 Preparation of BTHTT samples for in vivo testing Serum was deproteinized by mixing with methanol (1:1) and precipitating at −20℃ for 30 minutes, followed by centrifugation at 16000 rpm for 20 minutes. The supernatant was vacuum-dried and re-dissolved in 40% methanol to obtain the final sample. For the preparation of blank serum + BTHTT samples, an appropriate amount of blank serum was spiked with the in vitro BTHTT supernatant, and the remaining steps were consistent. 4.5 Establishment of analytical methods 4.5.1 Establishment of metabolomics analysis method The chromatographic separation was achieved using an ACQUITY UPLC BEH Amide column (1.7 μm, 2.1 mm × 100 mm; Waters, USA). The column temperature was maintained at 25℃. The mobile phase consisted of (A) water containing 25 mM ammonium acetate and 25 mM ammonia, and (B) acetonitrile. The flow rate was set at 0.5 mL/min, and the injection volume was 2 μL. The gradient elution program was as follows: 0-0.5 min, 95% B; 0.5-7 min, linear decrease of B from 95% to 65%; 7-8 min, linear decrease of B from 65% to 40%; 8-9 min, B held at 40%; 9-9.1 min, linear increase of B from 40% to 95%; 9.1-12 min, B held at 95%. During the entire analysis, samples were kept at 4℃ in the autosampler. To ensure the stability of the system and the reliability of the experimental data, samples were analyzed in a random sequence with quality control (QC) samples interspersed in the sample queue. The mass spectrometry analysis was performed using a Vanquish LC ultra-high-performance liquid chromatography system (UHPLC) coupled with an Orbitrap Exploris™ 480 mass spectrometer (Thermo). The samples were ionized using electrospray ionization (ESI) in both positive and negative ion modes. The ESI source and MS settings were as follows: nebulizer gas (Gas 1) was set at 50, auxiliary gas (Gas 2) at 2, ion source temperature at 350℃, and spray voltage (ISVF) at 3500 V in positive ion mode and 2800 V in negative ion mode. The mass range for MS1 was set from 70 to 1200 Da with a resolution of 60000 and a scan accumulation time of 100 ms. For MS2, data-dependent acquisition (DDA) with stepped collision energy was used. The mass range for MS2 was also set from 70 to 1200 Da with a resolution of 60000 and a scan accumulation time of 100 ms. The dynamic exclusion time was set at 4 s. 4.5.2 Establishment of analytical methods for in vivo and in vitro components Samples were separated using a Vanquish UHPLC system (Thermo Fisher Scientific, Bremen, Germany) equipped with an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 µm). The column temperature was maintained at 35°C, and the flow rate was set at 0.3 mL/min. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. Gradient elution was performed as shown in Table 1 . Table 1. Gradient elution method for in vivo and in vitro component analysis of BTHTT. Time (min) Mobile Phase A (%) Mobile Phase B (%) Initial 95 5 3 75 25 8.5 55 45 14 5 95 17 2 98 17.2 95 5 20.0 95 5 The Q-Exactive HFX mass spectrometer was used for the acquisition of both MS1 and MS2 spectra. The mass spectrometer was coupled with the UHPLC system and operated in both positive and negative electrospray ionization (ESI) modes. The ESI parameters were as follows: spray voltage 3800 V (ESI+) / 3500 V (ESI-), sheath gas pressure 45 arb, auxiliary gas pressure 20 arb, ion transfer tube temperature 320°C, and vaporizer temperature 350°C. The detection mode was set to full scan/data-dependent MS2 (Full-MS/dd-MS2) with resolutions of 60000 for MS1 and 15000 for MS2. The top 10 MS1 ions were selected for MS/MS fragmentation with stepped normalized collision energies of 20, 40, and 60. The mass range for MS1 was set from 90 to 1300 Da. For in vivo analysis, including blank group samples, dosed group samples, and blank group + BTHTT samples, 6 µL of each sample was precisely injected. For in vitro analysis of BTHTT, 2 µL of the sample was injected. Each batch of blank and dosed group samples was injected once, while the blank group + BTHTT samples were injected in triplicate, and the BTHTT samples were injected in quintuplicate. 4.6 Data processing and analysis 4.6.1 Metabolomics data analysis Raw metabolomics data were converted to the mzXML format using ProteoWizard and then processed with XCMS software for peak alignment, retention time correction, and peak area extraction. The data preprocessing workflow included the following steps: Firstly, ion peaks with a missing rate > 50% were removed. Secondly, remaining missing values were imputed using the KNN algorithm. Thirdly, metabolic features with a relative standard deviation (RSD) >50% were discarded. The quality of the experimental data was assessed using principal component analysis (PCA) and clustering of QC samples. Subsequent analyses included univariate statistics (e.g., t-tests), multivariate statistics (PLS-DA), differential metabolite screening (VIP > 1 and p <0.05), and KEGG pathway enrichment analysis (hypergeometric test) [38,39] . 4.6.2 In vivo and in vitro component data analysis Data in the mzXML format were processed using XCMS and compounds were identified based on a local high-resolution commercial traditional Chinese medicine mass spectrometry database. The criteria for identification were set as follows: a mass error of 0.7 for MS2 (where the Score reflects the similarity of fragment ions, with ≥ 0.7 being a reliable threshold) [40,41] . Statistical analysis included the counting of compounds and their classification (e.g., flavonoids, alkaloids), which was completed in conjunction with annotations from the mass spectrometry database [42] . 4.6.3 Transcriptomics data analysis Raw transcriptomics data generated by the Illumina platform (in fastq format) were processed using Perl scripts to remove adapter sequences and low-quality reads (reads with Q ≤ 25 bases accounting for > 60% or N rate > 5%). Clean reads were obtained after this filtering process. The clean reads were aligned to the reference genome using HISAT2, and gene expression levels were quantified using FeatureCounts to calculate FPKM values. Differential expression analysis was performed using DESeq2 (with screening criteria of |log2FC| > 1 and p -adj < 0.05). Transcription factor annotation was based on Animal TFDB or Pfam/DBD databases, matching gene IDs and protein domain information [43] . 4.7 Collection of COPD public data The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was utilized to retrieve information related to COPD patients. The involved datasets included GSE11784, GSE12472, GSE16972, GSE38974, and GSE222965. Platform data and matrix files that met the criteria were downloaded. Based on the annotation information in the platform files, the one-to-one correspondence between probes and gene files was identified to obtain the gene expression matrix files. The files were formatted with gene names as row names and sample names as column names for reading and forming the COPD database, i.e., the “gene-expression” analysis. Multiple COPD databases were merged to obtain the overall dataset and related expression levels for in-depth mining using various algorithms. 4.8 Establishment of statistical and molecular simulation analysis methods In this experiment, GraphPad Prism 8 software was used for statistical data analysis, with group calculations performed using t-tests or one-way analysis of variance (ANOVA). For molecular docking analysis, component data were downloaded from the PubChem database in the form of structural data information for active substances. The small molecule structures and energies were optimized using ChemOffice. Key target protein structure information was downloaded from the PDB database, and PyMol was used to separate the original protein ligands from the proteins, saving them separately after removing water molecules. AutoDockTool was employed to add hydrogens and charges to each protein structure, which were then saved in PDBQ format for subsequent docking analysis. Vina was selected as the docking software to perform molecular docking, generating 15 conformations each time, with the lowest binding energy used as the binding energy statistical summary for each protein-molecule docking. Finally, the interaction modes of receptor-ligand complexes were analyzed using the PLIP v2.3.0 online analysis website (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index), and pse format files were exported for visualization analysis using PyMol software. The topology files of IL1R2 and the potential active compound were prepared based on the GROMOS96 43a1 force field. An SPC water model was used to construct an icosahedral box, and water molecules were added. Ions were introduced into the system to ensure its electrical neutrality. The system underwent energy minimization, followed by temperature coupling using the V-rescale method, with an NVT ensemble set at 300 K, a time step of 2 fs, and a simulation time of 100 ps. Pressure coupling was performed using the Parrinello–Rahman method, with an NPT ensemble set at 1 bar and a simulation time of 100 ps. The molecular dynamics simulation was carried out for a total duration of 10 ns, with the trajectory being saved. Short-range electrostatic interactions were calculated using the cut-off method, with a cut-off radius of 1.2 nm. Long-range electrostatic interactions were computed using the particle mesh Ewald (PME) method. The molecular dynamics simulations of the spike protein and the potential active compound were performed using the GROMACS package. The stability of the constructed complex system was evaluated by performing least-squares fitting of the RMSD values. Declarations Supplementary Materials: Figure S1: Quality control analysis of metabonomics, Figure S2: The FPKM analysis for gene expression in RNA-seq, Figure S3: The batch effect analysis of transcriptome data for clinical patients based on GEO database, Figure S4: The correlation analysis of QC samples for BTHTT in positive and negative ion mode, Table S1: Identification and regulatory analysis of metabolic markers in COPD rat models, Table S2: Quality analysis of data from rat samples in each group in transcriptomics, Table S3: Differential gene analysis between the high-dose group and the model group rats, Table S4: Identification of high-content components in BTHTT based on base peak chromatogram analysis, Table S5: The analysis of transitional components in blood for BTHTT, Table S6: Screening and analysis of the therapeutic substance basis of BTHTT. Author Contributions: Xin Cha and Shao-bo Liu conducted the animal experiments and drafted the manuscript; Wen-wei Gong and Xue-ying Li assisted with the experiments and data analysis; Qing Xia contributed to the animal experiments; Jing-hua Ruan handled data processing; Zhu-sheng Zhu revised the manuscript; Xiang-chun Shen co-supervised the project. All authors reviewed and approved the final manuscript. Funding This work was supported by grants from the National key R&D plan project (2022YFC2503003); National Natural Science Foundation of China Youth Fund Project (82405036); Guizhou Provincial high-level innovative talents hundred level talents (GCC[2023]048); The unveiling and leading projects from State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine (JBGS-FAMP202304); The open fund of the State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine (Qian Jiao Ji [2023] No. 112); Guizhou Provincial Health Commission science and Technology (gzwkj2024-516); Launch of high-level talents in Guizhou Medical University (XBH J [2022] No. 008). Key Project of the Research and Development Program (LSLSKL20240101), National Key Laboratory of Classical Formulas and Modern TCM Integration and Innovation; Study on the mechanism of Chuanxiong protein-polysaccharide complex based on the characteristics of long-circulating Pickering milk to promote the treatment of headache with Chuanxiong (82360779); National and Provincial Science and Technology Innovation Talent Team Cultivation Program of Guizhou University of Traditional Chinese Medicine, Guizhou University of Traditional Chinese Medicine TD Hopes [2023] 005. Ethics approval and consent to participate: The animal experiments were approved by the experimental animal ethics committee of Guizhou Medical University (No.2303411). All animal experiments complied with the ARRIVE statement on animal use in biomedical research. We conffrmed that all experiments were performed in accordance with relevant guidelines and regulations. Institutional Review Board Statement: Not applicable. Data Availability Statement: Publicly available gene expression datasets used in this study were obtained from the Gene Expression Omnibus (GEO) under the following accession numbers: GSE11784, GSE12472, GSE16972, GSE38974, and GSE222965. Additionally, the original high-throughput sequencing data generated during the experimental phase of this study have been deposited in the NCBI BioProject database under accession number PRJNA1286104, accessible via the following link:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1286104. All datasets are publicly available and meet the journal’s data sharing requirements. Conflicts of Interest: The authors declare no conflicts of interest. References N.A.Negewo, et al.,COPD and its comorbidities: Impact, measurement and mechanisms. Respirology, 20 ,1160-1171 (2015). C. Raherison, et al., Epidemiology of COPD. Eur Respir Rev, 18 ,213-221. (2009) J.L.López-Campos, et al., Global burden of COPD. Respirology, 21 ,14-23.(2016). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7026355","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489222733,"identity":"bc2b4304-89ec-4e85-896c-feb8fdecf94b","order_by":0,"name":"Xin Zha","email":"","orcid":"","institution":"The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zha","suffix":""},{"id":489222734,"identity":"74153265-0f62-49e4-9082-02dc440f8246","order_by":1,"name":"Wen-wei Gong","email":"","orcid":"","institution":"The Third People's Hospital of Guizhou Province","correspondingAuthor":false,"prefix":"","firstName":"Wen-wei","middleName":"","lastName":"Gong","suffix":""},{"id":489222739,"identity":"a0d12e37-2987-4666-8b50-c63d17ebb1a2","order_by":2,"name":"Xue-ying Li","email":"","orcid":"","institution":"Mudanjiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xue-ying","middleName":"","lastName":"Li","suffix":""},{"id":489222740,"identity":"c9e0cc9e-3402-4bfc-840c-5ff7884f545b","order_by":3,"name":"Qing Xia","email":"","orcid":"","institution":"The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Xia","suffix":""},{"id":489222741,"identity":"a079deef-2d37-4d27-afad-a3d4cfe106f5","order_by":4,"name":"Jing-hua Ruan","email":"","orcid":"","institution":"The First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing-hua","middleName":"","lastName":"Ruan","suffix":""},{"id":489222742,"identity":"901c8488-08ef-481d-b325-b851f386635d","order_by":5,"name":"Zhu-sheng Zhu","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhu-sheng","middleName":"","lastName":"Zhu","suffix":""},{"id":489222743,"identity":"1811fa52-9279-4a00-b2fb-c69cb8f201e7","order_by":6,"name":"Xiang-chun Shen","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang-chun","middleName":"","lastName":"Shen","suffix":""},{"id":489222748,"identity":"a5dafa0a-3b31-4611-a280-b1114001ec71","order_by":7,"name":"Shao-bo Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACPmYGBiBiSGBgbz5w4EOFhJw8IS1scC08xxIPzjhjYWzYQEgLA0yLRI7xYd62ikSGA4S0sPMYfy6oscnj5zmWcHDmPIkExgbmh49u4HUYj5n0jGNpxZLtQL983CaRx87AZmycQ0ALMw/b4cQNZ0C2bJMoZmzgYZMmoMX4M88/oJYbOQaHeedIJDYcIKzFQJq3DaalgSgtbGXSvH1Av/QAHTbjmISxYTMBv/DzH978mecbMMTYmw9/+FBTJyfP3vzwMT4tWAAzacpHwSgYBaNgFGABAG1mS1ctk4nqAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shao-bo","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-07-02 07:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7026355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7026355/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87437062,"identity":"88c93ecc-ed13-4486-8aaa-83dfdb52ef63","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28805782,"visible":true,"origin":"","legend":"\u003cp\u003ePathophysiological validation of COPD rat model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A and B show the body weight and spontaneous activity analysis. C present the pro-inflammatory factors analysis in the serum（left half）and bronchoalveolar lavage fluid (BALF)（right half）. D shows the representative H\u0026amp;E-stained lung sections (200×). Control: normal architecture. Model: (i) thickened alveolar septa with granulocyte infiltration (black arrows), (ii) hydropic degeneration of bronchial epithelium (yellow asterisks), (iii) luminal mucin hypersecretion (blue arrows), and (iv) epithelioid cell proliferation forming cyst-like structures containing necrotic debris (red box), scale bars = 50 μm.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/37dbc18c82fedc4420161d06.png"},{"id":87437054,"identity":"aa075156-0672-4391-aa2e-e50713315a94","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3814640,"visible":true,"origin":"","legend":"\u003cp\u003eDose-dependent therapeutic effects of BTHTT on COPD rats\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A shows the analysis of weight measurement in during treatment for each group, compared with the model group, the difference between the blank group and the high-dose group remained extremely significant (p \u0026lt; 0.01), and there was a significant difference in the low-dose treatment group from the tenth day of treatment (p \u0026lt; 0.01). B shows the body weight and spontaneous activity analysis (C: Control group; M: Model group; H: High-dose group; L: Low-dose group). C present the pro-inflammatory factors analysis in the serum（left half）and bronchoalveolar lavage fluid (BALF)（right half）(CK: Control group; M: Model group; H: High-dose group; L: Low-dose group), after treatment, there were still significant differences between the low-dose group and the blank group (\u003csup\u003e#\u003c/sup\u003ep \u0026lt; 0.05) among the partial results. D shows Representative lung histopathology (H\u0026amp;E, 200×), model: peribronchiolar lymphocytic infiltration (yellow arrows), thickened alveolar walls with granulocytes (black arrowheads), peribronchiolar lymphocyte aggregation (green arrowheads), high-dose: near-normal architecture with intact alveoli (ALV) and bronchioles (BR), little inflammatory foci, low-dose: localized lymphocyte/neutrophil infiltration (dashed circles), mild alveolar thickening, scale bars = 50 μm.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/64137c72f57e6b08abfb598d.jpg"},{"id":87437057,"identity":"e2abf1e8-c90b-4ecd-864f-3af1651182a6","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1806272,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of metabolic markers in lung tissue of COPD rat model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A and B shows the PCA analysis in negative (A) and positive (B) ion mode. C and D shows the OPLS-DA analysis in negative (C) and positive (D) ion mode. E and F shows the Permutation tests analysis in negative (E) and positive (F) ion mode. G (negative ion mode) and H (positive ion mode) were the thermogram analysis of differential metabolites in relative content.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/41ec73ad54fea6330ca5b1da.png"},{"id":87437460,"identity":"34ca321e-f68c-4a27-a0c9-a0f0a7302f6f","added_by":"auto","created_at":"2025-07-23 19:01:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4992982,"visible":true,"origin":"","legend":"\u003cp\u003eThe key metabolic markers digging and key metabolic pathways determination in the lung of COPD rat model\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A was the correlation analysis for metabolic markers detection results in negative ion mode, among that a to n was O-phosphoethanolamine, Methionine sulfoxide, 2-keto-d-gluconic acid, Phosphoereatine, Dl-malic acid, Glu-Arg, Orotidine, Glyeeric acid, N-palmitoyltaurinc, Cysteine-s-sulfate, 4.alpha.-mannobiosc, D-fructose 1,6-bisphosphatd, L-(+)-Hactic acid, 3-Phospho-D-gtycerate. B was the correlation analysis for metabolic markers detection results in positive ion mode, among that a to o was L-palmitoylcarnitine, Pro-phe, Myristoy-l-earnitine, 2-cis-4-trans-abscisic acic, Phosphocholine, 3-methoxyprostaglandin f1.alpha., Glutathione, oxidized, L-beta-homomethionine, N-acetylneuraminate, 4-aminobenzoate, Betaine, L-pipecolic acid, N.epsilon.-methyl-1-1ysine, Pro-Ile-Lys, Tyrosine. C was the MetPA analysis for metabolic pathways in COPD rat model, where 1 to 7 represent the Phenylalanine, tyrosine and tryptophan biosynthesis (impact \u0026gt; 0.5), Tyrosine metabolism, One carbon pool by folate, Pyrimidine metabolism, Glycine, serine and threonine metabolism, Glycerophospholipid metabolism, Glutathione metabolism. D was the MSEA analysis for metabolic pathways in COPD rat model. E was the thermogram analysis of metabolic markers in each group after BTHTT treatment, among that 1 to 29 was 3-methoxyprostaglandin f1.alpha., L-palmitoylcarnitine, Betaine, Myristoyl-l-carnitine, Glu-Arg, Phosphocholine, Pro-phe, N.epsilon.-methyl-l-lysine, L-pipecolic acid, N-acetylneuraminate, L-beta-homomethionine, 4-aminobenzoate, Tyrosine, 2-cis-4-trans-abscisic acid, Pro-Ile-Lys, Phosphocreatine, L-(+)-lactic acid, Dl-malic acid, N-palmitoyltaurine, Orotidine, Cysteine-s-sulfate, Methionine sulfoxide, D-fructose 1,6-bisphosphatd, 3-Phospho-D-glycerate, Glyceric acid, 2-keto-d-gluconic acid, O-phosphoethanolamine, Glutathione, 4.alpha.-mannobiose. F was the MetPA analysis for metabolic pathways in High-dose group during treatment, where 1 to 6 represent the Phenylalanine, tyrosine and tryptophan biosynthesis (impact \u0026gt; 0.5), Tyrosine metabolism, One carbon pool by folate, Pyrimidine metabolism, Glycine, serine and threonine metabolism, Glutathione metabolism. G was the MSEA analysis for metabolic pathways in High-dose group during treatment.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/9c7a0039fc41c1fb2dfdb04c.png"},{"id":87437052,"identity":"81242775-97f2-439a-81c5-f78218f26210","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2316578,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic profiling of high-dose BTHTT against COPD\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A was the Volcano plot of differentially expressed genes (DEGs), with blue/red arrows indicating significantly upregulated (n=39) and downregulated (n=134) genes, respectively. B was the KEGG analysis（top 20）. C was the protein-protein interaction (PPI) network of DEGs. Node size and color intensity reflect degree centrality, with IL1β identified as the highest-degree hub node (degree ≥ 20).\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/400e4841c6657a52ae67a05f.png"},{"id":87437461,"identity":"77964f2d-1227-4d98-b0d9-08c113142ee8","added_by":"auto","created_at":"2025-07-23 19:01:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4207738,"visible":true,"origin":"","legend":"\u003cp\u003eMachine-learning-driven identification of COPD diagnostic biomarkers based on GEO database\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A was the Volcano plot of COPD-associated DEGs. B and C shows the LASSO regression for feature selection. D and E shows the SVM recursive feature elimination. F and G shows the Random forest variable importance. H was the Venn diagram intersecting LASSO, SVM and RF outputs, yielding 10 consensus genes. I was the expression heatmap of key gene signature in discovery cohort (treat vs. control). J was the ROC validation for the results from training set. K and L shows the expression and ROC curve analysis by validation set analysis base on training set results.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/fd1999789dd338c1cfa98428.jpg"},{"id":87437055,"identity":"ce48298b-4e1f-493d-99f4-8555655d478c","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2562312,"visible":true,"origin":"","legend":"\u003cp\u003eBTHTT exerts the therapeutic effect by mediating the competitive inhibition of IL1R2 on IL1 β – IL1R1 signaling pathway\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A was the expression changing of IL-1 β in the model group and the control group during COPD modeling from the blood to lung lavage fluid samples. B was the expression changing of IL-1 β in each group during treatment from the blood to lung lavage fluid samples. C was the comparative analysis of IL1R2 and IL1R1 transcriptional expression during modeling. D was the transcriptional expression of IL1R2and IL1R1 in high-dose group and model group during treatment. E was the immunohistochemical analysis of IL1R2protein in lung tissue.\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/3de4b593405beddd05eaf329.jpg"},{"id":87437059,"identity":"ef178966-72e4-4b89-b065-b03df2c5302d","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":12042688,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of BTHTT in vitro components, high content components and blood transitional components in vivo\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e: A and B were the base peak chromatogram analysis (BPC) of BTHTT in negative (A) and positive (B) ion mode. C was the Npclassifier distribution of main chemical categories of BTHTT in vitro (alkaloids: 24%; shikimate/phenylpropanoid derivatives: 24%). D and E were the Base peak chromatogram analysis of high content components in BTHTT in vitro, one of that was the negative mode (D), and the other was positive mode (E). F was the Npclassifier distribution of main chemical categories of high content components in BTHTT with shikimate/phenylpropanoid derivatives predominating (51%). G was the content components of BTHTT in vivo component classification with highlighting shikimate/phenylpropanoid derivatives (39%), alkaloids (20%), and terpenoids (16%).\u003c/p\u003e","description":"","filename":"figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/9b7021e917bbed021e476998.png"},{"id":87437463,"identity":"12c8d733-5852-4728-8232-d5d9ecc21bc6","added_by":"auto","created_at":"2025-07-23 19:01:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2086831,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulations of tanshinone IIA and cryptotanshinone with IL1R2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNotes\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e (A) tanshinone IIA—IL1R2, (B) cryptotanshinone—IL1R2.\u003c/p\u003e","description":"","filename":"figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/c6101db165ecf9d5d03f6d3b.png"},{"id":87437913,"identity":"50fdbbda-6d4f-4efa-964b-3f3e9874c972","added_by":"auto","created_at":"2025-07-23 19:09:46","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1559273,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the mechanism for BTHTT mediated IL1 β/IL1R1 axis in the treatment of COPD through IL1R2 competitive inhibition.\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/4c6fd4d1a62701d7c7e683e4.jpg"},{"id":89908898,"identity":"0f1ad0fb-befb-4e2a-8ac4-7957ac59e6cd","added_by":"auto","created_at":"2025-08-26 10:32:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":62464902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/2167be20-2647-46ea-aed8-e1eba34a0b62.pdf"},{"id":87437061,"identity":"fc3f3fdb-b162-4794-b6a9-819b511b1230","added_by":"auto","created_at":"2025-07-23 18:53:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1205776,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7026355/v1/32ef83c3b16719afab82ff6d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Therapeutic substance basis of Bu-Ti-Hua-Tan-Tang for chronic obstructive pulmonary disease: A targeted protein extraction approach","fulltext":[{"header":"1\tIntroduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD), one of the most common respiratory diseases in clinical practice, has become the third leading cause of death globally due to its extremely high disability rate and recurrent acute exacerbations \u003csup\u003e[1]\u003c/sup\u003e. COPD is a significant factor leading to the loss of labor capacity in the population and a sharp increase in family care costs, with social and economic losses far exceeding those of most chronic diseases \u003csup\u003e[2]\u003c/sup\u003e. In the “Healthy China Initiative 2030”, COPD is listed as a key disease for prevention and treatment. Due to the incomplete understanding of the mechanisms underlying COPD, the combined use of bronchodilators and corticosteroids remains the primary treatment for COPD. While this approach provides relatively obvious symptom relief, it cannot prevent the progressive decline in lung function. Moreover, the long-term use of corticosteroids inevitably increases the incidence of adverse reactions, further affecting the long-term efficacy of the drugs and the patients' tolerance \u003csup\u003e[3]\u003c/sup\u003e. Studies have shown that even with triple therapy, 28%–31% of patients still experience acute exacerbations or worsening of symptoms. Therefore, the development of effective drugs for COPD remains a key and primary issue in current COPD research \u003csup\u003e[4,5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTraditional Chinese Medicine (TCM) has a long history in the diagnosis and treatment of COPD \u003csup\u003e[6]\u003c/sup\u003e. It considers the pathogenesis of COPD to be primarily characterized by “qi and yin deficiency”, which is often manifested as the depletion of lung qi and insufficiency of yin fluids, resulting in the failure of the lung to disperse and descend qi, and the occurrence of dyspnea due to the reversal of qi. This is a classic type of TCM dyspnea syndrome. TCM believes that the onset of COPD is closely related to factors such as external pathogenic invasion, dysfunction of the viscera, and the interconnection of phlegm and blood stasis. Therefore, the classic “root deficiency with superficial excess” therapeutic theory has been proposed, that is, through the compound combination idea of “tonify, moisten, and astringe”, to tonify lung qi, nourish lung yin, and address the pathological characteristics of qi and yin deficiency in COPD \u003csup\u003e[7]\u003c/sup\u003e. At the same time, on this basis, the action of tonifying qi is strengthened to nourish yin and consolidate the foundation, and to improve the main pathogenesis of lung function decline \u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo address these needs, classic TCM formulas such as Bu-Ti-Hua-Tan-Tang (BTHTT), Shengmai Yin, and Bu Fei Tang have been developed. Among them, BTHTT is particularly notable for its significant effects in tonifying the lung, boosting energy, and resolving phlegm to relieve cough \u003csup\u003e[9,10]\u003c/sup\u003e. BTHTT is widely used in the clinical treatment of COPD, not only for its remarkable efficacy and minimal side effects, but also for its potential to reverse the progressive decline in lung function. It holds great promise in clinical and social contexts and has the potential for “secondary development” as a major drug, with significant scientific and economic value. However, due to the lack of systematic scientific research over the long term, studies on BTHTT have generally suffered from unclear understanding of its component basis and unidentified core targets of action. Fundamentally, this is due to the significant deficiencies in research on the therapeutic substance basis of BTHTT.\u003c/p\u003e\n\u003cp\u003eDisease is a pathological generalization, representing a comprehensive expression of the location, cause, nature, and trend of the disease at a certain stage. It is an overall functional state of the body in response to various external environmental changes and pathogenic factors. Essentially, it is the imbalance of key pathological targets in the body, leading to changes in proteins or protein networks. As treatment progresses, these characteristic changes are reflected through alterations in target proteins, which in turn cause further changes in the protein network. These changes are objectively manifested through the expression profiles of endogenous metabolic components, highlighting the body's response to treatment \u003csup\u003e[11,13]\u003c/sup\u003e. To address this, integrating multi-omics and multi-algorithm techniques such as metabolomics, transcriptomics, and multi-algorithm mining, focused on characteristic core protein targets within the body's internal environment, can help explore the essence of treatment. This approach also characterizes the metabolic profiles and biomarkers of diseases, and assesses the overall effects of herbal formulas based on these metabolic profiles and biomarkers \u003csup\u003e[14,16]\u003c/sup\u003e. Moreover, for TCM formulas like BTHTT, which are primarily taken orally, the entry of the drug into the bloodstream is the primary prerequisite for exerting therapeutic effects. The concentration of various pharmacologically active components in the body at steady-state determines the body's overall biological response. Therefore, the active components that truly exert therapeutic effects may differ from the inherent marker components of the formula. Thus, studying the blood-borne components under effective conditions is fundamental and essential for research on the therapeutic substance basis\u003csup\u003e[17,19]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, this study adopted a targeted protein extraction strategy, based on the premise of syndrome and formula correspondence. On one hand, it utilized TCM serum pharmacochemistry techniques to identify the blood-borne components of BTHTT under effective conditions. On the other hand, focusing on the key pathological targets of COPD, it comprehensively used tissue metabolomics technology to discover disease biomarkers from endogenous metabolic small molecules, and constructed a precise evaluation of the overall effects of the formula. Meanwhile, combined with multi-algorithm mining and transcriptomics identification analysis, it determined the core protein targets regulated by BTHTT under effective conditions. Through high-speed screening of components under molecular docking and molecular dynamics analysis, it resolved the intrinsic biological relationship between exogenous components and endogenous substances, and established a “dose–effect” correlation between components and the body. Ultimately, focusing on the core protein targets, it intelligently identified the therapeutic substance basis of BTHTT, determined its main mechanisms, and provided a new perspective and technical approach for the study of therapeutic substance basis and mechanisms.\u003c/p\u003e"},{"header":"2\tResults","content":"\u003cp\u003e\u003cstrong\u003e2.1 \u0026nbsp; \u0026nbsp; \u0026nbsp;The epigenetic pharmacological study of BTHTT in treating COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe phenotype, physiological and biochemical parameters, and histopathological analysis of the COPD rat model constructed by intratracheal instillation of LPS combined with cigarette smoke exposure revealed the following: Compared with the control group, the model group rats exhibited a significant decrease in body weight (\u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) \u003cstrong\u003e(Figure 1A)\u003c/strong\u003e, and the spontaneous activity analysis showed a highly significant decline (\u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) \u003cstrong\u003e(Figure 1B)\u003c/strong\u003e. Serum and bronchoalveolar lavage fluid (BALF) tests indicated that the levels of pro-inflammatory factors (TNF-\u0026alpha;, IL-6, IL-8, MCP-1, and OPN) were significantly elevated in the model group (\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) \u003cstrong\u003e(Figure 1C)\u003c/strong\u003e. Histopathological observations revealed extensive inflammatory infiltration in the lung tissue of the model group, characterized by thickening of the alveolar walls with granulocyte infiltration \u003cstrong\u003e(Figure 1D)\u003c/strong\u003e, lymphocyte aggregation around the bronchioles, and focal macrophage infiltration. Typical pathological changes included hydropic degeneration of the bronchial epithelium (pale cytoplasmic swelling), abnormal mucus secretion in the lumen, and proliferation of epithelioid cells forming cystic structures containing necrotic debris. No significant pathological changes were observed in the lung tissue of the blank control group. The animal model with typical pathological features of COPD was successfully established in this study.\u003c/p\u003e\n\u003cp\u003eAfter two weeks of intervention with BTHTT \u003cstrong\u003e(Figure 2)\u003c/strong\u003e, the body weight of rats in the model group increased slowly, while the weight gain in the treatment groups, especially the high-dose group, was significant (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), and the overall weight had returned to the level of the control group. Spontaneous activity analysis showed a significant improvement in the number of movements in the treatment groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01). Serum and BALF tests indicated that BTHTT intervention significantly reduced the elevated levels of TNF-\u0026alpha;, IL-6, IL-8, MCP-1, and OPN in the model group (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), and the levels of inflammatory factors in the high-dose group were no longer significantly different from those in the control group. Histopathological examination revealed persistent lymphocyte infiltration around the bronchioles and thickening of the alveolar walls with granulocyte infiltration in the model group; the low-dose group showed reduced inflammatory infiltration (local lymphocyte/neutrophil infiltration, slight alveolar thickening); and the high-dose group exhibited near-complete repair of pathological damage (no significant inflammatory cell infiltration or alveolar structural abnormalities). The results demonstrated that BTHTT has a dose-dependent therapeutic effect on COPD, with the high-dose group (twice the clinical dose) showing the best pathological repair capability.\u003c/p\u003e\n\u003cp\u003e3.2 Metabolomics study of BTHTT against COPD\u003c/p\u003e\n\u003cp\u003eThe total ion current (TIC) chromatograms of the QC samples are shown in \u003cstrong\u003eFigure S1A and S1B\u003c/strong\u003e. The response intensity and retention times of the chromatographic peaks largely overlapped, with correlation coefficients between QC samples exceeding 0.9, indicating good experimental reproducibility\u003cstrong\u003e\u0026nbsp;(Figure S1C and S1D)\u003c/strong\u003e. The proportion of peaks with relative standard deviation (RSD) \u0026le; 30% in the QC samples accounted for over 70% of the total number of peaks in the QC samples, confirming the stability of the analytical system and the suitability of the data for further analysis\u003cstrong\u003e\u0026nbsp;(Figure S1E and S1F)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ePCA analysis revealed distinct clustering within groups and clear separation between the blank and model groups, indicating successful induction of metabolic disturbances by the COPD model \u003cstrong\u003e(Figure 3A and 3B)\u003c/strong\u003e. The OPLS-DA model effectively distinguished the model group from the blank group\u003cstrong\u003e\u0026nbsp;(Figure 3C and 3D)\u003c/strong\u003e, with Q2 values of 0.659 and 0.757 in the positive and negative ion modes, respectively (Q2 \u0026gt; 0.5), demonstrating the model\u0026apos;s stability and reliability. Permutation tests \u003cstrong\u003e(Figure 3E and 3F)\u003c/strong\u003e showed that the R2 and Q2 values of the random models decreased with increasing permutation retention, confirming the absence of overfitting and the robustness of the original model. A total of 29 differential metabolites (positive/negative ion modes) were identified based on VIP \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05\u003cstrong\u003e\u0026nbsp;(Table S1)\u003c/strong\u003e, as shown in \u003cstrong\u003eFigure 3G and 3H\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003ePearson correlation analysis was performed on the identified small molecule metabolites to explore the metabolic relationships and regulatory interactions during the biological state changes. Metabolites with correlation coefficients greater than 0.8 were identified as key small molecules involved in synthesis and transformation. As shown in \u003cstrong\u003eFigures 4A and 4B\u003c/strong\u003e, glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine were identified as key endogenous metabolites according to the average correlation coefficient greater than 0.8. MetPA and MSEA enrichment analyses were conducted to identify the critical pathways associated with these metabolites and to uncover metabolically significant pathways with lower abundance changes. MetPA analysis \u003cstrong\u003e(Figure 4C)\u003c/strong\u003e identified 16 major metabolic pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis, tyrosine metabolism, pyrimidine metabolism, and glycine, serine, and threonine metabolism, with phenylalanine, tyrosine, and tryptophan biosynthesis (impact \u0026gt; 0.5) being the key metabolic pathways in the COPD rat model. MSEA analysis \u003cstrong\u003e(Figure 4D)\u003c/strong\u003e identified seven major metabolic pathways, including pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and melanogenesis, with pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism being the most critical (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). In summary, the five most critical metabolic pathways in the COPD rat model were identified as phenylalanine, tyrosine, and tryptophan biosynthesis; ketone acid metabolism; propionate metabolism; glycolysis/gluconeogenesis; and glycerolipid metabolism.\u003c/p\u003e\n\u003cp\u003eDuring BTHTT treatment, the metabolic markers in the treatment groups showed a trend towards normalization. In the high-dose group, 16 metabolic markers were significantly reversed (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), including the key endogenous metabolites glutathione (oxidized), phosphocholine, and L-palmitoylcarnitine (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), as shown in \u003cstrong\u003eFigure 4E\u003c/strong\u003e. In contrast, only seven metabolic markers were significantly reversed in the low-dose group, with no significant reversal observed for the key endogenous metabolites \u003cstrong\u003e(Table S1)\u003c/strong\u003e. These results further confirmed the superior therapeutic effects of the high-dose BTHTT treatment group. MetPA analysis revealed that the high-dose group significantly regulated 12 metabolic pathways, including phenylalanine, tyrosine, and tryptophan biosynthesis (impact \u0026gt; 0.5) \u003cstrong\u003e(Figure 4F)\u003c/strong\u003e. Compared with the control group, the MSEA analysis of the high-dose group showed no significant regulation of key metabolic pathways such as pyruvate metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism \u003cstrong\u003e(Figure 4G)\u003c/strong\u003e. In conclusion, BTHTT demonstrated significant therapeutic effects on COPD, with the high-dose group (twice the clinical dose) showing comprehensive reversal of the core metabolic disturbances in the COPD model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003eranscriptomics study of BTHTT against COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned results, further transcriptomics research was conducted on high-dose BTHTT for the treatment of COPD to identify key regulatory genes. The sequencing data and quality control met the experimental requirements, as shown in \u003cstrong\u003eTable S2\u003c/strong\u003e and \u003cstrong\u003eFigure S2\u003c/strong\u003e. The analysis results are presented in \u003cstrong\u003eFigure 5A\u003c/strong\u003e, with 173 regulatory genes identified using the criteria of \u003cem\u003ep\u003c/em\u003e-ad j \u0026lt; 0.05 and |log2(foldchange)| \u0026gt; 1, including 134 downregulated and 39 upregulated genes \u003cstrong\u003e(Table S3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSubsequently, functional annotation analysis was performed on the differentially expressed genes. The KEGG pathway annotation results of the differentially expressed genes are shown in\u003cstrong\u003e\u0026nbsp;Figure 5B\u003c/strong\u003e, revealing a strong association with the IL-17 signaling pathway, TNF signaling pathway, and cytokine-cytokine receptor interaction (\u003cem\u003ep\u003c/em\u003e-adj \u0026lt; 0.05). As illustrated in \u003cstrong\u003eFigure 5C\u003c/strong\u003e, protein-protein interaction network analysis of the differentially expressed genes indicated that, based on the centrality measure with a degree \u0026ge; 20, IL1\u0026beta; was identified as the central node of the protein interaction network. This suggests that IL1\u0026beta; is a core protein target for high-dose BTHTT in the treatment of COPD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCharacteristic study of IL1\u0026beta; in COPD via multi-algorithm integrated analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioinformatics methods were employed to conduct a characteristic study of COPD genes, particularly focusing on the key regulatory gene (IL1\u0026beta;) of BTHTT, through integrated multi-algorithm analysis.\u003c/p\u003e\n\u003cp\u003eBased on the GEO database, after batch correction analysis, the clinical patient experimental data from different chips were randomly ordered, indicating that the batch effect had been eliminated \u003cstrong\u003e(Figure S3)\u003c/strong\u003e. Following data correction, differential gene analysis was conducted with the criteria of |logFC|\u0026ge;1 and \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026le; 0.05. The results are shown in \u003cstrong\u003eFigure 6A\u003c/strong\u003e, identifying 32 upregulated and 2 downregulated differential genes, including SRPX2, CLDN10, TFF3, MUC1, TMEM45A, and LTF. Firstly, Lasso regression model was applied to screen the features of the 34 COPD differential genes. With the \u0026lambda; value corresponding to the minimum cross-validation error as the threshold, 16 core feature genes were identified \u003cstrong\u003e(Figures 6B and 6C)\u003c/strong\u003e, namely SRPX2, CLDN10, TFF3, HRASLS2, ALDH3A1, MUC1, MUC4, SERPINF1, TMEM45A, CYP1A1, IL1R2, MUC16, FOSB, SERPINB4, LTF, and CEACAM6. Secondly, based on SVM machine learning screening analysis, 30 characteristic genes of COPD disease were determined, as shown in\u003cstrong\u003e\u0026nbsp;Figure 6E\u003c/strong\u003e. The results were verified by \u003cstrong\u003eFigure 6D\u003c/strong\u003e, with the point of minimum error identified as the screening outcome. These genes included SERPINB4, ALDH3A1, FOSB, SRPX2, TFF3, CEACAM6, IL1R2, and HRASLS2. Lastly, using the random forest machine learning method \u003cstrong\u003e(Figures 6F and 6G)\u003c/strong\u003e, disease characteristic genes were screened based on their importance, including SRPX2, ATP6V0A4, TCN1, MUC1, IL1R2, CLDN10, TFF3, SERPINF1, FOSB, MUC4, CEACAM5, WFDC2, MSMB, ALDH1A3, TSPAN1, HRASLS2, S100P, ALDH3A1, GDF15, ADH7, and AGR2. Combining the above results, as shown in \u003cstrong\u003eFigure 6H\u003c/strong\u003e, 10 core characteristic genes of COPD were identified: SRPX2, CLDN10, TFF3, HRASLS2, ALDH3A1, MUC1, MUC4, SERPINF1, IL1R2, and FOSB. Differential expression analysis revealed significant differences in the expression of these genes between the COPD and control groups (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) \u003cstrong\u003e(Figure 6I)\u003c/strong\u003e. ROC curve analysis confirmed their good diagnostic performance (AUC \u0026gt; 0.7) \u003cstrong\u003e(Figure 6J)\u003c/strong\u003e. Validation set analysis further demonstrated significant differential expression of \u003cstrong\u003e\u003cem\u003eIL1R2, SRPX2,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eand \u003cstrong\u003e\u003cem\u003eTFF3\u0026nbsp;\u003c/em\u003e\u003c/strong\u003ein the validation set (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01)\u003cstrong\u003e\u0026nbsp;(Figure 6K)\u003c/strong\u003e, with AUC values of FOSB, \u003cstrong\u003e\u003cem\u003eIL1R2,\u003c/em\u003e\u003c/strong\u003e MUC4, \u003cstrong\u003e\u003cem\u003eSRPX2,\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eand \u003cstrong\u003e\u003cem\u003eTFF3\u003c/em\u003e\u003c/strong\u003e all \u0026gt;0.7, and an AUC of 0.816 for IL1R2 \u003cstrong\u003e(Figure 6L)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIn summary, the characteristic nature of IL1R2 in COPD detection was established. Although the characteristic results of IL1\u0026beta; were not directly found, combined with the literature, it can be revealed that IL1R2 is closely related to IL1\u0026beta;. Interleukin-1 receptor (IL-1R) is a cytokine receptor that binds to interleukin-1 (IL-1 \u0026alpha; or IL-1 \u0026beta;). It exists in two forms: type I receptor il-1r1 (IL1R1) and type II receptor il-1r2 (IL1R2). IL1R2 competes with IL1R1 for binding to IL1\u0026beta;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 \u0026nbsp; \u0026nbsp; \u0026nbsp;Validation analysis of IL1R2 competitive inhibition in IL1 \u0026beta; - IL1R1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the aforementioned results, we further verified the specific competitive role of IL1R2 in IL1 \u0026beta; - IL1R1. Firstly, we conducted detection studies of IL1\u0026beta; levels in blood and BALF during both the modeling and therapeutic intervention periods. The study revealed that during the modeling period, the levels of IL1\u0026beta; in the model group rats from BALF to blood were significantly elevated (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) \u003cstrong\u003e(Figure 7A)\u003c/strong\u003e. After therapeutic intervention \u003cstrong\u003e(Figure 7B)\u003c/strong\u003e, the levels of IL1\u0026beta; in all treatment groups significantly decreased (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01), with the most pronounced reduction observed in the high-dose group, which showed no significant difference compared to the blank control group. In the first part, the results showed that the occurrence and development of IL1 \u0026beta; were closely related to COPD in both key pathological tissues and systemic pathological changes. On the basis of treatment effectiveness, both could significantly reverse the content of IL1 \u0026beta; in all cases, and was in direct proportion to the treatment effect.\u003c/p\u003e\n\u003cp\u003eSecondly, based on the analysis results of IL1\u0026beta; in key pathological tissues, further transcriptome studies were carried out to observe the changes of IL1R2 and IL1R1 when the changes of IL1\u0026beta; happened with before and after treatment. In the transcriptomics study, during the modeling period \u003cstrong\u003e(Figure 7C)\u003c/strong\u003e, when IL1\u0026beta; levels in the model group rats were significantly elevated, IL1R2 levels were no significantly changing, while IL1R1 levels were significantly increased. However, after therapeutic intervention \u003cstrong\u003e(Figure 7D)\u003c/strong\u003e, the best therapeutic effect group（high group），the levels of IL1R2 has significant increased，IL1R1 was significantly reduced. This indicates that BTHTT have significantly increased IL1R2 and decreased IL1R1. The decrease of IL1R1 makes the binding of IL1 \u0026beta; - IL1R1 difficult, and the sudden increase of IL1R2 makes the binding of IL1 \u0026beta; - IL1R2 more possible. As a natural inhibitor, IL1R2 can bind and neutralize IL1\u0026beta;, further blocking its interaction with IL1R1, thereby terminating the related reactions of the downstream inflammatory cascade (see the IL-6, IL-8, TNF - \u0026alpha; and other results for details). This performance reached a related state similar to competitive inhibition.\u003c/p\u003e\n\u003cp\u003eFinally, based on the biochemical indicators and transcriptomic analysis results, immunohistochemical analysis of IL1R2 in lung tissue was conducted to observe the expression changes of IL1R2 protein in each group. Compared with the expression level of the best treatment group (high group), the expression level of IL1R2 positive cells in the lung tissue of rats in the high group was higher \u003cstrong\u003e(Figure 7E)\u003c/strong\u003e. The results further indicate that the complement phlegm resolving decoction can significantly increase the protein expression level of IL1R2 in the lungs of COPD rats.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 \u003cem\u003eIn vivo\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e component analysis of BTHTT\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;a\u003c/strong\u003e\u003cstrong\u003egainst COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUltra-high-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) was employed for the analysis. QC analysis demonstrated good experimental reproducibility with Pearson correlation coefficients greater than 0.9, indicating stable and reliable data (Figure S4). The base peak chromatograms (BPC) in both positive and negative ion modes are shown in \u003cstrong\u003eFigure 8A and 8B\u003c/strong\u003e. The serum samples from the high-dose group and the \u003cem\u003ein vitro\u003c/em\u003e test samples exhibited significant differences in the chromatograms. Moreover, clear distinctions were observed between these groups and the blank group as well as the blank serum plus \u003cem\u003ein vitro\u003c/em\u003e test samples.\u003c/p\u003e\n\u003cp\u003eThe acquired data, including mass, isotope distribution, and MS/MS fragmentation information, were compared with the commercial standard Traditional Chinese Medicine database from Shanghai Applied Protein Technology CO., Ltd. (Shanghai, China). The results were further matched with public databases such as GNPS \u003csup\u003e[20]\u003c/sup\u003e, ReSpect \u003csup\u003e[21]\u003c/sup\u003e, and Massbank \u003csup\u003e[22]\u003c/sup\u003e for compound identification and annotation. A total of 2547\u003cem\u003e\u0026nbsp;in vitro\u0026nbsp;\u003c/em\u003ecomponents of BTHTT were identified (1649 in positive ion mode and 968 in negative ion mode) using a mass error threshold of \u0026lt; 25 ppm for MS1 and a matching score \u0026gt; 0.7 for MS2. NPClassifier analysis indicated that the predominant \u003cem\u003ein vitro\u003c/em\u003e components were alkaloids (24%) and shikimate/phenylpropanoid derivatives (24%) \u003cstrong\u003e(Figure 8C)\u003c/strong\u003e. Further analysis of the base peak chromatograms led to the selection of 38 high-abundance \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003ecomponents of BTHTT\u003cstrong\u003e\u0026nbsp;(Figures 8D and 8E, Table S4)\u003c/strong\u003e, with shikimate/phenylpropanoid derivatives accounting for 51% \u003cstrong\u003e(Figure 8F)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the analysis and identification results of blank control serum and blank serum plus \u003cem\u003ein vitro\u003c/em\u003e test samples of BTHTT, combined with the \u003cem\u003ein vitro\u003c/em\u003e full component analysis results, a background subtraction algorithm in the chemometric module was applied to the high-dose group to ultimately determine the \u003cem\u003ein vivo\u003c/em\u003e components of BTHTT. A total of 283\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e components were identified (153 in positive ion mode and 132 in negative ion mode). NPClassifier chemical classification revealed that the predominant \u003cem\u003ein vivo\u003c/em\u003e components were shikimate/phenylpropanoid derivatives (39%), alkaloids (20%), and terpenoids (16%)\u003cstrong\u003e\u0026nbsp;(Figure 8G)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe results of the\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e components were further cross-referenced with public databases such as PubChem and ChemSpider. Using a mass error threshold of ppm \u0026le; \u0026plusmn;5 and a matching score \u0026gt; 0.9 for MS2, 71 \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ecomponents of BTHTT were ultimately identified\u003cstrong\u003e\u0026nbsp;(Table S5)\u003c/strong\u003e. Among them, seven high-abundance \u003cem\u003ein vitro\u003c/em\u003e components that were also detected \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ewere identified: Vitamin B1, magnoflorine, cryptotanshinone, tanshinone IIA, 3,4-dihydroxyphenylacetic acid, salvianolic acid A, and gibberellin A4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIdentification of the therapeutic substance basis of BTHTT a\u003c/strong\u003e\u003cstrong\u003egainst COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFocusing on the core protein targets associated with the critical pathological targets of COPD, molecular docking analysis was performed on the \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ecomponents of BTHTT using the SMINA software to establish a \u0026ldquo;dose-effect\u0026rdquo; relationship between the components and the body. Through the AI supercomputing platform, the therapeutic substance basis of BTHTT against COPD was intelligently identified. As shown in the\u003cstrong\u003e\u0026nbsp;Table S6\u003c/strong\u003e, with a binding energy threshold of \u0026le; \u0026minus;8 kcal/mol, seven important compounds in BTHTT were ultimately identified to potentially interact well with the core protein target (IL1\u0026beta;) via IL1R2, and it should be pointed out that tanshinone IIA and cryptotanshinone are not only the two components with the lowest molecular docking potential, but also the only two components with high content \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the results of molecular docking, molecular dynamics simulations were conducted to further confirm the binding affinity of tanshinone IIA and cryptotanshinone with IL1R2, providing a detailed description of the dynamic stability of their interactions. As shown in Figure 9A and B, after 300 ps, the system energies of tanshinone IIA and cryptotanshinone converge, reaching the minimum energy. After NVT equilibration, the temperature of both systems stabilized around 300 K, indicating proper temperature control. Following NPT equilibration, the pressure of both systems remained stable around 1 bar. Although fluctuations were observed, the system density remained relatively stable, suggesting proper pressure control. After 17 ns, the RMSD of both systems reached stability, fluctuating within a range of 0.2 nm. During the 20 ns simulation, the energy of both systems exhibited minimal fluctuation, indicating good stability of the simulated systems. In conclusion, tanshinone IIA and cryptotanshinone exhibit good stability upon binding with IL1R2.\u003c/p\u003e"},{"header":"3\tDiscussion","content":"\u003cp\u003eThis study, focusing on the therapeutic substance basis of BTHTT, for the first time adopted a \u0026ldquo;targeted protein extraction strategy\u0026rdquo;. Using the core protein targets associated with the critical pathological targets of COPD as the entry point, and integrating metabolomics, transcriptomics, multi-algorithm joint mining, and TCM serum pharmacochemistry techniques, a systematic research method was constructed that links \u0026ldquo;endogenous substance regulation\u0026rdquo; with \u0026ldquo;exogenous component action\u0026rdquo;. This strategy breaks through the traditional single-direction screening model of \u0026ldquo;component-target-disease\u0026rdquo; in the study of TCM formulas. By characteristically screening core targets (IL1\u0026beta;) in a reverse manner to lock in the therapeutic substances, the precision of the research was significantly enhanced.\u003c/p\u003e\n\u003cp\u003eThe study results reveal that IL1\u0026beta;, as a core protein target in the regulatory network of BTHTT, is closely associated with the pathological development of COPD. As a key pro-inflammatory cytokine of the IL-1 family, IL1\u0026beta; activates the IL-1 receptor (IL-1R1) and recruits accessory proteins (IL-1RAcP), directly triggering the activation of the downstream NF-\u0026kappa;B pathway \u003csup\u003e[2324]\u003c/sup\u003e. This activation induces the efficient expression of pro-inflammatory cytokines such as TNF-\u0026alpha; and IL-6. Subsequently, IL1\u0026beta; and IL-6 work together to promote the differentiation of Th17 cells and induce the secretion of IL-17. TNF-\u0026alpha; and IL-17 further collaborate to enhance the differentiation capacity of Th17 cells, thereby amplifying the inflammatory response \u003csup\u003e[25]\u003c/sup\u003e. The escalating inflammatory response induces the expression of MUC proteins (mucin family), which is successively confirmed by MUC1, MUC4, and MUC13 in the transcriptomics analysis. As transmembrane mucins, MUC proteins are crucial for the protection of the mucosal barrier. They can alter mucus viscosity by binding to extracellular matrix components, exacerbating airway obstruction. Airway obstruction is a core pathological feature of COPD and a direct driver of lung function decline\u003csup\u003e[26,27]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe metabolomics study further complements the aforementioned mechanistic hypothesis. Inflammation, by activating the NF-\u0026kappa;B pathway, inhibits the activity of phenylalanine hydroxylase (PAH), leading to the accumulation of phenylalanine and a reduction in tyrosine synthesis \u003csup\u003e[28]\u003c/sup\u003e. The decrease in phenylalanine results in an increase in intermediate products such as phenylpyruvate, with significant changes observed in the biosynthesis of phenylalanine, tyrosine, and tryptophan. Concurrently, the enhanced inflammatory response is inevitably accompanied by the generation of reactive oxygen species (ROS). The accumulation of ROS leads to the oxidation of reduced glutathione (GSH) to oxidized glutathione (GSSG), disrupting the intracellular redox balance and exacerbating tissue damage. The elevation of GSSG may further activate inflammasomes, promoting the cleavage of pro-IL1\u0026beta; and the release of mature IL1\u0026beta;, thus creating a vicious cycle \u003csup\u003e[29]\u003c/sup\u003e. The significance of GSSG is also highlighted in the metabolomics analysis. IL1\u0026beta;, by activating the inflammatory response, inhibits mitochondrial fatty acid \u0026beta;-oxidation, leading to the accumulation of long-chain acylcarnitines (e.g., L-palmitoylcarnitine) within cells. This accumulation exacerbates energy metabolism disorders and subsequently leads to disturbances in ketone acid metabolism, propionate metabolism, glycolysis/gluconeogenesis, and glycerolipid metabolism.\u003c/p\u003e\n\u003cp\u003eHowever, it is important to note that the activation of IL-1 receptors by IL1\u0026beta; involves not only IL-1R1 but also IL1R2, which can compete with IL-1R1. In fact, the relationship between IL1R2 and IL1\u0026beta; is not limited to simple receptor-ligand binding but is tightly linked through multiple mechanisms, including competitive inhibition, signaling pathway regulation, and immune microenvironment remodeling. Their interactions play a central role in inflammatory diseases, tumor progression, and immunotherapy, and targeted interventions against this axis (neutralizing antibodies and combination immunotherapy) have shown significant potential. This close functional dependence and pathological relevance make it a highly promising key target for the treatment of COPD. As a decoy receptor for IL1\u0026beta;, IL1R2 competitively binds to IL1\u0026beta; with high affinity through its extracellular domain, blocking the binding of IL1\u0026beta; to the functional receptor IL1R1. Since IL1R2 lacks intracellular signaling domains (TIR domains), this binding does not activate downstream pro-inflammatory signaling pathways (e.g., NF-\u0026kappa;B), thereby inhibiting a series of pro-inflammatory responses induced by IL1\u0026beta; \u003csup\u003e[30,33]\u003c/sup\u003e. We have comprehensively demonstrated through physiological and biochemical indicators, transcriptomics, and immunohistochemistry that BTHTT can significantly activate the increase of IL1R2 and the decrease of IL1R1. Moreover, the sudden increase of IL1R2 makes the binding of IL1 \u0026beta; - IL1R2 more likely. This competitive inhibitory state may be one of the main mechanisms of BTHTT. Therefore, molecular docking and molecular dynamics analysis were further employed to screen the \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ecomponents of BTHTT around IL1R2 and IL1R1. The results suggest that these components could serve as the therapeutic substance basis, they were efficiently binding to IL1R2 to competitively inhibit the functional binding of IL1R1 and IL1\u0026beta; in large probability.\u003c/p\u003e\n\u003cp\u003eIn summary, this study for the first time reveals the therapeutic substance basis of BTHTT and explores its main therapeutic effects against COPD through the competitive binding of IL1R2, which reverses the functional interaction between IL1R1 and IL1\u0026beta;. as shown in Figure 10.This provides a scientific basis for further investigation into its mechanisms and offers a new approach for the integrated multi-omics study of TCM formulas.\u003c/p\u003e"},{"header":"4\tMaterials and Methods","content":"\u003cp\u003e\u003cstrong\u003e4.1\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Instruments and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eeagents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVanquish UHPLC-Q-Exactive HFX MS and Vanquish Neo UHPLC-Orbitrap Exploris 480 MS systems (Thermo Scientific, USA), Nanodrop ND-2000 spectrophotometer, Qubit 4.0 fluorometer, and Agilent 4150 bioanalyzer (Agilent Technologies, USA), Illumina Novaseq 6000 sequencing platform (Illumina, USA), and Eppendorf Centrifuge 5430 R (Eppendorf, Germany) were used in this study. The small animal spontaneous activity recorder KW-ZF was purchased from Nanjing Karlvin Biotechnology (China).\u003c/p\u003e\n\u003cp\u003eBTHTT (Astragalus membranaceus, Pseudostellaria heterophylla, Cinnamomum cassia, Angelica sinensis, Salvia miltiorrhiza, Perilla frutescens, Lepidium sativum, Lepidium apetalum, Mahonia fortunei) was obtained from Guizhou Shanhaitongjia Pharmaceutical Co., Ltd. and authenticated to meet the pharmacopoeia standards by the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine. Materials for animal model construction, including LPS (Shandong Huino Pharmaceutical), experimental cigarettes (Harbin Taihua), and sodium pentobarbital (Shanghai Experimental Reagent Procurement Company), were purchased through regular channels. ELISA kits (TNF-α and IL-1β) were purchased from Shanghai Enzyme-Linked Biotechnology. Molecular biology reagents, such as Trizol (Magen, China) and RNA library construction kits (ABclonal, USA), were selected from commercial brands. Chromatographic-grade acetonitrile and methanol (Fisher, USA) and ammonium acetate (SIGMA, Germany) were used for LC-MS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 72 male Sprague-Dawley (SD) rats of SPF grade (230±20 g) were purchased from Tianqin Biotechnology (Changsha, China; SYXK2018-007). After 1 week of adaptive housing under standard conditions (25±1℃, 50±5% humidity, 12 h light-dark cycle), the rats were randomly divided into the control group (n = 24) and the model group (n = 48). The experimental protocol was approved by the Animal Ethics Committee of Guizhou Medical University (2303411) and followed the ARRIVE guidelines and animal welfare regulations.\u003c/p\u003e\n\u003cp\u003eThe model group was subjected to dual-factor modeling: Intermittent cigarette smoke exposure (9 weeks, 6 days per week, 3 Huangshan cigarettes per day divided into 2 sessions, 30 min per session). Intratracheal instillation of LPS (200 μg per instillation, once every other week)\u003csup\u003e[34,35]\u003c/sup\u003e . The control group received an equivalent volume of normal saline. After successful modeling, the model group was randomly divided into the model group (n = 12), the high-dose BTHTT group (High, 2× clinical dose, n = 12), and the low-dose group (Low, 1× clinical dose, n = 12), with continuous gavage intervention for 2 weeks. The control and model groups were given distilled water synchronously.\u003c/p\u003e\n\u003cp\u003eBody weight and spontaneous activity parameters (number of movements/time) were recorded weekly. At the end of modeling and treatment, lung tissue, serum, and bronchoalveolar lavage fluid (BALF) were collected. Levels of inflammatory markers (i.e., TNF-α, IL-1β, IL-6, IL-8, OPN, and MCP-1) were measured using ELISA according to the kit instructions. Tissue pathology and multi-omics analyses were also performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDetermination of BTHTT\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eosing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOPD case records revealed that, based on previous clinical dosing, the standard dose of BTHTT for adults is approximately 1.57 g/kg/day. Therefore, the corresponding dose for rats should be 9.9 g/kg/day. Considering the final concentrated volume of BTHTT as 50 mL, the clinical equivalent dose for rats was calculated to be 1.6 mL, administered via gavage once daily.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4\u0026nbsp; \u0026nbsp; \u0026nbsp; Sample preparation and collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4.4.1 Tissue sample collection\u003c/p\u003e\n\u003cp\u003eAt the experimental endpoints (Week 9 and Week 11), rats were anesthetized via intraperitoneal injection of 5% sodium pentobarbital (1 mL/100 g). Blood was collected from the portal vein, allowed to stand for 30 minutes at room temperature, and then centrifuged at 13000×g for 15 minutes at 4℃. The supernatant was stored at -80℃. Lung tissues were rapidly frozen in liquid nitrogen and stored at -80℃. BALF was collected by three consecutive lavages with cold PBS.\u003c/p\u003e\n\u003cp\u003e4.4.2 Sample preparation for metabolomics analysis\u003c/p\u003e\n\u003cp\u003eLung tissue (20-50 mg) was homogenized in pre-chilled methanol-acetonitrile-water (2:2:1, v/v) and sonicated in an ice bath. The homogenate was centrifuged at 13000 rpm for 20 minutes at 4℃. The supernatant was vacuum-concentrated, re-dissolved in acetonitrile-water (1:1, v/v), and filtered through a 0.22 μm membrane for LC-MS analysis\u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e4.4.3 Sample preparation for transcriptomics analysis\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from lung tissue using TRIzol reagent and quality-controlled using Nanodrop ND-2000 (A260/A280 \u0026gt; 1.8) and Agilent 4150 (RIN ≥ 7.0). RNA sequencing libraries were constructed using the ABclonal mRNA-seq Kit: mRNA was enriched with oligo(dT), fragmented, and used to synthesize double-stranded cDNA, which was then ligated with Illumina adapters and amplified by PCR. Libraries were quantified using Qubit and quality-checked with Agilent 4150 before sequencing on the Illumina Novaseq 6000 platform\u003csup\u003e[37]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e4.4.4 Preparation of BTHTT samples for \u003cem\u003ein vitro\u003c/em\u003e testing\u003c/p\u003e\n\u003cp\u003eBTHTT was extracted by decoction in water (twice, 30 minutes each), concentrated to 1.1-1.2 g/mL, and then freeze-dried. Before analysis, 600 μL of the freeze-dried powder solution was mixed with 400 μL of methanol, re-dissolved in 40% methanol, and centrifuged at 16000 rpm to collect the supernatant.\u003c/p\u003e\n\u003cp\u003e4.4.5 Preparation of BTHTT samples for\u003cem\u003e\u0026nbsp;in vivo\u0026nbsp;\u003c/em\u003etesting\u003c/p\u003e\n\u003cp\u003eSerum was deproteinized by mixing with methanol (1:1) and precipitating at −20℃ for 30 minutes, followed by centrifugation at 16000 rpm for 20 minutes. The supernatant was vacuum-dried and re-dissolved in 40% methanol to obtain the final sample. For the preparation of blank serum + BTHTT samples, an appropriate amount of blank serum was spiked with the\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e BTHTT supernatant, and the remaining steps were consistent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5\u003c/strong\u003e\u003cstrong\u003eEstablishment of analytical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4.5.1 Establishment of metabolomics analysis method\u003c/p\u003e\n\u003cp\u003eThe chromatographic separation was achieved using an ACQUITY UPLC BEH Amide column (1.7 μm, 2.1 mm × 100 mm; Waters, USA). The column temperature was maintained at 25℃. The mobile phase consisted of (A) water containing 25 mM ammonium acetate and 25 mM ammonia, and (B) acetonitrile. The flow rate was set at 0.5 mL/min, and the injection volume was 2 μL. The gradient elution program was as follows: 0-0.5 min, 95% B; 0.5-7 min, linear decrease of B from 95% to 65%; 7-8 min, linear decrease of B from 65% to 40%; 8-9 min, B held at 40%; 9-9.1 min, linear increase of B from 40% to 95%; 9.1-12 min, B held at 95%. During the entire analysis, samples were kept at 4℃ in the autosampler. To ensure the stability of the system and the reliability of the experimental data, samples were analyzed in a random sequence with quality control (QC) samples interspersed in the sample queue. The mass spectrometry analysis was performed using a Vanquish LC ultra-high-performance liquid chromatography system (UHPLC) coupled with an Orbitrap Exploris™ 480 mass spectrometer (Thermo). The samples were ionized using electrospray ionization (ESI) in both positive and negative ion modes. The ESI source and MS settings were as follows: nebulizer gas (Gas 1) was set at 50, auxiliary gas (Gas 2) at 2, ion source temperature at 350℃, and spray voltage (ISVF) at 3500 V in positive ion mode and 2800 V in negative ion mode. The mass range for MS1 was set from 70 to 1200 Da with a resolution of 60000 and a scan accumulation time of 100 ms. For MS2, data-dependent acquisition (DDA) with stepped collision energy was used. The mass range for MS2 was also set from 70 to 1200 Da with a resolution of 60000 and a scan accumulation time of 100 ms. The dynamic exclusion time was set at 4 s.\u003c/p\u003e\n\u003cp\u003e4.5.2 Establishment of analytical methods for \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e components\u003c/p\u003e\n\u003cp\u003eSamples were separated using a Vanquish UHPLC system (Thermo Fisher Scientific, Bremen, Germany) equipped with an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 µm). The column temperature was maintained at 35°C, and the flow rate was set at 0.3 mL/min. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. Gradient elution was performed as shown in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eGradient elution method for \u003cem\u003ein vivo\u003c/em\u003e and\u003cem\u003e\u0026nbsp;in vitro\u0026nbsp;\u003c/em\u003ecomponent analysis of BTHTT.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTime (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMobile Phase A (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMobile Phase B (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInitial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e17.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Q-Exactive HFX mass spectrometer was used for the acquisition of both MS1 and MS2 spectra. The mass spectrometer was coupled with the UHPLC system and operated in both positive and negative electrospray ionization (ESI) modes. The ESI parameters were as follows: spray voltage 3800 V (ESI+) / 3500 V (ESI-), sheath gas pressure 45 arb, auxiliary gas pressure 20 arb, ion transfer tube temperature 320°C, and vaporizer temperature 350°C. The detection mode was set to full scan/data-dependent MS2 (Full-MS/dd-MS2) with resolutions of 60000 for MS1 and 15000 for MS2. The top 10 MS1 ions were selected for MS/MS fragmentation with stepped normalized collision energies of 20, 40, and 60. The mass range for MS1 was set from 90 to 1300 Da. For \u003cem\u003ein vivo\u003c/em\u003e analysis, including blank group samples, dosed group samples, and blank group + BTHTT samples, 6 µL of each sample was precisely injected. For\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e analysis of BTHTT, 2 µL of the sample was injected. Each batch of blank and dosed group samples was injected once, while the blank group + BTHTT samples were injected in triplicate, and the BTHTT samples were injected in quintuplicate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.6 Data processing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e4.6.1 Metabolomics data analysis\u003c/p\u003e\n\u003cp\u003eRaw metabolomics data were converted to the mzXML format using ProteoWizard and then processed with XCMS software for peak alignment, retention time correction, and peak area extraction. The data preprocessing workflow included the following steps: Firstly, ion peaks with a missing rate \u0026gt; 50% were removed. Secondly, remaining missing values were imputed using the KNN algorithm. Thirdly, metabolic features with a relative standard deviation (RSD) \u0026gt;50% were discarded. The quality of the experimental data was assessed using principal component analysis (PCA) and clustering of QC samples. Subsequent analyses included univariate statistics (e.g., t-tests), multivariate statistics (PLS-DA), differential metabolite screening (VIP \u0026gt; 1 and \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt;0.05), and KEGG pathway enrichment analysis (hypergeometric test) \u003csup\u003e[38,39]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e4.6.2 \u003cem\u003eIn vivo\u003c/em\u003e and\u003cem\u003e\u0026nbsp;in vitro\u003c/em\u003e component data analysis\u003c/p\u003e\n\u003cp\u003eData in the mzXML format were processed using XCMS and compounds were identified based on a local high-resolution commercial traditional Chinese medicine mass spectrometry database. The criteria for identification were set as follows: a mass error of \u0026lt; 25 ppm for MS1 and a match score \u0026gt; 0.7 for MS2 (where the Score reflects the similarity of fragment ions, with ≥ 0.7 being a reliable threshold) \u003csup\u003e[40,41]\u003c/sup\u003e. Statistical analysis included the counting of compounds and their classification (e.g., flavonoids, alkaloids), which was completed in conjunction with annotations from the mass spectrometry database \u003csup\u003e[42]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e4.6.3 Transcriptomics data analysis\u003c/p\u003e\n\u003cp\u003eRaw transcriptomics data generated by the Illumina platform (in fastq format) were processed using Perl scripts to remove adapter sequences and low-quality reads (reads with Q ≤ 25 bases accounting for \u0026gt; 60% or N rate \u0026gt; 5%). Clean reads were obtained after this filtering process. The clean reads were aligned to the reference genome using HISAT2, and gene expression levels were quantified using FeatureCounts to calculate FPKM values. Differential expression analysis was performed using DESeq2 (with screening criteria of |log2FC| \u0026gt; 1 and \u003cem\u003ep\u003c/em\u003e-adj \u0026lt; 0.05). Transcription factor annotation was based on Animal TFDB or Pfam/DBD databases, matching gene IDs and protein domain information \u003csup\u003e[43]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 Collection of COPD public data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was utilized to retrieve information related to COPD patients. The involved datasets included GSE11784, GSE12472, GSE16972, GSE38974, and GSE222965. Platform data and matrix files that met the criteria were downloaded. Based on the annotation information in the platform files, the one-to-one correspondence between probes and gene files was identified to obtain the gene expression matrix files. The files were formatted with gene names as row names and sample names as column names for reading and forming the COPD database, i.e., the “gene-expression” analysis. Multiple COPD databases were merged to obtain the overall dataset and related expression levels for in-depth mining using various algorithms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.8 Establishment of statistical and molecular simulation analysis methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this experiment, GraphPad Prism 8 software was used for statistical data analysis, with group calculations performed using t-tests or one-way analysis of variance (ANOVA). For molecular docking analysis, component data were downloaded from the PubChem database in the form of structural data information for active substances. The small molecule structures and energies were optimized using ChemOffice. Key target protein structure information was downloaded from the PDB database, and PyMol was used to separate the original protein ligands from the proteins, saving them separately after removing water molecules. AutoDockTool was employed to add hydrogens and charges to each protein structure, which were then saved in PDBQ format for subsequent docking analysis. Vina was selected as the docking software to perform molecular docking, generating 15 conformations each time, with the lowest binding energy used as the binding energy statistical summary for each protein-molecule docking. Finally, the interaction modes of receptor-ligand complexes were analyzed using the PLIP v2.3.0 online analysis website (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index), and pse format files were exported for visualization analysis using PyMol software.\u003c/p\u003e\n\u003cp\u003eThe topology files of IL1R2 and the potential active compound were prepared based on the GROMOS96 43a1 force field. An SPC water model was used to construct an icosahedral box, and water molecules were added. Ions were introduced into the system to ensure its electrical neutrality. The system underwent energy minimization, followed by temperature coupling using the V-rescale method, with an NVT ensemble set at 300 K, a time step of 2 fs, and a simulation time of 100 ps. Pressure coupling was performed using the Parrinello–Rahman method, with an NPT ensemble set at 1 bar and a simulation time of 100 ps. The molecular dynamics simulation was carried out for a total duration of 10 ns, with the trajectory being saved. Short-range electrostatic interactions were calculated using the cut-off method, with a cut-off radius of 1.2 nm. Long-range electrostatic interactions were computed using the particle mesh Ewald (PME) method. The molecular dynamics simulations of the spike protein and the potential active compound were performed using the GROMACS package. The stability of the constructed complex system was evaluated by performing least-squares fitting of the RMSD values.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u0026nbsp;\u003c/strong\u003eFigure S1: Quality control analysis of metabonomics, Figure S2: The FPKM analysis for gene expression in RNA-seq, Figure S3: The batch effect analysis of transcriptome data for clinical patients based on GEO database, Figure S4: The correlation analysis of QC samples for BTHTT in positive and negative ion mode, Table S1: Identification and regulatory analysis of metabolic markers in COPD rat models, Table S2: Quality analysis of data from rat samples in each group in transcriptomics, Table S3: Differential gene analysis between the high-dose group and the model group rats, Table S4: Identification of high-content components in BTHTT based on base peak chromatogram analysis, Table S5: The analysis of transitional components in blood for BTHTT, Table S6: Screening and analysis of the therapeutic substance basis of BTHTT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eXin Cha and Shao-bo Liu conducted the animal experiments and drafted the manuscript; Wen-wei Gong and Xue-ying Li assisted with the experiments and data analysis; Qing Xia contributed to the animal experiments; Jing-hua Ruan handled data processing; Zhu-sheng Zhu revised the manuscript; Xiang-chun Shen co-supervised the project. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003eThis work was supported by grants from the National key R\u0026amp;D plan project (2022YFC2503003); National Natural Science Foundation of China Youth Fund Project (82405036); Guizhou Provincial high-level innovative talents hundred level talents (GCC[2023]048); The unveiling and leading projects from State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine (JBGS-FAMP202304); The open fund of the State Key Laboratory of Discovery and Utilization of Functional Components in Traditional Chinese Medicine (Qian Jiao Ji [2023] No. 112); Guizhou Provincial Health Commission science and Technology (gzwkj2024-516); Launch of high-level talents in Guizhou Medical University (XBH J [2022] No. 008). Key Project of the Research and Development Program (LSLSKL20240101), National Key Laboratory of Classical Formulas and Modern TCM Integration and Innovation; Study on the mechanism of Chuanxiong protein-polysaccharide complex based on the characteristics of long-circulating Pickering milk to promote the treatment of headache with Chuanxiong (82360779); National and Provincial Science and Technology Innovation Talent Team Cultivation Program of Guizhou University of Traditional Chinese Medicine, Guizhou University of Traditional Chinese Medicine TD Hopes [2023] 005.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe animal experiments were approved by the experimental animal ethics committee of Guizhou Medical University (No.2303411). All animal experiments complied with the ARRIVE statement on animal use in biomedical research. We conffrmed that all experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Publicly available gene expression datasets used in this study were obtained from the Gene Expression Omnibus (GEO) under the following accession numbers: GSE11784, GSE12472, GSE16972, GSE38974, and GSE222965. Additionally, the original high-throughput sequencing data generated during the experimental phase of this study have been deposited in the NCBI BioProject database under accession number PRJNA1286104, accessible via the following link:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1286104.\u003c/p\u003e\n\u003cp\u003eAll datasets are publicly available and meet the journal’s data sharing requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eN.A.Negewo, et al.,COPD and its comorbidities: Impact, measurement and mechanisms. Respirology, \u003cstrong\u003e20\u003c/strong\u003e,1160-1171 (2015).\u003c/li\u003e\n\u003cli\u003eC. Raherison, et al., Epidemiology of COPD. Eur Respir Rev, \u003cstrong\u003e18\u003c/strong\u003e,213-221. 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J Transl Med, \u003cstrong\u003e21\u003c/strong\u003e,330.(2023)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bu-Ti-Hua-Tan-Tang, chronic obstructive pulmonary disease, multiomics, multi-algorithm mining technology, therapeutic substance basis","lastPublishedDoi":"10.21203/rs.3.rs-7026355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7026355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is the third leading cause of death globally, and there are some limitations in the therapeutic effect of COPD triple therapy. Bu-Ti-Hua-Tan-Tang (BTHTT), a classic Traditional Chinese Medicine formula, has demonstrated significant clinical efficacy, yet its therapeutic substance basis and mechanisms remain unclear. This study employed a targeted protein extraction strategy to broadly screen for BTHTT components that migrate into the bloodstream, focusing on core protein targets associated with key pathological targets. We achieved intelligent identification of the therapeutic substance basis and mechanisms of BTHTT. Based on the significant therapeutic effects observed in the high-dose group, we identified key metabolic small molecules such as glutathione (oxidized form), phosphatidylcholine, and L-palmitoylcarnitine, and determined critical metabolic regulatory pathways of BTHTT, including phenylalanine, tyrosine, and tryptophan biosynthesis. Furthermore, we discovered IL1β as a core protein target within its protein regulatory interaction network and elucidated the primary mechanism of BTHTT, which involves competitive reversal of the functional binding of IL1β to IL1R1 via IL1R2. We identified seven components, including tanshinone IIA and cryptotanshinone, with high binding affinity (binding energy ≤−8 kcal/mol) to the IL1R2-IL1β and IL1β-IL1R1 interactions. This study is the first to reveal the potential therapeutic substance basis of BTHTT and explore its mechanism through competitive inhibition of IL1β binding to the functional receptor IL1R1 via IL1R2. It provides a scientific basis for further investigation into the mechanisms of BTHTT and offers a novel approach for the integrated multi-omics study of Traditional Chinese Medicine formulas.\u003c/p\u003e","manuscriptTitle":"Therapeutic substance basis of Bu-Ti-Hua-Tan-Tang for chronic obstructive pulmonary disease: A targeted protein extraction approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 18:53:41","doi":"10.21203/rs.3.rs-7026355/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"19251986-fc85-4642-819b-2c71c3811394","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51938059,"name":"Biological sciences/Biochemistry"},{"id":51938060,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":51938061,"name":"Health sciences/Diseases"},{"id":51938062,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2025-08-26T10:24:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 18:53:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7026355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7026355","identity":"rs-7026355","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}