Based on network meta-analysis (NMA) and network pharmacology approaches, we explored the clinical efficacy of different regimens, and clarified the pharmacological mechanisms of N-butylphthalide (NBP) in the treatment of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP).Firstly, NMA was conducted to obtain the ranking of the efficacy of different regimens for the treatment of DEACMP. Secondly, the drug with a relatively high efficacy ranking was selected and its mechanism of treatment for DEACMP was identified through a network pharmacology analysis. By the use of protein interaction and enrichment analysis, the pharmacological mechanism was predicted, and molecular docking was subsequently carried out to verify the reliability of the results.A total of 17 eligible randomized controlled trials (RCTs) involving 1293 patients and 16 interventions were eventually included in our analysis from NMA. Mesenchymal stem cells (MSCs) + NBP significantly increased mini-mental state examination (MMSE) and Barthel index (BI) scores; NBP + dexamethasone (DXM) was the most effective treatment in improving the activity of daily living (ADL) scores; NBP significantly decreased national institutes of health stroke scale (NIHSS) scores; Xingzhi-Yinao granules (XZYN) had more advantages in improving Montreal cognitive assessment (MoCA) scores, translational direct current stimulation (tDCS) had a significant effect in improving P300 latency and P300 amplitude and Kinnado + Citicoline had the most obvious effect in improving malondialdehyde (MDA). Meanwhile, by network pharmacology analysis, 33 interaction genes between NBP and DEACMP were obtained, and 4 of them were identified as possible key targets in the process of MCODE analysis. 516 Gene ontology (GO) entries and 116 Kyoto Encyclopedia of Gene and Genome (KEGG) entries were achieved by enrichment analysis. Molecular docking showed that NBP had good docking activity with the key targets.ALB, ESR1, EGFR, HSP90AA1, and other targets, and may play a role in neuroprotection for patients with DEACMP by modulating Lipid and atherosclerosis, IL-17 signaling pathway, MAPK signaling pathway, FoxO signaling pathway, PI3K/AKT signaling pathway.The NMA screened for regimens with better efficacy for each outcome indicator in order to provide a reference for clinical treatment. NBP can stably bind  Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a pathological phase after the “false recovery period,” in which patients with acute carbon monoxide (CO) poisoning have no any clinical symptoms or the clinical symptoms have been significantly alleviated for several days or even weeks after treatment, and then they reappear neuropsychiatric symptoms dominated by dementia and motor disorder. The cerebral white matter's extensive demyelination in DEACMP is the primary pathogenic alteration, which is specifically manifested as cognitive impairment, slow response, dyskinesia, autonomic nervous system dysfunction, Parkinson's syndrome, and abnormal behavior or mobility disorder. The most frequent and efficient treatment for acute CO poisoning is hyperbaric oxygen therapy (HOT) –3, but iThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  for NMA Diagnostic Criteria for DEACMP . Any of The inclusion criteria of the present study was set as follows: (1) Randomized controlled trials (RCTs); (2) Patients diagnosed with DEACMP; (3) Interventions included commonly used drugs for the treatment of DEACMP; and (4) At least one of the following outcomes was included: mini-mental state examination (MMSE), the national institutes of health stroke scale (NIHSS), the activity of daily living (ADL), Montreal cognitive assessment (MoCA), Barthel index (BI), P300 latency, P300 amplitude, malondialdehyde (MDA), and adverse reactions were measured with more than one outcome as their endpoints.Exclusion criteria of the present study was: (1) Animal experiments; (2) Inconsistent intervention measures; (3) Duplicate articles; (4) Diagnostic or efficacy evaluation criteria were not mentioned; (5) There were obvious statistical errors in the literature; and (6) Research whose full text cannot be obtained.According to the predetermined screening criteria, two researchers independently screened the papers, extracted data, and conducted cross-checks. Any disagreements among the researchers were discussed or arbitrated by a third party. Then articles were imported into Endnote, duplicates were removed, and preliminary screening was done by reading the titles and abstracts. If an article satisfied the requirements for inclusion, the reviewers carried out a full-text read. Finally, the first author, year of publication, sample size, age and course of the disease, intervention measures, outcome indicators, and course of treatment were mostly extracted using an Excel data extraction table.The risk of bias (ROB) of the included studies was assessed using the Cochrane Risk of Bias tool. These studies were estimated from seven domains of ROB, defined as having a high, low, or unclear ROB, and the seven domains included “random sequence generation,” “allocation concealment,” “blinding of participants and personnel,” “blinding of outcome assessment,” “incomplete outcome data,” “selective reporting,” and “other bias.” The judgment of ROB was carried out by two authors separately in Review Manager (Version 5.3).The statistical method of NMA was based on the frequency framework, and all outcome indicators were analyzed by the random effect model. For continuous variables, the standardized mean difference (SMD) was used as the effect size; for dichotomous variables, the odds ratio (OR) was as the effect size and the corresponding 95% confidence interval (CI) was calculated. Stata 16.0 software was applied to select the frequencyological framework random-effects model for the NMA, and the data were pre-processed using the network group command, and inconsistency tests were to assess the consistency of the results of direct and indirect comparisons. Efficacy ranking was then performed and the figure of the surface under the cumulative ranking curve (SUCRA) was plotted. A SUCRA of 100% indicated that the intervention was absolutely effective, whereas a SUCRA of 0 pointed out absolutely ineffective . If a clhttps://pubchem.ncbi.nlm.nih.gov/). Then the SDF file was submitted to Pharmmapper (http://www.lilab-ecust.cn/pharmmapper/) database for target prediction. All targets were transformed into recognized gene symbols using the UniProt database (http://www.Uniprot.org/), and complete protein names were corresponded to the gene abbreviations.The two-dimensional (2D) chemical structure of the drug was downloaded in Spatial Data Format (SDF) from the PubChem database  and the Oline Mendeline Inheritance in Man (OMIM) database (http://www.omim.org) were mined for potential targets related to DEACMP using the keyword “delayed encephalopathy after acute carbon monoxide poisoning.” The DEACMP-related targets were then obtained by integrating the targets from the two disease databases and removing duplicate values.The GeneCards database  (https:/database  (http://https://bioinfogp.cnb.csic.es/tools/venny/) software to obtain potential targets. Then the potential targets were submitted to the STRING11.0 database (https://string-db.org) to construct a PPI network model, with the organism species set to “Homo sapiens” and all settings set to default. The CytoNCA (The obtained drug targets and disease targets were intersected by Venny 2.1.0 (database  (https:/ CytoNCA  plugin i CytoNCA  plugin ihttp://metascape.org/gp/index.html) to examine the biological function and potential mechanisms of the potential targets.Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by the Metascape database  (http://https://www.rcsb.org). Then the key target proteins were imported into Molecular Operating Environment (MOE) software for removing water molecules and the original ligands, and molecular docking with the drug was then carried out. The binding activity is evaluated by the lowest binding energy.Molecular docking was performed among the drug and key target proteins. The three-dimensional (3D) structures of the key target proteins were downloaded from the Protein Data Bank (PDB) database  ,” 13 studies reported on the method of generating specific randomized sequences, four of them distributed treatment regimens according to the order of admission, therefore, it is defined as high risk. One study applied allocation concealment, four studies were blinded, all 17 studies reported on the outcome indicators expected to be measured and did not identify early termination of trials .Network plots of outcome indicators were constructed to exhibit all the available evidence of each treatment. All network relationships were generally centered on Conventional Therapy treatment, the dot size represented the sample size of the intervention, and the line thickness represented the number of RCTs using that treatment .The closed loops in the reticulation were three-armed studies from the same article and there was no inconsistency.P < 0.05, P < 0.05, P < 0.05, P < 0.05, P < 0.05, P < 0.05), P < 0.05, P < 0.05, The league plots of meta-analysis and probability ranking results are displayed in The SUCRA results indicated that: the probability of SUCRA for MMSE scores was ranked from highest to lowest: MSCs + NBP, MSCs, CB-MNCs, Nalmefene, Hydrocortisone, Kinnado, tDCS, Acupuncture, DXM, NBP, XZYN, NBP + DXM, Conventional Therapy , MSCs + NBP was probably the most effective intervention ; the proThe results of the inverted funnel plots showed a concentrated distribution of all studies in the interior of the “funnel,” the nodes symmetrically distributed on both sides of the axis, indicating a low likelihood of publication bias .Adverse reactions were reported in nine studies, while no serious adverse reactions were observed in all included studies. Adverse reactions that occurred during treatment period included hypothermia, dizziness, headache, swelling, pain in the ear, chest tightness, panic, and mild nausea, which recovered on their own without any specific treatment. Since most studies failed to report adverse reactions in a standardized manner, quantitative analysis of adverse reactions was not performed in this study.This study had some limitations: (1) Some of the included studies did not report baseline data on patients comprehensively, such as the duration of disease and sex ratio of patients; (2) A rigorous exclusion of the literature was carried out to ensure homogeneity between studies, but there was still some clinical heterogeneity in the interventions of the control group in the included studies, such as the medication regimen, dose, frequency, and duration of medication. Meanwhile, sampling error, differences in patient age and gender, differences in underlying medical conditions, and differences in smoking and alcohol history were also sources of heterogeneity; (3) The disease is not frequently-occurring, and the number of RCT articles on this disease in the database is small, the number of included studies was only 17, and the literature of some treatment options had only one article, so the ranking of efficacy was not stable. Further validation with high-quality, multi-center, large-sample RCTs is recommended in the future.To further investigate the pharmacological mechanism of NBP, a network pharmacology analysis was performed. One hundred and thirty-seven predicted targets of NBP were obtained from the Pharmmapper database. Cytoscape 3.9.2 software was used to map and analyze the relationship network between NBP and its targets, as shown in Five hundred and seventy-three DEACMP targets were obtained from the Genecards database and 618 DEACMP targets were achieved from the OMIM database, results were merged and the duplicate genes were eliminated, then 1154 DEACMP-related targets were identified.sarcoma (SRC), Heat Shock Protein 90α (HSP90AA1), mitogen-activated protein kinase 8 (MAPK8), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), estrogen receptor 1 (ESR1), kinase domain receptor (KDR), and another showing the relationship between protein tyrosine phosphatase nonreceptor 11 (PTPN11), fibroblast growth factor receptor 1 (FGFR1), mesenchymal-epithelial transition factor (MET) (NBP targets were intersected with DEACMP disease targets and 33 common targets for NBP-DEACMP were obtained by plotting Venn diagrams . The potor (MET) .P ≤ 0.01. Among them, there were 436 biological processes (BP) entries, 37 cellular components (CC) and 43 molecular functions (MF). Interleukin-17 (IL-17) signaling pathway, MAPK signaling pathway, forkhead box O (FoxO) signaling pathway, phosphatidylinositol 3' -kinase (PI3K)/ protein kinase B (AKT) signaling pathway, etc. , the values of the docking energy represented the affinity of the components with the targets and the stability of the conformation. The binding energy which <0 indicated that the two molecules were able to bind spontaneously, the lower the binding energy the more stable the structure. The docking results showed that NBP could be bound into the docking pocket, with good docking activity between the target proteins  include exactly the evaluation of cognition, consciousness, language, motor, sensory, ataxic-motor and visual field, thus these scales were used to assess the change of patients' condition. Different patients exhibited different dysfunctions, and this study screened for regimens with better efficacy for each outcome indicator in order to provide a reference for clinical treatment. Limited by the number and quality of included studies, further validation with high-quality, multi-center, large-sample RCTs is recommended in the future.NBP is widely used in the treatment of ischemic cerebrovascular diseases, and NBP can significantly improve the symptoms of neurological deficits . PreviouALB can prevent platelet activation and aggregation, and also reduce apoptosis of vascular endothelial cells. In addition, ALB may be an indicator of atherosclerosis (ESR can influence the expression of estrogen, thereby regulating different pathological processes. EGFR is distributed in all vascular epithelial cells, research has shown that EGFR could be transactivated during the phase of brain ischemia/reperfusion, which exerts neuroprotective effects (HSP90AA1. It is related to the regulation of cell death, apoptosis, viral processing, and drug responses (clerosis . Estrogeclerosis . Gene po effects . In eukaesponses .via the PI3K/AKT pathway by the mediation of ESR1 (EGFR can participate in the MAPK and FoxO signaling pathways, and HSP90 is a target protein in the IL-17 and PI3K/AKT signaling pathways. All the evidences stated above confirm indirectly that there is a close relationship between key targets and main pathways. Several of these signaling pathways have crosstalk effects, which are closely related to the pathological mechanism of DEACMP and the pharmacological mechanism of the drug.The IL-17 family proteins are prominent and pleiotropic to regulate both pro-inflammatory and anti-inflammatory responses. In the aftermath of a stroke, IL-17 is critical in boosting the inflammatory response and causing secondary brain damage . Studies of ESR1 . EGFR caALB, ESR1, EGFR, HSP90AA1, and other targets, and it may play a role in neuroprotection for patients with DEACMP by modulating Lipid and atherosclerosis, IL-17 signaling pathway, MAPK signaling pathway, FoxO signaling pathway, PI3K/AKT signaling pathway.The NMA screened for regimens with better efficacy for each outcome indicator in order to provide a reference for clinical treatment, but limited by the number and quality of included studies, further validation with high-quality, multi-center, large-sample RCTs is recommended in the future. NBP can stably bind ALB, ESR1, EGFR, HSP90AA1, and other targets, and it is involved in the cellular response to lipids, response to hormone, enzyme-linked receptor protein signaling pathway, protein phosphorylation, transmembrane receptor protein tyrosine kinase signaling pathway, and other important biological processes through the regulation of Lipid and atherosclerosis, IL-17 signaling pathway, MAPK signaling pathway, FoxO signaling pathway, PI3K/AKT signaling pathway, which plays a significant role in the treatment of DEACMP.In summary, this study screened for regimens with better efficacy for each outcome indicator and provided a reference for clinical treatment, and then predicted the pharmacological mechanism of NBP from a systematic perspective based on network pharmacology and molecular docking techniques. We found that NBP can stably bind The original contributions presented in the study are included in the article/Conception of the study: HS and QL. Entering data and analyzing and interpreting data: HS, AY, and XZ. Manuscript preparation: HS and AY. Critically revising the article: HS, QL, and WH. Administrative, technical, and material support: WH. All authors contributed to the article and approved the submitted version.


AKT1, MAPK3, HSP90AA1, VEGFA, CASP3, JUN, HIF1A, CCND1, PTGS2, and MDM2 were predicted as key targets. These 117 targets were enriched in the PI3K-AKT pathway, HIF-1 signaling pathway, apoptosis, and microRNAs in cancer. Astragaloside III, (R)-Isomucronulatol, Astragaloside I, Paeoniflorin, and β-sitosterol were selected as the main active components. Docking scores ranged from − 4.7 to − 10.7 kcal/mol, indicating a strong binding affinity between the main active compounds and key targets. In vivo studies have indeed shown that AM and RPR can alleviate the pathological lung fibrotic damage caused by bleomycin treatment. The treatment with AM and RPR resulted in a reduction of mRNA levels for key targets AKT1, HSP90AA1, CASP3, MAPK3, and VEGFA. Additionally, the protein expression levels of AKT1, HSP90AA1, and VEGFA were also reduced. These results support the therapeutic potential of AM and RPR in ameliorating pulmonary fibrosis and provide insight into the molecular mechanisms involved in their therapeutic effects.Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic disease with an unclear etiology and no effective treatment. This study aims to elucidate the pathogenic mechanism networks involving multiple targets and pathways in IPF. Extracts and metabolites of Astragalus membranaceus (AM) and Radix paeoniae rubra (RPR), two well-known traditional Chinese medicines, have demonstrated therapeutic effects on IPF. However, the underlying mechanisms of AM and RPR remain unclear. Utilizing network pharmacology analysis, differentially expressed genes (DEGs) associated with IPF were obtained from the GEO database. Targets of AM and RPR were identified using the TCM Systems Pharmacology Database and Analysis Platform and SwissTargetPrediction. A protein–protein interaction (PPI) network was subsequently constructed and analyzed using the STRING database and Cytoscape software. Gene ontology enrichment analysis and kyoto encyclopedia of genes and genomes analysis were conducted using Metascape. Additionally, a component-target-pathway network and a Sankey diagram were employed to identify the main active components, and molecular docking was performed between these components and proteins encoded by key targets. Finally, in vivo studies were conducted based on network pharmacology. A total of 117 common targets between DEGs of IPF and drug targets were identified and included in the PPI network, in which  IPF, a subtype of idiopathic interstitial pneumonia (IIP), is characterized by diffuse alveolitis and alveolar structural disorders, primarily affecting adults over 50 years old2. The main clinical manifestations include chronic, progressively worsening dyspnea, lung functions, and functional capacities. IPF is associated with poor survival and prognosis, exhibiting a median survival time of 3–5 years post-diagnosis3. Patients may succumb to respiratory failure, acute exacerbation, pulmonary hypertension, lung cancer, or other complications4. IPF incidence ranges from 2 to 29 per 100,000, as indicated by different studies6. Currently, there is no nationwide epidemiological data for IPF in Mainland China. Regional data from several large samples reveal that the incidence of interstitial lung disease (ILD) is significantly increasing in China. Pirfenidone and Nintedanib are the only treatment agents for IPF recommended by ATS/ERS/JRS/ALAT7. However, these agents mainly reduce the decline rate of lung functions but cannot reverse progressive deterioration or improve survival rates11. Adverse events and high medical costs must also be considered in clinical practice.Pulmonary fibrosis (PF) results from various interstitial lung diseases, with idiopathic pulmonary fibrosis (IPF) constituting the majority of cases13. Consequently, Chinese herbal medicines, with their thousands of years of history, are excellent candidates for exploring and evaluating IPF treatment. IPF can be classified as “the atrophic lung disease” in TCM due to its clinical characteristics, such as dyspnea, chronic cough, shortness of breath, and expectoration.Traditional Chinese Medicine (TCM) has increasingly been adopted as a complementary and alternative medicine worldwide. Chinese herbal formulas are the most commonly used TCM treatment in China, and have demonstrated positive effects on improving the quality of life and alleviating symptoms of ILD patients14. Over 100 active metabolites of AM, including flavonoids, saponins, polysaccharides, and amino acids, have been identified15. Numerous studies have demonstrated the antiviral, immunomodulatory, and anti-inflammatory properties of these metabolites both in vivo and in vitro18. Radix paeoniae rubra (RPR), also known as Chishao in China, is the dried root of Paeonia lactiflora Pall. or Paeonia veitchii Lynch19. Active ingredients of RPR, such as flavonoids, monoterpene, tannins, and phenolic acids, exhibit a variety of biological activities, including anti-inflammation, immunoregulation, antiviral, and antiallergic properties22. Some studies have suggested that certain components of AM and RPR may have therapeutic roles in IPF.Astragalus membranaceus (AM), also known as "Huangqi," is a Chinese herb that has been utilized by TCM practitioners for over two thousand yearsThe compatibility of Chinese herbs forms the basis of TCM for disease treatment, and the improved pharmacological effects of medicine pairs often result from the synergistic effect of different components with specific pharmacokinetic characteristics. However, the multi-ingredient and multi-target characteristics of herbs make identifying their molecular mechanisms difficult. Consequently, computer-aided identification methods, represented by network pharmacology and molecular docking, were employed in this study to predict the potential mechanisms of AM and RPR on IPF. A bleomycin (BLM)-induced pulmonary fibrosis rat model was then utilized to verify their therapeutic effects. The technical strategy of the study is illustrated in Fig. https://www.ncbi.nlm.nih.gov/geo/, 6/17/2022)23. The three data sets were read and standardized using the “Affy” and “Oligo” packages. Differentially expressed genes (DEGs) between IPF and control tissues were identified using the “limma” package, applying a threshold of |logFC|> 0.5 and FDR < 0.05.We downloaded 17 tissues (11 lung tissues with IPF and 6 control lung tissues) from GSE24206 (GPL570 platform), 10 tissues (7 lung tissues with IPF and 3 control lung tissues) from GSE101286 (GPL6947 platform), and 33 tissues (22 lung tissues with IPF and 11 control lung tissues) from GSE110147 (GPL6244 platform) from the GEO database  was utilized to determine the chemical components of AM and RPR based on two ADME parameters: oral bioavailability (OB) and drug-likeness (DL).24 Components that fulfilled the criteria of OB ≥ 30% and DL ≥ 0.18 were considered active components and downloaded from TCMSP for further analysis. Additionally, metabolites that did not meet the aforementioned screening criteria but possessed potential activity in IPF treatment were included by searching and reviewing relevant literature on PubMed, CNKI, and Web of Science. The 2D molecular structure files (.SDF) of active components were downloaded from the PubChem database  and submitted to SwissTargetPrediction (http://www.swisstargetprediction.ch/) with the species setting as “Homo Sapiens” to predict targets26. Ultimately, potential targets of each active component with a probability over 0.3 were retained.The Traditional Chinese Medicine Database and Analysis Platform  with the species setting as “Homo sapiens” and confidence level > 0.4, to construct a protein–protein interaction (PPI) network that reflected the physical and functional interactions between proteins.27 Results in the. tsv format were introduced into Cytoscape v3.7.2 to visualize the data and extract the hub genes according to the degree.The overlapping part of DEGs between IPF and control tissues and the targets of AM and RPR were obtained using a Venn diagram. Moreover, overlapping targets were imported into the STRING database (version 11.5) 28. Bio-enrichment results for these genes, encompassing molecular functions (MF), cellular components (CC), biological processes (BP), and signaling pathways, were obtained through this process. In this study, we selected the top 15 enrichment results based on the P-value and utilized bubble charts to visualize the results. The component-target-pathway (CTP) network was constructed based on target-component results acquired from SwissTargetPrediction and target-pathway results derived from KEGG analysis.The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted using Metascape , and all PDB files were converted to PDBQT files using AutoDock Tool (version 4.2) for further molecular docking in Autodock Vina30. Finally, the binding affinity, represented by the docking score.Proteins encoded by the 10 key genes and the DEG, which were significant in three data sets, were selected as macromolecular receptors. Active components that were predicted to have a close relationship with the 11 genes were chosen as ligands. We then visualized the relationship between these components and genes using a Sankey diagram. The 2D structure files of active components downloaded from PubChem were converted into PDB files using Chem3D software (version 18.0). The 3D crystal structure files of proteins were downloaded from the Worldwide Protein Data Bank  purchased from Sichuan New Green Pharmaceutical Technology Development Co., Ltd. . The control and BLM groups were given the same volume of normal saline. Rats were sacrificed 8 h after the final administration. Rats were anesthetized with an intraperitoneal injection of sodium pentobarbital (30 mg/kg), followed by laparotomy to expose the abdominal cavity. The rats were then euthanized through exsanguination from the abdominal aorta, and lung tissues were collected via thoracotomy. The left lung was fixed in 4% paraformaldehyde for 24 h and then embedded in paraffin for histological analysis and immunohistochemical detection, while the right lung was cryopreserved for colorimetric assay and qPCR.Specific pathogen-free SD male rats  were purchased from Chengdu Dashuo Experimental Animal Co., LTD. . After acclimating to experimental conditions for one week, all the animals were randomly divided into three groups: Blank control group (Control), Bleomycin model group (BLM), and Huangqi-Chishao group (HC), with 6 rats in each group. The BLM and HC groups were injected with Bleomycin sulfate  5 mg/kg by endotracheal intubation to construct the pulmonary fibrosis rat modelThe concentrations of HYP in the lung tissues were measured using a colorimetric assay kit . 100 mg lung tissues were collected, minced, and combined with 1 mL of 6 mol/L hydrochloric acid solution, followed by hydrolysis at 95 °C for 6 h. The pH of the hydrolyzed sample was adjusted to 6.5–7.0. A 2 mL aliquot of the hydrolyzed sample was mixed with 20 mg of Clarificant, centrifuged at 1500 rpm for 10 min, and 400 μL of the supernatant was collected for analysis. To the 400 μL of standard and test samples, 200 μL of Oxidant Agent was added, left to stand for 15 min at room temperature, followed by the addition of 400 μL of Chromogenic Agent. The samples were then incubated in a 60 ℃ water bath for 15 min before being transferred to an enzyme-linked immunosorbent assay plate. The optical density values at 558 nm were measured using an ELISA reader.Total RNA in lung tissues was extracted using the Animal Total RNA Isolation Kit . After determining the RNA concentration with a NanoDrop spectrophotometer, reverse transcription was performed to synthesize the cDNA strand. The PCR amplification conditions were as follows: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 10 s, annealing and extension at 60 °C for 30 s, and 40 cycles. Using β-Actin as an internal reference, the relative expression of the target gene was calculated using the 2− ΔΔCt method. The primers are shown in Table The rat lung tissues were harvested and immediately fixed in a 4% paraformaldehyde (PFA) solution. After fixation, the tissues were dehydrated and embedded in paraffin, and 5-μm-thick sections were cut. The sections were then stained with Hematoxylin–Eosin (H&E) for general histological assessment and with Masson’s trichrome for collagen detection.The antibodies used for immunohistochemical detection were MMP7 , HSP90AA1 , Caspase3 , AKT1 , MAPK3 , and VEGFA . The immunohistochemical procedure followed the standard protocol for tissue staining, including deparaffinization, antigen retrieval, blocking, primary antibody incubation, secondary antibody incubation, DAB development, counterstaining, dehydration, and mounting. The gray value analysis was carried out by randomly selecting 7 fields of each slice , and the mean optical density (MOD) of each field was measured using Image-Pro Plus 6.0 software. In order to automatically identify and select DAB-positive stained areas while excluding the influence of nuclei and other non-specific staining, we set an appropriate color threshold range based on the color characteristics of DAB staining. In the HSI color space, we set the Hue from 0 to 30, Saturation from 0 to 255, and Intensity from 0 to 220. Next, we used the color threshold function of the software to perform binary processing of the image according to the set color range. This process accurately identified DAB-positive stained areas while excluding cell nuclei and other non-specific stained regions. To further optimize the identification results, we applied morphological operations to the binary image, including opening (erosion followed by dilation) and closing (dilation followed by erosion), to remove noise and fill gaps. Finally, after completing the image processing, we calculated the MOD to assess the differences in protein expression levels between the groups, with higher MOD indicating higher expression levels of the target protein.P < 0.05; **, P < 0.01; and ****, P < 0.001.All data were analyzed with GraphPad Prism v9.4.1 and were presented as means ± SD. Shapiro–Wilk tests were performed to determine the normality of the data distribution. For normally distributed data, one-way ANOVA was used to analyze the differences between groups. For data not conforming to a normal distribution, Kruskal–Wallis analysis was used. The significance levels were defined as follows: *, A total of 4640 DEGs were identified in lung tissues of IPF patients compared with normal lung tissues , Astragaloside III , Astragaloside II , and Astragaloside I  were included in this study for their bioactivities reported in previous studies. Finally, 32 bioactive ingredients were included, as shown in Table AKT1, MAPK3, HSP90AA1, VEGFA, CASP3, JUN, HIF1A, CCND1, PTGS2, and MDM2 were predicted as the key targets according to the descending order of degree values  previously identified in the network pharmacology study provided insights into the potential molecular mechanisms underlying the therapeutic effects of AM and RPR on IPF. The expression levels of AKT1, HSP90AA1, CASP3, MAPK3, VEGFA, and MMP7 were significantly increased in the BLM group compared to the control group , a renowned Chinese formula for supplementing Qi and activating blood circulation. A recent study demonstrated the protective effect of BYHWD on a pulmonary fibrosis model in vitro, with its mechanism related to the inhibition of pulmonary inflammation, collagen deposition, and EMT by suppressing the TGF-β1 signaling pathway37. An AM extract injection has been reported to ameliorate pathological lung fibrotic damage caused by bleomycin in rats and improve lung function by reducing the expression levels of TGF-β1 and collagens I and III, while increasing those of MMP-3, MMP-9, TIMP-1, CXCL12, and CD9038.IPF, the most severe subtype of interstitial lung disease, is characterized by chronic inflammation, extracellular matrix (ECM) deposition, epithelial-mesenchymal transition (EMT) dysfunction, and abnormal lung tissue structure remodeling42. Targeting the PI3K/AKT pathway has already demonstrated benefits in IPF treatment43. However, extensive crosstalk and interactions between PI3K/AKT and other pathways, including TGF, VEGF, WNT, and Notch signaling pathways, suggest a complex IPF pathogenesis network44. Blocking a single node in this network may prove insufficient or ineffective.Consequently, we employed network pharmacology analysis to investigate the common transcription factor regulatory network in IPF, identifying AM and RPR as potential IPF treatments. The therapeutic effects and mechanisms of AM and RPR were verified in vivo, while network pharmacology analysis revealed their molecular functions and pharmacological targets for IPF treatment. The diverse and heterogeneous etiology and pathogenesis of IPF make it suitable for identifying pathways and targets through comparison of samples from IPF patients and control tissues based on microarray screening. Initially, 171 overlapping targets were identified among targets of AM, RPR, and differentially expressed genes (DEGs) in GSE110147, GSE101286, and GSE24206. Using these 171 targets, we revealed a regulatory network and key targets in IPF regulation. Subsequently, GO and KEGG enrichment analyses were performed, indicating that multiple biological processes and pathways contribute to the anti-IPF effects of AM and RPR. These pathways are associated with inflammation, autophagy, apoptosis, and cell survival, including the PI3K/AKT pathway, microRNAs in cancer, and apoptosis, among others. The PI3K/AKT pathway is implicated in numerous IPF-related pathological changes, such as alveolar epithelial cell damage, extracellular matrix overproduction, epithelial-mesenchymal transition, and apoptosis45. Quercetin has been demonstrated to counteract ROS damage and inflammation in the process of IPF46. Paeoniflorin has been found to inhibit the early stages of TGF-β-mediated EMT in alveolar epithelial cells by decreasing the expression of the transcription factor Snail through the upregulation of Smad747. Molecular docking validation was conducted, and the main active components were predicted to exhibit strong binding affinity with key targets.The use of natural products like AM and RPR, which can act on multiple targets and pathways, may provide a more promising approach for the treatment of IPF. Astragaloside III, (R)-Isomucronulatol, Astragaloside I, Paeoniflorin, and β-sitosterol were selected as the main active components according to the CTP network and Sankey diagram. Numerous metabolites of AM and RPR have already been proven to be promising agents for alleviating fibrosis progression in multiple organs. Astragaloside IV has been shown to significantly inhibit BLM-induced EMT in pulmonary fibrosis by targeting the PI3K/AKT pathwayAKT1, HSP90AA1, CASP3, MAPK3, VEGFA, and MMP7 after bleomycin treatment, consistent with the results of previous studies.Based on these findings, we further investigated the therapeutic effects of AM and RPR on IPF. In the experimental validation phase, we discovered that AM and RPR could alleviate pulmonary alveolar damage and, to some extent, reduce fibrosis. We then conducted quantitative measurements of hydroxyproline (HYP) and found that bleomycin tended to increase HYP content in lung tissue, while AM and RPR intervention led to a significant decrease in HYP, demonstrating their anti-fibrotic effects. qPCR revealed increased expression of AKT1 activation plays a crucial role in regulating pulmonary fibrosis, as it is strongly associated with the survival and differentiation of myofibroblasts. The function of AKT1 in the progression of pulmonary fibrosis is closely related to TGF-β. In alveolar macrophages, TGF-β induces AKT1 activation, which promotes mitochondrial reactive oxygen species (ROS) and mitophagy. This AKT1-mediated mitophagy leads to apoptosis resistance and prolonged survival of macrophages, which are necessary for pulmonary fibrosis progression48. However, apoptosis has the opposite effect in alveolar epithelial cells. Apoptosis of alveolar cells contributes to early fibrosis and lung injury. Increased levels of pro-apoptotic factors, such as Caspase-3, Bax, and PARP, have been observed in lipopolysaccharide-induced pulmonary fibrosis models, and induction of alveolar cell apoptosis can exacerbate pulmonary fibrosis49.Mitophagy and apoptosis exhibit cell type-specific features in the progression of pulmonary fibrosis. Apoptosis and mitophagy-induced type II alveolar epithelial cells, along with apoptosis-resistant and mitophagy-impaired macrophages, synergistically promote the development of pulmonary fibrosis. Several transcription factors contribute to these processes. MAPK and AKT by TGF-β1, promotes aspects of EMT50. The activation of the PI3K/AKT pathway has been identified to inhibit the function of FOXO3, and the repressed expression of FOXO3 in normal fibroblasts contributes to the IPF fibroblast phenotype51. HSP90, a member of stress-inducible proteins, consists of two isoforms, HSP90A and HSP90B, and participates in the regulation of TGF-β1 by inhibiting the activity of CHIP (carboxyl terminus of Hsc70-interacting protein) to ubiquitinate and degrade Smad352. HSP90 inhibition has been proven to abrogate TGF-β-induced fibroblast activation and ECM production53. Several MAPKs, including ERK, JNK, and p38, have been shown to activate activation protein 1 (AP-1), which is involved in the phenotypic transformation of human lung fibroblasts into myofibroblasts induced by TGF-β. VEGFA is a permeability and angiogenic factor. A recent study demonstrated that the level of VEGFA correlated with the TGF-β level and could be raised by bleomycin, which is consistent with our results54.Additionally, the activation of non-Smad signaling pathways, including MMPs) are metalloendopeptidases that can degrade components of the ECM and non-matrix proteins. Most MMPs have been shown to promote the development of IPF and are upregulated in IPF blood and/or lung samples. Although MMP7 is not the key target predicted by the PPI network, it is a DEG in all three series and a potential target of AM and RPR. The role of MMP7 in IPF includes promoting EMT, increasing lung levels or activity of profibrotic mediators, and reducing lung levels of antifibrotic mediators55.Matrix metalloproteinases (AKT1, CASP3, HSP90AA1, MAPK, and VEGFA, while drug treatment reduced their expression. However, we did not observe significant differences in MMP7 levels among the three groups, despite previous research suggesting that bleomycin injury triggers lung MMP7 overexpression52. Such inconsistency may be related to the limitations of semi-quantitative analysis. The downward trend of these factors after treatment implies a multi-target and multi-pathway characteristic of AM and RPR in anti-IPF treatment. We speculate that AM and RPR suppressed TGF-β1/PI3K/Akt, MAPK, and VEGF signaling pathways by targeting these pharmacological targets, subsequently ameliorating ECM deposition and inflammation in IPF. It is noteworthy that the research findings suggest AM and RPR may reduce inflammation in the BLM-challenged rats; however, inflammation is not the pathogenic mechanism underlying IPF. IPF is not an inflammatory disease, and although minor regions with inflammatory cells may be present, it does not undergo a transition from an inflammatory to a fibrotic phase. Moreover, IPF patients do not experience significant improvement following anti-inflammatory or corticosteroid treatments. Consequently, when discussing the potential of plant-derived compounds in targeting pharmacological agents to alleviate ECM deposition and inflammation in IPF, it is crucial to recognize that reducing inflammation in IPF would not enhance patients' quality of life; instead, ECM accumulation represents the genuine issue in IPF7.We subsequently performed immunohistochemical assays to measure the protein levels of the aforementioned targets in lung tissues. The results generally aligned with the PCR findings: BLM treatment induced the expression of Some limitations exist in this study. Certain active components of Chinese medicine may change during the decoction process, while other compounds with therapeutic effects that may be generated during the process are not included in this study. Additionally, we investigated several nodes in the complex network of multiple targets and pathways; further studies could link the upstream and downstream transcription factors of key targets to more clearly reveal the regulatory role of AM and RPR in the signaling pathways involved in IPF. It is anticipated that future researchers will overcome challenges and translate the extensive understanding of IPF pathogenetic mechanisms into effective therapeutic approaches.AKT1, HSP90AA1, and VEGFA.In this study, we investigated the potential pharmacological targets and therapeutic mechanisms of AM and RPR on IPF using network pharmacology and verified our findings through in vivo experiments. In conclusion, this study identified AM and RPR as potential therapeutic agents for IPF by regulating Supplementary Tables.


Introduction: Pharbitidis Semen (PS) has been widely used in traditional Chinese medicine to treat several diseases such as nephritis. PS is usually stir-fried to enhance its therapeutic efficacy before use in clinical practice. However, the changes in phenolic acids during stir-frying and the mechanisms of their therapeutic effects on nephritis are still unclear.Methods: Here, we studied the processing-induced chemical changes and elucidated the mechanism of PS in the treatment of nephritis. We determined the levels of the 7 phenolic acids in raw PS (RPS) and stir-fried PS (SPS) using high-performance liquid chromatography, analyzed the dynamic compositional changes during stir-frying, and used network analysis and molecular docking to predict and verify compound targets and pathways corresponding to nephritis.Results: The dynamic changes in the 7 phenolic acids in PS during stir-frying are suggestive of a transesterification reaction. Pathway analysis revealed that the targets of nephritis were mainly enriched in the AGE-RAGE, hypoxia-inducible factor-1, interleukin-17, and tumor necrosis factor signaling pathways among others. Molecular docking results showed that the 7 phenolic acids had good binding ability with the key nephritic targets.Discussion: The potential pharmaceutical basis, targets, and mechanisms of PS in treating nephritis were explored. Our findings provide a scientific basis for the clinical use of PS in treating nephritis. Pharbitis nil L.) Choisy or Pharbitis purpura L.) Voigt, a plant of the Convolvulaceae family, is a well-known traditional Chinese medicine (TCM) (Pharbitidis Semen (PS), the dried seeds of ne (TCM) . More thne (TCM) . Traditine (TCM) . The prone (TCM) . Specifine (TCM) . HoweverNephritis is a common renal disorder and a leading cause of end-stage renal disease. Nephritis can present in protean ways, with general features including proteinuria, hematuria, renal failure, and hypertension. Recent advances in nephritis management indicate that early therapeutic intervention can lead to the improvement in renal function and the long-term preservation of renal function, or slow the progression to end-stage renal failure in many cases. Several studies have reported the advantages of TCM in the treatment of nephritis, among which PS is a commonly used TCM . HoweverSystems pharmacology is developed based on the cross-disciplines of directional pharmacology, systems biology, and mathematics, and has the characteristics of integrity and synergy in the treatment process using TCM. Therefore, it is suitable for analyzing the key ingredients and mechanisms of TCM in treating complex diseases. Network analysis is an analytical tool that integrates systems biology, multidirectional pharmacology, computational biology, and other emerging concepts and methods. It has been widely used for the functional analysis of drugs . Here, wAll chemicals and reagents were of analytical grade, and the solvents used for chromatography were of HPLC grade. Methanol (MeOH) and formic acid were purchased from Shanghai Titan Technology Co., Ltd., Chromatographically pure MeOH was obtained from Sigma-Aldrich (Shanghai) Trading Co., Ltd., and purified water was acquired from Hangzhou Wahaha Group Co., Ltd. The standard chemicals included 3-CQA (batch: PS000627), CA (batch: PS010522), 5-CQA (batch: PS000974), 4-CQA (batch: PS001109), 3,5-DiCQA (batch: PS012051), 3,4-DiCQA (batch number: PS012052), and 4,5-DiCQA (batch number: PS001057), which were obtained from Chengdu Pusi Biotechnology Co., Ltd . PS sampRPS and SPS samples were crushed in an FW-40 high-speed multifunction pulverizer mill . The dried sample (1.0 g) was accurately weighed and dispersed in 50 mL MeOH. After 30 min of ultrasonication, the sample was cooled to room temperature (in 25°C) and filtered. The filtrate was collected and transferred to a 25-mL volumetric flask, and the volume was made up with MeOH. Then, the sample was filtered through a 0.22-µm filter after thorough mixing to obtain the sample solution.Samples were analyzed using an analytical SHIMADZU LC-20AD system  equipped with a Kromasil 100-5-C18 column  and an SPD-20A ultraviolet detector. The detection wavelength was 325 nm for the 7 phenolic acids. Analytes were eluted at a flow rate of 1 mL/min using a gradient profile at a column temperature of 25°C. The solvent system was composed of 0.1% aqueous formic acid solution A) and MeOH B). Gradient elution was performed as follows: 0–10 min: 10%–20% B), 20–30 min: 20%–30% B), 20–50 min: 30%–50% B), 50–51 min: 50%–10% B), 51–60 min: 10% B). Chromatographic data were processed using a Lab Solutions workstation .http://lsp.nwu.edu.cn/tcmsp.php). PubChem (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain SDF files with 2D structures of the 7 components, namely 3-CQA, CA, 5-CQA, 4-CQA, 3,5-DiCQA, 3,4-DiCQA, and 4,5-DiCQA. SwissTargetPrediction is an online web-based tool established in 2014 to perform ligand-based target prediction for small bioactive molecules. The SwissTargetPrediction model was trained by fitting multiple logistic regression on various size-related subsets of known actives to weigh 2D and 3D similarity parameters in a so-called Combined-Score. A Combined-Score >0.5 predicts that the molecules are likely to share a common protein target. In reverse screening, the Combined-Score allows to calculate for any query molecule assumed as bioactive and determine the probability to target a given protein. As the 2D and 3D descriptions of molecules are complementary, this dual scoring ligand–based reverse screening showed high performance in predicting the macromolecular targets in various test sets (http://www.swisstargetprediction.ch/), the Herbal Ingredients’ Targets (HIT) Platform 2.0 (http://www.badd-cao.net:2345/), and the HERB Materia Medica Database (http://herb.ac.cn/) to obtain UniProt IDs for predicting targets with the organism Homo sapiens , TTD (https://idrblab.net/ttd/), and GeneCards (https://www.genecards.org/) databases using “nephritis, glomerulonephritis” as the keywords (https://string-db.org/), “Multiple proteins” was selected, the species was set to “Homo Sapiens,” and the minimum interaction score was set to medium confidence (0.4) to obtain the protein–protein interaction network diagram. The resulting TSV file was imported into Cytoscape 3.7.1 for visual analysis of the network  was using p ≤ 0.01 (The Gene Ontology (GO) biological process (BP) was used to further validate that the potential targets were indeed matches for nephritis. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses were performed using the Database for Annotation, and visualization (p ≤ 0.01 . Compounp ≤ 0.01 .https://pubchem.ncbi.nlm.nih.gov/) and saved in the SDF format. The PDB database (https://www.rcsb.org/) was used to download the 3D structures of protein macromolecules in the PDB format. PyMoL 3.8.5 was used to remove water and ligands from the protein macromolecules. AutoDockTools 1.5.6 was used to convert macromolecules and compounds from the PDB to the PDBQT format, and the docking box position of protein macromolecular ligands was determined. AutoDock vina 1.1.2 script was used to dock protein macromolecules and compounds in sequence, obtain docking results, and draw molecular docking patterns (The 3D structures of phenolic acids were obtained using the PubChem database (patterns .An HPLC method was developed to determine the changes in the phenolic acids in PS during stir-frying . The totThe levels of three compounds, 3-CQA, CA, and 3,5-DiCQA decreased after processing, whereas those of the other four compounds increased significantly after processing. However, as only one processing temperature was used and it was for a short duration, the dynamic change rule of phenolic acid composition during the processing of PS cannot be fully revealed. Therefore, in this study, two processing temperatures were selected and the stir-frying time was extended to explore the change rule and trends of the 7 phenolic acids during stir-frying. The stir-frying temperatures were set at 120°C and 150°C, respectively, and the frying time was divided into 3, 5, 7, 9, 11, and 13 min. The % content of the 7 phenolic acids in the processed products , 4 and tNephritis-related genes were obtained by searching the keywords “Nephritis” and “Glomerulonephritis”; 129 disease targets were obtained from the OMIM database, 9 from the TTD database, and 575 from the GeneCards database after the median screening. A total of 641 nephritis-related disease targets were finally obtained after summarizing, deduplication, and union. A Venn diagram was drawn between the targets of the 7 phenolic acids and the targets of nephritis-related diseases, and a total of 33 crossover targets were obtained . Using SThe 33 compounds and disease intersection targets that were screened out were imported into the STRING database to generate a protein-protein interaction network map. Nodes represent the target proteins and edges represent the functional correlations between proteins. There are 33 nodes and 255 edges in total, with an average node degree of 15.5 and an average local clustering coefficient of 0.755 . The intp < 0.01 were screened out; there were 510 biological processes, 57 molecular functions, and 38 cellular components. Based on the p-value, the top 10 items with representative biological processes, molecular functions, and cellular compounds were screened out (p < 0.01 and an enrichment factor of >1.5. A total of 126 signaling pathways of the 7 phenolic acids were screened and found to interfere with nephritis. The top 15 pathways were filtered from the smallest to the largest p-value (The intersection targets were imported into the Metascape database for GO enrichment analysis, and entries with ened out –7, and Gened out . Biologi p-value . Nephritvia multiple targets and pathways.The 7 phenolic acids, intersection targets, and results from the KEGG pathway were integrated and processed using Cytoscape 3.7.1 to construct a component-target-signaling pathway network diagram and for visual analysis . There aMolecular docking was used to validate the results of the above network analysis, screen the network of the 7 phenolic acids with high degree values, and dock them with the targets of action on nephritis. Based on the analysis , TNF, VEThe pattern of molecular docking results between some of these components and the target is shown in Acanthopanax senticosus extract contains 3-CQA, 3,5-DiCQA, and 4,5-DiCQA, which have strong anti-inflammatory effects (Phenolic acids have good antinephritic effects ; network effects . 4-CQA e effects . Therefo effects .The protein interaction analysis network showed that TNF, VEGFA, PTGS2, MMP9, MAPK1, and other proteins were the key targets of the 7 phenolic acids in the treatment of nephritis. TNF-α from the TNF family is involved in the inflammatory response of chronic nephritis, and it is used as a detection index in the acute infectious phase of chronic nephritis . ActivatFindings from KEGG enrichment pathway analysis and bubble plots revealed that glomerulonephritis targets were mainly enriched in AGE-RAGE, HIF-1, IL-17, and TNF signaling pathways in diabetic complications. Inhibition of the inflammatory channels activated by the AGE-RAGE signaling pathway can reduce the inflammatory response in diabetic nephropathy, thereby protecting the kidneys . HIF priIn this study, the content changes in the 7 phenolic acids in PS during the stir-frying process were determined using HPLC. The 7 phenolic acids of PS could effectively treat nephritis through multiple pathways and multiple targets. The accuracy of the outcomes was further verified using subsequent molecular docking. The results showed that the 7 phenolic acids of PS could regulate the proliferation, metastasis, differentiation, senescence, apoptosis, and other biological processes of inflammatory cells by regulating the expression of related genes in nephritic cells, reflecting the antinephritic effect of PS. These findings illustrate the complexity of the pathological mechanisms of nephritis and the diversity of pharmacological activities of the phenolic acids in PS. However, our research results need to be further corroborated using animal experiments and other relevant studies to ensure their reliability. Overall, our study provides a novel basis for further exploration and subsequent experimental verification of PS in the treatment of nephritis.


ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins  and five microRNAs  as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes  were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches  and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs ( Chronic obstructive pulmonary disease (COPD), influenza, pneumonia, emphysema, lung cancers, asthma and covid-19 are the most common RD4. Among these lung diseases, severe acute respiratory syndrome coronavirus  is the very recent and world-threatening outbreak5.Around a million people die each year from respiratory diseases (RD), which are the most prevalent cause of disease-related mortality globallyNew therapeutic or preventive methods that are common for these lung diseases need to be proposed. To uncover novel therapeutics for patients with lung diseases, we need a complete understanding of their biological or molecular mechanisms and interconnected biological information among the RD. However, the biological or molecular information, as well as drug or non-drug mechanisms of lung diseases, are yet to be not fully understood by existing literature. But, a better understanding of the mechanisms of these diseases is required for diagnosing RD and finding novel drugs. The gene expression profiles, biomarker exploration and bio-functional pathways could be the novel method to get information about the identification of possible treatment strategies. Wet lab equipment and the use of this sophisticated equipment could be very costly. More experienced operators, periodic maintenance, longer start-up time and huge space requirements are the major problems of wet lab experiments. To address these issues, bioinformatics approaches are becoming increasingly tool-to-go in biomedical research day by day. However, high-volume bioinformatics data from clinical studies could promote the analysis of microarray and gene expression. The analysis could be helpful for identifying the different pathways, molecular mechanisms, biological biomarkers and appropriate treatment. The investigations can be easily done at a low cost with less time using bioinformatics analysis.12. In the present study, we would like to investigate the common molecular mechanisms of the RD by using microarray data through an integrated bioinformatics approach. This study attempts to find differentially expressed genes, HubGs, significant regulatory checkpoints, regulatory biomarkers, drug targets and drug agents. In the present study, we used seven datasets from the Gene Expression Omnibus (GEO) database  of RD in the bioinformatics analysis. First, differentially expressed genes (DEGs) for the seven datasets were identified. Next, common DEGs (CDEGs) for the seven datasets were discovered. The most important task is to identify HubGs from frequent CDEGs using protein–protein interaction (PPI) network analysis. Also, the CDEGs are largely responsible for identifying regulatory checkpoints (gene ontology and pathway), pharmacological targets, drug agents  and regulatory biomarkers . Finally, we screened potential drug targets (i.e. TFs and HubGs), drug agents and their compounds, which may be useful in fighting against all RD. The workflow of the present research is illustrated in Fig. Recently, few molecular mechanisms or pathogenesis of the individual RD (asthma or COPD or lung cancer or other lung diseases) were identified. However, they have not studied the common molecular pathogenesis of the RD, which still needs to be identified13, GSE2025714, GSE2701115, GSE3326716, GSE3571617, GSE37951, and GSE6981818 were taken from the GEO database (http://www.ncbi.nlm.nih.gov/geo/)19 for lung cancer, COPD, asthma, SARS-COV, pneumonia, influenza, emphysema, respectively. Next, the datasets were analyzed to identify DEGs between RD-infected cases and control samples. The above datasets were analyzed through GEO2R20 web tool, where the expression matrix was log2-transformed, and limma R packages to identify DEGs with P-value < 0.05 and “venn” package in R programming language.To explore RD-related biological or molecular information, the human gene expression profile was considered. Gene expression profile datasets GSE19188To find out regulatory components and HubGs from the CDEGs, we investigated the network-based molecular interaction analysis, including CDEGs with transcription factors (TFs), micro-RNA (miRNA), drugs, chemical components and protein–protein interaction (PPI). The specific detailed analysis of these molecular interactions is discussed in the following sub-sections.https://string-db.org/)21, with a confidence score of 0.9. To locate the HubGs in the PPI networks, we employed a topological degree of measurement (> 11).To analyze protein interactions, the PPI network was created. To create the PPI network, the CDEGs are inserted in the STRINGdb web portal  with a set of diseases based on miRNA-Disease association information by using miEAA web server [version 2.1]29.Regulatory biomarkers  are control genes at the post-transcriptional and transcriptional level in plenty of cellular functions and biological activities24. The CDEGs-chemical interaction network was performed from the Comparative Toxicogenomics Database (CTD)30 and the CDEGs-drugs interaction network was performed from DrugBank31 database. The significant chemicals were selected according to a topological degree > 25 and cDEGs-related drugs were found in the network.To meet expanding demand in pharmacogenomics and toxicogenomics, we also analyzed CDEGs-chemical and CDEGs-drugs interaction networks by using the NetworkAnalysis tool32 and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases33 through NetworkAnalyst. The statistical significance of the functional enrichment study was evaluated using a Fisher exact test with a cut-off p-value < 0.05.The principal reason for identifying functional and pathway enrichment terms is to understand the molecular action, cellular role and place in a cell. This study employed to identify significant functions and pathway enrichment analysis of CDEGs utilizing the Gene Ontology (GO)34 database, the 3D appropriate structures of 58 FDA-approved ligands were obtained, as seen in Table 35. The exhaustiveness value was set to −8.0 . The structure was generated, and the 2D protein–ligand interactions were visualized using the Discovery Studio Visualizer interface36.We used molecular docking simulation between the drug target and drug agents in order to suggest effective medicines for the RD. We used the 63 drug agents or ligands determined by CDEG-chemical and CDEG-Drug network and our hypothesized hub-proteins and TFs proteins were used as the drug target. From the PubChem37. Initially, the complexes were loaded into the simulation system, and the AMBER14 force field was used to minimize system energy38. In addition, water molecules and 0.9% NaCl were added at 310 K for system neutralization. To calculate the long-range electrostatic interaction, the particle mesh Ewald method (PME) was applied39. A cubic simulation cell was set up by 20 Å, and the system temperature was controlled by the Berendsen thermostat. The energy minimization of the system was carried out through the simulated annealing method40. The normal simulation time step (1.25 fs) was set, and trajectories were collected for every 100 ps. Finally, molecular dynamic simulation of the complexes was run for 100 ns and the radius of gyration (Rg), root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were calculated to understand the conformational stability and variation of the complex. It has become common practice to use the "Molecular Mechanics Poisson-Boltzmann Surface Area" (MM-PBSA) approach to determine the binding free energy of the protein–ligand complex41. Using the YASARA structure, the MM-PBSA has been used to calculate the binding free energy of the protein–ligand complex. The few snapshots (~ 100 ns) of the dynamic simulation trajectory were used to determine the MM-PBSA of the protein–ligand complex structure. The protein–ligand complex's binding free energy can be computed as follows:To validate the molecular docking study, we a performed molecular dynamics (MD) simulation of the docked complexes, which was implemented in the YASARA dynamics commercial packageThe gene expression dataset of seven RD datasets, i.e., GSE19188, GSE20257, GSE27011, GSE33267, GSE35716, GSE37951, and GSE69818 were used for the identification of DEGs, and a total of 18868, 8177, 2292, 14115, 15372, 12425 and 2931 DEGs were found, respectively. Collected DEGs for RD were identified by using R programming language and 73 common DEGs (CDEGs) were found. In Fig. ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2.The 73 CDEGs were used to build a PPI network in order to identify the hub genes (HubGs). We input CDEGs in the STRING database to collect interconnected proteins. According to the topological measure degree (> 11), the list of HubGs was chosen, as displayed in Fig. We carried out KEGG pathway and GO categories enrichment analyses to examine the biological roles of the CDEGs in more detail. Here, the GO-term enrichment analysis was performed by NetworkAnalyst. Here, we showed the top 5 significantly enriched terms in Biological Process (BP), Molecular Functions (MF), and Cellular Component (CC), which interacted with CDEGs Fig. .Figure 4ellular response to nutrient levels, Regulation of endocytosis, Cellular response to extracellular stimulus, Regulation of cell growth and Negative regulation of growth. We also identified the MF, including Collagen binding, Adenyl ribonucleotide binding, Double-stranded RNA binding, Phospholipid binding, and Adenyl nucleotide binding. We then identified the top five CC: Extrinsic to the plasma membrane, External side of the plasma membrane, Clathrin coated vesicle and Extrinsic to membrane and Axon.We identified the top five BP: CLongevity regulating pathway-multiple species, Insulin signalling pathway, Fatty acid elongation, Inflammatory mediator regulation of TRP channels and Proteoglycans in cancer.In the KEGG pathway analysis, we exposed significantly top 5 enriched pathways in Fig. . The topFOXC1, GATA2, FOXL1, YY1, POU2F2, and HINFP as shown in Fig. hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p, and hsa-mir-15b-5p in Fig. TFs and miRNAs control the transcriptional and post-transcriptional phases of gene regulation. To identify key regulators (TFs and miRNAs), we have established networks of CDEGs-TFs and CDEGs-miRNAs. This list of related TFs and miRNAs is summarized in Table . The analyses of the CDEGs-chemicals network found 15 chemicals according to a number of associated CDEGs  were retrieved from Protein Data Bank (PDB)42 with the PBD codes 3LXJ, 1s70, 3CO6, 3CQW, 3OXZ, 3G4L, 4WK0, 2PQU, 1uuh, 5DKC, 6akP, 1UBD, and 3CFS, respectively. The 3D structures of the proposed 63 drug agents were taken from the PubChem database34 listed in Table BCR + CID2336, CD44 + CID3827, FOXC1 + CID24826799, ITGB1 + CID2336, HINFP + CID2336, PDE4D + CID2336, PPP1CB + CID4521392, SMARCA2 + CID2336, PCBP2 + CID2336, ATAD2B + CID24826799, and YY1 + CID24826799 as the most plausible candidate drugs displayed in Table We took the proposed 10 HubGs and 6 key TF proteins as 16 drug target receptors as well as the recommended 15 chemicals and 48 drugs as ligands. The 3D structure of target proteins  and also shows very high deviation.The RMSD of Cα atoms have been computed for the proposed protein–ligand complexes to measure the protein structure stability. The average RMSD value of FOXC1 + CID24826799, ATAD2B + CID24826799, SMARCA2 + CID2336, PPP1CB + CID4521392, PDE4D + CID2336, BCR + CID2336, HINFP + CID2336 and PCBP2 + CID2336 complexes displaythe maximum area of the AA residue in their respective protein structures, which did not demonstrate fluctuation during the simulation  residues in a complex system are determined by the high RMSF value of the amino acid residues. Among the complexes, CD44 + CID3827 all the areas of the AA residue in the protein structure do not show fluctuation during the simulation. The ATAD2B + CID24826799, BCR + CID2336, CD44 + CID3827, FOXC1 + CID24826799, HINFP + CID2336, PCBP2 + CID2336, PDE4D + CID2336, PPP1CB + CID4521392, SMARCA2 + CID2336 and YY1 + CID24826799, respectively . The objective of our study is to detect common differential genes and potential HubGs, emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD, and potential candidate drugs for the treatment of RD. The study aims to reduce the toll of morbidity, disability and premature mortality by RD. In this study, we used the bioinformatics approach to examine the molecular mechanism of RD from gene expression data. We first identified common differentially expressed genes. In addition, we also focused on identifying responsible TFs, miRNAs, chemicals and HubGs by generating different interactions with the CDEGs, which play key roles in the regulation of CDEGs and drug development for RD. Later, we structured functional and pathway enrichment analysis with the CDEGs to determine the biological roles of CDEGs in those RD. Finally, we repositioned the drug candidate of RD through molecular docking and molecular dynamics simulation.Respiratory Diseases (RDs) affect the airways and other structures of the lungs. Some of the most common are asthma, COPD, lung cancer and other lung diseases. Currently, a number of treatments are available that can assist in managing symptoms and consequently ensuring better daily living. However, RDs are currently not fully curable  and RMSF scores of the protein–ligand complex indicate that the complexes could show better stability and would not show fluctuation. According to a prior study, ketotifen lessens obstructions of the conducting airways and may possibly have a direct impact on the small airways92. After a therapeutic trial, it was found that ketotifen shows a good impact on the asthmatic patient93. It has an antiviral activity that may play a potential role in the treatment of SARS-CoV-2 infection in humans94. Ketotifen contributes to reducing end-organ damage and mortality of mice infected by Influenza95. We may conclude from molecular docking studies that ketotifen can bind to CD44 in a satisfactory manner in a wet lab setting because the binding affinity score was −8.1 kcal/mol. It may show high compactness for low and stable Rg values of protein–ligand complexes. We may also draw the conclusion that the complex would not exhibit fluctuation because, in the simulation, none of the AA residue-containing regions in the protein structure exhibit fluctuation. The average RMSD value (1.997 Å) of the complex are specified that the complexes could show better stability. In previous research, the author’s shortlisted two FDA-approved drugs, where ponatinib is one of them to treat COVID-19 therapeutics96. According to the published study, ponatinib may serve as a new immunomodulator in the treatment of influenza, and it may be also effective for the treatment of lung cancer patients98. The binding affinity score was −8.5 kcal/mol which recommends that ponatinib may bind to ATAD2B well in a wet lab setting based on molecular docking studies. The low and stable Rg values of the ATAD2B-ponatinib complex suggest that the complex may exhibit high compactness. The protein–ligand complex's RMSD (1.945 Å) and RMSF scores suggest that the complex may be more stable and would not fluctuate. In order to effectively prevent RD, new technologies are being developed due to the difficulty of diagnosing and treating lung diseases. In light of this, the overall goal is defined as an integrative bioinformatic approach that includes multimodal diagnostics and disease-specific biomarker patterns. However, our identified biological information may shed light on the cause and progression of RD, as well as any new prospective therapeutic strategies.We picked the ten key proteins and their regulatory 6 TFs proteins as the drug target receptors as well as conducted their docking analysis with 63 drug agents. Then we selected the top-ranked five protein-drug complexes as the most probable repurposable candidate drugs complexes for RD based on their strong binding affinity scores  and molecular dynamics simulation. Among the identified candidate drugs, Benzo[a]pyrene might be encouraged RDhsa-miR-218-5p, hsa-miR-335-5p, hsa-miR-16-5p, hsa-miR-15b-5p and hsa-miR-106b-5p which are associated with lung injury and may play a role as a prognostic biomarker. Interconnection of significant key functions and pathway enrichment with CDEGs revealed the five biological processes , five molecular functions , five cellular components  and five pathways . The functions and pathways may play an important role in airway diseases/chronic lung diseases. According to interaction network, molecular docking and molecular dynamics simulation identified the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib as potential protein-drug complexes that will help to inhibit the RD. Therefore, the suggested molecular biomarkers and repurposing candidate drugs will be beneficial for the diagnosis and treatment of RD. The activity of drug complexes and biomarkers can be determined by further analysis using various lab-based trial techniques. The findings from this study will help to open up new avenues for cutting-edge treatments to reduce morbidity and future premature mortality from RD.Interaction network for regulatory elements with CDEGs exposed the Supplementary Tables.


As society continues to develop, women are more at risk of gonadotoxic substance exposure. Consequently, the incidence of premature ovarian insufficiency (POI) has increased significantly in the past decades. Hormone replacement therapy (HRT) is recommended as the standard treatment to relieve hypoestrogenic symptoms; however, its potential side effects and contraindications have drawn widespread controversy and concern. As such, the Chinese medicine Zishen Yutai Pill (ZSYTP) commonly used for treating miscarriage and menoxenia, is a highly promising alternative drug candidate against POI, however its therapeutic mechanism has not been completely elucidated.To systematically analyze the potential therapeutic targets of ZSYTP on POI, we combined network pharmacology analysis and molecular docking to predict critical target genes, with experimental validation on POI murine models.The active compounds of ZSYTP were collected from three online databases, and the candidate targets were predicted based on the chemical structure. The POI-related targets were obtained from four databases. A PPI network was constructed to find the key target genes between ZSYTP and POI, while GO and KEGG enrichment analyses were employed to study the mechanism of ZSYTP against POI. The binding capability of the key co-targets with active components was examined by molecular docking. We used a cyclophosphamide (CTX)-inducible POI mouse model to verify our predictions by histopathological observation, immunohistochemical staining , hormone determination  and ribonucleic acid sequencing (RNA Seq). Progynova was also used to study the difference between ZSYTP and HRT.In vivo, ZSYTP significantly reversed CTX-induced ovarian damage in follicle number, hormone level and apoptosis, with an overall improved therapeutic effect compared to Progynova. Results from RNA-Seq revealed that the PI3K-AKT, Hippo, AGE-RAGE, and Rap1 signaling pathways and regulation of inflammation, immune response, and lipid metabolism may mediate the protective effects of ZSYTP against POI, which is different than Progynova's mechanism of action.We identified 21 target genes as the hub between ZSYTP and POI. The GO and KEGG analyses revealed that the molecular mechanism of ZSYTP against POI were mainly based on the regulation of gene and protein expression. A variety of signaling pathways may be involved in the treatment of ZSYTP against POI, especially PI3K-AKT, HIF-1 and the AGE-RAGE cascades. Docking simulation showed that G1, C1, SR5, and F1 had relatively lower binding energy. Collectively, this study indicates that ZSYTP could be a highly promising alternative as a non-HRT-based therapy for POI. Its mechanism involves multiple signaling pathways, alleviating ovarian apoptosis and recovering AMH and FSH level. However, the discrepancy between different research techniques highlight the necessity of further experimental verification from other aspects such as translation and posttranslational modification. Thus, the progression of this natural phenomenon, namely premature ovarian insufficiency (POI), causes subfertility in women of reproductive age and lowers their quality of life. POI is defined as a pathological state wherein ovarian hypofunction occurs before age 40, and mainly characterized by abnormal menstruation, hypoestrogenic and hypergonadotropic conditions. One of the key symptoms in the pathogenesis of POI is follicular atresia that is primarily mediated by upregulation of apoptosis. Approximately 1% of women aged under 40 years and 0.1% of women aged under 30 years suffer from POI . AlthougZishen Yutai Pill (ZSYTP), consisting of 15 Chinese medicinal compounds, is a patented Chinese prescription now widely used to treat a diverse range of reproductive disorders, including absent and irregular menstruation . In the in silico predictions. In addition, to more confidently evaluate the accuracy of the network predictions, we employed RNA sequencing (RNA-Seq) to obtain a comprehensive transcriptome profiling. The overall workflow of this study is provided in In this study, we used network pharmacology analysis to predict the potential therapeutic targets of ZSYTP on POI. Animal experiments, including histopathology and immunochemistry, were also performed to verify our 22.12.1.1http://tcmspw.com/tcmsp.php, accessed September 2022) database . Its. Its52].In this study, we predicted potential targets of ZSYTP against POI using a network pharmacology approach. Although previous studies have performed network pharmacology analysis and concluded that ZSYTP could be ameliorating POI via glucose metabolism and the HIF-1 signaling pathway, these results were not supported by sufficient experimental validation . In addiTo compare the difference between ZSYTP and traditional HRT, we constructed a mouse model of POI by intraperitoneal injection with CTX, followed by administration of ZSYTP or Progynova. Although a commonly used drug for chemotherapy and immunosuppressive therapy, CTX can cause ovary damage and is widely employed to establish POI animal models. In regard to an analysis of published data, exposure to CTX had definite adverse effects on both the PMF pool and growing follicles, increasing follicular atresia and apoptosis in ovaries . The patHistopathological observation highlighted a definitive improvement in the POI model after treatment with ZSYTP, which slightly outperformed Progynova in terms of treatment results. To thoroughly detect changes in gene expression among each group, we performed transcriptome sequencing. Analysis of DEGs demonstrated the reliability of our network pharmacological analysis, revealing that a group of similar target genes may be involved in the treatment of POI using ZSYTP. Notably, the gene expression pattern of the Progynova group was rather different from that of the ZSYTP group, implying that these two drugs have diverse pharmacological mechanisms. We posited that ZSYTP might counter the CTX-induced ovarian histopathological damage and hypothalamus-pituitary-gonadal axis disorder mainly via the PI3K-Akt, Hippo, Rap1, and AGE-RAGE signaling pathways, which are implicated in cell proliferation, apoptosis, migration, lipid metabolism, immune response, and inflammation.The PI3K-AKT signaling pathway is crucial to mammals, covering a wide range of cellular processes, including cell growth, proliferation, apoptosis, and metabolism . In addiHIF-1 signaling pathway is another downstream pathway regulated both by PI3K-AKT and mTOR signaling pathway. Its core element HIF-1, which can be activated under oxygen deprivation and upregulate various downstream targets, including VEGF, to compensate for the low oxygen stress, consist of two protein subunit, HIF-1α and HIF-1β . HIF-1α The AGE-RAGE signaling axis is a well-studied cascade related to a diverse range of diseases, especially diabetes mellitus, and it activates various downstream effector pathways, such as the PI3K/AKT, MAPK/ERK, and NADPH oxidase signaling cascades, leading to the production of pro-inflammatory cytokines and reactive oxygen species (ROS), consequently resulting in cell damage in AGE-related diseases . FurtherHowever, several discrepancies were present between our bioinformatic prediction and experimental results. For instance, in the network analysis, HRAS and STAT3 were predicted as hub genes between ZSYTP and POI, and HIF-1 and VEGF signaling pathways were highly enriched, whereas RNA-seq suggested that they might have no significant function in ZSYTP against POI. One plausible explanation might be that when performing the PPI network analysis of the overlapping genes between ZSYTP and POI, we failed to consider whether these potential target genes were truly abundant in the drug or disease groups. Therefore, certain target genes of marginal compounds in ZSYTP might be highlighted, and vice versa. To minimize this deviation, we performed concurrent component-target network analyses. However, biases still existed between the prediction of pharmacological networks and the actual results, and therefore, certain conclusions drawn from the bioinformatics analysis may not be completely reliable. This further underscores the importance for reasonable follow-up experimental validation of computational predictions.This research had several limits. We used only RNA-seq to quantify gene expression without using other methods, such as PCR, to further verify the confidence level of the sequencing results. In the near future, we are planning to perform complementary Western blot and qPCR assays to further clarify the controversial results between network prediction and RNA-Seq during a follow-up study. Another potential pitfall was the limited number of mice used, which, to a certain extent, might have affected the authenticity of our conclusions. As different chemicals might induce different kinds of POI, we intend to compare the therapeutic effect of ZSYTP using a diverse range of POI-inducers in a future study. Overall, more basic research is needed to further understand how ZSYTP functions to treat POI and to promote TCM globally.536 77. Moreover, our work illustrated how ZSYTP ameliorates POI damage at the mRNA level based on RNA-seq. Based on the sequencing data, we demonstrated that ZSYTP could mediate anti-POI effects by downregulating PI3K-AKT, Hippo, AGE-RAGE, and Rap1 signaling pathways and inflammation, while concomitantly upregulating steroid biosynthesis, which was overall a markedly different mode of action from Progynova. These findings not only confirm the hypotheses presented in other studies but also contest several established views on the molecular mechanism of ZSYTP against POI, such as the HIF-1 signaling cascade. Based on these results, we believe that ZSYTP would be an excellent drug candidate as a complementary and alternative medicine for treating POI, while intending to investigate this in greater detail in the future.In our current research, we predicted the major target genes and pathways of ZSYTP against POI, which were consistent to an extent with the results of previous studiesJia Hu: Conceived and designed the experiments; Wrote the paper; Performed the experiments; Analyzed and interpreted the data. </p>Zifan Song: Performed the experiments; Analyzed and interpreted the data; Wrote the paper. </p>Kuangyu Song, Hongru Zhao: Performed the experiments; Analyzed and interpreted the data. </p>Yuanqiao He: Performed the experiments. </p>Data will be made available on request.JH conceived and designed the experiments. JH, ZS, KS, HZ and QH performed the experiments. ZS, KS, HZ and JH analyzed and interpreted the data.ZS and JH wrote the paper. All authors contributed to the article and approved the submitted version.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.