Convolutional neural networks (CNNs) have achieved great success in the areas of image processing and computer vision, handling grid-structured inputs and efficiently capturing local dependencies through multiple levels of abstraction. However, a lack of interpretability remains a key barrier to the adoption of deep neural networks, particularly in predictive modeling of disease outcomes. Moreover, because biological array data are generally represented in a non-grid structured format, CNNs cannot be applied directly.PathCNN, that constructs an interpretable CNN model on integrated multi-omics data using a newly defined pathway image. PathCNN showed promising predictive performance in differentiating between long-term survival (LTS) and non-LTS when applied to glioblastoma multiforme (GBM). The adoption of a visualization tool coupled with statistical analysis enabled the identification of plausible pathways associated with survival in GBM. In summary, PathCNN demonstrates that CNNs can be effectively applied to multi-omics data in an interpretable manner, resulting in promising predictive power while identifying key biological correlates of disease.To address these issues, we propose a novel method, called https://github.com/mskspi/PathCNN.The source code is freely available at:  Deep neural network methods provide the capability to perform non-linear modeling, while handling complex structures and dependencies in data, in order to learn informative representations through multiple levels of abstraction . On the Class Activation Mapping (CAM) was developed using global average pooling  to employ CAM, the CNN architecture used in modeling should be modified by removing fully connected layers and adding a global average pooling layer after the last convolutional layer where class activation maps are generated, and (ii) the modified network for CAM should be fine-tuned. A more generalizable approach, called Gradient-weighted Class Activation Mapping (Grad-CAM), was subsequently proposed, which uses the gradient information of any target class flowing into the last convolutional layer to produce class activation maps  are used, which are generated in the low dimensional space of integrated multi-omics data including mRNA expression, copy number variation (CNV) and DNA methylation. After modeling, to identify the biological pathways associated with outcomes, Grad-CAM is used, for which attention maps superimposed on pathway images are analyzed to pinpoint key pathways.In this study, we propose a novel method, called We demonstrate the method applied to predicting long-term survival in patients diagnosed and treated for glioblastoma multiforme (GBM). GBM is one of the most malignant tumors with poor prognosis and outcomes with a median survival of 14–15 months after diagnosis  was defined as survival > 2 years after diagnosis, whereas non-LTS was defined as survival Multi-omics data for GBM, including mRNA expression, CNV and DNA methylation, denoted as ip, the mRNA expression data of associated genes were extracted from the mRNA expression matrix (G), producing an intermediate matrix ir is the number of genes involved in the pathway ip. That is, the matrix B consists of samples in rows and genes for a given pathway in columns. Using principal component analysis (PCA), the matrix B was decomposed into uncorrelated components, yielding q is the number of principal components (PCs). This task for the pathway ip was also carried out on the CNV matrix (C) and DNA methylation matrix (M), yielding pG, pC and js, a set of matrices, pathway image of the sample js, where rows represent 146 pathways, and columns represent 3q = 1–5) were used; for example, a sample (pathway image) is represented by a matrix with 146 × 6 elements for q = 2, where the first and second columns are from mRNA expression, the third and fourth columns are from CNV, and the fifth and sixth columns are from DNA methylation, representing the first two PCs of each omics type. Each type of omics data at a gene level was converted into pathway level profiles. To do this, pathway information was first extracted, along with the associated genes for each pathway from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database , generated by combining all resultant pathway images. The two most correlated pathways were placed on the top two rows on the pathway images, and then a pathway that was the most correlated with the pathway in the second row was placed on the third row. This process was repeated for all 146 pathways. Note that the The input layer was provided with pathway images. The CNN architecture consisted of two convolutional layers with 32 and 64 filters, with a size of 3 × 3, followed by a 4 × 2 max-pooling layer, and a dropout layer with a dropout rate of 25%. Each convolutional layer was immediately followed by a rectified linear unit (ReLU) activation function . The outModeling used a 5-fold cross validation (CV) approach with 30 repeats. In each 5-fold CV, samples were randomly assigned as follows: 20% of data were assigned as a test set for performance evaluation, and the remaining data were further split into a training set (80% of the remaining data) and a so-called validation set (20% of the remaining data) used to tune hyper-parameters. The modeling was carried out using the training set and optimizing hyper-parameters in the CNN model using the validation set. The model was built in each CV and was evaluated on the test set. Predictive performance was quantified by the area under the curve (AUC).c with respect to feature maps A of the last convolutional layer. More specifically, neuron importance weights c were computed as follows:k is the number of feature maps and Z is the number of pixels in the feature map. An activation map for the class c was then produced using the following equation:cL is a weighted sum of feature maps followed by a ReLU. The motivation of the ReLU is to highlight pixels whose intensities contribute to increasing Grad-CAM for class-discriminative localization mapping was employed to identify the important pixels (pathways) on pathway images associated with LTS in GBM patients  . A difference map between the two activation maps was thereby produced. After repeating this process for all samples, a statistical analysis was conducted. For a given pixel, the statistical difference between the LTS and non-LTS groups was assessed using the Wilcoxon rank-sum test . The P-vThe predictive performance of PathCNN was compared with several machine learning methods, including logistic regression, support vector machines (SVMs), fully connected neural networks and the recently proposed Multi-omics Integrative Net (MiNet)  were used for 285 and 155 cases, respectively. Removing 5 duplicate cases, 435 cases were evaluable for DNA methylation data. In total, 8037 genes were common in the three omics types, and 343 cases had all three omics types. After removing individuals who survived with the last follow-up In total, 4989 unique genes were involved in 146 KEGG pathways with an average of 68 genes per pathway. PCA tests were performed for individual pathways separately on each omics type to convert gene level information to pathway level information. When genes belonging to a given pathway were not available, PCA was conducted without the missing genes. The number of missing genes in each pathway was on average 13, 2 and 20 for mRNA expression, CNV and DNA methylation, respectively.P-value < 0.001 using a two-sample t-test. Due to the significance of age in terms of survival, the variable was added to the fully connected layer in the CNN model. IDH-wildtype GBM is more common and has a worse prognosis, whereas IDH-mutant GBM is more frequently observed in younger patients and has been associated with longer survival  were used, the performance was saturated with an average AUC of 0.753, and there was no significant additional improvement with more PCs. When q = 2, a model without age achieved an average AUC of 0.677, demonstrating the significance of age.The CNN model trained to classify LTS and non-LTS groups in GBM using pathway images was tested in a 5-fold CV scheme. The pathway image representation consisted of 146 rows (each row represents the same pathway) and 3All possible arrangement combinations of mRNA expression, CNV and DNA methylation in pathway images with two PCs were tested. Interestingly, an arrangement of  resulted in the worst AUC of 0.736. Arrangements of  and  achieved average AUCs of 0.741 and 0.747, respectively. Other arrangements resulted in average AUCs of 0.755 which is similar to that of the arrangement order of  used in this study. The performance differences, though small, may be due to the relatedness of the different data types.q = 2. As shown in q = 3 was used due to the filter size of 3 × 3. Overall, the use of multi-omics types in modeling showed improved performance. However, DNA methylation did not significantly improve the predictive power compared to mRNA expression and CNV, which is likely due to the relatively high rate of missing genes in DNA methylation. By contrast, the low missing rate in CNV explains its contribution in predictive power.To assess which omics type is more informative to the model, CNN modeling on pathway images, generated using combinations of two omics types, was conducted with The predictive performance of PathCNN was compared with four different machine learning methods, including logistic regression, SVMs, fully connected neural networks and MiNet. In addition to GBM, multi-omics data, including RNASeq gene expression, CNV and DNA methylation, were downloaded from the TCGA and assessed for three other cancers, including kidney cancer, low-grade glioma (LGG) and lung adenocarcinoma (LUAD). is, respectively. If the model is well trained, and an input sample is belongs to the LTS group, n is the number of samples. Based on the original class of each sample, D was split into two groups, j, k), i.e. jth row and kth column on each matrix in the set D, the difference in values between P-value. After statistical tests for all pixels, P-values were corrected using the Bonferroni correction.To assist biological interpretations, the CNN model was retrained using all samples of the pathway images generated with the first two PCs for individual omics types. To identify the biological mechanisms associated with survival in GBM patients and independent from age, the clinical variable age was removed from the CNN model. Individual pathway images were fed into the trained model, as shown in P-values < 0.001 were found, which consisted of 15 pixels (pathways) with 10 unique pathways  and uniform manifold approximation and projection (UMAP), will be further considered in future work.In the 146 pathways, the number of genes involved in each pathway ranged from 10 (taurine and hypotaurine metabolism) to 389 (olfactory transduction). For the sake of simplicity and efficiency in CNN modeling and Grad-CAM based biological interpretation, the size of the pathway image for each sample was fixed with 146 pathways in rows and a predefined number of PCs in columns. For the dimensional reduction of each pathway, if there were missing genes in the omics data, PCA was performed without those genes, which removed the difficulty of handling missing data. Despite the linear nature of PCA, the non-linear relationships among pathways can be captured by the CNN model.The resulting pathway based CNN model effectively predicted LTS for GBM patients. When all three omics types were used, an average AUC of 0.753 was obtained, using the first two PCs for each omics type. When two omics types (mRNA expression with CNV and CNV with DNA methylation) were used, similar performance was achieved with average AUCs of 0.749 and 0.748, respectively. Predictive power worsened when mRNA expression with DNA methylation data were used, with an average AUC of 0.704. Overall, the use of multi-omics data significantly improved predictive performance compared to modeling using any single omics type.P-values < 0.05, indicating that all tests rejected the null hypothesis that the data came from a normal distribution at the 5% significance level. Therefore, non-parametric tests with a Wilcoxon rank-sum method were carried out.For the biological interpretation of the class activation maps that were derived using a Grad-CAM technique, a matrix was produced that represents the intensity difference between the two class activation maps for each sample. Then, for a specific pixel on the difference maps, a statistical analysis was conducted to assess the value differences between the LTS and non-LTS groups. Before that, a normality test, using the Kolmogorov-Smirnov method, was performed. For all pixels, the Kolmogorov-Smirnov test produced P-values < 0.0001 were found, in which at least two correlated pathways were localized together, consisting of 15 pixels and 10 unique pathways: (i) cytokine-cytokine receptor interaction, chemokine signaling pathway, NOD-like receptor signaling pathway, ECM-receptor interaction; (ii) regulation of autophagy, glycosylphosphatidylinositol (GPI) anchor biosynthesis; (iii) neuroactive ligand-receptor interaction, long term depression and (iv) alpha linolenic acid metabolism, linoleic acid metabolism.Using the Grad-CAM, four significant regions with adjusted et al. found 76 differentially expressed genes between low and high risk groups in GBM, and showed that these 76 genes were enriched in three KEGG pathways, including the cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway and chemokine signaling pathway  , Breast Cancer Research Foundation [BCRF-17-193] and the Ministry of Science [ICT 2019-0-01601].Conflict of Interest: none declared.


Tripterygium wilfordii (TW) in the treatment of diabetic nephropathy (DN) through network pharmacology and molecular biology. First, the active ingredients and potential targets of TW were obtained through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and related literature materials, and Cytoscape 3.7.2 software was used to construct the active ingredient-target network diagram of TW. Second, the target set of DN was obtained through the disease database, and the potential targets of TW in the treatment of DN were screened through a Venn diagram. A protein interaction network diagram (PPI) was constructed with the help of the String platform and Cytoscape 3.7.2. Third, the ClueGO plug-in tool was used to enrich the GO biological process and the KEGG metabolic pathway. Finally, molecular docking experiments and cell pathway analyses were performed. As a result, a total of 52 active ingredients of TW were screened, and 141 predicted targets and 49 target genes related to DN were identified. The biological process of GO is mediated mainly through the regulation of oxygen metabolism, endothelial cell proliferation, acute inflammation, apoptotic signal transduction pathway, fibroblast proliferation, positive regulation of cyclase activity, adipocyte differentiation and other biological processes. KEGG enrichment analysis showed that the main pathways involved were AGE-RAGE, vascular endothelial growth factor, HIF-1, IL-17, relaxin signalling pathway, TNF, Fc epsilon RI, insulin resistance and other signaling pathways. It can be concluded that TW may treat DN by reducing inflammation, reducing antioxidative stress, regulating immunity, improving vascular disease, reducing insulin resistance, delaying renal fibrosis, repairing podocytes, and reducing cell apoptosis, among others, with multicomponent, multitarget and multisystem characteristics.We aimed to explore the active ingredients and molecular mechanism of  According to data surveys, it is inferred that by 2030, diabetes will become the seventh most common cause of death in the world , and diaTripterygium wilfordii (TW) is the most commonly used. TW has been used in TCM for more than two thousand years for the treatment of rheumatoid arthritis, autoimmune diseases and kidney diseases  also provides an effective treatment for DN. In China, Chinese herbal medicine is widely used in the treatment of DN, among which diseases . Modern diseases . It can diseases . HoweverAll of the chemical constituents of TW were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The TCMSP database is the most commonly used database for the retrieval of Chinese medicine ingredients and it describes the relationships between drugs, targets and diseases . This dahttp://db.idrblab.net/ttd/) (https://www.drugbank.ca/) (https://www.disgenet.org/) (https://www.ncbi.nlm.nih.gov/) (Homo sapiens”.The DN-related target proteins were collected from the following four widely recognized disease databases: 1) Therapeutic Target Database (TTD) (et/ttd/) , 2) Drugank.ca/) , 3) DisGet.org/) , and 4) ih.gov/) . We searhttp://bioinfogp.cnb.csic.es/tools/venny/index.html) are commonly used to display list comparisons  is an international standard system used to classify gene functions. It divides gene functions into three aspects: molecular function (MF), cell composition (CC) and biological process (BP) . Kyoto Ep ≤ 0.05 was set for enrichment.To predict the molecular mechanism of TW in the treatment of DN, this paper adopted a focused analysis method and used ClueGO software  to analyThe GO biological process enrichment parameter "GO Tree Interval" was set to 4–9, the minimum gene of "GO Term Selection" was set to 5, the minimum gene proportion was set to 5%, and the kappa score was set to 0.5. The minimum gene of the KEGG pathway enrichment parameter "GO pathway selection" was 6, with the minimum gene accounting for 4%, and the kappa score was set as 0.5. After the selection parameters were run separately, the GO biological processes and KEGG pathway selection and their related target information were obtained.First, the top seven core compounds were selected, and the two-dimensional structure diagrams of the compounds were downloaded from the TCMSP database and saved in mol2 format. Then, the files were imported into AutoDockTools-1.5.6 software to add charge and display rotatable keys and then they were saved in pdbqt format. Second, the protein crystal structures corresponding to the core target genes were downloaded from the PDB database , importeA total of 144 chemical constituents and 51 active components of TW were screened by TCMSP, mainly alkaloids and terpenoids, as shown in A total of 755 target genes related to DN were retrieved from the TTD, DrugBank, DisGeNET and NCBI databases .The targets of TW and DN were input into a Venn diagram for mapping and intersection, and a total of 49 cross targets were obtained, that is, the potential targets of TW in treating DN, as shown in The 49 common targets of TW and DN were used to draw protein interaction diagrams through String and Cytoscape software. As shown in Through GO biological process analysis, 27 major biological processes of TW treating DN were obtained, mainly enriched in 11 categories, as shown in To further explore the specific molecular mechanism of TW in the treatment of DN, the KEGG pathway was explored. As shown in The results obtained by the molecular docking software are shown in In vivo experimental studies which shows that TW extract can improve the micro-inflammatory state of DN , HIF-1, IL-17, the relaxin signaling pathway, TNF, Fc epsilon RI, insulin resistance and other signaling pathways.The active component-target network diagram and related topological parameters show that the key active ingredients of TW are kaempferol, triptolide, nobiletin, and tripterine. TW mainly improves kidney damage and alleviates related symptoms by regulating oxidative stress, cell apoptosis, insulin resistance, etc. The 12 key compounds were mainly terpenoids, among which kaempferol and caseolin were flavonoids. Kaempferol has anti-inflammatory, antioxidant and lipolysis effects . RelatedThere were 16 core targets screened through PPI, such as AKT1, TP53, VEGFA, PTGS2, TNF, MMP9, JUN, FN1, CXCL8, NOS3, PPARG, RELA, ESR1, STAT1, MMP1, and CREB1, which may play a key role in the treatment of DN.In this study, it was found that AKT1 was directly linked to 51 of the 64 screened pathways, which may be one of the important targets for TW to regulate multiple pathways. AKT1 is a serine/threonine kinase that can regulate insulin metabolism. In addition, AKT1 is an important factor in the PI3K/AKT pathway and it plays an important role in regulating glucose homeostasis, lipid metabolism, protein synthesis, and cell survival . FurtherTargets such as VEGFA, CXCL8, and TNF are all proinflammatory factors, and they jointly participate in the chronic inflammatory response process of DN. Among them, VEGFA is the main contributor to the development of new blood vessels, and it is also a proinflammatory cytokine , which iin vivo and is a major target of NSAIDs for treatment , which is closely related to endothelial cell function, and vascular endothelial dysfunction has been considered an important factor in the pathogenesis of DN . Other sreatment . It mainreatment . In addireatment .in vitro" and animal studies have shown that renal fibrosis is positively correlated with the expression and activation of MMPs, and urine MMP-1, -2, -9 excretion and the urine MMP-1, -2, -9/TIMP–1 ratio can be used as early biomarkers of renal fibrosis (Both fibronectin (FN) and matrix metalloproteinases (MMPs) play a role in the treatment of DN by affecting extracellular matrix (ECM) proteins. The early pathological features of DN are glomerular hypertrophy, basement membrane thickening and mesangial dilatation, and late stages involve glomerular sclerosis and interstitial fibrosis. Excessive accumulation of ECM is the common pathological basis and it eventually causes glomerular sclerosis. FN, an important component of the ECM, is distributed in the glomerular basement membrane, mesangial membrane and plasma. The abnormal increase in fibronectin in the ECM plays an important role in the pathogenesis of DN . MMPs affibrosis . CREB1 ifibrosis , and renfibrosis . CREB1 ifibrosis .STAT1, RELA, JUN, are all transcription factors. STAT1 is a member of the STAT family and it acts as a signaling messenger and transcription factor . It reguPPARG involves the expression of various genes, such as insulin sensitivity, and it plays an important role in glucose and lipid metabolism . Recent The results of KEGG pathway enrichment analysis showed that the pathways of TW in treating DN were mainly enriched in five categories: inflammatory response, oxidative stress and immune regulation, anti-vascular disease, insulin resistance, renal fibrosis and apoptosis.First, the inflammatory response, oxidative stress and immune regulation: Although the pathogenesis of DN is not yet fully understood, it is certain that oxidative stress, inflammation, and immunity play an important role in the occurrence and development of DN . InflammSecond, improving vascular disease: DN is a progressive microvascular complication caused by diabetes  and is gThird, insulin resistance: Insulin resistance signalling pathway: Insulin resistance (IR) is an independent risk factor for the occurrence and development of DN . In the Fourth, renal fibrosis: The formation mechanism of renal fibrosis is mainly divided into transforming growth factor expression, extracellular matrix deposition, epithelial mesenchymal transformation, inflammatory response, and oxidative stress response . The NF-Fifth, apoptosis: TW regulates the cell cycle by regulating apoptosis and cellular senescence pathways, thereby playing a role in the treatment of DN.Further analysis of the enrichment pathways revealed that the top 9 pathways with the highest proportion of hit genes were AGE-RAGE (18.00%), VEGF (13.56%), HIF-1 (12.00%), fluid shear stress and atherosclerosis (11.43%), IL-17 (11.83%), relaxin (10.77%), TNF (10.00%), Fc epsilon RI signaling pathway (8.82%), and insulin resistance (8.33%). The age-rage, VEGF, HIF -1, relaxin, TNF, and insulin resistance pathways were selected to draw the cell pathway diagram, as shown in TW has complex chemical components and extensive pharmacological activities. Compared with immunosuppressant, TW has definite efficacy and fewer side effects and adverse reactions. It is widely used in autoimmune diseases and various skin diseases, and is one of the hot natural drugs in the research at home and abroad. This article provides theoretical support for TW to treat DN from the molecular biology level. At the same time, this paper also focuses on the plant itself rather than the extract of TW, aiming to promote further research and provide directions for finding new therapeutic targets.In summary, a total of 52 active ingredients of TW were screened, with 141 predicted targets, 755 targets for DN, 49 potential targets for TW treatment of DN and 12 key active ingredients. The key compounds are mainly terpenes, of which kaempferol and nobiletin are flavonoids, which highlights the fact that flavonoids cannot be ignored in the study of the efficacy of TW. These compounds affect VEGFA, TP53, PTGS2, TNF, MMP9, Jun, FN1, CXCL8, NOS3, PPARG and other core targets through AGE-RAGE, VEGF, HIF-1, IL-17, relaxin, insulin resistance, TNF and other signalling pathways. Reducing the inflammatory response and antioxidant stress, regulating immunity, improving vascular disease, reducing insulin resistance, delaying renal fibrosis, repairing podocytes, blocking cell apoptosis and other processes jointly improve DN. TW has complex chemical components and extensive pharmacological activities. Compared with immunosuppressants, TW has definite efficacy and fewer side effects and adverse reactions. It is widely used in treating autoimmune diseases and various skin diseases and is a popular natural drug in research. This article provides theoretical support for TW to treat DN at the molecular biology level. At the same time, this paper also focuses on the plant itself rather than the extract of TW, aiming to promote further research and provide directions for finding new therapeutic targets.


Traditional Chinese medicine (TCM) has a long history of treating glaucoma with remarkable effects, but there is no clear conclusion on its mechanism.Network pharmacology and molecular docking were used to analyze the mechanism and targets of TCM in the treatment of glaucoma, and baicalein was used to treat chronic ocular hypertension animal models rats for observation.The results of animal experiments showed that baicalein could significantly reduce intraocular pressure (IOP) in a rat model of chronic ocular hypertension and protect the structure of the retina and optic nerve, as shown by hematoxylin–eosin (H&E) staining and transmission electron microscopy (TEM). Reducing the apoptosis of retinal ganglion cells (RGCs) by upregulating the expression of the antiapoptotic protein BCL-2 is basically consistent with the results of molecular docking. In the network pharmacology analysis, many key proteins of biological pathways involved in the herbal therapeutic processes in glaucoma, such as threonine kinase 1 , tumor protein p53 , signal transducer and activator of transcription 3 , interleukin 6 (IL-6) and interleukin 17 , were identified. Their interactions built complicated chain reactions in the process of glaucoma.By combining the analysis of network pharmacology and animal experimental results, baicalein could effectively improve the symptoms of glaucoma and reduce RGC apoptosis, suggesting that the potential mechanism of TCM in treating glaucoma is related to regulating inflammation and cellular immunity and reducing apoptosis. Glaucoma, an optic neuropathy, has become a leading cause of irreversible vision loss due to its unpredictable morbidity and atypical initial symptoms, resulting in profound visual impairment worldwide. According to a report of the World Health Organization (WHO), approximately 76 million people (40–80 years old) suffer from vision loss caused by glaucoma, and glaucoma has become the fifth leading cause of vision impairment worldwide . The etiErigeron breviscapus, Radix salviae, Lycii fructus, Croci stigma and Ginkgo folium are 5 herbs that are commonly used in the treatment of glaucoma in China [The risks of glaucoma include family inheritance, black race, senior age, use of corticosteroids and high IOP –4. Howevin China , 13. Howin China . In the Erigeron breviscapus, Radix salviae, Lycii fructus, Croci stigma and Ginkgo folium were acquired from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database (http://tcmspw.com), which focuses on TCM [https://www.uniprot.org).The active ingredients of s on TCM , under thttps://omim.org), GeneCards Database (https://www.genecards.org) and GEO Datasets of NCBI (https://www.ncbi.nlm.nih.gov/gds) [P > 0.05 and |log2 fold change (FC)|> 0.5. Finally, we summarized the targets and differentially expressed genes above into known glaucoma-related targets (GRTs).Disease target information was collected from 3 databases: the Online Mendelian Inheritance in Man Database  . We searThe drug-disease common targets were collected via R software (version 3.6.0) and are shown as a Venn diagram. Then, we used Cytoscape software (version 3.7.2) to construct and visualize the Glaucoma-Targets-Drug network. Under the screening conditions of “Organism = Homo sapiens” and “High Confidence (> 0.9)”, the common targets were also used to build the PPI network using the STRING Platform (version 11.0). The nodes and edges between the nodes consisted of the network above, in which the degree of the node is the number of edges connected to the node, and a higher degree indicates greater importance of the node.http://www.bioconductor.org/). Based on the gene ratio, we show the top 20 processes (P < 0.05) as a histogram and bubble chart.To explore the functional processes of common drug-disease targets, we performed GO biological process enrichment analysis on these targets in the R/Bioconductor environment (https://metascape.org/), and we also created a heatmap of the top 20 KEGG pathways. Then, 8 pathways highly related to glaucoma were selected to construct the functional pathway network, which also involved related targets and active ingredients. The functional pathway network was constructed using Cytoscape software.To clarify the potential molecular mechanisms, KEGG pathway enrichment analysis was performed by Metascape Platform software (http://www.rcsb.org/), respectively. The proteins were dehydrated and hydrogenated, and a grid box was set via AutoDockTools software (version 1.5.6). To determine exhaustive docking patterns, the docking of ingredients and targets was performed in a flexible and unrestrained manner, allowing the ligand to move through the entire volume of the grid box. After the steps above, the docking results were visualized using PyMOL software (https://www.pymol.org).We performed molecular docking to verify the connective validity of active ingredients and core targets using AutoDock Vina software (version 1.1.2) . The 3D Male Sprague–Dawley (SD) rats (200 ± 20 g) from the Laboratory Animal Centre, Chengdu University of Traditional Chinese Medicine, were used, and the experiment was approved by the Committee of Scientific Research and the Committee of Animal Care of Chengdu University of Traditional Chinese Medicine (permission number: 2020–12). Animals were adaptively fed for one week at a cozy temperature of 23 ± 2 ℃ and temperate humidity of 40–60%. We abided by guidelines about animal use from the Association for Research in Vision and Ophthalmology (ARVO) throughout the study.Baicalein (S25956), sodium carboxymethyl cellulose  and balanced salt solution were purchased from Shanghai Yuanye Bio-Technology Co., Ltd. . A TdT-mediated dUTP nick-end labeling (TUNEL) staining kit (11684795910) was purchased from Roche Group . BCL-2 (bs-20351R) was procured from Bioss Co., Ltd. . FITC-conjugated goat anti-rabbit IgG (GB22303) and FAS eyeball fixative (G1109) were provided by Servicebio . DAPI (ZLI-9557) and citrate repair solution (ZLI-9065) were obtained from Zsbio Co., Ltd. . Newborn calf serum (22012-8612) was obtained from Tianhang Co., Ltd. .A total of 20 SD rats were anesthetized by intraperitoneal injection of 2% pentobarbital sodium solution at a dose of 50 mg/kg weight and then underwent episcleral vein cauterization (EVC) surgery in the right eye by the same skillful surgeon. EVC surgery complied with the procedures designed by Shareef : three sThe IOP assay was performed preoperatively (day surgery), the first day after surgery (before drug administration), the third day of surgery, the seventh day after surgery, the fourteenth day after surgery, and the twenty-first day after surgery by a TONOLAB tonometer TV02 . The measurements of IOP were performed at least three times on each eye, and the integer nearest to the mean value of the measurement results above was recorded as the final IOP of the eye. During the measurements, the animal was in a fully conscious state. According to the IOP results of the first day after surgery (before drug administration), we excluded with IOP (operated eye) < 22 mmHg from the subsequent experiments.H&E staining was performed to quantify the thickness of the ganglion cell complex (GCC) and changes in retinal structure. After 28 days, rats were sacrificed by anesthesia, and their eyeballs were removed. Ocular tissues were fixed by formaldehyde, acetic acid, and saline (FAS) eyeball fixative, dehydrated with gradient concentrations of ethanol, and then sliced into 7 μm-thick slides with a Rotary Slicer-RM2016 . Slides stained with H&E were sealed with neutral balsam, with 3 samples per group and 3 slices on average. The overall GCC thicknesses (from the ganglion cell layer (GCL), to the nerve fiber layer (NFL)) were measured. All measurements were carried out at distances of 1.0–1.5 mm  retina from optic disc with ImageJ software .Rats were sacrificed after 28 days, and three eyes were removed from each group. TUNEL assay was performed to quantify RGC apoptosis in the retina. TUNEL staining was processed by an In Situ Cell Death Detection Kit, Fluorescein . After dewaxing, the slides were repaired with citric acid microwave for 8 min, washed with phosphate buffer saline (PBS) three times, 5 min each time. Prepared with fluorescent TUNEL incubation solution in the dark (A:B = 1:30) and incubated for 1 h at the temperature of 37 °C, washed three times with PBS, and stained with DAPI for 15 min, followed by washing with PBS. The slides of the TUNEL stain were sealed by gelatin glycerin. The images of the slides above were collected and analyzed by Pannoramic 250 Flash .After the experiment, the rats were sacrificed, and the eyeballs were removed. Ocular tissues were fixed by formaldehyde, acetic acid, and saline (FAS) eyeball fixative, dehydrated in sucrose, and then sliced into 7 μm-thick slides with a Rotary Slicer-RM2016 . After dewaxing, the slides were immersed in 0.01 M citrate buffer (pH 6.0), washed three times with PBS for 5 min each time, and blocked at room temperature for 30 min in newborn calf serum. Primary antibody was added (BCL-2 1:100) dropwise, followed by refrigeration overnight at 4 °C, washing three times with PBS for 5 min each time, addition of the secondary antibody dropwise 37 °C for 30 min, washing with PBS the same as before, addition of DAPI and incubation at room temperature for 10 min, washing with PBS and sealing with anti-fluorescence attenuation reagent. The images of the slides above were collected and analyzed by a Pannoramic 250 Flash .Rats were sacrificed after 28 days, and three optic nerves were removed from each group. The optic nerve samples were fixed in 3% glutaraldehyde and postfixed in 1% osmium tetroxide . After dehydration by gradient acetone dehydration, the optic nerve samples were permeated in Epon 812 epoxy resin . Then, the permeated samples were embedded in flat molds and heating polymerized for embedded blocks. Ultrathin sections (50 nm) were created with an ultramicrotome and double-stained with Reynolds’s lead citrate and 0.5% aqueous uranyl acetate . The microscopic examination was performed with a JEM-1400PLUS TEM , and the images were analyzed with OlyVIA software (version 2.8).P < 0.05 was considered statistically significant.SPSS software (Version 26.0) was used for statistical analysis, and GraphPad Prism software (Version 7) was used for plotting. All of the images were measured by ImageJ software . Data are expressed as the mean ± standard deviation (SD). Measurement data conformed to a normal distribution and were compared by the independent sample t test analysis for two groups. One-way ANOVA was used for multigroup analysis; the measurement data did not conform to a normal distribution, and the nonparametric rank sum test was used. Erigeron breviscapus, Radix salviae, Lycii fructus, Croci stigma and Ginkgo folium) and obtained 226 DRTs from the TCMSP database. We list some active ingredients in Table Under the screening conditions of OB ≥ 30% and DL ≥ 0.18, we selected 143 active ingredients from the total ingredients of 5 traditional Chinese medicines , which were exhibited as heatmaps and volcano plots using R software Fig. a, b. AccCroci stigma, Erigeron breviscapus, Ginkgo folium, Lycii fructus and Radix salviae, respectively, but there are 7 ingredients that are contained in more than 2 herbs. The ingredients and targets were laid out as 2 and 3 concentric circles, respectively, according to their degrees (inner > outer). The top 10 active ingredients according to degree are listed in Fig. Using Cytoscape, we constructed a glaucoma-target drug network based on 146 common targets and 99 glaucoma-related active ingredients of 5 herbs Fig. a. The acAs shown in Fig. P < 0.05, and the gene count determined the importance of the process. We built a histogram and bubble chart  from the KEGG signaling pathway enrichment analysis performed by the Metascape platform. The top 20 biological signaling pathways are shown in Fig. 10(P). Then, we chose some signaling pathways highly related to glaucoma, the common targets involved in these pathways and the active ingredients of herbs to build the functional pathway network , BCL2 (2o22), HIF1A 4h6j), IL6 (4cni), MDM2 (5laz), STAT1 (3wwt) and STAT3 (6njs). We obtained the active ingredients related to these core targets from the analysis of the glaucoma-target-drug network, including quercetin (MOL000098), baicalein (MOL002714), luteolin (MOL000006), kaempferol (MOL000422), beta-sitosterol (MOL000358), tanshinone iia (MOL007154) and cryptotanshinone (MOL007088). The 3D structures of ingredient molecules and core target proteins were obtained from the TCMSP and RCSB PDB databases, respectively. After dehydration and hydrogenation, we performed docking with AutoDockTools and AutoDock Vina software. The absolute value of the binding energy is positively correlated with the connective stability of the ingredient molecule and target protein, and we show the most stable connective patterns in Fig. j, IL6 (4P < 0.0001). As shown in Table P < 0.01)  with one day after surgery, the COH and BAL group IOP increased to 26.56 ± 3.3 mmHg and 28.33 ± 2.2 mmHg, respectively (P < 0.0001)    Fig. . However64) Fig. c, the nu01) Fig. b, the stP < 0.0001) , but there was no significantly increased between COH and CON group (P > 0.05) , we evaluated the protein Fig. a express01) Fig. a, b, and05) Fig. d.Fig. 9aP < 0.0001). However, there was no statistically significant different between CON and BAL, as shown in Fig. CON group showed a normal optic nerve ultrastructural, axons and myelin sheaths are tightly arranged. Compared with CON, COH had obvious neurofibropathy features, axon swollen, myelin sheath thickness increased, myelin lamellae loosened and disrupted, but complete demyelination and axonal dissolution did not yet occur. BAL also had a slightly axons and myelin lamina changes but to a lesser extent than COH Fig. a. When tGlaucoma is an optic neuropathy, and its pathological mechanism is the loss and death of retinal ganglion cells due to the optic nerve axons damage. Previous studies have shown that increased intraocular pressure is a high-risk factor for glaucoma. However, recent studies have found that some patients’ disease continues to develop, although the IOP is under stable control . As we kErigeron breviscapus, Radix salviae, Lycii fructus, Croci stigma and Ginkgo folium are commonly used by practitioners of TCM in the treatment of chronic glaucoma [TCM has a history of thousands of years of clinical practice and is widely used to treat glaucoma in China –23. A meglaucoma –28, and glaucoma , 29 and glaucoma , increasglaucoma , 32. Howglaucoma . Based oThe PPI network constructed by common targets showed that many key proteins of biological pathways are involved in the herbal therapeutic processes in glaucoma, such as AKT1 , TP53 (a tumor suppressor gene coding tumor protein P53), STAT3 , IL6 and IL1B (proinflammatory factors). Their interactions build complicated chain reactions in the process of glaucoma. Li’s research showed that PI3K/Akt signaling activation were positive correlation with the survival of RGCs both in vitro and in vivo in the simulated ocular hypertension environment, the activation of the PI3K/Akt pathway can inhibit RGC apoptosis in glaucoma . An in vIn the present GO biological process enrichment analysis, most of the important processes are included in our results, such as activation of transcription factors and activators, cytokine and related receptor activation, ubiquitylation and protease activity, messenger molecule activity and phosphatase activity. These processes manage cell DNA replication and transcription, functional protein synthesis and metabolism, signaling transmissions and so on.As shown in KEGG enrichment analysis, multiple signaling pathways were involved in the treatment effects, such as the AGE-RAGE signaling pathway, IL-17 signaling pathway, HIF-1 signaling pathway, FoxO signaling pathway, and NF-κB signaling pathway, and the relevant mechanism included anti-neuroinflammation, antioxidative stress and glial activity regulation –45. We pP > 0.05)  Fig. c, and ax05) Fig. a. GlaucoIn this study, we explored the protective effects and potential molecular mechanisms of TCM in the treatment of glaucoma through network pharmacology and used baicalein to treat an animal model of COH for observation. Network pharmacology analysis demonstrated that the mechanisms of herbal therapy against glaucoma were multicomponent, multitarget, and multipathway. Their interactions built complicated chain reactions in the process of glaucoma. Moreover, AKT1, TP53, STAT3, IL-6, IL-1b and several pathways, which are closely related to inflammation and oxidative stress, are believed to play a significant role in the mechanism of action of herbal therapeutics against glaucoma. Molecular docking results showed that the core active ingredients showed good affinity for BCL-2, IL-6, STAT3, HIF-1α, AKT1 and so on since the binding sites had stable hydrogen bonds. However, there is no in vivo evidence to support these interactions. Further studies are needed. Experimental evidence revealed that baicalein treatment notably decreased the IOP, reduced the axon and myelin sheath damage. The therapeutic effects of baicalein on glaucoma might be related to reduced apoptosis in GCL and up-regulated the expression of BCL-2 in a COH rat model, at least partially verifying the predicted consequences of network pharmacology. These findings provide direct evidence for TCM therapy in the prevention and treatment of glaucoma.


Alpha-synuclein (α-syn) exhibits pathological misfolding in many human neurodegenerative disorders. We previously showed that α-syn is arginylated in the mouse brain and that lack of arginylation leads to neurodegeneration in mice.Here, we tested α-syn arginylation in human brain pathology using newly derived antibodies in combination with Western blotting, biochemical assays, and experiments in live neurons.We found that α-syn was arginylated in the human brain on E46 and E83, two sites previously implicated in α-syn pathology and familial cases of Parkinson’s disease. The levels of arginylation in different brain samples ranged between ~ 3% and ~ 50% of the total α-syn pool, and this arginylation nearly exclusively concentrated in the subcellular α-syn fraction that sedimented at low centrifugation speeds and appeared to be simultaneously targeted by multiple posttranslational modifications. Arginylated α-syn was less susceptible to S129 phosphorylation and pathological aggregation in neurons. The arginylation level inversely correlated with the overall α-syn levels and with patient age, suggesting a possible causal relationship between arginylation decline and α-syn-dependent neuropathology.We propose that α-syn arginylation constitutes a potential neuroprotective mechanism that prevents its abnormal accumulation during neurodegeneration and aging in the human brain.The online version contains supplementary material available at 10.1186/s40035-022-00295-0. Alpha-synuclein (α-syn) exhibits pathological misfolding and aggregation in human neurodegenerative disorders that are collectively called synucleinopathies . One of Protein arginylation mediated by arginyltransferase ATE1 is a PTM of emerging biological significance that consists of transfer ribonucleic acid (tRNA)-dependent transfer of Arg (R) to proteins and has been implicated in many key physiological events in vivo , 7. ATE1Here, we tested whether E46 and E83 arginylation targets α-syn in the human brain, and whether this arginylation correlates with any physiological changes in the patients, such as disease status or age. We also tested the potential interplay between α-syn arginylation and S129 phosphorylation, another PTM previously implicated in PD pathology. Our results uncover a potential new mechanism of α-syn regulation in the human brain and propose functional implications of arginylation in neurodegeneration and aging.Frozen postmortem brain samples of human frontal cortex were from patient brain donors who underwent autopsy at the Center for Neurodegenerative Disease Research (CNDR) at the University of Pennsylvania between 1992 and 2016 . Detaile3 and brine. The organic layer was dried over MgSO4 and evaporated to give rise to 460 mg oil, which was used directly for the next step. The obtained Fmoc-Glu(Arg(Pbf/OtBu)-OAll (0.5 mmol) was dissolved in 5 ml dichloromethane (DCM). To this flask was added 15 mg Pd(PPh3)4  and 617 μl PhSiH3 (5 mmol) under argon. The mixture was stirred for 4 h at room temperature and then evaporated under reduced pressure. The residue was re-dissolved in 20 ml DCM and washed with NaHCO3 and brine. The organic layer was dried over MgSO4 and evaporated to dryness. Further purification using silica gel gave rise to 375 mg pure Fmoc-Glu(Arg(Pbf)-OtBu)-OH with a total yield of 45%.To generate branched peptrides mimicking arginylated sites on α-syn, we synthesized Fmoc-Glu(Arg(Pbf)-OtBu)-OH, an arginylated Glu derivative to be used to synthesize the Glu-arginylated peptides. To do this we first generated a precursor, Fmoc-Glu(Arg(Pbf)-OtBu)-OAll. The procedure for precursor generation and final synthesis was as follows. Two hundred and five milligrams of Fmoc-Glu-OAll (0.5 mmol) were dissolved in 15 ml tetrahydrofuran (THF) in a flask and the flask was cooled to -20 °C. Next, 60 μl of N-methylmorpholine (0.5 mmol) was added to this flask, followed by dropwise addition of 65 μl isobutyl chloroformate (0.5 mmol). The mixture was stirred for 10 min, and then 259 mg Arg(Pbf)-OtBu·HCl (0.5 mmol) and additional 60 μl of N-methylmorpholine were added. The resulting slurry was stirred at − 20 °C for 1 h and then at room temperature for 3 h. The precipitate was filtered off and the filtrate was evaporated under reduced pressure. The residue was dissolved in 30 ml EtOAc and then washed with NaHCO+ 874.29; Fmoc-Glu(Arg(Pbf)-OtBu)-OH: [MH]+ 834.3750.The compounds were verified by matrix assisted laser desorption/ionization (MALDI) to determine their mass, as follows: Fmoc-Glu(Arg(Pbf)-OtBu)-OAll: [MH]RGVVH and CAVAQKTVERGAG were synthesized manually using standard Fmoc-based strategy with modification as reported in [−1 M−1). Subsequent amino acids were coupled by adding Fmoc-amino acid (5 equiv.) that was activated by HBTU (5 equiv.) and DIPEA (10 equiv.) and stirring for 30 min. For the on-resin coupling of Fmoc-Glu(Arg(Pbf)-OtBu)-OH, 2 equiv. of Fmoc-Glu(Arg(Pbf)-OtBu)-OH in DMF was activated by 2 equiv. of HBTU and 4 equiv. of DIPEA and then mixed with resin for 2 h. After further elongation, the peptides were cleaved from resin by treatment with 90%TFA/5%TIPS/5%DCM for 1.5 h. The cleavage solution was pooled into cold ether and the precipitate was collected and purified by reverse-phase HPLC using VyDAC C18 column and 0.1% TFA/0.1% acetonitrile as the mobile phase. The purity was checked by analytic HPLC , followed by boiling for 10 min. Then 2.5 μl of each sample was loaded on 15% SDS–polyacrylamide gels (PAGE) and transferred to a 0.2-μm nitrocellulose membrane at 250 mA for 20 min. Blots were blocked in 3% BSA in TBST, then incubated with primary antibodies  at 1:10 w/v ratio, and then centrifuged at 13,000 r/min in a tabletop microfuge at 4 °C for 30 min. The supernatant and pellet were collected as soluble and insoluble pool for Western blotting analysis. The supernatant was mixed at 1:1 ratio with 4× SDS loading buffer. The pellet was resuspended in 4× SDS loading buffer equal to the volume of the original extract, and both samples were boiled for 10 min prior to loading on the gel. Then 6 µl of pellet and 20 µl of supernatant were loaded on 10% bis–tris gels for mass spectrometry or 15% SDS-PAGE for Western blot.The tissue powder was lysed in reaction buffer  was mixed with recombinant α-syn at a ratio of 2 µl PLK2 per 72 µg α-syn in the reaction buffer containing 25 mM HEPES, pH 7.2, 50 mM NaCl, 20 mM MgClPrimary neurons were isolated from C57-Bl6 mouse neonatal brain as described , and incw/v uranyl acetate in water, and visualized by transmission electron microscopy.Non-arginylated or 5%-arginylated α-syn fibrils  were deposited on formvar-coated grids, stained with 2% Western blots were quantified by direct measurements of the secondary antibody fluorescence using Odyssey gel imager . Quantification of the band intensity over the background was calculated using the Odyssey gel imaging software integrated with the instrument.“total intensity” (following the command name in the Metamorph imaging software) measures the total gray level of each aggregate, thresholded against the background; this parameter directly depends on the area and density of the aggregate and thus quantitatively reflects the aggregate size.“average intensity” (following the command name in the Metamorph imaging software) measures the gray levels  per unit area, and this reflects the density of the aggregate, as more densely packed aggregate would have higher fluorescence per unit area.For description of intracellular S129-positive aggregates, the following parameters were measured:t-test between two groups with the P-value corrected by Bonferroni test.Calculations for Figs. Our previous work showed that α-syn is arginylated in the mouse brain on two conserved sites, E46 and E83, which are targeted for arginylation within the intact non-proteolyzed α-syn protein . Both ofWe next used these antibodies to probe extracts from the frontal cortex samples of patients, obtained from the biobank at the Center for Neurodegenerative Disease Research (CNDR) at the University of Pennsylvania. We analyzed healthy controls, as well as patients with PD and PD-related dementia . Strikingly, all brain samples showed prominent reactivity with both E46 and E83 antibodies Fig. , suggestSeveral interesting trends were observed by comparisons of the patient samples Fig. a, b. FirDuring the experiments described above, we noticed that the single band on the SDS-PAGE recognized by the antibodies for arginylated and S129-phosphorylated α-syn ran somewhat higher on the gel than the expected 14 kDa. At the same time, the total α-syn antibodies recognized a doublet in the ~ 14 kDa range, with the upper band in the doublet matching the position of the band positive for both arginylated and phosphorylated α-syn  α-syn is arginylated in the human brain on two conserved, functionally important sites – E46, previously implicated in familial PD, and E83, previously found to be critically important to α-syn pathology; (2) E46 and E83 arginylation targets a different percentage of α-syn in different patients, ranging from a few percent to nearly 50% of the total α-syn pool; (3) α-syn arginylation on these two sites strongly decreases with age, and negatively impacts α-syn pathological intracellular aggregation; and (4) α-syn arginylation counteracts S129 phosphorylation.Additional file 1. Table S1: Clinical profiles of donors with and without PD. Table S2: Primary antibodies used for western blot. Fig. S1: Characterization of E46- and E83-arginylated α-syn antibodies. Fig. S2: Both bands in the doublet recognized with α-syn antibodies contain α-syn. Fig. S3: S129 phosphorylation and E46 and E83 arginylation shows negative correlation with total α-syn levels. Fig. S4: E46 and E83 arginylation in the human brain decreases with patient age. Fig. S5: E46 and E83 arginylation levels show no correlation with ATE1 levels. Fig. S6: E46 and E83 arginylation alters the morphology and size of intracellular inclusions seeded by α-syn in cultured neurons. Fig. S7: E46 and E83 arginylation alters the morphology and size of intracellular inclusions seeded by a-syn in cultured neurons. Fig. S8: E46 and E83 5% arginylated a-syn fibrils are morphologically similar to wild type.Additional file 2. Dataset 1: HPLC conditions and analysis for the synthetic peptides used for antibody generation


The purpose of present study was to identify the differentially expressed genes (DEGs) associated with BMP-9-induced osteogenic differentiation of mesenchymal stem cells (MSCs) by using bioinformatics methods.Gene expression profiles of BMP-9-induced MSCs were compared between with GFP-induced MSCs and BMP-9-induced MSCs. GSE48882 containing two groups of gene expression profiles, 3 GFP-induced MSC samples and 3 from BMP-9-induced MSCs, was downloaded from the Gene Expression Omnibus (GEO) database. Then, DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that the identified DEGs were potentially involved in cytoplasm, nucleus, and extracellular exosome signaling pathway.A total of 1967 DEGs (1029 upregulated and 938 downregulated) were identified from GSE48882 datasets. R/Bioconductor package limma was used to identify the DEGs. Further analysis revealed that there were 35 common DEGs observed between the samples. GO function and KEGG pathway enrichment analysis, among which endoplasmic reticulum, protein export, RNA transport, and apoptosis was the most significant dysregulated pathway. The result of protein-protein interaction (PPI) network modules demonstrated that the Hspa5, P4hb, Sec61a1, Smarca2, Pdia3, Dnajc3, Hyou1, Smad7, Derl1, and Surf4 were the high-degree hub nodes.Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in BMP-9 induced MSCs, which could improve our understanding of the key genes and pathways for BMP-9-induced osteogenic of MSCs. Mesenchymal stem cells (MSCs) are non-hematopoietic multipotent cells and used for bone tissue regeneration , 2. MSCsThe purpose of our study was to compare the gene expression of BMP-9-induced osteogenic differentiation of MSCs in the GEO database. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses were performed to identify the potential pathway of BMP-9-induced osteogenic differentiation of MSCs. Furthermore, protein-protein interaction (PPI) was performed to identify relevant genes and pathways and molecular mechanisms.http://www.ncbi.nlm.nih.gov/geo/). GSE48882 was based on the Affymetrix GPL339 platform . The GSE48882 dataset contained six samples, including three GFP1-induced MSC samples and three BMP-9-induced MSC samples.The microarray data GSE48882 used in our study was downloaded from Gene Expression Omnibus  and bioconductor package limma [P < 0.05).The original array data were performed background correction and quartile data normalization . The rawge limma . The DEGhttps://david.ncifcrf.gov/) [The DEGs was entered into the online software Database for Annotation, Visualization and Integrated Discovery  . The DEGhttp://www.genome.jp/) and P < 0.05 was considered to indicate a statistically significant difference [DEGs functional and signaling pathway enrichment were conducted using online websites of KEGG (fference .http://www.string-db.org/) [www.cytoscape.org). P < 0.05 was considered to be a statistically significant difference [We downloaded the comprehensive interaction information of BMP-9-induced osteogenic differentiation of MSCs from the Search Tool for the Retrieval of Interacting Genes (STRING) database (db.org/) . A combifference .P < 0.05, logFC (fold control) > 2.0 or logFC < − 2.0 was used in current study, and we listed the first tenth DEGs in Table DAVID was used to carry out a gene ontology (GO) function enrichment for DEGs. Results show that the DEGs were significantly enriched in CC, including cytoplasm, nucleus, and extracellular exosome Fig. . For MF,Results show that the DEGs were enriched in protein processing in endoplasmic reticulum, protein export, RNA transport, apoptosis, Alzheimer’s disease, N-Glycan biosynthesis, Valine, leucine and isoleucine degradation, TNF signaling pathway, Toxoplasmosis, p53 signaling pathway, and AGE-RACE signaling pathway in diabetic complications Figs. .Fig. 5BAs shown in Fig. In the clinical application of rhBMP-2, adverse events such as tissue inflammation and bone resorption perplexed the physician –18. TherThe present study performed a comprehensive analysis and built a gene interaction network based on the gene expression profiles (GSE48882) comprising three GFP-MSC samples and three BMP-9-MSC samples. The results of the analysis demonstrated that 1967 genes were identified as DEGs. Moreover, GO and KEGG pathway analyses were performed to find the interactions of DEGs. Combining with the PPI network, key potential genes and pathways may be associated with BMP-9-induced osteogenic differentiation of MSCs.The results of GO analyses indicated cytoplasm, nucleus and extracellular exosome, protein binding and poly(A) and RNA binding. Nakamura et al.  revealedLin et al.  also fouChen et al.  found thHspa5 was the heat shock protein and plays an important role in protein folding and transport processes. Hspa5 plays a role in facilitating the assembly of multimeric protein complexes inside the endoplasmic reticulum. And thus, BMP-9 may through protein folding and transport and promote osteogenic differentiation of MSCs. Sharff et al.  revealedSeveral limitations existed in our study: (1) we did not verify the key gene and pathways through PCR and Western-blot method; (2) key miRNA and lncRNA were not further analyzed and should be performed in future research; and (3) optimal dose of BMP-9 was not explored in this gene expression microarray. Future studies should be focused on the specific mechanism BMP-9-induced osteogenic differentiation of MSCs. What is more, administration with small interference RNA to further confirms our conclusion.In conclusion, our study provides a comprehensive bioinformatics analysis of key genes and pathways of BMP-9 induced osteogenic differentiation of MSCs. Their related GO terms found that cytoplasm and nucleus may play important roles in BMP-9-induced osteogenic differentiation of MSCs and have the potential to be used as an potential agent for MSCs osteogenic differentiation. Further genetic and experimental studies with larger sample size are needed to confirm our results.


A novel hybrid biodegradable Nuss bar model was developed to surgically correct the pectus excavatum and reduce the associated pain during treatment. The scheme consisted of a three-dimensional (3D) printed biodegradable polylactide (PLA) Nuss bar as the surgical implant and electrospun polylactide–polyglycolide (PLGA) nanofibers loaded with lidocaine and ketorolac as the analgesic agents. The degradation rate and mechanical properties of the PLA Nuss bars were characterized after submersion in a buffered mixture for different time periods. In addition, the in vivo biocompatibility of the integrated PLA Nuss bars/analgesic-loaded PLGA nanofibers was assessed using a rabbit chest wall model. The outcomes of this work suggest that integration of PLA Nuss bar and PLGA/analgesic nanofibers could successfully enhance the results of pectus excavatum treatment in the animal model. The histological analysis also demonstrated good biocompatibility of the PLA Nuss bars with animal tissues. Eventually, the 3D printed biodegradable Nuss bars may have a potential role in pectus excavatum treatment in humans. Pectus excavatum (PE), also termed “funnel chest”, is a posterior depression of the sternum and adjacent costal cartilages and is responsible for more than 90% of congenital chest wall malformations. The source of PE may originate from unbalanced excessive growth in the costochondral regions. PE patients can manifest chest wall discomfort, exercise intolerance, and tachycardia. In severe cases, PE may also lead to cardiopulmonary impairment and physiological limitations. Previous studies have demonstrated that patients with asymmetric PE possess shorter ribs on the more severely depressed side of the defect. More than 43% of patients with PE possess a family history of the condition. PE is considered a multifactorial inheritance; however, the exact genes implicated in this process remain unclear .Up to the present, the minimal incursive therapy of PE, also named the Nuss process, has been widely used for patients who need surgical correction of PE ,3. By meAn ideal Nuss bar for the repair of PE should possess several characteristics: (1) possess good mechanical strengths to correct the chest wall, (2) deliver adequate and sustainable analgesics to the target site for pain relief, (3) be biodegradable after serving its purpose so that a secondary surgery to remove the bar is not needed, and (4) be biocompatible with human tissue so that the material breakdown process would not result in any tissue irritation.Polylactide (PLA), a biodegradable polymer with good strength, has been widely researched as an excellent biomaterial for load-bearing implants such as fracture fixation devices ,7. The mIn this work, we designed and exploited novel analgesics-loaded biodegradable Nuss bars using 3D printing and electrospinning techniques. We assumed that successful PE treatment can be effectively achieved using a biodegradable polylactide (PLA) Nuss bar.The degradation behavior of PLA Nuss bars was investigated. After submersion in buffered mixtures for various times, no evident dimensional variations were noted for the PLA Nuss bars. The mean maximum bending strengths of the bars decreased somewhat over time, namely 375.7 N, 361.9 N, 348.1 N, 341.2 N, 336.3 N, 337.6 N, 332.4 N corresponding to months 0 to 6 (N = 3). Meanwhile, the bending strengths of the retrieved bars from the animals at different times were also measured. The results suggest that the maximum bending strengths  after implantation of PLA bars was reduced in the animals as the time course of the experiment progressed.p < 0.05). p < 0.05) one month after implantation in the rabbits. Obviously, the bars degraded more rapidly in vivo than in vitro. Furthermore, as illustrated in The in vitro and in vivo molecular weight variations of PLA materials in the 3D printed bars were also characterized using a gel permeation chromatograph. p < 0.05) mainly due to the pain associated with the trauma of surgery. Meanwhile, the animals in Group B exhibited a greater number of sensor triggers than Group A (p < 0.05), demonstrating the efficacy of analgesics loaded on PLA for pain relief.p < 0.05); however, the animals received implantation of analgesic-loaded PLA bars that exhibited food and water intake comparable to that of the control. This finding further demonstrates that the lidocaine- and ketorolac-incorporated nanofibers could effectively ease the pain associated with the implantation of the PLA Nuss bars. Finally, the histological examination of the peri-Nuss bar tissue of Groups A and B , possibly due to the greater thickness of PLA bars  that induced more severe pain in the rabbit’s chest cavity. The outcome of activity analysis  printing method for commercial PLA filaments (weight average molecular weight (Mw) of 110,000 g/mol, Prolink Microsystems Corp., Taipei, Taiwan) on a 3D printer. The filament has a diameter of 1.75 ± 0.2 mm. In the printing process, the printer extruded melted polymeric material from a nozzle  at an extrusion speed of 30 mm/s. The nozzle head, which was set at a temperature of 230 °C, heated the material and adjusted the flow. Once the material was pushed out of the nozzle, it hardened and deposited layers. Two microprocessor-controlled step-motors were used to shift the printing head and regulate the flow of molten polymers.The in vitro variation of molecular weights in the PLA bars was assessed via submerging the bars in a phosphate buffered mixture at 37 °C. Specimens were retrieved from the mixture at 1–6 months (in increment of one month). After dehydration for 24 h, the molecular weights of PLA bars were evaluated using a Waters gel permeation chromatograph .The mechanical strengths of the 3D printed PLA Nuss bars were also characterized after being submerged in the phosphate buffer solution for various time points. Three-point bending tests of the bars were completed on a tensile test machine . During the measurements, the bars were compressed by the top loading pin at an increasing rate of 5 N until the end of the test. The ultimate strength and elongation at break were monitored. The evaluation was done in triplicate (N = 3) for each bar.Analgesics-eluting nanofibers, which were previously developed and fabricated in our lab by electrospinning of commercially available PLGA, were employed. Lidocaine and ketorolac were employed as the analgesics. Eighteen New Zealand white rabbits (weight ranging from 2.2 to 2.5 kg) were used in this study. The entire animal-related process acquired approval from Chang Gung University (CGU105-052), and all enrolled rabbits were cared for according to the regulations of the Department of Health and Welfare, Taiwan.The rabbits were separated into three distinct groups: (1) PLA bar group , (2) PLA bar with analgesic-eluting nanofibrous membrane group , and (3) control . Before implantation, the bars were disinfected with ethanol at a concentration of 75%. All 18 rabbits underwent general anesthesia via isoflurane respiration. After completing the surgical procedure, the animals received post-operatory analgesics  in the first 24 h. Thereafter, the general activity of each animal was assessed using an activity cage with dimensions of 120 cm (W) × 120 cm (L) × 60 cm (H). As illustrated in Furthermore, the daily food/water intakes of the animals were recorded. The 12 rabbits that received surgery underwent radiologic examination at 1-, 2-, and 3-months post-implantation.All 12 rabbits were euthanized by intravenous injections of lethal dosage of lidocaine at postoperative 1, 2, and 3 months for PLA bar retrieval for mechanical strength measurements and a histological examination.The mechanical properties of the retrieved PLA bars were characterized by the three-point bending test described earlier in t-test for statistical evaluation between different two-group combinations with SPSS software , with statistical significance set at p < 0.05. All data are presented as mean ± standard deviation.We used the paired We successfully developed a novel hybrid biodegradable Nuss bar model for the surgical correction of PE and a reduction of the associated pain during treatment. The experimental results show that the integration of PLA Nuss bar and PLGA/analgesics nanofibers can successfully enhance the therapy in a PE rabbit model. The histological analysis also demonstrated good biocompatibility of the PLA bars with animal tissues. The 3D-printed biodegradable Nuss bars may have a potential role in the treatment of PE in humans.


This study was designed to understand the pivotal anti-cerebral ischemia/reperfusion injury (CIRI) targets and pathways of calycosin through network pharmacology and molecular docking analyses. In this study, bioinformatics tools were employed to characterize and identify the pharmacological functions and mechanisms of calycosin for CIRI management. The network pharmacology data identified potential, merged CIRI-associated targets of calycosin including tumor protein p53 (TP53), protein kinase B (AKT1), vascular endothelial growth factor A (VEGFA), interleukin 6, tumor necrosis factor (TNF), and mitogen-activated protein kinase 1 (MAPK1). Molecular docking analysis indicated the binding efficacy of calycosin with three of the targets, namely TP53, AKT1, and VEGFA. The biological processes of calycosin for the treatment of CIRI are mainly involved in the improvement of endothelial cell proliferation and growth, inflammatory development, and cellular metabolism. In addition, the anti-CIRI actions of calycosin were primarily through suppression of the toll-like receptor, PI3K-AKT, TNF, MAPK, and VEGF signaling pathways. Taken together, the current bioinformatic findings revealed pivotal targets, biological functions, and pharmacological mechanisms of calycosin for the treatment of CIRI. In conclusion, calycosin, a functional phytoestrogen, can be potentially used for the treatment of CIRI in future clinical trials. Some coin vitro . Guo et in vitro . Howeverin vitro –14. Our in vitro –18.In the present study, we used bioinformatics tools including network pharmacology and molecular docking to uncover the pharmacological functions and therapeutic mechanisms of calycosin as an anti-CIRI agent. A flow chart was created to summarize the main findings of calycosin action for the treatment of CIRI, obtained via bioinformatics analysis .All calycosin-putative genes were identified through a series of traditional Chinese medicine systems pharmacology (TCMSP), Swiss Target Prediction, and SuperPred databases. Meanwhile, other CIRI-associated pathogenic genes were collected using the DisGeNET and Genecards databases. Calycosin and CIRI-related genes were merged using the UniProt database. After re-determination using the Funrich software, the calycosin-anti-CIRI targets were screened and specified as described in previous reports –20.All merged genes of calycosin-anti-CIRI targets were used for re-analysis to create a target-functional network via the STRING database following an associated algorithm . Using CThe pharmacological functions and signaling pathways of all pivotal targets of calycosin-anti-CIRI were identified and uncovered using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Subsequently, pivotal targets were determined using Cytoscape software to create a network diagram of anti-CIRI targets of calycosin. After determining the log P value using the OmicShare tool, the advanced bubble diagrams used for biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based molecular pathways were detailed and visualized, as reported previously –24.In brief, pivotal targets including tumor protein p53 (TP53), protein kinase B (AKT1), and vascular endothelial growth factor A (VEGFA) were screened and used for calycosin-associated molecular docking. The structure of these target proteins was retrieved from protein-data-bank (PDB) database with PDB IDs 2MWO, 3O96, and 5T89, respectively. Calycosin was docked to these proteins at their respective binding sites where their ligands are bound. The three-dimensional structure of calycosin prior to docking was demonstrated using ChemBio3D Draw setting of Chem Bio Office 2010 software. The software AutoDock Vina was used for all dockings and the molecular docked structures of calycosin with the target proteins of TP53, AKT1, and VEGFA were viewed using MGLTools in Autodock Vina software. The accuracy of the docking parameters was determined and identified using the root mean square deviation (RMSD) of the ligand molecules. The setting with an RMSD <4 Å is denoted as the threshold to conform to ligand molecules –26.After analyses of databases, 407 anti-diseased targets of calycosin and 198 diseased targets of CIRI were identified. In addition, a total of 51 merged targets of calycosin and CIRI are shown in a Venn diagram. The correlation network connected with these merged targets is shown in In the following analysis, the sub-network clusters obtained by the MCODE algorithm in Cytoscape software are shown in Based on the topological data and degree value to screen pivotal targets, the parameters of mean and large degrees of freedom in calycosin against anti-IRI target proteins were standardized. As a result, six core CIRI-associated targets of calycosin were screened and identified, including TP53, AKT1, VEGFA, interleukin 6 (IL6), tumor necrosis factor (TNF), and mitogen-activated protein kinase 1 (MAPK1) .We revealed the pharmacological mechanisms of calycosin for management of CIRI using enrichment analysis with pivotal targets. An advanced bubble chart showed top 20 biological processes of calycosin that can possibly be involved for treatment of CIRI, including its metabolic and apoptotic functions. The detailed mechanisms of calycocin action were determined from the enrichment analysis findings that revealed top 20 anti-CIRI pathways including modulation of cell proliferation and inflammation, and improvement of intracellular microenvironment . More daThe RMSD value of MLY, the original bound ligand in the TP53 protein (2MWO) was 2.675 Å. The ligand was bound at the target site through hydrogen bonding with the amino acid residues of the protein including ASP-1521 (2.7 Å), LYS-372 (2.6 Å), GLN-375 (1.8 Å), and LYS-373 (2.3 Å). In the calycosin-docked protein, calycosin was found to make hydrogen bonds with amino acid residues of ASP-1521 (2.5 Å), LYS-373 (2.4 Å), and SER-371 (2.5 Å) . In AKT1Calycosin formed hydrogen bonds with the amino acid residues of VAL-271 (3.2 Å), ASN-54 (2.9 Å), LYS-268 (2.4 Å), SER-205 (2.4 Å), TYR-272 (3.3 Å), and ASP-292 (2.3 Å) . In the Our current findings via bioinformatics analyses using network pharmacology and molecular docking revealed pivotal targets, biological functions, and molecular pathways of calycosin involved in CIRI management. We identified a total of six pivotal CIRI-associated targets of calycosin, including TP53, AKT1, VEGFA, IL6, TNF, and MAPK1. Moreover, molecular docking analysis demonstrated efficient binding of calycosin with thee of the targets, namely TP53, AKT1, and VEGFA. These findings strongly implicate an anti-CIRI action of calycosin. TP53, a well-reported anti-oncogene, can suppress intracellular DNA injury or genomic aberrations that are responsible for cell cycle arrest and cell growth . When muIn conclusion, the pivotal targets, biological functions, and molecular mechanisms of calycosin related to the treatment of CIRI are revealed through bioinformatics tools using network pharmacology and molecular docking. Furthermore, pharmacological targets, including TP53, AKT1, and VEGFA, have been identified before any experimental validation.Supplementary Tables


The Chinese herbal formula TiaoGanYiPi (TGYP) showed effective against chronic hepatitis B (CHB) caused by hepatitis B virus (HBV) infection. Hence, we aimed to clarify the mechanisms and potential targets between TGYP and CHB. The active compounds and related putative targets of TGYP, and disease targets of CHB were obtained from the public databases. The key targets between TGYP and CHB were identified through the network construction and module analysis. The expression of the key targets was detected in Gene Expression Omnibus (GEO) dataset and normal hepatocyte cell line LO2. We first obtained 11 key targets which were predominantly enriched in the Cancer, Cell cycle and HBV-related pathways. And the expression of the key targets was related to HBV infection and liver inflammation verified in GSE83148 database. Furthermore, the results of real-time quantitative PCR and CCK-8 assay indicated that TGYP could regulate the expression of key targets including CCNA2, ABL1, CDK4, CDKN1A, IGFR and MAP2K1, and promote proliferation of LO2 cells. In coclusion, we identified the active compounds and key targets btween TGYP and CHB, and found that the TGYP might exhibite curative effect on CHB via promoting hepatocyte proliferation and inhibiting the liver inflammatory processes. The worldwide estimates suggested that more than 2 billion people have been infected with HBV, with 248 million of these people are chronic hepatitis B (CHB) patients. And about 15–25% of CHB patients died from cirrhosis or liver cancer2, which caused a substantial global burden3. In China, there are 20 ~ 30 million CHB patients, and the mortality rate of HBV-related complications is higher than that in the world4. The disease process of CHB is affected by both HBV and the host immune system, while the pathogenesis of CHB is complex and still unclear. Meanwhile, existing treatment options including nucleos(t)ide analogs (NAs) and interferons (IFN) are limited in their efficacy and scope of application. At present, the discontinuation of NAs lacked operability in clinc5, and oral antiviral drugs also had limitations in improving complications6. Meanwhile, the contraindications and side effects hindered the clinical application of IFN7.It has been a global public health problem for the hepatitis B virus (HBV) infection for so long9. As for CHM prescriptions which contained a comlex compounds, often function with various multi-targeting and synergistic effects. Among CHM, the TiaoGanYiPi formula (TGYP) was used together with entecavir (ETV), and reported better efficacy for CHB treatment in our previous multi-center, double-blind, randomized placebo-controlled trial10. And in Chinese medicine theory, CHB is a complex-syndrome disease, and the TGYP is targeting at the core syndrome which is stagnation of Gan and deficiency of Pi qi, and damp-heat in the Gan and Dan11. However, the pharmacological mechanisms of TGYP regarding the therapeutic effect of CHB have not been fully explained. Here, we aimed to explore the relationship between CHB and TGYP, identify the key target genes and mechanisms of TGYP treatment on CHB through bioinformatics methods and in vitro experiments.For a long time, Chinese herbal medicine (CHM) plays a crucial role in CHB treatment and shows its superiorityIn this study, we first performed network pharmacology based on system biology and molecular docking, screened out 11 key target genes that exert essential functions during the treatment of TGYP on CHB. And the results of CCK-8 assay and real-time quantitative PCR (RT-qPCR) indicated that curative effect of TGYP could be related to promoting normal hepatocytes proliferation and regulation on the expression of partial key target genes. These results revealed the relationship between TGYP and CHB, further confirmed the effect of TGYP on hepatocytes, and indicated directions and targets for further research.Based on the previous work, which showed the anti-HBV effect of TGYP, we designed the flowchart of this study Fig. . As the To construct the compound-putative targets network of TGYP, we first analyzed the four main herbs in TGYP by CNKI and TCMSP databases and found 26 active components in Chaihu, 25 active compounds in Huangqi, 25 active compounds in Danggui, and 17 active compounds in Kudiding, respectively Figure A. Among To explore the biological function of TGYP target genes, the GO and KEGG pathway enrichment analysis were performed by DAVID. The GO enrichment analysis revealed that the GO-Cellular Component (CC) of TGYP target genes were predominantly involved in the Cytoplasm, Plasma membrane and Nucleus Fig. A; the GOWe analyzed and screened CHB disease targets, where we identified 8,037 targets in the GeneCards database, 415 targets in the DisGeNET database, and 236 targets in the NCBI Gene database, respectively Table . And we Since there was one gene lacked a corresponding protein structure and seven proteins did not interact with others based on PPI network, the remaining 1,192 proteins from STRING data were imported into the Cytoscape to further obtain interaction networks Fig. A. FilterFunctional enrichment analysis of the key target genes was performed after data screening to further understand the biological behaviors Table . The GO-To better understand the interactions between the compounds and target genes, we constructed an interaction network between the key target genes and corresponding active compounds in TGYP Fig. A. Accord12. Thus, we further analyzed the key target genes expressions in CHB liver tissues and their correlations with liver inflammation in the GEO dataset GSE83148. Compared to healthy controls, some key target genes were significantly higher expressed in CHB patients (P < 0.05), including ABL1, CASP8, CCNA2, CCNB1, CDK4, CDKN1A, HIF1A, and IGF1R . The active compounds in TCMSP were mainly filtered by integrating the pharmacokinetic properties comprising oral bioavailability (OB) ≥ 30%42, drug-likeness (DL) ≥ 0.1843, as well as half-life (HL) ≥ 4 h. The corresponding compounds chemical 2D structure SDF and canonical SMILES acquired from PubChem database (https://pubchem.ncbi.nlm.nih.gov). Data formats were transformed with Open Babel GUI software44. The Venn diagram was charted by the Venn tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).Compounds of the main herbs in TGYP, including Chaihu, Huangqi, Danggui, and Kudiding were searched in CNKI and TCMSP database (http://www.swisstargetprediction.ch/) and the STITCH databases (http://stitch.embl.de/), respectively. Putative targets of TGYP were collected under the following conditions: selecting the species is homo sapiens. The probability value of each potential target listed in the SwissTargetPrediction database was used to investigate the accuracy of the current predictions, whose probability value ≥ 0.1 was identified. In addition, the potential target proteins with confidence score ≥ 0.15 in the STITCH database.The canonical SMILES or SDF of the active compounds were imported into the SwissTargetPrediction (https://www.genecards.org/), the DisGeNET database (https://www.disgenet.org/) and the NCBI gene database (https://www.ncbi.nlm.nih.gov/gene/). The search condition was using the keyword “Chronic Hepatitis B” and selecting the organisms “Homo sapiens” from the three platforms. The above gene ID was identified and standardized in the UniProt database (https://www.uniprot.org). We used Retrieve/ID mapping tool of UniProt to convert identifiers, which are of a different type to UniProt identifiers.The target genes related to CHB were gathered from followed three databases. The GeneCards database (https://string-db.org/) with confidence scores ≥ 0.4 and the species limited to “Homo sapiens”, and exported protein–protein interaction (PPI) data. Analyzed the PPI data and constructed network by the Cytoscape 3.8.0 (https://cytoscape.org/), and the non-connection genes were removed. We use the Analyzer plugin to analyze the PPI network and get the degree, through taking the over a double median of degree to get the preliminary hub network45. Then, using the CytoNCA plugin to analyze the preliminary hub network to get network topological parameters: BC, CC, DC, LAC, NC and SC. We took the excess median of BC, CC, DC, LAC, NC, and SC to obtain hub network46. The MCODE application calculated the hub network to get the dense regions47. The genes in dense regions from MCODE is called key targets.The overlapped genes (OGEs) of the compound-putative targets and disease targets were charted by the Venn tool. We inputted the OGEs into the STRING database . The GO function enrichment and KEGG pathway enrichment were analyzed by DAVID database (https://david.ncifcrf.gov/). The filtering of retrieval results is with a threshold value of p < 0.05 and count in descending order. The value of P reflected the significance of protein biological function, and the purpose of filtering is to select the most enriched biological annotation from a given list of genes. Bubble Diagrams were drawn by Sangerbox tools (http://sangerbox.com/AllTools?tool_id=9697991).The screened out genes were analyzed by the GO function analysis included BP, MF and CChttps://www.rcsb.org). Moreover, the 2D structures of the compounds were obtained from the PubChem Database. AutoDock Vina could markedly improve the average accuracy of the combined mode prediction49. Thereby the Molecular docking simulation was performed by AutoDock Vina software, and the result was visualized by PyMOL software50. In this analysis, the value of the vina score indicates the binding activity between a compound and a protein, and the lower the vina score, the more stably the compound binds to the protein.Molecular docking simulation was performed to verify the critical components' binding ability to the key targets and explore their accurate binding modes. The macromolecular protein target receptors were acquired from the RCSB PDB database (http://www.ncbi.nlm.gov/geo/). The keywords were set as "hepatitis B" and "homo sapiens", the tissue type was limited to liver tissue, and the gene expression data of GSE8314851 was downloaded from the GEO database. The platform of dataset GSE83148 is the GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array, which includes 122 patients with CHB and 6 healthy liver samples, and includes 71 patients with abnormal transaminase and 34 patients with normal transaminase, which threshold of ALT was 40 and the threshold of AST was 35.The expression of key genes was verified in the GEO database  supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 µg/mL streptomycin and 5% CO2. For the acquisition of Chinese herbal compounds, the granules of TGYP formula were obtained and authenticated by the Nanning Peili Pharmaceutical Co., Ltd. The granules was dissolved in DMSO to 500 mg/mL and stored at -20 °C. The TGYP solution was added into cell culture with 500 µg/mL and use DMSO as control.5 LO2 cells were seeded in 6-well plates. After incubated for 24 h, the cells were treated with TGYP (500 μg/ml) for 48 h. Total RNAs were extracted using the RaPure Total RNA Mini Kit  according the manufacturer’s instructions. The reverse transcription of total RNA to cDNA was performed with qPCR RT Master Mix kit . RT-qPCR was performed using the Real-time PCR Detection System  with the SYBR Green Real-time PCR Master Mix . The primers used in this study are provided in Supplementary Table The 5 × 103 LO2 cells were seeded in 96-well plates with four duplications, and after TGYP treatment, CCK-8 assay kit  was carried out to assess the ability of cell growth through measuring the absorbance at the wavelength of 450 nm by the TECAN infinite M200 Multimode microplate reader .The 5 × 10Data for graphing was processed with GraphPad Prism 8.0 software. Statistical analyses were performed using the SPSS 25.0 statistical software package. For the comparison of quantitative data between groups, the Student’s t-test is used. Differences in means were considered statistically significant at P < 0.05. Significance levels are: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, not significant.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.