Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability.To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD.t-cell activation.The performance of PINNet was compared with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and https://github.com/DMCB-GIST/PINNet.By integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at  Alzheimer's disease (AD) is the most prevalent type of dementia and is distinguished by amyloid beta (Aβ) plaques and neurofibrillary tangles in the brain. Aβ and tau are clinical hallmarks of AD, in which abnormal accumulation precedes neurodegeneration and cognitive impairment in both sporadic and familial AD  to predict AD using a gene expression dataset from the brain and blood. The motivation for the PINNet is incorporating the explicit gene relationships via pathway information, as well as capturing implicit gene relationships through the fully connected layer in the model's structure. To determine the model's interpretability across various pathway contexts, we considered two sources of pathway information: Gene Ontology (GO), a literature-curated reference database with a hierarchical structure, or the Kyoto Encyclopedia of Genes and Genomes (KEGG), an alternative pathway ontology containing dynamics and interaction between the genes. The GO ontology design is interrelated through a hierarchical parent-child relationship, enabling us to examine functional clusters at different scales. KEGG represents networks of interacting molecules responsible for specific biological functions. In PINNet, pathway information was included in the structure of the model, thereby enhancing interpretation. The model was interpreted using Deep SHAP  , where m is the number of pathways and n is the number of genes.Pathway information was compiled from the GO biological process (BP)  is the same as the number of genes  in the dataset. The pathway layer consists of fully connected nodes f and pathway nodes p. Pathway nodes p represent relations between genes and pathways, and fully connected nodes f represent features for all genes regardless that they are included in the pathway. The number of nodes in p∈ℝm was the same as the number of pathways used. For instance, m is 7,470 when using the GO database and 186 when using the KEGG database. In order to represent the relationship between the gene and the pathway, we obtain pathway nodes p asPINNet is a four-layer deep neural network with biological pathway information as prior knowledge for AD classification. The PINNet comprises four layers: an input layer, a pathway layer, a hidden layer, and an output layer, as shown in Wp is weight matrices. The M∈ℝm×n with element-wise multiplication. Masking weights (Wp°M) produce a sparse network by weighting between a particular pathway and genes. Genes not included in the pathway are set to zero. Note that non-zero weights of Wp are updated by backpropagation. u∈ℝm is a normalization vector to prevent the values of pathway nodes are affected by the number of genes in the pathway and is defined as follows:where f, n input nodes are directly connected to the f through a linear layer as follows:For the fully connected nodes Wf is a weight matrix. p and f are concatenated after layer normalization (), and it passes through the hidden layer and the output layer. Then, output o∈ℝ is obtained. Tanh is used for the nonlinear transforming function, and batch normalization is utilized to decrease the effect of internal covariate shift induced by various weight scales. Dropout (α = 0.3) prevents overfitting of the model. We performed the training process by minimizing a cross entropy loss.where k-nearest neighborhood of the minority class. To train PINNet, we initialized all weights with the Xavier initialization. We optimized the loss using the ADAM optimizer, the ReduceLROnPlateau scheduler, and a stochastic gradient descent algorithm, with a mini-batch size of 64. Note that the same settings are applied to both PINNet and the neural network model used for the comparison. The model was implemented using Python  and Pytorch  on GeForce TITAN X GPUs.The training dataset was oversampled using SMOTE  values. The SHAP values for each model were computed using the DeepExplainer SHAP package in Python, which is based on Deep SHAP  is gene expression values in a test set. Furthermore, to compute the contribution of pathway nodes in the pathway layer, the values of pathway nodes p in Equation (3) are used as xi in Equation (5). For each pathway and brain and blood datasets, ten models are constructed by 10-fold cross-validation. Absolute mean SHAP values are normalized as z-scores, and the z-scores are averaged for the ten models. We defined it as an importance score. Importance scores are used to evaluate the contribution of the genes and pathways. The steps for evaluating and analyzing highly contributed genes are depicted in Reference inputs and outputs can be chosen for a given problem, and we set the reference to a training set with a balanced label distribution, where the reference output is the average of the outputs of the training set. The attribution score calculated from Deep SHAP can be adapted to an approximation of SHAP values. To obtain the contribution of the genes in the input layer, u: (1) u = 1 , (2) u = 1/n, and (3) n is the number of genes in each pathway, and compared the importance score of pathway nodes (Details about an importance score are described in Methods). Without normalization, pathways with a larger number of genes had higher importance scores in all four models. The absolute values of the Pearson correlation coefficient between the numbers of genes in pathways and importance scores were strongly correlated in the blood (GO) (|R| = 0.43) and blood (KEGG) (|R| = 0.30). However, when normalizing with R| < 0.1 was obtained in four models. This implies that the number of genes in the pathway has almost no effect on the importance scores of the pathways through normalization , support vector machines (SVM), and random forest (RF) methods. The DNN model architecture is identical to PINNet with the same number of the first hidden layer, except that it does not apply a pathway binary mask. The DNN model consists of two fully connected layers without a pathway binary mask, and the number of nodes and learning rate of the first hidden layer were selected using grid search in the validation set. Since the range of nodes in the first hidden layer of PINNet was 200 (168 + 32) to 4,154 , the candidate nodes in the first hidden layer include 128, 512, 1,024, and 4,096, and the learning rate used the same set of candidate values as PINNet. Similarly, for SVM and RF, the combination of hyperparameters and kernel type with the highest AUC for the validation set was chosen within each fold. Sets of hyperparameter values searched in the validation and subsequently selected best hyperparameter values are provided in A major objective of the analysis of gene expressions for the disease is to associate changes in the transcript with changes in phenotype. To reveal such associations, we interpreted PINNet models for AD prediction. We identified which genes played an important role in prediction in the models learned from 10-fold-cross validation in p-value ≤ 0.001). However, in the case of DNN models, the difference in importance score between AD-related genes and non-AD-related genes is only partially significant for brain data, and there is no difference in blood. In addition, we observed that the importance scores of known AD genes from PINNet were significantly higher than those from the DNN model for both KEGG and GO sources. These results shows the robustness of our approach. Unlike standard DNNs, PINNet captures important biological features based on prior biological knowledge. It represents that PINNet's biological prior knowledge leads to learning disease-related genes as essential features.We analyzed the importance scores of genes using two lists of known AD-related genes: AlzGene . Using a protein interaction network obtained from the STRING database, we examined genes with high importance scores. We selected 1% genes with the highest importance scores from the four models. We integrated genes selected in PINNet (GO) and PINNet (KEGG) models for the blood and brain, separately, and obtained 170 and 167 genes in blood and the brain, respectively. The highly contributed genes were mapped onto the STRING database (version 11.0) . We obtained 24 and 291 significantly different genes between blood and brain models (blood (GO) vs. brain (GO) and blood (KEGG) vs. brain (KEGG), respectively) with the adjusted p-values (BH) < 0.05. Next, we checked highly contributed genes (top 1%) that showed a clear distinction between the tissues. As a result, in the brain, CLU, PTPN11, ITPKB, PRKACB, IGF1, and MTHFD2 were more important compared to blood. CLU is related to several pathological states of AD, including a mediator of Aβ toxicity  in different tissues with a two-sided Wilcoxon test see , S3. Fron, n/2, n/4, 2}, where n is the number of input genes. We compared the performance of 10% of selected genes using PINNet with the same number of randomly selected genes and all genes for predicting AD samples. Genes selected using PINNet outperformed the random gene set in blood models , and a remaining set for testing see . Second,d models , suggestIn this study, we developed PINNet, a model with higher interpretability than a black box neural network with pathway information as prior knowledge. When designing a model, relationships between pathways and genes were represented as weights between the input layer and the pathway layer.We compared the performance of our proposed model with those of previous studies using blood and brain datasets. In the case of the brain dataset, prior studies employing the Weighted Gene Co-expression Network Analysis method for biomarker selection achieved an AUC ranging from 0.959 to 0.972  were >0.05 for all pathways  anchor-related pathway and immune response. The GPI anchor metabolic process was the highest-ranked GO BP, and similar KEGG pathways, such as glycerophospholipid metabolism and GPI-anchor biosynthesis, were also highly ranked among pathway nodes of blood (KEGG). In addition, several immune-related terms have been ranked top in blood. In recent studies, inflammation has been identified as an important contributor to AD pathology. Peripheral immune cells, such as T cells, are activated and infiltrate the inflamed brain area through the damaged blood-brain barrier (BBB) and accumulate in the AD brain (Togo et al., In the prediction of the brain dataset, the GO BP term with the highest importance score is the regulation of filopodium assembly. Filopodia is the protrusion at the end of the neuron and is related to neural plasticity Ozcan, . AD pathWe proposed a pathway information-based neural network model for the AD prediction. PINNet applied pathway-level of prior biological knowledge in constructing connections between genes and the AD status. We improved classification accuracies of AD and CN in blood and brain using PINNet, and the interpretation of the trained model revealed biological systems related to AD. Additionally, PINNet would be a promising model for various disease prediction tasks due to its possibility of model interpretability.The original contributions presented in the study are included in the article/HL initiated and supervised the study. YK developed the method and performed the experiments. All authors participated in writing the manuscript and have read and approved the manuscript.


Olive oil contains monounsaturated oleic acid up to 83% and phenolic compounds, making it an excellent source of fat. Due to its economic importance, the quantity and quality of olive oil should be improved in parallel with international standards. In this study, we analyzed the raw RNA-seq data with a meta-analysis approach to identify important genes and their metabolic pathways involved in olive oil quality.A deep search of RNA-seq published data shed light on thirty-nine experiments associated with the olive transcriptome, four of these proved to be ideal for meta-analysis. Meta-analysis confirmed the genes identified in previous studies and released new genes, which were not identified before. According to the IDR index, the meta-analysis had good power to identify new differentially expressed genes. The key genes were investigated in the metabolic pathways and were grouped into four classes based on the biosynthetic cycle of fatty acids and factors that affect oil quality. Galactose metabolism, glycolysis pathway, pyruvate metabolism, fatty acid biosynthesis, glycerolipid metabolism, and terpenoid backbone biosynthesis were the main pathways in olive oil quality. In galactose metabolism, raffinose is a suitable source of carbon along with other available sources for carbon in fruit development. The results showed that the biosynthesis of acetyl-CoA in glycolysis and pyruvate metabolism is a stable pathway to begin the biosynthesis of fatty acids. Key genes in oleic acid production as an indicator of oil quality and critical genes that played an important role in production of triacylglycerols were identified in different developmental stages. In the minor compound, the terpenoid backbone biosynthesis was investigated and important enzymes were identified as an interconnected network that produces important precursors for the synthesis of a monoterpene, diterpene, triterpene, tetraterpene, and sesquiterpene biosynthesis.The results of the current investigation can produce functional data related to the quality of olive oil and would be a useful step in reducing the time of cultivar screening by developing gene specific markers in olive breeding programs, releasing also new genes that could be applied in the genome editing approach.The online version contains supplementary material available at 10.1186/s12864-023-09673-y. Olive oil is a particularly important product because of its fatty acids and phenolic compounds, which are mainly responsible for the beneficial health aspects. Among these compounds, oleic acid and minor bioactive compounds have the highest impact on the quality and health effect of oil –3. The ISeveral studies have noted that the main factors that influence olive oil quality are genotype, climatic and agronomic conditions, edaphic factors, and the technological method applied for oil extraction. Among these factors, genotype has a preponderant influence –16. BesiOne of the most important goals of RNA-seq experiments is to investigate changes in gene expression profiles under two or more different experimental conditions. The most of the RNA-seq studies performed in olives were related to the study of biotic and abiotic stresses , 22, micTechnical variation in different experiments could affect the reproducibility of the research. Moreover, due to the cost of sequencing, RNA-seq experiments are mainly performed in a limited number of biological replicates, reducing the statistical power and the ability to detect and validate differences in gene expression. Accordingly, one of the most effective ways to improve reproducibility is to use multiple datasets through meta-analysis . TherefoC1): S1 vs S2, comparison 2 (C2): S1 vs S3, and comparison 3 (C3): S2 vs S3) and the results of each comparison were shown as an independent Venn diagram  for each comparison. The IDR for C1 and C2. Furthermore, the most important individuated metabolic pathways were pentose phosphate, pyruvate metabolism, glycolysis/gluconeogenesis, fatty acid biosynthesis, fatty acid elongation, and biotin metabolism. The comparisons’ results also showed that the terpenoid compounds biosynthesis pathways were specific to C2 including limonene and pinene degradation, diterpenoid biosynthesis, and monoterpenoid biosynthesis  and metabolic pathways (second step) described in Unver et al. . In the In the metabolic pathways containing down-regulated genes, 23 metabolic pathways were common between all developmental stages  and fatty acid synthase (FAS) is necessary to start the biosynthesis of fatty acids . It was III.Pathways related to fatty acids: In the third group, biosynthesis of the carbon chain of fatty acids, the key metabolic pathways of fatty acid biosynthesis, fatty acid elongation, and biotin metabolism were individuated in the present study. Metabolic pathway glycerolipid metabolism was identified as a key metabolic pathway in the assembling of fatty acids and the formation of complex lipids. In addition, two metabolic pathways alpha-linolenic acid metabolism and linoleic acid metabolism were identified as important pathways in the production of by-products of fatty acid biosynthesis.IV.Pathways related to non-fatty acids components (minor compound): The fourth group was related to metabolic pathways that have a direct impact on the quality of the oil and its taste. In this group, six key metabolic pathways were observed: terpenoid backbone biosynthesis, phenylpropanoid biosynthesis, flavonoid biosynthesis, limonene and pinene degradation, monoterpenoid biosynthesis, and carotenoid biosynthesis.The identified metabolic pathways were grouped according to the biosynthetic cycle of fatty acids and factors affecting oil quality, to provide more details about the most important identified pathways. In the study of oil biosynthesis, attention should be paid to the three key stages of carbon supply in the skeleton of triacylglycerols including the production of fatty acids, the assembly of fatty acids, and finally the formation of complex lipids .I.PathwaIn order to more closely examine the identified enriched metabolic pathways, the most important enriched metabolic pathways affecting oil quality were identified in each group and have been investigated independently. Finally, the key genes affecting each metabolic pathway have been identified and introduced Table . Also, tOlea europaea subsp. europaea var. europaea), the crossbreeding activities have been delayed by the particularly long generation time  9-desaturase (SAD) enzyme and it can affect the amount and quality of the oil. In the research of Zeng et al.  is converted to palmitic acid by the enzyme fatty acyl-ACP thioesterase A (FATA) (EC: 3.1.2.14) or it can convert to its unsaturated form by the activity of the SAD enzyme and then is converted to palmitoleic acid by FATA enzyme  (EC: 2.3.1.179), FabG (beta-ketoacyl-[acp] (ACP) reductase, KAR) (EC: 1.1.1.100), and FabI  (EC: 1.3.1.9 and EC: 1.3.1.10) participate in the production of palmitoyl-[acp] which are important enzymes in FAS complex enzyme Fig. . FurtherOleic acid produced at the end of fatty acid biosynthesis steps can be converted into linoleic acid by fatty acid desaturases (FAD) enzymes . For thiThe results showed that the expression of long-chain acyl-CoA synthetase (LACS) (EC: 6.2.1.3) enzyme increases in S3 stage and a CoA group is added to the palmitic acid and finally forming long-chain acyl-CoA. In other words, the production of long-chain acyl-CoA can also be the beginning of the fatty acids degradation via beta-oxidation with LACS (oeu00071)  as a precursor enter to reaction producing triacylglycerols (TAG). A review of the sources showed that G3P and dihydroxyacetone phosphate are key intermediates of oil biosynthesis . Our resPaeonia rockii and was announced DGAT3 mainly expressed in the vegetative organs and also showed DGAT3 preference unsaturated fatty acid (α-linolenic acid and linoleic acid) in the last step of TAG biosynthesis. According to the present results, the production of TAG by the PDAT pathway probably occurred in the mesocarp and played an important role in the production of TAG and oil in the mesocarp. The results also showed that in the TAG degradation pathway, the expression of acylglycerol lipase (EC: 3.1.1.23) enzyme increases in S3 stage and converts monoacylglycerol to glycerol and free fatty acids.Two pathways are suggested to produce TAG from DAG. The first pathway is dependent on the presence of fatty acids and DAG is converted to TAG by the enzyme diacylglycerol acyltransferase (DGAT), while the second pathway is independent of the presence of fatty acids and DAG is transformed to TAG by the enzyme phospholipid:diacylglycerol acyltransferase (PDAT). The results of the present study showed that the expression of PDAT (EC: 2.3.1.158) increased in growth stages S2 and S3 and the conversion of DAG to TAG is produced from this pathway and is independent of fatty acids . In the terpenoid backbone biosynthesis, there are two paths, the mevalonate pathway (MVA) and non-mevalonate pathway (MEP)  (EC: 2.3.3.10) increased up to a maximum in 130 days after flowering, after which its expression decreases. The results showed that in the MVA pathway the expression of two enzymes phosphomevalonate kinase (MVAPK) (EC: 2.7.4.2) and diphosphomevalonate decarboxylase (MVAPPD) (EC: 4.1.1.33) increased in the growth stage of S2 and S3 and Isopentenyl diphosphate (IPP) production increased. According to our results, HMGCS enzyme expression, which is located at the beginning of the MVA pathway, increased in S1 and S2. Most probably, it is a key enzyme in setting up MVA pathway and subsequent enzymes have their maximum activity in step S3. Increasing the expression of MVAK at all stages of development led to the production of mevalonic acid 5-phosphate known as the immediate precursor of IPP. Some previous studies have stated that there is no significant difference in the expression of this enzyme and others have shown that its expression increases at all stages of development , 76.Dimethylallyl diphosphate (DMAPP) is produced at the end of the MEP pathway, and IPP is produced at the end of the MVA pathway, and these components can turn to each other with isopentenyl-diphosphate Delta-isomerase (EC: 5.3.3.2) enzyme. This enzyme is known as the common enzyme between the two pathways and participating in the production of final products of both introduced pathways. The results of the meta-analysis showed that the expression of isopentenyl-diphosphate delta-isomerase enzyme increases in the growth stage of S3 which is consistent with the results of Alagna et al. . TherefoC1 (S1 vs S2), C2 (S1 vs S3), and C3 (S2 vs S3). The identified key genes were investigated in the metabolic pathways related to oil quality and the identified metabolic pathways were also grouped into four groups according to the pathway of oil biosynthesis and the factors affecting its quality. The metabolic pathways that affecting the quality of oil have been examined independently confirming the earlier published results through meta-analysis and contributing to the reproducibility of the results. The galactose metabolism pathway can be further investigated as a source of carbon supply in olive fruit, and due to the increase in the expression of its genes in the S3, it can help to supply carbon in the final stage of growth when the fruit photosynthesis is reduced. Moreover, the production of acetyl-CoA in various ways in different growth stage shows the high efficiency of olive in the production of fatty acids. In the fatty acid biosynthesis pathway FabF and SAD have a critical role in oil quality and second identified pathway is more suitable for production of high oleic acid. Furthermore, the production of TAG by the PDAT probably occurred in the mesocarp and played an important role in the production of TAG and oil in the mesocarp. Also, the results of present study showed biosynthesis of phenolic compounds in olives in terms of growth stages might be in the opposite of the biosynthesis of fatty acids and there are the highest amount of phenolic compounds after fruit formation, while the biosynthesis of oil is at its lowest. The identified and confirmed genes by meta-analysis approach provided a suitable dataset to draw on for future studies in different scientific sectors. The present research can also play a role in cultivar selection by applying potential molecular markers developed in the here individuated key genes, reducing the duration of breeding programs, and helping the genome editing by introducing key and essential genes for the development of healthy compounds.According to the economic importance of olive oil, the quantity and quality of oil should be improved in parallel based on international standards in commercial cultivars. Technical variation in different experiments and limited number of biological replicates in RNA-seq researches could affect the reproducibility and the statistical power of the experiments. Meta-analysis of RNA-seq data can increase the speed of production of functional data related to the quality of olive oil and produce useful information. The present work would be an effective and useful step in reducing the time of cultivar screening by developing key genes relating to oil quality in olive breeding programs. The IDR index indicated the meta-analysis had good power to identify new differentially expressed genes and identified 155, 473, and 241 differential expressed genes for the first time respectively in the To search for raw sequencing data and publications related to the olive oil using RNA-seq method, the SRA database was investigated. The title and abstract of SRA project were examined in the first step, and the relevant data with olive RNA-seq were chosen. The obtained SRA description were reviewed independently for sample type and sequencing information. Then the information was recorded in a local dataset. In the second step, literature review was performed to increase the depth of the search and find more results. For this purpose, several keywords such as olive transcriptome, olive RNA-seq, olive sequencing, olive fruit, quality of olive oil, quantity of olive oil, oil biosynthesis, key genes in oil biosynthesis, phenolic or polyphenolic compounds were searched and related articles were selected. In SRA database, 126 run accessions and 26 related publications were found using literature review. After matching the search results of both strategies, the information of thirty nine experiments was recorded in our dataset . The raw reads were trimmed by Trimmomatic software v0.32 by discarding adaptors, ambiguous nucleotides, low-quality (< 20), and short-length reads (< 50 nt) for all the experiments. FastQC was utilized after each trimming to assess the properties of processed reads and to validate trimming efficiency. The data of PRJNA184000 was discarded since our standards parameters for quality could not be passed. The filtered, high-quality reads (four experiments) were used for mapping analyses.The quality control of raw Illumina RNA-seq reads was performed by FastQC software v0.11.8 (O. europaea genome as the reference (OE6) (https://www.ncbi.nlm.nih.gov/assembly/GCA_900603015.1/) using the Hisat2 package. The aligned reads were sorted by position using the Samtools package. The read counts were calculated using the HTSeq package to estimate the count of uniquely mapped reads for each of the experiments. Finally, the read count matrix was used for the meta-analysis and differential expression analysis using edgeR.The clean reads were mapped onto the p-values from each experiment, it is necessary to perform a differential expression analysis using the uniform method per study. We used the edgeR package [p-values for differential analyses of each study independently. In the meta-analysis, the uniform distribution of raw p-values is a necessary assumption. Therefore, we checked this assumption by histograms of raw p-values for each experiment and confirmed the assumption for all studies. In the next step, the combination of p-values of experiments was performed by the Fisher method. Genes that showed contradictory different expressions in separate studies were removed from the expressed genes list [C1): S1 vs. S2; C2: S1 vs. S3; C3: S2 vs. S3) as indicated above.Meta-analysis was performed by metaRNASeq package . Before  package , 81 to ones list . FinallyThe protein sequence of differentially expressed genes identified in the meta-analysis was used for pathway enrichment analysis by local KOBAS pipeline and in-house scripts. The KEGG pathways were examined and significant pathways were selected for more investigation. Related metabolic pathways were grouped according to their function and role in the biosynthesis of oil or its minor compounds was examined in detail. Also, selected metabolic pathways images were received from KEGG –84. FinaAdditional file 1: Figure S1. Up- (A) and down-regulated (B) identified genes of the galactose metabolism pathway in the biosynthesis of raffinose family oligosaccharides.Additional file 2: Figure S2. Up- (A) and down-regulated (B) identified genes of the glycolysis pathway in production of acetyl-CoA.Additional file 3: Figure S3. Up- (A) and down-regulated (B) identified genes of the pyruvate pathway in production of acetyl-CoA.Additional file 4: Table S2. The accession numbers of the identified up- and down-regulated genes in each of the investigated comparisons.Additional file 5: Table S1. Identification of enriched genes in glycolysis and pentose phosphate pathways.Additional file 6: Figure S4. Up- (A) and down-regulated (B) identified genes of the biotin metabolism pathway in production of acetyl-CoA carboxylase biotin carboxyl carrier protein.Additional file 7: Figure S5. Up- (A) and down-regulated (B) identified genes in the Fatty acid biosynthesis pathway.Additional file 8: Figure S6. Up- (A) and down-regulated (B) identified genes of the biosynthesis of unsaturated fatty acid pathway in production of linoleic acid.Additional file 9: Figure S7. Up- (A) and down-regulated (B) identified genes in the Fatty acid degradation pathway.Additional file 10: Figure S8. Up- (A) and down-regulated (B) identified genes in the glycerolipid metabolism pathway.Additional file 11: Figure S9. Up- (A) and down-regulated (B) identified genes in the terpenoid backbone biosynthesis pathway. Additional file 12: Table S3. The information of thirty nine experiments was recorded. Table S4. The selected experiments were filtered based on the sequencing platform (Illumina) and tissue (fruit). Table S5. The experiments with at least two developmental stages for meta-analysis.Additional file 13. edgeR and metaRNASeq packages general code for differential expression and meta-analysis.


Objective: Colorectal cancer (CRC) is a common cancer that cannot be detected at an early stage and is a major challenge in oncology research. Studies have shown that vitamin D3 has some anti-cancer and preventive effects on colorectal cancer, but the exact anti-cancer mechanism is not clear. We applied the relevant research methods of network pharmacology to speculate and validate the possible potential pharmacological mechanisms of vitamin D3 for the prevention of colorectal cancer, and to provide more theoretical support for the clinical anticancer effects of vitamin D3.Methods: The relevant targets for vitamin D3 and CRC were obtained from the database of drug and disease targets, respectively. The target of vitamin D3 and the target of colorectal cancer were taken to intersect to obtain common targets. Then, the PPI network was constructed. In addition, the pathways of drug-disease interactions were predicted by GO and KEGG enrichment analysis. Finally, the obtained results were verified to ensure the reliability of the experiments.Results: 51 targets of vitamin D3 for the prevention of colorectal cancer were obtained. The 10 core targets were obtained from the PPI network. The 10 core targets include: ALB, SRC, MMP9, PPARG, HSP90AA1, IGF1, EGFR, MAPK1, MAP2K1 and IGF1R. The core targets were further validated by molecular docking and animal experiments. The results suggest that vitamin D3 plays a key role in the prevention of CRC through core targets, PI3K-Akt pathway, HIF-1 pathway, and FoxO pathway.Conclusion: This study will provide more theoretical support for vitamin D3 to reduce the incidence of CRC and is important to explore more pharmacological effects of vitamin D3. Colorectal cancer (CRC) is a malignant tumor that has the third highest incidence rate among cancers and the second highest mortality rate among malignant tumors . In ChinThe research process is shown , and theVitamin D3 is a class of steroid hormones that are usually synthesized by the body in response to UV radiation or supplemented by food. Studies have shown that vitamin D3 is necessary to maintain calcium homeostasis and bone mineralization in the body. Binding of vitamin D3 and its receptors can regulate specific gene expression and signal transduction in the body, exerting biological effects. The properties of vitamin D3 are shown , and thehttp://lilab-ecust.cn/pharmmapper/) (http://targetnet.scbdd.com/) (https://go.drugbank.com/) (https://www.uniprot.org/) (The English name of the drug was input to get its details in PubChem (https://pubchem. ncbi.nlm.nih.gov/) . The SDFmapper/) . In Phardd.com/) , and thenk.com/) . The tarot.org/) . Finallyhttps://www.disgenet.org/) (The targets were obtained in DisGeNET (et.org/)  and Geneet.org/) . The tarhttps://bioinfogp.cnb.csic.es/tools/venny/index.html). Finally, the intersecting targets obtained are potential targets of vitamin D3 for CRC prevention. The intersection targets are imported into String (https://cn.string-db.org/) to construct the PPI network. The tsv file of the PPI network was obtained and imported into Cytoscape 3.9.0  (The targets of vitamin D3 and CRC-related targets were taken intersected in Venny 2.1.0 (n 3.9.0)  for analhttps://david.ncifcrf.gov/)  for visualization and analysis.Core targets were imported into David (rf.gov/)  to obtairf.gov/) . The scrp < 0.05 was used as a screening criterion, and the top 20 signaling pathways were selected based on the ranking of p-values from smallest to largest. Data of vitamin D3, signaling pathways and core targets were made into network. xlsx and type. xlsx and the files were imported into Cytoscape for visualization. The vitamin D3-core target-pathway network was constructed. The red triangle represents vitamin D3, the blue diamond represents the core target, and the green circle represents the signaling pathway.To explore the possible mechanisms of vitamin D3 action on CRC. The strength and pattern of interactions between vitamin D3 and core targets were assessed by molecular docking in this study. The PDB ID of the core target was obtained from the RCSB PDB and its 3D structure was downloaded in PDB format. Molecular docking was performed using Discovery studio (2019 version) and the “Prepare Ligands” module was used to obtain 3D structures in this study. After removing the water of crystallization, the “Prepare Protein” module is used to remove multiple conformations of the target protein by adding hydrogen atoms. Then, the fast molecular docking option is selected in the “LibDock” module. The software calculates the corresponding docking scores in the columns of the LibDockScore. The docking score as a judgment criterion was used to evaluate the affinity of the target protein and the drug.To verify the reliability of molecular docking, the original ligands of 10 target protein complexes were separated and then reattached to the pockets of the complexes in this study. The root-means-square deviation (RMSD) of the ligand conformation after ligand docking and the ligand conformation in the original crystal structure were calculated. The RMSD value less than 2.0Å indicates that the docking method can well reproduce the original ligand-receptor binding mode and the docking parameters are reasonably set.http://gepia.cancer-pku.cn), the expression information of the core targets were obtained. The relevant information of core targets for different populations and different stages of cancer were obtained in the “Expression DIY” module (In GEPIA (” module .https://www.proteinatlas.org/) (Differences of core targets were studied at the protein expression level. The relevant data from the Human Protein Atlas database (as.org/)  were anahttps://www.cbioportal.org/)  samples in the database were analyzed using cBioPortal  . We obtaal.org/) . In addiIn GEPIA, the “General” module was selected and the core targets were entered to obtain the distribution and expression levels of the core targets in different cancers. The obtained results were presented in the form of a dot plot.https://kmplot.com/analysis/) was accessed. After the core targets were entered, the results of the overall survival of each target were obtained in this database.To explore the impact of core targets on overall survival in CRC, Kaplan-Meier Plotter .Vitamin D3 (Item No.: B2065) was purchased from APExBIO. Azoxymethane  was purchased from Abe Medical Device Trading (Shanghai) Co., LTD., and Dextran sulfate sodium  was purchased from Meilan Biotechnology  Co., LTD. RNAiso Plus was purchased from Takara bio, Japan, and RIPA cell lysate and protease inhibitors were purchased from Dingguo Changsheng Biotechnology (Beijing) Co., LTD. Protein phosphatase inhibitors were purchased from bimake Biotechnology Co., LTD. HiScript®II 1st Strand cDNA Synthesis Kit and ChamQ universal SYBR qPCR Master Mix were purchased from Novizam Biotechnology (Nanjing) Co., LTD. The corresponding primers of the core target were synthesized by Sangon Biotech (Shanghai).A completely randomized design was used to divide the mice into three groups. Physiological saline control group (Nacl): 6 mice, model group (AOM + DSS): 6 mice, vitamin D3 intervention group (AOM + DSS + vitaminD3): 6 mice. After 1 week of adaptive feeding, the mice were gavaged with vitamin D for 2 continuous weeks at a dose of 60 IU/g per week, 3 times per week. At week 4, mice of the model and vitamin D3 intervention groups were given a single intraperitoneal injection of AOM (10 mg/kg). At weeks 5, 8 and 11, the model and vitamin D3 intervention groups were given drinking water containing 2% DSS for 5 days and normal drinking water for the rest of the day. The mice were weighed twice a week. Mice were sacrificed at week 15, and colorectal tissues were collected for RT-qPCR experiments.−ΔΔCT method was used to calculate the expression of core targets. The corresponding primer sequences of the core targets were listed :90, CC(Cellular Component):23, MF(Molecular Function):21. The top 10 items in each category were selected for visual analysis . The bioThe core targets, vitamin D3 and KEGG enrichment pathways were categorized and organized to construct the drug-target-pathway network in Cytoscape 3.9.0 . In FiguTo investigate the interaction between the 10 core targets and vitamin D3, molecular docking was used. The relevant information of 10 core target proteins was downloaded from the PDB database . First, p < 0.05). The mRNA expression level of IGF1 was lower than normal in CRC (p < 0.05) (p < 0.05)  . The mRN < 0.05) .To understand the expression of core targets in CRC, the relevant immunohistochemical images were analyzed by HPA database. The protein levels of PPARG and MMP9 were found to be high in CRC by comparative analysis. The protein expression of ALB, IGF1R, and MAP2K1 was higher in normal tissues than in CRC. The protein expression of EGFR, HSP90AA1, and MAPK1 was equal in normal and CRC. Immunohistochemical data of IGF1 and SRC were not found in the HPA database .Among the samples with mutation and CNA data (105 samples/patient), a total of 36 samples (34%) had mutations in the relevant genes, including 10 core targets . It alsoDot plots of core target protein distribution were obtained in different cancers in the GEPIA database. The results showed that the expression levels of core target proteins differed in different cancers .The core targets were entered into Kaplan-Meier Plotter for analysis. The effect of expression levels of 10 targets on the overall survival of CRC patients were obtained . The resp < 0.05). The expression of EGFR is high in CRC tissues and decreased in CRC tissues after vitamin D3 intervention, but the difference was not statistically significant (p > 0.05). ALB, MAP2K1, and PPARG are low expression in CRC tissues, and their expression levels increased after the intervention of vitamin D3, which indicates that the treatment of vitamin D3 is effective and the difference is statistically significant (p < 0.05). The results of statistical analysis of RT-qPCR experiments are shown (Through the experiment, the following findings were obtained:SRC, IGF1, IGF1R, HSP90AA1, MAPK1 and MMP9 are high expression in CRC tissues and after the intervention of vitamin D3, the expression of these genes was reduced in cancer tissues, and the differences were statistically significant (re shown .The disease burden of CRC has been high and has increased in Asia in recent years . VitaminIn this study, we further investigated the molecular mechanism of vitamin D3 in the prevention of CRC through network pharmacology and animal experimental studies. The results of the enrichment analysis suggest that vitamin D3 may play a role in the prevention of CRC through the PI3K-Akt signaling pathway and the HIF signaling pathway. A study showed that vitamin D3 can inhibit cell proliferation, invasion, and metastasis and promote apoptosis through the PI3K-Akt pathway . VitaminThe mechanisms of action of vitamin D include inhibition of VDR-mediated IGF1, HIF1, NF-κB and PI3K/AKT activities, or activation of MAPK family-related genes, downregulation of cell cycle progression, anti-inflammation, inhibition of angiogenesis, induction of apoptosis, and other anti-tumor effects . Solid tA growing number of evidences suggest that activation of ERK MAPK pathway is closely associated with the occurrence, development and oncogenic behavior of human CRC . The antThese results suggest that vitamin D may be an effective chemical for the prevention of CRC. In summary, ALB, SRC, MMP9, PPARG, HSP90AA1, IGF1, EGFR, MAPK1, MAP2K1 and IGF1R play important roles in the pathogenesis of CRC. Therefore, based on the network pharmacological analysis, it is expected to discover more novel compounds that can prevent cancer or play an anti-cancer role. This study provides additional theoretical basis for the prevention of CRC with vitamin D3.In this study, vitamin D was found to be a multi-target medicine for the prevention of CRC. We predict that the main mechanism of vitamin D for the prevention of CRC is the regulation of tumor cell proliferation, apoptosis, migration and angiogenesis through signaling pathways such as HIF-1, FoxO and PI3K-Akt, thus playing a role in the prevention of CRC.Based on network pharmacological analysis, this study predicted 10 potential core targets of vitamin D3 for the prevention of CRC, suggesting that vitamin D3 is a single-component, multi-target, multi-pathway small molecule compound. Network pharmacological analysis suggests that vitamin D3 may play a role in CRC chemoprevention by regulating different targets, such as MAPK, IGF1 and IGF1R. Graphene oxide analysis of the core targets ALB, SRC, MMP9, PPARG, HSP90AA1, IGF1, EGFR, MAPK1, MAP2K1 and IGF1R showed that vitamin D3 exerts pharmacological effects by affecting different biological processes, such as endosomal transport and positive regulation of protein kinase B signaling. The analysis of KEGG pathway in this study suggests that vitamin D3 may exert pharmacological effects by simultaneously regulating different CRC-related signaling pathways, such as FoxO signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. These results support that vitamin D3 may mediate the inhibition of HIF-1 through inhibition of the upstream PI3K-AKT pathway.In summary, vitamin D3 may be a new safe, effective, multi-targeted medicine for the prevention of CRC. This study concludes from a network pharmacological analysis that vitamin D3 may play a preventive role in CRC by regulating cell metabolism, proliferation and apoptosis through multiple targets, pathways and biological processes. However, the clinical efficacy of vitamin D3 in the prevention of CRC and its mechanism of action need to be further validated.


Evodia rutaecarpa, a traditional herbal drug, is widely used as an analgesic and antiemetic. Many studies have confirmed that Evodia rutaecarpa has an anticancer effect. Here, our study explored the bioactive ingredients in Evodia rutaecarpa acting on colorectal cancer (CRC) by utilizing network pharmacology.Evodia rutaecarpa. CRC-related genes were obtained from several public databases to extract candidate targets. Candidate targets were used to construct a protein–protein interaction (PPI) network for screening out core targets with topological analysis, and then we selected the core targets and corresponding ingredients for molecular docking. Cell proliferation experiments and enzyme-linked immunosorbent assays (ELISAs) verified the anticancer effect of the bioactive ingredients and the results of molecular docking.We clarified the effective ingredients and corresponding targets of TNF, MAPK1, TP53, AKT1, RELA, RB1, ESR1, JUN, CCND1 and MYC—were screened out as core targets. In vitro experiments suggested that rutaecarpine excelled isorhamnetin, evodiamine and quercetin in the inhibition of CRC cells and the release of TNF-α was altered with the concentrations of rutaecarpine. Molecular docking showed that rutaecarpine could effectively bind with TNF-α.Our study obtained a total of 24 bioactive ingredients and 100 candidate targets after intersecting ingredient-related targets and CRC-related genes, and finally, 10 genes—Evodia rutaecarpa acted on CRC were excavated. Rutaecarpine as a bioactive ingredient of Evodia rutaecarpamight effectively inhibit the proliferation of CRC cells by suppressing TNF-α.The pairs of ingredients-targets in The online version contains supplementary material available at 10.1186/s12906-023-04254-8. Colorectal cancer (CRC) is a frequently occurring malignancy originating from the digestive tract with high invasiveness. In terms of global incidence, CRC ranks third in terms of cancer-related mortality . AdvanceEvodia rutaecarpa (Chinese name: Wuzhuyu), a traditional herbal drug, has been widely used to treat headaches and abdominal pain for a long time [Evodia rutaecarpa has been shown to be involved in the biological regulation of tumour growth [Evodia rutaecarpa was confirmed to induce apoptosis via activation of the endoplasmic reticulum stress signalling pathway in ovarian cancer [Evodia rutaecarpa, could overcome castration resistance to anti-androgen therapy in prostate cancer and significantly improve dextran sulfate sodium-induced colitis [Evodia rutaecarpa therefore might be potential tools for anticancer therapy.Herbal drugs are a critical part of traditional Chinese medicine. However, as a result of the complexity of the components, some side effects, including organ failure, accompany the significant efficacy of these drugs against diseases . Many biong time . As a nar growth . Evodiamn cancer . Rutaeca colitis , 14. TheNetwork pharmacology was developed based on the theory that compounds that selectively act on two or more targets should be more effective than agents that act on a single target . MappingEvodia rutaecarpa on CRC can be integrated with network pharmacology to map the unexplored target space and confirm the therapeutic potential. We focused on the functions and mechanisms of Evodia rutaecarpa in CRC. Network pharmacology was applied and revealed the ingredients and corresponding targets that were affected in CRC, and we selected pairs with strong correlations and prominent impacts for further validation, which included computational analysis and experimental verification. Our study indicates the anti-cancer application of rutaecarpine and expands the knowledge regarding promising therapeutics from Evodia rutaecarpa for the treatment of CRC.The effects of Evodia rutaecarpa were searched on the Traditional Chinese Medicine Database and Analysis Platform  [https://www.UniProt.org) [The bioactive ingredients of p-e.com) . Oral bip-e.com) . Corresprot.org) .Homo sapiens were collected from public databases, including GeneCards (https://www.genecards.org), Online Mendelian Inheritance in Man , Pharmacogenomics Knowledgebase , Therapeutic Target Database  and DrugBank (https://go.drugbank.com) [CRC-related targets of ank.com) –26. The Functional enrichment analyses were performed in R software (version 3.6.0). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to investigate the biological functions and pathways related to the candidate targets with the clusterProfiler package .https://string-db.org) was used to construct the protein–protein interaction (PPI) network. Candidate targets were uploaded to STRING with the organism Homo sapiens. The minimum required interaction score was set as the highest confidence (0.9), and disconnected nodes were removed from the network.The network platform STRING  and The Protein Data Bank  [The structures of the drug molecule and target macromolecule were downloaded from PubChem (csb.org) , 29, rescsb.org) . The res2 at 37 °C.The human CRC cell lines HT29 and LS180 were obtained from Cell Resource Center, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences. The cells were routinely grown in Dulbecco’s modified Eagle’s medium  containing 10% foetal bovine serum  and 1% penicillin-streptomycin solution  in an incubator with a humidified atmosphere of 5% CO3 per well) were seeded in 96-well plates. After attachment to the plates and culture in complete growth medium for 24 h, the cells were treated with isorhamnetin, evodiamine, quercetin, rutaecarpine  containing the concentrations of 0.5 µM, 1 µM, 2 µM, 4 µM, 8 µM and 16 µM.0.1% dimethyl sulfoxide  was used as a solvent control. Cell Counting Kit-8 (CCK-8) reagent  was added 48 h later, and the optical density (OD) value per well at 450 nm was detected with a Thermo Varioskan Flash reader after incubation for 2 h. The experiment was repeated three times. The relative viability was calculated as (treated sample OD-blank control OD)/ (negative control OD-blank control OD) *100.Cells (8 × 105 per well) were seeded in 6-well plates and grown in 2 mL complete medium for 24 h before treatment with 0.5 µM, 1 µM, 2 µM and 4 µM rutaecarpine for 48 h. The culture supernatant was collected for ELISA  following the manufacturers’ protocol.CRC cells . Data are shown as means ± standard deviation (SD). Statistical differences were analysed by one-way analysis of variance (ANOVA) followed by Fisher’s least significant difference (LSD) and t-test. A Evodia rutaecarpa are complex and mainly include berberine, rutaecarpine, obacunone, isorhamnetin and other chemical compounds. A total of 30 chemical compounds from Evodia rutaecarpa were identified according to the inclusion criteria (Additional file The chemical compounds of Evodia rutaecarpa-related targets and CRC-related targets, 100 overlapping targets and corresponding target-related bioactive ingredients were identified (Fig. Evodia rutaecarpa in the treatment of CRC was visualized by Cytoscape (Fig. After deleting duplicates, we obtained 1,628 CRC-related genes, including 36 from DrugBank, 1,261 from GeneCards, 168 from OMIM, 298 from PharmGKB and 90 from TTD. After mapping We performed GO analysis of the candidate targets, and the top three GO terms for biological processes were the cellular response to chemical stress, the response to oxidative stress and the response to lipopolysaccharide, all of which are closely associated with tumorigenesis and tumour progression Fig. A. The maEvodia rutaecarpa in CRC, a PPI network was built by importing the 100 overlapping targets into the STRING database. The target network had 100 nodes and 380 edges, with an average node degree of 7.6 (Fig. TNF, MAPK1, TP53, AKT1, RELA, RB1, ESR1, JUN, CCND1 and MYC were considered to play crucial roles in the treatment of CRC. Functional enrichment analysis suggested final 10 targets were associated with DNA-binding transcription and cell proliferation (Fig. To explore the mechanism of 7.6 Fig. A, B&C. Tion Fig. D.Evodia rutaecarpa, and the results showed that cell activity was distinctly affected by treatment with rutaecarpine comparing to isorhamnetin, evodiamine and quercetin (Fig. The CCK-8 assay was used to assess CRC cell proliferation under main ingredients of tin Fig. A.Evodia rutaecarpa [Previous study confirmed that rutaecarpine and TNF-α exert activity in colon diseases, and our study suggested TNF-α was proposed to be directly related to taecarpa , 31. BasThe TNF-α levels were reduced in the culture supernatant after CRC cells were pretreated with decreasing concentrations of rutaecarpine, indicating that rutaecarpine might block the activity of extracellular TNF-α, verifying the conclusion of the molecular docking data Fig. D.Evodia rutaecarpa is a prevalent clinical treatment, but the molecular mechanism of its therapeutic function is complex and partially unclear. The effect of the drug was summarized in practical applications, but the molecular mechanism has not been fully clarified. We attempted to identify new indications for established drugs with long-established safety in humans. Thorough research on Evodia rutaecarpa revealed valuable candidates. Rutaecarpine, an alkaloid constituent isolated from Evodia rutaecarpa, was screened out for further investigation in our study based on the network pharmacology results. It has been reported that rutaecarpine could inhibit angiogenesis as a potential drug candidate [As a traditional herbal drug, andidate . With reandidate . Rutaecaandidate . This fiTNF-α, a common inflammatory cytokine with important roles in homeostasis and pathogenesis, works as a double-edged sword in cancer behaviour . TNF-α cEvodia rutaecarpa. We aimed to identify a single ingredient of the herbal drug suitable for unambiguous investigation, and we screened out a few of the major ingredients strongly associated with CRC-related genes in paired form based on data extracted from several public databases. For further confirmation, topological analysis was used to select core genes in the tumorigenesis and progression of CRC as candidate targets. The structures of rutaecarpine and TNF-α were obtained for molecular docking analysis to assess their potential interaction. After this interaction was verified by computational analyses, we performed experimental validation. The anticancer effect of rutaecarpine was verified based on our finding that this molecule could inhibit CRC proliferation. Considering the contradictory functions of TNF-α, we exposed CRC cells to rutaecarpine, and we found that the effective concentrations of TNF-α were significantly upregulated. Thus, rutaecarpine might suppress the malignant behaviour of CRC by inhibiting TNF-α release.We applied network pharmacology to expand upon the anticancer function of There are some limitations to our study. Firstly, we found a number of significative pairs of ingredients-genes after database analysis and focused on rutaecarpine-TNF-α in our study. The other pairs can be investigated. Secondly, the promotive or inhibitive effects of drugs are often dose-dependent. The regulatory effect of TNF-α on rutaecarpine is still ambiguous, as it has dual roles and the dose may interfere with the final results. Thirdly, more biological experiments including in vivo model and further investigations of the downstream effectors of rutaecarpine for in-depth exploration may better support this function.Evodia rutaecarpa against CRC with network pharmacology, molecular docking and experiment. Our results indicated that one bioactive ingredient, rutaecarpine, might restrain the biological behaviour of CRC cells via the regulation of TNF-α. Taken together, our work uncovers the effect of rutaecarpine in CRC and contributes to the development of anticancer treatments from traditional herbal drugs.We conducted a systematic investigation of Below is the link to the electronic supplementary material.Supplementary Material 1


Ethnopharmacological relevance: Salidroside (SAL), a phenolic natural product present in Rhodiola rosea, are commonly used in the treatment of various ischemic-hypoxic diseases, including intestinal ischemia–reperfusion (IR) injury. However, their efficacy and potential mechanisms in the treatment of intestinal IR injury have not been investigated.Objective: The objective of the present study is to investigate the pharmacological mechanism of action of SAL on intestinal IR injury using a network pharmacology approach combined with experimental validation.in vivo model of mouse superior mesenteric artery ligation was established to assess the anti-intestinal IR injury effect of SAL by assessing histopathological changes in mouse small intestine by HE staining, detecting inflammatory factor expression by ELISA kit, and detecting the expression of key protein targets by Western blotting.Methods: In the present study, we used the Traditional Chinese Medicine Systematic Pharmacology (TCMSP) database and analysis platform and Comparative Toxicogenomics Database (CTD) to predict possible target genes of SAL, collected relevant target genes of intestinal IR injury from GeneCards and DisGenet websites, and collected summary data to screen common target genes. Then, the protein–protein interaction (PPI) target network was constructed and analyzed by STRING database and Cytoscape 3.8.2 with the above intersecting genes. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed and the component-target-pathway network was constructed, followed by the use of molecular docking and molecular dynamic simulation to verify the possible binding conformation between SAL and candidate targets to further explore the potential targets of SAL in the treatment of intestinal IR injury. Finally, an Results: A total of 166 SAL target genes and 1740 disease-related targets were retrieved, and 88 overlapping proteins were obtained as potential therapeutic targets. The pathway enrichment analysis revealed that the pharmacological effects of SAL on intestinal IR injury were anti-hypoxic, anti-inflammatory and metabolic pathway related, and the molecular docking and molecular dynamic simulation results showed that the core bioactive components had good binding affinity for TXNIP and AMPK, and the immunoblotting results indicated that the expression levels of TXNIP and AMPK in the small intestinal tissues of mice in the drug-treated group compared with the model group were significantly changed.Conclusion: SAL may target AMPK and TXNIP domains to act as a therapeutic agent for intestinal IR. These findings comprehensively reveal the potential therapeutic targets for SAL against intestinal IR and provide theoretical basis for the clinical application of SAL in the treatment of intestinal IR. Intestinal ischemia reperfusion (IR) injury is a complex pathological process with multifactorial involvement due to multiple etiologies, which not only causes local damage to the intestine, but also disruption of the intestinal mucosal barrier caused by tissue hypoxia, inflammation and cellular infiltration is the ‘trigger point’ for systemic organ damage . The inc14H20O7. SAL possesses various pharmacological effects including anti-hypoxia, antioxidant, anti-stress, anti-fatigue, anti-inflammatory, anti-cancer, anti-metabolic dysregulation, anti-immune stimulation and neuroprotection [Rhodiola rosea, also known as ‘rose root’, ‘golden root’ or ‘arctic root’, belongs to the plant family Rosaceae. It is a perennial yellow-flowered herb that grows naturally in dry, sandy soils, sea cliffs and crevices in the Arctic regions of Europe and Asia (mainly Siberia) and the eastern seaboard of North America. Rhodiola rosea is a phytomedicinal plant with an adaptogenic effect that enhances the body’s non-specific resilience to physical and mental stress and normalizes its function . The maiotection , which cotection  With theotection ,16. At tin vivo model of intestinal IR injury in mice, along with the identification of critical protein targets using immunoblotting analysis. Our study offers a theoretical basis for the precise application of SAL and the development of multi-targeted drugs. The overall idea of the study is illustrated in Network pharmacology is a novel, promising and cost-effective approach to drug development based on bioinformatics, systems biology and polypharmacology, with integrity and systematization . It examhttps://old.tcmsp-e.com/index.php), Comparative Toxicogenomics Database  and PhammerMap (http://lilab-ecust.cn/pharmmapper/submitfile.html) databases to generalize SAL-related target proteins. The collection of target genes related to intestinal IR injury was then performed by searching the DisGenet (https://www.disgenet.org/) and GeneCard (https://www.genecards.org/) databases to acquire disease-related genes. Due to the probable incomplete standardization of the nomenclature of targets obtained from different databases, the aforementioned targets were converted into standardized gene names using the Uniprot (https://www.genecards.org/) and restricted to the species ‘human’.By filtering the Traditional Chinese Medicine Systems Pharmacology database  to filter out SAL and intestinal IR intersection target genes by loading target genes of SAL and intestinal IR, automatically retrieving overlapping genes and drawing Venn diagrams.https://string-db.org/), the species was chosen as Homo sapiens, the minimization threshold was adjusted to high confidence 0.4, and the solitary targets with no linkage were excluded. The obtained protein interaction network data were inserted into Cytoscape 3.8.2 to create protein–protein interaction (PPI) network.The intersection targets were inserted into the String database (https://www.bioconductor.org/). GO analysis was conducted as an in-depth exploration of biological processes (BP), molecular functions (MF) and cellular components (CC). A filter was set at P<0.05 for further analysis, and the top 20 terms with the highest concentration in GO and KEGG were selected and visualized.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed with DAVID database and visualized with R packages. These R packages were obtained by downloading from Bioconductor’s open source platform , intersecting target genes and diseases , while edges stand for interactions.https://pubchem.ncbi.nlm.nih.gov/), imported into ChemBio3D 19.0 software, and saved in mol2 format. The crystal structures of the core targets were downloaded from protein data bank (PDB https://www.rcsb.org/), including the thioredoxin-interacting protein (TXNIP) (PDB code: 4GEI) and the adenosine monophosphate-activated protein kinase (AMPK) (PDB code: 2H6D). The target proteins were imported into Pymol 2.5.0 to eliminate the proligands, water molecules, pre-treated such as hydrogenation and searching for active pockets using AutodockTools-1.5.7, and saved as pdbqt format. The selection of candidate proteins according to the degree values obtained from PPI was performed on Autodock vina 1.5.7 platform for docking. The localization points were defined using a grid of 40,40,40 with a spacing of 1.000 Å per grid and other parameters using default values. The lower the score and the higher the affinity. The proteins with the lowest scores were finally selected and visualized by Pymol 2.5.0.Molecular docking was carried out to validate potential target–drug interactions. The 2D structure of SAL (MOL003341) was downloaded from PubChem (https://atb.uq.edu.au). The TXNIP system was placed in a cubic box and solventized using the SPC water model [+/Cl− was later included to make the system electrically neutral [Molecular dynamics (MD) simulations  were perer model , while N neutral . Prior tSalidroside  is ordered commercially. Rabbit anti-p-AMPK (Thr 172) , rabbit anti-AMPK  and TXNIP .n=6): sham-operated group (Sham group), SAL-treated group (SAL group), model group (IR group) and model + SAL-treated group (IR + SAL group). Mice in the IR and IR + SAL groups were constructed as mouse intestinal IR injury models, and mice in the Sham and SAL groups were only isolated from the superior mesenteric artery (SMA) without ligation. Mice in the SAL and IR + SAL groups were injected intraperitoneally with SAL (30 mg/kg) for 7 days before operation, and the mice in the Sham and IR groups were injected with equal amounts of saline at the same time point. After the mice in each group were executed, blood specimens and intestinal tissue specimens  were gathered for examination.A total of 24 healthy male C57BL/6J mice, weighing 25 ± 2 g, were purchased from the Experimental Animal Center of Lanzhou University for this experiment. All animal experiments took place at Basic Laboratory of Lanzhou University. All experimental procedures were performed in accordance with the International Guide for the Care and Use of Laboratory Animals and were approved by the Animal Ethics Committee of the Lanzhou University. The animals were housed in well-ventilated SPF-rated rooms  and were fed food and water ad libitum. After 1 week of adaptive feeding, the mouse were randomly divided into four groups  to anesthetize the mice, the mice were immobilized in the supine position on a small animal test bench. The abdominal region was prepared for surgery and disinfected by wiping with 75% alcohol. An approximately 2 cm long incision was created along the midline of the abdomen, and the intestine was exposed in layers, and the SMA was fully exposed and isolated using toothless forceps, dissection forceps, and a glass splitting needle, SMA was clamped with a saline-wetted non-invasive micro-arterial clip to block the blood flow. Visually, the color of intestinal tissue turns from bright red to pale white, signifying successful ischemia. Warm sterile gauze containing saline is used to cover the wound. The wound was covered with warm sterile gauze containing saline. After 45 min of ischemia, the non-invasive micro-artery clamps were removed to restore blood perfusion to the intestine. Visually a successful reperfusion is indicated by a change from pale to bright red intestinal tissue. The abdominal cavity of the mice was carefully examined to make sure there were no abnormalities, and then the abdomen was closed one by one with sterile silk thread. After 120 min of reperfusion, the mice were executed.All procedures for this study were performed and agreed upon in accordance with internationally accepted principles for the use and care of laboratory animals, and the protocol was approved by the Animal Protection and Ethics Committee of the First Hospital of Lanzhou University.Intestinal tissues were captured, fixed in 4% paraformaldehyde, and embedded in paraffin blocks. The degree of intestinal histopathological lesion in mice was assessed by double-blind method with hematoxylin and eosin (H&E) staining, referring to Chiu’s score .Measurement of serum and intestinal tissue levels of interleukin (IL)-6, IL-1β and tumor necrosis factor (TNF)-α. ELISA Kit  was used according to the manufacturer’s instructions.The bowel tissue was extracted from −80°C and immediately placed on ice. The tissue was sliced into small pieces, placed in Eppendorf tubes, and lysed on ice for 30 min with 100 μL/10kg lysis buffer, shaking back and forth frequently for adequate lysis. After centrifugation at 12,000 rpm for 15 min at 4°C, each supernatant was moved to another pre-cooled Eppendorf tube. Protein concentrations were calculated by the BCA assay kit. In each protein blot analysis, equal amounts of protein (100 μg) from each group were separated by SDS-PAGE and electrotransferred to polyvinylidene difluoride (PVDF) membranes . After being closed with TBST  with 5% skim milk for 1 h at room temperature, the membranes were incubated with primary antibodies for TXNIP, AMPK and β-actin, respectively, overnight at 4°C. After washing three times with TBST, the membranes were incubated with the corresponding secondary antibodies for 1 h at room temperature. Specific protein bands were observed by applying the ECL kit.t-test. One-way ANOVA was used for comparison between groups. P<0.05 was considered statistically significant.All statistical analyses were carried out using GraphPad Prism 8.0 software. Results are presented as mean ± standard deviation (SD). Each independent experiment was repeated at least three times to obtain the mean. Differences between the two groups were compared using Student’s A sum of 166 targets for SAL were obtained from TCMSP, Swisstarget prediction, and PhammerMap databases. Integrating the disease targets collected from each disease database, a total of 1740 intestine IR injury targets were obtained (Supplementary Table S1). The Venn Diagram was plotted by Venny 2.1, and 88 targets in SAL were found to be potential targets for the treatment of intestine IR injury .The interaction targets were channeled into the STRING database, and the network was further visualized and analyzed by Cytoscape 3.8.2 to build a protein interactions map . The nodP<0.05 as the selection condition. The BP outcomes indicate that the key cross-targets of SAL for the treatment of intestinal IR injury were mainly enriched in apoptotic process, response to hypoxia, regulation of protein phosphorylation, inflammatory response and positive regulation of phosphatidylinositol 3 kinase (PI3K) signaling. The CC results suggest that the potential targets of SAL for improving intestinal IR are mainly focused on the cell surface, components of the plasma membrane and cell junction. The enriched MF terms were mainly enzyme binding, protein serine/threonine kinase activity and ATP binding , 59 CC (Supplementary Table S3) and 91 MF (Supplementary Table S4) were acquired using  binding A–C. Withetrieved D. The pretrieved E. The reWe built the SAL-target gene-IR grid to visualize and elaborate the pharmacological potential of SAL to regulate IR. A total of 88 nodes in purple represent common target genes, red nodes represent SAL, and blue nodes represent IR .To further explore the binding affinity of the core target to SAL, molecular docking was performed in this study. According to previous findings , bindingRg) gives an indication of the tightness of the protein structure over the course of the simulation. Analyzing the MD process of free TXNIP and TXNIP-SAL complexes, we found that the Rg of both was relatively small, and both were less than 1.9 nm, with smooth and small fluctuations, and the protein structure was tighter, demonstrating that the conformation of the complexes did not swell or contract during the simulation (Root mean square deviation (RMSD) is considered to be a measure of the stability of the system. The lower the average RMSD value, the better the stability of the system. The RMSD values of the free TXNIP and TXNIP-SAL complexes varied during the first 10 ns of simulation and then stabilized after 20 ns. The RMSD values of both remained low and stable during the overall 100 ns simulation A. It shomulation B.To further evaluate the protective effect of SAL on mice intestinal IR, intestinal morphological staining was performed on each group of mice, and intestinal histopathological changes were examined by light microscopy. The results displayed that, compared with Sham group, the intestinal tissue of IR group mice showed disorganized villi arrangement, partially free villi surface, massive necrosis of intestinal epithelial cells, discontinuous structure and obvious destruction A, signifP<0.01), and SAL treatment obviously decreased the levels of TNF-α, IL-6 and IL-1β in serum and intestinal tissues after intestinal IR (P<0.01)  A–C. ThusPPI network and molecular docking suggest that TXNIP and AMPK are central target genes with high affinity to SAL, and our previous study showed that TXNIP expression was increased in the intestinal IR mouse model . To furtIntestinal IR injury is one of the clinically frequent IR injuries with a very high morbidity and mortality rate. It has been reported that the overall mortality rate in patients with acute mesenteric ischemia is 55.9% , and theAs we all know, Chinese herbal medicine, as an indispensable section of alternative medicine, has been deeply valued by Asian people in clinical practice for its high safety and low side effects . RhodiolInflammation occurs frequently in IR injury and it plays an important role in the pathophysiology of IR injury. Although inflammation is an essential component of the body’s defense system, over-activation of inflammatory cells and secretion of cytokines can lead to severe damage to intestinal epithelial cells ,38. The P-value in intestinal IR-related signaling pathways, and were strongly associated with other pathways. Importantly, the molecular docking analysis revealed that the docking of SAL with AMPK and TXNIP was < −5.0 kJ/mol, indicating that both possess good affinity for SAL. However, the docking pose of TXNIP seems to be unstable compared with AMPK, and SAL tends to fluctuate in the binding pocket of TXNIP; therefore, we performed SAL and TXNIP molecular dynamics simulations to verify whether such fluctuations would affect the conformation of the bound complex, and it was found that the conformation of the SAL-TXNIP complex did not swell or contract during the simulation.In the present study, the 88 potential targets of SAL for the treatment of intestinal IR were screened by searching diverse databases and further Venn diagram analysis, which were presented using PPI visual interaction. To clarify the multiple mechanisms of SAL for the treatment of CML, we performed GO and KEGG enrichment analysis of the intersecting targets. Among them, TXNIP, as one of the key molecules of thioredoxin reduction system, serves as a bridge between oxidative stress and inflammatory response, and AMPK signaling pathway, which maintains metabolic stability, were enriched with higher gene number and lower It is well known that TXNIP is one of the components of the thioredoxin reduction system , and itsin vivo validation experiments in our study, where AMPK was activated in the mouse IR model group, and after SAL treatment AMPK was further activated. However, Qin et al. [AMPK, an AMP-dependent protein kinase, is responsible for regulating cellular energy synthesis and catabolism and are key regulators of cell growth, reproduction and autophagy . AMPK isn et al.  discovern et al.  in whichin vivo experiments, suggesting that SAL has the ability to act simultaneously on multiple targets and signaling pathways to produce synergistic effects, and is an effective means to treat intestinal IR injury. Our study indicates that SAL may affect intestinal IR injury by regulating the expression of TXNIP and AMPK, and these molecules have important roles in regulating inflammatory responses and energy metabolism. Consequently, we could conclude that SAL might be an effective therapeutic agent for intestinal ischemia–reperfusion injury, but further experiments are still needed to explore its mechanism.In summary, the present study combined network pharmacology and in vivo experimental validation to predict the effective mechanism and main targets of SAL for the treatment of intestinal IR injury, and validated the predicted results by molecular docking, molecular dynamics simulation and Click here for additional data file.


Solanum nigrum L. (SNL) (Longkui) is a Chinese herb that can be used to treat colon cancer. The present study explored the components and mechanisms of SNL in treating colon cancer by using network pharmacology and molecular docking.via Cytoscape 3.7.2. Gene Ontology and KEGG pathways were enriched by using the David 6.8 online tool. Finally, the binding of key components and targets was verified by molecular docking, and the cellular thermal shift assay (CETSA) was used to detect the efficiency of apigenin and kaempferol binding to the AURKB protein in CT26 cells.The components of SNL were collected from the TCMSP, ETCM, HERB, and NPASS databases. Meanwhile, the target proteins of these ingredients were collected/predicted by the TCMSP, SEA, SwissTargetPrediction, and the STITCH databases colon cancer-related target genes were identified from TCGA and GTEx databases. The interaction networks were established A total of 37 SNL components, 796 SNL targets, 5,356 colon cancer genes, and 241 shared targets of SNL and colon cancer were identified. A total of 43 key targets were obtained through topology analysis. These key targets are involved in multiple biological processes, such as signal transduction and response to drug and protein phosphorylation. At the same time, 104 signaling pathways, such as pathways in cancer, human cytomegalovirus infection, and PI3K-Akt signaling pathway, are also involved. The binding of the four key components  and the key targets was verified by molecular docking. The CETSA results showed that apigenin and kaempferol were able to bind to the AURKB protein to exert anti-CRC effects.via signaling pathways, such as pathways in cancer, PI3K-Akt, and cell cycle signaling pathways.Quercetin, apigenin, kaempferol, and luteolin are the main components of SNL in treating colon cancer. SNL regulates multiple bioprocesses  Its incidence rate ranks top three among all malignant tumors, accounting for 10.0% of the incidence rate of all cancers, with a mortality rate of 9.4% , 3.Solanum nigrum L. (SNL) (Longkui) is a traditional Chinese medicine that is commonly applied to cancer treatment. SNL can inhibit the proliferation and metastatic potential of RKO CRC cells  (http://www.tcmip.cn/ETCM/) (http://bidd.group/NPASS/index.php) (http://www.symmap.org/) (http://herb.ac.cn/) (https://old.tcmsp-e.com/load_intro.php?id=29) or ETCM (drug-likeness weight (DW) ≥ 0.49).The SNL components were retrieved from the following databases: the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  , The Encn/ETCM/) , the Natdex.php) , SymMap ap.org/) , and HER.ac.cn/) . The com2.2https://sea.bkslab.org/) (http://stitch.embl.de/) (http://swisstargetprediction.ch/) (https://pubchem.ncbi.nlm.nih.gov/) and submitted into the SEA, STITCH, and SwissTargetPrediction databases. Next, the targets in active SNL components were predicted by taking Max Tc ≥ 0.4, confidence score ≥ 0.4, and probability value ≥ 0.5 as the criteria, and the gene name was converted into its official gene symbol.The target genes were retrieved from the TCMSP and ETCM. The target genes in the SNL components were also predicted using Similarity Ensemble Approach  , STITCH mbl.de/) , and Swiion.ch/)  based on2.3http://gepia2.cancer-pku.cn/#index) online tool  of colon adenocarcinoma (COAD) in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases.The Gene Expression Profiling Interactive Analysis , and overlapped targets were uploaded to the STRING database (https://string-db.org/) (via Cytoscape 3.7.2 (The intersection set between SNL and COAD target genes was acquired using the Venn online tool (db.org/) , the prope 3.7.2 , includi2.5https://david.ncifcrf.gov/) is an online tool for genetic function annotation  (The DAVID 6.8 database (notation . The keynotation  and Kyotnotation –25 enricme.html) .2.6https://www.rcsb.org/) database, and the file in PDB format was downloaded (via the PLIP website (https://projects.biotec.tu-dresden.de/plip-web/plip/) (The protein structures were inquired about in the Protein Data Bank (PDB)   and PyMO2.72 and saturated humidity. Logarithmic growth stage cells were used in the experiment. Cellular thermal shift assay (CETSA) was used to detect the binding efficiency of drugs to the corresponding target proteins in CT26 cells . Cell lysates were prepared by centrifugation at 12,000 rpm for 15 min at 4°C, and supernatants were collected. The supernatant was divided into three equal fractions and treated with DMSO, 100 μM Apigenin , and 100 μM Kaempferol , respectively. After incubating at room temperature for 1 h, the three parts were divided into nine parts (60 µl each) and heated at different temperatures  for 3 min, followed by cooling at room temperature for 3 min. The heated lysates were centrifuged at 15,000 rpm for 20 min at 4°C, and the precipitate and soluble fraction were separated in an ice bath. The supernatant was transferred to a new centrifuge tube, analyzed by SDS-PAGE, and Western blot analysis was performed using aurora kinase B .Mouse colon cancer CT26 cells were obtained from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences  and cultured in RPMI1640 medium  containing 10% fetal bovine serum  in an incubator at 37°C with 5% CO26 cells , 32. CT22.8t-test was used when the data satisfy normality distribution and homogeneity of variance. t’-test was used when the data satisfy normality distribution but not homogeneity of variance. Non-parametric rank sum test was used for non-normally distributed data. Statistically significant differences were considered at p < 0.05.The data were expressed as mean ± standard deviation (SD). A two independent sample 33.1A total of 39 SNL components were acquired from the TCMSP database, 35 from ETCM, 114 from HERB, and 60 from NPASS. Repeated components were deleted and subjected to the OB and DL/DW screening, and then 37 effective compounds were obtained. The targets were collected and predicted in the TCMSP, STITCH, SwissTargetPrediction, and SEA databases, and 796 targets were acquired in total Figure 13.2p < 0.05), and those ranking in the top 12 (gene number ≥ 50) included metabolic pathways, pathways in cancer, neuroactive ligand–receptor interaction, PI3K-Akt signaling pathway, non-alcoholic fatty liver disease (NAFLD), Alzheimer’s disease, hepatitis B, MAPK signaling pathway, HTLV-I infection, Huntington’s disease, Parkinson’s disease, and Ras signaling pathway   differentially expressed in COAD Table 2.3.4via DAVID6.8 online tool. The GO analysis manifested that these targets mainly existed in regions, such as the nucleus, cytoplasm, cytosol, nucleoplasm, plasma membrane, extracellular region, perinuclear region of cytoplasm, centrosome, an integral component of the plasma membrane, and chromatin   could stably bind to the corresponding key targets. Quercetin and CASP3 formed two hydrogen bonds through ASP-2 and SER-251. Apigenin and AURKB formed two hydrogen bonds through HIS-192 and GLY-193. Linoleic acid and CCNA2 formed a hydrogen bond through PRO-155. Kaempferol and AURKB formed two hydrogen bonds through LEU-83 and PHE-219. All of them form two hydrogen bonds each, except for linoleic acid and CCNA2, which form one hydrogen bond. The findings above indicated that quercetin, apigenin, and kaempferol are probably the core active components of SNL Figure 83.7The stability of compound-induced target proteins was examined by CETSA, and the interaction of apigenin and kaempferol with AURKB in CT26 cells was determined by a western blot assay. As shown in 4SNL is a commonly clinically used anticancer in traditional Chinese medicine. The antitumor effect of SNL is mainly manifested by repressing the proliferation, arresting the cell cycle, inducing cell apoptosis, and inhibiting the cell migration of tumor cells –36. Howein vitro and in vivo. The expression of miR-384 was downregulated and that of pleiotrophin (PTN) was upregulated compared with adjacent normal tissues, suggesting that the antitumor effects of luteolin were partly mediated through the miR-384/PTN axis  are serine-threonine kinases that could link extracellular signals to fundamental cellular processes such as cell growth, proliferation, differentiation, migration, and apoptosis . The MAP5via signaling pathways, including pathways in cancer, the PI3K-Akt signaling pathway, cell cycle, cellular senescence, and the chemokine signaling pathway. The CETSA confirmed that apigenin and kaempferol, the active components of SNL in colon cancer treatment, were able to bind to AURKB. The present study provides a basis for the treatment of CRC by SNL from the perspective of network pharmacology. We identified the main components, targets, and signaling pathways of SNL for the treatment of colon cancer and provided new clues for the R&D of anti-CRC drugs.In conclusion, the present study demonstrated that the active components of SNL for the treatment of colon cancer include quercetin, apigenin, kaempferol, and luteolin, and the effective targets include AURKB, PIK3R1, CDK1, CDK2, GSK3B, CCNA2, CCND1, CCNB1, CASP3, CCNA2, and STAT1. SNL regulates bioprocesses such as signal transduction, response to the drug, protein phosphorylation, gene expression, and apoptotic process The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.Conception and design: JFC and BH. Administrative support: BH. Provision of study materials: JFC and BH. Collection and assembly of data: JFC, SWW, YJQ and MRD. Data analysis and interpretation: JFC. All authors contributed to the article and approved the submitted version.


Asthma is a common chronic respiratory disease, which causes inflammation and airway stenosis, leading to dyspnea, wheezing and chest tightness. Using transgelin-2 as a target, we virtually screened the lead compound glycyrrhizin from the self-built database of anti-asthma compounds by molecular docking technology, and found that it had anti-inflammatory, anti-oxidative and anti-asthma pharmacological effects. Then, molecular dynamics simulations were used to confirm the stability of the glycyrrhizin-transgelin-2 complex from a dynamic perspective, and the hydrophilic domains of glycyrrhizin was found to have the effect of targeting transgelin-2. Due to the self-assembly properties of glycyrrhizin, we explored the formation process and mechanism of the self-assembly system using self-assembly simulations, and found that hydrogen bonding and hydrophobic interactions were the main driving forces. Because of the synergistic effect of glycyrrhizin and salbutamol in improving asthma, we revealed the mechanism through simulation, and believed that salbutamol adhered to the surface of the glycyrrhizin nano-drug delivery system through hydrogen bonding and hydrophobic interactions, using the targeting effect of the hydrophilic domains of glycyrrhizin to reach the pathological parts and play a synergistic anti-asthmatic role. Finally, we used network pharmacology to predict the molecular mechanisms of glycyrrhizin against asthma, which indicated the direction for its clinical transformation. Asthma is a common inflammatory disease of the airways that involves multiple types of cells and cellular components . In ChinTransgelin-2 was widely distributed in the human body, and had a variety of biological functions, including muscle contraction, cell migration, cell proliferation and apoptosis . It mainResearchers demonstrated the critical role of metallothionein-2 (MT-2) in the pathogenesis of asthma using a gene-knockout mouse model. Then, they identified transgelin-2 as the receptor for MT-2 on the bronchial smooth muscle. Furthermore, they confirmed transgelin-2 as a new target for anti-asthmatic therapy, using some techniques such as laser confocal microscopy, RNA interference, surface plasmon resonance, and gene-knockout animal models . AdditioCurrently, there were few active targets for asthma treatment, and the discovery of new targets and the development of small molecule drugs were particularly important. Based on this situation, this study efficiently screened potential active compounds targeting transgelin-2 and predicted the biological functions and signaling pathways for anti-asthma effects. At the same time, in-depth structural biology analysis and pharmaceutical studies were conducted on the lead compound to provide important references for the clinical translation of anti-asthma small molecule drugs.https://old.tcmsp-e.com/) database  database  from RCSB PDB (database . We useddatabase . Therefodatabase .http://davapc1.bioch.dundee.ac.uk/cgi-bin/prodrg) website  is a software package used for molecular dynamics simulations, known for its high computational speed and extensive features . In this website . GROMOS9 website . The sim website . The ste website . Particl website . Finallyhttp://atb.uq.edu.au/) website  online database for target prediction (https://go.drugbank.com/), TTD (https://db.idrblab.net/ttd/), and Genecards (https://www.genecards.org/) databases (https://cn.string-db.org/) database for protein-protein interaction (PPI) analysis (https://metascape.org/) database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to enrich and annotate the biological information and signaling pathways related to the lead compound , 31 cellular components (CC), and 48 molecular functions (MF) were obtained by GO analysis. BP, such as regulation of kinase activity, regulation of protein kinase activity, regulation of defense response, regulation of protein serine/threonine kinase activity, positive regulation of kinase activity, positive regulation of transferase activity, and regulation of MAPK cascade. CC, such as membrane raft, membrane microdomain, cell projection membrane, leading edge membrane, caveola, lytic vacuole, and lysosome. MF, such as endopeptidase activity, peptidase activity, protein tyrosine kinase activity, protein kinase activity, phosphotransferase activity, alcohol group as acceptor, kinase activity, and serine-type peptidase activity  may be important for the anti-asthmatic effect of glycyrrhizin, and some signaling pathways  are also promising research directions for improving asthma.