The development of high-throughput omics technologies has enabled the quantification of vast amounts of genes and gene products in the whole genome. Pathway enrichment analysis (PEA) provides an intuitive solution for extracting biological insights from massive amounts of data. Topology-based pathway analysis (TPA) represents the latest generation of PEA methods, which exploit pathway topology in addition to lists of differentially expressed genes and their expression profiles. A subset of these TPA methods, such as BPA, BNrich, and PROPS, reconstruct pathway structures by training Bayesian networks (BNs) from canonical biological pathways, providing superior representations that explain causal relationships between genes. However, these methods have never been compared for their differences in the PEA and their different topology reconstruction strategies. In this study, we aim to compare the BN reconstruction strategies of the BPA, BNrich, PROPS, Clipper, and Ensemble methods and their PEA and performance on tumor and non-tumor classification based on gene expression data. Our results indicate that they performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) yet with a varying ranking of pathways, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. This can be clearly seen from the reconstructed JAK-STAT networks by different strategies. In a nutshell, BNrich, which relies on expert intervention to remove loops and cyclic structures, produces BNs that best fit the biological facts. The plausibility of the Clipper strategy can also be partially explained by intuitive biological rules and theorems. Our results may offer an informed reference for the proper method for a given data analysis task. The development of high-throughput omics technologies has revolutionized the study of life. Often the quantification of the whole genome is just a first step in generating hypotheses based on differentially expressed genes (DEGs) and pathways. Pathway enrichment analysis (PEA) offers a set of convenient solutions for the dimensionality reduction of massive data and the systematic interpretation of the functional relations of the predefined subsets of biomolecules . They arChronologically, the past two decades have seen three generations of PEA approaches : over-reSince the first statistical approach to scoring changes in the activity of metabolic pathways from gene expression data , a numbeAnother set of TPA methods, such as BPA , BAPA-IGThe structures of real biological pathways are complicated, often consisting of loops or cyclic structures. The existence of cycles in the pathway networks conflicts with the requirement of DAGs in BNs . TherefoIn a recent study by Ihnatova et al., seven non-BN-based TPA methods were critically assessed with respect to their performance in identifying differentially expressed pathways between two contrary groups from gene expression data . HoweverExisting BN-based TPA approaches evaluate their PEA results and their performance in a disease state classification based on disparate gene expression data, making fair comparisons between the various approaches difficult. Moreover, the biological relevance of the reconstructed BNs is insufficiently or barely disclosed. In light of these perceived needs, we collected five sets of gene expression data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) containing at least 30 paired tumor and adjacent non-tumor samples, HCC, HNSC, THCA, KIRC, and LUAD, and performed PEA and disease state classification. The decision tree-based Random Forest was employed to calculate pathway rankings in the classification contribution. The collection of biomolecules and their causal relations of JAK-STAT signaling, a canonical pathway with complex negative feedback loop structures, was used to illustrate the differences of these approaches in the reconstructed BN structures and their relevance to the biological facts.As a result, BPA, BNrich, PROPS, Clipper, and Ensemble performed equally well in distinguishing tumor and non-tumor samples (AUC > 0.95) across all five datasets. However, their ranking of pathways by feature importance varies widely, which can be attributed to the different BN structures resulting from the different cyclic structure removal strategies. By involving intuitive biological rules, BNrich produces the best BNs that preserve causal relations between genes that best fit the biological facts, and the most reasonable ranking of enriched pathways. Clipper measures edge importance and preserves edges on the assumptions of linearity and conditional independence that are often not met in biology. Nonetheless, the plausibility of Clipper can be partially explained by the dominant signaling and transition state theorem in biology. Despite the excellent classification performance, the randomness of PROPS and BPA hamper their biological interpretability, as does the EN method due to its graph hierarchy-based strategy, which ignores gene expression quantification and biological knowledge.To summarize, this is the first comprehensive study that assesses the most recent and widely used BN-based strategies for biological pathway reconstruction. Their PEA and disease state classification performance were evaluated and compared on five benchmark gene expression datasets. A canonical pathway, JAK-STAT signaling, was used to illustrate the differences of the reconstructed BNs and their biological relevance. The advantages and limitations of each strategy were also thoroughly discussed, prompting possible future research directions for an optimized network reconstruction strategy.n = 140), head and neck squamous cell carcinoma , kidney renal clear cell carcinoma , lung adenocarcinoma , and thyroid carcinoma —were downloaded from GEO and TCGA to construct sets of benchmark data for the fair evaluation and comparison of reconstructed BNs using different strategies. RNA sequencing expression read counts downloaded from TCGA were normalized in FPKM (fragments per kilobase of transcript per million mapped reads). The processed expression levels were subject to log2 (n + 1) transformation. The paired samples in the dataset were randomly split into training (75%) and test (25%) sets ten times. Only data from the training set were utilized for BN reconstruction, BN parameter learning, and classification model training.Five datasets quantifying the gene expressions of paired tumor and adjacent non-tumor samples from multiple cancer types—hepatocellular carcinoma , among the most authoritative pathway databases, adds value to a systematic understanding of complex biological systems by providing a shared resource of known pathways. In total, 187 and 270 KEGG pathways are implemented in BNrich and PROPS, respectively. Overlapped pathways with more than five edges after reconstruction were preserved, resulting in 157 pathways that contribute to the downstream analysis.The first step in building a BN is to reconstruct the pathways using directed acyclic graphs (DAG). In our comparison, we used the preprocessed datasets to reconstruct the BN structure, depending on the underlying structure of the selected KEGG pathway. Five different reconstruction methods were incorporated in this study, including BPA, PROPS, Clipper, BNrich, and Ensemble method. The detailed strategies of these approaches are illustrated in the following subsections.BPA is one of the earliest efforts to model pathways as BNs. In BPA, cycles are not handled individually but are considered in groups. Specifically, sets of cycles sharing at least one node are first found using Tarjan’s algorithm and termed cyclegroups. Then, for each cyclegroup, all edges between nodes are removed. The nodes within a specific cyclegroup are randomly numbered without duplication. Directed edges pointing from the smaller numbered nodes to the larger numbered nodes are then added. In addition, for parent node(s) that are outside the cyclegroup, directed edges pointing to child node(s) are also added see also . Such reKEGGgraph R package. For each selected path, nodes (genes) and associated edges not included in the preprocessed dataset are removed. To convert the original pathway into a DAG for BN modeling, an empty graph is created with all genes in the pathway as nodes, and an edge is randomly selected from the pathway and added to the graph each time unless a cycle is triggered . Finally, pathway networks with more than five edges remain in the analysis.In PROPS, KEGG human pathways are extracted as reference pathways using the see also . The edgKEGGgraph R package, and genes not included in the preprocessed datasets are discarded. Instead of randomly removing edges that can form a cycle, Clipper employs the strength of linear relationships between nodes inferred from gene expression data to guide edge elimination. To derive a measure of the correlation between nodes connected by each edge, linear regression models are constructed using gene expression data with parent nodes as explanatory variables and child nodes as response variables. Specifically, the p-value of a standard significance test with zero coefficients is used as the strength of evidence. The list of edges is then sorted by their corresponding p-values from small (significant) to large (not significant). Finally, edges that do not generate cycles (checked by the built-in function in the bnlearn package) are sequentially added to the graph so that the removed edges represent minimal expression profile correlations  and a social agony metric, generating two hierarchical ranking scores for each node. TrueSkill treats every node as a player and every edge as a competition. The skill level of each node is formulated as normal distribution and updated according to the edge direction. If there is an edge from Ensemble method attempts to break cycles from a directed graph while preserving its graph hierarchy  as much as possible . First, networkx) and then decycles the SCCs using all six methods independently. The process is iteratively applied to resulting SCCs until no more SCCs can be generated  is applied to the directed graph until the graph becomes acyclic. Since there are two hierarchical ranking metrics and three edge removal strategies, combining them in pairs yields six decycling methods. In practice, the process first divides a pathway graph into strongly connected components (SCCs) using Tarjan’s algorithm  and mtry (number of variables available for splitting at each tree node) are optimized by grid search. The model equipped with optimized hyperparameters is used to perform training and prediction. The performance on the test set is reported in terms of accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). Except for the AUC, the threshold is determined by the best Matthews correlation coefficient. Feature importance is assessed by mean drop Gini (MDG), which should be a useful value to quantify and rank the enrichment scores for each pathway.The random divisions of paired samples into training and test sets were repeated ten times, and used for downstream PEA using five methods, BNrich, PROPS, Clipper, BPA, and Ensemble method, respectively. All five methods were generally equally good at distinguishing between tumor and non-tumor samples using log-likelihood values of their respective reconstructed BNs. The classification performance on each dataset reveals slight differences with AUC 0.95–0.97 on HCC and HNSC, 0.97–0.98 on THCA, and 0.99–1.0 on KIRC and LUAD .Although all five approaches produced consistently high accuracy in the PEA-based classification of tumor and non-tumor samples, there were significant differences in the rankings of pathways enriched by the five methods according to the feature importance based on MDG scores . Figure The main challenge in modeling pathways using BNs is to remove cyclic structures while preserving key biological context. In this study, we investigate five methods for determining the edges to be eliminated, BNrich, PROPS, Clipper, BPA, and Ensemble method. These five approaches represent four main directions, based on randomness, gene correlations inferred from expression data, graph hierarchies, and biological intuitive rules .PROPS provides the most straightforward method. For those edges that form a cyclic structure, PROPS assumes to be based on no prior knowledge about the edges, so every edge has an equal probability of being dropped a. This aIt is worth noting that BPA e, one ofOn the other hand, Clipper b computaThe Ensemble method d also cop-value threshold  in Clipper.Unlike Clipper and Ensemble, BNrich takes a biological approach rather than relying on computation to remove cyclic structures c. With eThe reconstructed BN structures of the JAK-STAT signaling pathway  clearly In the case of BNrich, it was not surprising to see that all the negative feedback edges representing SOCS inhibition of JAK tyrosine activity and JAK-STAT signal transduction were removed . Such a TrueSkill hierarchical ranking scores. Given the assumption of the Ensemble method, which prioritizes maintaining a network hierarchy that minimizes social agony, it is plausible that the negative feedback edges from SOCS to JAK-STATs were removed.The same removal of SOCS negative feedback edges was observed in the case of the Ensemble method, which can be explained by the nature of the Ensemble strategy. A signaling pathway is an artificially defined subset of chemical reactions in a cell that lead to a certain product or a change. In the spectrum of KEGG-defined JAK-STAT signaling, SOCS expression is regulated by the DNA binding of STAT proteins . JAK-STACompared to BNrich and Ensemble, Clipper removed more edges representing activated JAKs and/or the cytokine receptor phosphorylation of STATs . While tAnother difference in the Clipper- and Ensemble-reconstructed BNs is that they retained a number of edges representing state transitions . OncogenPROPS and BPA reconstruct BNs by adopting a randomness strategy. We were curious to what extent this randomness would affect the reconstructed network structure and whether it would impair downstream pathway analysis. To this end, we randomly generated ten reconstructed BNs and compared them in the case of PROPS and BPA, respectively . Figure In this section, we demonstrate the pros and cons of each approach based on a discussion of algorithmic differences, the experimental results on five gene expression datasets, and the reconstructed structures of the JAK-STAT signaling pathway. Given the apparent complementarity, we present a potential method combining existing methods as a suggestion for future research. In addition, potential links between the BN reconstruction of pathways and learning of the BN structure from gene expression data are discussed.Bayesian networks represent a major class of methods for performing topology-based pathway analysis, the latest generation of pathway enrichment analysis methods. While BN allows incorporating pathway topology for global pathway analysis, it requires the input network to be directed and acyclic. Therefore, eliminating cyclic structures from the experimentally identified pathways becomes a first and crucial step. In this study, we rigorously compared five different strategies for reconstructing pathways into BNs proposed by the BPA, PROPS, Clipper, BNrich, and Ensemble method, respectively. These strategies fall into four main directions to remove edges: random selection, linear relationship strength learned from expression data, biological intuitive rules, and graph hierarchies. In particular, in addition to BN reconstruction, BNrich also applies LASSO regression to discard edges whose linear correlations cannot be supported by the expression data, to remove redundant edges and simplify the network.These five strategies were examined in different aspects, including pathway-based disease state classification, PEA results, the fitness of the reconstructed BNs on non-tumor samples, and the plausibility of eliminated edges with the JAK-STAT signaling pathway. The BNs reconstructed by different methods all provide consistently high classification performance (AUC > 0.95); nonetheless, the pathways that played a major role in the classification were distinctly different. Since the reconstruction strategy is the only variable, these results suggest that there are tumor-influenced gene relationships that are inappropriately removed by some methods. This makes it urgent to dig deeper into the biological functions represented by edges preferentially removed by various methods. To this end, the canonical pathway JAK-STAT, which contains complex negative feedback loop structures, was analyzed. As expected, BNrich removed all edges representing negative feedback inhibition from SOCS to JAKs. The Ensemble method, which learns to infer and make decisions based on the graph hierarchy, also removes all edges from SOCS to JAK. This fits well with its core assumption that nodes receiving edges are ranked higher than nodes giving edges. In the case of biological pathways, genes such as JAKs and STATs that regulate a large number of downstream genes would be inferred by Ensemble as low-ranking, and thus edges pointing to them would be more likely to be dropped. Unlike BNrich and Ensemble, Clipper, another computational method that relies on a linear relationship between gene expression, preserves some negative feedback edges while removing more edges representing activated JAKs and/or cytokine receptor phosphorylation of STATs. This can be partially explained by the adaptive process, while maintaining feedback edges may also help to identify tumor-influenced inhibition. For the remaining two methods, PROPS and BPA, the random selection strategy makes them difficult to agree across different implementations, and edges representing key regulations are often omitted. As for BPA, while new edges are added randomly as a remedy, they are often excessive and can hinder true biological signaling representation.In short, BNrich relies on expert knowledge to provide the most biologically logical BN. The only limitation is that manual selection is required every time a new pathway is considered. The Ensemble method prefers to remove edges pointing to key regulators with a large number of downstream genes, and this needs to be carefully inspected when used. The data-driven method, Clipper, produces similar yet different results to Ensemble, which can be partially explained from the perspective of biological principles to some extent. Therefore, Clipper can serve as a useful alternative when hand-crafted acyclic pathways are not available.However, limitations still exist. Edge removal in Clipper and network simplification in BNrich are performed under the assumption of linear correlation, which is often not fulfilled in real scenarios. Some distribution-free methods, such as maximum local correlation and mutual information, should be incorporated in future research to detect the nonlinear relationships in the gene expression data . The PROOne of the immediate future directions for developing BN reconstruction methods for pathway analysis is to combine the advantages of current methods, using BPA as a template since randomly removing edges that form cyclic structure can impair important biological causality. The strength of evidence for each edge inferred from expression data, performed in Clipper, can also be considered as a useful alternative. The idea of adding edges to preserve associativity is worth keeping, but the way of adding at random and connecting parent nodes to all loop members brings superfluous low-quality edges and does not guarantee the preservation of high-quality edges. The LASSO-based network simplification strategy used in BNrich can be used to refine newly added edges. In addition to linear relationships, nonlinear relationships should also be considered throughout. Finally, the biological intuitive rules-based approach such as that proposed in BNrich can serve as a guideline for checking and improving edge elimination and addition.While we emphasize the comparison of methods based on an experimentally identified pathway, many existing BN methods for biological networks focus on reconstructing BN purely from expression data. They often do not take into account existing knowledge of experimentally determined pathways. Without pathway information as prior knowledge, they usually assume a uniform graph, which may be suboptimal. In addition, due to the computational complexity, they are often applicable to relatively small gene sets, whereas the volume of expression data generated by high-throughput platforms is often much larger. Therefore, the resulting high computational costs may limit the use of all available data and the reconstruction of complete network structures. Even so, their ability to infer underlying biological topology and causal relationships from the data and store them in BN in the form of edges is desirable for future study. One possible way to combine the context of known biological pathways with inference methods that learn structures from data is to perform Bayesian network structure refinement via carefully designed scores or constraints, starting from existing pathways. Given the high computational complexity resulting from a large number of genes, local learning may be preferred, which learns structures near one or more target genes that are of particular interest but not the rest.In this study, we comprehensively evaluate and compare five representative BN reconstruction strategies among existing TPA methods. Through rigorous testing on five gene expression datasets of different cancer types, we show that while all methods perform well in disease state classification, their pathway rankings vary widely, indicating the sensitivity of the edge removal. The preserved edges within the complex cyclic structure of the JAK-STAT pathway were visualized and analyzed to reveal the differences among methods. Based on the domain knowledge of experts, BNrich provides network structures that best fit the biological facts. The gene expression data-driven approach, Clipper, may be a useful alternative, the results of which can also be explained to some extent by biological principles and theorems. Finally, the limitations and advantages of all methods are summarized to serve as a useful reference to facilitate future research.


To elucidate the mechanism of action of berberine on ischaemic stroke based on network pharmacology, bioinformatics, and experimental verification. in vitro experiments.  Berberine-related long noncoding RNAs (lncRNAs) were screened from public databases. Differentially expressed lncRNAs in ischaemic stroke were retrieved from the Gene Expression Omnibus (GEO) database. GSE102541 was comprehensively analysed using GEO2R. The correlation between lncRNAs and ischaemic stroke was evaluated by the mammalian noncoding RNA-disease repository (MNDR) database. The component-target-disease network and protein-protein interaction (PPI) network of berberine in the treatment of ischaemic stroke were constructed by using network pharmacology. We then performed gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analyses. Finally, according to the molecular docking analysis and the binding probability between the lncRNA and key proteins, the effectiveness of the results was further verified by in vitro experiments demonstrated that lncRNA H19, p-JNK1/JNK1, p-c-Jun/c-Jun, and EGFR expressions were significantly increased in hypoxia-treated SH-SY5Y cells and were restored by berberine treatment.  After matching stroke-related lncRNAs with berberine-related lncRNAs, four genes were selected as potential targets of berberine in the treatment of ischaemic stroke. Subsequently, lncRNA H19 was identified as the potential crucial regulatory lncRNA of berberine. Here, 52 target proteins of berberine in the treatment of ischaemic stroke were identified through database mining. Through topological analysis, 20 key targets were identified which were enriched in inflammation, apoptosis, and immunity. Molecular docking results showed that MAPK8, JUN, and EGFR were central genes. Finally,  The potential targets and biological effects of berberine in the treatment of ischaemic stroke were predicted in this study. The lncRNA H19/EGFR/JNK1/c-Jun signalling pathway may be a key mechanism of berberine-induced neuroprotection in ischaemic stroke. Stroke is a type of cerebrovascular disease that causes a disability and even death worldwide. Clinically, ischaemic stroke is more common than haemorrhagic stroke, accounting for 87% of all cases, and it has become the focus of most research . IschaemRecent studies have shown that lncRNAs, as endogenous small molecules, are extensively involved in the pathogenesis of ischaemic stroke –8. A cliCoptis chinensis, Phellodendron amurense, and other Chinese herbal medicines, possesses various biological functions [Berberine, a natural isoquinoline alkaloid extracted from unctions –16. Moununctions –19. Zhu unctions . Clinicaunctions –23. ReceIn the past few years, bioinformatics and microarray techniques have been widely used to mine genetic targets for a variety of diseases to help researchers identify differentially expressed genes and potentially different signalling pathways. Based on these approaches, more lncRNAs will be discovered, which will expand our understanding of the molecular mechanisms underlying ischaemic stroke. Network pharmacology integrates the technology and content of systems biology, multidirectional pharmacology, network analysis, and other disciplines, and it systematically evaluates the interaction mechanisms between diseases and drugs , 25. TheHere, to elucidate the pharmacological mechanism of berberine, we adopted a systematic method based on bioinformatics analysis, network pharmacology, and experimental verification of berberine intervention on ischaemic stroke. This approach provides an effective strategy to explore the molecular mechanism of berberine against ischaemic stroke and to identify potential protein targets with synergistic effects. A flowchart of the study is shown in As of October 20, 2021, we conducted literature searches in PubMed, EMBASE, CNKI database, and Google Scholar database to search for qualified studies detailing the biological effects of berberine-related lncRNAs in diseases. The following MeSH or free text terms were used to search the databases: (“berberine” OR “BBR”) and (“long noncoding RNA” OR “lncRNA”).https://www.ncbi.nlm.nih.gov/geo/) [n = 6) and healthy controls (Con) (n = 3), and it was processed using the GEO2R online analysis tool. The diagram was plotted by an online platform (https://www.bioinformatics.com.cn) for data analysis and visualisation. The cut-off criteria in this analysis were set as P value <0.05 and |log2(fold change)| >1.Ischaemic stroke-related lncRNAs were obtained from the GEO database (ov/geo/) , 29. Thehttps://bioinformatics.psb.ugent.be/ webtools/Venn/). MNDR is a database that curates the associations between ncRNAs and disease [https://www.rna-society.org/mndr/home.html).The intersection of berberine-related lncRNAs and ischaemic stroke-related lncRNAs was visualised using an online mapping tool ( disease . To furthttps://pubchem.ncbi.nlm.nih.gov/) [http://www.swisstargetprediction.ch/) [https://www.Symmap.org/) [https://ctdbase.org/) [https://stitch.embl.de/) [https://sea.bkslab.org/) [https://targetnet.scbdd.com/) [https://www.UniProt.org/), the species was limited to “human” [Berberine structure information was obtained from NCBI PubChem (ih.gov/) . Therapeion.ch/) , SymMap ap.org/) , Comparase.org/) , STITCH mbl.de/) , SEA (htab.org/) , and Tardd.com/) . STITCH  “human” .https://db.idrblab.net/ttd/) [https://www.drugbank.ca/) [https://www.genecards.org/) [https://www.disgenet.org/) [https://bioinfogp.cnb.csic.es/tools/venny/) was used to map the component targets of berberine to the disease targets of ischaemic stroke [All targets associated with ischaemic stroke were collected from the Therapeutic Target Database (TTD) (et/ttd/) , DrugBanank.ca/) , GeneCards.org/) , and Diset.org/) . After ac stroke .https://string-db.org/) [https://www.cytoscape.org/) was used to draw a PPI network diagram for visual analysis [The potential targets of berberine in the treatment of ischaemic stroke were imported into the STRING database (db.org/) , and theanalysis .Combined with the related literature and with the help of topological parameters, such as closeness centrality (Cc), eigenvector centrality (EC), network centrality (NC), local average connectivity (LAC), betweenness centrality (BC), and degree (DC), the CytoNCA network topology analysis plug-in  was usedhttps://david.ncifcrf.gov/) to perform GO and KEGG enrichment analyses [P values <0.01 were considered the screening condition to screen out the potential pathway of berberine in the treatment of ischaemic stroke.To further explain the role of the target proteins in the active components of TCM on gene and pathway functions, we used the DAVID database  [The structure map of berberine was downloaded from the PubChem database, and the crystal structure of the key target proteins, based on DC, BC, Cc, EC, NC, and LAC, was the ligand and the core target protein was used as the receptor for molecular docking downloaded from the RCSB protein database (sb.org/) . Berberisb.org/) . Ligplotsb.org/) . Accordihttps://pridb.gdcb.iastate.edu/RPISeq/index.html) [We searched the nucleotide sequences of lncRNA H19 and key targets of molecular docking through the NCBI and UniProt databases. Based on the nucleotide sequence, the interaction probability between lncRNA H19 and key targets was predicted by the RNA-Protein Interaction Prediction (RPISeq) database (ex.html) .β-actin were purchased from Abcam (USA), and rabbit polyclonal antibodies against p-JNK1 were purchased from Cell Signaling Technology (USA).Sterile filtered dimethyl sulfoxide (DMSO) was obtained from Gibco (USA). Berberine was purchased from Yuanye  and was dissolved in DMSO . Dulbecc2 incubator . The hypoxia model was conducted according to previous studies [2) condition to mimic ischaemia stroke in vitro. Briefly, cells were seeded at a density of 5 × 104 cells/ml in culture dishes for 48 h. After reaching 80% confluence, cells were randomly divided into control group, model group, and different berberine groups. The medium was replaced by serum-free DMEM in each group. The berberine groups were treated with the final concentration of 10, 20, and 50 μM, respectively, before hypoxia. The model group and different berberine groups were exposed to hypoxia in a three-gas incubator  at 37°C for 24 h. The control group was incubated in normoxic conditions for the same time. The morphology of SH-SY5Y cells in each group was observed under an inverted microscope.Human neuroblastoma SH-SY5Y cells were obtained from the Cell Culture Centre at the Institute of Basic Medical Sciences (IBMS) of the Chinese Academy of Medical Sciences (CAMS) and cultured in DMEM containing 10% FBS in an automatic CO studies , 54. CelμM) in the hypoxia model. After treatment, the culture medium was removed from the wells, and 10 μl of CCK8 solution was added to each well in 100 μl of medium followed by incubation at 37°C for 2 h. The absorbance was subsequently measured at 490 nm with a microplate reader .Cell viability was detected by CCK8 assay according to the manufacturer's instructions. Briefly, cells were treated with berberine  of protein were then electrophoresed on a 10% gradient SDS‐PAGE gel and transferred to PVDF membranes . After the membranes were blocked with 5% skim milk or 5% BSA for 1 hour at room temperature, they were incubated at 4°C overnight with the following primary antibodies: JNK1 (1 : 1000), p-JNK1 (1 : 2000), c-Jun (1 : 5000), p-c-Jun (1 : 2000), EGFR (1 : 5000), and β‐actin (1 : 5000). The membranes were then incubated with secondary antibodies at room temperature for 1 hour. Super ECL Plus  was added to the membranes, and protein bands were visualised on a chemiluminescence imaging system . The optical density (OD) value of the protein bands was determined by ImageJ software.The concentration of protein extracted from the cells was determined by a BCA protein assay kit . Equal amounts . cDNA was synthesized using FastKing gDNA Dispelling RT SuperMix  according to the manufacturer's instructions. qRT-qPCR was performed with an Applied Biosystems 7500 using SuperReal PreMix Plus . The following primers were used: lncRNA H19 forward, 5′-CGCTTTTGAACCAGCAGGG-3′; lncRNA H19 reverse, 5′-TTCCCGAGGCTTT GGTGTG-3′; GAPDH forward, 5′-GGAGTCCACTGGCGTCTTCA-3′; and GAPDH reverse, 5′-GTCATGAGTCCTTCCACGATACC-3′. GAPDH was utilized as the reference gene.P values <0.05 were considered statistically significant.Data are expressed as the means ± SD. GraphPad Prism 8.0 was utilized for visualisation of data. Differences in multiple groups were analysed by ANOVA. Using “Berberine” and “lncRNA” as the keywords for searching PubMed, EMBASE, CNKI database, and Google Scholar database, CASC2, RP5-1057I20.5, MIAT, LINC00943, BACE1-AS, LASER, MRAK052686, H19, HOTAIR, and MALAT1 were found to be associated with berberine . FurtherA total of 13011 differentially expressed lncRNAs were screened from the GSE102541 dataset with 4732 upregulated genes and 8279 downregulated genes Figures . After mFor compound target identification, 422 targets of berberine were identified from the Swiss Target Prediction, SymMap, CTD, STITCH, SEA, and Targetnet databases. The 3387 targets identified in ischaemic stroke were obtained after sorting from the TTD, GeneCards, Drugbank, and DisGeNET databases. By using Venny 2.1 drawing software, 248 treatment targets were selected as potential targets of berberine in the treatment of ischaemic stroke . A PPI nP value <0.01, including 117 biological process (BP) terms, 14 cell composition (CC) terms, and 31 molecular function (MF) terms. The top 10 BP, CC, and MF terms are screened and are represented by a bar chart in In the GO enrichment analysis, 162 items were obtained from 20 core targets with a −1  or support vector machine (SVM). As shown in μM) reduced morphological damage and maintained the normal morphology of SH-SY5Y cells during cell hypoxia, and it had a significant protective effect on SH-SY5Y cell injury. The CCK8 assay indicated that cell viability was significantly enhanced after berberine treatment at concentrations of 10 μM and 20 μM (μM) was selected for subsequent signalling pathway studies. The expression levels of lncRNA H19, EGFR, p-JNK1/JNK1, and p-c-Jun/c-Jun in SH-SY5Y cells were then evaluated. The results indicated that the expression levels of lncRNA H19, EGFR, p-JNK1/JNK1, and p-c-Jun/c-Jun were significantly increased in SH-SY5Y cells after hypoxia injury and were normalised by berberine treatment (Figures To explore the neuroprotective effects of berberine by regulating lncRNA H19, we induced hypoxia injury in SH-SY5Y cells. As shown in nd 20 μM . Accordi Figures . These dCoptis chinensis, which is widely used as a hypoglycaemic, lipid-lowering, anti-inflammatory, and anticancer drug in China [Ischaemic stroke remains a main cause of death and disability worldwide, and more effective drug treatment is urgently needed , 70. Berin China –74. Recein China . In the in vitro experiments confirmed that berberine may have a good therapeutic effect on ischaemic stroke by regulating the lncRNA H19/EGFR/JNK1/c-Jun signalling pathway.This study investigated the synergistic effect of berberine on ischaemic stroke from four aspects. First, after matching stroke-related lncRNAs with berberine-related lncRNAs, four genes were selected as potential targets for berberine in the treatment of ischaemic stroke. We further evaluated their correlation using the MNDR3.1 database and found that lncRNA H19 may be the crucial regulatory lncRNA of berberine against ischaemic stroke. Second, Venny drawing software and the PPI network identified 248 treatment targets as potential targets of berberine against ischaemic stroke. The PPI network recognised MAPK8, JUN, EGFR, STAT3, MAPK1, SRC, MAPK14, MAPK3, AKT1, MYC, TP53, FOS, RELA, IL6, ESR1, TNF, CREBBP, EP300, SHC1, and RAC1 as hub genes. The PPI network revealed the interaction of berberine with ischaemic stroke-related targets and identified possible essential targets from a more detailed perspective according to the topological attributes of the network. GO and KEGG analyses illustrated that the main signalling pathways related to these targets were as follows: MAPK signalling pathway, Toll-like receptor signalling pathway, prolactin signalling pathway, TNF signalling pathway, and HIF-1 signalling pathway. These pathways are closely related to inflammation, immunity, and oxidative stress. Molecular docking analysis between the compound and targets further validated that berberine had good binding ability with these key proteins, and the JNK1/c-Jun signalling pathway may be the crucial functional pathway. Third, we evaluated the binding probability between lncRNA H19 and key proteins, and we found that lncRNA H19 may have a direct regulatory relationship with both JNK1 and EGFR. Finally, α axis [LncRNAs have been reported to actively participate in many important biological processes through cell cycle regulation, splicing regulation, RNA degradation, gene imprinting, and chromatin remodelling , 76. Lncα axis . LncRNA α axis . Gao et α axis . A cliniα axis . These dAt present, there are relatively few studies on lncRNAs in TCM. Previous studies have shown that resveratrol, curcumin, and other active components of TCM attenuate oxidative stress, inflammation, and apoptosis by regulating lncRNAs , 82. EmeBased on molecular docking and the correlation of lncRNA H19-proteins, we investigated the role of berberine-regulated lncRNA H19 in hypoxia-induced SH-SY5Y cells, focusing on the EGFR/JNK1/c-Jun signalling pathway. EGFR activates a variety of downstream signalling pathways, such as the JNK1/c-Jun pathway and PI3K/Akt pathway, which participate in the regulation of cell proliferation, differentiation, and angiogenesis –85. Studin vivo experiments need to be conducted in the future to verify these results. In addition, various high-throughput sequencing screening methods, such as sequencing and proteomic analysis, should be combined with target screening to provide more reliable evidence for these screening results.In conclusion, this study utilized network pharmacology, molecular docking, and bioinformatics analysis to elucidate the relationship between complex diseases, such as ischaemic stroke, and TCM intervention. We confirmed that berberine has an excellent neuroprotective effect via regulation of the lncRNA H19/EGFR/JNK1/c-Jun pathway in hypoxia-induced SH-SY5Y cell injury, making it a possible drug candidate for ischaemic stroke. This study provides a novel strategy for a comprehensive understanding of the mechanism of berberine in ischaemic stroke. However,


PI3K pathway is the most frequently mutated pathway in head and neck squamous cell carcinoma (HNSC), which plays a crucial role in tumorigenesis and progression. In the present study, we aimed to investigate the role of PI3K pathway mutation in clinical prognosis prediction and the relationship with immune microenvironment and response rate to immunotherapy.We collected 129 samples with immunotherapy information from MSKCC-2019 cohort as well as 501 and 40 samples from TCGA-HNSC and MD-Anderson non-immunotherapy cohorts, respectively. Somatic mutation data was utilized to characterize the mutational status of the PI3K pathway. Subsequently, we further analyzed the differences in prognosis, immunotherapy response, genomic alterations, functional characteristics, and immune microenvironment between the mutation and wild groups.p = 0.012) but did not reach statistical significance in the non-immunotherapy cohorts TCGA-HNSC (p = 0.68) and MD-Anderson (p = 0.68). After incorporating several clinicopathologic features such as age, gender, and tumor mutation burden (TMB), the results of multivariate Cox regression analysis also demonstrated that the PI3K pathway mutation could indicate better immunotherapy outcomes in HNSC patients with a hazard ratio (HR) of 0.533  in the immunotherapy cohort MSKCC-2019, compared with 0.888  and 1.939  in the non-immunotherapy cohorts TCGA-HNSC and MD-Anderson. In addition, the results of the subclass mapping (SubMap) and the tumor immune dysfunction and exclusion (TIDE) also consistently suggested that patients in the mutation group are more likely to benefit from immunotherapy. And further studies showed that the mutation group owned significantly higher TMB, activated immune-related pathways, richer abundance of immune cells, and higher expression levels of immunomodulators. To improve the prognosis of the wild group, we identified five relatively sensitive potential drugs for the wild group, including “BMS-536924,” “linsitinib,” “NVP-TAE684,” “PLX-4720,” and “clonazepam.”The Kaplan-Meier survival curves displayed that PI3K pathway mutation predicted observably prolonged overall survival (OS) in the immunotherapy cohort MSKCC-2019 (The PI3K pathway mutation status could be considered as a potential biomarker to predict better immunotherapeutic efficacy and clinical outcomes after immunotherapy in HNSC patients.The online version contains supplementary material available at 10.1186/s12957-023-02938-6. Head and neck squamous cell carcinoma (HNSC) is the sixth most common cancer worldwide and is mainly induced by smoking, alcohol consumption, and human papillomavirus . CurrentThe PI3K/AKT pathway is one of the most important pathways regulating many cellular processes, and its abnormal activation leads to cell cycle dysregulation, genomic instability, cell differentiation defects, and persistent mitotic signaling in HNSC patients, thereby enhancing the malignancy of tumors . In termFollowing the success of immunotherapy in melanoma, an increasing number of clinical trials have confirmed its remarkable efficacy in specific patients . RecentlIn this study, we characterized PI3K pathway mutation landscape in HNSC patients and analyze its prognostic value in immunotherapy and non-immunotherapy cohorts. After stratifying HNSC patients into the mutation and wild groups, we systematically evaluated the relationship between PI3K pathway mutation and immunological characteristics from multiple dimensions including TMB, immune cell infiltration, immunomodulator expression, and immunotherapy response prediction, respectively. In addition, we also explored potentially sensitive drugs in the wild group population that responded poorly to immunotherapy to significantly improve their prognosis. Overall, our study may provide a reference for early clinical identification of immunotherapy-sensitive HNSC patients to receive further individualized therapy.https://www.cbioportal.org/) and TCGA  databases [http://xena.ucsc.edu/) and further transformed to log2 (TPM + 1).Somatic mutation data and corresponding clinical information for the three independent HNSC cohorts, including the immunotherapy cohort MSKCC-2019 as well as the non-immunotherapy cohorts MD-Anderson and TCGA-HNSC, were obtained from the cBioPortal , we chose chromosome fragments with broad-level CNA frequencies greater than 33% and six classical genes located on chromosomes 7p11.2, 3p24.1, and 9p21.3 for display. In addition, we calculated copy number gain and loss loads occurring at focal and chromosomal arm levels to evaluate global CNA loads between the PI3K pathway mutation and wild groups.Based on the mutation and copy number alteration (CNA) data from the TCGA-HNSC cohort, we comprehensively analyzed the PI3K pathway mutation and wild groups. The analysis steps are as follows: (1) we first calculated the TMB for each tumor sample and selected the top 15 mutated genes for visualization; (2) referring to the previous study, we extracted the four most common mutational signatures  in HNSC using the quencies , 27; (3)We performed gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) to explore potential Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmark pathways associated with PI3K pathway mutation and wild phenotypes in HNSC. Afterward, we selected 10 GSVA pathways to visualize via heatmaps and 5 GSEA pathways to visualize via GSEA plots for the mutation and wild groups, respectively.We applied CIBERSORT and xCell algorithms to evaluate the abundance of immune cell infiltration in the PI3K pathway mutation and wild groups, respectively , 29. In http://tide.dfci.harvard.edu/).Using the subclass mapping (SubMap) algorithm , an unsupRRophetic package [P < 0.01 for CTRP and PRISM data.We then identified potential drugs for the PI3K pathway wild group using the following three steps: (1) the expression data of cancer cell lines (CCLs) were obtained from the Cancer Cell Line Encyclopedia (CCLE) database. The drug-sensitive data with AUC values were obtained from the Cancer Therapeutics Response Portal (CTRP) and PRISM repurposing databases; (2) afterward, we further estimated the drug sensitivity of each sample in the TCGA-HNSC cohort according to the  package ; (3) fint-test or Wilcoxon rank-sum test; categorical variables were analyzed by chi-square test or Fisher’s exact test. Differences in Kaplan-Meier survival curves between the two groups were evaluated using the log-rank test. All P values were two-sided, and P < 0.05 was considered statistically significant.All data processing and analysis for this study were performed using the R 4.1.1 software. Continuous variables between the PI3K pathway mutation and wild groups were analyzed by Student’s p = 0.021), whereas the HRs in the non-immunotherapy cohorts TCGA-HNSC and MD-Anderson were 0.888  and 1.939 , respectively , but there were not statistically significantly associated with OS and DFS in the non-immunotherapy cohorts TCGA-HNSC (n = 501) and MD-Anderson , while copy number amplifications failed to reach statistical significance, although there was a corresponding trend . However, stromal components were more enriched in the PI3K pathway wild group  and colorectal cancer (P = 0.0022). It is worth pointing out that since the MSKCC-2014 cohort had only 64 melanoma samples, although their Kaplan-Meier curves prominently separated, the result did not reach statistical significance  and MSKCC-2014 cohort (melanoma). As shown in Fig. P = 0.68); however, HPV-positive status was associated with significantly prolonged OS  infection status is a favorable prognostic factor in HNSC patients , 34, basHNSC is the sixth most common cancer worldwide. Currently, treatment modalities for HNSC include surgical resection, targeted therapy, adjuvant chemotherapy, radiotherapy, and immunotherapy , 36. MutIn this study, we divided HNSC patients into the mutation and wild groups according to the mutation status of PI3K pathway genes and found that the mutation group had longer OS observably in the immunotherapy cohort. However, the mutation status was not significantly associated with OS and DFS in the non-immunotherapy cohorts. Further studies exhibited that the PI3K pathway mutation group owned significantly higher TMB as well as mutation frequencies of oncogenes and tumor suppressor genes, relatively active immune-related pathways, richer immune cell infiltration, and higher expression levels of immunomodulators. The predicted results of TIDE and SubMap consistently indicated that HNSC patients in the PI3K pathway mutation group were more likely to benefit from immunotherapy. Thus, PI3K pathway mutation status may be a reliable biomarker to predict the efficacy of immunotherapy in HNSC patients.In HNSC patients, aberrant activation of the PI3K/AKT/MTOR pathway promotes malignant progression and impacts many vital aspects of immune cell development, differentiation, and activation of immunosuppressive factors –7, 42. MTo explore the potential mechanisms that may contribute to the differential efficacy of immunotherapy and prognosis in patients with PI3K pathway mutation and wild groups, we performed an integrated genomic variation analysis between the two groups. Consistent with Lui et al., the mutation group possessed significantly higher mutation rates of tumor suppressor gene CDKN2A and oncogenes TTN, NOTCH1, and MUC16 . Many stNext, we performed GSVA and GSEA enrichment analyses separately to explore the underlying biological mechanisms of the PI3K pathway mutation and wild groups. The results exhibited that the immune-related pathways, including B cell-mediated immunity, MHC protein binding, T cell receptor complex, and positive regulation of T cell proliferation, were significantly enriched in the PI3K pathway mutation group. On the other hand, specific pathways associated with malignant tumor progression such as angiogenesis, positive regulation of mitochondrial fission, and metabolism-related pathways such as fatty acid beta oxidation were relatively activated in the PI3K pathway wild group, indicating that we can develop specific drugs targeting tumor angiogenesis, cell proliferation, and metabolism for patients in the wild group.Considering the significant enrichment of immune-related biological processes in the PI3K pathway mutation group, we further evaluated the abundance of immune cell infiltration and immunomodulators expression levels between the two groups. The results from xCell and CIBERSORT consistently indicated that killer cells such as memory CD4 T cells and central memory CD8 T cells, which play critical roles in anti-tumor immunity and immunotherapy, were significantly richer in the PI3K pathway mutation group. Similarly, the expression levels of immunomodulators, including co-stimulatory, co-inhibitory, ligand, and receptor molecules, were also significantly higher in the PI3K pathway mutation group. Thus, we have reason to believe that anti-tumor immunity will be more active in the PI3K pathway mutation group after targeting these immunomodulators using ICIs, and reactivated killer immune cells will exert anti-tumor function and improve patient outcomes –52.Our previous findings exhibited that the PI3K pathway mutation group possessed significantly higher levels of TMB, killer immune cell infiltration, and immunomodulators expression, hinting that patients in the mutation group are more sensitive to immunotherapy , 53, 54.This study is the first to elaborate the mutation landscape of the PI3K pathway in HNSC and found that PI3K pathway mutation predicts better immunotherapeutic efficacy and significantly prolonged OS in HNSC patients after receiving immunotherapy. Second, we explored the utility and generalizability of our findings and confirmed that PI3K pathway mutation could still accurately predict better immunotherapeutic outcomes in other common tumors such as esophagogastric and colorectal cancers. However, our study also has some shortcomings. For instance, due to the lack of immunotherapy cohorts for HNSC, we collected only 129 samples with complete mutation, immunotherapy, and prognosis information from the MSKCC-2019 cohort, and more multi-center prospective studies are needed to validate our results in the future.Based on the mutation status of PI3K pathway genes, we found that PI3K pathway mutation predicted a better prognosis for HNSC patients in the immunotherapy cohort, whereas there was no prognostic difference in the non-immunotherapy cohorts. The PI3K pathway mutation group owned significantly higher TMB as well as mutation frequencies of oncogenes and tumor suppressor genes, enriched immune-related pathways, more affluent abundance of immune cells infiltration, and superior immunomodulators expression and was more sensitive to immunotherapy. Correspondingly, the PI3K pathway wild group had a relatively high copy number amplification and deletion loads, mainly enriched in metabolism-related pathways, and thereby we identified five potential specific drugs for this property. Overall, PI3K pathway mutation may be an underlying biomarker for immunotherapy in HNSC patients and could provide a reference for early identifying immunotherapy-sensitive populations to receive further immunotherapy.Additional file 1: Fig. S1. Overall workflow diagram of our research. Fig. S2. Landscape and clinical prognostic value of PI3K pathway mutation in the MD-Anderson cohort as well as heatmaps of immune cells infiltration and boxplots of immunomodulators expression. (A) Oncoplot depicts the landscape of PI3K pathway gene mutation in the MD-Anderson cohort. (B) Multivariate Cox regression analysis of PI3K pathway mutation phenotype in the MD-Anderson cohort. (C-D) Kaplan-Meier survival analysis of OS (C) and DFS (D) between the PI3K pathway mutation and wild groups in the MD-Anderson cohort. (E-F) Assess infiltration abundance of 22 immune cells calculated by CIBERSORT (E) as well as 39 immune cells and stromal cells calculated by xCell (F). (G) Boxplots represent different expression levels of 18 ligand molecules between the two groups. WT, wild type; MT, mutant type; *P < 0.05. Fig. S3. The relationship between PI3K pathway mutation and HPV status and patient prognosis in the TCGA-HNSC cohort. (A) Boxplots represent different expression levels of 19 receptor molecules between the PI3K pathway mutation and wild groups. (B) Composition percentage of HPV status between the PI3K pathway mutation and wild groups. (C) Composition percentage of PI3K pathway mutation status between the HPV-negative and HPV-positive groups. (D) Kaplan-Meier survival analysis of the HPV status in the TCGA-HNSC cohort. (E) Kaplan-Meier survival analysis of PI3K pathway mutation in the HPV-negative group patients of TCGA-HNSC cohort. (F-G) Kaplan-Meier survival analysis of HPV status in the PI3K pathway wild (F) and mutation (G) group patients of TCGA-HNSC cohort. WT, wild type; MT, mutant type; *P < 0.05; **P < 0.01. Table S1. The 29 PI3K pathway genes used to define samples as PI3K pathway mutation or wild groups.


Vascular dementia (VD), associated with cerebrovascular injury, is characterized by severe cognitive impairment. Jianpi Tianjing Decoction (JTD) has been widely used to treat VD. However, its molecular targets and mechanisms of action in this treatment remain unclear. This study integrated network pharmacology and proteomics to identify targets and mechanisms of JTD in the treatment of VD and to provide new insights and goals for clinical treatments.  Systematic network pharmacology was used to identify active chemical compositions, potential targets, and mechanisms of JTD in VD treatment. Then, a mouse model of VD was induced via transient bilateral common carotid artery occlusion to verify the identified targets and mechanisms of JTD against VD using 4D label-free quantitative proteomics. In vivo experiments showed that JTD improved learning and memory in mice. Proteomics also identified 112 differentially expressed proteins in the model and sham groups and the JTD and model groups. Integrating the network pharmacology and proteomics results revealed that JTD may regulate expressions of cytochrome c oxidase subunit 7C, metabotropic glutamate receptor 2, Slc30a1 zinc transporter 1, and apolipoprotein A-IV in VD mice and that their mechanisms involve biological processes like oxidative phosphorylation, regulation of neuron death, glutamate secretion, cellular ion homeostasis, and lipoprotein metabolism.  By screening active chemical compositions and potential targets in relevant databases, 187 active chemical compositions and 416 disease-related compound targets were identified.  JTD may suppress VD development via multiple components, targets, and pathways. It may thus serve as a complementary treatment option for patients with VD. Vascular dementia (VD) is a cognitive dysfunction associated with cerebrovascular injury and characterized by severe impairment of cognitive functions, including attention, memory, verbal fluency, and executive function . EpidemiPanax ginseng C.A. Meyer, Gastrodia elata, Atractylodes macrocephala, Morinda officinalis Radix, Acorus tatarinowii Schott, Rhizoma coptidis, and Semen cuscutae. Previous studies have shown that Gastrodia elata ameliorates vessel elasticity and prevents atherosclerosis [Panax ginseng C.A. Meyer extract attenuates neuronal injury and cognitive deficits in a VD rat model by upregulating the apoptosis regulator Bcl-2 and downregulating the apoptosis regulator BAX (Bax) protein expression [Rhizoma coptidis improves the rat neurological function after acute brain injury by increasing the hippocampal brain-derived neurotrophic factor expression [Chinese herbal formula (CHF) has multiple targets and few side effects, playing an active role in VD treatment . CHF shoclerosis . Panax gpression . Rhizomapression . Our prepression , 24. Anipression , 26. HowNetwork pharmacology is a novel method for studying CHM and CHF that combines systematic network analysis and pharmacology to identify interactions among compounds, genes, proteins, and diseases . Tian ethttps://www.old.tcmsp-e.com/tcmsp.php ), BATMAN-TCM ), ETCM ), chemical database ), and more recent research literature were used to collect the chemical compositions of Panax ginseng C.A. Meyer, Gastrodia elata, Atractylodes macrocephala, Morindae Officinalis Radix, Acorus tatarinowii Schott, Rhizoma coptidis, and Semen Cuscutae. Next, active chemical compositions were screened using the Swiss ADME database ) and selected based on oral bioavailability “> 30%,” gastrointestinal absorption level “high,” and “Yes” for atleast three of Lipinski, Ghose, Veber, Egan, or Muegge drug-likeness items or drug-likeness “>0.18.”The TCM systems pharmacology (TCMSP) database ), OMIM ) and Drugbank ) databases. The protein targets of active chemical compositions were obtained through the TCMSP and Swiss Target Prediction databases ). Target protein names were transformed into their equivalent official gene names using the UniProt database ). An online data analysis and visualization platform ) was used to plot Venn diagrams and access disease-related compound targets.VD genes were collected from GeneCards ), with species “Homo sapiens”, and the protein–protein interaction (PPI) network was generated based on a confidence level ≥0.7. The results were imported into Cytoscape 3.9.1 for visualization and analysis, and the top 30 hub genes were ranked with the CytoHubba plug-in of Cytoscape 3.9.1. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of potential targets were performed on the Metascape platform ).Cytoscape 3.9.1 software was used to construct the “herb-component-target” network diagram. Disease-related compound targets were imported into the String database  were purchased from the Shanghai Institute of Planned Parenthood Research Center for Animal Research  2018-0006). Animal experiments were approved by the Animal Experimentation Ethics Committee of Zhejiang Chinese Medical University (grant number: IACUC-20210906-12). The mice were kept at the Zhejiang Chinese Medical University Laboratory Animal Research Center (license number: SYXK (Zhejiang) 2021-0012). All mice were allowed to acclimatize for one week prior to being used in experiments. Mice were group housed (five per cage) at 22 ± 2°C and humidity 63 ± 2%, with noise level <55 dB and an alternating 12-h light/dark cycle. The mice had free access to standard laboratory food and tap water.Panax ginseng C.A. Meyer 9 g, Gastrodia elata 9 g, Atractylodes macrocephala 10 g, Morindae Officinalis Radix 6 g, Acorus tatarinowii Schott 6 g, Rhizoma coptidis 3 g, and Semen Cuscutae 12 g. The JTD granules are prepared in accordance with the previous methods [Panax ginseng C.A. Meyer lot no. 21021463, Gastrodia elata lot no. 21041643, Atractylodes macrocephala lot no. 21073513, Morindae Officinalis Radix lot no. 2101010, Acorus tatarinowii Schott lot no. 2104010, Rhizoma coptidis lot no. 21051343, and Semen Cuscutae lot no. 20111003). Previous studies have shown that the optimal therapeutic dose of JTD in mice is 20.160 g/kg/d [JTD granules were composed of  methods , 33. The methods . All com0 g/kg/d , 26 and n = 10), model (n = 15), and JTD (n = 15). The transient bilateral common carotid artery occlusion (BCCAO) surgery was performed as previously described with minor modifications [The 40 mice were randomly divided into three groups: sham surgery  with a circular escape platform  and an image acquisition system. The pool is divided into four quadrants, with the circular escape platform in the third quadrant. Powdered milk is added to make the water opaque. The mice were released from the four quadrants, respectively, and given 90 s (max) to find the platform. If the mice could not find the platform in 90 s, they were guided onto the platform and allowed to remain for 30 s. Training occurred on four consecutive days. At the end of this training period, the mice were randomly released into the first, second, or fourth quadrant, and their time to reach the platform, or escape latency (EL), was recorded. Testing lasted five days. On day 6, the circular escape platform was removed, and each mouse was placed in the first quadrant. Duration spent in the third quadrant and number of times crossing the platform (TCP) during 1 min were recorded.μm, 1.6 μm C18 beads) was used to desalt the peptide segment.Following Morris water maze testing, all mice were sacrificed, and the extracted brains were immediately placed in liquid nitrogen and stored at −80°C. Three samples from each group were randomly assigned to subsequent analysis. SDT buffer  was added to samples to extract proteins. The supernatant was quantified with the BCA Protein Assay Kit . Proteins were then digested using the filter-aided sample preparation procedure . The C18μm analytical column, 1.6 μm C18 beads with a packed emitter tip . The column was equilibrated using 4 column volumes before loading a sample in 100% buffer A (0.1% formic acid). Samples were separated at 300 nl/min using a linear gradient as follows: 2–22% buffer B (99.9% acetonitrile and 0.1% FA) for 75 min, 22–37% buffer B for 5 min, 37–80% buffer B for 5 min, and hold in 80% buffer B for 5 min. The TIMS TOF Pro was operated in parallel accumulation-serial fragmentation (PASEF) mode. Specifications were as follows: mass range 100–1700 m/z; 1/K0 start 0.75 V·s/cm2; end 1.4 V·s/cm2; ramp time 100 ms; lock duty cycle to 100%; capillary voltage 1500 V; dry gas 3 L/min; dry temp 180°C. PASEF settings were as follows: 10 MS/MS scans ; charge range 0–5, active exclusion for 0.5 min; scheduling target intensity 10,000; intensity threshold 2,500; and CID collision energy 20–59 eV.Samples were analyzed on a nanoElute  coupled to a TIMS TOF Pro  equipped with a CaptiveSpray source. Peptides were separated on a 25 cm × 75 p < 0.05 were considered DEPs. Hierarchical cluster analysis was performed using Matplotlib 3.5.1. GO annotations were performed on the DEPs using Blast2GO. The KEGG database (http://www.kegg.jp/) was used to obtain information on the biological pathways of DEPs. GO and KEGG pathway enrichment analyses were evaluated using Fisher's exact probability test. The DEPs were imported into the String database with the species “MusMusculus”, and results were imported into Cytoscape 3.9.1 for visual analysis.MS data were analyzed using MaxQuant software version 1.6.17.0. A label-free quantitation strategy  was used∗p < 0.05 or ∗∗p < 0.01. Normality was tested by the Shapiro–Wilk test. Homogeneity of variance was tested by the Levene test. A one-way analysis of variance (ANOVA) was used when variance was homogeneous. For samples with unequal variance, the Mann–Whitney test was used.All data were analyzed using IBM SPSS Statistics for Windows, Version 25.0 . Continuous data are expressed as mean ± standard deviation. Significant between-group differences are represented by In addition, the study protocol is shown in Panax ginseng C.A. Meyer had 322 potential targets, Gastrodia elata had 143 potential targets, Atractylodes macrocephala had 84 potential targets, Morindae Officinalis Radix had 88 potential targets, Acorus tatarinowii Schott had 149 potential targets, Rhizoma coptidis had 269 potential targets, and Semen Cuscutae had 139 potential targets , GO and KEGG pathways were analyzed for potential JTD targets. GO enrichment analysis showed that potential targets were involved in 2,766 GO terms: 3,414 in BP, 234 in cellular components (CC), and 395 in molecular functions (MF). The top 20 GO enrichment analyses are shown in No death was observed in the sham group mice, and three mice in each of the JTD and model groups died within three days post-surgery. The cause of death of mice may be accidental death caused by vagus nerve injury caused by excessive traction during operation. Two mice in each of the JTD and model groups died during the 2 weeks. During this period, the cause of death in mice may be due to the stress response, low mood, and inability to eat and drink independently. The rest of the experimental mice survived until the end of the experiment.p < 0.01). When the hidden platform was removed on day 6, compared with the sham group, TCP and time spent in the platform quadrant (TSPQ) of the model and JTD groups decreased significantly (p < 0.01) and increased significantly in the JTD group compared with the model group (p < 0.01).Morris water maze results are shown in Figures p < 0.05) . Compared with the model group, there were 39 upregulated proteins and 26 downregulated proteins in the JTD group (p < 0.05). The Venn diagram shows DEP overlap (MS analysis identified 5,236 protein groups and 50,097 unique peptides. There were 152 DEPs identified according to the stated criteria (fold-change ratio ≥1.2 or ≤0.833, and  Figures . Compare overlap . Cluster overlap . Alpha-1GO enrichment analysis revealed 163 BP terms, 35 CC terms, and 68 MF terms between the model and sham groups; there were 169 BP terms, 20 CC terms, and 53 MF terms between the JTD and model groups .GO analysis of the DEPs of the model and sham groups showed that negative regulation of endopeptidase activity, negative regulation of complement activation, the lectin pathway, positive regulation of the fatty acid biosynthetic process, and negative regulation of ATPase activity were the primary BPs. These DEPs are mainly in the extracellular space, the external plasma membrane, and the hemoglobin complex. They are also associated with receptor binding, ion exchange, and functions .Next, GO enrichment analysis was performed on the DEPs of the JTD and model groups . GO-BP aKEGG pathway enrichment analysis showed that the top five signaling pathways were complement and coagulation cascades, African trypanosomiasis, tuberculosis, legionellosis, and systemic lupus erythematosus in the DEPs of the model and sham groups . SimilarDEPs of the model and sham groups and the JTD and model groups were combined and de-duplicated to obtain 112 common DEPs. Next, Cytoscape 3.9.1 was used to construct a PPI network diagram for these DEPs . MetascaVD is a cluster of cognitive disorder syndromes caused by cerebrovascular lesions. Current risk factors for VD include advanced age, diabetes, hypertension, hyperlipidemia, atherosclerosis, and stroke . In partMitochondrial dysfunction has also been reported to be a significant factor in VD . Under aA main cause of VD-induced cognitive dysfunction is excitotoxicity. Glutamate excitotoxicity has been hypothesized to be excessively activated by excitatory glutamate receptors, causing neuronal dysfunction or death . Grm2 enCerebral ischemia enhances synaptic activity, leading to increased zinc release. However, high intracellular zinc levels may become toxic to neurons and neuroglia , rapidlyHerein, the KEGG pathway enrichment analysis revealed that ApoA4 participates in several signaling pathways, including lipid and atherosclerosis, fat digestion and absorption, and cholesterol metabolism. Carotid artery stenosis and atherosclerosis are risk factors for cognitive impairment . It is wIn vivo mechanisms may be involved in attenuating mitochondrial dysfunction, reducing excitotoxicity, maintaining cellular ion homeostasis and antiatherosclerosis.Integrated network pharmacology and proteomics analysis revealed that Cox7c, Grm2, Slc30a1, and ApoA4 are critical targets of JTD in VD treatment.


In spite of substantial investigation, the biological link between periodontitis and rheumatoid arthritis remains unexplained. This study intended to correlate periodontitis and rheumatoid arthritis gene expression patterns to find shared targets for both the disease. We identified the differentially expressed genes (DEGs) in periodontitis and rheumatoid arthritis. The network was built by integrating DEGs and ranking the genes using GeneMANIA. FINDSITEcomb2.0 was used to find a possible inhibitor for the top-ranked gene. Further, the binding effectiveness and protein-ligand complex stability were then determined by molecular docking and molecular dynamics. The network analysis showed IFI44L as a highly ranking gene implicated in most immunological pathways. A virtual screening of 6507 compounds revealed vemurafenib as the best candidate for the IFI44L target. Molecular docking and molecular dynamics modelling revealed the stability of the IFI44L-vemurafenib complex, which suggest IFI44L is potential target and vemurafenib could be the better candidate to treat both diseases.comb2.0. Subsequently, the molecular docking and molecular dynamics were performed to determine the binding efficiency and protein-ligand complex stability, respectively. Results: From the network analysis, IFN-induced protein 44-like (IFI44L) was identified as a top ranked gene involved in most of the immunological pathway. With further virtual screening of 6507 molecules, vemurafenib was identified to be the best fit against the IFI44L target. The binding energy and stability of IFI44L with vemurafenib were investigated using molecular docking and molecular dynamics simulation. Docking results show binding energy of −7.7 Kcal/mol, and the simulation results show stability till 100 ns. Conclusions: The identified IFI44L may represent a common drug target for periodontitis and rheumatoid arthritis. Vemurafenib could be a potent anti-inflammatory drug for both diseases.Objective: Despite extensive research on periodontitis and rheumatoid arthritis, the underlying molecular connectivity between these condition remains largely unknown. This research aimed to integrate periodontitis and rheumatoid arthritis gene expression profiles to identify interconnecting genes and focus to develop a common lead molecule against these inflammatory conditions. Materials and Methods: Differentially expressed genes (DEGs) of periodontitis and rheumatoid arthritis were identified from the datasets retrieved from the Gene Expression Omnibus database. The network was constructed by merging DEGs, and the interconnecting genes were identified and ranked using GeneMANIA. For the selected top ranked gene, the potential inhibitor was searched using FINDSITE Periodontitis is an inflammatory polymicrobial disease characterized by gradually destroying supporting tooth structures and bone. If untreated, periodontitis may lead to adult tooth loss ,2. The bRheumatoid arthritis (RA) is an autoimmune condition that leads to bone and cartilage degeneration and breakdown. It is a continuous, debilitating, and progressive disease. Common defects, inflammatory joint inflammation, bone resorption, and cartilage destruction are the primary pathophysiological features of RA. The onset and progression of RA may be influenced by genetic factors, inflammation, and immunological dysfunction . The etiSeveral studies demonstrate remarkable pathological and clinical similarities between RA and periodontitis . At the This study aims to add to the knowledge base regarding molecular connectivity between periodontal disease and rheumatoid arthritis. Additionally, our work presents a common drug target between them by analyzing its DEGs network, which enables us to predict a potential inhibitor based on virtual screening and subsequent molecular modeling methods.Gene expression datasets for periodontitis (GSE7451) and rheumatoid arthritis (GSE133422) was selected from the GEO database. GEO2R was used to determine two hundred and fifty differentially expressed genes (DEGs) from a dataset of rheumatoid arthritis and periodontitis by comparing with its respective controls available within the datasets. Merging the DEGs of both the conditions, an interaction network was generated  using thTo investigate the function of the network, GO enrichment analysis was executed using the DAVID online tool. The enrichment was categorized into biological processes (BP), molecular function (MF) and cellular component (CC). The network enrichment based on biological process showed involvement in type I interferon signaling, cellular response to type I interferon, cytokine-mediated signaling, response to cytokine, immune effector process, cellular response to cytokine stimulus . Similarcomb(http://cssb2.biology.gatech.edu/FINDSITE-COMB-II/index_vls.html)(accessed on 12 February 2022). Among the 6507molecules, vemurafenib was found best compound suited against IFI44Lwith the precision score of 0.967031. Further, to test the binding efficiency of the vemurafenib with IFI44L, the molecular docking was performed usingAutoDock Vina. Due to the lack of IFI144L 3D structure in Protein Data Bank, the structure of IFI144L was used to retrieve from the Alpha Fold database. From the 3D structure, water molecules, ions and other hetero-molecules were removed before docking. Following the standard protocol of AutoDock Vina docking, the binding affinity of vemurafenib with IFI44L was found −7.7 Kcal/mol. The interaction between the vemurafenib with IFI44Lprotein was depictedin Virtual screening of ligands against IFI44L protein was performed using the FINDSITEY-axis denotes protein RMSD, while the right Y-axis denotes ligand RMSD. At 18 ns, the complex achieves stability, and thereafter the RMSD values remain with minimal drift. After equilibrium, the ligand match to protein RMSD values varies around 1.0. These results indicate that the ligands stayed securely attached to the receptor’s binding all through the simulation. Additionally, root mean square fluctuation (RMSF) was assessed, which provides the deviation of a particle on the portions of the structure that fluctuate from the original structure. Lower values denote the stability of the protein–ligand binding. MD simulation is potentially used to assess the dynamic behavior of ligand–protein complexes. Porphyromonas gingivalis in both conditions by the presence of peptidylarginine deiminase contributing to RA development [Expression of genes play a critical role in the pathogenesis of disease, which is increasingly used in medicine to characterize the complicated diseases and aid understanding of the disease mechanism, and development of novel biomarkersand drug targets ,25,26. Welopment . In addielopment . The bacOur systematic analysis of network and functional enrichment suggest that most genes in the network were functionally linked to the pathway of innate immunity, the toll-like receptor pathway, and the RIG-like receptor pathway. Among the network genes, IFI44L was ranked highest as the gene that could act as a common target for both of the diseases. IFI44L is a crucial gene connecting rheumatoid arthritis and periodontitis. IFI44L genes play vital roles in inflammatory and immune pathway genes in periodontitis and rheumatoid arthritis. The IFN-induced type 1 gene (IFI44L) promoter DNA methylation level was substantially increased in patients with RA, which is consistent with a previous study in which it was demonstrated thatIFN regulated genes have more robust methylation in patients with RA involvement . SimilarScreening of 6507 known drugs from the DrugBank database showed vemurafenib as a potential ligand against IFI44L based on the deep convolutional neural network method . VemurafFinally, molecular dynamics modeling was used to investigate their stability in the simulation environment. The findings revealed that the potential energy of vemurafenib gradually stabilises over time based on the RMSD and RMSF plots. From the plot, it is clear that vemurafenib interact with IFI44L and their complexes are stable in the simulated environment. According to this finding, vemurafenib might be useful for inflammatory diseases like rheumatoid arthritis and periodontal disease. To our knowledge, this study is novel in that it uses an integrative approach towards periodontitis and rheumatoid arthritis for drug target discovery with no experimental data currently available. However, our extensive insilico approach proves that vemurafenib can act as an anti-inflammatory drug by targeting IFN-induced protein 44, and can be a proposed as a potential drug for diseases through the drug repurposing approach.http://www.ncbi.nlm.nih.gov/geo/) GEO database (accessed on 10 February 2022). Upon extensive searching using key terms related to the study objective, GSE133422 and GSE7451 were selected for our analysis. GSE133422 demonstrate the gene expression of human gingiva epithelial cells exposed to pathogenic biofilms. Similarly, GSE7451 provide the gene expression of patients with primary Sojgren’s syndrome, a condition related to rheumatoid arthritis. Using GEO2R, which is an online R-based tool, the DEGs in each condition were identified by comparing against the control available within the datasets. The DEGs were extracted with the cut-off criterion of logFC > 2 and adj p-value set at p < 0.05.The gene expression datasets were collected from the  module was used to construct the integrated network of periodontitis and rheumatoid arthritis based on DEGs. Additionally, GeneMANIA support ranking of genes in the network to identify the potential genes that link both the conditions, contributing to the common drug target. Afunctional enrichment was carried out for molecules in the merged network using Database for Annotation Visualization and Interactive Discovery . The functional enrichment was classified into biological process and molecular function. Additionally, REACTOME pathway enrichment was executed, whichconveys the involvement of a network molecule in the molecular signaling and metabolic pathways.https://pubchem.ncbi.nlm.nih.gov/) (accessed on 15 February 2022). Similarly, the 3D structure of protein was searched using a relevant protein name or uniprot id (Q53G44) in the PDB database. If there was unavailability of protein structure in PDB, we used a similar search term to collect the predicted structure from the Alphafold database. Using AutoDock Vina , vemurafenib was docked with the IFI44Lto determine the binding efficiency, and their interaction was visualized using the BIOVIA Discovery Studio Visualizer.The FINDSITEcomb tool was used for drug screening against the identified top ranked gene(IFI44L) in the network. FINDSITEcomb receives a target sequence as input and applies the convolution neural network (CNN) algorithm to determine the best molecule from the 6507 molecules available in the DrugBank library. Further, the screened ligand was subjected to molecular docking to determine the binding efficiency with the target (IFI44L). For docking, the structure of vemurafenib (Compound CID: 42611257) was retrieved in SDF format from the PubChem database  setup for the high-speed molecular dynamics simulation for the analysis of our model. In general, MD simulations estimate atom movements over time by incorporating Newton’s classical equation of motion. The ligand-binding position in the physiological context was estimated using simulations ,41. In oIn conclusion, several differentially expressed genes are involved in functions and signaling pathways in rheumatoid arthritis and periodontitis, contributing to their progression and development. Our developed integrated network showed several common linkages between both diseases. Additionally, our network provides IFI44Las a common therapeutic target that can be inhibited by vemurafenib on virtual screening and molecular docking. However, further cell line studies are needed to confirm results. These findings extend our knowledge of these two inflammatory diseases and can pave the way for future studies on drug discovery.