This study explored the curative effect and possible mechanism of Acori graminei rhizoma on Alzheimer's disease. In this paper, 8 active components of Acori graminei rhizoma were collected by consulting literature and using the TCMSP database, and 272 targets were screened using the PubChem and Swiss Target Prediction databases. Introduce it into the software of Cytoscape 3.7.2 and establish the graph of “drug-active ingredient-ingredient target.” A total of 276 AD targets were obtained from OMIM, Gene Cards, and DisGeNET databases. Import the intersection targets of drugs and diseases into STRING database for enrichment analysis, and build PPI network in the Cytoscape 3.7.2 software, whose core targets involve APP, AMPK, NOS3, etc. GO analysis and KEGG analysis showed that there were 195 GO items and 30 AD-related pathways, including Alzheimer's disease pathway, serotonin synapse, estrogen signaling pathway, dopaminergic synapse, and PI3K-Akt signaling pathway. Finally, molecular docking was carried out to verify the binding ability between Acori graminei rhizoma and core genes. Our results predict that Acori graminei rhizoma can treat AD mainly by mediating Alzheimer's signal pathway, thus reducing the production of A The main pathological features were abnormal deposition of A mulation , 2. At pmulation , but themulation . In disemulation . As a mumulation , 7.β-amyloid protein (A β) deposition, anticentral cholinergic nerve function damage, and antineuronal apoptosis [In traditional Chinese medicine, AD is often classified as “dementia” caused by brain marrow deficiency and evil, disturbing brain orifices . The datpoptosis , 12. Howhttp://lsp.nwu.edu.cn/tcmsp.php). In this study, the chemical components of Acori graminei rhizoma were screened based on the pharmacokinetic parameters: absorption, distribution, metabolism, and excretion (ADME). The screening conditions were oral bioavailability (OB) ≥ 30% and drug − like (DL) ≥ 0.18. Additionally, a chemical acting on a specific part of the brain must effectively penetrate the blood-brain barrier (BBB) to reach the brain target. To study the mechanism of action of Acori graminei rhizoma on nervous system diseases, BBB can be used as a screening index, and the threshold value of BBB ≥ −0.30 (it is considered that the molecule has certain permeability) is used as a threshold. Finally, the active components of Acori graminei rhizoma, which are not included, are collected and supplemented by literature.Acori graminei rhizoma's chemical constituents were obtained by consulting the literature and TCMSP database (https://Pubchem.ncbi.nlm.nih.gov/), search and collect the 2D structure of the components, import them into the Swiss Target Prediction database (http://www.swisstargetprediction.ch/), and obtain the component targets. Then, using the target protein name in the Uniprot database (http://www.Uniprot.org), the genes were standardized and sorted out, and finally, possible target genes for the compound's action were downloaded and sorted out.Import the components into the PubChem database (https://www.omim.org/), CTD database (http://CTD.mdibl.org/), and Gene Cards database (https://www.genecards.org/). Using Excel combined data, the target protein was introduced into the Uniprot database for gene standardization, and coincident genes were removed to collect AD disease genes.Search for “Alzheimer's disease” in OMIM database (The molecular formulas of the Acori graminei rhizoma components were expressed by AGR1-AGRn (AGR is the abbreviation of “Acori graminei rhizoma”). The components and corresponding targets were introduced into the Cytoscape3.7.2 software to construct the “drug-active ingredient-target” network and then analyze it. In the network, the active components and protein targets are represented by a node, and an edge connects the nodes. Acori graminei rhizoma's core targets in AD treatment were analyzed according to the betweenness centrality (BC), closeness centrality (CC), and the median of Degree.http://bioinfogp.cnb.csic.es/tools/Venn/index.html), and the Venn diagram was used to show the common target of AGR and AD. The intersection gene was introduced into the STRING database (https://string-db.org/) and searched under the condition of Homo sapiens. 0.40 and 0.70 were used as the moderate correlation threshold and high correlation threshold of Acori graminei rhizoma. The Node1 and Node2 in the results were introduced into the Cytoscape 3.7.2 software. The topological analysis and calculation were carried out using the Network Analyzer tool to construct the “component-target-disease” network. The core target was predicted according to the median of BC, CC, and Degree value.The intersection gene was introduced into Wei Shengxin website (https://david.ncifcrf.gov/). According to “P ≤ 0.01” on the Wei Shengxin website (http://www.bioinformatics.com.cn/), significantly enriched biological annotations were screened out according to biological information. GO analysis chart and KEGG analysis bubble chart were drawn to predict the pathway of Acori graminei rhizoma in AD treatment.Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) analysis can provide gene expression data and systematic visualization information of target genes. In this study, GO analysis and KEGG functional annotation and enrichment analysis were carried out by using DAVID 6.8 database  network between drugs and diseases and then gained the visual analysis through the Cytoscape 3.7.2 software. The size and color of the node are correlated with the Degree value positively. The larger the Degree value, the larger the node and the darker the color, indicating that the target is more important in this network relationship. A PPI network with a threshold of 0.4 is constructed . BC ≥ 0.P value were selected for mapping (P < 0.05), as shown in Y-axis represents the GO entry, and the area size of the X-axis and bar chart represents the number of genes belonging to GO in the target gene set. In the biological process (BP), the typical targets are mainly concentrated in the response to drugs, the positive regulation of cell proliferation, the response to nicotine, the response to hypoxia, the negative regulation of apoptosis, and so on. In the cellular component (CC), the typical targets are mainly concentrated in the plasma membrane, cytoplasm, extracellular body, membrane, etc. In terms of molecular functional (MF), the typical targets are mainly related to protein binding, enzyme binding, same protein binding, protein homodimerization activity, drug binding, and so on.In this study, a total of 195 GO items were enriched, and the first 15 GO items with the lowest P value combined with biological annotations  and core targets  is less than 5 kJ mol−1, indicating that the core compounds have good binding activity with core targets. The docking mode between β-asarone and nine core targets is shown in The study used ten core targets with a high score in protein-protein interaction for molecular docking. Since there was no data on LRRK2, the rest nine core targets were selected to conduct molecular docking with Acori graminei rhizoma. The docking score is shown in α-asarone, β-asarone, and eugenol in Acori graminei rhizoma were screened in this study. The “drug-active ingredient-target” network diagram was constructed by the Cytoscape 3.7.2 software, and it was found that a complex interaction between components and target. The PPI network of “component-target-disease” is constructed based on the STRING database. According to the topology parameters, the factors with high scores are APP, CASP3, MAPK1, MAPT, VEGFA, ACHE, GSK3B, ESR1, LRRK2, DRD2, and so on. A close relationship exists between amyloid precursor protein (APP) and β-amyloid protein (A β) accumulation. APP produces A β through secretase cleavage. Excessive secretion and accumulation of A β in the brain will cause cytotoxicity and injury, cause an inflammatory reaction, and finally lead to nerve cell apoptosis and degenerative lesions [β formation [β aggregation and amyloid fibril formation [β) activity affects tau hyperphosphorylation and participates in AD's formation and development [β is regulated by the classical antiapoptotic signal pathway phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway [β deposition, and activating caspase-3 to induce apoptosis of hippocampal neurons [Studies have shown that among the single drugs of traditional Chinese medicine in treating Alzheimer's disease, Acori graminei rhizoma appears with the highest frequency , 14, so  lesions –17. Caspormation . Interesormation . The D2 ormation . The chaelopment . The act pathway –24. At t neurons .β-asarone has a strong binding activity with APP, and it also has a good performance in the docking diagram. In the process of consulting the relevant literature, we found that β-asarone can indeed affect the related targets of AD, which suggests that β-asarone may be a key component in treating AD. β-asarone may reduce the production of A β and reduce the toxic damage of A β to neurons and synaptic ultrastructure by inhibiting the overexpression of APP or promoting the decomposition and excretion of APP [β-asarone can inhibit the phosphorylation of JNK in hippocampal neurons, upregulate the expression of Bcl-2 protein, and downregulate the expression of caspase-3 at the transcriptional level, thus play a role in antiapoptosis of hippocampal neurons [β-asarone in the volatile oil of Acori graminei rhizoma can restore the phosphorylation level of GSK-3 β and activate Wnt/β-catenin pathway, thus reducing AD caused by tau hyperphosphorylation and A β accumulation [β-asarone has the effect of acetylcholine inhibitor to reduce AChE and can inhibit the production of A β 42 [κB and MAPK pathway and has antiapoptosis and antioxidant activities and may also play a role in treating AD [α-Asarone can protect glial cells by inhibiting the release of inflammatory cytokines [In this study, 10 key targets of Acori graminei rhizoma in treating AD were obtained, but the LRRK2 lacked relevant data so that the rest nine targets were selected for molecular docking with drugs. The molecular docking of 2′-O-methylisoglycine to the core target is very strong, which suggests that it has a research prospect. In molecular docking, we found that n of APP . At the  neurons . Other smulation . Furtherf A β 42 , which cating AD , 31. α-Aytokines , 33.β-asarone combined with L-dopa, it was found that β-asarone may reduce dopaminergic neurons' damage by regulating the HSP70/MEF2D/Beclin1 pathway in rats [To explain the selected key targets' role in gene function and signal pathway, GO functional enrichment analysis and KEGG pathway enrichment analysis were carried out in this study. According to the analysis results, some common targets are enriched in Alzheimer's disease, serotonergic synapses, HIF-1 signal pathways, estrogen signal pathways, dopaminergic synapses, neuroactive ligand-receptor interactions, and so on. Serotonin can maintain nerve excitability. When its expression decreased, it will aggravate brain neuron damage and clinical manifestation of cognitive impairment . Eugenol in rats . It corrβ-Asarone, one of the main components of Acori graminei rhizoma, decreased the concentration of calcium, inhibited apoptosis, and alleviated the dementia damage of vascular endothelial cells induced by β-amyloid protein in the AD cell model [Interestingly, only moderate dopamine signals can optimize the cognitive function of brain . Estrogell model . Studiesα-asarone and β-asarone. The therapeutic effect of Acori graminei rhizoma on AD has been carried out clinically, and the curative effect is remarkable. On this basis, this study preliminarily verified the pharmacological mechanism of Acori graminei rhizoma in the treatment of AD, which laid a foundation for further research from the level of cell biology.To sum up, in this study, the network pharmacology method was used to analyze the complex network relationship between multicomponents as well as multitargets of Acori graminei rhizoma and AD disease targets, which was to explore the molecular mechanism of Acori graminei rhizoma in the treatment of AD. As a result, we found prominent signal molecules such as APP, CASP3, and MAPK1, as well as key signal pathways like Alzheimer's disease and serotonergic synapses. Among them, pathways, for instance, alcoholism and cocaine addiction, are closely related to the pathogenesis, suggesting that these potential pathways are worth studying. At the same time, we predict that the main components of Acori graminei rhizoma in treating of AD are


Telomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact. Telomeres perform protective functions at the ends of linear eukaryotic chromosomes. They constitute one of the basic molecular factors conditioning the ability of cells to divide. Excessive cell divisions lead to incomplete reconstitution of telomeres resulting in telomere shortening and loss of proper structure of telomeric ends . Highly Current experimental assays for TMM phenotyping have several shortcomings. For example, the telomeric-repeat amplification protocol (TRAP) assay for estimating telomerase activity is not very sensitive and is time and resource consuming . Assays Here we were set to develop a complementary approach to TMM detection that utilizes widely available gene expression data in combination with molecular interaction topologies in the TMM pathways. Establishment and analysis of TMM pathway topologies is not a trivial issue, because there is no holistic understanding of the functional context of the molecular factors, of their interactions and of the mechanisms triggering TMM activation. Previous research has identified transcriptional regulators of telomerase complex assembly , howeverIn the first part of the manuscript, we show how gene expression data can be used for TMM phenotyping making use of the TEL and ALT TMM pathways. We have constructed these pathways based on available knowledge about molecular factors and interactions involved in TMMs by further developing our previous work . For demIn order to address the current lack of signaling pathway representations of telomere maintenance mechanisms (TMMs), we have performed a literature search to identify genes involved in TEL and/or ALT TMMs and to define their interaction partners and functional role . OverallThe ALT pathway is represented through a series of branches describing DNA damage and assembly of APB bodies, which then promote separation of one of the telomere strands and invasion to another telomeric template from sister chromatids, other chromosomes or extra-chromosomal telomeric sequences, followed by DNA polymerase delta assisted telomere synthesis and ultimate processing of Holliday junctions, which are formed during strand invasion . The ALTOur pathway model is based on the following general assumptions: (i) TMM pathways are subnets of a global network of cellular processes. We only include genes that have unambiguous effect on either TEL or ALT pathway. (ii) Alternative splicing and/or post-transcriptional regulatory effects mediated, e.g., via transcription factors, non-coding RNA (miRNA or lncRNA) or interactions on protein level, are not explicitly considered in our TMM pathways. We assume that these factors to a certain degree are implicitly taken into account by the measured expression levels of the genes in the TMM pathways. (iii) The TMM pathways are treated as directed graphs to mimic signal transduction from source to sink as described below.PSFC and TMM; see “Materials and Methods” section for details). The algorithm computes the PSF score in each of the pathway nodes by considering signal propagation through all upstream activating and inhibiting interactions and complex and linker node types making use of the fold change (FC) expression values of the involved pathway genes with respect to their mean expression in the respective data set. The PSF score thus reflects the activity of all upstream events and of their topology in contrast to gene set overexpression measures often used alone for functional assessment  algorithm as implemented in Cytoscape  alongside with the gene expression measurements. Using support vector machine (SVM) learning, TEL positive and negative samples were separated by a vertical line, and ALT positive and negative samples by a horizontal line in both data sets  and ALT–/TEL+ (blue) samples one finds their accumulation in the respective top-left and down-right quadrants, respectively, as expected. A certain fraction of theses phenotypes is found “displaced” in the left-down and top-right quadrants assigning them to double negative and double positive TMM cases, respectively. For some samples, we observed discordance in TMM PSF values between technical replicates, which was also noticeable on the level of gene expression  indeed locate in the ALTpression , suggestFor a better understanding of particular reasons of the diversity observed in the ALT/TEL plots we visualized pathway activity patterns for selected samples in +/ALT+ pathway phenotype. The C33 cell line (T2), derived from cervical squamous cell carcinoma, shows clear activation of the major TEL pathway nodes, while the main ALT events, such as telomere recruitment to APB, strand invasion and telomere synthesis, are suppressed, pushing it to the TEL+/ALT– quadrant in agreement with experimental assignment (SUSM1 (A4), a transformed ALT-activated  cell line derived from fetal liver, shows activation of all the nodes involved in TEL and ALT , which psignment . The blaTERC or TERT  of each gene on TMM pathway activities . PI enables understanding of which genes act as triggers to activate or to deactivate selected nodes in the pathways. Genes, increasing or decreasing the PSF of the target node have positive or negative PI’s, respectively. Activating nodes with log fold change (FC) expression above or below zero in a given sample will thus have a positive or negative PI, respectively. Nodes with inhibitory effect, on the other hand, will have the reverse association with PI .NAF1, WRAP53 and PARN . It is important to mention that the used microarray gene expression datasets did not contain expression values for the TERC gene itself, and only the expression of hTR processing factors contributed to the hTR branch in this case.We have examined the PIs in the TEL pathway of the 5,637 (T4) bladder carcinoma cell line , where band PARN  were reslue of 1  was suffA similar branch activation and PI pattern was observed for the liposarcoma tissue T8, where the TMM-PSF diverged from the experimental assay results . In the vice versa.PI analysis also identified that various genes were responsible for activation of the APB branch of the ALT pathway in the A6 and A7 liposarcoma tissues, while down-regulation of RAD51 and CHEK1 led to suppression of the downstream strand invasion events leading to low ALT PSF activity in both samples . Hence, –/TEL– from the single positive ALT and TEL samples on one hand, and also the latters each from another. Between 82% and 85% of the samples were properly annotated compared with the experimental annotations. Agreement with PSF-based annotations is high . Hence, gene clustering and PSF based classifications and experimental annotations are well-aligned . As an alternative option we made use of TelNet database  which co-aligned , and tho1. Together they contained 138 unique genes, either involved in the pathways curated by us or in the TelNet database with ambiguous role related to TMM and not included in the pathways by design .We have also explored the TMM related 12 gene sets from the C5 collection of the Molecular Signatures DatabaseIt is worth mentioning that gene sets are typically applied using enrichment or overexpression signature metrics, which are conceptually different but complementary to the PSF-approach that explicitly considers interaction topologies see . PathwayWe have previously applied our PSF approach to study telomere maintenance states in Lynch syndrome and sporadic colorectal cancer subtypes . Here weN = 17–80 samples per tissue taken from donors evenly distributed across age and sex groups) making use of RNA-seq data taken from the GTEx portal  according to the age of the donors (p = 0.01). On the other hand, we observed a clear bi-modal distribution of samples with high or low TEL pathway activities regardless of age , a previously unrecognized APB body component, is crucial for survival of ALT+ cells, as it protects replaced POT1 from proteasomal degradation. We also found high expression of TSPYL5 in our testis samples showing slight correlation with ALT activity  in 80–85% agreement with independent experimental annotations of TMM in the samples. The absence of TERC expression data due to the lack of microarray probes for this gene may have limited the accuracy of the TEL pathway activity estimation in part of the samples.The TMM pathway topologies proved by quantitative assessment of the TEL and ALT TMMs activity in cancer cell lines and tissues based on two gene expression data taken from a previous study . Our patOur TMM-PSF method stands out with a couple of advantages: (a) it helps to easily annotate samples based on ALT/TEL pathway activity values, and (b) it provides molecular details for dissecting the role of genes and sub-events in the overall activation of the pathway. For example, we could show that for some samples despite their low ALT activity, the APB-pathway branch was highly active, which may explain why these samples were detected as ALT positive in the independent APB assay. This is in agreement with recent studies showing that the existence of APBs does not ensure telomere synthesis and many APBs in the cell may lack ALT activity . In consin vitro. However, it has low sensitivity, is not well suited for single-cell analysis and does not account for downstream processes, such as recruitment of the enzyme to the telomeres and synthesis of the complementary strand , a recently identified APB body component that is crucial for survival of ALT+ cells  nature, meaning that the resulting TMM phenotype can be “dissected” at gene level. In other words, we can explore gene-specific effects on sub-processes or events triggering activation of TMM. The method estimates TEL and ALT pathway activity in the same sample, thus enabling to establish its state in a TEL/ALT continuum with impact for investigating co-activation and switching events between the two pathways, e.g., upon cancer treatment and development. Of importance, owing to its sensitivity, it detects subtle activation of TEL and ALT in healthy human tissues. Despite the actuality of our pathway topologies it is important to note that new studies about additional factors affecting TMMs are permanently appearing with possible consequences for the pathways reconstructed here. Notably, the topologies were reconstructed in a generic manner: as the pathways can be activated via different mechanisms, not all the genes are required for the pathway activation in different situation. Future applications will show what branches and components are important in different situations. Single cell transcriptomics is one important field to essentially improve resolution of our TMM pathway method. Expression based methods thus potentially provide an independent and complementary approach to assess the TMM state. Because of the complex nature of TEL and ALT TMM, single gene expression markers or gene set signatures are potentially insufficient in most cases. Our TMM pathway method paves a new way for omics-based evaluation of TMM with a series of applications ranging from cell experiments to cancer diagnostics.TEL and ALT pathways presented in this publication were carefully constructed based on comprehensive literature search resulting in 31 publications which provided relevant knowledge about TMM. To find and select publications describing factors involved in the telomere maintenance mechanisms, we searched the PubMed database with terms “telomerase” or “alternative lengthening of telomeres”. The first phase was based on “review” articles published before 2020. These reviews provided the order of molecular processes involved in the pathways and thus their basic topology. The TMM genes mentioned in the review articles were chosen and then used in an extended search of the form (gene or protein) AND . We also included genes not yet mentioned in review articles, by repeating the initial search with “telomerase” or “alternative lengthening of telomeres”, concentrating on the original research articles of the last 3 years (2017–2020).The articles were read in chronological order and in case of current consensus about the functional role of the mentioned genes they were included in the respective pathway, otherwise they were ignored. To ensure quality, we confirmed each interaction by inter-researcher agreement between the two authors of this manuscript that have curated the pathways. The present version of ALT and TEL pathways (TMMv2.0) is based on a previous version (TMMv1.0) , and mak–/TEL– (ALT and TEL inactive) or single positive ALT+/TEL–(ALT active) or ALT–/TEL+ (TEL active) using independent experiments. The gene expression matrix files were downloaded from the Gene Expression Omnibus (GEO) repository (accession GSE14533). This dataset contains microarray gene expression profiling data for ten cell lines cultured from different tissues, and seventeen liposarcoma tumor samples along with four human Mesenchymal Stem Cells (hMSC) samples isolated from the bone marrow of healthy individuals. ALT activation was assessed by the presence of ALT-associated promyelocytic leukemia bodies (APBs) (+/TEL–), four were telomerase positive (ALT–/TEL+), and two were ALT/TEL double inactive (ALT–/TEL–). Among the liposarcoma samples, nine were ALT positive and eight were telomerase positive. Most of the samples had two technical replicates. In case of multiple microarray probes mapping to the same gene, the probe with highest standard deviation of values was considered. Cell line and tissue data were processed separately. Gene expression values higher than the 0.9 percentile in each of these sets were limited to that percentile value.For approval of ALT and TEL TMM pathways we made use of gene expression data on cell lines and liposarcoma tissues taken from the study by s (APBs) , while Ts (APBs) . Among tHealthy human tissue RNA-seq gene expression data was obtained from the GTEx portal (release V8) in units of transcripts per million (TPM). Fold changes were computed in comparison to the average of non-zero TPM values per gene. Data of the top 15 most common tissues extracted from subjects suffered violent or fast death from natural causes were selected. They were grouped by age and sex. For cross-tissue analysis we selected a maximum number of 10 subjects per age and sex group). For age-dependent analysis of the testis transcriptome all 129 available testis samples were chosen.2. It compares TMM pathway activation patterns with experimental annotations, uses PSFC for pathway activity computation and it also produces reports for TMM phenotype comparison across samples. The app is written in Java (major version 8), the source code is available at https://github.com/lilit-nersisyan/tmm. The app user guide, along with the example datasets and network files can be accessed at the project homepage http://big.sci.am/software/tmm/.The pathway signal flow (PSF) algorithm , 2017 waFC = 1, and calculating the log ratio of PSF at the target node before and after neutralizing its expression  of a source gene estimates the extent to which its expression affects the activity of a downstream target node in the pathway. The PI-value depends on the expression value of the source gene, pathway topology and the expression of other pathway members. It is computed by neutralizing the fold change of the gene to pression . To comphttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14533); GTeX portal RNA-seq data .Publicly available datasets were analyzed in this study. This data can be found here: GSE14533 (LN, AA, and HB conceived the study. LN and AS performed pathway curation and data analysis. LN designed the software packages and prepared the figures. All authors contributed to data interpretation and manuscript preparation.LN and AA were co-founders of a start-up team Pathverse, which uses the algorithms described herein for further developments. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Ziziphi Spinosae Semen (ZSS) is a common natural medicine used to treat insomnia, and to show clearly its method of action, we managed and did an in-depth discussion. Network pharmacology research is very suitable for the analysis of multiple components, multiple targets, and multiple pathways of Traditional Chinese Medicine (TCM). According to the relevant theory, we first carefully collected and screened the active ingredients in ZSS and received 11 active ingredients that may work. The targets going along with these active components were also strongly related to insomnia targets, 108 common genes were identified, and drug-compound-gene symbol-disease visualization network and protein-protein interaction network were constructed. Forty-eight core genes were identified by PPI analysis and subjected to GO functional analysis with KEGG pathway analysis. The results of GO analysis pointed that there were 998 gene ontology items for the treatment of insomnia, including terms of 892 biological processes, 47 cellular components, and 59 molecular functions. It mainly shows the coupling effect and transport mode of some proteins in the biological pathways of ZSS in the treatment of insomnia and explains the mechanism of action through the connection between the target and the cell biomembrane. KEGG enrichment analyzed 19 signaling pathways, which were collectively classified into seven categories. We have identified the potential pathways of ZSS against insomnia and obtained the regulatory relationship between core genes and pathways and know that the same target can be regulated by multiple components at the same time. The results of molecular docking also prove this conclusion. We sought to provide a new analytical approach to explore TCM treatments for diseases using network pharmacology analysis tools. Insomnia refers to the symptoms of unable to get commonly and regular sleep or difficulty falling asleep, insufficient sleep time, lack of deep sleep, or even sleepless nights. Sleep deprivation is a potential factor for a variety of risk sicknesses. General medicine-based treatments such as cognitive behavioral therapy for insomnia have limitations, and better alternatives have also been searched for . There aZiziphus jujuba Mill.var spinosa (Bunge) Hu ex H. F. Chou; according to TCM theory, ZSS is a gentle temperament drug that can nourish the heart and benefit the liver, tranquilize the mind, converge sweat, and generate fluid [The effective drugs commonly used clinically are benzodiazepines, but they also have obvious side effects, even increasing the death rate of the elderly , 4. As ate fluid . ZSS is te fluid .Chinese herbal medicine has been used to treat diseases in China for thousands of years. Drugs regulate the immune system or nervous system by eliminating pathogenic factors and strengthening vital energy, so that various organs of the human body can operate normally and maintain the balance between the body and nature. Traditional Chinese herbs, on the other hand, are often multicomponent, multitarget, and multicenter of action. This paper also explains the treatment of insomnia by ZSS with the help of tools and analysis methods .http://lsp.nwu.edu.cn/tcmsp.php); this platform relatively comprehensively covers the vast majority of TCM compounds' described information, while including the links between related targets and diseases [To get the compounds in ZSS directly and effectively, we used the most commonly used specialized Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  [The SMILES structural formulas of the compounds were obtained with the ZINC 15 database  and TCMShttps://www.genecards.org/) and the Online Mendelian Inheritance in Man (OMIM) database (http://www.omim.org/). GeneCards database automatically integrates data from about 125 network-derived genes including genome, transcriptomics, and proteomics, with very powerful functions [In order to ensure that the genes we obtain that are associated with the disease are more comprehensive, we used “Insomnia” as a keyword to search on these two reliable database platforms, respectively, from the GeneCards database  based on the interaction between drugs (ZSS), compounds, gene symbols, and diseases (insomnia) by Cytoscape software (version 3.8.0) and made the corresponding schematic.https://string-db.org/). STRING is commonly used to filter and evaluate relevant data for functional genomics, setting the organism species to Homo sapiens (human) before retrieval [Discovering and annotating the interaction relationships of all functional targets in proteins enable system-level learning and understanding of the functions of proteins, and protein-protein interaction data were obtained from the STRING database . Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using ClueGO plug-in in Cytoscape [Gene Ontology (GO) enrichment analyses was performed with R software (version 4.0.2), and the entries enriched were determined by the Bioconductor database (ytoscape .https://www.rcsb.org). We chose the appropriate protein crystal overall principle to be structurally intact and stable and obtained X-ray crystal structures of TNF  and IL-2 (interleukin-2), which have a PDBID of 5 UUI and 1 M4B, respectively. In addition, to further confirm the reliability of the docking results, we also docked these two proteins with their positive inhibitors curcumin and upadacitinib. After processing targets and compounds using PyMOL software (version 1.3), docking work was performed using AutoDock Vina software (version 1.1.2), and the presentation files were processed by AutoDockTools.As a feasible and modern verification method, molecular docking is a method for drug design through the characteristics of receptors and the interaction mode between receptors and drug molecules and is widely used in the field of drug binding to protein receptors . We seleWe collated the collected data and compared the obtained component names, CAS numbers, and structures one by one to remove some inaccurate information. Nine active ingredients that met the above conditions were obtained by screening according to the previous parameters of OB ≥ 30% and DL ≥ 0.18. In addition, two components, jujuboside A and spinosin, were included in the provisions of Chinese Pharmacopoeia 2020. Studies have shown that saponins and flavonoids are the two most important active components in ZSS , 24, theAfter collecting related proteins from the STP database and simplifying (probability>0), we obtained 11 components in the ZSS and 331 known target symbols associated with them  of 48 core target genes was classified and enriched according to three modules: biological process (BP), cellular component (CC), and molecular function (MF), with 998 GO terms enriched, of which the BP term accounted for a relatively high proportion, with 892 GO terms, mainly showing the coupling role and transport mode of some proteins in biological pathways, such as G protein-coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger (GO:0007187), adenylate cyclase-modulating G protein-coupled receptor signaling pathway (GO:0007188), monoamine transport (GO:0015844), and organic hydroxy compound transport (GO:0015850). CC is rich in 47 GO terms and analyzes that these core genes are closely related to those cell biofilms, mainly involving a variety of synaptic membranes, such as integral component of synaptic membrane (GO:0099699), intrinsic component of synaptic membrane (GO:0099240), and integral component of presynaptic membrane (GO:0099056). Similarly, we enriched 59 GO terms for MF, and in MF analysis, we could understand which receptor activities the core genes would affect and the forms of partial protein and target binding, mainly G protein-coupled amine receptor activity (GO:0008227), neurotransmitter receptor activity (GO:0030594), and catecholamine binding (GO:1901338). To present the GO enrichment results more directly, we based on p. Adjust intercepts for the top 10 terms from small to large for an abbreviated presentation , as shown in KEGG pathway enrichment analysis can help us further figure out the potential pathways of ZSS against insomnia; after all core genes are entered and after applying Based on the above prediction and analysis, we selected three classical components, swertisin, jujuboside A, and spinosin, in ZSS for molecular docking with two important proteins, TNF and IL-2, to validate our analysis results. According to the docking results, we know that the binding energy of swertisin to TNF is −6.4 kcal/mol, the binding energy of jujuboside A to IL-2 is −7.8 kcal/mol, and the binding energy of spinosin to TNF and IL-2 is −6.9 and −5.9 kcal/mol, respectively. The lower binding energy between molecules represents that the stronger force binding energy between them  is carefully thought about being conducive to the binding reaction, which shows that our analysis is accurate and reliable, and the compounds can better act on the related proteins. The binding energy of IL-2 and its inhibitor curcumin is −6.5 kcal/mol, and the binding energy of TNF and the inhibitor upadacitinib is −6.4 kcal/mol. The compound and the inhibitor are docked into the same pocket of the protein, which means our results are more reliable. in vivo [With the acceleration of people's modern life rhythm, the number of insomnia population showed an increasing trend. Despite developed medical conditions and increasingly diverse means of treating psychiatric disorders, the problem of insomnia still cannot be effectively treated , 38. Monin vivo , which aγ-amino butyric acid receptor plays an important role in controlling different vigilance states in the human body [GO functional analysis makes a simple annotation of gene products, BP enrichment results in the coupling effect and transport mode of proteins in biological pathways, for example, G protein-coupled receptors can mediate the normal function of the brain, of which H3 and H4 receptors are high-affinity receptors in the brain and immune system, respectively, and H3 receptors can regulate histamine and neurotransmitters, which control sleep quality . The resman body . KEGG paman body . Besidesman body . Renin sman body . In addiman body . ClassifComplex diseases often have many symptoms, and sometimes, the use of monomeric components is less effective to treat complex diseases and has significant side effects. TCM has been used to treat diseases for thousands of years, with significant results. Meanwhile, the special philosophical ideas of Chinese medicine and the very complex composition of TCM limit its further development, and the rise of network pharmacology brings another idea for the study of TCM. By constructing a drug-target-disease network, higher levels of information can be obtained by calculating the mechanism of simultaneous action of multiple components, multiple targets, and multiple pathways, and then using the construction of visual network charts can enable researchers to quickly and clearly know the mechanism of complex components in the treatment of diseases, which is a very practical new research tool. All of our work is dedicated to establishing a new analytical method with the help of network pharmacology, and besides exploring ZSS for the treatment of insomnia, we also hope to apply it in more traditional Chinese medicine research.


Our goal is to bring additional attention to these still not well researched but important pathways in the regulation of bone biology in the hope of prompting additional research in the area of non-canonical Wnt signalling pathways.The Wnt signalling pathway is one of the central signalling pathways in bone development, homeostasis and regulation of bone mineral density. It consists of numerous Wnt ligands, receptors and co-receptors, which ensure tight spatiotemporal regulation of Wnt signalling pathway activity and thus tight regulation of bone tissue homeostasis. This enables maintenance of optimal mineral density, tissue healing and adaptation to changes in bone loading. While the role of the canonical/β-catenin Wnt signalling pathway in bone homeostasis is relatively well researched, Wnt ligands can also activate several non-canonical, β-catenin independent signalling pathways with important effects on bone tissue. In this review, we will provide a thorough overview of the current knowledge on different non-canonical Wnt signalling pathways involved in bone biology, focusing especially on the pathways that affect bone cell differentiation, maturation and function, processes involved in bone tissue structure regulation. We will describe the role of the two most known non-canonical pathways (Wnt/planar cell polarity pathways and Wnt/Ca The wingless-type mouse mammary tumour virus (MMTV) integration site family (Wnt) signalling pathway is a central and evolutionary conserved signalling pathway involved in several developmental and physiological processes in the organisms. It plays an essential role in embryonal development, the establishment of polarity axes and cell fate determinations, tissue and organ development, as well as tissue regeneration and repair in post-embryonal and adult life. On a cellular level, the pathway regulates different stages of cell differentiation and/or stem cell renewal, cell proliferation, cell adhesion, motility and apoptosis ,2. Due tThe involvement of the Wnt signalling pathway in bone biology and disease was first discovered through a mutation in the LRP5 co-receptor, which was associated with osteoporosis pseudoglioma syndrome (OPPG), an autosomal recessive disorder resulting in low bone mass, frequent fractures and bone deformations . Since t2+ pathway. In bone cells, the activity of the canonical Wnt signalling pathway generally leads to bone tissue formation and maintenance of bone mineral density in adults [Wnt signalling pathways are in general divided into the canonical, β-catenin dependent Wnt signalling pathway, and non-canonical, β-catenin-independent signalling pathways, which are historically divided into Wnt/planar cell polarity (PCP) pathway and Wnt/Can adults ,14. Canon adults ,16,17 whn adults . On the n adults . Moreove2+ pathways, as well as other signalling pathways that communicate with Wnt signalling pathway through non-canonical Wnt signalling. We will focus on pathways that are downstream of Wnt activation and signalling, such as RANK/RANKL NF-κB pathway with a crucial role in osteoclast differentiation, mTOR pathways important for adjustments of cell metabolism during cell differentiation, as well as the Hippo and MAPK signalling pathways, which seem to be required for a complete response to Wnt activation.In this review, we will explore the current knowledge on different non-canonical Wnt signalling pathways involved in bone biology, focusing especially on the pathways that affect bone cell differentiation, maturation and function, processes involved in bone tissue structure regulation. We will describe the signal transduction pathways and roles of Wnt/PCP and Wnt/CaThe canonical Wnt/β-catenin signalling pathway is the most thoroughly researched Wnt signalling pathway, especially in relation to bone physiology. It is initiated by binding of a Wnt ligand to the frizzled (Fzd) receptor and two LDL receptor-related protein (LRP) co-receptors (LRP5/6), forming a tripartite complex . This re2+ pathway, resulting in the release of intracellular calcium and activation of calcium-dependent processes, and the Wnt/PCP pathway, which leads to the establishment of cell polarity, changes in cytoskeleton arrangement, cell migration and attachments, although activation of several other transcription factors have also been observed [In comparison, the non-canonical Wnt signalling pathway is a generic term that refers to signalling pathways triggered by Wnt ligands but not mediated by β-catenin. They are activated by specific Wnt ligands. However, due to the complexity, significant overlap and utilisation of the same core proteins and multitude of different outcomes, it is still not completely clear how many and which pathways there are. Two historically defined signalling pathways are Wnt/Caobserved , which wAlthough the intracellular transduction pathways are markedly different, non-canonical and canonical Wnt signalling pathways share several extracellular and membrane components, especially Wnt ligands, certain receptors and Wnt endogenous inhibitors. In humans, 19 different Wnt ligands exist, and while certain Wnts activate only the canonical pathway, Wnt3a, Wnt4, Wnt5a, Wnt5b, Wnt7b and Wnt16 activate both the canonical and non-canonical pathways. Which pathway will be activated is most probably dependent on the quantity of Wnt ligands, expressed receptors, co-receptors and endogenous inhibitors ,28,29,30Similar to the canonical Wnt signalling pathway, non-canonical signalling also requires an Fzd receptor. The frizzled protein family consists of 10 Fzd receptors, hepta-span transmembrane receptors, which can bind multiple Wnt ligands ,38,39. NThe last major component shared by canonical and non-canonical Wnt signalling pathways are endogenous Wnt inhibitors. While certain inhibitors, such as sclerostin (SOST) ,46 and tThe large number of available ligands and receptors enable tight spatial and temporal regulation through ligand/receptor/co-receptor specificity, regulation of their transcription, as well as through secretion of endogenous Wnt inhibitors—the pathway will thus be activated only when all required molecules are present . However2+ signalling pathway is a group of Wnt ligand-induced signalling pathways that result in an increase in intracellular Ca2+ concentration and the subsequent Ca2+-dependent cell signalling [2+ concentration might promote canonical Wnt signalling by facilitating the transport of larger molecules (>10 kDa) across the nuclear envelope, thus increasing also the translocation of β-catenin [The Wnt/Cagnalling . It regugnalling  and diffgnalling ,90. Calcgnalling , and a s-catenin .2+ signalling. They bind to ROR2 co-receptors and one of the Fzd receptors. For activation of calcium signalling, Wnt5a requires receptors Fzd2, 3, 4, 5 or 6 [2) into inositol-1,4,5-trisphosphate (IP3) and 1,2 diacylglycerol (DAG). PI3 diffuses into the cytosol and binds to the IP3 receptors (IP3Rs), which function as calcium channels on the endoplasmatic reticulum, triggering the release of Ca2+ from intracellular stores [2+ together with calmodulin, a phospho-serine/threonine-specific protein phosphatase, activates calcium-sensitive enzymes, such as calmodulin-dependent protein kinase II (CaMKII) and calcineurin [2+, activates protein kinase C (PKC) [Wnt5a and Wnt3a are considered prototypical activators of Wnt/Ca, 5 or 6 ,92,93, o, 5 or 6 ,94,95 an, 5 or 6 ,61,96 ha, 5 or 6 ,98,99. Tr stores . Increas C (PKC) ,99. Thes2+ signalling pathway is also required for osteoblast and osteoclast cell differentiation and function. In osteoblasts, Wnt3a and Wnt7b activate a Gαq/11/PLCβ/PKCδ pathway, through which they promote osteoblast differentiation and mineralisation in a β-catenin independent way. Inhibition of this pathway through KO of PKCδ or Wnt7b results in lower embryonal ossification, delayed chondrocyte maturation in long bones and reduced osteoblast differentiation [2+ signalling also controls chondrogenesis. Bradley and Drissi have shown that Wnt5a promotes or inhibits chondrogenesis in a differentiation stage-specific manner. In the early stages, Wnt5a increases CaMK/NFAT and inhibits NF-κB-dependent signalling to promote chondrogenesis through an increase in SRY-Box transcription factor 9 (Sox9) expression. On the other hand, in later stages of differentiation, Wnt5a activates the PI3K/Akt/NF-κB signalling pathway and represses chondrocyte hypertrophy via NF-κB-dependent inhibition of RUNX family transcription factor 2 (Runx2) expression [The Wnt/Cantiation . Wnt/Ca2pression . The twoBoth canonical and non-canonical Wnt signalling pathways are also involved in Wnt suppression of RANKL induced osteoclast differentiation in osteoclast precursor cells. The non-canonical signalling is involved in the suppression of RANKL-mediated activation of the nuclear factor of activated T cells 1 (NFATc1) transcription factor. Wnt3a treatment led to a rapid increase in cyclic adenosine monophosphate (cAMP) accumulation, which induced phosphorylation and thus activation of protein kinase A (PKA), increasing phosphorylation (inactivation) of NFATc1 .Interestingly, while there is ample evidence for tight cross-communication between osteoblast, osteocytes and osteoclast through both canonical and non-canonical Wnt signalling pathways, so far, no study has addressed the role of these pathways in communication with osteal macrophages , resident macrophages in bone tissue. Even though osteal macrophages were shown to provide significant anabolic support during bone remodelling and fracture healing and are emerging as important players in bone homeostasis ,102, theThe PCP signalling pathway is a mechanism that provides global and local directional information that is required for cell and tissue polarisation, which is especially important during gastrulation and embryonal tissue formation. As such, it regulates cytoskeletal organisation, cell attachment and downstream cell signalling . In vertThrough mutual inhibition, two complexes—Celsr-Vangl2-Prickle and Fzd-Celsr-DVL-Ankrd6—are established on the opposite sites of the cell ,104,105.In bone tissue, the PCP signalling pathway and tissue polarisation have been associated with embryonal bone and joint formation, which involves cell migration, elongation and gradient-dependent differentiation . For exaThe establishment of cell and tissue polarity is thus mainly involved in tissue organisation, growth and elongation, but not regulation of BMD. Instead, most effects of PCP signalling on osteoblast differentiation and function are attributed to changes in gene expression mediated by PCP pathway activation. It is not yet clear exactly how non-canonical Wnt ligands activate PCP-related signalling; however, several studies confirmed that Fzd receptor and ROR2 co-receptor are both required for Wnt/PCP signalling pathway activation, as is phosphorylation of Vangl ,106,109.Apart from the cytoskeletal organisation, the Wnt/PCP signalling pathway can also activate the NH2-terminal kinase (JNK) and transcription factor c-jun, affecting downstream target genes ,104,105.The Wnt signalling pathway is part of an extensive and highly complex network of signalling pathways inside each cell. As such, the Wnt pathway can be stimulated or inhibited by other signalling pathways and vice versa; Wnt signalling activity can transactivate other signalling pathways . In boneThe RANK/RANKL signalling pathway is one of the main pathways regulating bone resorption. The receptor RANK is widely expressed in several cell types, including osteoclast precursor cells. It binds the RANK ligand (RANKL), which is in bone tissue secreted by mature osteoblasts, together with osteoprotegerin (OPG), a secreted non-signalling decoy receptor for RANKL that helps regulate osteoclast differentiation and function . RANK/RAThe communication between RANK/RANKL and the Wnt signalling pathway can also occur on the level of signalling transduction pathways. For example, Wnt4 inhibits osteoclast differentiation of primary bone marrow macrophages and RAW264.7 cells induced by RANKL by repressing NF-κB-dependent genes. This is achieved by Wnt4 promoting the formation of Tak1-Tab2-NLK complex, a MAPK-related pathway, that was also shown to inhibit the canonical Wnt signalling pathway by inhibiting the activity of TCF. In this pathway, transforming growth factor beta-activated kinase 1 (Tak1) kinase activates the Serine/threonine-protein Nemo like kinase (NLK), which, in turn, phosphorylates LEF1/TCF family proteins and prevents their binding to DNA ,141. HowThe Wnt signalling pathway has also been shown to interact with mTORC1 (mechanistic target of rapamycin complex 1) and mTORC2, the two complexes formed by mTOR. The two complexes have distinctive functions; while mTORC1 is involved in the regulation of protein synthesis, ribosomal biogenesis and nutrient transport, mTORC2 controls glucose and lipid metabolism, survival and cytoskeletal organisation ,143. mTOEsen and co-workers showed that Wnt3a/mTORC2 signalling could induce the Warburg effect in differentiating cells, a switch to glycolytic metabolism despite the presence of oxygen . They deThe mTORC1 and Wnt signalling pathways in osteoblasts also communicate in the opposite direction. mTORC1 activity has been shown to inhibit the Wnt signalling pathway activity by decreasing cell surface levels of Fzd receptors. It influences the association of DVL with clathrin AP-2 adaptor , which iThe role of the Hippo signalling pathway in bone biology has been recognised only in recent years. The pathway is involved in the regulation of cell proliferation and differentiation, especially in response to changes in cell adhesion and mechanical stress ,165, butThe Wnt and Hippo signalling pathways are tightly connected and regulate each other’s activities. YAP/TAZ, the main effectors of the Hippo signalling pathway, regulates β-catenin levels and activity through physically interacting with β-catenin or DVL by blocking the activation of DVL and/or the cytoplasmic sequestration of β-catenin ,176. Oth12/13 G protein, which lead to downstream activation of RhoA and Rac kinases and subsequent inhibition of YAP/TAZ inhibitors Lats1/2 [Most of these interactions are mediated through the canonical Wnt signalling pathway; however, one study indicated that non-canonical Wnt ligands Wnt5a, Wnt5b, Wnt4 and Wnt3a are also strong activators of YAP/TAZ activity. The YAP/TAZ activation was independent of LRP5/6 co-receptors and β-catenin but required Fzd2 or Fzd5 receptor, ROR1 co-receptor and Gα Lats1/2  (Scheme  Lats1/2 . The twoThe mitogen-activated protein kinase (MAPK) pathway is a cascade involving small GTPases Ras and a series of sequentially activated protein kinases (MAPKKK → MAPKK → MAPK), which lead to the activation of MAPK level kinases: extracellular signal-regulated kinases 1/2 (ERK1/2), JNK and p38 MAPK. The pathway is involved in responses to extracellular signals and affects skeletal development and bone homeostasis through regulation of osteoblast commitment and differentiation . Both ERMAPK and Wnt signalling pathways mainly communicate through the β-catenin destruction complex of the canonical Wnt signalling pathway d. InhibiNevertheless, several studies indicate MAPK kinase activity can also be influenced through the non-canonical Wnt signalling pathway, which is especially important for the regulation of MSC function. For example, Wnt3a has been shown to induce both canonical and non-canonical Wnt signalling pathways ,32, and Another Wnt-MAPK crosstalk was reported by Chang and colleagues, who showed that non-canonical Wnt4 signalling promoted osteoblast differentiation from MSCs, increased mineralisation in vitro and stimulated bone formation in vivo. Observed p38 MAPK activation was dependent on axin but did not affect JNK activity . SimilarMost clinical research has been focused on canonical Wnt signalling in association with skeletal dysplasia’s, such as Osteogenesis Imperfecta type XV (mutations in Wnt1), Sclerosteosis and Van Buchem disease (mutations in SOST), osteoporosis pseudoglioma syndrome, juvenile osteoporosis (mutations in LRP5) and many more . Up to nOut of endogenous Wnt inhibitors, only mutations in sFRPs, a family of Wnt ligand protein inhibitors, affect both canonical and non-canonical Wnt signalling. Loss of function mutations in sFRP4 were associated with Pyle disease (OMIM 265900), a rare recessive disorder resulting in metaphyseal widening of long bones, cortex thinning, increased trabecular bone, decreased BMD and a consequently increased bone fragility ,201. CorMuch work thus remains to be done in clinical and basic research to better understand all connections between different canonical and non-canonical Wnt signalling pathways and better define Wnt ligand/receptor specificity and expression patterns, as non-canonical Wnt signalling pathways are emerging as an important support of canonical Wnt signalling and represent a potential target for the treatment of skeletal diseases. Unfortunately, Wnt signalling pathways are not crucial only for bone development, and any increase or decrease in signalling activity could result in adverse effects on other organ systems or cancer. The possible novel Wnt-based therapies would thus have to adjust the delivery system in order to limit the effects of the therapy to the target tissue, or target bone-specific protein targets, as was achieved with anti-SOST therapeutics. Despite that, even FDA-approved SOST inhibitor EVENITY comes with a warning for potential risk of myocardial infarction, stroke or cardiovascular death.The Wnt signalling pathway has been shown to have an enormous impact on bone development and remodeling. Variations in several Wnt-related genomic regions have been associated with changes in BMD or different bone disorders, and changes in signalling activity can easily tip the balance between bone formation and bone resorption into a pathological state. Despite the fact that most studies primarily attribute this to the canonical Wnt signalling pathway, non-canonical Wnt signalling has been time and again shown to also have an important role in the regulation of this balance, either through its direct effects, through interactions with the canonical Wnt signalling pathway, or through crosstalk with other signalling pathways crucial for appropriate bone development. After all, Wnt signalling is just a part of a complex network of signal transduction events that occur during processes such as differentiation. Dysregulation of any of the connected pathways might thus propagate a change to the whole signalling network and induce effects that cannot be explained by the observed pathway only.Thus, when researching the many roles of Wnt signalling pathways in bone biology, it is important to consider that Wnt ligands can not only activate canonical and non-canonical signalling at the same time, as was demonstrated for Wnt3a, but could also activate more than one non-canonical Wnt signalling pathway. Future studies exploring the effects of Wnt signalling pathways should thus consider analysing also the possible activation of other  Wnt signalling pathways or even strongly interconnected pathways, such as Hippo or MAPK signalling pathways. This will result in a better understanding of the importance and impact of each pathway’s crosstalk, shine more light on the roles of GWAS hits associated with BMD and possibly provide the groundwork for novel anabolic bone therapies targeting more than one signalling pathway.


Animal venoms offer a valuable source of potent new drug leads, but their mechanisms of action are largely unknown. We therefore developed a novel network pharmacology approach based on multi-omics functional data integration to predict how stingray venom disrupts the physiological systems of target animals. We integrated 10 million transcripts from five stingray venom transcriptomes and 848,640 records from three high-content venom bioactivity datasets into a large functional data network. The network featured 216 signaling pathways, 29 of which were shared and targeted by 70 transcripts and 70 bioactivity hits. The network revealed clusters for single envenomation outcomes, such as pain, cardiotoxicity and hemorrhage. We carried out a detailed analysis of the pain cluster representing a primary envenomation symptom, revealing bibrotoxin and cholecystotoxin-like transcripts encoding pain-inducing candidate proteins in stingray venom. The cluster also suggested that such pain-inducing toxins primarily activate the inositol-3-phosphate receptor cascade, inducing intracellular calcium release. We also found strong evidence for synergistic activity among these candidates, with nerve growth factors cooperating with the most abundant translationally-controlled tumor proteins to activate pain signaling pathways. Our network pharmacology approach, here applied to stingray venom, can be used as a template for drug discovery in neglected venomous species. More than 200,000 animal species produce venom, mostly for defense and/or predation ,2,3,4. VAnimal toxins interact with a vast array of targets , but MoANetwork pharmacology is a drug discovery tool that integrates multiple omics methods on a functional level to achieve a holistic interpretation of biological responses to specific molecules . Here weDasyatis pastinaca, Himantura leoparda, and Pteroplatytrygon violacea) and Potamotrygonidae . An average of 30.5 million raw reads were assembled into an average of 2.1 million transcripts per species transcriptome. The bioinformatics workflow is shown in RNA was isolated from five stingray species representing the two most important stingray habitats and the two largest families: Dasyatidae  families, and the widely distributed venom kunitz-type, venom metalloproteinase (M12B), and peptidase S1 toxin families , representing only ~6% of the annotated hits against ToxProt when fragments were ignored  and freshwater (Potamotrygonidae) species B. DifferD. pastinaca, P. violacea, and P. leopoldi) was tested in a high-content screening (HCS) assay based on HeLa cells. The species were selected based on their comparability in the transcriptome dataset and the availability of sufficient fresh venom samples for bioactivity screenings. The raw data were reduced in a stepwise manner to 31 highly informative cytological parameters  mainly target the nervous and cardiovascular systems, but also the immune system .The cytological profiles of all three stingray venom extracts revealed dose-dependent effects against the NF-κB pathway and the cytoskeleton, as well as the nucleus, mitochondria, endoplasmic reticulum (ER) and lysosomes C,D. ThesP. violacea venom triggered the translocation of p53 into the nucleus, membrane disruption (resulting in the cytoplasmic redistribution of the membrane marker), ~40% cell death, cell cycle arrest during the G0/G1 phase, and a strong increase in the number of cells with a low DNA content, indicating damaged (necrotic) or apoptotic cells. Interestingly, 11 of 12 reference compounds clustering with the (marine) D. pastinaca venom profile overlapped with those clustering with (marine) P. violacea venom but none of them overlapped with (freshwater) P. leopoldi venom.The comparison of marine and freshwater species revealed that marine but not freshwater stingray venom induces effects against the ER and lysosomes. In addition, Nervous system, Circulatory system, Cancer, Signal Transduction, and the Immune system. We focused on this shared dataset, which included 70 transcripts representing 16 toxin families and 70 reference drugs clustered by HCS mostly neuropsychiatric and cardiovascular agents. The core dataset included the interlaced toxin-like transcripts, cellular bioactivities, and functional data enabling the prediction of stingray toxins MoAs against individual target systems and pathways in the context of specific envenomation outcomes.The transcriptome and bioactivity datasets each offer a limited perspective on the MoA of stingray toxins. However, the integration of both data topologies at the same functional level allows a more holistic interpretation. Therefore, stingray toxin-like transcripts and reference compounds with similar MoAs to our stingray venom extracts were screened against the KEGG database, linking the transcripts and clustered drugs to molecules with known targets, target systems, pathways and associated diseases. An average of 32% of all stingray transcripts and 58% of clustered reference compounds were annotated in this manner. The datasets were integrated at a pathway level because this was the most informative and abundantly inferred functional category. The pathways inferred by transcript and reference compound annotation were compared, resolving the integrated dataset to a total of 216 pathways among which 29 were shared and represented 15 target systems, including the Signal transduction, Nervous system and Sensory system categories and were targeted by 20 transcripts and 41 HCS-clustered reference drugs  calcium signaling is a key MoA for pain-inducing stingray toxins. Specifically, the transcriptome hits bibrotoxin and cholecystoxin, and 16 of the HCS hits  were found to target distinct GPCRs , subsequently activating a Gα protein (Gαq). This in turn causes phospholipase C (PLC) to hydrolyze a membrane substrate into inositol-1,4,5-triphosphate (IP3) and diacylglycerol (DAG). IP3 acts as a second messenger, binding to its receptor in the ER or sarcoplasmic reticulum (SR) and triggering intracellular calcium release. DAG activates protein kinase C (PKC), which phosphorylates targets such as TRPV1, a non-selective membrane cation channel, contributing to further intracellular calcium accumulation.The activation of calcium signaling was supported by the remaining pain cluster hits, albeit acting via different signaling pathways. Three hits in the transcriptome dataset represented venom nerve growth factor 1 (vNGF), which binds a receptor tyrosine kinase (RTK) and directly activates either PLC or PKC . NGFs caOur novel network pharmacology approach has a remarkable multiplexing capacity. Depending on the focus, multiple clusters can be built from our integrated shared core functional network, including cardiovascular and hemostasis clusters . BrieflyAnimal venoms are a rich source of potent and diverse new drug candidates that could provide large insights into novel MoAs and synergistic activities.Potamotrygon  ,30 Potameviously  and the P. motoro transcriptome study [Potamotrygon [Potamotrygon transcriptome data [Our transcriptome analysis revealed several intriguing results. First, the augerpeptide was present in all of our transcriptome datasets and was often the most abundant transcript, supporting its detection in the first me study . Howevermotrygon ,30, but motrygon . Howeverome data ,30, suppome data , the augPastinachus (Dasyatis) sephen alters mitochondrial membrane potential by enhancing the production of reactive oxygen species [P. violacea venom, as also reported for cnidarian toxins [P. violacea crude venom in our bioactivity assay. Contradictorily, it appears that the Potamotrygon crude venom did not induce cell death on our bioassay, although necrosis in freshwater envenomations is typically remarkably severe [Our bioactivity assay revealed compartmental signals that provide insight into the cellular responses to stingray venom. Although the precise MoAs remain unknown, the translocation of Nf-κB, mitochondrial impairment, and the disruption of the ER and lysosomes indicate oxidative stress at the cellular level . The mit species . In the  species ,46,47,48n toxins . The losy severe ,34. Givey severe .Each dataset provided only limited insight into the MoAs and synergistic activity of stingray venom components, so to achieve a more holistic interpretation we integrated the gene expression and bioactivity data in a novel network pharmacology approach. Several fields, including natural product research, drug discovery, and pharmacology, typically involve large-scale screens based on target binding or MoAs , but morThe primary effect induced by stingray venom is pain. The induction or modulation of peripheral pain by animal toxins is often linked to ligand-gated or voltage-gated ion channels, such as TRPV1 and Nav1.7, respectively . HoweverP. leopoldi.The perception of severe pain may involve the combined activation of several pathways . In our We also found strong evidence for the polypharmacology of stingray venom components. The candidate pain-inducing toxins in our pain cluster were bibrotoxin and cholecystoxin, which are associated with neuropathic pain and inflammation but also cardiovascular disorders ,63. AccoTaken together, our results agree with previous network pharmacology data showing that multi-omics data integration can provide robust predictions of MoAs and synergies in animal venoms that have not been characterized in detail. Our transcriptome and bioactivity data indicate that stingray venom includes enzymes, other proteins and a small number of peptides that can induce pain, disrupt the tissue matrix and hemostasis, and induce pro-inflammatory and cardiotoxic activity. Our integrative approach relies on the quality and abundance of functional data as well as additional manual curation. However, the integrated data network and resulting clusters comprehensively unraveled the mechanisms underlying major stingray envenomation symptoms and their timeline. First, pain is mainly induced via GPCR-IP3 signaling and potentiated by GPCR-PKA, NGF-ERK/IP3, and voltage-gated ion channel activation. Second, inflammation triggers further pain based on the massive release of intracellular histamine, for example by strongly expressed TCTP toxins. Finally, tissue disruption leads to the typical necrotic profile probably reflecting the combination of strongly expressed hyaluronidases and massive inflammation. Our network pharmacology approach was therefore able to identify several routes via which stingray venom synergistically induces system perturbations, but we also found initial evidence for the polypharmacological nature of stingray toxins that trigger multiple systems such as nociception, cardiovascular and immune.P. leopoldi and P. motoro; Potamotrygonidae) and three marine stingrays  following Directive 2010/63/EU on the protection of animals used for scientific purposes. Total RNA was extracted for mRNA isolation, cDNA library construction and sequencing (50 million paired-end reads) carried out by Vertis Biotechnologie  and Macrogen  using the Ilumina HiSeq2000 platform. For high-content bioactivity screening, crude venom was obtained from 30–104 mg of spine tissue from mature live specimens of P. leopoldi, D. pastinaca and P. violacea using a standardized methanol-based bioactive compound extraction protocol (Venomous tissue (45–212 mg) was scraped from the tail spine of mature live specimens of two freshwater stingrays  in a HeLa cell bioactivity assay. Briefly, serially-diluted venom extracts were tested in four replicates. After 24 h, four fluorescence-based cell-staining protocols  were useFor each reference compound and crude venom sample, the induced cell responses were aggregated into high-resolution cytological profiles, defined as unique fingerprints of cellular perturbations generated by joining distinct features derived from spatially-resolved measures of fluorescence intensities relative to control treatments . These pThe functional data inferred independently by transcriptome analysis and bioactivity screening were integrated to create a dataset of shared signaling pathways including functionally annotated hits generated by both approaches. From this integrated dataset, single clusters were identified for each envenomation effect, as herein, but can also be identified for target systems and toxin families/classes. The cluster for the primary stingray envenomation effect (pain) was created by combining all pathways potentially related to this effect and using the hits in these pathways to reveal potential molecular targets and induced cellular responses either by functional annotation in KEGG or additional manual curation. Finally, the venom transcripts, molecular targets and cellular response data were compiled in specific clusters to identify drug candidates in the stingray venom and predict their major MoAs.