Accumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to model the interplay between genome, transcriptome, and proteome. We introduce Markov affinity-based proteogenomic signal diffusion (MAPSD), a method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain. We predicted a set of high-confidence SCZ risk loci followed by characterizing the subcellular localization of proteins encoded by candidate SCZ risk genes, and illustrated that most are enriched in neuronal cells in the cerebral cortex as well as Purkinje cells in the cerebellum. We demonstrated how the identified genes may be involved in neurodevelopment, how they may alter SCZ-related biological pathways, and how they facilitate drug repurposing. MAPSD is applicable in other polygenic diseases and can facilitate our understanding of disease mechanisms.  •MAPSD models protein trafficking for disease modeling.•Integrated proteome-genome modeling identifies novel schizophrenia risk genes.•Schizophrenia risk genes are involved in different stages of neurodevelopment.•Schizophrenia risk genes may converge in modules in interaction networks. Proteins constitute the functional machinery in a cell. Genetic aberrations may cause disrupting the normal functionality of the proteins. On the other hand, biophysical and biochemical properties of proteins vary in distinct tissues mandating separate modeling of proteomic features given the tissue being studied, e.g. brain in case of schizophrenia. Using the concept of signal diffusion in graph theory, we proposed a model, termed MAPSD, which enables us to leverage proteomic properties of different tissues at single cell resolution along with genomic and epigenomic features of a disease in order to predict potential risk genes which cannot be annotated using common univariate approaches. Taking this approach helps create novel therapeutic hypotheses for precision medicine so that more effective treatments with less side effects on other organs can be developed. Application of MAPSD is not restricted to schizophrenia and most of complex diseases can benefit from the method. Proteome studies are lagging behind the research on nucleic acids in neuropsychiatric disorders. We present a novel data integration method  to model protein trafficking maps within the cellular micro-domains in conjunction with other available biological data such as gene expression data to identify novel schizophrenia risk genes. We showed that targeted modeling of disease signatures in sub-regions of each tissue enables us to discover critical therapeutic insights that may not be revealed by the existing approaches. DRD2) for SCZ has been re-identified by GWAS.The emergence of omics technologies has revolutionized neuropsychiatric research,,,,In addition to genetic association studies, tremendous efforts have been made over the years to understand the machinery of gene regulation. Whole-body proteomics data, such as the Human Protein Atlas,,,,,The current available omics data on SCZ are predominantly related to those of nucleic acids, e.g., genomics, transcriptomics, and epigenomics, while the use of proteomics information is quite limited.,,de novo mutations,In this study, we introduce MAPSD , a multi-omics network-based computational method to identify novel risk genes for polygenic diseases. MAPSD leverages multiple layers of omics information, as well as the under-studied proteome subcellular localization patterns and tissue-wise cell-specific abundances of proteins in tens of different tissues and a wide range of cells, followed by propagating the biological signals across the human interactome to characterize potential disease-associated risk genes. The proposed model has several unique advantages, including (1) it uses protein trafficking information in subcellular micro-domains in 131 tissues and cell types, including multiple regions in the brain from the Human Protein Atlas;M is created. Using graph Laplacian concept in graph theory, a one-step probability distribution from each node to its neighbors is computed , 3,915 genes were curated to be associated with SCZ with various degrees of signal intensities (DGKZ) has a single intensity of 4 and six genes were found to have a signal intensity of 3, including DNAJA4, TCF4, CHRNA2, CPNE8, GRIN2A, and ZNF536. Notably, in a recent studyTCF4 to act as a transcriptional master regulator in SCZ, based on expression network analysis of human dorsolateral prefrontal cortex. Upon initiating the diffusion process, MAPSD terminated the diffusion at the time step t = 3 . We further compared the differences between the proportions of the major subcellular domains indicated above in neuronal cells within the cerebral cortex. Except vesicles , no significant difference was observed between their proportions: plasma membrane , cytosol , nucleus , and nucleoplasm . These observations extend further support, regarding efficacy of MAPSD in modeling, a more realistic map of proteomic properties of SCZ at the cellular resolution.DGKZ and ST8SIA2) to show the highest signal intensity levels in the brain. DGKZ showed the highest signal intensity of 4. DGKZ is a well-studied SCZ risk gene demonstrated to be DE,de novo mutations.ST8SIA2 . These genes include NLGN1, NLGN2, NLGN3, CASK, AFDN, and PAX4. NLGN1, NLGN2, and NLGN3 belong to the family of neuronal cell surface proteins, Neuroligin, and are involved in formation of CNS synapses.NLGN1 and NLGN2 were input to the model as SCZ risk genes, yet NLGN3 was identified by MAPSD as a susceptibility disease risk gene. This finding is in concordance with the well-established observations that Neuroligin protein members act as ligands for Neuroxins, resulting in the connections between neurons and generation of synapses.CASK and AFDN have also been implicated in CNS diseases such intellectual disabilities,,AFDN interacts with NRXN3,As a proof of concept, we picked ng MAPSD C. The au,To further gain evidence supporting their disease relevance, we analyzed the tissue-specific expression levels of the identified SCZ risk genes at mRNA level. For this analysis, we used gene expression levels on 53 different tissues from the Genotype-Tissue Expression (GTEx) project.−8, FER = 15.4; CBC: FDR = 8.5 × 10−9, FER = 13.8; HIP: FDR = 2.8 × 10−7, FER = 11.8), calcium signaling pathway , circadian entertainment , and cholinergic synapse .Because SCZ is likely a neurodevelopmental disorder, we next investigated if the brain-specific MAPSD genes are dysregulated during various developmental stages in human brain. We used the Atlas of the Developing Human Brain (BrainSpan)−4). We found multiple calcium channel mRNAs to be of high-risk signal intensities, such as CACNB1, CACNG2, CACNG3, and CACNG7. These genes are known to be the targets of fragile X mental retardation protein, which cause fragile X syndrome and autistic symptoms.SCN1A, a voltage-dependent sodium channel gene is known to be associated with epilepsy.,SCN3A, an epilepsy gene was picked up by MAPSD in neuronal cells in the cerebral cortex and hippocampus. These two genes have been widely studied in epilepsy as well as mental retardation and other neuropsychiatric disorders.We were interested in whether the MAPSD-identified SCZ risk genes act as targets of known drugs related to CNS. We used the list of US Food and Drug Administration (FDA)-approved drug targets by Santos et al.HRH1. This gene was found to be the target of 51 drugs, of which 10 were developed for CNS diseases. This gene showed the highest MAPSD signal intensity in neuronal cells in the cerebral cortex despite not being used initially as an SCZ signature in MAPSD. A few studies have investigated its association with SCZ. For example, Nakai et al.HRH1 and SCZ, despite borderline evidence for an association in GWAS.ADRA1B through two antipsychotic drugs chlorpromazine and trimipramine. Such interdependencies between the original SCZ risk genes supplied to MAPSD and the identified high signal genes further supports an orchestrated mechanism of the disease through interactions in convergent modules in the human interactome.Another highly connected gene within the created drug target network was CHRM1 and CHRM2 were found to be targeted by over 30 drugs, 8 related to CNS. These genes are implicated in alcohol dependence,SLC12A1, a solute carrier transporter, was found with the highest signal intensity in the brain to be targeted by five drugs. This gene is essentially targeted to reduce edema caused by kidney or heart failure. However, granted the role of such membrane-bound proteins in transferring substrates within the cell, such as dopamine and serotonin,Among the identified genes to be drug targets, In our view, the extreme polygenic nature of complex psychiatric disorders, such as SCZ, necessitates taking a more holistic view on the overall system of the diseases. One critical component of such a system is the proteome and its dynamics, given that proteins are in fact work horses of intra-cellular activities. Proteins reflect the genetic, epigenetic, and transcriptomic alterations that are caused by the disease. Yet, research on the proteome lags behind other omics data types, especially those generated on DNA and RNA levels,de novo mutations, and chromatin accessibility data followed by diffusing this repertoire of information into each dedicated cell-specific PPI network to predict the signal intensities of novel candidate genes and their potential role in the disease onset and progression. The Markov affinity-based criterion borrowed from graph theory as well as the designed termination criterion ensures accurate transition of information across the network, while avoiding over-smoothing the signal intensities. Therefore, the highest amount of information will flow through the network while preventing the signals at each node are distinctive enough. MAPSD enables ranking the genes related to SCZ given their signal intensity levels in the brain.MAPSD has unique characteristics that are worth further discussion. MAPSD features modeling the protein localization in subcellular micro-domains as well as tissue-wise cell-specific distribution of protein abundances in the human body. Taking all this information into account, MAPSD creates a dedicated cell-specific PPI network for tens of distinct human tissues. This allowed us to create more realistic PPI networks that can lead to more accurate prediction of disease drivers. MAPSD jointly uses GWAS hits, DE genes, rare and −4, enrichment ratio = 3.2). Among them, 85% of the genes were enriched in neuronal cells in the cerebral cortex, 7.5% in Purkinje cells in the cerebellum, and 7.5% in neuronal cells in the caudate. This observation further supports the mechanism introduced in MAPSD to jointly model mutual interactions between omics data modalities for identification of novel risk genes and susceptibility risk modules in PPI networks.An important strength of MAPSD is that the identified novel disease risk gene may not be immediate neighbors of known SCZ risk genes. For example, 217 genes out of 514 (∼42%) identified risk genes by MAPSD are not directly connected to disease susceptibility loci. We checked the topology of the PPI network on the identified MAPSD risk genes, which were connected to at least one SCZ risk gene. Given the direct neighbors of MAPSD genes, we categorized them into four groups D followede novo mutations, common variants, methylated loci, and open chromatin regions across the entire signature genes. Except for a mild correlation between DE and methylation signals (correlation coefficient = −0.43), we did not observe significant correlations between these data types. Incorporating the biophysical properties of proteins plays a critical role in precision predictions made by MAPSD. To evaluate the effects of removing the cell-specific PPI adjustment step in the performance of MAPSD, we ran MAPSD while disabling this stage followed by comparing the results with the original findings where the PPI network was adjusted for protein localization information. First, we looked for the known SCZ risk factors showing the highest signal intensity in the brain. We found that 108 genes, compared with 190 genes when applying this stage, share the highest signal intensity in the brain demonstrating a 43% loss in reproducibility power of MAPSD. While all of the previous 190 genes were unique to the brain, we found 11 genes having unique signal intensities in tissues other than the brain. Regarding the prediction power of MAPSD, we came up with 450 genes to share the highest signal intensity in the brain compared with 514 predicted risk genes (12.5% decrease). These observations suggest a loss of power in reproducing a good portion of the predictions. Moreover, we evaluated the effect adjusting the signal vector using the cell-specific protein abundances. For this, we directly used the initial signal vector in the diffusion process in tandem with the adjusted PPI network. We found 69 SCZ risk genes, compared with 190 genes in the original experiment, to share the highest signal intensity in the brain. However, we found that 97 SCZ risk genes also show the highest signal intensity in other tissues, such as heart muscle, lung, and liver. Next, we checked the status of the 514 predicted hits in the original analysis. We found only 109 (21.2%) genes to be significantly enriched in the brain and 213 genes to be significantly enriched in other tissues. Collectively, it can be concluded that removing the effect of modeling the cell-specific protein abundances has a radical impact on the overall performance of MAPSD in distinguishing cell-specific hits. Moreover, we found, from above, that relaxing the PPI adjustment stage in MAPSD has serious negative impacts on the reproducibility power of the algorithm and diminished the overall reliability of the predictions.Given that MAPSD takes an additive approach to combine signals from a variety of omics data types, we sought to explore if there were any correlations between these data types. We calculated the pairwise Matthews correlation among DE genes, DGKZ, GRIN2A, and CHRNA2 (DGKZ and GRIN2A) showed high signals in the brain after signal diffusion on the I2D network demonstrating a 67% overlap with the previous findings. Although the I2D PPI is significantly smaller  than the original PPI used previously, we were able to re-identify ∼11% of the original predictions while only ∼16% of the predictions existed in the I2D network. Therefore, our findings suggest that MAPSD is resilient to changing the networks being used. However, using a more detailed network will certainly lead to more robust predictions. In addition, conducting a randomized trial with 10 signal vectors each containing 3,915 signatures where none of which are SCZ risk loci led to an average of 473 genes with the highest signal intensity in the brain. We did not observe a significant overlap with the original SCZ findings (p = 0.398) suggesting the robustness of MAPSD.To evaluate how MAPSD can be resilient to the networks being used, we conducted a secondary analysis using a second independent PPI network from the IMEx consortiumd CHRNA2  keep theThere are some factors that may influence the overall quality of the prediction performance of MAPSD. First, the quality of the networks fed to the model. Since there are multiple compendia for PPIs, strict thresholds should be applied on the quality and reliability of pairwise interactions. This will ensure more accurate signal propagation through the network and will reveal more reliable outcomes. Second, more data types being fed to the model equates to more enriched signal matrices, which will in turn potentially lead to more concrete predictions regarding associations of the novel risk genes with the disease. If there is not enough evidence regarding each initial risk factor, then the Markov process will immediately converge while being over-smooth. Therefore, it would be difficult to interpret the findings, and the identified hits will likely be false negatives. Third, availability of high-resolution single-cell proteome data can increasingly improve the overall performance of MAPSD. Current data in the Human Protein Atlas are the major resource for profiling proteins across a wide range of tissues and cells. We acknowledge that the current data are not quite comprehensive, yet with the advent of technologies, generating more in-depth proteome data across more tissues has become possible. Therefore, we will be continuously updating MAPSD with more additional data. Given that a large number of the identified risk genes by MAPSD co-localize in various cell types in the cerebral cortex, we made sure that the results are not driven by the bias in the proteome data used. We used the Human Protein Atlas data and extracted the genes whose protein products are highly expressed in various cerebral cell types. Among 13,150 protein products, 2,894 (22.0%) proteins showed high expression in various cell types in the cerebral cortex. Therefore, the dataset used is not biased toward the cerebral cortex. MAPSD had predicted 514 novel risk genes among which 390 (∼76%) are highly expressed in the cerebral cortex which is equivalent to an odds ratio of 3.45. Therefore, MAPSD findings are bias-free.MAPSD takes advantage of high-dimensional omics data and is not tied to specific phenotypes. Therefore, it can effectively be applied to any complex disease, such as ASDs or autoimmune diseases, when necessary multi-omics datasets are available. MAPSD provides an ideal platform to leverage the outcomes of ongoing massive-scale projects, such as the PGC,wangk@email.chop.edu).Kai Wang, PhD . ThereforeThe initial signal matrix E, we can create S as follows: i is DE and differentially methylated, then Si = 2. We should make sure that the data being collected to create the signal vector have been generated from the same tissue or appropriate surrogate tissues to avoid generating spurious signals.Next, using ,A was created. A is an n×n binary matrix where aij = 1 if two proteins i and j are connected in the network, otherwise aij = 0. n denotes the total number of unique proteins in the PPI network. MAPSD scans the entire elements of A and checks its localization micro-domain. If two proteins i and j are connected in the network while co-localizing in the same micro-domain, then aij = 1.5. However, If two proteins i and j are connected in the network while not being co-localized in the same micro-domain, then aij = 1. Note that A is a symmetric matrix, i.e., aij = aji.Subcellular localization data used in MAPSD were downloaded from the Human Protein Atlas project.M). M is an n×n transition probability matrix whose element mij denoted the probability of single-step random walk from the node i to the node j. Leveraging random walk Laplacian in the graph theory,M can be obtained as follows: M =D−1A, where A denotes the adjusted affinity matrix above which consists subcellular localization information on all of the edges in the network and D represents the degree matrix. D is a diagonal matrix of the degree n, generated from A whose non-zero elements can be obtained as follows: D equals the row-wise summation of its corresponding protein in the affinity matrix A.Upon adjusting the raw affinity matrix to contain the subcellular localization information, MAPSD obtains the Markov operator matrix  as follows: SSE is the sum of square error and SST is the sum of square total and can be calculated as follows:e denotes a single element of the error matrix E = Mt+1S∗−MtS∗. k denotes a single element of the total matrix K=Mt+1S∗+MtS∗. MAPSD terminates the diffusion process if R ≤ 0.05. In other words, if the normalized difference of changes between signal intensities do not change at a certain threshold, then MAPSD stop the diffusion to avoid over-smoothing the signals of the protein across the network.MAPSD uses the Markov operator matrix Pathway enrichment and gene ontology analysis were conducted using WebGestalt


Accumulating amount of evidence has highlighted the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in tumor pathogenesis. However, the roles of long non coding RNAs (lncRNAs) in the lncRNA-related ceRNA network of intrahepatic cholangiocarcinoma (ICC) still remain enigmatic. The current study aims to identify prognostic factors in the lncRNA-related ceRNA network of ICC.The transcriptome sequencing data of lncRNAs, messenger RNA (mRNA) and microRNA (miR) were downloaded from the SRA and TCGA databases. Differentially expressed lncRNAs (DElncRNAs), DEmiRs and DEmRNAs were identified and adopted to construct an lncRNA-miR-mRNA ceRNA network. ICC-associated DEmRNAs were adopted to construct the protein–protein interaction (PPI) network. The expression of the top 6 genes in the hub module was validated with mRNA transcriptome sequencing data and ICC-related gene expression dataset GSE45001, followed by GO and KEGG pathway enrichment analysis. The relationship between the hub gene-associated ceRNA network and the overall survival of patients with ICC was predicted by conducting a Kaplan–Meier survival analysis.Sixty co-expressed DEmRNAs were identified in the ceRNA network. The top 6 hub genes consisted of downregulated FOS, IGF2, FOXO1 and NTF3, upregulated IGF1R, and insignificantly downregulated HGF in ICC tissues, when compared to that of normal adjacent tissues, followed by the successful construction of lncRNA-miR-hub network consisting of 86 ceRNA modules. MME-AS1 and hsa-miR-182 were associated with overall survival in ICC patients. FOS, IGF1R, IGF2, FOXO1, and NTF3 might target “TGF-β signaling pathway”, “the hedgehog signaling pathway”, “retinol metabolism”, or “type II diabetes mellitus” pathways respectively.These results indicate that FOS, IGF1R, IGF2, FOXO1, and NTF3 were useful prognostic factors in determining the prognosis of patients with ICC. Cholangiocarcinoma is an aggressive malignancy that frequently occurs at the biliary tract, with an unfavorable prognosis . IntraheThe methylation of DLEC1 cilia and flagella associated protein is engaged in a favorable clinical outcome and prognosis in patients with small duct ICC . HoweverIn this study, we adopted the transcriptome sequencing data of the lncRNAs, messenger RNA (mRNA) and miR from the Sequence Read Archive (SRA), and Cancer Genome Atlas (TCGA) database, respectively. Differentially expressed lncRNAs (DElncRNAs), DEmiRs and DEmRNAs were identified and adopted to construct an lncRNA-miR-mRNA ceRNA network. ICC-associated DEmRNAs were adopted to construct the protein–protein interaction (PPI) network. The expression of the top 6 genes was identified in the hub module provided by the PPI network, followed by GO and KEGG pathway enrichment analyses. The potential function of genes in the hub module of the ceRNA network was finally subjected to the Gene Set Enrichment Analysis (GSEA). Thus, the purpose of this study was to identify genetic alterations that underlie the prognostic factors of ICC.https://www.ncbi.nlm.nih.gov/sra/) and were then subjected to the SRP126672 dataset. The SRP126672 dataset was composed of 30 ICC tissues and 27 adjacent normal tissues. FastQC and Trimmomatic applications were adopted to quality-control and filter the original sequencing data. LncRNAs were quantified based on the Genome Research Project of Encyclopedia of DNA Elements (GENCODE) (GRCh37) catalog (http://www.gencodegenes.org/). The RNA transcriptome sequencing data for ICC were downloaded from TCGA database. MiR sequencing and mRNA sequencing data were downloaded using a data transfer management tool (provided by GDC Apps) (https://tcga-data.nci.nih.gov/). The miR sequencing data and mRNA sequencing data both consisted of 33 ICC tissues and 8 normal tissues, respectively. In addition, the GSE26566 dataset for ICC, which was downloaded from the Gene Expression Omnibus (GEO) database, included 10 samples of ICC and 10 non-tumor liver samples to determine the expression of the hub gene.The original transcriptome sequencing data of the lncRNAs from human ICC and adjacent normal tissues were retrieved until September 2019 from the SRA database | > 2 and FDR < 0.01.The RNA-seq original data of ICC tissues and adjacent normal tissues were corrected, and normalized, with their expression calculated. DElncRNAs were screened using a DESeq 2 package. The adjusted standard was |log 2 (fold change [FC])| > 2, http://www.mircode.org/) were adopted to predict the markedly downregulated lncRNAs targeted by upregulated miRs and markedly elevated lncRNAs targeted by repressed miRs in ICC. DEGs with the correct trends and targeting relationships served as candidate genes. Next, the TargetScan (http://www.targetscan.org/), miRDB (http://www.mirdb.org/) and miRTarBase online databases (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) were employed to predict markedly downregulated mRNAs targeted by upregulated miRs and markedly elevated mRNAs targeted by repressed miRs in ICC. The mRNAs with the correct trends in intersection among the three databases served as candidate genes. The predicted lncRNA-miR and miR-mRNA were combined to construct the lncRNA-miR-mRNA ceRNA network. Finally, the Cytoscape v3.6.1 software was adopted to visualize and map out the whole constructed network.The ceRNA network was constructed based on the DElncRNAs, DEmiRs and DEmRNAs. The following databases: DElncRNAs, DEmiRs and DEmRNAs, and the miRcode (https://david.ncifcrf.gov/) was used to perform a GO enrichment analysis of DEmRNAs by setting the default parameters. The GO function, enriched by p < 0.05, was considered significant among the available transcriptome sequencing data. In order to understand the potential pathways of DEmRNAs involved in the ceRNA network, the KOBAS database (http://kobas.cbi.pku.edu.cn/index.php) was employed to perform a KEGG pathway enrichment analysis on DEmRNAs, in which the significance of the KEGG pathway was evaluated at p < 0.001.To elucidate the potential biological processes of DEmRNAs related to the ceRNA network in the development of ICC, the DAVID database (http://www.string-db.org/) and a PPI network was subsequently built using the Cytoscape v3.6.1 software. Lastly, the top six key genes in the hub module were obtained from the PPI network using the MCC network topology belonging to the cytoHubba plug-in in Cytoscape.The interaction between DEGs identified key genes in modules involved in the development of ICC, with a combined score of > 0.4 for a PPI network to be considered as the threshold. The PPI information of DEmRNAs was obtained from the STRING database , as well as the overall survival (OS) curve of patients. The level of The ICC gene expression dataset GSE45001, from the GEO database, was used to verify the expression of hub genes. The gene expression dataset GSE26566 was also used for the prediction of the hub gene function.p < 0.05.To better elucidate the potential function of the hub genes in the ceRNA network, a GSEA was performed. According to the median expression of hub genes in the RNA sequencing data, 33 ICC samples from the TCGA database were assigned into the high expression group and the low expression group. The reference gene set is the annotated c2.cp.kegg.v7.0.symbols.gm gene set in the Molecular Signature Database (MSigDB), and the critical criterion was To identify the presence of DEmRNAs, DEmiRs and DElncRNAs in ICC, the SRP126672 dataset of RNA-seq sequencing data, composed of 30 ICC tissues and 27 adjacent normal tissues, was downloaded from SRA database. FastQC and Trimmomatic applications were used to quality-control and filter the original sequencing data, and the DESeq 2 package was used to screen DElncRNAs. A total of 912 DElncRNAs were obtained, specifically with 524 upregulated and 388 downregulated DElncRNAs . DEmRNAs that were related to cellular components had a close resemblance with mitochondrial inner membrane (p < 0.05). DEmRNAs that were related to molecular functions were mainly enriched in GO terms of chemoattractant activity, growth factor activity, and transcriptional coactivator binding (p < 0.05)   Fig. a. The KE01) Fig. b. CohereThe PPI network was constructed based on DEmRNAs, comprising of 60 nodes and 41 edges , and associated DElncRNAs and DEmiRNAs with OS in patients with ICC, a Kaplan–Meier curve analysis was performed. The results indicated that only DElncRNA MME-AS1 and DEmiRNA hsa-miR-182 were correlated with OS of patients with ICC Fig. a, b. MorTo better validate our analysis, the expression of 6 hub genes were evaluated in 33 ICC tissues and 8 adjacent normal tissues. The results  expedite glucose metabolism with their availability modulated by IGF binding proteins, and function as prognostic factors for type 1 diabetes . The indIGF1 maintains the phenotype of the tumor and allows the transformed murine pheochromocytoma cells to survive . The inhFOXO1 transcription factors orchestrate various cell types that are important in the host response . MoreoveIn conclusion, our research identified several novel genetic alterations and pathways associated with the prognosis of ICC, as well as the potential roles of miRs, lncRNAs and mRNAs in the development of ICC via bioinformatic analysis. Based on the ceRNA network, we also discovered that FOS, IGF1R, IGF2, FOXO1, and NTF3 might target the “TGF-β signaling pathway”, “the hedgehog signaling pathway”, “retinol metabolism”, or “type II diabetes mellitus” pathways respectively, thereby modulating the subsequent development of ICC. A substantial insight gained from the understanding of the molecules involved in the prognosis of ICC contributed to an increased efficacy of the available treatments for patients with ICC. However, further studies with more selective candidate genes and a larger sample size are required to clearly define their roles in the development of ICC. Additional studies also need to be performed to examine the underlying mechanisms of FOS and TGF-β signaling pathway.


Thyroid-associated ophthalmopathy (TAO) is an autoimmune inflammatory disorder, which lacks effective treatment currently. Spica Prunellae (SP) is popularly used for its anti-inflammatory and immune-regulating properties, indicating SP may have potential therapeutic value in TAO. Therefore, the purpose of this study is to identify the efficiency and potential mechanism of SP in treating TAO.A network pharmacology integrated molecular docking strategy was used to predict the underlying molecular mechanism of treating TAO. Firstly, the active compounds of SP were obtained from TCMSP database and literature research. Then we collected the putative targets of SP and TAO based on multi-sources databases to generate networks. Network topology analysis, GO and KEGG pathway enrichment analysis were performed to screen the key targets and mechanism. Furthermore, molecular docking simulation provided an assessment tool for verifying drug and target binding.Our results showed that 8 targets  were recognized as key therapeutic targets with excellent binding affinity after network analysis and molecular docking-based virtual screening. The results of enrichment analysis suggested that the underlying mechanism was mainly focused on the biological processes and pathways associated with immune inflammation, proliferation, and apoptosis. Notably, the key pathway was considered as the PI3K-AKT signaling pathway.In summary, the present study elucidates that SP may suppress inflammation and proliferation and promote apoptosis through the PI3K-AKT pathway, which makes SP a potential treatment against TAO. And this study offers new reference points for future experimental research and provides a scientific basis for more widespread clinical application. Thyroid associated ophthalmopathy (TAO), an inflammatory disorder affecting the orbit and ocular adnexa, is occurred in autoimmune thyroid disease, mainly Graves’ disease . A prospPrunella vulgaris L., is a traditional antipyretic Chinese herb with a wide distribution in Northeast Asia. According to the theory of traditional Chinese medicine (TCM), SP has long been thought to have benefits involving clearing the fire and eyesight, relieving edema and removing stasis. Moreover, SP has been extensively used in food additives and pharmaceuticals [Spica Prunellae (SP), the fruit-spikes of the euticals . Previoueuticals , anti-caeuticals , 20 and euticals . Nowadayeuticals , 23. Indeuticals . HoweverBased on the development of bioinformatics, network biology and pharmacology analysis, network pharmacology becomes a novel strategy to understand the law of interactions between multicomponent and multitarget systematically and comprehensively , 26. In http://lsp.nwu.edu.cn/tcmsp.php), a systematic pharmacology platform of Chinese herbal medicine. Based on their ADME  features, Oral bioavailability (OB) and Drug-likeness (DL) were set as the key parameters [To collect the pharmacologically active compounds of SP, we searched Traditional Chinese Medicine System Pharmacology Database  , the PubChem database [http://pubchem.ncbi.nlm.nih.gov), the Swiss Target Prediction database [http://www.swisstargetprediction.ch/), and the STITCH database [http://stitch.embl.de/). Then, all the targets names were put into uniprot sites (http://www.uniprot.org/) and selected by Homo Saipan species to normalize the gene information. Detailed information of putative targets is provided in Table SThe putative targets of active compounds were extracted from the TCMSP database  (http://database  (http://database  (http://database  (http://https://db.idrblab.org/ttd/); (2) the Online Mendelian Inheritance in Man database [http://omim.org/); (3) the Comparative Toxicogenomics Database [http://ctdbase.org/); (4) the DrugBank database [https://www.drugbank.ca/); (5) the GeneCards database [https://www.genecards.org/). Detailed information is described in Table SRelated TAO targets were gathered from multi-sources databases using “Thyroid-associated ophthalmopathy” and “graves ophthalmopathy” as keywords. Five database are as follow: (1) the Therapeutic Target Database  ;  the Onlim.org/);  the CompTo obtain the candidate targets responsible for TAO therapy, the putative targets of SP mapped to the TAO related targets to obtain overlapping targets. For overlapping targets, numbers were visualized with a Venn diagram generated in R version 3.2.0, a freely available, open-source statistical programming language and environment for statistical computing , with Vehttps://string-db.org/), an online platform that critically predict functional protein association networks, to generate protein interaction network. Network data were used for the topological properties analysis to identify important targets in anti-TAO system, and Table SThese related targets of SP in treating TAO were then put in STRING tools  (https:/https://david.ncifcrf.gov/) to perform GO functional analysis and KEGG pathways analysis. The enrichment P values of functional annotations were corrected by both Bonferroni (P < 0.05) and Benjamini (P < 0.05) correction. The plots of GO enrichment and KEGG pathway enrichment were then carried out with R version 3.2.0.To further reveal the potential mechanism of SP in TAO treating, we imported overlapping targets into the Functional Annotation tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8  (https:/http://cytoscape.org/), a software environment for integrated models of biomolecular interaction networks, was utilized to construct the following networks:(1) Compound-target network; (2) PPI network of TAO targets; (3) PPI network of compound-TAO targets; (4) Compound-target-pathway network.The Cytoscape3.6.1   “Degree” refers to the number of nodes that directly interact with that node in a network , (2) “Behttp://www.pdb.org/). The downloaded complexes were embellished by PyMol2.3.0 (the PyMol Molecular Graphics System) to remove original ligand, water molecules and phosphates. Moreover, the AutoDock Tools1.5.6 [http://mgltools.scripps.edu/documentation/links/autodock) was used to prepare receptors, including adding hydrogen and seting docking paraments. “Grid box” was set to maximum to perform the blind docking.The crystal structures of hub genes were downloaded from RCSB Protein Data Bank    and actiIn conclusion, up till now, TAO treatments have been an intractable problem, but TCM may be a potential alternative treatment to a certain extent. In the present study, we predicted 8 key targets from complex networks through the integration of network pharmacology and molecular docking, and performed comprehensive explanation of the therapeutic mechanism of SP in TAO, which is likely to focus on three aspects: immunoinflammatory suppression and regulation of proliferation and apoptosis. In addition, we found that PI3K-AKT signaling pathway may occupy core status in anti-TAO system. Our research would be enlightening for developing therapeutic methods targeted TAO. However, further extensive experiments will be necessary to unravel the above possibility, such as in vivo and in vitro efficacy experiments of SP in TAO therapy.Additional file 1: Table S1 The putative targets of SP.Additional file 2: Table S2 TAO related targetsAdditional file 3: Table S3 Information for TAO targets after PPI analysis.Additional file 4: Table S4 Information for overlapped targets after PPI analysis.


The network pharmacology method was used to predict the active components of Banxia Xiexin decoction, its targets and the key signalling pathways that are activated in the treatment of depression and ulcerative colitis to explore the common mechanism.The active components and targets of Banxia Xiexin decoction were obtained by searching the ETCM,TCMSP and TCMIP database. The disease targets of depression and ulcerative colitis were obtained by combining the following the DisGeNET, OMIM,Drugbank,CTD and PharmGKB disease databases. The drug and disease target genes were obtained from the intersection of the herbal medicine targets and the disease targets and were imported into the STRING platform for the analysis of PPI network. The network modules were constructed using Cytoscape software. An analysis of the functional annotations of GO terms and KEGG signalling pathways was performed for each network module. Then, the tissue distribution, sub-cellular distribution and protein attributes of the key targets in the pathway were analysed by the BioGPS, Genecards and DisGeNET databases.The mechanism of Banxia Xiexin Decoction in the treatment of depression and ulcerative colitis is related to drug reaction, steroid metabolism, lipid metabolism, inflammatory response, oxidative stress response, cell response to lipopolysaccharide, insulin secretion regulation, estradiol response and other biological functions, mainly through the regulation of 5-hydroxytryptamine synaptic, arachidonic acid metabolism, HIF-1 signaling pathway and NF-kappa B signaling pathway can achieve the effect of same treatment for different diseases.The mechanism of Banxia Xiexin Decoction in treating different diseases involves direct or indirect correlation of multiple signal pathways, mainly involved in drug metabolism and lipid metabolism, but also through comprehensive intervention of the body’s nervous system, immune system, digestive system and other systems. The effective components of Banxia Xiexin Decoction are mainly act on eight key target proteins  to carry out multi-target effect mechanism, biological mechanism of treating different diseases with the same treatment, and related mechanism of overall treatment, which provide theoretical reference for further research on the material basis and mechanism of Banxiaxiexin decoction on antidepressant and prevention and treatment of ulcerative colitis. Depression is a chronic and recurrent mental disorder. The main clinical characteristics of patients are marked and lasting depression, accompanied by physical symptoms of gastrointestinal diseases, which can impair social functions and lead to suicidal tendencies. Thus, depression is very harmful to the physical and mental health of patients –3. Ulcer8+ and regulate CD4 + / CD8 + balance in UC model rats [With the rapid development of modern society and the economy, the accelerated pace of life, and the intense pressure of competition, the number of patients with the two diseases has shown an increasing trend, which not only seriously distress the normal life and work of patients but also imposes a heavy burden on the family and society. Currently, Standard treatment often uses an aminosalicylic acid preparation, corticosteroid hormones, and immunosuppressants as the main treatment, combined with antidepressants. Although these drugs display short-term clinical efficacy, different degrees of side effects occur with the prolongation of the treatment, which will seriously reduce the compliance of patients with the treatment process and the curative effect of the disease. Traditional Chinese medicine has the unique advantages of overall regulation, safety and effectiveness in the treatment of depression and ulcerative colitis. Traditional Chinese medicine not only has a profound foundation in clinical practice but also has led to the development of a large number of classic prescriptions for treatment. Thus, traditional Chinese medicine has gradually become more widely used. Banxia Xiexin decoction, which was first published in the Treatise on Cold Damage and Miscellaneous Disease, is composed of seven herbs, namely, Banxia, Ganjiang, Scutellaria, Coptis, ginseng, liquorice and jujube. This prescription can effectively improve gastrointestinal inflammation, regulate gastrointestinal motility and anti-ulcer effectively in clinical –12. Reladel rats . It can del rats . Relevandel rats . At the del rats , 17. Accdel rats , 19.Based on previous clinical experience, modern clinical practice and animal experiments using traditional Chinese medicine, Banxia Xiexin decoction effectively displays the comprehensive advantages of treating different diseases, treating the viscera and treating specimens with the same formulation through component coordination, target coordination and efficacy coordination among various herbs. However, there is a lack of comprehensive and systematic understanding on the material basis and molecular mechanism of its efficacy, and there is no study on the biological mechanism of Banxia Xiexin decoction in treating depression and ulcerative colitis with different diseases. Therefore,this study aims to screen the active components and predicted targets of the herbs contained in Banxia Xiexin decoction by using the biological module analysis of network pharmacology to better understand the common targets, molecular mechanism, tissue localization and subcellular distribution of the traditional Chinese medicine in the treatment of different diseases. In addition, the relevant networks and biological functions are analysed to explore the multi-target mechanism, the biological mechanism of simultaneously treating different diseases, and the related mechanism of the overall treatment with the classical prescriptions in a comprehensive, multi-level and systematic manner. We aim to provide a reliable reference value for subsequent experimental verification, clinical application, and new drug research and development.Panax ginseng C. A. Mey), ginger , jujube (Ziziphus jujuba Mill) and liquorice . The active components and targets of Banxia Xiexin decoction were obtained by searching the Encyclopedia of Traditional Chinese Medicine  [http://lsp.nwu.edu.cn/tcmsp.php) database was used to search all the chemical composition information of the single Chinese medicine. According to the recommended screening threshold conditions of the platform, when OB ≥ 30% and DL ≥ 0.18, get the key active ingredients of the Chinese medicine were obtained [https://pubchem.ncbi.nlm.nih.gov/) database to search and download the molecular structure formula and PubChem CID of the corresponding chemical composition, and then select the corresponding target of the compound whose species is “Homo sapiens” in SwissTargetPrediction (http://swisstargetprediction.ch/) platform according to the chemical similarity between the active compound ligands. Seven herbs were added to TCMIP (http://www.tcmip.cn/) database to establish the chemical composition database of Banxia Xiexin Decoction. According to the target prediction interface of traditional Chinese medicine, all chemical composition prediction targets with drug similarity score ≥ 0.8 were selected [http://www.uniprot.org/) database was used to input the names of the predicted target proteins, and the species was set to Homo sapiens. Then, the target protein of each herbal medicine was corrected to the official symbol.Banxia Xiexin decoction contains seven Chinese herbal medicines: Banxia (Arum Ternatum Thunb), Scutellaria , Coptis (Coptis chinensis Franch), ginseng (0/ETCM/) , 21, thiobtained . The canselected –25. The Using the disease databases gene disease association database (DisGeNET), comprehensive human Mendelian genetics database (OMIM), drug target database (Drugbank), comparative toxicogenomic database (CTD), and pharmacogenetics and pharmacogenomics database (PharmGKB), we input the disease gene keywords for depressive disorder, depression and ulcerative colitis. The target genes of the two diseases were searched and screened, and the target genes of each disease database were combined to eliminate the repeated disease targets. Finally, the known disease targets of depression and ulcerative colitis were obtained.The herbal medicine targets in Banxia Xiexin decoction and the disease targets of depression and ulcerative colitis were separately imported into the OmicShare online tool to generate a Venn diagram. The target genes of Banxia Xiexin decoction for treating depression and ulcerative colitis were mapped, and the target gene database of compounds for treating depression and ulcerative colitis was constructed by extracting the intersecting target genes. The disease target genes that were not related to the components of Banxia Xiexin decoction were removed from the table, and the components that did not match the components of depression and ulcerative colitis were removed. Finally, the disease target genes of the herbs were obtained, and a common target database was established.Homo sapiens”, and the minimum interaction threshold was set to “Medium Confidence”. The data for the protein-protein interactions were obtained, listed in columns, and saved in. TSV format, and the PPI network was visualized by constructing a graph.The PPI was constructed and clarified using the STRING network database platform to further elucidate the interactions of the common target genes in the treatment of depression and ulcerative colitis with Banxia xiexin decoction. The protein species was set to “P-value of each function and pathway was obtained by the method and was corrected using the Benjamini-Hochberg algorithm. The biological signaling pathways with P < 0.05 were regarded as statistically significant. The results were sorted according to the P-value, and the top three results were retained.As a closely connected functional group, the biological network module performs the corresponding functions through the interactions of multiple targets in each network module. The data presented in columns of the TSV file were imported into the Cytoscape software, and the key network modules were constructed by using the MCODE Cluster analysis plug-in in the toolbar Apps. By using the online Database for Annotation, Visualization and Integrated Discovery (DAVID6.8), the target genes in the key network modules were analysed by determining the related GO biological processes and signalling pathways that were enriched in the KEGG. The DAVID database integrates various database resources and uses an improved fisher to accurately assess and calculate the results. The http://biogps.org), Genecards (http://www.genecards.org/) and DisGeNET (http://www.disenet.org) databases to obtain the tissue distribution, subcellular distribution and protein classification information, respectively.The key targets in the selected signalling pathways were imported into the STRING database to obtain the protein interaction relationship, and the protein interaction network was drawn with the Cytoscape software. According to the Degree value, the key targets were selected and imported into the BioGPS . Using the related target prediction technology in each software to screen the prediction targets of the above-mentioned active ingredients, eliminate the repeated targets, and finally obtain a total of 1551 herbal prediction targets.We searched the DisGeNET, OMIM, Drugbank, CTD and PharmGKB databases and screened the known target genes related to depression and ulcerative colitis. We combined the target genes from each database, deleted the duplicate disease targets, and retrieved 876 disease targets for depression and 914 disease targets for ulcerative colitis. The intersection of 1551 predicted herbal medicine targets and two disease targets was obtained to obtain 318 therapeutic targets of Banxia Xiexin decoction for depression and 198 therapeutic targets for ulcerative colitis. Then, 318 depression targets and 198 ulcerative colitis targets were imported into the OmicShare online tool to extract the intersecting target genes and construct a Venn diagram, as shown in Fig. The 66 intersecting target genes listed above were imported into the STRING software platform to obtain the data columns of protein-protein interactions, construct a PPI network diagram, and better understand the potential mechanism of Banxia xiexin decoction in the treatment of depression and ulcerative colitis. The network contains 65 protein-protein interaction targets and 464 edges that represent protein-protein interactions. The average node Degree value in the network is 14.30, and the average centre clustering coefficient is 0.614, as shown in Fig. P-values obtained from the enrichment analysis. Module A was mainly related to the positive regulation of biosynthesis, including pathway in cancer, proteoglycan signaling pathway in cancer and arachidonic acid metabolism pathway. Module B was mainly related to metabolism pathway, including HIF-1 signaling pathway, arachidonic acid metabolism pathway and NF-kappa B signaling Pathway. Module C was mainly related to plasma membrane regulation, mainly involving neuroactive ligand-receptor interaction signaling pathway, serotonergic synapse and calcium signaling pathway. Module D was related to related metabolic processes, mainly involving drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450 and retinol metabolism signaling pathway.The protein interaction data listed above were imported into the Cytoscape software, and the protein interaction network of Banxia Xiexin decoction in the treatment of depression and ulcerative colitis was divided into modules by using the MCODE Cluster plug-in tool, involving 63 nodes and 464 edges. Four key network modules and 90 targets were finally obtained, among which the largest network module was composed of 35 target nodes. The nodal target proteins contained in these modules may play important roles in the treatment of depression and ulcerative colitis with Banxia Xiexin decoction, as shown in Fig. The 66 common target genes screened above were mapped for an enrichment analysis of the GO terms and KEGG pathways and displayed in the chart shown in Fig. The targets in the four key signaling pathways selected above were imported into the STRING platform, and the columns of protein-protein interaction data were obtained. The data columns were imported into the Cytoscape software for a network topology analysis. The targets were sorted according to the node degree value, and the frequency of the targets in each pathway was counted. Finally, the key targets with a value higher than the degree median value and the highest frequency were selected. Eight key targets meet these criteria, as shown in Table The eight key targets described above were imported into the BioGPS platform to obtain the relevant tissue distribution information. Obtain the tissue information with correlation cutoff value above 0.9 and each gene expression ranking in the top five to make a data list. The obtained data list is imported into the Cytoscape software to draw the “tissue-target”network diagram, as shown in Fig. Import the key targets into the Genecards database to obtain the subcellular distribution information. The subcellular location of Confidence = 5 of the key target was selected and included. Import the subcellular distribution information table into the Cytoscape software to draw the “subcellular-target” network diagram, as shown in Fig. Import the key targets into the DisGeNET database to obtain the protein information of each target. The results show that the key targets mainly involve transfer/carrier protein, signaling molecule, oxidoreductase, transferase and transcription factor. See Table As the basic principle of guiding the clinical diagnosis and treatment of diseases, the treatment method of “treating different diseases with the same treatment” of traditional Chinese medicine is also the essence of syndrome differentiation and treatment in traditional Chinese medicine. The explanation for the simultaneous treatment of different diseases is mainly attributed to the similar aetiology, pathogenesis, symptoms and disease location during the occurrence and development of different diseases. Therefore, different diseases can be cured with the same prescription, completely reflecting the advantages of traditional Chinese medicine in syndrome differentiation, holistic treatment and comprehensive treatment. Nowadays, the traditional Chinese medicine Banxia Xiexin Decoction is often used to treat ulcerative colitis , 11 and According to the analysis of enriched biological processes and signalling pathways of common targets of the classical prescription used to treat two diseases, the GO biological processes, KEGG pathways, and the results of the biological network module analysis are overlapped and were enriched in the 5-HT synaptic signalling pathway, arachidonic acid (AA) metabolism, HIF-1 signaling pathway and NF-kappa B signalling pathway. Thus, different diseases are able to be treated with the same prescriptions. 5-hydroxytryptamine (5-HT), as an indole derivative, is mainly produced in brain tissue by tryptophan hydroxylase-2 (TPH2) and plays an important role. 95% of 5-HT is mainly produced in gastrointestinal tract of digestive system, and this process is mainly produced by further metabolism of tryptophan hydroxylase-1 (TPH1) . 5-HT noThe targets of Banxia Xiexin decoction were also significantly enriched in the HIF-1 signaling pathway and NF-kappa B signalling pathway. Studies have confirmed that acute stress can promote animals to keep excited for a certain period of time, while long time in this state of tension will make the sympathetic nerve in the body in a continuous state of tension, the hypothalamic-pituitary-adrenocortical (HPA) axis over excitation will cause human body to be in a negative state, produce anxiety and despair, reduce the ability of learning and memory, and then the nervous system and immune system of the body will be in different levels of disorder, leading to the occurrence of various diseases, and serious mental diseases such as depression, which will become a serious threat to our own and social development . HoweverActivated NF-kB, an important nuclear transcription factor in cells, participates in the mechanisms regulating the expression of various genes involved in the inflammatory response and immune response, and it regulate cell apoptosis, the stress response, synaptic plasticity and memory. In recent years, an imbalance in the intestinal flora has been reported to be closely related to the occurrence of ulcerative colitis. When ulcerative colitis occurs, it will activate the immune system in vivo and lead to the occurrence of systemic inflammation. T-lymphocytes will accumulate in the damaged intestinal mucosa and lead to a defect in intestinal barrier function. The immune system is activated by various microbial antigens after intestinal pathogens invade the submucosa. The activation of inflammatory cells leads to the inflammation of the intestinal mucosa. This process is mediated by Toll-like receptors, which activate the NF-kappa B signalling pathway, resulting in the release of the pro-inflammatory cytokines IL-1 and TNF-α. The inflammatory state caused by the abnormal immune function also plays a role in the pathogenesis of depression. The number of immune cells in patients with depression often changes the immune function of the body, disrupting the immune function of peripheral cells and increasing the levels of pro-inflammatory cytokines. Subsequently, the pro-inflammatory cytokines alter the local brain structure and neuroendocrine function. Therefore, Banxia Xiexin decoction may induce the expression of inflammatory cytokines and participate in the immune and inflammatory responses of patients with depression by activating the NF-kappa B signalling pathway to effectively improve the gastrointestinal inflammatory response and the effective regulation of the central nervous system , 54.In this study, we used the tools of BioGPS, Genecards and DisGeNET to analyze the tissue distribution, subcellular distribution, and protein attribution. It can be seen from the results that in the treatment of depression and ulcerative colitis, the tissue distribution mainly involves the amygdala, pancreas and lung of brain, while the subcellular localization mainly includes myeloid cells, monocytes, cardiomyocytes, lymphoblasts and bronchial epithelial cells. Therefore, the prediction results not only provide a reference value for the clinical application of traditional Chinese medicine in the treatment of different diseases, but also provide guidance for the next animal and cell experimental verification stage of the research group, and also open up a new research idea for the traditional Chinese medicine to study the potential pharmacological mechanism of depression and ulcerative colitis from the perspective of brain, lung and pancreas.In summary, this paper studies the common mechanism of the treatment of ulcerative colitis and depression using a network pharmacological biological module analysis by investigating biological processes, signalling pathways, tissue distribution, subcellular distribution, protein attributes, etc. Banxia Xiexin decoction not only directly participated in drug and lipid metabolism but also exerted the effect of “treating different diseases with the same treatment” by comprehensively regulating the functions of the nervous system, immune system, digestive system and other systems of the body. In addition, from the perspective of integration, we also explored the efficacy of this herbal medicine through multiple channels and effects. The results were consistent with the process of disease induction by multiple factors and interactions, and a certain correlation was observed between the key signalling pathways that work synergistically. The traditional concept of “treating different diseases with the same treatment: of traditional Chinese medicine is integrated with the research technology of modern network pharmacology to not only take full advantage of traditional Chinese medicine in overall treatment and comprehensive prevention and treatment but also systematically analyse the mechanism underlying the efficacy of ancient traditional Chinese medicine prescriptions using of modern visualization technology. Our study provides a new area of research for exploring the mechanism of synergism in the treatment of different diseases, provides a reliable reference value for the subsequent experimental verification of our findings, and ensures that the overall research results are highly credible and persuasive.


These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by  The key group of effective components (KGEC) in a formula of traditional Chinese medicine (TCM) refer to the pharmacologically active components that are closely related to the positive response to the therapy of the diseases. How to determine the KGEC that play leading roles in the treatment of specific disease is a difficult problem due to the high complexity of the chemical composition and the incompletely understanding the complex multi-targets mechanism of TCM. Selecting the KGEC in the formula of TCM is an important direction in the reduction of non-active components and analysis of the treatment mechanism of formula. At present, there are several formula optimization models based on network pharmacology have been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug or components, and ignore the optimized functional space, which couldrepresent effective relationships between drug targets and pathogenetic genes . IncreasSystemic Lupus Erythematosus (SLE) is an autoimmune disease involving multiple systems and organs, with complicated clinical manifestations and persistent. Most of them are found in young women, and the incidence ratio of male to female is 1: 5 ~ 10 . PreviouLonicera japonica Thunb. , Forsythia suspensa (Thunb.) Vahl , Taraxacum mongolicum Hand. , Coptis chinensis Franch. , Rehjnannia glutinosa Libosch. , Rheum officinale Baill. , Glycyrrhiza uralensis Fisch. , Scolopendra subspinipes mutilans L. Koch , Paeonia ladiflora Pall. , Angelica sinensis (Oliv.) Diels , Salvia miltiorrhiza Bge. , Scrophularia ningpoensis Hemsl. , Prunus persica (L.) Batsch , Carthamus tinctorius L. , Cryptotympana pustulata Fabricius , and Fritillaria thunbergii Miq. . In this Chinese formula, Flos Lonicerae, Fructus Forsythiae and Herba Taraxaci have the function of clearing heat-toxin, eliminating carbuncles, and resolving masses, Coptidis Rhizoma has the function of clearing heart fire, the Rehmanniae Radix mainly focus on cooling blood, nourishing yin and promoting fluid production, Radix et Rhizoma Rhei play roles in clearing heat cooling blood promoting blood circulation; Glycyrrhrizae Radix used for clearing heat-toxin harmonizing various drugs; Radix Paeoniae Rubra, Saviae Miltiorrhizae Radix and Carthami Flos usually used for clearing heat cooling blood; Scolopendra used for removing toxic substance resolving masses, detumescence, and relieving pain; Radix Angelicae Sinensis used for nourishing and activating blood; Radix Scrophulariae used for clearing heat-toxin nourishing yin for lowering fire; Semen Persicae used for promoting blood circulation removing blood stasis; Periostracum Cicadae used for clearing heat; Bulbus Fritillariae Thunbergii used for clearing heat dissipating phlegm and resolving masses. The whole recipe has the functions of clearing away heat and toxic materials, cooling blood and promoting blood circulation. In pharmacologic studies, LCW may has the effect of inhibiting humoral immune function and enhancing cellular immune function. Additionally, LCW also obvious inhibitory effect on acute and chronic inflammation and allergy in rats, and can promote fibrinolytic activity. These studies confirmed that the LCW could be beneficial in the treatment of patients with SLE at comprehensive level. Nevertheless, not any document expounds the key components and underlying therapeutic mechanism of LCW for clinically benefit to SLE  network. The weighted gene regulatory network and C-T networks were used to construct optimization space to determine the effective proteins. The effective proteins selected from optimization space were used to screen the effective components. The contribution index (CI) module was employed to optimize effective components and get the KGEC, which would be used to illustrate the molecular mechanism of LCW in the therapy of SLE. Finally, the key components in KGEC of LCW were evaluated by In order to construct comprehensive weight gene network of SLE, the PPI data were derived from public databases BioGRID, STRING, Dip, HPRD, Intact, Mint and Reactome . Genes fHigh performance liquid chromatography (HPLC)-grade acetonitrile and HPLC grade formic acid were obtained from Thermo-Fisher (USA). Reference standards provided by the National Institute on Drug Abuse of China:Hydroxysafflor-Yellow-A (batch number: 111637-200502), amygdalin (batch number: 110820-200403), paeoniflorin (batch number: 0905-9805), caffeic acid (batch number: 110728-200506), phillyrin (batch number: 120908-200914), liquiritin (batch number:111610-200503), peimisine (batch number: 0750-9303), harpagoside (batch number: 712-9403), rhein (batch number: 0902-200207), Z-Ligustilide (batch number: 927-9908), berberine (batch number: 0713-200107), tanshinone II a (batch number: 0766-200011), catalpol (batch number: 0808-9602). The purities of all standards were no less than 98% and suitable for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. LCW was purchased from Changchun Overseas Pharmaceutical Co. Ltd.LCW (batch number: 20190601) was grinded into powder. Each sample of LCW powder (0.5 g) was weighed precisely and ultrasonically extracted in 50 ml hydrochloric-acid methanol (1:100) for 30 min. Supplement the weight lost with hydrochloric acid-methanol (1:100) solution and then filtered through 0.22 μm nylon membrane filters. The filtrate was analyzed directly by UPLC-ESI-MS/MS. At the same time, a stock solution containing 13 reference standards was prepared in methanol. All solutions were stored at 4°C prior to analysis.Chemical profiling of LCW and reference standards were detected by an Agilent ultra-performance liquid chromatography system (UHPLC)  coupled to a Q-trap 3200 . Chromatography separation was carried out on a Waters ACQUITYUPLC HSS T3 column  maintained at 40°C. The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), and run under the following program: 0 ~ 1 min, 15% B; 1 ~ 4 min, 15 ~ 45% B; 4 ~ 10 min, 45 ~ 60% B, 10 ~ 15 min, 60 ~ 90% B; 15 ~ 18 min, 90 ~ 95% B. The sample injection volume was 5 μl and the flow rate was set at 0.2 ml/min. The mass spectrometer was fitted with an electrospray ionization source and the mass detection was operated in both positive and negative ion modes with the following setting: ion source temperature, 500°C; Sheath gas velocity, 50 psi; Auxiliary gas flow rate, 12 L/min; scan range, m/z 150–900.All components of LCW were collected from four published natural product data sources: TCMSP database , Traditiin vitro model to study the passive diffusion of drugs across the intestinal epithelium, the ingredients’ transport rates (nm/s) in Caco-2 monolayers represent the intestinal epithelial permeability in TCMSP. Components with Caco-2>-0.4 were chosen as the candidate components, because the components with a Caco-2 value less than -0.4 were not permeable. Drug-likeness is a qualitative concept used in drug design for an estimate on how “drug-like” a prospective compound is, which helps to optimize pharmacokinetic and pharmaceutical properties, such as solubility and chemical stability. The “drug-like” level of the components is 0.18, which is used as a selection criterion for the “drug-like” components in the traditional Chinese herbs  represents the percentage of an orally administered dose of unchanged drug that reaches the systemic circulation, which reveals the convergence of the ADME process . High orse herbs . After ATo obtain the targets of active components in LCW, the commonly used prediction tools, i.e., Similarity Ensemble Approach (SEA) , HitPickConstruction of the optimization space is able to maximize the search for targets of small molecules that are highly relevant to pathogenetic genes. Firstly, we used the weighted gene regulatory network and active components targets network to construct the disease-targets-components network. Degree is an important topological property in the network that can be used to evaluate the importance of nodes in the network. The nodes with higher degree than the average degree of all nodes in the disease-targets-components network were identified as hub nodes. Following this rule, the passed nodes and their edges in the disease-targets-components network were kept and defined as optimization space. The whole process can be described as follows:ppi = {N, E}, N means nodes that represent proteins, E means edges that represent protein–protein interactions derived from public databases BioGRID, STRING, Dip, HPRD, Intact, Mint, and Reactome. tarT = {P1tar, P2tar… … ntarP} means the predicted targets of active components. disD = {G1dis, G2dis… … ndisG} means the pathogenic genes of SLE. The optimization space can be calculated by the following steps.We defined NetavgD is the average degree of all nodes in the disease-targets-components network. k means the number of nodes in the disease-targets-components network. OptS represent the optimization space. The nodes in the optimization space were identified as effective proteins.Where 1, λ2, λ3… … λm}, The target number of each active compounds be defined as ω = {ω1, ω2, ω3… …ωm}, then coverage of the target number for each active compounds defined as ν = {v1, v2, v3… … vm}, the dynamic-0-1 knapsack algorithm can be described as:To optimize effective components and get the KGEC, which would be used to illustrate the molecular mechanism of LCW in the therapy of SLE. Active components which are associated with effective proteins were extracted as λ = {λν and ω, the number of active components m and the number of effective ν proteins W.Input: Output: The optimal KGEC.p-values were set at 0.05 as the cut-off criterion. The clusterProfiler package of R software . The results of analysis were annotated by Pathview (https://www.bioconductor.org/).To analyze the main function of the targets, the clusterProfiler package of R software was used to perform Gene Ontology (GO) analysis. software  was emplsoftware  for KEGGPathview  in the RLiquiritin and ferulic acid . Resiquimod . Fetal bovine serum (FBS) (Sangon Biotech (Shanghai) Co., Ltd., China). RPMI-1640 (Sangon Biotech (Shanghai) Co., Ltd., China). β-actin, Phospho-ERK1/2, Phospho-AKT, and Phospho-PI3K .2, humidified 95%. RAW 264.7 cells were seeded on 96-well plates with 1×104 per/well, 100 mm dishes with 1×106 per/dishes, and cultured for 24 h. After incubation, the RAW 264.7 cells were co-incubated with different concentrations of liquiritin, ferulic acid, and resiquimod for 24 h. The concentration of resiquimod was selected at 0.1 μg/ml.RAW 264.7 cells  were cultured in DMEM-H with 10% FBS at 37°C, in an atmosphere containing 5% CORAW 264.7 cells were seeded in 96-well plates. After drug treatment, the culture medium was removed, 100 μl 0.5 mg/ml MTT (Sangon Biotech (Shanghai) Co., Ltd., China) solution was added. After 4 h, the culture medium was removed and 100 μl DMSO (Sangon Biotech (Shanghai) Co., Ltd., China) was added. The absorbance at 570 nm was measured with a microplate reader . Cell viability is expressed as a percentage of the control.The cells were cultured in 6-well plates. After relevant treatment, the cells were collected and centrifuged to obtain a cell pellet and supernatant. The cell pellet and supernatant were stored at −80°C until required for analysis. The level of IL-6 was determined by commercial assay kits .RAW 264.7 cells were seeded in 100 mm dishes. At the end of the treatments, the cells were harvested and washed twice with cold PBS. The cells were lysed with RIPA lysis buffer  containing 1% phenylmethylsulfonylfluoride . The whole-cell lysates were centrifuged at 12,000 rpm/min for 15 min at 4°C, and the supernatants were collected. Protein concentrations were determined by bicinchoninic acid assay. Equal amounts of protein (50 μg) were separated by electrophoresis on 12% sodium dodecyl sulphate polyacrylamide gels and transferred onto PVDF membranes. These membranes were incubated with 5% (w/v) non-fat milk powder in Tris-buffered saline containing 0.1% (v/v) Tween-20 (TBST) for 2 h to block nonspecific binding sites. The membranes were then incubated overnight at 4°C with the primary antibodies. After washing with TBST, the membranes were incubated for 2 h at room temperature with the fluorescent secondary antibodies. After rewashing with TBST, the membranes were scanning by a fluorescent scanner .in vitro experiments.In this report, a novel network pharmacology module was designed to detect the KGEC and elucidate the therapeutic mechanisms of LCW in the treatment of SLE Figure 1Construction and analysis of weighted gene regulatory network is the basis and key step to understand the pathogenesis and provide intervention strategies of SLE. In order to construct a comprehensive weighted gene network of SLE, the PPI data sets from public databases BioGRID, STRING, Dip, HPRD, Intact, Mint and Reactome were used to construct the PPI network. 1201 genes from DisGeNET which confirmed associated with SLE were extracted and mapped to the PPI network to construct the weighted gene regulatory network of SLE. The weighted gene regulatory network contains 950 nodes and 6,984 edges Figure 2Thirteen components in the chromatograms of the LCW sample were identified and assigned by comparing the retention time with those of the reference compounds Figure 4By a systematic search of components from public databases of 16 herbs, a total of 1693 components were retrieved in LCW in As can be seen from via suppressing IKBα/NF-kappa B signaling pathway, which has been reported associating with SLE. Phillyrin (LCW187), one of the most effective constituents in LQ, has good antibacterial and anti-inflammatory activity, which can regulate MyD88/IκBα/NF-kappa B signaling pathway by controlling the expression of IκBα, IL-1β, IL-6 and TNF-α, which would be a benefit to SLE , the specific component of JYH, protects against vascular inflammation in mice and TNF-α induced monocyte adhesion to endothelial cells t to SLE . As the t to SLE  which sut to SLE . Thus, tin vitro , and the pathogenic genes enriched in 131 pathways (p < 0.05). The effective proteins enriched pathways were found to cover 96% of the pathogenic genes enriched pathways , Th17 cell differentiation (hsa04659), T cell receptor signaling pathway (hsa04660), TNF signaling pathway (hsa04668), MAPK signaling pathway (hsa04010), Toll-like receptor signaling pathway (hsa04620), NF-kappa B signaling pathway (hsa04064), IL-17 signaling pathway (hsa04657), and B cell receptor signaling pathway (hsa04662) . This deThe CI module was established to capture the KGEC, which would be used to illustrate the molecular mechanism of LCW in the therapy of SLE. According to the contribution accumulation results, the top 11 components including vanillic acid (LCW190), pinoresinol monomethyl ether (LCW169), phillyrin (LCW187), oleic acid (LCW160), palmitic acid (LCW100), stearic acid (LCW139), ferulic acid (LCW75), methyl caffeate (LCW137), p-coumaric acid (LCW186), ellagic acid (LCW8), and wogonin (LCW130) contribute to 51.89% target coverage of effective proteins. For further analysis, 82 components which can contribute to 90.18% targets coverage of effective proteins were selected as KGEC Table 3.p < 0.05), and the pathogenic genes enriched in 131 pathways (p < 0.05). The KGEC targets enriched pathways were found to cover 80.15% of the pathogenic genes enriched pathways , leukocyte activation involved in inflammatory response , production of molecular mediator involved in inflammatory response , and inflammatory response to antigenic stimulus . Important genes involved in inflammation in these pathways include TNF, NOS2, TLR2, etc. these genes are involved in inflammation of SLE . The patInterestingly, LCW regulates the GO cellular component of SLE, including mitochondrial respiratory chain complex I , mitochondrial respiratory chain , and oxidoreductase complex . Accumulating evidence indicates that mitochondrial dysfunction plays important roles in the pathogenesis of SLE, including mitochondrial DNA damage, mitochondrial dynamics change, abnormal mitochondrial biogenesis and energy metabolism, oxidative stress, inflammatory reactions . Given tMoreover, LCW regulates the GO molecular function of SLE including protein serine/threonine kinase activity , transcription factor activity , and protein tyrosine kinase activity . Increasing evidence confirmed enzymes play different roles in regulating inflammation and immunity . Our resSLE is a chronic autoimmune disease involving multiple organs and systems characterized by the production of multiple autoantibodies. Previous studies confirmed that SLE-associated pathways could be decomposed into several functional modules such as immune response, synthesis of inflammatory mediators, autoimmune pathology, neutrophil recruitment, immunity to extracellular pathogens, cell cycle progression, protein synthesis, apoptosis, and so forth. Increasing evidence indicate that PI3K-Akt signaling pathway (hsa04151), TNF signaling pathway (hsa04668), NF-kappa B signaling pathway (hsa04064) and IL-17 signaling pathway (hsa04657) response to these functional modules. Such as, PI3K-Akt signaling pathway (hsa04151) has been reported involved in the inhibition of apoptosis, cell proliferation and expression of inflammatory cytokines. TNF signaling pathway (hsa04668) can induce a wide range of intracellular signal pathways including apoptosis and cell survival as well as inflammation and immunity. NF-kappa B signaling pathway (hsa04064) is the generic name of a family of transcription factors that function as dimers and regulate genes involved in immunity, inflammation and cell survival. While IL-17 signaling pathway (hsa04657) work as a subset of cytokines consisting of IL-17A-F, plays crucial roles in both acute and chronic inflammatory responses. For exploring the mechanism of LCW in the treatment of SLE at the system level, we constructed a comprehensive signaling pathway use four important molecular pathways  is one of the top pathways in the treatment of SLE with KGEC in LCW. KGEC regulates 10 targets located upstream of PI3K-Akt signaling pathway (hsa04151), such as RTK, TLR2/4 and JAK, and 24 targets located downstream pathways, such as PI3K, AKT, and mTOR. The downstream targets account for more than 70%. KGEC may activate downstream of the PI3K and AKT proteins through the upstream TLR2/4, resulting in downstream GSK3, RXRα, and CREB cascade amplification, which are closely related to SLE immune response, cell proliferation and protein synthesis . Most ofNF-kappa B signaling pathway (hsa04064) and IL-17 signaling pathway (hsa04657) are also important pathways in the treatment of SLE by LCW. The targets regulated by KGEC are more downstream of the pathway. For example, 19 targets such as TAK1, IL-1 β, and COX2 of KGEC are located downstream of NF-kappa B signaling pathway (hsa04064). KGEC in LCW can affect upstream BCR, and then activate downstream AP-1, to further affect a series of inflammatory and immune-related proteins such as IL-1 β, TNF α, and COX2 related to SLE. The 12 targets of KGEC, such as MAPKs, AP-1 and LCN2, in the IL-17 signaling pathway (hsa04657) are located downstream of the pathway. KGEC in LCW can activate downstream AP-1, through upstream MAPKs, and affect a series of inflammatory and immune-related proteins such as LCN2, MMP1, and MMP3 related to SLE . Therefop < 0.01). In contrast, pretreatment with liquiritin and ferulic acid significantly attenuated the phenomenon induced by resiquimod  and SDF19-0402-P02], Hong Kong Baptist University Interdisciplinary Research Matching Scheme [grant No. RC/IRCs/17-18/04], the General Research Fund of Hong Kong Research Grants Council  and the National S&T Major Project for “Major New Drugs Innovation and Development” [2017ZX09301047].The 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.