Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology

Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology Basavaraj Mallikarjunayya Vastrad, Shivaling Pattanashetti, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7391829/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A Protein-Protein Interaction (PPI) network and modules were constructed and analyzed. We predicted regulatory miRNAs and TFs of hub-genes through miRNet and NetworkAnalyst online database. Drug predicted for BD treatment was screened out from the DrugBank through NetworkAnalyst. Molecular docking studies were carried out for predicting novel drug molecules. Receiver operating characteristic curve (ROC) curves was drawn to elucidate the diagnostic value of hub genes. In this investigation, total of 957 DEGs, including 477 up regulated and 480 down regulated genes. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. We screened hub genes include UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11. We also predicted miRNAs, TFs and drugs include hsa-mir-8085, hsa-mir-4514, HMG20B, STAT3, phenserine and roflumilast. Molecular docking technology screened out three small molecule compounds, including Kakkalide, Divaricatol and Brucine small molecule compounds. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD. Bioinformatics bioinformatics analysis enrichment analysis bipolar disorder differentially expressed genes next generation sequencing molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Introduction Bipolar disorder (BD) is a psychiatric disorder characterized by recurrent manic or hypomanic and depressive episodes [ 1 ]. According to the report of World Health Organization (WHO), BD is the sixth cause of disability-adjusted life years among all psychiatric diseases [ 2 ]. The numbers of cases of BD are rising worldwide and it has become an important mental health concern. It is estimated that the incidence of BD was 30–69% in Europe and in the United States [ 3 ]. The risk factors associated with BD are mainly caused by obesity [ 4 ], anxiety [ 5 ], depression [ 6 ], cognitive dysfunction [ 7 ], pregnancy [ 8 ], hypertension [ 9 ], cardiovascular diseases [ 10 ], diabetes mellitus [ 11 ] and genetic factor [ 12 ]. A numerous tests are available for screening and detecting BD, but not yet achieved satisfactory results [ 13 ]. Therefore, there is an urgent need to develop novel diagnostic strategies and therapeutic agents to improve the prognosis of patients with BD. In recent years, genes and signaling pathway have been found to be associated with changes in neuron structure and function in BD patients. For example, studies have shown that genes include CACNA1C [ 14 ], BDNF (brain derived neurotrophic factor) [ 15 ], GSK3 (glycogen synthetase kinase-3) [ 16 ], DUSP6 [ 17 ] and SYNE1 [ 18 ] were associated BD. Signaling pathways include kynurenine signaling pathway [ 19 ], Wnt and GSK3 signaling pathways [ 20 ], cAMP–CREB signaling pathways [ 21 ] PI3K/AKT/HIF1-a signaling pathway [ 22 ], MAP kinase and phosphoinositide signaling pathway [ 23 ], and Notch signaling pathway [ 24 ] have been shown to be associated with BD. Consequently, it is crucial to fully understand the molecular pathogenesis of BD to improve the early diagnosis, treatment, and prognosis of BD. Bioinformatics approaches based on next generation sequencing (NGS) data provide novel opportunities to uncover the underlying molecular mechanism of various diseases [ 25 – 26 ]. NGS data are usually deposited and available in free public website NCBI-Gene Expression Omnibus database (NCBI-GEO) ( https://www.ncbi.nlm.nih.gov/geo ) [ 27 ]. Integrated bioinformatics analyses of NGS data derived from investigation of BD could help identify the hub genes and further demonstrate their related functions and potential therapeutic targets in BD. In the current investigation, NGS data GSE124326 [ 28 ] download from NCBI-GEO database. A total of 240 BD samples and 240 normal control samples were available. Differentially expressed genes (DEGs) between BD and normal control samples were filtered and obtained using the R bioconductor tool DESeq2. Gene Ontology (GO) and REACTOME pathway enrichment analysis were conducted. The functions of the DEGs were further assessed by PPI network and modules to identify the hub genes in BD. Moreover, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network of the hub genes were established, Molecular docking studies was carried out to predict novel drug molecules. The diagnostic roles of hub genes were analyzed using receiver operating characteristic curve (ROC) analysis. With the above approaches, it is hoped that our results might provide a preliminary insight into the molecular mechanism of BD and a search for possible novel biomarkers and drug molecules. Materials and methods Data resources The NGS dataset GSE124326 [ 28 ] based on GPL16791 Illumina HiSeq 2500 (Homo sapiens) was acquired from the Gene Expression Ominibus (GEO) database. The GSE124326 dataset contained 240 BD samples and 240 normal control samples. Identification of DEGs DESeq2 package of R software [ 29 ] was used to analyze the DEGs between PD and normal control in the NGS data of GSE124326. The adjusted P-value and [log FC] were calculated. The Benjamini & Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [ 30 ]. The statistically significant DEGs were identified according to adjusted P-value 0.235 for up regulated genes and [logFC] < -0.52 for down regulated genes. The DEGs are presented as volcano plot and heat map generated using ggplot2 and gplot in R Bioconductor. GO and pathway enrichment analyses of DEGs g:Profiler ( http://biit.cs.ut.ee/gprofiler/ ) [ 31 ] was used to perform GO functional and REACTOME pathway enrichment analyses. GO ( http://www.geneontology.org ) [ 32 ] annotation was applied to define gene functions in three terms: biological process (BP), cellular component (CC) and molecular function (MF). REACTOME ( https://reactome.org/ ) [ 33 ] is a pathway database resource for understanding high-level biological functions and utilities. p < 0.05 was considered as statistically significant. Construction of the PPI network and module analysis In order to obtain interacting proteins related to DEGs, the STRING version 11.5 database ( https://string-db.org/ ) was used [ 34 ]. Cytoscape ( http://www.cytoscape.org/ ) (version 3.9.1) [ 35 ] was used to visualize the PPI network of DEGs. The Network Analyzer plug-in was used to explore hub genes, and the hub genes were generated using four topological parameters include node degree [ 36 ], betweenness [ 37 ], stress [ 38 ] and closeness [ 39 ]. The intersect function was used to identify the hub genes. The PEWCC1 [ 40 ] was used to search modules of the PPI network. MiRNA-hub gene regulatory network construction MiRNA regulates gene expression under defined disease conditions through interaction with hub genes during the post transcriptional stage was analyzed. We applied miRNet database ( https://www.mirnet.ca/ ) [ 41 ] to integrate miRNA databases (TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0.). We visualized miRNA-hub gene regulatory network by employing Cytoscape software [ 35 ]. TF-hub gene regulatory network construction TF regulates gene expression under defined disease conditions through interaction with hub genes during the transcriptional stage was analyzed. We applied NetworkAnalyst database ( https://www.networkanalyst.ca/ ) [ 42 ] to integrate TF database (ENCODE). We visualized TF-hub gene regulatory network by employing Cytoscape software [ 35 ]. Drug-hub gene interaction network construction Drugs molecules predicted for BD treatment through interaction with hub genes. We applied NetworkAnalyst database ( https://www.networkanalyst.ca/ ) [ 42 ] to integrate drug database (DrugBank). We visualized drug-hub gene interaction network by employing Cytoscape software [ 35 ]. Receiver operating characteristic curve (ROC) analysis The hub genes were used to identify biomarkers with high sensitivity and specificity for BD diagnosis. The ROC curves were plotted and area under curve (AUC) was calculated separately to evaluate the performance of each model using the R packages “pROC” [ 43 ]. A AUC > 0.9 indicated that the model had a good fitting effect. Insilico molecular docking studies Swiss-model, RCSB PDB, Prank web, NCBI Gene, ChEMBL, BindingDB, CASTp, Fpocket, DoGSiteScorer, ChemDraw, Avogadro tool, Autodock 1.7.1, and Autodock Vina tools, Biovia Discovery Studio client 2021, ADMET lab 3.0 web server. Receptor Selection Based on Gene Information The selection of target receptor structures for molecular docking was initiated by identifying genes of interest relevant to the disease condition under study. Genes showing differential expression or known functional involvement in disease pathophysiology were prioritized based on genomic and transcriptomic data [ 44 – 45 ]. The corresponding protein products of these genes were then retrieved using UniProt ( https://www.uniprot.org/ ). This databases provide curated information on gene-protein relationships, functional domains, isoforms, and organism-specific variants [ 46 ]. Once the protein names and UniProt accessions were determined, three-dimensional structures of these proteins were searched in the Protein Data Bank (PDB) ( https://www.rcsb.org/ ). Priority was given to experimentally determined structures (X-ray crystallography or cryo-EM) derived from Homo sapiens , with resolutions ≤ 2.5 Å and co-crystallized ligands when available. Structures were evaluated for completeness, presence of functional domains, and biologically relevant binding conformations. In cases where multiple structures were available, the one with the most complete and biologically relevant ligand-protein interaction site was selected [ 47 – 48 ]. If co-crystallized ligands were present in the selected PDB entry, these were examined to confirm their biological relevance (e.g., substrate, inhibitor, agonist). The functional nature of the ligand was cross-validated using ligand bioactivity databases such as ChEMBL ( https://www.ebi.ac.uk/chembl/ ) and BindingDB ( https://www.bindingdb.org/ ), where half-maximal inhibitory concentration (IC₅₀), binding affinity (K i ), or other pharmacological data are reported [ 49 – 50 ]. To confirm the presence and accessibility of druggable binding pockets, cavity detection tools such as CASTp, Fpocket, or DoGSiteScorer were used [ 51 – 52 ]. This ensured that the selected structure was suitable for molecular docking, with a validated and accessible ligand-binding domain. Overall, this systematic approach starting from gene selection to receptor structure identification ensured biological relevance, structural accuracy, and docking compatibility of the chosen targets. In present study, receptor structures corresponding to selected genes were identified through a systematic database-driven approach prioritizing experimentally determined human protein structures. For the CNBD2 gene, which encodes a cyclic nucleotide-binding domain protein involved in ion channel regulation, the crystal structure of the homologous human HCN2 channel domain (PDB ID: 3U10) bound to cyclic AMP (cAMP) was selected. This structure, determined by X-ray crystallography at 2.35 Å resolution, was used as a representative template due to its functional and structural similarity to CNBD2. For the ubiquitin B (UBB) gene, which encodes a non-druggable ubiquitin protein, a functionally relevant receptor protein ubiquitin-specific protease 7 (USP7) was selected instead. The human USP7 structure (PDB ID: 5NGE), co-crystallized with the potent small-molecule inhibitor FT671 (IC₅₀ ≈ 52 nM), was chosen due to its direct interaction with ubiquitin and its critical role in the deubiquitination pathway. In the case of UBE2D1, a ubiquitin-conjugating E2 enzyme, no crystal structure was available; therefore, a highly homologous protein, UBE2D3 (sharing > 95% sequence identity), was used as a proxy. The human UBE2D3–CBL-B complex structure (PDB ID: 5TRF), resolved by X-ray crystallography at 2.4 Å resolution, was selected, as it includes a small-molecule modulator and represents the active conformation of the E2-E3 ubiquitination interface. For Caveolin-1 (CAV1), a membrane scaffolding protein implicated in signalling and endocytosis, the high-resolution cryo-EM structure of the oligomeric 8S complex (PDB ID: 7RLE) was chosen to study its biologically active multimeric conformation. Although no small-molecule ligand is present, this structure provides critical insight into the structural organization of CAV1. All structures were obtained from the RCSB Protein Data Bank ( https://www.rcsb.org ), filtered for Homo sapiens origin, high resolution (≤ 3.7 Å), and functional relevance. Receptor and ligand preparation The selected targets with their structural information are presented in Table 1. Using the Swiss-Model web server, the missing residues were remodelled and downloaded in pdb format [ 53 ]. Following the identification of the binding sites utilizing the server Prank web [ 54 ],the protein's pdb format was entered into Software Auto Dock Tools 1.7.1, and water molecules and atoms were eliminated. The receptor has also been checked for missing amino acid residues, Kollman charges have been fixed, and only polar hydrogens have been inserted. In order to cover the entire receptor, the grid was fixed using the Autogrid program. The grid dimension file [ 55 ] was then saved. Ligand structures were created using ChemDraw and saved as SMILES. The phytoconstituents which are used in present study were shown in Fig. 1. They were then loaded into Avogadro software to convert them from 2D to 3D structures in PDB format [ 56 ]. The ligand's pdb format was then entered into Auto Dock Tools 1.7.1, and the ligand molecule's root was identified and selected. Lastly, the pdbqt format was used to save the ligand molecule. Performing AutodockVina Auto Dock Vina can be executed using the command line (cmd) or the Autodock tool. The configuration file was ready for Autodock Vina to execute; the grid dimension file that was previously saved includes the protein's n-points, active site, and x, y, and z coordinates. That information was added to the configuration file, which was made to contain the protein's active site details. It also comes with an output file in pdbqt format and a log file in .txt format. The command line was used to run Autodock Vina, “vina.exe -- config config.txt” was the command used to launch Auto Dock Vina. Docked coordinates were output in the pdbqt format when the program was finished. Receptor-ligand interactions were then visualized using the file, and binding affinity was ascertained using the log.txt file [ 57 ]. Visualization The Biovia Discovery Studio Client 2021 program has been used to visualize the docking results. Next, a PNG file is created from the 3D image of the docked ligand and the 2D image of the docked ligand that is attached to various amino acids [ 58 ]. In silico ADMET properties However, high binding affinity alone is insufficient. ADMET profiling, evaluating Absorption, Distribution, Metabolism, Excretion, and Toxicity,is essential to ensure that candidate compounds possess favourable pharmacokinetic properties and safety profiles. Without this, compounds may fail in later stages despite strong docking results. Integration of docking with in silico ADMET prediction streamlines compound selection by filtering out unsuitable leads early in the discovery pipeline [ 59 ].ADMET properties were predicted with the help of the ADMET lab free web server, as previously reported [ 60 ]. Results Identification of DEGs GSE124326 was selected and underwent DEGs analysis using “DESeq2” package in R software. There was a total of 957 DEGs between BD and normal control samples, including 477 up regulated DEGs and 480 down regulated genes (Table 2). A volcano plot was constructed for the DEGs and is presented in Fig. 2. The DEGs are presented by a heat map in Fig. 3. GO and pathway enrichment analyses of DEGs To obtain a deeper insight into the biological functions of DEGs, GO annotation and REACTOME pathway enrichment analyses were performed. The enriched GO terms were shown in Table 3. In the present investigation, up regulated genes were mainly enriched in transport (BP), localization (BP), cytoplasm (CC), intrinsic component (CC), enzyme regulator activity (MF) and protein binding (MF) and shown in Fig. 4. Down regulated genes were mainly involved in multicellular organismal process (BP), developmental process (BP), cell periphery (CC), plasma membrane (CC), metal ion binding (MF) and cation binding (MF) and shown in Fig. 5. The up regulated and down regulated genes from the REACTOME pathway enrichment analysis are shown in Table 4, The up regulated genes from the REACTOME pathway enrichment analysis were those for the neutrophil degranulation and immune system and shown in Fig. 4. The most down regulated genes from the REACTOME pathway enrichment were for extracellular matrix organization and diseases of metabolism and shown in Fig. 5. Construction of the PPI and module analysis Based on the information in the STRING database, the hub nodes with higher node degree, betweenness, stress and closeness were screened (Table 5). The PPI network contained 4360 nodes and 9761 edges (Fig. 6). UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 were the hub genes with the highest values of topological parameters (node degree, betweenness, stress and closeness). Furthermore, the two significant modules were extracted from the PPI network. Module 1 contained 71 gene nodes, including RPL39, RPL31, RPL23, RPL35A, RPL11, RPS24, UBB and RPL34 with 434 edges (Fig. 7). Functional enrichment analysis of the hub genes in this module was mainly related to cellular responses to stress, axon guidance, metabolism, transport, localization, cytoplasm, immune system and protein binding. Module 2 contained 70 gene nodes, including FZD4, MSTN, EGR3, NOTCH3, PAX8, SFRP5 and WNT4 with 132 edges (Fig. 8). Functional enrichment analysis of the hub genes in this module was mainly related to multicellular organismal process, developmental process, signaling by receptor tyrosine, diseases of metabolism and cell periphery (Fig. 9). MiRNA-hub gene regulatory network construction To predict the miRNAs for the hub genes, we used independent online tool (miRNet). The miRNA-hub gene regulatory network contained 2378 nodes, including 2090 miRNAs and 288 hub genes, and 11478 edges (Fig. 11). TUBB2A that was modulated by 206 miRNAs (ex; hsa-mir-8085), BCL2L1 that was modulated by 179 miRNAs (ex; hsa-mir-6735-5p), UBE2D1 that was modulated by 84 miRNAs (ex; hsa-mir-548ap-5p), UBB that was modulated by 81 miRNAs (ex; hsa-mir-132-3p), RPS24 that was modulated by 79 miRNAs (ex; hsa-mir-27a-3p), CAV1 that was modulated by 115 miRNAs (ex; hsa-mir-4514), EPHA2 that was modulated by 90 miRNAs (ex; hsa-mir-3133), ERBB3 that was modulated by 53 miRNAs (ex; hsa-mir-4328), MYH11 that was modulated by 50 miRNAs (ex; hsa-mir-643) and WNT4 that was modulated by 42 miRNAs (ex; hsa-mir-6749-3p) and are listed in Table 6 TF-hub gene regulatory network construction To predict the TF for the hub genes, we used independent online tool (NetworkAnalyst). The TF-hub gene regulatory network contained 582 nodes, including 336 TFs and 246 hub genes, and 6606 edges (Fig. 12). BCL2L1 that was modulated by 115 TFs (ex; HMG20B), RPL23 that was modulated by 108 TFs (ex; ATF3), TUBB2A that was modulated by 59 TFs (ex; SMARCE1), UBB that was modulated by 59 TFs (ex; NCOR1), RPL26 that was modulated by 49 TFs (ex; POLR2A), EPHA2 that was modulated by 91 TFs (ex; STAT3), FOXA1 that was modulated by 79 TFs (ex; RARA), ERBB3 that was modulated by 54 TFs (ex; KLF4), FLNC that was modulated by 53 TFs (ex; HIC1) and CAV1 that was modulated by 24 TFs (ex; ESRRA) and are listed in Table 6. Drug-hub gene interaction network construction To predict the drug molecule to target hub genes, we used independent online tool (NetworkAnalyst) (Fig. 13). ACHE that was targeted by 54 drugs (ex; Phenserine), KCNH2 that was targeted by 29 drugs (ex; Amiodarone), UQCRB that was targeted by 9 drugs (ex; Famoxadone), EPHB4 that was targeted by 9 drugs (ex; 3-({4-[(5-chloro-1,3-benzodioxol-4-yl)amino]pyrimidin-2-yl}amino)benzamide), TUBA1A that was targeted by 7 drugs (ex; Vinblastine), PDE4C that was targeted by 8 drugs (ex; Roflumilast), CACNA1B that was targeted by 6 drugs (ex; Spironolactone), SCN4A that was targeted by 5 drugs (ex; Zonisamide), GCK that was targeted by 5 drugs (ex; 2-(methylamino)-N-(4-methyl-1,3-thiazol-2-yl)-5-[(4-methyl-4H-1,2,4-triazol-3-yl)sulfanyl]benzamide) and RYR1 that was targeted by 4 drugs (ex; Suramin) and are listed in Table 7. Receiver operating characteristic curve (ROC) analysis ROC curve analyses were performed to verify the hub genes, and area under the curve (AUC) values was calculated. The diagnostic value of hub genes in BD samples and normal control samples are as follow: UBB (AUC: 0.902), UBE2D1 (AUC:0.907), TUBA1A (AUC:0.920), RPL11 (AUC:0.948), RPS24 (AUC:0.940), NOTCH3 (AUC:0.942), CAV1 (AUC:0.917), CNBD2 (AUC:0.922), CCNA1 (AUC: 0.915) and MYH11 (AUC: 0.905) (Fig. 14). Therefore, we hypothesise that UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 might be biomarkers For BD. Insilico molecular docking studies Molecular docking studies were conducted to assess the binding affinity and interaction profiles of phytoconstituentsKakkalide, Divaricatol, and Brucine Bwith proteins encoded by genes implicated in bipolar disorder, namely UBB, CNBD2, UBE2D1, and CAV1. The UBB gene, which is up regulated in bipolar disorder, showed the strongest binding with its co-crystallized ligand FT671 (-9.6 kcal/mol) via hydrogen bonds at PHE409 and ASN418. Kakkalide also showed good binding (-7.9 kcal/mol) and formed multiple hydrogen bonds with LEU267, GLN268, VAL531, ARG239, and ASN236. In the case of CNBD2, which is down regulated, the co-crystallized ligand cAMP demonstrated weaker binding (-6.5 kcal/mol) with limited hydrogen bonding to ARG507. Kakkalide, in contrast, had a stronger affinity (-8.4 kcal/mol) and formed multiple hydrogen bonds with GLN509, TYR631, GLU515, and ARG507, and ILE341. Brucine B generally showed weaker or incomplete binding profiles across targets. Binding affinity and amino acid interaction were given in Supplementary Table S1, and 2D and 3D amino interaction images were given in Fig. 14 to Fig. 15 . For the upregulated gene UBE2D1, the standard ligand 7HC bound with an affinity of − 8.8 kcal/mol, forming hydrogen bonds at HIS14 and VAL93, while Kakkalide displayed a moderate affinity (-7.9 kcal/mol) and interacted via LEU54. Regarding CAV1, which is downregulated, the co-crystallized ligand EDK had a moderate affinity of -7.4 kcal/mol with a single hydrogen bond at LEU453. However, Divaricatol showed stronger binding (-8.2 kcal/mol) and formed hydrogen bonds with SER342, CYS285, LEU340, and ILE341. Brucine B generally showed weaker or incomplete binding profiles across targets. Binding affinity and amino acid interaction were given in Supplementary Table S2, and 2D and 3D amino interaction images were given in Fig. 16 to Fig. 17. ADMET Results The ADMET profiles of seven compoundsKakkalide, Brucine B, Divaricatol, FT671, cAMP, 7HC, and EDKwere evaluated using ADMETlab 2.0. Brucine B demonstrated high bioavailability, FT671 and cAMP showed moderate scores, while 7HC and EDK exhibited very low bioavailability. Solubility varied, with most compounds being moderately to poorly soluble. EDK and 7HC were the least soluble (Log S -5.02 and − 4.79 respectively). Lipophilicity was highest in 7HC and EDK, indicating poor aqueous solubility, while cAMP was distinctly hydrophilic. Only cAMP displayed high human intestinal absorption (~ 94.5%), whereas other compounds had poor absorption values. FT671, 7HC, and EDK were BBB permeant; cAMP showed partial CNS penetration. Among them, Brucine B had the highest unbound plasma fraction, while EDK had the lowest. EDK and 7HC acted as multiCYP inhibitors, showing potential for drug-drug interactions. Conversely, Divaricatol, FT671, Kakkalide, and Brucine B were metabolically safer. EDK showed the highest total clearance, indicating rapid systemic elimination. Kakkalide exhibited the lowest LD50 value (~ 0.038 mol/kg), suggesting higher acute toxicity. Hepatotoxicity was predicted for nearly all compounds except 7HC, which showed a moderate risk all the values were given in Supplementary Table S3. Discussion The BD is the most common psychiatric disorder worldwide. Although numerous advances have been made in the treatment of BD, the prognosis has remained poor. Therefore, it is crucial to elucidate the molecular mechanism of BD for understanding of the disease progression to develop novel therapeutic targets. Due to the rapid advancement of NGS technology, bioinformatics analysis might contribute to identifying the DEGs and functional pathways involved in the BD. In this investigation, NGS dataset was selected to identify the DEGs between BD and normal control samples. As a result, 957 DEGs including 477 up regulated and 480 down regulated genes were identified. Wang et al. [ 61 ] and Joshi et al. [ 62 ] showed altered expression of HBG1 and S100A8 in cardiovascular diseases. Lu et al [ 63 ] reported that S100A8 promoted cognitive dysfunction. S100A8 [ 64 ] was altered expression in obesity and might serve as a potential prognostic biomarker of obesity. S100A8 [ 65 ] and KLHL40 [ 66 ] plays an emerging role in pregnancy. The expression of S100A8 [ 67 ] is altered in diabetes mellitus. The above findings might contribute to a better understanding of the molecular mechanisms underlying the pathogenesis of BD. GO and REACTOME pathway enrichment analysis were conducted to demonstrate interactions of the DEGs. Immune system [ 68 ], axon guidance [ 69 ], metabolism [ 70 ], diseases of metabolism [ 71 ] and neuronal system [ 72 ] were lined with advancement of BD. Altered expression of enriched genes include SLC6A9 [ 73 ], RHD (Rh blood group D antigen) [ 74 ], CHRFAM7A [ 75 ], ANXA3 [ 76 ], SLC1A5 [ 77 ], KCNH2 [ 78 ], HP (haptoglobin) [ 79 ], SELENBP1 [ 80 ], SNCA (synuclein alpha) [ 81 ], TGM2 [ 82 ], PINK1 [ 83 ], B2M [ 84 ], QPCT (glutaminyl-peptide cyclotransferase) [ 85 ], CBS (cystathionine beta-synthase) [ 86 ], NQO2 [ 87 ], GLRX5 [ 88 ], BASP1 [ 89 ], GAS7 [ 90 ], GPX1 [ 91 ], OLIG2 [ 92 ], RPTN (repetin) [ 93 ], IL33 [ 94 ], SOX10 [ 95 ], GRIK1 [ 96 ], ZFPM2 [ 97 ], SHANK3 [ 98 ], ERBB3 [ 99 ], ARC (activity regulated cytoskeleton associated protein) [ 100 ], GFAP (glial fibrillary acidic protein) [ 101 ], PLAT (plasminogen activator, tissue type) [ 102 ], GRIK5 [ 103 ], CACNB2 [ 104 ], NRXN2 [ 105 ], TERT (telomerase reverse transcriptase) [ 106 ], GNB3 [ 107 ], L1CAM [ 108 ], EGR3 [ 109 ], CAV1 [ 110 ], CACNA1B [ 111 ], MAGI1 [ 112 ], KIR2DL1 [ 113 ], PAH (phenylalanine hydroxylase) [ 114 ] and CYP3A5 [ 115 ] have been shown in schizophrenia. Recent studies showed that SLC6A9 [ 116 ], FKBP1B [ 117 ], S100A12 [ 118 ], SLC6A19 [ 119 ], TXN (thioredoxin) [ 120 ], TLR9 [ 121 ], HP (haptoglobin) [ 122 ], ARG1 [ 123 ], PINK1 [ 124 ], B2M [ 125 ], C5AR1 [ 126 ], MYADM (myeloid associated differentiation marker) [ 127 ], CBS (cystathionine beta-synthase) [ 128 ], GPX1 [ 129 ], SIAH2 [ 130 ], PRDX2 [ 131 ], RDH8 [ 132 ], CYP11B2 [ 133 ], RARRES2 [ 134 ], NOX1 [ 135 ], IL33 [ 136 ], OTC (ornithine transcarbamylase) [ 137 ], CYP1A1 [ 138 ], NFATC4 [ 139 ], TSLP (thymic stromal lymphopoietin) [ 140 ], WNT4 [ 141 ], MGP (matrix Gla protein) [ 142 ], FGFBP1 [ 143 ], GHR (growth hormone receptor) [ 144 ], ERBB3 [ 145 ], GFAP (glial fibrillary acidic protein) [ 146 ], CCDC40 [ 147 ], CACNB2 [ 148 ], CD34 [ 149 ], NOTCH3 [ 150 ], TERT (telomerase reverse transcriptase) [ 151 ], GNB3 [ 152 ], TP73 [ 153 ], RYR2 [ 154 ], ENPEP (glutamyl aminopeptidase) [ 155 ], SCN7A [ 156 ], WNK4 [ 157 ], SFRP5 [ 158 ], GDF15 [ 159 ], CAV1 [ 160 ], KCNA5 [ 161 ], FOXC1 [ 162 ], ASIC1 [ 163 ], VASH2 [ 164 ], CXCL8 [ 165 ], PAPPA2 [ 166 ], KCNMA1 [ 167 ], LOX (lysyl oxidase) [ 168 ], SPARCL1 [ 169 ] and CYP3A5 [ 170 ] might have the potential to be used as diagnostic biomarkers of hypertension. Previous studies have reported that enriched genes include RHD (Rh blood group D antigen) [ 171 ], S100A12 [ 172 ], TXN (thioredoxin) [ 173 ], TLR9 [ 174 ], S100P [ 175 ], TAGLN2 [ 176 ], S100A9 [ 177 ], CA1 [ 178 ], HP (haptoglobin) [ 179 ], RPL39 [ 180 ], F5 [ 181 ], PINK1 [ 182 ], B2M [ 183 ], S100A11 [ 184 ], SLC4A1 [ 185 ], CBS (cystathionine beta-synthase) [ 186 ], AHSP (alpha hemoglobin stabilizing protein) [ 187 ], F12 [ 188 ], EPHB4 [ 189 ], NFE2 [ 190 ], VRK1 [ 191 ], GPX1 [ 192 ], FOXA1 [ 193 ], CYP11B2 [ 194 ], NOX1 [ 195 ], IL33 [ 196 ], NPHS1 [ 197 ], OTC (ornithine transcarbamylase) [ 198 ], SULF1 [ 199 ], CYP1A1 [ 200 ], DCN (decorin) [ 201 ], ADAMTS7 [ 202 ], WNT4 [ 203 ], LAMA4 [ 204 ], SCN4A [ 205 ], CACNB2 [ 206 ], GNB3 [ 207 ], ENPEP (glutamyl aminopeptidase) [ 208 ], WNK4 [ 209 ], FOXC1 [ 210 ], PAX8 [ 211 ], ROBO1 [ 212 ], CXCL8 [ 213 ], PAPPA2 [ 214 ], LOX (lysyl oxidase) [ 215 ], NOSTRIN (nitric oxide synthase trafficking) [ 216 ], MUC16 [ 217 ], MIOX (myo-inositol oxygenase) [ 218 ], CYP11A1 [ 219 ] and CYP3A5 [ 220 ] are involved in the pregnancy complications. Modification in the activity and expression of enriched genes include RHD (Rh blood group D antigen) [ 221 ], S100A12 [ 222 ], TLR9 [ 223 ], ANXA3 [ 224 ], S100P [ 225 ], TAGLN2 [ 226 ], S100A9 [ 227 ], KCNH2 [ 228 ], HP (haptoglobin) [ 229 ], SELENBP1 [ 230 ], TANGO2 [ 231 ], PINK1 [ 232 ], TFR2 [ 233 ], B2M [ 234 ], LTBP2 [ 235 ], PGLYRP1 [ 236 ], HRH2 [ 237 ], CLEC5A [ 238 ], PLSCR4 [ 239 ], S100A11 [ 240 ], PPBP (pro-platelet basic protein) [ 241 ], RAP1GAP [ 242 ], CBS (cystathionine beta-synthase) [ 243 ], RBM38 [ 244 ], MYL4 [ 245 ], EPHB4 [ 246 ], TNNT1 [ 247 ], KBTBD7 [ 248 ], GPX1 [ 249 ], SIAH2 [ 130 ], FOXO4 [ 250 ], PRDX2 [ 251 ], MAF1 [ 252 ], KANK2 [ 253 ], SFRP2 [ 254 ], BGN (biglycan) [ 255 ], HSPB7 [ 256 ], ABCG8 [ 257 ], CYP11B2 [ 258 ], RARRES2 [ 259 ], NOX1 [ 260 ], CPE (carboxypeptidase E) [ 261 ], IL33 [ 262 ], OTC (ornithine transcarbamylase) [ 137 ], CYP1A1 [ 263 ], LRIG3 [ 264 ], GJA1 [ 265 ], NFATC4 [ 139 ], SEMA3F [ 266 ], CDH11 [ 267 ], DCN (decorin) [ 268 ], TSLP (thymic stromal lymphopoietin) [ 269 ], ADAMTS7 [ 270 ], C1QTNF1 [ 271 ], SFTPB (surfactant protein B) [ 272 ], WNT4 [ 273 ], MGP (matrix Gla protein) [ 274 ], SHANK3 [ 275 ], ALOX12B [ 276 ], MYBPHL (myosin binding protein H like) [ 277 ], GHR (growth hormone receptor) [ 278 ], ERBB3 [ 279 ], PLAT (plasminogen activator, tissue type) [ 280 ], ADAMTS2 [ 281 ], CACNB2 [ 282 ], DAB2IP [ 283 ], CD34 [ 284 ], COL15A1 [ 285 ], MSTN (myostatin) [ 286 ], NOTCH3 [ 287 ], HSPG2 [ 288 ], TERT (telomerase reverse transcriptase) [ 289 ], GNB3 [ 290 ], MMP15 [ 291 ], COL4A2 [ 292 ], EGR3 [ 293 ], RBM20 [ 294 ], RYR2 [ 295 ], EPHA2 [ 296 ], NDRG4 [ 297 ], UNC5B [ 298 ], CTNNA3 [ 299 ], SFRP5 [ 300 ], CUX2 [ 301 ], GDF15 [ 302 ], AXL (AXL receptor tyrosine kinase) [ 303 ], CAV1 [ 304 ], KCNA5 [ 305 ], FOXC1 [ 306 ], RYR1 [ 307 ], ROBO1 [ 308 ], XIRP2 [ 309 ], FZD4 [ 310 ], VASH2 [ 311 ], CXCL8 [ 312 ], KCNMA1 [ 167 ], LOX (lysyl oxidase) [ 313 ], DNAH11 [ 314 ], FMOD (fibromodulin) [ 315 ], MFAP4 [ 316 ], FLNC (filamin C) [ 317 ], LIFR (LIF receptor subunit alpha) [ 318 ], MAGI1 [ 319 ], SLC39A2 [ 320 ] and CYP3A5 [ 321 ] are linked to cardiovascular diseases. Studies have reported that enriched genes include CHRFAM7A [ 75 ], ACHE (acetylcholinesterase (Cartwright blood group) [ 322 ], HP (haptoglobin) [ 323 ], SELENBP1 [ 80 ], BASP1 [ 89 ], CHRNA1 [ 324 ], RPTN (repetin) [ 93 ], IL33 [ 325 ], TACR1[ 326 ], SHANK3 [ 327 ], ALOX12B [ 328 ], HPN (hepsin) [ 329 ], GFAP (glial fibrillary acidic protein) [ 101 ], CACNB2 [ 330 ], MYT1 [ 331 ], NOTCH3 [ 332 ], TERT (telomerase reverse transcriptase) [ 333 ], GNB3 [ 334 ], EGR3 [ 335 ], BSN (bassoon presynaptic cytomatrix protein) [ 336 ], CUX2 [ 337 ], GDF15 [ 338 ], CXCL8 [ 339 ], MAGI1 [ 112 ] and PAH (phenylalanine hydroxylase) [ 340 ] are necessary for BD development. Enriched genes include S100A12 [ 341 ], CDH1 [ 342 ], S100A9 [ 343 ], ANK1 [ 344 ], KCNH2 [ 345 ], HP (haptoglobin) [ 346 ], FRMD4A [ 347 ], SNCA (synuclein alpha) [ 348 ], FBXO7 [ 349 ], PINK1 [ 350 ], B2M [ 351 ], KCNH3 [ 352 ], CA2 [ 353 ], FUZ (fuzzy planar cell polarity protein) [ 354 ], UBB (ubiquitin B) [ 355 ], C5AR1 [ 356 ], CBS (cystathionine beta-synthase) [ 357 ], F12 [ 358 ], TSPAN5 [ 359 ], NQO2 [ 360 ], NDUFA1 [ 361 ], SRXN1 [ 362 ], BASP1 [ 363 ], GPX1 [ 364 ], OLIG2 [ 365 ], EFHC2 [ 366 ], FOXA1 [ 367 ], PAX4 [ 368 ], BGN (biglycan) [ 369 ], IL33 [ 370 ], LRIG3 [ 371 ], GJA1 [ 372 ], SHANK3 [ 373 ], ARC (activity regulated cytoskeleton associated protein) [ 374 ], GFAP (glial fibrillary acidic protein) [ 375 ], UNC13A [ 376 ], MSTN (myostatin) [ 377 ], HSPG2 [ 378 ], TERT (telomerase reverse transcriptase) [ 379 ], GNB3 [ 380 ], L1CAM [ 381 ], CALB1 [ 382 ], RYR2 [ 383 ], MYO15A [ 384 ], MYH11 [ 385 ], GDF15 [ 386 ], CAV1 [ 387 ], KIRREL3 [ 388 ], COBL (cordon-bleu WH2 repeat protein) [ 389 ], LOX (lysyl oxidase) [ 390 ], SPARCL1 [ 391 ], FLNC (filamin C) [ 392 ], TMPRSS4 [ 393 ], VWA2 [ 394 ], OGDHL (oxoglutarate dehydrogenase L) [ 395 ], CYP3A5 [ 396 ] and FBXO40 [ 397 ] were revealed to be associated with cognitive dysfunction. Enriched genes include S100A12 [ 398 ], SLC6A19 [ 399 ], TXN (thioredoxin) [ 400 ], TLR9 [ 401 ], S100P [ 402 ], S100A9 [ 403 ], ANK1 [ 404 ], CA1 [ 405 ], HP (haptoglobin) [ 406 ], ARG1 [ 407 ], SNCA (synuclein alpha) [ 408 ], STARD10 [ 409 ], PINK1 [ 410 ], TFR2 [ 411 ], B2M [ 412 ], PHOSPHO1 [ 413 ], CBS (cystathionine beta-synthase) [ 414 ], WNT6 [ 415 ], NFE2 [ 416 ], RNF10 [ 417 ], CARM1 [ 418 ], GLRX5 [ 419 ], PPP2R5B [ 420 ], GPX1 [ 421 ], FOXO4 [ 422 ], ARHGEF12 [ 423 ], FOXA1 [ 424 ], PAX4 [ 368 ], ABCG8 [ 425 ], RARRES2 [ 426 ], NOX1 [ 427 ], CPE (carboxypeptidase E) [ 428 ], IL33 [ 429 ], ADAMTS7 [ 430 ], SFTPB (surfactant protein B) [ 272 ], MGP (matrix Gla protein) [ 274 ], VTN (vitronectin) [ 431 ], PRSS1 [ 432 ], GHR (growth hormone receptor) [ 433 ], ERBB3 [ 434 ], GFAP (glial fibrillary acidic protein) [ 435 ], CD34 [ 436 ], MSTN (myostatin) [ 437 ], NOTCH3 [ 438 ], NR5A2 [ 439 ], SMOC1 [ 440 ], GNB3 [ 441 ], CTNNA3 [ 442 ], SFRP5 [ 300 ], GDF15 [ 302 ], AXL (AXL receptor tyrosine kinase) [ 443 ], CAV1 [ 444 ], LCT (lactase) [ 445 ], FZD4 [ 446 ], KCNMA1 [ 447 ], FMOD (fibromodulin) [ 448 ] and CPA6 [ 449 ] were revealed to be correlated with disease outcome in patients with diabetes mellitus. Previous studies have demonstrated that enriched genes include SLC6A19 [ 450 ], DNAJC6 [ 451 ], TXN (thioredoxin) [ 400 ], S100A9 [ 452 ], HP (haptoglobin) [ 453 ], ARG1 [ 454 ], SNCA (synuclein alpha) [ 408 ], CEP19 [ 455 ], CAMP (cathelicidin antimicrobial peptide) [ 456 ], PINK1 [ 457 ], UBB (ubiquitin B) [ 458 ], PHOSPHO1 [ 413 ], CBS (cystathionine beta-synthase) [ 414 ], PPP2R5B [ 420 ], GPX1 [ 459 ], PRDX2 [ 460 ], LXN (latexin) [ 461 ], RGS6 [ 462 ], MAF1 [ 463 ], HSPB7 [ 464 ], PNLIP (pancreatic lipase) [ 465 ], NOX1 [ 427 ], CPE (carboxypeptidase E) [ 428 ], IL33 [ 466 ], SP7 [ 467 ], ZFPM2 [ 468 ], TSLP (thymic stromal lymphopoietin) [ 140 ], TACR1 [ 469 ], WNT4 [ 470 ], MFAP5 [ 471 ], GHR (growth hormone receptor) [ 472 ], GFAP (glial fibrillary acidic protein) [ 473 ], ACTN3 [ 474 ], MSTN (myostatin) [ 437 ], GNB3 [ 475 ], MMP15 [ 476 ], KCP (kielin cysteine rich BMP regulator) [ 477 ], WNK4 [ 478 ], SFRP5 [ 300 ], GDF15 [ 479 ], CAV1 [ 444 ], LCT (lactase) [ 445 ], CXCL8 [ 480 ], LOX (lysyl oxidase) [ 481 ], AIF1L [ 482 ] and CYP3A5 [ 483 ] are linked with the development mechanisms of obesity. Recent studies have proposed that the enriched genes include DNAJC6 [ 484 ], DNAJB2 [ 485 ], GPR4 [ 486 ] and ZFPM2 [ 97 ] are associated with Parkinson Disease. Vauthier et al [ 451 ], Cushion et al [ 487 ], Barone et al [ 488 ], Marcoli et al [ 489 ], Qin et al [ 490 ], de Nijs et al [ 491 ], Bergareche et al [ 492 ], Hu et al [ 493 ], Liu et al [ 494 ], Zhang et al [ 495 ], Gorman et al [ 496 ], Cherian et al [ 497 ], Gururaj et al [ 498 ], Belhedi et al [ 499 ] and Park et al [ 500 ] demonstrated that enriched genes include DNAJC6, TUBB2A, DPM2, SMOX (spermine oxidase), CDYL (chromodomain Y like), EFHC1, SCN4A, DOC2A, ADGRV1, SCAMP5, CACNA1B, KCNT1, KCNT2, CPA6 and CYP3A5 could induce epilepsy. These enriched genes might play essential roles in the advancement of BD and act as novel diagnosis biomarkers or treatment targets of BD. Construction of PPI network and its modules of DEGs might be helpful for understanding the relationship of developmental BD. Hub genes include UBB (ubiquitin B) [ 355 ], CAV1 [ 387 ], MYH11 [ 385 ] and MSTN (myostatin) [ 377 ] have been demonstrated to accelerate cognitive dysfunction. Hub genes include UBB (ubiquitin B) [ 458 ], CAV1 [ 444 ], MSTN (myostatin) [ 437 ], SFRP5 [ 300 ] and WNT4 [ 470 ] expression might be regarded as an indicator of susceptibility to obesity. Hub genes include NOTCH3 [ 150 ], CAV1 [ 110 ], EGR3 [ 109 ], SFRP5 [ 158 ] and WNT4 [ 141 ] are involved in hypertension progression. Altered expression of hub genes include NOTCH3 [ 287 ], CAV1 [ 304 ], FZD4 [ 310 ], MSTN (myostatin) [ 286 ], EGR3 [ 293 ], SFRP5 [ 300 ] and WNT4 [ 273 ] are associated with prognosis in patients with cardiovascular diseases. Hub genes include NOTCH3 [ 332 ] and EGR3 [ 335 ] are a potential markers for the detection and prognosis of BD. Altered expression of hub genes include RPL39 [ 180 ], PAX8 [ 211 ] and WNT4 [ 203 ] promotes pregnancy complications. A previous study reported that hub genes include NOTCH3 [ 438 ], CAV1 [ 444 ], FZD4 [ 446 ], MSTN (myostatin) [ 437 ] and SFRP5 [ 300 ] are altered expressed in diabetes mellitus. However, the roles of novel biomarkers include in BD have not been reported until now and listed in supplementary Table S4. MiRNA-hub gene regulatory network and TF-hub gene regulatory network analyses predicted hub genes, miRNAs and TFs. hsa-mir-27a-3p [ 501 ], STAT3 [ 502 ], KLF4 [ 503 ] and ESRRA (estrogen related receptor alpha) [ 504 ] were reported to be associated with the prognosis of cognitive dysfunction. Altered expression of ATF3 [ 505 ], STAT3 [ 506 ] and KLF4 [ 507 ] are observed in hypertension, indicating that these biomarkers play a role in hypertension. ATF3 [ 508 ], NCOR1 [ 509 ], STAT3 [ 510 ] and KLF4 [ 511 ] have been positively correlated with cardiovascular diseases ATF3 [ 512 ], STAT3 [ 510 ] and ESRRA (estrogen related receptor alpha) [ 513 ] were previously reported to be critical for the development of diabetes mellitus. Ku et al. [ 514 ], Su et al. [ 515 ], Redonnet et al. [ 516 ], Deng et al. [ 517 ] and Larsen et al [ 513 ] concluded that ATF3, STAT3, retinoic acid receptor, alpha (RARA), KLF4 and ESRRA (estrogen related receptor alpha) were an important participant in obesity. Previous studies have reported that STAT3 [ 518 ] and retinoic acid receptor, alpha (RARA) [ 519 ] are related to schizophrenia. STAT3 [ 520 ] levels are correlated with pregnancy complications. Novel biomarkers include BCL2L1, RPL26, hsa-mir-8085, hsa-mir-6735-5p, hsa-mir-548ap-5p, hsa-mir-132-3p, hsa-mir-4514, hsa-mir-3133, hsa-mir-4328, hsa-mir-6749-3p, HMG20B, SMARCE1, POLR2A and HIC1 might play important roles in the development of BD and act as early diagnosis biomarkers or treatment targets of BD. Therefore, our further study will investigate the interactions of hub genes, miRNAs and TFs to shed new light on the molecular mechanisms involved in BD pathology. Our investigation focused on understanding the action of drug on expression of hub genes. Our findings suggest that drugs- Phenserine, Amiodarone, famoxadone, 3-({4-[(5-chloro-1,3-benzodioxol-4-yl)amino]pyrimidin-2-yl}amino)benzamide, Vinblastine, Roflumilast, Spironolactone, Zonisamide, 2-(methylamino)-N-(4-methyl-1,3-thiazol-2-yl)-5-[(4-methyl-4H-1,2,4-triazol-3-yl)sulfanyl]benzamide and Suramin concurrently target to hub genes include HTR2A, GRIA2, TNF and LYZ, potentially controlling the development of MDD. The docking results indicate that Kakkalide and Divaricatol share notable structural and functional similarities with standard drugs based on both binding affinity and hydrogen bonding interactions. Kakkalide’s interaction with UBB, despite being slightly weaker in binding energy compared to FT671, involved a greater number of hydrogen bonds, suggesting a potentially stable and effective interaction. This implies a likelihood of functional mimicry and downregulation of the upregulated UBB gene. Its interaction with CNBD2 was even more promising, where it not only surpassed the standard ligand cAMP in binding strength but also formed several key hydrogen bonds. This supports the possibility that Kakkalide could restore or upregulate the expression of downregulated CNBD2. For UBE2D1, although the standard ligand exhibited higher affinity, Kakkalide’s moderate binding suggests it may still provide partial regulatory effects, potentially normalizing the gene's overexpression. In the case of CAV1, Divaricatol exhibited stronger affinity and a more diverse hydrogen bonding profile than the standard ligand, indicating a higher potential to upregulate the downregulated gene. These findings point to a dual-direction regulatory capability of the ligands: suppressing overactive genes while boosting underactive ones. Brucine B, however, showed limited binding and fewer interactions, indicatespoor therapeutic candidate. The ADMET evaluation reveals significant pharmacokinetic variability among the test compounds. Brucine B, despite limited absorption (~ 3.9%), showed high bioavailability and a favourable metabolic profile, indicating its potential as a lead compound with minimal interaction risk. Kakkalide and Divaricatol showed safer metabolic properties but limited systemic exposure and potential little hepatotoxicity. cAMP, a co-crystallized ligand, displayed the most balanced ADMET characteristics, including high absorption, moderate clearance, and low CYP interaction, but was flagged for high DILI risk. 7HC and EDK, while BBB permeable and CYP inhibitors, presented multiple drawbacks such as low absorption, high lipophilicity, and systemic toxicity risks. FT671 showed moderate promise but requires enhancement of absorption and reduction of hepatic risk. Conclusions In summary, five crucial candidate genes (UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11) might play key roles in the occurrence and development of BD, suggesting they might serve as potential biomarkers and therapeutic targets in the BD. Overall, Kakkalide and Divaricatol emerge as promising phytoconstituents capable of interacting with key molecular targets involved in bipolar disorder. Their comparable or superior interaction profiles relative to co-crystallized ligands suggest a high potential for therapeutic modulation of gene expression. Kakkalide appears to effectively target both upregulated (UBB, UBE2D1) and downregulated (CNBD2) genes, indicating a normalizing effect on gene dysregulation. Similarly, Divaricatol shows strong potential to restore the expression of downregulated CAV1. These properties position both compounds as viable candidates for multi-target drug development, offering a novel therapeutic approach for the treatment of bipolar disorder. Further experimental validation is recommended to confirm their biological activity and regulatory impact at the cellular and systemic levels. cAMP and Brucine B emerge as promising candidates for further investigation based on their ADMET profiles. cAMP shows strong absorption and metabolic safety with caution toward hepatotoxicity. Brucine B offers favourable plasma exposure and low interaction risk, despite its low intestinal absorption. In contrast, 7HC and EDK require substantial structural or formulation-based optimization to overcome significant ADMET liabilities. These results provide a pharmacokinetic basis for prioritizing compounds for further in vivo evaluation in bipolar disorder research. Declarations Acknowledgement I thank Krebs CE, Loohuis LM, Ophoff RA, UCLA, Center for Neurobehavioral Genetics, Los Angeles, CA, USA, very much, the author who deposited their NGS dataset GSE124326, into the public GEO database. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Research involving Human Participants and/or Animals Not applicable. Informed consent No informed consent because this study does not contain human or animals participants. Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE124326) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124326)] Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding No funding Consent to participate Not applicable. Author Contributions B. V. - Writing original draft, and review and editing S.P. - Formal analysis and validation C. 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Psychiatry Res 133(1):13–21. 10.1016/j.psychres.2004.11.003 Luo JY, Fu D, Wu YQ, Gao Y (2016) Inhibition of the JAK2/STAT3/SOSC1 Signaling Pathway Improves Secretion Function of Vascular Endothelial Cells in a Rat Model of Pregnancy-Induced Hypertension. Cell Physiol Biochem 40(3–4):527–537. 10.1159/000452566 Tables Tables 1 to 7 are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files ResearchSquareTables.docx SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx SupplementaryTableS4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7391829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501459321,"identity":"267a364d-513d-42dc-ab93-55e257dee1c3","order_by":0,"name":"Basavaraj Mallikarjunayya Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Pharmaceutical Chemistry, K.L.E. 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Kakkalide, B. Divaricatol, C. Brucine B which are used in molecular docking\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/e0a89b4a1ca2290dac070f08.png"},{"id":89357696,"identity":"b4c9d480-8169-42bf-8a7b-38f50f1a7346","added_by":"auto","created_at":"2025-08-19 07:44:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81340,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/0d734ab8035f5511d7a8cf0f.png"},{"id":89357959,"identity":"b56a5e73-4139-4349-804b-53d079b8d46b","added_by":"auto","created_at":"2025-08-19 07:52:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158455,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A240 = normal control \u0026nbsp;samples; B1 – B240 = BD samples)\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/463d1968a168abc718d72913.png"},{"id":89356781,"identity":"169b7614-81b4-40c1-a824-e5f3d927951c","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230416,"visible":true,"origin":"","legend":"\u003cp\u003eGO and REACTOME pathway enrichment analysis for up regulated genes. p \u0026lt; 0.05. Abbreviations: BP, biological process; CC, cell component; MF, molecular function. GO, Gene Ontology; REAC, REACTOME. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/1b68a884a0a19702d23fa977.png"},{"id":89356784,"identity":"421972fc-592e-48b9-a056-f58c89c9ec73","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256707,"visible":true,"origin":"","legend":"\u003cp\u003eGO and REACTOME pathway enrichment analysis for down regulated genes. p \u0026lt; 0.05. Abbreviations: BP, biological process; CC, cell component; MF, molecular function. GO, Gene Ontology; REAC, REACTOME. The size of the circle represents the number of genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/ae94c58ef535678b927f6824.png"},{"id":89357703,"identity":"f6062e17-8e76-44bb-bd52-03bd486e8402","added_by":"auto","created_at":"2025-08-19 07:44:48","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":580623,"visible":true,"origin":"","legend":"\u003cp\u003ePPI network of DEGs. Up regulated genes are marked in green; down regulated genes are marked in red\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/b487f9b2cb608d82aeebbcfd.jpeg"},{"id":89358836,"identity":"476b866d-dc37-457f-b9a9-d123e379e33c","added_by":"auto","created_at":"2025-08-19 08:00:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":414099,"visible":true,"origin":"","legend":"\u003cp\u003eModules 1 was isolated form PPI of up regulated genes. Module 1 has 71 nodes and 434 edges for up regulated genes. Up regulated genes are marked in green.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/bdabc4efc4233f35ae30091b.png"},{"id":89356797,"identity":"9d6dbdf2-2648-4e29-bf37-538f9e63e937","added_by":"auto","created_at":"2025-08-19 07:36:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":230568,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 1. The size of the circle represents the number of up regulated genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/c8493741b164a36383ae23ad.png"},{"id":89357964,"identity":"8a50079f-3c61-49ae-b960-fd46ddb98830","added_by":"auto","created_at":"2025-08-19 07:52:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":350561,"visible":true,"origin":"","legend":"\u003cp\u003eModules 2 was isolated form PPI of down regulated genes. Module 2 has 70 nodes and 132 edges for down regulated genes. Down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/1be3dae5e31f7a5b795522e2.png"},{"id":89357709,"identity":"fc40bf92-360b-4893-ab76-0d7dbc9dd184","added_by":"auto","created_at":"2025-08-19 07:44:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":203364,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis for module 2. The size of the circle represents the number of down regulated genes involved, and the abscissa represents the frequency of the genes involved in the term total genes.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/78889c02f34815ae1f7989f3.png"},{"id":89356795,"identity":"15664eaa-91a0-4111-9299-72b7b5444f62","added_by":"auto","created_at":"2025-08-19 07:36:49","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":659955,"visible":true,"origin":"","legend":"\u003cp\u003eTarget gene - miRNA regulatory network between target genes. The purple color diamond nodes represent the key miRNAs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/eb121daabbb67481a2cb551b.jpeg"},{"id":89357962,"identity":"d1a20ea1-18fc-4aef-943a-cbbd3c25b3fa","added_by":"auto","created_at":"2025-08-19 07:52:48","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":515433,"visible":true,"origin":"","legend":"\u003cp\u003eHub gene - TF regulatory network between target genes. The yellow color triangle nodes represent the key TFs; up regulated genes are marked in green; down regulated genes are marked in red.\u003c/p\u003e","description":"","filename":"image12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/26293c0dc113eba0cd7a3b50.jpeg"},{"id":89357701,"identity":"f4dfc071-99c5-4fd7-bc59-ea38162341f0","added_by":"auto","created_at":"2025-08-19 07:44:48","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":135432,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses of hub genes. A) \u0026nbsp;UBB B) UBE2D1 C) \u0026nbsp;TUBA1A D) \u0026nbsp;RPL11 E) \u0026nbsp;RPS24 F) NOTCH3 G) CAV1 H) \u0026nbsp;CNBD2 I) \u0026nbsp;CCNA1 J) MYH11\u003c/p\u003e","description":"","filename":"image13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/4526440e9bfeaa52ffe7b8d4.jpeg"},{"id":89357705,"identity":"df624887-24ae-4e2a-a34d-9fc42d779d2a","added_by":"auto","created_at":"2025-08-19 07:44:48","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":356609,"visible":true,"origin":"","legend":"\u003cp\u003e2D amino acid interaction and 3D hydrogen bonding interaction images of 5NGE and phytoconstituents\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/5a62f56f4f9c527300097ff4.png"},{"id":89357708,"identity":"13d6829f-3c95-4efe-aad5-0c86b3193c9d","added_by":"auto","created_at":"2025-08-19 07:44:48","extension":"jpeg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":307544,"visible":true,"origin":"","legend":"\u003cp\u003e2D amino acid interaction and 3D hydrogen bonding interaction images of 3U10 and phytoconstituents.\u003c/p\u003e","description":"","filename":"image15.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/60b40cabd5bea06ddbbb88d7.jpeg"},{"id":89356791,"identity":"904a61b1-f221-4a17-9548-3d054c5e6d62","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":415207,"visible":true,"origin":"","legend":"\u003cp\u003e2D amino acid interaction and 3D hydrogen bonding interaction images of 5TRFand phytoconstituents.\u003c/p\u003e","description":"","filename":"image16.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/b739895da28d045af3a9afc1.png"},{"id":89356796,"identity":"af800589-32b7-40c6-a516-c4b27479f95f","added_by":"auto","created_at":"2025-08-19 07:36:49","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":318625,"visible":true,"origin":"","legend":"\u003cp\u003e2D amino acid interaction and 3D hydrogen bonding interaction images of 7RLEand phytoconstituents.\u003c/p\u003e","description":"","filename":"image17.png","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/a532d0591b0d33f462696673.png"},{"id":89359671,"identity":"b1a6e0e8-12ab-4994-b032-52e2dfcdb655","added_by":"auto","created_at":"2025-08-19 08:08:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7020653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/f895591c-9253-45b7-ba6d-26ffbb988a57.pdf"},{"id":89356773,"identity":"704eeb48-2acb-4c0f-a769-9fce22fab34c","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":209310,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchSquareTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/9c982efca5f431ab70137b7f.docx"},{"id":89356776,"identity":"860a7915-9d5c-419e-b09e-24452b442421","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14664,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/27491fb03454e1431c0892ee.docx"},{"id":89356778,"identity":"51e0433b-e431-4e22-a244-aa23a39027c3","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14700,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/beb091816b65b9dda3d968f7.docx"},{"id":89357960,"identity":"0e6f728c-07ef-458f-bc31-91fc72cf383d","added_by":"auto","created_at":"2025-08-19 07:52:48","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15452,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/9ab5ebdc211d5922f9cea90d.docx"},{"id":89356780,"identity":"7934f83f-c590-4e94-83eb-87e8889b3275","added_by":"auto","created_at":"2025-08-19 07:36:48","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12809,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7391829/v1/bc557a177018cb842f5c4c67.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBipolar disorder (BD) is a psychiatric disorder characterized by recurrent manic or hypomanic and depressive episodes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the report of World Health Organization (WHO), BD is the sixth cause of disability-adjusted life years among all psychiatric diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The numbers of cases of BD are rising worldwide and it has become an important mental health concern. It is estimated that the incidence of BD was 30\u0026ndash;69% in Europe and in the United States [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The risk factors associated with BD are mainly caused by obesity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], anxiety [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], depression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], cognitive dysfunction [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], pregnancy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], hypertension [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], cardiovascular diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], diabetes mellitus [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and genetic factor [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A numerous tests are available for screening and detecting BD, but not yet achieved satisfactory results [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, there is an urgent need to develop novel diagnostic strategies and therapeutic agents to improve the prognosis of patients with BD.\u003c/p\u003e\u003cp\u003eIn recent years, genes and signaling pathway have been found to be associated with changes in neuron structure and function in BD patients. For example, studies have shown that genes include CACNA1C [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], BDNF (brain derived neurotrophic factor) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], GSK3 (glycogen synthetase kinase-3) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], DUSP6 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and SYNE1 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] were associated BD. Signaling pathways include kynurenine signaling pathway [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Wnt and GSK3 signaling pathways [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], cAMP\u0026ndash;CREB signaling pathways [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] PI3K/AKT/HIF1-a signaling pathway [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], MAP kinase and phosphoinositide signaling pathway [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and Notch signaling pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] have been shown to be associated with BD. Consequently, it is crucial to fully understand the molecular pathogenesis of BD to improve the early diagnosis, treatment, and prognosis of BD.\u003c/p\u003e\u003cp\u003eBioinformatics approaches based on next generation sequencing (NGS) data provide novel opportunities to uncover the underlying molecular mechanism of various diseases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. NGS data are usually deposited and available in free public website NCBI-Gene Expression Omnibus database (NCBI-GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Integrated bioinformatics analyses of NGS data derived from investigation of BD could help identify the hub genes and further demonstrate their related functions and potential therapeutic targets in BD.\u003c/p\u003e\u003cp\u003eIn the current investigation, NGS data GSE124326 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] download from NCBI-GEO database. A total of 240 BD samples and 240 normal control samples were available. Differentially expressed genes (DEGs) between BD and normal control samples were filtered and obtained using the R bioconductor tool DESeq2. Gene Ontology (GO) and REACTOME pathway enrichment analysis were conducted. The functions of the DEGs were further assessed by PPI network and modules to identify the hub genes in BD. Moreover, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network of the hub genes were established, Molecular docking studies was carried out to predict novel drug molecules. The diagnostic roles of hub genes were analyzed using receiver operating characteristic curve (ROC) analysis. With the above approaches, it is hoped that our results might provide a preliminary insight into the molecular mechanism of BD and a search for possible novel biomarkers and drug molecules.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData resources\u003c/h2\u003e\u003cp\u003eThe NGS dataset GSE124326 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] based on GPL16791 Illumina HiSeq 2500 (Homo sapiens) was acquired from the Gene Expression Ominibus (GEO) database. The GSE124326 dataset contained 240 BD samples and 240 normal control samples.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of DEGs\u003c/h3\u003e\n\u003cp\u003eDESeq2 package of R software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used to analyze the DEGs between PD and normal control in the NGS data of GSE124326. The adjusted P-value and [log FC] were calculated. The Benjamini \u0026amp; Hochberg false discovery rate method was used as a correction factor for the adjusted P-value in DESeq2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The statistically significant DEGs were identified according to adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, [logFC]\u0026thinsp;\u0026gt;\u0026thinsp;0.235 for up regulated genes and [logFC] \u0026lt; -0.52 for down regulated genes. The DEGs are presented as volcano plot and heat map generated using ggplot2 and gplot in R Bioconductor.\u003c/p\u003e\n\u003ch3\u003eGO and pathway enrichment analyses of DEGs\u003c/h3\u003e\n\u003cp\u003eg:Profiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biit.cs.ut.ee/gprofiler/\u003c/span\u003e\u003cspan address=\"http://biit.cs.ut.ee/gprofiler/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was used to perform GO functional and REACTOME pathway enrichment analyses. GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] annotation was applied to define gene functions in three terms: biological process (BP), cellular component (CC) and molecular function (MF). REACTOME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://reactome.org/\u003c/span\u003e\u003cspan address=\"https://reactome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] is a pathway database resource for understanding high-level biological functions and utilities. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant.\u003c/p\u003e\n\u003ch3\u003eConstruction of the PPI network and module analysis\u003c/h3\u003e\n\u003cp\u003eIn order to obtain interacting proteins related to DEGs, the STRING version 11.5 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cytoscape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (version 3.9.1) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] was used to visualize the PPI network of DEGs. The Network Analyzer plug-in was used to explore hub genes, and the hub genes were generated using four topological parameters include node degree [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], betweenness [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and closeness [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The intersect function was used to identify the hub genes. The PEWCC1 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] was used to search modules of the PPI network.\u003c/p\u003e\n\u003ch3\u003eMiRNA-hub gene regulatory network construction\u003c/h3\u003e\n\u003cp\u003eMiRNA regulates gene expression under defined disease conditions through interaction with hub genes during the post transcriptional stage was analyzed. We applied miRNet database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirnet.ca/\u003c/span\u003e\u003cspan address=\"https://www.mirnet.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] to integrate miRNA databases (TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2.0.). We visualized miRNA-hub gene regulatory network by employing Cytoscape software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eTF-hub gene regulatory network construction\u003c/h2\u003e\u003cp\u003eTF regulates gene expression under defined disease conditions through interaction with hub genes during the transcriptional stage was analyzed. We applied NetworkAnalyst database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] to integrate TF database (ENCODE). We visualized TF-hub gene regulatory network by employing Cytoscape software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDrug-hub gene interaction network construction\u003c/h3\u003e\n\u003cp\u003eDrugs molecules predicted for BD treatment through interaction with hub genes. We applied NetworkAnalyst database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] to integrate drug database (DrugBank). We visualized drug-hub gene interaction network by employing Cytoscape software [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h3\u003e\n\u003cp\u003eThe hub genes were used to identify biomarkers with high sensitivity and specificity for BD diagnosis. The ROC curves were plotted and area under curve (AUC) was calculated separately to evaluate the performance of each model using the R packages \u0026ldquo;pROC\u0026rdquo; [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9 indicated that the model had a good fitting effect.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsilico\u003c/b\u003e \u003cb\u003emolecular docking studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSwiss-model, RCSB PDB, Prank web, NCBI Gene, ChEMBL, BindingDB, CASTp, Fpocket, DoGSiteScorer, ChemDraw, Avogadro tool, Autodock 1.7.1, and Autodock Vina tools, Biovia Discovery Studio client 2021, ADMET lab 3.0 web server.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eReceptor Selection Based on Gene Information\u003c/h2\u003e\u003cp\u003eThe selection of target receptor structures for molecular docking was initiated by identifying genes of interest relevant to the disease condition under study. Genes showing differential expression or known functional involvement in disease pathophysiology were prioritized based on genomic and transcriptomic data [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The corresponding protein products of these genes were then retrieved using UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This databases provide curated information on gene-protein relationships, functional domains, isoforms, and organism-specific variants [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOnce the protein names and UniProt accessions were determined, three-dimensional structures of these proteins were searched in the Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Priority was given to experimentally determined structures (X-ray crystallography or cryo-EM) derived from \u003cem\u003eHomo sapiens\u003c/em\u003e, with resolutions\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026Aring; and co-crystallized ligands when available. Structures were evaluated for completeness, presence of functional domains, and biologically relevant binding conformations. In cases where multiple structures were available, the one with the most complete and biologically relevant ligand-protein interaction site was selected [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIf co-crystallized ligands were present in the selected PDB entry, these were examined to confirm their biological relevance (e.g., substrate, inhibitor, agonist). The functional nature of the ligand was cross-validated using ligand bioactivity databases such as ChEMBL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/chembl/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/chembl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and BindingDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bindingdb.org/\u003c/span\u003e\u003cspan address=\"https://www.bindingdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), where half-maximal inhibitory concentration (IC₅₀), binding affinity (K\u003csub\u003ei\u003c/sub\u003e), or other pharmacological data are reported [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo confirm the presence and accessibility of druggable binding pockets, cavity detection tools such as CASTp, Fpocket, or DoGSiteScorer were used [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This ensured that the selected structure was suitable for molecular docking, with a validated and accessible ligand-binding domain. Overall, this systematic approach starting from gene selection to receptor structure identification ensured biological relevance, structural accuracy, and docking compatibility of the chosen targets.\u003c/p\u003e\u003cp\u003eIn present study, receptor structures corresponding to selected genes were identified through a systematic database-driven approach prioritizing experimentally determined human protein structures. For the CNBD2 gene, which encodes a cyclic nucleotide-binding domain protein involved in ion channel regulation, the crystal structure of the homologous human HCN2 channel domain (PDB ID: 3U10) bound to cyclic AMP (cAMP) was selected. This structure, determined by X-ray crystallography at 2.35 \u0026Aring; resolution, was used as a representative template due to its functional and structural similarity to CNBD2. For the ubiquitin B (UBB) gene, which encodes a non-druggable ubiquitin protein, a functionally relevant receptor protein ubiquitin-specific protease 7 (USP7) was selected instead. The human USP7 structure (PDB ID: 5NGE), co-crystallized with the potent small-molecule inhibitor FT671 (IC₅₀ \u0026asymp; 52 nM), was chosen due to its direct interaction with ubiquitin and its critical role in the deubiquitination pathway.\u003c/p\u003e\u003cp\u003eIn the case of UBE2D1, a ubiquitin-conjugating E2 enzyme, no crystal structure was available; therefore, a highly homologous protein, UBE2D3 (sharing\u0026thinsp;\u0026gt;\u0026thinsp;95% sequence identity), was used as a proxy. The human UBE2D3\u0026ndash;CBL-B complex structure (PDB ID: 5TRF), resolved by X-ray crystallography at 2.4 \u0026Aring; resolution, was selected, as it includes a small-molecule modulator and represents the active conformation of the E2-E3 ubiquitination interface. For Caveolin-1 (CAV1), a membrane scaffolding protein implicated in signalling and endocytosis, the high-resolution cryo-EM structure of the oligomeric 8S complex (PDB ID: 7RLE) was chosen to study its biologically active multimeric conformation. Although no small-molecule ligand is present, this structure provides critical insight into the structural organization of CAV1.\u003c/p\u003e\u003cp\u003eAll structures were obtained from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), filtered for \u003cem\u003eHomo sapiens\u003c/em\u003e origin, high resolution (\u0026le;\u0026thinsp;3.7 \u0026Aring;), and functional relevance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eReceptor and ligand preparation\u003c/h2\u003e\u003cp\u003eThe selected targets with their structural information are presented in Table\u0026nbsp;1. Using the Swiss-Model web server, the missing residues were remodelled and downloaded in pdb format [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Following the identification of the binding sites utilizing the server Prank web [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e],the protein's pdb format was entered into Software Auto Dock Tools 1.7.1, and water molecules and atoms were eliminated. The receptor has also been checked for missing amino acid residues, Kollman charges have been fixed, and only polar hydrogens have been inserted. In order to cover the entire receptor, the grid was fixed using the Autogrid program. The grid dimension file [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] was then saved. Ligand structures were created using ChemDraw and saved as SMILES. The phytoconstituents which are used in present study were shown in Fig.\u0026nbsp;1. They were then loaded into Avogadro software to convert them from 2D to 3D structures in PDB format [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The ligand's pdb format was then entered into Auto Dock Tools 1.7.1, and the ligand molecule's root was identified and selected. Lastly, the pdbqt format was used to save the ligand molecule.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePerforming AutodockVina\u003c/h2\u003e\u003cp\u003eAuto Dock Vina can be executed using the command line (cmd) or the Autodock tool. The configuration file was ready for Autodock Vina to execute; the grid dimension file that was previously saved includes the protein's n-points, active site, and x, y, and z coordinates. That information was added to the configuration file, which was made to contain the protein's active site details. It also comes with an output file in pdbqt format and a log file in .txt format. The command line was used to run Autodock Vina, \u0026ldquo;vina.exe -- config config.txt\u0026rdquo; was the command used to launch Auto Dock Vina. Docked coordinates were output in the pdbqt format when the program was finished. Receptor-ligand interactions were then visualized using the file, and binding affinity was ascertained using the log.txt file [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eVisualization\u003c/h2\u003e\u003cp\u003eThe Biovia Discovery Studio Client 2021 program has been used to visualize the docking results. Next, a PNG file is created from the 3D image of the docked ligand and the 2D image of the docked ligand that is attached to various amino acids [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eADMET properties\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHowever, high binding affinity alone is insufficient. ADMET profiling, evaluating Absorption, Distribution, Metabolism, Excretion, and Toxicity,is essential to ensure that candidate compounds possess favourable pharmacokinetic properties and safety profiles. Without this, compounds may fail in later stages despite strong docking results. Integration of docking with in silico ADMET prediction streamlines compound selection by filtering out unsuitable leads early in the discovery pipeline [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].ADMET properties were predicted with the help of the ADMET lab free web server, as previously reported [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of DEGs\u003c/h2\u003e\u003cp\u003eGSE124326 was selected and underwent DEGs analysis using \u0026ldquo;DESeq2\u0026rdquo; package in R software. There was a total of 957 DEGs between BD and normal control samples, including 477 up regulated DEGs and 480 down regulated genes (Table\u0026nbsp;2). A volcano plot was constructed for the DEGs and is presented in Fig.\u0026nbsp;2. The DEGs are presented by a heat map in Fig.\u0026nbsp;3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eGO and pathway enrichment analyses of DEGs\u003c/h2\u003e\u003cp\u003eTo obtain a deeper insight into the biological functions of DEGs, GO annotation and REACTOME pathway enrichment analyses were performed. The enriched GO terms were shown in Table\u0026nbsp;3. In the present investigation, up regulated genes were mainly enriched in transport (BP), localization (BP), cytoplasm (CC), intrinsic component (CC), enzyme regulator activity (MF) and protein binding (MF) and shown in Fig.\u0026nbsp;4. Down regulated genes were mainly involved in multicellular organismal process (BP), developmental process (BP), cell periphery (CC), plasma membrane (CC), metal ion binding (MF) and cation binding (MF) and shown in Fig.\u0026nbsp;5. The up regulated and down regulated genes from the REACTOME pathway enrichment analysis are shown in Table\u0026nbsp;4, The up regulated genes from the REACTOME pathway enrichment analysis were those for the neutrophil degranulation and immune system and shown in Fig.\u0026nbsp;4. The most down regulated genes from the REACTOME pathway enrichment were for extracellular matrix organization and diseases of metabolism and shown in Fig.\u0026nbsp;5.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of the PPI and module analysis\u003c/h2\u003e\u003cp\u003eBased on the information in the STRING database, the hub nodes with higher node degree, betweenness, stress and closeness were screened (Table\u0026nbsp;5). The PPI network contained 4360 nodes and 9761 edges (Fig.\u0026nbsp;6). UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 were the hub genes with the highest values of topological parameters (node degree, betweenness, stress and closeness). Furthermore, the two significant modules were extracted from the PPI network. Module 1 contained 71 gene nodes, including RPL39, RPL31, RPL23, RPL35A, RPL11, RPS24, UBB and RPL34 with 434 edges (Fig.\u0026nbsp;7). Functional enrichment analysis of the hub genes in this module was mainly related to cellular responses to stress, axon guidance, metabolism, transport, localization, cytoplasm, immune system and protein binding. Module 2 contained 70 gene nodes, including FZD4, MSTN, EGR3, NOTCH3, PAX8, SFRP5 and WNT4 with 132 edges (Fig.\u0026nbsp;8). Functional enrichment analysis of the hub genes in this module was mainly related to multicellular organismal process, developmental process, signaling by receptor tyrosine, diseases of metabolism and cell periphery (Fig.\u0026nbsp;9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMiRNA-hub gene regulatory network construction\u003c/h2\u003e\u003cp\u003eTo predict the miRNAs for the hub genes, we used independent online tool (miRNet). The miRNA-hub gene regulatory network contained 2378 nodes, including 2090 miRNAs and 288 hub genes, and 11478 edges (Fig.\u0026nbsp;11). TUBB2A that was modulated by 206 miRNAs (ex; hsa-mir-8085), BCL2L1 that was modulated by 179 miRNAs (ex; hsa-mir-6735-5p), UBE2D1 that was modulated by 84 miRNAs (ex; hsa-mir-548ap-5p), UBB that was modulated by 81 miRNAs (ex; hsa-mir-132-3p), RPS24 that was modulated by 79 miRNAs (ex; hsa-mir-27a-3p), CAV1 that was modulated by 115 miRNAs (ex; hsa-mir-4514), EPHA2 that was modulated by 90 miRNAs (ex; hsa-mir-3133), ERBB3 that was modulated by 53 miRNAs (ex; hsa-mir-4328), MYH11 that was modulated by 50 miRNAs (ex; hsa-mir-643) and WNT4 that was modulated by 42 miRNAs (ex; hsa-mir-6749-3p) and are listed in Table\u0026nbsp;6\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eTF-hub gene regulatory network construction\u003c/h2\u003e\u003cp\u003eTo predict the TF for the hub genes, we used independent online tool (NetworkAnalyst). The TF-hub gene regulatory network contained 582 nodes, including 336 TFs and 246 hub genes, and 6606 edges (Fig.\u0026nbsp;12). BCL2L1 that was modulated by 115 TFs (ex; HMG20B), RPL23 that was modulated by 108 TFs (ex; ATF3), TUBB2A that was modulated by 59 TFs (ex; SMARCE1), UBB that was modulated by 59 TFs (ex; NCOR1), RPL26 that was modulated by 49 TFs (ex; POLR2A), EPHA2 that was modulated by 91 TFs (ex; STAT3), FOXA1 that was modulated by 79 TFs (ex; RARA), ERBB3 that was modulated by 54 TFs (ex; KLF4), FLNC that was modulated by 53 TFs (ex; HIC1) and CAV1 that was modulated by 24 TFs (ex; ESRRA) and are listed in Table\u0026nbsp;6.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDrug-hub gene interaction network construction\u003c/h2\u003e\u003cp\u003eTo predict the drug molecule to target hub genes, we used independent online tool (NetworkAnalyst) (Fig.\u0026nbsp;13). ACHE that was targeted by 54 drugs (ex; Phenserine), KCNH2 that was targeted by 29 drugs (ex; Amiodarone), UQCRB that was targeted by 9 drugs (ex; Famoxadone), EPHB4 that was targeted by 9 drugs (ex; 3-({4-[(5-chloro-1,3-benzodioxol-4-yl)amino]pyrimidin-2-yl}amino)benzamide), TUBA1A that was targeted by 7 drugs (ex; Vinblastine), PDE4C that was targeted by 8 drugs (ex; Roflumilast), CACNA1B that was targeted by 6 drugs (ex; Spironolactone), SCN4A that was targeted by 5 drugs (ex; Zonisamide), GCK that was targeted by 5 drugs (ex; 2-(methylamino)-N-(4-methyl-1,3-thiazol-2-yl)-5-[(4-methyl-4H-1,2,4-triazol-3-yl)sulfanyl]benzamide) and RYR1 that was targeted by 4 drugs (ex; Suramin) and are listed in Table\u0026nbsp;7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eReceiver operating characteristic curve (ROC) analysis\u003c/h2\u003e\u003cp\u003eROC curve analyses were performed to verify the hub genes, and area under the curve (AUC) values was calculated. The diagnostic value of hub genes in BD samples and normal control samples are as follow: UBB (AUC: 0.902), UBE2D1 (AUC:0.907), TUBA1A (AUC:0.920), RPL11 (AUC:0.948), RPS24 (AUC:0.940), NOTCH3 (AUC:0.942), CAV1 (AUC:0.917), CNBD2 (AUC:0.922), CCNA1 (AUC: 0.915) and MYH11 (AUC: 0.905) (Fig.\u0026nbsp;14). Therefore, we hypothesise that UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 might be biomarkers For BD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInsilico\u003c/b\u003e \u003cb\u003emolecular docking studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular docking studies were conducted to assess the binding affinity and interaction profiles of phytoconstituentsKakkalide, Divaricatol, and Brucine Bwith proteins encoded by genes implicated in bipolar disorder, namely UBB, CNBD2, UBE2D1, and CAV1. The UBB gene, which is up regulated in bipolar disorder, showed the strongest binding with its co-crystallized ligand FT671 (-9.6 kcal/mol) via hydrogen bonds at PHE409 and ASN418. Kakkalide also showed good binding (-7.9 kcal/mol) and formed multiple hydrogen bonds with LEU267, GLN268, VAL531, ARG239, and ASN236. In the case of CNBD2, which is down regulated, the co-crystallized ligand cAMP demonstrated weaker binding (-6.5 kcal/mol) with limited hydrogen bonding to ARG507. Kakkalide, in contrast, had a stronger affinity (-8.4 kcal/mol) and formed multiple hydrogen bonds with GLN509, TYR631, GLU515, and ARG507, and ILE341. Brucine B generally showed weaker or incomplete binding profiles across targets. Binding affinity and amino acid interaction were given in Supplementary Table S1, and 2D and 3D amino interaction images were given in \u003cb\u003eFig.\u0026nbsp;14 to Fig.\u0026nbsp;15\u003c/b\u003e. For the upregulated gene UBE2D1, the standard ligand 7HC bound with an affinity of \u0026minus;\u0026thinsp;8.8 kcal/mol, forming hydrogen bonds at HIS14 and VAL93, while Kakkalide displayed a moderate affinity (-7.9 kcal/mol) and interacted via LEU54. Regarding CAV1, which is downregulated, the co-crystallized ligand EDK had a moderate affinity of -7.4 kcal/mol with a single hydrogen bond at LEU453. However, Divaricatol showed stronger binding (-8.2 kcal/mol) and formed hydrogen bonds with SER342, CYS285, LEU340, and ILE341. Brucine B generally showed weaker or incomplete binding profiles across targets. Binding affinity and amino acid interaction were given in Supplementary Table S2, and 2D and 3D amino interaction images were given in Fig.\u0026nbsp;16 to Fig.\u0026nbsp;17.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eADMET Results\u003c/h2\u003e\u003cp\u003eThe ADMET profiles of seven compoundsKakkalide, Brucine B, Divaricatol, FT671, cAMP, 7HC, and EDKwere evaluated using ADMETlab 2.0. Brucine B demonstrated high bioavailability, FT671 and cAMP showed moderate scores, while 7HC and EDK exhibited very low bioavailability. Solubility varied, with most compounds being moderately to poorly soluble. EDK and 7HC were the least soluble (Log S -5.02 and \u0026minus;\u0026thinsp;4.79 respectively). Lipophilicity was highest in 7HC and EDK, indicating poor aqueous solubility, while cAMP was distinctly hydrophilic. Only cAMP displayed high human intestinal absorption (~\u0026thinsp;94.5%), whereas other compounds had poor absorption values. FT671, 7HC, and EDK were BBB permeant; cAMP showed partial CNS penetration. Among them, Brucine B had the highest unbound plasma fraction, while EDK had the lowest. EDK and 7HC acted as multiCYP inhibitors, showing potential for drug-drug interactions. Conversely, Divaricatol, FT671, Kakkalide, and Brucine B were metabolically safer. EDK showed the highest total clearance, indicating rapid systemic elimination. Kakkalide exhibited the lowest LD50 value (~\u0026thinsp;0.038 mol/kg), suggesting higher acute toxicity. Hepatotoxicity was predicted for nearly all compounds except 7HC, which showed a moderate risk all the values were given in Supplementary Table S3.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe BD is the most common psychiatric disorder worldwide. Although numerous advances have been made in the treatment of BD, the prognosis has remained poor. Therefore, it is crucial to elucidate the molecular mechanism of BD for understanding of the disease progression to develop novel therapeutic targets. Due to the rapid advancement of NGS technology, bioinformatics analysis might contribute to identifying the DEGs and functional pathways involved in the BD.\u003c/p\u003e\u003cp\u003eIn this investigation, NGS dataset was selected to identify the DEGs between BD and normal control samples. As a result, 957 DEGs including 477 up regulated and 480 down regulated genes were identified. Wang et al. [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and Joshi et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] showed altered expression of HBG1 and S100A8 in cardiovascular diseases. Lu et al [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] reported that S100A8 promoted cognitive dysfunction. S100A8 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] was altered expression in obesity and might serve as a potential prognostic biomarker of obesity. S100A8 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] and KLHL40 [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] plays an emerging role in pregnancy. The expression of S100A8 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] is altered in diabetes mellitus. The above findings might contribute to a better understanding of the molecular mechanisms underlying the pathogenesis of BD.\u003c/p\u003e\u003cp\u003eGO and REACTOME pathway enrichment analysis were conducted to demonstrate interactions of the DEGs. Immune system [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], axon guidance [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], metabolism [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], diseases of metabolism [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and neuronal system [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] were lined with advancement of BD. Altered expression of enriched genes include SLC6A9 [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], RHD (Rh blood group D antigen) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], CHRFAM7A [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], ANXA3 [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], SLC1A5 [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], KCNH2 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], SELENBP1 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], SNCA (synuclein alpha) [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], TGM2 [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], B2M [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], QPCT (glutaminyl-peptide cyclotransferase) [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], NQO2 [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], GLRX5 [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], BASP1 [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], GAS7 [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], OLIG2 [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], RPTN (repetin) [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], SOX10 [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], GRIK1 [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], ZFPM2 [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], SHANK3 [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], ERBB3 [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], ARC (activity regulated cytoskeleton associated protein) [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], PLAT (plasminogen activator, tissue type) [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], GRIK5 [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], CACNB2 [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], NRXN2 [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], TERT (telomerase reverse transcriptase) [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e], L1CAM [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e], EGR3 [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e], CACNA1B [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], MAGI1 [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e], KIR2DL1 [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], PAH (phenylalanine hydroxylase) [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e] and CYP3A5 [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e] have been shown in schizophrenia. Recent studies showed that SLC6A9 [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e], FKBP1B [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e], S100A12 [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], SLC6A19 [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], TXN (thioredoxin) [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e], TLR9 [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e], ARG1 [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e], B2M [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e], C5AR1 [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e], MYADM (myeloid associated differentiation marker) [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e], SIAH2 [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], PRDX2 [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e], RDH8 [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e], CYP11B2 [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e], RARRES2 [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e], NOX1 [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e], OTC (ornithine transcarbamylase) [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e], CYP1A1 [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e], NFATC4 [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e], TSLP (thymic stromal lymphopoietin) [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e], WNT4 [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e], MGP (matrix Gla protein) [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e], FGFBP1 [\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e], GHR (growth hormone receptor) [\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e], ERBB3 [\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e], CCDC40 [\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e], CACNB2 [\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e], CD34 [\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e], NOTCH3 [\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e], TERT (telomerase reverse transcriptase) [\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e], TP73 [\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e], RYR2 [\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e], ENPEP (glutamyl aminopeptidase) [\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e], SCN7A [\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e], WNK4 [\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e], KCNA5 [\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e], FOXC1 [\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e], ASIC1 [\u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e], VASH2 [\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e164\u003c/span\u003e], CXCL8 [\u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e165\u003c/span\u003e], PAPPA2 [\u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e166\u003c/span\u003e], KCNMA1 [\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e], LOX (lysyl oxidase) [\u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e168\u003c/span\u003e], SPARCL1 [\u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e169\u003c/span\u003e] and CYP3A5 [\u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e170\u003c/span\u003e] might have the potential to be used as diagnostic biomarkers of hypertension. Previous studies have reported that enriched genes include RHD (Rh blood group D antigen) [\u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e171\u003c/span\u003e], S100A12 [\u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e172\u003c/span\u003e], TXN (thioredoxin) [\u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e173\u003c/span\u003e], TLR9 [\u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e174\u003c/span\u003e], S100P [\u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e175\u003c/span\u003e], TAGLN2 [\u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e176\u003c/span\u003e], S100A9 [\u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e177\u003c/span\u003e], CA1 [\u003cspan citationid=\"CR178\" class=\"CitationRef\"\u003e178\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR179\" class=\"CitationRef\"\u003e179\u003c/span\u003e], RPL39 [\u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e180\u003c/span\u003e], F5 [\u003cspan citationid=\"CR181\" class=\"CitationRef\"\u003e181\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR182\" class=\"CitationRef\"\u003e182\u003c/span\u003e], B2M [\u003cspan citationid=\"CR183\" class=\"CitationRef\"\u003e183\u003c/span\u003e], S100A11 [\u003cspan citationid=\"CR184\" class=\"CitationRef\"\u003e184\u003c/span\u003e], SLC4A1 [\u003cspan citationid=\"CR185\" class=\"CitationRef\"\u003e185\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR186\" class=\"CitationRef\"\u003e186\u003c/span\u003e], AHSP (alpha hemoglobin stabilizing protein) [\u003cspan citationid=\"CR187\" class=\"CitationRef\"\u003e187\u003c/span\u003e], F12 [\u003cspan citationid=\"CR188\" class=\"CitationRef\"\u003e188\u003c/span\u003e], EPHB4 [\u003cspan citationid=\"CR189\" class=\"CitationRef\"\u003e189\u003c/span\u003e], NFE2 [\u003cspan citationid=\"CR190\" class=\"CitationRef\"\u003e190\u003c/span\u003e], VRK1 [\u003cspan citationid=\"CR191\" class=\"CitationRef\"\u003e191\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR192\" class=\"CitationRef\"\u003e192\u003c/span\u003e], FOXA1 [\u003cspan citationid=\"CR193\" class=\"CitationRef\"\u003e193\u003c/span\u003e], CYP11B2 [\u003cspan citationid=\"CR194\" class=\"CitationRef\"\u003e194\u003c/span\u003e], NOX1 [\u003cspan citationid=\"CR195\" class=\"CitationRef\"\u003e195\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR196\" class=\"CitationRef\"\u003e196\u003c/span\u003e], NPHS1 [\u003cspan citationid=\"CR197\" class=\"CitationRef\"\u003e197\u003c/span\u003e], OTC (ornithine transcarbamylase) [\u003cspan citationid=\"CR198\" class=\"CitationRef\"\u003e198\u003c/span\u003e], SULF1 [\u003cspan citationid=\"CR199\" class=\"CitationRef\"\u003e199\u003c/span\u003e], CYP1A1 [\u003cspan citationid=\"CR200\" class=\"CitationRef\"\u003e200\u003c/span\u003e], DCN (decorin) [\u003cspan citationid=\"CR201\" class=\"CitationRef\"\u003e201\u003c/span\u003e], ADAMTS7 [\u003cspan citationid=\"CR202\" class=\"CitationRef\"\u003e202\u003c/span\u003e], WNT4 [\u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e203\u003c/span\u003e], LAMA4 [\u003cspan citationid=\"CR204\" class=\"CitationRef\"\u003e204\u003c/span\u003e], SCN4A [\u003cspan citationid=\"CR205\" class=\"CitationRef\"\u003e205\u003c/span\u003e], CACNB2 [\u003cspan citationid=\"CR206\" class=\"CitationRef\"\u003e206\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR207\" class=\"CitationRef\"\u003e207\u003c/span\u003e], ENPEP (glutamyl aminopeptidase) [\u003cspan citationid=\"CR208\" class=\"CitationRef\"\u003e208\u003c/span\u003e], WNK4 [\u003cspan citationid=\"CR209\" class=\"CitationRef\"\u003e209\u003c/span\u003e], FOXC1 [\u003cspan citationid=\"CR210\" class=\"CitationRef\"\u003e210\u003c/span\u003e], PAX8 [\u003cspan citationid=\"CR211\" class=\"CitationRef\"\u003e211\u003c/span\u003e], ROBO1 [\u003cspan citationid=\"CR212\" class=\"CitationRef\"\u003e212\u003c/span\u003e], CXCL8 [\u003cspan citationid=\"CR213\" class=\"CitationRef\"\u003e213\u003c/span\u003e], PAPPA2 [\u003cspan citationid=\"CR214\" class=\"CitationRef\"\u003e214\u003c/span\u003e], LOX (lysyl oxidase) [\u003cspan citationid=\"CR215\" class=\"CitationRef\"\u003e215\u003c/span\u003e], NOSTRIN (nitric oxide synthase trafficking) [\u003cspan citationid=\"CR216\" class=\"CitationRef\"\u003e216\u003c/span\u003e], MUC16 [\u003cspan citationid=\"CR217\" class=\"CitationRef\"\u003e217\u003c/span\u003e], MIOX (myo-inositol oxygenase) [\u003cspan citationid=\"CR218\" class=\"CitationRef\"\u003e218\u003c/span\u003e], CYP11A1 [\u003cspan citationid=\"CR219\" class=\"CitationRef\"\u003e219\u003c/span\u003e] and CYP3A5 [\u003cspan citationid=\"CR220\" class=\"CitationRef\"\u003e220\u003c/span\u003e] are involved in the pregnancy complications. Modification in the activity and expression of enriched genes include RHD (Rh blood group D antigen) [\u003cspan citationid=\"CR221\" class=\"CitationRef\"\u003e221\u003c/span\u003e], S100A12 [\u003cspan citationid=\"CR222\" class=\"CitationRef\"\u003e222\u003c/span\u003e], TLR9 [\u003cspan citationid=\"CR223\" class=\"CitationRef\"\u003e223\u003c/span\u003e], ANXA3 [\u003cspan citationid=\"CR224\" class=\"CitationRef\"\u003e224\u003c/span\u003e], S100P [\u003cspan citationid=\"CR225\" class=\"CitationRef\"\u003e225\u003c/span\u003e], TAGLN2 [\u003cspan citationid=\"CR226\" class=\"CitationRef\"\u003e226\u003c/span\u003e], S100A9 [\u003cspan citationid=\"CR227\" class=\"CitationRef\"\u003e227\u003c/span\u003e], KCNH2 [\u003cspan citationid=\"CR228\" class=\"CitationRef\"\u003e228\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR229\" class=\"CitationRef\"\u003e229\u003c/span\u003e], SELENBP1 [\u003cspan citationid=\"CR230\" class=\"CitationRef\"\u003e230\u003c/span\u003e], TANGO2 [\u003cspan citationid=\"CR231\" class=\"CitationRef\"\u003e231\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR232\" class=\"CitationRef\"\u003e232\u003c/span\u003e], TFR2 [\u003cspan citationid=\"CR233\" class=\"CitationRef\"\u003e233\u003c/span\u003e], B2M [\u003cspan citationid=\"CR234\" class=\"CitationRef\"\u003e234\u003c/span\u003e], LTBP2 [\u003cspan citationid=\"CR235\" class=\"CitationRef\"\u003e235\u003c/span\u003e], PGLYRP1 [\u003cspan citationid=\"CR236\" class=\"CitationRef\"\u003e236\u003c/span\u003e], HRH2 [\u003cspan citationid=\"CR237\" class=\"CitationRef\"\u003e237\u003c/span\u003e], CLEC5A [\u003cspan citationid=\"CR238\" class=\"CitationRef\"\u003e238\u003c/span\u003e], PLSCR4 [\u003cspan citationid=\"CR239\" class=\"CitationRef\"\u003e239\u003c/span\u003e], S100A11 [\u003cspan citationid=\"CR240\" class=\"CitationRef\"\u003e240\u003c/span\u003e], PPBP (pro-platelet basic protein) [\u003cspan citationid=\"CR241\" class=\"CitationRef\"\u003e241\u003c/span\u003e], RAP1GAP [\u003cspan citationid=\"CR242\" class=\"CitationRef\"\u003e242\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR243\" class=\"CitationRef\"\u003e243\u003c/span\u003e], RBM38 [\u003cspan citationid=\"CR244\" class=\"CitationRef\"\u003e244\u003c/span\u003e], MYL4 [\u003cspan citationid=\"CR245\" class=\"CitationRef\"\u003e245\u003c/span\u003e], EPHB4 [\u003cspan citationid=\"CR246\" class=\"CitationRef\"\u003e246\u003c/span\u003e], TNNT1 [\u003cspan citationid=\"CR247\" class=\"CitationRef\"\u003e247\u003c/span\u003e], KBTBD7 [\u003cspan citationid=\"CR248\" class=\"CitationRef\"\u003e248\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR249\" class=\"CitationRef\"\u003e249\u003c/span\u003e], SIAH2 [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], FOXO4 [\u003cspan citationid=\"CR250\" class=\"CitationRef\"\u003e250\u003c/span\u003e], PRDX2 [\u003cspan citationid=\"CR251\" class=\"CitationRef\"\u003e251\u003c/span\u003e], MAF1 [\u003cspan citationid=\"CR252\" class=\"CitationRef\"\u003e252\u003c/span\u003e], KANK2 [\u003cspan citationid=\"CR253\" class=\"CitationRef\"\u003e253\u003c/span\u003e], SFRP2 [\u003cspan citationid=\"CR254\" class=\"CitationRef\"\u003e254\u003c/span\u003e], BGN (biglycan) [\u003cspan citationid=\"CR255\" class=\"CitationRef\"\u003e255\u003c/span\u003e], HSPB7 [\u003cspan citationid=\"CR256\" class=\"CitationRef\"\u003e256\u003c/span\u003e], ABCG8 [\u003cspan citationid=\"CR257\" class=\"CitationRef\"\u003e257\u003c/span\u003e], CYP11B2 [\u003cspan citationid=\"CR258\" class=\"CitationRef\"\u003e258\u003c/span\u003e], RARRES2 [\u003cspan citationid=\"CR259\" class=\"CitationRef\"\u003e259\u003c/span\u003e], NOX1 [\u003cspan citationid=\"CR260\" class=\"CitationRef\"\u003e260\u003c/span\u003e], CPE (carboxypeptidase E) [\u003cspan citationid=\"CR261\" class=\"CitationRef\"\u003e261\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR262\" class=\"CitationRef\"\u003e262\u003c/span\u003e], OTC (ornithine transcarbamylase) [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e], CYP1A1 [\u003cspan citationid=\"CR263\" class=\"CitationRef\"\u003e263\u003c/span\u003e], LRIG3 [\u003cspan citationid=\"CR264\" class=\"CitationRef\"\u003e264\u003c/span\u003e], GJA1 [\u003cspan citationid=\"CR265\" class=\"CitationRef\"\u003e265\u003c/span\u003e], NFATC4 [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e], SEMA3F [\u003cspan citationid=\"CR266\" class=\"CitationRef\"\u003e266\u003c/span\u003e], CDH11 [\u003cspan citationid=\"CR267\" class=\"CitationRef\"\u003e267\u003c/span\u003e], DCN (decorin) [\u003cspan citationid=\"CR268\" class=\"CitationRef\"\u003e268\u003c/span\u003e], TSLP (thymic stromal lymphopoietin) [\u003cspan citationid=\"CR269\" class=\"CitationRef\"\u003e269\u003c/span\u003e], ADAMTS7 [\u003cspan citationid=\"CR270\" class=\"CitationRef\"\u003e270\u003c/span\u003e], C1QTNF1 [\u003cspan citationid=\"CR271\" class=\"CitationRef\"\u003e271\u003c/span\u003e], SFTPB (surfactant protein B) [\u003cspan citationid=\"CR272\" class=\"CitationRef\"\u003e272\u003c/span\u003e], WNT4 [\u003cspan citationid=\"CR273\" class=\"CitationRef\"\u003e273\u003c/span\u003e], MGP (matrix Gla protein) [\u003cspan citationid=\"CR274\" class=\"CitationRef\"\u003e274\u003c/span\u003e], SHANK3 [\u003cspan citationid=\"CR275\" class=\"CitationRef\"\u003e275\u003c/span\u003e], ALOX12B [\u003cspan citationid=\"CR276\" class=\"CitationRef\"\u003e276\u003c/span\u003e], MYBPHL (myosin binding protein H like) [\u003cspan citationid=\"CR277\" class=\"CitationRef\"\u003e277\u003c/span\u003e], GHR (growth hormone receptor) [\u003cspan citationid=\"CR278\" class=\"CitationRef\"\u003e278\u003c/span\u003e], ERBB3 [\u003cspan citationid=\"CR279\" class=\"CitationRef\"\u003e279\u003c/span\u003e], PLAT (plasminogen activator, tissue type) [\u003cspan citationid=\"CR280\" class=\"CitationRef\"\u003e280\u003c/span\u003e], ADAMTS2 [\u003cspan citationid=\"CR281\" class=\"CitationRef\"\u003e281\u003c/span\u003e], CACNB2 [\u003cspan citationid=\"CR282\" class=\"CitationRef\"\u003e282\u003c/span\u003e], DAB2IP [\u003cspan citationid=\"CR283\" class=\"CitationRef\"\u003e283\u003c/span\u003e], CD34 [\u003cspan citationid=\"CR284\" class=\"CitationRef\"\u003e284\u003c/span\u003e], COL15A1 [\u003cspan citationid=\"CR285\" class=\"CitationRef\"\u003e285\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR286\" class=\"CitationRef\"\u003e286\u003c/span\u003e], NOTCH3 [\u003cspan citationid=\"CR287\" class=\"CitationRef\"\u003e287\u003c/span\u003e], HSPG2 [\u003cspan citationid=\"CR288\" class=\"CitationRef\"\u003e288\u003c/span\u003e], TERT (telomerase reverse transcriptase) [\u003cspan citationid=\"CR289\" class=\"CitationRef\"\u003e289\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR290\" class=\"CitationRef\"\u003e290\u003c/span\u003e], MMP15 [\u003cspan citationid=\"CR291\" class=\"CitationRef\"\u003e291\u003c/span\u003e], COL4A2 [\u003cspan citationid=\"CR292\" class=\"CitationRef\"\u003e292\u003c/span\u003e], EGR3 [\u003cspan citationid=\"CR293\" class=\"CitationRef\"\u003e293\u003c/span\u003e], RBM20 [\u003cspan citationid=\"CR294\" class=\"CitationRef\"\u003e294\u003c/span\u003e], RYR2 [\u003cspan citationid=\"CR295\" class=\"CitationRef\"\u003e295\u003c/span\u003e], EPHA2 [\u003cspan citationid=\"CR296\" class=\"CitationRef\"\u003e296\u003c/span\u003e], NDRG4 [\u003cspan citationid=\"CR297\" class=\"CitationRef\"\u003e297\u003c/span\u003e], UNC5B [\u003cspan citationid=\"CR298\" class=\"CitationRef\"\u003e298\u003c/span\u003e], CTNNA3 [\u003cspan citationid=\"CR299\" class=\"CitationRef\"\u003e299\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e], CUX2 [\u003cspan citationid=\"CR301\" class=\"CitationRef\"\u003e301\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR302\" class=\"CitationRef\"\u003e302\u003c/span\u003e], AXL (AXL receptor tyrosine kinase) [\u003cspan citationid=\"CR303\" class=\"CitationRef\"\u003e303\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR304\" class=\"CitationRef\"\u003e304\u003c/span\u003e], KCNA5 [\u003cspan citationid=\"CR305\" class=\"CitationRef\"\u003e305\u003c/span\u003e], FOXC1 [\u003cspan citationid=\"CR306\" class=\"CitationRef\"\u003e306\u003c/span\u003e], RYR1 [\u003cspan citationid=\"CR307\" class=\"CitationRef\"\u003e307\u003c/span\u003e], ROBO1 [\u003cspan citationid=\"CR308\" class=\"CitationRef\"\u003e308\u003c/span\u003e], XIRP2 [\u003cspan citationid=\"CR309\" class=\"CitationRef\"\u003e309\u003c/span\u003e], FZD4 [\u003cspan citationid=\"CR310\" class=\"CitationRef\"\u003e310\u003c/span\u003e], VASH2 [\u003cspan citationid=\"CR311\" class=\"CitationRef\"\u003e311\u003c/span\u003e], CXCL8 [\u003cspan citationid=\"CR312\" class=\"CitationRef\"\u003e312\u003c/span\u003e], KCNMA1 [\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e], LOX (lysyl oxidase) [\u003cspan citationid=\"CR313\" class=\"CitationRef\"\u003e313\u003c/span\u003e], DNAH11 [\u003cspan citationid=\"CR314\" class=\"CitationRef\"\u003e314\u003c/span\u003e], FMOD (fibromodulin) [\u003cspan citationid=\"CR315\" class=\"CitationRef\"\u003e315\u003c/span\u003e], MFAP4 [\u003cspan citationid=\"CR316\" class=\"CitationRef\"\u003e316\u003c/span\u003e], FLNC (filamin C) [\u003cspan citationid=\"CR317\" class=\"CitationRef\"\u003e317\u003c/span\u003e], LIFR (LIF receptor subunit alpha) [\u003cspan citationid=\"CR318\" class=\"CitationRef\"\u003e318\u003c/span\u003e], MAGI1 [\u003cspan citationid=\"CR319\" class=\"CitationRef\"\u003e319\u003c/span\u003e], SLC39A2 [\u003cspan citationid=\"CR320\" class=\"CitationRef\"\u003e320\u003c/span\u003e] and CYP3A5 [\u003cspan citationid=\"CR321\" class=\"CitationRef\"\u003e321\u003c/span\u003e] are linked to cardiovascular diseases. Studies have reported that enriched genes include CHRFAM7A [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], ACHE (acetylcholinesterase (Cartwright blood group) [\u003cspan citationid=\"CR322\" class=\"CitationRef\"\u003e322\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR323\" class=\"CitationRef\"\u003e323\u003c/span\u003e], SELENBP1 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e], BASP1 [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], CHRNA1 [\u003cspan citationid=\"CR324\" class=\"CitationRef\"\u003e324\u003c/span\u003e], RPTN (repetin) [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR325\" class=\"CitationRef\"\u003e325\u003c/span\u003e], TACR1[\u003cspan citationid=\"CR326\" class=\"CitationRef\"\u003e326\u003c/span\u003e], SHANK3 [\u003cspan citationid=\"CR327\" class=\"CitationRef\"\u003e327\u003c/span\u003e], ALOX12B [\u003cspan citationid=\"CR328\" class=\"CitationRef\"\u003e328\u003c/span\u003e], HPN (hepsin) [\u003cspan citationid=\"CR329\" class=\"CitationRef\"\u003e329\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], CACNB2 [\u003cspan citationid=\"CR330\" class=\"CitationRef\"\u003e330\u003c/span\u003e], MYT1 [\u003cspan citationid=\"CR331\" class=\"CitationRef\"\u003e331\u003c/span\u003e], NOTCH3 [\u003cspan citationid=\"CR332\" class=\"CitationRef\"\u003e332\u003c/span\u003e], TERT (telomerase reverse transcriptase) [\u003cspan citationid=\"CR333\" class=\"CitationRef\"\u003e333\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR334\" class=\"CitationRef\"\u003e334\u003c/span\u003e], EGR3 [\u003cspan citationid=\"CR335\" class=\"CitationRef\"\u003e335\u003c/span\u003e], BSN (bassoon presynaptic cytomatrix protein) [\u003cspan citationid=\"CR336\" class=\"CitationRef\"\u003e336\u003c/span\u003e], CUX2 [\u003cspan citationid=\"CR337\" class=\"CitationRef\"\u003e337\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR338\" class=\"CitationRef\"\u003e338\u003c/span\u003e], CXCL8 [\u003cspan citationid=\"CR339\" class=\"CitationRef\"\u003e339\u003c/span\u003e], MAGI1 [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] and PAH (phenylalanine hydroxylase) [\u003cspan citationid=\"CR340\" class=\"CitationRef\"\u003e340\u003c/span\u003e] are necessary for BD development. Enriched genes include S100A12 [\u003cspan citationid=\"CR341\" class=\"CitationRef\"\u003e341\u003c/span\u003e], CDH1 [\u003cspan citationid=\"CR342\" class=\"CitationRef\"\u003e342\u003c/span\u003e], S100A9 [\u003cspan citationid=\"CR343\" class=\"CitationRef\"\u003e343\u003c/span\u003e], ANK1 [\u003cspan citationid=\"CR344\" class=\"CitationRef\"\u003e344\u003c/span\u003e], KCNH2 [\u003cspan citationid=\"CR345\" class=\"CitationRef\"\u003e345\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR346\" class=\"CitationRef\"\u003e346\u003c/span\u003e], FRMD4A [\u003cspan citationid=\"CR347\" class=\"CitationRef\"\u003e347\u003c/span\u003e], SNCA (synuclein alpha) [\u003cspan citationid=\"CR348\" class=\"CitationRef\"\u003e348\u003c/span\u003e], FBXO7 [\u003cspan citationid=\"CR349\" class=\"CitationRef\"\u003e349\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR350\" class=\"CitationRef\"\u003e350\u003c/span\u003e], B2M [\u003cspan citationid=\"CR351\" class=\"CitationRef\"\u003e351\u003c/span\u003e], KCNH3 [\u003cspan citationid=\"CR352\" class=\"CitationRef\"\u003e352\u003c/span\u003e], CA2 [\u003cspan citationid=\"CR353\" class=\"CitationRef\"\u003e353\u003c/span\u003e], FUZ (fuzzy planar cell polarity protein) [\u003cspan citationid=\"CR354\" class=\"CitationRef\"\u003e354\u003c/span\u003e], UBB (ubiquitin B) [\u003cspan citationid=\"CR355\" class=\"CitationRef\"\u003e355\u003c/span\u003e], C5AR1 [\u003cspan citationid=\"CR356\" class=\"CitationRef\"\u003e356\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR357\" class=\"CitationRef\"\u003e357\u003c/span\u003e], F12 [\u003cspan citationid=\"CR358\" class=\"CitationRef\"\u003e358\u003c/span\u003e], TSPAN5 [\u003cspan citationid=\"CR359\" class=\"CitationRef\"\u003e359\u003c/span\u003e], NQO2 [\u003cspan citationid=\"CR360\" class=\"CitationRef\"\u003e360\u003c/span\u003e], NDUFA1 [\u003cspan citationid=\"CR361\" class=\"CitationRef\"\u003e361\u003c/span\u003e], SRXN1 [\u003cspan citationid=\"CR362\" class=\"CitationRef\"\u003e362\u003c/span\u003e], BASP1 [\u003cspan citationid=\"CR363\" class=\"CitationRef\"\u003e363\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR364\" class=\"CitationRef\"\u003e364\u003c/span\u003e], OLIG2 [\u003cspan citationid=\"CR365\" class=\"CitationRef\"\u003e365\u003c/span\u003e], EFHC2 [\u003cspan citationid=\"CR366\" class=\"CitationRef\"\u003e366\u003c/span\u003e], FOXA1 [\u003cspan citationid=\"CR367\" class=\"CitationRef\"\u003e367\u003c/span\u003e], PAX4 [\u003cspan citationid=\"CR368\" class=\"CitationRef\"\u003e368\u003c/span\u003e], BGN (biglycan) [\u003cspan citationid=\"CR369\" class=\"CitationRef\"\u003e369\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR370\" class=\"CitationRef\"\u003e370\u003c/span\u003e], LRIG3 [\u003cspan citationid=\"CR371\" class=\"CitationRef\"\u003e371\u003c/span\u003e], GJA1 [\u003cspan citationid=\"CR372\" class=\"CitationRef\"\u003e372\u003c/span\u003e], SHANK3 [\u003cspan citationid=\"CR373\" class=\"CitationRef\"\u003e373\u003c/span\u003e], ARC (activity regulated cytoskeleton associated protein) [\u003cspan citationid=\"CR374\" class=\"CitationRef\"\u003e374\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR375\" class=\"CitationRef\"\u003e375\u003c/span\u003e], UNC13A [\u003cspan citationid=\"CR376\" class=\"CitationRef\"\u003e376\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR377\" class=\"CitationRef\"\u003e377\u003c/span\u003e], HSPG2 [\u003cspan citationid=\"CR378\" class=\"CitationRef\"\u003e378\u003c/span\u003e], TERT (telomerase reverse transcriptase) [\u003cspan citationid=\"CR379\" class=\"CitationRef\"\u003e379\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR380\" class=\"CitationRef\"\u003e380\u003c/span\u003e], L1CAM [\u003cspan citationid=\"CR381\" class=\"CitationRef\"\u003e381\u003c/span\u003e], CALB1 [\u003cspan citationid=\"CR382\" class=\"CitationRef\"\u003e382\u003c/span\u003e], RYR2 [\u003cspan citationid=\"CR383\" class=\"CitationRef\"\u003e383\u003c/span\u003e], MYO15A [\u003cspan citationid=\"CR384\" class=\"CitationRef\"\u003e384\u003c/span\u003e], MYH11 [\u003cspan citationid=\"CR385\" class=\"CitationRef\"\u003e385\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR386\" class=\"CitationRef\"\u003e386\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR387\" class=\"CitationRef\"\u003e387\u003c/span\u003e], KIRREL3 [\u003cspan citationid=\"CR388\" class=\"CitationRef\"\u003e388\u003c/span\u003e], COBL (cordon-bleu WH2 repeat protein) [\u003cspan citationid=\"CR389\" class=\"CitationRef\"\u003e389\u003c/span\u003e], LOX (lysyl oxidase) [\u003cspan citationid=\"CR390\" class=\"CitationRef\"\u003e390\u003c/span\u003e], SPARCL1 [\u003cspan citationid=\"CR391\" class=\"CitationRef\"\u003e391\u003c/span\u003e], FLNC (filamin C) [\u003cspan citationid=\"CR392\" class=\"CitationRef\"\u003e392\u003c/span\u003e], TMPRSS4 [\u003cspan citationid=\"CR393\" class=\"CitationRef\"\u003e393\u003c/span\u003e], VWA2 [\u003cspan citationid=\"CR394\" class=\"CitationRef\"\u003e394\u003c/span\u003e], OGDHL (oxoglutarate dehydrogenase L) [\u003cspan citationid=\"CR395\" class=\"CitationRef\"\u003e395\u003c/span\u003e], CYP3A5 [\u003cspan citationid=\"CR396\" class=\"CitationRef\"\u003e396\u003c/span\u003e] and FBXO40 [\u003cspan citationid=\"CR397\" class=\"CitationRef\"\u003e397\u003c/span\u003e] were revealed to be associated with cognitive dysfunction. Enriched genes include S100A12 [\u003cspan citationid=\"CR398\" class=\"CitationRef\"\u003e398\u003c/span\u003e], SLC6A19 [\u003cspan citationid=\"CR399\" class=\"CitationRef\"\u003e399\u003c/span\u003e], TXN (thioredoxin) [\u003cspan citationid=\"CR400\" class=\"CitationRef\"\u003e400\u003c/span\u003e], TLR9 [\u003cspan citationid=\"CR401\" class=\"CitationRef\"\u003e401\u003c/span\u003e], S100P [\u003cspan citationid=\"CR402\" class=\"CitationRef\"\u003e402\u003c/span\u003e], S100A9 [\u003cspan citationid=\"CR403\" class=\"CitationRef\"\u003e403\u003c/span\u003e], ANK1 [\u003cspan citationid=\"CR404\" class=\"CitationRef\"\u003e404\u003c/span\u003e], CA1 [\u003cspan citationid=\"CR405\" class=\"CitationRef\"\u003e405\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR406\" class=\"CitationRef\"\u003e406\u003c/span\u003e], ARG1 [\u003cspan citationid=\"CR407\" class=\"CitationRef\"\u003e407\u003c/span\u003e], SNCA (synuclein alpha) [\u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e408\u003c/span\u003e], STARD10 [\u003cspan citationid=\"CR409\" class=\"CitationRef\"\u003e409\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR410\" class=\"CitationRef\"\u003e410\u003c/span\u003e], TFR2 [\u003cspan citationid=\"CR411\" class=\"CitationRef\"\u003e411\u003c/span\u003e], B2M [\u003cspan citationid=\"CR412\" class=\"CitationRef\"\u003e412\u003c/span\u003e], PHOSPHO1 [\u003cspan citationid=\"CR413\" class=\"CitationRef\"\u003e413\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR414\" class=\"CitationRef\"\u003e414\u003c/span\u003e], WNT6 [\u003cspan citationid=\"CR415\" class=\"CitationRef\"\u003e415\u003c/span\u003e], NFE2 [\u003cspan citationid=\"CR416\" class=\"CitationRef\"\u003e416\u003c/span\u003e], RNF10 [\u003cspan citationid=\"CR417\" class=\"CitationRef\"\u003e417\u003c/span\u003e], CARM1 [\u003cspan citationid=\"CR418\" class=\"CitationRef\"\u003e418\u003c/span\u003e], GLRX5 [\u003cspan citationid=\"CR419\" class=\"CitationRef\"\u003e419\u003c/span\u003e], PPP2R5B [\u003cspan citationid=\"CR420\" class=\"CitationRef\"\u003e420\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR421\" class=\"CitationRef\"\u003e421\u003c/span\u003e], FOXO4 [\u003cspan citationid=\"CR422\" class=\"CitationRef\"\u003e422\u003c/span\u003e], ARHGEF12 [\u003cspan citationid=\"CR423\" class=\"CitationRef\"\u003e423\u003c/span\u003e], FOXA1 [\u003cspan citationid=\"CR424\" class=\"CitationRef\"\u003e424\u003c/span\u003e], PAX4 [\u003cspan citationid=\"CR368\" class=\"CitationRef\"\u003e368\u003c/span\u003e], ABCG8 [\u003cspan citationid=\"CR425\" class=\"CitationRef\"\u003e425\u003c/span\u003e], RARRES2 [\u003cspan citationid=\"CR426\" class=\"CitationRef\"\u003e426\u003c/span\u003e], NOX1 [\u003cspan citationid=\"CR427\" class=\"CitationRef\"\u003e427\u003c/span\u003e], CPE (carboxypeptidase E) [\u003cspan citationid=\"CR428\" class=\"CitationRef\"\u003e428\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR429\" class=\"CitationRef\"\u003e429\u003c/span\u003e], ADAMTS7 [\u003cspan citationid=\"CR430\" class=\"CitationRef\"\u003e430\u003c/span\u003e], SFTPB (surfactant protein B) [\u003cspan citationid=\"CR272\" class=\"CitationRef\"\u003e272\u003c/span\u003e], MGP (matrix Gla protein) [\u003cspan citationid=\"CR274\" class=\"CitationRef\"\u003e274\u003c/span\u003e], VTN (vitronectin) [\u003cspan citationid=\"CR431\" class=\"CitationRef\"\u003e431\u003c/span\u003e], PRSS1 [\u003cspan citationid=\"CR432\" class=\"CitationRef\"\u003e432\u003c/span\u003e], GHR (growth hormone receptor) [\u003cspan citationid=\"CR433\" class=\"CitationRef\"\u003e433\u003c/span\u003e], ERBB3 [\u003cspan citationid=\"CR434\" class=\"CitationRef\"\u003e434\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR435\" class=\"CitationRef\"\u003e435\u003c/span\u003e], CD34 [\u003cspan citationid=\"CR436\" class=\"CitationRef\"\u003e436\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e437\u003c/span\u003e], NOTCH3 [\u003cspan citationid=\"CR438\" class=\"CitationRef\"\u003e438\u003c/span\u003e], NR5A2 [\u003cspan citationid=\"CR439\" class=\"CitationRef\"\u003e439\u003c/span\u003e], SMOC1 [\u003cspan citationid=\"CR440\" class=\"CitationRef\"\u003e440\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR441\" class=\"CitationRef\"\u003e441\u003c/span\u003e], CTNNA3 [\u003cspan citationid=\"CR442\" class=\"CitationRef\"\u003e442\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR302\" class=\"CitationRef\"\u003e302\u003c/span\u003e], AXL (AXL receptor tyrosine kinase) [\u003cspan citationid=\"CR443\" class=\"CitationRef\"\u003e443\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR444\" class=\"CitationRef\"\u003e444\u003c/span\u003e], LCT (lactase) [\u003cspan citationid=\"CR445\" class=\"CitationRef\"\u003e445\u003c/span\u003e], FZD4 [\u003cspan citationid=\"CR446\" class=\"CitationRef\"\u003e446\u003c/span\u003e], KCNMA1 [\u003cspan citationid=\"CR447\" class=\"CitationRef\"\u003e447\u003c/span\u003e], FMOD (fibromodulin) [\u003cspan citationid=\"CR448\" class=\"CitationRef\"\u003e448\u003c/span\u003e] and CPA6 [\u003cspan citationid=\"CR449\" class=\"CitationRef\"\u003e449\u003c/span\u003e] were revealed to be correlated with disease outcome in patients with diabetes mellitus. Previous studies have demonstrated that enriched genes include SLC6A19 [\u003cspan citationid=\"CR450\" class=\"CitationRef\"\u003e450\u003c/span\u003e], DNAJC6 [\u003cspan citationid=\"CR451\" class=\"CitationRef\"\u003e451\u003c/span\u003e], TXN (thioredoxin) [\u003cspan citationid=\"CR400\" class=\"CitationRef\"\u003e400\u003c/span\u003e], S100A9 [\u003cspan citationid=\"CR452\" class=\"CitationRef\"\u003e452\u003c/span\u003e], HP (haptoglobin) [\u003cspan citationid=\"CR453\" class=\"CitationRef\"\u003e453\u003c/span\u003e], ARG1 [\u003cspan citationid=\"CR454\" class=\"CitationRef\"\u003e454\u003c/span\u003e], SNCA (synuclein alpha) [\u003cspan citationid=\"CR408\" class=\"CitationRef\"\u003e408\u003c/span\u003e], CEP19 [\u003cspan citationid=\"CR455\" class=\"CitationRef\"\u003e455\u003c/span\u003e], CAMP (cathelicidin antimicrobial peptide) [\u003cspan citationid=\"CR456\" class=\"CitationRef\"\u003e456\u003c/span\u003e], PINK1 [\u003cspan citationid=\"CR457\" class=\"CitationRef\"\u003e457\u003c/span\u003e], UBB (ubiquitin B) [\u003cspan citationid=\"CR458\" class=\"CitationRef\"\u003e458\u003c/span\u003e], PHOSPHO1 [\u003cspan citationid=\"CR413\" class=\"CitationRef\"\u003e413\u003c/span\u003e], CBS (cystathionine beta-synthase) [\u003cspan citationid=\"CR414\" class=\"CitationRef\"\u003e414\u003c/span\u003e], PPP2R5B [\u003cspan citationid=\"CR420\" class=\"CitationRef\"\u003e420\u003c/span\u003e], GPX1 [\u003cspan citationid=\"CR459\" class=\"CitationRef\"\u003e459\u003c/span\u003e], PRDX2 [\u003cspan citationid=\"CR460\" class=\"CitationRef\"\u003e460\u003c/span\u003e], LXN (latexin) [\u003cspan citationid=\"CR461\" class=\"CitationRef\"\u003e461\u003c/span\u003e], RGS6 [\u003cspan citationid=\"CR462\" class=\"CitationRef\"\u003e462\u003c/span\u003e], MAF1 [\u003cspan citationid=\"CR463\" class=\"CitationRef\"\u003e463\u003c/span\u003e], HSPB7 [\u003cspan citationid=\"CR464\" class=\"CitationRef\"\u003e464\u003c/span\u003e], PNLIP (pancreatic lipase) [\u003cspan citationid=\"CR465\" class=\"CitationRef\"\u003e465\u003c/span\u003e], NOX1 [\u003cspan citationid=\"CR427\" class=\"CitationRef\"\u003e427\u003c/span\u003e], CPE (carboxypeptidase E) [\u003cspan citationid=\"CR428\" class=\"CitationRef\"\u003e428\u003c/span\u003e], IL33 [\u003cspan citationid=\"CR466\" class=\"CitationRef\"\u003e466\u003c/span\u003e], SP7 [\u003cspan citationid=\"CR467\" class=\"CitationRef\"\u003e467\u003c/span\u003e], ZFPM2 [\u003cspan citationid=\"CR468\" class=\"CitationRef\"\u003e468\u003c/span\u003e], TSLP (thymic stromal lymphopoietin) [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e], TACR1 [\u003cspan citationid=\"CR469\" class=\"CitationRef\"\u003e469\u003c/span\u003e], WNT4 [\u003cspan citationid=\"CR470\" class=\"CitationRef\"\u003e470\u003c/span\u003e], MFAP5 [\u003cspan citationid=\"CR471\" class=\"CitationRef\"\u003e471\u003c/span\u003e], GHR (growth hormone receptor) [\u003cspan citationid=\"CR472\" class=\"CitationRef\"\u003e472\u003c/span\u003e], GFAP (glial fibrillary acidic protein) [\u003cspan citationid=\"CR473\" class=\"CitationRef\"\u003e473\u003c/span\u003e], ACTN3 [\u003cspan citationid=\"CR474\" class=\"CitationRef\"\u003e474\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e437\u003c/span\u003e], GNB3 [\u003cspan citationid=\"CR475\" class=\"CitationRef\"\u003e475\u003c/span\u003e], MMP15 [\u003cspan citationid=\"CR476\" class=\"CitationRef\"\u003e476\u003c/span\u003e], KCP (kielin cysteine rich BMP regulator) [\u003cspan citationid=\"CR477\" class=\"CitationRef\"\u003e477\u003c/span\u003e], WNK4 [\u003cspan citationid=\"CR478\" class=\"CitationRef\"\u003e478\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e], GDF15 [\u003cspan citationid=\"CR479\" class=\"CitationRef\"\u003e479\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR444\" class=\"CitationRef\"\u003e444\u003c/span\u003e], LCT (lactase) [\u003cspan citationid=\"CR445\" class=\"CitationRef\"\u003e445\u003c/span\u003e], CXCL8 [\u003cspan citationid=\"CR480\" class=\"CitationRef\"\u003e480\u003c/span\u003e], LOX (lysyl oxidase) [\u003cspan citationid=\"CR481\" class=\"CitationRef\"\u003e481\u003c/span\u003e], AIF1L [\u003cspan citationid=\"CR482\" class=\"CitationRef\"\u003e482\u003c/span\u003e] and CYP3A5 [\u003cspan citationid=\"CR483\" class=\"CitationRef\"\u003e483\u003c/span\u003e] are linked with the development mechanisms of obesity. Recent studies have proposed that the enriched genes include DNAJC6 [\u003cspan citationid=\"CR484\" class=\"CitationRef\"\u003e484\u003c/span\u003e], DNAJB2 [\u003cspan citationid=\"CR485\" class=\"CitationRef\"\u003e485\u003c/span\u003e], GPR4 [\u003cspan citationid=\"CR486\" class=\"CitationRef\"\u003e486\u003c/span\u003e] and ZFPM2 [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] are associated with Parkinson Disease. Vauthier et al [\u003cspan citationid=\"CR451\" class=\"CitationRef\"\u003e451\u003c/span\u003e], Cushion et al [\u003cspan citationid=\"CR487\" class=\"CitationRef\"\u003e487\u003c/span\u003e], Barone et al [\u003cspan citationid=\"CR488\" class=\"CitationRef\"\u003e488\u003c/span\u003e], Marcoli et al [\u003cspan citationid=\"CR489\" class=\"CitationRef\"\u003e489\u003c/span\u003e], Qin et al [\u003cspan citationid=\"CR490\" class=\"CitationRef\"\u003e490\u003c/span\u003e], de Nijs et al [\u003cspan citationid=\"CR491\" class=\"CitationRef\"\u003e491\u003c/span\u003e], Bergareche et al [\u003cspan citationid=\"CR492\" class=\"CitationRef\"\u003e492\u003c/span\u003e], Hu et al [\u003cspan citationid=\"CR493\" class=\"CitationRef\"\u003e493\u003c/span\u003e], Liu et al [\u003cspan citationid=\"CR494\" class=\"CitationRef\"\u003e494\u003c/span\u003e], Zhang et al [\u003cspan citationid=\"CR495\" class=\"CitationRef\"\u003e495\u003c/span\u003e], Gorman et al [\u003cspan citationid=\"CR496\" class=\"CitationRef\"\u003e496\u003c/span\u003e], Cherian et al [\u003cspan citationid=\"CR497\" class=\"CitationRef\"\u003e497\u003c/span\u003e], Gururaj et al [\u003cspan citationid=\"CR498\" class=\"CitationRef\"\u003e498\u003c/span\u003e], Belhedi et al [\u003cspan citationid=\"CR499\" class=\"CitationRef\"\u003e499\u003c/span\u003e] and Park et al [\u003cspan citationid=\"CR500\" class=\"CitationRef\"\u003e500\u003c/span\u003e] demonstrated that enriched genes include DNAJC6, TUBB2A, DPM2, SMOX (spermine oxidase), CDYL (chromodomain Y like), EFHC1, SCN4A, DOC2A, ADGRV1, SCAMP5, CACNA1B, KCNT1, KCNT2, CPA6 and CYP3A5 could induce epilepsy. These enriched genes might play essential roles in the advancement of BD and act as novel diagnosis biomarkers or treatment targets of BD.\u003c/p\u003e\u003cp\u003eConstruction of PPI network and its modules of DEGs might be helpful for understanding the relationship of developmental BD. Hub genes include UBB (ubiquitin B) [\u003cspan citationid=\"CR355\" class=\"CitationRef\"\u003e355\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR387\" class=\"CitationRef\"\u003e387\u003c/span\u003e], MYH11 [\u003cspan citationid=\"CR385\" class=\"CitationRef\"\u003e385\u003c/span\u003e] and MSTN (myostatin) [\u003cspan citationid=\"CR377\" class=\"CitationRef\"\u003e377\u003c/span\u003e] have been demonstrated to accelerate cognitive dysfunction. Hub genes include UBB (ubiquitin B) [\u003cspan citationid=\"CR458\" class=\"CitationRef\"\u003e458\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR444\" class=\"CitationRef\"\u003e444\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e437\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e] and WNT4 [\u003cspan citationid=\"CR470\" class=\"CitationRef\"\u003e470\u003c/span\u003e] expression might be regarded as an indicator of susceptibility to obesity. Hub genes include NOTCH3 [\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e], EGR3 [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e] and WNT4 [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e] are involved in hypertension progression. Altered expression of hub genes include NOTCH3 [\u003cspan citationid=\"CR287\" class=\"CitationRef\"\u003e287\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR304\" class=\"CitationRef\"\u003e304\u003c/span\u003e], FZD4 [\u003cspan citationid=\"CR310\" class=\"CitationRef\"\u003e310\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR286\" class=\"CitationRef\"\u003e286\u003c/span\u003e], EGR3 [\u003cspan citationid=\"CR293\" class=\"CitationRef\"\u003e293\u003c/span\u003e], SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e] and WNT4 [\u003cspan citationid=\"CR273\" class=\"CitationRef\"\u003e273\u003c/span\u003e] are associated with prognosis in patients with cardiovascular diseases. Hub genes include NOTCH3 [\u003cspan citationid=\"CR332\" class=\"CitationRef\"\u003e332\u003c/span\u003e] and EGR3 [\u003cspan citationid=\"CR335\" class=\"CitationRef\"\u003e335\u003c/span\u003e] are a potential markers for the detection and prognosis of BD. Altered expression of hub genes include RPL39 [\u003cspan citationid=\"CR180\" class=\"CitationRef\"\u003e180\u003c/span\u003e], PAX8 [\u003cspan citationid=\"CR211\" class=\"CitationRef\"\u003e211\u003c/span\u003e] and WNT4 [\u003cspan citationid=\"CR203\" class=\"CitationRef\"\u003e203\u003c/span\u003e] promotes pregnancy complications. A previous study reported that hub genes include NOTCH3 [\u003cspan citationid=\"CR438\" class=\"CitationRef\"\u003e438\u003c/span\u003e], CAV1 [\u003cspan citationid=\"CR444\" class=\"CitationRef\"\u003e444\u003c/span\u003e], FZD4 [\u003cspan citationid=\"CR446\" class=\"CitationRef\"\u003e446\u003c/span\u003e], MSTN (myostatin) [\u003cspan citationid=\"CR437\" class=\"CitationRef\"\u003e437\u003c/span\u003e] and SFRP5 [\u003cspan citationid=\"CR300\" class=\"CitationRef\"\u003e300\u003c/span\u003e] are altered expressed in diabetes mellitus. However, the roles of novel biomarkers include in BD have not been reported until now and listed in supplementary Table S4.\u003c/p\u003e\u003cp\u003eMiRNA-hub gene regulatory network and TF-hub gene regulatory network analyses predicted hub genes, miRNAs and TFs. hsa-mir-27a-3p [\u003cspan citationid=\"CR501\" class=\"CitationRef\"\u003e501\u003c/span\u003e], STAT3 [\u003cspan citationid=\"CR502\" class=\"CitationRef\"\u003e502\u003c/span\u003e], KLF4 [\u003cspan citationid=\"CR503\" class=\"CitationRef\"\u003e503\u003c/span\u003e] and ESRRA (estrogen related receptor alpha) [\u003cspan citationid=\"CR504\" class=\"CitationRef\"\u003e504\u003c/span\u003e] were reported to be associated with the prognosis of cognitive dysfunction. Altered expression of ATF3 [\u003cspan citationid=\"CR505\" class=\"CitationRef\"\u003e505\u003c/span\u003e], STAT3 [\u003cspan citationid=\"CR506\" class=\"CitationRef\"\u003e506\u003c/span\u003e] and KLF4 [\u003cspan citationid=\"CR507\" class=\"CitationRef\"\u003e507\u003c/span\u003e] are observed in hypertension, indicating that these biomarkers play a role in hypertension. ATF3 [\u003cspan citationid=\"CR508\" class=\"CitationRef\"\u003e508\u003c/span\u003e], NCOR1 [\u003cspan citationid=\"CR509\" class=\"CitationRef\"\u003e509\u003c/span\u003e], STAT3 [\u003cspan citationid=\"CR510\" class=\"CitationRef\"\u003e510\u003c/span\u003e] and KLF4 [\u003cspan citationid=\"CR511\" class=\"CitationRef\"\u003e511\u003c/span\u003e] have been positively correlated with cardiovascular diseases ATF3 [\u003cspan citationid=\"CR512\" class=\"CitationRef\"\u003e512\u003c/span\u003e], STAT3 [\u003cspan citationid=\"CR510\" class=\"CitationRef\"\u003e510\u003c/span\u003e] and ESRRA (estrogen related receptor alpha) [\u003cspan citationid=\"CR513\" class=\"CitationRef\"\u003e513\u003c/span\u003e] were previously reported to be critical for the development of diabetes mellitus. Ku et al. [\u003cspan citationid=\"CR514\" class=\"CitationRef\"\u003e514\u003c/span\u003e], Su et al. [\u003cspan citationid=\"CR515\" class=\"CitationRef\"\u003e515\u003c/span\u003e], Redonnet et al. [\u003cspan citationid=\"CR516\" class=\"CitationRef\"\u003e516\u003c/span\u003e], Deng et al. [\u003cspan citationid=\"CR517\" class=\"CitationRef\"\u003e517\u003c/span\u003e] and Larsen et al [\u003cspan citationid=\"CR513\" class=\"CitationRef\"\u003e513\u003c/span\u003e] concluded that ATF3, STAT3, retinoic acid receptor, alpha (RARA), KLF4 and ESRRA (estrogen related receptor alpha) were an important participant in obesity. Previous studies have reported that STAT3 [\u003cspan citationid=\"CR518\" class=\"CitationRef\"\u003e518\u003c/span\u003e] and retinoic acid receptor, alpha (RARA) [\u003cspan citationid=\"CR519\" class=\"CitationRef\"\u003e519\u003c/span\u003e] are related to schizophrenia. STAT3 [\u003cspan citationid=\"CR520\" class=\"CitationRef\"\u003e520\u003c/span\u003e] levels are correlated with pregnancy complications. Novel biomarkers include BCL2L1, RPL26, hsa-mir-8085, hsa-mir-6735-5p, hsa-mir-548ap-5p, hsa-mir-132-3p, hsa-mir-4514, hsa-mir-3133, hsa-mir-4328, hsa-mir-6749-3p, HMG20B, SMARCE1, POLR2A and HIC1 might play important roles in the development of BD and act as early diagnosis biomarkers or treatment targets of BD. Therefore, our further study will investigate the interactions of hub genes, miRNAs and TFs to shed new light on the molecular mechanisms involved in BD pathology.\u003c/p\u003e\u003cp\u003eOur investigation focused on understanding the action of drug on expression of hub genes. Our findings suggest that drugs- Phenserine, Amiodarone, famoxadone, 3-({4-[(5-chloro-1,3-benzodioxol-4-yl)amino]pyrimidin-2-yl}amino)benzamide, Vinblastine, Roflumilast, Spironolactone, Zonisamide, 2-(methylamino)-N-(4-methyl-1,3-thiazol-2-yl)-5-[(4-methyl-4H-1,2,4-triazol-3-yl)sulfanyl]benzamide and Suramin concurrently target to hub genes include HTR2A, GRIA2, TNF and LYZ, potentially controlling the development of MDD.\u003c/p\u003e\u003cp\u003eThe docking results indicate that Kakkalide and Divaricatol share notable structural and functional similarities with standard drugs based on both binding affinity and hydrogen bonding interactions. Kakkalide\u0026rsquo;s interaction with UBB, despite being slightly weaker in binding energy compared to FT671, involved a greater number of hydrogen bonds, suggesting a potentially stable and effective interaction. This implies a likelihood of functional mimicry and downregulation of the upregulated UBB gene. Its interaction with CNBD2 was even more promising, where it not only surpassed the standard ligand cAMP in binding strength but also formed several key hydrogen bonds. This supports the possibility that Kakkalide could restore or upregulate the expression of downregulated CNBD2. For UBE2D1, although the standard ligand exhibited higher affinity, Kakkalide\u0026rsquo;s moderate binding suggests it may still provide partial regulatory effects, potentially normalizing the gene's overexpression. In the case of CAV1, Divaricatol exhibited stronger affinity and a more diverse hydrogen bonding profile than the standard ligand, indicating a higher potential to upregulate the downregulated gene. These findings point to a dual-direction regulatory capability of the ligands: suppressing overactive genes while boosting underactive ones. Brucine B, however, showed limited binding and fewer interactions, indicatespoor therapeutic candidate.\u003c/p\u003e\u003cp\u003eThe ADMET evaluation reveals significant pharmacokinetic variability among the test compounds. Brucine B, despite limited absorption (~\u0026thinsp;3.9%), showed high bioavailability and a favourable metabolic profile, indicating its potential as a lead compound with minimal interaction risk. Kakkalide and Divaricatol showed safer metabolic properties but limited systemic exposure and potential little hepatotoxicity. cAMP, a co-crystallized ligand, displayed the most balanced ADMET characteristics, including high absorption, moderate clearance, and low CYP interaction, but was flagged for high DILI risk. 7HC and EDK, while BBB permeable and CYP inhibitors, presented multiple drawbacks such as low absorption, high lipophilicity, and systemic toxicity risks. FT671 showed moderate promise but requires enhancement of absorption and reduction of hepatic risk.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, five crucial candidate genes (UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11) might play key roles in the occurrence and development of BD, suggesting they might serve as potential biomarkers and therapeutic targets in the BD.\u003c/p\u003e\u003cp\u003eOverall, Kakkalide and Divaricatol emerge as promising phytoconstituents capable of interacting with key molecular targets involved in bipolar disorder. Their comparable or superior interaction profiles relative to co-crystallized ligands suggest a high potential for therapeutic modulation of gene expression. Kakkalide appears to effectively target both upregulated (UBB, UBE2D1) and downregulated (CNBD2) genes, indicating a normalizing effect on gene dysregulation. Similarly, Divaricatol shows strong potential to restore the expression of downregulated CAV1. These properties position both compounds as viable candidates for multi-target drug development, offering a novel therapeutic approach for the treatment of bipolar disorder. Further experimental validation is recommended to confirm their biological activity and regulatory impact at the cellular and systemic levels.\u003c/p\u003e\u003cp\u003ecAMP and Brucine B emerge as promising candidates for further investigation based on their ADMET profiles. cAMP shows strong absorption and metabolic safety with caution toward hepatotoxicity. Brucine B offers favourable plasma exposure and low interaction risk, despite its low intestinal absorption. In contrast, 7HC and EDK require substantial structural or formulation-based optimization to overcome significant ADMET liabilities. These results provide a pharmacokinetic basis for prioritizing compounds for further in vivo evaluation in bipolar disorder research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI thank Krebs CE, Loohuis LM, Ophoff RA, UCLA, Center for Neurobehavioral Genetics, Los Angeles, CA, USA, very much, the author who deposited their NGS dataset GSE124326, into the public GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch involving Human Participants and/or Animals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo informed consent because this study does not contain human or animals participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE124326) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124326)]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB. V. \u0026nbsp; \u0026nbsp;- Writing original draft, and review and editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.P. \u0026nbsp; \u0026nbsp; - Formal analysis and validation\u003c/p\u003e\n\u003cp\u003eC. V. \u0026nbsp; - Software and investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBasavaraj Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ORCID ID: 0000-0003-2202-7637\u003c/p\u003e\n\u003cp\u003eShivaling Pattanashetti \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; ORCID ID: 0009-0003-9246-1604\u003c/p\u003e\n\u003cp\u003eChanabasayya \u0026nbsp; Vastrad \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;ORCID ID: 0000-0003-3615-4450\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDuan J, Yang R, Lu W, Zhao L, Hu S, Hu C (2021) Comorbid Bipolar Disorder and Migraine: From Mechanisms to Treatment. 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Cell Physiol Biochem 40(3\u0026ndash;4):527\u0026ndash;537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000452566\u003c/span\u003e\u003cspan address=\"10.1159/000452566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 7 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"KLE College of Pharmacy, Gadag , karanataka, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"bioinformatics analysis, enrichment analysis, bipolar disorder, differentially expressed genes, next generation sequencing, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-7391829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7391829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A Protein-Protein Interaction (PPI) network and modules were constructed and analyzed. We predicted regulatory miRNAs and TFs of hub-genes through miRNet and NetworkAnalyst online database. Drug predicted for BD treatment was screened out from the DrugBank through NetworkAnalyst. Molecular docking studies were carried out for predicting novel drug molecules. Receiver operating characteristic curve (ROC) curves was drawn to elucidate the diagnostic value of hub genes. In this investigation, total of 957 DEGs, including 477 up regulated and 480 down regulated genes. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. We screened hub genes include UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11. We also predicted miRNAs, TFs and drugs include hsa-mir-8085, hsa-mir-4514, HMG20B, STAT3, phenserine and roflumilast. Molecular docking technology screened out three small molecule compounds, including Kakkalide, Divaricatol and Brucine small molecule compounds. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.\u003c/p\u003e","manuscriptTitle":"Identification of bipolar disorder related biomarkers, signaling pathways and potential therapeutic compounds based on bioinformatics methods and molecular docking technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 07:36:43","doi":"10.21203/rs.3.rs-7391829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53260734,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2025-08-19T07:36:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 07:36:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7391829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7391829","identity":"rs-7391829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}