Abstract: Intervertebral disc degeneration (IDD) is considered the basis of serious clinical symptoms, especially for low back pain (LBP). Therefore, it is essential to explore the regulatory role and diagnostic performance of dysregulated genes and potential drugs in IDD. Through WGCNA co-expression analysis, 36 co-expression modules were obtained. Among them, MidnightBlue and Red modules were the most related to IDD. Functional enrichment analysis showed that the Red module was mainly related to neutrophil activation and regulation of cytokine-mediated signaling pathway and apoptosis, whereas the MidnightBlue module was mainly related to extracellular matrix organization, bone development, extracellular matrix, extracellular matrix component, and other extracellular matrices. Furthermore, 356 genes highly related to the module were screened to construct a protein interaction network. Network degree distribution analysis showed that the known IDD-related genes had a higher degree of distribution. Enrichment analysis demonstrated that these genes were enriched in MAPK_SIGNALING_PATHWAY (FDR = 0.012), CHEMOKINE_SIGNALING_PATHWAY, and some other pathways. By constructing a disease-gene interaction network, three disease-specific genes were finally identified. Through combining with the drug-target gene interaction network, two potential therapeutic drugs, entrectinib and larotrectinib, were determined. Finally, based on these genes, the diagnostic model in the training dataset, test dataset, and verification dataset all showed a high diagnostic performance. The findings of this study contributed to the diagnosis of IDD and personalized treatment of IDD.

Abstract: Oral cancer (OC) is one of the most pernicious cancers with increasing incidence and mortality worldwide. Surgery is the primary approach for the treatment of early-stage OC, which reduces the quality of life of the patients. Therefore, there is an urgent need to discover novel treatments for OC. Targeting ferroptosis to induce cell death through the modulation of lipid oxidation has been used as a new approach to treat many cancers. Glutathione (GSH) is a coenzyme factor of GSH peroxidase 4, and it carries potential applicability in treating OC. By using network pharmacology and molecular docking followed by systematic bioinformatic analysis, we aimed to study GSH-targeting ferroptosis to treat OC. We identified 14 core molecular targets, namely, EGFR, PTGS2, HIF1A, VEGFA, TFRC, SLC2A1, CAV1, CDKN2A, SLC3A2, IFNG, NOX4, DDIT4, CA9, and DUSP1, involved in ferroptosis that were targeted by GSH for OC treatment. Functional characterization of these molecular targets showed their importance in the control of cell apoptosis, cell proliferation, and immune responses through various kinase activities such as the mitogen-activated protein kinase activity (e.g., ERK1 and ERK2 cascades) and modulation of TOR signaling (e.g., the HIF-1 signaling pathway). Molecular docking further revealed the direct binding of GSH with EGFR, PTGS2, and HIF1A proteins. These findings provide a novel insight into the targets of GSH in ferroptosis as well as possible molecular mechanisms involved, suggesting the possible use of GSH as a combined therapy for treating OC.

Abstract: Objective: This study aimed to explore the shared mechanism and candidate drugs of multiple sclerosis (MS) and Sjogren's syndrome (SS). Methods: MS- and SS-related susceptibility genes and differentially expressed genes (DEGs) were identified by bioinformatics analysis based on genome-wide association studies (GWAS) and transcriptome data from GWAS catalog and Gene Expression Omnibus (GEO) database. Pathway enrichment, Gene Ontology (GO) analysis, and protein-protein interaction analysis for susceptibility genes and DEGs were performed. The drugs targeting common pathways/genes were obtained through Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. The target genes of approved/investigational drugs for MS and SS were obtained through DrugBank and compared with the common susceptibility genes. Results: Based on GWAS data, we found 14 hub common susceptibility genes (HLA-DRB1, HLA-DRA, STAT3, JAK1, HLA-B, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, TYK2, IL2RA, and MAPK1), with 8 drugs targeting two or more than two genes, and 28 common susceptibility pathways, with 15 drugs targeting three or more than three pathways. Based on transcriptome data, we found 3 hub common DEGs (STAT1, GATA3, PIK3CA) with 3 drugs and 10 common risk pathways with 435 drugs. "JAK-STAT signaling pathway" was included in common susceptibility pathways and common risk pathways at the same time. There were 133 overlaps including JAK-STAT inhibitors between agents from GWAS and transcriptome data. Besides, we found that IL2RA and HLA-DRB1, identified as hub common susceptibility genes, were the targets of daclizumab and glatiramer that were used for MS, indicating that daclizumab and glatiramer may be therapeutic for SS. Conclusion: We observed the shared mechanism of MS and SS, in which JAK-STAT signaling pathway played a vital role, which may be the genetic and molecular bases of comorbidity of MS with SS. Moreover, JAK-STAT inhibitors were potential therapies for MS and SS, especially for their comorbidity.

Abstract: The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.

Abstract: Aims: Ulcerative colitis (UC) is a type of chronic idiopathic inflammatory bowel disease with a multifactorial pathogenesis and limited treatment options. The aim of the present study is to investigate the hydrogen deuterium exchange mass spectrometry (HDX-MS) behaviors of fermented deoxyanthocyanidins and their molecular mechanisms to alleviate UC by using quantum chemistry and network pharmacology. Methods: Tandem MS indicated at least two fragmentation pathways through which deuterated vinylphenol-deoxyanthocyanidins could generate different product ions. Quantum calculations were conducted to determine the transition states of the relevant molecules and analyze their optimized configuration, vibrational characteristics, intrinsic reaction coordinates, and corresponding energies. The potential targets of deoxyanthocyanidins in UC were screened from a public database. The R package was used for Gene Ontology (GO) and KEGG pathway analyses, and the protein-protein interactions (PPIs) of the targets were assessed using Search Tool for the Retrieval of Interacting Genes (STRING). Finally, molecular docking was implemented to analyze the binding energies and action modes of the target compounds through the online tool CB-Dock. Results: Quantum calculations indicated two potential fragmentation pathways involving the six-membered ring and dihydrogen cooperative transfer reactions of the vinylphenol-deoxyanthocyanidins. A total of 146 and 57 intersecting targets of natural and fermented deoxyanthocyanidins were separately screened out from the UC database and significant overlaps in GO terms and KEGG pathways were noted. Three shared hub targets (i.e., PTGS2, ESR1, and EGFR) were selected from the two PPI networks by STRING. Molecular docking results showed that all deoxyanthocyanidins have a good binding potential with the hub target proteins and that fermented deoxyanthocyanidins have lower binding energies and more stable conformations compared with natural ones. Conclusions: Deoxyanthocyanidins may provide anti-inflammatory, antioxidative, and immune system regulatory effects to suppress UC progression. It is proposed for the first time that fermentation of deoxyanthocyanidins can help adjust the structure of the intestinal microbiota and increase the biological activity of the natural compounds against UC. Furthermore, HDX-MS is a helpful strategy to analyze deoxyanthocyanidin metabolites with unknown structures.

Abstract: DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.

Abstract: There is yet no effective drug for Alzheimer's disease (AD) which is one of the world's most common neurodegenerative diseases. The Qin-Zhi-Zhu-Dan Formula (QZZD) is derived from a widely used Chinese patent drug-Qing-Kai-Ling Injection. It consists of Radix Scutellariae, Fructus Gardeniae, and Pulvis Fellis Suis. Recent study showed that QZZD and its effective components played important roles in anti-inflammation, antioxidative stress and preventing brain injury. It was noted that QZZD had protective effects on the brain, but the mechanism remained unclear. This study aims to investigate the mechanism of QZZD in the treatment of AD combining network pharmacology approach with experimental validation. In the network pharmacology analysis, a total of 15 active compounds of QZZD and 135 putative targets against AD were first obtained. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were then applied to clarify the biological mechanism. The anti-inflammatory mechanism of QZZD was proved, and a synthetic pathway-TNFR1-ERK1/2-NF-kappaBp65 signaling pathway was obtained. On the basis of the above discoveries, we further validated the protective effects QZZD on neurons with an APP/PS1 double transgenic mouse model. Weight change of the mice was monitored to assess QZZD's influence on the digestive system; water maze experiment was used for evaluating the effects on spatial learning and memory; Western blotting and immunohistochemistry analysis were used to detect the predicted key proteins in network pharmacology analysis, including Abeta, IL-6, NF-kappaBp65, TNFR1, p-ERK1/2, and ERK1/2. We proved that QZZD could improve neuroinflammation and attenuate neuronal death without influencing the digestive system in APP/PS1 double transgenic mice with dementia. Combining animal pharmacodynamic experiments with network pharmacology analysis, we confirmed the importance of inflammation in pathogenesis of AD, clarified the pharmacodynamic characteristics of QZZD in treating AD, and proved its neuroprotective effects through the regulation of TNFR1-ERK1/2-NF-kappaBp65 signaling pathway, which might provide reference for studies on treatment of AD in the future.

Abstract: Objective: Finding target genes and target pathways of existing drugs for drug repositioning in multiple sclerosis (MS) based on transcriptomic changes in MS immune cells. Materials and Methods: Based on transcriptome data from Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) in MS patients without treatment were identified by bioinformatics analysis according to the type of immune cells, as well as DEGs in MS patients before and after drug administration. Hub target genes of the drug for MS were analyzed by constructing the protein-protein interaction network, and candidate drugs targeting 2 or more hub target genes were obtained through the connectivity map (CMap) database and Drugbank database. Then, the enriched pathways of MS patients without treatment and the enriched pathways of MS patients before and after drug administration were intersected to obtain the target pathways of the drug for MS, and the candidate drugs targeting 2 or more target pathways were obtained through Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results: We obtained 50 hub target genes for CD4+ T cells in Fingolimod for MS, 15 hub target genes for Plasmacytoid dendritic cells (pDCs) and 7 hub target genes for Peripheral blood mononuclear cells (PBMC) in interferon-beta (IFN-beta) for MS. 6 candidate drugs targeting two or more hub targets (Fostamatinib, Copper, Artenimol, Phenethyl isothiocyanate, Aspirin and Zinc) were obtained. In addition, we obtained 4 target pathways for CD19+ B cells and 15 target pathways for CD4+ T cells in Fingolimod for MS, 7 target pathways for pDCs and 6 target pathways for PBMC in IFN-beta for MS, most of which belong to the immune system and viral infectious disease pathways. We obtained 69 candidate drugs targeting two target pathways. Conclusion: We found that applying candidate drugs that target both the "PI3K-Akt signaling pathway" and "Chemokine signaling pathway" (e.g., Nemiralisib and Umbralisib) or applying tyrosine kinase inhibitors (e.g., Fostamatinib) may be potential therapies for the treatment of MS.

Abstract: BACKGROUND: Alzheimer's disease (AD) is the most common form of age-related neurodegenerative disease. Unfortunately, due to the complexity of pathological types and clinical heterogeneity of AD, there is a lack of satisfactory treatment for AD. Previous studies have shown that microRNAs and transcription factors can modulate genes associated with AD, but the underlying pathophysiology remains unclear. METHODS: The datasets GSE1297 and GSE5281 were downloaded from the gene expression omnibus (GEO) database and analyzed to obtain the differentially expressed genes (DEGs) through the "R" language "limma" package. The GSE1297 dataset was analyzed by weighted correlation network analysis (WGCNA), and the key gene modules were selected. Next, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis for the key gene modules were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, for the GSE150693 dataset, the "R" package "survivation" was used to integrate the data of survival time, AD transformation status and 35 characteristics, and the key microRNAs (miRNAs) were selected by Cox method. We also performed regression analysis using least absolute shrinkage and selection operator (Lasso)-Cox to construct and validate prognostic features associated with the four key genes using different databases. We also tried to find drugs targeting key genes through DrugBank database. RESULTS: GO and KEGG enrichment analysis showed that DEGs were mainly enriched in pathways regulating chemical synaptic transmission, glutamatergic synapses and Huntington's disease. In addition, 10 hub genes were selected from the PPI network by using the algorithm Between Centrality. Then, four core genes (TBP, CDK7, GRM5, and GRIA1) were selected by correlation with clinical information, and the established model had very good prognosis in different databases. Finally, hsa-miR-425-5p and hsa-miR-186-5p were determined by COX regression, AD transformation status and aberrant miRNAs. CONCLUSION: In conclusion, we tried to construct a network in which miRNAs and transcription factors jointly regulate pathogenic genes, and described the process that abnormal miRNAs and abnormal transcription factors TBP and CDK7 jointly regulate the transcription of AD central genes GRM5 and GRIA1. The insights gained from this study offer the potential AD biomarkers, which may be of assistance to the diagnose and therapy of AD.

Abstract: Hepatocellular carcinoma (HCC) is a highly malignant tumor with a poor prognosis. More than 30% of patients with diagnosed HCC have abnormally high expression of fibroblast growth factor receptor 4 (FGFR4). Currently, clinical trials for a variety of FGFR4-specific inhibitors have started. However, the effect of these inhibitors is not ideal, and it is necessary to find a drug combination to synergistically exert anti-tumor effects. We found strong correlations between FGFR4 and HCC clinicopathological characteristics in the present study. After grouping patients according to FGFR4 expression, the key gene signatures were inputted the drug-gene related databases, which predicted several potential drug candidates. More importantly, to achieve the reliable and high throughput drug cytotoxicity assessment, we developed an efficient and reproducible agarose hydrogel microwells to generate uniform-sized multicellular tumor spheroids, which provide better mimicry of conventional solid tumors that can precisely represent anticancer drug candidates' effects. Using high content screening, we quickly evaluated the enhanced anti-tumor effects of these combinations. Finally, we demonstrated that Parthenolide is a potential drug that can significantly enhance the clinical efficacy of FGFR4 receptor inhibitors. In general, we offered a new therapeutic way for FGFR4 positive HCC patients.