Ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and molecular network analysis alongside network pharmacology to elucidate the active constituents and mechanism of action of Huahong tablet in treating pelvic inflammatory disease.

RATIONALE: Huahong tablet, a commonly used clinical Chinese patent medicine, shows good efficacy in treating pelvic inflammation and other gynaecological infectious diseases. However, the specific composition of Huahong tablets, which are complex herbal formulations, remains unclear. Therefore, this study aims to identify the active compounds and targets of Huahong tablets and investigate their mechanism of action in pelvic inflammatory diseases. METHODS: We utilised ultrahigh-performance liquid chromatography Q-Exactive-Orbitrap mass spectrometry and the relevant literature to identify the chemical components of Huahong tablets. The GNPS database was employed to further analyse and speculate on the components. Potential molecular targets of the active ingredients were predicted using the SwissTargetPrediction website. Protein-protein interaction analysis was conducted using the STRING database, with visualisation in Cytoscape 3.9.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the DAVID database. Additionally, a traditional Chinese medicine-ingredient-target-pathway network was constructed using Cytoscape 3.10.1. Molecular docking validation was carried out to investigate the interaction between core target and specific active ingredient. RESULTS: A total of 66 chemical components were identified, and 41 compounds were selected as potential active components based on the literature and the TCMSP database. Moreover, 38 core targets were identified as key targets in the treatment of pelvic inflammatory diseases with Huahong tablets. GO and KEGG enrichment analysis revealed 986 different biological functions and 167 signalling pathways. CONCLUSION: The active ingredients in Huahong tablets exert therapeutic effects on pelvic inflammatory diseases by acting on multiple targets and utilising different pathways. Molecular docking confirmed the high affinity between the specific active ingredients and disease targets.

On the mechanism of wogonin against acute monocytic leukemia using network pharmacology and experimental validation.

Wogonin is a natural flavone compound from the plant Scutellaria baicalensis, which has a variety of pharmacological activities such as anti-cancer, anti-virus, anti-inflammatory, and immune regulation. However, the potential mechanism of wogonin remains unknown. This study was to confirm the molecular mechanism of wogonin for acute monocytic leukemia treatment, known as AML-M5. The potential action targets between wogonin and acute monocytic leukemia were predicted from databases. The compound-target-pathway network and protein-protein interaction network (PPI) were constructed. The enrichment analysis of related targets and molecular docking were performed. The network pharmacological results of wogonin for AML-M5 treatment were verified using the THP-1 cell line. 71 target genes of wogonin associated with AML-M5 were found. The key genes TP53, SRC, AKT1, RELA, HSP90AA1, JUN, PIK3R1, and CCND1 were preliminarily found to be the potential central targets of wogonin for AML-M5 treatment. The PPI network analysis, GO analysis and KEGG pathway enrichment analysis demonstrated that the PI3K/AKT signaling pathway was the significant pathway in the wogonin for AML-M5 treatment. The antiproliferative effects of wogonin on THP-1 cells of AML-M5 presented a dose-dependent and time-dependent manner, inducing apoptosis, blocking the cell cycle at the G2/M phase, decreasing the expressions of CCND1, CDK2, and CyclinA2 mRNA, as well as AKT and p-AKT proteins. The mechanisms of wogonin on AML-M5 treatment may be associated with inhibiting cell proliferation and regulating the cell cycle via the PI3K/AKT signaling pathway.

Exploring the mechanism of Jinlida granules against type 2 diabetes mellitus by an integrative pharmacology strategy.

Jinlida granule (JLD) is a Traditional Chinese Medicine (TCM) formula used for the treatment of type 2 diabetes mellitus (T2DM). However, the mechanism of JLD treatment for T2DM is not fully revealed. In this study, we explored the mechanism of JLD against T2DM by an integrative pharmacology strategy. Active components and corresponding targets were retrieved from Traditional Chinese Medicine System Pharmacology (TCMSP), SwissADME and Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine Database (BATMAN-TCM) database. T2DM-related targets were obtained from Drugbank and Genecards databases. The protein-protein interaction (PPI) network was constructed and analyzed with STRING (Search Toll for the Retrieval of Interacting Genes/proteins) and Cytoscape to get the key targets. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID). Lastly, the binding capacities and reliability between potential active components and the targets were verified with molecular docking and molecular dynamics simulation. In total, 185 active components and 337 targets of JLD were obtained. 317 targets overlapped with T2DM-related targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), interleukin-6 (IL-6), cellular tumor antigen p53 (TP53), prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3) and signal transducer and activator of transcription 3 (STAT3) were identified as seven key targets by the topological analysis of the PPI network. GO and KEGG enrichment analyses showed that the effects were primarily associated with gene expression, signal transduction, apoptosis and inflammation. The pathways were mainly enriched in PI3K-AKT signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Molecular docking and molecular dynamics simulation verified the good binding affinity between the key components and targets. The predicted results may provide a theoretical basis for drug screening of JLD and a new insight for the therapeutic effect of JLD on T2DM.

Mechanistic insights into targeting caspase-3 activation and alveolar macrophage pyroptosis by Ephedra and bitter almond compounds for treating pediatric pneumonia via network pharmacology and bioinformatics.

This study investigates the molecular mechanism of Ma Huang-Ku Xing Ren, a traditional Chinese medicine formula, in treating pediatric pneumonia. The focus is on the regulation of caspase-3 activation and reduction of alveolar macrophage necrosis through network pharmacology and bioinformatics analyses of Ephedra and bitter almond components. Active compounds and targets from ephedrine and bitter almond were obtained using TCMSP, TCMID, and GeneCards databases, identifying pediatric pneumonia-related genes. A protein-protein interaction (PPI) network was constructed, and core targets were screened. GO and KEGG pathway enrichment analyses identified relevant genes and pathways. An acute pneumonia mouse model was created using the lipopolysaccharide (LPS) inhalation method, with caspase-3 overexpression induced by a lentivirus. The mice were treated with Ephedra and bitter almond through gastric lavage. Lung tissue damage, inflammatory markers (IL-18 and IL-1ß), and cell death-related gene activation were assessed through H&E staining, ELISA, western blot, flow cytometry, and immunofluorescence. The study identified 128 active compounds and 121 gene targets from Ephedra and bitter almond. The PPI network revealed 13 core proteins, and pathway analysis indicated involvement in inflammation, apoptosis, and cell necrosis, particularly the caspase-3 pathway. In vivo results showed that Ephedra and bitter almond treatment significantly mitigated LPS-induced lung injury in mice, reducing lung injury scores and inflammatory marker levels. It also decreased caspase-3 activity and cell death in alveolar macrophages. In conclusion, the active ingredients of Ma Huang-Ku Xing Ren, particularly targeting caspase-3, may effectively treat pediatric pneumonia by reducing apoptosis in alveolar macrophages, as demonstrated by both network pharmacology, bioinformatics analyses, and experimental data.

In silico and network pharmacology analysis of fucosterol: a potent anticancer bioactive compound against HCC.

The Fucaceae family of marine brown algae includes Ascophyllum nodosum. Fucosterol (FSL) is a unique bioactive component that was identified through GC-MS analysis of the hydroalcoholic extract of A. nodosum. Fucosterol's mechanism of action towards hepatocellular cancer was clarified using network pharmacology and docking study techniques. The probable target gene of FSL has been predicted using the TargetNet and SwissTargetPred databases. GeneCards and the DisGNet database were used to check the targeted genes of FSL. By using the web programme Venny 2.1, the overlaps of FSL and HCC disease demonstrated that 18 genes (1.3%) were obtained as targeted genes Via the STRING database, a protein-protein interaction (PPI) network with 18 common target genes was constructed. With the aid of CytoNCA, hub genes were screened using the Cytoscape software, and the targets' hub genes were exported into the ShinyGo online tool for study of KEGG and gene ontology enrichment. Using the software AutoDock, a hub gene molecular docking study was performed. Ten genes, including AR, CYP19A1, ESR1, ESR2, TNF, PPARA, PPARG, HMGCR, SRC, and IGF1R, were obtained. The 10 targeted hubs docked with FSL successfully. The active components FSL of ASD, the FSL, are engaged in fatty liver disease, cancer pathways, and other signalling pathways, which could prove beneficial for the management of HCC.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Explore the Potential Mechanism of Gualou Xiebai Banxia Decoction against Myocardial Infarction.

The aim of this study was to investigate targets through which Gualou Xiebai Banxia decoction aids in treating myocardial infarction (MI) using network pharmacology in combination with molecular docking. The principal active ingredients of Gualou Xiebai Banxia decoction were identified from the TCMSP database using the criteria of drug-likeness ≥30% and oral bioavailability ≥0.18. Interactions and pathway enrichment were investigated using protein-protein interaction (PPI) networks and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, respectively. Active component structures were docked with those of potential protein targets using AutoDock molecular docking relative softwares. HIF1A was of particular interest as it was identified by the PPI network, GO and KEGG pathway enrichment analyses. In conclusion, the use of network pharmacology prediction and molecular docking assessments provides further information on the active components and mechanisms of action Gualou Xiebai Banxia decoction.

Exploring the mechanism of Corbrin capsules in the intervention of AKI-COVID-19 based on network pharmacology combined with GEO dataset.

AIM: of the study: This study used network pharmacology and the Gene Expression Omnibus (GEO) database to investigate the therapeutic effects of Corbrin capsules on acute kidney injury (AKI)-COVID-19 (coronavirus disease 2019). MATERIALS AND METHODS: The active constituents and specific molecular targets of Corbrin capsules were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Swiss Target Prediction databases. The targets related to AKI and COVID-19 disease were obtained from the Online Mendelian Inheritance in Man (OMIM), GeneCards, and GEO databases. A protein-protein interaction (PPI) network was constructed by utilizing Cytoscape. To enhance the analysis of pathways associated with the pathogenesis of AKI-COVID-19, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Furthermore, immune infiltration analysis was performed by using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Molecular docking was used to assess interactions between differentially expressed genes and active ingredients. Verification was performed by utilizing GEO databases and in vivo assays. RESULTS: This study revealed an overlap of 18 significantly differentially expressed genes between the Corbrin capsules group and the AKI-COVID-19 target group. Analysis of the PPI network identified TP53, JAK2, PIK3CA, PTGS2, KEAP1, and MCL1 as the top six core protein targets with the highest degrees. The results obtained from GO and KEGG analyses demonstrated that the target genes were primarily enriched in the apoptosis and JAK-STAT signaling pathways. Moreover, the analysis of immune infiltration revealed a notable disparity in the percentage of quiescent memory CD4 + T cells. Western blot analyses provided compelling evidence suggesting that the dysregulation of 6 core protein targets could be effectively reversed by Corbrin capsules. CONCLUSION: This study revealed the key components, targets, and pathways involved in treating AKI-related COVID-19 using Corbrin capsules. This study also provided a new understanding of the molecular mechanisms underlying this treatment.

Multi-level bioinformatics resources support drug target discovery of protein-protein interactions.

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Proximity interactome analysis of Lassa polymerase reveals eRF3a/GSPT1 as a druggable target for host-directed antivirals.

Completion of the Lassa virus (LASV) life cycle critically depends on the activities of the virally encoded, RNA-dependent RNA polymerase in replication and transcription of the viral RNA genome in the cytoplasm of infected cells. The contribution of cellular proteins to these processes remains unclear. Here, we applied proximity proteomics to define the interactome of LASV polymerase in cells under conditions that recreate LASV RNA synthesis. We engineered a LASV polymerase-biotin ligase (TurboID) fusion protein that retained polymerase activity and successfully biotinylated the proximal proteome, which allowed the identification of 42 high-confidence LASV polymerase interactors. We subsequently performed a small interfering RNA (siRNA) screen to identify those interactors that have functional roles in authentic LASV infection. As proof of principle, we characterized eukaryotic peptide chain release factor subunit 3a (eRF3a/GSPT1), which we found to be a proviral factor that physically associates with LASV polymerase. Targeted degradation of GSPT1 by a small-molecule drug candidate, CC-90009, resulted in strong inhibition of LASV infection in cultured cells. Our work demonstrates the feasibility of using proximity proteomics to illuminate and characterize yet-to-be-defined host-pathogen interactome, which can reveal new biology and uncover novel targets for the development of antivirals against highly pathogenic RNA viruses.

Proximity Profiling of the CFTR Interaction Landscape in Response to Orkambi.

Deletion of phenylalanine 508 (∆F508) of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) anion channel protein is the leading cause of Cystic Fibrosis (CF). Here, we report the analysis of CFTR and ∆F508-CFTR interactomes using BioID (proximity-dependent biotin identification), a technique that can also detect transient associations. We identified 474 high-confidence CFTR proximity-interactors, 57 of which have been previously validated, with the remainder representing novel interaction space. The ∆F508 interactome, comprising 626 proximity-interactors was markedly different from its wild type counterpart, with numerous alterations in protein associations categorized in membrane trafficking and cellular stress functions. Furthermore, analysis of the ∆F508 interactome in cells treated with Orkambi identified several interactions that were altered as a result of this drug therapy. We examined two candidate CFTR proximity interactors, VAPB and NOS1AP, in functional assays designed to assess surface delivery and overall chloride efflux. VAPB depletion impacted both CFTR surface delivery and chloride efflux, whereas NOS1AP depletion only affected the latter. The wild type and ∆F508-CFTR interactomes represent rich datasets that could be further mined to reveal additional candidates for the functional rescue of ∆F508-CFTR.

Mechanism of action of Tripterygium wilfordii for treatment of idiopathic membranous nephropathy based on network pharmacology.

BACKGROUND: Although thunder god vine (Tripterygium wilfordii) has been widely used for treatment of idiopathic membranous nephropathy (IMN), the pharmacological mechanisms underlying its effects are still unclear. This study investigated potential therapeutic targets and the pharmacological mechanism of T. wilfordii for the treatment of IMN based on network pharmacology. METHODS: Active components of T. wilfordii were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. IMN-associated target genes were collected from the GeneCards, DisGeNET, and OMIM databases. VENNY 2.1 was used to identify the overlapping genes between active compounds of T. wilfordii and IMN target genes. The STRING database and Cytoscape 3.7.2 software were used to analyze interactions among overlapping genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the targets were performed using Rx64 4.0.2 software, colorspace, stringi, DOSE, clusterProfiler, and enrichplot packages. RESULTS: A total of 153 compound-related genes and 1485 IMN-related genes were obtained, and 45 core genes that overlapped between both categories were identified. The protein-protein interaction network and MCODE results indicated that the targets TP53, MAPK8, MAPK14, STAT3, IFNG, ICAM1, IL4, TGFB1, PPARG, and MMP1 play important roles in the treatment of T. wilfordii on IMN. Enrichment analysis showed that the main pathways of targets were the AGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, and Toll-like receptor signaling pathway. CONCLUSION: This study revealed potential multi-component and multi-target mechanisms of T. wilfordii for the treatment of IMN based on network pharmacological, and provided a scientific basis for further experimental studies.

Challenges in Discovering Drugs That Target the Protein-Protein Interactions of Disordered Proteins.

Protein-protein interactions (PPIs) outnumber proteins and are crucial to many fundamental processes; in consequence, PPIs are associated with several pathological conditions including neurodegeneration and modulating them by drugs constitutes a potentially major class of therapy. Classically, however, the discovery of small molecules for use as drugs entails targeting individual proteins rather than targeting PPIs. This is largely because discovering small molecules to modulate PPIs has been seen as extremely challenging. Here, we review the difficulties and limitations of strategies to discover drugs that target PPIs directly or indirectly, taking as examples the disordered proteins involved in neurodegenerative diseases.

Inhibition of ACE2-Spike Interaction by an ACE2 Binder Suppresses SARS-CoV-2 Entry.

The emergence of SARS-CoV-2 variants is a significant concern in developing effective therapeutics and vaccines in the middle of the ongoing COVID-19 pandemic. Here, we have identified a novel small molecule that inhibited the interactions between SARS-CoV-2 spike RBDs and ACE2 by modulating ACE2 without impairing its enzymatic activity necessary for normal physiological functions. Furthermore, the identified compounds suppressed viral infection in cultured cells by inhibiting the entry of ancestral and variant SARS-CoV-2. Our study suggests that targeting ACE2 could be a novel therapeutic strategy to inhibit SARS-CoV-2 entry into host cells and prevent the development of COVID-19.

Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis.

INTRODUCTION: The coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19. MATERIALS AND METHODS: Based on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs. RESULTS: A total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01). CONCLUSION: Identifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.

Reveals of quercetin's therapeutic effects on oral lichen planus based on network pharmacology approach and experimental validation.

Oral lichen planus (OLP) is a localized autoimmune disease of the oral mucosa, with an incidence of up to 2%. Although corticosteroids are the first-line treatment, they cause several adverse effects. Quercetin, a naturally occurring compound, has fewer side-effects and provides long-term benefits. Besides, it has powerful anti­inflammatory activities. Here, we combined network pharmacology with experimental verification to predict and verify the key targets of quercetin against OLP. First, 66 quercetin-OLP common targets were analyzed from various databases. The protein-protein interaction (PPI) network was constructed. Topology analysis and MCODE cluster analysis of common targets were conducted to identify 12 key targets including TP53, IL-6 and IFN-γ and their connections. Gene functions and key signaling pathways, including reactive oxygen species metabolism, IL-17 pathway and AGE-RAGE pathway, were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, in vitro experiments showed that quercetin interfered with Th1/Th2 balance by acting on IL-6 and IFN-γ to modulate the immune system in treating OLP. Quercetin considerably affected the apoptosis and migration of T lymphocytes in OLP patients. Our study reveals the potential therapeutic targets and signaling pathways of quercetin associated with OLP, and establishes the groundwork for future clinical applications.

Utilizing network pharmacology and experimental validation to explore the potential molecular mechanisms of BanXia-YiYiRen in treating insomnia.

BanXia-YiYiRen (Pinellia Ternata and Coix Seed, BX-YYR) has been clinically proven to be an effective Chinese medicine compatible with the treatment of insomnia. However, the underlying mechanism of BX-YYR against insomnia remains unclear. This study aimed to explore the pharmacological mechanisms of BX-YYR in treating insomnia based on network pharmacology and experimental validation. The drug-disease targets were obtained using publicly available databases. The analysis revealed 21 active compounds and 101 potential targets of BX-YYR from the pharmacological database of Chinese medicine system and analysis platform (TCMSP) and 1020 related targets of insomnia from the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Furthermore, 38 common targets of BX-YYR against insomnia were identified, and these common targets were used to construct a protein-protein interaction (PPI) network. The visual PPI network was constructed by Cytoscape software. The top three genes from PPI according to degree value are FOS, AKT1, and CASP3. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were applied to reveal the potential targets and signaling pathways involved in BX-YYR against insomnia, especially the serotonergic pathway. In addition, molecular docking revealed that baicalein, beta-sitosterol, and stigmasterol displayed strong binding to AKT1, FOS, PRKCA, and VEGFA. Experimental study found that BX-YYR against insomnia might play a role in improving sleep by modulating the serotonergic pathway. In summary, our findings revealed the underlying mechanism of BX-YYR against insomnia and provided an objective basis for further experimental study and clinical application.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma.

OBJECTIVE: To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. METHODS: The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. RESULTS: 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. CONCLUSION: The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

Network controllability solutions for computational drug repurposing using genetic algorithms.

Control theory has seen recently impactful applications in network science, especially in connections with applications in network medicine. A key topic of research is that of finding minimal external interventions that offer control over the dynamics of a given network, a problem known as network controllability. We propose in this article a new solution for this problem based on genetic algorithms. We tailor our solution for applications in computational drug repurposing, seeking to maximize its use of FDA-approved drug targets in a given disease-specific protein-protein interaction network. We demonstrate our algorithm on several cancer networks and on several random networks with their edges distributed according to the Erdos-Rényi, the Scale-Free, and the Small World properties. Overall, we show that our new algorithm is more efficient in identifying relevant drug targets in a disease network, advancing the computational solutions needed for new therapeutic and drug repurposing approaches.

In vitro anti-synovial sarcoma effect of diallyl trisulfide and mRNA profiling.

OBJECTIVE: Synovial sarcoma (SS) is a malignant soft tissue sarcoma and its natural history is a long, indolent clinical course followed by high rate of local recurrence and distant metastasis. Current therapies are still limited in increasing satisfactory of 5-year survival, especially for patients with recurrence and metastasis. Accordingly, finding new therapeutic drug for SS treatment is clinically urgent need. Diallyl trisulfide (DATS), a bioactive compound derived from garlic, is reported as a promising anti-cancer agent for various carcinomas. However, its effect on anti-SS remains unknown. This study investigated the anti-SS effect of DATS in human synovial sarcoma SW982 cells. METHODS: CCK-8 assay were used to examine the cell viability. High-content Imaging System was used to examine the apoptosis, intracellular ROS and autophagy. Flow cytometry was used to detect cell cycle. qPCR and Western blot were used to examine the expression of related mRNA and protein. High-throughput RNA-sequencing and bio-information analysis were used to investigate the mRNA profiling. RESULTS: The results showed a suppressive effect of DATS on tumor biology of SW982 cells including inducing apoptosis, triggering G2/M cell cycle arrest, elevating intracellular ROS and damaging mitochondria. Further high-throughput RNA-sequencing analysis clarified a comprehensive molecular portrait for DATS-induced transcriptional regulation. Besides, protein-protein interaction (PPI) analysis demonstrated that a network consisted of FOXM1, CCNA2, CCNB1, MYBL2, PLK1 and CDK1 might be response for DATS-induced G2/M cell cycle arrest and increased intracellular ROS. Notably, protein feature analysis revealed structure enrichment in microtubule network like kinesin motors domain, and tubulin domain. Molecular function analysis suggested that DATS-induced dysfunction of microtubule network might be the major cause for its effect on cell cycle arrest and successive apoptosis. Furthermore, 28 hub genes (including KIF2C, PLK1, CDK1, BIRC5, CCNB2, CENPF, TPX2, TOP2A and so on) were determined. Finally, pathway analysis showed that DATS-induced differentially expressed genes were mainly involved in cell cycle. CONCLUSION: Collectively, our findings for the first time provided the DATS-induced cellular response and transcriptional profiling of SW982 cells, which proposes that suppression of DATS on SS is multi-targeted and represent a therapeutic evidence for SS.

Interrelationship between 2019-nCov receptor DPP4 and diabetes mellitus targets based on protein interaction network.

Patients with diabetes are more likely to be infected with Coronavirus disease 2019 (COVID-19), and the risk of death is significantly higher than ordinary patients. Dipeptidyl peptidase-4 (DPP4) is one of the functional receptor of human coronavirus. Exploring the relationship between diabetes mellitus targets and DPP4 is particularly important for the management of patients with diabetes and COVID-19. We intend to study the protein interaction through the protein interaction network in order to find a new clue for the management of patients with diabetes with COVID-19. Diabetes mellitus targets were obtained from GeneCards database. Targets with a relevance score exceeding 20 were included, and DPP4 protein was added manually. The initial protein interaction network was obtained through String. The targets directly related to DPP4 were selected as the final analysis targets. Importing them into String again to obtain the protein interaction network. Module identification, gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were carried out respectively. The impact of DPP4 on the whole network was analyzed by scoring the module where it located. 43 DPP4-related proteins were finally selected from the diabetes mellitus targets and three functional modules were found by the cluster analysis. Module 1 was involved in insulin secretion and glucagon signaling pathway, module 2 and module 3 were involved in signaling receptor binding. The scoring results showed that LEP and apoB in module 1 were the highest, and the scores of INS, IL6 and ALB of cross module associated proteins of module 1 were the highest. DPP4 is widely associated with key proteins in diabetes mellitus. COVID-19 may affect DPP4 in patients with diabetes mellitus, leading to high mortality of diabetes mellitus combined with COVID-19. DPP4 inhibitors and IL-6 antagonists can be considered to reduce the effect of COVID-19 infection on patients with diabetes.