Interactome Rewiring Following Pharmacological Targeting of BET Bromodomains.

Targeting bromodomains (BRDs) of the bromo-and-extra-terminal (BET) family offers opportunities for therapeutic intervention in cancer and other diseases. Here, we profile the interactomes of BRD2, BRD3, BRD4, and BRDT following treatment with the pan-BET BRD inhibitor JQ1, revealing broad rewiring of the interaction landscape, with three distinct classes of behavior for the 603 unique interactors identified. A group of proteins associate in a JQ1-sensitive manner with BET BRDs through canonical and new binding modes, while two classes of extra-terminal (ET)-domain binding motifs mediate acetylation-independent interactions. Last, we identify an unexpected increase in several interactions following JQ1 treatment that define negative functions for BRD3 in the regulation of rRNA synthesis and potentially RNAPII-dependent gene expression that result in decreased cell proliferation. Together, our data highlight the contributions of BET protein modules to their interactomes allowing for a better understanding of pharmacological rewiring in response to JQ1.

Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein-protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28-5.52, P = 8.84 × 10-3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06-3.75, P = 3.24 × 10-2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04-0.58, P = 5.59 × 10-3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.

Novel systems biology experimental pipeline reveals matairesinol's antimetastatic potential in prostate cancer: an integrated approach of network pharmacology, bioinformatics, and experimental validation.

Matairesinol (MAT), a plant lignan renowned for its anticancer properties in hormone-sensitive cancers like breast and prostate cancers, presents a promising yet underexplored avenue in the treatment of metastatic prostate cancer (mPC). To elucidate its specific therapeutic targets and mechanisms, our study adopted an integrative approach, amalgamating network pharmacology (NP), bioinformatics, GeneMANIA-based functional association (GMFA), and experimental validation. By mining online databases, we identified 27 common targets of mPC and MAT, constructing a MAT-mPC protein-protein interaction network via STRING and pinpointing 11 hub targets such as EGFR, AKT1, ERBB2, MET, IGF1, CASP3, HSP90AA1, HIF1A, MMP2, HGF, and MMP9 with CytoHuba. Utilizing DAVID, Gene Ontology (GO) analysis highlighted metastasis-related processes such as epithelial-mesenchymal transition, positive regulation of cell migration, and key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including cancer, prostate cancer, PI3K-Akt, and MAPK signaling, while the web resources such as UALCAN and GEPIA2 affirmed the clinical significance of the top 11 hub targets in mPC patient survival analysis and gene expression patterns. Our innovative GMFA enrichment method further enriched network pharmacology findings. Molecular docking analyses demonstrated substantial interactions between MAT and 11 hub targets. Simulation studies confirmed the stable interactions of MAT with selected targets. Experimental validation in PC3 cells, employing quantitative real-time reverse-transcription PCR and various cell-based assays, corroborated MAT's antimetastatic effects on mPC. Thus, this exhaustive NP analysis, complemented by GMFA, molecular docking, molecular dynamics simulations, and experimental validations, underscores MAT's multifaceted role in targeting mPC through diverse therapeutic avenues. Nevertheless, comprehensive in vitro validation is imperative to solidify these findings.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

SRC family kinase inhibition rescues molecular and behavioral phenotypes, but not protein interaction network dynamics, in a mouse model of Fragile X syndrome.

Glutamatergic synapses encode information from extracellular inputs using dynamic protein interaction networks (PINs) that undergo widespread reorganization following synaptic activity, allowing cells to distinguish between signaling inputs and generate coordinated cellular responses. Here, we investigate how Fragile X Messenger Ribonucleoprotein (FMRP) deficiency disrupts signal transduction through a glutamatergic synapse PIN downstream of NMDA receptor or metabotropic glutamate receptor (mGluR) stimulation. In cultured cortical neurons or acute cortical slices from P7, P17 and P60 FMR1-/y mice, the unstimulated protein interaction network state resembled that of wildtype littermates stimulated with mGluR agonists, demonstrating resting state pre-activation of mGluR signaling networks. In contrast, interactions downstream of NMDAR stimulation were similar to WT. We identified the Src family kinase (SFK) Fyn as a network hub, because many interactions involving Fyn were pre-activated in FMR1-/y animals. We tested whether targeting SFKs in FMR1-/y mice could modify disease phenotypes, and found that Saracatinib (SCB), an SFK inhibitor, normalized elevated basal protein synthesis, novel object recognition memory and social behavior in FMR1-/y mice. However, SCB treatment did not normalize the PIN to a wild-type-like state in vitro or in vivo, but rather induced extensive changes to protein complexes containing Shank3, NMDARs and Fyn. We conclude that targeting abnormal nodes of a PIN can identify potential disease-modifying drugs, but behavioral rescue does not correlate with PIN normalization.

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.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Ethanol extract of Vanilla planifolia stems reduces PAK6 expression and induces cell death in glioblastoma cells.

Glioblastoma multiforme (GBM) is a malignant tumour with a poor prognosis. Therefore, potential treatment strategies and novel therapeutic targets have gained increased attention. Our data showed that the ethanol extract of Vanilla planifolia stem (VAS) significantly decreased the viability and the colony formation of GBM cells. Moreover, VAS induced the cleavage of MAP1LC3, a marker of autophagy. Further RNA-seq and bioinformatic analysis revealed 4248 differentially expressed genes (DEGs) between VAS-treated GBM cells and the control cells. Protein-protein interactions between DEGs with fold changes less than -3 and more than 5 were further analysed, and we found that 16 and 9 hub DEGs, respectively, were correlated with other DEGs. Further qPCR experiments confirmed that 14 hub DEGs was significantly downregulated and 9 hub DEGs was significantly upregulated. In addition, another significantly downregulated DEG, p21-activated kinase 6 (PAK6), was correlated with the overall survival of GBM patients. Further validation experiments confirmed that VAS significantly reduced the mRNA and protein expression of PAK6, which led to the abolition of cell viability and colony formation. These findings demonstrated that VAS reduced cell viability, suppressed colony formation and induced autophagy and revealed PAK6 and other DEGs as potential therapeutic targets for GBM treatment.

Icariside II in NSCLC and COVID-19: Network pharmacology and molecular docking study.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) are susceptible to coronavirus disease-2019 (COVID-19), but current treatments are limited. Icariside II (IS), a flavonoid compound derived from the plant epimedin, showed anti-cancer,anti-inflammation and immunoregulation effects. The present study aimed to evaluate the possible effect and underlying mechanisms of IS on NSCLC patients with COVID-19 (NSCLC/COVID-19). METHODS: NSCLC/COVID-19 targets were defined as the common targets of NSCLC (collected from The Cancer Genome Atlas database) and COVID-19 targets (collected from disease database of Genecards, OMIM, and NCBI). The correlations of NSCLC/COVID-19 targets and survival rates in patients with NSCLC were analyzed using the survival R package. Prognostic analyses were performed using univariate and multivariate Cox proportional hazards regression models. Furthermore, the targets in IS treatment of NSCLC/COVID-19 were defined as the overlapping targets of IS (predicted from drug database of TMSCP, HERBs, SwissTarget Prediction) and NSCLC/COVID-19 targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of these treatment targets were performed aiming to understand the biological process, cellular component, molecular function and signaling pathway. The hub targets were analyzed by a protein-protein interaction network and the binding capacity with IS was characterized by molecular docking. RESULTS: The hub targets for IS in the treatment of NSCLC/COVID-19 includes F2, SELE, MMP1, MMP2, AGTR1 and AGTR2, and the molecular docking results showed that the above target proteins had a good binding degree to IS. Network pharmacology showed that IS might affect the leucocytes migration, inflammation response and active oxygen species metabolic process, as well as regulate the interleukin-17, tumor necrosus factor and hypoxia-inducible factor-1 signaling pathway in NSCLC/COVID-19. CONCLUSIONS: IS may enhance the therapeutic efficacy of current clinical anti-inflammatory and anti-cancer therapy to benefit patients with NSCLC combined with COVID-19.

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.

Study on the mechanism of Huangqi Guizhi Wuwu Decoction in treating diabetes peripheral neuropathy based on network pharmacology and molecular docking.

This study aimed to explore the effective substances and mechanism network of Huangqi Guizhi Wuwu Decoction in treating diabetes peripheral neuropathy. Based on the TCM systemic pharmacological analysis platform (TCMP) and UniProt database, the database of active Huqarqu Decoction was constructed, and the related targets of diabetic peripheral neuropathy were collected through the OMIM, CTD, DisGeNET, TTD and GeneCards databases. The intersection targets were obtained to construct the network diagram of Huangqi dis Guizhi Wuwu Decoction-Active Through the String database, the interaction between target proteins was analyzed, and molecular docking between active components and potential targets was carried out. Combined with the DAVID v6.8 database, GO function analysis and KEGG pathway analysis were performed on the targets. Guizhi Wuwu Decoction mainly acts on core targets such as IL6, MAPK3, VE GFA, JUN and ESR1 through quercetin, kaempferol and naringin and regulates the TNF signaling pathway, estrogen signaling pathway and MAPK signaling pathway, thus achieving the effect of treating diabetes peripheral neuropathy. Huangqi Guizhi Wu has multiple targets and regulates multiple signaling pathways in neuropathy, which lays a foundation for future pharmacological research.

Network pharmacology and transcriptomics analysis reveal the mechanism of BushenHuoxue formula attenuates premature ovarian failure via modulation PI3K/AKT pathway.

This study aims to analyze the active components and mechanism of Bushen Huoxue (BSHX) formula on the autoimmune premature ovarian insufficiency (POI) by combining network pharmacology and Transcriptomics. The active components and targets of BSHXF were screened through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). POI-related targets were identified through Therapeutic Targets Database (TTD), DisGeNET and drugbank database. The Veen diagram was performed to obtain the action targets. The active compound-target network and Protein-Protein Interaction (PPI) network were built by using STRING database and Cytoscape software. Key targets and active compounds were further identified by topological analysis. Molecular docking shows that Kaempferol, Isorhamnetin and Anhydroicaritin have strong binding to AKT. Finally, a zp3-induced autoimmune ovarian function deficiency mouse model was used to explore the potential mechanism of POI. The potential pathways of BSHXF for the treatment of POI were identified by Transcriptomic analysis. PI3K-AKT and NF-kb pathways were the common pathways between network pharmacology and transcriptomics. Our results revealed that BSHXF could reduce the FSH expression levels and raise the E2, and AMH levels in the serum. Western bloting demonstrates that BSHXF could upregulate the expression of p-PI3K and p-AKT.

A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Deciphering the Anticancer Arsenal of Piper longum: Network Pharmacology and Molecular Docking Unveil Phytochemical Targets Against Lung Cancer.

Introduction: Lung cancer, characterized by uncontrolled cellular proliferation within the lung tissues, is the predominant cause of cancer-related fatalities worldwide. The traditional medicinal herb Piper longum has emerged as a significant contender in oncological research because of its documented anticancer attributes, suggesting its potential for novel therapeutic development. Methods: This study adopted network pharmacology and omics methodology to elucidate the anti-lung cancer potential of P. longum by identifying its bioactive constituents and their corresponding molecular targets. Results: Through a comprehensive literature review and the Integrated Medicinal Plant Phytochemistry and Therapeutics database (IMPPAT), we identified 33 bioactive molecules from P. longum. Subsequent analyses employing tools such as SwissTargetPrediction, SuperPred, and DIGEP-Pred facilitated the isolation of 676 potential targets, among which 72 intersected with 666 lung cancer-associated genetic markers identified through databases including the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards. Further validation through protein-protein interaction (PPI) networks, gene ontology, pathway analyses, boxplots, and overall survival metrics underscored the therapeutic potential of compounds such as 7-epi-eudesm-4(15)-ene-1ß, demethoxypiplartine, methyl 3,4,5-trimethoxycinnamate, 6-alpha-diol, and aristolodione. Notably, our findings reaffirm the relevance of lung cancer genes, such as CTNNB1, STAT3, HIF1A, HSP90AA1, and ERBB2, integral to various cellular processes and pivotal in cancer genesis and advancement. Molecular docking assessments revealed pronounced affinity between 6-alpha-diol and HIF1A, underscoring their potential as therapeutic agents for lung cancer. Conclusion: This study not only highlights the bioactive compounds of P. longum but also reinforces the molecular underpinnings of its anticancer mechanism, paving the way for future lung cancer therapeutics.

Combination of UPLC-Q-TOF/MS and network pharmacology to reveal the mechanism of Chaihu-jia-Longgu-Muli decoction for treating vertigo with anxiety disorder.

Chaihu-jia-Longgu-Muli decoction (CLMD) has been proven clinically effective in treating vertigo with anxiety disorder. However, the mechanism is not clear. This study aimed to explore the mechanism of CLMD in treating vertigo with anxiety disorder based on ultra-performance liquid chromatography-quadrupole time-of-flight/mass spectrometry (UPLC-Q-TOF/MS) and network pharmacology. UPLC-Q-TOF/MS was performed to identify the compounds in blood and the targets of compounds of CLMD in vertigo and anxiety were searched using databases. A protein-protein interaction network was built to screen the core targets. The core targets were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. In addition, the vertigo with anxiety rat model was used to verify the results. A total of 22 compounds were absorbed into the blood. Eighty-one potential targets associated with CLMD for vertigo with anxiety disorder were identified through network pharmacological analysis. Subsequently, GO and KEGG analysis showed that CLMD treatment for vertigo with anxiety disorder is associated with neurotransmitter levels and other pertinent physiological processes. The results of the animal experiments showed that CLMD decreased the levels of serotonin, norepinephrine and dopamine, alleviating the symptoms of vertigo and anxiety disorder in model rats. The study revealed CLMD could alleviate vertigo and anxiety symptoms through reducing the levels of neurotransmitters.

Network pharmacology study on fermented soybeans for the prevention of Alzheimer's disease in older individuals.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the disruption of synaptic communication among millions of neurons. Recent research has highlighted the potential therapeutic effectiveness of natural polyphenolic compounds in addressing AD. Soybeans are abundant in polyphenols, and their polyphenolic composition undergoes significant alteration through fermentation by Eurotium cristatum. Through comprehensive database searches, we identified active components within fermented soybean polyphenols and genes associated with AD. Subsequently, we utilized Venn diagrams to analyze the overlap between AD-related genes and these components. Furthermore, we visualized the network between intersecting targets and proteins using Cytoscape software. The anti-AD effects of soybeans were further explored through comprehensive analysis, including protein-protein interaction analysis, pathway enrichment analysis, and molecular docking studies. Our investigation unveiled 6-hydroxydaidzein as a major component of fermented soybean polyphenols, shedding light on its potential therapeutic significance in combating AD. The intersection between target proteins of fermented soybeans and disease-related targets in AD comprised 34 genes. Protein-protein interaction analysis highlighted key potential targets, including glyceraldehyde-3-phosphate dehydrogenase (GAPDH), glycogen synthase kinase 3 beta (GSK3B), amyloid precursor protein (APP), cyclin-dependent kinase 5 (CDK5), and beta-site APP cleaving enzyme 1 (BACE1). Molecular docking results demonstrated a robust binding effect between major components from fermented soybeans and the aforesaid key targets implicated in AD treatment. These findings suggest that fermented soybeans demonstrate a degree of efficacy and present promising prospects in the prevention of AD.

Renal toxicity of Aconitum plants? A study based on a new mass spectrometry scanning strategy and computer virtual screening.

BACKGROUND: Radix Aconiti Lateralis (Fuzi), a mono-herbal preparation of Aconitum herbs in the genus Aconitum, is commonly used in traditional Chinese medicine (TCM) to treat critical illnesses. The curative effect of Fuzi is remarkable. However, the toxic effects of Fuzi are still a key clinical focus, and the substances inducing nephrotoxicity are still unclear. Therefore, this study proposes a research model combining "in vitro and in vivo component mining-virtual multi-target screening-active component prediction-literature verification" to screen potential nephrotoxic substances rapidly. METHOD: The UHPLC-Q-Exactive-Orbitrap MS analysis method was used for the correlation analysis of Fuzi's in vitro-in vivo chemical substance groups. On this basis, the key targets of nephrotoxicity were screened by combining online disease databases and a protein-protein interaction (PPI) network. The computer screening technique was used to verify the binding mode and affinity of Fuzi's components with nephrotoxic targets. Finally, the potential material basis of Fuzi-induced nephrotoxicity was screened. RESULTS: Eighty-one Fuzi components were identified. Among them, 35 components were absorbed into the blood. Based on the network biology method, 21 important chemical components and three potential key targets were screened. Computer virtual screening revealed that mesaconine, benzoylaconine, aconitine, deoxyaconitine, hypaconitine, benzoylhypaconine, benzoylmesaconine, and hypaconitine may be potential nephrotoxic substances of Fuzi. CONCLUSIONS: Fuzi may interact with multiple components and targets in the process of inducing nephrotoxicity. In the future, experiments can be designed to explore further. This study provides a reference for screening Fuzi nephrotoxic components and has certain significance for the safe use of Fuzi.

Non-Hereditary Obesity Type Networks and New Drug Targets: An In Silico Approach.

Obesity, a chronic, preventable disease, has significant comorbidities that are associated with a great human and financial cost for society. The aim of the present work is to reconstruct the interactomes of non-hereditary obesity to highlight recent advances of its pathogenesis, and discover potential therapeutic targets. Obesity and biological-clock-related genes and/or gene products were extracted from the biomedical literature databases PubMed, GeneCards and OMIM. Their interactions were investigated using STRING v11.0 (a database of known and predicted physical and indirect associations among genes/proteins), and a high confidence interaction score of >0.7 was set. We also applied virtual screening to discover natural compounds targeting obesity- and circadian-clock-associated proteins. Two updated and comprehensive interactomes, the (a) stress- and (b) inflammation-induced obesidomes involving 85 and 93 gene/gene products of known and/or predicted interactions with an average node degree of 9.41 and 10.8, respectively, were produced. Moreover, 15 of these were common between the two non-hereditary entities, namely, ADIPOQ, ADRB2/3, CCK, CRH, CXCL8, FOS, GCG, GNRH1, IGF1, INS, LEP, MC4R, NPY and POMC, while phelligridin E, a natural product, may function as a potent FOX1-DBD interaction blocker. Molecular networks may contribute to the understanding of the integrated regulation of energy balance/obesity pathogenesis and may associate chronopharmacology schemes with natural products.

Anti-Influenza A Potential of Tagetes erecta Linn. Extract Based on Bioinformatics Analysis and In Vitro Assays.

Tagetes erecta Linn. (TE) is traditionally used to treat cardiovascular, renal, and gastrointestinal diseases. In this study, we investigated the active compounds and targets of TE extract that may exert antiviral effects against influenza A. Active compounds and targets of TE extract were identified using the Traditional Chinese Medicine Systems Pharmacology database (TCSMP). The influenza A-related gene set was screened using GeneCards and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was built to establish the hub targets. Pathway and target studies were conducted using Gene Expression Omnibus (GEO). The interactions between active compounds and potential targets were assessed by molecular docking. An in vitro study was performed using antiviral and plaque reduction assays. From the compound and target search, we identified 6 active compounds and 95 potential targets. We retrieved 887 influenza-associated target genes and determined 14 intersecting core targets between TE and influenza. After constructing a compound-target network, we discovered lutein and beta-carotene to be the key compounds. Next, PPI network analysis identified the top three hub genes associated with influenza (IL-6, HIF1A, and IL-1ß). Similarly, GEO analysis revealed IL-6, TGFB1, and CXCL8 to be the top three target genes. In our docking study, we identified that lutein and IL-6 had the strongest bindings. Our in vitro experimental results revealed that the TE extract exhibited therapeutic rather than prophylactic effects on influenza disease. We identified lutein as a main active compound in TE extract, and IL-6 as an important target associated with influenza, by using data mining and bioinformatics. Our in vitro findings indicated that TE extract exerted protective properties against the influenza A virus. We speculated that lutein, as a key active component in TE extract, is largely responsible for its antiviral effects. Therefore, we suggest TE extract as an alternative in the treatment of influenza.

Efficacy of Glycyrrhetinic Acid in the Treatment of Acne Vulgaris Based on Network Pharmacology and Experimental Validation.

Glycyrrhetinic acid (GA) is a saponin compound, isolated from licorice (Glycyrrhiza glabra), which has been wildly explored for its intriguing pharmacological and medicinal effects. GA is a triterpenoid glycoside displaying an array of pharmacological and biological activities, including anti-inflammatory, anti-bacterial, antiviral and antioxidative properties. In this study, we investigated the underlying mechanisms of GA on acne vulgaris through network pharmacology and proteomics. After the intersection of the 154 drug targets and 581 disease targets, 37 therapeutic targets for GA against acne were obtained. A protein-protein interaction (PPI) network analysis highlighted TNF, IL1B, IL6, ESR1, PPARG, NFKB1, STAT3 and TLR4 as key targets of GA against acne, which is further verified by molecular docking. The experimental results showed that GA inhibited lipid synthesis in vitro and in vivo, improved the histopathological damage of skin, prevented mast cell infiltration and decreased the level of pro-inflammatory cytokines, including TNF-α, IL-1ß and IL-6. This study indicates that GA may regulate multiple pathways to improve acne symptoms, and the beneficial effects of GA against acne vulgaris might be through the regulation of sebogenesis and inflammatory responses.