Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification

Background

Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC.

Methods

In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro.

Results

Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2.

Conclusions

This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03530-3.

Genetic associations of protein-coding variants in human disease

Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes1. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery.

Effects and Mechanisms of Rhus chinensis Mill. Fruits on Suppressing RANKL-Induced Osteoclastogenesis by Network Pharmacology and Validation in RAW264.7 Cells

Rhus chinensis Mill. fruits are a kind of widely distributed edible seasoning, which have been documented to possess a variety of biological activities. However, its inhibitory effect on osteoclast formation has not been determined. The objective of this study was to evaluate the effect of the fruits on osteoclast differentiation of RAW264.7 cells, induced by receptor activator of nuclear factor-κB ligand (RANKL) and to illuminate the potential mechanisms using network pharmacology and western blots. Results showed that the extract containing two organic acids and twelve phenolic substances could effectively inhibit osteoclast differentiation in RANKL-induced RAW264.7 cells. Network pharmacology examination and western blot investigation showed that the concentrate essentially decreased the expression levels of osteoclast-specific proteins, chiefly through nuclear factor kappa-B, protein kinase B, and mitogen-activated protein kinase signaling pathways, particularly protein kinase B α and mitogen-activated protein kinase 1 targets. Moreover, the extract likewise directly down regulated the expression of cellular oncogene Fos and nuclear factor of activated T-cells cytoplasmic 1 proteins. Citric acid, quercetin, myricetin-3-O-galactoside, and quercetin-3-O-rhamnoside were considered as the predominant bioactive ingredients. Results of this work may provide a scientific basis for the development and utilization of R. chinensis fruits as a natural edible material to prevent and/or alleviate osteoporosis-related diseases.

Network Pharmacology-Based and Experimental Identification of the Effects of Paeoniflorin on Major Depressive Disorder

Objective: Major depressive disorder (MDD) is one of the most common psychiatric disorders, the diagnosis and treatment of MDD are major clinical issues. However, there is a lack of effective biomarkers and drugs diagnosis and therapeutics of MDD. In the present study, bioinformatics analysis combined with an experimental verification strategy was used to identify biomarkers and paeoniflorin targets for MDD diagnosis and treatment.

Methods: Based on network pharmacology, we obtained potential targets and pathways of paeoniflorin as an antidepressant through multiple databases. We then constructed a protein-protein interaction network and performed enrichment analyses. According to the results, we performed in vivo and in vitro experimental validation.

Results: The results showed that paeoniflorin may exert an antidepressant effect by regulating cell inflammation, synaptic function, NF-κB signaling pathway, and intestinal inflammation.

Conclusion: NPM1, HSPA8, HSPA5, HNRNPU, and TNF are the targets of paeoniflorin treatment. In addition, we demonstrated that paeoniflorin inhibits inflammatory cytokine production via the p38MAPK/NF-κB pathway and has neuroprotective effects on the synaptic structure. Our findings provide valuable evidence for the diagnosis and treatment of MDD.

Network Pharmacology and Molecular Docking-Based Mechanism Study to Reveal the Protective Effect of Salvianolic Acid C in a Rat Model of Ischemic Stroke

Salvianolic acid C (SAC) is a major bioactive component of Salvia miltiorrhiza Bunge (Danshen), a Chinese herb for treating ischemic stroke (IS). However, the mechanism by which SAC affects the IS has not yet been evaluated, thus a network pharmacology integrated molecular docking strategy was performed to systematically evaluate its pharmacological mechanisms, which were further validated in rats with cerebral ischemia. A total of 361 potential SAC-related targets were predicted by SwissTargetPrediction and PharmMapper, and a total of 443 IS-related targets were obtained from DisGeNET, DrugBank, OMIM, and Therapeutic Target database (TTD) databases. SAC-related targets were hit by the 60 targets associated with IS. By Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment combined with the protein-protein interaction (PPI) network and cytoHubba plug-ins, nine related signaling pathways (proteoglycans in cancer, pathways in cancer, PI3K-Akt signaling pathway, Focal adhesion, etc.), and 20 hub genes were identified. Consequently, molecular docking indicated that SAC may interact with the nine targets (F2, MMP7, KDR, IGF1, REN, PPARG, PLG, ACE and MMP1). Four of the target proteins (VEGFR2, MMP1, PPARγ and IGF1) were verified using western blot. This study comprehensively analyzed pathways and targets related to the treatment of IS by SAC. The results of western blot also confirmed that the SAC against IS is mainly related to anti-inflammatory and angiogenesis, which provides a reference for us to find and explore the effective anti-IS drugs.

Uncovering a Hub Signaling Pathway of Antimicrobial-Antifungal-Anticancer Peptides’ Axis on Short Cationic Peptides via Network Pharmacology Study

Short cationic peptides (SCPs) with therapeutic efficacy of antimicrobial peptides (AMPs), antifungal peptides (AFPs), and anticancer peptides (ACPs) are known as an enhancement of the host defense system. Here, we investigated the uppermost peptide(s), hub signaling pathway(s), and their associated target(s) through network pharmacology. Firstly, we selected SCPs with positive amino acid residues on N- and C- terminals under 500 Dalton via RStudio. Secondly, the overlapping targets between the bacteria-responsive targets (TTD and OMIM) and AMPs’ targets were visualized by VENNY 2.1. Thirdly, the overlapping targets between AFPs’ targets and fungal-responsive targets were exhibited by VENNY 2.1. Fourthly, the overlapping targets between cancer-related targets (TTD and OMIM) and fungal-responsive targets were displayed by VENNY 2.1. Finally, a molecular docking study (MDS) was carried out to discover the most potent peptides on a hub signaling pathway. A total of 1833 SCPs were identified, and AMPs’, AFPs’, and ACPs’ filtration suggested that 197 peptides (30 targets), 81 peptides (6 targets), and 59 peptides (4 targets) were connected, respectively. The AMPs―AFPs―ACPs’ axis indicated that 27 peptides (2 targets) were associated. Each hub signaling pathway for the enhancement of the host defense system was “Inactivation of Rap1 signaling pathway on AMPs”, “Activation of Notch signaling pathway on AMPs―AFPs’ axis”, and “Inactivation of HIF-1 signaling pathway on AMPs―AFPs―ACPs’ axis”. The most potent peptides were assessed via MDS; finally, HPIK on STAT3 and HVTK on NOS2 and on HIF-1 signaling pathway were the most stable complexes. Furthermore, the two peptides had better affinity scores than standard inhibitors (Stattic, 1400 W). Overall, the most potent SCPs for the human defense system were HPIK on STAT3 and HVTK on NOS2, which might inactivate the HIF-1 signaling pathway.

A Practical Strategy for Exploring the Pharmacological Mechanism of Luteolin Against COVID-19/Asthma Comorbidity: Findings of System Pharmacology and Bioinformatics Analysis

Asthma patients may increase their susceptibility to SARS-CoV-2 infection and the poor prognosis of coronavirus disease 2019 (COVID-19). However, anti-COVID-19/asthma comorbidity approaches are restricted on condition. Existing evidence indicates that luteolin has antiviral, anti-inflammatory, and immune regulation capabilities. We aimed to evaluate the possibility of luteolin evolving into an ideal drug and explore the underlying molecular mechanisms of luteolin against COVID-19/asthma comorbidity. We used system pharmacology and bioinformatics analysis to assess the physicochemical properties and biological activities of luteolin and further analyze the binding activities, targets, biological functions, and mechanisms of luteolin against COVID-19/asthma comorbidity. We found that luteolin may exert ideal physicochemical properties and bioactivity, and molecular docking analysis confirmed that luteolin performed effective binding activities in COVID-19/asthma comorbidity. Furthermore, a protein–protein interaction network of 538 common targets between drug and disease was constructed and 264 hub targets were obtained. Then, the top 6 hub targets of luteolin against COVID-19/asthma comorbidity were identified, namely, TP53, AKT1, ALB, IL-6, TNF, and VEGFA. Furthermore, the enrichment analysis suggested that luteolin may exert effects on virus defense, regulation of inflammation, cell growth and cell replication, and immune responses, reducing oxidative stress and regulating blood circulation through the Toll-like receptor; MAPK, TNF, AGE/RAGE, EGFR, ErbB, HIF-1, and PI3K–AKT signaling pathways; PD-L1 expression; and PD-1 checkpoint pathway in cancer. The possible “dangerous liaison” between COVID-19 and asthma is still a potential threat to world health. This research is the first to explore whether luteolin could evolve into a drug candidate for COVID-19/asthma comorbidity. This study indicated that luteolin with superior drug likeness and bioactivity has great potential to be used for treating COVID-19/asthma comorbidity, but the predicted results still need to be rigorously verified by experiments.

Elucidation of Potential Targets of San-Miao-San in the Treatment of Osteoarthritis Based on Network Pharmacology and Molecular Docking Analysis

Background

To examine the potential therapeutic targets of Chinese medicine formula San-Miao-San (SMS) in the treatment of osteoarthritis (OA), we analyzed the active compounds of SMS and key targets of OA and investigated the interacting pathways using network pharmacological approaches and molecular docking analysis.

Methods

The active compounds of SMS and OA-related targets were searched and screened by TCMSP, DrugBank, Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. Venn analysis and PPI were performed for evaluating the interaction of the targets. The topological analysis and molecular docking were used to confirm the subnetworks and binding affinity between active compounds and key targets, respectively. The GO and KEGG functional enrichment analysis for all targets of each subnetwork were conducted.

Results

A total of 57 active compounds and 203 targets of SMS were identified by the TCMSP and DrugBank database, while 1791 OA-related targets were collected from the Genecards, OMIM, DisGeNet, TTD, and PharmGKB databases. By Venn analysis, 108 intersection targets between SMS targets and OA targets were obtained. Most of these intersecting targets involve quercetin, kaempferol, and wogonin. Moreover, intersecting targets identified by PPI analysis were introduced into Cytoscape plug-in CytoNCA for topological analysis. Hence, nine key targets of SMS for OA treatment were obtained. Furthermore, the potential binding conformations between active compounds and key targets were found through molecular docking analysis. According to the DAVID enrichment analysis, the main biological processes of SMS in the treatment of OA include oxidative stress, response to reactive oxygen species, and apoptotic signaling pathways. Finally, we found wogonin, the key compound in SMS, might play a pivotal role on Toll-like receptor, IL-17, TNF, osteoclast differentiation, and apoptosis signaling pathways through interacting with four key targets.

Conclusions

Therefore, this study elucidated the potential active compounds and key targets of SMS in the treatment of OA based on network pharmacology.

Comparison between Heat-Clearing Medicine and Antirheumatic Medicine in Treatment of Gastric Cancer Based on Network Pharmacology, Molecular Docking, and Tumor Immune Infiltration Analysis

Background

Clinical research found that TCM is therapeutic in treating gastric cancer. Clearing heat is the most common method, while some antirheumatic medicines are widely used in treatment as well. To explore the pharmacological mechanism, we researched the comparison between heat-clearing medicine and antirheumatic medicine in treating gastric cancer.

Methods

First, related ingredients and targets were searched, respectively, and are shown in an active ingredient-target network. Combining the relevant targets of gastric cancer, we constructed a PPI network and MCODE network. Then, GO and KEGG enrichment analyses were conducted. Molecular docking experiments were performed to verify the affinity of targets and ligands. Finally, we analyzed the tumor immune infiltration on gene expression, somatic CNA, and clinical outcome.

Results

A total of 31 ingredients and 90 targets of heat-clearing medicine, 31 ingredients and 186 targets of antirheumatic medicine, and 12,155 targets of gastric cancer were collected. Antirheumatic medicine ranked the top in all the enrichment analyses. In the KEGG pathway, both types of medicines were related to pathways in cancer. In the KEGG map, AR, MMP2, ERBB2, and TP53 were the most crucial targets. Key targets and ligands were docked with low binding energy. Analysis of tumor immune infiltration showed that the expressions of AR and ERBB2 were correlated with the abundance of immune infiltration and made a difference in clinical outcomes.

Conclusions

Quercetin is an important ingredient in both heat-clearing medicine and antirheumatic medicine. AR signaling pathway exists in both types of medicines. The mechanism of the antitumor effect in antirheumatic medicine was similar to trastuzumab, a targeted drug aimed at ERBB2. Both types of medicines were significant in tumor immune infiltration. The immunology of gastric tumor deserves further research.

Diosgenin From Dioscorea Nipponica Rhizoma Against Graves’ Disease—On Network Pharmacology and Experimental Evaluation

Dioscorea nipponica rhizoma (DNR) is commonly used for the cure of hyperthyroidism resulting from Graves’ disease (GD) or thyroid nodules. However, its therapeutic mechanism remains unclear. This study aimed to utilize network pharmacology integrated molecular docking and experimental verification to reveal the potential pharmacological mechanism of DNR against GD. First, the active componds of DNR were collected from the HERB database and a literature search was conducted. Then, according to multisource database, the predicted genes of DNR and GD were collected to generate networks. The analysis of protein–protein interaction and GO enrichment and KEGG pathway were employed to discover main mechanisms associated with therapeutic targets. Moreover, molecular docking simulation was applied in order to verify the interactions between the drug and target. Finally, our experiments validated the ameliorated effects of diosgenin, the main component of DNR, in terms of phosphorylation deactivation in IGF-1R, which in turn inhibited the phosphorylation and activation of PI3K-AKT and Rap1-MEK signaling pathways, promoting cell apoptosis and GD remission. Our present study provided a foundation for further investigation of the in-depth mechanisms of diosgenin in GD and will provide new scientific evidence for clinical application.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

Molecular Mechanism of Gelsemium elegans (Gardner and Champ.) Benth. Against Neuropathic Pain Based on Network Pharmacology and Experimental Evidence

Gelsemium elegans (Gardner and Champ.) Benth. (Gelsemiaceae) (GEB) is a toxic plant indigenous to Southeast Asia especially China, and has long been used as Chinese folk medicine for the treatment of various types of pain, including neuropathic pain (NPP). Nevertheless, limited data are available on the understanding of the interactions between ingredients-targets-pathways. The present study integrated network pharmacology and experimental evidence to decipher molecular mechanisms of GEB against NPP. The candidate ingredients of GEB were collected from the published literature and online databases. Potentially active targets of GEB were predicted using the SwissTargetPrediction database. NPP-associated targets were retrieved from GeneCards, Therapeutic Target database, and DrugBank. Then the protein-protein interaction network was constructed. The DAVID database was applied to Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis. Molecular docking was employed to validate the interaction between ingredients and targets. Subsequently, a 50 ns molecular dynamics simulation was performed to analyze the conformational stability of the protein-ligand complex. Furthermore, the potential anti-NPP mechanisms of GEB were evaluated in the rat chronic constriction injury model. A total of 47 alkaloids and 52 core targets were successfully identified for GEB in the treatment of NPP. Functional enrichment analysis showed that GEB was mainly involved in phosphorylation reactions and nitric oxide synthesis processes. It also participated in 73 pathways in the pathogenesis of NPP, including the neuroactive ligand-receptor interaction signaling pathway, calcium signaling pathway, and MAPK signaling pathway. Interestingly, 11-Hydroxyrankinidin well matched the active pockets of crucial targets, such as EGFR, JAK1, and AKT1. The 11-hydroxyrankinidin-EGFR complex was stable throughout the entire molecular dynamics simulation. Besides, the expression of EGFR and JAK1 could be regulated by koumine to achieve the anti-NPP action. These findings revealed the complex network relationship of GEB in the "multi-ingredient, multi-target, multi-pathway" mode, and explained the synergistic regulatory effect of each complex ingredient of GEB based on the holistic view of traditional Chinese medicine. The present study would provide a scientific approach and strategy for further studies of GEB in the treatment of NPP in the future.

Integrating Network Analysis and Metabolomics to Reveal Mechanism of Huaganjian Decoction in Treatment of Cholestatic Hepatic Injury

Huaganjian decoction (HGJD) was first recorded in the classic "Jing Yue Quan Shu" during the Ming dynasty, and it has been extensively applied in clinical practice to treat liver diseases for over 300 years in China. However, its bioactive constituents and relevant pharmacological mechanism are still unclear. In this study, a strategy integrating network analysis and metabolomics was applied to reveal mechanism of HGJD in treating cholestatic hepatic injury (CHI). Firstly, we observed the therapeutic effect of HGJD against CHI with an alpha-naphthylisothiocyanate (ANIT) induced CHI rat model. Then, we utilized UPLC-Q-Exactive MS/MS method to analyze the serum migrant compounds of HGJD in CHI rats. Based on these compounds, network analysis was conducted to screen for potential active components, and key signaling pathways interrelated to therapeutic effect of HGJD. Meanwhile, serum metabolomics was utilized to investigate the underlying metabolic mechanism of HGJD against CHI. Finally, the predicted key pathway was verified by western blot and biochemical analysis using rat liver tissue from in vivo efficacy experiment. Our results showed that HGJD significantly alleviated ANIT induced CHI. Totally, 31 compounds originated from HGJD have been identified in the serum sample. PI3K/Akt/Nrf2 signaling pathway related to GSH synthesis was demonstrated as one of the major pathways interrelated to therapeutic effect of HGJD against CHI. This research supplied a helpful strategy to determine the potential bioactive compounds and mechanism of traditional Chinese medicine.

Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach

Purpose

Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment.

Methods

All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot.

Results

Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis (p < 0.05) and 119 signaling pathways (p < 0.05) in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS.

Conclusions

This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.

RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection

Some studies reported that genomic RNA of SARS-CoV-2 can absorb a few host miRNAs that regulate immune-related genes and then deprive their function. In this perspective, we conjecture that the absorption of the SARS-CoV-2 genome to host miRNAs is not a coincidence, which may be an indispensable approach leading to viral survival and development in host. In our study, we collected five datasets of miRNAs that were predicted to interact with the genome of SARS-CoV-2. The targets of these miRNAs in the five groups were consistently enriched immune-related pathways and virus-infectious diseases. Interestingly, the five datasets shared no one miRNA but their targets shared 168 genes. The signaling pathway enrichment of 168 shared targets implied an unbalanced immune response that the most of interleukin signaling pathways and none of the interferon signaling pathways were significantly different. Protein-protein interaction (PPI) network using the shared targets showed that PPI pairs, including IL6-IL6R, were related to the process of SARS-CoV-2 infection and pathogenesis. In addition, we found that SARS-CoV-2 absorption to host miRNA could benefit two popular mutant strains for more infectivity and pathogenicity. Conclusively, our results suggest that genomic RNA absorption to host miRNAs may be a vital approach by which SARS-CoV-2 disturbs the host immune system and infects host cells.

Deciphering the involvement of iron targets in colorectal cancer: a network biology approach

Several studies suggested the role of heme iron, but not non-heme iron in colorectal cancer. A network and system biology-based approach was used to understand the role of heme and non-heme iron on colorectal cancer etiology. Heme and non-heme iron targets were screened in addition to CRC targets. The protein-protein interaction map of both iron targets was prepared with CRC targets. Moreover, functional enrichment analysis was performed in order to understand their role in cancer etiology. The heme iron is predicted to modulate several cancer-associated pathways. Our results indicate several targets and pathways, including IL-4/IL-13, ACE, and HIF-1 signaling, that may have an important role in heme iron-mediated CRC and must be given consideration for understanding their role in colorectal cancer.

Identification of Anticancer and Anti-inflammatory Drugs from Drug-target Interaction Descriptors by Machine Learning

Background:

Drug repositioning is an important subject in drug-disease research. In the past, most studies simply used drug descriptors as the feature vector to classify drugs or targets, or used qualitative data about drug-target or drug-disease to predict drug-target interactions. These data provide limited information for drug repositioning.

Objective:

Considering both drugs and targets and constructing quantitative drug-target interaction descriptors as a method of drug characteristics are of great significance to the study of drug repositioning.

Methods:

Taking anticancer and anti-inflammatory drugs as research objects, the interaction sites between drugs and targets were determined by molecular docking. Sixty-seven drug-target interaction descriptors were calculated to describe the drug-target interactions, and 22 important descriptors were screened for drug classification by SVM, LightGBM and MLP.

Results:

The accuracy of SVM, LightGBM and MLP reached 93.29%, 92.68% and 94.51%, their Matthews correlation coefficients reached 0.852, 0.840 and 0.882, and their areas under the ROC curve reached 0.977, 0.969 and 0.968, respectively.

Conclusion:

Using drug-target interaction descriptors to build machine learning models can obtain better results for drug classification. Number of atom pairs, force field, hydrophobic interactions and bSASA are the four types of key features for the classification of anticancer and anti-inflammatory drugs.

Investigating the Mechanisms of Pollen Typhae in the Treatment of Diabetic Retinopathy Based on Network Pharmacology and Molecular Docking

OBJECTIVE

To explore the main bioactive compounds and investigate the underlying mechanism of Pollen Typhae (PT) against diabetic retinopathy (DR) by network pharmacology and molecular docking analysis.

METHODS

Bioactive ingredients and the target proteins of PT were obtained from TCMSP, and the related target genes were acquired from the SwissTargetPrediction database. The target genes of DR were obtained from GeneCards, TTD database, DisGeNET database, and DrugBank. The compound-target interaction network was established based on Cytoscape 3.7.2. The protein-protein interaction (PPI) network was constructed via STRING database and Cytoscape 3.7.2. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were visualized through DAVID database and Bioinformatics. Ingredient-gene-pathway network analysis was conducted to further screen the ingredients, target proteins, and pathways closely related to the biological mechanism on PT for DR, and molecular docking analysis was performed by SYBYL-X 2.1.1 software. Finally, the mechanism and underlying targets of PT in the treatment of DR were predicted.

RESULTS

A total of 8 compounds and 171 intersection targets were obtained based on the online network database. 7 main compounds were screened from compound-target network, and 53 targets including the top six key targets (PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR) were further acquired from PPI analysis. The 53 key targets covered 80 signaling pathways, among which PI3K-Akt signaling pathway, focal adhesion, Rap1 signaling pathway, VEGF signaling pathway, and HIF-1 signaling pathway were closely connected with the biological mechanism involved in the alleviation of DR by PT. Ingredient-gene-pathway network shows that AKTI, EGFR, and VEGFA were core genes, kaempferol and isorhamnetin were pivotal ingredients, and VEGF signaling pathway and Rap1 signaling pathway were closely involved in anti-DR. The docking results indicated that five main compounds (arachidonic acid, isorhamnetin, quercetin, kaempferol, and (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one) had good binding activity with EGFR and AKT1 targets.

CONCLUSION

The active ingredients in PT may regulate the levels of inflammatory factors, suppress the oxidative stress, and inhibit the proliferation, migration, and invasion of retinal pericytes by acting on PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR targets through VEGF signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and HIF-1 signaling pathway to play a therapeutic role in diabetic retinopathy.

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

HIT 2.0: an enhanced platform for Herbal Ingredients' Targets

Literature-described targets of herbal ingredients have been explored to facilitate the mechanistic study of herbs, as well as the new drug discovery. Though several databases provided similar information, the majority of them are limited to literatures before 2010 and need to be updated urgently. HIT 2.0 was here constructed as the latest curated dataset focusing on Herbal Ingredients’ Targets covering PubMed literatures 2000–2020. Currently, HIT 2.0 hosts 10 031 compound-target activity pairs with quality indicators between 2208 targets and 1237 ingredients from more than 1250 reputable herbs. The molecular targets cover those genes/proteins being directly/indirectly activated/inhibited, protein binders, and enzymes substrates or products. Also included are those genes regulated under the treatment of individual ingredient. Crosslinks were made to databases of TTD, DrugBank, KEGG, PDB, UniProt, Pfam, NCBI, TCM-ID and others. More importantly, HIT enables automatic Target-mining and My-target curation from daily released PubMed literatures. Thus, users can retrieve and download the latest abstracts containing potential targets for interested compounds, even for those not yet covered in HIT. Further, users can log into ‘My-target’ system, to curate personal target-profiling on line based on retrieved abstracts. HIT can be accessible at http://hit2.badd-cao.net.

Network Pharmacology-Based Investigation and Experimental Exploration of the Antiapoptotic Mechanism of Colchicine on Myocardial Ischemia Reperfusion Injury

Background: The beneficial effects of colchicine on cardiovascular disease have been widely reported in recent studies. Previous research demonstrated that colchicine has a certain protective effect on ischemic myocardium and has the potential to treat myocardial ischemia reperfusion injury (MIRI). However, the potential targets and pharmacological mechanism of colchicine to treat MIRI has not been reported.

Methods: In this study, we used network pharmacology and experimental verification to investigate the pharmacological mechanisms of colchicine for the treatment of MIRI. Potential targets of colchicine and MIRI related genes were screened from public databases. The mechanism of colchicine in the treatment of MIRI was determined by protein-protein interaction (PPI), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Additionally, we evaluated the effect of colchicine on H9C2 cell activity using CCK-8 assays, observed the effect of colchicine on H9C2 cell apoptosis via flow cytometry, and further verified the expression of key targets after colchicine treated by Western blot.

Results: A total of 626 target genes for colchicine and 1549 MIRI disease targets were obtained. 138 overlapping genes were determined as potential targets of colchicine in treating MIRI. the PPI network analysis demonstrated that the targets linked to MIRI were ALB, TNF, ACTB, AKT1, IL6, TP53, IL1B, CASP3 and these targets showed nice affinity with colchicine in molecular docking experiments. The results of GO analysis and KEGG pathway enrichment demonstrated that the anti-MIRI effect of colchicine involves in apoptotic signaling pathway. Further tests suggested that colchicine can protect H9C2 cell from Hypoxia/Reoxygenation (H/R) injury through anti-apoptotic effects. Western blot results demonstrated that colchicine can inhibited MIRI induced apoptosis of H9C2 cell by enhancing the decreased levels of Caspase-3 in myocardial injure model induced by H/R and activating the PI3K/AKT/eNOS pathway.

Conclusions: we performed network pharmacology and experimental evaluation to reveal the pharmacological mechanism of colchicine against MIRI. The results from this study could provide a theoretical basis for the development and clinical application of colchicine.

An Unbiased Functional Genetics Screen Identifies Rare Activating ERBB4 Mutations

Despite the relatively high frequency of somatic ERBB4 mutations in various cancer types, only a few activating ERBB4 mutations have been characterized, primarily due to lack of mutational hotspots in the ERBB4 gene. Here, we utilized our previously published pipeline, an in vitro screen for activating mutations, to perform an unbiased functional screen to identify potential activating ERBB4 mutations from a randomly mutated ERBB4 expression library. Ten potentially activating ERBB4 mutations were identified and subjected to validation by functional and structural analyses. Two of the 10 ERBB4 mutants, E715K and R687K, demonstrated hyperactivity in all tested cell models and promoted cellular growth under two-dimensional and three-dimensional culture conditions. ERBB4 E715K also promoted tumor growth in in vivo Ba/F3 cell mouse allografts. Importantly, all tested ERBB4 mutants were sensitive to the pan-ERBB tyrosine kinase inhibitors afatinib, neratinib, and dacomitinib. Our data indicate that rare ERBB4 mutations are potential candidates for ERBB4-targeted therapy with pan-ERBB inhibitors.

STATEMENT OF SIGNIFICANCE

ERBB4 is a member of the ERBB family of oncogenes that is frequently mutated in different cancer types but the functional impact of its somatic mutations remains unknown. Here, we have analyzed the function of over 8,000 randomly mutated ERBB4 variants in an unbiased functional genetics screen. The data indicate the presence of rare activating ERBB4 mutations in cancer, with potential to be targeted with clinically approved pan-ERBB inhibitors.

Chinese Medicine Meets Conventional Medicine in Targeting COVID-19 Pathophysiology, Complications and Comorbidities

OBJECTIVE

To investigate how the National Health Commission of China (NHCC)-recommended Chinese medicines (CMs) modulate the major maladjustments of coronavirus disease 2019 (COVID-19), particularly the clinically observed complications and comorbidities.

METHODS

By focusing on the potent targets in common with the conventional medicines, we investigated the mechanisms of 11 NHCC-recommended CMs in the modulation of the major COVID-19 pathophysiology (hyperinflammations, viral replication), complications (pain, headache) and comorbidities (hypertension, obesity, diabetes). The constituent herbs of these CMs and their chemical ingredients were from the Traditional Chinese Medicine Information Database. The experimentally-determined targets and the activity values of the chemical ingredients of these CMs were from the Natural Product Activity and Species Source Database. The approved and clinical trial drugs against these targets were searched from the Therapeutic Target Database and DrugBank Database. Pathways of the targets was obtained from Kyoto Encyclopedia of Genes and Genomes and additional literature search.

RESULTS

Overall, 9 CMs modulated 6 targets discovered by the COVID-19 target discovery studies, 8 and 11 CMs modulated 8 and 6 targets of the approved or clinical trial drugs for the treatment of the major COVID-19 complications and comorbidities, respectively.

CONCLUSION

The coordinated actions of each NHCC-recommended CM against a few targets of the major COVID-19 pathophysiology, complications and comorbidities, partly have common mechanisms with the conventional medicines.

Feature, Function, and Information of Drug Transporter-Related Databases

With the rapid progress in pharmaceutical experiments and clinical investigations, extensive knowledge of drug transporters (DTs) has accumulated, which is valuable data for the understanding of drug metabolism and disposition. However, such data are largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis. A variety of databases have, therefore, been constructed to provide DT-related data, and they were reviewed in this study. First, several knowledge bases providing data regarding clinically important drugs and their corresponding transporters were discussed, which constituted the most important resources of DT-centered data. Second, some databases describing the general transporters and their functional families were reviewed. Third, various databases offering transporter information as part of their entire data collection were described. Finally, customized database functions that are available to facilitate DT-related research were discussed. This review provided an overview of the whole collection of DT-related databases, which might facilitate research on precision medicine and rational drug use. SIGNIFICANCE STATEMENT: A collection of well established databases related to drug transporters were comprehensively reviewed, which were organized according to their importance in drug absorption, distribution, metabolism, and excretion research. These databases could collectively contribute to the research on rational drug use.

Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis

BACKGROUND

Patients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis.

METHODS

The disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein-protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD.

RESULTS

This study identified 495 risk genes for DKD complicated with IBD. After constructing a protein-protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C-C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product-receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway.

CONCLUSION

There is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.

Molecular Insights on Bioactive Compounds against Covid-19: A Network Pharmacological and Computational Study

BACKGROUND

Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in the inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2.

METHODS

SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, proteinphytochemical/ drugs network and up-down regulation of pathological host target proteins. This led to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g: Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explain the efficiency and stability of the interactions.

RESULTS

The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity, thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of a human host. Further exploration through virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets.

CONCLUSION

This work illustrates a probable strategy for the identification of phytochemical-based cocktails and off-targets which are effective against SARS_CoV 2.

Anticancer Action of Xiaoxianxiong Tang in Non-Small Cell Lung Cancer by Pharmacological Analysis and Experimental Validation

Xiaoxianxiong Tang (XXXT) is a well-known traditional Chinese medicine formula. Evidence is emerging supporting the benefits of XXXT in ameliorating therapy for non-small cell lung cancer (NSCLC). The purpose of this study aimed to explore the effects and mechanisms of XXXT through network pharmacological analysis and biological validation. TCMSP database was used to identify potentially active compounds in XXXT with absorption, distribution, metabolism, excretion screening, and their potential targets. The disease targets related to NSCLC were predicted by searching for Therapeutic Target database, GeneCards database, DrugBank database, and DisGeNET database. Of the 4385 NSCLC-related targets, 156 targets were also the targets of compounds present in XXXT. Subsequently, GO function and KEGG pathway enrichment and PPI network analyses revealed that, of the 95 targets and 20 pathways influenced by 20 ingredients in XXXT, 20 targets were associated with patient survival, and XXXT could exert an inhibitory action on the PI3K-AKT signaling pathway. Moreover, XXXT restrained the proliferation of A549 and H460 cells in a concentration-dependent manner and suppressed the mRNA and protein levels of key targets CCNA2, FOSL2, and BIRC5 closely linked to the PI3K-AKT pathway. Hence, XXXT has the potential to improve therapy for NSCLC by targeting the PI3K-AKT signaling pathway.

Computer-Based Drug Design of Positive Modulators of Store-Operated Calcium Channels to Prevent Synaptic Dysfunction in Alzheimer's Disease

Store-operated calcium entry (SOCE) constitutes a fine-tuning mechanism responsible for the replenishment of intracellular stores. Hippocampal SOCE is regulated by store-operated channels (SOC) organized in tripartite complex TRPC6/ORAI2/STIM2. It is suggested that in neurons, SOCE maintains intracellular homeostatic Ca2+ concentration at resting conditions and is needed to support the structure of dendritic spines. Recent evidence suggests that positive modulators of SOC are prospective drug candidates to treat Alzheimer’s disease (AD) at early stages. Although STIM2 and ORAI2 are definitely involved in the regulation of nSOC amplitude and a play major role in AD pathogenesis, growing evidence suggest that it is not easy to target these proteins pharmacologically. Existing positive modulators of TRPC6 are unsuitable for drug development due to either bad pharmacokinetics or side effects. Thus, we concentrate the review on perspectives to develop specific nSOC modulators based on available 3D structures of TRPC6, ORAI2, and STIM2. We shortly describe the structural features of existing models and the methods used to prepare them. We provide commonly used steps applied for drug design based on 3D structures of target proteins that might be used to develop novel AD preventing therapy.

Integrated Strategy From In Vitro, In Situ, In Vivo to In Silico for Predicting Active Constituents and Exploring Molecular Mechanisms of Tongfengding Capsule for Treating Gout by Inhibiting Inflammatory Responses

The study of screening active constituents from traditional Chinese medicine (TCM) is important for explicating the mechanism of action of TCM and further evaluating the safety and efficacy effectively. However, detecting and identifying the active constituents from complicated biological samples still remain a challenge. Here, a practical, quick, and novel integrated strategy from in vitro, in situ, in vivo to in silico for rapidly screening the active constituents was developed. Firstly, the chemical profile of TCM in vitro was identified using UPLC-Q Exactive-Orbitrap HRMS. Secondly, the in situ intestinal perfusion with venous sampling (IPVS) method was used to investigate the intestinal absorption components. Thirdly, after intragastric administration of the TCM extract, the in vivo absorbed prototype components were detected and identified. Finally, the target network pharmacology approach was applied to explore the potential targets and possible mechanisms of the absorbed components from TCM. The reliability and availability of this approach was demonstrated using Tongfengding capsule (TFDC) as an example of herbal medicine. A total of 141 compounds were detected and identified in TFDC, and among them, 64 components were absorbed into the plasma. Then, a total of 35 absorbed bioactive components and 50 related targets shared commonly by compounds and gout were integrated via target network pharmacology analysis. Ultimately, the effects of the absorbed components on metabolism pathways were verified by experiments. These results demonstrated that this original method may provide a practical tool for screening bioactive compounds from TCM treating particular diseases. Furthermore, it also can clarify the potential mechanism of action of TCM and rationalize the application of TFDC as an effective herbal therapy for gout.

Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.

XDeathDB: a visualization platform for cell death molecular interactions

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

Objective:

To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking.

Methods:

The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19.

Results:

A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6.

Conclusions:

This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

Exploring the Antiglioma Mechanisms of Luteolin Based on Network Pharmacology and Experimental Verification

Luteolin, a natural flavone compound, exists in a variety of fruits and vegetables, and its anticancer effect has been shown in many studies. However, its use in glioma treatment is hampered due to the fact that the underlying mechanism of action has not been fully explored. Therefore, we elucidated the potential antiglioma targets and pathways of luteolin systematically with the help of network pharmacology and molecular docking technology. The druggability of luteolin, including absorption, excretion, distribution, and metabolism, was assessed via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The potential targets of luteolin and glioma were extracted from public databases, and the intersecting targets between luteolin and glioma were integrated and visualized by a Venn diagram. In addition, GO and KEGG pathway analysis was engaged in Metascape. The network of the luteolin-target-pathway was visualized by Cytoscape. Ultimately, the interactions between luteolin and predicted key targets were confirmed by Discovery studio software. According to the ADME results, luteolin shows great potential for development into a drug. 4860 glioma-associated targets and 280 targets of luteolin were identified, of which 205 were intersection targets. 6 core targets of luteolin against glioma, including AKT1, JUN, ALB, MAPK3, MAPK1, and TNF, were identified via PPI network analysis of which AKT1, JUN, ALB, MAPK1, and TNF harbor diagnostic value. The biological processes of luteolin are mainly involved in the response to inorganic substances, response to oxidative stress, and apoptotic signaling pathway. The essential pathways of luteolin against glioma involve pathways in cancer, the PI3K-Akt signaling pathway, the TNF signaling pathway, and more. Meanwhile, luteolin's interaction with six core targets was verified by molecular docking simulation and its antiglioma effect was verified by in vitro experiments. This study suggests that luteolin has a promising potential for development into a drug and, moreover, it displays preventive effects against glioma by targeting various genes and pathways.

The Pharmacological Mechanism of Guchangzhixie Capsule Against Experimental Colitis

Ulcerative colitis (UC) is the major type of inflammatory bowel disease (IBD) characterized by an overactive immune response and destruction of colorectal epithelium with intricate pathological factors. Guchangzhixie (GCZX) capsule, included in the Chinese Pharmacopoeia 2020, has been widely utilized against UC. However, the underlying molecular mechanisms have not been elucidated. In the present study, a murine model of experimental colitis was established by orally feeding 4% dextran sodium sulfate (DSS) for 5 days and subsequently subjecting to GCZX treatment for another 15 days. Network pharmacology analysis was performed to predict the pertinent mechanisms of GCZX capsule. Cellular experiments examining the functional changes of intestinal organoids (IOs), macrophages (Mφs), and human colon epithelial cell cells (NCM460 cell line) after GCZX therapy were performed. Sequencing of 16S rRNA was conducted on the stools from the mouse model. Liquid chromatography-mass spectrometry (LC–MS) was utilized to detect serum metabolites. As a result, DSS induced experimental colitis, and this induction was alleviated by GCZX treatment, as evidenced by rescued pathological symptoms in UC mouse models, such as rectal bleeding stopping, decreased levels of albumin, interleukin-17, as well as chemokine (C-X-C motif) ligand 1 (CXCL1), and reduction in colon length. Network pharmacology analysis showed that GCZX-target genes were enriched in pathogen-induced infections, inflammatory pathways, as well as neoplastic processes. DSS treatment decreased microbial diversity and led to the accumulation of pathological bacterial, which was reversed by GCZX capsule. PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) based on profiles of microbiota composition demonstrated a decreased incidence of infectious disease and cancers after GCZX therapy. In full accordance with these data, GCZX administration suppressed Mφ transition to pro-inflammatory phenotype, alleviated tumor necrosis factor-α (TNFα)-compromised IOs functions, and decreased the recruitment of Mφs by epithelial cells. We conclude that GCZX capsule is an effective drug for UC and its pharmacological mechanisms involve re-establishing an anti-inflammatory milieu and favoring mucosal healing.

The Identification of the Biomarkers of Sheng-Ji Hua-Yu Formula Treated Diabetic Wound Healing Using Modular Pharmacology

Sheng-Ji Hua-Yu (SJHY) formula has been proved to reduce the severity of diabetic wound healing without significant adverse events in our previous clinical trials. However, based on multi-target characteristics, the regulatory network among herbs, ingredients, and hub genes remains to be elucidated. The current study aims to identify the biomarkers of the SJHY formula for the treatment of diabetic wound healing. First, a network of components and targets for the SJHY formula was constructed using network pharmacology. Second, the ClusterONE algorithm was used to build a modular network and identify hub genes along with kernel pathways. Third, we verified the kernel targets by molecular docking to select hub genes. In addition, the biomarkers of the SJHY formula were validated by animal experiments in a diabetic wound healing mice model. The results revealed that the SJHY formula downregulated the mRNA expression of Cxcr4, Oprd1, and Htr2a, while upregulated Adrb2, Drd, Drd4, and Hrh1. Besides, the SJHY formula upregulated the kernel pathways, neuroactive ligand-receptor interaction, and cAMP signaling pathway in the skin tissue homogenate of the diabetic wound healing mice model. In summary, this study identified the potential targets and kernel pathways, providing additional evidence for the clinical application of the SJHY formula for the treatment of diabetic wound healing.

Exploring the Therapeutic Mechanism of Tingli Dazao Xiefei Decoction on Heart Failure Based on Network Pharmacology and Experimental Study

Background

Tingli Dazao Xiefei decoction (TDXD) has been shown to have a therapeutic effect on heart failure (HF). Nevertheless, its molecular mechanism for treating HF is still unclear.

Materials and Methods

TDXD and HF targets were collected from the databases, and protein-protein interaction (PPI) analysis and enrichment analysis were performed on the overlapping targets. Then, AutoDock was employed for molecular docking. Finally, we used the left anterior descending coronary artery (LAD) ligation to induce HF model rats for in vivo experiments and verified the effect and mechanism of TDXD on HF.

Results

Network pharmacological analysis showed that the main active components of TDXD in treating HF were quercetin, kaempferol, beta-carotene, isorhamnetin, and beta-sitosterol, and the core targets were IL-6, VEGFA, TNF, AKT1, and MAPK1. Multiple gene functions and signaling pathways were obtained by enrichment analysis, among which inflammation-related, PI3K/Akt, and MAPK signaling pathways were closely related to HF. Furthermore, the molecular docking results showed that the core targets had good binding ability with the main active components. Animal experiments showed that TDXD could effectively improve left ventricular ejection fraction (EF) and left ventricular fractional shortening (FS), decrease left ventricular internal diastolic diameter (LVIDd) and left ventricular internal systolic diameter (LVIDs), reduce the area of myocardial fibrosis, and decrease serum BNP, LDH, CK-MB, IL-6, IL-1β, and TNF-α levels in HF rats. Meanwhile, TDXD could upregulate the expression of Bcl-2, downregulate the expression of Bax, and reduce cardiomyocyte apoptosis. At the same time, it was verified that TDXD could significantly decrease the expression of PI3K, P-Akt, and P-MAPK. Captopril showed similar effects.

Conclusions

Combining network pharmacological analysis and experimental validation, this study verified that TDXD could improve cardiac function and protect against cardiac injury by inhibiting the activation of PI3K/Akt and MAPK signaling pathways.

The effect and mechanism of Jiao-tai-wan in the treatment of diabetes mellitus with depression based on network pharmacology and experimental analysis

BACKGROUND

The incidence of diabetes mellitus (DM) and depression is increasing year by year around the world, bringing a serious burden to patients and their families. Jiao-tai-wan (JTW), a well-known traditional Chinese medicine (TCM), has been approved to have hypoglycemic and antidepressant effects, respectively, but whether JTW has such dual effects and its potential mechanisms is still unknown. This study is to evaluate the dual therapeutic effects of JTW on chronic restraint stress (CRS)-induced DM combined with depression mice, and to explore the underlying mechanisms through network pharmacology.

METHODS

CRS was used on db/db mice for 21 days to induce depression-like behaviors, so as to obtain the DM combined with depression mouse model. Mice were treated with 0.9% saline (0.1 ml/10 g), JTW (3.2 mg/kg) and Fluoxetine (2.0 mg/kg), respectively. The effect of JTW was accessed by measuring fasting blood glucose (FBG) levels, conducting behavioral tests and observing histopathological change. The ELISA assay was used to evaluate the levels of inflammatory cytokines and the UHPLC-MS/MS method was used to determine the depression-related neurotransmitters levels in serum. The mechanism exploration of JTW against DM and depression were performed via a network pharmacological method.

RESULTS

The results of blood glucose measurement showed that JTW has a therapeutic effect on db/db mice. Behavioral tests and the levels of depression-related neurotransmitters proved that JTW can effectively ameliorate depression-like symptoms in mice induced by CRS. In addition, JTW can also improve the inflammatory state and reduce the number of apoptotic cells in the hippocampus. According to network pharmacology, 28 active compounds and 484 corresponding targets of JTW, 1407 DM targets and 1842 depression targets were collected by screening the databases, and a total of 117 targets were obtained after taking the intersection. JTW plays a role in reducing blood glucose level and antidepressant mainly through active compounds such as quercetin, styrene, cinnamic acid, ethyl cinnamate, (R)-Canadine, palmatine and berberine, etc., the key targets of its therapeutic effect include INS, AKT1, IL-6, VEGF-A, TNF and so on, mainly involved in HIF-1 signal pathway, pathways in cancer, Hepatitis B, TNF signal pathway, PI3K-Akt signal pathway and MAPK signaling pathway, etc. CONCLUSION: Our experimental study showed that JTW has hypoglycemic and antidepressant effects. The possible mechanism was explored by network pharmacology, reflecting the characteristics of multi-component, multi-target and multi-pathway, which provides a theoretical basis for the experimental research and clinical application of JTW in the future.

Antidepressant Mechanism of Traditional Chinese Medicine Formula Xiaoyaosan in CUMS-Induced Depressed Mouse Model via RIPK1-RIPK3-MLKL Mediated Necroptosis Based on Network Pharmacology Analysis

Background: Depression is a stress-related disorder that seriously threatens people's physical and mental health. Xiaoyaosan is a classical traditional Chinese medicine formula, which has been used to treat mental depression since ancient times. More and more notice has been given to the relationship between the occurrence of necroptosis and the pathogenesis of mental disorders. Objective: The purpose of present study is to explore the potential mechanism of Xiaoyaosan for the treatment of depression using network pharmacology and experimental research, and identify the potential targets of necroptosis underlying the antidepressant mechanism of Xiaoyaosan. Methods: The mice model of depression was induced by chronic unpredictable mild stress (CUMS) for 6 weeks. Adult C57BL/6 mice were randomly divided into five groups, including control group, chronic unpredictable mild stress group, Xiaoyaosan treatment group, necrostatin-1 (Nec-1) group and solvent group. Drug intervention performed from 4^th to 6^th week of modeling. The mice in Xiaoyaosan treatment group received Xiaoyaosan by intragastric administration (0.254 g/kg/d), and mice in CUMS group received 0.5 ml physiological saline. Meanwhile, the mice in Nec-1 group were injected intraperitoneally (i.p.) with Nec-1 (10 mg/kg/d), and the equivalent volume of DMSO/PBS (8.3%) was injected into solvent group mice. The behavior tests such as sucrose preference test, forced swimming test and novelty-suppressed feeding test were measured to evaluate depressive-like behaviors of model mice. Then, the active ingredients in Xiaoyaosan and the related targets of depression and necroptosis were compiled through appropriate databases, while the "botanical drugs-active ingredients-target genes" network was constructed by network pharmacology analysis. The expressions of RIPK1, RIPK3, MLKL, p-MLKL were detected as critical target genes of necroptosis and the potential therapeutic target compounds of Xiaoyaosan. Furthermore, the levels of neuroinflammation and microglial activation of hippocampus were measured by detecting the expressions of IL-1β, Lipocalin-2 and IBA1, and the hematoxylin and eosin (H&E) stained was used to observe the morphology in hippocampus sections. Results: After 6-weeks of modeling, the behavioral data showed that mice in CUMS group and solvent group had obvious depressive-like behaviors, and the medication of Xiaoyaosan or Nec-1 could improve these behavioral changes. A total of 96 active ingredients in Xiaoyaosan which could regulate the 23 key target genes were selected from databases. Xiaoyaosan could alleviate the core target genes in necroptosis and improve the hippocampal function and neuroinflammation in depressed mice. Conclusion: The activation of necroptosis existed in the hippocampus of CUMS-induced mice, which was closely related to the pathogenesis of depression. The antidepressant mechanism of Xiaoyaosan included the regulation of multiple targets in necroptosis. It also suggested that necroptosis could be a new potential target for the treatment of depression.

Large-scale integration of the plasma proteome with genetics and disease

The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.

Genome-Wide RNAi Screening Identifies Novel Pathways/Genes Involved in Oxidative Stress and Repurposable Drugs to Preserve Cystic Fibrosis Airway Epithelial Cell Integrity

Recurrent infection-inflammation cycles in cystic fibrosis (CF) patients generate a highly oxidative environment, leading to progressive destruction of the airway epithelia. The identification of novel modifier genes involved in oxidative stress susceptibility in the CF airways might contribute to devise new therapeutic approaches. We performed an unbiased genome-wide RNAi screen using a randomized siRNA library to identify oxidative stress modulators in CF airway epithelial cells. We monitored changes in cell viability after a lethal dose of hydrogen peroxide. Local similarity and protein-protein interaction network analyses uncovered siRNA target genes/pathways involved in oxidative stress. Further mining against public drug databases allowed identifying and validating commercially available drugs conferring oxidative stress resistance. Accordingly, a catalog of 167 siRNAs able to confer oxidative stress resistance in CF submucosal gland cells targeted 444 host genes and multiple circuitries involved in oxidative stress. The most significant processes were related to alternative splicing and cell communication, motility, and remodeling (impacting cilia structure/function, and cell guidance complexes). Other relevant pathways included DNA repair and PI3K/AKT/mTOR signaling. The mTOR inhibitor everolimus, the α1-adrenergic receptor antagonist doxazosin, and the Syk inhibitor fostamatinib significantly increased the viability of CF submucosal gland cells under strong oxidative stress pressure. Thus, novel therapeutic strategies to preserve airway cell integrity from the harsh oxidative milieu of CF airways could stem from a deep understanding of the complex consequences of oxidative stress at the molecular level, followed by a rational repurposing of existing "protective" drugs. This approach could also prove useful to other respiratory pathologies.

Exploration of the mechanism of luteolin against ischemic stroke based on network pharmacology, molecular docking and experimental verification

Stroke is a leading cause of morbidity and mortality worldwide. As the most common type of stroke cases, treatment effectiveness is still limited despite intensive research. Recently, traditional Chinese medicine has attracted attention because of potential benefits for stroke treatment. Among these, luteolin, a natural plant flavonoid compound, offers neuroprotection following against ischemic stroke, although the specific mechanisms are unknown. Here we used network pharmacology, molecular docking, and experimental verification to explore the mechanisms whereby luteolin can benefit stroke recovery. The pharmacological and molecular properties of luteolin were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The potential targets of luteolin and ischemic stroke were collected from interrogating public databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed by Funrich and Database for Annotation, Visualization and Integrated Discovery respectively, a luteolin-target-pathway network constructed using Cytoscape, Autodock vina was used for molecular docking simulation with Discovery Studio was used to visualize and analyze the docked conformations. Lastly, we employed an in vitro model of stroke injury to evaluate the effects of luteolin on cell survival and expression of the putative targets. From 95 candidate luteolin target genes, our analysis identified six core targets . KEGG analysis of the candidate targets identified that luteolin provides therapeutic effects on stroke through TNF signaling and other pathways. Our experimental analyses confirmed the conclusions analyzed above. In summary, the molecular and pharmacological mechanisms of luteolin against stroke are indicated in our study from a systematic perspective.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation

BACKGROUND

Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated.

METHODS

Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis.

RESULTS

The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis.

CONCLUSIONS

Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.

The whole-genome sequencing in predicting Mycobacterium tuberculosis drug susceptibility and resistance in Papua, Indonesia

BACKGROUND

Tuberculosis is one of the deadliest disease caused by Mycobacterium tuberculosis. Its treatment still becomes a burden for many countries including Indonesia. Drug resistance is one of the problems in TB treatment. However, a development in the molecular field through Whole-genome sequencing (WGS) can be used as a solution in detecting mutations associated with TB- drugs. This investigation intended to implement this data for supporting the scientific community in deeply understanding any TB epidemiology and evolution in Papua along with detecting any mutations in genes associated with TB-Drugs.

RESULT

A whole-genome sequencing was performed on the random samples from TB Referral Laboratory in Papua utilizing MiSeq 600 cycle Reagent Kit (V3). Furthermore, TBProfiler was used for genome analysis, RAST Server was employed for annotation, while Gview server was applied for BLAST genome mapping and a Microscope server was implemented for Regions of Genomic Plasticity (RGP). The largest genome of M. tuberculosis obtained was at the size of 4,396,040 bp with subsystems number at 309 and the number of coding sequences at 4326. One sample (TB751) contained one RGP. The drug resistance analysis revealed that several mutations associated with TB-drug resistance existed. In details, mutations of rpoB gene which were identified as S450L, D435Y, H445Y, L430P, and Q432K had caused the reduced effectiveness of rifampicin; while the mutases in katG (S315T), kasA (312S), inhA (I21V), and Rv1482c-fabG1 (C-15 T) genes had contributed to the resistance in isoniazid. In streptomycin, the resistance was triggered by the mutations in rpsL (K43R) and rrs (A514C, A514T) genes, and, in Amikacin, its resistance was led by mutations in rrs (A514C) gene. Additionally, in Ethambutol and Pyrazinamide, their reduced effectiveness was provoked by embB gene mutases (M306L, M306V, D1024N) and pncA (W119R).

CONCLUSIONS

The results from whole-genome sequencing of TB clinical sample in Papua, Indonesia could contribute to the surveillance of TB-drug resistance. In the drug resistance profile, there were 15 Multi Drugs Resistance (MDR) samples. However, Extensively Drug-resistant (XDR) samples have not been found, but samples were resistant to only Amikacin, a second-line drug.

Chamaejasmenin E from Stellera chamaejasme induces apoptosis of hepatocellular carcinoma cells by targeting c-Met in vitro and in vivo

Hepatocellular carcinoma (HCC), the most prevalent liver cancer, is considered one of the most lethal malignancies with a dismal outcome. There is an urgent need to find novel therapeutic approaches to treat HCC. At present, natural products have served as a valuable source for drug discovery. Here, we obtained five known biflavones from the root of Stellera chamaejasme and evaluated their activities against HCC Hep3B cells in vitro. Chamaejasmenin E (CE) exhibited the strongest inhibitory effect among these biflavones. Furthermore, we found that CE could suppress the cell proliferation and colony formation, as well as the migration ability of HCC cells, but there was no significant toxicity on normal liver cells. Additionally, CE induced mitochondrial dysfunction and oxidative stress, eventually leading to cellular apoptosis. Mechanistically, the potential target of CE was predicted by database screening, showing that the compound might exert an inhibitory effect by targeting at c-Met. Next, this result was confirmed by molecular docking, cellular thermal shift assay (CETSA), as well as RT-PCR and Western blot analysis. Meanwhile, CE also reduced the downstream proteins of c-Met in HCC cells. In concordance with above results, CE is efficacious and non-toxic in tumor xenograft model. Taken together, our findings revealed an underlying tumor-suppressive mechanism of CE, which provided a foundation for identifying the target of biflavones.

Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

Fungal pathogens pose a global threat to human health, with Candida albicans among the leading killers. Systematic analysis of essential genes provides a powerful strategy to discover potential antifungal targets. Here, we build a machine learning model to generate genome-wide gene essentiality predictions for C. albicans and expand the largest functional genomics resource in this pathogen (the GRACE collection) by 866 genes. Using this model and chemogenomic analyses, we define the function of three uncharacterized essential genes with roles in kinetochore function, mitochondrial integrity, and translation, and identify the glutaminyl-tRNA synthetase Gln4 as the target of N-pyrimidinyl-β-thiophenylacrylamide (NP-BTA), an antifungal compound.

Study on the Mechanism of Liuwei Dihuang Pills in Treating Parkinson's Disease Based on Network Pharmacology

Background

Parkinson's disease (PD) is a common neurodegenerative disease in middle-aged and elderly people. Liuwei Dihuang (LWDH) pills have a good effect on PD, but its mechanism remains unclear. Network pharmacology is the result of integrating basic theories and research methods of medicine, biology, computer science, bioinformatics, and other disciplines, which can systematically and comprehensively reflect the mechanism of drug intervention in disease networks.

Methods

The main components and targets of herbs in LWDH pills were obtained through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Its active components were screened based on absorption, distribution, metabolism, and excretion (ADME); the PD-related targets were obtained from the Genecards, OMIM, TTD, and DRUGBANK databases. We used R to take the intersection of LWDH- and PD-related targets and Cytoscape software to construct the drug-component-target network. Moreover, STRING and Cytoscape software was used to analyze protein-protein interactions (PPI), construct a PPI network, and explore potential protein functional modules in the network. The Metascape platform was used to perform KEGG pathway and GO function enrichment analyses. Finally, molecular docking was performed to verify whether the compound and target have good binding activity.

Results

After screening and deduplication, 210 effective active ingredients, 204 drug targets, 4333 disease targets, and 162 drug-disease targets were obtained. We consequently constructed a drug-component-targets network and a PPI-drug-disease-targets network. The results showed that the hub components of LWDH pills were quercetin, stigmasterol, kaempferol, and beta-sitosterol; the hub targets were AKT1, VEGFA, and IL6. GO and KEGG enrichment analyses showed that these targets are involved in neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic processes, membrane rafts, MAPK signaling pathways, cellular senescence, and other biological processes. Molecular docking showed that the hub components were in good agreement with the hub targets.

Conclusion

LWDH pills have implications for the treatment of PD since they contain several active components, target multiple ligands, and activate various pathways. The hub components possibly include quercetin, stigmasterol, kaempferol, and beta-sitosterol and act through pairing with hub targets, such as AKT1, VEGFA, and IL6, to regulate neuronal death, G protein-coupled amine receptor activity, reactive oxygen species metabolic process, membrane raft, MAPK signaling pathway, and cellular senescence for the treatment of PD.

Anticancer Potential of Green Synthesized Silver Nanoparticles of the Soft Coral Cladiella pachyclados Supported by Network Pharmacology and In Silico Analyses

Cladiella-derived natural products have shown promising anticancer properties against many human cancer cell lines. In the present investigation, we found that an ethyl acetate extract of Cladiella pachyclados (CE) collected from the Red Sea could inhibit the human breast cancer (BC) cells (MCF and MDA-MB-231) in vitro (IC50 24.32 ± 1.1 and 9.55 ± 0.19 µg/mL, respectively). The subsequent incorporation of the Cladiella extract into the green synthesis of silver nanoparticles (AgNPs) resulted in significantly more activity against both cancer cell lines (IC50 5.62 ± 0.89 and 1.72 ± 0.36, respectively); the efficacy was comparable to that of doxorubicin with much-enhanced selectivity. To explore the mode of action of this extract, various in silico and network-pharmacology-based analyses were performed in the light of the LC-HRESIMS-identified compounds in the CE extract. Firstly, using two independent machine-learning-based prediction software platforms, most of the identified compounds in CE were predicted to inhibit both MCF7 and MDA-MB-231. Moreover, they were predicted to have low toxicity towards normal cell lines. Secondly, approximately 242 BC-related molecular targets were collected from various databases and used to construct a protein-protein interaction (PPI) network, which revealed the most important molecular targets and signaling pathways in the pathogenesis of BC. All the identified compounds in the extract were then subjected to inverse docking against all proteins hosted in the Protein Data bank (PDB) to discover the BC-related proteins that these compounds can target. Approximately, 10.74% of the collected BC-related proteins were potential targets for 70% of the compounds identified in CE. Further validation of the docking results using molecular dynamic simulations (MDS) and binding free energy calculations revealed that only 2.47% of the collected BC-related proteins could be targeted by 30% of the CE-derived compounds. According to docking and MDS experiments, protein-pathway and compound-protein interaction networks were constructed to determine the signaling pathways that the CE compounds could influence. This paper highlights the potential of marine natural products as effective anticancer agents and reports the discovery of novel anti-breast cancer AgNPs.

Path4Drug: Data Science Workflow for Identification of Tissue-Specific Biological Pathways Modulated by Toxic Drugs

The early prediction of drug adverse effects is of great interest to pharmaceutical research, as toxicity is one of the leading reasons for drug attrition. Understanding the cell signaling and regulatory pathways affected by a drug candidate is crucial to the study of drug toxicity. In this study, we present a computational technique that employs the propagation of drug-protein interactions to connect compounds to biological pathways. Target profiles for drugs were built by retrieving drug target proteins from public repositories such as ChEMBL, DrugBank, IUPHAR, PharmGKB, and TTD. Subsequent enrichment test of the protein pool using Reactome revealed potential pathways affected by the drugs. Furthermore, an optional tissue filter utilizing the Human Protein Atlas was applied to identify tissue-specific pathways. The analysis pipeline was implemented in an open-source KNIME workflow called Path4Drug to allow automated data retrieval and reconstruction for any given drug present in ChEMBL. The pipeline was applied to withdrawn drugs and cardio- and hepatotoxic drugs with black box warnings to identify biochemical pathways they affect and to find pathways that can be potentially connected to the toxic events. To complement this approach, drugs used in cardiac therapy without any record of toxicity were also analyzed. The results provide already known associations as well as a large amount of additional potential connections. Consequently, our approach can link drugs to biological pathways by leveraging big data available in public resources. The developed tool is openly available and modifiable to support other systems biology analyses.

A Feature Fusion Predictor for RNA Pseudouridine Sites with Particle Swarm Optimizer Based Feature Selection and Ensemble Learning Approach

RNA pseudouridine modification is particularly important in a variety of cellular biological and physiological processes. It plays a significant role in understanding RNA functions, RNA structure stabilization, translation processes, etc. To understand its functional mechanisms, it is necessary to accurately identify pseudouridine sites in RNA sequences. Although some computational methods have been proposed for the identification of pseudouridine sites, it is still a challenge to improve the identification accuracy and generalization ability. To address this challenge, a novel feature fusion predictor, named PsoEL-PseU, is proposed for the prediction of pseudouridine sites. Firstly, this study systematically and comprehensively explored different types of feature descriptors and determined six feature descriptors with various properties. To improve the feature representation ability, a binary particle swarm optimizer was used to capture the optimal feature subset for six feature descriptors. Secondly, six individual predictors were trained by using the six optimal feature subsets. Finally, to fuse the effects of all six features, six individual predictors were fused into an ensemble predictor by a parallel fusion strategy. Ten-fold cross-validation on three benchmark datasets indicated that the PsoEL-PseU predictor significantly outperformed the current state-of-the-art predictors. Additionally, the new predictor achieved better accuracy in the independent dataset evaluation-accuracy which is significantly higher than that of its existing counterparts-and the user-friendly webserver developed by the PsoEL-PseU predictor has been made freely accessible.

Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19

This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.