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Chen X, Guo Y, Chen X. iGMDR: Integrated Pharmacogenetic Resource Guide to Cancer Therapy and Research. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:150-160. [PMID: 32916316 PMCID: PMC7646137 DOI: 10.1016/j.gpb.2019.11.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 09/27/2019] [Accepted: 11/29/2019] [Indexed: 10/25/2022]
Abstract
Current pharmacogenetic studies have obtained many genetic models that can predict the therapeutic efficacy of anticancer drugs. Although some of these models are of crucial importance and have been used in clinical practice, these very valuable models have not been well adopted into cancer research to promote the development of cancer therapies due to the lack of integration and standards for the existing data of the pharmacogenetic studies. For this purpose, we built a resource investigating genetic model of drug response (iGMDR), which integrates the models from in vitro and in vivo pharmacogenetic studies with different omics data from a variety of technical systems. In this study, we introduced a standardized process for all integrations, and described how users can utilize these models to gain insights into cancer. iGMDR is freely accessible at https://igmdr.modellab.cn.
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Affiliation(s)
- Xiang Chen
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou 310058, China.
| | - Yi Guo
- Department of Polymer Science and Engineering and Key Laboratory of Adsorption and Separation Materials and Technologies of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
| | - Xin Chen
- Institute of Pharmaceutical Biotechnology and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou 310058, China; Joint Institute for Genetics and Genome Medicine between Zhejiang University and University of Toronto, Zhejiang University, Hangzhou 310058, China.
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202
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Chen XF, Guo MR, Duan YY, Jiang F, Wu H, Dong SS, Zhou XR, Thynn HN, Liu CC, Zhang L, Guo Y, Yang TL. Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs. JCI Insight 2020; 5:136477. [PMID: 32879140 PMCID: PMC7526455 DOI: 10.1172/jci.insight.136477] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 07/16/2020] [Indexed: 12/16/2022] Open
Abstract
More than 90% of autoimmune-associated variants are located in noncoding regions, leading to challenges in deciphering the underlying causal roles of functional variants and genes and biological mechanisms. Therefore, to reduce the gap between traditional genetic findings and mechanistic understanding of disease etiologies and clinical drug development, it is important to translate systematically the regulatory mechanisms underlying noncoding variants. Here, we prioritized functional noncoding SNPs with regulatory gene targets associated with 19 autoimmune diseases by incorporating hundreds of immune cell-specific multiomics data. The prioritized SNPs are associated with transcription factor (TF) binding, histone modification, or chromatin accessibility, indicating their allele-specific regulatory roles. Their target genes are significantly enriched in immunologically related pathways and other known immunologically related functions. We found that 90.1% of target genes are regulated by distal SNPs involving several TFs (e.g., the DNA-binding protein CCCTC-binding factor [CTCF]), suggesting the importance of long-range chromatin interaction in autoimmune diseases. Moreover, we predicted potential drug targets for autoimmune diseases, including 2 genes (NFKB1 and SH2B3) with known drug indications on other diseases, highlighting their potential drug repurposing opportunities. Taken together, these findings may provide useful information for future experimental follow-up and drug applications on autoimmune diseases.
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203
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De Cesco S, Davis JB, Brennan PE. TargetDB: A target information aggregation tool and tractability predictor. PLoS One 2020; 15:e0232644. [PMID: 32877430 PMCID: PMC7467329 DOI: 10.1371/journal.pone.0232644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 07/28/2020] [Indexed: 12/15/2022] Open
Abstract
When trying to identify new potential therapeutic protein targets, access to data and knowledge is increasingly important. In a field where new resources and data sources become available every day, it is crucial to be able to take a step back and look at the wider picture in order to identify potential drug targets. While this task is routinely performed by bespoke literature searches, it is often time-consuming and lacks uniformity when comparing multiple targets at one time. To address this challenge, we developed TargetDB, a tool that aggregates public information available on given target(s) (links to disease, safety, 3D structures, ligandability, novelty, etc.) and assembles it in an easy to read output ready for the researcher to analyze. In addition, we developed a target scoring system based on the desirable attributes of good therapeutic targets and machine learning classification system to categorize novel targets as having promising or challenging tractrability. In this manuscript, we present the methodology used to develop TargetDB as well as test cases.
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Affiliation(s)
- Stephane De Cesco
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
| | - John B. Davis
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
| | - Paul E. Brennan
- Nuffield Department of Medicine, ARUK Oxford Drug Discovery Institute, Target Discovery Institute, University of Oxford, Oxford, United-Kingdom
- * E-mail: (PEB); (SDC)
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204
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Dong S, Liang J, Zhai W, Yu Z. Development and Validation of an Individualized Nomogram for Predicting Overall Survival in Patients With Typical Lung Carcinoid Tumors. Am J Clin Oncol 2020; 43:607-614. [PMID: 32889829 PMCID: PMC7515482 DOI: 10.1097/coc.0000000000000715] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE We aim to develop and validate an effective nomogram prognostic model for patients with typical lung carcinoid tumors using a large patient cohort from the Surveillance, Epidemiology, and End Results (SEER) database. MATERIALS AND METHODS Data from patients with typical lung carcinoid tumors between 2010 and 2015 were selected from the SEER database for retrospective analysis. Univariate and multivariate Cox analysis was performed to clarify independent prognostic factors. Next, a nomogram was formulated to predict the probability of 3- and 5-year overall survival (OS). Concordance indexes (c-index), receiver operating characteristic analysis and calibration curves were used to evaluate the model. RESULTS The selected patients were randomly divided into a training and a validation cohort. A nomogram was established based on the training cohort. Cox analysis results indicated that age, sex, T stage, N stage, surgery, and bone metastasis were independent variables for OS. All these factors, except surgery, were included in the nomogram model for predicting 3- and 5-year OS. The internally and externally validated c-indexes were 0.787 and 0.817, respectively. For the 3-year survival prediction, receiver operating characteristic analysis showed that the areas under the curve in the training and validation cohorts were 0.824 and 0.795, respectively. For the 5-year survival prediction, the area under the curve in the training and validation cohorts were 0.812 and 0.787, respectively. The calibration plots for probability of survival were in good agreement. CONCLUSION The nomogram brings us closer to personalized medicine and the maximization of predictive accuracy in the prediction of OS in patients with typical lung carcinoid tumors.
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Affiliation(s)
- Shenghua Dong
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province
| | - Jun Liang
- Department of Oncology, Peking University International Hospital, Beijing, China
| | - Wenxin Zhai
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province
| | - Zhuang Yu
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province
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Jiang T, Kong B, Yan W, Wu C, Jiang M, Xu X, Xi X. Network Pharmacology to Identify the Pharmacological Mechanisms of a Traditional Chinese Medicine Derived from Trachelospermum jasminoides in Patients with Rheumatoid Arthritis. Med Sci Monit 2020; 26:e922639. [PMID: 32840241 PMCID: PMC7466841 DOI: 10.12659/msm.922639] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND This study used a network pharmacology approach to identify the pharmacological mechanisms of a traditional Chinese medicine derived from Trachelospermum jasminoides (Lindl.) Lem. in patients with rheumatoid arthritis (RA). MATERIAL AND METHODS Known compounds of T. jasminoides were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Shanghai Institute of Organic Chemistry of Chinese Academy of Science, Chemistry (CASC) database, and a literature search. Putative targets of identified compounds were predicted by SwissTargetPrediction. RA-related targets were achieved from the Therapeutic Target database, Drugbank database, Pharmacogenomics Knowledgebase, and Online Mendelian Inheritance in Man database. The protein-protein interaction (PPI) network was built by STRING. CluGO was utilized for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. RESULTS A total of 354 potential targets were predicted for the 17 bioactive compounds in T. jasminoides; 69 of these targets overlapped with RA-related targets. A PPI network was composed and 2 clusters of 59 and 42 nodes each were excavated. GO and KEGG enrichment analysis of the overlapping targets and the 2 clusters was mainly grouped into immunity, inflammation, estrogen, anxiety, and depression processes. CONCLUSIONS Our study illustrated that T. jasminoides alleviates RA through the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and other immune and inflammatory-related processes. It also may exert effects in regulating cell differentiation and potentially has anti-anxiety, anti-depression, and estrogen-like effects.
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Affiliation(s)
- Tao Jiang
- Department of Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland).,Shanghai Key Laboratory for Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Bo Kong
- Department of Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Wei Yan
- Department of Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Changgui Wu
- Department of Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Min Jiang
- Shanghai Key Laboratory for Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Xing Xu
- Shanghai Key Laboratory for Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
| | - Xiaobing Xi
- Department of Traumatology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland).,Shanghai Key Laboratory for Bone and Joint Diseases, Shanghai Institute of Traumatology and Orthopaedics, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (mainland)
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206
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Computational Chemogenomics Drug Repositioning Strategy Enables the Discovery of Epirubicin as a New Repurposed Hit for Plasmodium falciparum and P. vivax. Antimicrob Agents Chemother 2020; 64:AAC.02041-19. [PMID: 32601162 PMCID: PMC7449180 DOI: 10.1128/aac.02041-19] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 06/19/2020] [Indexed: 12/13/2022] Open
Abstract
Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity. Widespread resistance against antimalarial drugs thwarts current efforts for controlling the disease and urges the discovery of new effective treatments. Drug repositioning is increasingly becoming an attractive strategy since it can reduce costs, risks, and time-to-market. Herein, we have used this strategy to identify novel antimalarial hits. We used a comparative in silico chemogenomics approach to select Plasmodium falciparum and Plasmodium vivax proteins as potential drug targets and analyzed them using a computer-assisted drug repositioning pipeline to identify approved drugs with potential antimalarial activity. Among the seven drugs identified as promising antimalarial candidates, the anthracycline epirubicin was selected for further experimental validation. Epirubicin was shown to be potent in vitro against sensitive and multidrug-resistant P. falciparum strains and P. vivax field isolates in the nanomolar range, as well as being effective against an in vivo murine model of Plasmodium yoelii. Transmission-blocking activity was observed for epirubicin in vitro and in vivo. Finally, using yeast-based haploinsufficiency chemical genomic profiling, we aimed to get insights into the mechanism of action of epirubicin. Beyond the target predicted in silico (a DNA gyrase in the apicoplast), functional assays suggested a GlcNac-1-P-transferase (GPT) enzyme as a potential target. Docking calculations predicted the binding mode of epirubicin with DNA gyrase and GPT proteins. Epirubicin is originally an antitumoral agent and presents associated toxicity. However, its antiplasmodial activity against not only P. falciparum but also P. vivax in different stages of the parasite life cycle supports the use of this drug as a scaffold for hit-to-lead optimization in malaria drug discovery.
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207
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Enhancing and Complementary Mechanisms of Synergistic Action of Acori Tatarinowii Rhizoma and Codonopsis Radix for Alzheimer's Disease Based on Systems Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2020; 2020:6317230. [PMID: 32802132 PMCID: PMC7334796 DOI: 10.1155/2020/6317230] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 05/20/2020] [Accepted: 06/01/2020] [Indexed: 12/18/2022]
Abstract
Materials and Methods In this study, a systems pharmacology-based strategy was used to elucidate the synergistic mechanism of Acori Tatarinowii Rhizoma and Codonopsis Radix for the treatment of AD. This novel systems pharmacology model consisted of component information, pharmacokinetic analysis, and pharmacological data. Additionally, the related pathways were compressed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and the organ distributions were determined in the BioGPS bank. Results Sixty-eight active ingredients with suitable pharmacokinetic profiles and biological activities were selected through ADME screening in silico. Based on 62 AD-related targets, such as APP, CHRM1, and PTGS1, systematic analysis showed that these two herbs were mainly involved in the PI3K-Akt signaling pathway, MAPK signaling pathway, neuroactive ligand-receptor interaction, and fluid shear stress and atherosclerosis, indicating that they had a synergistic effect on AD. However, ATR acted on the KDR gene, while CR acted on IGF1R, MET, IL1B, and CHUK, showing that they also had complementary effects on AD. The ingredient contribution score involved 29 ingredients contributing 90.14% of the total contribution score of this formula for AD treatment, which emphasized that the effective therapeutic effects of these herbs for AD were derived from both ATR and CR, not a single herb. Organ distribution showed that the targets of the active ingredients were mainly located in the whole blood, the brain, and the muscle, which are associated with AD. Conclusions In sum, our findings suggest that the systems pharmacology methods successfully revealed the synergistic and complementary mechanisms of ATR and CR for the treatment of AD.
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208
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A Network Pharmacology Approach to Investigate the Active Compounds and Mechanisms of Musk for Ischemic Stroke. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2020; 2020:4063180. [PMID: 32714405 PMCID: PMC7354650 DOI: 10.1155/2020/4063180] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 05/14/2020] [Accepted: 06/10/2020] [Indexed: 12/28/2022]
Abstract
Objectives This study aims to study the material basis and effective mechanism of musk for ischemic stroke (IS) based on the network pharmacology approach. Methods We collected the chemical components and target gene of musk from the BATMAN-TCM analytical platform and identified ischemic stroke-related targets from the following databases: DisGeNET, NCBI-Gene, HPO, OMIM, DrugBank, and TTD. The targets of musk and IS were uploaded to the String database to construct the protein-protein interaction (PPI) network, and then, the key targets were analyzed by topological methods. At last, the function biological process and signaling pathways of key targets were carried out by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and cluster analysis by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server and Metascape platform. Results A total of 29 active compounds involving 1081 predicted targets were identified in musk and there were 1104 IS-related targets. And 88 key targets of musk for IS were obtained including AKT1, MAPK1/3, TP53, TNF, SRC, FOS, CASP3, JUN, NOS3, and IL1B. The GO and KEGG enrichment analysis suggested that these key targets are mainly involved in multiple pathways which participated in TNF signaling pathway, estrogen signaling pathway, prolactin signaling pathway, neurotrophin signaling pathway, T-cell receptor signaling pathway, cAMP signaling pathway, FoxO signaling pathway, and HIF1 signaling pathway. Conclusion This study revealed that the effective mechanisms of musk against IS would be associated with the regulation of apoptosis, inflammatory response, and gene transcription.
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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210
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Xu L, Zhang L, Wang T, Wu Y, Pu X, Li M, Guo Y. ExoceRNA atlas: A database of cancer ceRNAs in human blood exosomes. Life Sci 2020; 257:118092. [PMID: 32681912 DOI: 10.1016/j.lfs.2020.118092] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/07/2020] [Accepted: 07/09/2020] [Indexed: 01/08/2023]
Abstract
AIMS Competing endogenous RNAs (ceRNAs) play essential roles in cancer pathogenesis and those in exosomes have been the promising biomarkers for cancer diagnose and therapy. We aim to identify potential active ceRNA pairs in cancer blood exosomes by combining TCGA and exoRBase. MAIN METHODS Two strict screening criteria were implemented, including hypergeometric test on the targets predicted by RNA22 for differential miRNAs and Pearson test on the candidate mRNAs and lncRNAs for each cancer. Then2638292, 4925485 and 70669 ceRNAs in blood exosomes are available for colorectal cancer (CRC), hepatocellular carcinoma (HCC) and pancreatic adenocarcinoma (PAAD), respectively. KEY FINDINGS A comprehensive functional analysis on differential miRNAs in cancer blood exosomes indicates that they play important roles in development of cancer by degrading or inhibiting the post-transcription translation level of mRNA or by acting as mediators to regulate the expression of mRNA. Topological and biological functional analysis of ceRNA networks demonstrate that hub ceRNAs involve in cancer-related biological pathways and processes, so as to influence the occurrence and development of cancer and would be the potential biomarkers for three cancers. Finally, we designed a web-accessible database, ExoceRNA Atlas (https://www.exocerna-atlas.com/exoceRNA#/) as a repository of ceRNAs in blood exosomes. It can friendly search, browse and visualize ceRNA networks of the query genes along with giving the detailed functional analysis results. The entire ceRNA data can also be freely downloaded. SIGNIFICANCE ExoceRNA Atlas will serve as a powerful public resource for identifying ceRNAs and greatly deepen our understanding their functions in cancer exosomes.
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Affiliation(s)
- Lei Xu
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Lei Zhang
- College of Cybersecurity, Sichuan University, Chengdu, Sichuan, China
| | - Tian Wang
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Yanling Wu
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China.
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211
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Zhang J, Liu X, Zhou W, Cheng G, Wu J, Guo S, Jia S, Liu Y, Li B, Zhang X, Wang M. A bioinformatics investigation into molecular mechanism of Yinzhihuang granules for treating hepatitis B by network pharmacology and molecular docking verification. Sci Rep 2020; 10:11448. [PMID: 32651427 PMCID: PMC7351787 DOI: 10.1038/s41598-020-68224-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/22/2020] [Indexed: 12/17/2022] Open
Abstract
Yinzhihuang granules (YZHG) is a patented Chinese medicine for the treatment of hepatitis B. This study aimed to investigate the intrinsic mechanisms of YZHG in the treatment of hepatitis B and to provide new evidence and insights for its clinical application. The chemical compounds of YZHG were searched in the CNKI and PUBMED databases, and their putative targets were then predicted through a search of the SuperPred and Swiss Target Prediction databases. In addition, the targets of hepatitis B were obtained from TTD, PharmGKB and DisGeNET. The abovementioned data were visualized using Cytoscape 3.7.1, and network construction identified a total of 13 potential targets of YZHG in the treatment of hepatitis B. Molecular docking verification showed that CDK6, CDK2, TP53 and BRCA1 might be strongly correlated with hepatitis B treatment. Furthermore, GO and KEGG analyses indicated that the treatment of hepatitis B by YZHG might be related to positive regulation of transcription, positive regulation of gene expression, the hepatitis B pathway and the viral carcinogenesis pathway. Network pharmacology intuitively shows the multicomponent, multitarget and multichannel pharmacological effects of YZHG in the treatment of hepatitis B and provides a scientific basis for its mechanism of action.
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Affiliation(s)
- Jingyuan Zhang
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Xinkui Liu
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Wei Zhou
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Guoliang Cheng
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Linyi, 276000, China
| | - Jiarui Wu
- Beijing University of Chinese Medicine, Beijing, 100102, China.
| | - Siyu Guo
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Shanshan Jia
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Yingying Liu
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Bingbing Li
- State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine, Linyi, 276000, China
| | - Xiaomeng Zhang
- Beijing University of Chinese Medicine, Beijing, 100102, China
| | - Miaomiao Wang
- Beijing University of Chinese Medicine, Beijing, 100102, China
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Zhang H, Song Y, Du Z, Li X, Zhang J, Chen S, Chen F, Li T, Zhan Q. Exome sequencing identifies new somatic alterations and mutation patterns of tongue squamous cell carcinoma in a Chinese population. J Pathol 2020; 251:353-364. [PMID: 32432340 DOI: 10.1002/path.5467] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 04/08/2020] [Accepted: 05/07/2020] [Indexed: 12/21/2022]
Abstract
Tongue squamous cell carcinoma (TSCC) is an aggressive group of tumors characterized by high rates of regional lymph node metastasis and local recurrence. Emerging evidence has revealed genetic variations of TSCC across different geographical regions due to the impact of multiple risk factors such as chewing betel-quid. However, we know little of the mutational processes of TSCC in the Chinese population without the history of chewing betel-quid/tobacco. To explore the mutational spectrum of this disease, we performed whole-exome sequencing of sample pairs, comprising tumors and normal tissue, from 82 TSCC patients. In addition to identifying seven previously known TSCC-associated genes (TP53, CDKN2A, PIK3CA, NOTCH1, ASXL1, USH2A, and CSMD3), the analysis revealed six new genes (GNAQ, PRG4, RP1, ZNF16, NBEA, and PTPRC) that had not been reported previously in TSCC. Our in vitro experiments identified ZNF16 for the first time as a solid tumor associated gene to promote malignancy of TSCC cells. We also identified a microRNA (miR-585-5p) encoded by the 5q35.1 region and characterized it as a tumor suppressor by targeting SOX9. At least one non-silent mutation of genes involved in the 10 canonical oncogenic pathways (Notch, RTK-RAS, PI3K, Wnt, Cell cycle, p53, Myc, Hippo, TGFβ, and Nrf2) was found in 82.9% of cases. Collectively, our data extend the spectrum of TSCC mutations and define novel diagnosis markers and potential clinical targets for TSCC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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Affiliation(s)
- Heyu Zhang
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, PR China.,National Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, Beijing, PR China.,Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, PR China
| | - Yongmei Song
- State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
| | - Zhenglin Du
- China National Center for Bioinformation & National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, PR China
| | - Xuefen Li
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, Beijing, PR China.,Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, PR China
| | - Shuai Chen
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Feng Chen
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, PR China
| | - Tiejun Li
- Central Laboratory, Peking University School and Hospital of Stomatology, Beijing, PR China.,Department of Oral Pathology, Peking University School and Hospital of Stomatology, Beijing, PR China.,National Clinical Research Center for Oral Diseases, Peking University School and Hospital of Stomatology, Beijing, PR China.,Research Unit of Precision Pathologic Diagnosis in Tumors of the Oral and Maxillofacial Regions, Chinese Academy of Medical Sciences (2019RU034), Beijing, PR China
| | - Qimin Zhan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Molecular Oncology, Peking University Cancer Hospital & Institute, Beijing, PR China.,State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, PR China
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213
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Tao Q, Du J, Li X, Zeng J, Tan B, Xu J, Lin W, Chen XL. Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19. Drug Dev Ind Pharm 2020; 46:1345-1353. [PMID: 32643448 PMCID: PMC7441778 DOI: 10.1080/03639045.2020.1788070] [Citation(s) in RCA: 137] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Purpose Huashi Baidu formula (HSBDF) was developed to treat the patients with severe COVID-19
in China. The purpose of this study was to explore its active compounds and demonstrate
its mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
through network pharmacology and molecular docking. Methods All the components of HSBDF were retrieved from the pharmacology database of TCM
system. The genes corresponding to the targets were retrieved using UniProt and
GeneCards database. The herb–compound–target network was constructed by Cytoscape. The
target protein–protein interaction network was built using STRING database. The core
targets of HSBDF were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
Genomes (KEGG). The main active compounds of HSBDF were docked with SARS-CoV-2 and
angiotensin converting enzyme II (ACE2). Results Compound–target network mainly contained 178 compounds and 272 corresponding targets.
Key targets contained MAPK3, MAPK8, TP53, CASP3, IL6, TNF, MAPK1, CCL2, PTGS2, etc.
There were 522 GO items in GO enrichment analysis (p < .05) and 168 signaling pathways (p < .05) in KEGG, mainly including TNF signaling pathway, PI3K–Akt
signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, and
HIF-1 signaling pathway. The results of molecular docking showed that baicalein and
quercetin were the top two compounds of HSBDF, which had high affinity with ACE2. Conclusion Baicalein and quercetin in HSBDF may regulate multiple signaling pathways through ACE2,
which might play a therapeutic role on COVID-19.
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Affiliation(s)
- Quyuan Tao
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiaxin Du
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xiantao Li
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jingyan Zeng
- The First Clinical School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Bo Tan
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jianhua Xu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Wenjia Lin
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xin-Lin Chen
- School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, China
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214
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Bao H, Guo H, Feng Z, Li X. Deciphering the underlying mechanism of Xianlinggubao capsule against osteoporosis by network pharmacology. BMC Complement Med Ther 2020; 20:208. [PMID: 32620113 PMCID: PMC7333287 DOI: 10.1186/s12906-020-03007-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/26/2020] [Indexed: 12/18/2022] Open
Abstract
Background Xianlinggubao formula (XLGB), a Chinese State Food and Drug Administration-permitted traditional Chinese herbal medicine, has been extensively used to treat osteoporosis. Although XLGB was shown to improve bone mass in ovariectomized rats and clinically alleviate osteoporosis symptoms, its pharmacological mechanisms remain unclear. Methods In this study, we used a network pharmacological approach to explore the potential mechanism of XLGB in treating osteoporosis. We obtained XLGB compounds from the TCMSP and TCMID databases and identified potential targets of these compounds through target fishing based on the TCMSP and Swiss Target Prediction databases. Next, we identified the osteoporosis targets by using the CTD, TTD, GeneCards, OMIM and PharmGKB databases. Then, the overlapping genes between the XLGB potential targets and the osteoporosis targets were used to establish a protein-protein interaction (PPI) network and to analyze their interactions and identify the major hub genes in this network. Subsequently, the Metascape database was utilized to conduct the enrichment of Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results There were 104 active compounds and 295 related targets identified overall. After the Metascape enrichment analysis, we identified the top 25 cellular biological processes and top 15 pathways based on the logP value and found that the XLGB-mediated anti-osteoporosis effect was mainly associated with reactive oxygen species, organonitrogen compound response and cell migration. Furthermore, 36 hub genes of XLGB, such as EGF, EGFR, MTOR, MAPK14 and NFKB1, were considered potential therapeutic targets, suggesting the underlying mechanisms of XLGB acting on osteoporosis. Conclusion We investigated the possible therapeutic mechanisms of XLGB from a systemic perspective. These key targets and pathways provide promising directions for future research to reveal the exact regulatory mechanisms of XLGB.
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Affiliation(s)
- Hangsheng Bao
- Foshan Hospital of Traditional Chinese Medicine, Foshan, 528000, China
| | - Huizhi Guo
- First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Zongquan Feng
- Foshan Hospital of Traditional Chinese Medicine, Foshan, 528000, China
| | - Xin Li
- Department of Nephrology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
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215
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Xue W, Fu T, Zheng G, Tu G, Zhang Y, Yang F, Tao L, Yao L, Zhu F. Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters. Curr Med Chem 2020; 27:3830-3876. [DOI: 10.2174/0929867325666181009123218] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 09/30/2018] [Accepted: 10/03/2018] [Indexed: 01/06/2023]
Abstract
Background:
The human Monoamine Transporters (hMATs), primarily including hSERT,
hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders
with more than the availability of 30 approved drugs.
Objective:
This paper is to review the recent progress in the binding mode and inhibitory mechanism of
hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor
design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds
to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted.
Methods:
PubMed and Web of Science databases were searched for protein-ligand interaction, novel
inhibitors design and synthesis studies related to hMATs.
Results:
Literature data indicate that since the first crystal structure determinations of the homologous
bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental
structures or computational models has been accumulated that now defines a substantial degree
of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs
inhibitors have been discovered by medicinal chemistry with significant help from computational models.
Conclusion:
The reported new compounds act on hMATs as well as the structures of the transporters
complexed with diverse ligands by either experiment or computational modeling have shed light on the
poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies
will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high
activity and selectivity for hMATs.
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Affiliation(s)
- Weiwei Xue
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Tingting Fu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Guoxun Zheng
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Gao Tu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Yang Zhang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Fengyuan Yang
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou 310036, China
| | - Lixia Yao
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, United States
| | - Feng Zhu
- Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Chongqing Key Laboratory of Natural Drug Research, Chongqing University, Chongqing 401331, China
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216
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Schneider L, Kehl T, Thedinga K, Grammes NL, Backes C, Mohr C, Schubert B, Lenhof K, Gerstner N, Hartkopf AD, Wallwiener M, Kohlbacher O, Keller A, Meese E, Graf N, Lenhof HP. ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification. Bioinformatics 2020; 35:5171-5181. [PMID: 31038669 PMCID: PMC6954665 DOI: 10.1093/bioinformatics/btz302] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 04/02/2019] [Accepted: 04/26/2019] [Indexed: 01/10/2023] Open
Abstract
Motivation Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. Results Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. Availability and implementation ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lara Schneider
- Center for Bioinformatics, Saarbrücken, Germany.,Saarbrücken Graduate School of Computer Science, Saarbrücken, Germany
| | - Tim Kehl
- Center for Bioinformatics, Saarbrücken, Germany.,Saarbrücken Graduate School of Computer Science, Saarbrücken, Germany
| | | | | | - Christina Backes
- Center for Bioinformatics, Saarbrücken, Germany.,Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Christopher Mohr
- Quantitative Biology Center (QBiC), Tübingen, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Benjamin Schubert
- Department of Systems Biology, Boston, MA, USA.,Department of Cell Biology, Harvard Medical School, Boston, MA, USA.,cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kerstin Lenhof
- Center for Bioinformatics, Saarbrücken, Germany.,Saarbrücken Graduate School of Computer Science, Saarbrücken, Germany
| | - Nico Gerstner
- Center for Bioinformatics, Saarbrücken, Germany.,Saarbrücken Graduate School of Computer Science, Saarbrücken, Germany
| | | | - Markus Wallwiener
- Department of Obstetrics and Gynecology, University of Heidelberg, Heidelberg, Germany.,National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
| | - Oliver Kohlbacher
- Quantitative Biology Center (QBiC), Tübingen, Germany.,Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany.,Center for Bioinformatics, University of Tübingen, Tübingen, Germany.,Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.,Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Andreas Keller
- Center for Bioinformatics, Saarbrücken, Germany.,Chair for Clinical Bioinformatics, Saarland Informatics Campus, Saarland University, Saarbrücken, Germany
| | - Eckart Meese
- Center for Bioinformatics, Saarbrücken, Germany.,Human Genetics, Saarland University, Homburg, Germany
| | - Norbert Graf
- Center for Bioinformatics, Saarbrücken, Germany.,Department of Pediatric Oncology and Hematology, Medical School, Saarland University, Homburg, Germany
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217
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Gong J, Chen Y, Pu F, Sun P, He F, Zhang L, Li Y, Ma Z, Wang H. Understanding Membrane Protein Drug Targets in Computational Perspective. Curr Drug Targets 2020; 20:551-564. [PMID: 30516106 DOI: 10.2174/1389450120666181204164721] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 09/03/2018] [Accepted: 09/04/2018] [Indexed: 01/16/2023]
Abstract
Membrane proteins play crucial physiological roles in vivo and are the major category of drug targets for pharmaceuticals. The research on membrane protein is a significant part in the drug discovery. The biological process is a cycled network, and the membrane protein is a vital hub in the network since most drugs achieve the therapeutic effect via interacting with the membrane protein. In this review, typical membrane protein targets are described, including GPCRs, transporters and ion channels. Also, we conclude network servers and databases that are referring to the drug, drug-target information and their relevant data. Furthermore, we chiefly introduce the development and practice of modern medicines, particularly demonstrating a series of state-of-the-art computational models for the prediction of drug-target interaction containing network-based approach and machine-learningbased approach as well as showing current achievements. Finally, we discuss the prospective orientation of drug repurposing and drug discovery as well as propose some improved framework in bioactivity data, created or improved predicted approaches, alternative understanding approaches of drugs bioactivity and their biological processes.
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Affiliation(s)
- Jianting Gong
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Yongbing Chen
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Feng Pu
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Pingping Sun
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Fei He
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun, China
| | - Yanwen Li
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Zhiqiang Ma
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, China.,Institution of Computational Biology, Northeast Normal University, Changchun, China
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218
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Liu L, Wang G, Wang L, Yu C, Li M, Song S, Hao L, Ma L, Zhang Z. Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling. Biol Direct 2020; 15:10. [PMID: 32539851 PMCID: PMC7294636 DOI: 10.1186/s13062-020-00264-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 06/04/2020] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes. RESULTS In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner. CONCLUSIONS PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.
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Affiliation(s)
- Lin Liu
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Guangyu Wang
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
- Present Address: The Methodist Hospital Research Institute, 6670 Bertner Ave, Houston, TX, 77030, USA
| | - Liguo Wang
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA
| | - Chunlei Yu
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengwei Li
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Shuhui Song
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Lili Hao
- China National Center for Bioinformation, Beijing, 100101, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100101, China
| | - Lina Ma
- China National Center for Bioinformation, Beijing, 100101, China.
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100101, China.
| | - Zhang Zhang
- China National Center for Bioinformation, Beijing, 100101, China.
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100101, China.
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219
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Sakaue S, Okada Y. GREP: genome for REPositioning drugs. Bioinformatics 2020; 35:3821-3823. [PMID: 30859178 PMCID: PMC6761931 DOI: 10.1093/bioinformatics/btz166] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 02/14/2019] [Accepted: 03/09/2019] [Indexed: 02/01/2023] Open
Abstract
Summary Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics. Availability and implementation GREP is available at https://github.com/saorisakaue/GREP as a python source code. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Department of Allergy and Rheumatology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan.,Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
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220
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Tang J, Mou M, Wang Y, Luo Y, Zhu F. MetaFS: Performance assessment of biomarker discovery in metaproteomics. Brief Bioinform 2020; 22:5854399. [PMID: 32510556 DOI: 10.1093/bib/bbaa105] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/17/2020] [Accepted: 05/05/2020] [Indexed: 12/19/2022] Open
Abstract
Metaproteomics suffers from the issues of dimensionality and sparsity. Data reduction methods can maximally identify the relevant subset of significant differential features and reduce data redundancy. Feature selection (FS) methods were applied to obtain the significant differential subset. So far, a variety of feature selection methods have been developed for metaproteomic study. However, due to FS's performance depended heavily on the data characteristics of a given research, the well-suitable feature selection method must be carefully selected to obtain the reproducible differential proteins. Moreover, it is critical to evaluate the performance of each FS method according to comprehensive criteria, because the single criterion is not sufficient to reflect the overall performance of the FS method. Therefore, we developed an online tool named MetaFS, which provided 13 types of FS methods and conducted the comprehensive evaluation on the complex FS methods using four widely accepted and independent criteria. Furthermore, the function and reliability of MetaFS were systematically tested and validated via two case studies. In sum, MetaFS could be a distinguished tool for discovering the overall well-performed FS method for selecting the potential biomarkers in microbiome studies. The online tool is freely available at https://idrblab.org/metafs/.
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221
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Gong Y, Yang Y. Activation of Nrf2/AREs-mediated antioxidant signalling, and suppression of profibrotic TGF-β1/Smad3 pathway: a promising therapeutic strategy for hepatic fibrosis - A review. Life Sci 2020; 256:117909. [PMID: 32512009 DOI: 10.1016/j.lfs.2020.117909] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/19/2020] [Accepted: 06/01/2020] [Indexed: 02/07/2023]
Abstract
Hepatic fibrosis (HF) is a wound-healing response that occurs during chronic liver injury and features by an excessive accumulation of extracellular matrix (ECM) components. Activation of hepatic stellate cell (HSC), the leading effector in HF, is responsible for overproduction of ECM. It has been documented that transforming growth factor-β1 (TGF-β1) stimulates superfluous accumulation of ECM and triggers HSCs activation mainly via canonical Smad-dependent pathway. Also, the pro-fibrogenic TGF-β1 is correlated with generation of reactive oxygen species (ROS) and inhibition of antioxidant mechanisms. Moreover, involvement of oxidative stress (OS) can be clearly elucidated as a fundamental event in liver fibrogenesis. Nuclear factor erythroid 2-related factor 2-antioxidant response elements (Nrf2-AREs) pathway, a group of OS-mediated transcription factors with diverse downstream targets, is associated with the induction of diverse detoxifying enzymes and the most pivotal endogenous antioxidative system. More specifically, Nrf2-AREs pathway has recently assigned as a new therapeutic target for cure of HF. The overall goal of this review will focus on recent findings about activation of Nrf2-AREs-mediated antioxidant and suppression of profibrotic TGF-β1/Smad3 pathway in the liver, providing an overview of recent advances in transcriptional repressors that dislocated during HF formation, and highlighting possible novel therapeutic targets for liver fibrosis.
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Affiliation(s)
- Yongfang Gong
- Department of Pharmacology, Anhui Medical University, Key Laboratory of Anti-inflammatory and Immunopharmacology, Ministry of Education, Hefei 230032, China
| | - Yan Yang
- Department of Pharmacology, Anhui Medical University, Key Laboratory of Anti-inflammatory and Immunopharmacology, Ministry of Education, Hefei 230032, China.
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222
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Xu J, Bai C, Huang L, Liu T, Wan Y, Zheng Z, Ma X, Gao F, Yu H, Gu X. Network pharmacology to dissect the mechanisms of Yinlai Decoction for pneumonia. BMC Complement Med Ther 2020; 20:168. [PMID: 32493296 PMCID: PMC7267769 DOI: 10.1186/s12906-020-02954-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 05/14/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Pneumonia is a common respiratory disorder, which brings an enormous financial burden to the medical system. However, the current treatment options for pneumonia are limited because of drug resistance and side effects. Our previous study preliminarily confirmed that Yinlai Decoction (YD), a common prescription for pneumonia in clinical practice, can regulate the expression of inflammatory factors, but the mechanisms are unknown yet. METHODS In our work, a method named network pharmacology was applied, which investigated the underlying mechanisms of herbs based on a variety of databases. We obtained bioactive ingredients of YD on TCMSP database and collected potential targets of these ingredients by target fishing. Then the pneumonia-related targets database was built by TTD, Drugbank, HPO, OMIM, and CTD. Based on the matching targets between YD and pneumonia, the PPI network was built by STRING to analyze the interactions among these targets and then input into Cytoscape for further topological analysis. DAVID and KEGG were utilized for GO and pathway enrichment analysis. Then rat model based on LPS stimulated pneumonia was used to verify the possible mechanism of YD in treating pneumonia. RESULTS Sixty-eight active ingredients, 103 potential targets and 8 related pathways, which likely exert a number of effects, were identified. Three networks were constructed using Cytoscape, which were herb-component-network, YD-pneumonia target network, and herb-component-YD target-pneumonia network. YD was verified to treat LPS-induced pneumonia by regulating the inflammatory factor IL-6, which was a predicted target. CONCLUSION Network analysis indicated that YD could alleviate the symptoms and signs of pneumonia through regulating host immune inflammatory response, angiogenesis and vascular permeability, the barrier function of the airway epithelial cells, hormone releasing and cell growth, proliferation, and apoptosis.
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Affiliation(s)
- Jingnan Xu
- Department of Acupuncture and Mini-invasive Oncology, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, 100029, People's Republic of China
| | - Chen Bai
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - Ling Huang
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - Tiegang Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - Yuxiang Wan
- Department of Acupuncture and Mini-invasive Oncology, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, 100029, People's Republic of China
| | - Zian Zheng
- School of Basic Medical Sciences, Guiyang University of Chinese Medicine, Guiyang, Guizhou, 550025, People's Republic of China
| | - Xueyan Ma
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - Fei Gao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - He Yu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China
| | - Xiaohong Gu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China.
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223
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Eckhardt M, Hultquist JF, Kaake RM, Hüttenhain R, Krogan NJ. A systems approach to infectious disease. Nat Rev Genet 2020; 21:339-354. [PMID: 32060427 PMCID: PMC7839161 DOI: 10.1038/s41576-020-0212-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/08/2020] [Indexed: 01/01/2023]
Abstract
Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.
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Affiliation(s)
- Manon Eckhardt
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Judd F Hultquist
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
- Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
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Bray SA, Lucas X, Kumar A, Grüning BA. The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform. J Cheminform 2020; 12:40. [PMID: 33431029 PMCID: PMC7268608 DOI: 10.1186/s13321-020-00442-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 05/16/2020] [Indexed: 01/14/2023] Open
Abstract
Here, we introduce the ChemicalToolbox, a publicly available web server for performing cheminformatics analysis. The ChemicalToolbox provides an intuitive, graphical interface for common tools for downloading, filtering, visualizing and simulating small molecules and proteins. The ChemicalToolbox is based on Galaxy, an open-source web-based platform which enables accessible and reproducible data analysis. There is already an active Galaxy cheminformatics community using and developing tools. Based on their work, we provide four example workflows which illustrate the capabilities of the ChemicalToolbox, covering assembly of a compound library, hole filling, protein-ligand docking, and construction of a quantitative structure-activity relationship (QSAR) model. These workflows may be modified and combined flexibly, together with the many other tools available, to fit the needs of a particular project. The ChemicalToolbox is hosted on the European Galaxy server and may be accessed via https://cheminformatics.usegalaxy.eu.
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Affiliation(s)
- Simon A Bray
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany.
| | - Xavier Lucas
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, Basel, Switzerland
| | - Anup Kumar
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
| | - Björn A Grüning
- Department of Computer Science, University of Freiburg, Georges-Köhler-Allee 106, Freiburg, Germany
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Zhuang Z, Chen Q, Huang C, Wen J, Huang H, Liu Z. A Comprehensive Network Pharmacology-Based Strategy to Investigate Multiple Mechanisms of HeChan Tablet on Lung Cancer. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:7658342. [PMID: 32595734 PMCID: PMC7277035 DOI: 10.1155/2020/7658342] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 05/03/2020] [Accepted: 05/14/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND HeChan tablet (HCT) is a traditional Chinese medicine preparation extensively prescribed to treat lung cancer in China. However, the pharmacological mechanisms of HCT on lung cancer remain to be elucidated. METHODS A comprehensive network pharmacology-based strategy was conducted to explore underlying mechanisms of HCT on lung cancer. Putative targets and compounds of HCT were retrieved from TCMSP and BATMAN-TCM databases; related genes of lung cancer were retrieved from OMIM and DisGeNET databases; known therapeutic target genes of lung cancer were retrieved from TTD and DrugBank databases; PPI networks among target genes were constructed to filter hub genes by STRING. Furthermore, the pathway and GO enrichment analysis of hub genes was performed by clusterProfiler, and the clinical significance of hub genes was identified by The Cancer Genome Atlas. RESULT A total of 206 compounds and 2,433 target genes of HCT were obtained. 5,317 related genes of lung cancer and 77 known therapeutic target genes of lung cancer were identified. 507 unique target genes were identified among HCT-related genes of lung cancer and 34 unique target genes were identified among HCT-known therapeutic target genes of lung cancer. By PPI networks, 11 target genes AKT1, TP53, MAPK8, JUN, EGFR, TNF, INS, IL-6, MYC, VEGFA, and MAPK1 were identified as major hub genes. IL-6, JUN, EGFR, and MYC were shown to associate with the survival of lung cancer patients. Five compounds of HCT, quercetin, luteolin, kaempferol, beta-sitosterol, and baicalein were recognized as key compounds of HCT on lung cancer. The gene enrichment analysis implied that HCT probably benefitted patients with lung cancer by modulating the MAPK and PI3K-Akt pathways. CONCLUSION This study predicted pharmacological and molecular mechanisms of HCT against lung cancer and could pave the way for further experimental research and clinical application of HCT.
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Affiliation(s)
- Zhenjie Zhuang
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qianying Chen
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Cihui Huang
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Junmao Wen
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haifu Huang
- Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Zhanhua Liu
- Department of Oncology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
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226
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Yin Z, Yan X, Wang Q, Deng Z, Tang K, Cao Z, Qiu T. Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling. Front Genet 2020; 11:524. [PMID: 32528533 PMCID: PMC7264416 DOI: 10.3389/fgene.2020.00524] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/29/2020] [Indexed: 12/13/2022] Open
Abstract
Background Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs’ detection in other cancer types. Aim This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics. Materials and Methods The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein–protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study. Results By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs. Conclusions The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.
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Affiliation(s)
- Zuojing Yin
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Xinmiao Yan
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Qiming Wang
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Zeliang Deng
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Kailin Tang
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Zhiwei Cao
- Department of Gastroenterology, Shanghai Tenth People's Hospital, College of Life Science and Technology, Tongji University, Shanghai, China
| | - Tianyi Qiu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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227
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Liu H, Zhang W, Zou B, Wang J, Deng Y, Deng L. DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy. Nucleic Acids Res 2020; 48:D871-D881. [PMID: 31665429 PMCID: PMC7145671 DOI: 10.1093/nar/gkz1007] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 10/14/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023] Open
Abstract
Drug combinations have demonstrated high efficacy and low adverse side effects compared to single drug administration in cancer therapies and thus have drawn intensive attention from researchers and pharmaceutical enterprises. Due to the rapid development of high-throughput screening (HTS), the number of drug combination datasets available has increased tremendously in recent years. Therefore, there is an urgent need for a comprehensive database that is crucial to both experimental and computational screening of synergistic drug combinations. In this paper, we present DrugCombDB, a comprehensive database devoted to the curation of drug combinations from various data sources: (i) HTS assays of drug combinations; (ii) manual curations from the literature; and (iii) FDA Orange Book and external databases. Specifically, DrugCombDB includes 448 555 drug combinations derived from HTS assays, covering 2887 unique drugs and 124 human cancer cell lines. In particular, DrugCombDB has more than 6000 000 quantitative dose responses from which we computed multiple synergy scores to determine the overall synergistic or antagonistic effects of drug combinations. In addition to the combinations extracted from existing databases, we manually curated 457 drug combinations from thousands of PubMed publications. To benefit the further experimental validation and development of computational models, multiple datasets that are ready to train prediction models for classification and regression analysis were constructed and other significant related data were gathered. A website with a user-friendly graphical visualization has been developed for users to access the wealth of data and download prebuilt datasets. Our database is available at http://drugcombdb.denglab.org/.
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Affiliation(s)
- Hui Liu
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Wenhao Zhang
- Lab of Information Management, Changzhou University, Changzhou 213164, China
| | - Bo Zou
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Jinxian Wang
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Yuanyuan Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410075, China.,School of Software, Xinjiang University, Urumqi 830008, China
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228
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Yin Z, Wang Q, Yan X, Zhang L, Tang K, Cao Z, Qiu T. Reveal the Regulation Patterns of Prognosis-Related miRNAs and lncRNAs Across Solid Tumors in the Cancer Genome Atlas. Front Cell Dev Biol 2020; 8:368. [PMID: 32523951 PMCID: PMC7261917 DOI: 10.3389/fcell.2020.00368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/24/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The dysregulation of non-coding RNAs (ncRNAs) such as miRNAs and lncRNAs are associated with the pathogenesis and progression in multiple cancers including solid tumors. Comprehensive investigations of prognosis-related ncRNA markers could promote the development of therapeutic strategies for solid tumors, but rarely reported. METHODS By taking advantage of The Cancer Genome Atlas (TCGA), pan-cancer prognosis analysis (PCPA) models were firstly constructed based on miRNA and lncRNA expression profiles of 8,450 samples in 19 solid tumors. Further, the co-occurrence and exclusivity among ncRNA markers were systematically analyzed for different cancers. RESULTS In identified ncRNA makers, 71% of the miRNA markers were shared in multiple cancers, whereas 96% of the lncRNA markers were cancer-specific. Moreover, to analyze the regulation patterns of prognosis-related ncRNAs at the pan-cancer level, miRNA markers were further annotated into eight carcinogenic pathways. Results represented that approximately 86% of these miRNA markers could regulate the PI3K-Akt signaling pathway, while only 48% for the Notch signaling pathway. Finally, among 126 common genes that participated in eight carcinogenic pathways, BCL2, CSNK2A1, EGFR, PDGFRA, and VEGFA were proposed as potential drug targets for multiple cancers. CONCLUSION The prognosis analysis and regulation characteristics of ncRNAs presented in this study may help to facilitate the discovery of anti-cancer drugs for multiple solid tumors.
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Affiliation(s)
- Zuojing Yin
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Qiming Wang
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Xinmiao Yan
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Lu Zhang
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Kailin Tang
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Zhiwei Cao
- Department of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Tianyi Qiu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
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229
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Revealing the Common Mechanisms of Scutellarin in Angina Pectoris and Ischemic Stroke Treatment via a Network Pharmacology Approach. Chin J Integr Med 2020; 27:62-69. [PMID: 32447519 DOI: 10.1007/s11655-020-2716-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/28/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To investigate the shared mechanisms of scutellarin in angina pectoris (AP) and ischemic stroke (IS) treatment. METHODS A network pharmacology approach was used to detect the potential mechanisms of scutellarin in AP and IS treatment by target prediction, protein-protein interaction (PPI) data collection, network construction, network analysis, and enrichment analysis. Furthermore, molecular docking simulation was employed to analyze the interaction between scutellarin and core targets. RESULTS Two networks were established, including a disease-target network and a PPI network of scutellarin targets against AP and IS. Network analysis showed that 14 targets, namely, AKT1, VEGFA, JUN, ALB, MTOR, ESR1, MAPK8, HSP90AA1, NOS3, SERPINE1, FGA, F2, FOXO3, and STAT1, might be the therapeutic targets of scutellarin in AP and IS. Among them, NOS3 and F2 were recognized as the core targets. Additionally, molecular docking simulation confifirmed that scutellarin exhibited a relatively high potential for binding to the active sites of NOS3 and F2. Furthermore, enrichment analysis indicated that scutellarin might exert a therapeutic role in both AP and IS by regulating several important pathways, such as coagulation cascades, mitogen-activated protein kinase (MAPK) signaling pathway, phosphatidylinositol 3 kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) signaling pathway, Toll-like receptor signaling pathway, hypoxia inducible factor-1 (HIF-1) signaling pathway, forkhead box O (FoxO) signaling pathway, tumor necrosis factor (TNF) signaling pathway, adipocytokine signaling pathway, insulin signaling pathway, insulin resistance, and estrogen signaling pathway. CONCLUSIONS The shared underlying mechanisms of scutellarin on AP and IS treatment might be strongly associated with its vasorelaxant, anticoagulant, anti-inflammatory, and antioxidative effects as well as its effect on improving lipid metabolism.
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230
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Ulaganathan VK. TraPS-VarI: Identifying genetic variants altering phosphotyrosine based signalling motifs. Sci Rep 2020; 10:8453. [PMID: 32439998 PMCID: PMC7242328 DOI: 10.1038/s41598-020-65146-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 04/26/2020] [Indexed: 12/17/2022] Open
Abstract
Patient stratification and individualized therapeutic strategies rely on the established knowledge of genotype-specific molecular and cellular alterations of biological and therapeutic significance. Whilst almost all approved drugs have been developed based on the Reference Sequence protein database (RefSeq), the latest genome sequencing studies establish the substantial prevalence of non-synonymous genetic mutations in the general population, including stop-insertion and frame shift mutations within the coding regions of membrane proteins. While the availability of individual genotypes are becoming increasingly common, the biological and clinical interpretations of mutations among individual genomes is largely lagging behind. Lately, transmembrane proteins of haematopoietic (myeloid and lymphoid) derived immune cells have attracted much attention as important targets for cancer immunotherapies. As such, the signalling properties of haematological transmembrane receptors rely on the membrane-proximal phosphotyrosine based sequence motifs (TBSMs) such as ITAM (immunoreceptor tyrosine-based activation motif), ITIM (immunoreceptor tyrosine-based inhibition motif) and signal transducer and activator of transcription 3 (STAT3)-recruiting YxxQ motifs. However, mutations that alter the coding regions of transmembrane proteins, resulting in either insertion or deletion of crucial signal modulating TBSMs, remains unknown. To conveniently identify individual cell line-specific or patient-specific membrane protein altering mutations, we present the Transmembrane Protein Sequence Variant Identifier (TraPS-VarI). TraPS-VarI is an annotation tool for accurate mapping of the effect of an individual’s mutation in the transmembrane protein sequence, and to identify the prevalence of TBSMs. TraPS-VarI is a biologist and clinician-friendly algorithm with a web interface and an associated database browser (https://www.traps-vari.org/).
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Affiliation(s)
- Vijay Kumar Ulaganathan
- Department of Molecular Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, Martinsried, 82152, Germany. .,Department of Neuroimmunology, Universitätsmedizin Göttingen, Von-Siebold-Str. 3A, Göttingen, 37075, Germany.
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231
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Yin J, Sun W, Li F, Hong J, Li X, Zhou Y, Lu Y, Liu M, Zhang X, Chen N, Jin X, Xue J, Zeng S, Yu L, Zhu F. VARIDT 1.0: variability of drug transporter database. Nucleic Acids Res 2020; 48:D1042-D1050. [PMID: 31495872 PMCID: PMC6943059 DOI: 10.1093/nar/gkz779] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/20/2019] [Accepted: 08/29/2019] [Indexed: 12/11/2022] Open
Abstract
The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes crucial in precision medicine. However, no database is constructed to provide the comprehensive data of all aspects of DT variability. Herein, the Variability of Drug Transporter Database (VARIDT) was introduced to provide such data. First, 177 and 146 DTs were confirmed, for the first time, by the transporting drugs approved and in clinical/preclinical, respectively. Second, for the confirmed DTs, VARIDT comprehensively collected all aspects of their variability (23 947 DNA methylations, 7317 noncoding RNA/histone regulations, 1278 genetic polymorphisms, differential abundance profiles of 257 DTs in 21 781 patients/healthy individuals, expression of 245 DTs in 67 tissues of human/model organism, 1225 exogenous factors altering the activity of 148 DTs), which allowed mutual connection between any aspects. Due to huge amount of accumulated data, VARIDT made it possible to generalize characteristics to reveal disease etiology and optimize clinical treatment, and is freely accessible at: https://db.idrblab.org/varidt/ and http://varidt.idrblab.net/.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Wen Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiaoxu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Ying Zhou
- The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yinjing Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Mengzhi Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xue Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Na Chen
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiuping Jin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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232
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Zeng X, Zhang P, Wang Y, Qin C, Chen S, He W, Tao L, Tan Y, Gao D, Wang B, Chen Z, Chen W, Jiang YY, Chen YZ. CMAUP: a database of collective molecular activities of useful plants. Nucleic Acids Res 2020; 47:D1118-D1127. [PMID: 30357356 PMCID: PMC6324012 DOI: 10.1093/nar/gky965] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/05/2018] [Indexed: 01/03/2023] Open
Abstract
The beneficial effects of functionally useful plants (e.g. medicinal and food plants) arise from the multi-target activities of multiple ingredients of these plants. The knowledge of the collective molecular activities of these plants facilitates mechanistic studies and expanded applications. A number of databases provide information about the effects and targets of various plants and ingredients. More comprehensive information is needed for broader classes of plants and for the landscapes of individual plant’s multiple targets, collective activities and regulated biological pathways, processes and diseases. We therefore developed a new database, Collective Molecular Activities of Useful Plants (CMAUP), to provide the collective landscapes of multiple targets (ChEMBL target classes) and activity levels (in 2D target-ingredient heatmap), and regulated gene ontologies (GO categories), biological pathways (KEGG categories) and diseases (ICD blocks) for 5645 plants (2567 medicinal, 170 food, 1567 edible, 3 agricultural and 119 garden plants) collected from or traditionally used in 153 countries and regions. These landscapes were derived from 47 645 plant ingredients active against 646 targets in 234 KEGG pathways associated with 2473 gene ontologies and 656 diseases. CMAUP (http://bidd2.nus.edu.sg/CMAUP/) is freely accessible and searchable by keywords, plant usage classes, species families, targets, KEGG pathways, gene ontologies, diseases (ICD code) and geographical locations.
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Affiliation(s)
- Xian Zeng
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua University Shenzhen Graduate School, Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, Shenzhen Kivita Innovative Drug Discovery Institute, Guangdong 518055, P. R. China.,Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Peng Zhang
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Yali Wang
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Chu Qin
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Shangying Chen
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Weidong He
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Lin Tao
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore.,Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, School of Medicine, Hangzhou Normal University, Hangzhou 310006, R. P. China
| | - Ying Tan
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua University Shenzhen Graduate School, Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, Shenzhen Kivita Innovative Drug Discovery Institute, Guangdong 518055, P. R. China
| | - Dan Gao
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua University Shenzhen Graduate School, Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, Shenzhen Kivita Innovative Drug Discovery Institute, Guangdong 518055, P. R. China
| | - Bohua Wang
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang 330045, P. R. China.,College of Life and Environmental Sciences, Collaborative Innovation Center for Efficient and Health Production of Fisheries in Hunan Province, Hunan University of Arts and Science, Changde, Hunan 415000, P. R. China
| | - Zhe Chen
- Zhejiang Key Laboratory of Gastro-intestinal Pathophysiology, Zhejiang Hospital of Traditional Chinese Medicine, Zhejiang Chinese Medical University, School of Medicine, Hangzhou Normal University, Hangzhou 310006, R. P. China
| | - Weiping Chen
- Key Lab of Agricultural Products Processing and Quality Control of Nanchang City, Jiangxi Agricultural University, Nanchang 330045, P. R. China
| | - Yu Yang Jiang
- The State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Tsinghua University Shenzhen Graduate School, Shenzhen Technology and Engineering Laboratory for Personalized Cancer Diagnostics and Therapeutics, Shenzhen Kivita Innovative Drug Discovery Institute, Guangdong 518055, P. R. China
| | - Yu Zong Chen
- Bioinformatics and Drug Design group, Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
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233
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Yu Y, Wang Y, Xia Z, Zhang X, Jin K, Yang J, Ren L, Zhou Z, Yu D, Qing T, Zhang C, Jin L, Zheng Y, Guo L, Shi L. PreMedKB: an integrated precision medicine knowledgebase for interpreting relationships between diseases, genes, variants and drugs. Nucleic Acids Res 2020; 47:D1090-D1101. [PMID: 30407536 PMCID: PMC6324052 DOI: 10.1093/nar/gky1042] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/19/2018] [Indexed: 01/01/2023] Open
Abstract
One important aspect of precision medicine aims to deliver the right medicine to the right patient at the right dose at the right time based on the unique ‘omics’ features of each individual patient, thus maximizing drug efficacy and minimizing adverse drug reactions. However, fragmentation and heterogeneity of available data makes it challenging to readily obtain first-hand information regarding some particular diseases, drugs, genes and variants of interest. Therefore, we developed the Precision Medicine Knowledgebase (PreMedKB) by seamlessly integrating the four fundamental components of precision medicine: diseases, genes, variants and drugs. PreMedKB allows for search of comprehensive information within each of the four components, the relationships between any two or more components, and importantly, the interpretation of the clinical meanings of a patient's genetic variants. PreMedKB is an efficient and user-friendly tool to assist researchers, clinicians or patients in interpreting a patient's genetic profile in terms of discovering potential pathogenic variants, recommending therapeutic regimens, designing panels for genetic testing kits, and matching patients for clinical trials. PreMedKB is freely accessible and available at http://www.fudan-pgx.org/premedkb/index.html#/home.
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Affiliation(s)
- Ying Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Yunjin Wang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Zhaojie Xia
- State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | | | | | - Jingcheng Yang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Luyao Ren
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Zheng Zhou
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Dong Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Tao Qing
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Chengdong Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China.,Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Yuanting Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China.,Human Phenome Institute, Fudan University, Shanghai 201203, China.,Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai 200438, China
| | - Li Guo
- State Key Laboratory of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China.,School of Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leming Shi
- State Key Laboratory of Genetic Engineering, School of Life Sciences and Shanghai Cancer Hospital/Cancer Institute, Fudan University, Shanghai 200438, China.,Human Phenome Institute, Fudan University, Shanghai 201203, China.,Fudan-Gospel Joint Research Center for Precision Medicine, Fudan University, Shanghai 200438, China
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234
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Sun C, Yang Z, Su L, Wang L, Zhang Y, Lin H, Wang J. Chemical–protein interaction extraction via Gaussian probability distribution and external biomedical knowledge. Bioinformatics 2020; 36:4323-4330. [DOI: 10.1093/bioinformatics/btaa491] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/22/2020] [Accepted: 05/06/2020] [Indexed: 01/25/2023] Open
Abstract
Abstract
Motivation
The biomedical literature contains a wealth of chemical–protein interactions (CPIs). Automatically extracting CPIs described in biomedical literature is essential for drug discovery, precision medicine, as well as basic biomedical research. Most existing methods focus only on the sentence sequence to identify these CPIs. However, the local structure of sentences and external biomedical knowledge also contain valuable information. Effective use of such information may improve the performance of CPI extraction.
Results
In this article, we propose a novel neural network-based approach to improve CPI extraction. Specifically, the approach first employs BERT to generate high-quality contextual representations of the title sequence, instance sequence and knowledge sequence. Then, the Gaussian probability distribution is introduced to capture the local structure of the instance. Meanwhile, the attention mechanism is applied to fuse the title information and biomedical knowledge, respectively. Finally, the related representations are concatenated and fed into the softmax function to extract CPIs. We evaluate our proposed model on the CHEMPROT corpus. Our proposed model is superior in performance as compared with other state-of-the-art models. The experimental results show that the Gaussian probability distribution and external knowledge are complementary to each other. Integrating them can effectively improve the CPI extraction performance. Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks.
Availability and implementation
Data and code are available at https://github.com/CongSun-dlut/CPI_extraction.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cong Sun
- School of Computer Science and Technology
| | | | - Leilei Su
- School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China
| | - Lei Wang
- Beijing Institute of Health Administration and Medical Information, Beijing 100850, China
| | - Yin Zhang
- Beijing Institute of Health Administration and Medical Information, Beijing 100850, China
| | | | - Jian Wang
- School of Computer Science and Technology
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235
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Ab Ghani NS, Ramlan EI, Firdaus-Raih M. Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Res 2020; 47:W350-W356. [PMID: 31106379 PMCID: PMC6602481 DOI: 10.1093/nar/gkz391] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 04/24/2019] [Accepted: 05/02/2019] [Indexed: 11/29/2022] Open
Abstract
A common drug repositioning strategy is the re-application of an existing drug to address alternative targets. A crucial aspect to enable such repurposing is that the drug's binding site on the original target is similar to that on the alternative target. Based on the assumption that proteins with similar binding sites may bind to similar drugs, the 3D substructure similarity data can be used to identify similar sites in other proteins that are not known targets. The Drug ReposER (DRug REPOSitioning Exploration Resource) web server is designed to identify potential targets for drug repurposing based on sub-structural similarity to the binding interfaces of known drug binding sites. The application has pre-computed amino acid arrangements from protein structures in the Protein Data Bank that are similar to the 3D arrangements of known drug binding sites thus allowing users to explore them as alternative targets. Users can annotate new structures for sites that are similarly arranged to the residues found in known drug binding interfaces. The search results are presented as mappings of matched sidechain superpositions. The results of the searches can be visualized using an integrated NGL viewer. The Drug ReposER server has no access restrictions and is available at http://mfrlab.org/drugreposer/.
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Affiliation(s)
- Nur Syatila Ab Ghani
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
| | - Effirul Ikhwan Ramlan
- School of Computing, Engineering and Intelligent Systems, Ulster University, Northlands Road, Magee Campus, Londonderry BT48 7JL, UK.,Malaysia Genome Institute, National Institutes of Biotechnology Malaysia, Jalan Bangi, 43000 Kajang, Selangor, Malaysia
| | - Mohd Firdaus-Raih
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia.,Institute of Systems Biology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
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236
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Agoni C, Olotu FA, Ramharack P, Soliman ME. Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say? J Mol Model 2020; 26:120. [PMID: 32382800 DOI: 10.1007/s00894-020-04385-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/22/2020] [Indexed: 11/29/2022]
Abstract
The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.
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Affiliation(s)
- Clement Agoni
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Fisayo A Olotu
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Pritika Ramharack
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa
| | - Mahmoud E Soliman
- Molecular Bio-Computation & Drug Design Lab, School of Health Sciences, University of KwaZulu-Natal, Westville, Durban, 4000, South Africa.
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237
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Comprehensive germline genomic profiles of children, adolescents and young adults with solid tumors. Nat Commun 2020; 11:2206. [PMID: 32371905 PMCID: PMC7200683 DOI: 10.1038/s41467-020-16067-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/08/2020] [Indexed: 02/07/2023] Open
Abstract
Compared to adult carcinomas, there is a paucity of targeted treatments for solid tumors in children, adolescents, and young adults (C-AYA). The impact of germline genomic signatures has implications for heritability, but its impact on targeted therapies has not been fully appreciated. Performing variant-prioritization analysis on germline DNA of 1,507 C-AYA patients with solid tumors, we show 12% of these patients carrying germline pathogenic and/or likely pathogenic variants (P/LP) in known cancer-predisposing genes (KCPG). An additional 61% have germline pathogenic variants in non-KCPG genes, including PRKN, SMARCAL1, SMAD7, which we refer to as candidate genes. Despite germline variants in a broad gene spectrum, pathway analysis leads to top networks centering around p53. Our drug-target analysis shows 1/3 of patients with germline P/LP variants have at least one druggable alteration, while more than half of them are from our candidate gene group, which would otherwise go unidentified in routine clinical care. Targeted therapies for solid tumors in children, adolescents, and young adults (C-AYA) lag behind that of adult carcinomas. Here, the authors study the germline genomic signatures of 1,507 C-AYA patients with solid tumors and find pathogenic/likely pathogenic germline variants in diverse genes of which 1/3 of these alterations are druggable.
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238
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Ceddia G, Pinoli P, Ceri S, Masseroli M. Matrix Factorization-based Technique for Drug Repurposing Predictions. IEEE J Biomed Health Inform 2020; 24:3162-3172. [PMID: 32365039 DOI: 10.1109/jbhi.2020.2991763] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for already approved drugs that leverage computational methods are of crucial relevance. We previously demonstrated the efficacy of the Non-negative Matrix Tri-Factorization, a method that allows exploiting both data integration and machine learning, to infer novel indications for approved drugs. In this work, we present an innovative enhancement of the NMTF method that consists of a shortest-path evaluation of drug-protein pairs using the protein-to-protein interaction network. This approach allows inferring novel protein targets that were never considered as drug targets before, increasing the information fed to the NMTF method. Indeed, this novel advance enables the investigation of drug-centric predictions, simultaneously identifying therapeutic classes, protein targets and diseases associated with a particular drug. To test our methodology, we applied the NMTF and shortest-path enhancement methods to an outdated collection of data and compared the predictions against the most updated version, obtaining very good performance, with an Average Precision Score of 0.82. The data enhancement strategy allowed increasing the number of putative protein targets from 3,691 to 15,295, while the predictive performance of the method is slightly increased. Finally, we also validated our top-scored predictions according to the literature, finding relevant confirmation of predicted interactions between drugs and protein targets, as well as of predicted annotations between drugs and both therapeutic classes and diseases.
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239
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An Analysis of the Anti-Neuropathic Effects of Qi She Pill Based on Network Pharmacology. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2020; 2020:7193832. [PMID: 32454869 PMCID: PMC7222608 DOI: 10.1155/2020/7193832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/16/2020] [Accepted: 01/30/2020] [Indexed: 12/12/2022]
Abstract
Background Qi She Pill (QSP) is a traditional prescription for the treatment of neuropathic pain (NP) that is widely used in China. However, no network pharmacology studies of QSP in the treatment of NP have been conducted to date. Objective To verify the potential pharmacological effects of QSP on NP, its components were analyzed via target docking and network analysis, and network pharmacology methods were used to study the interactions of its components. Materials and Methods Information on pharmaceutically active compounds in QSP and gene information related to NP were obtained from public databases, and a compound-target network and protein-protein interaction network were constructed to study the mechanism of action of QSP in the treatment of NP. The mechanism of action of QSP in the treatment of NP was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment, and the drug-like component-target-pathway network was constructed. Results The compound-target network contained 60 compounds and 444 corresponding targets. The key active compounds included quercetin and beta-sitosterol. Key targets included PTGS2 and PTGS1. The protein-protein interaction network of the active ingredients of QSP in the treatment of NP featured 48 proteins, including DRD2, CHRM, β2-adrenergic receptor, HTR2A, and calcitonin gene-related peptide. In total, 53 GO entries, including 35 biological process items, 7 molecular function items, and 11 cell related items, were identified. In addition, eight relevant (KEGG) pathways were identified, including calcium, neuroactive ligand-receptor interaction, and cAMP signaling pathways. Conclusion Network pharmacology can help clarify the role and mechanism of QSP in the treatment of NP and provide a foundation for further research.
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240
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Zhou H, Cao H, Matyunina L, Shelby M, Cassels L, McDonald JF, Skolnick J. MEDICASCY: A Machine Learning Approach for Predicting Small-Molecule Drug Side Effects, Indications, Efficacy, and Modes of Action. Mol Pharm 2020; 17:1558-1574. [PMID: 32237745 PMCID: PMC7319183 DOI: 10.1021/acs.molpharmaceut.9b01248] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
To improve the drug discovery yield, a method which is implemented at the beginning of drug discovery that accurately predicts drug side effects, indications, efficacy, and mode of action based solely on the input of the drug's chemical structure is needed. In contrast, extant predictive methods do not comprehensively address these aspects of drug discovery and rely on features derived from extensive, often unavailable experimental information for novel molecules. To address these issues, we developed MEDICASCY, a multilabel-based boosted random forest machine learning method that only requires the small molecule's chemical structure for the drug side effect, indication, efficacy, and probable mode of action target predictions; however, it has comparable or even significantly better performance than existing approaches requiring far more information. In retrospective benchmarking on high confidence predictions, MEDICASCY shows about 78% precision and recall for predicting at least one severe side effect and 72% precision drug efficacy. Experimental validation of MEDICASCY's efficacy predictions on novel molecules shows close to 80% precision for the inhibition of growth in ovarian, breast, and prostate cancer cell lines. Thus, MEDICASCY should improve the success rate for new drug approval. A web service for academic users is available at http://pwp.gatech.edu/cssb/MEDICASCY.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
| | - Hongnan Cao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
| | - Lilya Matyunina
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Madelyn Shelby
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Lauren Cassels
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - John F. McDonald
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, 30332-0230, USA
| | - Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, N.W., Atlanta, GA 30332
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241
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SSizer: Determining the Sample Sufficiency for Comparative Biological Study. J Mol Biol 2020; 432:3411-3421. [DOI: 10.1016/j.jmb.2020.01.027] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 12/26/2019] [Accepted: 01/18/2020] [Indexed: 01/25/2023]
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242
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Wang D, Lu C, Yu J, Zhang M, Zhu W, Gu J. Chinese Medicine for Psoriasis Vulgaris Based on Syndrome Pattern: A Network Pharmacological Study. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2020; 2020:5239854. [PMID: 32419809 PMCID: PMC7204377 DOI: 10.1155/2020/5239854] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/13/2020] [Accepted: 01/23/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND The long-term use of conventional therapy for psoriasis vulgaris remains a challenge due to limited or no patient response and severe side effects. Complementary and alternative treatments such as traditional Chinese medicine (TCM) are widely used in East Asia. TCM treatment is based on individual syndrome types. Three TCM formulae, Compound Qingdai Pills (F1), Yujin Yinxie Tablets (F2), and Xiaoyin Tablets (F3), are used for blood heat, blood stasis, and blood dryness type of psoriasis vulgaris, respectively. OBJECTIVES To explore the mechanism of three TCM formulae for three syndrome types of psoriasis vulgaris. METHODS The compounds of the three TCM formulae were retrieved from the Psoriasis Database of Traditional Chinese Medicine (PDTCM). Their molecular properties of absorption, distribution, metabolism, excretion and toxicity (ADME/T), and drug-likeness were compared by analyzing the distribution of compounds in the chemical space. The cellular targets of the compounds were predicted by molecular docking. By constructing the compound-target network and analyzing network centrality, key targets and compounds for each formula were screened. Three syndrome types of psoriasis vulgaris related pathways and biological processes (BPs) were enriched by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8. RESULTS The compounds of the three formulae exhibited structural diversity, good drug-like properties, and ADME/T properties. A total of 72, 97 and 85 targets were found to have interactions with compounds of F1, F2, and F3, respectively. The three formulae were all related to 53 targets, 8 pathways, 9 biological processes, and 10 molecular functions (MFs). In addition, each formula had unique targets and regulated different pathways and BPs. CONCLUSION The three TCM formulae exhibited common mechanisms to some extent. The differences at molecular and systems levels may contribute to their unique applications in individualized treatment.
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Affiliation(s)
- Dongmei Wang
- Dermatology Hospital of Southern Medical University, Guangzhou 510091, China
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Chuanjian Lu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jingjie Yu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Miaomiao Zhang
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Wei Zhu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
| | - Jiangyong Gu
- Guangdong Provincial Academy of Chinese Medical Sciences, Guangzhou 510006, China
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
- Department of Biochemistry, School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou 510006, China
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243
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Liu F, Peng L, Tian G, Yang J, Chen H, Hu Q, Liu X, Zhou L. Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk. Front Bioeng Biotechnol 2020; 8:131. [PMID: 32258003 PMCID: PMC7090022 DOI: 10.3389/fbioe.2020.00131] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/10/2020] [Indexed: 12/05/2022] Open
Abstract
Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.
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Affiliation(s)
- Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing, China
| | | | - Hui Chen
- College of Chemical Engineering, Xiangtan University, Xiangtan, China
| | - Qi Hu
- Xiangya Second Hospital, Central South University, Changsha, Hunan, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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244
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Wang P, Zhang X, Sun N, Zhao Z, He J. Comprehensive Analysis of the Tumor Microenvironment in Cutaneous Melanoma associated with Immune Infiltration. J Cancer 2020; 11:3858-3870. [PMID: 32328190 PMCID: PMC7171484 DOI: 10.7150/jca.44413] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/27/2020] [Indexed: 12/25/2022] Open
Abstract
Accumulating evidence suggests that the malignant phenotypes of cancers are determined not only by the intrinsic properties of cancer cells but also by components in the tumor microenvironment (TME). In this study, we comprehensively characterized the TME of cutaneous melanoma (CM). As a result, tumor stage, tissue site, ulceration, thickness as well as patient age, sex were associated with immune infiltration. Patients of higher immune infiltration exhibited better survival outcomes, and antitumor effector cells, such as CD8 T cells and M1 macrophages, were found in significantly higher numbers in those tissues. Differential expression of mRNAs and long non-coding RNAs (lncRNAs) was analyzed and utilized to construct an immune-related competing endogenous RNA network, in which a lncRNA-associated subnetwork that could positively regulate the expression of IFN-γ was highlighted. Functional analysis confirmed that this network was remarkably enriched in functional terms related to both immune response and tumor-intrinsic pathways. Finally, a total of 109 high-confidence prognostic genes were identified, and a gene module that contained several key immune checkpoint molecules or modulators (PD-1, PD-L1, PD-L2, and LCK) was screened, which confers survival benefit for CM patients as supported by both overall and relapse-free survival rates from different datasets.
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Affiliation(s)
- Pan Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Xinyu Zhang
- Department of body contouring and liposuction center, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100144, China
| | - Nan Sun
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zhihong Zhao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
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245
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Zhang S, Zhou Y, Wang Y, Wang Z, Xiao Q, Zhang Y, Lou Y, Qiu Y, Zhu F. The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years. Brief Bioinform 2020; 22:1860-1883. [PMID: 32249290 DOI: 10.1093/bib/bbaa023] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/09/2020] [Accepted: 02/12/2020] [Indexed: 02/07/2023] Open
Abstract
Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.
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Affiliation(s)
- Song Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China.,College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Yanan Wang
- School of Life Sciences in Nanchang University, China
| | - Zhengwen Wang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Qitao Xiao
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Yan Lou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Feng Zhu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China.,College of Pharmaceutical Sciences in Zhejiang University, China
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246
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Gu L, Lu J, Li Q, Wu N, Zhang L, Li H, Xing W, Zhang X. A network-based analysis of key pharmacological pathways of Andrographis paniculata acting on Alzheimer's disease and experimental validation. JOURNAL OF ETHNOPHARMACOLOGY 2020; 251:112488. [PMID: 31866509 DOI: 10.1016/j.jep.2019.112488] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 12/13/2019] [Accepted: 12/15/2019] [Indexed: 05/26/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Andrographis paniculata (AP) is a native plant with anti-inflammatory and antioxidant properties and used as an official herbal medicine. Recently more and more researches have indicated that AP shows pharmacological effects on Alzheimer's disease (AD) but its mechanism is unclear. AIMS OF THE STUDY Network pharmacology approach combined with experimental validation was developed to reveal the underlying molecular mechanisms of AP in treating AD. MATERIALS AND METHODS The compounds of AP from TCM database, the AD-related targets from disease database and the targets corresponding to compounds from swissTargetPrediction were collected. Then DAVID database was used for annotation and enrichment pathways, meanwhile the compound-target, protein-protein interaction from String database and compound-target-pathway network was constructed, molecular modeling was performed using Sybyl-x. Okadaic acid (OKA)-induced cytotoxicity model in PC12 cells was established to verify the mechanism of AP and the key proteins were detected by western blotting. RESULTS 28 AP components were identified after ADME filter analysis and 52 targets were gained via mapping predicted targets into AD-related proteins. In addition, after multiple network analysis, the 22 hub target genes were enriched onto pathways involved in AD, such as neuroactive ligand-receptor interaction, serotonergic synapse, Alzheimer's disease, PI3K-Akt and NF-kB signaling pathway. Interestingly, molecular docking simulation revealed that the targets including PTGS2, BACE1, GSK3B and IKBKB had good ability to combine with AP components. Experimental validation in an in vitro system proved that AP treatment obviously increased in levels inactive of p-GSK3β (P < 0.05) and decreased in levels of BACE (P < 0.05), PTGS2 (namely COX2, P < 0.05) and NF-kB protein (P < 0.05) compare with OKA treated group. CONCLUSION Our data provided convincing evidence that the neuroprotective effects of AP might be partially related to their regulation of the APP-BACE1-GSK3B signal axis and inflammation, which should be the focus of study in this field in the future.
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Affiliation(s)
- Lili Gu
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Jiaqi Lu
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Qin Li
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Ningzi Wu
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Lingxi Zhang
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Hongxing Li
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China
| | - Wenmin Xing
- Zhejiang Provincial Key Lab of Geriatrics, Department of Geriatrics, Zhejiang Hospital, Hangzhou, 310013, Zhejiang, PR China
| | - Xinyue Zhang
- Key Laboratory of Neuropsychiatric Drug Research of Zhejiang Province, Institute of Materia Medica, Zhejiang Academy of Medical Sciences, Hangzhou, 310013, Zhejiang, PR China.
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247
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Sama IE, Woolley RJ, Nauta JF, Romaine SPR, Tromp J, Ter Maaten JM, van der Meer P, Lam CSP, Samani NJ, Ng LL, Metra M, Dickstein K, Anker SD, Zannad F, Lang CC, Cleland JGF, van Veldhuisen DJ, Hillege HL, Voors AA. A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure. Eur J Heart Fail 2020; 22:821-833. [PMID: 32243695 PMCID: PMC7319432 DOI: 10.1002/ejhf.1811] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/11/2019] [Accepted: 12/11/2019] [Indexed: 12/12/2022] Open
Abstract
Aims Heart failure (HF) is frequently caused by an ischaemic event (e.g. myocardial infarction) but might also be caused by a primary disease of the myocardium (cardiomyopathy). In order to identify targeted therapies specific for either ischaemic or non‐ischaemic HF, it is important to better understand differences in underlying molecular mechanisms. Methods and results We performed a biological physical protein–protein interaction network analysis to identify pathophysiological pathways distinguishing ischaemic from non‐ischaemic HF. First, differentially expressed plasma protein biomarkers were identified in 1160 patients enrolled in the BIOSTAT‐CHF study, 715 of whom had ischaemic HF and 445 had non‐ischaemic HF. Second, we constructed an enriched physical protein–protein interaction network, followed by a pathway over‐representation analysis. Finally, we identified key network proteins. Data were validated in an independent HF cohort comprised of 765 ischaemic and 100 non‐ischaemic HF patients. We found 21/92 proteins to be up‐regulated and 2/92 down‐regulated in ischaemic relative to non‐ischaemic HF patients. An enriched network of 18 proteins that were specific for ischaemic heart disease yielded six pathways, which are related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. We identified five key network proteins: acid phosphatase 5, epidermal growth factor receptor, insulin‐like growth factor binding protein‐1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1. Similar results were observed in the independent validation cohort. Conclusions Pathophysiological pathways distinguishing patients with ischaemic HF from those with non‐ischaemic HF were related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. The five key pathway proteins identified are potential treatment targets specifically for patients with ischaemic
HF.
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Affiliation(s)
- Iziah E Sama
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rebecca J Woolley
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan F Nauta
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Jasper Tromp
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Cardiology, National Heart Centre Singapore, Singapore.,Singapore Duke-NUS Graduate Medical School, Singapore
| | - Jozine M Ter Maaten
- Robertson Centre for Biostatistics & Clinical Trials Unit, University of Glasgow and Clinical Cardiology, National Heart & Lung Institute, Imperial College London, London, UK
| | - Peter van der Meer
- Robertson Centre for Biostatistics & Clinical Trials Unit, University of Glasgow and Clinical Cardiology, National Heart & Lung Institute, Imperial College London, London, UK
| | - Carolyn S P Lam
- Singapore Duke-NUS Graduate Medical School, Singapore.,Robertson Centre for Biostatistics & Clinical Trials Unit, University of Glasgow and Clinical Cardiology, National Heart & Lung Institute, Imperial College London, London, UK
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Leong L Ng
- Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, and NIHR Leicester Biomedical Research Centre, Leicester, UK
| | - Marco Metra
- Institute of Cardiology, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy
| | - Kenneth Dickstein
- University of Bergen, Bergen, Norway.,Stavanger University Hospital, Stavanger, Norway
| | - Stefan D Anker
- Department of Cardiology (CVK) and Berlin-Brandenburg Center for Regenerative Therapies (BCRT); German Centre for Cardiovascular Research (DZHK) partner site Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Faiez Zannad
- CHU de Nancy, Inserm CIC 1433, Université de Lorrain, CHRU de Nancy, F-CRIN INI-CRCT, Nancy, France
| | - Chim C Lang
- Division of Molecular and Clinical Medicine, School of Medicine, University of Dundee Ninewells Hospital and Medical School, Dundee, UK
| | - John G F Cleland
- Robertson Centre for Biostatistics & Clinical Trials Unit, University of Glasgow and Clinical Cardiology, National Heart & Lung Institute, Imperial College London, London, UK
| | - Dirk J van Veldhuisen
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Hans L Hillege
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Adriaan A Voors
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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248
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Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides. Front Bioeng Biotechnol 2020; 8:245. [PMID: 32296690 PMCID: PMC7137786 DOI: 10.3389/fbioe.2020.00245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yang Hu
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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249
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Dezső Z, Ceccarelli M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinformatics 2020; 21:104. [PMID: 32171238 PMCID: PMC7071582 DOI: 10.1186/s12859-020-3442-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Accepted: 03/04/2020] [Indexed: 01/12/2023] Open
Abstract
Background The selection and prioritization of drug targets is a central problem in drug discovery. Computational approaches can leverage the growing number of large-scale human genomics and proteomics data to make in-silico target identification, reducing the cost and the time needed. Results We developed a machine learning approach to score proteins to generate a druggability score of novel targets. In our model we incorporated 70 protein features which included properties derived from the sequence, features characterizing protein functions as well as network properties derived from the protein-protein interaction network. The advantage of this approach is that it is unbiased and even less studied proteins with limited information about their function can score well as most of the features are independent of the accumulated literature. We build models on a training set which consist of targets with approved drugs and a negative set of non-drug targets. The machine learning techniques help to identify the most important combination of features differentiating validated targets from non-targets. We validated our predictions on an independent set of clinical trial drug targets, achieving a high accuracy characterized by an Area Under the Curve (AUC) of 0.89. Our most predictive features included biological function of proteins, network centrality measures, protein essentiality, tissue specificity, localization and solvent accessibility. Our predictions, based on a small set of 102 validated oncology targets, recovered the majority of known drug targets and identifies a novel set of proteins as drug target candidates. Conclusions We developed a machine learning approach to prioritize proteins according to their similarity to approved drug targets. We have shown that the method proposed is highly predictive on a validation dataset consisting of 277 targets of clinical trial drug confirming that our computational approach is an efficient and cost-effective tool for drug target discovery and prioritization. Our predictions were based on oncology targets and cancer relevant biological functions, resulting in significantly higher scores for targets of oncology clinical trial drugs compared to the scores of targets of trial drugs for other indications. Our approach can be used to make indication specific drug-target prediction by combining generic druggability features with indication specific biological functions.
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Affiliation(s)
- Zoltán Dezső
- Computational Biology-Genomic Research Center, ABBVIE, Redwood City, CA, USA.
| | - Michele Ceccarelli
- Computational Biology-Genomic Research Center, ABBVIE, Redwood City, CA, USA. .,Department of Electrical Engineering and Information Technology (DIETI), University of Naples "Federico II", 80128, Naples, Italy. .,Istituto di Ricerche Genetiche "G. Salvatore", Biogem s.c.ar.l, 83031, Ariano Irpino, Italy.
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250
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Dai Y, Hu R, Pei G, Zhang H, Zhao Z, Jia P. Diverse types of genomic evidence converge on alcohol use disorder risk genes. J Med Genet 2020; 57:733-743. [PMID: 32170004 DOI: 10.1136/jmedgenet-2019-106490] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 01/08/2020] [Accepted: 02/10/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND Alcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%-60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations. METHODS To explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes). RESULTS We identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them. CONCLUSION We characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.
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Affiliation(s)
- Yulin Dai
- School of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Ruifeng Hu
- School of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Guangsheng Pei
- School of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Huiping Zhang
- Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA.,Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, Massachusetts, USA
| | - Zhongming Zhao
- School of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Peilin Jia
- School of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
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