scholarly journals Drug target gene-based analyses of drug repositionability in rare and intractable diseases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryuichi Sakate ◽  
Tomonori Kimura
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Target Gene ◽  
Target Genes ◽  
Drug Information ◽  
Drug Repositioning ◽  
Clinical Trial Data ◽  
Insufficient Information ◽  
Disease Pair ◽  
Low Prevalence

AbstractDrug development for rare and intractable diseases has been challenging for decades due to the low prevalence and insufficient information on these diseases. Drug repositioning is increasingly being used as a promising option in drug development. We aimed to analyze the trend of drug repositioning and inter-disease drug repositionability among rare and intractable diseases. We created a list of rare and intractable diseases based on the designated diseases in Japan. Drug information extracted from clinical trial data were integrated with information of drug target genes, which represent the mechanism of drug action. We obtained 753 drugs and 551 drug target genes from 8307 clinical trials for 189 diseases or disease groups. Trend analysis of drug sharing between a disease pair revealed that 1676 drug repositioning events occurred in 4401 disease pairs. A score, Rgene, was invented to investigate the proportion of drug target genes shared between a disease pair. Annual changes of Rgene corresponded to the trend of drug repositioning and predicted drug repositioning events occurring within a year or two. Drug target gene-based analyses well visualized the drug repositioning landscape. This approach facilitates drug development for rare and intractable diseases.

scholarly journals Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shingo Tsuji ◽  
Takeshi Hase ◽  
Ayako Yachie-Kinoshita ◽  
Taiko Nishino ◽  
Samik Ghosh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Drug Target ◽  
Target Genes ◽  
Drug Repositioning ◽  
Therapeutic Targets ◽  
Machine Learning Techniques ◽  
Computational Framework ◽  
Novel Therapeutic

Abstract Background Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets for FDA-approved drugs. As a result, it is inevitable to develop powerful computational tools that can identify potential novel therapeutic targets. Fortunately, the human protein-protein interaction network (PIN) could be a useful resource to achieve this objective. Methods In this study, we developed a deep learning-based computational framework that extracts low-dimensional representations of high-dimensional PIN data. Our computational framework uses latent features and state-of-the-art machine learning techniques to infer potential drug target genes. Results We applied our computational framework to prioritize novel putative target genes for Alzheimer’s disease and successfully identified key genes that may serve as novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we could infer repositionable candidate-compounds for the disease (e.g., tamoxifen, bosutinib, and dasatinib). Conclusions Our deep learning-based computational framework could be a powerful tool to efficiently prioritize new therapeutic targets and enhance the drug repositioning strategy.

scholarly journals Identification of key genes and associated pathways in neuroendocrine tumors through bioinformatics analysis and predictions of small drug molecules

2020 ◽  
Author(s):  
Praveenkumar Devarbhavi ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Drug Target ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Regulatory Elements ◽  
Future Research ◽  
High Morbidity ◽  
Hub Genes ◽  
Go Enrichment ◽  
Significant Difference

AbstractNeuroendocrine tumor (NET) is one of malignant cancer and is identified with high morbidity and mortality rates around the world. With indigent clinical outcomes, potential biomarkers for diagnosis, prognosis and drug target are crucial to explore. The aim of this study is to examine the gene expression module of NET and to identify potential diagnostic and prognostic biomarkers as well as to find out new drug target. The differentially expressed genes (DEGs) identified from GSE65286 dataset was used for pathway enrichment analyses and gene ontology (GO) enrichment analyses and protein - protein interaction (PPI) analysis and module analysis. Moreover, miRNAs and transcription factors (TFs) that regulated the up and down regulated genes were predicted. Furthermore, validation of hub genes was performed. Finally, molecular docking studies were performed. DEGs were identified, including 453 down regulated and 459 up regulated genes. Pathway and GO enrichment analysis revealed that DEGs were enriched in sucrose degradation, creatine biosynthesis, anion transport and modulation of chemical synaptic transmission. Important hub genes and target genes were identified through PPI network, modules, target gene - miRNA network and target gene - TF network. Finally, survival analyses, receiver operating characteristic (ROC) curve and RT-PCR validated the significant difference of ATP1A1, LGALS3, LDHA, SYK, VDR, OBSL1, KRT40, WWOX, NINL and PPP2R2B between metastatic NET and normal controls. In conclusion, the DEGs and hub genes with their regulatory elements identified in this study will help us understand the molecular mechanisms underlying NET and provide candidate targets for future research.

scholarly journals A landscape for drug-target interactions based on network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247018
Author(s):  
Edgardo Galan-Vasquez ◽  
Ernesto Perez-Rueda
Keyword(s):  
Drug Target ◽  
Target Genes ◽  
Metabolic Diseases ◽  
Drug Repositioning ◽  
Topological Analysis ◽  
Interaction Network ◽  
Clustering Coefficient ◽  
Connected Components ◽  
Target Interaction ◽  
Target Network

In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.

scholarly journals Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs

2019 ◽  
Author(s):  
Daniele Parisi ◽  
Melissa F. Adasme ◽  
Anastasia Sveshnikova ◽  
Yves Moreau ◽  
Michael Schroeder
Keyword(s):  
Drug Development ◽  
Drug Target ◽  
Drug Repositioning ◽  
Close Relation ◽  
Full Potential ◽  
Development Cost ◽  
Computational Power ◽  
Future Developments ◽  
Current Drug ◽  
Repurposed Drugs

ABSTRACTDrug repositioning aims to find new indications for existing drugs, in order to reduce drug development cost and time. Currently, numerous successful stories of drug repositioning have been reported and many drugs are already available on the market. Although many of those cases are products of serendipitous findings, repositioning opportunities can be uncovered systematically by following either a disease-centric approach, as a result of a close relation between an old and new indication, a target-centric one, which links a known target and its established drug to a new indication, or a drug-centric approach, which connects a known drug to a new target and its associated indication. The three approaches differ in their complexity, potential, and limits, and most important the necessary starting information and computational power. Which one is predominant in current drug repositioning and what does this imply for future developments? To address these questions, we systematically evaluated over 100 drugs, 200 targets structures and over 300 indications from the Drug Repositioning Database. Each of the analysed cases has been classified based on one of the three repositioning approaches, showing that the majority, more than 60%, falls within the disease-centric definition, around 30% within the target-centric, and only less than 10% within the drug-centric. We therefore concluded that so far repositioning has mainly been disease and target repositioning and not, as drug repositioning, as expected by definition. We discuss the reasons and suggest directions to exploit the full potential of techniques useful for drug-centric in order to sustain future rationale repositioning pipelines.

scholarly journals NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

2018 ◽  
Author(s):  
Fangping Wan ◽  
Lixiang Hong ◽  
An Xiao ◽  
Tao Jiang ◽  
Jianyang Zeng
Keyword(s):  
Drug Development ◽  
Biological Networks ◽  
Heterogeneous Network ◽  
Drug Target ◽  
Large Scale ◽  
Drug Repositioning ◽  
Supplementary Information ◽  
Functional Roles ◽  
Aggregation Techniques ◽  
Wide Range

AbstractMotivationAccurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks.ResultsInspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks (CNNs) to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g., compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.Availability and implementationThe source code and data used in NeoDTI are available at: https://github.com/FangpingWan/[email protected] informationSupplementary data are available at Bioinformatics online.

Artificial microRNA (amiRNA) induced gene silencing in alfalfa (Medicago sativa)

Botany ◽  
2013 ◽  
Vol 91 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Julian C. Verdonk ◽  
Michael L. Sullivan
Keyword(s):  
Gene Silencing ◽  
Medicago Sativa ◽  
Target Gene ◽  
Target Genes ◽  
Mirna Precursor ◽  
Crop Species ◽  
Forage Crop ◽  
Ribonucleic Acids ◽  
Endogenous Gene ◽  
Medicago Sativa L

Gene silencing is a powerful technique that allows the study of the function of specific genes by selectively reducing their transcription. Several different approaches can be used, however they all have in common the artificial generation of single stranded small ribonucleic acids (RNAs) that are utilized by the endogenous gene silencing machinery of the organism. Artificial microRNAs (amiRNA) can be used to very specifically target genes for silencing because only a short sequence of 21 nucleotides of the gene of interest is used. Gene silencing via amiRNA has been developed for Arabidopsis thaliana (L.) Heynh. and rice using endogenous microRNA (miRNA) precursors and has been shown to also work effectively in other dicot species using the arabidopsis miRNA precursor. Here, we demonstrate that the arabidopsis miR319 precursor can be used to silence genes in the important forage crop species alfalfa (Medicago sativa L.) by silencing the expression of a transgenic beta-glucuronidase (GUSPlus) target gene.

scholarly journals CHOP-Dependent Stress-Inducible Expression of a Novel Form of Carbonic Anhydrase VI

1999 ◽  
Vol 19 (1) ◽  
pp. 495-504 ◽  
Author(s):  
John Sok ◽  
Xiao-Zhong Wang ◽  
Nikoleta Batchvarova ◽  
Masahiko Kuroda ◽  
Heather Harding ◽  
...  
Keyword(s):  
Carbonic Anhydrase ◽  
Target Gene ◽  
Target Genes ◽  
Direct Evidence ◽  
Nuclear Protein ◽  
Intracellular Form ◽  
Carbonic Anhydrase Vi ◽  
Responsive Element

ABSTRACT CHOP (also called GADD153) is a stress-inducible nuclear protein that dimerizes with members of the C/EBP family of transcription factors and was initially identified as an inhibitor of C/EBP binding to classic C/EBP target genes. Subsequent experiments suggested a role for CHOP-C/EBP heterodimers in positively regulating gene expression; however, direct evidence that this is the case has so far not been uncovered. Here we describe the identification of a positively regulated direct CHOP-C/EBP target gene, that encoding murine carbonic anhydrase VI (CA-VI). The stress-inducible form of the gene is expressed from an internal promoter and encodes a novel intracellular form of what is normally a secreted protein. Stress-induced expression of CA-VI is both CHOP and C/EBPβ dependent in that it does not occur in cells deficient in either gene. A CHOP-responsive element was mapped to the inducibleCA-VI promoter, and in vitro footprinting revealed binding of CHOP-C/EBP heterodimers to that site. Rescue of CA-VIexpression in c/ebpβ−/− cells by exogenous C/EBPβ and a shorter, normally inhibitory isoform of the protein known as LIP suggests that the role of the C/EBP partner is limited to targeting the CHOP-containing heterodimer to the response element and points to a preeminent role for CHOP in CA-VI induction during stress.

scholarly journals Mutant Alleles of the Drosophila trithorax Gene Produce Common and Unusual Homeotic and Other Developmental Phenotypes

Genetics ◽  
1999 ◽  
Vol 152 (1) ◽  
pp. 319-344
Author(s):  
Thomas R Breen
Keyword(s):  
Target Gene ◽  
Target Genes ◽  
Protein Isoforms ◽  
Homeotic Genes ◽  
Set Domain ◽  
Human Homologue ◽  
Terminal Set ◽  
Hypomorphic Alleles ◽  
Different Levels ◽  
Target Gene Transcription

Abstract trithorax (trx) encodes chromosome-binding proteins required throughout embryogenesis and imaginal development for tissue- and cell-specific levels of transcription of many genes including homeotic genes of the ANT-C and BX-C. trx encodes two protein isoforms that contain conserved motifs including a C-terminal SET domain, central PHD fingers, an N-terminal DNA-binding homology, and two short motifs also found in the TRX human homologue, ALL1. As a first step to characterizing specific developmental functions of TRX, I examined phenotypes of 420 combinations of 21 trx alleles. Among these are 8 hypomorphic alleles that are sufficient for embryogenesis but provide different levels of trx function at homeotic genes in imaginal cells. One allele alters the N terminus of TRX, which severely impairs larval and imaginal growth. Hypomorphic alleles that alter different regions of TRX equivalently reduce function at affected genes, suggesting TRX interacts with common factors at different target genes. All hypomorphic alleles examined complement one another, suggesting cooperative TRX function at target genes. Comparative effects of hypomorphic genotypes support previous findings that TRX has tissue-specific interactions with other factors at each target gene. Some hypomorphic genotypes also produce phenotypes that suggest TRX may be a component of signal transduction pathways that provide tissue- and cell-specific levels of target gene transcription.

scholarly journals Integrated network analysis identifying potential novel drug candidates and targets for Parkinson's disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pusheng Quan ◽  
Kai Wang ◽  
Shi Yan ◽  
Shirong Wen ◽  
Chengqun Wei ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Target ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Control Group ◽  
Drug Candidates ◽  
Novel Drug

AbstractThis study aimed to identify potential novel drug candidates and targets for Parkinson’s disease. First, 970 genes that have been reported to be related to PD were collected from five databases, and functional enrichment analysis of these genes was conducted to investigate their potential mechanisms. Then, we collected drugs and related targets from DrugBank, narrowed the list by proximity scores and Inverted Gene Set Enrichment analysis of drug targets, and identified potential drug candidates for PD treatment. Finally, we compared the expression distribution of the candidate drug-target genes between the PD group and the control group in the public dataset with the largest sample size (GSE99039) in Gene Expression Omnibus. Ten drugs with an FDR < 0.1 and their corresponding targets were identified. Some target genes of the ten drugs significantly overlapped with PD-related genes or already known therapeutic targets for PD. Nine differentially expressed drug-target genes with p < 0.05 were screened. This work will facilitate further research into the possible efficacy of new drugs for PD and will provide valuable clues for drug design.

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