scholarly journals Molecular Network Approach Reveals Rictor as a Central Target of Cardiac ProtectomiRs

2021 ◽  
Vol 22 (17) ◽  
pp. 9539
Author(s):  
András Makkos ◽  
Bence Ágg ◽  
Zoltán V. Varga ◽  
Zoltán Giricz ◽  
Mariann Gyöngyösi ◽  
...  
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Ischemic Postconditioning ◽  
Molecular Network ◽  
Pig Model ◽  
Node Degree ◽  
Central Target ◽  
Mrna Targets ◽  
Novel Drug

Cardioprotective medications are still unmet clinical needs. We have previously identified several cardioprotective microRNAs (termed ProtectomiRs), the mRNA targets of which may reveal new drug targets for cardioprotection. Here we aimed to identify key molecular targets of ProtectomiRs and confirm their association with cardioprotection in a translational pig model of acute myocardial infarction (AMI). By using a network theoretical approach, we identified 882 potential target genes of 18 previously identified protectomiRs. The Rictor gene was the most central and it was ranked first in the protectomiR-target mRNA molecular network with the highest node degree of 5. Therefore, Rictor and its targeting microRNAs were further validated in heart samples obtained from a translational pig model of AMI and cardioprotection induced by pre- or postconditioning. Three out of five Rictor-targeting pig homologue of rat ProtectomiRs showed significant upregulation in postconditioned but not in preconditioned pig hearts. Rictor was downregulated at the mRNA and protein level in ischemic postconditioning but not in ischemic preconditioning. This is the first demonstration that Rictor is the central molecular target of ProtectomiRs and that decreased Rictor expression may regulate ischemic postconditioning-, but not preconditioning-induced acute cardioprotection. We conclude that Rictor is a potential novel drug target for acute cardioprotection.

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.

scholarly journals 4E Interacting Protein as a Potential Novel Drug Target for Nucleoside Analogues in Trypanosoma Brucei

2021 ◽  
Vol 9 (4) ◽  
pp. 826
Author(s):  
Dorien Mabille ◽  
Camila Cardoso Santos ◽  
Rik Hendrickx ◽  
Mathieu Claes ◽  
Peter Takac ◽  
...  
Keyword(s):  
Mode Of Action ◽  
Drug Target ◽  
Drug Targets ◽  
De Novo ◽  
Treatment Options ◽  
Adenosine Kinase ◽  
Nucleoside Analogues ◽  
Purine Salvage ◽  
Interacting Protein ◽  
Novel Drug

Human African trypanosomiasis is a neglected parasitic disease for which the current treatment options are quite limited. Trypanosomes are not able to synthesize purines de novo and thus solely depend on purine salvage from the host environment. This characteristic makes players of the purine salvage pathway putative drug targets. The activity of known nucleoside analogues such as tubercidin and cordycepin led to the development of a series of C7-substituted nucleoside analogues. Here, we use RNA interference (RNAi) libraries to gain insight into the mode-of-action of these novel nucleoside analogues. Whole-genome RNAi screening revealed the involvement of adenosine kinase and 4E interacting protein into the mode-of-action of certain antitrypanosomal nucleoside analogues. Using RNAi lines and gene-deficient parasites, 4E interacting protein was found to be essential for parasite growth and infectivity in the vertebrate host. The essential nature of this gene product and involvement in the activity of certain nucleoside analogues indicates that it represents a potential novel drug target.

scholarly journals Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jeremy J. Yang ◽  
Christopher R. Gessner ◽  
Joel L. Duerksen ◽  
Daniel Biber ◽  
Jessica L. Binder ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Human Health ◽  
Molecular Basis ◽  
Drug Target ◽  
Drug Targets ◽  
Complex Diseases ◽  
Knowledge Graph ◽  
Graph Analytics ◽  
Novel Drug ◽  
Novel Drug Targets

Abstract Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

scholarly journals Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

2018 ◽  
Author(s):  
Phuong A. Nguyen ◽  
Aimee M. Deaton ◽  
Paul Nioi ◽  
Lucas D. Ward
Keyword(s):  
Side Effects ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Organ System ◽  
Genetic Evidence ◽  
Genetic Syndromes ◽  
Medical Needs ◽  
Organ Systems ◽  
Genes Encoding

ABSTRACTBiomedical scientists face major challenges in developing novel drugs for unmet medical needs. Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict drug and target safety as early as possible using a variety of assays in vitro and in preclinical species. In principle, characterizing the effect of natural variation in the genes encoding drug targets should present a powerful alternate approach to predict not only whether a protein will be an effective drug target, but also whether a protein will be an inherently safe drug target, while avoiding the challenges of translating biology from experiments in non-human species. We have embarked on a retrospective analysis, demonstrating for the first time a statistical link between the organ systems involved in genetic syndromes of drug target genes and the organ systems in which side effects are observed clinically. Across 1,819 drugs and 21 organ system phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0% vs 19.2%; OR = 1.80). Conversely, we find that having genetic evidence of a Mendelian syndrome involving a drug target in which a certain organ system is unaffected decreases the likelihood that side effects will manifest in that organ system, relative to having no informative syndrome (18.5% vs 20.2%; OR = 0.89). We find a relationship between genetics and side effects even when controlling for known confounders such as drug delivery route and indication. We highlight examples where genetics of drug targets could have anticipated side effects observed during clinical trials. This result suggests that human genetic data should be routinely used to predict potential safety issues associated with novel drug targets. This may lead to selection of better targets, appropriate monitoring of putative side effects early in development, reduction of the use of preclinical animal experiments, and ultimately increased success of molecules. Furthermore, deeply phenotyping human knockouts will be critically important to understand the full spectrum of effects that a new drug may elicit.

scholarly journals Identification of Candidate Drugs for Heart Failure using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression between Heart Failure and DrugMatrix Datasets

2017 ◽  
Author(s):  
Y-h. Taguchi
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Feature Extraction ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Expression Profiles ◽  
Tensor Decomposition ◽  
Integrated Analysis ◽  
Unsupervised Feature Extraction

AbstractIdentifying drug target genes in gene expression profiles is not straightforward. Because a drug targets not mRNAs but proteins, mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data on gene expression during various drug treatments of rats were reported. I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported.

scholarly journals Sleep and Alzheimer’s Disease: Shared Genetic Risk Factors, Drug Targets, Molecular Mechanisms, and Causal Effects

2021 ◽  
Author(s):  
Dongze Chen ◽  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Sleep Duration ◽  
Drug Target ◽  
Drug Targets ◽  
Target Genes ◽  
Lipoprotein Particle ◽  
Level Data ◽  
Particle Clearance

Abstract Background: Alzheimer’s disease (AD) was associated with sleep-related phenotypes (SRPs). Whether they share common genetic etiology remains largely unknown. We explored the shared genetics and causality between AD and SRPs by using high-definition likelihood (HDL), cross phenotype association study (CPASSOC), transcriptome wide association study (TWAS), and bidirectional Mendelian randomization (MR) in summary-level data for AD (n = 79145) and summary-level data for seven SRPs (sample size ranges from 345552 to 386577). Results: AD shared strong genetic basis with insomnia (rg = 0.20; P = 9.70×10-5), snoring (rg = 0.13; P = 2.45×10-3), and sleep duration (rg = -0.11; P = 1.18×10-3). CPASSOC identifies 31 independent loci shared between AD and SRPs, including four novel shared loci. Functional analysis and TWAS showed shared genes were enriched in liver, brain, breast, and heart tissues, and highlighted the regulatory role of immunological disorders, very-low-density lipoprotein particle clearance, triglyceride-rich lipoprotein particle clearance, chylomicron remnant clearance and positive regulation of T cell mediated cytotoxicity pathways. Protein-protein interaction analysis provided three potential drug target genes (APOE, MARK4 and HLA-DRA) that interacted with known FDA-approved drug target genes. CPASSOC and TWAS demonstrated three regions 11p11.2, 6p22.3 and 16p11.2 may account for the shared basis between AD and sleep duration or snoring. MR showed AD had causal effect on sleep duration (βIVW = -0.056, PIVW = 1.03×10-3). Conclusion: Our findings provide strong evidence of shared genetics and causation between AD and sleep, and advance our understanding the genetic overlap between them. Identifying shared drug targets and molecular pathways can be beneficial to treat AD and sleep disorders more efficiently.

HPV-associated penile cancer: Impact of copy number alterations in miRNA/mRNA interactions and potential druggable targets

2021 ◽  
pp. 1-14
Author(s):  
Jenilson da Silva ◽  
Leudivan Nogueira ◽  
Ronald Coelho ◽  
Amanda Deus ◽  
André Khayat ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Copy Number ◽  
Drug Targets ◽  
Penile Cancer ◽  
Target Genes ◽  
Kegg Pathways ◽  
Mrna Targets ◽  
Genome Wide ◽  
Target Treatment ◽  
Druggable Targets

BACKGROUND: Penile cancer (PeCa) is a rare disease, but its incidence has increased worldwide, mostly in HPV+ patients. Nevertheless, there is still no targeted treatment for this carcinoma. OBJECTIVE: To predict the main signaling pathways involved in penile tumorigenesis and its potential drug targets. METHODS: Genome-wide copy number profiling was performed in 28 PeCa. Integration analysis of CNAs and miRNAs and mRNA targets was performed by DIANA-TarBase v.8. The potential impact of the miRNAs/target genes on biological pathways was assessed by DIANA-miRPath v.3.0. For each miRNA, KEGG pathways were generated based on the tarbase and microT-CDS algorithms. Pharmaco-miR was used to identify associations between miRNAs and their target genes to predict druggable targets. RESULTS: 269 miRNAs and 2,395 genes were mapped in cytobands with CNAs. The comparison of the miRNAs mapped at these cytobands and the miRNAs that were predicted to regulate the genes also mapped in these regions, resulted in a set of common 35 miRNAs and 292 genes. Enrichment pathway revealed their involvement in five top signaling pathways. EGFR and COX2 were identified as potential druggable targets. CONCLUSION: Our data indicate the potential use of EGFR and COX2 inhibitors as a target treatment for PeCa patients.

scholarly journals Virtual Screening and Docking Studies of Identified Potential Drug Target: Polysaccharide Deacetylase in Bacillus anthracis

2015 ◽  
Vol 34 ◽  
pp. 70-77
Author(s):  
K. Zaveri ◽  
A. Krishna Chaitanya ◽  
I. Bhaskar Reddy
Keyword(s):  
Virtual Screening ◽  
Bacillus Anthracis ◽  
Drug Target ◽  
Drug Targets ◽  
Docking Studies ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Subtractive Genomics ◽  
Subtractive Genomics Approach ◽  
Novel Drug

In recent years, insilico approaches have been predicting novel drug targets. The present day development in pharmaceutics mainly ponders on target based drugs and this has been aided by structure based drug designing and subtractive genomics. In the present study, the computational genome subtraction methodology was applied for identification of novel, potential drug target against Bacillus anthracis, cause of deadly anthrax. The potential drug target identified through subtractive genomics approach was considered as polysaccharide deacetylase. By virtual screening against NCI database and Drugbank chemical libraries, two potential lead molecules were predicted. Further the potential lead molecules and target protein were subjected for docking studies using Autodock.

scholarly journals Identification and Validation of ERK5 as a DNA Damage Modulating Drug Target in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 944
Author(s):  
Natasha Carmell ◽  
Ola Rominiyi ◽  
Katie N. Myers ◽  
Connor McGarrity-Cottrell ◽  
Aurelie Vanderlinden ◽  
...  
Keyword(s):  
Brain Tumours ◽  
Drug Target ◽  
Drug Targets ◽  
Standard Of Care ◽  
Dna Lesions ◽  
Validation Data ◽  
Primary Brain Tumours ◽  
Incurable Disease ◽  
Dismal Prognosis ◽  
Novel Drug

Brain tumours kill more children and adults under 40 than any other cancer, with approximately half of primary brain tumours being diagnosed as high-grade malignancies known as glioblastomas. Despite de-bulking surgery combined with chemo-/radiotherapy regimens, the mean survival for these patients is only around 15 months, with less than 10% surviving over 5 years. This dismal prognosis highlights the urgent need to develop novel agents to improve the treatment of these tumours. To address this need, we carried out a human kinome siRNA screen to identify potential drug targets that augment the effectiveness of temozolomide (TMZ)—the standard-of-care chemotherapeutic agent used to treat glioblastoma. From this we identified ERK5/MAPK7, which we subsequently validated using a range of siRNA and small molecule inhibitors within a panel of glioma cells. Mechanistically, we find that ERK5 promotes efficient repair of TMZ-induced DNA lesions to confer cell survival and clonogenic capacity. Finally, using several glioblastoma patient cohorts we provide target validation data for ERK5 as a novel drug target, revealing that heightened ERK5 expression at both the mRNA and protein level is associated with increased tumour grade and poorer patient survival. Collectively, these findings provide a foundation to develop clinically effective ERK5 targeting strategies in glioblastomas and establish much-needed enhancement of the therapeutic repertoire used to treat this currently incurable disease.

Drug Target Interplay: A Network-based Analysis of Human Diseases and the Drug Targets

2018 ◽  
Vol 18 (13) ◽  
pp. 1053-1061 ◽  
Author(s):  
Bhushan Jain ◽  
Utkarsh Raj ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Disease Gene ◽  
Search Space ◽  
Disease Genes ◽  
Data Generation ◽  
Human Interactome ◽  
Experimental Drugs ◽  
Novel Drug ◽  
Disease Specific

Screening and identifying a disease-specific novel drug target is the first step towards a rational drug designing approach. Due to the advent of high throughput data generation techniques, the protein search space has now exceeded 24,500 human protein coding genes, which encodes approximately 1804proteins. This work aims at mining out the relationship between target proteins, drugs, and diseases genes through a network-based systems biology approach. A network of all FDA approved drugs, along with their targets were utilized to construct the proposed Drug Target (DT) network. Further, the experimental drugs were mapped into the DT network to infer the functional relationship by utilizing the respective network attributes. Similar to the DT network, a network of disease genes was created through OMIM Gene Map and Morbid Map, to link the binary associations of disorder-disease genes. In the proposed model of Human Interactome Network, shortest path length between the target protein and disease gene was used to infer the correlation between ‘Drug Targets’ and ‘Disease-Gene’. This network-based study will help researchers to analyze, infer and identify disease-specific novel drug targets through harnessing the graph theory based network attributes.

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