scholarly journals An Integrating Approach for Genome-Wide Screening of MicroRNA Polymorphisms Mediated Drug Response Alterations

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xianyue Wang ◽  
Hong Jiang ◽  
Wei Wu ◽  
Rongxin Zhang ◽  
Lingxiang Wu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Drug Response ◽  
Mirna Target ◽  
Mrna Translation ◽  
Gene Interaction ◽  
Mirna Regulation ◽  
Small Noncoding Rnas ◽  
Target Sites ◽  
Drug Efficiency

MicroRNAs (miRNAs) are a class of evolutionarily conserved small noncoding RNAs, ~22 nt in length, and found in diverse organisms and play important roles in the regulation of mRNA translation and degradation. It was shown that miRNAs were involved in many key biological processes through regulating the expression of targets. Genetic polymorphisms in miRNA target sites may alter miRNA regulation and therefore result in the alterations of the drug targets. Recent studies have demonstrated that SNPs in miRNA target sites can affect drug efficiency. However, there are still a large number of specific genetic variants related to drug efficiency that are yet to be discovered. We integrated large scale of genetic variations, drug targets, gene interaction networks, biological pathways, and seeds region of miRNA to identify miRNA polymorphisms affecting drug response. In addition, harnessing the abundant high quality biological network/pathways, we evaluated the cascade distribution of tarSNP impacts. We showed that the predictions can uncover most of the known experimentally supported cases as well as provide informative candidates complementary to existing methods/tools. Although there are several existing databases predicting the gain or loss of targeting function of miRNA mediated by SNPs, such as PolymiRTS, miRNASNP, MicroSNiPer, and MirSNP, none of them evaluated the influences of tarSNPs on drug response alterations. We developed a user-friendly online database of this approach named Mir2Drug.

scholarly journals Linking drug target and pathway activation for effective therapy using multi-task learning

2017 ◽  
Author(s):  
Mi Yang ◽  
Jaak Simm ◽  
Chi Chung Lam ◽  
Pooya Zakeri ◽  
Gerard J.P. van Westen ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Response Data ◽  
Combination Strategies ◽  
Pathway Activation ◽  
Protein X

ABSTRACTDespite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways’ activation. A typical insight would be: “Activation of pathway Y will confer sensitivity to any drug targeting protein X”. We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Daniel J. Panyard ◽  
Kyeong Mo Kim ◽  
Burcu F. Darst ◽  
Yuetiva K. Deming ◽  
Xiaoyuan Zhong ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Drug Targets ◽  
Large Scale ◽  
Prediction Models ◽  
Genome Wide Association ◽  
Large Samples ◽  
Genome Wide ◽  
Metabolomic Data ◽  
Related Phenotype ◽  
Omic Data

AbstractThe study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.

scholarly journals A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Friedemann Krentel ◽  
Franziska Singer ◽  
María Lourdes Rosano-Gonzalez ◽  
Ewan A. Gibb ◽  
Yang Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
In Silico ◽  
Drug Response ◽  
Gene Interaction ◽  
Response Prediction ◽  
Manual Curation ◽  
Genetic Landscape ◽  
Promising Treatment ◽  
Muscle Invasive ◽  
The Individual

AbstractImproved and cheaper molecular diagnostics allow the shift from “one size fits all” therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients—including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

scholarly journals Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

2019 ◽  
Vol 25 (1) ◽  
pp. 9-20 ◽  
Author(s):  
Olivia W. Lee ◽  
Shelley Austin ◽  
Madison Gamma ◽  
Dorian M. Cheff ◽  
Tobie D. Lee ◽  
...  
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
High Throughput ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Large Scale ◽  
Early Stage ◽  
Cancer Cell Line ◽  
Hek 293 ◽  
Cytotoxic Compounds

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

scholarly journals MirSNP, a database of polymorphisms altering miRNA target sites, identifies miRNA-related SNPs in GWAS SNPs and eQTLs

BMC Genomics ◽  
2012 ◽  
Vol 13 (1) ◽  
pp. 661 ◽  
Author(s):  
Chenxing Liu ◽  
Fuquan Zhang ◽  
Tingting Li ◽  
Ming Lu ◽  
Lifang Wang ◽  
...  
Keyword(s):  
Mirna Target ◽  
Gwas Snps ◽  
Target Sites

scholarly journals KSHV-Encoded MicroRNAs: Lessons for Viral Cancer Pathogenesis and Emerging Concepts

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Zhiqiang Qin ◽  
Andrew Jakymiw ◽  
Victoria Findlay ◽  
Chris Parsons
Keyword(s):  
Cancer Progression ◽  
Recent Literature ◽  
Regulation Of Gene Expression ◽  
Molecular Signatures ◽  
Mirna Regulation ◽  
Small Noncoding Rnas ◽  
Human Mirnas ◽  
Cancer Pathogenesis ◽  
Promote Disease Progression

The human genome contains microRNAs (miRNAs), small noncoding RNAs that orchestrate a number of physiologic processes through regulation of gene expression. Burgeoning evidence suggests that dysregulation of miRNAs may promote disease progression and cancer pathogenesis. Virus-encoded miRNAs, exhibiting unique molecular signatures and functions, have been increasingly recognized as contributors to viral cancer pathogenesis. A large segment of the existing knowledge in this area has been generated through characterization of miRNAs encoded by the human gamma-herpesviruses, including the Kaposi’s sarcoma-associated herpesvirus (KSHV). Recent studies focusing on KSHV miRNAs have led to a better understanding of viral miRNA expression in human tumors, the identification of novel pathologic check points regulated by viral miRNAs, and new insights for viral miRNA interactions with cellular (“human”) miRNAs. Elucidating the functional effects of inhibiting KSHV miRNAs has also provided a foundation for further translational efforts and consideration of clinical applications. This paper summarizes recent literature outlining mechanisms for KSHV miRNA regulation of cellular function and cancer-associated pathogenesis, as well as implications for interactions between KSHV and human miRNAs that may facilitate cancer progression. Finally, insights are offered for the clinical feasibility of targeting miRNAs as a therapeutic approach for viral cancers.

scholarly journals Global Analysis of the Genetic Variations in miRNA-Targeted Sites and Their Correlations With Agronomic Traits in Rapeseed

2021 ◽  
Vol 12 ◽  
Author(s):  
Pengfei Xu ◽  
Yantao Zhu ◽  
Yanfeng Zhang ◽  
Jianxia Jiang ◽  
Liyong Yang ◽  
...  
Keyword(s):  
Mirna Target ◽  
Target Genes ◽  
Rare Variants ◽  
Agronomic Traits ◽  
Global Analysis ◽  
Variant Calling ◽  
Genetic Variations ◽  
Nucleotide Polymorphisms ◽  
A Genome ◽  
Target Sites

MicroRNAs (miRNAs) and their target genes play vital roles in crops. However, the genetic variations in miRNA-targeted sites that affect miRNA cleavage efficiency and their correlations with agronomic traits in crops remain unexplored. On the basis of a genome-wide DNA re-sequencing of 210 elite rapeseed (Brassica napus) accessions, we identified the single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs) in miRNA-targeted sites complementary to miRNAs. Variant calling revealed 7.14 million SNPs and 2.89 million INDELs throughout the genomes of 210 rapeseed accessions. Furthermore, we detected 330 SNPs and 79 INDELs in 357 miRNA target sites, of which 33.50% were rare variants. We also analyzed the correlation between the genetic variations in miRNA target sites and 12 rapeseed agronomic traits. Eleven SNPs in miRNA target sites were significantly correlated with phenotypes in three consecutive years. More specifically, three correlated SNPs within the miRNA-binding regions of BnSPL9-3, BnSPL13-2, and BnCUC1-2 were in the loci associated with the branch angle, seed weight, and silique number, respectively; expression profiling suggested that the variation at these 3 miRNA target sites significantly affected the expression level of the corresponding target genes. Taken together, the results of this study provide researchers and breeders with a global view of the genetic variations in miRNA-targeted sites in rapeseed and reveal the potential effects of these genetic variations on elite agronomic traits.

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

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