scholarly journals Encircling the regions of the pharmacogenomic landscape that determine drug response

2018 ◽  
Author(s):  
Adrià Fernández-Torras ◽  
Miquel Duran-Frigola ◽  
Patrick Aloy
Keyword(s):  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Drug Repositioning ◽  
Gene Sets ◽  
Diffusion Analysis ◽  
Genome Wide ◽  
Molecular Determinants ◽  
Gene Modules

AbstractBackgroundThe integration of large-scale drug sensitivity screens and genome-wide experiments is changing the field of pharmacogenomics, revealing molecular determinants of drug response without the need for previous knowledge about drug action. In particular, transcriptional signatures of drug sensitivity may guide drug repositioning, prioritize drug combinations and point to new therapeutic biomarkers. However, the inherent complexity of transcriptional signatures, with thousands of differentially expressed genes, makes them hard to interpret, thus giving poor mechanistic insights and hampering translation to clinics.MethodsTo simplify drug signatures, we have developed a network-based methodology to identify functionally coherent gene modules. Our strategy starts with the calculation of drug-gene correlations and is followed by a pathway-oriented filtering and a network-diffusion analysis across the interactome.ResultsWe apply our approach to 189 drugs tested in 671 cancer cell lines and observe a connection between gene expression levels of the modules and mechanisms of action of the drugs. Further, we characterize multiple aspects of the modules, including their functional categories, tissue-specificity and prevalence in clinics. Finally, we prove the predictive capability of the modules and demonstrate how they can be used as gene sets in conventional enrichment analyses.ConclusionsNetwork biology strategies like module detection are able to digest the outcome of large-scale pharmacogenomic initiatives, thereby contributing to their interpretability and improving the characterization of the drugs screened.

scholarly journals Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Peanut

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 536 ◽  
Author(s):  
Xiaobo Zhao ◽  
Liming Gan ◽  
Caixia Yan ◽  
Chunjuan Li ◽  
Quanxi Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Target Genes ◽  
Sequencing Data ◽  
Regulatory Processes ◽  
Genome Wide ◽  
Non Coding Rnas ◽  
Identification And Characterization ◽  
Lower Expression ◽  
Weighted Correlation

Long non-coding RNAs (lncRNAs) are involved in various regulatory processes although they do not encode protein. Presently, there is little information regarding the identification of lncRNAs in peanut (Arachis hypogaea Linn.). In this study, 50,873 lncRNAs of peanut were identified from large-scale published RNA sequencing data that belonged to 124 samples involving 15 different tissues. The average lengths of lncRNA and mRNA were 4335 bp and 954 bp, respectively. Compared to the mRNAs, the lncRNAs were shorter, with fewer exons and lower expression levels. The 4713 co-expression lncRNAs (expressed in all samples) were used to construct co-expression networks by using the weighted correlation network analysis (WGCNA). LncRNAs correlating with the growth and development of different peanut tissues were obtained, and target genes for 386 hub lncRNAs of all lncRNAs co-expressions were predicted. Taken together, these findings can provide a comprehensive identification of lncRNAs in peanut.

scholarly journals Identifying Drug Sensitivity Subnetworks with NETPHLIX

2019 ◽  
Author(s):  
Yoo-Ah Kim ◽  
Rebecca Sarto Basso ◽  
Damian Wojtowicz ◽  
Dorit S. Hochbaum ◽  
Fabio Vandin ◽  
...  
Keyword(s):  
Drug Response ◽  
Genetic Alterations ◽  
Previous Method ◽  
Computational Method ◽  
Cancer Subtypes ◽  
Network Information ◽  
Genome Wide ◽  
Network Identification ◽  
Cancer Phenotype ◽  
Gene Modules

AbstractPhenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. One of the important challenges in the area is to predict drug response on a personalized level. The pathway-centric view of cancer significantly advanced the understanding of genotype-phenotype relationships. However, most of network identification methods in cancer focus on identifying subnetworks that include general cancer drivers or are associated with discrete features such as cancer subtypes, hence cannot be applied directly for the analysis of continuous features like drug response. On the other hand, existing genome wide association approaches do not fully utilize the complex proprieties of cancer mutational landscape. To address these challenges, we propose a computational method, named NETPHLIX (NETwork-to-PHenotpe mapping LeveragIng eXlusivity), which aims to identify mutated subnetworks that are associated with drug response (or any continuous cancer phenotype). Utilizing properties such as mutual exclusivity and interactions among genes, we formulate the problem as an integer linear program and solve it optimally to obtain a set of genes satisfying the constraints. NETPHLIX identified gene modules significantly associated with many drugs, including interesting response modules to MEK1/2 inhibitors in both directions (increased and decreased sensitivity to the drug) that the previous method, which does not utilize network information, failed to identify. The genes in the modules belong to MAPK/ERK signaling pathway, which is the targeted pathway of the drug.

scholarly journals Efficient strategies for screening large-scale genetic interaction networks

2017 ◽  
Author(s):  
Raamesh Deshpande ◽  
Justin Nelson ◽  
Scott W. Simpkins ◽  
Michael Costanzo ◽  
Jeff S. Piotrowski ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimal Algorithm ◽  
Genetic Interaction ◽  
Genetic Screens ◽  
Chemical Genetic ◽  
Genome Wide ◽  
Powerful Approach ◽  
Interaction Screening ◽  
Genetic Interaction Data

Large-scale genetic interaction screening is a powerful approach for unbiased characterization of gene function and understanding systems-level cellular organization. While genome-wide screens are desirable as they provide the most comprehensive interaction profiles, they are resource and time-intensive and sometimes infeasible, depending on the species and experimental platform. For these scenarios, optimal methods for more efficient screening while still producing the maximal amount of information from the resulting profiles are of interest.To address this problem, we developed an optimal algorithm, called COMPRESS-GI, which selects a small but informative set of genes that captures most of the functional information contained within genome-wide genetic interaction profiles. The utility of this algorithm is demonstrated through an application of the approach to define a diagnostic mutant set for large-scale chemical genetic screens, where more than 13,000 compound screens were achieved through the increased throughput enabled by the approach. COMPRESS-GI can be broadly applied for directing genetic interaction screens in other contexts, including in species with little or no prior genetic-interaction data.

scholarly journals Genome-Wide Characterization of Lectin Receptor Kinases in Saccharum spontaneum L. and Their Responses to Stagonospora tainanensis Infection

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 322
Author(s):  
Zhoutao Wang ◽  
Hui Ren ◽  
Fu Xu ◽  
Guilong Lu ◽  
Wei Cheng ◽  
...  
Keyword(s):  
Tandem Repeats ◽  
Saccharum Spontaneum ◽  
Unequal Distribution ◽  
Lectin Receptor ◽  
Gene Sets ◽  
Genome Wide ◽  
Receptor Kinases ◽  
Plant Pathogen Interaction ◽  
Basic Reference

Sugarcane is an important sugar and bioenergy ethanol crop, and the hyperploidy has led to stagnant progress in sugarcane genome decipherment, which also hindered the genome-wide analyses of versatile lectin receptor kinases (LecRKs). The published genome of Saccharum spontaneum, one of the two sugarcane ancestor species, enables us to study the characterization of LecRKs and their responses to sugarcane leaf blight (SLB) triggered by Stagonospora tainanensis. A total of 429 allelic and non-allelic LecRKs, which were classified into evolved independently three types according to signal domains and phylogeny, were identified based on the genome. Regarding those closely related LecRKs in the phylogenetic tree, their motifs and exon architectures of representative L- and G-types were similar or identical. LecRKs showed an unequal distribution on chromosomes and more G-type tandem repeats may come from the gene expansion. Comparing the differentially expressed LecRKs (DELs) in response to SLB in sugarcane hybrid and ancestor species S. spontaneum, we found that the DEL number in the shared gene sets was highly variable among each sugarcane accession, which indicated that the expression dynamics of LecRKs in response to SLB were quite different between hybrids and particularly between sugarcane hybrid and S. spontaneum. In addition, C-type LecRKs may participate in metabolic processes of plant–pathogen interaction, mainly including pathogenicity and plant resistance, indicating their putative roles in sugarcane responses to SLB infection. The present study provides a basic reference and global insight into the further study and utilization of LecRKs in plants.

scholarly journals Gene isoforms as expression-based biomarkers predictive of drug response in vitro

2017 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Kelsie L. Thu ◽  
Jennifer Silvester ◽  
Petr Smirnov ◽  
Mathieu Lupien ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Biomarker Discovery ◽  
Kinase Inhibitor ◽  
Tissue Type ◽  
Breast Cancer Cell Lines ◽  
Molecular Features

ABSTRACTBackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.MethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.ResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.ConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

scholarly journals funMotifs: Tissue-specific transcription factor motifs

2019 ◽  
Author(s):  
Husen M. Umer ◽  
Karolina Smolinska-Garbulowska ◽  
Nour-al-dain Marzouka ◽  
Zeeshan Khaliq ◽  
Claes Wadelius ◽  
...  
Keyword(s):  
Large Scale ◽  
Tissue Specificity ◽  
Functional Characterization ◽  
Regulatory Elements ◽  
Web Based ◽  
Tissue Specific ◽  
Functional Variants ◽  
Genome Wide ◽  
Reporter Assays

ABSTRACTTranscription factors (TF) regulate gene expression by binding to specific sequences known as motifs. A bottleneck in our knowledge of gene regulation is the lack of functional characterization of TF motifs, which is mainly due to the large number of predicted TF motifs, and tissue specificity of TF binding. We built a framework to identify tissue-specific functional motifs (funMotifs) across the genome based on thousands of annotation tracks obtained from large-scale genomics projects including ENCODE, RoadMap Epigenomics and FANTOM. The annotations were weighted using a logistic regression model trained on regulatory elements obtained from massively parallel reporter assays. Overall, genome-wide predicted motifs of 519 TFs were characterized across fifteen tissue types. funMotifs summarizes the weighted annotations into a functional activity score for each of the predicted motifs. funMotifs enabled us to measure tissue specificity of different TFs and to identify candidate functional variants in TF motifs from the 1000 genomes project, the GTEx project, the GWAS catalogue, and in 2,515 cancer samples from the Pan-cancer analysis of whole genome sequences (PCAWG) cohort. To enable researchers annotate genomic variants or regions of interest, we have implemented a command-line pipeline and a web-based interface that can publicly be accessed on: http://bioinf.icm.uu.se/funmotifs.

scholarly journals Estimation and inference for the indirect effect in high-dimensional linear mediation models

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 573-589
Author(s):  
Ruixuan Rachel Zhou ◽  
Liewei Wang ◽  
Sihai Dave Zhao
Keyword(s):  
Direct Effect ◽  
Indirect Effect ◽  
Drug Response ◽  
Molecular Mechanisms ◽  
Total Effect ◽  
High Dimensional ◽  
Gene Sets ◽  
Genome Wide ◽  
A Genome ◽  
Mediation Models

Summary Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.

scholarly journals Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Fiona Allum ◽  
◽  
Xiaojian Shao ◽  
Frédéric Guénard ◽  
Marie-Michelle Simon ◽  
...  
Keyword(s):  
Large Scale ◽  
Hdl Cholesterol ◽  
Cost Effective ◽  
Strongly Correlated ◽  
Genome Wide ◽  
Cost Effective Approach ◽  
Genome Bisulfite Sequencing ◽  
Comparable Accuracy ◽  
Capture Sequencing

Abstract Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targeted arrays or enrichment methodologies preferentially covering CpG-dense regions, to characterize sufficiently large samples. To overcome this limitation, we present here a new customizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), for sequencing functional methylomes, while simultaneously providing genetic variation information. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adipose tissue (AT) samples and public databases to design AT-specific panels. We establish its efficiency for high-density interrogation of methylome variability by systematic comparisons with other approaches and demonstrate its applicability by identifying novel methylation variation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol, including at CD36. Our more comprehensive AT panel assesses tissue methylation and genotypes in parallel at ∼4 and ∼3 M sites, respectively. Our study demonstrates that MCC-Seq provides comparable accuracy to alternative approaches but enables more efficient cataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS.

scholarly journals Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics

2021 ◽  
Author(s):  
Max Lam ◽  
Chia-Yen Chen ◽  
Tian Ge ◽  
Yan Xia ◽  
David W. Hill ◽  
...  
Keyword(s):  
Drug Targets ◽  
Large Scale ◽  
Association Studies ◽  
Drug Repurposing ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Nootropic Drug ◽  
Gene Sets ◽  
Genome Wide ◽  
Novel Drug

AbstractBroad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify “druggable” targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

scholarly journals Characterization of Sleeping Beauty Transposition and Its Application to Genetic Screening in Mice

2003 ◽  
Vol 23 (24) ◽  
pp. 9189-9207 ◽  
Author(s):  
Kyoji Horie ◽  
Kosuke Yusa ◽  
Kojiro Yae ◽  
Junko Odajima ◽  
Sylvia E. J. Fischer ◽  
...  
Keyword(s):  
Large Scale ◽  
Fluorescent Protein ◽  
Germ Line ◽  
Donor Site ◽  
Insertion Site ◽  
Sleeping Beauty ◽  
Mutant Mice ◽  
Genome Wide ◽  
Green Fluorescent

ABSTRACT The use of mutant mice plays a pivotal role in determining the function of genes, and the recently reported germ line transposition of the Sleeping Beauty (SB) transposon would provide a novel system to facilitate this approach. In this study, we characterized SB transposition in the mouse germ line and assessed its potential for generating mutant mice. Transposition sites not only were clustered within 3 Mb near the donor site but also were widely distributed outside this cluster, indicating that the SB transposon can be utilized for both region-specific and genome-wide mutagenesis. The complexity of transposition sites in the germ line was high enough for large-scale generation of mutant mice. Based on these initial results, we conducted germ line mutagenesis by using a gene trap scheme, and the use of a green fluorescent protein reporter made it possible to select for mutant mice rapidly and noninvasively. Interestingly, mice with mutations in the same gene, each with a different insertion site, were obtained by local transposition events, demonstrating the feasibility of the SB transposon system for region-specific mutagenesis. Our results indicate that the SB transposon system has unique features that complement other mutagenesis approaches.

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