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 The cellular response to drug perturbation is limited: comparison of large-scale chemogenomic fitness signatures

2021 ◽  
Author(s):  
Marjan Barazandeh ◽  
Divya Kriti ◽  
Corey Nislow ◽  
Guri Giaever
Keyword(s):  
Small Molecules ◽  
Mammalian Cells ◽  
Cellular Response ◽  
Large Scale ◽  
Genetic Interaction ◽  
Common Mechanism ◽  
Genome Wide ◽  
Powerful Approach ◽  
Response Systems ◽  
Academic Laboratory

Abstract BackgroundChemogenomic profiling is a powerful approach towards understanding the genome-wide cellular response to small molecules. Developed in Saccharomyces cerevisiae, chemogenomic screens provide direct, unbiased identification of drug target candidates as well as genes required for drug resistance. While many laboratories have performed chemogenomic fitness assays, they have not been assessed for reproducibility and accuracy. Here we analyze the two largest independent yeast chemogenomic datasets comprising over 35 million gene-drug interactions and more than 6000 unique chemogenomic profiles; the first from our own academic laboratory and the second from the Novartis Institute of Biomedical Research (NIBR).ResultsCombining the datasets revealed robust genetic interaction response signatures that point to common mechanism of action, despite the substantial differences in experimental and analytical pipelines. We previously reported that the cellular response to small molecules is limited and can be described by a network of 45 chemogenomic signatures. In the present study, we show that the majority of these signatures (66%) are also found in the companion dataset, providing further support for their biological relevance as systems-level, small molecule response systems. ConclusionsOur results demonstrate the robustness of chemogenomic fitness profiling in yeast, while offering guidelines for performing other high-dimensional comparisons including parallel CRISPR screens in mammalian cells.

scholarly journals MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action

2017 ◽  
Vol 34 (7) ◽  
pp. 1251-1252 ◽  
Author(s):  
Justin Nelson ◽  
Scott W Simpkins ◽  
Hamid Safizadeh ◽  
Sheena C Li ◽  
Jeff S Piotrowski ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Data Repository ◽  
Interaction Data ◽  
Chemical Genetic ◽  
Web Resource ◽  
Modes Of Action ◽  
Genetic Interaction Data

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 Systematic analysis of Ca2+homeostasis inSaccharomyces cerevisiaebased on chemical-genetic interaction profiles

2017 ◽  
Vol 28 (23) ◽  
pp. 3415-3427 ◽  
Author(s):  
Farzan Ghanegolmohammadi ◽  
Mitsunori Yoshida ◽  
Shinsuke Ohnuki ◽  
Yuko Sukegawa ◽  
Hiroki Okada ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Principal Component ◽  
Analysis Of Covariance ◽  
High Dimensional ◽  
High Concentration ◽  
Systematic Analysis ◽  
Chemical Genetic ◽  
Cellular Processes ◽  
Cellular Compartments

We investigated the global landscape of Ca2+homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+. After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+–cls interactions. We found that high-dimensional, morphological Ca2+–cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+–cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+–cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+homeostasis.

scholarly journals Transferrin-a modulates hepcidin expression in zebrafish embryos

Blood ◽  
2009 ◽  
Vol 113 (12) ◽  
pp. 2843-2850 ◽  
Author(s):  
Paula G. Fraenkel ◽  
Yann Gibert ◽  
Jason L. Holzheimer ◽  
Victoria J. Lattanzi ◽  
Sarah F. Burnett ◽  
...  
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Hypochromic Anemia ◽  
Zebrafish Embryos ◽  
Genetic Screens ◽  
Hepcidin Expression ◽  
Morpholino Knockdown ◽  
Low Levels ◽  
Identification And Characterization

Abstract The iron regulatory hormone hepcidin is transcriptionally up-regulated in response to iron loading, but the mechanisms by which iron levels are sensed are not well understood. Large-scale genetic screens in the zebrafish have resulted in the identification of hypochromic anemia mutants with a range of mutations affecting conserved pathways in iron metabolism and heme synthesis. We hypothesized that transferrin plays a critical role both in iron transport and in regulating hepcidin expression in zebrafish embryos. Here we report the identification and characterization of the zebrafish hypochromic anemia mutant, gavi, which exhibits transferrin deficiency due to mutations in transferrin-a. Morpholino knockdown of transferrin-a in wild-type embryos reproduced the anemia phenotype and decreased somite and terminal gut iron staining, while coinjection of transferrin-a cRNA partially restored these defects. Embryos with transferrin-a or transferrin receptor 2 (TfR2) deficiency exhibited low levels of hepcidin expression, however anemia, in the absence of a defect in the transferrin pathway, failed to impair hepcidin expression. These data indicate that transferrin-a transports iron and that hepcidin expression is regulated by a transferrin-a–dependent pathway in the zebrafish embryo.

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.

Chemical Genetics: Drug Screens in Zebrafish

2005 ◽  
Vol 25 (5-6) ◽  
pp. 289-297 ◽  
Author(s):  
Jeroen den Hertog
Keyword(s):  
Biological Activity ◽  
High Throughput ◽  
Large Scale ◽  
Chemical Genetics ◽  
Zebrafish Embryos ◽  
Genetic Screens ◽  
Zebrafish Development ◽  
Chemical Genetic

High throughput chemical genetic screens for compounds with specific biological activity in a whole organism are feasible using zebrafish embryos. At least two medium to large scale drug screens have been carried out to date, leading to the identification of compounds that disturb zebrafish development. Chemical genetics using zebrafish embryos may become an important step in the discovery of drugs and their targets.

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