Chemical Genetic Screens for TDP-43 Modifiers and ALS Drug Discovery

2013 ◽  
Author(s):  
Pierre Drapeau ◽  
Alexandre Parker ◽  
Edor Kabashi ◽  
Jean-Pierre Julien
Keyword(s):  
Drug Discovery ◽  
Genetic Screens ◽  
Chemical Genetic

Chemical Genetic Screens for TDP-43 Modifiers and ALS Drug Discovery

2015 ◽  
Author(s):  
Pierre Drapeau ◽  
Alexandre Parker ◽  
Edor Kabashi ◽  
Jean-Pierre Julien
Keyword(s):  
Drug Discovery ◽  
Genetic Screens ◽  
Chemical Genetic

scholarly journals Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent

2017 ◽  
Vol 68 (1) ◽  
pp. 210-223.e6 ◽  
Author(s):  
Marco Jost ◽  
Yuwen Chen ◽  
Luke A. Gilbert ◽  
Max A. Horlbeck ◽  
Lenno Krenning ◽  
...  
Keyword(s):  
Genetic Screens ◽  
Chemical Genetic

Xenopus as a model organism in developmental chemical genetic screens

2005 ◽  
Vol 1 (3) ◽  
pp. 223 ◽  
Author(s):  
Matthew L. Tomlinson ◽  
Robert A. Field ◽  
Grant N. Wheeler
Keyword(s):  
Model Organism ◽  
Genetic Screens ◽  
Chemical Genetic

scholarly journals Combination of Reverse and Chemical Genetic Screens Reveals Angiogenesis Inhibitors and Targets

2009 ◽  
Vol 16 (4) ◽  
pp. 432-441 ◽  
Author(s):  
Mattias Kalén ◽  
Elisabet Wallgard ◽  
Noomi Asker ◽  
Aidas Nasevicius ◽  
Elisabet Athley ◽  
...  
Keyword(s):  
Angiogenesis Inhibitors ◽  
Genetic Screens ◽  
Chemical Genetic

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 Matrix linear models for high-throughput chemical genetic screens

2018 ◽  
Author(s):  
Jane W. Liang ◽  
Robert J. Nichols ◽  
Śaunak Sen
Keyword(s):  
High Throughput ◽  
Linear Models ◽  
Natural Extension ◽  
Attractive Alternative ◽  
Genetic Screens ◽  
Computationally Efficient ◽  
Model Framework ◽  
Chemical Genetic ◽  
Link Type ◽  
Univariate Approach

AbstractWe develop a flexible and computationally efficient approach for analysing high throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. The goal is to detect interactions between genes and stresses. Typically, this is achieved by grouping the mutants and stresses into categories, and performing modified t-tests for each combination. This approach does not have a natural extension if mutants or stresses have quantitative or non-overlapping annotations (eg. if conditions have doses, or a mutant falls into more than one category simultaneously). We develop a matrix linear model framework that allows us to model relationships between mutants and conditions in a simple, yet flexible multivariate framework. It encodes both categorical and continuous relationships to enhance detection of associations. To handle large datasets, we develop a fast estimation approach that takes advantage of the structure of matrix linear models. We evaluate our method’s performance in simulations and in an E. coli chemical genetic screen, comparing it with an existing univariate approach based on modified t-tests. We show that matrix linear models perform slightly better than the univariate approach when mutants and conditions are classified in non-overlapping categories, and substantially better when conditions can be ordered in dosage categories. Our approach is much faster computationally and is scalable to larger datasets. It is an attractive alternative to current methods, and provides a natural framework extensible to larger, and more complex chemical genetic screens. A Julia implementation of matrix linear models and the code used for the analysis in this paper can be found at https://bitbucket.org/jwliang/mlm_packages and https://bitbucket.org/jwliang/mlm_gs_supplement, respectively.

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.

scholarly journals Pharmaceutical-grade Rigosertib is a Microtubule-destabilizing Agent

2020 ◽  
Author(s):  
Marco Jost ◽  
Yuwen Chen ◽  
Luke A. Gilbert ◽  
Max A. Horlbeck ◽  
Lenno Krenning ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Phase Iii ◽  
Genetic Screens ◽  
Microtubule Network ◽  
Chemical Genetic ◽  
Phase Iii Clinical Trials ◽  
Anti Cancer ◽  
Cancer Agent ◽  
Reduced Toxicity

SummaryWe recently used CRISPRi/a-based chemical-genetic screens and targeted cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. In a recent manuscript, Reddy and co-workers suggest that this microtubule-destabilizing activity of rigosertib is mediated not by rigosertib itself but by a contaminating degradation product of rigosertib, ON01500, present in formulations obtained from commercial vendors (Baker et al., 2019). Here, we demonstrate that treatment of cells with pharmaceutical-grade rigosertib (>99.9% purity) results in qualitatively indistinguishable phenotypes as treatment with commercially obtained rigosertib across multiple assays. The two compounds have indistinguishable chemical-genetic interactions with genes involved in modulating the microtubule network (KIF2C and TACC3), both destabilize microtubules in cells and in vitro, and both show substantially reduced toxicity in cell lines expressing a rationally-designed mutant of tubulin (L240F TUBB mutant), in which the rigosertib binding site in tubulin is mutated. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents, which is disputed by Reddy and co-workers, was recently confirmed by an independent research group (Patterson et al., 2019). We conclude that rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

Sign in / Sign up

Export Citation Format

Share Document