Dataset Information

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.

ABSTRACT: Small molecules often affect multiple targets, elicit off-target effects, and induce genotype-specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high-content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical-genetic interaction analysis, we observed phenotypic gene-drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off-target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti-alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off-target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.

SUBMITTER: Breinig M

PROVIDER: S-EPMC4704494 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.

Breinig Marco M Klein Felix A FA Huber Wolfgang W Boutros Michael M

Molecular systems biology 20151223 12

Small molecules often affect multiple targets, elicit off-target effects, and induce genotype-specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high-content screening and image analysis to measure effects of 1,280 pharmac ...[more]

PMID: 26700849

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Project description:Image-based high-throughput plant phenotyping in greenhouse has the potential to relieve the bottleneck currently presented by phenotypic scoring which limits the throughput of gene discovery and crop improvement efforts. Numerous studies have employed automated RGB imaging to characterize biomass and growth of agronomically important crops. The objective of this study was to investigate the utility of hyperspectral imaging for quantifying chemical properties of maize and soybean plants in vivo. These properties included leaf water content, as well as concentrations of macronutrients nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), calcium (Ca), and sulfur (S), and micronutrients sodium (Na), iron (Fe), manganese (Mn), boron (B), copper (Cu), and zinc (Zn). Hyperspectral images were collected from 60 maize and 60 soybean plants, each subjected to varying levels of either water deficit or nutrient limitation stress with the goal of creating a wide range of variation in the chemical properties of plant leaves. Plants were imaged on an automated conveyor belt system using a hyperspectral imager with a spectral range from 550 to 1,700 nm. Images were processed to extract reflectance spectrum from each plant and partial least squares regression models were developed to correlate spectral data with chemical data. Among all the chemical properties investigated, water content was predicted with the highest accuracy [R2 = 0.93 and RPD (Ratio of Performance to Deviation) = 3.8]. All macronutrients were also quantified satisfactorily (R2 from 0.69 to 0.92, RPD from 1.62 to 3.62), with N predicted best followed by P, K, and S. The micronutrients group showed lower prediction accuracy (R2 from 0.19 to 0.86, RPD from 1.09 to 2.69) than the macronutrient groups. Cu and Zn were best predicted, followed by Fe and Mn. Na and B were the only two properties that hyperspectral imaging was not able to quantify satisfactorily (R2 < 0.3 and RPD < 1.2). This study suggested the potential usefulness of hyperspectral imaging as a high-throughput phenotyping technology for plant chemical traits. Future research is needed to test the method more thoroughly by designing experiments to vary plant nutrients individually and cover more plant species, genotypes, and growth stages.

Dataset Information

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.

Publications

A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.

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