scholarly journals Towards an Integrated Map of Genetic Interactions in Cancer Cells

2017 ◽  
Author(s):  
Benedikt Rauscher ◽  
Florian Heigwer ◽  
Luisa Henkel ◽  
Thomas Hielscher ◽  
Oksana Voloshanenko ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Genetic Variants ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Network ◽  
Genetic Interactions ◽  
Model Organisms ◽  
Genetic Screens ◽  
New Genotype ◽  
Analytical Approaches

ABSTRACTCancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/β-catenin signaling identifiedGANABandPRKCSHas new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

scholarly journals Decoding directional genetic dependencies through orthogonal CRISPR/Cas screens

2017 ◽  
Author(s):  
Michael Boettcher ◽  
Ruilin Tian ◽  
James Blau ◽  
Evan Markegard ◽  
David Wu ◽  
...  
Keyword(s):  
Genetic Information ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Interactions ◽  
Combinatorial Screening ◽  
Human Cancer Cells ◽  
Functional Interactions ◽  
Powerful Approach ◽  
Screening Platform ◽  
Interaction Approach

SummaryGenetic interaction studies are a powerful approach to identify functional interactions between genes. This approach can reveal networks of regulatory hubs and connect uncharacterised genes to well-studied pathways. However, this approach has previously been limited to simple gene inactivation studies. Here, we present an orthogonal CRISPR/Cas-mediated genetic interaction approach that allows the systematic activation of one gene while simultaneously knocking out a second gene in the same cell. We have developed this concept into a quantitative and scalable combinatorial screening platform that allows the parallel interrogation of hundreds of thousands of genetic interactions. We demonstrate that the established platform works robustly to uncover genetic interactions in human cancer cells and to interpret the direction of the flow of genetic information.

scholarly journals A Time-dependent Genome-Wide SNP-SNP Interaction Analysis of Chicken Body Weight

2019 ◽  
Author(s):  
Fang-Ge Li ◽  
Hui Li
Keyword(s):  
Body Weight ◽  
Genetic Interaction ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Genetic Network ◽  
Time Dependent ◽  
Model Organisms ◽  
Multiple Time ◽  
Genome Wide ◽  
Snp Interaction

Abstract Background The important property of the quantitative traits of model organisms is time-dependent. However, the methodology for investigating the genetic interaction network over time is still lacking. Our study aims to provide insights into the mechanistic basis of epistatic interactions affecting the phenotypes of model organisms. Results We performed an exhaustive genome-wide search for significant SNP-SNP interactions associated with male birds’ body weight (BW) (n=475) at multiple time points (day of hatch (BW0) and one, three, five, and seven weeks (BW1, BW3, BW5, and BW7)). Statistical analysis detected 67, four, and two significant SNP pairs associated with BW0, BW1, and BW3, respectively, with a significance threshold at 8.67×10 −12 (Bonferroni-adjusted: 1%). Meanwhile, no significant SNP pairs associated with BW5 and BW7 were found. The SNP-SNP interaction networks of BW0, BW1, and BW3 were built and annotated. Conclusions With strong annotated information and a strict significant threshold, SNP-SNP interactions underpinned the gene-gene interactions that might occur between chromosomes or within the same chromosome. Comparing and combing the networks, the results indicated that the genetic network for chicken body weight was dynamic and time-dependent.

scholarly journals A time-dependent genome-wide SNP-SNP interaction analysis of chicken body weight

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fang-Ge Li ◽  
Hui Li
Keyword(s):  
Body Weight ◽  
Genetic Interaction ◽  
Interaction Analysis ◽  
Interaction Network ◽  
Genetic Network ◽  
Time Dependent ◽  
Model Organisms ◽  
Multiple Time ◽  
Genome Wide ◽  
Snp Interaction

Abstract Background The important property of the quantitative traits of model organisms is time-dependent. However, the methodology for investigating the genetic interaction network over time is still lacking. Our study aims to provide insights into the mechanistic basis of epistatic interactions affecting the phenotypes of model organisms. Results We performed an exhaustive genome-wide search for significant SNP-SNP interactions associated with male birds’ body weight (BW) (n = 475) at multiple time points (day of hatch (BW0) and 1, 3, 5, and 7 weeks (BW1, BW3, BW5, and BW7)). Statistical analysis detected 67, four, and two significant SNP pairs associated with BW0, BW1, and BW3, respectively, with a significance threshold at 8.67 × 10− 12 (Bonferroni-adjusted: 1%). Meanwhile, no significant SNP pairs associated with BW5 and BW7 were found. The SNP-SNP interaction networks of BW0, BW1, and BW3 were built and annotated. Conclusions With strong annotated information and a strict significant threshold, SNP-SNP interactions underpinned the gene-gene interactions that might occur between chromosomes or within the same chromosome. Comparing and combing the networks, the results indicated that the genetic network for chicken body weight was dynamic and time-dependent.

Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping

2013 ◽  
Vol 10 (5) ◽  
pp. 427-431 ◽  
Author(s):  
Christina Laufer ◽  
Bernd Fischer ◽  
Maximilian Billmann ◽  
Wolfgang Huber ◽  
Michael Boutros
Keyword(s):  
Cancer Cells ◽  
Human Cancer ◽  
Genetic Interactions ◽  
Human Cancer Cells

scholarly journals A map of directional genetic interactions in a metazoan cell

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Bernd Fischer ◽  
Thomas Sandmann ◽  
Thomas Horn ◽  
Maximilian Billmann ◽  
Varun Chaudhary ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Genetic Interactions ◽  
Gene Interactions ◽  
Human Cancer Cells ◽  
Multiple Phenotypes ◽  
Interaction Map

Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
Author(s):  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
Biological Networks ◽  
Genetic Interaction ◽  
Initial Conditions ◽  
Genetic Interactions ◽  
Interaction Networks ◽  
Stochastic Simulations ◽  
Model Organisms ◽  
Loss Of Function ◽  
Boolean Models ◽  
Computational Tools

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

scholarly journals Hierarchical organization of the human cell from a cancer coessentiality network

2018 ◽  
Author(s):  
Eiru Kim ◽  
Merve Dede ◽  
Walter F. Lenoir ◽  
Gang Wang ◽  
Sanjana Srinivasan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Gene Function ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Hierarchical Organization ◽  
Model Organisms ◽  
Function Modules ◽  
Strong Inference ◽  
Highly Correlated ◽  
Insight Into

AbstractGenetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

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