scholarly journals Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens

2019 ◽  
Author(s):  
Atray Dixit ◽  
Olena Kuksenko ◽  
David Feldman ◽  
Aviv Regev
Keyword(s):  
Large Scale ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Genetic Interactions ◽  
Pairwise Interactions ◽  
Affect Cell Viability ◽  
Melanoma Model ◽  
Dividing Cells ◽  
Combinatorial Encoding ◽  
Massive Number

AbstractGenetic interactions, defined as the non-additive phenotypic impact of combinations of genes, are a hallmark of the mapping from genotype to phenotype. However, genetic interactions remain challenging to systematically test given the massive number of possible combinations. In particular, while large-scale screening efforts in yeast have quantified pairwise interactions that affect cell viability, or synthetic lethality, between all pairs of genes as well as for a limited number of three-way interactions, it has previously been intractable to perform the large screens needed to comprehensively assess interactions in a mammalian genome. Here, we develop Shuffle-Seq, a scalable method to assay genetic interactions. Shuffle-Seq leverages the co-inheritance of genetically encoded barcodes in dividing cells and can scale in proportion to sequencing throughput. We demonstrate the technical validity of Shuffle-Seq and apply it to screening for mechanisms underlying drug resistance in a melanoma model. Shuffle-Seq should allow screens of hundreds of millions of combinatorial perturbations and facilitate the understanding of genetic dependencies and drug sensitivities.

scholarly journals Interrogation of kinase genetic interactions provides a global view of PAK1-mediated signal transduction pathways

2020 ◽  
Vol 295 (50) ◽  
pp. 16906-16919
Author(s):  
Jae-Hong Kim ◽  
Yeojin Seo ◽  
Myungjin Jo ◽  
Hyejin Jeon ◽  
Young-Seop Kim ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Cell Migration ◽  
Intracellular Signaling ◽  
Mammalian Cells ◽  
Drug Targets ◽  
Large Scale ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Signal Transduction Pathways ◽  
Global View

Kinases are critical components of intracellular signaling pathways and have been extensively investigated with regard to their roles in cancer. p21-activated kinase-1 (PAK1) is a serine/threonine kinase that has been previously implicated in numerous biological processes, such as cell migration, cell cycle progression, cell motility, invasion, and angiogenesis, in glioma and other cancers. However, the signaling network linked to PAK1 is not fully defined. We previously reported a large-scale yeast genetic interaction screen using toxicity as a readout to identify candidate PAK1 genetic interactions. En masse transformation of the PAK1 gene into 4,653 homozygous diploid Saccharomyces cerevisiae yeast deletion mutants identified ∼400 candidates that suppressed yeast toxicity. Here we selected 19 candidate PAK1 genetic interactions that had human orthologs and were expressed in glioma for further examination in mammalian cells, brain slice cultures, and orthotopic glioma models. RNAi and pharmacological inhibition of potential PAK1 interactors confirmed that DPP4, KIF11, mTOR, PKM2, SGPP1, TTK, and YWHAE regulate PAK1-induced cell migration and revealed the importance of genes related to the mitotic spindle, proteolysis, autophagy, and metabolism in PAK1-mediated glioma cell migration, drug resistance, and proliferation. AKT1 was further identified as a downstream mediator of the PAK1-TTK genetic interaction. Taken together, these data provide a global view of PAK1-mediated signal transduction pathways and point to potential new drug targets for glioma therapy.

scholarly journals Mapping genetic interactions in cancer: a road to rational combination therapies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Beril Tutuncuoglu ◽  
Nevan J. Krogan
Keyword(s):  
Anticancer Drugs ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Parp Inhibitors ◽  
Genetic Interactions ◽  
Parp Inhibition ◽  
Functional Dependencies ◽  
Combination Therapies ◽  
Systematic Screening ◽  
Rational Combination

Abstract The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.

Protein Interaction Networks

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Large Scale ◽  
Scientific Literature ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Protein Protein Interactions ◽  
Expression Array ◽  
Biological Responses ◽  
Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

scholarly journals Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

2019 ◽  
Author(s):  
Christopher J. Lord ◽  
Niall Quinn ◽  
Colm J. Ryan
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Genetic Interactions ◽  
Molecular Heterogeneity ◽  
Loss Of Function ◽  
Driver Genes ◽  
Lethal Effects ◽  
Synthetic Lethal

AbstractGenetic interactions, such as synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Understanding which genetic interactions are robust in the face of the molecular heterogeneity observed in tumours and what factors influence this robustness could streamline the identification of therapeutic targets. Here, we develop a computational approach to identify robust genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. We used this approach to evaluate >140,000 potential genetic interactions involving cancer driver genes and identified 1,520 that are significant in at least one study but only 220 that reproduce across multiple studies. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions in cancer are enriched for gene pairs whose protein products physically interact. This suggests that protein-protein interactions can be used not only to understand the mechanistic basis of genetic interaction effects, but also to prioritise robust targets for further development. To explore the utility of this approach, we used a protein-protein interaction network to guide the search for robust synthetic lethal interactions associated with passenger gene alterations and validated two novel robust synthetic lethalities.

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 Paralogous synthetic lethality underlies genetic dependencies of the cancer-mutated gene STAG2

2021 ◽  
Vol 4 (11) ◽  
pp. e202101083
Author(s):  
Melanie L Bailey ◽  
David Tieu ◽  
Andrea Habsid ◽  
Amy Hin Yan Tong ◽  
Katherine Chan ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Regulatory Gene ◽  
Chromosome Instability ◽  
Genetic Interactions ◽  
Complex Nature ◽  
Whole Genome ◽  
Cohesin Complex ◽  
Synthetic Lethal ◽  
Cancer Types

STAG2, a component of the mitotically essential cohesin complex, is highly mutated in several different tumour types, including glioblastoma and bladder cancer. Whereas cohesin has roles in many cancer-related pathways, such as chromosome instability, DNA repair and gene expression, the complex nature of cohesin function has made it difficult to determine how STAG2 loss might either promote tumorigenesis or be leveraged therapeutically across divergent cancer types. Here, we have performed whole-genome CRISPR-Cas9 screens for STAG2-dependent genetic interactions in three distinct cellular backgrounds. Surprisingly, STAG1, the paralog of STAG2, was the only negative genetic interaction that was shared across all three backgrounds. We also uncovered a paralogous synthetic lethal mechanism behind a genetic interaction between STAG2 and the iron regulatory gene IREB2. Finally, investigation of an unusually strong context-dependent genetic interaction in HAP1 cells revealed factors that could be important for alleviating cohesin loading stress. Together, our results reveal new facets of STAG2 and cohesin function across a variety of genetic contexts.

scholarly journals DANGO: Predicting higher-order genetic interactions

2020 ◽  
Author(s):  
Ruochi Zhang ◽  
Jianzhu Ma ◽  
Jian Ma
Keyword(s):  
Genetic Interaction ◽  
Phenotypic Diversity ◽  
Search Space ◽  
Higher Order ◽  
Genetic Interactions ◽  
Combinatorial Search ◽  
Pairwise Interactions ◽  
Phenotypic Variations ◽  
Almost All

AbstractHigher-order genetic interactions, which have profound impact on phenotypic variations, remain poorly characterized. Almost all studies to date have primarily reported pairwise interactions because it is dauntingly difficult to design high-throughput genetic screenings of the large combinatorial search space for higher-order interactions. Here, we develop an algorithm named Dango, based on a self-attention hypergraph neural network, to effectively predict the higher-order genetic interaction for a group of genes. As a proof-of-concept, we make comprehensive prediction of >400 million trigenic interactions in the yeast S. cerevisiae, significantly expanding the quantitative characterization of trigenic interactions. We find that Dango can accurately predict trigenic interactions that reveal both known and new biological functions related to cell growth. The predicted trigenic interactions can also serve as powerful genetic markers to predict growth response to many distinct conditions. Dango enables unveiling a more complete map of complex genetic interactions that impinge upon phenotypic diversity.

scholarly journals A comprehensive map of genetic interactions in childhood cancer reveals multiple underlying biological mechanisms

2020 ◽  
Author(s):  
Josephine T. Daub ◽  
Saman Amini ◽  
Denise J.E. Kersjes ◽  
Xiaotu Ma ◽  
Natalie Jäger ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Child Death ◽  
Developed Countries ◽  
Genetic Interactions ◽  
Cancer Development ◽  
Biological Mechanisms ◽  
Treatment Research ◽  
Interaction Map

AbstractChildhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological mechanisms underlying most candidates are either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting results of genetic interaction tests.

scholarly journals A systematic analysis of genetic interactions and their underlying biology in childhood cancer

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Josephine T. Daub ◽  
Saman Amini ◽  
Denise J. E. Kersjes ◽  
Xiaotu Ma ◽  
Natalie Jäger ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Child Death ◽  
Developed Countries ◽  
Genetic Interactions ◽  
Cancer Development ◽  
Systematic Analysis ◽  
Treatment Research ◽  
Interaction Map

AbstractChildhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

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