scholarly journals Exploring genetic interaction manifolds constructed from rich phenotypes

2019 ◽  
Author(s):  
Thomas M. Norman ◽  
Max A. Horlbeck ◽  
Joseph M. Replogle ◽  
Alex Y. Ge ◽  
Albert Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Genetic Interaction ◽  
Erythroid Differentiation ◽  
Analytical Framework ◽  
Genetic Interactions ◽  
Synthetic Lethal ◽  
Regulatory Pathways ◽  
Synergistic Interactions ◽  
Dimensional Interaction ◽  
Synthetic Lethal Interactions

AbstractSynergistic interactions between gene functions drive cellular complexity. However, the combinatorial explosion of possible genetic interactions (GIs) has necessitated the use of scalar interaction readouts (e.g. growth) that conflate diverse outcomes. Here we present an analytical framework for interpreting manifolds constructed from high-dimensional interaction phenotypes. We applied this framework to rich phenotypes obtained by Perturb-seq (single-cell RNA-seq pooled CRISPR screens) profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying true suppressors), and mechanistic elucidation of synthetic lethal interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.One Sentence SummaryPrinciples and mechanisms of genetic interactions are revealed by rich phenotyping using single-cell RNA sequencing.

scholarly journals Exploring genetic interaction manifolds constructed from rich single-cell phenotypes

Science ◽  
2019 ◽  
Vol 365 (6455) ◽  
pp. 786-793 ◽  
Author(s):  
Thomas M. Norman ◽  
Max A. Horlbeck ◽  
Joseph M. Replogle ◽  
Alex Y. Ge ◽  
Albert Xu ◽  
...  
Keyword(s):  
Single Cell ◽  
Genetic Interaction ◽  
Erythroid Differentiation ◽  
Analytical Framework ◽  
Regulatory Pathways ◽  
Synergistic Interactions ◽  
Expression Of Genes ◽  
Organismal Complexity ◽  
Cell Phenotypes

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

scholarly journals Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

2014 ◽  
Vol 42 (15) ◽  
pp. 9838-9853 ◽  
Author(s):  
Saeed Kaboli ◽  
Takuya Yamakawa ◽  
Keisuke Sunada ◽  
Tao Takagaki ◽  
Yu Sasano ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Essential Genes ◽  
Genetic Interactions ◽  
Lethal Gene ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

scholarly journals Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Morgan W. B. Kirzinger ◽  
Frederick S. Vizeacoumar ◽  
Bjorn Haave ◽  
Cristina Gonzalez-Lopez ◽  
Keith Bonham ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Interaction ◽  
Synthetic Lethal ◽  
Synthetic Lethal Interactions ◽  
Yeast Genetic

scholarly journals SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery

2021 ◽  
Author(s):  
Bahar Tercan ◽  
Guangrong Qin ◽  
Taekkyun Kim ◽  
Boris Aguilar ◽  
Christopher J. Kemp ◽  
...  
Keyword(s):  
Genetic Interactions ◽  
Computational Resource ◽  
Synthetic Lethal Interaction ◽  
Synthetic Lethal ◽  
Damage Repair ◽  
Interaction Partners ◽  
Synthetic Lethal Interactions ◽  
Multiple Prediction ◽  
Context Specific ◽  
Repair Genes

Synthetic lethal interactions (SLIs), genetic interactions whereby the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer. We present SL-Cloud, an integrated resource and framework to facilitate prediction of context-specific synthetic lethal interactions using cloud-based technologies. This resource addresses two main challenges related to SLI inference, namely, the need to wrangle and preprocess large multi-omic datasets and the ability to integrate multiple prediction approaches, each of which comes with its own assumptions. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential synthetic lethal interaction partners using multiple computational strategies. Context specific SLI potential can also be studied using the framework. The SL-Cloud computational resource demonstrates a variety of use cases and demonstrates the utility of this approach for customizable and extensible in silico inference of SLIs.

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 Single-cell genomics reveals the genetic and molecular bases for escape from mutational epistasis in myeloid neoplasms

Blood ◽  
2020 ◽  
Vol 136 (13) ◽  
pp. 1477-1486 ◽  
Author(s):  
Justin Taylor ◽  
Xiaoli Mi ◽  
Khrystyna North ◽  
Moritz Binder ◽  
Alexander Penson ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Splicing ◽  
Large Scale ◽  
Hematologic Malignancies ◽  
Splicing Factor ◽  
Splicing Factors ◽  
Synthetic Lethal ◽  
Myeloid Neoplasms ◽  
Sequencing Studies ◽  
Synthetic Lethal Interactions

Abstract Large-scale sequencing studies of hematologic malignancies have revealed notable epistasis among high-frequency mutations. One of the most striking examples of epistasis occurs for mutations in RNA splicing factors. These lesions are among the most common alterations in myeloid neoplasms and generally occur in a mutually exclusive manner, a finding attributed to their synthetic lethal interactions and/or convergent effects. Curiously, however, patients with multiple-concomitant splicing factor mutations have been observed, challenging our understanding of one of the most common examples of epistasis in hematologic malignancies. In this study, we performed bulk and single-cell analyses of patients with myeloid malignancy who were harboring ≥2 splicing factor mutations, to understand the frequency and basis for the coexistence of these mutations. Although mutations in splicing factors were strongly mutually exclusive across 4231 patients (q < .001), 0.85% harbored 2 concomitant bona fide splicing factor mutations, ∼50% of which were present in the same individual cells. However, the distribution of mutations in patients with double mutations deviated from that in those with single mutations, with selection against the most common alleles, SF3B1K700E and SRSF2P95H/L/R, and selection for less common alleles, such as SF3B1 non-K700E mutations, rare amino acid substitutions at SRSF2P95, and combined U2AF1S34/Q157 mutations. SF3B1 and SRSF2 alleles enriched in those with double-mutations had reduced effects on RNA splicing and/or binding compared with the most common alleles. Moreover, dual U2AF1 mutations occurred in cis with preservation of the wild-type allele. These data highlight allele-specific differences as critical in regulating the molecular effects of splicing factor mutations as well as their cooccurrences/exclusivities with one another.

scholarly journals Genetic screens in isogenic mammalian cell lines without single cell cloning

2019 ◽  
Author(s):  
Peter C DeWeirdt ◽  
Kendall R Sanson ◽  
Ruth E Hanna ◽  
Mudra Hegde ◽  
Annabel K Sangree ◽  
...  
Keyword(s):  
Cell Lines ◽  
Mammalian Cells ◽  
Genetic Interaction ◽  
Cell Types ◽  
Repair Gene ◽  
Synthetic Lethal ◽  
Mammalian Cell Lines ◽  
Genome Wide ◽  
Single Cell Cloning ◽  
Synthetic Lethal Interactions

Isogenic pairs of cell lines, which differ by a single genetic modification, are powerful tools for understanding gene function. Generating such pairs for mammalian cells, however, is labor-intensive, time-consuming, and impossible in some cell types. Here we present an approach to create isogenic pairs of cells and screen them with genome-wide CRISPR-Cas9 libraries to generate genetic interaction maps. We queried the anti-apoptotic genes BCL2L1 and MCL1, and the DNA damage repair gene PARP1, via 25 genome-wide screens across 4 cell lines. For all three genes, we identify a rich set of both expected and novel buffering and synthetic lethal interactions. Further, we compare the interactions observed in genetic space to those found when targeting these genes with small molecules and identify hits that may inform the clinical uses for these inhibitors. We anticipate that this methodology will be broadly useful to comprehensively study genes of interest across many cell types.

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 Combined Proteomic and Genetic Interaction Mapping Reveals New RAS Effector Pathways and Susceptibilities

2020 ◽  
Author(s):  
Marcus R. Kelly ◽  
Kaja Kostyrko ◽  
Kyuho Han ◽  
Nancie Mooney ◽  
Edwin E. Jeng ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Affinity Purification ◽  
Gene Families ◽  
Genetic Interactions ◽  
New Combination ◽  
Screening Process ◽  
Ras Pathway ◽  
Synthetic Lethal ◽  
Therapy Targets ◽  
Multi Stage

ABSTRACTActivating mutations in RAS GTPases drive one fifth of cancers, but poor understanding of many RAS effectors and regulators, and of the roles of their different paralogs, continues to impede drug development. We developed a multi-stage discovery and screening process to understand RAS function and identify RAS-related susceptibilities in lung adenocarcinoma. Using affinity purification mass spectrometry (AP/MS), we generated a protein-protein interaction map of the RAS pathway containing thousands of interactions. From this network we constructed a CRISPR dual knockout library targeting 119 RAS-related genes that we screened for genetic interactions (GIs). We found important new effectors of RAS-driven cellular functions, RADIL and the GEF RIN1, and over 250 synthetic lethal GIs, including a potent KRAS-dependent interaction between RAP1GDS1 and RHOA. Many GIs link specific paralogs within and between gene families. These findings illustrate the power of the multiomic approach to identify synthetic lethal combinations for hitherto undruggable cancers.STATEMENT OF SIGNIFICANCEWe present a thorough survey of protein-protein and genetic interactions in the Ras pathway. These interactions suggested new discoveries that we validate here, and demonstrate important new paralog specificities and redundancies. By comparing synthetic lethal interactions across KRAS-dependent and -independent tumors, we identify new combination therapy targets against Ras-driven cancers.

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