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

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

Nucleic Acids Research ◽

10.1093/nar/gku576 ◽

2014 ◽

Vol 42 (15) ◽

pp. 9838-9853 ◽

Cited By ~ 6

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.

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

10.1101/646810 ◽

2019 ◽

Cited By ~ 1

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.

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Exploring genetic interaction manifolds constructed from rich phenotypes

10.1101/601096 ◽

2019 ◽

Cited By ~ 3

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.

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Predictability of Genetic Interactions from Functional Gene Modules

10.1101/049627 ◽

2016 ◽

Author(s):

Jonathan H. Young ◽

Edward M. Marcotte

Keyword(s):

Web Application ◽

Genetic Interaction ◽

Homo Sapiens ◽

Interaction Network ◽

Genetic Interactions ◽

Functional Relationships ◽

Therapy Targets ◽

Genetic Interaction Network ◽

Gene Level ◽

Interaction Partners

AbstractCharacterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets. Yet experimentally determining whether genes interact is technically non-trivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally-related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high-predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

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

Life Science Alliance ◽

10.26508/lsa.202101083 ◽

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.

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Discovery of synthetic lethal and tumor suppressive paralog pairs in the human genome

10.1101/2020.12.20.423710 ◽

2020 ◽

Author(s):

Phoebe C. R. Parrish ◽

James D. Thomas ◽

Shriya Kamlapurkar ◽

Austin Gabel ◽

Robert K. Bradley ◽

...

Keyword(s):

Drug Targets ◽

Genetic Interaction ◽

Synthetic Lethality ◽

Essential Gene ◽

Single Gene ◽

Gene Families ◽

Blind Spot ◽

List Type ◽

Double Knockout ◽

Synthetic Lethal

AbstractCRISPR knockout screens have accelerated the discovery of important cancer genetic dependencies. However, traditional CRISPR-Cas9 screens are limited in their ability to assay the function of redundant or duplicated genes. Paralogs in multi-gene families constitute two-thirds of the protein-coding genome, so this blind spot is the rule, not the exception. To overcome the limitations of single gene CRISPR knockout screens, we developed paired guide RNAs for Paralog gENetic interaction mapping (pgPEN), a pooled CRISPR/Cas9 approach which targets over a thousand duplicated human paralogs in single knockout and double knockout configurations. We applied pgPEN to two cell lineages and discovered that over 10% of human paralogs exhibit synthetic lethality in at least one cellular context. We recovered known synthetic lethal paralogs such as MAP2K1/MAP2K2, important drug targets such as CDK4/CDK6, and numerous other synthetic lethal pairs such as CCNL1/CCNL2. In addition, we identified ten tumor suppressive paralog pairs whose compound loss promotes cell growth. These findings identify a large number of previously unidentified essential gene families and nominate new druggable targets for oncology drug discovery.HighlightsComprehensive genetic interaction mapping of 1,030 human duplicated paralogs using a dual targeting CRISPR/Cas9 approachDuplicated paralogs are highly enriched for genetic interactionsSynthetic lethal paralogs include CCNL1/CCNL2, CDK4/CDK6, and GSK3A/GSK3BTumor suppressor paralog pairs include CDKN2A/CDKN2B and FBXO25/FBXO32

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Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

Current Biotechnology ◽

10.2174/2211550109999201008125800 ◽

2020 ◽

Vol 9 (3) ◽

pp. 177-191

Author(s):

Sridharan Priya ◽

Radha K. Manavalan

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Cardiovascular Diseases ◽

Computational Models ◽

Genetic Interaction ◽

Association Studies ◽

Genetic Interactions ◽

Computational Techniques ◽

Genome Wide Association Studies ◽

Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

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Stress Mediating Genes in Aging, Health, and Longevity Traits: Effects of Multiple Interactions

Innovation in Aging ◽

10.1093/geroni/igaa057.915 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 286-286

Author(s):

Anatoliy Yashin ◽

Dequing Wu ◽

Konstantin Arbeev ◽

Arseniy Yashkin ◽

Galina Gorbunova ◽

...

Keyword(s):

Strong Interaction ◽

Genetic Interaction ◽

Interaction Effects ◽

Genetic Interactions ◽

Risk Scores ◽

Data Sets ◽

Interaction Patterns ◽

Polygenic Risk ◽

Drug Candidates ◽

Aging Health

Abstract Persistent stress of external or internal origin accelerates aging, increases risk of aging related health disorders, and shortens lifespan. Stressors activate stress response genes, and their products collectively influence traits. The variability of stressors and responses to them contribute to trait heterogeneity, which may cause the failure of clinical trials for drug candidates. The objectives of this paper are: to address the heterogeneity issue; to evaluate collective interaction effects of genetic factors on Alzheimer’s disease (AD) and longevity using HRS data; to identify differences and similarities in patterns of genetic interactions within two genders; and to compare AD related genetic interaction patterns in HRS and LOADFS data. To reach these objectives we: selected candidate genes from stress related pathways affecting AD/longevity; implemented logistic regression model with interaction term to evaluate effects of SNP-pairs on these traits for males and females; constructed the novel interaction polygenic risk scores for SNPs, which showed strong interaction potential, and evaluated effects of these scores on AD/longevity; and compared patterns of genetic interactions within the two genders and within two datasets. We found there were many genes involved in highly significant interactions that were the same and that were different within the two genders. The effects of interaction polygenic risk scores on AD were strong and highly statistically significant. These conclusions were confirmed in analyses of interaction effects on longevity trait using HRS data. Comparison of HRS to LOADFS data showed that many genes had strong interaction effects on AD in both data sets.

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Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0806261105 ◽

2008 ◽

Vol 105 (43) ◽

pp. 16653-16658 ◽

Cited By ~ 124

Author(s):

S. J. Dixon ◽

Y. Fedyshyn ◽

J. L. Y. Koh ◽

T. S. K. Prasad ◽

C. Chahwan ◽

...

Keyword(s):

Genetic Interaction ◽

Interaction Networks ◽

Synthetic Lethal

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A Screen for Dynein Synthetic Lethals in Aspergillus nidulans Identifies Spindle Assembly Checkpoint Genes and Other Genes Involved in Mitosis

Genetics ◽

10.1093/genetics/149.1.101 ◽

1998 ◽

Vol 149 (1) ◽

pp. 101-116

Author(s):

Vladimir P Efimov ◽

N Ronald Morris

Keyword(s):

Aspergillus Nidulans ◽

Spindle Assembly Checkpoint ◽

Genetic Interaction ◽

Synthetic Lethality ◽

Spindle Assembly ◽

Nuclear Migration ◽

Cytoplasmic Dynein ◽

Vesicle Transport ◽

Synthetic Lethal ◽

Checkpoint Genes

Abstract Cytoplasmic dynein is a ubiquitously expressed microtubule motor involved in vesicle transport, mitosis, nuclear migration, and spindle orientation. In the filamentous fungus Aspergillus nidulans, inactivation of cytoplasmic dynein, although not lethal, severely impairs nuclear migration. The role of dynein in mitosis and vesicle transport in this organism is unclear. To investigate the complete range of dynein function in A. nidulans, we searched for synthetic lethal mutations that significantly reduced growth in the absence of dynein but had little effect on their own. We isolated 19 sld (synthetic lethality without dynein) mutations in nine different genes. Mutations in two genes exacerbate the nuclear migration defect seen in the absence of dynein. Mutations in six other genes, including sldA and sldB, show a strong synthetic lethal interaction with a mutation in the mitotic kinesin bimC and, thus, are likely to play a role in mitosis. Mutations in sldA and sldB also confer hypersensitivity to the microtubule-destabilizing drug benomyl. sldA and sldB were cloned by complementation of their mutant phenotypes using an A. nidulans autonomously replicating vector. Sequencing revealed homology to the spindle assembly checkpoint genes BUB1 and BUB3 from Saccharomyces cerevisiae. Genetic interaction between dynein and spindle assembly checkpoint genes, as well as other mitotic genes, indicates that A. nidulans dynein plays a role in mitosis. We suggest a model for dynein motor action in A. nidulans that can explain dynein involvement in both mitosis and nuclear distribution.

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