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

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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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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Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BMC Bioinformatics ◽

10.1186/s12859-019-3197-3 ◽

2019 ◽

Vol 20 (S19) ◽

Author(s):

Jiang Huang ◽

Min Wu ◽

Fan Lu ◽

Le Ou-Yang ◽

Zexuan Zhu

Keyword(s):

Matrix Factorization ◽

Drug Targets ◽

Synthetic Lethality ◽

Cancer Therapeutics ◽

The Self ◽

Interaction Data ◽

Synthetic Lethal Interaction ◽

Synthetic Lethal ◽

Interaction Prediction ◽

Synthetic Lethal Interactions

Abstract Background Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. Results In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. Conclusions In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

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Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

10.1101/2021.09.14.460408 ◽

2021 ◽

Author(s):

Lipika R. Pal ◽

Kuoyuan Cheng ◽

Nishanth U Nair ◽

Laura Martin-Sancho ◽

Sanju Sinha ◽

...

Keyword(s):

Drug Targets ◽

Synthetic Lethality ◽

Host Cells ◽

Synthetic Lethal ◽

Viral Propagation ◽

In Vivo Testing ◽

Altered Gene ◽

Host Genes

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

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Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality

Molecular Cancer ◽

10.1186/s12943-021-01405-8 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Salvatore Benfatto ◽

Özdemirhan Serçin ◽

Francesca R. Dejure ◽

Amir Abdollahi ◽

Frank T. Zenke ◽

...

Keyword(s):

Machine Learning ◽

Cancer Genomics ◽

Genetic Interaction ◽

Synthetic Lethality ◽

Interacting Protein ◽

Synthetic Lethal ◽

Damage Repair ◽

Genome Wide ◽

Machine Learning Approach ◽

Transcriptomics Data

Abstract Background Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing. Methods Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map. Results Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2. Conclusions PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.

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Syn-Lethality: An Integrative Knowledge Base of Synthetic Lethality towards Discovery of Selective Anticancer Therapies

BioMed Research International ◽

10.1155/2014/196034 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Xue-juan Li ◽

Shital K. Mishra ◽

Min Wu ◽

Fan Zhang ◽

Jie Zheng

Keyword(s):

Knowledge Base ◽

Drug Targets ◽

Molecular Mechanisms ◽

Synthetic Lethality ◽

Human Cancer ◽

Single Gene ◽

Drug Induced ◽

Human Genes ◽

A Cell ◽

Novel Strategy

Synthetic lethality (SL) is a novel strategy for anticancer therapies, whereby mutations of two genes will kill a cell but mutation of a single gene will not. Therefore, a cancer-specific mutation combined with a drug-induced mutation, if they have SL interactions, will selectively kill cancer cells. While numerous SL interactions have been identified in yeast, only a few have been known in human. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. In this paper, we present Syn-Lethality, the first integrative knowledge base of SL that is dedicated to human cancer. It integrates experimentally discovered and verified human SL gene pairs into a network, associated with annotations of gene function, pathway, and molecular mechanisms. It also includes yeast SL genes from high-throughput screenings which are mapped to orthologous human genes. Such an integrative knowledge base, organized as a relational database with user interface for searching and network visualization, will greatly expedite the discovery of novel anticancer drug targets based on synthetic lethality interactions. The database can be downloaded as a stand-alone Java application.

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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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VRK1 is a Paralog Synthetic Lethal Target in VRK2-methylated Glioblastoma

10.1101/2021.12.30.474571 ◽

2022 ◽

Author(s):

Julie A Shields ◽

Samuel R Meier ◽

Madhavi Bandi ◽

Maria Dam Ferdinez ◽

Justin L Engel ◽

...

Keyword(s):

Kinase Activity ◽

Genetic Interaction ◽

Synthetic Lethality ◽

Cancer Therapeutics ◽

Tumor Growth Inhibition ◽

Synthetic Lethal Interaction ◽

Synthetic Lethal ◽

Aggressive Cancer ◽

Nuclear Envelope Formation ◽

Redundant Functions

Synthetic lethality - a genetic interaction that results in cell death when two genetic deficiencies co-occur but not when either deficiency occurs alone - can be co-opted for cancer therapeutics. A pair of paralog genes is among the most straightforward synthetic lethal interaction by virtue of their redundant functions. Here we demonstrate a paralog-based synthetic lethality by targeting Vaccinia-Related Kinase 1 (VRK1) in Vaccinia-Related Kinase 2 (VRK2)-methylated glioblastoma (GBM). VRK2 is silenced by promoter methylation in approximately two-thirds of GBM, an aggressive cancer with few available targeted therapies. Genetic knockdown of VRK1 in VRK2-null or VRK2-methylated cells results in decreased activity of the downstream substrate Barrier to Autointegration Factor (BAF), a regulator of post-mitotic nuclear envelope formation. VRK1 knockdown, and thus reduced BAF activity, causes nuclear lobulation, blebbing and micronucleation, which subsequently results in G2/M arrest and DNA damage. The VRK1-VRK2 synthetic lethal interaction is dependent on VRK1 kinase activity and is rescued by ectopic VRK2 expression. Knockdown of VRK1 leads to robust tumor growth inhibition in VRK2-methylated GBM xenografts. These results indicate that inhibiting VRK1 kinase activity could be a viable therapeutic strategy in VRK2-methylated GBM.

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Integration of pathway, cellular and genetic context reveals principles of synthetic lethality that affect reproducibility

10.1101/591677 ◽

2019 ◽

Author(s):

Angel A. Ku ◽

Sameera Kongara ◽

Hsien-Ming Hu ◽

Xin Zhao ◽

Di Wu ◽

...

Keyword(s):

Genetic Interaction ◽

Synthetic Lethality ◽

Synthetic Lethal ◽

Testing Framework ◽

Gene Level ◽

Cancer Mutations ◽

Experimental Approaches ◽

Interaction Map ◽

Mutant Cells ◽

Genetic Context

ABSTRACTSynthetic lethal screens have the potential to identify new vulnerabilities incurred by specific cancer mutations but have been hindered by lack of agreement between studies. Using KRAS as a model, we identified that published synthetic lethal screens significantly overlap at the pathway rather than gene level. Analysis of pathways encoded as protein networks identified synthetic lethal candidates that were more reproducible than those previously reported. Lack of overlap likely stems from biological rather than technical limitations as most synthetic lethal phenotypes were strongly modulated by changes in cellular conditions or genetic context, the latter determined using a pairwise genetic interaction map that identified numerous interactions that suppress synthetic lethal effects. Accounting for pathway, cellular and genetic context nominates a DNA repair dependency in KRAS-mutant cells, mediated by a network containing BRCA1. We provide evidence for why most reported synthetic lethals are not reproducible which is addressable using a multi-faceted testing framework.STATEMENT OF SIGNIFICANCESynthetic lethal screens have the power to identify new targets in cancer, although have thus far come up short of expectation. We use computational and experimental approaches to delineate principles for identifying robust synthetic lethal targets that could aid in the development of effective new therapeutic strategies for genetically defined cancers.

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

10.1101/2020.04.20.051623 ◽

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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An Internal Domain of Exo70p Is Required for Actin-independent Localization and Mediates Assembly of Specific Exocyst Components

Molecular Biology of the Cell ◽

10.1091/mbc.e08-02-0157 ◽

2009 ◽

Vol 20 (1) ◽

pp. 153-163 ◽

Cited By ~ 24

Author(s):

Alex H. Hutagalung ◽

Jeff Coleman ◽

Marc Pypaert ◽

Peter J. Novick

Keyword(s):

Plasma Membrane ◽

Synthetic Lethality ◽

Single Gene ◽

Synthetic Lethal ◽

Amino Terminal ◽

Synthetic Lethal Interactions ◽

Synthetically Lethal ◽

Actin Cables ◽

Amino Terminal Domain ◽

Internal Domain

The exocyst consists of eight rod-shaped subunits that align in a side-by-side manner to tether secretory vesicles to the plasma membrane in preparation for fusion. Two subunits, Sec3p and Exo70p, localize to exocytic sites by an actin-independent pathway, whereas the other six ride on vesicles along actin cables. Here, we demonstrate that three of the four domains of Exo70p are essential for growth. The remaining domain, domain C, is not essential but when deleted, it leads to synthetic lethality with many secretory mutations, defects in exocyst assembly of exocyst components Sec5p and Sec6p, and loss of actin-independent localization. This is analogous to a deletion of the amino-terminal domain of Sec3p, which prevents an interaction with Cdc42p or Rho1p and blocks its actin-independent localization. The two mutations are synthetically lethal, even in the presence of high copy number suppressors that can bypass complete deletions of either single gene. Although domain C binds Rho3p, loss of the Exo70p-Rho3p interaction does not account for the synthetic lethal interactions or the exocyst assembly defects. The results suggest that either Exo70p or Sec3p must associate with the plasma membrane for the exocyst to function as a vesicle tether.

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