Genetic Interaction Mapping Research Articles

Genetic interaction mapping of Aurora protein kinases in mouse oocytes.

The Aurora Kinases (AURKs) are a family of serine-threonine protein kinases critical for cell division. Somatic cells express only AURKA and AURKB. However, mammalian germ cells and some cancer cells express all three isoforms. A major question in the field has been determining the molecular and cellular changes when cells express three instead of two aurora kinases. Using a systematic genetic approach involving different Aurora kinase oocyte-specific knockout combinations, we completed an oocyte-AURK genetic interaction map and show that one genomic copy of Aurka is necessary and sufficient to support female fertility and oocyte meiosis. We further confirm that AURKB and AURKC alone cannot compensate for AURKA. These results highlight the importance of AURKA in mouse oocytes, demonstrating that it is required for spindle formation and proper chromosome segregation. Surprisingly, a percentage of oocytes that lack AURKB can complete meiosis I, but the quality of those eggs is compromised, suggesting a role in AURKB to regulate spindle assembly checkpoint or control the cell cycle. Together with our previous studies, we wholly define the genetic interplay among the Aurora kinases and reinforce the importance of AURKA expression in oocyte meiosis.

Genetic interaction mapping reveals functional relationships between peptidoglycan endopeptidases and carboxypeptidases.

Peptidoglycan (PG) is the main component of the bacterial cell wall; it maintains cell shape while protecting the cell from internal osmotic pressure and external environmental challenges. PG synthesis is essential for bacterial growth and survival, and a series of PG modifications are required to allow expansion of the sacculus. Endopeptidases (EPs), for example, cleave the crosslinks between adjacent PG strands to allow the incorporation of newly synthesized PG. EPs are collectively essential for bacterial growth and must likely be carefully regulated to prevent sacculus degradation and cell death. However, EP regulation mechanisms are poorly understood. Here, we used TnSeq to uncover novel EP regulators in Vibrio cholerae. This screen revealed that the carboxypeptidase DacA1 (PBP5) alleviates EP toxicity. dacA1 is essential for viability on LB medium, and this essentiality was suppressed by EP overexpression, revealing that EP toxicity both mitigates, and is mitigated by, a defect in dacA1. A subsequent suppressor screen to restore viability of ΔdacA1 in LB medium identified hypomorphic mutants in the PG synthesis pathway, as well as mutations that promote EP activation. Our data thus reveal a more complex role of DacA1 in maintaining PG homeostasis than previously assumed.

Mapping of DDX11 genetic interactions defines sister chromatid cohesion as the major dependency.

DDX11/Chl1R is a conserved DNA helicase with roles in genome maintenance, DNA replication, and chromatid cohesion. Loss of DDX11 in humans leads to the rare cohesinopathy Warsaw breakage syndrome. DDX11 has also been implicated in human cancer where it has been proposed to have an oncogenic role and possibly to constitute a therapeutic target. Given the multiple roles of DDX11 in genome stability and its potential as an anticancer target, we set out to define a complete genetic interaction profile of DDX11 loss in human cell lines. Screening the human genome with clustered regularly interspaced short palindromic repeats (CRISPR) guide RNA drop out screens in DDX11-wildtype (WT) or DDX11-deficient cells revealed a strong enrichment of genes with functions related to sister chromatid cohesion. We confirm synthetic lethal relationships between DDX11 and the tumor suppressor cohesin subunit STAG2, which is frequently mutated in several cancer types and the kinase HASPIN. This screen highlights the importance of cohesion in cells lacking DDX11 and suggests DDX11 may be a therapeutic target for tumors with mutations in STAG2.

A global genetic interaction network by single-cell imaging and machine learning.

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Genetic interactions under the microscope.

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

Genetic Interactions of Progranulin Across the ALS-FTD Spectrum and Beyond.

Progranulin (PGRN) is a growth factor in which mutations are one of the leading causes of frontotemporal dementia (FTD), and has been implicated in an assortment of neurodegenerative diseases. Conversely, higher levels of the protein have shown potential as a general neuronal protective factor. While examining its neuroprotective applications on a broader scale would be unfeasible in mammalian models, we turned to the nematode C. elegans to map the interactions of PGRN across multiple genetic models of neurodegenerative diseases. Our results indicate that while the overexpression of PGRN appears to be protective across all models tested, the loss of PGRN exacerbated the disease phenotypes of all but three of the models tested. Given the ease of genetic analysis in nematodes, we propose this model organism as an efficient tool to build a comprehensive map of PGRN's genetic interactions.

CRISPR screens for functional interrogation of immunity.

CRISPR-based technologies represent a major breakthrough in biomedical science as they offer a powerful platform for unbiased screening and functional genomics in various fields, including immunology. Pooled and arrayed CRISPR screens have uncovered previously unknown intracellular drivers in innate and adaptive immune cells for immune regulation as well as intercellular regulators mediating cell-cell interactions. Recent single-cell CRISPR screening platforms expand the readouts to the transcriptome and enable the inference of gene regulatory networks for better mechanistic insights. CRISPR screens also allow for mapping of genetic interactions to identify genes that synergize or alleviate complex immune phenotypes. Here, we review the progress in and emerging adaptation of CRISPR technologies to advance our fundamental immunological knowledge and identify novel disease targets for immunotherapy of infection, inflammation and cancer.

A genetic map of the chromatin regulators to drug response in cancer cells

BackgroundDiverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging.MethodsIn order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets.ResultsTotally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment.ConclusionsThe CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

Abstract 2278: Expanding cancer therapy options by leveraging synthetic lethal interactions between druggable paralogs

Abstract Synthetic lethal therapies are a promising approach to expand therapeutic options for cancer patients. Since synthetic lethal therapies target tumor cells specifically, they have fewer off-target effects than oncogene targeted approaches. To identify new cancer drug targets, we focused on paralogs, ancestrally-duplicated genes that frequently retain redundant or overlapping functions. To find synthetic lethal human paralogs, we developed paired guide RNAs for Paralog gENetic interaction mapping (pgPEN), a pooled CRISPR-Cas9 single and double knockout approach targeting over 2,000 paralogs. We applied pgPEN to two cancer cell lines and found that 12% of human paralogs exhibit synthetic lethality in at least one context. To our knowledge, pgPEN represents the largest experimental assessment of human paralog synthetic lethality to date. We next identified paralog pairs to prioritize for follow-up study. A key drawback to synthetic lethal therapies is that many genetic interactions are context-dependent. To identify likely penetrant interactions, we compared our data to other published paralog screens and computational predictions of paralog synthetic lethality. We found that over 75% (n=96) of pgPEN hits were predicted to be broadly synthetic lethal, and nearly 10% (n=10) of pairs were synthetic lethal in multiple paralog screens. Finally, we prioritized paralogs targeted by existing small molecule therapies. Of the 122 synthetic lethal pairs identified by pgPEN, 16% (n=20) are currently druggable. We mined drug repurposing data from DepMap to find pairs where a paralog-targeting drug showed a stronger effect in cell lines with low target gene copy number or expression relative to cell lines with normal target gene copy number or expression. These drugs could selectively target cancer cells in cases where one or both paralogs is lost in tumors but retained in normal tissue. We also leveraged The Cancer Genome Atlas tumor sequencing data to find paralog pairs where one member is recurrently lost in cancer. Taken together, these studies identify druggable, highly penetrant synthetic lethal paralog interactions. In combination with tumor sequencing data, we identify high-priority paralog drug targets that can be further tested and translated to the clinic. Paralog synthetic lethal therapies could provide a relatively low-toxicity therapeutic approach to improve the efficacy of cancer treatments and prevent the emergence of acquired resistance. Citation Format: Phoebe C. Parrish, James D. Thomas, Austin M. Gabel, Shriya Kamlapurkar, Robert K. Bradley, Alice H. Berger. Expanding cancer therapy options by leveraging synthetic lethal interactions between druggable paralogs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2278.

Chemical-genetic interaction mapping links carbon metabolism and cell wall structure to tuberculosis drug efficacy.

Current chemotherapy against Mycobacterium tuberculosis (Mtb), an important human pathogen, requires a multidrug regimen lasting several months. While efforts have been made to optimize therapy by exploiting drug–drug synergies, testing new drug combinations in relevant host environments remains arduous. In particular, host environments profoundly affect the bacterial metabolic state and drug efficacy, limiting the accuracy of predictions based on in vitro assays alone. In this study, we utilized conditional Mtb knockdown mutants of essential genes as an experimentally tractable surrogate for drug treatment and probe the relationship between Mtb carbon metabolism and chemical–genetic interactions (CGIs). We examined the antitubercular drugs isoniazid, rifampicin, and moxifloxacin and found that CGIs are differentially responsive to the metabolic state, defining both environment-independent and -dependent interactions. Specifically, growth on the in vivo–relevant carbon source, cholesterol, reduced rifampicin efficacy by altering mycobacterial cell surface lipid composition. We report that a variety of perturbations in cell wall synthesis pathways restore rifampicin efficacy during growth on cholesterol, and that both environment-independent and cholesterol-dependent in vitro CGIs could be leveraged to enhance bacterial clearance in the mouse infection model. Our findings present an atlas of chemical–genetic–environmental interactions that can be used to optimize drug–drug interactions, as well as provide a framework for understanding in vitro correlates of in vivo efficacy.

Genetic interaction mapping highlights key roles of the Tol-Pal complex.

The conserved Tol-Pal trans-envelope complex is important for outer membrane (OM) stability and cell division in Gram-negative bacteria. It is proposed to mediate OM constriction during cell division via cell wall tethering. Yet, recent studies suggest the complex has additional roles in OM lipid homeostasis and septal wall separation. How Tol-Pal facilitates all these processes is unclear. To gain insights into its function(s), we applied transposon-insertion sequencing, and report here a detailed network of genetic interactions with the tol-pal locus in Escherichia coli. We found one positive and>20 negative strong interactions based on fitness. Disrupting osmoregulated-periplasmic glucan biosynthesis restores fitness and OM barrier function, but not proper division, in tol-pal mutants. In contrast, deleting genes involved in OM homeostasis and cell wall remodeling causes synthetic growth defects in strains lacking Tol-Pal, especially exacerbating OM barrier and/or division phenotypes. Notably, the ΔtolA mutant having additional defects in OM protein assembly (ΔbamB) exhibited severe division phenotypes, even when single mutants divided normally; this highlights the possibility for OM phenotypes to indirectly impact cell division. Overall, our work underscores the intricate nature of Tol-Pal function, and reinforces its key roles in cell wall-OM tethering, cell wall remodeling, and in particular, OM homeostasis.

From systems to structure - using genetic data to model protein structures.

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical–genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Using Synthetic Genetic Interactions in Candida glabrata as a Novel Method to Detect Genes with Roles in Antifungal Drug Resistance.

Synthetic genetic interaction analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the interactions between genes, categorize into biological pathways, and characterize genes of unknown function. It has been extensively employed in model organisms for fundamental, systems-level assessment of the interactions between genes. However, more recently, genetic interaction mapping has been applied to fungal pathogens and has been instrumental for the study of clinically important infectious organisms. This protocol herein explains in the detail the methodology and analysis that can be employed to develop interaction maps in microbial pathogens. Such techniques can aid in bridging our understanding of complex genetic networks, with applications to diverse microbial pathogens to further our understanding of virulence, the use of antimicrobial therapies, and host-pathogen interactions.

Conducting genetic interaction analysis with CRISPR-Cas9-based strategies in Candida albicans

Candida albicans is an opportunistic fungal pathogen found in the oral mucosa, the gut, the vaginal mucosa, and humans' skin. While C. albicans can cause superficial infections, severe invasive infections can occur in immunocompromised individuals. Understanding the survival mechanisms and pathogenesis of C. albicans is critical for novel antifungal drug discovery. Determining the relationships between different genes can create a genetic interaction map, which can identify complementary gene sets, central to C. albicans survival, as potential drug targets in combination therapy. A genetic approach using the CRISPR-Cas9-based genome editing platform will focus on genetic interaction analysis of C. albicans stress response genes. The ultimate goal is to create a stress response gene deletion library to study its pathogen survival role. This library of single and double stress response gene mutants will be screened under diverse growth conditions to assess their relative fitness. Genetic interaction analysis will help map out epistatic interactions between fungal genes involved in growth, survival, and pathogenesis and uncover putative targets for combination antifungal therapy based on negative or synthetic lethal genetic interactions.

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

Childhood 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.

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening.

CRISPR-based forward genetic screening represents a powerful approach for the systematic characterization of gene function. Recent efforts have been directed toward establishing CRISPR-based tools for the programmable delivery of combinatorial genetic perturbations, most of which are mediated by a single nuclease and the expression of structurally identical guide backbones from two promoters. In contrast, we have developed CHyMErA (Cas hybrid for multiplexed editing and screening applications), which is based on the co-expression of Cas9 and Cas12a nucleases in conjunction with a hybrid guide RNA (hgRNA) engineered by the fusion of Cas9 and Cas12a guides and expressed from a single U6 promoter. CHyMErA is suitable for the high-throughput deletion of genetic segments including the excision of individual exons. Furthermore, CHyMErA enables the concomitant targeting of two or more genes and can thus be used for the systematic mapping of genetic interactions in mammalian cells. CHyMErA can also be applied for the perturbation of paralogous gene pairs, thereby allowing the capturing of phenotypic roles that would otherwise be masked because of genetic redundancy. Here, we provide instructions for the cloning of hgRNA screening libraries and individual hgRNA constructs and offer guidelines for designing and performing combinatorial pooled genetic screens using CHyMErA. Starting with the generation of Cas9- and Cas12a-expressing cell lines, CHyMErA screening can be implemented within 15-20 weeks.

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast.

Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with query mutants related with RNA metabolism and estimated the growth rates of about 700 000 double mutant Saccharomyces cerevisiae strains. The tested targets included the gene deletion collection and 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results, we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1471 genes and 117 biological processes. In addition to these large-scale results, we validated both enhancement and suppression of slow growth measured for specific RNA-related pathways. Thus, negative genetic interactions identified a role for the OCA inositol polyphosphate hydrolase complex in mRNA translation initiation. By analysis of suppressors, we found that Puf4, a Pumilio family RNA binding protein, inhibits ribosomal protein Rpl9 function, by acting on a conserved UGUAcauUA motif located downstream the stop codon of the RPL9B mRNA. Altogether, the results and their analysis should represent a useful resource for discovery of gene function in yeast.

Cost-Effective Mapping of Genetic Interactions in Mammalian Cells.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

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

SUMMARYCRISPR screens have accelerated the discovery of important cancer vulnerabilities. However, single-gene knockout phenotypes can be masked by redundancy among related genes. Paralogs constitute two-thirds of the human protein-coding genome, so existing methods are likely inadequate for assaying a large portion of gene function. Here, we develop paired guide RNAs for paralog genetic interaction mapping (pgPEN), a pooled CRISPR-Cas9 single- and double-knockout approach targeting more than 2,000 human paralogs. We apply pgPEN to two cell types and discover that 12% of human paralogs exhibit synthetic lethality in at least one context. We recover known synthetic lethal paralogs MEK1/MEK2, important drug targets CDK4/CDK6, and other synthetic lethal pairs including CCNL1/CCNL2. Additionally, we identify ten tumor suppressor paralog pairs whose compound loss promotes cell proliferation. These findings nominate drug targets and suggest that paralog genetic interactions could shape the landscape of positive and negative selection in cancer.

Abstract 2133: Systematic mapping of genetic interactions in human cancer cells reveals context dependent cancer signaling pathways

Abstract Genetic interaction screens have long been used to map gene functions and cellular pathways in model organisms and more recently in human cells. However, due to the technical challenges of these maps, most studies in human cells have focused on a single cell line, limiting the ability to determine how different genetic and cellular contexts influence genetic interactions. Here, we engineered a 110,728-element combinatorial CRISPR-Cas9 library to systematically map 12,282 genetic interactions among tumor suppressor genes and druggable targets. We mapped the landscape of genetic interactions in cell lines from breast cancer, head and neck squamous carcinoma, and non-small cell lung cancer as well as in non-cancerous, epithelial cells. The resulting genetic interaction maps emphasize the context dependency of these interactions. Of the interactions that are conserved across multiple cell lines, many include frequently essential genes, suggesting that these interactions may be critical for fundamental cellular functions. We found that genetic interaction maps from cancer cell lines are more similar to one another than non-cancer interaction maps, even between cancer and normal cells from the same tissue. Subsequent targeted chemogenetic screens confirmed interaction hubs across multiple cell lines while highlighting the robustness of some of the strongest interactions. Many of the identified genetic interactions suggest therapeutic interventions, such as the combination of CDK4 and MAPK inhibitors. Using the tissue-specific genetic interaction networks, we also built a deep learning model that accurately predicts patient outcomes, outperforming models that use accepted biomarkers such as MammaPrint. In summary, these genetic interaction maps provide a broad resource to investigate context dependency in cancer signaling pathways and in cancer drug response. Citation Format: Samson H. Fong, Brent M. Kuenzi, John Lee, Kyle Sanchez, Ana Bojorquez-Gomez, Kyle Ford, Brenton P. Munson, Katherine Licon, Jeff Hager, John Paul Shen, Jason F. Kreisberg, Prashant Mali, Trey Ideker. Systematic mapping of genetic interactions in human cancer cells reveals context dependent cancer signaling pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2133.