Exploring the conservation of synthetic lethal genetic interaction networks

Targeting fungal lipid synthesis for antifungal drug development and potentiation of contemporary antifungals

Two of the three most commonly used classes of antifungal drugs target the fungal membrane through perturbation of sterol biosynthesis or function. In addition to these triazole and polyene antifungals, recent research is identifying new antifungal molecules that perturb lipid biosynthesis and function. Here, we review fungal lipid biosynthesis pathways and their potential as targets for antifungal drug development. An emerging goal is discovering new molecules that potentiate contemporary antifungal drugs in part through perturbation of lipid form and function.

Phenotypic landscape of a fungal meningitis pathogen reveals its unique biology

Cryptococcus neoformans is the most common cause of fungal meningitis and the top-ranked W.H.O. priority fungal pathogen. Only distantly related to model fungi, C. neoformans is also a powerful experimental system for exploring conserved eukaryotic mechanisms lost from specialist model yeast lineages. To decipher its biology globally, we constructed 4328 gene deletions and measured-with exceptional precision--the fitness of each mutant under 141 diverse growth-limiting in vitro conditions and during murine infection. We defined functional modules by clustering genes based on their phenotypic signatures. In-depth studies leveraged these data in two ways. First, we defined and investigated new components of key signaling pathways, which revealed animal-like pathways/components not predicted from studies of model yeasts. Second, we identified environmental adaptation mechanisms repurposed to promote mammalian virulence by C. neoformans, which lacks a known animal reservoir. Our work provides an unprecedented resource for deciphering a deadly human pathogen.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins as viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal (SL) partners of cancer-specific mutations. Similarly, SL interactions of viral-induced hypomorphs can potentially be targeted as host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for SL partners of GBF1 revealed ARF1 as the top hit, disruption of which selectively killed cells that synthesize 3A alone or in the context of a poliovirus replicon. Thus, viral protein interactions can induce hypomorphs that render host cells selectively vulnerable to perturbations that leave uninfected cells otherwise unscathed. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal partners of cancer-specific mutations. Synthetic lethal interactions of viral-induced hypomorphs have the potential to be similarly targeted for the development of host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for synthetic lethal partners of GBF1 revealed ARF1 as the top hit, disruption of which, selectively killed cells that synthesize poliovirus 3A. Thus, viral protein interactions can induce hypomorphs that render host cells vulnerable to perturbations that leave uninfected cells intact. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

SUMMARY

Using a viral-induced hypomorph of GBF1, Navare et al., demonstrate that the principle of synthetic lethality is a mechanism to selectively kill virus-infected cells.

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

Background

Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics.

Methods

We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response.

Results

Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib.

Conclusions

This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.

The Causes and Consequences of Genetic Interactions (Epistasis)

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

Evolutionary design principles in metabolism

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

Using genetic buffering relationships identified in fission yeast to reveal susceptibilities in cells lacking hamartin or tuberin function

Tuberous sclerosis complex is an autosomal dominant disorder characterized by benign tumors arising from the abnormal activation of mTOR signaling in cells lacking TSC1 (hamartin) or TSC2 (tuberin) activity. To expand the genetic framework surrounding this group of growth regulators, we utilized the model eukaryote Schizosaccharomyces pombe to uncover and characterize genes that buffer the phenotypic effects of mutations in the orthologous tsc1 or tsc2 loci. Our study identified two genes: fft3 (encoding a DNA helicase) and ypa1 (encoding a peptidyle-prolyl cis/trans isomerase). While the deletion of fft3 or ypa1 has little effect in wild-type fission yeast cells, their loss in tsc1Δ or tsc2Δ backgrounds results in severe growth inhibition. These data suggest that the inhibition of Ypa1p or Fft3p might represent an 'Achilles' heel' of cells defective in hamartin/tuberin function. Furthermore, we demonstrate that the interaction between tsc1/tsc2 and ypa1 can be rescued through treatment with the mTOR inhibitor, torin-1, and that ypa1Δ cells are resistant to the glycolytic inhibitor, 2-deoxyglucose. This identifies ypa1 as a novel upstream regulator of mTOR and suggests that the effects of ypa1 loss, together with mTOR activation, combine to result in a cellular maladaptation in energy metabolism that is profoundly inhibitory to growth.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Synthetic lethality and cancer

A synthetic lethal interaction occurs between two genes when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability. Key to exploiting synthetic lethality in cancer treatment are the identification and the mechanistic characterization of robust synthetic lethal genetic interactions. Advances in next-generation sequencing technologies are enabling the identification of hundreds of tumour-specific mutations and alterations in gene expression that could be targeted by a synthetic lethality approach. The translation of synthetic lethality to therapy will be assisted by the synthesis of genetic interaction data from model organisms, tumour genomes and human cell lines.

'Big data' approaches for novel anti-cancer drug discovery

INTRODUCTION

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

Microtubule-associated proteins, Bik1 and Bim1, are required for faithful partitioning of the endogenous 2 micron plasmids in budding yeast

The 2 μ plasmid of budding yeast shows high mitotic stability similar to that of chromosomes by using its self-encoded systems, namely partitioning and amplification. The partitioning system consists of the plasmid-borne proteins Rep1, Rep2 and a cis-acting locus STB that, along with several host factors, ensures efficient segregation of the plasmid. The plasmids show high stability as they presumably co-segregate with chromosomes through utilization of various host factors. To acquire these host factors, the plasmids are thought to localize to a certain sub-nuclear locale probably assisted by the motor protein, Kip1 and microtubules. Here, we show that the microtubule-associated proteins Bik1 and Bim1 are also important host factors in this process, perhaps by acting as an adapter between the plasmid and the motor and thus helping to anchor the plasmid to microtubules. Abrogation of Kip1 recruitment at STB in the absence of Bik1 argues for its function at STB upstream of Kip1. Consistent with this, both Bik1 and Bim1 associate with plasmids without any assistance from the Rep proteins. As observed earlier with other host factors, lack of Bik1 or Bim1 also causes a cohesion defect between sister plasmids leading to plasmid missegregation.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution.

Network biology has focused for years on protein-protein interaction (PPI) networks, identifying nodes with central structural functions and modules associated to bioprocesses, phenotypes and diseases. Network biology field moved to a higher level of abstraction, and started characterizing a less intuitive kind of interactions, called genetic interactions (GIs) or epistasis. Mostly due to technical challenges associated to the genome-wide mapping of GIs, these studies primarily focused on unicellular organisms. They uncovered modules embedded within the structure of these networks and started characterizing their relationship with PPI-network and biological functions. We provide here the first in-depth characterization of a network composed of ~600 GIs within signaling and metabolic pathways of a multicellular organism, the nematode Caenorhabditis elegans. We characterize the structure of this network, and the function of GI classes found in this network. We also discuss how these GI classes contribute to the genomic robustness and the adaptive evolution of multicellular organisms.

Prediction of Genetic Interactions Using Machine Learning and Network Properties

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

RNAi screening comes of age: improved techniques and complementary approaches

Gene silencing through sequence-specific targeting of mRNAs by RNAi has enabled genome-wide functional screens in cultured cells and in vivo in model organisms. These screens have resulted in the identification of new cellular pathways and potential drug targets. Considerable progress has been made to improve the quality of RNAi screen data through the development of new experimental and bioinformatics approaches. The recent availability of genome-editing strategies, such as the CRISPR (clustered regularly interspaced short palindromic repeats)-Cas9 system, when combined with RNAi, could lead to further improvements in screen data quality and follow-up experiments, thus promoting our understanding of gene function and gene regulatory networks.

Quantitative maps of genetic interactions in yeast - comparative evaluation and integrative analysis

Background

High-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.

Results

Using large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.

Conclusions

We have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

Exploration of synthetic lethal interactions as cancer drug targets

In cancer research the quest continues to identify the Achilles’ heel of cancer. The ideal cancer drug targets are those that are essential in tumor cells but not in normal cells. Such targets are defined as cancer-specific vulnerabilities or as synthetic lethal interactions with cancer-specific genetic lesions. The search for synthetic lethal interactions focuses on proteins that are frequently mutated but elude pharmacological inhibition, for example, RAS, or proteins that are lost in cancer cells and by definition cannot be targeted, such as the tumor suppressor genes p53, APC and RB. These genetic interactions could yield alternative, effective targets for cancer treatment. However, it remains very difficult to predict or extrapolate these synthetic lethal interactions based on existing knowledge. With the discovery of RNAi, unbiased large-scale functional genomic screens for the identification of such targets have become possible potentially leading to major advances in the treatment of cancers. In this review we will discuss the biological basis of synthetic lethal interactions in relation to existing targeted therapeutics, lessons taught by targeted therapeutics already used in the clinic and the implementation of RNAi as tool to identify such synthetic lethal interactions.

Synthetic lethal hubs associated with vincristine resistant neuroblastoma

Chemotherapy of cancer experiences a number of shortcomings including development of drug resistance. This fact also holds true for neuroblastoma utilizing chemotherapeutics as vincristine. We performed a comparative analysis of molecular and cellular mechanisms associated with vincristine resistance utilizing cell line as well as human tissue data. Differential gene expression analysis revealed molecular features, processes and pathways afflicted with drug resistance mechanisms in general, and specifically with vincristine significantly involving actin associated features. However, specific mode of resistance as well as underlying genotype of parental, vincristine sensitive cells apparently exhibited significant heterogeneity. No consensus profile for vincristine resistance could be derived, but resistance-associated changes on the level of individual neuroblastoma cell lines as well as individual patient profiles became clearly evident. Based on these prerequisites we utilized the concept of synthetic lethality aimed at identifying hub proteins which when inhibited promise to induce cell death due to a synthetic lethal interaction with down-regulated, chemoresistance associated features. Our screening procedure identified synthetic lethal hub proteins afflicted with actin associated processes holding synthetic lethal interactions to down-regulated features individually found in all chemoresistant cell lines tested, therefore promising an improved therapeutic window. Verification of such synthetic lethal hub candidates in human neuroblastoma tissue expression profiles indicated the feasibility of this screening approach for addressing vincristine resistance in neuroblastoma.