Chromosomal arrangement of synthetic lethal gene pairs: repulsion or attraction?

AbstractSynthetic lethal interactions are of paramount importance both in biology and in medicine, and hence increasing efforts have been devoted to their systematic identification. Our previous computational analysis revealed that in prokaryotic species, synthetic lethal genes tend to be further away in chromosomes than random (i.e. repulsion), which was shown to provide bacterial genomes with greater robustness to large-scale DNA deletions. To test the generalizability of this observation in eukaryotic genomes, we leveraged the wealth of experimentally determined synthetic lethal genetic interactions of yeast that are curated in the BioGRID (Biological General Repository for Interaction Datasets) database. We observed an opposite trend that is the genomic proximity of synthetic lethal gene pairs both on the 2D and 3D chromosomal space of the yeast genome (i.e. 2D and 3D attraction). To gain mechanistic insights into the origin of the attraction of synthetic lethal gene pairs in S. cerevisiae, we characterized four classes of genes, in which synthetic lethal interactions are enriched and partly explain the observed patterns of genomic attraction: i) gene pairs operating on the same pathways, 2) co-expressed genes, 3) gene pairs whose protein products physically interact and 4) the paralogs. However, our analysis revealed that the contribution of these four types of genes is not sufficient to fully explain the observed 2D and 3D attraction of synthetic lethal gene pairs and hence its evolutionary origin still remains as an open question.Significance statementUnravelling the organizing principles underlying gene arrangements is one of the fundamental questions of research in evolutionary biology. One understudied aspect of this organization is the relative chromosomal arrangement of synthetic lethal gene pairs. In this study, by analyzing a wealth of synthetic lethality data in yeast, we provide evidence that synthetic lethal gene pairs tend to be attracted to each other both on 2D and 3D chromosomal space of the yeast genome. This observation is in sharp contrast with the repulsion of synthetic lethal (metabolic) gene pairs that we observed previously in bacterial genomes. Characterizing the evolutionary forces underlying this genomic pattern in yeast can open the door towards an evolutionary theory of genome organization in eukaryotes.

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Using Yeast Synthetic Lethality To Inform Drug Combination for Malaria

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01533-17 ◽

2018 ◽

Vol 62 (4) ◽

Author(s):

Suvitha Subramaniam ◽

Christoph D. Schmid ◽

Xue Li Guan ◽

Pascal Mäser

Keyword(s):

Plasmodium Falciparum ◽

Drug Combination ◽

Synthetic Lethality ◽

Drug Combinations ◽

Synthetic Lethal Interaction ◽

Content Type ◽

Synthetic Lethal ◽

Gene Pairs ◽

Phosphatidylinositol 4

ABSTRACT Combinatorial chemotherapy is necessary for the treatment of malaria. However, finding a suitable partner drug for a new candidate is challenging. Here we develop an algorithm that identifies all of the gene pairs of Plasmodium falciparum that possess orthologues in yeast that have a synthetic lethal interaction but are absent in humans. This suggests new options for drug combinations, particularly for inhibitors of targets such as P. falciparum calcineurin, cation ATPase 4, or phosphatidylinositol 4-kinase.

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A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

10.1101/2021.09.01.458495 ◽

2021 ◽

Author(s):

Iñigo Apaolaza ◽

Edurne San José-Enériz ◽

Luis Valcarcel ◽

Xabier Agirre ◽

Felipe Prosper ◽

...

Keyword(s):

Cancer Metabolism ◽

Synthetic Lethality ◽

Experimental Proof ◽

Synthetic Lethal ◽

New Family ◽

Novel Approach ◽

Synthetic Lethal Interactions ◽

Nutrient Environment ◽

Tumor Environment ◽

Definition Of

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

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Synthetic Lethality: From Research to Precision Cancer Nanomedicine

Current Cancer Drug Targets ◽

10.2174/1568009617666170630141931 ◽

2018 ◽

Vol 18 (4) ◽

pp. 337-346 ◽

Cited By ~ 12

Author(s):

Anuradha Gupta ◽

Anas Ahmad ◽

Aqib Iqbal Dar ◽

Rehan Khan

Keyword(s):

Cancer Cell ◽

Mutational Analysis ◽

Synthetic Lethality ◽

Therapeutic Index ◽

Genetic Alterations ◽

Cancer Drug ◽

Lethal Gene ◽

Synthetic Lethal ◽

Therapeutic Molecules ◽

Drug Resistant Phenotype

Cancer is an evolutionary disease with multiple genetic alterations, accumulated due to chromosomal instability and/or aneuploidy and it sometimes acquires drug-resistant phenotype also. Whole genome sequencing and mutational analysis helped in understanding the differences among persons for predisposition of a disease and its treatment non-responsiveness. Thus, molecular targeted therapies came into existence. Among them, the concept of synthetic lethality have enthralled great attention as it is a pragmatic approach towards exploiting cancer cell specific mutations to specifically kill cancer cells without affecting normal cells and thus enhancing anti-cancer drug therapeutic index. Thus, this approach helped in discovering new therapeutic molecules for development of precision medicine. Nanotechnology helped in delivering these molecules to the target site in an effective concentration thus reducing off target effects of drugs, dose and dosage frequency drugs. Researchers have tried to deliver siRNA targeting synthetic lethal partner for target cancer cell killing by incorporating it in nanoparticles and it has shown efficacy by preventing tumor progression. This review summarizes the brief introduction of synthetic lethality, and synthetic lethal gene interactions, with a major focus on its therapeutic anticancer potential with the application of nanotechnology for development of personalized medicine.

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SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment

Cancers ◽

10.3390/cancers11111682 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1682 ◽

Cited By ~ 4

Author(s):

Xiang Deng ◽

Shaoli Das ◽

Kristin Valdez ◽

Kevin Camphausen ◽

Uma Shankavaram

Keyword(s):

Clinical Relevance ◽

Synthetic Lethality ◽

Drug Efficacy ◽

Synthetic Lethal ◽

Cancer Data ◽

Cancer Pathways ◽

Kaplan Meier ◽

Survival Pathways ◽

Synthetic Lethal Interactions ◽

Cancer Genome Atlas

Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer’s genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan–Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs.

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Abstract 4171: Integrating pan-molecular data sets by bimodality to nominate synthetic lethal gene pairs and biomarkers of drug response

10.1158/1538-7445.am2014-4171 ◽

2014 ◽

Author(s):

Jonathan R. Dry ◽

Mark Wappett ◽

Ron Yang

Keyword(s):

Drug Response ◽

Molecular Data ◽

Data Sets ◽

Lethal Gene ◽

Synthetic Lethal ◽

Gene Pairs

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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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Cross-species synthetic lethal interaction screening as a strategy for the identification of novel therapeutic targets in cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.11105 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. 11105-11105

Author(s):

John P. Shen ◽

Rohith Srivas ◽

Ana Bojorquez-Gomez ◽

Katherine Licon ◽

Jian Feng Li ◽

...

Keyword(s):

Tumor Suppressor ◽

Tumor Suppressor Genes ◽

Synthetic Lethality ◽

Control Cell ◽

Therapeutic Targets ◽

Model Organisms ◽

Suppressor Genes ◽

Synthetic Lethal ◽

Synthetic Lethal Interactions ◽

Novel Therapeutic

11105 Background: Mutation, deletion, or epigenetic silencing of tumor suppressor genes is a near universal feature of malignant cells. However, therapeutic strategies for restoring the function of mutated or deleted genes have proven difficult. Synthetic lethality, an event in which the simultaneous perturbation of two genes results in cellular death, has been proposed as a method to selectively target cancer cells. Identifying and pharmacologically inhibiting proteins encoded by genes that are synthetic lethal with known tumor suppressor mutations should result in selective toxicity to tumor cells. Methods: To identify candidate target proteins we measured all pair-wise genetic interactions between all known orthologs of human tumor suppressor genes (162 genes) and all orthologs of druggable human proteins (~400 genes) in the model organism S. Cerevisiae. Analysis of the data uncovered 2,087 distinct synthetic lethal interactions between a tumor suppressor and druggable gene. A computational algorithm was then developed to identify those interactions which were likely to be conserved in humans based on conservation of the synthetic lethal relationship in the distant fission yeast S. pombe. Results: Our bioinformatic analysis suggested a high probability of conservation of the synthetic lethal interactions between the yeast RAD51 (ortholog of BRCA1) and RAD57 (ortholog of XRCC3) with HDA1 (a histone deacetylase; HDAC). We confirmed this by treating LN428 cells with stable lentiviral knockdown of BRCA1 or XRCC3 with the HDAC inhibitors vorinostat (SAHA) and entinostat (MS-275). Both the BRCA1 and XRCC3 knockdown cell lines were significantly more sensitive to HDAC inhibition relative to wild-type (non-silencing lentiviral control) cell line (Table). Conclusions: These results demonstrate that high-throughput approaches for screening synthetic lethal interactions in model organisms such as S. cerevisiae and S. pombecan serve as a valuable resource in helping to identify novel therapeutic targets in human cancer. [Table: see text]

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Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs

BMC Genomics ◽

10.1186/s12864-016-2375-1 ◽

2016 ◽

Vol 17 (1) ◽

Cited By ~ 10

Author(s):

Mark Wappett ◽

Austin Dulak ◽

Zheng Rong Yang ◽

Abdullatif Al-Watban ◽

James R. Bradford ◽

...

Keyword(s):

Lethal Gene ◽

Loss Of Function ◽

Synthetic Lethal ◽

Gene Pairs

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Targeting mutant p53 for cancer therapy: direct and indirect strategies

Journal of Hematology & Oncology ◽

10.1186/s13045-021-01169-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Jiahao Hu ◽

Jiasheng Cao ◽

Win Topatana ◽

Sarun Juengpanich ◽

Shijie Li ◽

...

Keyword(s):

Noncoding Rna ◽

Synthetic Lethality ◽

Noncoding Rnas ◽

Research Progress ◽

Mutant P53 ◽

Wild Type ◽

Synthetic Lethal ◽

Therapeutic Benefits ◽

Synthetic Lethal Interactions ◽

Wild Type P53

AbstractTP53 is a critical tumor-suppressor gene that is mutated in more than half of all human cancers. Mutations in TP53 not only impair its antitumor activity, but also confer mutant p53 protein oncogenic properties. The p53-targeted therapy approach began with the identification of compounds capable of restoring/reactivating wild-type p53 functions or eliminating mutant p53. Treatments that directly target mutant p53 are extremely structure and drug-species-dependent. Due to the mutation of wild-type p53, multiple survival pathways that are normally maintained by wild-type p53 are disrupted, necessitating the activation of compensatory genes or pathways to promote cancer cell survival. Additionally, because the oncogenic functions of mutant p53 contribute to cancer proliferation and metastasis, targeting the signaling pathways altered by p53 mutation appears to be an attractive strategy. Synthetic lethality implies that while disruption of either gene alone is permissible among two genes with synthetic lethal interactions, complete disruption of both genes results in cell death. Thus, rather than directly targeting p53, exploiting mutant p53 synthetic lethal genes may provide additional therapeutic benefits. Additionally, research progress on the functions of noncoding RNAs has made it clear that disrupting noncoding RNA networks has a favorable antitumor effect, supporting the hypothesis that targeting noncoding RNAs may have potential synthetic lethal effects in cancers with p53 mutations. The purpose of this review is to discuss treatments for cancers with mutant p53 that focus on directly targeting mutant p53, restoring wild-type functions, and exploiting synthetic lethal interactions with mutant p53. Additionally, the possibility of noncoding RNAs acting as synthetic lethal targets for mutant p53 will be discussed.

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