scholarly journals Variable dose analysis: A novel RNAi-based method for detection of synthetic lethal interactions

2017 ◽  
Author(s):  
Benjamin E. Housden ◽  
Zhongchi Li ◽  
Colleen Kelley ◽  
Yuanli Wang ◽  
Yanhui Hu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Limited Range ◽  
Synthetic Lethal ◽  
Candidate Drug ◽  
Highly Sensitive ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Variable Dose

AbstractSynthetic sick or synthetic lethal (SS/L) screens are a powerful way to identify candidate drug targets to specifically kill tumor cells but such screens generally suffer from low reproducibility. We found that many SS/L interactions involve essential genes and are therefore detectable within a limited range of knockdown efficiency. Such interactions are often missed by overly efficient RNAi reagents. We therefore developed an assay that measures viability over a range of knockdown efficiency within a cell population. This method, called variable dose analysis (VDA), is highly sensitive to viability phenotypes and reproducibly detects SS/L interactions. We applied the VDA method to search for SS/L interactions with TSC1 and TSC2, the two tumor suppressors underlying tuberous sclerosis complex (TSC) and generated a SS/L network for TSC. Using this network, we identified four FDA-approved drugs that selectively affect viability of TSC deficient cells, representing promising candidates for repurposing to treat TSC-related tumors.

scholarly journals Improved detection of synthetic lethal interactions in Drosophila cells using variable dose analysis (VDA)

2017 ◽  
Vol 114 (50) ◽  
pp. E10755-E10762 ◽  
Author(s):  
Benjamin E. Housden ◽  
Zhongchi Li ◽  
Colleen Kelley ◽  
Yuanli Wang ◽  
Yanhui Hu ◽  
...  
Keyword(s):  
Drug Targets ◽  
Limited Range ◽  
Synthetic Lethal ◽  
Candidate Drug ◽  
Highly Sensitive ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Approved Drugs ◽  
Drosophila Cells ◽  
Variable Dose

Synthetic sick or synthetic lethal (SS/L) screens are a powerful way to identify candidate drug targets to specifically kill tumor cells, but this approach generally suffers from low consistency between screens. We found that many SS/L interactions involve essential genes and are therefore detectable within a limited range of knockdown efficiency. Such interactions are often missed by overly efficient RNAi reagents. We therefore developed an assay that measures viability over a range of knockdown efficiency within a cell population. This method, called Variable Dose Analysis (VDA), is highly sensitive to viability phenotypes and reproducibly detects SS/L interactions. We applied the VDA method to search for SS/L interactions with TSC1 and TSC2, the two tumor suppressors underlying tuberous sclerosis complex (TSC), and generated a SS/L network for TSC. Using this network, we identified four Food and Drug Administration-approved drugs that selectively affect viability of TSC-deficient cells, representing promising candidates for repurposing to treat TSC-related tumors.

scholarly journals Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Merve Dede ◽  
Megan McLaughlin ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
Cell Lines ◽  
Protein Complexes ◽  
Gene Knockout ◽  
Essential Genes ◽  
Loss Of Function ◽  
Systematic Analysis ◽  
Synthetic Lethal ◽  
Synthetic Lethal Interactions ◽  
Crispr Screen ◽  
A Cell

Abstract Background Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell. Results Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Conclusions Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.

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

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.

scholarly journals Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens

2020 ◽  
Author(s):  
Merve Dede ◽  
Megan McLaughlin ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
Cell Lines ◽  
Protein Complexes ◽  
Essential Genes ◽  
Systematic Analysis ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
Synthetic Lethal Interactions ◽  
Crispr Screen ◽  
A Cell ◽  
Genetic Buffering

AbstractMajor efforts on pooled library CRISPR knockout screening across hundreds of cell lines have identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens are very low compared to the number of constitutively expressed genes in a cell, raising the question of why there are so few essential genes. Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observed that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. We investigated paralog buffering through systematic dual-gene CRISPR knockout screening by testing algorithmically defined ~400 candidate paralog pairs with the enCas12a multiplex knockout system in three cell lines. We observed 24 synthetic lethal paralog pairs which have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions were present in at least two out of three cell lines and 14 of 24 (58%) were present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Together these observations strongly suggest that paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes due to genetic buffering in monogenic CRISPR-based mammalian functional genomics approaches.

scholarly journals MYC Oncogene Contributions to Release of Cell Cycle Brakes

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 244 ◽  
Author(s):  
Lucía García-Gutiérrez ◽  
María Dolores Delgado ◽  
Javier León
Keyword(s):  
Cell Cycle ◽  
Transcriptional Level ◽  
Synthetic Lethal ◽  
Myc Oncogene ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Functional Inactivation ◽  
P27 Gene ◽  
Cell Cycle Inhibitors

Promotion of the cell cycle is a major oncogenic mechanism of the oncogene c-MYC (MYC). MYC promotes the cell cycle by not only activating or inducing cyclins and CDKs but also through the downregulation or the impairment of the activity of a set of proteins that act as cell-cycle brakes. This review is focused on the role of MYC as a cell-cycle brake releaser i.e., how MYC stimulates the cell cycle mainly through the functional inactivation of cell cycle inhibitors. MYC antagonizes the activities and/or the expression levels of p15, ARF, p21, and p27. The mechanism involved differs for each protein. p15 (encoded by CDKN2B) and p21 (CDKN1A) are repressed by MYC at the transcriptional level. In contrast, MYC activates ARF, which contributes to the apoptosis induced by high MYC levels. At least in some cells types, MYC inhibits the transcription of the p27 gene (CDKN1B) but also enhances p27’s degradation through the upregulation of components of ubiquitin ligases complexes. The effect of MYC on cell-cycle brakes also opens the possibility of antitumoral therapies based on synthetic lethal interactions involving MYC and CDKs, for which a series of inhibitors are being developed and tested in clinical trials

scholarly journals Predicting synthetic lethal interactions using heterogeneous data sources

2019 ◽  
Vol 36 (7) ◽  
pp. 2209-2216 ◽  
Author(s):  
Herty Liany ◽  
Anand Jeyasekharan ◽  
Vaibhav Rajan
Keyword(s):  
Drug Targets ◽  
Matrix Completion ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Supplementary Information ◽  
Potential Candidate ◽  
Synthetic Lethal ◽  
Heterogeneous Data Sources ◽  
Synthetic Lethal Interactions ◽  
Latent Representations

Abstract Motivation A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources. Results In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering. Availability and implementation Software available at https://github.com/lianyh. Supplementary information Supplementary data are available at Bioinformatics online.

Review and Prospect of Tissue-agnostic targeted Strategies in Anticancer therapies

2020 ◽  
Vol 20 ◽  
Author(s):  
Peng Yu ◽  
Tao Hongxun ◽  
Gao Yuanqing ◽  
Yang Yuanyuan ◽  
Chen Zhiyong
Keyword(s):  
Targeted Therapy ◽  
Drug Targets ◽  
Anatomical Location ◽  
Tumor Characteristics ◽  
Genetic Characteristics ◽  
Drug Candidates ◽  
Specific Tumor ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Prevalence Of Cancer

: Due to the increasing prevalence of cancer year by year, and the complexity and refractory nature of the disease itself, it is required to constantly innovate the development of new cancer treatment schemes. At the same time, the understanding of cancers has deepened, from the use of chemotherapy regimens with high toxicity and side effects, to the popularity of targeted drugs with specific targets, to precise treatments based on tumor characteristics rather than traditional anatomical location classification. In precision medical, in the view of the specific tumor diseases and their biological characteristics, it has great potential to develop tissue-agnostic targeted therapy with broad-spectrum anticancer significance. The present review has discussed tissue-agnostic targeted therapy based on the biological and genetic characteristics of cancers, expounded its theoretical basis and strategies for drug development. And the feasible drug targets, FDA-approved drugs, as well as drug candidates in clinical trials have also been summarized. In conclusion, the “tissue-agnostic targeted therapy” is a breakthrough in anticancer therapies.

Coronavirus Disease 2019: Virology and Drug Targets

2020 ◽  
Vol 20 ◽  
Author(s):  
Praveen Thaggikuppe Krishnamurthy
Keyword(s):  
High Throughput Screening ◽  
Drug Targets ◽  
Vaccine Development ◽  
Antiviral Drug ◽  
Drug Repurposing ◽  
Structural Proteins ◽  
Current Status ◽  
Approved Drugs ◽  
Viral Target ◽  
Fda Approved Drugs

: The Coronavirus Disease 2019, a pandemic caused by novel Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is seriously affecting global health and the economy. As the vaccine development takes time, the current research is focused on repurposing FDA approved drugs against the viral target proteins. This review discusses the current understanding of SARS-CoV-2 virology, its target structural proteins (S- glycoprotein), non-structural proteins (3- chymotrypsin-like protease, papain-like protease, RNA-dependent RNA polymerase, and helicase) and accessory proteins, drug discovery strategies (drug repurposing, artificial intelligence, and high-throughput screening), and the current status of antiviral drug development.

scholarly journals An Exon Skipping Screen Identifies Antitumor Drugs That Are Potent Modulators of Pre-mRNA Splicing, Suggesting New Therapeutic Applications

2019 ◽  
Author(s):  
Yihui Shi ◽  
Walter Bray ◽  
Alexander J. Smith ◽  
Wei Zhou ◽  
Joy Calaoagan ◽  
...  
Keyword(s):  
Small Molecule ◽  
Exon Skipping ◽  
Mrna Splicing ◽  
Luciferase Reporter ◽  
Antitumor Drugs ◽  
Small Molecule Ligands ◽  
A Cell ◽  
Displacement Assay ◽  
Approved Drugs ◽  
Fda Approved Drugs

ABSTRACTAgents that modulate pre-mRNA splicing are of interest in multiple therapeutic areas, including cancer. We report our recent screening results with the application of a cell-based Triple Exon Skipping Luciferase Reporter (TESLR) using a library that is composed of FDA approved drugs, clinical compounds, and mechanistically characterized tool compounds. Confirmatory assays showed that three clinical antitumor therapeutic candidates (milciclib, PF-3758309 and PF-030871) are potent splicing modulators and that these drugs are, in fact, nanomolar inhibitors of multiple kinases involved in the regulation the spliceosome. We also report the identification of new SF3B1 antagonists (sudemycinol C and E) and show that these antagonists can be used to develop a displacement assay for SF3B1 small molecule ligands. These results further supports the broad potential for the development of agents that target the spliceosome for the treatment of cancer and other diseases, as well as new avenues for chemotherapeutic discovery.

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