SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through CRISPR screening, a knowledge graph about human SLs, and new web interface, etc. Over 16,000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14,100 genes, 53 cancers, and 1,898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful APIs. The database is accessible online at http://synlethdb.sist.shanghaitech.edu.cn/v2.

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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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Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool

10.1158/1538-7445.compsysbio-b1-35 ◽

2015 ◽

Author(s):

Matthew R. Jones

Keyword(s):

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Next Generation ◽

Gene Set Enrichment ◽

Web Based ◽

Gene Set

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Activation of LXR Receptors and Inhibition of TRAP1 Causes Synthetic Lethality in Solid Tumors

Cancers ◽

10.3390/cancers11060788 ◽

2019 ◽

Vol 11 (6) ◽

pp. 788 ◽

Cited By ~ 5

Author(s):

Trang Thi Thu Nguyen ◽

Chiaki Tsuge Ishida ◽

Enyuan Shang ◽

Chang Shu ◽

Elena Bianchetti ◽

...

Keyword(s):

Cell Death ◽

Transcription Factor ◽

Tumor Growth ◽

Solid Tumors ◽

Combination Treatment ◽

Synthetic Lethality ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Simultaneous Activation ◽

Lxr Agonists

Cholesterol is a pivotal factor for cancer cells to entertain their relentless growth. In this case, we provide a novel strategy to inhibit tumor growth by simultaneous activation of liver-X-receptors and interference with Tumor Necrosis Factor Receptor-associated Protein 1 (TRAP1). Informed by a transcriptomic and subsequent gene set enrichment analysis, we demonstrate that inhibition of TRAP1 results in suppression of the cholesterol synthesis pathway in stem-like and established glioblastoma (GBM) cells by destabilizing the transcription factor SREBP2. Notably, TRAP1 inhibition induced cell death, which was rescued by cholesterol and mevalonate. Activation of liver X receptor (LXR) by a clinically validated LXR agonist, LXR623, along with the TRAP1 inhibitor, gamitrinib (GTPP), results in synergistic reduction of tumor growth and cell death induction in a broad range of solid tumors, which is rescued by exogenous cholesterol. The LXR agonist and TRAP1 inhibitor mediated cell death is regulated at the level of Bcl-2 family proteins with an elevation of pro-apoptotic Noxa. Silencing of Noxa and its effector BAK attenuates cell death mediated by the combination treatment of LXR agonists and TRAP1 inhibition. Combined inhibition of TRAP1 and LXR agonists elicits a synergistic activation of the integrated stress response with an increase in activating transcription factor 4 (ATF4) driven by protein kinase RNA-like endoplasmic reticulum kinase (PERK). Silencing of ATF4 attenuates the increase of Noxa by using the combination treatment. Lastly, we demonstrate in patient-derived xenografts that the combination treatment of LXR623 and gamitrinib reduces tumor growth more potent than each compound. Taken together, these results suggest that TRAP1 inhibition and simultaneous activation of LXR might be a potent novel treatment strategy for solid malignancies.

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A novel dysregulated pathway-identification analysis based on global influence of within-pathway effects and crosstalk between pathways

Journal of The Royal Society Interface ◽

10.1098/rsif.2014.0937 ◽

2015 ◽

Vol 12 (102) ◽

pp. 20140937 ◽

Cited By ~ 10

Author(s):

Junwei Han ◽

Chunquan Li ◽

Haixiu Yang ◽

Yanjun Xu ◽

Chunlong Zhang ◽

...

Keyword(s):

Enrichment Analysis ◽

Distribution Functions ◽

Gene Set Enrichment Analysis ◽

Cumulative Distribution ◽

Global Network ◽

Web Based ◽

Novel Method ◽

Microarray Datasets ◽

Identification Analysis ◽

Global Influence

Identifying dysregulated pathways from high-throughput experimental data in order to infer underlying biological insights is an important task. Current pathway-identification methods focus on single pathways in isolation; however, consideration of crosstalk between pathways could improve our understanding of alterations in biological states. We propose a novel method of pathway analysis based on global influence (PAGI) to identify dysregulated pathways, by considering both within-pathway effects and crosstalk between pathways. We constructed a global gene–gene network based on the relationships among genes extracted from a pathway database. We then evaluated the extent of differential expression for each gene, and mapped them to the global network. The random walk with restart algorithm was used to calculate the extent of genes affected by global influence. Finally, we used cumulative distribution functions to determine the significance values of the dysregulated pathways. We applied the PAGI method to five cancer microarray datasets, and compared our results with gene set enrichment analysis and five other methods. Based on these analyses, we demonstrated that PAGI can effectively identify dysregulated pathways associated with cancer, with strong reproducibility and robustness. We implemented PAGI using the freely available R-based and Web-based tools ( http://bioinfo.hrbmu.edu.cn/PAGI ).

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

10.1101/2020.05.05.078626 ◽

2020 ◽

Author(s):

Sayed-Rzgar Hosseini ◽

Bishoy Wadie ◽

Evangelia Petsalaki

Keyword(s):

Synthetic Lethality ◽

Yeast Genome ◽

Lethal Gene ◽

Bacterial Genomes ◽

Synthetic Lethal ◽

Evolutionary Forces ◽

Gene Pairs ◽

Chromosomal Arrangement ◽

Synthetic Lethal Interactions ◽

2D And 3D

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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A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

10.1101/2021.01.08.425918 ◽

2021 ◽

Author(s):

Christopher H Yogodzinski ◽

Abolfazl Arab ◽

Justin R Pritchard ◽

Hani Goodarzi ◽

Luke Gilbert

Keyword(s):

Cancer Cell ◽

Cell Biology ◽

Cancer Biology ◽

Large Scale ◽

Synthetic Lethality ◽

Data Retrieval ◽

Lethal Gene ◽

Synthetic Lethal ◽

Cancer Data ◽

Sex Disparity

Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Beyond a database search, our approach empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Our approach is straightforward to implement, well documented and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology.

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DrugPattern: a web-based tool for drug set enrichment analysis

10.1101/178632 ◽

2017 ◽

Author(s):

Chuanbo Huang ◽

Weili Yang ◽

Junpei Wang ◽

Yuan Zhou ◽

Bin Geng ◽

...

Keyword(s):

Low Density Lipoprotein ◽

Animal Experiments ◽

Density Lipoprotein ◽

Enrichment Analysis ◽

The Body ◽

Gene Set Enrichment Analysis ◽

Web Based ◽

Beneficial Effects ◽

Regular Patterns

ABSTRACTSet enrichment analysis based methods (e.g. gene set enrichment analysis) have provided great helps in mining patterns in biomedical datasets, however, tools for inferring regular patterns in drug-related datasets are still limited. For the above purpose, here we developed a web-based tool, DrugPattern. DrugPattern first collected and curated 7019 drug sets, including indications, adverse reaction, targets, pathways etc. For a list of interested drugs, DrugPattern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we applied it to predict the potential protective roles of oxidized low-density lipoprotein (oxLDL), a widely accepted deleterious factor for the body. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by literature except type 2 diabetes (T2D), in which oxLDL was previously believed to be a risk factor. Animal experiments further validated that oxLDL indeed has beneficial effects on T2D. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases including T2D. This study provides a tool to infer regular patterns in biomedical datasets based on drug set enrichment analysis.

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Systematical Analysis of the Protein Targets of Lactoferricin B and Histatin-5 Using Yeast Proteome Microarrays

International Journal of Molecular Sciences ◽

10.3390/ijms20174218 ◽

2019 ◽

Vol 20 (17) ◽

pp. 4218 ◽

Cited By ~ 1

Author(s):

Pramod Shah ◽

Wei-Sheng Wu ◽

Chien-Sheng Chen

Keyword(s):

Protein Complex ◽

Synthetic Lethality ◽

Enrichment Analysis ◽

Growth Inhibition Assay ◽

Protein Targets ◽

Histatin 5 ◽

Synthetic Lethal ◽

Yeast Proteome ◽

Lower Minimum Inhibitory Concentration ◽

Cellular Components

Antimicrobial peptides (AMPs) have potential antifungal activities; however, their intracellular protein targets are poorly reported. Proteome microarray is an effective tool with high-throughput and rapid platform that systematically identifies the protein targets. In this study, we have used yeast proteome microarrays for systematical identification of the yeast protein targets of Lactoferricin B (Lfcin B) and Histatin-5. A total of 140 and 137 protein targets were identified from the triplicate yeast proteome microarray assays for Lfcin B and Histatin-5, respectively. The Gene Ontology (GO) enrichment analysis showed that Lfcin B targeted more enrichment categories than Histatin-5 did in all GO biological processes, molecular functions, and cellular components. This might be one of the reasons that Lfcin B has a lower minimum inhibitory concentration (MIC) than Histatin-5. Moreover, pairwise essential proteins that have lethal effects on yeast were analyzed through synthetic lethality. A total of 11 synthetic lethal pairs were identified within the protein targets of Lfcin B. However, only three synthetic lethal pairs were identified within the protein targets of Histatin-5. The higher number of synthetic lethal pairs identified within the protein targets of Lfcin B might also be the reason for Lfcin B to have lower MIC than Histatin-5. Furthermore, two synthetic lethal pairs were identified between the unique protein targets of Lfcin B and Histatin-5. Both the identified synthetic lethal pairs proteins are part of the Spt-Ada-Gcn5 acetyltransferase (SAGA) protein complex that regulates gene expression via histone modification. Identification of synthetic lethal pairs between Lfcin B and Histatin-5 and their involvement in the same protein complex indicated synergistic combination between Lfcin B and Histatin-5. This hypothesis was experimentally confirmed by growth inhibition assay.

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A cell atlas of the adult Drosophila midgut

10.1101/410423 ◽

2018 ◽

Cited By ~ 2

Author(s):

Ruei-Jiun Hung ◽

Yanhui Hu ◽

Rory Kirchner ◽

Fangge Li ◽

Chiwei Xu ◽

...

Keyword(s):

Cell Fate ◽

Single Cells ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Web Based ◽

Molecular Features ◽

Gut Hormone ◽

A Cell ◽

Fate Decision

AbstractStudies of the adult Drosophila midgut have provided a number of insights on cell type diversity, stem cell regeneration, tissue homeostasis and cell fate decision. Advances in single-cell RNA sequencing (scRNA-seq) provide opportunities to identify new cell types and molecular features. We used inDrop to characterize the transcriptome of midgut epithelial cells and identified 12 distinct clusters representing intestinal stem cells (ISCs), enteroblasts (EBs), enteroendocrine cells (EEs), enterocytes (ECs) from different regions, and cardia. This unbiased approach recovered 90% of the known ISCs/EBs markers, highlighting the high quality of the dataset. Gene set enrichment analysis in conjunction with electron micrographs revealed that ISCs are enriched in free ribosomes and possess mitochondria with fewer cristae. We demonstrate that a subset of EEs in the middle region of the midgut expresses the progenitor marker esg and that individual EEs are capable of expressing up to 4 different gut hormone peptides. We also show that the transcription factor klumpfuss (klu) is expressed in EBs and functions to suppress EE formation. Lastly, we provide a web-based resource for visualization of gene expression in single cells. Altogether, our study provides a comprehensive resource for addressing novel functions of genes in the midgut epithelium.

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