scholarly journals Syn-Lethality: An Integrative Knowledge Base of Synthetic Lethality towards Discovery of Selective Anticancer Therapies

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xue-juan Li ◽  
Shital K. Mishra ◽  
Min Wu ◽  
Fan Zhang ◽  
Jie Zheng
Keyword(s):  
Knowledge Base ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Synthetic Lethality ◽  
Human Cancer ◽  
Single Gene ◽  
Drug Induced ◽  
Human Genes ◽  
A Cell ◽  
Novel Strategy

Synthetic lethality (SL) is a novel strategy for anticancer therapies, whereby mutations of two genes will kill a cell but mutation of a single gene will not. Therefore, a cancer-specific mutation combined with a drug-induced mutation, if they have SL interactions, will selectively kill cancer cells. While numerous SL interactions have been identified in yeast, only a few have been known in human. There is a pressing need to systematically discover and understand SL interactions specific to human cancer. In this paper, we present Syn-Lethality, the first integrative knowledge base of SL that is dedicated to human cancer. It integrates experimentally discovered and verified human SL gene pairs into a network, associated with annotations of gene function, pathway, and molecular mechanisms. It also includes yeast SL genes from high-throughput screenings which are mapped to orthologous human genes. Such an integrative knowledge base, organized as a relational database with user interface for searching and network visualization, will greatly expedite the discovery of novel anticancer drug targets based on synthetic lethality interactions. The database can be downloaded as a stand-alone Java application.

scholarly journals In Silico Prediction of Synthetic Lethality by Meta-Analysis of Genetic Interactions, Functions, and Pathways in Yeast and Human Cancer

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14026 ◽  
Author(s):  
Min Wu ◽  
Xuejuan Li ◽  
Fan Zhang ◽  
Xiaoli Li ◽  
Chee-Keong Kwoh ◽  
...  
Keyword(s):  
Large Scale ◽  
Synthetic Lethality ◽  
Human Cancer ◽  
Meta Analysis ◽  
Operating Characteristics ◽  
Human Genes ◽  
Yeast Data ◽  
Anti Cancer ◽  
A Cell ◽  
Wet Lab

A major goal in cancer medicine is to find selective drugs with reduced side effect. A pair of genes is called synthetic lethality (SL) if mutations of both genes will kill a cell while mutation of either gene alone will not. Hence, a gene in SL interactions with a cancer-specific mutated gene will be a promising drug target with anti-cancer selectivity. Wet-lab screening approach is still so costly that even for yeast only a small fraction of gene pairs has been covered. Computational methods are therefore important for large-scale discovery of SL interactions. Most existing approaches focus on individual features or machine-learning methods, which are prone to noise or overfitting. In this paper, we propose an approach named MetaSL for predicting yeast SL, which integrates 17 genomic and proteomic features and the outputs of 10 classification methods. MetaSL thus combines the strengths of existing methods and achieves the highest area under the Receiver Operating Characteristics (ROC) curve (AUC) of 87.1% among all competitors on yeast data. Moreover, through orthologous mapping from yeast to human genes, we then predicted several lists of candidate SL pairs in human cancer. Our method and predictions would thus shed light on mechanisms of SL and lead to discovery of novel anti-cancer drugs. In addition, all the experimental results can be downloaded from http://www.ntu.edu.sg/home/zhengjie/data/MetaSL .

scholarly journals Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Emmanuel Bresso ◽  
Pierre Monnin ◽  
Cédric Bousquet ◽  
François-Elie Calvier ◽  
Ndeye-Coumba Ndiaye ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Drug Targets ◽  
Graph Mining ◽  
Molecular Mechanisms ◽  
Randomized Clinical Trials ◽  
Knowledge Graph ◽  
Drug Induced ◽  
Liver Injuries ◽  
Explainable Ai ◽  
Knowledge Graphs

Abstract Background Adverse drug reactions (ADRs) are statistically characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. This is true even for hepatic or skin toxicities, which are classically monitored during drug design. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs, such as their properties, interactions, or involvements in pathways. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. Methods We propose in this paper to mine knowledge graphs for identifying biomolecular features that may enable automatically reproducing expert classifications that distinguish drugs causative or not for a given type of ADR. In an Explainable AI perspective, we explore simple classification techniques such as Decision Trees and Classification Rules because they provide human-readable models, which explain the classification itself, but may also provide elements of explanation for molecular mechanisms behind ADRs. In summary, (1) we mine a knowledge graph for features; (2) we train classifiers at distinguishing, on the basis of extracted features, drugs associated or not with two commonly monitored ADRs: drug-induced liver injuries (DILI) and severe cutaneous adverse reactions (SCAR); (3) we isolate features that are both efficient in reproducing expert classifications and interpretable by experts (i.e., Gene Ontology terms, drug targets, or pathway names); and (4) we manually evaluate in a mini-study how they may be explanatory. Results Extracted features reproduce with a good fidelity classifications of drugs causative or not for DILI and SCAR (Accuracy = 0.74 and 0.81, respectively). Experts fully agreed that 73% and 38% of the most discriminative features are possibly explanatory for DILI and SCAR, respectively; and partially agreed (2/3) for 90% and 77% of them. Conclusion Knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not for ADRs. In addition to explaining classifications, most discriminative features appear to be good candidates for investigating ADR mechanisms further.

Melatonin as an armament against non-steroidal anti-inflammatory drug induced gastric injury: An overview

2019 ◽  
Vol 2 (1) ◽  
pp. 115-137
Author(s):  
Palash Kumar Pal ◽  
Bharati Bhattacharjee ◽  
Aindrila Chattopadhyay ◽  
Debasish Bandyopadhyay
Keyword(s):  
Molecular Mechanisms ◽  
Antioxidant Properties ◽  
Protective Role ◽  
Gastric Injury ◽  
Clear Understanding ◽  
Drug Induced ◽  
Microvascular Damage ◽  
Tissue Damages ◽  
Anti Inflammatory ◽  
Novel Strategy

Non-steroidal anti-inflammatory drugs (NSAIDs) are the most widely prescribed medicines to treat numerous pathophysiological conditions clinically. However, growing evidence indicates the adverse effects of NSAIDs on the different vital organs, among which gastrointestinal (GI) tract seems to be the utmost target in most of the cases. NSAIDs promote over production of harmful reactive oxygen species (ROS) and reactive nitrogen species (RNS) in the gastric mucosa. These toxic species cause microvascular damage, increasing intestinal permeability, leading to the development of gastric lesions including ulcerations. Several strategies have been proposed to reduce the side effects of NSAIDs on the GI tissue, but most of them have failed to achieve this goal. Identification of an appropriate therapeutic strategy is urgently required. It is our opinion that this novel strategy to target GI damage induced by NSAID should include both anti-inflammatory and antioxidant properties. Under such a circumstance melatonin probably is the best choice for this purpose. Melatonin is a broad spectrum antioxidant and anti-inflammatory molecule. Numerous studies have reported the protective role of melatonin against gastric tissue damages caused by NSAIDs in animals or clinically. However, the underlying molecular mechanisms are not fully clarified. Thus, the present review attempts to gather the available information on this topic to provide a clear understanding on the exact scenario of this aspect. 

scholarly journals Anthracyclines as Topoisomerase II Poisons: From Early Studies to New Perspectives

2018 ◽  
Vol 19 (11) ◽  
pp. 3480 ◽  
Author(s):  
Jessica Marinello ◽  
Maria Delcuratolo ◽  
Giovanni Capranico
Keyword(s):  
Immune System ◽  
Anticancer Activity ◽  
Drug Targets ◽  
Topoisomerase Ii ◽  
Molecular Mechanisms ◽  
Human Cancer ◽  
Immune Therapy ◽  
Therapeutic Index ◽  
Cellular Effects ◽  
Topoisomerase Ii Poisons

Mammalian DNA topoisomerases II are targets of anticancer anthracyclines that act by stabilizing enzyme-DNA complexes wherein DNA strands are cut and covalently linked to the protein. This molecular mechanism is the molecular basis of anthracycline anticancer activity as well as the toxic effects such as cardiomyopathy and induction of secondary cancers. Even though anthracyclines have been used in the clinic for more than 50 years for solid and blood cancers, the search of breakthrough analogs has substantially failed. The recent developments of personalized medicine, availability of individual genomic information, and immune therapy are expected to change significantly human cancer therapy. Here, we discuss the knowledge of anthracyclines as Topoisomerase II poisons, their molecular and cellular effects and toxicity along with current efforts to improve the therapeutic index. Then, we discuss the contribution of the immune system in the anticancer activity of anthracyclines, and the need to increase our knowledge of molecular mechanisms connecting the drug targets to the immune stimulatory pathways in cancer cells. We propose that the complete definition of the molecular interaction of anthracyclines with the immune system may open up more effective and safer ways to treat patients with these drugs.

scholarly journals Mechanisms of imipridones in targeting mitochondrial metabolism in cancer cells

2020 ◽  
Author(s):  
Erin R Bonner ◽  
Sebastian M Waszak ◽  
Michael A Grotzer ◽  
Sabine Mueller ◽  
Javad Nazarian
Keyword(s):  
Drug Targets ◽  
Molecular Mechanisms ◽  
Human Cancer ◽  
Meta Analysis ◽  
Single Agent ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Mitochondrial Metabolism ◽  
Response To Treatment ◽  
Human Cancer Cell Lines

Abstract ONC201 is the first member of the imipridone family of anticancer drugs to enter the clinic for the treatment of diverse solid and hematologic cancers. A subset of pediatric and adult patients with highly aggressive brain tumors has shown remarkable clinical responses to ONC201, and recently, the more potent derivative ONC206 entered clinical trials as a single agent for the treatment of CNS cancers. Despite the emerging clinical interest in the utility of imipridones, their exact molecular mechanisms are not fully described. In fact, the existing literature points to multiple pathways (e.g. TRAIL signaling, dopamine receptor antagonism, and mitochondrial metabolism) as putative drug targets. We have performed a comprehensive literature review and highlighted mitochondrial metabolism as the major target of imipridones. In support of this, we performed a meta-analysis of an ONC201 screen across 539 human cancer cell lines and showed that the mitochondrial protease ClpP is the most significant predictive biomarker of response to treatment. Herein, we summarize the main findings on the anticancer mechanisms of this potent class of drugs, provide clarity on their role, and identify clinically relevant predictive biomarkers of response.

scholarly journals Bayesian Integrative Modeling of Genome-Scale Metabolic and Regulatory Networks

Informatics ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Hanen Mhamdi ◽  
Jérémie Bourdon ◽  
Abdelhalim Larhlimi ◽  
Mourad Elloumi
Keyword(s):  
Cancer Cells ◽  
Drug Targets ◽  
Regulatory Networks ◽  
Computational Models ◽  
Cancer Metabolism ◽  
Human Cancer ◽  
Genetic Regulation ◽  
Kinetic Properties ◽  
Drug Induced ◽  
Integrative Modeling

The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.

scholarly journals Acquired resistance to PRMT5 inhibition induces concomitant collateral sensitivity to paclitaxel

2021 ◽  
Vol 118 (34) ◽  
pp. e2024055118
Author(s):  
Helen S. Mueller ◽  
Colin E. Fowler ◽  
Simona Dalin ◽  
Enrico Moiso ◽  
Tee Udomlumleart ◽  
...  
Keyword(s):  
Cell Lines ◽  
Human Cancer ◽  
Single Gene ◽  
Acquired Resistance ◽  
The Cancer Genome Atlas ◽  
Complete Regression ◽  
Drug Induced ◽  
Human Cancer Cell Lines ◽  
Collateral Sensitivity ◽  
Resistant State

Epigenetic regulators play key roles in cancer and are increasingly being targeted for treatment. However, for many, little is known about mechanisms of resistance to the inhibition of these regulators. We have generated a model of resistance to inhibitors of protein arginine methyltransferase 5 (PRMT5). This study was conducted in KrasG12D;Tp53-null lung adenocarcinoma (LUAD) cell lines. Resistance to PRMT5 inhibitors (PRMT5i) arose rapidly, and barcoding experiments showed that this resulted from a drug-induced transcriptional state switch, not selection of a preexisting population. This resistant state is both stable and conserved across variants arising from distinct LUAD lines. Moreover, it brought with it vulnerabilities to other chemotherapeutics, especially the taxane paclitaxel. This paclitaxel sensitivity depended on the presence of stathmin 2 (STMN2), a microtubule regulator that is specifically expressed in the resistant state. Remarkably, STMN2 was also essential for resistance to PRMT5 inhibition. Thus, a single gene is required for both acquisition of resistance to PRMT5i and collateral sensitivity to paclitaxel in our LUAD cells. Accordingly, the combination of PRMT5i and paclitaxel yielded potent and synergistic killing of the murine LUAD cells. Importantly, the synergy between PRMT5i and paclitaxel also extended to human cancer cell lines. Finally, analysis of The Cancer Genome Atlas patient data showed that high STMN2 levels correlate with complete regression of tumors in response to taxane treatment. Collectively, this study reveals a recurring mechanism of PRMT5i resistance in LUAD and identifies collateral sensitivities that have potential clinical relevance.

scholarly journals Discovery of synthetic lethal and tumor suppressive paralog pairs in the human genome

2020 ◽  
Author(s):  
Phoebe C. R. Parrish ◽  
James D. Thomas ◽  
Shriya Kamlapurkar ◽  
Austin Gabel ◽  
Robert K. Bradley ◽  
...  
Keyword(s):  
Drug Targets ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Essential Gene ◽  
Single Gene ◽  
Gene Families ◽  
Blind Spot ◽  
List Type ◽  
Double Knockout ◽  
Synthetic Lethal

AbstractCRISPR knockout screens have accelerated the discovery of important cancer genetic dependencies. However, traditional CRISPR-Cas9 screens are limited in their ability to assay the function of redundant or duplicated genes. Paralogs in multi-gene families constitute two-thirds of the protein-coding genome, so this blind spot is the rule, not the exception. To overcome the limitations of single gene CRISPR knockout screens, we developed paired guide RNAs for Paralog gENetic interaction mapping (pgPEN), a pooled CRISPR/Cas9 approach which targets over a thousand duplicated human paralogs in single knockout and double knockout configurations. We applied pgPEN to two cell lineages and discovered that over 10% of human paralogs exhibit synthetic lethality in at least one cellular context. We recovered known synthetic lethal paralogs such as MAP2K1/MAP2K2, important drug targets such as CDK4/CDK6, and numerous other synthetic lethal pairs such as CCNL1/CCNL2. In addition, we identified ten tumor suppressive paralog pairs whose compound loss promotes cell growth. These findings identify a large number of previously unidentified essential gene families and nominate new druggable targets for oncology drug discovery.HighlightsComprehensive genetic interaction mapping of 1,030 human duplicated paralogs using a dual targeting CRISPR/Cas9 approachDuplicated paralogs are highly enriched for genetic interactionsSynthetic lethal paralogs include CCNL1/CCNL2, CDK4/CDK6, and GSK3A/GSK3BTumor suppressor paralog pairs include CDKN2A/CDKN2B and FBXO25/FBXO32

scholarly journals RNA binding protein hnRNP-U is required for physiological hypertrophy of skeletal muscle

2020 ◽  
Author(s):  
Debalina Bagchi ◽  
Benjamin D Mason ◽  
Kodilichi Baldino ◽  
Bin Li ◽  
Eun-Joo Lee ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Molecular Mechanisms ◽  
Rna Binding ◽  
Single Gene ◽  
Systemic Disease ◽  
Protein Isoforms ◽  
Anabolic Resistance ◽  
Multiple Protein ◽  
A Cell ◽  
Cell Type Specific

AbstractSkeletal muscle has the remarkable ability to modulate its mass in response to physiological changes associated with nutritional input, functional utilization, systemic disease, and age. A decreased responsiveness to anabolic stimuli is thought to contribute significantly to the loss of skeletal muscle mass and strength associated with sarcopenia, however the molecular mechanisms precipitating this are unclear. The signal transduction pathways that control the relative balance between anabolic and catabolic processes are tightly regulated at the transcriptional and post-transcriptional levels. Alternative splicing produces multiple protein isoforms from a single gene in a cell-type-specific manner and in response to environmental cues. We show that sustained activation of Akt1 in Hnrnpu deficient mice leads to premature muscle wasting, in part, through impaired autophagy while providing mechanistic insights into the development of anabolic resistance.

A Mucin-Specific Protease Enables Molecular and Functional Analysis of Human Cancer-Associated Mucins

2018 ◽  
Author(s):  
Stacy A. Malaker ◽  
Kayvon Pedram ◽  
Michael J. Ferracane ◽  
Elliot C. Woods ◽  
Jessica Kramer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Molecular Mechanisms ◽  
Cultured Cells ◽  
High Resolution Mass Spectrometry ◽  
Human Cancer ◽  
Glycosylation Sites ◽  
Bacterial Protease ◽  
Specific Protease ◽  
To Come ◽  
And Function

<div> <div> <div> <p>Mucins are a class of highly O-glycosylated proteins that are ubiquitously expressed on cellular surfaces and are important for human health, especially in the context of carcinomas. However, the molecular mechanisms by which aberrant mucin structures lead to tumor progression and immune evasion have been slow to come to light, in part because methods for selective mucin degradation are lacking. Here we employ high resolution mass spectrometry, polymer synthesis, and computational peptide docking to demonstrate that a bacterial protease, called StcE, cleaves mucin domains by recognizing a discrete peptide-, glycan-, and secondary structure- based motif. We exploited StcE’s unique properties to map glycosylation sites and structures of purified and recombinant human mucins by mass spectrometry. As well, we found that StcE will digest cancer-associated mucins from cultured cells and from ovarian cancer patient-derived ascites fluid. Finally, using StcE we discovered that Siglec-7, a glyco-immune checkpoint receptor, specifically binds sialomucins as biological ligands, whereas the related Siglec-9 receptor does not. Mucin-specific proteolysis, as exemplified by StcE, is therefore a powerful tool for the study of glycoprotein structure and function and for deorphanizing mucin-binding receptors. </p> </div> </div> </div>

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