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Showing 2831 to 2834 of 5186 entries
Published on November 27, 2019
An Autophagy Modifier Screen Identifies Small Molecules Capable of Reducing Autophagosome Accumulation in a Model of CLN3-Mediated Neurodegeneration.
Abstract: Alterations in the autophagosomal-lysosomal pathway are a major pathophysiological feature of CLN3 disease, which is the most common form of childhood-onset neurodegeneration. Accumulating autofluorescent lysosomal storage material in CLN3 disease, consisting of dolichols, lipids, biometals, and a protein that normally resides in the mitochondria, subunit c of the mitochondrial ATPase, provides evidence that autophagosomal-lysosomal turnover of cellular components is disrupted upon loss of CLN3 protein function. Using a murine neuronal cell model of the disease, which accurately mimics the major gene defect and the hallmark features of CLN3 disease, we conducted an unbiased search for modifiers of autophagy, extending previous work by further optimizing a GFP-LC3 based assay and performing a high-content screen on a library of ~2000 bioactive compounds. Here we corroborate our earlier screening results and identify expanded, independent sets of autophagy modifiers that increase or decrease the accumulation of autophagosomes in the CLN3 disease cells, highlighting several pathways of interest, including the regulation of calcium signaling, microtubule dynamics, and the mevalonate pathway. Follow-up analysis on fluspirilene, nicardipine, and verapamil, in particular, confirmed activity in reducing GFP-LC3 vesicle burden, while also demonstrating activity in normalizing lysosomal positioning and, for verapamil, in promoting storage material clearance in CLN3 disease neuronal cells. This study demonstrates the potential for cell-based screening studies to identify candidate molecules and pathways for further work to understand CLN3 disease pathogenesis and in drug development efforts.
Published on November 22, 2019
Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.
Abstract: Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, beta-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.
Published on November 22, 2019
Targeted Drug-Loaded Chemical Probe Staining Assay to Predict Therapy Response and Function as an Independent Pathological Marker.
Abstract: Multi-targeted kinase inhibitors, such as sorafenib, have been used in various malignancies, but their efficacy in clinical applications varies among individuals and lacks pretherapeutic prediction measures. We applied the concept of "click chemistry" to pathological staining and established a drug-loaded probe staining assay. We stained the cells and different types of pathological sections and demonstrated that the assay was reliable. We further verified in cells, cell-derived xenograft model, and clinical level that the staining intensity of the probe could reflect drug sensitivity. The stained samples from 300 patients who suffered from hepatocellular carcinoma and used the sorafenib probe also indicated that staining intensity was closely related to clinical information and could be used as an independent marker without undergoing sorafenib therapy for prognosis. This assay provided new ideas for multi-target drug clinical trials, pre-medication prediction, and pathological research.
Published on November 22, 2019
MiRNA therapeutics based on logic circuits of biological pathways.
Abstract: BACKGROUND: In silico experiments, with the aid of computer simulation, speed up the process of in vitro or in vivo experiments. Cancer therapy design is often based on signalling pathway. MicroRNAs (miRNA) are small non-coding RNA molecules. In several kinds of diseases, including cancer, hepatitis and cardiovascular diseases, they are often deregulated, acting as oncogenes or tumor suppressors. miRNA therapeutics is based on two main kinds of molecules injection: miRNA mimics, which consists of injection of molecules that mimic the targeted miRNA, and antagomiR, which consists of injection of molecules inhibiting the targeted miRNA. Nowadays, the research is focused on miRNA therapeutics. This paper addresses cancer related signalling pathways to investigate miRNA therapeutics. RESULTS: In order to prove our approach, we present two different case studies: non-small cell lung cancer and melanoma. KEGG signalling pathways are modelled by a digital circuit. A logic value of 1 is linked to the expression of the corresponding gene. A logic value of 0 is linked to the absence (not expressed) gene. All possible relationships provided by a signalling pathway are modelled by logic gates. Mutations, derived according to the literature, are introduced and modelled as well. The modelling approach and analysis are widely discussed within the paper. MiRNA therapeutics is investigated by the digital circuit analysis. The most effective miRNA and combination of miRNAs, in terms of reduction of pathogenic conditions, are obtained. A discussion of obtained results in comparison with literature data is provided. Results are confirmed by existing data. CONCLUSIONS: The proposed study is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation of a cancer pathway. Using this simulation, the most effective combination of drugs and miRNAs for mutated cancer therapy design are obtained and these results were validated by the literature. The proposed modelling and analysis approach can be applied to each human disease, starting from the corresponding signalling pathway.
Published on November 20, 2019
Predicting drug-disease associations via sigmoid kernel-based convolutional neural networks.
Abstract: BACKGROUND: In the process of drug development, computational drug repositioning is effective and resource-saving with regards to its important functions on identifying new drug-disease associations. Recent years have witnessed a great progression in the field of data mining with the advent of deep learning. An increasing number of deep learning-based techniques have been proposed to develop computational tools in bioinformatics. METHODS: Along this promising direction, we here propose a drug repositioning computational method combining the techniques of Sigmoid Kernel and Convolutional Neural Network (SKCNN) which is able to learn new features effectively representing drug-disease associations via its hidden layers. Specifically, we first construct similarity metric of drugs using drug sigmoid similarity and drug structural similarity, and that of disease using disease sigmoid similarity and disease semantic similarity. Based on the combined similarities of drugs and diseases, we then use SKCNN to learn hidden representations for each drug-disease pair whose labels are finally predicted by a classifier based on random forest. RESULTS: A series of experiments were implemented for performance evaluation and their results show that the proposed SKCNN improves the prediction accuracy compared with other state-of-the-art approaches. Case studies of two selected disease are also conducted through which we prove the superior performance of our method in terms of the actual discovery of potential drug indications. CONCLUSION: The aim of this study was to establish an effective predictive model for finding new drug-disease associations. These experimental results show that SKCNN can effectively predict the association between drugs and diseases.
Published on November 19, 2019
Sparganin A alleviates blood stasis syndrome and its key targets by molecular docking.
Abstract: Blood stasis syndrome is implicated in the development of chronic conditions, including cardio- and cerebrovascular diseases. Cyclo-(Tyr-Leu), named Sparganin A (SA), is a compound isolated from the ethanol extract of Rhizoma Sparganii. Here, the successful extraction of SA from Rhizoma Sparganii was verified by extensive spectral analysis using (1)H NMR and (13)C NMR. To determine the biological effects of SA, a mouse model of acute blood stasis was established by subcutaneous injection of adrenaline hydrochloride and placing the animals in an ice water bath. In this model, the concentration of TXB2, PAI-1, FIB, ET-1 was measured by ELISA, and thymus index (TI), hepatic index (HI), and spleen index (SI) were calculated. Molecular docking by SYBYL and functional analysis of the putative targets by STRING and Cytoscape were employed to identify the key targets of SA. The accumulated results documented that SA exhibits anticoagulative activity, and its key targets are VEGFA and SERPINE1. SA may be involved in the pathological process of complement and coagulation cascades. This study demonstrates that SA may be a promising drug to control coagulation in blood stasis syndrome.
Published on November 19, 2019
A Bayesian machine learning approach for drug target identification using diverse data types.
Abstract: Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201-an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201's target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.
Published on November 18, 2019
Synthesis of Indomorphan Pseudo-Natural Product Inhibitors of Glucose Transporters GLUT-1 and -3.
Abstract: Bioactive compound design based on natural product (NP) structure may be limited because of partial coverage of NP-like chemical space and biological target space. These limitations can be overcome by combining NP-centered strategies with fragment-based compound design through combination of NP-derived fragments to afford structurally unprecedented "pseudo-natural products" (pseudo-NPs). The design, synthesis, and biological evaluation of a collection of indomorphan pseudo-NPs that combine biosynthetically unrelated indole- and morphan-alkaloid fragments are described. Indomorphane derivative Glupin was identified as a potent inhibitor of glucose uptake by selectively targeting and upregulating glucose transporters GLUT-1 and GLUT-3. Glupin suppresses glycolysis, reduces the levels of glucose-derived metabolites, and attenuates the growth of various cancer cell lines. Our findings underscore the importance of dual GLUT-1 and GLUT-3 inhibition to efficiently suppress tumor cell growth and the cellular rescue mechanism, which counteracts glucose scarcity.
Published on November 18, 2019
Machine learning-based chemical binding similarity using evolutionary relationships of target genes.
Abstract: Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional activity related to target binding often fails. To overcome this limitation, we developed a novel machine learning-based chemical binding similarity score by using various evolutionary relationships of binding targets. The chemical similarity was defined by the probability of chemical compounds binding to identical targets. Comprehensive and heterogeneous multiple target-binding chemical data were integrated into a paired data format and processed using multiple classification similarity-learning models with various levels of target evolutionary information. Encoding evolutionary information to chemical compounds through their binding targets substantially expanded available chemical-target interaction data and significantly improved model performance. The output probability of our integrated model, referred to as ensemble evolutionary chemical binding similarity (ensECBS), was effective for finding hidden chemical relationships. The developed method can serve as a novel chemical similarity tool that uses evolutionarily conserved target binding information.
Published on November 18, 2019
A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome.
Abstract: Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.
Showing 2831 to 2834 of 5186 entries