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Showing 2201 to 2204 of 5186 entries
Published on December 10, 2020
Whole-genome sequencing analysis of the cardiometabolic proteome.
Abstract: The human proteome is a crucial intermediate between complex diseases and their genetic and environmental components, and an important source of drug development targets and biomarkers. Here, we comprehensively assess the genetic architecture of 257 circulating protein biomarkers of cardiometabolic relevance through high-depth (22.5x) whole-genome sequencing (WGS) in 1328 individuals. We discover 131 independent sequence variant associations (P < 7.45 x 10(-11)) across the allele frequency spectrum, all of which replicate in an independent cohort (n = 1605, 18.4x WGS). We identify for the first time replicating evidence for rare-variant cis-acting protein quantitative trait loci for five genes, involving both coding and noncoding variation. We construct and validate polygenic scores that explain up to 45% of protein level variation. We find causal links between protein levels and disease risk, identifying high-value biomarkers and drug development targets.
Published on December 9, 2020
Hybrid attentional memory network for computational drug repositioning.
Abstract: BACKGROUND: Drug repositioning has been an important and efficient method for discovering new uses of known drugs. Researchers have been limited to one certain type of collaborative filtering (CF) models for drug repositioning, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model which can offer the advantages of both. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. RESULTS: Inspired by the memory network, we propose the hybrid attentional memory network (HAMN) model, a deep architecture combining two classes of CF models in a nonlinear manner. First, the memory unit and the attention mechanism are combined to generate a neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. During this process, ancillary information of drugs and diseases can help alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is coupled with the drug latent factor and disease latent factor to produce predicted values. Comprehensive experimental results on two data sets demonstrate that our proposed HAMN model outperforms other comparison models based on the AUC, AUPR and HR indicators. CONCLUSIONS: Through the performance on two drug repositioning data sets, we believe that the HAMN model proposes a new solution to improve the prediction accuracy of drug-disease associations and give pharmaceutical personnel a new perspective to develop new drugs.
Published on December 9, 2020
New information of dopaminergic agents based on quantum chemistry calculations.
Abstract: Dopamine is an important neurotransmitter that plays a key role in a wide range of both locomotive and cognitive functions in humans. Disturbances on the dopaminergic system cause, among others, psychosis, Parkinson's disease and Huntington's disease. Antipsychotics are drugs that interact primarily with the dopamine receptors and are thus important for the control of psychosis and related disorders. These drugs function as agonists or antagonists and are classified as such in the literature. However, there is still much to learn about the underlying mechanism of action of these drugs. The goal of this investigation is to analyze the intrinsic chemical reactivity, more specifically, the electron donor-acceptor capacity of 217 molecules used as dopaminergic substances, particularly focusing on drugs used to treat psychosis. We analyzed 86 molecules categorized as agonists and 131 molecules classified as antagonists, applying Density Functional Theory calculations. Results show that most of the agonists are electron donors, as is dopamine, whereas most of the antagonists are electron acceptors. Therefore, a new characterization based on the electron transfer capacity is proposed in this study. This new classification can guide the clinical decision-making process based on the physiopathological knowledge of the dopaminergic diseases.
Published on December 8, 2020
Understanding the biological role of PqqB in Pseudomonas stutzeri using molecular dynamics simulation approach.
Abstract: Phosphate solubilization is an important and widely studied plant growth promoting trait exhibited by many bacteria. Pyrroloquinoline quinone (PQQ), a redox cofactor of methanol and glucose dehydrogenases has been well established as essential for phosphate solubilization. PQQ operon has been well studied in growth promoting rhizobacteria like Pseudomonas spp., Gluconobacter oxydans, Klebsiella pneumoniae, etc. However, the role of PqqB is quite ambiguous as its functional role has been contradicted in many studies. In the present study, we selected Pseudomonas stutzeri - a well-known P solubilizing bacterium as a representative species of the Pseudomonas genus on the basis of phylogenetic and statistical analyses of PqqB proteins. A 3 D model was generated for this protein. Docking of PqqB with PQQ showed good interaction with a theoretical binding affinity of -7.4 kcal/mol. On the other hand, docking of PqqC with 3a-(2-amino-2-carboxy-ethyl)-4,5-dioxo-4,5,6,7,8,9-hexahydro-quinoline-7,9-dicarb oxylic acid (AHQQ, immediate precursor of PQQ) showed strong interaction (-10.4 kcal/mol) but the same was low with PQQ (-6.4 kcal/mol). Molecular dynamic simulation of both the complexes showed stable conformation. The binding energy of PqqB-PQQ complex (-182.710 +/- 16.585 kJ/mol) was greater than PqqC-PQQ complex (-166.114 +/- 12.027 kJ/mol). The results clearly indicated that kinetically there is a possibility that after cyclization of AHQQ to PQQ by PqqC, PQQ can be taken up by PqqB and transported to periplasm for the oxidation of glucose. To the best of our knowledge, this is the first attempt to understand the biological role of PqqB on the basis of molecular interactions and dynamics.Communicated by Ramaswamy H. Sarma.
Published on December 7, 2020
Improving the informativeness of Mendelian disease-derived pathogenicity scores for common disease.
Abstract: Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease.
Published on December 7, 2020
Proteomic signatures of 16 major types of human cancer reveal universal and cancer-type-specific proteins for the identification of potential therapeutic targets.
Abstract: BACKGROUND: Proteomic characterization of cancers is essential for a comprehensive understanding of key molecular aberrations. However, proteomic profiling of a large cohort of cancer tissues is often limited by the conventional approaches. METHODS: We present a proteomic landscape of 16 major types of human cancer, based on the analysis of 126 treatment-naive primary tumor tissues, 94 tumor-matched normal adjacent tissues, and 12 normal tissues, using mass spectrometry-based data-independent acquisition approach. RESULTS: In our study, a total of 8527 proteins were mapped to brain, head and neck, breast, lung (both small cell and non-small cell lung cancers), esophagus, stomach, pancreas, liver, colon, kidney, bladder, prostate, uterus and ovary cancers, including 2458 tissue-enriched proteins. Our DIA-based proteomic approach has characterized major human cancers and identified universally expressed proteins as well as tissue-type-specific and cancer-type-specific proteins. In addition, 1139 therapeutic targetable proteins and 21 cancer/testis (CT) antigens were observed. CONCLUSIONS: Our discoveries not only advance our understanding of human cancers, but also have implications for the design of future large-scale cancer proteomic studies to assist the development of diagnostic and/or therapeutic targets in multiple cancers.
Published on December 5, 2020
Enhancing the Low Oral Bioavailability of Sulpiride via Fast Orally Disintegrating Tablets: Formulation, Optimization and In Vivo Characterization.
Abstract: Sulpiride (SUL) is a dopamine D2-receptor antagonist used for management of GIT disturbance and it has anti-psychotic activities based on the administered dose. SUL undergoes P-glycoprotein efflux, which lead to poor bioavailability and erratic absorption. Therefore, the objective of this research was an attempt to enhance the oral bioavailability of SUL via formulation of fast disintegrating tablets (SUL-FDTs) with a rapid onset of action. A 3(2) full-factorial design was performed for optimization of SUL-FDTs using desirability function. The concentration of superdisintegrant (X1) and Prosolv((R)) (X2) were selected as independent formulation variables for the preparation and optimization of SUL-FDTs using direct compression technique. The prepared SUL-FDTs were investigated regarding their mechanical strength, disintegration time, drug release and in vivo pharmacokinetic analysis in rabbits. The optimized formulation has hardness of 4.58 +/- 0.52 KP, friability of 0.73 +/- 0.158%, disintegration time of 37.5 +/- 1.87 s and drug release of 100.51 +/- 1.34% after 30 min. In addition, the optimized SUL-FDTs showed a significant (p < 0.01) increase in Cmax and AUC(0-infinity) and a relative bioavailability of about 9.3 fold compared to the commercial product. It could be concluded that SUL-FDTs are a promising formulation for enhancing the oral bioavailability of SUL concomitant with a fast action.
Published on December 4, 2020
Integrated Bioinformatics Analysis for the Screening of Associated Pathways and Therapeutic Drugs in Coronavirus Disease 2019.
Abstract: BACKGROUND: COVID-19 caused by a novel coronavirus, a severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has recently broken out worldwide. Up to now, the development of vaccine is still in the stage of clinical research, and there is no clinically approved specific antiviral drug for human coronavirus infection. The purpose of this study is to investigate the key molecules involved in response during SARS-CoV-2 infection and provide references for the treatment of SARS-CoV-2. METHODS: We conducted in-depth and comprehensive bioinformatics analysis of human proteins identified with SARS-CoV-2, including functional enrichment analysis, protein interaction network analysis, screening of hub genes, and evaluation of their potential as therapeutic targets. In addition, we used the gene-drug database to search for inhibitors of related biological targets. RESULTS: Several significant pathways, such as PKA, centrosome and transcriptional regulation, may greatly contribute to the development and progression of COVID-2019 disease. Taken together 15 drugs and 18 herb ingredients were screened as potential drugs for viral treatment. Specially, the trans-resveratrol can significantly reduce the expression of N protein of MERS-CoV and inhibit MERS-CoV. In addition, trans-resveratrol, Epigallocatechin-3-gallate (EGCG) and BX795 all show good anti multiple virus effects. CONCLUSION: Some drugs selected through our methods have been proven to have antiviral effects in previous studies. We aim to use global bioinformatics analysis to provide insights to assist in the design of new drugs and provide new choices for clinical treatment.
Published on December 4, 2020
Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments.
Abstract: Chemosensitivity assays are commonly used for preclinical drug discovery and clinical trial optimization. However, data from independent assays are often discordant, largely attributed to uncharacterized variation in the experimental materials and protocols. Spurred by the annotation of minimum information (MI) for ensuring data reproducibility, we report here the launching of MICHA (Minimal Information for Chemosensitivity Assays), accessed via https://micha-protocol.org . Distinguished from existing MI efforts that are often lack of support from data integration tools, MICHA can automatically extract publicly available information to facilitate the assay annotation including: 1) compounds, 2) samples, 3) reagents, and 4) data processing methods. For example, MICHA provides an integrative web server and database to obtain compound annotation including chemical structures, targets and disease indications. In addition, the annotation of cell line samples, assay protocols and literature references can be greatly eased by retrieving manually curated catalogues. Once the annotation is complete, MICHA can export a report that conforms to the FAIR principle (Findable, Accessible, Interoperable and Reusable) of drug screening studies. To consolidate the utility of MICHA, we provide FAIRified protocols from several major cancer drug screening studies, as well as recently conducted COVID-19 studies. With the integrative webserver and database, we envisage a wider adoption of the MICHA strategy to foster a community-driven effort to improve the open access of drug sensitivity assays.
Published on December 2, 2020
signatureSearch: environment for gene expression signature searching and functional interpretation.
Abstract: signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.
Showing 2201 to 2204 of 5186 entries