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Showing 3461 to 3464 of 5186 entries
Published in February 2018
Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma.
Abstract: The lack of early diagnostic markers and novel therapeutic targets for clear cell renal cell carcinoma (ccRCC) negatively affects patient prognosis. Cancer metabolism is an attractive area for the understanding of the molecular mechanism of carcinogenesis. The present study attempted to identify metabolic changes from the view of the expression of metabolism-associated genes between control samples and those of ccRCC at different disease stages. Data concerning ccRCC gene expression obtained by RNA-sequencing was obtained from The Cancer Genome Atlas and data on metabolism-associated genes were extracted using the Recon2 model. Following analysis of differential gene expression, multiple differentially expressed metabolic genes at each tumor-node-metastasis disease stage were identified, compared with control non-disease samples: Metabolic genes (305) were differentially expressed in stage I disease, 323 in stage II disease, 355 in stage III disease and 363 in stage IV disease. Following enrichment analysis for differential metabolic genes, 22 metabolic pathways were identified to be dysregulated in multiple stages of ccRCC. Abnormalities in hormone, vitamin, glucose and lipid metabolism were present in the early stages of the disease, with dysregulation to reactive oxygen species detoxification and amino acid metabolism pathways occurring with advanced disease stages, particularly to valine, leucine, and isoleucine metabolism, which was substantially dysregulated in stage IV disease. The xenobiotic metabolism pathway, associated with multiple cytochrome P450 family genes, was dysregulated in each stage of the disease. This pathway is worthy of substantial attention since it may aid understanding of drug resistance in ccRCC. The results of the present study offer information to aid further research into early diagnostic biomarkers and therapeutic targets of ccRCC.
Published in February 2018
Translational Biomedical Informatics and Pharmacometrics Approaches in the Drug Interactions Research.
Abstract: Drug interaction is a leading cause of adverse drug events and a major obstacle for current clinical practice. Pharmacovigilance data mining, pharmacokinetic modeling, and text mining are computation and informatic tools on integrating drug interaction knowledge and generating drug interaction hypothesis. We provide a comprehensive overview of these translational biomedical informatics methodologies with related databases. We hope this review illustrates the complementary nature of these informatic approaches and facilitates the translational drug interaction research.
Published on February 23, 2018
Identification and preclinical evaluation of the small molecule, NSC745887, for treating glioblastomas via suppressing DcR3-associated signaling pathways.
Abstract: The small-molecule naphtha [2,3-f]quinoxaline-7,12-dione (NSC745887) can effectively inhibit the proliferation of various cancers by trapping DNA-topoisomerase cleavage. The aim of this study was to elucidate cellular responses of NSC745887 in human glioblastoma multiforme (GBM, U118MG and U87MG cells) and investigate the underlying molecular mechanisms. NSC745887 reduced the cell survival rate and increased the sub-G1 population in dose- and time-dependent manners in GBM cells. Moreover, NSC745887 increased expression of gammaH2AX and caused DNA fragmentation leading to DNA damage. Furthermore, Annexin V/propidium iodide and Br-dTP staining showed the apoptotic effect of NSC745887 in GBM cells. DNA repair proteins of ataxia-telangiectasia mutated (ATM), ATM and Rad3-related, and decoy receptor 3 also decreased with NSC745887 treatment. In addition, NSC745887 caused apoptosis by the caspase-8/9-caspase-3-poly(ADP-ribose) polymerase cascade. An in vivo study indicated that NSC745887 suppressed the [(18)F]-FDG-specific uptake value in brain tumors. Histological staining also indicated a decrease in Ki-67 and increases in gammaH2AX and cleaved caspase-3 in the brain tumor area. These data provide preclinical evidence for NSC745887 as a potential new small molecule drug for managing glioblastomas.
Published on February 13, 2018
Method to generate highly stable D-amino acid analogs of bioactive helical peptides using a mirror image of the entire PDB.
Abstract: Biologics are a rapidly growing class of therapeutics with many advantages over traditional small molecule drugs. A major obstacle to their development is that proteins and peptides are easily destroyed by proteases and, thus, typically have prohibitively short half-lives in human gut, plasma, and cells. One of the most effective ways to prevent degradation is to engineer analogs from dextrorotary (D)-amino acids, with up to 10(5)-fold improvements in potency reported. We here propose a general peptide-engineering platform that overcomes limitations of previous methods. By creating a mirror image of every structure in the Protein Data Bank (PDB), we generate a database of approximately 2.8 million D-peptides. To obtain a D-analog of a given peptide, we search the (D)-PDB for similar configurations of its critical-"hotspot"-residues. As a proof of concept, we apply our method to two peptides that are Food and Drug Administration approved as therapeutics for diabetes and osteoporosis, respectively. We obtain D-analogs that activate the GLP1 and PTH1 receptors with the same efficacy as their natural counterparts and show greatly increased half-life.
Published on February 9, 2018
Guanosine monophosphate reductase 1 is a potential therapeutic target for Alzheimer's disease.
Abstract: Alzheimer's disease (AD) is a severe neurodegenerative disorder for which identification of differentially expressed genes is one way to find new therapeutic targets. Here, we conducted analysis to identify age-independent, AD-specific genes. We found that the MET, WIF1, and NPTX2 genes are downregulated in AD. WIF1 and MET are implicated in Wnt and MET signaling and regulate GSK3beta activity and are thus linked with AD. Importantly, we found that the GMPR gene exhibited a gradual increase in AD progression. A logistic model based on GMPR has good ability to classify AD cases. GMPR's product GMPR1 is in the AMPK and adenosine receptor pathways and is thus associated with Tau phosphorylation in AD. This allows GMPR1 to be a therapeutic target. Therefore, we screened five possible inhibitors to GMPR1 by docking GMPR1 with 1,174 approved drugs. Among them, lumacaftor is ideal. We then tested the effects of lumacaftor on AD model mice. After 20 days of oral administration, we observed that beta-Amyloid accumulation was slowed down, and phosphorylation of Tau was almost eliminated in the treated mice. We highlight the elevated expression level of GMPR in AD and propose a therapeutic strategy of inhibiting GMPR1 with lumacaftor.
Published on February 6, 2018
Analysis of a gene panel for targeted sequencing of colorectal cancer samples.
Abstract: Colorectal cancer (CRC) is a leading cause of death worldwide. Surgical intervention is a successful treatment for stage I patients, whereas other more advanced cases may require adjuvant chemotherapy. The selection of effective adjuvant treatments remains, however, challenging. Accurate patient stratification is necessary for the identification of the subset of patients likely responding to treatment, while sparing others from pernicious treatment. Targeted sequencing approaches may help in this regard, enabling rapid genetic investigation, and at the same time easily applicable in routine diagnosis. We propose a set of guidelines for the identification, including variant calling and filtering, of somatic mutations driving tumorigenesis in the absence of matched healthy tissue. We also discuss the inclusion criteria for the generation of our gene panel. Furthermore, we evaluate the prognostic impact of individual genes, using Cox regression models in the context of overall survival and disease-free survival. These analyses confirmed the role of commonly used biomarkers, and shed light on controversial genes such as CYP2C8. Applying those guidelines, we created a novel gene panel to investigate the onset and progression of CRC in 273 patients. Our comprehensive biomarker set includes 266 genes that may play a role in the progression through the different stages of the disease. Tracing the developmental state of the tumour, and its resistances, is instrumental in patient stratification and reliable decision making in precision clinical practice.
Published on February 6, 2018
Revisiting the Therapeutic Potential of Bothrops jararaca Venom: Screening for Novel Activities Using Connectivity Mapping.
Abstract: Snake venoms are sources of molecules with proven and potential therapeutic applications. However, most activities assayed in venoms (or their components) are of hemorrhagic, hypotensive, edematogenic, neurotoxic or myotoxic natures. Thus, other relevant activities might remain unknown. Using functional genomics coupled to the connectivity map (C-map) approach, we undertook a wide range indirect search for biological activities within the venom of the South American pit viper Bothrops jararaca. For that effect, venom was incubated with human breast adenocarcinoma cell line (MCF7) followed by RNA extraction and gene expression analysis. A list of 90 differentially expressed genes was submitted to biosimilar drug discovery based on pattern recognition. Among the 100 highest-ranked positively correlated drugs, only the antihypertensive, antimicrobial (both antibiotic and antiparasitic), and antitumor classes had been previously reported for B. jararaca venom. The majority of drug classes identified were related to (1) antimicrobial activity; (2) treatment of neuropsychiatric illnesses (Parkinson's disease, schizophrenia, depression, and epilepsy); (3) treatment of cardiovascular diseases, and (4) anti-inflammatory action. The C-map results also indicated that B. jararaca venom may have components that target G-protein-coupled receptors (muscarinic, serotonergic, histaminergic, dopaminergic, GABA, and adrenergic) and ion channels. Although validation experiments are still necessary, the C-map correlation to drugs with activities previously linked to snake venoms supports the efficacy of this strategy as a broad-spectrum approach for biological activity screening, and rekindles the snake venom-based search for new therapeutic agents.
Published on February 5, 2018
Prediction of Effective Drug Combinations by an Improved Naive Bayesian Algorithm.
Abstract: Drug combinatorial therapy is a promising strategy for combating complex diseases due to its fewer side effects, lower toxicity and better efficacy. However, it is not feasible to determine all the effective drug combinations in the vast space of possible combinations given the increasing number of approved drugs in the market, since the experimental methods for identification of effective drug combinations are both labor- and time-consuming. In this study, we conducted systematic analysis of various types of features to characterize pairs of drugs. These features included information about the targets of the drugs, the pathway in which the target protein of a drug was involved in, side effects of drugs, metabolic enzymes of the drugs, and drug transporters. The latter two features (metabolic enzymes and drug transporters) were related to the metabolism and transportation properties of drugs, which were not analyzed or used in previous studies. Then, we devised a novel improved naive Bayesian algorithm to construct classification models to predict effective drug combinations by using the individual types of features mentioned above. Our results indicated that the performance of our proposed method was indeed better than the naive Bayesian algorithm and other conventional classification algorithms such as support vector machine and K-nearest neighbor.
Published on February 1, 2018
Tissue specificity of in vitro drug sensitivity.
Abstract: Objectives: We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications. Materials and Methods: We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity. Results: We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.05). By restricting the drug-tissue interactions to those with AUC > 0.2, we found that in 52% of interactions, the tissue was predictive of drug sensitivity (concordance index > 0.65). When compared with clinical indications, the observed overlap was weak (Matthew correlation coefficient, MCC = 0.0003, P > .10). Discussion: While drugs exhibit significant tissue specificity in vitro, there is little overlap with clinical indications. This can be attributed to factors such as underlying biological differences between in vitro models and patient tumors, or the inability of tissue-specific drugs to bring additional benefits beyond gold standard treatments during clinical trials. Conclusion: Our meta-analysis of pan-cancer drug screening datasets indicates that most tested drugs exhibit tissue-specific sensitivities in a large panel of cancer cell lines. However, the observed preclinical results do not translate to the clinical setting. Our results suggest that additional research into showing parallels between preclinical and clinical data is required to increase the translational potential of in vitro drug screening.
Published in January 2018
Integration among databases and data sets to support productive nanotechnology: Challenges and recommendations.
Abstract: Many groups within the broad field of nanoinformatics are already developing data repositories and analytical tools driven by their individual organizational goals. Integrating these data resources across disciplines and with non-nanotechnology resources can support multiple objectives by enabling the reuse of the same information. Integration can also serve as the impetus for novel scientific discoveries by providing the framework to support deeper data analyses. This article discusses current data integration practices in nanoinformatics and in comparable mature fields, and nanotechnology-specific challenges impacting data integration. Based on results from a nanoinformatics-community-wide survey, recommendations for achieving integration of existing operational nanotechnology resources are presented. Nanotechnology-specific data integration challenges, if effectively resolved, can foster the application and validation of nanotechnology within and across disciplines. This paper is one of a series of articles by the Nanomaterial Data Curation Initiative that address data issues such as data curation workflows, data completeness and quality, curator responsibilities, and metadata.
Showing 3461 to 3464 of 5186 entries