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Showing 1581 to 1584 of 5186 entries
Published in July 2021
Transcriptomic analysis and molecular docking reveal genes involved in the response of Aedes aegypti larvae to an essential oil extracted from Eucalyptus.
Abstract: BACKGROUND: Aedes aegypti (L.) is an urban mosquito, vector of several arboviruses that cause severe diseases in hundreds of million people each year. The resistance to synthetic insecticides developed by Ae. aegypti populations worldwide has contributed to failures in vector control campaigns, increasing the impact of arbovirus diseases. In this context, plant-derived essential oils with larvicidal activity could be an attractive alternative for vector control. However, the mode of action and the detoxificant response of mosquitoes to plant derived compounds have not been established, impairing the optimization of their use. METHODS AND FINDINGS: Here we compare gene expression in Ae. aegypti larvae after 14 hrs of exposure to Eucalyptus camaldulensis essential oil with a control group exposed to vehicle (acetone) for the same lapse, by using RNA-Seq. We found differentially expressed genes encoding for cuticle proteins, fatty-acid synthesis, membrane transporters and detoxificant related gene families (i.e. heat shock proteins, cytochromes P450, glutathione transferases, UDP-glycosyltransferases and ABC transporters). Finally, our RNA-Seq and molecular docking results provide evidence pointing to a central involvement of chemosensory proteins in the detoxificant response in mosquitoes. CONCLUSIONS AND SIGNIFICANCE: Our work contributes to the understanding of the physiological response of Ae. aegypti larvae to an intoxication with a natural toxic distilled from Eucalyptus leafs. The results suggest an involvement of most of the gene families associated to detoxification of xenobiotics in insects. Noteworthy, this work provides important information regarding the implication of chemosensory proteins in the detoxification of a natural larvicide. Understanding the mode of detoxification of Eucalyptus distilled compounds could contribute to their implementation as a tool in mosquito control.
Published in July 2021
Gentamicin-induced hearing loss: A retrospective study using the Food and Drug Administration Adverse Event Reporting System and a toxicological study using drug-gene network analysis.
Abstract: The objectives of the study were to evaluate the relationship between gentamicin (GEN) and hearing loss using the Food and Drug Administration Adverse Event Reporting system (FAERS) database and elucidate the potential toxicological mechanism of GEN-induced hearing loss through a drug-gene network analysis. Using the preferred terms and standardized queries from the Medical Dictionary for Regulatory Activities, we calculated the reporting odds ratios (RORs). We extracted GEN-associated genes (seed genes) and analyzed drug-gene interactions using the ClueGO plug-in in the Cytoscape software and the DIseAse MOdule Detection (DIAMOnD) algorithm. The lower limit of the 95% confidence interval (CI) of the ROR for aminoglycosides (AG) antibacterials was over 1, and the ROR was 5.5 (5.1-6.0). We retrieved 17 seed genes related to GEN from the PharmGKB and Drug Gene Interaction databases. In total, 1018 human genes interacting with GEN were investigated using ClueGO. Through Molecular Complex Detection (MCODE) analysis, we identified 17 local gene clusters. The nodes and edges of the highest-ranked local gene cluster named "Cluster 1" were 30 and 433, respectively. According to the ClueGO analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), Cluster 1 genes were highly enriched in "oxidative phosphorylation." According to the ClueGO analysis using ClinVar, Cluster 1 genes were highly enriched in "mitochondrial diseases," "mitochondrial complex I deficiency," "hereditary hearing loss and deafness," and "Leigh syndrome." We identified 60 GEN-associated genes using the DIAMOnD algorithm. Several GEN-associated genes in the DIAMOnD algorithm were highly enriched in "PI3K-Akt signaling pathway," "Ras signaling pathway," "focal adhesion," "MAPK signaling pathway," "regulation of actin cytoskeleton," "oxidative phosphorylation," and "ECM-receptor interaction." Our analysis demonstrated an association between several AGs and hearing loss using the FAERS database. Drug-gene network analysis demonstrated that GEN may be associated with oxidative phosphorylation-associated genes and integrin genes, which may be associated with hearing loss.
Published on July 30, 2021
Identifying risk of opioid use disorder for patients taking opioid medications with deep learning.
Abstract: OBJECTIVE: The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. METHODS: Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. RESULTS: The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). CONCLUSIONS: LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
Published on July 30, 2021
Advances in the computational landscape for repurposed drugs against COVID-19.
Abstract: The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
Published on July 30, 2021
Drug-induced phospholipidosis confounds drug repurposing for SARS-CoV-2.
Abstract: Repurposing drugs as treatments for COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has drawn much attention. Beginning with sigma receptor ligands and expanding to other drugs from screening in the field, we became concerned that phospholipidosis was a shared mechanism underlying the antiviral activity of many repurposed drugs. For all of the 23 cationic amphiphilic drugs we tested, including hydroxychloroquine, azithromycin, amiodarone, and four others already in clinical trials, phospholipidosis was monotonically correlated with antiviral efficacy. Conversely, drugs active against the same targets that did not induce phospholipidosis were not antiviral. Phospholipidosis depends on the physicochemical properties of drugs and does not reflect specific target-based activities-rather, it may be considered a toxic confound in early drug discovery. Early detection of phospholipidosis could eliminate these artifacts, enabling a focus on molecules with therapeutic potential.
Published on July 29, 2021
Comprehensive virtual screening of 4.8 k flavonoids reveals novel insights into allosteric inhibition of SARS-CoV-2 M(PRO).
Abstract: SARS-CoV-2 main protease is a common target for inhibition assays due to its high conservation among coronaviruses. Since flavonoids show antiviral activity, several in silico works have proposed them as potential SARS-CoV-2 main protease inhibitors. Nonetheless, there is reason to doubt certain results given the lack of consideration for flavonoid promiscuity or main protease plasticity, usage of short library sizes, absence of control molecules and/or the limitation of the methodology to a single target site. Here, we report a virtual screening study where dorsilurin E, euchrenone a11, sanggenol O and CHEMBL2171598 are proposed to inhibit main protease through different pathways. Remarkably, novel structural mechanisms were observed after sanggenol O and CHEMBL2171598 bound to experimentally proven allosteric sites. The former drastically affected the active site, while the latter triggered a hinge movement which has been previously reported for an inactive SARS-CoV main protease mutant. The use of a curated database of 4.8 k flavonoids, combining two well-known docking software (AutoDock Vina and AutoDock4.2), molecular dynamics and MMPBSA, guaranteed an adequate analysis and robust interpretation. These criteria can be considered for future screening campaigns against SARS-CoV-2 main protease.
Published on July 29, 2021
Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach.
Abstract: Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.
Published on July 29, 2021
Structural Refinement of Carbimazole by NMR Crystallography.
Abstract: The characterization of the three-dimensional structure of solids is of major importance, especially in the pharmaceutical field. In the present work, NMR crystallography methods are applied with the aim to refine the crystal structure of carbimazole, an active pharmaceutical ingredient used for the treatment of hyperthyroidism and Grave's disease. Starting from previously reported X-ray diffraction data, two refined structures were obtained by geometry optimization methods. Experimental (1)H and (13)C isotropic chemical shift measured by the suitable (1)H and (13)C high-resolution solid state NMR techniques were compared with DFT-GIPAW calculated values, allowing the quality of the obtained structure to be experimentally checked. The refined structure was further validated through the analysis of (1)H-(1)H and (1)H-(13)C 2D NMR correlation experiments. The final structure differs from that previously obtained from X-ray diffraction data mostly for the position of hydrogen atoms.
Published on July 28, 2021
DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.
Abstract: MOTIVATION: In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding affinities. Both approaches can be combined with information about interaction networks. RESULTS: We developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein-protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major effects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods. AVAILABILITY: DTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Published on July 28, 2021
In silico drug repurposing in COVID-19: A network-based analysis.
Abstract: The SARS-CoV-2 pandemic is a worldwide public health emergency. Despite the beginning of a vaccination campaign, the search for new drugs to appropriately treat COVID-19 patients remains a priority. Drug repurposing represents a faster and cheaper method than de novo drug discovery. In this study, we examined three different network-based approaches to identify potentially repurposable drugs to treat COVID-19. We analyzed transcriptomic data from whole blood cells of patients with COVID-19 and 21 other related conditions, as compared with those of healthy subjects. In addition to conventionally used drugs (e.g., anticoagulants, antihistaminics, anti-TNFalpha antibodies, corticosteroids), unconventional candidate compounds, such as SCN5A inhibitors and drugs active in the central nervous system, were identified. Clinical judgment and validation through clinical trials are always mandatory before use of the identified drugs in a clinical setting.
Showing 1581 to 1584 of 5186 entries