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Showing 711 to 714 of 5186 entries
Published on July 30, 2022
Repurposing of a human antibody-based microarray to explore conserved components of the signalome of the parasitic nematode Haemonchus contortus.
Abstract: BACKGROUND: Gaining insight into molecular signalling pathways of socioeconomically important parasitic nematodes has implications for understanding their molecular biology and for developing novel anthelmintic interventions. METHODS: Here, we evaluated the use of a human antibody-based microarray to explore conserved elements of the signalome in the barber's pole worm Haemonchus contortus. To do this, we prepared extracts from mixed-sex (female and male) adult worms and third-stage larvae (L3s), incubated these extracts on the antibody microarray and then measured the amounts of antibody-bound proteins ('signal intensity'). RESULTS: In total, 878 signals were classified into two distinct categories: signals that were higher for adults than for larvae of H. contortus (n = 376), and signals that were higher for larvae than for adults of this species (n = 502). Following a data-filtering step, high confidence ('specific') signals were obtained for subsequent analyses. In total, 39 pan-specific signals (linked to antibodies that recognise target proteins irrespective of their phosphorylation status) and 65 phosphorylation-specific signals were higher in the adult stage, and 82 pan-specific signals and 183 phosphorylation-specific signals were higher in L3s. Thus, notably more signals were higher in L3s than in the adult worms. Using publicly available information, we then inferred H. contortus proteins that were detected (with high confidence) by specific antibodies directed against human homologues, and revealed relatively high structural conservation between the two species, with some variability for select proteins. We also in silico-matched 763 compound structures (listed in the DrugBank and Kinase SARfari public databases) to four H. contortus proteins (designated HCON_00005760, HCON_00079680, HCON_00013590 and HCON_00105100). CONCLUSIONS: We conclude that the present antibody-based microarray provides a useful tool for comparative analyses of signalling pathways between/among developmental stages and/or species, as well as opportunities to explore nematocidal target candidates in H. contortus and related parasites.
Published on July 29, 2022
Gene networks under circadian control exhibit diurnal organization in primate organs.
Abstract: Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.
Published on July 29, 2022
Learning size-adaptive molecular substructures for explainable drug-drug interaction prediction by substructure-aware graph neural network.
Abstract: Drug-drug interactions (DDIs) can trigger unexpected pharmacological effects on the body, and the causal mechanisms are often unknown. Graph neural networks (GNNs) have been developed to better understand DDIs. However, identifying key substructures that contribute most to the DDI prediction is a challenge for GNNs. In this study, we presented a substructure-aware graph neural network, a message passing neural network equipped with a novel substructure attention mechanism and a substructure-substructure interaction module (SSIM) for DDI prediction (SA-DDI). Specifically, the substructure attention was designed to capture size- and shape-adaptive substructures based on the chemical intuition that the sizes and shapes are often irregular for functional groups in molecules. DDIs are fundamentally caused by chemical substructure interactions. Thus, the SSIM was used to model the substructure-substructure interactions by highlighting important substructures while de-emphasizing the minor ones for DDI prediction. We evaluated our approach in two real-world datasets and compared the proposed method with the state-of-the-art DDI prediction models. The SA-DDI surpassed other approaches on the two datasets. Moreover, the visual interpretation results showed that the SA-DDI was sensitive to the structure information of drugs and was able to detect the key substructures for DDIs. These advantages demonstrated that the proposed method improved the generalization and interpretation capability of DDI prediction modeling.
Published on July 29, 2022
Andrographolide in atherosclerosis: integrating network pharmacology and in vitro pharmacological evaluation.
Abstract: OBJECTIVE: Andrographis paniculata (Burm.f.) Nees is a medicinal plant that has been traditionally used as an anti-inflammatory and antibacterial remedy for several conditions. Andrographolide (AG), the active constituent of A. paniculata (Burm.f.) Nees, has anti-lipidic and anti-inflammatory properties as well as cardiovascular protective effects. The present study aimed to explore the effects of AG on the progression of atherosclerosis and to investigate related mechanisms via network pharmacology. MATERIALS AND METHODS: Compound-related information was obtained from the PubChem database. Potential target genes were identified using STITCH, SwissTargetPrediction, Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine, and Comparative Toxicogenomics Database. Genes involved in atherosclerosis were obtained from DisGeNet and compared with AG target genes to obtain an overlapping set. Protein-protein interactions were determined by STRING. Gene ontology (GO) analysis was performed at WebGestalt, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was analyzed using Metascape. The final network showing the relationship between compounds, targets, and pathways was constructed using Cytoscape. After that, oxLDL-induced RAW264.7 cells were used to further validate a part of the network pharmacology results. RESULT: Eighty-one potential AG target genes were identified. PPI, GO, and KEGG enrichment revealed genes closely related to tumor progression, lipid transport, inflammation, and related pathways. AG improves the reverse cholesterol transport (RCT) through NF-kappaB/CEBPB/PPARG signaling in oxLDL-induced RAW264.7 cells. CONCLUSION: We successfully predict AG's potential targets and pathways in atherosclerosis and illustrate the mechanism of action. AG may regulate NF-kappaB/CEBPB/PPARG signaling to alleviate atherosclerosis.
Published on July 28, 2022
Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma.
Abstract: Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein-protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.
Published on July 27, 2022
A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening.
Abstract: Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.
Published on July 27, 2022
Small Molecular Drug Screening Based on Clinical Therapeutic Effect.
Abstract: Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard-Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.
Published on July 26, 2022
An automated 13.5 hour system for scalable diagnosis and acute management guidance for genetic diseases.
Abstract: While many genetic diseases have effective treatments, they frequently progress rapidly to severe morbidity or mortality if those treatments are not implemented immediately. Since front-line physicians frequently lack familiarity with these diseases, timely molecular diagnosis may not improve outcomes. Herein we describe Genome-to-Treatment, an automated, virtual system for genetic disease diagnosis and acute management guidance. Diagnosis is achieved in 13.5 h by expedited whole genome sequencing, with superior analytic performance for structural and copy number variants. An expert panel adjudicated the indications, contraindications, efficacy, and evidence-of-efficacy of 9911 drug, device, dietary, and surgical interventions for 563 severe, childhood, genetic diseases. The 421 (75%) diseases and 1527 (15%) effective interventions retained are integrated with 13 genetic disease information resources and appended to diagnostic reports ( https://gtrx.radygenomiclab.com ). This system provided correct diagnoses in four retrospectively and two prospectively tested infants. The Genome-to-Treatment system facilitates optimal outcomes in children with rapidly progressive genetic diseases.
Published on July 25, 2022
Population pharmacokinetic model to generate mechanistic insights in bile acid homeostasis and drug-induced cholestasis.
Abstract: Bile acids (BA) fulfill a wide range of physiological functions, but are also involved in pathologies, such as cholestasis. Cholestasis is characterized by an intrahepatic accumulation of BAs and subsequent spillage to the systemic circulation. The aim of the present study was to develop physiologically based kinetic (PBK) models that would provide a tool to predict dose-dependent BA accumulation in humans upon treatment with a Bile Salt Export Pump (BSEP) inhibitor. We developed a PBK model describing the BA homeostasis using glycochenodeoxycholic acid as an exemplary BA. Population wide distributions of BSEP abundances were incorporated in the PBK model using Markov Chain Monte Carlo simulations, and alternatively the total amount of BAs was scaled empirically to describe interindividual differences in plasma BA levels. Next, the effects of the BSEP inhibitor bosentan on the BA levels were simulated. The PBK model developed adequately predicted the in vivo BA dynamics. Both the Markov Chain Monte Carlo simulations based on a distribution of BSEP abundances and empirical scaling of the total BA pool readily described the variations within and between data in human volunteers. Bosentan treatment disproportionally increased the maximum BA concentration in individuals with a large total BA pool or low BSEP abundance. Especially individuals having a large total BA pool size and a low BSEP abundance were predicted to be at risk for rapid saturation of BSEP and subsequent intrahepatic BA accumulation. This model provides a first estimate of personalized safe therapeutic external dose levels of compounds with BSEP-inhibitory properties.
Published on July 25, 2022
Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates.
Abstract: Meningitis creates a life-threatening clinical crisis. Moreover, the administered antibiotics result into multi-drug resistance, thereby necessitating development of alternative therapeutic strategies. This study aimed at identifying novel-drug targets in Neisseria meningitidis and therapeutic molecules which can be exploited for the treatment of meningitis. Novel targets were identified by applying a pathogenomic approach involving protein data-set mining, subtractive channel analysis and subsequent qualitative analysis comprising of in silico pharmacokinetics, molecular docking and pharmacophore generation. Pathogenomic studies revealed Neisserial Surface Protein A (NSP-A) and Iron-III-Substrate Binding Protein (Fe-IIISBP) as potential targets. Two pharmacophore models comprising of 2-(biaryl) carbapenems, efavirenz, praziquantel and pyrimethamine for NSP-A and 2-(biaryl) carbapenems, trimipramine and pyrimethamine for Fe-IIISBP, showed successful docking, followed drug-likeness criteria and generated pharmacophore model with a score of 8.08 and 8.818, respectively, which had further been docked to the target stably. Thus, our study identifies NSP-A and Fe-IIISBP as novel targets in Neisseria meningitidis for which 2-(biaryl) carbapenems, efavirenz, praziquantel, trimipramine and pyrimethamine may be employed for effective treatment of meningitis.
Showing 711 to 714 of 5186 entries