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Showing 3351 to 3354 of 5186 entries
Published on July 9, 2018
NetControl4BioMed: a pipeline for biomedical data acquisition and analysis of network controllability.
Abstract: BACKGROUND: Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer. RESULT: We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user's query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php . CONCLUSION: The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.
Published on July 4, 2018
Identification of Antifungal Targets Based on Computer Modeling.
Abstract: Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host(-)pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
Published on July 3, 2018
ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures.
Abstract: The assessment of bioactivity and toxicity for mixtures remains a challenging work. Although several computational models have been developed to accelerate the evaluation of chemical-chemical interaction, a specific biological endpoint should be defined before applying the models that usually relies on clinical and experimental data. The development of computational methods is desirable for identifying potential biological endpoints of mixture interactions. To facilitate the identification of potential effects of mixture interactions, a novel online system named ChemDIS-Mixture is proposed to analyze the shared target proteins, and common enriched functions, pathways, and diseases affected by multiple chemicals. Venn diagram tools have been implemented for easy analysis and visualization of interaction targets and effects. Case studies have been provided to demonstrate the capability of ChemDIS-Mixture for identifying potential effects of mixture interactions in clinical studies. ChemDIS-Mixture provides useful functions for the identification of potential effects of coexposure to multiple chemicals. ChemDIS-Mixture is freely accessible at http://cwtung.kmu.edu.tw/chemdis/mixture .
Published on July 3, 2018
Identification of Missing Carbon Fixation Enzymes as Potential Drug Targets in Mycobacterium Tuberculosis.
Abstract: Metabolic adaptation to the host environment has been recognized as an essential mechanism of pathogenicity and the growth of Mycobacterium tuberculosis (Mtb) in the lungs for decades. The Mtb uses CO2 as a source of carbon during the dormant or non-replicative state. However, there is a lack of biochemical knowledge of its metabolic networks. In this study, we investigated the CO2 fixation pathways (such as ko00710 and ko00720) most likely involved in the energy production and conversion of CO2 in Mtb. Extensive pathway evaluation of 23 completely sequenced strains of Mtb confirmed the existence of a complete list of genes encoding the relevant enzymes of the reductive tricarboxylic acid (rTCA) cycle. This provides the evidence that an rTCA cycle may function to fix CO2 in this bacterium. We also proposed that as CO2 is plentiful in the lungs, inhibition of CO2 fixation pathways (by targeting the relevant CO2 fixation enzymes) could be used in the expansion of new drugs against the dormant Mtb. In support of the suggested hypothesis, the CO2 fixation enzymes were confirmed as a potential drug target by analyzing a number of attributes necessary to be a good bacterial target.
Published in June 2018
Exploring the Viral Channel KcvPBCV-1 Function via Computation.
Abstract: Viral potassium channels (Kcv) are homologous to the pore module of complex [Formula: see text]-selective ion channels of cellular organisms. Due to their relative simplicity, they have attracted interest towards understanding the principles of [Formula: see text] conduction and channel gating. In this work, we construct a homology model of the [Formula: see text] open state, which we validate by studying the binding of known blockers and by monitoring ion conduction through the channel. Molecular dynamics simulations of this model reveal that the re-orientation of selectivity filter carbonyl groups coincides with the transport of potassium ions, suggesting a possible mechanism for fast gating. In addition, we show that the voltage sensitivity of this mechanism can originate from the relocation of potassium ions inside the selectivity filter. We also explore the interaction of [Formula: see text] with the surrounding bilayer and observe the binding of lipids in the area between two adjacent subunits. The model is available to the scientific community to further explore the structure/function relationship of Kcv channels.
Published in June 2018
Main active constituent identification in Guanxinjing capsule, a traditional Chinese medicine, for the treatment of coronary heart disease complicated with depression.
Abstract: Guanxinjing capsules (GXJCs) are used in traditional Chinese medicine as a common therapy for coronary heart disease (CHD) complicated with depression. In this study, we aimed to identify the main active constituents in GXJCs and to investigate the mechanisms of GXJC action on CHD complicated with depression. The chemical constituent profile of the GXJC was identified by UHPLC-LTQ-Orbitrap assay, and oral bioavailability was evaluated to screen the GXJC drug-like chemical constituents. A total of 16 GXJC drug-like chemical constituents were identified. Then, putative targets of the GXJC drug-like chemical constituents were predicted using MedChem Studio, with 870 genes found to be the putative targets of these molecules. After that, a GXJC putative target-known CHD/depression therapeutic target network was constructed, and four topological features, including degree, betweenness, closeness and K-coreness, were calculated. According to the topological feature values of the GXJC putative targets, 14 main active constituents were identified because their corresponding putative targets had topological importance in the GXJC putative target-known CHD/depression therapeutic target network, which were defined as the candidate targets of GXJC against CHD complicated with depression. Functionally, these candidate targets were significantly involved in several CHD/depression-related pathways, including repairing pathological vascular changes, reducing platelet aggregation and inflammation, and affecting patient depression. This study identified a list of main active constituents of GXJC acting on CHD complicated with depression using an integrative pharmacology-based approach that combined active chemical constituent identification, drug target prediction and network analysis. This method may offer an efficient way to understand the pharmacological mechanisms of traditional Chinese medicine prescriptions.
Published in June 2018
Exploration of the molecular mechanisms of cervical cancer based on mRNA expression profiles and predicted microRNA interactions.
Abstract: The molecular mechanisms of cervical cancer have been minimally explored with multi-omics data. In the present study, mRNA expression profiles were analyzed and combined with predicted miRNA interactions to contribute to the characterization of the underlying regulatory mechanisms of cervical cancer. A total of 92 significantly differentially expressed genes (DEGs) were identified in 33 tumor samples by comparison with 29 normal samples. mRNA-miRNA interaction network analysis revealed that 16 out of the 92 DEGs, including checkpoint kinase 1 (CHEK1), SRY-box 17 (SOX17), centrosomal protein 55, cyclin dependent kinase inhibitor 2A (CDKN2A), and inhibitor of DNA binding 4, were the targets of 4 miRNAs which were previously reported to be involved in the regulation of cervical cancer. Tumor and normal samples could be distinctly classified into two groups based on the expression of the 16 DEGs. Furthermore, survival analysis using the SurvExpress database indicated that the 16 DEGs could individually significantly differentiate low- and high-risk cervical cancer groups. Overall, multiple biological processes are likely to participate in the progression of cervical cancer based on the pathway and function enrichment identified for the DEGs. The dysregulation of SOX17 is associated with the regulation of embryonic development, the determination of cell fate and likely promotes cancer cell transformation. The dysregulation of CHEK1 and CDKN2A further promote cancer cell proliferation by affecting the cell cycle checkpoint in response to DNA damage. The identification of critical genes and biological processes associated with cervical cancer may be beneficial for the exploration of the molecular mechanisms.
Published in June 2018
A Computational Systems Biology Approach for Identifying Candidate Drugs for Repositioning for Cardiovascular Disease.
Abstract: We report an in silico method to screen for receptors or pathways that could be targeted to elicit beneficial transcriptional changes in a cellular model of a disease of interest. In our method, we integrate: (1) a dataset of transcriptome responses of a cell line to a panel of drugs; (2) two sets of genes for the disease; and (3) mappings between drugs and the receptors or pathways that they target. We carried out a gene set enrichment analysis (GSEA) test for each of the two gene sets against a list of genes ordered by fold-change in response to a drug in a relevant cell line (HL60), with the overall score for a drug being the difference of the two enrichment scores. Next, we applied GSEA for drug targets based on drugs that have been ranked by their differential enrichment scores. The method ranks drugs by the degree of anti-correlation of their gene-level transcriptional effects on the cell line with the genes in the disease gene sets. We applied the method to data from (1) CMap 2.0; (2) gene sets from two transcriptome profiling studies of atherosclerosis; and (3) a combined dataset of drug/target information. Our analysis recapitulated known targets related to CVD (e.g., PPARgamma; HMG-CoA reductase, HDACs) and novel targets (e.g., amine oxidase A, delta-opioid receptor). We conclude that combining disease-associated gene sets, drug-transcriptome-responses datasets and drug-target annotations can potentially be useful as a screening tool for diseases that lack an accepted cellular model for in vitro screening.
Published in June 2018
Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error.
Abstract: Refractive errors, including myopia, are the most frequent eye disorders worldwide and an increasingly common cause of blindness. This genome-wide association meta-analysis in 160,420 participants and replication in 95,505 participants increased the number of established independent signals from 37 to 161 and showed high genetic correlation between Europeans and Asians (>0.78). Expression experiments and comprehensive in silico analyses identified retinal cell physiology and light processing as prominent mechanisms, and also identified functional contributions to refractive-error development in all cell types of the neurosensory retina, retinal pigment epithelium, vascular endothelium and extracellular matrix. Newly identified genes implicate novel mechanisms such as rod-and-cone bipolar synaptic neurotransmission, anterior-segment morphology and angiogenesis. Thirty-one loci resided in or near regions transcribing small RNAs, thus suggesting a role for post-transcriptional regulation. Our results support the notion that refractive errors are caused by a light-dependent retina-to-sclera signaling cascade and delineate potential pathobiological molecular drivers.
Published in June 2018
From gene networks to drugs: systems pharmacology approaches for AUD.
Abstract: The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.
Showing 3351 to 3354 of 5186 entries