131,831 research outputs found

    Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

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    Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naĂŻve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations

    Role of the Bifunctional Aminoacyl-tRNA Synthetase EPRS in Human Disease

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    Aminoacyl-tRNA synthetases (AARS) are a class of enzymes that catalyze the charging of tRNAs with cognate amino acids, a critical step that contributes to the fidelity of protein synthesis. Many AARSs also possess noncanonical functions such as regulation of apoptosis, mRNA translation, and RNA splicing. Some AARSs have evolved new domains with no apparent connection to their charging functions. For example, WHEP domains were originally identified in tryptophanyl-tRNA synthetase (WRS), histidyl-tRNA synthetase (HRS), and glutamyl-prolyl-tRNA synthetase (EPRS). EPRS is a unique bifunctional AARS, found only in higher eukaryotes, and consists of glutamyl-tRNA synthetase (ERS) and prolyl-tRNA synthetase (PRS) joined by a non-catalytic linker containing three WHEP domains in humans. Two compound heterozygous point mutations within human ERS (P14R and E205G) have been identified in the genomes of two patients with type 1 diabetes and bone disease. However, the mechanism by which these mutations contribute to disease is unknown. Our goal is to determine whether the point mutations affect the canonical catalytic activity of EPRS responsible for tRNA charging or noncanonical functions. Both P14 and E205 are highly conserved residues located in the GST and catalytic domain, respectively. An ERS variant appended to 2.5 WHEP domains (ERS 2.5W) has been purified and shown to display robust tRNA binding and aminoacylation activity in vitro. The P14R and E205G single mutants display the same binding affinity for tRNAGlu as WT ERS 2.5W, suggesting that the observed defect is at the catalytic step. Whereas the ERS 2.5W P14R mutant has near wild-type (WT) aminoacylation activity, the ERS 2.5W E205G variant has a severe aminoacylation defect. Both mutations, however, lead to reduced amino acid activation. Together with a collaborator, we are currently characterizing the effect of these two mutations on cell proliferation and the integrated stress response. Taken together, this work has important implications for the understanding of AARS-related human disease mechanisms and development of new therapeutics.College of Arts & SciencesOffice of Undergraduate Research & Creative InquiryNo embargoAcademic Major: Biochemistr

    Grammar-based Representation and Identification of Dynamical Systems

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    In this paper we propose a novel approach to identify dynamical systems. The method estimates the model structure and the parameters of the model simultaneously, automating the critical decisions involved in identification such as model structure and complexity selection. In order to solve the combined model structure and model parameter estimation problem, a new representation of dynamical systems is proposed. The proposed representation is based on Tree Adjoining Grammar, a formalism that was developed from linguistic considerations. Using the proposed representation, the identification problem can be interpreted as a multi-objective optimization problem and we propose a Evolutionary Algorithm-based approach to solve the problem. A benchmark example is used to demonstrate the proposed approach. The results were found to be comparable to that obtained by state-of-the-art non-linear system identification methods, without making use of knowledge of the system description.Comment: Submitted to European Control Conference (ECC) 201

    Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

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    Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

    Associative memory in gene regulation networks

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    The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems

    Targeted therapies to improve CFTR function in cystic fibrosis

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    Cystic fibrosis is the most common genetically determined, life-limiting disorder in populations of European ancestry. The genetic basis of cystic fibrosis is well established to be mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene that codes for an apical membrane chloride channel principally expressed by epithelial cells. Conventional approaches to cystic fibrosis care involve a heavy daily burden of supportive treatments to combat lung infection, help clear airway secretions and maintain nutritional status. In 2012, a new era of precision medicine in cystic fibrosis therapeutics began with the licensing of a small molecule, ivacaftor, which successfully targets the underlying defect and improves CFTR function in a subgroup of patients in a genotype-specific manner. Here, we review the three main targeted approaches that have been adopted to improve CFTR function: potentiators, which recover the function of CFTR at the apical surface of epithelial cells that is disrupted in class III and IV genetic mutations; correctors, which improve intracellular processing of CFTR, increasing surface expression, in class II mutations; and production correctors or read-through agents, which promote transcription of CFTR in class I mutations. The further development of such approaches offers great promise for future therapeutic strategies in cystic fibrosis
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