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Chan S.W.P.,Computational Biology Research Laboratory | Hung S.-P.,Verdezyne | Raman S.K.,Computational Biology Research Laboratory | Hatfield G.W.,Institute for Genomics and Bioinformatics | And 4 more authors.
Biomacromolecules | Year: 2010

A collagen-mimetic polymer that can be easily engineered with specific cell-responsive and mechanical properties would be of significant interest for fundamental cell-matrix studies and applications in regenerative medicine. However, oligonucleotide-based synthesis of full-length collagen has been encumbered by the characteristic glycine-X-Y sequence repetition, which promotes mismatched oligonucleotide hybridizations during de novo gene assembly. In this work, we report a novel, modular synthesis strategy that yields full-length human collagen III and specifically defined variants. We used a computational algorithm that applies codon degeneracy to design oligonucleotides that favor correct hybridizations while disrupting incorrect ones for gene synthesis. The resulting recombinant polymers were expressed in Saccharomyces cereVisiae engineered with prolyl-4-hydroxylase. Our modular approach enabled mixing-and-matching domains to fabricate different combinations of collagen variants that contained different secretion signals at the N-terminus and cysteine residues imbedded within the triple-helical domain at precisely defined locations. This work shows the flexibility of our strategy for designing and assembling specifically tailored biomimetic collagen polymers with re-engineered properties. © 2010 American Chemical Society. Source

Masri S.,University of California at Irvine | Papagiannakopoulos T.,Massachusetts Institute of Technology | Papagiannakopoulos T.,New York University | Kinouchi K.,University of California at Irvine | And 6 more authors.
Cell | Year: 2016

The circadian clock controls metabolic and physiological processes through finely tuned molecular mechanisms. The clock is remarkably plastic and adapts to exogenous "zeitgebers," such as light and nutrition. How a pathological condition in a given tissue influences systemic circadian homeostasis in other tissues remains an unanswered question of conceptual and biomedical importance. Here, we show that lung adenocarcinoma operates as an endogenous reorganizer of circadian metabolism. High-throughput transcriptomics and metabolomics revealed unique signatures of transcripts and metabolites cycling exclusively in livers of tumor-bearing mice. Remarkably, lung cancer has no effect on the core clock but rather reprograms hepatic metabolism through altered pro-inflammatory response via the STAT3-Socs3 pathway. This results in disruption of AKT, AMPK, and SREBP signaling, leading to altered insulin, glucose, and lipid metabolism. Thus, lung adenocarcinoma functions as a potent endogenous circadian organizer (ECO), which rewires the pathophysiological dimension of a distal tissue such as the liver. PaperClip © 2016 Elsevier Inc. Source

Zaragosi L.-E.,University of Nice Sophia Antipolis | Wdziekonski B.,University of Nice Sophia Antipolis | Villageois P.,University of Nice Sophia Antipolis | Keophiphath M.,University Pierre and Marie Curie | And 17 more authors.
Diabetes | Year: 2010

OBJECTIVE - Growth of white adipose tissue takes place in normal development and in obesity. A pool of adipose progenitors is responsible for the formation of new adipocytes and for the potential of this tissue to expand in response to chronic energy overload. However, factors controlling self-renewal of human adipose progenitors are largely unknown. We investigated the expression profile and the role of activin A in this process. RESEARCH DESIGN AND METHODS - Expression of INHBA/activin A was investigated in three types of human adipose progenitors. We then analyzed at the molecular level the function of activin A during human adipogenesis. We finally investigated the status of activin A in adipose tissues of lean and obese subjects and analyzed macrophage-induced regulation of its expression. RESULTS - INHBA/activin A is expressed by adipose progenitors from various fat depots, and its expression dramatically decreases as progenitors differentiate into adipocytes. Activin A regulates the number of undifferentiated progenitors. Sustained activation or inhibition of the activin A pathway impairs or promotes, respectively, adipocyte differentiation via the C/EBPβ-LAP and Smad2 pathway in an autocrine/paracrine manner. Activin A is expressed at higher levels in adipose tissue of obese patients compared with the expression levels in lean subjects. Indeed, activin A levels in adipose progenitors are dramatically increased by factors secreted by macrophages derived from obese adipose tissue. CONCLUSIONS - Altogether, our data show that activin A plays a significant role in human adipogenesis. We propose a model in which macrophages that are located in adipose tissue regulate adipose progenitor self-renewal through activin A. © 2010 by the American Diabetes Association. Source

Andronico A.,Institute for Genomics and Bioinformatics | Randall A.,Institute for Genomics and Bioinformatics | Benz R.W.,Institute for Genomics and Bioinformatics | Benz R.W.,University of California at Irvine | And 3 more authors.
Journal of Chemical Information and Modeling | Year: 2011

Accurate prediction of the 3-D structure of small molecules is essential in order to understand their physical, chemical, and biological properties, including how they interact with other molecules. Here, we survey the field of high-throughput methods for 3-D structure prediction and set up new target specifications for the next generation of methods. We then introduce COSMOS, a novel data-driven prediction method that utilizes libraries of fragment and torsion angle parameters. We illustrate COSMOS using parameters extracted from the Cambridge Structural Database (CSD) by analyzing their distribution and then evaluating the system's performance in terms of speed, coverage, and accuracy. Results show that COSMOS represents a significant improvement when compared to state-of-the-art prediction methods, particularly in terms of coverage of complex molecular structures, including metal-organics. COSMOS can predict structures for 96.4% of the molecules in the CSD (99.6% organic, 94.6% metal-organic), whereas the widely used commercial method CORINA predicts structures for 68.5% (98.5% organic, 51.6% metal-organic). On the common subset of molecules predicted by both methods, COSMOS makes predictions with an average speed per molecule of 0.15 s (0.10 s organic, 0.21 s metal-organic) and an average rmsd of 1.57 Å (1.26 Å organic, 1.90 Å metal-organic), and CORINA makes predictions with an average speed per molecule of 0.13s (0.18s organic, 0.08s metal-organic) and an average rmsd of 1.60 Å (1.13 Å organic, 2.11 Å metal-organic). COSMOS is available through the ChemDB chemoinformatics Web portal at http://cdb.ics.uci.edu/. © 2011 American Chemical Society. Source

Magnan C.N.,Institute for Genomics and Bioinformatics | Zeller M.,Institute for Genomics and Bioinformatics | Kayala M.A.,Institute for Genomics and Bioinformatics | Vigil A.,University of California at Irvine | And 4 more authors.
Bioinformatics | Year: 2010

Motivation: Discovery of novel protective antigens is fundamental to the development of vaccines for existing and emerging pathogens. Most computational methods for predicting protein antigenicity rely directly on homology with previously characterized protective antigens; however, homology-based methods will fail to discover truly novel protective antigens. Thus, there is a significant need for homology-free methods capable of screening entire proteomes for the antigens most likely to generate a protective humoral immune response.Results: Here we begin by curating two types of positive data: (i) antigens that elicit a strong antibody response in protected individuals but not in unprotected individuals, using human immunoglobulin reactivity data obtained from protein microarray analyses; and (ii) known protective antigens from the literature. The resulting datasets are used to train a sequence-based prediction model, ANTIGENpro, to predict the likelihood that a protein is a protective antigen. ANTIGENpro correctly classifies 82% of the known protective antigens when trained using only the protein microarray datasets. The accuracy on the combined dataset is estimated at 76% by cross-validation experiments. Finally, ANTIGENpro performs well when evaluated on an external pathogen proteome for which protein microarray data were obtained after the initial development of ANTIGENpro. © The Author 2010. Published by Oxford University Press. All rights reserved. Source

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