Organizational Unit:
Wallace H. Coulter Department of Biomedical Engineering

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https://ror.org/02j15s898
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Publication Search Results

Now showing 1 - 10 of 123
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    High-Throughput Screening Identifies Micrornas That Target Nox2 and Improve Function Following Acute Myocardial Infarction
    (Georgia Institute of Technology, 2017-02-24) Yang, Junyu ; Brown, Milton E. ; Zhang, Hanshuo ; Martinez, Mario ; Zhao, Zhihua ; Bhutani, Srishti ; Yin, Shenyi ; Trac, David ; Xi, Jianzhong Jeff ; Davis, Michael E.
    Myocardial infarction (MI) is the most common cause of heart failure. Excessive production of reactive oxygen species plays a key role in the pathogenesis of cardiac remodeling after MI. NADPH with Nox2 as the catalytic subunit is a major source of superoxide production and expression is significantly increased in the infarcted myocardium, especially by infiltrating macrophages. While microRNAs (miRNAs) are potent regulators of gene expression, and play an important role in heart disease, there still lacks efficient ways to identify miRNAs that target important pathological genes for treating MI. Thus, the overall objective was to establish a miRNA screening and delivery system for improving heart function after MI using Nox2 as a critical target.
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    P-Selectin Glycoprotein Ligand-1 Forms Dimeric Interactions with E-Selectin but Monomeric Interactions with L-Selectin on Cell Surfaces
    (Georgia Institute of Technology, 2013-02-25) Zhang, Yan ; Jiang, Ning ; Zarnitsyna, Veronika I. ; Klopocki, Arkadiusz G. ; McEver, Rodger P. ; Zhu, Cheng
    Interactions of selectins with cell surface glycoconjugates mediate the first step of the adhesion and signaling cascade that recruits circulating leukocytes to sites of infection or injury. P-selectin dimerizes on the surface of endothelial cells and forms dimeric bonds with P-selectin glycoprotein ligand-1 (PSGL-1), a homodimeric sialomucin on leukocytes. It is not known whether leukocyte L-selectin or endothelial cell E-selectin are monomeric or oligomeric. Here we used the micropipette technique to analyze two-dimensional binding of monomeric or dimeric L- and E-selectin with monomeric or dimeric PSGL- 1. Adhesion frequency analysis demonstrated that E-selectin on human aortic endothelial cells supported dimeric interactions with dimeric PSGL-1 and monomeric interactions with monomeric PSGL-1. In contrast, L-selectin on human neutrophils supported monomeric interactions with dimeric or monomeric PSGL-1. Our work provides a new method to analyze oligomeric cross-junctional molecular binding at the interface of two interacting cells.
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    GeneTack database: genes with frameshifts in prokaryotic genomes and eukaryotic mRNA sequences
    (Georgia Institute of Technology, 2013) Antonov, Ivan ; Baranov, Pavel ; Borodovsky, Mark
    Database annotations of prokaryotic genomes and eukaryotic mRNA sequences pay relatively low attention to frame transitions that disrupt protein-coding genes. Frame transitions (frameshifts) could be caused by sequencing errors or indel mutations inside protein-coding regions. Other observed frameshifts are related to recoding events (that evolved to control expression of some genes). Earlier, we have developed an algorithm and software program GeneTack for ab initio frameshift finding in intronless genes. Here, we describe a database (freely available at http://topaz.gatech. edu/GeneTack/db.html) containing genes with frameshifts (fs-genes) predicted by GeneTack. The database includes 206 991 fs-genes from 1106 complete prokaryotic genomes and 45 295 frameshifts predicted in mRNA sequences from 100 eukaryotic genomes. The whole set of fs-genes was grouped into clusters based on sequence similarity between fs-proteins (conceptually translated fs-genes), conservation of the frameshift position and frameshift direction (_1, +1). The fs-genes can be retrieved by similarity search to a given query sequence via a web interface, by fs-gene cluster browsing, etc. Clusters of fs-genes are characterized with respect to their likely origin, such as pseudogenization, phase variation, etc. The largest clusters contain fs-genes with programed frameshifts (related to recoding events).
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    Histological image classification using biologically interpretable shape-based features
    (Georgia Institute of Technology, 2013) Kothari, Sonal ; Phan, John H. ; Young, Andrew N. ; Wang, May Dongmei
    Background: Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. Methods: We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. Results: The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. Conclusions: Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.
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    TrueSight: a new algorithm for splice junction detection using RNA-seq
    (Georgia Institute of Technology, 2012) Li, Yang ; Li-Byarlay, Hongmei ; Burns, Paul ; Borodovsky, Mark ; Robinson, Gene E. ; Ma, Jian
    RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the short length of NGS reads, it is challenging to accurately map RNA-seq reads to splice junctions (SJs), which is a critically important step in the analysis of alternative splicing (AS) and isoform construction. In this article, we describe a new method, called TrueSight, which for the first time combines RNA-seq read mapping quality and coding potential of genomic sequences into a unified model. The model is further utilized in a machine-learning approach to precisely identify SJs. Both simulations and real data evaluations showed that TrueSight achieved higher sensitivity and specificity than other methods. We applied TrueSight to new high coverage honey bee RNA-seq data to discover novel splice forms. We found that 60.3% of honey bee multi-exon genes are alternatively spliced. By utilizing gene models improved by TrueSight, we characterized AS types in honey bee transcriptome. We believe that TrueSight will be highly useful to comprehensively study the biology of alternative splicing.
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    The genome of the polar eukaryotic microalga coccomyxa subellipsoidea reveals traits of cold adaptation
    (Georgia Institute of Technology, 2012) Blanc, Guillaume ; Agarkova, Irina ; Grimwood, Jane ; Kuo, Alan ; Brueggeman, Andrew ; Dunigan, David D. ; Gurnon, James ; Ladunga, Istvan ; Lindquist, Erika ; Lucas, Susan ; Pangilinan, Jasmyn ; Pröschold, Thomas ; Salamov, Asaf ; Schmutz, Jeremy ; Weeks, Donald ; Yamada, Takashi ; Lomsadze, Alexandre ; Borodovsky, Mark ; Claverie, Jean-Michel ; Grigoriev, Igor V. ; Van Etten, James L.
    Background: Little is known about the mechanisms of adaptation of life to the extreme environmental conditions encountered in polar regions. Here we present the genome sequence of a unicellular green alga from the division chlorophyta, Coccomyxa subellipsoidea C-169, which we will hereafter refer to as C-169. This is the first eukaryotic microorganism from a polar environment to have its genome sequenced. Results: The 48.8 Mb genome contained in 20 chromosomes exhibits significant synteny conservation with the chromosomes of its relatives Chlorella variabilis and Chlamydomonas reinhardtii. The order of the genes is highly reshuffled within synteny blocks, suggesting that intra-chromosomal rearrangements were more prevalent than inter-chromosomal rearrangements. Remarkably, Zepp retrotransposons occur in clusters of nested elements with strictly one cluster per chromosome probably residing at the centromere. Several protein families overrepresented in C. subellipsoidae include proteins involved in lipid metabolism, transporters, cellulose synthases and short alcohol dehydrogenases. Conversely, C-169 lacks proteins that exist in all other sequenced chlorophytes, including components of the glycosyl phosphatidyl inositol anchoring system, pyruvate phosphate dikinase and the photosystem 1 reaction center subunit N (PsaN). Conclusions: We suggest that some of these gene losses and gains could have contributed to adaptation to low temperatures. Comparison of these genomic features with the adaptive strategies of psychrophilic microbes suggests that prokaryotes and eukaryotes followed comparable evolutionary routes to adapt to cold environments.
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    Constructing stochastic models from deterministic process equations by propensity adjustment
    (Georgia Institute of Technology, 2011-11) Wu, Jialiang ; Vidakovic, Brani ; Voit, Eberhard O.
    BACKGROUND: Gillespie's stochastic simulation algorithm (SSA) for chemical reactions admits three kinds of elementary processes, namely, mass action reactions of 0th, 1st or 2nd order. All other types of reaction processes, for instance those containing non-integer kinetic orders or following other types of kinetic laws, are assumed to be convertible to one of the three elementary kinds, so that SSA can validly be applied. However, the conversion to elementary reactions is often difficult, if not impossible. Within deterministic contexts, a strategy of model reduction is often used. Such a reduction simplifies the actual system of reactions by merging or approximating intermediate steps and omitting reactants such as transient complexes. It would be valuable to adopt a similar reduction strategy to stochastic modelling. Indeed, efforts have been devoted to manipulating the chemical master equation (CME) in order to achieve a proper propensity function for a reduced stochastic system. However, manipulations of CME are almost always complicated, and successes have been limited to relative simple cases. RESULTS: We propose a rather general strategy for converting a deterministic process model into a corresponding stochastic model and characterize the mathematical connections between the two. The deterministic framework is assumed to be a generalized mass action system and the stochastic analogue is in the format of the chemical master equation. The analysis identifies situations: where a direct conversion is valid; where internal noise affecting the system needs to be taken into account; and where the propensity function must be mathematically adjusted. The conversion from deterministic to stochastic models is illustrated with several representative examples, including reversible reactions with feedback controls, Michaelis-Menten enzyme kinetics, a genetic regulatory motif, and stochastic focusing. CONCLUSIONS: The construction of a stochastic model for a biochemical network requires the utilization of information associated with an equation-based model. The conversion strategy proposed here guides a model design process that ensures a valid transition between deterministic and stochastic models.
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    Integrative Analysis of Transgenic Alfalfa (Medicago sativa L.) Suggests New Metabolic Control Mechanisms for Monolignol Biosynthesis
    (Georgia Institute of Technology, 2011-05) Lee, Yun ; Chen, Fang ; Gallego-Giraldo, Lina ; Dixon, Richard A. ; Voit, Eberhard O.
    The entanglement of lignin polymers with cellulose and hemicellulose in plant cell walls is a major biological barrier to the economically viable production of biofuels from woody biomass. Recent efforts of reducing this recalcitrance with transgenic techniques have been showing promise for ameliorating or even obviating the need for costly pretreatments that are otherwise required to remove lignin from cellulose and hemicelluloses. At the same time, genetic manipulations of lignin biosynthetic enzymes have sometimes yielded unforeseen consequences on lignin composition, thus raising the question of whether the current understanding of the pathway is indeed correct. To address this question systemically, we developed and applied a novel modeling approach that, instead of analyzing the pathway within a single target context, permits a comprehensive, simultaneous investigation of different datasets in wild type and transgenic plants. Specifically, the proposed approach combines static flux-based analysis with a Monte Carlo simulation in which very many randomly chosen sets of parameter values are evaluated against kinetic models of lignin biosynthesis in different stem internodes of wild type and lignin-modified alfalfa plants. In addition to four new postulates that address the reversibility of some key reactions, the modeling effort led to two novel postulates regarding the control of the lignin biosynthetic pathway. The first posits functionally independent pathways toward the synthesis of different lignin monomers, while the second postulate proposes a novel feedforward regulatory mechanism. Subsequent laboratory experiments have identified the signaling molecule salicylic acid as a potential mediator of the postulated control mechanism. Overall, the results demonstrate that mathematical modeling can be a valuable complement to conventional transgenic approaches and that it can provide biological insights that are otherwise difficult to obtain.
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    Statistical Inference Methods for Sparse Biological Time Series Data
    (Georgia Institute of Technology, 2011-04) Ndukum, Juliet ; Fonseca, Luís L. ; Santos, Helena ; Voit, Eberhard O. ; Datta, Susmita
    Background: Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. Results: The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been–or had not been–preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). Conclusion: We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.
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    The Genome Sequence of the North-European Cucumber (Cucumis sativus L.) Unravels Evolutionary Adaptation Mechanisms in Plants
    (Georgia Institute of Technology, 2011) Wóycicki, Rafał ; Witkowicz, Justyna ; Gawroński, Piotr ; Dąbrowska, Joanna ; Lomsadze, Alexandre ; Pawełkowicz, Magdalena ; Siedlecka, Ewa ; Yagi, Kohei ; Pląder, Wojciech ; Seroczyńska, Anna ; Śmiech, Mieczysław ; Gutman, Wojciech ; Niemirowicz-Szczytt, Katarzyna ; Bartoszewski, Grzegorz ; Tagashira, Norikazu ; Hoshi, Yoshikazu ; Borodovsky, Mark ; Karpiński, Stanisław ; Malepszy, Stefan ; Przybecki, Zbigniew
    Cucumber (Cucumis sativus L.), a widely cultivated crop, has originated from Eastern Himalayas and secondary domestication regions includes highly divergent climate conditions e.g. temperate and subtropical. We wanted to uncover adaptive genome differences between the cucumber cultivars and what sort of evolutionary molecular mechanisms regulate genetic adaptation of plants to different ecosystems and organism biodiversity. Here we present the draft genome sequence of the Cucumis sativus genome of the North-European Borszczagowski cultivar (line B10) and comparative genomics studies with the known genomes of: C. sativus (Chinese cultivar – Chinese Long (line 9930)), Arabidopsis thaliana, Populus trichocarpa and Oryza sativa. Cucumber genomes show extensive chromosomal rearrangements, distinct differences in quantity of the particular genes (e.g. involved in photosynthesis, respiration, sugar metabolism, chlorophyll degradation, regulation of gene expression, photooxidative stress tolerance, higher non-optimal temperatures tolerance and ammonium ion assimilation) as well as in distributions of abscisic acid-, dehydration- and ethylene-responsive cis-regulatory elements (CREs) in promoters of orthologous group of genes, which lead to the specific adaptation features. Abscisic acid treatment of non-acclimated Arabidopsis and C. sativus seedlings induced moderate freezing tolerance in Arabidopsis but not in C. sativus. This experiment together with analysis of abscisic acid-specific CRE distributions give a clue why C. sativus is much more susceptible to moderate freezing stresses than A. thaliana. Comparative analysis of all the five genomes showed that, each species and/or cultivars has a specific profile of CRE content in promoters of orthologous genes. Our results constitute the substantial and original resource for the basic and applied research on environmental adaptations of plants, which could facilitate creation of new crops with improved growth and yield in divergent conditions.