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Building an Accurate Chromosome Segregation Machine in Fission Yeast
Tuesday, March 1, 2016 well-documented, the alterations in cardiac intracellular signaling that can lead to cardiotoxicity remain unclear. To elucidate the mechanisms underlying TKI-induced cardiotoxic events, we have initiated a large-scale project that aims to uncover drug-specific cellular ‘‘signatures’’ in cardiac myocytes. These signatures are produced by integrating experimental studies with a unique computational pipeline. Experimental measurements primarily consist of transcriptome-wide, druginduced changes in gene expression, quantified by mRNA-seq. The computational analysis involves: (1) identification of differentially-expressed genes under each condition; (2) network analysis to identify which pathways are altered, and (3) integration of expression measurements into dynamical mathematical models to generate predictions of altered intracellular signaling. Results obtained to date indicate that: (1) TKIs produce common alterations in gene expression compared with non-cardiotoxic drugs; (2) Genes altered by TKIs tend to share the protein-protein interaction neighborhoods that define cardiac specific disease pathways; (3) Simulated dynamic activation patterns exhibit both features that are common between TKIs and characteristics that are specific to particular drugs and physiological stimuli. Together these results suggest that the integrated analysis approach discussed here is a promising way to delineate the cellular mechanism behind the druginduced toxicity and to predict the phenotypic abnormality. 2348-Pos Board B492 Building an Accurate Chromosome Segregation Machine in Fission Yeast Hadrien Mary1, Guillaume Gay2, Thibault Courthe´oux3, Jonathan Fouchard1, Reyes Ce´line1, Sylvie Tournier1, Yannick Gachet1. 1 LBCMCP, Univ Paul Sabatier, toulouse, France, 2DAMCB, 43 rue Horace Bertin, 13005, DAMCB, Marseille, France, 3LBCMCP, Univ Paul Sabatier, Toulouse, France. In fission yeast, erroneous attachments of spindle microtubules to kinetochores are frequent in early mitosis. Most are corrected before anaphase onset by a mechanism involving the protein kinase Aurora B which destabilizes kinetochore microtubules (ktMT) in the absence of tension between sister chromatids. We previously described a minimal mathematical model of fission yeast chromosome segregation based on the stochastic attachment and detachment of kinetochore microtubules. This model accurately reproduces the timing of correct chromosome bi-orientation and segregation seen in fission yeast. This force-balance model of mitosis describes global spindle dynamics and predicts chromosome segregation defects (Courtheoux J.Cell Biol 2009; Gay, J.Cell Biol 2012). We recently implemented new features to our model that describe kinetochore alignment during metaphase. In higher eukaryotes, efficient chromosome congression relies, among other players, on the activity of chromokinesins. We provide a quantitative analysis of kinetochore oscillations and positioning in S. pombe, a model lacking chromokinesins. In wild type cells, chromosomes align during prophase and while oscillating, maintain this alignment throughout metaphase. Chromosome oscillations are dispensable both for kinetochore congression and stable kinetochore alignment during metaphase. We propose that Kinesin-8 aligns chromosomes by controlling pulling forces in a length dependent manner. Our chromosome segregation model implemented with a lengthdependent process that controls the force at kinetochores is sufficient to mimic congression and to prevent aneuploidy. These model illustrate how a motor protein at kinetochores provides spatial cues within the spindle to align chromosomes. Altogether, our work illustrates how a simplified force balance model, with stochastic attachment and detachment events, correction mechanisms (Aurora B, and kinetochore orientation effect) and a length dependent process for kinetochore alignment, can explain the segregation of chromosomes with a timing and accuracy similar to chromosome segregation in living wild-type fission yeast cells. 2349-Pos Board B493 A Synthetic Knob for Modulating Antibiotic Resistance Dilay Hazal Ayhan, Yusuf Talha Tamer, Mohammed Akbar, David E. Greenberg, Erdal Toprak. Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Antibiotic resistance is a worldwide public health problem. The lack of effective antibiotic therapies against resistant pathogens has led to prolonged treatments, increased morbidity, an increased number of hospital admissions, and burgeoning health care costs [1]. According to the CDC report entitled ‘‘Antibiotic Resistance Threats in the United States, 2013’’, antibi-
477a
otic resistance causes over 23,000 deaths and two million infections annually in the United States alone. Pathogenic bacteria are increasingly resistant to existing antibiotics leaving clinicians with few or no options for treatment. For combating both the short- and long-term ineffectiveness of antibiotics, we have developed a novel approach to modulate the intrinsic antibiotic resistance level of bacteria. By utilizing this novel synthetic knob, we can decrease bacterial antibiotic resistance and develop antibiotic therapies at doses that were previously unachievable. The ability to reduce the dose of antibiotics that are currently in use or utilize current antibiotics that currently could not be used will have important implications for treatment of infectious diseases. 2350-Pos Board B494 The Effects of Population Density on Antibiotic Efficacy in E. Faecalis Jason Karslake, Kevin Wood. Biophysics, University of Michigan, Ann Arbor, MI, USA. Resistance to current antibiotics is a major public health issue in the United States and around the world. A better understanding of the complex relationship between the dynamics of bacterial populations and the response of these populations to antibiotics is crucial for mitigating the rapid decline in antibiotic efficacy. While significant research has focused on the molecular mechanisms of antibiotic resistance, relatively little work has addressed the population-level dynamics that arise from interactions between cells. One of the classic manifestations of such community-wide behavior is the inoculum effect, where the starting size of a cell population modulates the efficacy of some antibiotics, with larger inoculums exhibiting higher drug tolerance. The inoculum effect suggests that there is a relationship between bacterial population density and drug efficacy. However, traditional measurements of the inoculum effect are unable to provide an explicit measure of this density-dependence because cell density is itself changing throughout the experiments. As a result, there is debate about the magnitude of the effect, and it is typically ignored in clinical management of infections. Here, we examine the effect of density on drug inhibitory effects through the use of a continuous culture device designed to control cellular population densities. We are able to quantify the effect of density on several classes of important antibiotics. Further, we also investigate possible general mechanisms behind this effect, and determine using mathematical models the how density affect the dynamics of growing populations exposed to antibiotics. 2351-Pos Board B495 Rule-Based Modeling with Virtual Cell: Effect of UBE3A on Dendritic Spine Morphogenesis Judy E. Bloom1, Michael L. Blinov2, Leslie M. Loew2. 1 Department of Neuroscience, UCONN Health, Farmington, CT, USA, 2 Center for Cell Analysis and Modeling, UCONN Health, Farmington, CT, USA. Many biological systems are marked by an effect of combinatorial complexity, when a small number of interacting molecules can generate a large number of chemical species. One of the examples is a model for regulation of dendritic spine morphogenesis that requires the ubiquitin ligase UBE3A. The degradation of the RhoA GEF Ephexin5 by UBE3A is an important step in synapse development. In Angelman Syndrome (AS), the lack of UBE3A causes an increase in Ephexin5 and a reduction of dendritic spines density. Understanding of how UBE3A concentrations affect substrate degradation is very important for understanding of dendritic spine morphogenesis. However, an E3 ubiquitin ligase must transfer a variable number of ubiquitin molecules in multiple configurations to a substrate such as Ephexin5 to enable its targeting for degradation by proteasomes. This leads to an explosion in the number of possible chemical species to consider in a model, and makes manual specification impractical. To model such systems, a rule-based modeling approach is required; the model is expressed in the form of rules capable of accounting for all the potential molecular complexes and interactions among them without explicit enumeration. Here we present a ubiquitin model implemented using a recently-introduced rule-based features of Virtual Cell modeling and simulation framework. A graphical user interface is used to specify and visualize model elements without necessity to use a scripting language. Reactions and rules are used together within the same graphical interface. Simulations are performed using both deterministic (using network generation algorithms) and stochastic (using network-free algorithm) simulation engines. This model represents a first critical step toward understanding how UBE3A expression levels are linked to dendritic spine density.
477a
otic resistance causes over 23,000 deaths and two million infections annually in the United States alone. Pathogenic bacteria are increasingly resistant to existing antibiotics leaving clinicians with few or no options for treatment. For combating both the short- and long-term ineffectiveness of antibiotics, we have developed a novel approach to modulate the intrinsic antibiotic resistance level of bacteria. By utilizing this novel synthetic knob, we can decrease bacterial antibiotic resistance and develop antibiotic therapies at doses that were previously unachievable. The ability to reduce the dose of antibiotics that are currently in use or utilize current antibiotics that currently could not be used will have important implications for treatment of infectious diseases. 2350-Pos Board B494 The Effects of Population Density on Antibiotic Efficacy in E. Faecalis Jason Karslake, Kevin Wood. Biophysics, University of Michigan, Ann Arbor, MI, USA. Resistance to current antibiotics is a major public health issue in the United States and around the world. A better understanding of the complex relationship between the dynamics of bacterial populations and the response of these populations to antibiotics is crucial for mitigating the rapid decline in antibiotic efficacy. While significant research has focused on the molecular mechanisms of antibiotic resistance, relatively little work has addressed the population-level dynamics that arise from interactions between cells. One of the classic manifestations of such community-wide behavior is the inoculum effect, where the starting size of a cell population modulates the efficacy of some antibiotics, with larger inoculums exhibiting higher drug tolerance. The inoculum effect suggests that there is a relationship between bacterial population density and drug efficacy. However, traditional measurements of the inoculum effect are unable to provide an explicit measure of this density-dependence because cell density is itself changing throughout the experiments. As a result, there is debate about the magnitude of the effect, and it is typically ignored in clinical management of infections. Here, we examine the effect of density on drug inhibitory effects through the use of a continuous culture device designed to control cellular population densities. We are able to quantify the effect of density on several classes of important antibiotics. Further, we also investigate possible general mechanisms behind this effect, and determine using mathematical models the how density affect the dynamics of growing populations exposed to antibiotics. 2351-Pos Board B495 Rule-Based Modeling with Virtual Cell: Effect of UBE3A on Dendritic Spine Morphogenesis Judy E. Bloom1, Michael L. Blinov2, Leslie M. Loew2. 1 Department of Neuroscience, UCONN Health, Farmington, CT, USA, 2 Center for Cell Analysis and Modeling, UCONN Health, Farmington, CT, USA. Many biological systems are marked by an effect of combinatorial complexity, when a small number of interacting molecules can generate a large number of chemical species. One of the examples is a model for regulation of dendritic spine morphogenesis that requires the ubiquitin ligase UBE3A. The degradation of the RhoA GEF Ephexin5 by UBE3A is an important step in synapse development. In Angelman Syndrome (AS), the lack of UBE3A causes an increase in Ephexin5 and a reduction of dendritic spines density. Understanding of how UBE3A concentrations affect substrate degradation is very important for understanding of dendritic spine morphogenesis. However, an E3 ubiquitin ligase must transfer a variable number of ubiquitin molecules in multiple configurations to a substrate such as Ephexin5 to enable its targeting for degradation by proteasomes. This leads to an explosion in the number of possible chemical species to consider in a model, and makes manual specification impractical. To model such systems, a rule-based modeling approach is required; the model is expressed in the form of rules capable of accounting for all the potential molecular complexes and interactions among them without explicit enumeration. Here we present a ubiquitin model implemented using a recently-introduced rule-based features of Virtual Cell modeling and simulation framework. A graphical user interface is used to specify and visualize model elements without necessity to use a scripting language. Reactions and rules are used together within the same graphical interface. Simulations are performed using both deterministic (using network generation algorithms) and stochastic (using network-free algorithm) simulation engines. This model represents a first critical step toward understanding how UBE3A expression levels are linked to dendritic spine density.