Zscan4 regulates telomere elongation and genomic stability in ES cells

Exceptional genomic stability is one of the hallmarks of mouse embryonic stem (ES) cells. However, the genes contributing to this stability remain obscure. We previously identified Zscan4 as a specific marker for two-cell embryo and ES cells. Here we show that Zscan4 is involved in telomere maintenance and long-term genomic stability in ES cells. Only 5% of ES cells express Zscan4 at a given time, but nearly all ES cells activate Zscan4 at least once during nine passages. The transient Zscan4-positive state is associated with rapid telomere extension by telomere recombination and upregulation of meiosis-specific homologous recombination genes, which encode proteins that are colocalized with ZSCAN4 on telomeres. Furthermore, Zscan4 knockdown shortens telomeres, increases karyotype abnormalities and spontaneous sister chromatid exchange, and slows down cell proliferation until reaching crisis by passage eight. Together, our data show a unique mode of genome maintenance in ES cells. © 2010 Macmillan Publishers Limited. All rights reserved

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data

A network perspective on the topological importance of enzymes and their phylogenetic conservation

<p>Abstract</p> <p>Background</p> <p>A metabolic network is the sum of all chemical transformations or reactions in the cell, with the metabolites being interconnected by enzyme-catalyzed reactions. Many enzymes exist in numerous species while others occur only in a few. We ask if there are relationships between the phylogenetic profile of an enzyme, or the number of different bacterial species that contain it, and its topological importance in the metabolic network. Our null hypothesis is that phylogenetic profile is independent of topological importance. To test our null hypothesis we constructed an enzyme network from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. We calculated three network indices of topological importance: the degree or the number of connections of a network node; closeness centrality, which measures how close a node is to others; and betweenness centrality measuring how frequently a node appears on all shortest paths between two other nodes.</p> <p>Results</p> <p>Enzyme phylogenetic profile correlates best with betweenness centrality and also quite closely with degree, but poorly with closeness centrality. Both betweenness and closeness centralities are non-local measures of topological importance and it is intriguing that they have contrasting power of predicting phylogenetic profile in bacterial species. We speculate that redundancy in an enzyme network may be reflected by betweenness centrality but not by closeness centrality. We also discuss factors influencing the correlation between phylogenetic profile and topological importance.</p> <p>Conclusion</p> <p>Our analysis falsifies the hypothesis that phylogenetic profile of enzymes is independent of enzyme network importance. Our results show that phylogenetic profile correlates better with degree and betweenness centrality, but less so with closeness centrality. Enzymes that occur in many bacterial species tend to be those that have high network importance. We speculate that this phenomenon originates in mechanisms driving network evolution. Closeness centrality reflects phylogenetic profile poorly. This is because metabolic networks often consist of distinct functional modules and some are not in the centre of the network. Enzymes in these peripheral parts of a network might be important for cell survival and should therefore occur in many bacterial species. They are, however, distant from other enzymes in the same network.</p

An Analytical Rate Expression for the Kinetics of Gene Transcription Mediated by Dimeric Transcription Factors

[[sponsorship]]化學研究所[[note]]已出版;[SCI];有審查制度;具代表性[[note]]http://gateway.isiknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Drexel&SrcApp=hagerty_opac&KeyRecord=0021-924X&DestApp=JCR&RQ=IF_CAT_BOXPLO

Transition diagrams for Markov-chain model (MCM).

<p>(a) A 2-state MCM, redrawn from the original <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#pone-0032376-g002" target="_blank">Figure 2</a> in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#pone.0032376-Ko2" target="_blank">[21]</a>. <i>p₁</i> and <i>p₂</i> are probabilities of transitions between a state of active transcription (ON), where TFs bind to a promoter and form a stable transcription initiation complex, and a state of no transcription (OFF). (b) 3-state MCM. To account for both activator-bound and repressor-bound states, two 2-state MCMs are combined, where unbound state (i.e., neither activator nor repressor bound) represents a state of basal-level transcription. <i>P_A1</i>, <i>P_A2</i>, <i>P_R1</i> and <i>P_R2</i> are transition probabilities between the states.</p

Model prediction matches more closely to the experimental observation.

<p>(a) Simulated dose-response relationship between [dox] and promoter activity (normalized gene induction levels). (b) Hill functions showing estimated switching probabilities (<i>P_A1, P_A2, P_R1,</i> and <i>P_R2</i>) against [dox]. Values of P_A1+P_R1 against [dox] are also shown. To show the relationship between (a) and (b), these graphs are aligned by the [dox]. (c) Comparisons between model predictions and experimental observations.</p

A strategy of parameter estimation for a 3-state MCM.

<p>All the parameters for the 3-state MCM were estimated from the published data only on the dose-response experiments of activator only and repressor only <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#pone.0032376-Rossi1" target="_blank">[20]</a>. In the case of activator only, parameters in <i>P_Act</i> (Eq. <b>3</b> or Eq. <b>10</b>) were estimated by using the observed dose-response curve (<i>OBS_Act</i>: a Hill function of [dox]) represented by the equation (Eq. <b>4</b> or Eq. <b>11</b>) in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#s4" target="_blank">Materials and Methods</a> section. The repressor only case (<i>OBS_Rep</i>, <i>P</i>_Rep) was handled in the same manner.</p

Stochastic simulation using a 3-state MCM yields all-or-none responses.

<p>In a cell population, steady-state distributions of gene induction were stochastically simulated by the 3-state MCM using the estimated parameters described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#pone-0032376-g002" target="_blank">Figure 2</a>. Red, black, and green lines present the peaks of transcription levels in the “repressor-bound,” “unbound,” and “activator-bound” states, respectively. A gray vertical bar indicates the increasing concentrations of [dox], which correspond to the experimental conditions reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032376#pone.0032376-Rossi1" target="_blank">[20]</a>.</p