RNA Stimulates Aurora B Kinase Activity during Mitosis

Accurate chromosome segregation is essential for cell viability. The mitotic spindle is crucial for chromosome segregation, but much remains unknown about factors that regulate spindle assembly. Recent work implicates RNA in promoting proper spindle assembly independently of mRNA translation; however, the mechanism by which RNA performs this function is currently unknown. Here, we show that RNA regulates both the localization and catalytic activity of the mitotic kinase, Aurora-B (AurB), which is present in a ribonucleoprotein (RNP) complex with many mRNAs. Interestingly, AurB kinase activity is reduced in Xenopus egg extracts treated with RNase, and its activity is stimulated in vitro by RNA binding. Spindle assembly defects following RNase-treatment are partially rescued by inhibiting MCAK, a microtubule depolymerase that is inactivated by AurB-dependent phosphorylation. These findings implicate AurB as an important RNA-dependent spindle assembly factor, and demonstrate a translation-independent role for RNA in stimulating AurB

A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

Cis-acting determinants of asymmetric, cytoplasmic RNA transport

Asymmetric subcellular distribution of RNA is used by many organisms to establish cell polarity, differences in cell fate, or to sequester protein activity. Accurate localization of RNA requires specific sequence and/or structural elements in the localized RNA, as well as proteins that recognize these elements and link the RNA to the appropriate molecular motors. Recent advances in biochemistry, molecular biology, and cell imaging have enabled the identification of many RNA localization elements, or “zipcodes,” from a variety of systems. This review focuses on the mechanisms by which various zipcodes direct RNA transport and on the known sequence/structural requirements for their recognition by transport complexes. Computational and experimental methods for predicting and identifying zipcodes are also discussed

Figure 2

<p>The Bayesian average network representation of the models. (a, b) initial model. (c, d) second model. (e, f) third model. (a, c, e) network graphic representation. The green nodes represent gene expression clusters. Representative genes of each cluster are shown in the box below each node. ESR: environmental stress response cluster. energy: glucose metabolism cluster. oxidative stress: the application of H₂O₂ or menadione. Nitric oxide: the duration of NO· exposure. galactose: galactose utilization. diauxic shift: shift between anaerobic growth and aerobic respiration. Nodes with missing values are colored in gray. The CPD table shows the conditional probability distribution of Fzf1p activity. The red edges represent novel predictions from the first network model. (b, d, f) edge confidence score histogram. The dot-filled columns represent edges excluded from a model by structural constraints.</p

Figure 3

<p>The change of gene expression in Fzf1p response clusters, <i>FZF1</i> and galactose utilization genes in response to galactose. (a) Wild type and gal promoter driven <i>FZF1</i> over-expression strains in response to the change from glucose to galactose (experiment E2). (b) Wild type and <i>fzf1</i>Δ strains in response to the change from glucose to galactose (experiment E3). (c) Wild type and <i>fzf1</i>Δ strains in response to the change from raffinose to galactose (experiment E4). Color unit is fold change of gene expression. Gene expression too low to be detected is colored in blue.</p

Figure 4

<p>Glucose repression and derepression of Yhb1p-GFP measured by flow cytometry. (a) wild type strain. (b) <i>tup1</i> deletion strain. To calculate a mean GFP intensity, a minimum 100,000 cells were measured for each time point.</p

Figure 1

<p>Illustration of the iterative network learning and experimental feedback algorithm.</p