Nejvíce citovaný článek - PubMed ID 9843569
Yeast cells must grow to a critical size before committing to division. It is unknown how size is measured. We find that as cells grow, mRNAs for some cell-cycle activators scale faster than size, increasing in concentration, while mRNAs for some inhibitors scale slower than size, decreasing in concentration. Size-scaled gene expression could cause an increasing ratio of activators to inhibitors with size, triggering cell-cycle entry. Consistent with this, expression of the CLN2 activator from the promoter of the WHI5 inhibitor, or vice versa, interfered with cell size homeostasis, yielding a broader distribution of cell sizes. We suggest that size homeostasis comes from differential scaling of gene expression with size. Differential regulation of gene expression as a function of cell size could affect many cellular processes.
- Klíčová slova
- Cln3, cell cycle, cell cycle control, cell cycle regulation, cell size control, growth Whi5, growth control of division, size homeostasis, start, yeast cell cycle,
- MeSH
- buněčné dělení genetika MeSH
- buněčný cyklus genetika MeSH
- cykliny genetika MeSH
- G1 fáze genetika MeSH
- regulace genové exprese u hub genetika MeSH
- Saccharomyces cerevisiae - proteiny genetika MeSH
- Saccharomyces cerevisiae genetika růst a vývoj MeSH
- velikost buňky * MeSH
- vývojová regulace genové exprese genetika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Názvy látek
- CLN2 protein, S cerevisiae MeSH Prohlížeč
- cykliny MeSH
- Saccharomyces cerevisiae - proteiny MeSH
BACKGROUND: Identifying regulons of sigma factors is a vital subtask of gene network inference. Integrating multiple sources of data is essential for correct identification of regulons and complete gene regulatory networks. Time series of expression data measured with microarrays or RNA-seq combined with static binding experiments (e.g., ChIP-seq) or literature mining may be used for inference of sigma factor regulatory networks. RESULTS: We introduce Genexpi: a tool to identify sigma factors by combining candidates obtained from ChIP experiments or literature mining with time-course gene expression data. While Genexpi can be used to infer other types of regulatory interactions, it was designed and validated on real biological data from bacterial regulons. In this paper, we put primary focus on CyGenexpi: a plugin integrating Genexpi with the Cytoscape software for ease of use. As a part of this effort, a plugin for handling time series data in Cytoscape called CyDataseries has been developed and made available. Genexpi is also available as a standalone command line tool and an R package. CONCLUSIONS: Genexpi is a useful part of gene network inference toolbox. It provides meaningful information about the composition of regulons and delivers biologically interpretable results.
- Klíčová slova
- Cytoscape, Gene network inference, Time series, Transcription regulation,
- MeSH
- Bacteria genetika MeSH
- časové faktory MeSH
- databáze genetické * MeSH
- Eukaryota genetika MeSH
- genové regulační sítě * MeSH
- lidé MeSH
- regulace genové exprese * MeSH
- regulon genetika MeSH
- reprodukovatelnost výsledků MeSH
- Saccharomyces cerevisiae genetika MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Cell cycle is controlled by the activity of protein family of cyclins and cyclin-dependent kinases that are periodically expressed during cell cycle and that are conserved among different species. Genome-wide location analysis found that cyclins are controlled by a small number of transcription factors that form closed network of genes controlling each other. To investigate gene expression dynamics of this network, we developed a general procedure for stochastic simulation of gene expression process. Using the binding data, we simulated gene expression of all genes of the network for all possible combinations of regulatory interactions and by statistical comparison with experimentally measured time series excluded those interactions that formed gene expression temporal profiles significantly different from the measured ones. These experiments led to a new definition of the cyclins regulatory network coherent with the binding experiments which are kinetically plausible. Level of influence of individual regulators in control of the regulated genes is defined. Simulation results indicate particular mechanism of regulatory activity of protein complexes involved in the control of cyclins.
- MeSH
- cyklin-dependentní kinasy genetika MeSH
- cykliny biosyntéza genetika MeSH
- genetická transkripce MeSH
- genové regulační sítě * MeSH
- regulace genové exprese u hub * MeSH
- Saccharomyces cerevisiae genetika MeSH
- stochastické procesy MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- cyklin-dependentní kinasy MeSH
- cykliny MeSH
Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network--the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly.
- MeSH
- časové faktory MeSH
- cykliny genetika metabolismus MeSH
- delece genu MeSH
- genové regulační sítě genetika MeSH
- modely genetické MeSH
- mutageneze genetika MeSH
- regulace genové exprese u hub MeSH
- Saccharomyces cerevisiae - proteiny genetika metabolismus MeSH
- Saccharomyces cerevisiae genetika MeSH
- stanovení celkové genové exprese MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- cykliny MeSH
- Saccharomyces cerevisiae - proteiny MeSH
Strains of Saccharomyces cerevisiae lacking Isw2, the catalytic subunit of the Isw2 chromatin remodeling complex, show the mating type-independent activation of the cell wall integrity (CWI) signaling pathway. Since the CWI pathway activation usually reflects cell wall defects, we searched for the cell wall-related genes changed in expression. The genes DSE1, CTS1, and CHS1 were upregulated as a result of the absence of Isw2, according to previously published gene expression profiles (I. Frydlova, M. Basler, P. Vasicova, I. Malcova, and J. Hasek, Curr. Genet. 52:87-95, 2007). Western blot analyses of double deletion mutants, however, did not indicate the contribution of the chitin metabolism-related genes CTS1 and CHS1 to the CWI pathway activation. Nevertheless, the deletion of the DSE1 gene encoding a daughter cell-specific protein with unknown function suppressed CWI pathway activation in isw2Delta cells. In addition, the deletion of DSE1 also abolished the budding-within-the-birth-scar phenotype of isw2Delta cells. The plasmid-driven overexpression proved that the deregulation of Dse1 synthesis was also responsible for CWI pathway activation and manifestation of the budding-within-the-birth-scar phenotype in wild-type cells. The overproduced Dse1-green fluorescent protein localized to both sides of the septum and persisted in unbudded cells. Although the exact cellular role of this daughter cell-specific protein has to be elucidated, our data point to the involvement of Dse1 in bud site selection in haploid cells.
BACKGROUND: Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks. RESULTS: The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed. CONCLUSION: Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.
- MeSH
- algoritmy MeSH
- biologické modely * MeSH
- mapování interakce mezi proteiny metody MeSH
- počítačová simulace MeSH
- proteom metabolismus MeSH
- regulace genové exprese fyziologie MeSH
- rozpoznávání automatizované metody MeSH
- signální transdukce fyziologie MeSH
- umělá inteligence * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- proteom MeSH
Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found.
- MeSH
- algoritmy * MeSH
- CDC geny MeSH
- genetická transkripce MeSH
- genové regulační sítě MeSH
- lineární modely MeSH
- metoda nejmenších čtverců MeSH
- modely genetické * MeSH
- regulace genové exprese u hub * MeSH
- Saccharomyces cerevisiae genetika MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
- stanovení celkové genové exprese MeSH
- transkripční faktory analýza MeSH
- výpočetní biologie MeSH
- Publikační typ
- časopisecké články MeSH
- hodnotící studie MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
- Názvy látek
- transkripční faktory MeSH
BACKGROUND: Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process. RESULTS: We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data. CONCLUSION: Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.
- MeSH
- algoritmy MeSH
- bakteriální chromozomy genetika MeSH
- počítačová simulace MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
- stanovení celkové genové exprese * MeSH
- Streptomyces coelicolor klasifikace genetika metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The plasma membrane H(+)-ATPase activity was determined under various growth conditions using the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe. Under early batch-growth conditions in a rich medium, the budding yeast S. cerevisiae ATPase specific activity increased 2- to 3-fold during exponential growth. During late exponential growth, a peak of ATPase activity, followed by a sudden decrease, was observed and termed "growth-arrest control". The growth arrest phenomenon of S. cerevisiae could not be related to the acidification of the culture medium or to glucose exhaustion in the medium or to variation of glucose activation of the H(+)-ATPase. Addition of ammonium to a proline minimum medium also stimulated transiently the ATPase activity of S. cerevisiae. Specific activity of the fission yeast S. pombe ATPase did not show a similar profile and steadily increased to reach a plateau in stationary growth. Under synchronous mitotic growth conditions, the ATPase activity of S. cerevisiae increased during the cell division cycle according to the "peak" type cycle, while that of S. pombe was of the "step" type.
- MeSH
- buněčná membrána enzymologie MeSH
- glukosa metabolismus MeSH
- kinetika MeSH
- kultivační média MeSH
- protonové ATPasy metabolismus MeSH
- Saccharomyces cerevisiae enzymologie růst a vývoj MeSH
- Schizosaccharomyces enzymologie růst a vývoj MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- glukosa MeSH
- kultivační média MeSH
- protonové ATPasy MeSH
