Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

Background  

Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.     

Modeling the evolution of a classic genetic switch

Background  

The regulatory network underlying the yeast galactose-use pathway has emerged as a model system for the study of regulatory network evolution. Evidence has recently been provided for adaptive evolution in this network following a whole genome duplication event. An ancestral gene encoding a bi-functional galactokinase and co-inducer protein molecule has become subfunctionalized as paralogous genes (GAL1 and GAL3) in Saccharomyces cerevisiae, with most fitness gains being attributable to changes in cis-regulatory elements. However, the quantitative functional implications of the evolutionary changes in this regulatory network remain unexplored.     

Display of wasp venom allergens on the cell surface of <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>

Background  

Yeast surface display is a technique, where the proteins of interest are expressed as fusions with yeast surface proteins and thus remain attached to the yeast cell wall after expression. Our purpose was to study whether allergens expressed on the cell surface of baker's yeast Saccharomyces cerevisiae preserve their native allergenic properties and whether the yeast native surface glycoproteins interfere with IgE binding. We chose to use the major allergens from the common wasp Vespula vulgaris venom: phospholipase A1, hyaluronidase and antigen 5 as the model.     

A single sequence context cannot satisfy all non-AUG initiator codons in yeast<Superscript>†</Superscript>

Background  

Previous studies in Saccharomyces cerevisiae showed that ALA1 (encoding alanyl-tRNA synthetase) and GRS1 (encoding glycyl-tRNA synthetase) respectively use ACG and TTG as their alternative translation initiator codons. To explore if any other non-ATG triplets can act as initiator codons in yeast, ALA1 was used as a reporter for screening.     

Improving the <Emphasis Type="Italic">i</Emphasis>MM904 <Emphasis Type="Italic">S. cerevisiae</Emphasis> metabolic model using essentiality and synthetic lethality data

Background  

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., i MM904) remains significantly less predictive than the latest E. coli model (i.e., i AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.     

Functional,genetic and bioinformatic characterization of a calcium/calmodulin kinase gene in <Emphasis Type="Italic">Sporothrix schenckii</Emphasis>

Background  

Sporothrix schenckiiis a pathogenic, dimorphic fungus, the etiological agent of sporotrichosis, a subcutaneous lymphatic mycosis. Dimorphism inS. schenckiiresponds to second messengers such as cAMP and calcium, suggesting the possible involvement of a calcium/calmodulin kinase in its regulation. In this study we describe a novel calcium/calmodulin-dependent protein kinase gene inS. schenckii, sscmk1, and the effects of inhibitors of calmodulin and calcium/calmodulin kinases on the yeast to mycelium transition and the yeast cell cycle.     

YODA: Software to facilitate high-throughput analysis of chronological life span,growth rate,and survival in budding yeast

Background  

The budding yeast Saccharomyces cerevisiae is one of the most widely studied model organisms in aging-related science. Although several genetic modifiers of yeast longevity have been identified, the utility of this system for longevity studies has been limited by a lack of high-throughput assays for quantitatively measuring survival of individual yeast cells during aging.     

Global nucleosome occupancy in yeast

Background  

Although eukaryotic genomes are generally thought to be entirely chromatin-associated, the activated PHO5 promoter in yeast is largely devoid of nucleosomes. We systematically evaluated nucleosome occupancy in yeast promoters by immunoprecipitating nucleosomal DNA and quantifying enrichment by microarrays.     

The outcome of Cryptococcus neoformans intracellular pathogenesis in human monocytes

Background  

Cryptococcus neoformans is an encapsulated yeast that is a facultative intracellular pathogen. The interaction between macrophages and C. neoformans is critical for extrapulmonary dissemination of this pathogenic yeast. C. neoformans can either lyse macrophages or escape from within them through a process known as phagosomal extrusion. However, most studies of intracellular pathogenesis have been made with mouse cells and their relevance to human infection is uncertain. In this study we extended studies of C. neoformans-macrophage cellular interaction/s to human peripheral blood monocytes.     

Gene network landscape of the ciliate Tetrahymena thermophila

Background

Genome-wide expression data of gene microarrays can be used to infer gene networks. At a cellular level, a gene network provides a picture of the modules in which genes are densely connected, and of the hub genes, which are highly connected with other genes. A gene network is useful to identify the genes involved in the same pathway, in a protein complex or that are co-regulated. In this study, we used different methods to find gene networks in the ciliate Tetrahymena thermophila, and describe some important properties of this network, such as modules and hubs.

Methodology/Principal Findings

Using 67 single channel microarrays, we constructed the Tetrahymena gene network (TGN) using three methods: the Pearson correlation coefficient (PCC), the Spearman correlation coefficient (SCC) and the context likelihood of relatedness (CLR) algorithm. The accuracy and coverage of the three networks were evaluated using four conserved protein complexes in yeast. The CLR network with a Z-score threshold 3.49 was determined to be the most robust. The TGN was partitioned, and 55 modules were found. In addition, analysis of the arbitrarily determined 1200 hubs showed that these hubs could be sorted into six groups according to their expression profiles. We also investigated human disease orthologs in Tetrahymena that are missing in yeast and provide evidence indicating that some of these are involved in the same process in Tetrahymena as in human.

Conclusions/Significance

This study constructed a Tetrahymena gene network, provided new insights to the properties of this biological network, and presents an important resource to study Tetrahymena genes at the pathway level.     

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Background

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

Results

Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G₁ fixed point was found for deterministic updating schemes.

Conclusions

In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.     

Differential gene expression in recombinant <Emphasis Type="Italic">Pichia pastoris</Emphasis> analysed by heterologous DNA microarray hybridisation

Background  

Pichia pastoris is a well established yeast host for heterologous protein expression, however, the physiological and genetic information about this yeast remains scanty. The lack of a published genome sequence renders DNA arrays unavailable, thereby hampering more global investigations of P. pastoris from the beginning. Here, we examine the suitability of Saccharomyces cerevisiae DNA microarrays for heterologous hybridisation with P. pastoris cDNA.     

A network-based approach on elucidating the multi-faceted nature of chronological aging in S. cerevisiae

Background

Cellular mechanisms leading to aging and therefore increasing susceptibility to age-related diseases are a central topic of research since aging is the ultimate, yet not understood mechanism of the fate of a cell. Studies with model organisms have been conducted to ellucidate these mechanisms, and chronological aging of yeast has been extensively used as a model for oxidative stress and aging of postmitotic tissues in higher eukaryotes.

Methodology/Principal Findings

The chronological aging network of yeast was reconstructed by integrating protein-protein interaction data with gene ontology terms. The reconstructed network was then statistically “tuned” based on the betweenness centrality values of the nodes to compensate for the computer automated method. Both the originally reconstructed and tuned networks were subjected to topological and modular analyses. Finally, an ultimate “heart” network was obtained via pooling the step specific key proteins, which resulted from the decomposition of the linear paths depicting several signaling routes in the tuned network.

Conclusions/Significance

The reconstructed networks are of scale-free and hierarchical nature, following a power law model with γ  =  1.49. The results of modular and topological analyses verified that the tuning method was successful. The significantly enriched gene ontology terms of the modular analysis confirmed also that the multifactorial nature of chronological aging was captured by the tuned network. The interplay between various signaling pathways such as TOR, Akt/PKB and cAMP/Protein kinase A was summarized in the “heart” network originated from linear path analysis. The deletion of four genes, TCB3, SNA3, PST2 and YGR130C, was found to increase the chronological life span of yeast. The reconstructed networks can also give insight about the effect of other cellular machineries on chronological aging by targeting different signaling pathways in the linear path analysis, along with unraveling of novel proteins playing part in these pathways.     

PhiC31 recombination system demonstrates heritable germinal transmission of site-specific excision from the <Emphasis Type="Italic">Arabidopsis</Emphasis> genome

Background  

The large serine recombinase phiC31 from broad host range Streptomyces temperate phage, catalyzes the site-specific recombination of two recognition sites that differ in sequence, typically known as attachment sites attB and attP. Previously, we characterized the phiC31 catalytic activity and modes of action in the fission yeast Schizosaccharomyces pombe.     

Evidence from comparative genomics for a complete sexual cycle in the 'asexual' pathogenic yeast <Emphasis Type="Italic">Candida glabrata</Emphasis>

Background  

Candida glabrata is a pathogenic yeast of increasing medical concern. It has been regarded as asexual since it was first described in 1917, yet phylogenetic analyses have revealed that it is more closely related to sexual yeasts than other Candida species. We show here that the C. glabrata genome contains many genes apparently involved in sexual reproduction.     

Biochemical and molecular characterisation of Tetrahymena thermophila extracellular cysteine proteases

Background  

Over the last decades molecular biologic techniques have been developed to alter the genome and proteome of Tetrahymena thermophila thereby providing the basis for recombinant protein expression including functional human enzymes. The biotechnological potential of Tetrahymena has been proved in numerous publications, demonstrating fast growth, high biomass, fermentation in ordinary bacterial/yeast equipment, up-scalability, existence of cheap and chemical defined media. For these reasons Tetrahymena offers promising opportunities for the development of a high expression system. Yet optimised high yield strains with protease deficiency such as commonly used in yeast and bacterial systems are not available.     

Sds22, a PP1 phosphatase regulatory subunit, regulates epithelial cell polarity and shape [Sds22 in epithelial morphology]

Background  

How epithelial cells adopt their particular polarised forms is poorly understood. In a screen for genes regulating epithelial morphology in Drosophila, we identified sds22, a conserved gene previously characterised in yeast.     

A systematic survey of centrality measures for protein-protein interaction networks

Background

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results

We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions

The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.