New insights into protein-protein interaction data lead to increased estimates of the <Emphasis Type="Italic">S. cerevisiae</Emphasis> interactome size

Background  

As protein interactions mediate most cellular mechanisms, protein-protein interaction networks are essential in the study of cellular processes. Consequently, several large-scale interactome mapping projects have been undertaken, and protein-protein interactions are being distilled into databases through literature curation; yet protein-protein interaction data are still far from comprehensive, even in the model organism Saccharomyces cerevisiae. Estimating the interactome size is important for evaluating the completeness of current datasets, in order to measure the remaining efforts that are required.     

Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation

Background  

Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties.     

A genome-wide survey of changes in protein evolutionary rates across four closely related species of <Emphasis Type="Italic">Saccharomyces</Emphasis> sensu stricto group

Background  

Changes in protein evolutionary rates among lineages have been frequently observed during periods of notable phenotypic evolution. It is also known that, following gene duplication and loss, the protein evolutionary rates of genes involved in such events changed because of changes in functional constraints acting on the genes. However, in the evolution of closely related species, excluding the aforementioned situations, the frequency of changes in protein evolutionary rates is still not clear at the genome-wide level. Here we examine the constancy of protein evolutionary rates in the evolution of four closely related species of the Saccharomyces sensu stricto group (S. cerevisiae, S. paradoxus, S. mikatae and S. bayanus).     

Switching the mode of sucrose utilization by <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>

Background  

Overflow metabolism is an undesirable characteristic of aerobic cultures of Saccharomyces cerevisiae during biomass-directed processes. It results from elevated sugar consumption rates that cause a high substrate conversion to ethanol and other bi-products, severely affecting cell physiology, bioprocess performance, and biomass yields. Fed-batch culture, where sucrose consumption rates are controlled by the external addition of sugar aiming at its low concentrations in the fermentor, is the classical bioprocessing alternative to prevent sugar fermentation by yeasts. However, fed-batch fermentations present drawbacks that could be overcome by simpler batch cultures at relatively high (e.g. 20 g/L) initial sugar concentrations. In this study, a S. cerevisiae strain lacking invertase activity was engineered to transport sucrose into the cells through a low-affinity and low-capacity sucrose-H⁺ symport activity, and the growth kinetics and biomass yields on sucrose analyzed using simple batch cultures.     

<Emphasis Type="Italic">Xenopus</Emphasis> Cdc14α/β are localized to the nucleolus and centrosome and are required for embryonic cell division

Background  

The dual specificity phosphatase Cdc14 has been shown to be a critical regulator of late mitotic events in several eukaryotes, including S. cerevisiae, S. pombe. C. elegans and H. sapiens. However, Cdc14 homologs have clearly evolved to regulate distinct cellular processes and to respond to regulatory signals important for these processes. The human paralogs hCdc14A and B are the only vertebrate Cdc14 homologues studied to date, but their functions are not well understood. Therefore, it is of great interest to examine the function Cdc14 homologs in other vertebrate species.     

Optimization of conditions for cadmium selenide quantum dot biosynthesis in Saccharomyces cerevisiae

The biosynthesis of quantum dots has been explored as an alternative to traditional physicochemical methods; however, relatively few studies have determined optimal synthesis parameters. Saccharomyces cerevisiae sequentially treated with sodium selenite and cadmium chloride synthesized CdSe quantum dots in the cytoplasm. These nanoparticles displayed a prominent yellow fluorescence, with an emission maximum of approximately 540 nm. The requirement for glutathione in the biosynthetic mechanism was explored by depleting its intracellular content through cellular treatments with 1-chloro-2,4-dinitrobenzene and buthionine sulfoximine. Synthesis was significantly inhibited by both of these reagents when they were applied after selenite treatment prior to the addition of cadmium, thereby indicating that glutathione contributes to the biosynthetic process. Determining the optimum conditions for biosynthesis revealed that quantum dots were produced most efficiently at entry into stationary phase followed by direct addition of 1 mM selenite for only 6 h and then immediately incubating these cells in fresh growth medium containing 3 mM Cd (II). Synthesis of quantum dots reached a maximum at 84 h of reaction time. Biosynthesis of 800-μg g⁻¹ fresh weight cells was achieved. For the first time, significant efforts have been undertaken to optimize each aspect of the CdSe biosynthetic procedure in S. cerevisiae, resulting in a 70% increased production.

     

Extension of the yeast metabolic model to include iron metabolism and its use to estimate global levels of iron-recruiting enzyme abundance from cofactor requirements

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 ⁻¹¹ mmol·(g biomass) ⁻¹·h ⁻¹.     

Mining protein networks for synthetic genetic interactions

Background  

The local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.     

Random Phenotypic Variation of Yeast (Saccharomyces cerevisiae) Single-Gene Knockouts Fits a Double Pareto-Lognormal Distribution

Background

Distributed robustness is thought to influence the buffering of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and protein-protein interaction networks. If this hypothesis is true, then the phenotypic response to the perturbation of particular nodes in such a network should be proportional to the number of links those nodes make with neighboring nodes. This suggests a probability distribution approximating an inverse power-law of random phenotypic variation. Zero phenotypic variation, however, is impossible, because random molecular and cellular processes are essential to normal development. Consequently, a more realistic distribution should have a y-intercept close to zero in the lower tail, a mode greater than zero, and a long (fat) upper tail. The double Pareto-lognormal (DPLN) distribution is an ideal candidate distribution. It consists of a mixture of a lognormal body and upper and lower power-law tails.

Objective and Methods

If our assumptions are true, the DPLN distribution should provide a better fit to random phenotypic variation in a large series of single-gene knockout lines than other skewed or symmetrical distributions. We fit a large published data set of single-gene knockout lines in Saccharomyces cerevisiae to seven different probability distributions: DPLN, right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pareto. The best model was judged by the Akaike Information Criterion (AIC).

Results

Phenotypic variation among gene knockouts in S. cerevisiae fits a double Pareto-lognormal (DPLN) distribution better than any of the alternative distributions, including the right Pareto-lognormal and lognormal distributions.

Conclusions and Significance

A DPLN distribution is consistent with the hypothesis that developmental stability is mediated, in part, by distributed robustness, the resilience of gene regulatory, metabolic, and protein-protein interaction networks. Alternatively, multiplicative cell growth, and the mixing of lognormal distributions having different variances, may generate a DPLN distribution.     

Towards a genome-scale kinetic model of cellular metabolism

Background  

Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.     

Protein kinases associated with the yeast phosphoproteome

Background  

Protein phosphorylation is an extremely important mechanism of cellular regulation. A large-scale study of phosphoproteins in a whole-cell lysate of Saccharomyces cerevisiae has previously identified 383 phosphorylation sites in 216 peptide sequences. However, the protein kinases responsible for the phosphorylation of the identified proteins have not previously been assigned.     

Genome-wide identification of <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> genes required for tolerance to acetic acid

Background  

Acetic acid is a byproduct of Saccharomyces cerevisiae alcoholic fermentation. Together with high concentrations of ethanol and other toxic metabolites, acetic acid may contribute to fermentation arrest and reduced ethanol productivity. This weak acid is also a present in lignocellulosic hydrolysates, a highly interesting non-feedstock substrate in industrial biotechnology. Therefore, the better understanding of the molecular mechanisms underlying S. cerevisiae tolerance to acetic acid is essential for the rational selection of optimal fermentation conditions and the engineering of more robust industrial strains to be used in processes in which yeast is explored as cell factory.     

Differential selection on gene translation efficiency between the filamentous fungus <Emphasis Type="Italic">Ashbya gossypii</Emphasis> and yeasts

Background  

The filamentous fungus Ashbya gossypii grows into a multicellular mycelium that is distinct from the unicellular morphology of its closely related yeast species. It has been proposed that genes important for cell cycle regulation play central roles for such phenotypic differences. Because A. gossypii shares an almost identical set of cell cycle genes with the typical yeast Saccharomyces cerevisiae, the differences might occur at the level of orthologous gene regulation. Codon usage patterns were compared to identify orthologous genes with different gene regulation between A. gossypii and nine closely related yeast species.     

<Emphasis Type="Italic">Candida albicans</Emphasis> virulence and drug-resistance requires the <Emphasis Type="Italic">O</Emphasis>-acyltransferase Gup1p

Background  

GUP1 gene was primarily identified in Saccharomyces cerevisiae being connected with glycerol uptake defects in association with osmotic stress response. Soon after, Gup1p was implicated in a complex and extensive series of phenotypes involving major cellular processes. These include membrane and wall maintenance, lipid composition, bud-site selection, cytoskeleton orientation, vacuole morphology, secretory/endocytic pathway, GPI anchors remodelling, and lipid-ordered domains assembly, which is compatible with their inclusion in the Membrane Bound O-acyl transferases (MBOAT) family. In mammals, it has been described as a negative regulator of the Sonic hedgehog pathway involved in morphogenesis, differentiation, proliferation, among other processes.