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Cyclo Olefin Polymer (COP) based microbioreactors on a microfluidic chip were produced in house by hot-embossing and thermo-compression bonding methods. The chip allows two different experiments to be performed on trapped cells at the same time. On one side of the chip, red fluorescent protein (RFP) tagged nucleolar Nop56 protein was used to track changes in cell cycle as well as protein synthesis within the yeast cells under the application of the anti-tumor agent hydroxyurea (HU). Simultaneously, on the other side of the chip, the response of yeast cells to the drug metformin, mTOR inhibitor, was investigated to reveal the role of TOR signaling in ribosome biogenesis and cell proliferation. The results of 20 h long experiments are captured by taking brightfield and fluorescent microscopy images of the trapped cells every 9 min. The expression of Nop56 protein of ribosome assembly and synthesis was densely observed during G1 phase of cell cycle, and later towards the end of cell cycle the ribosomal protein expression slowed down. Under HU treatment, the morphology of yeast cells changed, but after cessation of HU, the biomass synthesis rate was sustained as monitored by the cell perimeter. Under metformin treatment, the perimeters of single cells were observed to decrease, implying a decrease in biomass growth; however these cells continued their proliferation during and after the drug application. The relation between ribosome biogenesis and cell cycle was successfully investigated on single cell basis, capturing cell-to-cell variations, which cannot be tracked by regular macroscale bioreactors.

A microfluidic platform is designed and fabricated to investigate the role of uncharacterized YOR060C (Sld7) protein in aging in yeast cells for the first time. Saccharomyces cerevisiae yeast cells are trapped in the series of C-shaped regions (0.5 nL) of COP (cyclo olefin polymer), PMMA (poly methylmethacrylate), or PS (polystyrene) microbioreactors. The devices are fabricated using hot embossing and thermo-compression bonding methods. Photolithography and electrochemical etching are used to form the steel mold needed for hot embossing. The cell cycle processes are investigated by monitoring green fluorescent protein (GFP) tagged Sld7 expressions under normal as well as calorie restricted conditions. The cells are loaded at 1 μL/min flowrate and trapped successfully within each chamber. The medium is continuously fed at 0.1 μL/min throughout the experiments. Fluorescent signals of the low abundant Sld7 proteins could be distinguished only on COP devices. The background fluorescence of COP is found 1.22 and 7.24 times lower than that of PMMA, and PS, respectively. Hence, experiments are continued with COP, and lasted for more than 40 h without any contamination. The doubling time of the yeast cells are found as 72 min and 150 min, and the growth rates as 9.63 × 10 -3  min -1 and 4.62 × 10 -3  min -1 , in 2% glucose containing YPD and YNB medium, respectively. The product concentration (Sld7p:GFP) increased in accordance with cell growth. The dual role of Sld7 protein in both cell cycle and chronological aging needs to be further investigated following the preliminary experimental results.

One of the factors affecting chronological life span (CLS) in budding yeast is nutrient, especially carbon limitation. Aside from metabolites in the growth medium such as glucose, amino acids, and acetic acid, many pharmaceuticals have also been proven to alter CLS. Besides their impact on life span, these drugs are also prospective chemicals to treat the age-associated diseases, so the identification of their action mechanism and their potential side effects is of crucial importance. In this study, the effects of caloric restriction and metformin, a dietary mimetic pharmaceutical, on yeast CLS are compared. Saccharomyces cerevisiae cells grown in synthetic dextrose complete (SDC) up to mid-exponential phase were either treated with metformin or were subjected to glucose limitation. The impacts of these perturbations were analyzed via transcriptomics, and the common (stimulation of glucose uptake, induction of mitochondrial maintenance, and reduction of protein translation) and divergent (stimulation of aerobic respiration and reprogramming of respiratory electron transport chain (ETC)) cellular responses specific to each treatment were determined. These results revealed that both glucose limitation and metformin treatment stimulate CLS extension and involve the mitochondrial function, probably by creating an efficient mitochondria-to-nucleus signaling of either aerobic respiration or ETC signaling stimulation, respectively.

Because of its multifactorial nature, aging is one of the most complicated cell phenomena known. A systems biology approach, which aims to understand the organism as a whole rather than concentrate on the behaviors of individual genes, thus comprises a seamless tool for investigating the aging machinery, which arises mainly as a result of degeneration of the collaboration between signaling and regulatory pathways. In the present study, the effects of medium buffering on the chronological life span are investigated via transcriptome analyses and subsequent integration of the data obtained with the chronological aging network of yeast. The comparative inquiry of transcriptome data of young and old cells grown in buffered and unbuffered media reveals new roles for pH control (e.g. the re-organization of lipid metabolism and intracellular signaling cascades) that have beneficial consequences on chronological longevity. Integration of the transcriptome data onto the aging network, as well as validation experiments, suggest that Snf1p is a possible intermediate player in the interjunction of sphingolipid and ergosterol metabolisms with extracellular pH control with respect to regulation of the chronological life span. Consequently, a more detailed insight of the chronological aging mechanism of yeast is obtained. The results of the present study provide a solid basis for further research focusing on uncovering the agents that affect aging and age-related diseases in humans., (© 2014 FEBS.)

Sphingolipids are essential building blocks of the plasma membranes and are highly bioactive in the regulation of diverse cellular functions and pathological processes, a fact which renders the sphingolipid metabolism an important research area. In this study, a computational framework was recruited for the reconstruction of a functional interaction network for sphingolipid metabolism in Baker's yeast, SSN. Gene Ontology (GO) annotations were integrated with functional interaction data of the BIOGRID database and the reconstructed protein interaction network was subjected to topological and descriptive analyses. SSN was of a scale-free nature, following a power law model with γ=1.41. Prominent processes of SSN revealed that the reconstructed network encapsulated the involvement of sphingolipid metabolism in vital cellular processes such as energy homeostasis, cell growth and/or death and synthesis of building blocks. To investigate the potential of SSN for predicting signal transduction pathways regulating and/or being regulated by sphingolipid biosynthesis in yeast, a case study involving the S. cerevisiae counterpart of AMP-activated protein kinase, the Snf1 kinase complex, was conducted. The mutant strain lacking the catalytic α subunit, snf1Δ/snf1Δ, had elevated inositol phosphorylceramide and mannosyl-inositol phosphorylceramide levels, and decreased mannosyl-diinositol phosphorylceramide levels compared to the wild type strain, revealing that Snf1p has a regulatory role in the sphingolipid metabolism. Transcriptome data belonging to that strain available in the literature were mapped onto SSN and the correlated SSN was further investigated to evaluate the possible crosstalk machineries where sphingolipids and Snf1p function in coordination, in other words the crosstalk points between sphingolipid-mediated and Snf1 kinase signalling. The subsequent investigation of the discovered candidate crosstalk processes by performing sensitivity experiments imply a tight interconnection between sphingolipids and Snf1p in the regulation of calcineurin activity, cellular metal ion homeostasis and response to cell wall and endoplasmic reticulum stresses in yeast.

The first elaborate metabolic model of Saccharomyces cerevisiae sphingolipid metabolism was reconstructed in silico. The model considers five different states of sphingolipid hydroxylation, rendering it unique among other models. It is aimed to clarify the significance of hydroxylation on sphingolipids and hence to interpret the preferences of the cell between different metabolic pathway branches under different stress conditions. The newly constructed model was validated by single, double and triple gene deletions with experimentally verified phenotypes. Calcium sensitivity and deletion mutations that may suppress calcium sensitivity were examined by CSG1 and CSG2 related deletions. The model enabled the analysis of complex sphingolipid content of the plasma membrane coupled with diacylglycerol and phosphatidic acid biosynthesis and ATP consumption in in silico cell. The flux data belonging to these critically important key metabolites are integrated with the fact of phytoceramide induced cell death to propose novel potential drug targets for cancer therapeutics. In conclusion, we propose that IPT1, GDA1, CSG and AUR1 gene deletions may be novel candidates of drug targets for cancer therapy according to the results of flux balance and variability analyses coupled with robustness analysis., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)

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., (© 2011 Borklu Yucel, Ulgen.)

The transcriptional and metabolic impact of deleting one or both copies of a respiration-related gene has been studied in glucose-limited chemostats. Integration of literature information on phenotype with our exometabolome and transcriptome data enabled the identification of novel relationships between gene copy number, transcriptional regulation and phenotype. We found that the effect of complete respiratory deficiency on transcription was limited to downregulation of genes involved in oxidoreductase activity and iron assimilation. Partial respiratory deficiency had no significant impact on gene transcription. Changes in the copy number of two transcription-factor genes, HAP4 and MIG1, had a major impact on genes involved in mitochondrial function. Regulation of respiratory chain components encoded in the nucleus and mitochondria appears to be divided between Hap4p and Oxa1p, respectively. Similarly, repression of respiration may be imposed by the action of Mig1p and Mba1p on nuclear and mitochondrial gene expression, respectively. However, it is not clear whether Oxa1p and Mba1p regulate mitochondrial gene expression via their interaction with mitochondrial ribosomes or by some indirect means. The phenotype of nuclear petite mutants may not simply be due to the absence of respiration; e.g. Oxa1p or Bcs1p may play a role in the regulation of ribosome assembly in the nucleolus. Integration between respiration and cell growth may also result from the action of a single transcription factor. Thus, Hap4p targets genes that are required for respiration and for fitness in nutrient-limited conditions. This suggests that Hap4p may enable cells to adapt to nutrient limitation as well as diauxy.

Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4Delta/hap4Delta, mig1Delta/mig1Delta, qdr3Delta/qdr3Delta, pdr3Delta/pdr3Delta, qcr7Delta/qcr7Delta, cyt1Delta/cyt1Delta, and rip1Delta/rip1Delta mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3Delta/qdr3Delta mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7Delta/qcr7Delta mutant compared to the rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD(+)/NADH imbalance in rip1Delta/rip1Delta and cyt1Delta/cyt1Delta mutants, but not the qcr7Delta/qcr7Delta mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism., ((c) 2007 Wiley Periodicals, Inc.)

Background: Control effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism., Results: The effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products (ethanol, glycerol, acetate, succinate) were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied., Conclusion: The analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments.

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.

Background: New analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods., Results: Transcriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures--PLS) using PLS toolbox in Matlab., Conclusion: The experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure., (Copyright 2004 Wiley Periodicals, Inc.)