Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin.

Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug.

Dynamic transcriptional response of Saccharomyces cerevisiae cells to copper.

Copper is a crucial trace element for all living systems and any deficiency in copper homeostasis leads to the development of severe diseases in humans. The observation of extensive evolutionary conservation in copper homeostatic systems between human and Saccharomyces cerevisiae made this organism a suitable model organism for elucidating molecular mechanisms of copper transport and homeostasis. In this study, the dynamic transcriptional response of both the reference strain and homozygous deletion mutant strain of CCC2, which encodes a Cu-transporting P-type ATPase, were investigated following the introduction of copper impulse to reach a copper concentration which was shown to improve the respiration capacity of CCC2 deletion mutants.

Insights Into the Mechanism of Anticancer Drug Imatinib Revealed Through Multi-Omic Analyses in Yeast.

Imatinib mesylate is a receptor tyrosine kinase inhibitor drug with broad applications in cancer therapeutics, for example, in chronic myeloid leukemia and gastrointestinal stromal tumors. In this study, new multi-omics findings in yeast on the mechanism of imatinib are reported, using the model organism . Whole-genome analysis of the transcriptional response of yeast cells following long-term exposure to imatinib, flux-balance analysis (FBA), and modular analysis of protein/protein interaction network consisting of proteins encoded by differentially expressed genes (DEGs) were performed.

Identification of Novel Components of Target-of-Rapamycin Signaling Pathway by Network-Based Multi-Omics Integrative Analysis.

Target of rapamycin (TOR) is a major signaling pathway and regulator of cell growth. TOR serves as a hub of many signaling routes, and is implicated in the pathophysiology of numerous human diseases, including cancer, diabetes, and neurodegeneration. Therefore, elucidation of unknown components of TOR signaling that could serve as potential biomarkers and drug targets has a great clinical importance.

Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism.

Background: Rapamycin is a potent inhibitor of the highly conserved TOR kinase, the nutrient-sensitive controller of growth and aging. It has been utilised as a chemotherapeutic agent due to its anti-proliferative properties and as an immunosuppressive drug, and is also known to extend lifespan in a range of eukaryotes from yeast to mammals. However, the mechanisms through which eukaryotic cells adapt to sustained exposure to rapamycin have not yet been thoroughly investigated.

Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells.

Doxorubicin is one of the most effective chemotherapy drugs used against solid tumors in the treatment of several cancer types. Two different mechanisms, (i) intercalation of doxorubicin into DNA and inhibition of topoisomerase II leading to changes in chromatin structure, (ii) generation of free radicals and oxidative damage to biomolecules, have been proposed to explain the mode of action of this drug in cancer cells. A genome-wide integrative systems biology approach used in the present study to investigate the long-term effect of doxorubicin in Saccharomyces cerevisiae cells indicated the up-regulation of genes involved in response to oxidative stress as well as in Rad53 checkpoint sensing and signaling pathway.

Transcriptomic response of yeast cells to ATX1 deletion under different copper levels.

Background: Iron and copper homeostatic pathways are tightly linked since copper is required as a cofactor for high affinity iron transport. Atx1p plays an important role in the intracellular copper transport as a copper chaperone transferring copper from the transporters to Ccc2p for its subsequent insertion into Fet3p, which is required for high affinity iron transport.

Results: In this study, genome-wide transcriptional landscape of ATX1 deletants grown in media either lacking copper or having excess copper was investigated.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations.

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms.

A novel topological centrality measure capturing biologically important proteins.

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality.

An integrative analysis of transcriptomic response of ethanol tolerant strains to ethanol in Saccharomyces cerevisiae.

The accumulation of ethanol is one of the main environmental stresses that Saccharomyces cerevisiae cells are exposed to in industrial alcoholic beverage and bioethanol production processes. Despite the known impacts of ethanol, the molecular mechanisms underlying ethanol tolerance are still not fully understood. Novel gene targets leading to ethanol tolerance were previously identified via a network approach and the investigations of the deletions of these genes resulted in the improved ethanol tolerance of pmt7Δ/pmt7Δ and yhl042wΔ/yhl042wΔ strains.

Biomass composition: the "elephant in the room" of metabolic modelling.

Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for , the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation.

CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.

Motivation: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets.

Results: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need.

A system based network approach to ethanol tolerance in Saccharomyces cerevisiae.

Background: Saccharomyces cerevisiae has been widely used for bio-ethanol production and development of rational genetic engineering strategies leading both to the improvement of productivity and ethanol tolerance is very important for cost-effective bio-ethanol production. Studies on the identification of the genes that are up- or down-regulated in the presence of ethanol indicated that the genes may be involved to protect the cells against ethanol stress, but not necessarily required for ethanol tolerance.

Results: In the present study, a novel network based approach was developed to identify candidate genes involved in ethanol tolerance.

Yeast cells with impaired drug resistance accumulate glycerol and glucose.

Multiple drug resistance (MDR) in yeast is effected by two major superfamilies of membrane transporters: the major facilitator superfamily (MFS) and the ATP-binding cassette (ABC) superfamily. In the present work, we investigated the cellular responses to disruptions in both MFS (by deleting the transporter gene, QDR3) and ABC (by deleting the gene for the Pdr3 transcription factor) transporter systems by growing diploid homozygous deletion yeast strains in glucose- or ammonium-limited continuous cultures. The transcriptome and the metabolome profiles of these strains, as well as the flux distributions in the optimal solution space, reveal novel insights into the underlying mechanisms of action of QDR3 and PDR3.

Transcriptional remodelling in response to changing copper levels in the Wilson and Menkes disease model of Saccharomyces cerevisiae.

A high degree of conservation of the copper homeostasis pathway between yeast and humans makes yeast an ideal model organism for studying copper-related disorders. In this study, a system based integrative approach was used to investigate the genome-wide effects of the deletion of the yeast ortholog of Wilson and Menkes diseases encoding a Cu(2+)-transporting P-type ATPase (CCC2) in different copper containing media and to compare with the wild type. The experimental design applied in this study enabled the observation of the effect of CCC2 deletion, extracellular copper levels and interactive effects of both factors in S.

Complex disease interventions from a network model for type 2 diabetes.

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network are informative to reveal significant metabolic processes and disease's associations with other complex disorders. In the current study, Type 2 diabetes associated functional linkage network (T2DFN) containing 2770 proteins and 15041 linkages was constructed.

A novel strategy for selection and validation of reference genes in dynamic multidimensional experimental design in yeast.

Background: Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response.

Short- and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment.

Quantitative data on the dynamic changes in the transcriptome and the metabolome of yeast in response to an impulse-like perturbation in nutrient availability was integrated with the metabolic pathway information in order to elucidate the long-term dynamic re-organization of the cells. This study revealed that, in addition to the dynamic re-organization of the de novo biosynthetic pathways, salvage pathways were also re-organized in a time-dependent manner upon catabolite repression. The transcriptional and the metabolic responses observed for nitrogen catabolite repression were not as severe as those observed for carbon catabolite repression.

How yeast re-programmes its transcriptional profile in response to different nutrient impulses.

Background: A microorganism is able to adapt to changes in its physicochemical or nutritional environment and this is crucial for its survival. The yeast, Saccharomyces cerevisiae, has developed mechanisms to respond to such environmental changes in a rapid and effective manner; such responses may demand a widespread re-programming of gene activity. The dynamics of the re-organization of the cellular activities of S.

A novel integrative network approach to understand the interplay between cardiovascular disease and other complex disorders.

There is accumulating evidence that the proteins encoded by the genes associated with a common disorder interact with each other, participate in similar pathways and share GO terms. It has been anticipated that the functional modules in a disease related functional linkage network can be integrated with bibliomics to reveal association with other complex disorders. In this study, the cardiovascular disease functional linkage network (CFN) containing 1536 nodes and 3345 interactions was constructed using proteins encoded by 234 genes associated with the disease.

Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains.

The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory.

A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism.

Exometabolic and transcriptional response in relation to phenotype and gene copy number in respiration-related deletion mutants of S. cerevisiae.

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.

Integration of metabolic modeling and phenotypic data in evaluation and improvement of ethanol production using respiration-deficient mutants of Saccharomyces cerevisiae.

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.

Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae.

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.