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. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.

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 Cu2+-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. The analysis of data by using different clustering algorithms revealed significantly affected processes and pathways in response to a switch from copper deficient environment to elevated copper levels. Sulfur compound, methionine and cysteine biosynthetic processes were identified as significantly affected processes for the first time in this study. Stress response, cellular response to DNA damage, iron ion homeostasis, ubiquitin dependent proteolysis, autophagy and regulation of macroautophagy, DNA repair and replication, as well as organization of mitochondrial respiratory chain complex IV, mitochondrial organization and translation were identified as significantly affected processes in only CCC2 deleted strain. The integration of the transcriptomic data with regulome revealed the differences in the extensive re-wiring of dynamic transcriptional organization and regulation in these strains.

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 Saccharomyces cerevisiae. 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. DEGs indicated that carbon, nitrogen, starch, sucrose, glyoxylate/dicarboxylate metabolism, valine and leucine degradation, and tricarboxylic acid cycle (TCA) were significantly upregulated. By contrast, ribosome biogenesis, pentose/glucuronate interconversion, tryptophan/pyruvate metabolic pathways, and meiosis were significantly downregulated. FBA revealed that a large set of metabolic pathways was altered by imatinib to compensate cancer-associated metabolic changes. Integration of transcriptome and interactome (protein/protein interactions) data helped to identify the core regulatory genes and pathways through elucidation of the active subnetworks. It appears that imatinib may also contribute to antitumoral immune response in the tumor microenvironment and most of the metabolic rearrangements are at posttranscriptional level. Furthermore, additional support for possible contribution of thiamine/pyridoxal phosphate biosynthesis and mitogen-activated protein kinase pathway to drug resistance is noted. This report advances multi-omics understanding of the mechanism of imatinib, and by extension, offers new molecular avenues toward precision medicine and discovery of novel drug targets in cancer therapeutics.

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. In this study, our aim is to integrate transcriptomics, interactomics, and regulomics data in Saccharomyces cerevisiae using a network-based multiomics approach to enlighten previously unidentified, potential components of TOR signaling. We constructed the TOR-signaling protein interaction network, which was used as a template to search for TOR-mediated rapamycin and caffeine signaling paths. We scored the paths passing from at least one component of TOR Complex 1 or 2 (TORC1/TORC2) using the co-expression levels of the genes in the transcriptome data of the cells grown in the presence of rapamycin or caffeine. The resultant network revealed seven hitherto unannotated proteins, namely, Atg14p, Rim20p, Ret2p, Spt21p, Ylr257wp, Ymr295cp, and Ygr017wp, as potential components of TOR-mediated rapamycin and caffeine signaling in yeast. Among these proteins, we suggest further deciphering of the role of Ylr257wp will be particularly informative in the future because it was the only protein whose removal from the constructed network hindered the signal transduction to the TORC1 effector kinase Npr1p. In conclusion, this study underlines the value of network-based multiomics integrative data analysis in discovering previously unidentified components of the signaling networks by revealing potential components of TOR signaling for future experimental validation.

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. METHODS: Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture. RESULTS: Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition. CONCLUSION: Oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

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. Modular analysis of the active sub-network has also revealed the induction of the genes significantly associated with nucleosome assembly/disassembly and DNA repair in response to doxorubicin. Furthermore, an extensive re-wiring of the metabolism was observed. In addition to glycolysis, and sulfate assimilation, several pathways related to ribosome biogenesis/translation, amino acid biosynthesis, nucleotide biosynthesis, de novo IMP biosynthesis and one-carbon metabolism were significantly repressed. Pentose phosphate pathway, MAPK signaling pathway biological processes associated with meiosis and sporulation were found to be induced in response to long-term exposure to doxorubicin in yeast cells.

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. ATX1 deletants were allowed to recover full respiratory capacity in the presence of excess copper in growth environment. The present study revealed that iron ion homeostasis was not significantly affected by the absence of ATX1 either at the transcriptional or metabolic levels, suggesting other possible roles for Atx1p in addition to its function as a chaperone in copper-dependent iron absorption. The analysis of the transcriptomic response of atx1∆/atx1∆ and its integration with the genetic interaction network highlighted for the first time, the possible role of ATX1 in cell cycle regulation, likewise its mammalian counterpart ATOX1, which was reported to play an important role in the copper-stimulated proliferation of non-small lung cancer cells. CONCLUSIONS: The present finding revealed the dispensability of Atx1p for the transfer of copper ions to Ccc2p and highlighted its possible role in the cell cycle regulation. The results also showed the potential of Saccharomyces cerevisiae as a model organism in studying the capacity of ATOX1 as a therapeutic target for lung cancer therapy.

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. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

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. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. AVAILABILITY AND IMPLEMENTATION: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. CONTACT: sgo24@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. In the present study, an integrative system based approach was used to investigate the global transcriptional changes in these two ethanol tolerant strains in response to ethanol and hence to elucidate the mechanisms leading to the observed tolerant phenotypes. In addition to strain specific biological processes, a number of common and already reported biological processes were found to be affected in the reference and both ethanol tolerant strains. However, the integrative analysis of the transcriptome with the transcriptional regulatory network and the ethanol tolerance network revealed that each ethanol tolerant strain had a specific organization of the transcriptomic response. Transcription factors around which most important changes occur were determined and active subnetworks in response to ethanol and functional clusters were identified in all strains.

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. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.

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 Saccharomyces cerevisiae, 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. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast's biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.

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. Protein-protein interaction (PPI) network associated with ethanol tolerance (tETN) was reconstructed by integrating PPI data with Gene Ontology (GO) terms. Modular analysis of the constructed networks revealed genes with no previously reported experimental evidence related to ethanol tolerance and resulted in the identification of 17 genes with previously unknown biological functions. We have randomly selected four of these genes and deletion strains of two genes (YDR307W and YHL042W) were found to exhibit improved tolerance to ethanol when compared to wild type strain. The genome-wide transcriptomic response of yeast cells to the deletions of YDR307W and YHL042W in the absence of ethanol revealed that the deletion of YDR307W and YHL042W genes resulted in the transcriptional re-programming of the metabolism resulting from a mis-perception of the nutritional environment. Yeast cells perceived an excess amount of glucose and a deficiency of methionine or sulfur in the absence of YDR307W and YHL042W, respectively, possibly resulting from a defect in the nutritional sensing and signaling or transport mechanisms. Mutations leading to an increase in ribosome biogenesis were found to be important for the improvement of ethanol tolerance. Modulations of chronological life span were also identified to contribute to ethanol tolerance in yeast. CONCLUSIONS: The system based network approach developed allows the identification of novel gene targets for improved ethanol tolerance and supports the highly complex nature of ethanol tolerance in yeast.

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. Our results show how cells rearrange their metabolism to cope with the problems that arise from the loss of these drug-resistance genes, which likely evolved to combat chemical attack from bacterial or fungal competitors. This is achieved through the accumulation of intracellular glucose, glycerol, and inorganic phosphate, as well as by repurposing genes that are known to function in other parts of metabolism in order to minimise the effects of toxic compounds.

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. cerevisiae cells. The integrative analysis of the transcriptome with the interactome and regulome further elucidated the pathways affected by the disturbance of copper homeostasis. The results demonstrated that iron homeostasis is disturbed in the absence of CCC2 under copper deficient conditions and also revealed the importance of this gene in the maintenance of iron homeostasis under high copper conditions. NAD(+) metabolism was observed to be affected both by the deletion of CCC2 and the level of bio-available extracellular copper. The regulation of glucose transporters was also affected in the absence of CCC2 and a starvation-like response was observed in a copper level dependent manner. Alterations in the amino acid metabolism and specifically in the arginine metabolic process observed at the transcriptional level provided further support through the integration of the metabolomic data. This study also highlighted pyridoxine deficiency caused by the absence of CCC2. The observation of the improvement in the respiratory capacity of CCC2 deleted cells by supplementation with pyridoxine as well as with nicotinic acid may shed light on novel therapeutic interventions for Wilson and Menkes diseases.

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. The functional modules in this network were scored and evaluated in terms of shared pathways, co-localization, co-expression and associations with similar diseases. The assembly of top scoring overlapping members in the functional modules revealed that, along with the well known biological pathways, circadian rhythm, diverse actions of nuclear receptors in steroid and retinoic acid metabolisms have significant occurrence in the pathophysiology of the disease. The disease's association with other metabolic and neuromuscular disorders was established through shared proteins. Nuclear receptor NRIP1 has a pivotal role in lipid and carbohydrate metabolism, indicating the need to investigate subsequent effects of NRIP1 on Type 2 diabetes. Our study also revealed that CREB binding protein (CREBBP) and cardiotrophin-1 (CTF1) have suggestive roles in linking Type 2 diabetes and neuromuscular diseases.

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. PRINCIPAL FINDINGS: In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in Saccharomyces cerevisiae was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (TPI1, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes ACT1 and TDH3. The most stable reference genes from this pool were determined as TPI1, FBA1, CDC19 and ACT1 in response to a perturbation in the amount of available glucose and as FBA1, TDH3, CCW12 and ACT1 in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, HAP4 and MEP2, in response to relaxation from glucose and ammonium limitations, respectively. CONCLUSIONS: A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest.

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. Selective up- or down regulation of a single member of a paralogous gene pair during the response to the relaxation from nutritional limitation was identified indicating a differentiation of functions among paralogs. Our study highlighted the role of inosine accumulation and recycling in energy homeostasis and indicated possible bottlenecks in the process.

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. cerevisiae in response to the sudden and transient removal of either carbon or nitrogen limitation has been studied by following both the short- and long-term changes in yeast's transcriptomic profiles. RESULTS: The study, which spans timescales from seconds to hours, has revealed the hierarchy of metabolic and genetic regulatory switches that allow yeast to adapt to, and recover from, a pulse of a previously limiting nutrient. At the transcriptome level, a glucose impulse evoked significant changes in the expression of genes concerned with glycolysis, carboxylic acid metabolism, oxidative phosphorylation, and nucleic acid and sulphur metabolism. In ammonium-limited cultures, an ammonium impulse resulted in the significant changes in the expression of genes involved in nitrogen metabolism and ion transport. Although both perturbations evoked significant changes in the expression of genes involved in the machinery and process of protein synthesis, the transcriptomic response was delayed and less complex in the case of an ammonium impulse. Analysis of the regulatory events by two different system-level, network-based approaches provided further information about dynamic organization of yeast cells as a response to a nutritional change. CONCLUSIONS: The study provided important information on the temporal organization of transcriptomic organization and underlying regulatory events as a response to both carbon and nitrogen impulse. It has also revealed the importance of a long-term dynamic analysis of the response to the relaxation of a nutritional limitation to understand the molecular basis of the cells' dynamic behaviour.

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. Integration of CFN with bibliomics showed that 227 out of 566 functional modules are significantly associated with one or more diseases. Analysis of functional modules revealed the possible regulatory roles of SP1 and CXCL12 in the pathogenesis of cardiovascular disease (CVD) and modulation of their activities may be considered as potential therapeutic tools. The integration of CFN with bibliomics also indicated significant relations of CVD with other complex disorders. In a stratified map the members of 227 functional modules and 58 diseases in 15 disease classes were combined. In this map, leprosy, listeria monocytogenes, myasthenia, hemorrhagic diathesis and Protein S deficiency, which were not previously reported to be associated with CVD, showed significant associations. Several cancers arising from epithelial cells were also found to be linked to other diseases through hub proteins, VEGFA and PTGS2.