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

An excess of glycolytic enzymes under glucose-limited conditions may enable Saccharomyces cerevisiae to adapt to nutrient availability

Microorganisms, including the budding yeast Saccharomyces cerevisiae, express glycolytic proteins to a maximal capacity that (largely) exceeds the actual flux through the enzymes, especially at low growth rates. An open question is if this apparent expression level is really an overcapacity, or maintains the (optimal) enzyme capacity needed to carry flux at (very) low substrate availability. Here, we use computational modelling to suggest that yeast maintains a genuine excess of glycolytic enzymes at low specific growth rates. During fast fermentative growth at high glucose levels, the observed expression of the glycolytic enzymes matched the predicted optimal levels. We suggest that the excess glycolytic capacity at low glucose levels is a preparatory strategy in the adaptation to sugar fluctuations in the environment.

Multi-Omics Analysis of Multiple Glucose-Sensing Receptor Systems in Yeast

The yeast Saccharomyces cerevisiae has long been used to produce alcohol from glucose and other sugars. While much is known about glucose metabolism, relatively little is known about the receptors and signaling pathways that indicate glucose availability. Here, we compare the two glucose receptor systems in S. cerevisiae. The first is a heterodimer of transporter-like proteins (transceptors), while the second is a seven-transmembrane receptor coupled to a large G protein (Gpa2) that acts in coordination with two small G proteins (Ras1 and Ras2). Through comprehensive measurements of glucose-dependent transcription and metabolism, we demonstrate that the two receptor systems have distinct roles in glucose signaling: the G-protein-coupled receptor directs carbohydrate and energy metabolism, while the transceptors regulate ancillary processes such as ribosome, amino acids, cofactor and vitamin metabolism. The large G-protein transmits the signal from its cognate receptor, while the small G-protein Ras2 (but not Ras1) integrates responses from both receptor pathways. Collectively, our analysis reveals the molecular basis for glucose detection and the earliest events of glucose-dependent signal transduction in yeast.

Methylated PP2A stabilizes Gcn4 to enable a methionine-induced anabolic program

Methionine, through S-adenosylmethionine, activates a multifaceted growth program in which ribosome biogenesis, carbon metabolism, and amino acid and nucleotide biosynthesis are induced. This growth program requires the activity of the Gcn4 transcription factor (called ATF4 in mammals), which facilitates the supply of metabolic precursors that are essential for anabolism. However, how Gcn4 itself is regulated in the presence of methionine is unknown. Here, we discover that Gcn4 protein levels are increased by methionine, despite conditions of high cell growth and translation (in which the roles of Gcn4 are not well-studied). We demonstrate that this mechanism of Gcn4 induction is independent of transcription, as well as the conventional Gcn2/eIF2α-mediated increased translation of Gcn4. Instead, when methionine is abundant, Gcn4 phosphorylation is decreased, which reduces its ubiquitination and therefore degradation. Gcn4 is dephosphorylated by the protein phosphatase 2A (PP2A); our data show that when methionine is abundant, the conserved methyltransferase Ppm1 methylates and alters the activity of the catalytic subunit of PP2A, shifting the balance of Gcn4 toward a dephosphorylated, stable state. The absence of Ppm1 or the loss of the PP2A methylation destabilizes Gcn4 even when methionine is abundant, leading to collapse of the Gcn4-dependent anabolic program. These findings reveal a novel, methionine-dependent signaling and regulatory axis. Here methionine directs the conserved methyltransferase Ppm1 via its target phosphatase PP2A to selectively stabilize Gcn4. Through this, cells conditionally modify a major phosphatase to stabilize a metabolic master regulator and drive anabolism.

Genome-scale reconstruction of Gcn4/ATF4 networks driving a growth program

Growth and starvation are considered opposite ends of a spectrum. To sustain growth, cells use coordinated gene expression programs and manage biomolecule supply in order to match the demands of metabolism and translation. Global growth programs complement increased ribosomal biogenesis with sufficient carbon metabolism, amino acid and nucleotide biosynthesis. How these resources are collectively managed is a fundamental question. The role of the Gcn4/ATF4 transcription factor has been best studied in contexts where cells encounter amino acid starvation. However, high Gcn4 activity has been observed in contexts of rapid cell proliferation, and the roles of Gcn4 in such growth contexts are unclear. Here, using a methionine-induced growth program in yeast, we show that Gcn4/ATF4 is the fulcrum that maintains metabolic supply in order to sustain translation outputs. By integrating matched transcriptome and ChIP-Seq analysis, we decipher genome-wide direct and indirect roles for Gcn4 in this growth program. Genes that enable metabolic precursor biosynthesis indispensably require Gcn4; contrastingly ribosomal genes are partly repressed by Gcn4. Gcn4 directly binds promoter-regions and transcribes a subset of metabolic genes, particularly driving lysine and arginine biosynthesis. Gcn4 also globally represses lysine and arginine enriched transcripts, which include genes encoding the translation machinery. The Gcn4 dependent lysine and arginine supply thereby maintains the synthesis of the translation machinery. This is required to maintain translation capacity. Gcn4 consequently enables metabolic-precursor supply to bolster protein synthesis, and drive a growth program. Thus, we illustrate how growth and starvation outcomes are both controlled using the same Gcn4 transcriptional outputs that function in distinct contexts.

Candida albicans Culture, Cell Harvesting, and Total RNA Extraction

Transcriptional analysis has become a cornerstone of biological research, and with the advent of cheaper and more efficient sequencing technology over the last decade, there exists a need for high-yield and efficient RNA extraction techniques. Fungi such as the human pathogen Candida albicans present a unique obstacle to RNA purification in the form of the tough cell wall made up of many different components such as chitin that are resistant to many common mammalian or bacterial cell lysis methods. Typical in vitro C. albicans cell harvesting methods can be time consuming and expensive if many samples are being processed with multiple opportunities for product loss or sample variation. Harvesting cells via vacuum filtration rather than centrifugation cuts down on time before the cells are frozen and therefore the available time for the RNA expression profile to change. Vacuum filtration is preferred for C. albicans for two main reasons: cell lysis is faster on non-pelleted cells due to increased exposed surface area, and filamentous cells are difficult to pellet in the first place unlike yeast or bacterial cells. Using mechanical cell lysis, by way of zirconia/silica beads, cuts down on time for processing as well as overall cost compared to enzymatic treatments. Overall, this method is a fast, efficient, and high-yield way to extract total RNA from in vitro cultures of C. albicans.

In Vivo Analysis of NH4+ Transport and Central Nitrogen Metabolism in Saccharomyces cerevisiae during Aerobic Nitrogen-Limited Growth

Ammonium is the most common N source for yeast fermentations. Although its transport and assimilation mechanisms are well documented, there have been only a few attempts to measure the in vivo intracellular concentration of ammonium and assess its impact on gene expression. Using an isotope dilution mass spectrometry (IDMS)-based method, we were able to measure the intracellular ammonium concentration in N-limited aerobic chemostat cultivations using three different N sources (ammonium, urea, and glutamate) at the same growth rate (0.05 h^-1). The experimental results suggest that, at this growth rate, a similar concentration of intracellular (IC) ammonium, about 3.6 mmol NH₄⁺/liter_IC, is required to supply the reactions in the central N metabolism, independent of the N source. Based on the experimental results and different assumptions, the vacuolar and cytosolic ammonium concentrations were estimated. Furthermore, we identified a futile cycle caused by NH₃ leakage into the extracellular space, which can cost up to 30% of the ATP production of the cell under N-limited conditions, and a futile redox cycle between Gdh1 and Gdh2 reactions. Finally, using shotgun proteomics with protein expression determined relative to a labeled reference, differences between the various environmental conditions were identified and correlated with previously identified N compound-sensing mechanisms.IMPORTANCE In our work, we studied central N metabolism using quantitative approaches. First, intracellular ammonium was measured under different N sources. The results suggest that Saccharomyces cerevisiae cells maintain a constant NH₄⁺ concentration (around 3 mmol NH₄⁺/liter_IC), independent of the applied nitrogen source. We hypothesize that this amount of intracellular ammonium is required to obtain sufficient thermodynamic driving force. Furthermore, our calculations based on thermodynamic analysis of the transport mechanisms of ammonium suggest that ammonium is not equally distributed, indicating a high degree of compartmentalization in the vacuole. Additionally, metabolomic analysis results were used to calculate the thermodynamic driving forces in the central N metabolism reactions, revealing that the main reactions in the central N metabolism are far from equilibrium. Using proteomics approaches, we were able to identify major changes, not only in N metabolism, but also in C metabolism and regulation.

Bacterial metabolism in immediate response to nutritional perturbation with temporal and network view of metabolites

In this study, the initial propagation of metabolic perturbation in Escherichia coli was visualized to understand the dynamic characteristics of the metabolic pathways without the association of transcription alterations. E. coli cells were exposed to the sudden relief of glucose starvation, and time-dependent variances in metabolite balances were traced in the second scale. The acquired time-course data were represented by structural variations of the metabolite-metabolite correlation network. The initial correlation structure was altered immediately by the glucose pulse, followed by further structural variations within a few minutes. It was demonstrated that one metabolite temporally correlated with distinct metabolites with different timings, and such a behavior could imply a regulatory role for the metabolite in the metabolic network. Centrality analysis of the networks and partial correlation analysis indicated that preparation for growth and oxidative stress could be coupled as a structural property of the metabolic pathways.

Accurate Measurement of the in vivo Ammonium Concentration in Saccharomyces cerevisiae

Ammonium (NH₄⁺) is the most common N-source for yeast fermentations, and N-limitation is frequently applied to reduce growth and increase product yields. While there is significant molecular knowledge on NH₄⁺ transport and assimilation, there have been few attempts to measure the in vivo concentration of this metabolite. In this article, we present a sensitive and accurate analytical method to quantify the in vivo intracellular ammonium concentration in Saccharomycescerevisiae based on standard rapid sampling and metabolomics techniques. The method validation experiments required the development of a proper sample processing protocol to minimize ammonium production/consumption during biomass extraction by assessing the impact of amino acid degradation—an element that is often overlooked. The resulting cold chloroform metabolite extraction method, together with quantification using ultra high performance liquid chromatography-isotope dilution mass spectrometry (UHPLC-IDMS), was not only more sensitive than most of the existing methods but also more accurate than methods that use electrodes, enzymatic reactions, or boiling water or boiling ethanol biomass extraction because it minimized ammonium consumption/production during sampling processing and interference from other metabolites in the quantification of intracellular ammonium. Finally, our validation experiments showed that other metabolites such as pyruvate or 2-oxoglutarate (αKG) need to be extracted with cold chloroform to avoid measurements being biased by the degradation of other metabolites (e.g., amino acids).

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.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets.

Results

Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn.

Conclusions

The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0614-0) contains supplementary material, which is available to authorized users.

Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis

BACKGROUND

The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.

RESULTS

In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.

CONCLUSIONS

The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Bridging physiological and evolutionary time-scales in a gene regulatory network

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32,423 expressed sequences to drought and to abscisic acid (ABA) and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST ) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of the sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time-scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments.

Construction of a metabolome library for transcription factor-related single gene mutants of Saccharomyces cerevisiae

Transcription factors (TFs) play an important role in gene regulation, providing control for cells to adapt to ever changing environments and different physiological states. Although great effort has been taken to study TFs through DNA-protein binding and microarray gene expression experiments, the understanding of transcriptional regulation is still lacking, due to lack of information that links TF regulatory events and final phenotypic change. Here, we focused on metabolites as the final readouts of gene transcription process. We performed metabolite profiling of 154 Saccharomyces cerevisiae's single gene knockouts each defective in a gene encoding transcription factor and built a metabolome library consists of 84 metabolites with good reproducibility. Using the metabolome dataset, we obtained significant correlations and identified differential strains that exhibit altered metabolism compared to control. This work presents a novel metabolome dataset library which will be invaluable for researchers working on transcriptional regulation and yeast biology in general.

Metabolic and transcriptomic response of the wine yeast Saccharomyces cerevisiae strain EC1118 after an oxygen impulse under carbon-sufficient, nitrogen-limited fermentative conditions

During alcoholic fermentation, Saccharomyces cerevisiae is exposed to continuously changing environmental conditions, such as decreasing sugar and increasing ethanol concentrations. Oxygen, a critical nutrient to avoid stuck and sluggish fermentations, is only discretely available throughout the process after pump-over operation. In this work, we studied the physiological response of the wine yeast S. cerevisiae strain EC1118 to a sudden increase in dissolved oxygen, simulating pump-over operation. With this aim, an impulse of dissolved oxygen was added to carbon-sufficient, nitrogen-limited anaerobic continuous cultures. Results showed that genes related to mitochondrial respiration, ergosterol biosynthesis, and oxidative stress, among other metabolic pathways, were induced after the oxygen impulse. On the other hand, mannoprotein coding genes were repressed. The changes in the expression of these genes are coordinated responses that share common elements at the level of transcriptional regulation. Beneficial and detrimental effects of these physiological processes on wine quality highlight the dual role of oxygen in 'making or breaking wines'. These findings will facilitate the development of oxygen addition strategies to optimize yeast performance in industrial fermentations.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

L-Proline uptake in Saccharomyces cerevisiae mitochondria can contribute to bioenergetics during nutrient stress as alternative mitochondrial fuel

L-Proline (pyrrolidine-2-carboxylic acid) is a distinctive metabolite both biochemically and biotechnologically and is currently recognized to have a cardinal role in gene expression and cellular signaling pathways in stress response. Proline-fueled mitochondrial metabolism involves the oxidative conversion of L-Proline to L-Glutamate in two enzymatic steps by means of Put1p and Put2p that help Saccharomyces cerevisiae to respond to changes in the nutritional environment by initiating the breakdown of L-Proline as a source for nitrogen, carbon, and energy. Compartmentalization of L-Proline catabolic pathway implies that extensive L-Proline transport must take place between the cytosol where its biogenesis via Pro1p, Pro2p, Pro3p occurs and mitochondria. L-Proline uptake in S. cerevisiae purified and active mitochondria was investigated by swelling experiments, oxygen uptake and fluorimetric measurement of a membrane potential generation (ΔΨ). Our results strongly suggest that L-Proline uptake occurs via a carried-mediated process as demonstrated by saturation kinetics and experiments with N-ethylmaleimide, a pharmacological compound that is a cysteine-modifying reagent in hydrophobic protein domains and that inhibited mitochondrial transport. Plasticity of S. cerevisiae cell biochemistry according to background fluctuations is an important factor of adaptation to stress. Thus L-Proline → Glutamate route feeds Krebs cycle providing energy and anaplerotic carbon for yeast survival.

Linking RNA measurements and proteomics with genome-scale models

Genome-scale metabolic models (GMMs) have been recognized as being powerful tools for capturing system-wide metabolic phenomena and connecting those phenomena to underlying genetic and regulatory changes. By formalizing and codifying the relationship between the levels of gene expression, protein concentration, and reaction flux, metabolic models are able to translate changes in gene expression to their effects on the metabolic network. A number of methods are then available to interpret how those changes are manifest in the metabolic flux distribution. In addition to discussing how gene expression datasets can be interpreted in the context of a metabolic model, this chapter discusses two of the most common methods for analyzing the resulting metabolic network. The chapter begins by demonstrating how a typical microarray dataset can be processed for incorporation into a GMM of the yeast Saccharomyces cerevisiae. Once the expression states of the reactions in the model are available, the method of directly trimming the metabolic model by removing or constraining reactions with low expression states is demonstrated. This is the simplest and most direct approach to interpret gene expression states, but it is prone to overvaluing the effects of down regulation and it can propagate false negative errors. We therefore also include a more advanced method that uses a mixed-integer linear programming optimization to find a flux distribution that maximizes agreement with global gene expression states. Sample MATLAB code for use with the COBRA toolbox is provided for all methods used.

Gene network requirements for regulation of metabolic gene expression to a desired state

Gene circuits that control metabolism should restore metabolic functions upon environmental changes. Whether gene networks are capable of steering metabolism to optimal states is an open question. Here we present a method to identify such optimal gene networks. We show that metabolic network optimisation over a range of environments results in an input-output relationship for the gene network that guarantees optimal metabolic states. Optimal control is possible if the gene network can achieve this input-output relationship. We illustrate our approach with the best-studied regulatory network in yeast, the galactose network. We find that over the entire range of external galactose concentrations, the regulatory network is able to optimally steer galactose metabolism. Only a few gene network parameters affect this optimal regulation. The other parameters can be tuned independently for optimisation of other functions, such as fast and low-noise gene expression. This study highlights gene network plasticity, evolvability, and modular functionality.

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.