Transcriptional regulation of respiration in yeast metabolizing differently repressive carbon substrates

Proteome-wide quantitative multiplexed profiling of protein expression: carbon-source dependency in Saccharomyces cerevisiae

A mass spectrometry–based tandem mass tag 9-plex strategy was used to determine alterations in relative protein abundance due to three carbon sources—glucose, galactose, and raffinose. More than 4700 proteins were quantified across all nine samples; 1003 demonstrated statistically significant differences in abundance in at least one condition.

The global proteomic alterations in the budding yeast Saccharomyces cerevisiae due to differences in carbon sources can be comprehensively examined using mass spectrometry–based multiplexing strategies. In this study, we investigate changes in the S. cerevisiae proteome resulting from cultures grown in minimal media using galactose, glucose, or raffinose as the carbon source. We used a tandem mass tag 9-plex strategy to determine alterations in relative protein abundance due to a particular carbon source, in triplicate, thereby permitting subsequent statistical analyses. We quantified more than 4700 proteins across all nine samples; 1003 proteins demonstrated statistically significant differences in abundance in at least one condition. The majority of altered proteins were classified as functioning in metabolic processes and as having cellular origins of plasma membrane and mitochondria. In contrast, proteins remaining relatively unchanged in abundance included those having nucleic acid–related processes, such as transcription and RNA processing. In addition, the comprehensiveness of the data set enabled the analysis of subsets of functionally related proteins, such as phosphatases, kinases, and transcription factors. As a resource, these data can be mined further in efforts to understand better the roles of carbon source fermentation in yeast metabolic pathways and the alterations observed therein, potentially for industrial applications, such as biofuel feedstock production.

Dynamic metabolic flux analysis using B-splines to study the effects of temperature shift on CHO cell metabolism

Metabolic flux analysis (MFA) is widely used to estimate intracellular fluxes. Conventional MFA, however, is limited to continuous cultures and the mid-exponential growth phase of batch cultures. Dynamic MFA (DMFA) has emerged to characterize time-resolved metabolic fluxes for the entire culture period. Here, the linear DMFA approach was extended using B-spline fitting (B-DMFA) to estimate mass balanced fluxes. Smoother fits were achieved using reduced number of knots and parameters. Additionally, computation time was greatly reduced using a new heuristic algorithm for knot placement. B-DMFA revealed that Chinese hamster ovary cells shifted from 37 °C to 32 °C maintained a constant IgG volume-specific productivity, whereas the productivity for the controls peaked during mid-exponential growth phase and declined afterward. The observed 42% increase in product titer at 32 °C was explained by a prolonged cell growth with high cell viability, a larger cell volume and a more stable volume-specific productivity.

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New dynamic MFA framework using B-spline (B-DMFA) generates smooth fit.

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B-DMFA performs better than linear DMFA when fitting fast dynamic changes.

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Heuristic algorithm for knot placement dramatically reduced computation time.

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Temperature shifted cultures maintain a constant IgG volume specific productivity.

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CHO cells shifted to 32 °C have a 42% higher IgG titer due to larger cell volume.

Protein degradation and dynamic tRNA thiolation fine-tune translation at elevated temperatures

Maintenance of protein quality control has implications in various processes such as neurodegeneration and ageing. To investigate how environmental insults affect this process, we analysed the proteome of yeast continuously exposed to mild heat stress. In agreement with previous transcriptomics studies, amongst the most marked changes, we found up-regulation of cytoprotective factors; a shift from oxidative phosphorylation to fermentation; and down-regulation of translation. Importantly, we also identified a novel, post-translationally controlled, component of the heat shock response. The abundance of Ncs2p and Ncs6p, two members of the URM1 pathway responsible for the thiolation of wobble uridines in cytoplasmic tRNAs tK^UUU, tQ^UUG and tE^UUC, is down-regulated in a proteasomal dependent fashion. Using random forests we show that this results in differential translation of transcripts with a biased content for the corresponding codons. We propose that the role of this pathway in promoting catabolic and inhibiting anabolic processes, affords cells with additional time and resources needed to attain proper protein folding under periods of stress.

Proteasomes, Sir2, and Hxk2 form an interconnected aging network that impinges on the AMPK/Snf1-regulated transcriptional repressor Mig1

Elevated proteasome activity extends lifespan in model organisms such as yeast, worms and flies. This pro-longevity effect might be mediated by improved protein homeostasis, as this protease is an integral module of the protein homeostasis network. Proteasomes also regulate cellular processes through temporal and spatial degradation of signaling pathway components. Here we demonstrate that the regulatory function of the proteasome plays an essential role in aging cells and that the beneficial impact of elevated proteasome capacity on lifespan partially originates from deregulation of the AMPK signaling pathway. Proteasome-mediated lifespan extension activity was carbon-source dependent and cells with enhancement proteasome function exhibited increased respiratory activity and oxidative stress response. These findings suggested that the pro-aging impact of proteasome upregulation might be related to changes in the metabolic state through a premature induction of respiration. Deletion of yeast AMPK, SNF1, or its activator SNF4 abrogated proteasome-mediated lifespan extension, supporting this hypothesis as the AMPK pathway regulates metabolism. We found that the premature induction of respiration in cells with increased proteasome activity originates from enhanced turnover of Mig1, an AMPK/Snf1 regulated transcriptional repressor that prevents the induction of genes required for respiration. Increasing proteasome activity also resulted in partial relocation of Mig1 from the nucleus to the mitochondria. Collectively, the results argue for a model in which elevated proteasome activity leads to the uncoupling of Snf1-mediated Mig1 regulation, resulting in a premature activation of respiration and thus the induction of a mitohormetic response, beneficial to lifespan. In addition, we observed incorrect Mig1 localization in two other long-lived yeast aging models: cells that overexpress SIR2 or deleted for the Mig1-regulator HXK2. Finally, compromised proteasome function blocks lifespan extension in both strains. Thus, our findings suggest that proteasomes, Sir2, Snf1 and Hxk2 form an interconnected aging network that controls metabolism through coordinated regulation of Mig1.

Advanced cellular age is associated with decreased efficiency of the proteostasis network. The proteasome, a protease in the cytoplasm and nuclei of eukaryotic cells, is an important component of this network. Recent studies demonstrate that increased proteasome capacity has a positive impact on longevity. The underlying mechanisms, however, have not been fully identified. Here we report that proteasomes are involved in regulating the AMP-activated kinase (AMPK) pathway and thus participate in correct metabolic adaptation. We find that Mig1, a transcriptional repressor downstream of yeast AMPK, Snf1, is a proteasome target and a negative regulator of lifespan. Increased proteasome activity results in enhanced turnover and incorrect localization of Mig1. The reduced Mig1 levels result in the induction of respiration and upregulation of the oxidative stress response. Premature Mig1 inactivation is also observed in two additional long-lived strains that overexpress SIR2 or are deleted for HXK2 and lifespan extension in both strains requires correct proteasome function. Our results uncover an interconnected network comprised of the proteasome, Sir2 and AMPK/Hxk2 signaling that impacts longevity through regulation of Mig1 and modulates respiratory metabolism. Mechanistic information on the cross-communication between these pathways is expected to facilitate the identification of novel pro-aging interventions.

Molecular characterization of hap complex components responsible for methanol-inducible gene expression in the methylotrophic yeast Candida boidinii

We identified genes encoding components of the Hap complex, CbHAP2, CbHAP3, and CbHAP5, as transcription factors regulating methanol-inducible gene expression in the methylotrophic yeast Candida boidinii. We found that the Cbhap2Δ, Cbhap3Δ, and Cbhap5Δ gene-disrupted strains showed severe growth defects on methanol but not on glucose and nonfermentable carbon sources such as ethanol and glycerol. In these disruptants, the transcriptional activities of methanol-inducible promoters were significantly decreased compared to those of the wild-type strain, indicating that CbHap2p, CbHap3p, and CbHap5p play indispensable roles in methanol-inducible gene expression. Further molecular and biochemical analyses demonstrated that CbHap2p, CbHap3p, and CbHap5p localized to the nucleus and bound to the promoter regions of methanol-inducible genes regardless of the carbon source, and heterotrimer formation was suggested to be necessary for binding to DNA. Unexpectedly, distinct from Saccharomyces cerevisiae, the Hap complex functioned in methanol-specific induction rather than glucose derepression in C. boidinii. Our results shed light on a novel function of the Hap complex in methanol-inducible gene expression in methylotrophic yeasts.

Multi-tasking of biosynthetic and energetic functions of glycolysis explained by supply and demand logic

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free-energy carriers, and precursors for biosynthesis and stress-related compounds. Here we will review the regulation of multi-tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components.

Stress-induced nuclear RNA degradation pathways regulate yeast bromodomain factor 2 to promote cell survival

Bromodomain proteins are key regulators of gene expression. How the levels of these factors are regulated in specific environmental conditions is unknown. Previous work has established that expression of yeast Bromodomain factor 2 (BDF2) is limited by spliceosome-mediated decay (SMD). Here we show that BDF2 is subject to an additional layer of post-transcriptional control through RNase III-mediated decay (RMD). We found that the yeast RNase III Rnt1p cleaves a stem-loop structure within the BDF2 mRNA to down-regulate its expression. However, these two nuclear RNA degradation pathways play distinct roles in the regulation of BDF2 expression, as we show that the RMD and SMD pathways of the BDF2 mRNA are differentially activated or repressed in specific environmental conditions. RMD is hyper-activated by salt stress and repressed by hydroxyurea-induced DNA damage while SMD is inactivated by salt stress and predominates during DNA damage. Mutations of cis-acting signals that control SMD and RMD rescue numerous growth defects of cells lacking Bdf1p, and show that SMD plays an important role in the DNA damage response. These results demonstrate that specific environmental conditions modulate nuclear RNA degradation pathways to control BDF2 expression and Bdf2p-mediated gene regulation. Moreover, these results show that precise dosage of Bromodomain factors is essential for cell survival in specific environmental conditions, emphasizing their importance for controlling chromatin structure and gene expression in response to environmental stress.

Cells adapt to changes in the environment through modulating gene expression at both the RNA and protein levels. RNA degradation plays a central role in this adaption response, by controlling the stability of specific mRNAs to optimize protein production in different conditions. In this study, we show that the gene encoding Bromodomain Factor 2 (BDF2) is tightly regulated according to environmental conditions by two distinct RNA degradation mechanisms. We show that these RNA degradation pathways are critical for cell growth in specific conditions. Our study suggests that environmental modulation of nuclear RNA degradation pathways is a previously unappreciated aspect of gene expression control.

Coordination of microbial metabolism

Beyond fuelling cellular activities with building blocks and energy, metabolism also integrates environmental conditions into intracellular signals. The underlying regulatory network is complex and multifaceted: it ranges from slow interactions, such as changing gene expression, to rapid ones, such as the modulation of protein activity via post-translational modification or the allosteric binding of small molecules. In this Review, we outline the coordination of common metabolic tasks, including nutrient uptake, central metabolism, the generation of energy, the supply of amino acids and protein synthesis. Increasingly, a set of key metabolites is recognized to control individual regulatory circuits, which carry out specific functions of information input and regulatory output. Such a modular view of microbial metabolism facilitates an intuitive understanding of the molecular mechanisms that underlie cellular decision making.

Exploiting cell metabolism for biocatalytic whole-cell transamination by recombinant Saccharomyces cerevisiae

The potential of Saccharomyces cerevisiae for biocatalytic whole-cell transamination was investigated using the kinetic resolution of racemic 1-phenylethylamine (1-PEA) to (R)-1-PEA as a model reaction. As native yeast do not possess any ω-transaminase activity for the reaction, a recombinant yeast biocatalyst was constructed by overexpressing the gene coding for vanillin aminotransferase from Capsicum chinense. The yeast-based biocatalyst could use glucose as the sole co-substrate for the supply of amine acceptor via cell metabolism. In addition, the biocatalyst was functional without addition of the co-factor pyridoxal-5′-phosphate (PLP), which can be explained by a high inherent cellular capacity to sustain PLP-dependent reactions in living cells. In contrast, external PLP supplementation was required when cell viability was low, as it was the case when using pyruvate as a co-substrate. Overall, the results indicate a potential for engineered S. cerevisiae as a biocatalyst for whole-cell transamination and with glucose as the only co-substrate for the supply of amine acceptor and PLP.

Lost in transition: start-up of glycolysis yields subpopulations of nongrowing cells

Cells need to adapt to dynamic environments. Yeast that fail to cope with dynamic changes in the abundance of glucose can undergo growth arrest. We show that this failure is caused by imbalanced reactions in glycolysis, the essential pathway in energy metabolism in most organisms. The imbalance arises largely from the fundamental design of glycolysis, making this state of glycolysis a generic risk. Cells with unbalanced glycolysis coexisted with vital cells. Spontaneous, nongenetic metabolic variability among individual cells determines which state is reached and, consequently, which cells survive. Transient ATP (adenosine 5'-triphosphate) hydrolysis through futile cycling reduces the probability of reaching the imbalanced state. Our results reveal dynamic behavior of glycolysis and indicate that cell fate can be determined by heterogeneity purely at the metabolic level.

Customization of ¹³C-MFA strategy according to cell culture system

(13)C-MFA is far from being a simple assay for quantifying metabolic activity. It requires considerable up-front experimental planning and familiarity with the cell culture system in question, as well as optimized analytics and adequate computation frameworks. The success of a (13)C-MFA experiment is ultimately rated by the ability to accurately quantify the flux of one or more reactions of interest. In this chapter, we describe the different (13)C-MFA strategies that have been developed for the various fermentation or cell culture systems, as well as the limitations of the respective strategies. The strategies are affected by many factors and the (13)C-MFA modeling and experimental strategy must be tailored to conditions. The prevailing philosophy in the computation process is that any metabolic processes that produce significant systematic bias in the labeling pattern of the metabolites being measured must be described in the model. It is equally important to plan a labeling strategy by analytical screening or by heuristics.

Metabolic constraint-based refinement of transcriptional regulatory networks

There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10⁻¹⁷²), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10⁻¹⁴) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/

Cellular networks, such as metabolic and transcriptional regulatory networks (TRNs), do not operate independently but work together in unison to determine cellular phenotypes. Further, the phenotype and architecture of one network constrains the topology of other networks. Hence, it is critical to study network components and interactions in the context of the entire cell. Typically, efforts to reconstruct TRNs focus only on immediately proximal data such as gene co-expression and transcription factor (TF)-binding. Herein, we take a different strategy by linking candidate TRNs with the metabolic network to predict systems-level responses such as growth phenotypes of TF knockout strains, and compare predictions with experimental phenotype data to select amongst the candidate TRNs. Our approach goes beyond traditional data integration approaches for network inference and refinement by using a predictive network model (metabolism) to refine another network model (regulation) – thus providing an alternative avenue to this area of research. Understanding how the networks function together in a cell will pave the way for synthetic biology and has a wide-range of applications in biotechnology, drug discovery and diagnostics. Further we demonstrate how metabolic models can integrate and reconcile inconsistencies across different data-types.

Promoters maintain their relative activity levels under different growth conditions

Most genes change expression levels across conditions, but it is unclear which of these changes represents specific regulation and what determines their quantitative degree. Here, we accurately measured activities of ~900 S. cerevisiae and ~1800 E. coli promoters using fluorescent reporters. We show that in both organisms 60-90% of promoters change their expression between conditions by a constant global scaling factor that depends only on the conditions and not on the promoter's identity. Quantifying such global effects allows precise characterization of specific regulation-promoters deviating from the global scale line. These are organized into few functionally related groups that also adhere to scale lines and preserve their relative activities across conditions. Thus, only several scaling factors suffice to accurately describe genome-wide expression profiles across conditions. We present a parameter-free passive resource allocation model that quantitatively accounts for the global scaling factors. It suggests that many changes in expression across conditions result from global effects and not specific regulation, and provides means for quantitative interpretation of expression profiles.

Optimised protocols for the metabolic profiling of S. cerevisiae by 1H-NMR and HRMAS spectroscopy

An optimised extraction protocol for the analysis of Saccharomyces cerevisiae aqueous and organic metabolites by nuclear magnetic resonance spectroscopy that allows the identification and quantification of up to 50 different compounds is presented. The method was compared with other metabolic profiling protocols for S. cerevisiae, where generally different analytical techniques are applied for metabolite quantification. In addition, the analysis of intact S. cerevisiae cells by HRMAS was implemented for the first time as a complementary method. The optimised protocols were applied to study the metabolic effect of glucose and galactose on S. cerevisiae growth. Furthermore, the metabolic reaction of S. cerevisiae to osmotic stress has been studied.

Genetic and nongenetic determinants of cell growth variation assessed by high-throughput microscopy

In microbial populations, growth initiation and proliferation rates are major components of fitness and therefore likely targets of selection. We used a high-throughput microscopy assay, which enables simultaneous analysis of tens of thousands of microcolonies, to determine the sources and extent of growth rate variation in the budding yeast (Saccharomyces cerevisiae) in different glucose environments. We find that cell growth rates are regulated by the extracellular concentration of glucose as proposed by Monod (1949), but that significant heterogeneity in growth rates is observed among genetically identical individuals within an environment. Yeast strains isolated from different geographic locations and habitats differ in their growth rate responses to different glucose concentrations. Inheritance patterns suggest that the genetic determinants of growth rates in different glucose concentrations are distinct. In addition, we identified genotypes that differ in the extent of variation in growth rate within an environment despite nearly identical mean growth rates, providing evidence that alleles controlling phenotypic variability segregate in yeast populations. We find that the time to reinitiation of growth (lag) is negatively correlated with growth rate, yet this relationship is strain-dependent. Between environments, the respirative activity of individual cells negatively correlates with glucose abundance and growth rate, but within an environment respirative activity and growth rate show a positive correlation, which we propose reflects differences in protein expression capacity. Our study quantifies the sources of genetic and nongenetic variation in cell growth rates in different glucose environments with unprecedented precision, facilitating their molecular genetic dissection.

Kinetic isotope effects significantly influence intracellular metabolite (13) C labeling patterns and flux determination

Rigorous mathematical modeling of carbon-labeling experiments allows estimation of fluxes through the pathways of central carbon metabolism, yielding powerful information for basic scientific studies as well as for a wide range of applications. However, the mathematical models that have been developed for flux determination from (13) C labeling data have commonly neglected the influence of kinetic isotope effects on the distribution of (13) C label in intracellular metabolites, as these effects have often been assumed to be inconsequential. We have used measurements of the (13) C isotope effects on the pyruvate dehydrogenase enzyme from the literature to model isotopic fractionation at the pyruvate node and quantify the modeling errors expected to result from the assumption that isotope effects are negligible. We show that under some conditions kinetic isotope effects have a significant impact on the (13) C labeling patterns of intracellular metabolites, and the errors associated with neglecting isotope effects in (13) C-metabolic flux analysis models can be comparable in size to measurement errors associated with GC-MS. Thus, kinetic isotope effects must be considered in any rigorous assessment of errors in (13) C labeling data, goodness-of-fit between model and data, confidence intervals of estimated metabolic fluxes, and statistical significance of differences between estimated metabolic flux distributions.

Functioning of a metabolic flux sensor in Escherichia coli

Regulation of metabolic operation in response to extracellular cues is crucial for cells' survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular "metabolic flux" was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems ("flux sensors") in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this "flux-signaling metabolite" with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses.

The Hog1 SAPK controls the Rtg1/Rtg3 transcriptional complex activity by multiple regulatory mechanisms

The retrograde (RTG) pathway transcription factors Rtg1 and Rtg3 are shown to be targets of the Hog1 stress-activated protein kinase (SAPK). Hog1 acts on the RTG complex at multiple levels to mediate gene expression upon stress. The SAPK is required for the nuclear accumulation of the complex, the recruitment of the complex at RTG-responsive promoters, and the regulation of Rtg3 transcriptional activity.

Cells modulate expression of nuclear genes in response to alterations in mitochondrial function, a response termed retrograde (RTG) regulation. In budding yeast, the RTG pathway relies on Rtg1 and Rtg3 basic helix-loop-helix leucine Zipper transcription factors. Exposure of yeast to external hyperosmolarity activates the Hog1 stress-activated protein kinase (SAPK), which is a key player in the regulation of gene expression upon stress. Several transcription factors, including Sko1, Hot1, the redundant Msn2 and Msn4, and Smp1, have been shown to be directly controlled by the Hog1 SAPK. The mechanisms by which Hog1 regulates their activity differ from one to another. In this paper, we show that Rtg1 and Rtg3 transcription factors are new targets of the Hog1 SAPK. In response to osmostress, RTG-dependent genes are induced in a Hog1-dependent manner, and Hog1 is required for Rtg1/3 complex nuclear accumulation. In addition, Hog1 activity regulates Rtg1/3 binding to chromatin and transcriptional activity. Therefore Hog1 modulates Rtg1/3 complex activity by multiple mechanisms in response to stress. Overall our data suggest that Hog1, through activation of the RTG pathway, contributes to ensure mitochondrial function as part of the Hog1-mediated osmoadaptive response.

System-level insights into yeast metabolism by thermodynamic analysis of elementary flux modes

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis impossible using current methods. To gain more insight into the metabolic operation of S. cerevisiae we developed a new computational approach where we characterize the flux solution space by determining elementary flux modes (EFMs) that are subsequently classified as thermodynamically feasible or infeasible on the basis of experimental metabolome data. This allows us to provably rule out the contribution of certain EFMs to the in vivo flux distribution. From the 71 million EFMs in a medium size metabolic network of S. cerevisiae, we classified 54% as thermodynamically feasible. By comparing the thermodynamically feasible and infeasible EFMs, we could identify reaction combinations that span the cytosol and mitochondrion and, as a system, cannot operate under the investigated glucose batch conditions. Besides conclusions on single reactions, we found that thermodynamic constraints prevent the import of redox cofactor equivalents into the mitochondrion due to limits on compartmental cofactor concentrations. Our novel approach of incorporating quantitative metabolite concentrations into the analysis of the space of all stoichiometrically feasible flux distributions allows generating new insights into the system-level operation of the intracellular fluxes without making assumptions on metabolic objectives of the cell.

Fluxes in metabolic pathways are a highly informative aspect of an organism's phenotype. The experimental determination of such fluxes is well established and has proven very useful. To address some of the limitations of experimental flux analysis, such as when the cell is divided in multiple compartments, stoichiometric modeling provides a valuable addition. The approach that we take is based on stoichiometric modeling where we consider the thermodynamic feasibility of many different possible routes through the metabolic network of Saccharomyces cerevisiae using experimentally determined metabolite concentrations. We show that next to conclusions on single biochemical reactions in the metabolic network, we obtain system-level insights on thermodynamically infeasible flux patterns. We found that the compartmental concentrations of and NADH are the causes for the system-level infeasibilities. With the current advances in quantitative metabolomics and biochemical thermodynamics, we envision that the presented method will help gaining more insight into complex metabolic systems.

Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions

Background

Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution.

Results

The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated ¹³C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells.

Conclusions

The analysis of compartmentation of metabolic networks based on the measured ¹³C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured ¹³C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis.

Chemical and Synthetic Genetic Array Analysis Identifies Genes that Suppress Xylose Utilization and Fermentation in Saccharomyces cerevisiae

Though highly efficient at fermenting hexose sugars, Saccharomyces cerevisiae has limited ability to ferment five-carbon sugars. As a significant portion of sugars found in cellulosic biomass is the five-carbon sugar xylose, S. cerevisiae must be engineered to metabolize pentose sugars, commonly by the addition of exogenous genes from xylose fermenting fungi. However, these recombinant strains grow poorly on xylose and require further improvement through rational engineering or evolutionary adaptation. To identify unknown genes that contribute to improved xylose fermentation in these recombinant S. cerevisiae, we performed genome-wide synthetic interaction screens to identify deletion mutants that impact xylose utilization of strains expressing the xylose isomerase gene XYLA from Piromyces sp. E2 alone or with an additional copy of the endogenous xylulokinase gene XKS1. We also screened the deletion mutant array to identify mutants whose growth is affected by xylose. Our genetic network reveals that more than 80 nonessential genes from a diverse range of cellular processes impact xylose utilization. Surprisingly, we identified four genes, ALP1, ISC1, RPL20B, and BUD21, that when individually deleted improved xylose utilization of both S. cerevisiae S288C and CEN.PK strains. We further characterized BUD21 deletion mutant cells in batch fermentations and found that they produce ethanol even the absence of exogenous XYLA. We have demonstrated that the ability of laboratory strains of S. cerevisiae to utilize xylose as a sole carbon source is suppressed, which implies that S. cerevisiae may not require the addition of exogenous genes for efficient xylose fermentation.

Caloric restriction improves efficiency and capacity of the mitochondrial electron transport chain in Saccharomyces cerevisiae

Caloric restriction (CR) is known to extend lifespan in a variety of species; however, the mechanism remains unclear. In this study, we found that CR potentiated the mitochondrial electron transport chain (ETC) at both the transcriptional and translational levels. Indeed, mitochondrial membrane potential (MMP) was increased by CR, and, regardless of ages, overall reactive oxygen species (ROS) generation was decreased by CR. With these changes, overall growth rate of cells was maintained under various CR conditions, just like cells under a non-restricted condition. All of these data support increased efficiency and capacity of the ETC by CR, and this change might lead to extension of lifespan.

Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast

While typically many expression levels change in transcription factor mutants, only few of these changes lead to functional changes. The predictive capability of expression and DNA binding data for such functional changes in metabolism is very limited.

Large-scale ¹³C-flux data reveal the condition specificity of transcriptional control of metabolic function.

Transcription control in yeast focuses on the switch between respiration and fermentation.

Follow-up modeling on the basis of transcriptomics and proteomics data suggest the newly discovered Gcn4 control of respiration to be mediated via PKA and Snf1.

Effective control and modulation of cellular behavior is of paramount importance in medicine (Kreeger and Lauffenburger, 2010) and biotechnology (Haynes and Silver, 2009), and requires profound understanding of control mechanisms. In this study, we aim to elucidate the extent to which transcription factors control the operation of yeast metabolism. As a quantitative readout of metabolic function, we monitored the traffic of small molecules through various pathways of central metabolism by ¹³C-flux analysis (Sauer, 2006). The choosen growth conditions represent two different regulatory states of reduced (galactose) and maximal carbon source repression (glucose), as well as a different nitrogen metabolism and two common, permanent stress conditions.

Depending on the growth condition, between 7 and 13% of the deleted transcription factors altered the determined flux ratios (Figure 3). Of the six quantified flux ratios, only the glycolysis/pentose phosphate pathway split, and the convergent ratio of anaplerosis and TCA cycle were controlled by the deleted transcription factors. Thus, we concluded that 23 transcription factors control flux distributions under at least one of the tested growth conditions, leading to 42 condition-dependent interactions of transcription factors with metabolic pathway activity (Figure 4). With two exceptions, all other identified transcription factors interactions controlled the TCA cycle flux. This condition-specific active control of metabolic function could not have been predicted from DNA binding and expression data; that is, 26.1% false negatives, 48.6% true positives.

Of the 23 transcription factors that controlled TCA cycle flux distributions under the tested conditions, only Bas1, Gcn4, Gcr2 and Pho2 exerted control under more than one condition. We identified Cit1, Mdh1 and Idh1/2 with a proteomics approach as the relevant target enzyme that increase the TCA cycle flux. Next, we asked whether Bas1, Gcr2, Gcn4 and Pho2 act directly on the TCA cycle or mediate their effect indirectly. Based on the transcriptomics data, the pattern of differentially activated transcription factors inferred by the differential expression of their target genes suggested reduced glucose repression in all four mutants as the common mechanism.

Starting from the currently largest set of ¹³C-based flux distributions, we identified networks of individual transcription factors that control metabolic pathway activity. These networks of active metabolic control have the following properties. First, they are highly condition dependent, as at most four transcription factors control the same metabolic flux distribution under more than one growth conditions. Second, they focus almost exclusively on the TCA cycle, thereby controlling the switch between respiratory and fermentative metabolism. Third, with four to 14 active transcription factors, they are small compared with gene regulation networks that were obtained from expression and DNA binding data. For the metabolic network studied here, robustness is also apparent from the fact that upregulated TCA cycle fluxes were not sufficient to achieve full respiratory metabolism; that is, absent or low ethanol formation. Several explanations could potentially explain the observed robustness. The most likely explanation is that environmental signals might be transmitted by different signaling pathways to several transcription factors, whose orchestrated action on multiple target genes is necessary to achieve a functional flux response. This hypothesis would explain why several transcription factors exert flux effects on the same pathway, but each flux effect is relatively small, as further, coordinated manipulations would be necessary to further improve the respiratory flux. Our findings demonstrate the importance of identifying and quantifying the extent to which regulatory effectors alter cellular function.

Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition-dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition-specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional ¹³C-data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.

Subunits Rip1p and Cox9p of the respiratory chain contribute to diclofenac-induced mitochondrial dysfunction

The widely used drug diclofenac can cause serious heart, liver and kidney injury, which may be related to its ability to cause mitochondrial dysfunction. Using Saccharomyces cerevisiae as a model system, we studied the mechanisms of diclofenac toxicity and the role of mitochondria therein. We found that diclofenac reduced cell growth and viability and increased levels of reactive oxygen species (ROS). Strains increasingly relying on respiration for their energy production showed enhanced sensitivity to diclofenac. Furthermore, oxygen consumption was inhibited by diclofenac, suggesting that the drug inhibits respiration. To identify the site of respiratory inhibition, we investigated the effects of deletion of respiratory chain subunits on diclofenac toxicity. Whereas deletion of most subunits had no effect, loss of either Rip1p of complex III or Cox9p of complex IV resulted in enhanced resistance to diclofenac. In these deletion strains, diclofenac did not increase ROS formation as severely as in the wild-type. Our data are consistent with a mechanism of toxicity in which diclofenac inhibits respiration by interfering with Rip1p and Cox9p in the respiratory chain, resulting in ROS production that causes cell death.

Intracellular characterization of aerobic glucose metabolism in seven yeast species by 13C flux analysis and metabolomics

Key distinguishing characteristics of yeast glucose metabolism are the relative proportions of fermentation and respiration. Crabtree-positive yeast species exhibit a respirofermentative metabolism, whereas aerobic species respire fully without secretion of fermentation byproducts. Physiological data suggest a gradual transition in different species between these two states. Here, we investigate whether this gradual transition also occurs at the intracellular level by quantifying the intracellular metabolism of Saccharomyces cerevisiae, Saccharomyces bayanus, Saccharomyces exiguus, Kluyveromyces thermotolerans, Yarrowia lipolytica, Pichia angusta and Candida rugosa by (13)C-flux analysis and metabolomics. Different from the extracellular physiology, the intracellular fluxes through the tricarboxylic acid cycle fall into two classes where the aerobic species exhibit much higher respiratory fluxes at otherwise similar glycolytic fluxes. More generally, we found the intracellular metabolite concentrations to be primarily species-specific. The sole exception of a metabolite-flux correlation in a species-overarching manner was found for fructose-1,6-bisphosphate and dihydroxyacetone-phosphate, indicating a conservation of the functional properties around these two metabolites.