Differential glucose repression in common yeast strains in response to HXK2 deletion

A Dive Into Yeast's Sugar Diet-Comparing the Metabolic Response of Glucose, Fructose, Sucrose, and Maltose Under Dynamic Feast/Famine Conditions

Microbes experience dynamic conditions in natural habitats as well as in engineered environments, such as large-scale bioreactors, which exhibit increased mixing times and inhomogeneities. While single perturbations have been studied for several organisms and substrates, the impact of recurring short-term perturbations remains largely unknown. In this study, we investigated the response of Saccharomyces cerevisiae to repetitive gradients of four different sugars: glucose, fructose, sucrose, and maltose. Due to different transport mechanisms and metabolic routes, nonglucose sugars lead to varied intracellular responses. To characterize the impact of the carbon sources and the dynamic substrate gradients, we applied both steady-state and dynamic cultivation conditions, comparing the physiology, intracellular metabolome, and proteome. For maltose, the repeated concentration gradients led to a significant decrease in biomass yield. Under glucose, fructose, and sucrose conditions, S. cerevisiae maintained the biomass yield observed under steady-state conditions. Although the physiology was very similar across the different sugars, the intracellular metabolome and proteome were clearly differentiated. Notably, the concentration of upper glycolytic enzymes decreased for glucose and maltose (up to -60% and -40%, respectively), while an increase was observed for sucrose and fructose when exposed to gradients. Nevertheless, for all sugar gradient conditions, a stable energy charge was maintained, ranging between 0.78 and 0.89. This response to maltose is particularly distinct compared to previous single-substrate pulse experiments or limitation to excess shifts, which led to maltose-accelerated death in earlier studies. At the same time, enzymes of lower glycolysis were elevated. Interestingly, common stress-related proteins (GO term: cellular response to oxidative stress) decreased during dynamic conditions.

Review of Recent Advances in Microbial Production and Applications of Nerolidol

Nerolidol, an oxygenated sesquiterpene (C15H26O) that occurs in plants, exhibits significant bioactivities such as antioxidant, anti-inflammatory, antimicrobial, and neuroprotective activities. It is a U.S. Food and Drug Administration-approved flavoring agent and a common ingredient in several commercial products such as toiletries and detergents. In addition, the potential applications of nerolidol that may prove beneficial for human health, agriculture, and the food industry have garnered increasing attention from researchers in these fields. Recent years have witnessed the application of metabolic engineering and synthetic biology strategies for constructing microbial cell factories that can produce nerolidol, which is considered a sustainable and economical approach. This review summarizes recent research on the biological activities and applications of nerolidol as well as nerolidol production using microbial cell factories. In addition, the synthesis of bioactive derivatives of nerolidol is addressed. In summary, this review provides readers with an updated understanding of the potential applications and green production prospects of nerolidol.

Virtually identical does not mean exactly identical: Discrepancy in energy metabolism between glucose and fructose fermentation influences the reproductive potential of yeast cells

The physiological efficiency of cells largely depends on the possibility of metabolic adaptations to changing conditions, especially on the availability of nutrients. Central carbon metabolism has an essential role in cellular function. In most cells is based on glucose, which is the primary energy source, provides the carbon skeleton for the biosynthesis of important cell macromolecules, and acts as a signaling molecule. The metabolic flux between pathways of carbon metabolism such as glycolysis, pentose phosphate pathway, and mitochondrial oxidative phosphorylation is dynamically adjusted by specific cellular economics responding to extracellular conditions and intracellular demands. Using Saccharomyces cerevisiae yeast cells and potentially similar fermentable carbon sources i.e. glucose and fructose we analyzed the parameters concerning the metabolic status of the cells and connected with them alteration in cell reproductive potential. Those parameters were related to the specific metabolic network: the hexose uptake - glycolysis and activity of the cAMP/PKA pathway - pentose phosphate pathway and biosynthetic capacities - the oxidative respiration and energy generation. The results showed that yeast cells growing in a fructose medium slightly increased metabolism redirection toward respiratory activity, which decreased pentose phosphate pathway activity and cellular biosynthetic capabilities. These differences between the fermentative metabolism of glucose and fructose, lead to long-term effects, manifested by changes in the maximum reproductive potential of cells.

Profiling proteomic responses to hexokinase-II depletion in terpene-producing Saccharomyces cerevisiae

Hexokinase II (Hxk2) is a master protein in glucose-mediated transcriptional repression signaling pathway. Degrading Hxk2 through an auxin-inducible protein degradation previously doubled sesquiterpene (nerolidol) production at gram-per-liter levels in Saccharomyces cerevisiae. Global transcriptomics/proteomics profiles in Hxk2-deficient background are important to understanding genetic and molecular mechanisms for improved nerolidol production and guiding further strain optimization. Here, proteomic responses to Hxk2 depletion are investigated in the yeast strains harboring a GAL promoters-controlled nerolidol synthetic pathway, at the exponential and ethanol growth phases and in GAL80-wildtype and gal80Δ backgrounds. Carbon metabolic pathways and amino acid metabolic pathways show diversified responses to Hxk2 depletion and growth on ethanol, including upregulation of alternative carbon catabolism and respiration as well as downregulation of amino acid synthesis. De-repression of GAL genes may contribute to improved nerolidol production in Hxk2-depleted strains. Seventeen transcription factors associated with upregulated genes are enriched. Validating Ash1-mediated repression on the RIM4 promoter shows the variation on the regulatory effects of different Ash1-binding sites and the synergistic effect of Ash1 and Hxk2-mediated repression. Further validation of individual promoters shows that HXT1 promoter activities are glucose-dependent in hxk2Δ background, but much weaker than those in HXK2-wildtype background. In summary, inactivating HXK2 may relieve glucose repression on respiration and GAL promoters for improved bioproduction under aerobic conditions in S. cerevisiae. The proteomics profiles provide a better genetics overview for a better metabolic engineering design in Hxk2-deficient backgrounds.

Alleviating glucose repression and enhancing respiratory capacity to increase itaconic acid production

The Crabtree effect products ethanol and acetic acid can be used for itaconic acid (IA) production in Saccharomyces cerevisiae. However, both the IA synthesis and oxidative phosphorylation pathways were hampered by glucose repression when glucose was used as the substrate. This study aimed to improve IA titer by increasing gene expressions related to glucose derepression without impairing yeast growth on glucose. Engineering the acetyl-CoA synthesis pathway increased the titer of IA to 257 mg/L in a urea-based medium. Instead of entire pathway overexpression, we found that some signaling pathways regulating glucose repression were effective targets to improve IA production and respiratory capacity. As a consequence of the reduced inhibition, IA titer was further increased by knocking out a negative regulator of the mitochondrial retrograde signaling MKS1. SNF1/MIG1 signaling was disturbed by deleting the hexokinase HXK2 or an endoplasmic reticulum membrane protein GSF2. The shaking results showed that XYY286 (BY4741, HO::cadA, Y::Dz.ada, 208a::Mt.acs, Δhxk2, pRS415-cadA, pRS423-aac2) accumulated 535 mg/L IA in 168 h in the YSCGLU medium. qRT-PCR results verified that deletion of MKS1 or HXK2 upregulated the gene expressions of the IA synthesis and respiratory pathways during the growth on glucose.

Hexokinase 2: The preferential target of trehalose-6-phosphate over hexokinase 1

Cancer-related metabolic features are in part maintained by hexokinase 2 upregulation, which leads to high levels of glucose-6-phosphate (G6P) and is needed to provide energy and biomass to support rapid proliferation. Using a humanized model of the yeast Saccharomyces cerevisiae, we explored how human hexokinase 2 (HK2) behaves under different nutritional conditions. At high glucose levels, yeast presents aerobic glycolysis through a regulatory mechanism known as catabolic repression, which exerts a metabolic adaptation like the Warburg effect. At high glucose concentrations, HK2 did not translocate into the nucleus and was not able to shift the metabolism toward a highly glycolytic state, in contrast to the effect of yeast hexokinase 2 (Hxk2), which is a crucial protein for the control of aerobic glycolysis in S. cerevisiae. During the stationary phase, when glucose is exhausted, Hxk2 is shuttled out of the nucleus, ceasing catabolic repression. Cells harvested at this condition display low glucose consumption rates. However, glucose-starved cells expressing HK2 had an increased capacity to consume glucose. In those cells, HK2 localized to mitochondria, becoming insensitive to G6P inhibition. We also found that the sugar trehalose-6-phosphate (T6P) is a human HK2 inhibitor, like yeast Hxk2, but was not able to inhibit human HK1, the isoform that is ubiquitously expressed in almost all mammalian tissues. In contrast to G6P, T6P inhibited HK2 even when HK2 was associated with mitochondria. The binding of HK2 to mitochondria is crucial for cancer survival and proliferation. T6P was able to reduce the cell viability of tumor cells, although its toxicity was not impressive. This was expected as cell absorption of phosphorylated sugars is low, which might be counteracted using nanotechnology. Altogether, these data suggest that T6P may offer a new paradigm for cancer treatment based on specific inhibition of HK2.

Post-transcriptional regulation of ATG1 is a critical node that modulates autophagy during distinct nutrient stresses

Macroautophagy/autophagy is a highly conserved nutrient-recycling pathway that eukaryotes utilize to combat diverse stresses including nutrient depletion. Dysregulation of autophagy disrupts cellular homeostasis leading to starvation susceptibility in yeast and disease development in humans. In yeast, the robust autophagy response to starvation is controlled by the upregulation of ATG genes, via regulatory processes involving multiple levels of gene expression. Despite the identification of several regulators through genetic studies, the predominant mechanism of regulation modulating the autophagy response to subtle differences in nutrient status remains undefined. Here, we report the unexpected finding that subtle changes in nutrient availability can cause large differences in autophagy flux, governed by hitherto unknown post-transcriptional regulatory mechanisms affecting the expression of the key autophagyinducing kinase Atg1 (ULK1/ULK2 in mammals). We have identified two novel post-transcriptional regulators of ATG1 expression, the kinase Rad53 and the RNA-binding protein Ded1 (DDX3 in mammals). Furthermore, we show that DDX3 regulates ULK1 expression post-transcriptionally, establishing mechanistic conservation and highlighting the power of yeast biology in uncovering regulatory mechanisms that can inform therapeutic approaches.

Efficient production of shinorine, a natural sunscreen material, from glucose and xylose by deleting HXK2 encoding hexokinase in Saccharomyces cerevisiae

Mycosporine-like amino acids (MAAs), microbial secondary metabolites with ultraviolet (UV) absorption properties, are promising natural sunscreen materials. Due to the low efficiency of extracting MAAs from natural producers, production in heterologous hosts has recently received attention. Shinorine is a well characterized MAA with strong UV-A absorption property. Previous, we developed Saccharomyces cerevisiae strain producing shinorine by introducing four shinorine biosynthetic genes from cyanobacterium Nostoc punctiforme. Shinorine is produced from sedoheptulose 7-phosphate (S7P), an intermediate in the pentose phosphate pathway. Shinorine production was greatly improved by using xylose as a co-substrate, which can increase the S7P pool. However, due to a limited xylose-utilizing capacity of the engineered strain, glucose was used as a co-substrate to support cell growth. In this study, we further improved shinorine production by attenuating glucose catabolism via glycolysis, which can redirect the carbon flux from glucose to the pentose phosphate pathway favoring shinorine production. Of the strategies we examined to reduce glycolytic flux, deletion of HXK2, encoding hexokinase, was most effective in increasing shinorine production. Furthermore, by additional expression of Ava3858 from Anabaena variabilis, encoding a rate-limiting enzyme 2-demethyl 4-deoxygadusol synthase, 68.4 mg/L of shinorine was produced in an optimized medium containing 14 g/L glucose and 6 g/L xylose, achieving a 2.2-fold increase compared with the previous strain.

AMPK Phosphorylation Is Controlled by Glucose Transport Rate in a PKA-Independent Manner

To achieve growth, microbial organisms must cope with stresses and adapt to the environment, exploiting the available nutrients with the highest efficiency. In Saccharomyces cerevisiae, Ras/PKA and Snf1/AMPK pathways regulate cellular metabolism according to the supply of glucose, alternatively supporting fermentation or mitochondrial respiration. Many reports have highlighted crosstalk between these two pathways, even without providing a comprehensive mechanism of regulation. Here, we show that glucose-dependent inactivation of Snf1/AMPK is independent from the Ras/PKA pathway. Decoupling glucose uptake rate from glucose concentration, we highlight a strong coordination between glycolytic metabolism and Snf1/AMPK, with an inverse correlation between Snf1/AMPK phosphorylation state and glucose uptake rate, regardless of glucose concentration in the medium. Despite fructose-1,6-bisphosphate (F1,6BP) being proposed as a glycolytic flux sensor, we demonstrate that glucose-6-phosphate (G6P), and not F1,6BP, is involved in the control of Snf1/AMPK phosphorylation state. Altogether, this study supports a model by which Snf1/AMPK senses glucose flux independently from PKA activity, and thanks to conversion of glucose into G6P.

Auxin-mediated protein depletion for metabolic engineering in terpene-producing yeast

In metabolic engineering, loss-of-function experiments are used to understand and optimise metabolism. A conditional gene inactivation tool is required when gene deletion is lethal or detrimental to growth. Here, we exploit auxin-inducible protein degradation as a metabolic engineering approach in yeast. We demonstrate its effectiveness using terpenoid production. First, we target an essential prenyl-pyrophosphate metabolism protein, farnesyl pyrophosphate synthase (Erg20p). Degradation successfully redirects metabolic flux toward monoterpene (C10) production. Second, depleting hexokinase-2, a key protein in glucose signalling transduction, lifts glucose repression and boosts production of sesquiterpene (C15) nerolidol to 3.5 g L-1 in flask cultivation. Third, depleting acetyl-CoA carboxylase (Acc1p), another essential protein, delivers growth arrest without diminishing production capacity in nerolidol-producing yeast, providing a strategy to decouple growth and production. These studies demonstrate auxin-mediated protein degradation as an advanced tool for metabolic engineering. It also has potential for broader metabolic perturbation studies to better understand metabolism.

A proteome-integrated, carbon source dependent genetic regulatory network in Saccharomyces cerevisiae

Integrated regulatory networks can be powerful tools to examine and test properties of cellular systems, such as modelling environmental effects on the molecular bioeconomy, where protein levels are altered in response to changes in growth conditions. Although extensive regulatory pathways and protein interaction data sets exist which represent such networks, few have formally considered quantitative proteomics data to validate and extend them. We generate and consider such data here using a label-free proteomics strategy to quantify alterations in protein abundance for S. cerevisiae when grown on minimal media using glucose, galactose, maltose and trehalose as sole carbon sources. Using a high quality-controlled subset of proteins observed to be differentially abundant, we constructed a proteome-informed network, comprising 1850 transcription factor interactions and 37 chaperone interactions, which defines the major changes in the cellular proteome when growing under different carbon sources. Analysis of the differentially abundant proteins involved in the regulatory network pointed to their significant roles in specific metabolic pathways and function, including glucose homeostasis, amino acid biosynthesis, and carbohydrate metabolic process. We noted strong statistical enrichment in the differentially abundant proteome of targets of known transcription factors associated with stress responses and altered carbon metabolism. This shows how such integrated analysis can lend further experimental support to annotated regulatory interactions, since the proteomic changes capture both magnitude and direction of gene expression change at the level of the affected proteins. Overall this study highlights the power of quantitative proteomics to help define regulatory systems pertinent to environmental conditions.

Reproductive Potential of Yeast Cells Depends on Overall Action of Interconnected Changes in Central Carbon Metabolism, Cellular Biosynthetic Capacity, and Proteostasis

Carbon metabolism is a crucial aspect of cell life. Glucose, as the primary source of energy and carbon skeleton, determines the type of cell metabolism and biosynthetic capabilities, which, through the regulation of cell size, may affect the reproductive capacity of the yeast cell. Calorie restriction is considered as the most effective way to improve cellular physiological capacity, and its molecular mechanisms are complex and include several nutrient signaling pathways. It is widely assumed that the metabolic shift from fermentation to respiration is treated as a substantial driving force for the mechanism of calorie restriction and its influence on reproductive capabilities of cells. In this paper, we propose another approach to this issue based on analysis the connection between energy-producing and biomass formation pathways which are closed in the metabolic triangle, i.e., the respiration-glycolysis-pentose phosphate pathway. The analyses were based on the use of cells lacking hexokinase 2 (∆hxk2) and conditions of different glucose concentration corresponding to the calorie restriction and the calorie excess. Hexokinase 2 is the key enzyme involved in central carbon metabolism and is also treated as a calorie restriction mimetic. The experimental model used allows us to explain both the role of increased respiration as an effect of calorie restriction but also other aspects of carbon metabolism and the related metabolic flux in regulation of reproductive potential of the cells. The obtained results reveal that increased respiration is not a prerequisite for reproductive potential extension but rather an accompanying effect of the positive role of calorie restriction. More important seems to be the changes connected with fluxes in central carbon metabolic pathways resulting in low biosynthetic capabilities and improved proteostasis.

Energy metabolism controls phenotypes by protein efficiency and allocation

Cells require energy for growth and maintenance and have evolved to have multiple pathways to produce energy in response to varying conditions. A basic question in this context is how cells organize energy metabolism, which is, however, challenging to elucidate due to its complexity, i.e., the energy-producing pathways overlap with each other and even intertwine with biomass formation pathways. Here, we propose a modeling concept that decomposes energy metabolism into biomass formation and ATP-producing pathways. The latter can be further decomposed into a high-yield and a low-yield pathway. This enables independent estimation of protein efficiency for each pathway. With this concept, we modeled energy metabolism for Escherichia coli and Saccharomyces cerevisiae and found that the high-yield pathway shows lower protein efficiency than the low-yield pathway. Taken together with a fixed protein constraint, we predict overflow metabolism in E. coli and the Crabtree effect in S. cerevisiae, meaning that energy metabolism is sufficient to explain the metabolic switches. The static protein constraint is supported by the findings that protein mass of energy metabolism is conserved across conditions based on absolute proteomics data. This also suggests that enzymes may have decreased saturation or activity at low glucose uptake rates. Finally, our analyses point out three ways to improve growth, i.e., increasing protein allocation to energy metabolism, decreasing ATP demand, or increasing activity for key enzymes.

Reassessment of requirements for anaerobic xylose fermentation by engineered, non-evolved Saccharomyces cerevisiae strains

Expression of a heterologous xylose isomerase, deletion of the GRE3 aldose-reductase gene and overexpression of genes encoding xylulokinase (XKS1) and non-oxidative pentose-phosphate-pathway enzymes (RKI1, RPE1, TAL1, TKL1) enables aerobic growth of Saccharomyces cerevisiae on d-xylose. However, literature reports differ on whether anaerobic growth on d-xylose requires additional mutations. Here, CRISPR-Cas9-assisted reconstruction and physiological analysis confirmed an early report that this basic set of genetic modifications suffices to enable anaerobic growth on d-xylose in the CEN.PK genetic background. Strains that additionally carried overexpression cassettes for the transaldolase and transketolase paralogs NQM1 and TKL2 only exhibited anaerobic growth on d-xylose after a 7–10 day lag phase. This extended lag phase was eliminated by increasing inoculum concentrations from 0.02 to 0.2 g biomass L⁻¹. Alternatively, a long lag phase could be prevented by sparging low-inoculum-density bioreactor cultures with a CO₂/N₂-mixture, thus mimicking initial CO₂ concentrations in high-inoculum-density, nitrogen-sparged cultures, or by using l-aspartate instead of ammonium as nitrogen source. This study resolves apparent contradictions in the literature on the genetic interventions required for anaerobic growth of CEN.PK-derived strains on d-xylose. Additionally, it indicates the potential relevance of CO₂ availability and anaplerotic carboxylation reactions for anaerobic growth of engineered S. cerevisiae strains on d-xylose.

An upper limit on Gibbs energy dissipation governs cellular metabolism

The principles governing cellular metabolic operation are poorly understood. Because diverse organisms show similar metabolic flux patterns, we hypothesized that a fundamental thermodynamic constraint might shape cellular metabolism. Here, we develop a constraint-based model for Saccharomyces cerevisiae with a comprehensive description of biochemical thermodynamics including a Gibbs energy balance. Non-linear regression analyses of quantitative metabolome and physiology data reveal the existence of an upper rate limit for cellular Gibbs energy dissipation. By applying this limit in flux balance analyses with growth maximization as the objective function, our model correctly predicts the physiology and intracellular metabolic fluxes for different glucose uptake rates as well as the maximal growth rate. We find that cells arrange their intracellular metabolic fluxes in such a way that, with increasing glucose uptake rates, they can accomplish optimal growth rates but stay below the critical rate limit on Gibbs energy dissipation. Once all possibilities for intracellular flux redistribution are exhausted, cells reach their maximal growth rate. This principle also holds for Escherichia coli and different carbon sources. Our work proposes that metabolic reaction stoichiometry, a limit on the cellular Gibbs energy dissipation rate, and the objective of growth maximization shape metabolism across organisms and conditions.

Methionine supplementation stimulates mitochondrial respiration

Mitochondria play essential metabolic functions in eukaryotes. Although their major role is the generation of energy in the form of ATP, they are also involved in maintenance of cellular redox state, conversion and biosynthesis of metabolites and signal transduction. Most mitochondrial functions are conserved in eukaryotic systems and mitochondrial dysfunctions trigger several human diseases. By using multi-omics approach, we investigate the effect of methionine supplementation on yeast cellular metabolism, considering its role in the regulation of key cellular processes. Methionine supplementation induces an up-regulation of proteins related to mitochondrial functions such as TCA cycle, electron transport chain and respiration, combined with an enhancement of mitochondrial pyruvate uptake and TCA cycle activity. This metabolic signature is more noticeable in cells lacking Snf1/AMPK, the conserved signalling regulator of energy homeostasis. Remarkably, snf1Δ cells strongly depend on mitochondrial respiration and suppression of pyruvate transport is detrimental for this mutant in methionine condition, indicating that respiration mostly relies on pyruvate flux into mitochondrial pathways. These data provide new insights into the regulation of mitochondrial metabolism and extends our understanding on the role of methionine in regulating energy signalling pathways.

Multi-omic characterization of laboratory-evolved Saccharomyces cerevisiae HJ7-14 with high ability of algae-based ethanol production

In this study, an evolved Saccharomyces cerevisiae HJ7-14 with high ability of algae-based ethanol production was characterized by multi-omic approaches. Genome sequencing of the HJ7-14 revealed a point mutation in the GAL83 gene (G703A) involved in the catabolite repression as well as the galactose metabolism. Cultural and transcriptional analyses of a S. cerevisiae mutant with chromosomal GAL83(G703A) indicated that the catabolite repression onto the galactose metabolism was considerably relieved in all cell growth stages. Untargeted metabolomic approach revealed that metabolic phenotypes between the control D452-2 and HJ7-14 strains were clearly discriminated in time-dependent manner. Especially in early growth stage at 6 h, the HJ7-14 showed dramatic and coordinated alteration in central carbon and amino acid metabolisms. Through metabolomic re-organization, fold changes in fatty acid metabolism and metabolites related to stress response system were also found upon glucose depletion and active galactose utilization. Multi-omic characterization using genome sequencing, transcription, and metabolome profiling clearly unveiled that the GAL83 gene mutation partially relieved glucose-dependent catabolite repression and allowed the evolved HJ7-14 to efficiently convert algal sugars to ethanol. Our finding could be applicable for engineering of S. cerevisiae able to covert red algal biomass to other biofuels and biochemicals.

A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

BACKGROUND

Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.

RESULTS

We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.

CONCLUSIONS

We present a design-build-test cycle composed of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated "modeling and experiments" systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

Autonomous Metabolic Oscillations Robustly Gate the Early and Late Cell Cycle

Eukaryotic cell division is known to be controlled by the cyclin/cyclin dependent kinase (CDK) machinery. However, eukaryotes have evolved prior to CDKs, and cells can divide in the absence of major cyclin/CDK components. We hypothesized that an autonomous metabolic oscillator provides dynamic triggers for cell-cycle initiation and progression. Using microfluidics, cell-cycle reporters, and single-cell metabolite measurements, we found that metabolism of budding yeast is a CDK-independent oscillator that oscillates across different growth conditions, both in synchrony with and also in the absence of the cell cycle. Using environmental perturbations and dynamic single-protein depletion experiments, we found that the metabolic oscillator and the cell cycle form a system of coupled oscillators, with the metabolic oscillator separately gating and maintaining synchrony with the early and late cell cycle. Establishing metabolism as a dynamic component within the cell-cycle network opens new avenues for cell-cycle research and therapeutic interventions for proliferative disorders.

The transcription factors ADR1 or CAT8 are required for RTG pathway activation and evasion from yeast acetic acid-induced programmed cell death in raffinose

Yeast Saccharomyces cerevisiae grown on glucose undergoes programmed cell death (PCD) induced by acetic acid (AA-PCD), but evades PCD when grown in raffinose. This is due to concomitant relief of carbon catabolite repression (CCR) and activation of mitochondrial retrograde signaling, a mitochondria-to-nucleus communication pathway causing up-regulation of various nuclear target genes, such as CIT2, encoding peroxisomal citrate synthase, dependent on the positive regulator RTG2 in response to mitochondrial dysfunction. CCR down-regulates genes mainly involved in mitochondrial respiratory metabolism. In this work, we investigated the relationships between the RTG and CCR pathways in the modulation of AA-PCD sensitivity under glucose repression or de-repression conditions. Yeast single and double mutants lacking RTG2 and/or certain factors regulating carbon source utilization, including MIG1, HXK2, ADR1, CAT8, and HAP4, have been analyzed for their survival and CIT2 expression after acetic acid treatment. ADR1 and CAT8 were identified as positive regulators of RTG-dependent gene transcription. ADR1 and CAT8 interact with RTG2 and with each other in inducing cell resistance to AA-PCD in raffinose and controlling the nature of cell death. In the absence of ADR1 and CAT8, AA-PCD evasion is acquired through activation of an alternative factor/pathway repressed by RTG2, suggesting that RTG2 may play a function in promoting necrotic cell death in repressing conditions when RTG pathway is inactive. Moreover, our data show that simultaneous mitochondrial retrograde pathway activation and SNF1-dependent relief of CCR have a key role in central carbon metabolism reprogramming which modulates the yeast acetic acid-stress response.

Protein abundance changes of Zygosaccharomyces rouxii in different sugar concentrations

Zygosaccharomyces rouxii is a yeast which can cause spoilage in the concentrated juice industries. It exhibits resistance to high sugar concentrations but genome- and proteome-wide studies on Z. rouxii in response to high sugar concentrations have been poorly investigated. Herein, by using a 2-D electrophoresis based workflow, the proteome of a wild strain of Z. rouxii under different sugar concentrations has been analyzed. Proteins were extracted, quantified, and subjected to 2-DE analysis in the pH range 4-7. Differences in growth (lag phase), protein content (13.97-19.23mg/g cell dry weight) and number of resolved spots (196-296) were found between sugar concentrations. ANOVA test showed that 168 spots were different, and 47 spots, corresponding to 40 unique gene products have been identified. These protein species are involved in carbohydrate and energy metabolism, amino acid metabolism, response to stimulus, protein transport and vesicle organization, cell morphogenesis regulation, transcription and translation, nucleotide metabolism, amino-sugar nucleotide-sugar pathways, oxidoreductases balancing, and ribosome biogenesis. The present study provides important information about how Z. rouxii acts to cope with high sugar concentration at molecular levels, which might enhance our global understanding of Z. rouxii's high sugar-tolerance trait.

Evolutionary engineering of Saccharomyces cerevisiae for efficient conversion of red algal biosugars to bioethanol

The aim of this work was to apply the evolutionary engineering to construct a mutant Saccharomyces cerevisiae HJ7-14 resistant on 2-deoxy-D-glucose and with an enhanced ability of bioethanol production from galactose, a mono-sugar in red algae. In batch and repeated-batch fermentations, HJ7-14 metabolized 5-fold more galactose and produced ethanol 2.1-fold faster than the parental D452-2 strain. Transcriptional analysis of genes involved in the galactose metabolism revealed that moderate relief from the glucose-mediated repression of the transcription of the GAL genes might enable HJ7-14 to metabolize galactose rapidly. HJ7-14 produced 7.4 g/L ethanol from hydrolysates of the red alga Gelidium amansii within 12 h, which was 1.5-times faster than that observed with D452-2. We demonstrate conclusively that evolutionary engineering is a promising tool to manipulate the complex galactose metabolism in S. cerevisiae to produce bioethanol from red alga.

¹³C-based metabolic flux analysis of Saccharomyces cerevisiae with a reduced Crabtree effect

Saccharomyces cerevisiae shows a Crabtree effect that produces ethanol in a high glucose concentration even under fully aerobic condition. For efficient production of cake yeast or compressed yeast for baking, ethanol by-production is not desired since glucose limited chemostat or fed-batch cultivations are performed to suppress the Crabtree effect. In this study, the (13)C-based metabolic flux analysis ((13)C-MFA) was performed for the S288C derived S. cerevisiae strain to characterize a metabolic state under the reduced Crabtree effect. S. cerevisiae cells were cultured at a low dilution rate (0.1 h(-1)) under the glucose-limited chemostat condition. The estimated metabolic flux distribution showed that the acetyl-CoA in mitochondria was mainly produced from pyruvate by pyruvate dehydrogenase (PDH) reaction and that the level of the metabolic flux through the pentose phosphate pathway was much higher than that of the Embden-Meyerhof-Parnas pathway, which contributes to high biomass yield at low dilution rate by supplying NADPH required for cell growth.

Calorie restriction does not elicit a robust extension of replicative lifespan in Saccharomyces cerevisiae

Calorie restriction (CR) is often described as the most robust manner to extend lifespan in a large variety of organisms. Hence, considerable research effort is directed toward understanding the mechanisms underlying CR, especially in the yeast Saccharomyces cerevisiae. However, the effect of CR on lifespan has never been systematically reviewed in this organism. Here, we performed a meta-analysis of replicative lifespan (RLS) data published in more than 40 different papers. Our analysis revealed that there is significant variation in the reported RLS data, which appears to be mainly due to the low number of cells analyzed per experiment. Furthermore, we found that the RLS measured at 2% (wt/vol) glucose in CR experiments is partly biased toward shorter lifespans compared with identical lifespan measurements from other studies. Excluding the 2% (wt/vol) glucose experiments from CR experiments, we determined that the average RLS of the yeast strains BY4741 and BY4742 is 25.9 buds at 2% (wt/vol) glucose and 30.2 buds under CR conditions. RLS measurements with a microfluidic dissection platform produced identical RLS data at 2% (wt/vol) glucose. However, CR conditions did not induce lifespan extension. As we excluded obvious methodological differences, such as temperature and medium, as causes, we conclude that subtle method-specific factors are crucial to induce lifespan extension under CR conditions in S. cerevisiae.

Leveraging transcription factors to speed cellobiose fermentation by Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae, a key organism used for the manufacture of renewable fuels and chemicals, has been engineered to utilize non-native sugars derived from plant cell walls, such as cellobiose and xylose. However, the rates and efficiencies of these non-native sugar fermentations pale in comparison with those of glucose. Systems biology methods, used to understand biological networks, hold promise for rational microbial strain development in metabolic engineering. Here, we present a systematic strategy for optimizing non-native sugar fermentation by recombinant S. cerevisiae, using cellobiose as a model.

RESULTS

Differences in gene expression between cellobiose and glucose metabolism revealed by RNA deep sequencing indicated that cellobiose metabolism induces mitochondrial activation and reduces amino acid biosynthesis under fermentation conditions. Furthermore, glucose-sensing and signaling pathways and their target genes, including the cAMP-dependent protein kinase A pathway controlling the majority of glucose-induced changes, the Snf3-Rgt2-Rgt1 pathway regulating hexose transport, and the Snf1-Mig1 glucose repression pathway, were at most only partially activated under cellobiose conditions. To separate correlations from causative effects, the expression levels of 19 transcription factors perturbed under cellobiose conditions were modulated, and the three strongest promoters under cellobiose conditions were applied to fine-tune expression of the heterologous cellobiose-utilizing pathway. Of the changes in these 19 transcription factors, only overexpression of SUT1 or deletion of HAP4 consistently improved cellobiose fermentation. SUT1 overexpression and HAP4 deletion were not synergistic, suggesting that SUT1 and HAP4 may regulate overlapping genes important for improved cellobiose fermentation. Transcription factor modulation coupled with rational tuning of the cellobiose consumption pathway significantly improved cellobiose fermentation.

CONCLUSIONS

We used systems-level input to reveal the regulatory mechanisms underlying suboptimal metabolism of the non-glucose sugar cellobiose. By identifying key transcription factors that cause suboptimal cellobiose fermentation in engineered S. cerevisiae, and by fine-tuning the expression of a heterologous cellobiose consumption pathway, we were able to greatly improve cellobiose fermentation by engineered S. cerevisiae. Our results demonstrate a powerful strategy for applying systems biology methods to rapidly identify metabolic engineering targets and overcome bottlenecks in performance of engineered strains.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Transcriptional regulation of fermentative and respiratory metabolism in Saccharomyces cerevisiae industrial bakers' strains

Bakers' yeast-producing companies grow cells under respiratory conditions, at a very high growth rate. Some desirable properties of bakers' yeast may be altered if fermentation rather than respiration occurs during biomass production. That is why differences in gene expression patterns that take place when industrial bakers' yeasts are grown under fermentative, rather than respiratory conditions, were examined. Macroarray analysis of V1 strain indicated changes in gene expression similar to those already described in laboratory Saccharomyces cerevisiae strains: repression of most genes related to respiration and oxidative metabolism and derepression of genes related to ribosome biogenesis and stress resistance in fermentation. Under respiratory conditions, genes related to the glyoxylate and Krebs cycles, respiration, gluconeogenesis, and energy production are activated. DOG21 strain, a partly catabolite-derepressed mutant derived from V1, displayed gene expression patterns quite similar to those of V1, although lower levels of gene expression and changes in fewer number of genes as compared to V1 were both detected in all cases. However, under fermentative conditions, DOG21 mutant significantly increased the expression of SNF1 -controlled genes and other genes involved in stress resistance, whereas the expression of the HXK2 gene, involved in catabolite repression, was considerably reduced, according to the pleiotropic stress-resistant phenotype of this mutant. These results also seemed to suggest that stress-resistant genes control desirable bakers' yeast qualities.

An integrated open framework for thermodynamics of reactions that combines accuracy and coverage

Motivation: The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions.

Results: Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG^○ of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K′ and pK_a data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM–3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions.

Availability: Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux.

Contact:ron.milo@weizmann.ac.il

Supplementary Information:Supplementary data are available at Bioinformatics online.

Exploring the metabolic state of microorganisms using metabolomics

Microorganisms depend on their ability to modulate their metabolic composition according to specific circumstances, such as different phases of the growth cycle and circadian rhythms, fluctuations in environmental conditions, as well as experimental perturbations. A thorough understanding of these metabolic adaptations requires the ability to comprehensively identify and quantify the metabolome of bacterial cells in different states. In this review, we present an overview of the diverse metabolomics approaches recently adopted to explore the metabolism of a wide variety of microorganisms. Focusing on a selection of illustrative case studies, we assess the different experimental designs used and explore the major achievements and remaining challenges in the field. We conclude by discussing the important complementary information provided by computational methods such as genome-scale metabolic modeling, which enable an integrated analysis of metabolic state changes in the context of overall cellular physiology.

Hydrophobicity and charge shape cellular metabolite concentrations

What governs the concentrations of metabolites within living cells? Beyond specific metabolic and enzymatic considerations, are there global trends that affect their values? We hypothesize that the physico-chemical properties of metabolites considerably affect their in-vivo concentrations. The recently achieved experimental capability to measure the concentrations of many metabolites simultaneously has made the testing of this hypothesis possible. Here, we analyze such recently available data sets of metabolite concentrations within E. coli, S. cerevisiae, B. subtilis and human. Overall, these data sets encompass more than twenty conditions, each containing dozens (28-108) of simultaneously measured metabolites. We test for correlations with various physico-chemical properties and find that the number of charged atoms, non-polar surface area, lipophilicity and solubility consistently correlate with concentration. In most data sets, a change in one of these properties elicits a ~100 fold increase in metabolite concentrations. We find that the non-polar surface area and number of charged atoms account for almost half of the variation in concentrations in the most reliable and comprehensive data set. Analyzing specific groups of metabolites, such as amino-acids or phosphorylated nucleotides, reveals even a higher dependence of concentration on hydrophobicity. We suggest that these findings can be explained by evolutionary constraints imposed on metabolite concentrations and discuss possible selective pressures that can account for them. These include the reduction of solute leakage through the lipid membrane, avoidance of deleterious aggregates and reduction of non-specific hydrophobic binding. By highlighting the global constraints imposed on metabolic pathways, future research could shed light onto aspects of biochemical evolution and the chemical constraints that bound metabolic engineering efforts.

Cryptococcus neoformans requires a functional glycolytic pathway for disease but not persistence in the host

Cryptococcus neoformans is an important fungal pathogen of immunocompromised individuals, with a close relative, Cryptococcus gattii, emerging as a serious threat for the immunocompetent. During initial infection, C. neoformans colonizes the airspaces of the lungs, resulting in pneumonia, and subsequently migrates to the central nervous system (CNS). We sought to understand fungal carbon utilization during colonization of these fundamentally different niches within the host, in particular the roles of gluconeogenesis and glycolysis. We created mutants at key points in the gluconeogenesis/glycolysis metabolic pathways that are restricted for growth on lactate and glucose, respectively. A phosphoenolpyruvate carboxykinase mutant (the pck1∆ mutant), blocked for entry of 2- and 3-carbon substrates into gluconeogenesis and attenuated for virulence in a murine inhalation model, showed wild-type (WT) persistence in a rabbit cerebrospinal fluid (CSF) model of cryptococcosis. Conversely, both the pyruvate kinase (pyk1∆) and the hexose kinase I and II (hxk1∆/hxk2∆) mutants, which show impaired glucose utilization, exhibited severely attenuated virulence in the murine inhalation model of cryptococcosis and decreased persistence in the CNS in both the rabbit CSF and the murine inhalation models while displaying adequate persistence in the lungs of mice. These data suggest that glucose utilization is critical for virulence of C. neoformans and persistence of the yeast in the CNS.

Cryptococcus neoformans is an emerging fungal pathogen of humans and is responsible for approximately 625,000 deaths annually among those suffering from HIV infection/AIDS. The ability of this fungus to persist in the host, coupled with its propensity to colonize the CNS, makes the understanding of nutrient acquisition in the host a primary concern. In this study, we report a requirement of glucose utilization for virulence of C. neoformans that is separate from its role in ATP production in the pathogen. Furthermore, we show that inhibition of glycolysis is a viable antifungal drug target, and impaired ATP production via the PYK1 deletion may serve as a model for dormant/chronic fungal infection in the host. Taken together, these results demonstrate the critical importance of understanding basic metabolic processes of the fungus in the context of host-pathogen interactions.

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.

2-DE based proteomic analysis of Saccharomyces cerevisiae wild and K+ transport-affected mutant (trk1,2) strains at the growth exponential and stationary phases

By using a 2-DE based workflow, the proteome of wild and potassium transport mutant trk1,2 under optimal growth potassium concentration (50mM) has been analyzed. At the exponential and stationary phases, both strains showed similar growth, morphology potassium content, and Vmax of rubidium transport, the only difference found being the Km values for this potassium analogue transport, higher for the mutant (20mM) than for the wild (3-6mM) cells. Proteins were buffer-extracted, precipitated, solubilized, quantified, and subjected to 2-DE analysis in the 5-8 pH range. More differences in protein content (37-64mgg(-1) cell dry weight) and number of resolved spots (178-307) were found between growth phases than between strains. In all, 164 spots showed no differences between samples and a total of 105 were considered to be differential after ANOVA test. 171 proteins, corresponding to 71 unique gene products have been identified, this set being dominated by cytosolic species and glycolitic enzymes. The ranking of the more abundant spots revealed no differences between samples and indicated fermentative metabolism, and active cell wall biosynthesis, redox homeostasis, biosynthesis of amino acids, coenzymes, nucleotides, and RNA, and protein turnover, apart from cell division and growth. PCA analysis allowed the separation of growth phases (PC1 and 2) and strains at the stationary phase (PC3 and 4), but not at the exponential one. These results are also supported by clustering analysis. As a general tendency, a number of spots newly appeared at the stationary phase in wild type, and to a lesser extent, in the mutant. These up-accumulated spots corresponded to glycolitic enzymes, indicating a more active glucose catabolism, accompanied by an accumulation of methylglyoxal detoxification, and redox-homeostasis enzymes. Also, more extensive proteolysis was observed at the stationary phase with this resulting in an accumulation of low Mr protein species.

Transcriptional regulation of respiration in yeast metabolizing differently repressive carbon substrates

Background

Depending on the carbon source, Saccharomyces cerevisiae displays various degrees of respiration. These range from complete respiration as in the case of ethanol, to almost complete fermentation, and thus very low degrees of respiration on glucose. While many key regulators are known for these extreme cases, we focus here on regulators that are relevant at intermediate levels of respiration.

Results

We address this question by linking the functional degree of respiration to transcriptional regulation via enzyme abundances. Specifically, we investigated aerobic batch cultures with the differently repressive carbon sources glucose, mannose, galactose and pyruvate. Based on ¹³C flux analysis, we found that the respiratory contribution to cellular energy production was largely absent on glucose and mannose, intermediate on galactose and highest on pyruvate. In vivo abundances of 40 respiratory enzymes were quantified by GFP-fusions under each condition. During growth on the partly and fully respired substrates galactose and pyruvate, several TCA cycle and respiratory chain enzymes were significantly up-regulated. From these enzyme levels and the known regulatory network structure, we determined the probability for a given transcription factor to cause the coordinated expression changes. The most probable transcription factors to regulate the different degrees of respiration were Gcr1p, Cat8p, the Rtg-proteins and the Hap-complex. For the latter three ones we confirmed their importance for respiration by quantifying the degree of respiration and biomass yields in the corresponding deletion strains.

Conclusions

Cat8p is required for wild-type like respiration, independent of its known activation of gluconeogenic genes. The Rtg-proteins and the Hap-complex are essential for wild-type like respiration under partially respiratory conditions. Under fully respiratory conditions, the Hap-complex, but not the Rtg-proteins are essential for respiration.