Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae

From yeast screening for suitability as single cell protein to fed-batch cultures

PURPOSE

Fed-batch cultures have rarely been used in single cell protein (SCP) research. This work evaluated multiple yeast species for suitability as SCP cultivated using glucose- and sucrose-based substrate and performed in-depth studies of fed-batch SCP cultivation kinetics for selected yeasts, including determination of specific crude nitrogen-to-protein conversion factors.

METHODS

SCP was cultivated using fully synthetic media in flask batch or bioreactor fed-batch cultures. Crude nitrogen and nucleic acid content were determined using the Dumas method and fluorescence assay kits, respectively.

RESULTS

C. utilis compared favorably to other yeasts in flask batch cultures in terms of process yield (0.52 ± 0.01 gx gs-1) and crude nitrogen content (10.0 ± 0.5 and 9.9 ± 0.5%CDW for glucose and sucrose, respectively). This is the first time biomass composition data was reported for SCP cultivated in fed-batch mode. C. utilis crude nitrogen content was consistent across the tested conditions (protein content stabilized around 50%CDW in fed-batch), while that of the benchmark yeast S. cerevisiae was higher in batch cultures and at the beginning of fed-batch relative to the end (protein content decreased over time and stabilized around 43%CDW). Total nucleic acid content of the yeasts was similar (6.8%CDW and 6.3%CDW, for C. utilis and S. cerevisiae, respectively), with crude nitrogen-to-protein conversion factors of 4.97 and 5.80.

CONCLUSION

This study demonstrated the suitability of C. utilis as SCP, notably the robustness of its crude nitrogen content (as an indicator of protein content) across batch and fed-batch conditions, compared to that of the benchmark yeast S. cerevisiae.

Development of an Integrated Bioprocess System for Bioethanol and Arabitol Production from Sugar Beet Cossettes

Research background

An innovative integrated bioprocess system for bioethanol production from raw sugar beet cossettes (SBC) and arabitol from remaining exhausted sugar beet cossettes (ESBC) was studied. This integrated three-stage bioprocess system is an example of the biorefinery concept to maximise the use of raw SBC for the production of high value-added products such as sugar alcohols and bioethanol.

Experimental approach

The first stage of the integrated bioprocess system was simultaneous sugar extraction from SBC and its alcoholic fermentation to produce bioethanol in an integrated bioreactor system (vertical column bioreactor and stirred tank bioreactor) containing a high-density suspension of yeast Saccharomyces cerevisiae (30 g/L). The second stage was the pretreatment of ESBC with dilute sulfuric acid to release fermentable sugars. The resulting liquid hydrolysate of ESBC was used in the third stage as a nutrient medium for arabitol production by non-Saccharomyces yeasts (Spathaspora passalidarum CBS 10155 and Spathaspora arborariae CBS 11463).

Results and conclusions

The obtained results show that the efficiency of bioethanol production increased with increasing temperature and prolonged residence time in the integrated bioreactor system. The maximum bioethanol production efficiency (87.22 %) was observed at a time of 60 min and a temperature of 36 °C. Further increase in residence time (above 60 min) did not result in the significant increase of bioethanol production efficiency. Weak acid hydrolysis was used for ESBC pretreatment and the highest sugar yield was reached at 200 °C and residence time of 1 min. The inhibitors of the weak acid pretreatment were produced below bioprocess inhibition threshold. The use of the obtained liqiud phase of ESBC hydrolysate for the production of arabitol in the stirred tank bioreactor under constant aeration clearly showed that S. passalidarum CBS 10155 with 8.48 g/L of arabitol (YP/S=0.603 g/g and bioprocess productivity of 0.176 g/(L.h)) is a better arabitol producer than Spathaspora arborariae CBS 10155.

Novelty and scientific contribution

An innovative integrated bioprocess system for the production of bioethanol and arabitol was developed based on the biorefinery concept. This three-stage bioprocess system shows great potential for maximum use of SBC as a feedstock for bioethanol and arabitol production and it could be an example of a sustainable 'zero waste' production system.

A multilayer microfluidic system for studies of the dynamic responses of cellular proteins to oxygen switches at the single-cell level

Oxygen levels vary in the environment. Oxygen availability has a major effect on almost all organisms, and oxygen is far more than a substrate for energy production. However, less is known about related biological processes under hypoxic conditions and about the adaptations to changing oxygen concentrations. The yeast Saccharomyces cerevisiae can adapt its metabolism for growth under different oxygen concentrations and can grow even under anaerobic conditions. Therefore, we developed a microfluidic device that can generate serial, accurately controlled oxygen concentrations for single-cell studies of multiple yeast strains. This device can construct a broad range of oxygen concentrations, [O2] through on-chip gas-mixing channels from two gases fed to the inlets. Gas diffusion through thin polydimethylsiloxane (PDMS) can lead to the equilibration of [O2] in the medium in the cell culture layer under gas cover regions within 2 min. Here, we established six different and stable [O2] varying between ~0.1 and 20.9% in the corresponding layers of the device designed for multiple parallel single-cell culture of four different yeast strains. Using this device, the dynamic responses of different yeast transcription factors and metabolism-related proteins were studied when the [O2] decreased from 20.9% to serial hypoxic concentrations. We showed that different hypoxic conditions induced varying degrees of transcription factor responses and changes in respiratory metabolism levels. This device can also be used in studies of the aging and physiology of yeast under different oxygen conditions and can provide new insights into the relationship between oxygen and organisms. Integration, innovation and insight: Most living cells are sensitive to the oxygen concentration because they depend on oxygen for survival and proper cellular functions. Here, a composite microfluidic device was designed for yeast single-cell studies at a series of accurately controlled oxygen concentrations. Using this device, we studied the dynamic responses of various transcription factors and proteins to changes in the oxygen concentration. This study is the first to examine protein dynamics and temporal behaviors under different hypoxic conditions at the single yeast cell level, which may provide insights into the processes involved in yeast and even mammalian cells. This device also provides a base model that can be extended to oxygen-related biology and can acquire more information about the complex networks of organisms.

Saccharomyces boulardii promoters for control of gene expression in vivo

BACKGROUND

Interest in the use of engineered microbes to deliver therapeutic activities has increased in recent years. The probiotic yeast Saccharomyces boulardii has been investigated for production of therapeutics in the gastrointestinal tract. Well-characterised promoters are a prerequisite for robust therapeutic expression in the gut; however, S. boulardii promoters have not yet been thoroughly characterised in vitro and in vivo.

RESULTS

We present a thorough characterisation of the expression activities of 12 S. boulardii promoters in vitro in glucose, fructose, sucrose, inulin and acetate, under both aerobic and anaerobic conditions, as well as in the murine gastrointestinal tract. Green fluorescent protein was used to report on promoter activity. Promoter expression was found to be carbon-source dependent, with inulin emerging as a favourable carbon source. Furthermore, relative promoter expression in vivo was highly correlated with expression in sucrose (R = 0.99).

CONCLUSIONS

These findings provide insights into S. boulardii promoter activity and aid in promoter selection in future studies utilising S. boulardii to produce therapeutics in the gut.

Proteome dynamics during transition from exponential to stationary phase under aerobic and anaerobic conditions in yeast

The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

Temporal segregation of biosynthetic processes is responsible for metabolic oscillations during the budding yeast cell cycle

Many cell biological and biochemical mechanisms controlling the fundamental process of eukaryotic cell division have been identified; however, the temporal dynamics of biosynthetic processes during the cell division cycle are still elusive. Here, we show that key biosynthetic processes are temporally segregated along the cell cycle. Using budding yeast as a model and single-cell methods to dynamically measure metabolic activity, we observe two peaks in protein synthesis, in the G1 and S/G2/M phase, whereas lipid and polysaccharide synthesis peaks only once, during the S/G2/M phase. Integrating the inferred biosynthetic rates into a thermodynamic-stoichiometric metabolic model, we find that this temporal segregation in biosynthetic processes causes flux changes in primary metabolism, with an acceleration of glucose-uptake flux in G1 and phase-shifted oscillations of oxygen and carbon dioxide exchanges. Through experimental validation of the model predictions, we demonstrate that primary metabolism oscillates with cell-cycle periodicity to satisfy the changing demands of biosynthetic processes exhibiting unexpected dynamics during the cell cycle.

Elevated energy costs of biomass production in mitochondrial respiration-deficient Saccharomyces cerevisia

Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeast Saccharomyces cerevisiae, although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model of S. cerevisiae to compare the optimal metabolic strategies between different conditions. We found that S. cerevisiae with respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants of S. cerevisiae. Collectively, these results point to elevated costs of resolving the redox imbalance caused by de novo biosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.

Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

BACKGROUND

Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values.

RESULTS

Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes.

CONCLUSION

We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

Yeast grown in continuous culture systems can detect mutagens with improved sensitivity relative to the Ames test

Continuous culture systems allow for the controlled growth of microorganisms over a long period of time. Here, we develop a novel test for mutagenicity that involves growing yeast in continuous culture systems exposed to low levels of mutagen for a period of approximately 20 days. In contrast, most microorganism-based tests for mutagenicity expose the potential mutagen to the biological reporter at a high concentration of mutagen for a short period of time. Our test improves upon the sensitivity of the well-established Ames test by at least 20-fold for each of two mutagens that act by different mechanisms (the intercalator ethidium bromide and alkylating agent methyl methanesulfonate). To conduct the tests, cultures were grown in small, inexpensive continuous culture systems in media containing (potential) mutagen, and the resulting mutagenicity of the added compound was assessed via two methods: a canavanine-based plate assay and whole genome sequencing. In the canavanine-based plate assay, we were able to detect a clear relationship between the amount of mutagen and the number of canavanine-resistant mutant colonies over a period of one to three weeks of exposure. Whole genome sequencing of yeast grown in continuous culture systems exposed to methyl methanesulfonate demonstrated that quantification of mutations is possible by identifying the number of unique variants across each strain. However, this method had lower sensitivity than the plate-based assay and failed to distinguish the different concentrations of mutagen. In conclusion, we propose that yeast grown in continuous culture systems can provide an improved and more sensitive test for mutagenicity.

DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks

The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

Thiamine: a key nutrient for yeasts during wine alcoholic fermentation

Alcoholic fermentation is a crucial step of winemaking, during which yeasts convert sugars to alcohol and also produce or biotransform numerous flavour compounds. In this context, nutrients are essential compounds to support yeast growth and ultimately ensure complete fermentation, as well as optimized production of flavour compounds over that of off-flavour compounds. In particular, the vitamin thiamine not only plays an essential cofactor role for several enzymes involved in various metabolic pathways, including those leading to the production of wine-relevant flavour compounds, but also aids yeast survival via thiamine-dependent stress protection functions. Most yeast species are able to both assimilate exogenous thiamine into the cell and synthesize thiamine de novo. However, the mechanism and level of thiamine accumulation depend on several factors. This review provides an in-depth overview of thiamine utilization and metabolism in the model yeast species Saccharomyces cerevisiae, as well as the current knowledge on (1) the intracellular functions of thiamine, (2) the balance between and regulation of uptake and synthesis of thiamine and (3) the multitude of factors influencing thiamine availability and utilization. For the latter, a particular emphasis is placed on conditions occurring during wine fermentation. The adequacy of thiamine concentration in grape must to ensure successful fermentation is discussed together with the effect of thiamine concentration on fermentation kinetics and on wine sensory properties. This knowledge may serve as a resource to optimise thiamine concentrations for optimal industrial application of yeasts. KEY POINTS: • Thiamine uptake is preferred over biosynthesis and is transcriptionally repressed. • Multiple factors affect thiamine synthesis, availability and uptake for wine yeast. • Thiamine availability impacts fermentation kinetics and wine's sensory properties.

Genomic Insights of Cryobacterium Isolated From Ice Core Reveal Genome Dynamics for Adaptation in Glacier

Glacier is the dominant cold habitat in terrestrial environments, providing a model ecosystem to explore extremophilic strategies and study early lives on Earth. The dominant form of life in glaciers is bacteria. However, little is known about past evolutionary processes that bacteria underwent during adaptation to the cryosphere and the connection of their genomic traits to environmental stressors. Aiming to test the hypothesis that bacterial genomic content and dynamics are driven by glacial environmental stressors, we compared genomes of 21 psychrophilic Cryobacterium strains, including 14 that we isolated from three Tibetan ice cores, to their mesophilic counterparts from the same family Microbacteriaceae of Actinobacteria. The results show that psychrophilic Cryobacterium underwent more dynamic changes in genome content, and their genomes have a significantly higher number of genes involved in stress response, motility, and chemotaxis than their mesophilic counterparts (P < 0.05). The phylogenetic birth-and-death model imposed on the phylogenomic tree indicates a vast surge in recent common ancestor of psychrophilic Cryobacterium (gained the greatest number of genes by 1,168) after the division of the mesophilic strain Cryobacterium mesophilum. The expansion in genome content brought in key genes primarily of the categories “cofactors, vitamins, prosthetic groups, pigments,” “monosaccharides metabolism,” and “membrane transport.” The amino acid substitution rates of psychrophilic Cryobacterium strains are two orders of magnitude lower than those in mesophilic strains. However, no significantly higher number of cold shock genes was found in psychrophilic Cryobacterium strains, indicating that multi-copy is not a key factor for cold adaptation in the family Microbacteriaceae, although cold shock genes are indispensable for psychrophiles. Extensive gene acquisition and low amino acid substitution rate might be the strategies of psychrophilic Cryobacterium to resist low temperature, oligotrophy, and high UV radiation on glaciers. The exploration of genome evolution and survival strategies of psychrophilic Cryobacterium deepens our understanding of bacterial cold adaptation.

Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis

Motivation

Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models.

Results

In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA.

Availability and implementation

Code is available in the Supplementary data available at Bioinformatics online.

Supplementary information

Supplementary data are available at Bioinformatics online.

Principal metabolic flux mode analysis

Motivation

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples.

Results

We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches.

Availability and implementation

Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine

In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

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

BACKGROUND

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

METHODS

Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture.

RESULTS

Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition.

CONCLUSION

Oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

Signature pathway expression of xylose utilization in the genetically engineered industrial yeast Saccharomyces cerevisiae

Haploid laboratory strains of Saccharomyces cerevisiae are commonly used for genetic engineering to enable their xylose utilization but little is known about the industrial yeast which is often recognized as diploid and as well as haploid and tetraploid. Here we report three unique signature pathway expression patterns and gene interactions in the centre metabolic pathways that signify xylose utilization of genetically engineered industrial yeast S. cerevisiae NRRL Y-50463, a diploid yeast. Quantitative expression analysis revealed outstanding high levels of constitutive expression of YXI, a synthesized yeast codon-optimized xylose isomerase gene integrated into chromosome XV of strain Y-50463. Comparative expression analysis indicated that the YXI was necessary to initiate the xylose metabolic pathway along with a set of heterologous xylose transporter and utilization facilitating genes including XUT4, XUT6, XKS1 and XYL2. The highly activated transketolase and transaldolase genes TKL1, TKL2, TAL1 and NQM1 as well as their complex interactions in the non-oxidative pentose phosphate pathway branch were critical for the serial of sugar transformation to drive the metabolic flow into glycolysis for increased ethanol production. The significantly increased expression of the entire PRS gene family facilitates functions of the life cycle and biosynthesis superpathway for the yeast. The outstanding higher levels of constitutive expression of YXI and the first insight into the signature pathway expression and the gene interactions in the closely related centre metabolic pathways from the industrial yeast aid continued efforts for development of the next-generation biocatalyst. Our results further suggest the industrial yeast is a desirable delivery vehicle for new strain development for efficient lignocellulose-to-advanced biofuels production.

Integrating transcriptional activity in genome-scale models of metabolism

Background

Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.

Results

We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.

Conclusions

Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.

TRFBA: an algorithm to integrate genome-scale metabolic and transcriptional regulatory networks with incorporation of expression data

Motivation

Integration of different biological networks and data-types has been a major challenge in systems biology. The present study introduces the transcriptional regulated flux balance analysis (TRFBA) algorithm that integrates transcriptional regulatory and metabolic models using a set of expression data for various perturbations.

Results

TRFBA considers the expression levels of genes as a new continuous variable and introduces two new linear constraints. The first constraint limits the rate of reaction(s) supported by a metabolic gene using a constant parameter (C) that converts the expression levels to the upper bounds of the reactions. Considering the concept of constraint-based modeling, the second set of constraints correlates the expression level of each target gene with that of its regulating genes. A set of constraints and binary variables was also added to prevent the second set of constraints from overlapping. TRFBA was implemented on Escherichia coli and Saccharomyces cerevisiae models to estimate growth rates under various environmental and genetic perturbations. The error sensitivity to the algorithm parameter was evaluated to find the best value of C. The results indicate a significant improvement in the quantitative prediction of growth in comparison with previously presented algorithms. The robustness of the algorithm to change in the expression data and the regulatory network was tested to evaluate the effect of noisy and incomplete data. Furthermore, the use of added constraints for perturbations without their gene expression profile demonstrates that these constraints can be applied to improve the growth prediction of FBA.

Availability and Implementation

TRFBA is implemented in Matlab software and requires COBRA toolbox. Source code is freely available at http://sbme.modares.ac.ir .

Contact

: motamedian@modares.ac.ir.

Supplementary information

Supplementary data are available at Bioinformatics online.

E-Flux2 and SPOT: Validated Methods for Inferring Intracellular Metabolic Flux Distributions from Transcriptomic Data

Background

Several methods have been developed to predict system-wide and condition-specific intracellular metabolic fluxes by integrating transcriptomic data with genome-scale metabolic models. While powerful in many settings, existing methods have several shortcomings, and it is unclear which method has the best accuracy in general because of limited validation against experimentally measured intracellular fluxes.

Results

We present a general optimization strategy for inferring intracellular metabolic flux distributions from transcriptomic data coupled with genome-scale metabolic reconstructions. It consists of two different template models called DC (determined carbon source model) and AC (all possible carbon sources model) and two different new methods called E-Flux2 (E-Flux method combined with minimization of l² norm) and SPOT (Simplified Pearson cOrrelation with Transcriptomic data), which can be chosen and combined depending on the availability of knowledge on carbon source or objective function. This enables us to simulate a broad range of experimental conditions. We examined E. coli and S. cerevisiae as representative prokaryotic and eukaryotic microorganisms respectively. The predictive accuracy of our algorithm was validated by calculating the uncentered Pearson correlation between predicted fluxes and measured fluxes. To this end, we compiled 20 experimental conditions (11 in E. coli and 9 in S. cerevisiae), of transcriptome measurements coupled with corresponding central carbon metabolism intracellular flux measurements determined by ¹³C metabolic flux analysis (¹³C-MFA), which is the largest dataset assembled to date for the purpose of validating inference methods for predicting intracellular fluxes. In both organisms, our method achieves an average correlation coefficient ranging from 0.59 to 0.87, outperforming a representative sample of competing methods. Easy-to-use implementations of E-Flux2 and SPOT are available as part of the open-source package MOST (http://most.ccib.rutgers.edu/).

Conclusion

Our method represents a significant advance over existing methods for inferring intracellular metabolic flux from transcriptomic data. It not only achieves higher accuracy, but it also combines into a single method a number of other desirable characteristics including applicability to a wide range of experimental conditions, production of a unique solution, fast running time, and the availability of a user-friendly implementation.

Disruption of a cystine transporter downregulates expression of genes involved in sulfur regulation and cellular respiration

Cystine and cysteine are important molecules for pathways such as redox signaling and regulation, and thus identifying cellular deficits upon deletion of the Saccharomyces cerevisiae cystine transporter Ers1p allows for a further understanding of cystine homeostasis. Previous complementation studies using the human ortholog suggest yeast Ers1p is a cystine transporter. Human CTNS encodes the protein Cystinosin, a cystine transporter that is embedded in the lysosomal membrane and facilitates the export of cystine from the lysosome. When CTNS is mutated, cystine transport is disrupted, leading to cystine accumulation, the diagnostic hallmark of the lysosomal storage disorder cystinosis. Here, we provide biochemical evidence for Ers1p-dependent cystine transport. However, the accumulation of intracellular cystine is not observed when the ERS1 gene is deleted from ers1-Δ yeast, supporting the existence of modifier genes that provide a mechanism in ers1-Δ yeast that prevents or corrects cystine accumulation. Upon comparison of the transcriptomes of isogenic ERS1+ and ers1-Δ strains of S. cerevisiae by DNA microarray followed by targeted qPCR, sixteen genes were identified as being differentially expressed between the two genotypes. Genes that encode proteins functioning in sulfur regulation, cellular respiration, and general transport were enriched in our screen, demonstrating pleiotropic effects of ers1-Δ. These results give insight into yeast cystine regulation and the multiple, seemingly distal, pathways that involve proper cystine recycling.

Summary: We identify genes that are differentially expressed in yeast lacking vacuolar cystine transporter Ers1p in order to find pathways, such as respiration and sulfur regulation, that are associated with cystine homeostasis.

Estimating Metabolic Fluxes Using a Maximum Network Flexibility Paradigm

Motivation

Genome-scale metabolic networks can be modeled in a constraint-based fashion. Reaction stoichiometry combined with flux capacity constraints determine the space of allowable reaction rates. This space is often large and a central challenge in metabolic modeling is finding the biologically most relevant flux distributions. A widely used method is flux balance analysis (FBA), which optimizes a biologically relevant objective such as growth or ATP production. Although FBA has proven to be highly useful for predicting growth and byproduct secretion, it cannot predict the intracellular fluxes under all environmental conditions. Therefore, alternative strategies have been developed to select flux distributions that are in agreement with experimental “omics” data, or by incorporating experimental flux measurements. The latter, unfortunately can only be applied to a limited set of reactions and is currently not feasible at the genome-scale. On the other hand, it has been observed that micro-organisms favor a suboptimal growth rate, possibly in exchange for a more “flexible” metabolic network. Instead of dedicating the internal network state to an optimal growth rate in one condition, a suboptimal growth rate is used, that allows for an easier switch to other nutrient sources. A small decrease in growth rate is exchanged for a relatively large gain in metabolic capability to adapt to changing environmental conditions.

Results

Here, we propose Maximum Metabolic Flexibility (MMF) a computational method that utilizes this observation to find the most probable intracellular flux distributions. By mapping measured flux data from central metabolism to the genome-scale models of Escherichia coli and Saccharomyces cerevisiae we show that i) indeed, most of the measured fluxes agree with a high adaptability of the network, ii) this result can be used to further reduce the space of feasible solutions iii) this reduced space improves the quantitative predictions made by FBA and contains a significantly larger fraction of the measured fluxes compared to the flux space that was reduced by a uniform sampling approach and iv) MMF can be used to select reactions in the network that contribute most to the steady-state flux space. Constraining the selected reactions improves the quantitative predictions of FBA considerably more than adding an equal amount of flux constraints, selected using a more naïve approach. Our method can be applied to any cell type without requiring prior information.

Availability

MMF is freely available as a MATLAB plugin at: http://cs.ru.nl/~wmegchel/mmf.

Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach

A systems-based investigation of the effect of perturbations on metabolic machinery is crucial to elucidate the mechanism behind perturbations. One way to investigate the perturbation-induced changes within the cell metabolism is to focus on pathway-level effects. In this study, three different perturbation types (genetic, environmental and disease-based) are analyzed to compute a list of reporter pathways, metabolic pathways which are significantly affected from a perturbation. The most common omics data type, transcriptome, is used as an input to the bioinformatic analysis. The pathways are scored by two alternative approaches: by averaging the changes in the expression levels of the genes controlling the associated reactions (reaction-centric), and by averaging the changes in the associated metabolites which were scored based on the associated genes (metabolite-centric). The analysis reveals the superiority of the novel metabolite-centric approach over the commonly used reaction-centric approach since it is based on metabolites which better represent the cross-talk among different pathways, enabling a more global and realistic cataloguing of network-wide perturbation effects.

Instability of succinate dehydrogenase in SDHD polymorphism connects reactive oxygen species production to nuclear and mitochondrial genomic mutations in yeast

AIMS

Mitochondrial succinate dehydrogenase (SDH) is an essential complex of the electron transport chain and tricarboxylic acid cycle. Mutations in the human SDH subunit D frequently lead to paraganglioma (PGL), but the mechanistic consequences of the majority of SDHD polymorphisms have yet to be unraveled. In addition to the originally discovered yeast SDHD subunit Sdh4, a conserved homolog, Shh4, has recently been identified in budding yeast. To assess the pathogenic significance of SDHD mutations in PGL patients, we performed functional studies in yeast.

RESULTS

SDHD protein expression was reduced in SDHD-related carotid body tumor tissues. A BLAST search of SDHD to the yeast protein database revealed a novel protein, Shh4, that may have a function similar to human SDHD and yeast Sdh4. The missense SDHD mutations identified in PGL patients were created in Sdh4 and Shh4, and, surprisingly, a severe respiratory incompetence and reduced expression of the mutant protein was observed in the sdh4Δ strain expressing shh4. Although shh4Δ cells showed no respiratory-deficient phenotypes, deletion of SHH4 in sdh4Δ cells further abolished mitochondrial function. Remarkably, sdh4Δ shh4Δ strains exhibited increased reactive oxygen species (ROS) production, nuclear DNA instability, mtDNA mutability, and decreased chronological lifespan.

INNOVATION AND CONCLUSION

SDHD mutations are associated with protein and nuclear and mitochondrial genomic instability and increase ROS production in our yeast model. These findings reinforce our understanding of the mechanisms underlying PGL tumorigenesis and point to the yeast Shh4 as a good model to investigate the possible pathogenic relevance of SDHD in PGL polymorphisms.

Impact of two ionic liquids, 1-ethyl-3-methylimidazolium acetate and 1-ethyl-3-methylimidazolium methylphosphonate, on Saccharomyces cerevisiae: metabolic, physiologic, and morphological investigations

BACKGROUND

Ionic liquids (ILs) are considered as suitable candidates for lignocellulosic biomass pretreatment prior enzymatic saccharification and, obviously, for second-generation bioethanol production. However, several reports showed toxic or inhibitory effects of residual ILs on microorganisms, plants, and animal cells which could affect a subsequent enzymatic saccharification and fermentation process.

RESULTS

In this context, the impact of two hydrophilic imidazolium-based ILs already used in lignocellulosic biomass pretreatment was investigated: 1-ethyl-3-methylimidazolium acetate [Emim][OAc] and 1-ethyl-3-methylimidazolium methylphosphonate [Emim][MeO(H)PO2]. Their effects were assessed on the model yeast for ethanolic fermentation, Saccharomyces cerevisiae, grown in a culture medium containing glucose as carbon source and various IL concentrations. Classical fermentation parameters were followed: growth, glucose consumption and ethanol production, and two original factors: the respiratory status with the oxygen transfer rate (OTR) and carbon dioxide transfer rate (CTR) of yeasts which were monitored online by respiratory activity monitoring systems (RAMOS). In addition, yeast morphology was characterized by environmental scanning electron microscope (ESEM). The addition of ILs to the growth medium inhibited the OTR and switched the metabolism from respiration (conversion of glucose into biomass) to fermentation (conversion of glucose to ethanol). This behavior could be observed at low IL concentrations (≤5% IL) while above there is no significant growth or ethanol production. The presence of IL in the growth medium also induced changes of yeast morphology, which exhibited wrinkled, softened, and holed shapes. Both ILs showed the same effects, but [Emim][MeO(H)PO2] was more biocompatible than [Emim][OAc] and could be better tolerated by S. cerevisiae.

CONCLUSIONS

These two imidazolium-derived ILs were appropriate candidates for useful pretreatment of lignocellulosic biomass in the context of second-generation bioethanol production. This fundamental study provides additional information about the toxic effects of ILs. Indeed, the investigations highlighted the better tolerance by S. cerevisiae of [Emim][MeO(H)PO2] than [Emim][OAc].

Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

Exometabolome analysis reveals hypoxia at the up-scaling of a Saccharomyces cerevisiae high-cell density fed-batch biopharmaceutical process

BACKGROUND

Scale-up to industrial production level of a fermentation process occurs after optimization at small scale, a critical transition for successful technology transfer and commercialization of a product of interest. At the large scale a number of important bioprocess engineering problems arise that should be taken into account to match the values obtained at the small scale and achieve the highest productivity and quality possible. However, the changes of the host strain's physiological and metabolic behavior in response to the scale transition are still not clear.

RESULTS

Heterogeneity in substrate and oxygen distribution is an inherent factor at industrial scale (10,000 L) which affects the success of process up-scaling. To counteract these detrimental effects, changes in dissolved oxygen and pressure set points and addition of diluents were applied to 10,000 L scale to enable a successful process scale-up. A comprehensive semi-quantitative and time-dependent analysis of the exometabolome was performed to understand the impact of the scale-up on the metabolic/physiological behavior of the host microorganism. Intermediates from central carbon catabolism and mevalonate/ergosterol synthesis pathways were found to accumulate in both the 10 L and 10,000 L scale cultures in a time-dependent manner. Moreover, excreted metabolites analysis revealed that hypoxic conditions prevailed at the 10,000 L scale. The specific product yield increased at the 10,000 L scale, in spite of metabolic stress and catabolic-anabolic uncoupling unveiled by the decrease in biomass yield on consumed oxygen.

CONCLUSIONS

An optimized S. cerevisiae fermentation process was successfully scaled-up to an industrial scale bioreactor. The oxygen uptake rate (OUR) and overall growth profiles were matched between scales. The major remaining differences between scales were wet cell weight and culture apparent viscosity. The metabolic and physiological behavior of the host microorganism at the 10,000 L scale was investigated with exometabolomics, indicating that reduced oxygen availability affected oxidative phosphorylation cascading into down- and up-stream pathways producing overflow metabolism. Our study revealed striking metabolic and physiological changes in response to hypoxia exerted by industrial bioprocess up-scaling.

Integration of transcription and flux data reveals molecular paths associated with differences in oxygen-dependent phenotypes of Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.

RESULTS

The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.

CONCLUSIONS

The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

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

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

Identification of a novel deletion mutant strain in Saccharomyces cerevisiae that results in a microsatellite instability phenotype

The DNA mismatch repair (MMR) pathway corrects specific types of DNA replication errors that affect microsatellites and thus is critical for maintaining genomic integrity. The genes of the MMR pathway are highly conserved across different organisms. Likewise, defective MMR function universally results in microsatellite instability (MSI) which is a hallmark of certain types of cancer associated with the Mendelian disorder hereditary nonpolyposis colorectal cancer. (Lynch syndrome). To identify previously unrecognized deleted genes or loci that can lead to MSI, we developed a functional genomics screen utilizing a plasmid containing a microsatellite sequence that is a host spot for MSI mutations and the comprehensive homozygous diploid deletion mutant resource for Saccharomyces cerevisiae. This pool represents a collection of non-essential homozygous yeast diploid (2N) mutants in which there are deletions for over four thousand yeast open reading frames (ORFs). From our screen, we identified a deletion mutant strain of the PAU24 gene that leads to MSI. In a series of validation experiments, we determined that this PAU24 mutant strain had an increased MSI-specific mutation rate in comparison to the original background wildtype strain, other deletion mutants and comparable to a MMR mutant involving the MLH1 gene. Likewise, in yeast strains with a deletion of PAU24, we identified specific de novo indel mutations that occurred within the targeted microsatellite used for this screen.

Cell differentiation within a yeast colony: metabolic and regulatory parallels with a tumor-affected organism

Nutrient sensing and metabolic reprogramming are crucial for metazoan cell aging and tumor growth. Here, we identify metabolic and regulatory parallels between a layered, multicellular yeast colony and a tumor-affected organism. During development, a yeast colony stratifies into U and L cells occupying the upper and lower colony regions, respectively. U cells activate a unique metabolism controlled by the glutamine-induced TOR pathway, amino acid-sensing systems (SPS and Gcn4p) and signaling from mitochondria with lowered respiration. These systems jointly modulate U cell physiology, which adapts to nutrient limitations and utilize the nutrients released from L cells. Stress-resistant U cells share metabolic pathways and other similar characteristics with tumor cells, including the ability to proliferate. L cells behave similarly to stressed and starving cells, which activate degradative mechanisms to provide nutrients to U cells. Our data suggest a nutrient flow between both cell types, resembling the Cori cycle and glutamine-NH(4)(+) shuttle between tumor and healthy metazoan cells.

Rational diversification of a promoter providing fine-tuned expression and orthogonal regulation for synthetic biology

Yeast is an ideal organism for the development and application of synthetic biology, yet there remain relatively few well-characterised biological parts suitable for precise engineering of this chassis. In order to address this current need, we present here a strategy that takes a single biological part, a promoter, and re-engineers it to produce a fine-graded output range promoter library and new regulated promoters desirable for orthogonal synthetic biology applications. A highly constitutive Saccharomyces cerevisiae promoter, PFY1p, was identified by bioinformatic approaches, characterised in vivo and diversified at its core sequence to create a 36-member promoter library. TetR regulation was introduced into PFY1p to create a synthetic inducible promoter (iPFY1p) that functions in an inverter device. Orthogonal and scalable regulation of synthetic promoters was then demonstrated for the first time using customisable Transcription Activator-Like Effectors (TALEs) modified and designed to act as orthogonal repressors for specific PFY1-based promoters. The ability to diversify a promoter at its core sequences and then independently target Transcription Activator-Like Orthogonal Repressors (TALORs) to virtually any of these sequences shows great promise toward the design and construction of future synthetic gene networks that encode complex "multi-wire" logic functions.

Transcription profiling of sparkling wine second fermentation

There is a specific set of stress factors that yeast cells must overcome under second fermentation conditions, during the production of sparkling wines by the traditional (Champenoise) method. Some of them are the same as those of the primary fermentation of still wines, although perhaps with a different intensity (high ethanol concentration, low pH, nitrogen starvation) while others are more specific to second fermentation (low temperature, CO(2) overpressure). The transcription profile of Saccharomyces cerevisiae during primary wine fermentation has been studied by several research groups, but this is the first report on yeast transcriptome under second fermentation conditions. Our results indicate that the main pathways affected by these particular conditions are related to aerobic respiration, but genes related to vacuolar and peroxisomal functions were also highlighted in this study. A parallelism between the transcription profile of wine yeast during primary and second fermentation is appreciated, with ethanol appearing as the main factor driving gene transcription during second fermentation. Low temperature seems to also influence yeast transcription profile under these particular winemaking conditions.

Protein trafficking, ergosterol biosynthesis and membrane physics impact recombinant protein secretion in Pichia pastoris

Background

The increasing availability of 'omics' databases provide important platforms for yeast engineering strategies since they offer a lot of information on the physiology of the cells under diverse growth conditions, including environmental stresses. Notably, only a few of these approaches have considered a performance under recombinant protein production conditions. Recently, we have identified a beneficial effect of low oxygen availability on the expression of a human Fab fragment in Pichia pastoris. Transcriptional analysis and data mining allowed for the selection of potential targets for strain improvement. A first selection of these candidates has been evaluated as recombinant protein secretion enhancers.

Results

Based on previous transcriptomics analyses, we selected 8 genes for co-expression in the P. pastoris strain already secreting a recombinant Fab fragment. Notably, WSC4 (which is involved in trafficking through the ER) has been identified as a novel potential target gene for strain improvement, with up to a 1.2-fold increase of product yield in shake flask cultures. A further transcriptomics-based strategy to modify the yeast secretion system was focused on the ergosterol pathway, an aerobic process strongly affected by oxygen depletion. By specifically partially inhibiting ergosterol synthesis with the antifungal agent fluconazole (inhibiting Erg11p), we tried to mimic the hypoxic conditions, in which the cellular ergosterol content was significantly decreased. This strategy led to an improved Fab yield (2-fold) without impairing cellular growth. Since ergosterol shortage provokes alterations in the plasma membrane composition, an important role of this cellular structure in protein secretion is suggested. This hypothesis was additionally supported by the fact that the addition of non-ionic surfactants also enhanced Fab secretion.

Conclusions

The current study presents a systems biotechnology-based strategy for the engineering of the industrially important yeast P. pastoris combining the use of host specific DNA microarray technologies and physiological studies under well defined environmental conditions. Such studies allowed for the identification of novel targets related with protein trafficking and ergosterol biosynthesis for improved recombinant protein production. Nevertheless, further studies will be required to elucidate the precise mechanisms whereby membrane biogenesis and composition impact on protein secretion in P. pastoris.

The impact of oxygen on the transcriptome of recombinant S. cerevisiae and P. pastoris - a comparative analysis

Background

Saccharomyces cerevisiae and Pichia pastoris are two of the most relevant microbial eukaryotic platforms for the production of recombinant proteins. Their known genome sequences enabled several transcriptomic profiling studies under many different environmental conditions, thus mimicking not only perturbations and adaptations which occur in their natural surroundings, but also in industrial processes. Notably, the majority of such transcriptome analyses were performed using non-engineered strains.

In this comparative study, the gene expression profiles of S. cerevisiae and P. pastoris, a Crabtree positive and Crabtree negative yeast, respectively, were analyzed for three different oxygenation conditions (normoxic, oxygen-limited and hypoxic) under recombinant protein producing conditions in chemostat cultivations.

Results

The major differences in the transcriptomes of S. cerevisiae and P. pastoris were observed between hypoxic and normoxic conditions, where the availability of oxygen strongly affected ergosterol biosynthesis, central carbon metabolism and stress responses, particularly the unfolded protein response. Steady state conditions under low oxygen set-points seemed to perturb the transcriptome of S. cerevisiae to a much lesser extent than the one of P. pastoris, reflecting the major tolerance of the baker's yeast towards oxygen limitation, and a higher fermentative capacity. Further important differences were related to Fab production, which was not significantly affected by oxygen availability in S. cerevisiae, while a clear productivity increase had been previously reported for hypoxically grown P. pastoris.

Conclusions

The effect of three different levels of oxygen availability on the physiology of P. pastoris and S. cerevisiae revealed a very distinct remodelling of the transcriptional program, leading to novel insights into the different adaptive responses of Crabtree negative and positive yeasts to oxygen availability. Moreover, the application of such comparative genomic studies to recombinant hosts grown in different environments might lead to the identification of key factors for efficient protein production.

Analysis of the Aspergillus fumigatus proteome reveals metabolic changes and the activation of the pseurotin A biosynthesis gene cluster in response to hypoxia

The mold Aspergillus fumigatus is the most important airborne fungal pathogen. Adaptation to hypoxia represents an important virulence attribute for A. fumigatus. Therefore, we aimed at obtaining a comprehensive overview about this process on the proteome level. To ensure highly reproducible growth conditions, an oxygen-controlled, glucose-limited chemostat cultivation was established. Two-dimensional gel electrophoresis analysis of mycelial and mitochondrial proteins as well as two-dimensional Blue Native/SDS-gel separation of mitochondrial membrane proteins led to the identification of 117 proteins with an altered abundance under hypoxic in comparison to normoxic conditions. Hypoxia induced an increased activity of glycolysis, the TCA-cycle, respiration, and amino acid metabolism. Consistently, the cellular contents in heme, iron, copper, and zinc increased. Furthermore, hypoxia induced biosynthesis of the secondary metabolite pseurotin A as demonstrated at proteomic, transcriptional, and metabolite levels. The observed and so far not reported stimulation of the biosynthesis of a secondary metabolite by oxygen depletion may also affect the survival of A. fumigatus in hypoxic niches of the human host. Among the proteins so far not implicated in hypoxia adaptation, an NO-detoxifying flavohemoprotein was one of the most highly up-regulated proteins which indicates a link between hypoxia and the generation of nitrosative stress in A. fumigatus.

Aspergillus fumigatus is a ubiquitously distributed filamentous fungus and an important human pathogen. To colonize the human lung, A. fumigatus has to adapt to low oxygen concentrations. We analyzed the cytosolic and mitochondrial proteome of A. fumigatus under normoxic in comparison to hypoxic conditions using an oxygen-controlled chemostat. Hypoxia led to an increased respiratory capacity, induction of the biosynthesis of the secondary metabolite pseurotin A and presumably nitrosative stress.

Biosynthetic and iron metabolism is regulated by thiol proteome changes dependent on glutaredoxin-2 and mitochondrial peroxiredoxin-1 in Saccharomyces cerevisiae

Redoxins are involved in maintenance of thiol redox homeostasis, but their exact sites of action are only partly known. We have applied a combined redox proteomics and transcriptomics experimental strategy to discover specific functions of two interacting redoxins: dually localized glutaredoxin 2 (Grx2p) and mitochondrial peroxiredoxin 1 (Prx1p). We have identified 139 proteins showing differential postranslational thiol redox modifications when the cells do not express Grx2p, Prx1p, or both and have mapped the precise cysteines involved in each case. Some of these modifications constitute functional switches that affect metabolic and signaling pathways as the primary effect, leading to gene transcription remodeling as the secondary adaptive effect as demonstrated by a parallel high throughput gene expression analysis. The results suggest that in the absence of Grx2p, the metabolic flow toward nucleotide and aromatic amino acid biosynthesis is slowed down by redox modification of the key enzymes Rpe1p (D-ribulose-5-phosphate 3-epimerase), Tkl1p (transketolase) and Aro4p (3-deoxy-D-arabino-heptulosonate-7-phosphate synthase). The glycolytic mainstream is then diverted toward carbohydrate storage by induction of trehalose and glycogen biosynthesis genes. Porphyrin biosynthesis may also be compromised by inactivation of the redox-sensitive cytosolic enzymes Hem12p (uroporphyrinogen decarboxylase) and Sam1p (S-adenosyl methionine synthetase) and a battery of respiratory genes sensitive to low heme levels are induced. Genes of the Aft1p-dependent iron regulon were induced specifically in the absence of Prx1p despite optimal mitochondrial Fe-S biogenesis, suggesting dysfunction of the mitochondria to the cytosol signaling pathway. Strikingly, requirement of Grx2p for these events places dithiolic Grx2 in the framework of iron metabolism.

Transcriptional responses of Saccharomyces cerevisiae to shift from respiratory and respirofermentative to fully fermentative metabolism

In industrial fermentations of Saccharomyces cerevisiae, transient changes in oxygen concentration commonly occur and it is important to understand the behavior of cells during these changes. Glucose-limited chemostat cultivations were used to study the time-dependent effect of sudden oxygen depletion on the transcriptome of S. cerevisiae cells initially in fully aerobic or oxygen-limited conditions. The overall responses to anaerobic conditions of cells initially in different conditions were very similar. Independent of initial culture conditions, transient downregulation of genes related to growth and cell proliferation, mitochondrial translation and protein import, and sulphate assimilation was seen. In addition, transient or permanent upregulation of genes related to protein degradation, and phosphate and amino acid uptake was observed in all cultures. However, only in the initially oxygen-limited cultures was a transient upregulation of genes related to fatty acid oxidation, peroxisomal biogenesis, oxidative phosphorylation, TCA cycle, response to oxidative stress, and pentose phosphate pathway observed. Furthermore, from the initially oxygen-limited conditions, a rapid response around the metabolites of upper glycolysis and the pentose phosphate pathway was seen, while from the initially fully aerobic conditions, a slower response around the pathways for utilization of respiratory carbon sources was observed.