Duygu Dikicioglu

Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains

The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the high-throughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism.

A novel strategy for selection and validation of reference genes in dynamic multidimensional experimental design in yeast.

Background Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response. Principal Findings In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in Saccharomyces cerevisiae was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (TPI1, FBA1, CCW12, CDC19, ADH1, PGK1, GCN4, PDC1, RPS26A and ARF1) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes ACT1 and TDH3. The most stable reference genes from this pool were determined as TPI1, FBA1, CDC19 and ACT1 in response to a perturbation in the amount of available glucose and as FBA1, TDH3, CCW12 and ACT1 in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, HAP4 and MEP2, in response to relaxation from glucose and ammonium limitations, respectively. Conclusions A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest.

Flux balance analysis of a genome-scale yeast model constrained by exometabolomic data allows metabolic system identification of genetically different strains

A systems approach to biology requires a principled approach to pathway identification. In this study, the two nuclear petite yeast mutants K1Δpet191a and K1Δpet191ab and their parental industrial strain K1 were cultured in glucose‐containing microaerobic chemostats. Exometabolomic profiles were used to infer the differences in the fermentation characteristics and respiration capacity of the strains. The ability of the metabolite measurement information to describe genetically different strains was investigated using a genome‐scale yeast model. Flux balance analysis (FBA) of the model reveals that the objective function of minimal oxygen consumption enables the identification of the effect of genotypic differences when combined with the knowledge of the extracellular state of metabolism. The predicted decrease in oxygen consumption flux of K1Δpet191a and K1Δpet191ab strains with respect to the parental strain is about 80% and 100%, respectively, which coincides with the respiratory deficiencies of the strains. The expected increase in ethanol production rates in response to the decrease in the respiratory capacity was also predicted to be very close to the experimental values. This study shows the predictive power of the integrated analysis of genome‐scale models with exometabolomic profiles, since accurate predictions could be made without any information about the respiration capacity of the strains. The FBA approach thereby enables identification of responsive pathways and so permits the elucidation of the genetic characteristics of strains in terms of expressed metabolite profiles.

Integration of Metabolic Modeling and Phenotypic Data in Evaluation and Improvement of Ethanol Production Using Respiration-Deficient Mutants of Saccharomyces cerevisiae

ABSTRACT Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4Δ/hap4Δ, mig1Δ/mig1Δ, qdr3Δ/qdr3Δ, pdr3Δ/pdr3Δ, qcr7Δ/qcr7Δ, cyt1Δ/cyt1Δ, and rip1Δ/rip1Δ mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3Δ/qdr3Δ mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7Δ/qcr7Δ mutant compared to the rip1Δ/rip1Δ and cyt1Δ/cyt1Δ mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD+/NADH imbalance in rip1Δ/rip1Δ and cyt1Δ/cyt1Δ mutants, but not the qcr7Δ/qcr7Δ mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

Biomass composition: the "elephant in the room" of metabolic modelling.

Abstract Genome-scale stoichiometric models, constrained to optimise biomass production are often used to predict mutant phenotypes. However, for Saccharomyces cerevisiae, the representation of biomass in its metabolic model has hardly changed in over a decade, despite major advances in analytical technologies. Here, we use the stoichiometric model of the yeast metabolic network to show that its ability to predict mutant phenotypes is particularly poor for genes encoding enzymes involved in energy generation. We then identify apparently inefficient energy-generating pathways in the model and demonstrate that the network suffers from the high energy burden associated with the generation of biomass. This is tightly connected to the availability of phosphate since this macronutrient links energy generation and structural biomass components. Variations in yeast’s biomass composition, within experimentally-determined bounds, demonstrated that flux distributions are very sensitive to such changes and to the identity of the growth-limiting nutrient. The predictive accuracy of the yeast metabolic model is, therefore, compromised by its failure to represent biomass composition in an accurate and context-dependent manner.

Improving functional annotation for industrial microbes: a case study with Pichia pastoris

Highlights • The current status of the Pichia pastoris genome is shown to lack extensive functional annotation.• GO annotation transfer and literature curation pipelines improve the functional annotation of genomes.• Pipelines and tools that can improve the annotation status of the genomes of Pichia pastoris and many industrial microbes are considered.• Well-annotated genome sequences will facilitate the utilization of these microbes in a broader range of synthetic biology applications.

Predicting complex phenotype–genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both “red” and “white” biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype–phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Dynamic Proteomic Profiling of Extra-Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells

During mammalian preimplantation development, the cells of the blastocysts inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra‐embryonic tissues, respectively. Extra‐embryonic endoderm (XEN) differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here, we use this GATA‐inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo‐derived XEN cells. Using mass spectrometry‐based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates of the pluripotent state and XEN differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin‐modifying enzymes, the reorganization of membrane trafficking machinery, and the breakdown of cell–cell adhesion are successive steps of the extra‐embryonic differentiation process. Thus, applying a range of sophisticated clustering approaches to a time‐resolved proteomic dataset has allowed the elucidation of complex biological processes which characterize stem cell differentiation and could establish a general paradigm for the investigation of these processes. Stem Cells 2015;33:2712—2725

Yeast cells with impaired drug resistance accumulate glycerol and glucose

Multiple drug resistance (MDR) in yeast is effected by two major superfamilies of membrane transporters: the major facilitator superfamily (MFS) and the ATP-binding cassette (ABC) superfamily. In the present work, we investigated the cellular responses to disruptions in both MFS (by deleting the transporter gene, QDR3) and ABC (by deleting the gene for the Pdr3 transcription factor) transporter systems by growing diploid homozygous deletion yeast strains in glucose- or ammonium-limited continuous cultures. The transcriptome and the metabolome profiles of these strains, as well as the flux distributions in the optimal solution space, reveal novel insights into the underlying mechanisms of action of QDR3 and PDR3. Our results show how cells rearrange their metabolism to cope with the problems that arise from the loss of these drug-resistance genes, which likely evolved to combat chemical attack from bacterial or fungal competitors. This is achieved through the accumulation of intracellular glucose, glycerol, and inorganic phosphate, as well as by repurposing genes that are known to function in other parts of metabolism in order to minimise the effects of toxic compounds.

Short- and long-term dynamic responses of the metabolic network and gene expression in yeast to a transient change in the nutrient environment

Quantitative data on the dynamic changes in the transcriptome and the metabolome of yeast in response to an impulse-like perturbation in nutrient availability was integrated with the metabolic pathway information in order to elucidate the long-term dynamic re-organization of the cells. This study revealed that, in addition to the dynamic re-organization of the de novo biosynthetic pathways, salvage pathways were also re-organized in a time-dependent manner upon catabolite repression. The transcriptional and the metabolic responses observed for nitrogen catabolite repression were not as severe as those observed for carbon catabolite repression. Selective up- or down regulation of a single member of a paralogous gene pair during the response to the relaxation from nutritional limitation was identified indicating a differentiation of functions among paralogs. Our study highlighted the role of inosine accumulation and recycling in energy homeostasis and indicated possible bottlenecks in the process.