Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: a three factor design

The fitness trade-off between growth and stress resistance determines the phenotypic landscape

BACKGROUND

A central challenge in biology is to discover a principle that determines individual phenotypic differences within a species. The growth rate is particularly important for a unicellular organism, and the growth rate under a certain condition is negatively associated with that of another condition, termed fitness trade-off. Therefore, there should exist a common molecular mechanism that regulates multiple growth rates under various conditions, but most studies so far have focused on discovering those genes associated with growth rates under a specific condition.

RESULTS

In this study, we found that there exists a recurrent gene expression signature whose expression levels are related to the fitness trade-off between growth preference and stress resistance across various yeast strains and multiple conditions. We further found that the genomic variation of stress-response, ribosomal, and cell cycle regulators are potential causal genes that determine the sensitivity between growth and survival. Intriguingly, we further observed that the same principle holds for human cells using anticancer drug sensitivities across multiple cancer cell lines.

CONCLUSIONS

Together, we suggest that the fitness trade-off is an evolutionary trait that determines individual growth phenotype within a species. By using this trait, we can possibly overcome anticancer drug resistance in cancer cells.

Mimicked Mixing-Induced Heterogeneities of Industrial Bioreactors Stimulate Long-Lasting Adaption Programs in Ethanol-Producing Yeasts

Commercial-scale bioreactors create an unnatural environment for microbes from an evolutionary point of view. Mixing insufficiencies expose individual cells to fluctuating nutrient concentrations on a second-to-minute scale while transcriptional and translational capacities limit the microbial adaptation time from minutes to hours. This mismatch carries the risk of inadequate adaptation effects, especially considering that nutrients are available at optimal concentrations on average. Consequently, industrial bioprocesses that strive to maintain microbes in a phenotypic sweet spot, during lab-scale development, might suffer performance losses when said adaptive misconfigurations arise during scale-up. Here, we investigated the influence of fluctuating glucose availability on the gene-expression profile in the industrial yeast Ethanol Red™. The stimulus-response experiment introduced 2 min glucose depletion phases to cells growing under glucose limitation in a chemostat. Even though Ethanol Red™ displayed robust growth and productivity, a single 2 min depletion of glucose transiently triggered the environmental stress response. Furthermore, a new growth phenotype with an increased ribosome portfolio emerged after complete adaptation to recurring glucose shortages. The results of this study serve a twofold purpose. First, it highlights the necessity to consider the large-scale environment already at the experimental development stage, even when process-related stressors are moderate. Second, it allowed the deduction of strain engineering guidelines to optimize the genetic background of large-scale production hosts.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

JAZF1: A metabolic actor subunit of the NuA4/TIP60 chromatin modifying complex

The multisubunit NuA4/TIP60 complex is a lysine acetyltransferase, chromatin modifying factor and gene co-activator involved in diverse biological processes. The past decade has seen a growing appreciation for its role as a metabolic effector and modulator. However, molecular insights are scarce and often contradictory, underscoring the need for further mechanistic investigation. A particularly exciting route emerged with the recent identification of a novel subunit, JAZF1, which has been extensively linked to metabolic homeostasis. This review summarizes the major findings implicating NuA4/TIP60 in metabolism, especially in light of JAZF1 as part of the complex.

Complex genetic and epigenetic regulation deviates gene expression from a unifying global transcriptional program

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

How can we understand expression changes observed as a result of environmental or genetic perturbations? This issue has been conventionally answered by examining small groups of genes whose expression becomes qualitatively altered after these perturbations. But this approach is too simplistic, as we now know that extensive variation is typically observed. To explain this variation, recent works proposed a model in which genome-wide changes are explained by the action of a general program of transcription. Our manuscript reasons that given the complexity of eukaryotic transcriptional control, a unifying program of regulation cannot be achievable. Instead, we propose within an integrated framework of resource allocation that a rich structure of deviations from it exists and that by characterizing these deviations we can fully appreciate large-scale expression change.

Lack of G1/S control destabilizes the yeast genome via replication stress-induced DSBs and illegitimate recombination

The protein Swi6 in Saccharomyces cerevisiae is a cofactor in two complexes that regulate the transcription of the genes controlling the G1/S transition. It also ensures proper oxidative and cell wall stress responses. Previously, we found that Swi6 was crucial for the survival of genotoxic stress. Here, we show that a lack of Swi6 causes replication stress leading to double-strand break (DSB) formation, inefficient DNA repair and DNA content alterations, resulting in high cell mortality. Comparative genome hybridization experiments revealed that there was a random genome rearrangement in swi6Δ cells, whereas in diploid swi6Δ/swi6Δ cells, chromosome V is duplicated. SWI4 and PAB1, which are located on chromosome V and are known multicopy suppressors of swi6Δ phenotypes, partially reverse swi6Δ genome instability when overexpressed. Another gene on chromosome V, RAD51, also supports swi6Δ survival, but at a high cost; Rad51-dependent illegitimate recombination in swi6Δ cells appears to connect DSBs, leading to genome rearrangement and preventing cell death.This article has an associated First Person interview with the first author of the paper.

Maintenance-energy requirements and robustness of Saccharomyces cerevisiae at aerobic near-zero specific growth rates

Background

Saccharomyces cerevisiae is an established microbial platform for production of native and non-native compounds. When product pathways compete with growth for precursors and energy, uncoupling of growth and product formation could increase product yields and decrease formation of biomass as a by-product. Studying non-growing, metabolically active yeast cultures is a first step towards developing S. cerevisiae as a robust, non-growing cell factory. Microbial physiology at near-zero growth rates can be studied in retentostats, which are continuous-cultivation systems with full biomass retention. Hitherto, retentostat studies on S. cerevisiae have focused on anaerobic conditions, which bear limited relevance for aerobic industrial processes. The present study uses aerobic, glucose-limited retentostats to explore the physiology of non-dividing, respiring S. cerevisiae cultures, with a focus on industrially relevant features.

Results

Retentostat feeding regimes for smooth transition from exponential growth in glucose-limited chemostat cultures to near-zero growth rates were obtained by model-aided experimental design. During 20 days of retentostats cultivation, the specific growth rate gradually decreased from 0.025 h⁻¹ to below 0.001 h⁻¹, while culture viability remained above 80 %. The maintenance requirement for ATP (m_ATP) was estimated at 0.63 ± 0.04 mmol ATP (g biomass)⁻¹ h⁻¹, which is ca. 35 % lower than previously estimated for anaerobic retentostats. Concomitant with decreasing growth rate in aerobic retentostats, transcriptional down-regulation of genes involved in biosynthesis and up-regulation of stress-responsive genes resembled transcriptional regulation patterns observed for anaerobic retentostats. The heat-shock tolerance in aerobic retentostats far exceeded previously reported levels in stationary-phase batch cultures. While in situ metabolic fluxes in retentostats were intentionally low due to extreme caloric restriction, off-line measurements revealed that cultures retained a high metabolic capacity.

Conclusions

This study provides the most accurate estimation yet of the maintenance-energy coefficient in aerobic cultures of S. cerevisiae, which is a key parameter for modelling of industrial aerobic, glucose-limited fed-batch processes. The observed extreme heat-shock tolerance and high metabolic capacity at near-zero growth rates demonstrate the intrinsic potential of S. cerevisiae as a robust, non-dividing microbial cell factory for energy-intensive products.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0501-z) contains supplementary material, which is available to authorized users.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Bacterial transcriptome reorganization in thermal adaptive evolution

Background

Evolution optimizes a living system at both the genome and transcriptome levels. Few studies have investigated transcriptome evolution, whereas many studies have explored genome evolution in experimentally evolved cells. However, a comprehensive understanding of evolutionary mechanisms requires knowledge of how evolution shapes gene expression. Here, we analyzed Escherichia coli strains acquired during long-term thermal adaptive evolution.

Results

Evolved and ancestor Escherichia coli cells were exponentially grown under normal and high temperatures for subsequent transcriptome analysis. We found that both the ancestor and evolved cells had comparable magnitudes of transcriptional change in response to heat shock, although the evolutionary progression of their expression patterns during exponential growth was different at either normal or high temperatures. We also identified inverse transcriptional changes that were mediated by differences in growth temperatures and genotypes, as well as negative epistasis between genotype—and heat shock-induced transcriptional changes. Principal component analysis revealed that transcriptome evolution neither approached the responsive state at the high temperature nor returned to the steady state at the regular temperature. We propose that the molecular mechanisms of thermal adaptive evolution involve the optimization of steady-state transcriptomes at high temperatures without disturbing the heat shock response.

Conclusions

Our results suggest that transcriptome evolution works to maintain steady-state gene expression during constrained differentiation at various evolutionary stages, while also maintaining responsiveness to environmental stimuli and transcriptome homeostasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1999-x) contains supplementary material, which is available to authorized users.

Controllability analysis of transcriptional regulatory networks reveals circular control patterns among transcription factors

Transcriptional regulation is the most committed type of regulation in living cells where transcription factors (TFs) control the expression of their target genes and TF expression is controlled by other TFs forming complex transcriptional regulatory networks that can be highly interconnected. Here we analyze the topology and organization of nine transcriptional regulatory networks for E. coli, yeast, mouse and human, and we evaluate how the structure of these networks influences two of their key properties, namely controllability and stability. We calculate the controllability for each network as a measure of the organization and interconnectivity of the network. We find that the number of driver nodes nD needed to control the whole network is 64% of the TFs in the E. coli transcriptional regulatory network in contrast to only 17% for the yeast network, 4% for the mouse network and 8% for the human network. The high controllability (low number of drivers needed to control the system) in yeast, mouse and human is due to the presence of internal loops in their regulatory networks where the TFs regulate each other in a circular fashion. We refer to these internal loops as circular control motifs (CCM). The E. coli transcriptional regulatory network, which does not have any CCMs, shows a hierarchical structure of the transcriptional regulatory network in contrast to the eukaryal networks. The presence of CCMs also has influence on the stability of these networks, as the presence of cycles can be associated with potential unstable steady-states where even small changes in binding affinities can cause dramatic rearrangements of the state of the network.

Evaluation of metatranscriptomic protocols and application to the study of freshwater microbial communities

Metatranscriptomics of environmental samples enables the identification of community activities without a priori knowledge of taxonomic or functional composition. However, several technical challenges associated with the RNA preparation protocols can affect the relative representation of transcripts and data interpretation. Here, seven replicate metatranscriptomes from planktonic freshwater samples (Lake Lanier, USA) were sequenced to evaluate technical and biological reproducibility of different RNA extraction protocols. Organic versus bead-beating extraction showed significant enrichment for low versus high G + C% mRNA populations respectively. The sequencing data were best modelled by a negative binomial distribution to account for the large technical and biological variation observed. Despite the variation, the transcriptional activities of populations that persisted in year-round metagenomes from the same site consistently showed distinct expression patterns, reflecting different ecologic strategies and allowing us to test prevailing models on the contribution of both rare biosphere and abundant members to community activity. For instance, abundant members of the Verrucomicrobia phylum systematically showed low transcriptional activity compared with other abundant taxa. Our results provide a practical guide to the analysis of metatranscriptomes and advance understanding of the activity and ecology of abundant and rare members of temperate freshwater microbial communities.

In Pichia pastoris, growth rate regulates protein synthesis and secretion, mating and stress response

Protein production in yeasts is related to the specific growth rate μ. To elucidate on this correlation, we studied the transcriptome of Pichia pastoris at different specific growth rates by cultivating a strain secreting human serum albumin at μ = 0.015 to 0.15 h(-1) in glucose-limited chemostats. Genome-wide regulation revealed that translation-related as well as mitochondrial genes were upregulated with increasing μ, while autophagy and other proteolytic processes, carbon source-responsive genes and other targets of the TOR pathway as well as many transcriptional regulators were downregulated at higher μ. Mating and sporulation genes were most active at intermediate μ of 0.05 and 0.075 h(-1) . At very slow growth (μ = 0.015 h(-1) ) gene regulation differs significantly, affecting many transporters and glucose sensing. Analysis of a subset of genes related to protein folding and secretion reveals that unfolded protein response targets such as translocation, endoplasmic reticulum genes, and cytosolic chaperones are upregulated with increasing growth rate while proteolytic degradation of secretory proteins is downregulated. We conclude that a high μ positively affects specific protein secretion rates by acting on multiple cellular processes.

Growth rate-coordinated transcriptome reorganization in bacteria

Background

Cell growth rate reflects an organism’s physiological state and largely relies on the ability of gene expression to respond to the environment. The relationship between cellular growth rate and gene expression remains unknown.

Results

Growth rate-coordinated changes in gene expression were discovered by analyzing exponentially growing Escherichia coli cells cultured under multiple defined environments, in which osmotic pressure, temperature and starvation status were varied. Gene expression analyses showed that all 3,740 genes in the genome could be simply divided into three clusters (C1, C2 and C3), which were accompanied by a generic trend in the growth rate that was coordinated with transcriptional changes. The direction of transcriptional change in C1 indicated environmental specificity, whereas those in C2 and C3 were correlated negatively and positively with growth rates, respectively. The three clusters exhibited differentiated gene functions and gene regulation task division.

Conclusions

We identified three gene clusters, exhibiting differential gene functions and distinct directions in their correlations with growth rates. Reverses in the direction of the growth rate correlated transcriptional changes and the distinguished duties of the three clusters indicated how transcriptome homeostasis is maintained to balance the total expression cost for sustaining life in new habitats.

Mapping condition-dependent regulation of lipid metabolism in Saccharomyces cerevisiae

Lipids play a central role in cellular function as constituents of membranes, as signaling molecules, and as storage materials. Although much is known about the role of lipids in regulating specific steps of metabolism, comprehensive studies integrating genome-wide expression data, metabolite levels, and lipid levels are currently lacking. Here, we map condition-dependent regulation controlling lipid metabolism in Saccharomyces cerevisiae by measuring 5636 mRNAs, 50 metabolites, 97 lipids, and 57 (13)C-reaction fluxes in yeast using a three-factor full-factorial design. Correlation analysis across eight environmental conditions revealed 2279 gene expression level-metabolite/lipid relationships that characterize the extent of transcriptional regulation in lipid metabolism relative to major metabolic hubs within the cell. To query this network, we developed integrative methods for correlation of multi-omics datasets that elucidate global regulatory signatures. Our data highlight many characterized regulators of lipid metabolism and reveal that sterols are regulated more at the transcriptional level than are amino acids. Beyond providing insights into the systems-level organization of lipid metabolism, we anticipate that our dataset and approach can join an emerging number of studies to be widely used for interrogating cellular systems through the combination of mathematical modeling and experimental biology.

Promoters maintain their relative activity levels under different growth conditions

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

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.

Correlation of cell growth and heterologous protein production by Saccharomyces cerevisiae

With the increasing demand for biopharmaceutical proteins and industrial enzymes, it is necessary to optimize the production by microbial fermentation or cell cultures. Yeasts are well established for the production of a wide range of recombinant proteins, but there are also some limitations; e.g., metabolic and cellular stresses have a strong impact on recombinant protein production. In this work, we investigated the effect of the specific growth rate on the production of two different recombinant proteins. Our results show that human insulin precursor is produced in a growth-associated manner, whereas α-amylase tends to have a higher yield on substrate at low specific growth rates. Based on transcriptional analysis, we found that the difference in the production of the two proteins as function of the specific growth rate is mainly due to differences in endoplasmic reticulum processing, protein turnover, cell cycle, and global stress response. We also found that there is a shift at a specific growth rate of 0.1 h(-1) that influences protein production. Thus, for lower specific growth rates, the α-amylase and insulin precursor-producing strains present similar cell responses and phenotypes, whereas for higher specific growth rates, the two strains respond differently to changes in the specific growth rate.

Pattern and synchrony of gene expression among sympatric marine microbial populations

Planktonic marine microbes live in dynamic habitats that demand rapid sensing and response to periodic as well as stochastic environmental change. The kinetics, regularity, and specificity of microbial responses in situ, however, are not well-described. We report here simultaneous multitaxon genome-wide transcriptome profiling in a naturally occurring picoplankton community. An in situ robotic sampler using a Lagrangian sampling strategy enabled continuous tracking and repeated sampling of coherent microbial populations over 2 d. Subsequent RNA sequencing analyses yielded genome-wide transcriptome profiles of eukaryotic (Ostreococcus) and bacterial (Synechococcus) photosynthetic picoplankton as well as proteorhodopsin-containing heterotrophs, including Pelagibacter, SAR86-cluster Gammaproteobacteria, and marine Euryarchaea. The photosynthetic picoplankton exhibited strong diel rhythms over thousands of gene transcripts that were remarkably consistent with diel cycling observed in laboratory pure cultures. In contrast, the heterotrophs did not cycle diurnally. Instead, heterotrophic picoplankton populations exhibited cross-species synchronous, tightly regulated, temporally variable patterns of gene expression for many genes, particularly those genes associated with growth and nutrient acquisition. This multitaxon, population-wide gene regulation seemed to reflect sporadic, short-term, reversible responses to high-frequency environmental variability. Although the timing of the environmental responses among different heterotrophic species seemed synchronous, the specific metabolic genes that were expressed varied from taxon to taxon. In aggregate, these results provide insights into the kinetics, diversity, and functional patterns of microbial community response to environmental change. Our results also suggest a means by which complex multispecies metabolic processes could be coordinated, facilitating the regulation of matter and energy processing in a dynamically changing environment.

Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory

Saccharomyces cerevisiae is the most well characterized eukaryote, the preferred microbial cell factory for the largest industrial biotechnology product (bioethanol), and a robust commerically compatible scaffold to be exploitted for diverse chemical production. Succinic acid is a highly sought after added-value chemical for which there is no native pre-disposition for production and accmulation in S. cerevisiae. The genome-scale metabolic network reconstruction of S. cerevisiae enabled in silico gene deletion predictions using an evolutionary programming method to couple biomass and succinate production. Glycine and serine, both essential amino acids required for biomass formation, are formed from both glycolytic and TCA cycle intermediates. Succinate formation results from the isocitrate lyase catalyzed conversion of isocitrate, and from the α-keto-glutarate dehydrogenase catalyzed conversion of α-keto-glutarate. Succinate is subsequently depleted by the succinate dehydrogenase complex. The metabolic engineering strategy identified included deletion of the primary succinate consuming reaction, Sdh3p, and interruption of glycolysis derived serine by deletion of 3-phosphoglycerate dehydrogenase, Ser3p/Ser33p. Pursuing these targets, a multi-gene deletion strain was constructed, and directed evolution with selection used to identify a succinate producing mutant. Physiological characterization coupled with integrated data analysis of transcriptome data in the metabolically engineered strain were used to identify 2(nd)-round metabolic engineering targets. The resulting strain represents a 30-fold improvement in succinate titer, and a 43-fold improvement in succinate yield on biomass, with only a 2.8-fold decrease in the specific growth rate compared to the reference strain. Intuitive genetic targets for either over-expression or interruption of succinate producing or consuming pathways, respectively, do not lead to increased succinate. Rather, we demonstrate how systems biology tools coupled with directed evolution and selection allows non-intuitive, rapid and substantial re-direction of carbon fluxes in S. cerevisiae, and hence show proof of concept that this is a potentially attractive cell factory for over-producing different platform chemicals.

Physiological heterogeneities in microbial populations and implications for physical stress tolerance

BACKGROUND

Traditionally average values of the whole population are considered when analysing microbial cell cultivations. However, a typical microbial population in a bioreactor is heterogeneous in most phenotypes measurable at a single-cell level. There are indications that such heterogeneity may be unfavourable on the one hand (reduces yields and productivities), but also beneficial on the other hand (facilitates quick adaptation to new conditions--i.e. increases the robustness of the fermentation process). Understanding and control of microbial population heterogeneity is thus of major importance for improving microbial cell factory processes.

RESULTS

In this work, a dual reporter system was developed and applied to map growth and cell fitness heterogeneities within budding yeast populations during aerobic cultivation in well-mixed bioreactors. The reporter strain, which was based on the expression of green fluorescent protein (GFP) under the control of the ribosomal protein RPL22a promoter, made it possible to distinguish cell growth phases by the level of fluorescence intensity. Furthermore, by exploiting the strong correlation of intracellular GFP level and cell membrane integrity it was possible to distinguish subpopulations with high and low cell membrane robustness and hence ability to withstand freeze-thaw stress. A strong inverse correlation between growth and cell membrane robustness was observed, which further supports the hypothesis that cellular resources are limited and need to be distributed as a trade-off between two functions: growth and robustness. In addition, the trade-off was shown to vary within the population, and the occurrence of two distinct subpopulations shifting between these two antagonistic modes of cell operation could be distinguished.

CONCLUSIONS

The reporter strain enabled mapping of population heterogeneities in growth and cell membrane robustness towards freeze-thaw stress at different phases of cell cultivation. The described reporter system is a valuable tool for understanding the effect of environmental conditions on population heterogeneity of microbial cells and thereby to understand cell responses during industrial process-like conditions. It may be applied to identify more robust subpopulations, and for developing novel strategies for strain improvement and process design for more effective bioprocessing.

Genome-wide analytical approaches for reverse metabolic engineering of industrially relevant phenotypes in yeast

Successful reverse engineering of mutants that have been obtained by nontargeted strain improvement has long presented a major challenge in yeast biotechnology. This paper reviews the use of genome-wide approaches for analysis of Saccharomyces cerevisiae strains originating from evolutionary engineering or random mutagenesis. On the basis of an evaluation of the strengths and weaknesses of different methods, we conclude that for the initial identification of relevant genetic changes, whole genome sequencing is superior to other analytical techniques, such as transcriptome, metabolome, proteome, or array-based genome analysis. Key advantages of this technique over gene expression analysis include the independency of genome sequences on experimental context and the possibility to directly and precisely reproduce the identified changes in naive strains. The predictive value of genome-wide analysis of strains with industrially relevant characteristics can be further improved by classical genetics or simultaneous analysis of strains derived from parallel, independent strain improvement lineages.

Cellular responses of Saccharomyces cerevisiae at near-zero growth rates: transcriptome analysis of anaerobic retentostat cultures

Extremely low specific growth rates (below 0.01 h(-1) ) represent a largely unexplored area of microbial physiology. In this study, anaerobic, glucose-limited retentostats were used to analyse physiological and genome-wide transcriptional responses of Saccharomyces cerevisiae to cultivation at near-zero specific growth rates. While quiescence is typically investigated as a result of carbon starvation, cells in retentostat are fed by small, but continuous carbon and energy supply. Yeast cells cultivated near-zero specific growth rates, while metabolically active, exhibited characteristics previously associated with quiescence, including accumulation of storage polymers and an increased expression of genes involved in exit from the cell cycle into G(0) . Unexpectedly, analysis of transcriptome data from retentostat and chemostat cultures showed, as specific growth rate was decreased, that quiescence-related transcriptional responses were already set in at specific growth rates above 0.025 h(-1) . These observations stress the need for systematic dissection of physiological responses to slow growth, quiescence, ageing and starvation and indicate that controlled cultivation systems such as retentostats can contribute to this goal. Furthermore, cells in retentostat do not (or hardly) divide while remaining metabolically active, which emulates the physiological status of metazoan post-mitotic cells. We propose retentostat as a powerful cultivation tool to investigate chronological ageing-related processes.

Mapping the interaction of Snf1 with TORC1 in Saccharomyces cerevisiae

Nutrient sensing and coordination of metabolic pathways are crucial functions for living cells. A combined analysis of the yeast transcriptome, phosphoproteome and metabolome is used to investigate the interactions between the Snf1 and TORC1 pathways under nutrient-limited conditions.

Snf1 regulates a broad range of biological processes, while target of rapamycin complex 1 (TORC1) seems to be repressed under both glucose- and ammonium-limited conditions.

Snf1 has a role in regulating amino acids by upregulating the NADP+-dependent glutamate dehydrogenase (encoded by GDH3) under glucose-limited condition.

In addition to the accepted role of Snf1 in regulating fatty acid (FA) metabolism, TORC1 may also regulate FA metabolism.

Direct interactions between Snf1 and TORC1 pathways are unlikely under nutrient-limited conditions and TORC1 might be repressed in a manner that is independent of Snf1.

Nutrient sensing and coordination of metabolic pathways are crucial functions for all living cells, but details of the coordination under different environmental conditions remain elusive. We therefore undertook a systems biology approach to investigate the interactions between the Snf1 and the target of rapamycin complex 1 (TORC1) in Saccharomyces cerevisiae. We show that Snf1 regulates a much broader range of biological processes compared with TORC1 under both glucose- and ammonium-limited conditions. We also find that Snf1 has a role in upregulating the NADP⁺-dependent glutamate dehydrogenase (encoded by GDH3) under derepressing condition, and therefore may also have a role in ammonium assimilation and amino-acid biosynthesis, which can be considered as a convergence of Snf1 and TORC1 pathways. In addition to the accepted role of Snf1 in regulating fatty acid (FA) metabolism, we show that TORC1 also regulates FA metabolism, likely through modulating the peroxisome and β-oxidation. Finally, we conclude that direct interactions between Snf1 and TORC1 pathways are unlikely under nutrient-limited conditions and propose that TORC1 is repressed in a manner that is independent of Snf1.

Genome-wide analysis of yeast stress survival and tolerance acquisition to analyze the central trade-off between growth rate and cellular robustness

A genome-wide analysis of the acquisition of stress cross-tolerance shows that reduction of growth rate is an important determinant of severe stress survival. Cellular functions important for the coupling of growth rate to stress resistance are identified, as are those required for cross-tolerance acquisition independent of growth rate reduction.

All organisms have evolved to cope with changes in environmental conditions, ensuring the optimal combination of proliferation and survival. In yeast, exposure to a mild stress leads to an increased tolerance for other stresses. This suggests that yeast uses information from the environment to prepare for future threats. We used the yeast knockout collection to systematically investigate the genes and functions involved in severe stress survival and in the acquisition of stress (cross-) tolerance. Besides genes and functions relevant for survival of heat, acid, and oxidative stress, we found an inverse correlation between mutant growth rate and stress survival. Using chemostat cultures, we confirmed that growth rate governs stress tolerance, with higher growth efficiency at low growth rates liberating the energy for these investments. Cellular functions required for stress tolerance acquisition, independent of the reduction in growth rate, were involved in vesicular transport, the Rpd3 histone deacetylase complex, and the mitotic cell cycle. Stress resistance and acquired stress tolerance in Saccharomyces cerevisiae are governed by a combination of stress-specific and general processes. The reduction of growth rate, irrespective of the cause of this reduction, leads to redistribution of resources toward stress tolerance functions, thus preparing the cells for impending change.

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

Background

A microorganism is able to adapt to changes in its physicochemical or nutritional environment and this is crucial for its survival. The yeast, Saccharomyces cerevisiae, has developed mechanisms to respond to such environmental changes in a rapid and effective manner; such responses may demand a widespread re-programming of gene activity. The dynamics of the re-organization of the cellular activities of S. cerevisiae in response to the sudden and transient removal of either carbon or nitrogen limitation has been studied by following both the short- and long-term changes in yeast's transcriptomic profiles.

Results

The study, which spans timescales from seconds to hours, has revealed the hierarchy of metabolic and genetic regulatory switches that allow yeast to adapt to, and recover from, a pulse of a previously limiting nutrient. At the transcriptome level, a glucose impulse evoked significant changes in the expression of genes concerned with glycolysis, carboxylic acid metabolism, oxidative phosphorylation, and nucleic acid and sulphur metabolism. In ammonium-limited cultures, an ammonium impulse resulted in the significant changes in the expression of genes involved in nitrogen metabolism and ion transport. Although both perturbations evoked significant changes in the expression of genes involved in the machinery and process of protein synthesis, the transcriptomic response was delayed and less complex in the case of an ammonium impulse. Analysis of the regulatory events by two different system-level, network-based approaches provided further information about dynamic organization of yeast cells as a response to a nutritional change.

Conclusions

The study provided important information on the temporal organization of transcriptomic organization and underlying regulatory events as a response to both carbon and nitrogen impulse. It has also revealed the importance of a long-term dynamic analysis of the response to the relaxation of a nutritional limitation to understand the molecular basis of the cells' dynamic behaviour.

Resolution of gene regulatory conflicts caused by combinations of antibiotics

Regulatory conflicts occur when two signals that individually trigger opposite cellular responses are present simultaneously. Here, we investigate regulatory conflicts in the bacterial response to antibiotic combinations. We use an Escherichia coli promoter-GFP library to study the transcriptional response of many promoters to either additive or antagonistic drug pairs at fine two-dimensional (2D) resolution of drug concentration. Surprisingly, we find that this data set can be characterized as a linear sum of only two principal components. Component one, accounting for over 70% of the response, represents the response to growth inhibition by the drugs. Component two describes how regulatory conflicts are resolved. For the additive drug pair, conflicts are resolved by linearly interpolating the single drug responses, while for the antagonistic drug pair, the growth-limiting drug dominates the response. Importantly, for a given drug pair, the same conflict resolution strategy applies to almost all genes. These results provide a recipe for predicting gene expression responses to antibiotic combinations.

Extreme calorie restriction and energy source starvation in Saccharomyces cerevisiae represent distinct physiological states

Cultivation methods used to investigate microbial calorie restriction often result in carbon and energy starvation. This study aims to dissect cellular responses to calorie restriction and starvation in Saccharomyces cerevisiae by using retentostat cultivation. In retentostats, cells are continuously supplied with a small, constant carbon and energy supply, sufficient for maintenance of cellular viability and integrity but insufficient for growth. When glucose-limited retentostats cultivated under extreme calorie restriction were subjected to glucose starvation, calorie-restricted and glucose-starved cells were found to share characteristics such as increased heat-shock tolerance and expression of quiescence-related genes. However, they also displayed strikingly different features. While calorie-restricted yeast cultures remained metabolically active and viable for prolonged periods of time, glucose starvation resulted in rapid consumption of reserve carbohydrates, population heterogeneity due to appearance of senescent cells and, ultimately, loss of viability. Moreover, during starvation, calculated rates of ATP synthesis from reserve carbohydrates were 2-3 orders of magnitude lower than steady-state ATP-turnover rates calculated under extreme calorie restriction in retentostats. Stringent reduction of ATP turnover during glucose starvation was accompanied by a strong down-regulation of genes involved in protein synthesis. These results demonstrate that extreme calorie restriction and carbon starvation represent different physiological states in S. cerevisiae.

Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast

We discovered that the relative durations of the phases of the yeast metabolic cycle change with the growth rate. These changes can explain mechanistically the transcriptional growth-rate responses of all yeast genes (25% of the genome) that we find to be the same across all studied nutrient limitations in either ethanol or glucose media.

We studied the steady-state responses to changes in growth rate of yeast when ethanol is the sole source of carbon and energy. Analysis of these data, together with data from studies where glucose was the carbon source, allowed us to distinguish a “universal” growth rate response (GRR) common to all media studied from a GRR specific to the carbon source. Genes with positive universal GRR include ribosomal, translation, and mitochondrial genes, and those with negative GRR include autophagy, vacuolar, and stress response genes. The carbon source–specific GRR genes control mitochondrial function, peroxisomes, and synthesis of vitamins and cofactors, suggesting this response may reflect the intensity of oxidative metabolism. All genes with universal GRR, which comprise 25% of the genome, are expressed periodically in the yeast metabolic cycle (YMC). We propose that the universal GRR may be accounted for by changes in the relative durations of the YMC phases. This idea is supported by oxygen consumption data from metabolically synchronized cultures with doubling times ranging from 5 to 14 h. We found that the high oxygen consumption phase of the YMC can coincide exactly with the S phase of the cell division cycle, suggesting that oxidative metabolism and DNA replication are not incompatible.

LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

All cellular processes are regulated by condition-specific and time-dependent interactions between transcription factors and their target genes. While in simple organisms, e.g. bacteria and yeast, a large amount of experimental data is available to support functional transcription regulatory interactions, in mammalian systems reconstruction of gene regulatory networks still heavily depends on the accurate prediction of transcription factor binding sites. Here, we present a new method, log-linear modeling of 3D contingency tables (LLM3D), to predict functional transcription factor binding sites. LLM3D combines gene expression data, gene ontology annotation and computationally predicted transcription factor binding sites in a single statistical analysis, and offers a methodological improvement over existing enrichment-based methods. We show that LLM3D successfully identifies novel transcriptional regulators of the yeast metabolic cycle, and correctly predicts key regulators of mouse embryonic stem cell self-renewal more accurately than existing enrichment-based methods. Moreover, in a clinically relevant in vivo injury model of mammalian neurons, LLM3D identified peroxisome proliferator-activated receptor γ (PPARγ) as a neuron-intrinsic transcriptional regulator of regenerative axon growth. In conclusion, LLM3D provides a significant improvement over existing methods in predicting functional transcription regulatory interactions in the absence of experimental transcription factor binding data.

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.

Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation

Background

Several protein-protein interaction studies have been performed for the yeast Saccharomyces cerevisiae using different high-throughput experimental techniques. All these results are collected in the BioGRID database and the SGD database provide detailed annotation of the different proteins. Despite the value of BioGRID for studying protein-protein interactions, there is a need for manual curation of these interactions in order to remove false positives.

Results

Here we describe an annotated reconstruction of the protein-protein interactions around four key nutrient-sensing and metabolic regulatory signal transduction pathways (STP) operating in Saccharomyces cerevisiae. The reconstructed STP network includes a full protein-protein interaction network including the key nodes Snf1, Tor1, Hog1 and Pka1. The network includes a total of 623 structural open reading frames (ORFs) and 779 protein-protein interactions. A number of proteins were identified having interactions with more than one of the protein kinases. The fully reconstructed interaction network includes all the information available in separate databases for all the proteins included in the network (nodes) and for all the interactions between them (edges). The annotated information is readily available utilizing the functionalities of network modelling tools such as Cytoscape and CellDesigner.

Conclusions

The reported fully annotated interaction model serves as a platform for integrated systems biology studies of nutrient sensing and regulation in S. cerevisiae. Furthermore, we propose this annotated reconstruction as a first step towards generation of an extensive annotated protein-protein interaction network of signal transduction and metabolic regulation in this yeast.

Nutrient control of eukaryote cell growth: a systems biology study in yeast

Background

To elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions.

Results

We find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals.

Conclusions

Our results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways.

See associated commentary http://www.biomedcentral.com/1741-7007/8/62

Coordination of gene expression with growth rate: a feedback or a feed-forward strategy?

In the budding yeast, a large fraction of genes is coordinately regulated with growth rate. We argue that this correlation does not reflect a direct feedback from growth rate to gene expression. Rather, what appears to be a response to growth rate is dominated by environmental sensing. External parameters, such as nutrition or temperature, feed-forward to define gene expression pattern that is tuned to the evolutionary-predicted growth rate. While such a feed-forward strategy requires fine-tuning of signaling mechanisms, and is limited in the range of environments that can be monitored, it enables advanced preparation to physiological changes that predictably occur following environmental switching. The capacity to anticipate and prepare for changing conditions was probably a major selection force during yeast evolution.

Increased expression of the oxidative pentose phosphate pathway and gluconeogenesis in anaerobically growing xylose-utilizing Saccharomyces cerevisiae

Background

Fermentation of xylose to ethanol has been achieved in S. cerevisiae by genetic engineering. Xylose utilization is however slow compared to glucose, and during anaerobic conditions addition of glucose has been necessary for cellular growth. In the current study, the xylose-utilizing strain TMB 3415 was employed to investigate differences between anaerobic utilization of glucose and xylose. This strain carried a xylose reductase (XYL1 K270R) engineered for increased NADH utilization and was capable of sustained anaerobic growth on xylose as sole carbon source. Metabolic and transcriptional characterization could thus for the first time be performed without addition of a co-substrate or oxygen.

Results

Analysis of metabolic fluxes showed that although the specific ethanol productivity was an order of magnitude lower on xylose than on glucose, product yields were similar for the two substrates. In addition, transcription analysis identified clear regulatory differences between glucose and xylose. Respiro-fermentative metabolism on glucose during aerobic conditions caused repression of cellular respiration, while metabolism on xylose under the same conditions was fully respiratory. During anaerobic conditions, xylose repressed respiratory pathways, although notably more weakly than glucose. It was also observed that anaerobic xylose growth caused up-regulation of the oxidative pentose phosphate pathway and gluconeogenesis, which may be driven by an increased demand for NADPH during anaerobic xylose catabolism.

Conclusion

Co-factor imbalance in the initial twp steps of xylose utilization may reduce ethanol productivity by increasing the need for NADP+ reduction and consequently increase reverse flux in glycolysis.

Quantitative physiology of Saccharomyces cerevisiae at near-zero specific growth rates

Growth at near-zero specific growth rates is a largely unexplored area of yeast physiology. To investigate the physiology of Saccharomyces cerevisiae under these conditions, the effluent removal pipe of anaerobic, glucose-limited chemostat culture (dilution rate, 0.025 h(-1)) was fitted with a 0.22-microm-pore-size polypropylene filter unit. This setup enabled prolonged cultivation with complete cell retention. After 22 days of cultivation, specific growth rates had decreased below 0.001 h(-1) (doubling time of >700 h). Over this period, viability of the retentostat cultures decreased to ca. 80%. The viable biomass concentration in the retentostats could be accurately predicted by a maintenance coefficient of 0.50 mmol of glucose g(-1) of biomass h(-1) calculated from anaerobic, glucose-limited chemostat cultures grown at dilution rates of 0.025 to 0.20 h(-1). This indicated that, in contrast to the situation in several prokaryotes, maintenance energy requirements in S. cerevisiae do not substantially change at near-zero specific growth rates. After 22 days of retentostat cultivation, glucose metabolism was predominantly geared toward alcoholic fermentation to meet maintenance energy requirements. The strict correlation between glycerol production and biomass formation observed at higher specific growth rates was not maintained at the near-zero growth rates reached in the retentostat cultures. In addition to glycerol, the organic acids acetate, d-lactate, and succinate were produced at low rates during prolonged retentostat cultivation. This study identifies robustness and by-product formation as key issues in attempts to uncouple growth and product formation in S. cerevisiae.

Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein-protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.