Dynamics and design principles of a basic regulatory architecture controlling metabolic pathways

Genome-wide promoter responses to CRISPR perturbations of regulators reveal regulatory networks in Escherichia coli

Elucidating genome-scale regulatory networks requires a comprehensive collection of gene expression profiles, yet measuring gene expression responses for every transcription factor (TF)-gene pair in living prokaryotic cells remains challenging. Here, we develop pooled promoter responses to TF perturbation sequencing (PPTP-seq) via CRISPR interference to address this challenge. Using PPTP-seq, we systematically measure the activity of 1372 Escherichia coli promoters under single knockdown of 183 TF genes, illustrating more than 200,000 possible TF-gene responses in one experiment. We perform PPTP-seq for E. coli growing in three different media. The PPTP-seq data reveal robust steady-state promoter activities under most single TF knockdown conditions. PPTP-seq also enables identifications of, to the best of our knowledge, previously unknown TF autoregulatory responses and complex transcriptional control on one-carbon metabolism. We further find context-dependent promoter regulation by multiple TFs whose relative binding strengths determined promoter activities. Additionally, PPTP-seq reveals different promoter responses in different growth media, suggesting condition-specific gene regulation. Overall, PPTP-seq provides a powerful method to examine genome-wide transcriptional regulatory networks and can be potentially expanded to reveal gene expression responses to other genetic elements.

Discovering adaptation-capable biological network structures using control-theoretic approaches

Constructing biological networks capable of performing specific biological functionalities has been of sustained interest in synthetic biology. Adaptation is one such ubiquitous functional property, which enables every living organism to sense a change in its surroundings and return to its operating condition prior to the disturbance. In this paper, we present a generic systems theory-driven method for designing adaptive protein networks. First, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as ‘design requirements’ for the underlying networks. We go on to prove that a protein network with different input–output nodes (proteins) needs to be at least of third-order in order to provide adaptation. Next, we show that the necessary design principles obtained for a three-node network in adaptation consist of negative feedback or a feed-forward realization. We argue that presence of a particular class of negative feedback or feed-forward realization is necessary for a network of any size to provide adaptation. Further, we claim that the necessary structural conditions derived in this work are the strictest among the ones hitherto existed in the literature. Finally, we prove that the capability of producing adaptation is retained for the admissible motifs even when the output node is connected with a downstream system in a feedback fashion. This result explains how complex biological networks achieve robustness while keeping the core motifs unchanged in the context of a particular functionality. We corroborate our theoretical results with detailed and thorough numerical simulations. Overall, our results present a generic, systematic and robust framework for designing various kinds of biological networks.

Biological systems display a remarkable diversity of functionalities, many of which can be conceived as the response of a large network composed of small interconnecting modules. Unravelling the connection pattern, i.e. design principles, behind important biological functionalities is one of the most challenging problems in systems biology. One such phenomenon is perfect adaptation, which merits special attention owing to its universal presence ranging from chemotaxis in bacterial cells to calcium homeostasis in mammalian cells. The present work focuses on finding the design principles for perfect adaptation in the presence of a stair-case type disturbance. To this end, the current work proposes a systems-theoretic approach to deduce precise mathematical (hence structural) conditions that comply with the key performance parameters for adaptation. The approach is agnostic to the particularities of the reaction kinetics, underlining the dominant role of the topological structure on the response of the network. Notably, the design principles obtained in this work serve as the most strict necessary structural conditions for a network of any size to provide perfect adaptation.

The transcription regulator BrsR serves as a network hub of natural competence protein-protein interactions in Streptococcus mutans

Genome evolution is an essential and stringently regulated aspect of biological fitness. For bacteria, natural competence is one of the principal mechanisms of genome evolution and is frequently subject to multiple layers of regulation derived from a plethora of environmental and physiological stimuli. Here, we present a regulatory mechanism that illustrates how such disparate stimuli can be integrated into the Streptococcus mutans natural competence phenotype. S. mutans possesses an intriguing, but poorly understood ability to coordinately control its independently regulated natural competence and bacteriocin genetic pathways as a means to acquire DNA released from closely related, bacteriocin-susceptible streptococci. Our results reveal how the bacteriocin-specific transcription activator BrsR directly mediates this coordination by serving as an anti-adaptor protein responsible for antagonizing the proteolysis of the inherently unstable, natural competence-specific alternative sigma factor ComX. This BrsR ability functions entirely independent of its transcription regulator function and directly modulates the timing and severity of the natural competence phenotype. Additionally, many of the DNA uptake proteins produced by the competence system were surprisingly found to possess adaptor abilities, which are employed to terminate the BrsR regulatory circuit via negative feedback. BrsR-competence protein heteromeric complexes directly inhibit nascent brsR transcription as well as stimulate the Clp-dependent proteolysis of extant BrsR proteins. This study illustrates how critical genetic regulatory abilities can evolve in a potentially limitless variety of proteins without disrupting their conserved ancestral functions. These unrecognized regulatory abilities are likely fundamental for transducing information through complex genetic networks.

Dynamic control in metabolic engineering: Theories, tools, and applications

Metabolic engineering has allowed the production of a diverse number of valuable chemicals using microbial organisms. Many biological challenges for improving bio-production exist which limit performance and slow the commercialization of metabolically engineered systems. Dynamic metabolic engineering is a rapidly developing field that seeks to address these challenges through the design of genetically encoded metabolic control systems which allow cells to autonomously adjust their flux in response to their external and internal metabolic state. This review first discusses theoretical works which provide mechanistic insights and design choices for dynamic control systems including two-stage, continuous, and population behavior control strategies. Next, we summarize molecular mechanisms for various sensors and actuators which enable dynamic metabolic control in microbial systems. Finally, important applications of dynamic control to the production of several metabolite products are highlighted, including fatty acids, aromatics, and terpene compounds. Altogether, this review provides a comprehensive overview of the progress, advances, and prospects in the design of dynamic control systems for improved titer, rate, and yield metrics in metabolic engineering.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Metabolite Sequestration Enables Rapid Recovery from Fatty Acid Depletion in Escherichia coli

Microbes adapt their metabolism to take advantage of nutrients in their environment. Such adaptations control specific metabolic pathways to match energetic demands with nutrient availability. Upon depletion of nutrients, rapid pathway recovery is key to release cellular resources required for survival under the new nutritional conditions. Yet, little is known about the regulatory strategies that microbes employ to accelerate pathway recovery in response to nutrient depletion. Using the fatty acid catabolic pathway in Escherichia coli, here, we show that fast recovery can be achieved by rapid release of a transcriptional regulator from a metabolite-sequestered complex. With a combination of mathematical modeling and experiments, we show that recovery dynamics depend critically on the rate of metabolite consumption and the exposure time to nutrients. We constructed strains with rewired transcriptional regulatory architectures that highlight the metabolic benefits of negative autoregulation over constitutive and positive autoregulation. Our results have wide-ranging implications for our understanding of metabolic adaptations, as well as for guiding the design of gene circuitry for synthetic biology and metabolic engineering.IMPORTANCE Rapid metabolic recovery during nutrient shift is critical to microbial survival, cell fitness, and competition among microbiota, yet little is known about the regulatory mechanisms of rapid metabolic recovery. This work demonstrates a previously unknown mechanism where rapid release of a transcriptional regulator from a metabolite-sequestered complex enables fast recovery to nutrient depletion. The work identified key regulatory architectures and parameters that control the speed of recovery, with wide-ranging implications for the understanding of metabolic adaptations as well as synthetic biology and metabolic engineering.

Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference

One of the central tasks in systems biology is to understand how cells regulate their metabolism. Hierarchical regulation analysis is a powerful tool to study this regulation at the metabolic, gene-expression, and signaling levels. It has been widely applied to study steady-state regulation, but analysis of the metabolic dynamics remains challenging because it is difficult to measure time-dependent metabolic flux. Here, we develop a nonparametric method that uses Gaussian processes to accurately infer the dynamics of a metabolic pathway based only on metabolite measurements; from this, we then go on to obtain a dynamical view of the hierarchical regulation processes invoked over time to control the activity in a pathway. Our approach allows us to use hierarchical regulation analysis in a dynamic setting but without the need for explicitly time-dependent flux measurements.

Distinct segregation patterns of yeast cell-peripheral proteins uncovered by a method for protein segregatome analysis

Protein segregation contributes to various cellular processes such as polarization, differentiation, and aging. However, the difficulty in global determination of protein segregation hampers our understanding of its mechanisms and physiological roles. Here, by developing a quantitative proteomics technique, we globally monitored segregation of preexisting and newly synthesized proteins during cell division of budding yeast, and identified crucial domains that determine the segregation of cell-peripheral proteins. Remarkably, the proteomic and subsequent microscopic analyses demonstrated that the flow through the bud neck of the proteins that harbor both endoplasmic reticulum (ER) membrane-spanning and plasma membrane (PM)-binding domains is not restricted by the previously suggested ER membrane or PM diffusion barriers but by septin-mediated partitioning of the PM-associated ER (pmaER). Furthermore, the proteomic analysis revealed that although the PM-spanning t-SNARE Sso2 was retained in mother cells, its paralog Sso1 unexpectedly showed symmetric localization. We found that the transport of Sso1 to buds was required for enhancement of polarized cell growth and resistance to cell-wall stress. Taken together, these data resolve long-standing questions about septin-mediated compartmentalization of the cell periphery, and provide new mechanistic insights into the segregation of cell-periphery proteins and their cellular functions.

Dynamic metabolic control: towards precision engineering of metabolism

Advances in metabolic engineering have led to the synthesis of a wide variety of valuable chemicals in microorganisms. The key to commercializing these processes is the improvement of titer, productivity, yield, and robustness. Traditional approaches to enhancing production use the “push–pull-block” strategy that modulates enzyme expression under static control. However, strains are often optimized for specific laboratory set-up and are sensitive to environmental fluctuations. Exposure to sub-optimal growth conditions during large-scale fermentation often reduces their production capacity. Moreover, static control of engineered pathways may imbalance cofactors or cause the accumulation of toxic intermediates, which imposes burden on the host and results in decreased production. To overcome these problems, the last decade has witnessed the emergence of a new technology that uses synthetic regulation to control heterologous pathways dynamically, in ways akin to regulatory networks found in nature. Here, we review natural metabolic control strategies and recent developments in how they inspire the engineering of dynamically regulated pathways. We further discuss the challenges of designing and engineering dynamic control and highlight how model-based design can provide a powerful formalism to engineer dynamic control circuits, which together with the tools of synthetic biology, can work to enhance microbial production.

Metabolic Feedback Circuits Provide Rapid Control of Metabolite Dynamics

Metabolism constitutes the basis of life, and the dynamics of metabolism dictate various cellular processes. However, exactly how metabolite dynamics are controlled remains poorly understood. By studying an engineered fatty acid-producing pathway as a model, we found that upon transcription activation a metabolic product from an unregulated pathway required seven cell cycles to reach to its steady state level, with the speed mostly limited by enzyme expression dynamics. To overcome this limit, we designed metabolic feedback circuits (MeFCs) with three different architectures, and experimentally measured and modeled their metabolite dynamics. Our engineered MeFCs could dramatically shorten the rise-time of metabolites, decreasing it by as much as 12-fold. The findings of this study provide a systematic understanding of metabolite dynamics in different architectures of MeFCs and have potentially immense applications in designing synthetic circuits to improve the productivities of engineered metabolic pathways.

Engineering Microbial Metabolite Dynamics and Heterogeneity

As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.

Diversification of Transcriptional Regulation Determines Subfunctionalization of Paralogous Branched Chain Aminotransferases in the Yeast Saccharomyces cerevisiae

Saccharomyces cerevisiae harbors BAT1 and BAT2 paralogous genes that encode branched chain aminotransferases and have opposed expression profiles and physiological roles . Accordingly, in primary nitrogen sources such as glutamine, BAT1 expression is induced, supporting Bat1-dependent valine-isoleucine-leucine (VIL) biosynthesis, while BAT2 expression is repressed. Conversely, in the presence of VIL as the sole nitrogen source, BAT1 expression is hindered while that of BAT2 is activated, resulting in Bat2-dependent VIL catabolism. The presented results confirm that BAT1 expression is determined by transcriptional activation through the action of the Leu3-α-isopropylmalate (α-IPM) active isoform, and uncovers the existence of a novel α-IPM biosynthetic pathway operating in a put3Δ mutant grown on VIL, through Bat2-Leu2-Leu1 consecutive action. The classic α-IPM biosynthetic route operates in glutamine through the action of the leucine-sensitive α-IPM synthases. The presented results also show that BAT2 repression in glutamine can be alleviated in a ure2Δ mutant or through Gcn4-dependent transcriptional activation. Thus, when S. cerevisiae is grown on glutamine, VIL biosynthesis is predominant and is preferentially achieved through BAT1; while on VIL as the sole nitrogen source, catabolism prevails and is mainly afforded by BAT2.

Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth

Dynamic control of gene expression can have far-reaching implications for biotechnological applications and biological discovery. Thanks to the advantages of light, optogenetics has emerged as an ideal technology for this task. Current state-of-the-art methods for optical expression control fail to combine precision with repeatability and cannot withstand changing operating culture conditions. Here, we present a novel fully automatic experimental platform for the robust and precise long-term optogenetic regulation of protein production in liquid Escherichia coli cultures. Using a computer-controlled light-responsive two-component system, we accurately track prescribed dynamic green fluorescent protein expression profiles through the application of feedback control, and show that the system adapts to global perturbations such as nutrient and temperature changes. We demonstrate the efficacy and potential utility of our approach by placing a key metabolic enzyme under optogenetic control, thus enabling dynamic regulation of the culture growth rate with potential applications in bacterial physiology studies and biotechnology.

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering

Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.

Regulating ehrlich and demethiolation pathways for alcohols production by the expression of ubiquitin-protein ligase gene HUWE1

Ehrlich and demethiolation pathways as two competing branches converted amino acid into alcohols. Controlling both pathways offers considerable potential for industrial applications including alcohols overproduction, flavor-quality control and developing new flavors. While how to regulate ehrlich and demethiolation pathways is still not applicable. Taking the conversion of methionine into methionol and methanethiol for example, we constructed two suppression subtractive cDNA libraries of Clonostachys rosea by using suppression subtractive hybridization (SSH) technology for screening regulators controlling the conversion. E3 ubiquitin-protein ligase gene HUWE1 screened from forward SSH library was validated to be related with the biosynthesis of end products. Overexpressing HUWE1 in C. rosea and S. cerevisiae significantly increased the biosynthesis of methanethiol and its derivatives in demethiolation pathway, while suppressed the biosynthesis of methional and methionol in ehrlich pathway. These results attained the directional regulation of both pathways by overexpressing HUWE1. Thus, HUWE1 has potential to be a key target for controlling and enhancing alcohols production by metabolic engineering.

Mapping eQTL networks with mixed graphical Markov models

Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular, and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this article we approach this challenge with mixed graphical Markov models, higher-order conditional independences, and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene-gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.

Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry

Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis

Background

Metabolic control analysis (MCA) and supply–demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply–demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.

Results

This study integrates control engineering and classical MCA augmented with supply–demand theory and HCA. Because gene-expression regulation involves time integration, it is identified as a natural instantiation of the ‘integral control’ (or near integral control) known in control engineering. This study then focuses on robustness against and adaptation to perturbations of process activities in the network, which could result from environmental perturbations, mutations or slow noise. It is shown however that this type of ‘integral control’ should rarely be expected to lead to the ‘perfect adaptation’: although the gene-expression regulation increases the robustness of important metabolite concentrations, it rarely makes them infinitely robust. For perfect adaptation to occur, the protein degradation reactions should be zero order in the concentration of the protein, which may be rare biologically for cells growing steadily.

Conclusions

A proposed new framework integrating the methodologies of control engineering and metabolic and hierarchical control analysis, improves the understanding of biological systems that are regulated both metabolically and by gene expression. In particular, the new approach enables one to address the issue whether the intracellular biochemical networks that have been and are being identified by genomics and systems biology, correspond to the ‘perfect’ regulatory structures designed by control engineering vis-à-vis optimal functions such as robustness. To the extent that they are not, the analyses suggest how they may become so and this in turn should facilitate synthetic biology and metabolic engineering.

The integrated response of primary metabolites to gene deletions and the environment

Intracellular metabolites arise from the molecular integration of genomic and environmental factors that jointly determine metabolic activity. However, it is not clear how the interplay of genotype, nutrients, growth, and fluxes affect metabolite concentrations globally. Here we used quantitative metabolomics to assess the combined effect of environment and genotype on the metabolite composition of a yeast cell. We analyzed a panel of 34 yeast single-enzyme knockout mutants grown on three archetypical carbon sources, generating a dataset of 400 unique metabolome samples. The different carbon sources globally affected the concentrations of intermediates, both directly, by changing the thermodynamic potentials (Δ(r)G) as a result of the substrate influx, and indirectly, by cellular regulation. In contrast, enzyme deletion elicited only local accumulation of the metabolic substrate immediately upstream of the lesion. Key biosynthetic precursors and cofactors were generally robust under all tested perturbations in spite of changes in fluxes and growth rate.

Regulatory architecture determines optimal regulation of gene expression in metabolic pathways

In response to environmental changes, the connections ("arrows") in gene regulatory networks determine which genes modulate their expression, but the quantitative parameters of the network ("the numbers on the arrows") are equally important in determining the resulting phenotype. What are the objectives and constraints by which evolution determines these parameters? We explore these issues by analyzing gene expression changes in a number of yeast metabolic pathways in response to nutrient depletion. We find that a striking pattern emerges that couples the regulatory architecture of the pathway to the gene expression response. In particular, we find that pathways controlled by the intermediate metabolite activation (IMA) architecture, in which an intermediate metabolite activates transcription of pathway genes, exhibit the following response: the enzyme immediately downstream of the regulatory metabolite is under the strongest transcriptional control, whereas the induction of the enzymes upstream of the regulatory intermediate is relatively weak. This pattern of responses is absent in pathways not controlled by an IMA architecture. The observation can be explained by the constraint imposed by the fundamental feedback structure of the network, which places downstream enzymes under a negative feedback loop and upstream ones under a positive feedback loop. This general design principle for transcriptional control of a metabolic pathway can be derived from a simple cost/benefit model of gene expression, in which the observed pattern is an optimal solution. Our results suggest that the parameters regulating metabolic enzyme expression are optimized by evolution, under the strong constraint of the underlying regulatory architecture.

Analysis of hypoxia and hypoxia-like states through metabolite profiling

Background

In diverse organisms, adaptation to low oxygen (hypoxia) is mediated through complex gene expression changes that can, in part, be mimicked by exposure to metals such as cobalt. Although much is known about the transcriptional response to hypoxia and cobalt, little is known about the all-important cell metabolism effects that trigger these responses.

Methods and Findings

Herein we use a low molecular weight metabolome profiling approach to identify classes of metabolites in yeast cells that are altered as a consequence of hypoxia or cobalt exposures. Key findings on metabolites were followed-up by measuring expression of relevant proteins and enzyme activities. We find that both hypoxia and cobalt result in a loss of essential sterols and unsaturated fatty acids, but the basis for these changes are disparate. While hypoxia can affect a variety of enzymatic steps requiring oxygen and heme, cobalt specifically interferes with diiron-oxo enzymatic steps for sterol synthesis and fatty acid desaturation. In addition to diiron-oxo enzymes, cobalt but not hypoxia results in loss of labile 4Fe-4S dehydratases in the mitochondria, but has no effect on homologous 4Fe-4S dehydratases in the cytosol. Most striking, hypoxia but not cobalt affected cellular pools of amino acids. Amino acids such as aromatics were elevated whereas leucine and methionine, essential to the strain used here, dramatically decreased due to hypoxia induced down-regulation of amino acid permeases.

Conclusions

These studies underscore the notion that cobalt targets a specific class of iron proteins and provide the first evidence for hypoxia effects on amino acid regulation. This research illustrates the power of metabolite profiling for uncovering new adaptations to environmental stress.

Regulation and control of metabolic fluxes in microbes

After about ten years of research renaissance in metabolism, the present challenge is to understand how metabolic fluxes are controlled by a complex interplay of overlapping regulatory mechanisms. Reconstruction of various regulatory network topologies is steaming, illustrating that we underestimated the broad importance of post-translational modifications such as enzyme phosphorylation or acetylation for microbial metabolism. With the growing topological knowledge, the functional relevance of these regulatory events becomes an even more pressing need. A major knowledge gap resides in the regulatory network of protein-metabolite interactions, simply because we lacked pertinent methods for systematic analyses - but a start has now been made. Perhaps most dramatic was the conceptual shift in our perception of metabolism from an engine of cellular operation to a generator of input and feedback signals for regulatory circuits that govern many important decisions on cell proliferation, differentiation, death, and naturally metabolism.

Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics

With a targeted proteomic approach, we could quantify 90% of the enzymes involved in carbon and amino-acid metabolism in yeast, including complete isoenzyme families, throughout a set of different metabolic states.

The data, interpreted through flux balance modeling, indicate that S. cerevisiae expresses enzymes, not necessarily used in a particular metabolic condition.

For many isoenzymes our data suggest functional diversification, which might explain their parallel presence in the S. cerevisiae genome.

The metabolic network in the yeast Saccharomyces cerevisiae has been very well characterized in terms of components and topology. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood.

In this study, we exploited quantitative proteomic assays based on selected reaction monitoring (SRM) mass spectrometry to comprehensively analyze the set of enzymes involved in carbon and amino-acid metabolism in yeast (Figure 1), throughout a set of different metabolic states. To elucidate how this metabolic network of proteins adapts to environmental challenges, we chose five nutritional conditions resulting in maximal difference in magnitude and direction of metabolic fluxes. We could reproducibly detect and quantify across the different conditions, 90% of the targeted metabolic proteome, including complete families of isoenzymes, sharing up to 99.5% sequence identity and multi-subunit enzyme complexes. This yielded an information-rich proteomic data set that represents a nutritionally perturbed biological system with high coverage of its components.

Interpreted through flux balance modeling, the data indicate that S. cerevisiae expresses—at least at a basal level—more proteins than are actually necessary for sustaining a given metabolic condition. One potential explanation for the presence of non-necessary proteins is that these enzymes could realize immediate basal metabolic fluxes upon a change to new environmental conditions.

Next, we asked whether our data set could contribute to unravel the function of isoenzymes in the metabolic set. Previously proposed roles for isoenzymes include redundancy to buffer against mutations, a means to gene dosage or facilitation of evolutionary innovation and functional diversification. To address the role of isoenzymes in central metabolism, we used hierarchical clustering to analyze the abundance patterns of the metabolic proteins and their relationship to different functional classes and metabolic pathways. Interestingly, while subunits of the same protein complex preferably cluster in proximate branches, members of the same isoenzyme family often clustered in distant branches (Figure 5). The data therefore suggested functional diversification within most isoenzyme families and allowed to propose different functions of divergent isoenzymes.

We expect that the comprehensive data set presented in this study will be an ideal blueprint for further developing models of yeast metabolism and for follow-up studies on the function of target metabolic proteins.

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95–99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes.

Selective control of amino acid metabolism by the GCN2 eIF2 kinase pathway in Saccharomyces cerevisiae

Background

When eukaryotic cells are deprived of amino acids, uncharged tRNAs accumulate and activate the conserved GCN2 protein kinase. Activated Gcn2p up-regulates the general amino acid control pathway through phosphorylation of the translational initiation factor eIF2. In Saccharomyces cerevisiae, Gcn2p is the only kinase that phosphorylates eIF2 to regulate translation through this mechanism. We addressed changes in yeast growth and tRNA aminoacylation, or charging, during amino acid depletion in the presence and absence of GCN2. tRNA charging was measured using a microarray technique which simultaneously measures all cytosolic tRNAs. A fully prototrophic strain, and its isogenic gcn2Δ counterpart, were used to study depletion for each of the 20 amino acids, with a focus on Trp, Arg, His and Leu, which are metabolically distinct and together provide a good overview on amino acid metabolism.

Results

While the wild-type strain had no observable phenotype upon depletion for any amino acid, the gcn2Δ strain showed slow growth in media devoid of only Trp or Arg. Consistent with the growth phenotypes, profiles of genome-wide tRNA charging revealed significant decrease in cognate tRNA charging only in the gcn2Δ strain upon depletion for Trp or Arg. In contrast, there was no change in tRNA charging during His and Leu depletion in either the wild-type or gcn2Δ strains, consistent with the null effect on growth during loss of these amino acids. We determined that the growth phenotype of Trp depletion is derived from feedback inhibition of aromatic amino acid biosynthesis. By removing Phe and Tyr from the media in addition to Trp, regular growth was restored and tRNA^Trp charging no longer decreased. The growth phenotype of Arg depletion is derived from unbalanced nitrogen metabolism. By supplementing ornithine upon Arg depletion, both growth and tRNA^Arg charging were partially restored.

Conclusion

Under mild stress conditions the basal activity of Gcn2p is sufficient to allow for proper adaptation to amino acid depletion. This study highlights the importance of the GCN2 eIF2 kinase pathway for maintaining metabolic homeostasis, contributing to appropriate tRNA charging and growth adaptation in response to culture conditions deficient for the central amino acids, tryptophan and arginine.

Modelling the optimal timing in metabolic pathway activation-use of Pontryagin's Maximum Principle and role of the Golden section

The time course of enzyme concentrations in metabolic pathways can be predicted on the basis of the optimality criterion of minimizing the time period in which an essential product is generated. This criterion is in line with the widely accepted view that high fitness requires high pathway flux. Here, based on Pontryagin's Maximum Principle, a method is developed to solve the corresponding constrained optimal control problem in an almost exclusively analytical way and, thus, to calculate optimal enzyme profiles, when linear, irreversible rate laws are assumed. Three different problem formulations are considered and the corresponding optimization results are derived. Besides the minimization of transition time, we consider an operation time in which 90% of the substrate has been converted into product. In that case, only the enzyme at the lower end of the pathway rather than all enzymes are active in the last phase. In all cases, biphasic or multiphasic time courses are obtained. The biological meaning of the results in terms of a consecutive just-in-time expression of metabolic genes is discussed. For the special case of two-enzyme systems, the role of the Golden section in the solution is outlined.

Systems biology of microbial metabolism

One current challenge in metabolic systems biology is to map out the regulation networks that control metabolism. From progress in this area, we conclude that non-transcriptional mechanisms (e.g. metabolite-protein interactions and protein phosphorylation) are highly relevant in actually controlling metabolic function. Furthermore, recent results highlight more functions of enzymes and metabolites than currently appreciated in genome-scale metabolic reconstructions, thereby adding another level of complexity. Combining experimental analyses and modeling efforts we are also beginning to understand how metabolic behavior emerges. Particularly, we recognize that metabolism is not simply a dull workhorse process but rather takes very active control of itself and other cellular processes, rendering true system-level understanding of metabolism possibly more difficult than for other cellular systems.

Engineering static and dynamic control of synthetic pathways

Maximizing the production of a desired small molecule is one of the primary goals in metabolic engineering. Recent advances in the nascent field of synthetic biology have increased the predictability of small-molecule production in engineered cells growing under constant conditions. The next frontier is to create synthetic pathways that adapt to changing environments.

Toolbox model of evolution of prokaryotic metabolic networks and their regulation

It has been reported that the number of transcription factors encoded in prokaryotic genomes scales approximately quadratically with their total number of genes. We propose a conceptual explanation of this finding and illustrate it using a simple model in which metabolic and regulatory networks of prokaryotes are shaped by horizontal gene transfer of coregulated metabolic pathways. Adapting to a new environmental condition monitored by a new transcription factor (e.g., learning to use another nutrient) involves both acquiring new enzymes and reusing some of the enzymes already encoded in the genome. As the repertoire of enzymes of an organism (its toolbox) grows larger, it can reuse its enzyme tools more often and thus needs to get fewer new ones to master each new task. From this observation, it logically follows that the number of functional tasks and their regulators increases faster than linearly with the total number of genes encoding enzymes. Genomes can also shrink, e.g., because of a loss of a nutrient from the environment, followed by deletion of its regulator and all enzymes that become redundant. We propose several simple models of network evolution elaborating on this toolbox argument and reproducing the empirically observed quadratic scaling. The distribution of lengths of pathway branches in our model agrees with that of the real-life metabolic network of Escherichia coli. Thus, our model provides a qualitative explanation for broad distributions of regulon sizes in prokaryotes.

Gene regulatory network interactions in sea urchin endomesoderm induction

A major goal of contemporary studies of embryonic development is to understand large sets of regulatory changes that accompany the phenomenon of embryonic induction. The highly resolved sea urchin pregastrular endomesoderm-gene regulatory network (EM-GRN) provides a unique framework to study the global regulatory interactions underlying endomesoderm induction. Vegetal micromeres of the sea urchin embryo constitute a classic endomesoderm signaling center, whose potential to induce archenteron formation from presumptive ectoderm was demonstrated almost a century ago. In this work, we ectopically activate the primary mesenchyme cell-GRN (PMC-GRN) that operates in micromere progeny by misexpressing the micromere determinant Pmar1 and identify the responding EM-GRN that is induced in animal blastomeres. Using localized loss-of -function analyses in conjunction with expression of endo16, the molecular definition of micromere-dependent endomesoderm specification, we show that the TGFbeta cytokine, ActivinB, is an essential component of this induction in blastomeres that emit this signal, as well as in cells that respond to it. We report that normal pregastrular endomesoderm specification requires activation of the Pmar1-inducible subset of the EM-GRN by the same cytokine, strongly suggesting that early micromere-mediated endomesoderm specification, which regulates timely gastrulation in the sea urchin embryo, is also ActivinB dependent. This study unexpectedly uncovers the existence of an additional uncharacterized micromere signal to endomesoderm progenitors, significantly revising existing models. In one of the first network-level characterizations of an intercellular inductive phenomenon, we describe an important in vivo model of the requirement of ActivinB signaling in the earliest steps of embryonic endomesoderm progenitor specification.

Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network

Significant insight about biological networks arises from the study of network motifs--overly abundant network subgraphs--but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction. Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions.

Lighting up the stressed ER

Balancing the capacity for protein maturation with changes in protein flux through the endoplasmic reticulum (ER) is crucial for maintaining ER homeostasis. In this issue, Merksamer et al. (2008) exploit a redox-sensitive fluorescent protein to monitor the environment inside the ER of living yeast, illuminating how this organelle responds to different perturbations.

Real-time redox measurements during endoplasmic reticulum stress reveal interlinked protein folding functions

Disruption of protein folding in the endoplasmic reticulum (ER) causes unfolded proteins to accumulate, triggering the unfolded protein response (UPR). UPR outputs in turn decrease ER unfolded proteins to close a negative feedback loop. However, because it is infeasible to directly measure the concentration of unfolded proteins in vivo, cells are generically described as experiencing "ER stress" whenever the UPR is active. Because ER redox potential is optimized for oxidative protein folding, we reasoned that measureable redox changes should accompany unfolded protein accumulation. To test this concept, we employed fluorescent protein reporters to dynamically measure ER redox status and UPR activity in single cells. Using these tools, we show that diverse stressors, both experimental and physiological, compromise ER protein oxidation when UPR-imposed homeostatic control is lost. Using genetic analysis we uncovered redox heterogeneities in isogenic cell populations, and revealed functional interlinks between ER protein folding, modification, and quality control systems.