Coordination of the dynamics of yeast sphingolipid metabolism during the diauxic shift

High-throughput metabolomics for the design and validation of a diauxic shift model

Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput.

A dynamic compartment model for xylem loading and long-distance transport of iron explains the effect of kanamycin on metal uptake in Arabidopsis

Arabidopsis plants exposed to the antibiotic kanamycin (Kan) display altered metal homeostasis. Further, mutation of the WBC19 gene leads to increased sensitivity to kanamycin and changes in iron (Fe) and zinc (Zn) uptake. Here we propose a model that explain this surprising relationship between metal uptake and exposure to Kan. We first use knowledge about the metal uptake phenomenon to devise a transport and interaction diagram on which we base the construction of a dynamic compartment model. The model has three pathways for loading Fe and its chelators into the xylem. One pathway, involving an unknown transporter, loads Fe as a chelate with citrate (Ci) into the xylem. This transport step can be significantly inhibited by Kan. In parallel, FRD3 transports Ci into the xylem where it can chelate with free Fe. A third critical pathway involves WBC19, which transports metal-nicotianamine (NA), mainly as Fe-NA chelate, and possibly NA itself. To permit quantitative exploration and analysis, we use experimental time series data to parameterize this explanatory and predictive model. Its numerical analysis allows us to predict responses by a double mutant and explain the observed differences between data from wildtype, mutants and Kan inhibition experiments. Importantly, the model provides novel insights into metal homeostasis by permitting the reverse-engineering of mechanistic strategies with which the plant counteracts the effects of mutations and of the inhibition of iron transport by kanamycin.

System level modeling and analysis of TNF-α mediated sphingolipid signaling pathway in neurological disorders for the prediction of therapeutic targets

Sphingomyelin (SM) belongs to a class of lipids termed sphingolipids. The disruption in the sphingomyelin signaling pathway is associated with various neurodegenerative disorders. TNF-α, a potent pro-inflammatory cytokine generated in response to various neurological disorders like Alzheimer’s disease (AD), Parkinson’s disease (PD), and Multiple Sclerosis (MS), is an eminent regulator of the sphingomyelin metabolic pathway. The immune-triggered regulation of the sphingomyelin metabolic pathway via TNF-α constitutes the sphingomyelin signaling pathway. In this pathway, sphingomyelin and its downstream sphingolipids activate various signaling cascades like PI3K/AKT and MAPK/ERK pathways, thus, controlling diverse processes coupled with neuronal viability, survival, and death. The holistic analysis of the immune-triggered sphingomyelin signaling pathway is imperative to make necessary predictions about its pivotal components and for the formulation of disease-related therapeutics. The current work offers a comprehensive in silico systems analysis of TNF-α mediated sphingomyelin and downstream signaling cascades via a model-based quantitative approach. We incorporated the intensity values of genes from the microarray data of control individuals from the AD study in the input entities of the pathway model. Computational modeling and simulation of the inflammatory pathway enabled the comprehensive study of the system dynamics. Network and sensitivity analysis of the model unveiled essential interaction parameters and entities during neuroinflammation. Scanning of the key entities and parameters allowed us to determine their ultimate impact on neuronal apoptosis and survival. Moreover, the efficacy and potency of the FDA-approved drugs, namely Etanercept, Nivocasan, and Scyphostatin allowed us to study the model’s response towards inhibition of the respective proteins/enzymes. The network analysis revealed the pivotal model entities with high betweenness and closeness centrality values including recruit FADD, TNFR_TRADD, act CASP2, actCASP8, actCASP3 and 9, cytochrome C, and RIP_RAIDD which profoundly impacted the neuronal apoptosis. Whereas some of the entities with high betweenness and closeness centrality values like Gi-coupled receptor, actS1PR, Sphingosine, S1P, actAKT, and actERK produced a high influence on neuronal survival. However, the current study inferred the dual role of ceramide, both on neuronal survival and apoptosis. Moreover, the drug Nivocasan effectively reduces neuronal apoptosis via its inhibitory mechanism on the caspases.

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

BACKGROUND

Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell-cell and metabolic coupling lead to functionally optimized structures is still limited.

RESULTS

Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation.

CONCLUSIONS

We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

Aging-related elevation of sphingoid bases shortens yeast chronological life span by compromising mitochondrial function

Sphingoid bases (SBs) as bioactive sphingolipids, have been implicated in aging in yeast. However, we know neither how SBs are regulated during yeast aging nor how they, in turn, regulate it. Herein, we demonstrate that the yeast alkaline ceramidases (YPC1 and YDC1) and SB kinases (LCB4 and LCB5) cooperate in regulating SBs during the aging process and that SBs shortens chronological life span (CLS) by compromising mitochondrial functions. With a lipidomics approach, we found that SBs were increased in a time-dependent manner during yeast aging. We also demonstrated that among the enzymes known for being responsible for the metabolism of SBs, YPC1 was upregulated whereas LCB4/5 were downregulated in the course of aging. This inverse regulation of YPC1 and LCB4/5 led to the aging-related upregulation of SBs in yeast and a reduction in CLS. With the proteomics-based approach (SILAC), we revealed that increased SBs altered the levels of proteins related to mitochondria. Further mechanistic studies demonstrated that increased SBs inhibited mitochondrial fusion and caused fragmentation, resulting in decreases in mtDNA copy numbers, ATP levels, mitochondrial membrane potentials, and oxygen consumption. Taken together, these results suggest that increased SBs mediate the aging process by impairing mitochondrial structural integrity and functions.

A method for analysis and design of metabolism using metabolomics data and kinetic models: Application on lipidomics using a novel kinetic model of sphingolipid metabolism

We present a model-based method, designated Inverse Metabolic Control Analysis (IMCA), which can be used in conjunction with classical Metabolic Control Analysis for the analysis and design of cellular metabolism. We demonstrate the capabilities of the method by first developing a comprehensively curated kinetic model of sphingolipid biosynthesis in the yeast Saccharomyces cerevisiae. Next we apply IMCA using the model and integrating lipidomics data. The combinatorial complexity of the synthesis of sphingolipid molecules, along with the operational complexity of the participating enzymes of the pathway, presents an excellent case study for testing the capabilities of the IMCA. The exceptional agreement of the predictions of the method with genome-wide data highlights the importance and value of a comprehensive and consistent engineering approach for the development of such methods and models. Based on the analysis, we identified the class of enzymes regulating the distribution of sphingolipids among species and hydroxylation states, with the D-phospholipase SPO14 being one of the most prominent. The method and the applications presented here can be used for a broader, model-based inverse metabolic engineering approach.

Analysis of the Involvement of Different Ceramide Variants in the Response to Hydroxyurea Stress in Baker's Yeast

Sphingolipids have been identified as important signaling compounds in stress responses. However, it is not always clear how different sphingolipid profiles are achieved in a particular stress situation. Here we propose a detailed mass action model, containing 42 dependent variables and 137 reactions, that offers explanations of the roles of variant ceramides species, which differ in the lengths of their fatty acyl chains and their saturation state, in the response to hydroxyurea stress. The simulations demonstrate that the cells manage to achieve hydroxyurea tolerance through a well-coordinated, differential usage of the variant ceramide species. Moreover, the results suggest that key enzymes have different affinities toward saturated and unsaturated fatty acyl chains, which implies that the saturation state affords the cells with an additional mode of regulation that had not been recognized so far. These conclusions from our computational analysis are yet to be validated experimentally.

Harnessing the power of yeast to elucidate the role of sphingolipids in metabolic and signaling processes pertinent to psychiatric disorders

The development of therapies for neuropsychiatric disorders is hampered by the lack of understanding of the mechanisms underlying their pathologies. While aberrant sphingolipid metabolism is associated with psychiatric illness, the role of sphingolipids in these disorders is not understood. The genetically tractable yeast model can be exploited in order to elucidate the cellular consequences of sphingolipid perturbation. Hypotheses generated from studies in yeast and tested in mammalian cells may contribute to our understanding of the role of sphingolipids in psychiatric disorders and to the development of new treatments. Here, we compare sphingolipid metabolism in yeast and mammalian cells, discuss studies implicating sphingolipids in psychiatric disorders and propose approaches that utilize yeast in order to elucidate sphingolipid function and identify drugs that target sphingolipid synthesis.

Systematic lipidomic analysis of yeast protein kinase and phosphatase mutants reveals novel insights into regulation of lipid homeostasis

The regulatory pathways required to maintain eukaryotic lipid homeostasis are largely unknown. We developed a systematic approach to uncover new players in the regulation of lipid homeostasis. Through an unbiased mass spectrometry-based lipidomic screening, we quantified hundreds of lipid species, including glycerophospholipids, sphingolipids, and sterols, from a collection of 129 mutants in protein kinase and phosphatase genes of Saccharomyces cerevisiae. Our approach successfully identified known kinases involved in lipid homeostasis and uncovered new ones. By clustering analysis, we found connections between nutrient-sensing pathways and regulation of glycerophospholipids. Deletion of members of glucose- and nitrogen-sensing pathways showed reciprocal changes in glycerophospholipid acyl chain lengths. We also found several new candidates for the regulation of sphingolipid homeostasis, including a connection between inositol pyrophosphate metabolism and complex sphingolipid homeostasis through transcriptional regulation of AUR1 and SUR1. This robust, systematic lipidomic approach constitutes a rich, new source of biological information and can be used to identify novel gene associations and function.

Re-configuration of sphingolipid metabolism by oncogenic transformation

The sphingolipids are one of the major lipid families in eukaryotes, incorporating a diverse array of structural variants that exert a powerful influence over cell fate and physiology. Increased expression of sphingosine kinase 1 (SPHK1), which catalyses the synthesis of the pro-survival, pro-angiogenic metabolite sphingosine 1-phosphate (S1P), is well established as a hallmark of multiple cancers. Metabolic alterations that reduce levels of the pro-apoptotic lipid ceramide, particularly its glucosylation by glucosylceramide synthase (GCS), have frequently been associated with cancer drug resistance. However, the simple notion that the balance between ceramide and S1P, often referred to as the sphingolipid rheostat, dictates cell survival contrasts with recent studies showing that highly potent and selective SPHK1 inhibitors do not affect cancer cell proliferation or survival, and studies demonstrating higher ceramide levels in some metastatic cancers. Recent reports have implicated other sphingolipid metabolic enzymes such as acid sphingomyelinase (ASM) more strongly in cancer pathogenesis, and highlight lysosomal sphingolipid metabolism as a possible weak point for therapeutic targeting in cancer. This review describes the evidence implicating different sphingolipid metabolic enzymes and their products in cancer pathogenesis, and suggests how newer systems-level approaches may improve our overall understanding of how oncogenic transformation reconfigures sphingolipid metabolism.

Coordination of rapid sphingolipid responses to heat stress in yeast

The regulatory roles of sphingolipids in diverse cell functions have been characterized extensively. However, the dynamics and interactions among the different sphingolipid species are difficult to assess, because de novo biosynthesis, metabolic inter-conversions, and the retrieval of sphingolipids from membranes form a complex, highly regulated pathway system. Here we analyze the heat stress response of this system in the yeast Saccharomyces cerevisiae and demonstrate how the cell dynamically adjusts its enzyme profile so that it is appropriate for operation under stress conditions before changes in gene expression become effective. The analysis uses metabolic time series data, a complex mathematical model, and a custom-tailored optimization strategy. The results demonstrate that all enzyme activities rapidly increase in an immediate response to the elevated temperature. After just a few minutes, different functional clusters of enzymes follow distinct activity patterns. Interestingly, starting after about six minutes, both de novo biosynthesis and all exit routes from central sphingolipid metabolism become blocked, and the remaining metabolic activity consists entirely of an internal redistribution among different sphingoid base and ceramide pools. After about 30 minutes, heat stress is still in effect and the enzyme activity profile is still significantly changed. Importantly, however, the metabolites have regained concentrations that are essentially the same as those under optimal conditions.

Sphingolipids play dual roles by serving as components of membrane rafts and by regulating numerous key cell functions. Although sphingolipids have been studied extensively, the details of their functioning are difficult to understand, because their synthesis, pathways of inter-conversion, and utilization constitute a complex, dynamically changing system. We analyze the role of yeast sphingolipids in the response to heat stress. Data show that the profile of these lipids changes almost immediately with the initiation of the stress, but it is a priori unclear how this response is organized. Using experimental data, a sophisticated dynamic model, and a novel optimization strategy, we show how changes in enzyme activities are temporally organized. Intriguingly, the results show that the cells take up as much material as possible in the first few minutes of heat stress and then shut down entry and exit routes of the biosynthetic pathway system. After about 30 minutes, when heat stress is still in effect, the enzyme activity profile is still significantly changed, but the metabolites have regained concentrations that are essentially the same as those under optimal conditions. The results demonstrate how novel insights are achievable with an effective combination of experimental and theoretical research.

Biochemical Systems Theory: A Review

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools that facilitate the analysis of dynamic biological systems. This paper depicts major developments in BST up to the current state of the art in 2012. It discusses its rationale, describes the typical strategies and methods of designing, diagnosing, analyzing, and utilizing BST models, and reviews areas of application. The paper is intended as a guide for investigators entering the fascinating field of biological systems analysis and as a resource for practitioners and experts.

Iron, glucose and intrinsic factors alter sphingolipid composition as yeast cells enter stationary phase

Survival of Saccharomyces cerevisiae cells, like most microorganisms, requires switching from a rapidly dividing to a non-dividing or stationary state. To further understand how cells navigate this switch, we examined sphingolipids since they are key structural elements of membranes and also regulate signaling pathways vital for survival. During and after the switch to a non-dividing state there is a large increase in total free and sphingolipid-bound long chain-bases and an even larger increase in free and bound C20-long-chain bases, which are nearly undetectable in dividing cells. These changes are due to intrinsic factors including Orm1 and Orm2, ceramide synthase, Lcb4 kinase and the Tsc3 subunit of serine palmitoyltransferase as well as extrinsic factors including glucose and iron. Lowering the concentration of glucose, a form of calorie restriction, decreases the level of LCBs, which is consistent with the idea that reducing the level of some sphingolipids enhances lifespan. In contrast, iron deprivation increases LCB levels and decreases long term survival; however, these phenomena may not be related because iron deprivation disrupts many metabolic pathways. The correlation between increased LCBs and shorter lifespan is unsupported at this time. The physiological rise in LCBs that we observe may serve to modulate nutrient transporters and possibly other membrane phenomena that contribute to enhanced stress resistance and survival in stationary phase.

Programmed cell death in Saccharomyces cerevisiae is hampered by the deletion of GUP1 gene

Background

During the past years, yeast has been successfully established as a model to study mechanisms of programmed cell death regulation. Saccharomyces cerevisiae commits to cell death showing typical hallmarks of metazoan apoptosis, in response to different stimuli. Gup1p, an O-acyltransferase, is required for several cellular processes that are related to apoptosis development, such as rafts integrity and stability, lipid metabolism including GPI anchor correct remodeling, proper mitochondrial and vacuole function, bud site selection and actin dynamics. Therefore, we hypothesize that apoptotic process would be affected by GUP1 deletion.

Results

In the present work we used two known apoptosis inducing conditions, chronological aging and acetic acid, to assess several apoptotic markers in gup1∆ mutant strain. We found that this mutant presents a significantly reduced chronological lifespan as compared to Wt and it is also highly sensitive to acetic acid treatment. In addition, it presents extremely high levels of ROS. There were notorious differences on apoptotic markers between Wt and gup1∆ mutant strains, namely on the maintenance of plasma membrane integrity, on the phosphatidylserine externalization, on the depolarization of mitochondrial membrane and on the chromatin condensation. Those suggested that the mutant, under either condition, probably dies of necrosis and not from apoptosis.

Conclusions

To Gup1p has been assigned an important function on lipid rafts assembly/integrity, lipid metabolism and GPI anchor remodeling. Our results provide, for the first time, the connection of the integrity of yeast lipid rafts and apoptosis induction and/or signaling, giving new insights into the molecular mechanisms underlying this process in yeast.

Mathematical modeling and validation of the ergosterol pathway in Saccharomyces cerevisiae

The de novo biosynthetic machinery for both sphingolipid and ergosterol production in yeast is localized in the endoplasmic reticulum (ER) and Golgi. The interconnections between the two pathways are still poorly understood, but they may be connected in specialized membrane domains, and specific knockouts strongly suggest that both routes have different layers of mutual control and are co-affected by drugs. With the goal of shedding light on the functional integration of the yeast sphingolipid-ergosterol (SL-E) pathway, we constructed a dynamic model of the ergosterol pathway using the guidelines of Biochemical Systems Theory (BST) (Savageau., J. theor. Biol., 25, 365–9, 1969). The resulting model was merged with a previous mathematical model of sphingolipid metabolism in yeast (Alvarez-Vasquez et al., J. theor. Biol., 226, 265–91, 2004; Alvarez-Vasquez et al., Nature433, 425–30, 2005). The S-system format within BST was used for analyses of consistency, stability, and sensitivity of the SL-E model, while the GMA format was used for dynamic simulations and predictions. Model validation was accomplished by comparing predictions from the model with published results on sterol and sterol-ester dynamics in yeast. The validated model was used to predict the metabolomic dynamics of the SL-E pathway after drug treatment. Specifically, we simulated the action of drugs affecting sphingolipids in the endoplasmic reticulum and studied changes in ergosterol associated with microdomains of the plasma membrane (PM).

Analysis of operating principles with S-system models

Operating principles address general questions regarding the response dynamics of biological systems as we observe or hypothesize them, in comparison to a priori equally valid alternatives. In analogy to design principles, the question arises: Why are some operating strategies encountered more frequently than others and in what sense might they be superior? It is at this point impossible to study operation principles in complete generality, but the work here discusses the important situation where a biological system must shift operation from its normal steady state to a new steady state. This situation is quite common and includes many stress responses. We present two distinct methods for determining different solutions to this task of achieving a new target steady state. Both methods utilize the property of S-system models within Biochemical Systems Theory (BST) that steady states can be explicitly represented as systems of linear algebraic equations. The first method uses matrix inversion, a pseudo-inverse, or regression to characterize the entire admissible solution space. Operations on the basis of the solution space permit modest alterations of the transients toward the target steady state. The second method uses standard or mixed integer linear programming to determine admissible solutions that satisfy criteria of functional effectiveness, which are specified beforehand. As an illustration, we use both methods to characterize alternative response patterns of yeast subjected to heat stress, and compare them with observations from the literature.

Recent developments in parameter estimation and structure identification of biochemical and genomic systems

The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.