A Computational Toolbox to Investigate the Metabolic Potential and Resource Allocation in Fission Yeast.

The fission yeast, Schizosaccharomyces pombe, is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the baker's yeast, Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually curated high-quality reconstruction of S. pombe's metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behavior and the role of resource allocation in driving different strategies in fission yeast. IMPORTANCE Our understanding of microbial metabolism relies mostly on the knowledge we have obtained from a limited number of model organisms, and the diversity of metabolism beyond the handful of model species thus remains largely unexplored in mechanistic terms. Computational modeling of metabolic networks offers an attractive platform to bridge the knowledge gap and gain new insights into physiology of lesser-studied organisms. Here we showcase an example of successful knowledge transfer from the budding yeast Saccharomyces cerevisiae to a popular model organism in molecular and cell biology, fission yeast Schizosaccharomyces pombe, using computational models.

pH dependencies of glycolytic enzymes of yeast under in vivo-like assay conditions.

Under carbon source transitions, the intracellular pH of Saccharomyces cerevisiae is subject to change. Dynamics in pH modulate the activity of the glycolytic enzymes, resulting in a change in glycolytic flux and ultimately cell growth. To understand how pH affects the global behavior of glycolysis and ethanol fermentation, we measured the activity of the glycolytic and fermentative enzymes in S. cerevisiae under in vivo-like conditions at different pH. We demonstrate that glycolytic enzymes exhibit differential pH dependencies, and optima, in the pH range observed during carbon source transitions. The forward reaction of GAPDH shows the highest decrease in activity, 83%, during a simulated feast/famine regime upon glucose removal (cytosolic pH drop from 7.1 to 6.4). We complement our biochemical characterization of the glycolytic enzymes by fitting the Vmax to the progression curves of product formation or decay over time. The fitting analysis shows that the observed changes in enzyme activities require changes in Vmax , but changes in Km cannot be excluded. Our study highlights the relevance of pH as a key player in metabolic regulation and provides a large set of quantitative data that can be explored to improve our understanding of metabolism in dynamic environments.

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation-known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.

A yeast FRET biosensor enlightens cAMP signaling.

The cAMP-PKA signaling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions and is rather homogenous among single cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows the Weber-Fechner law; its height scales with the relative, and not the absolute, change in glucose. Last, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signaling, and metabolic adaptation.

Serial propagation in water-in-oil emulsions selects for Saccharomyces cerevisiae strains with a reduced cell size or an increased biomass yield on glucose.

In S. cerevisiae and many other micro-organisms an increase in metabolic efficiency (i.e. ATP yield on carbon) is accompanied by a decrease in growth rate. From a fundamental point of view, studying these yield-rate trade-offs provides insight in for example microbial evolution and cellular regulation. From a biotechnological point of view, increasing the ATP yield on carbon might increase the yield of anabolic products. We here aimed to select S. cerevisiae mutants with an increased biomass yield. Serial propagation of individual cells in water-in-oil emulsions previously enabled the selection of lactococci with increased biomass yields, and adapting this protocol for yeast allowed us to enrich an engineered Crabtree-negative S. cerevisiae strain with a high biomass yield on glucose. When we started the selection with an S. cerevisiae deletion collection, serial propagation in emulsion enriched hxk2Δ and reg1Δ strains with an increased biomass yield on glucose. Surprisingly, a tps1Δ strain was highly abundant in both emulsion- and suspension-propagated populations. In a separate experiment we propagated a chemically mutagenized S. cerevisiae population in emulsion, which resulted in mutants with a higher cell number yield on glucose, but no significantly changed biomass yield. Genome analyses indicate that genes involved in glucose repression and cell cycle processes play a role in the selected phenotypes. The repeated identification of mutations in genes involved in glucose-repression indicates that serial propagation in emulsion is a valuable tool to study metabolic efficiency in S. cerevisiae.

Biphasic Cell-Size and Growth-Rate Homeostasis by Single Bacillus subtilis Cells.

The growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these growth-rate disturbances. Here, we quantified growth and constitutive protein expression dynamics of single Bacillus subtilis cells as a function of cell-cycle progression. We found that, even though growth at the population level is exponential, close inspection of the cell cycle of thousands of single Bacillus subtilis cells reveals systematic deviations from exponential growth. Newborn cells display varying growth rates that depend on their size. When they divide, growth-rate variation has decreased, and growth rates have become birth size independent. Thus, cells indeed compensate for growth-rate disturbances and achieve growth-rate homeostasis. Protein synthesis and growth of single cells displayed correlated, biphasic dynamics from cell birth to division. During a first phase of variable duration, the absolute rates were approximately constant and cells behaved as sizers. In the second phase, rates increased, and growth behavior exhibited characteristics of a timer strategy. These findings demonstrate that, just like size homeostasis, growth-rate homeostasis is an inherent property of single cells that is achieved by cell-cycle-dependent rate adjustments of biosynthesis and growth.

An Improved ATP FRET Sensor For Yeast Shows Heterogeneity During Nutrient Transitions.

Adenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at a single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and we show that a small subpopulation of wild-type cells reached an imbalanced state during glycolytic startup, characterized by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments.

Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli.

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Bacillus subtilis and Escherichia coli. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments.

Natural selection has shaped the strategies for survival and growth of microorganisms. The success of microorganisms depends not only on slow evolutionary tuning but also on the ability to adapt to unpredictable changes in their environment. In principle, adaptive strategies range from purely deterministic mechanisms to those that exploit the randomness intrinsic to many cellular and molecular processes. Depending on the environment and selective pressures, particular strategies can lie somewhere along this continuum. In recent years, non-genetic cell-to-cell differences have received a lot of attention, not least because of their potential impact on the ability of microbial populations to survive in dynamic environments. Using several examples, we describe the origins of spontaneous and induced mechanisms of phenotypic adaptation. We identify some of the commonalities of these examples and consider the potential role of chance and constraints in microbial phenotypic adaptation.

Effects of growth rate and promoter activity on single-cell protein expression.

Protein expression in a single cell depends on its global physiological state. Moreover, genetically-identical cells exhibit variability (noise) in protein expression, arising from the stochastic nature of biochemical processes, cell growth and division. While it is well understood how cellular growth rate influences mean protein expression, little is known about the relationship between growth rate and noise in protein expression. Here we quantify this relationship in Bacillus subtilis by a novel combination of experiments and theory. We measure the effects of promoter activity and growth rate on the expression of a fluorescent protein in single cells. We disentangle the observed protein expression noise into protein-specific and systemic contributions, using theory and variance decomposition. We find that noise in protein expression depends solely on mean expression levels, regardless of whether expression is set by promoter activity or growth rate, and that noise increases linearly with growth rate. Our results can aid studies of (synthetic) gene circuits of single cells and their condition dependence.

The effect of group exercise frequency on health related quality of life in institutionalized elderly.

INTRODUCTION: The study aimed to determine the effect of group exercise frequency on health related quality of life in institutionalized elderly. METHODS: One hundred participants were recruited for voluntary participation from five aged care facilities, with inclusion being based on the outcome of a medical assessment by a sports physician. A quasi-experimental design was used to compare the effect of a 12 week group exercise programme on two groups of participants using pre-test and post-test procedures. RESULTS: A significant difference was noted in social function post training 2X/week (MD = -13.85, 95% CI [-24.66, -3.38], p = 0.017, d = 0.674) and 3X/week (MD = -13.30, 95% CI [-21.81, -5.59], p = 0.003, d = 0.712) a week. Training 3X/week a week provided an additional benefit in vitality (MD = -7.55, 95% CI [-13.16, -1.91], p = 0.018, d =0. 379). Improvements in mental component summary scale post training 2X/week (MD = -4.08, 95% CI [-7.67, -0.42], p = 0.033, d = 0.425) and 3X/week (MD = -6.67, 95% CI [-10.92, -2.33], p = 0.005, d = 0.567) a week was further noted. CONCLUSION: Mental health and social health benefits can be obtained irrespective of exercise frequency 2X/week or 3X/week. The exercise intervention at a frequency 3X/ week was more effective in improving mental component summary due to a larger effect size obtained compared to the exercise frequency of 2X/week. Additional benefits in vitality were achieved by exercising 3X/week. This may assist the elderly in preserving their independence.

Metabolism at evolutionary optimal States.

Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.

Multi-tasking of biosynthetic and energetic functions of glycolysis explained by supply and demand logic.

After more than a century of research on glycolysis, we have detailed descriptions of its molecular organization, but despite this wealth of knowledge, linking the enzyme properties to metabolic pathway behavior remains challenging. These challenges arise from multi-layered regulation and the context and time dependence of component functions. However, when viewed as a system that functions according to the principles of supply and demand, a simplifying theoretical framework can be applied to study its regulation logic and to assess the coherence of experimental interpretations. These principles are universally applicable, as they emphasize the common metabolic tasks of glycolysis: the provision of free-energy carriers, and precursors for biosynthesis and stress-related compounds. Here we will review the regulation of multi-tasking by glycolysis and consider how an understanding of this central metabolic pathway can be pursued using general principles, rather than focusing on the biochemical details of constituent components.

Lost in transition: start-up of glycolysis yields subpopulations of nongrowing cells.

Cells need to adapt to dynamic environments. Yeast that fail to cope with dynamic changes in the abundance of glucose can undergo growth arrest. We show that this failure is caused by imbalanced reactions in glycolysis, the essential pathway in energy metabolism in most organisms. The imbalance arises largely from the fundamental design of glycolysis, making this state of glycolysis a generic risk. Cells with unbalanced glycolysis coexisted with vital cells. Spontaneous, nongenetic metabolic variability among individual cells determines which state is reached and, consequently, which cells survive. Transient ATP (adenosine 5'-triphosphate) hydrolysis through futile cycling reduces the probability of reaching the imbalanced state. Our results reveal dynamic behavior of glycolysis and indicate that cell fate can be determined by heterogeneity purely at the metabolic level.

Fog as a fresh-water resource: overview and perspectives.

The collection of fog water is a simple and sustainable technology to obtain fresh water for afforestation, gardening, and as a drinking water source for human and animal consumption. In regions where fresh water is sparse and fog frequently occurs, it is feasible to set up a passive mesh system for fog water collection. The mesh is directly exposed to the atmosphere, and the foggy air is pushed through the mesh by the wind. Fog droplets are deposited on the mesh, combine to form larger droplets, and run down passing into a storage tank. Fog water collection rates vary dramatically from site to site but yearly averages from 3 to 10 l m(-2) of mesh per day are typical of operational projects. The scope of this article is to review fog collection projects worldwide, to analyze factors of success, and to evaluate the prospects of this technology.

Clinical practice guidelines within the Southern African Development Community: a descriptive study of the quality of guideline development and concordance with best evidence for five priority diseases.

BACKGROUND: Reducing the burden of disease relies on availability of evidence-based clinical practice guidelines (CPGs). There is limited data on availability, quality and content of guidelines within the Southern African Development Community (SADC). This evaluation aims to address this gap in knowledge and provide recommendations for regional guideline development. METHODS: We prioritised five diseases: HIV in adults, malaria in children and adults, pre-eclampsia, diarrhoea in children and hypertension in primary care. A comprehensive electronic search to locate guidelines was conducted between June and October 2010 and augmented with email contact with SADC Ministries of Health. Independent reviewers used the AGREE II tool to score six quality domains reporting the guideline development process. Alignment of the evidence-base of the guidelines was evaluated by comparing their content with key recommendations from accepted reference guidelines, identified with a content expert, and percentage scores were calculated. FINDINGS: We identified 30 guidelines from 13 countries, publication dates ranging from 2003-2010. Overall the 'scope and purpose' and 'clarity and presentation' domains of the AGREE II instrument scored highest, median 58%(range 19-92) and 83%(range 17-100) respectively. 'Stakeholder involvement' followed with median 39%(range 6-75). 'Applicability', 'rigour of development' and 'editorial independence' scored poorly, all below 25%. Alignment with evidence was variable across member states, the lowest scores occurring in older guidelines or where the guideline being evaluated was part of broader primary healthcare CPG rather than a disease-specific guideline. CONCLUSION: This review identified quality gaps and variable alignment with best evidence in available guidelines within SADC for five priority diseases. Future guideline development processes within SADC should better adhere to global reporting norms requiring broader consultation of stakeholders and transparency of process. A regional guideline support committee could harness local capacity to support context appropriate guideline development.

Structural and biochemical characterization of a nitrilase from the thermophilic bacterium, Geobacillus pallidus RAPc8.

Geobacillus pallidus RAPc8 (NRRL: B-59396) is a moderately thermophilic gram-positive bacterium, originally isolated from Australian lake sediment. The G. pallidus RAPc8 gene encoding an inducible nitrilase was located and cloned using degenerate primers coding for well-conserved nitrilase sequences, coupled with inverse PCR. The nitrilase open reading frame was cloned into an expression plasmid and the expressed recombinant enzyme purified and characterized. The protein had a monomer molecular weight of 35,790 Da, and the purified functional enzyme had an apparent molecular weight of approximately 600 kDa by size exclusion chromatography. Similar to several plant nitrilases and some bacterial nitrilases, the recombinant G. pallidus RAPc8 enzyme produced both acid and amide products from nitrile substrates. The ratios of acid to amide produced from the substrates we tested are significantly different to those reported for other enzymes, and this has implications for our understanding of the mechanism of the nitrilases which may assist with rational design of these enzymes. Electron microscopy and image classification showed complexes having crescent-like, "c-shaped", circular and "figure-8" shapes. Protein models suggested that the various complexes were composed of 6, 8, 10 and 20 subunits, respectively.

Evaluating the behavioural consequences of early maternal separation in adult C57BL/6 mice; the importance of time.

Research in rats has shown that early maternal separation can have a significant effect on stress-associated neuro- and endocrine mechanisms in adulthood. However, despite a growing body of evidence on the neurobiology of early MS, showing significant overlap in data from rat, non-human primate and human studies, there is still some uncertainty about the validity of this model in mice. Here we present evidence in support of long lasting effects of early MS on adult mouse behaviour, which were only apparent when time was included as an analytical component. In the elevated plus maze (EPM), conventional statistical strategies, which typically evaluate behaviour as a summed test-session total, were not sufficient to reveal more complex time-dependent behavioural profiles. Specifically, the spatially more complex nature of the EPM test underscored treatment-related differences in the time-dependent adjustments of open arm exploration and risk-assessment behaviours. In contrast, the open field elicited an immediate and consistent divergence in risk-assessment behaviours, between MS animals and controls. Finally, plasma corticosterone further underscored MS-associated alterations in adult mouse stress profiles, with significantly higher concentrations in the MS group, post-restraint stress. The extension of conventional analysis strategies, to include time as a significant dimension of behaviour on the EPM, identified behavioural nuances, which could reflect adaptive aspects of stress-driven behaviours in MS mice.

Parallel changes in gene expression in peripheral blood mononuclear cells and the brain after maternal separation in the mouse.

BACKGROUND: The functional integration of the neuro-, endocrine- and immune-systems suggests that the transcriptome of white blood cells may reflect neuropsychiatric states, and be used as a non-invasive diagnostic indicator. We used a mouse maternal separation model, a paradigm of early adversity, to test the hypothesis that transcriptional changes in peripheral blood mononuclear cells (PBMCs) are paralleled by specific gene expression changes in prefrontal cortex (PFC), hippocampus (Hic) and hypothalamus (Hyp). Furthermore, we evaluated whether gene expression profiles of PBMCs could be used to predict the separation status of individual animals. FINDINGS: Microarray gene expression profiles of all three brain regions provided substantial evidence of stress-related neural differences between maternally separated and control animals. For example, changes in expression of genes involved in the glutamatergic and GABAergic systems were identified in the PFC and Hic, supporting a stress-related hyperglutamatergic state within the separated group. The expression of 50 genes selected from the PBMC microarray data provided sufficient information to predict treatment classes with 95% accuracy. Importantly, stress-related transcriptome differences in PBMC populations were paralleled by stress-related gene expression changes in CNS target tissues. CONCLUSION: These results confirm that the transcriptional profiles of peripheral immune tissues occur in parallel to changes in the brain and contain sufficient information for the efficient diagnostic prediction of stress-related neural states in mice. Future studies will need to evaluate the relevance of the predictor set of 50 genes within clinical settings, specifically within a context of stress-related disorders.

Antimicrobial coating agents: can biofilm formation on a breast implant be prevented?

BACKGROUND: Numerous clinical studies have shown that biofilm formation by Staphylococcus epidermidis on the outer surface of a silicone breast implant is strongly associated with capsular contracture formation. Traditional administration of systemic antibiotics and antiseptic washing are not necessarily the most effective methods for the prevention of initial biofilm formation on implants in the clinical scenario. In this study an alternative or supplement was sought for preventing or delaying bacterial colonisation and adherence to the outer surface of a breast implant, by establishing an in vitro model for investigating this complex problem. The in vitro antimicrobial activity of several antimicrobial agents was investigated for inhibitory effects on biofilm formation by S. epidermidis. METHODS: The study consisted of two experiments. The first experiment consisted of two groups (A and B) of seven discs each whilst the second experiment was divided into three groups (C, D and E) of 14 discs each. Each group of 14 consisted of seven smooth and seven textured discs. Discs (biopsies) of each implant were individually coated with one of six different antimicrobial agents. Controls that received no agent were included in the various experimental groups. In the first experiment disc diffusion sensitivity testing was performed and inhibition zone sizes were measured. In the second experiment the discs were cultured in broth. The degree of biofilm formation was evaluated by scanning electron microscopy (SEM). RESULTS: In the first in vitro experiment, all six agents showed a measurable antimicrobial effect against the biofilm-forming strain of S. epidermidis when compared to the effect against the American Type Culture Collection strain. In the second in vitro experiment, discs coated with Chloramex, Fucidin and Terramycin did not allow biofilm formation to take place for at least 7 days. CONCLUSIONS: Staphylococcus epidermidis biofilm formation on the outer surface of a silicone breast implant was prevented in vitro for at least 7 days by coating with an appropriate antimicrobial agent. Further evaluation of the interaction between antimicrobial coating agents and S. epidermidis biofilm formation needs to be made before conclusions regarding the clinical scenario can be drawn.