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

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Availability of the Molecular Switch XylR Controls Phenotypic Heterogeneity and Lag Duration during Escherichia coli Adaptation from Glucose to Xylose

The glucose-xylose metabolic transition is of growing interest as a model to explore cellular adaption since these molecules are the main substrates resulting from the deconstruction of lignocellulosic biomass. Here, we investigated the role of the XylR transcription factor in the length of the lag phases when the bacterium Escherichia coli needs to adapt from glucose- to xylose-based growth. First, a variety of lag times were observed when different strains of E. coli were switched from glucose to xylose. These lag times were shown to be controlled by XylR availability in the cells with no further effect on the growth rate on xylose. XylR titration provoked long lag times demonstrated to result from phenotypic heterogeneity during the switch from glucose to xylose, with a subpopulation unable to resume exponential growth, whereas the other subpopulation grew exponentially on xylose. A stochastic model was then constructed based on the assumption that XylR availability influences the probability of individual cells to switch to xylose growth. The model was used to understand how XylR behaves as a molecular switch determining the bistability set-up. This work shows that the length of lag phases in E. coli is controllable and reinforces the role of stochastic mechanism in cellular adaptation, paving the way for new strategies for the better use of sustainable carbon sources in bioeconomy.

Computation of Single-Cell Metabolite Distributions Using Mixture Models

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

Non-invasive biomarkers for monitoring the immunotherapeutic response to cancer

Immunotherapy is an efficient way to cure cancer by modulating the patient’s immune response. However, the immunotherapy response is heterogeneous and varies between individual patients and cancer subtypes, reinforcing the need for early benefit predictors. Evaluating the infiltration of immune cells in the tumor and changes in cell-intrinsic tumor characteristics provide potential response markers to treatment. However, this approach requires invasive sampling and may not be suitable for real-time monitoring of treatment response. The recent emergence of quantitative imaging biomarkers provides promising opportunities. In vivo imaging technologies that interrogate T cell responses, metabolic activities, and immune microenvironment could offer a powerful tool to monitor the cancer response to immunotherapy. Advances in imaging techniques to identify tumors' immunological characteristics can help stratify patients who are more likely to respond to immunotherapy. This review discusses the metabolic events that occur during T cell activation and differentiation, anti-cancer immunotherapy-induced T cell responses, focusing on non-invasive imaging techniques to monitor T cell metabolism in the search for novel biomarkers of response to cancer immunotherapy.

Microbial metabolic noise

From the time a cell was first placed under the microscope, it became apparent that identifying two clonal cells that "look" identical is extremely challenging. Since then, cell-to-cell differences in shape, size, and protein content have been carefully examined, informing us of the ultimate limits that hinder two cells from occupying an identical phenotypic state. Here, we present recent experimental and computational evidence that similar limits emerge also in cellular metabolism. These limits pertain to stochastic metabolic dynamics and, thus, cell-to-cell metabolic variability, including the resulting adapting benefits. We review these phenomena with a focus on microbial metabolism and conclude with a brief outlook on the potential relationship between metabolic noise and adaptive evolution. This article is categorized under: Metabolic Diseases > Computational Models Metabolic Diseases > Biomedical Engineering.

Aberrant Intracellular pH Regulation Limiting Glyceraldehyde-3-Phosphate Dehydrogenase Activity in the Glucose-Sensitive Yeast tps1Δ Mutant

Glucose catabolism is the backbone of metabolism in most organisms. In spite of numerous studies and extensive knowledge, major controls on glycolysis and its connections to the other metabolic pathways remain to be discovered. A striking example is provided by the extreme glucose sensitivity of the yeast tps1Δ mutant, which undergoes apoptosis in the presence of just a few millimolar glucose. Previous work has shown that the conspicuous glucose-induced hyperaccumulation of the glycolytic metabolite fructose-1,6-bisphosphate (Fru1,6bisP) in tps1Δ cells triggers apoptosis through activation of the Ras-cAMP-protein kinase A (PKA) signaling pathway. However, the molecular cause of this Fru1,6bisP hyperaccumulation has remained unclear. We now provide evidence that the persistent drop in intracellular pH upon glucose addition to tps1Δ cells likely compromises the activity of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a major glycolytic enzyme downstream of Fru1,6bisP, due to its unusually high pH optimum. Our work highlights the potential importance of intracellular pH fluctuations for control of major metabolic pathways.

Whereas the yeast Saccharomyces cerevisiae shows great preference for glucose as a carbon source, a deletion mutant in trehalose-6-phosphate synthase, tps1Δ, is highly sensitive to even a few millimolar glucose, which triggers apoptosis and cell death. Glucose addition to tps1Δ cells causes deregulation of glycolysis with hyperaccumulation of metabolites upstream and depletion downstream of glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The apparent metabolic barrier at the level of GAPDH has been difficult to explain. We show that GAPDH isozyme deletion, especially Tdh3, further aggravates glucose sensitivity and metabolic deregulation of tps1Δ cells, but overexpression does not rescue glucose sensitivity. GAPDH has an unusually high pH optimum of 8.0 to 8.5, which is not altered by tps1Δ. Whereas glucose causes short, transient intracellular acidification in wild-type cells, in tps1Δ cells, it causes permanent intracellular acidification. The hxk2Δ and snf1Δ suppressors of tps1Δ restore the transient acidification. These results suggest that GAPDH activity in the tps1Δ mutant may be compromised by the persistently low intracellular pH. Addition of NH₄Cl together with glucose at high extracellular pH to tps1Δ cells abolishes the pH drop and reduces glucose-6-phosphate (Glu6P) and fructose-1,6-bisphosphate (Fru1,6bisP) hyperaccumulation. It also reduces the glucose uptake rate, but a similar reduction in glucose uptake rate in a tps1Δ hxt2,4,5,6,7Δ strain does not prevent glucose sensitivity and Fru1,6bisP hyperaccumulation. Hence, our results suggest that the glucose-induced intracellular acidification in tps1Δ cells may explain, at least in part, the apparent glycolytic bottleneck at GAPDH but does not appear to fully explain the extreme glucose sensitivity of the tps1Δ mutant.

Nutrient sensing and cAMP signaling in yeast: G-protein coupled receptor versus transceptor activation of PKA

A major signal transduction pathway regulating cell growth and many associated physiological properties as a function of nutrient availability in the yeast Saccharomyces cerevisiae is the protein kinase A (PKA) pathway. Glucose activation of PKA is mediated by G-protein coupled receptor (GPCR) Gpr1, and secondary messenger cAMP. Other nutrients, including nitrogen, phosphate and sulfate, activate PKA in accordingly-starved cells through nutrient transceptors, but apparently without cAMP signaling. We have now used an optimized EPAC-based fluorescence resonance energy transfer (FRET) sensor to precisely monitor in vivo cAMP levels after nutrient addition. We show that GPCR-mediated glucose activation of PKA is correlated with a rapid transient increase in the cAMP level in vivo, whereas nutrient transceptor-mediated activation by nitrogen, phosphate or sulfate, is not associated with any significant increase in cAMP in vivo. We also demonstrate direct physical interaction between the Gap1 amino acid transceptor and the catalytic subunits of PKA, Tpk1, 2 and 3. In addition, we reveal a conserved consensus motif in the nutrient transceptors that is also present in Bcy1, the regulatory subunit of PKA. This suggests that nutrient transceptor activation of PKA may be mediated by direct release of bound PKA catalytic subunits, triggered by the conformational changes occurring during transport of the substrate by the transceptor. Our results support a model in which nutrient transceptors are evolutionary ancestors of GPCRs, employing a more primitive direct signaling mechanism compared to the indirect cAMP second-messenger signaling mechanism used by GPCRs for activation of PKA.

Lipophilic Cations Rescue the Growth of Yeast under the Conditions of Glycolysis Overflow

Chemicals inducing a mild decrease in the ATP/ADP ratio are considered as caloric restriction mimetics as well as treatments against obesity. Screening for such chemicals in animal model systems requires a lot of time and labor. Here, we present a system for the rapid screening of non-toxic substances causing such a de-energization of cells. We looked for chemicals allowing the growth of yeast lacking trehalose phosphate synthase on a non-fermentable carbon source in the presence of glucose. Under such conditions, the cells cannot grow because the cellular phosphate is mostly being used to phosphorylate the sugars in upper glycolysis, while the biosynthesis of bisphosphoglycerate is blocked. We reasoned that by decreasing the ATP/ADP ratio, one might prevent the phosphorylation of the sugars and also boost bisphosphoglycerate synthesis by providing the substrate, i.e., inorganic phosphate. We confirmed that a complete inhibition of oxidative phosphorylation alleviates the block. As our system includes a non-fermentable carbon source, only the chemicals that did not cause a complete block of mitochondrial ATP synthesis allowed the initial depletion of glucose followed by respiratory growth. Using this system, we found two novel compounds, dodecylmethyl diphenylamine (FS1) and diethyl (tetradecyl) phenyl ammonium bromide (Kor105), which possess a mild membrane-depolarizing activity.

Alpha-ketoglutarate utilization in Saccharomyces cerevisiae: transport, compartmentation and catabolism

α-Ketoglutarate (αKG) is a metabolite of the tricarboxylic acid cycle, important for biomass synthesis and a precursor for biotechnological products like 1,4-butanediol. In the eukaryote Saccharomyces cerevisiae αKG is present in different compartments. Compartmentation and (intra-)cellular transport could interfere with heterologous product pathways, generate futile cycles and reduce product yields. Batch and chemostat cultivations at low pH (≤ 5) showed that αKG can be transported, catabolized and used for biomass synthesis. The uptake mechanism of αKG was further investigated under αKG limited chemostat conditions at different pH (3, 4, 5, and 6). At very low pH (3, 4) there is a fraction of undissociated αKG that could diffuse over the periplasmic membrane. At pH 5 this fraction is very low, and the observed growth and residual concentration requires a permease/facilitated uptake mechanism of the mono-dissociated form of αKG. Consumption of αKG under mixed substrate conditions was only observed for low glucose concentrations in chemostat cultivations, suggesting that the putative αKG transporter is repressed by glucose. Fully 13C-labeled αKG was introduced as a tracer during a glucose/αKG co-feeding chemostat to trace αKG transport and utilization. The measured 13C enrichments suggest the major part of the consumed 13C αKG was used for the synthesis of glutamate, and the remainder was transported into the mitochondria and fully oxidized. There was no enrichment observed in glycolytic intermediates, suggesting that there was no gluconeogenic activity under the co-feeding conditions. 13C based flux analysis suggests that the intracellular transport is bi-directional, i.e. there is a fast exchange between the cytosol and mitochondria. The model further estimates that most intracellular αKG (88%) was present in the cytosol. Using literature reported volume fractions, the mitochondria/cytosol concentration ratio was 1.33. Such ratio will not require energy investment for transport towards the mitochondria (based on thermodynamic driving forces calculated with literature pH values). Growth on αKG as sole carbon source was observed, suggesting that S. cerevisiae is not fully Krebs-negative. Using 13C tracing and modelling the intracellular use of αKG under co-feeding conditions showed a link with biomass synthesis, transport into the mitochondria and catabolism. For the engineering of strains that use cytosolic αKG as precursor, both observed sinks should be minimized to increase the putative yields.

A scalable method for parameter identification in kinetic models of metabolism using steady-state data

MOTIVATION

In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models.

RESULTS

Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform.

AVAILABILITY AND IMPLEMENTATION

https://github.com/LMSE/ident.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bacterial metabolic heterogeneity: origins and applications in engineering and infectious disease

Bacteria within an isoclonal population display significant heterogeneity in metabolism, even under tightly controlled environmental conditions. Metabolic heterogeneity enables influential functions not possible or measurable at the ensemble scale. Several molecular and cellular mechanisms are likely to give rise to metabolic heterogeneity including molecular noise in metabolic enzyme expression, positive feedback loops, and asymmetric partitioning of cellular components during cell division. Dissection of the mechanistic origins of metabolic heterogeneity has been enabled by recent developments in single-cell analytical tools. Finally, we provide a discussion of recent studies examining the importance of metabolic heterogeneity in applied settings such as infectious disease and metabolic engineering.

A tps1Δ persister-like state in Saccharomyces cerevisiae is regulated by MKT1

Trehalose metabolism in yeast has been linked to a variety of phenotypes, including heat resistance, desiccation tolerance, carbon-source utilization, and sporulation. The relationships among the several phenotypes of mutants unable to synthesize trehalose are not understood, even though the pathway is highly conserved. One of these phenotypes is that tps1Δ strains cannot reportedly grow on media containing glucose or fructose, even when another carbon source they can use (e.g. galactose) is present. Here we corroborate the recent observation that a small fraction of yeast tps1Δ cells do grow on glucose, unlike the majority of the population. This is not due to a genetic alteration, but instead resembles the persister phenotype documented in many microorganisms and cancer cells undergoing lethal stress. We extend these observations to show that this phenomenon is glucose-specific, as it does not occur on another highly fermented carbon source, fructose. We further demonstrate that this phenomenon appears to be related to mitochondrial complex III function, but unrelated to inorganic phosphate levels in the cell, as had previously been suggested. Finally, we found that this phenomenon is specific to S288C-derived strains, and is the consequence of a variant in the MKT1 gene.

Polymerization in the actin ATPase clan regulates hexokinase activity in yeast

The actin fold is found in cytoskeletal polymers, chaperones, and various metabolic enzymes. Many actin-fold proteins, such as the carbohydrate kinases, do not polymerize. We found that Glk1, a Saccharomyces cerevisiae glucokinase, forms two-stranded filaments with ultrastructure that is distinct from that of cytoskeletal polymers. In cells, Glk1 polymerized upon sugar addition and depolymerized upon sugar withdrawal. Polymerization inhibits enzymatic activity; the Glk1 monomer-polymer equilibrium sets a maximum rate of glucose phosphorylation regardless of Glk1 concentration. A mutation that eliminated Glk1 polymerization alleviated concentration-dependent enzyme inhibition. Yeast containing nonpolymerizing Glk1 were less fit when growing on sugars and more likely to die when refed glucose. Glk1 polymerization arose independently from other actin-related filaments and may allow yeast to rapidly modulate glucokinase activity as nutrient availability changes.

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.

Dynamic 13C Flux Analysis Captures the Reorganization of Adipocyte Glucose Metabolism in Response to Insulin

Cellular metabolism is dynamic, but quantifying non-steady metabolic fluxes by stable isotope tracers presents unique computational challenges. Here, we developed an efficient 13C-tracer dynamic metabolic flux analysis (13C-DMFA) framework for modeling central carbon fluxes that vary over time. We used B-splines to generalize the flux parameterization system and to improve the stability of the optimization algorithm. As proof of concept, we investigated how 3T3-L1 cultured adipocytes acutely metabolize glucose in response to insulin. Insulin rapidly stimulates glucose uptake, but intracellular pathways responded with differing speeds and magnitudes. Fluxes in lower glycolysis increased faster than those in upper glycolysis. Glycolysis fluxes rose disproportionally larger and faster than the tricarboxylic acid cycle, with lactate a primary glucose end product. The uncovered array of flux dynamics suggests that glucose catabolism is additionally regulated beyond uptake to help shunt glucose into appropriate pathways. This work demonstrates the value of using dynamic intracellular fluxes to understand metabolic function and pathway regulation.

Regulation of trehalose, a typical stress protectant, on central metabolisms, cell growth and division of Saccharomyces cerevisiae CEN.PK113-7D

Trehalose could protect the typical food microorganism Saccharomyces cerevisiae cell against environmental stresses; however, the other regulation effects of trehalose on yeast cells during the fermentation are still poorly understood. In this manuscript, different concentrations (i.e., 0, 2 and 5% g/v) of trehalose were respectively added into the medium to evaluate the effect of trehalose on growth, central metabolisms and division of S. cerevisiae CEN.PK113-7D strain that could uptake exogenous trehalose. Results indicated that addition of trehalose could inhibit yeast cell growth in the presence or absence of 8% v/v ethanol stress. Exogenous trehalose inhibited the glucose transporting efficiency and reduced intracellular glucose content. Simultaneously, increased intracellular trehalose content destroyed the steady state of trehalose cycle and caused the imbalance between the upper glycolysis part and the lower part, thereby leading to the dysfunction of glycolysis and further inhibiting the normal yeast cell growth. Moreover, energy metabolisms were impaired and the ATP production was reduced by addition of trehalose. Finally, exogenous trehalose-associated inhibition on yeast cell growth and metabolisms delayed cell cycle. These results also highlighted our knowledge about relationship between trehalose and growth, metabolisms and division of S. cerevisiae cells during fermentation.

Metabolic engineering of the cellulolytic thermophilic fungus Myceliophthora thermophila to produce ethanol from cellobiose

BACKGROUND

Cellulosic biomass is a promising resource for bioethanol production. However, various sugars in plant biomass hydrolysates including cellodextrins, cellobiose, glucose, xylose, and arabinose, are poorly fermented by microbes. The commonly used ethanol-producing microbe Saccharomyces cerevisiae can usually only utilize glucose, although metabolically engineered strains that utilize xylose have been developed. Direct fermentation of cellobiose could avoid glucose repression during biomass fermentation, but applications of an engineered cellobiose-utilizing S. cerevisiae are still limited because of its long lag phase. Bioethanol production from biomass-derived sugars by a cellulolytic filamentous fungus would have many advantages for the biorefinery industry.

RESULTS

We selected Myceliophthora thermophila, a cellulolytic thermophilic filamentous fungus for metabolic engineering to produce ethanol from glucose and cellobiose. Ethanol production was increased by 57% from glucose but not cellobiose after introduction of ScADH1 into the wild-type (WT) strain. Further overexpression of a glucose transporter GLT-1 or the cellodextrin transport system (CDT-1/CDT-2) from N. crassa increased ethanol production by 131% from glucose or by 200% from cellobiose, respectively. Transcriptomic analysis of the engineered cellobiose-utilizing strain and WT when grown on cellobiose showed that genes involved in oxidation-reduction reactions and the stress response were downregulated, whereas those involved in protein biosynthesis were upregulated in this effective ethanol production strain. Turning down the expression of pyc gene results the final engineered strain with the ethanol production was further increased by 23%, reaching up to 11.3 g/L on cellobiose.

CONCLUSIONS

This is the first attempt to engineer the cellulolytic fungus M. thermophila to produce bioethanol from biomass-derived sugars such as glucose and cellobiose. The ethanol production can be improved about 4 times up to 11 grams per liter on cellobiose after a couple of genetic engineering. These results show that M. thermophila is a promising platform for bioethanol production from cellulosic materials in the future.

Evolved resistance to partial GAPDH inhibition results in loss of the Warburg effect and in a different state of glycolysis

Aerobic glycolysis or the Warburg effect (WE) is characterized by increased glucose uptake and incomplete oxidation to lactate. Although the WE is ubiquitous, its biological role remains controversial, and whether glucose metabolism is functionally different during fully oxidative glycolysis or during the WE is unknown. To investigate this question, here we evolved resistance to koningic acid (KA), a natural product that specifically inhibits glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a rate-controlling glycolytic enzyme, during the WE. We found that KA-resistant cells lose the WE but continue to conduct glycolysis and surprisingly remain dependent on glucose as a carbon source and also on central carbon metabolism. Consequently, this altered state of glycolysis led to differential metabolic activity and requirements, including emergent activities in and dependences on fatty acid metabolism. These findings reveal that aerobic glycolysis is a process functionally distinct from conventional glucose metabolism and leads to distinct metabolic requirements and biological functions.

Steady-state and Flux-based Trehalose Estimation as an Indicator of Carbon Flow from Gluconeogenesis or Glycolysis

Trehalose (and glycogen) is a major storage carbohydrate in many cells, including S. cerevisiae. Typically, trehalose (a disaccharide of glucose) is synthesized and stored through gluconeogenesis. However, trehalose can also be made directly from glucose, if glucose-6-phosphate is channeled away from glycolysis or pentose phosphate pathway. Therefore, analyzing trehalose synthesis, utilization or its accumulation, can be used as a sentinel read-out for either gluconeogenesis or rewired glucose utilization. However, the steady-state measurements alone of trehalose cannot unambiguously distinguish the nature of carbon flux in a system. Here, we first summarize simple steady-state enzymatic assays to measure trehalose (and glycogen), that will have very wide uses. Subsequently, we describe methods of highly sensitive, quantitative LC-MS/MS based to measure trehalose. We include methods of ¹³C stable-isotope based pulse-labeling experiments (using different carbon sources) with which to measure rates of trehalose synthesis, from different carbon metabolism pathways. This approach can be used to unambiguously determine the extent of carbon flux into trehalose coming from gluconeogenesis, or directly from glucose/glycolysis. These protocols collectively enable comprehensive steady-state as well as carbon flux based measurements of trehalose. This permits a dissection of carbon flux to distinguish between cells in a gluconeogenic state (conventionally leading to trehalose synthesis), or cells with rewired glucose metabolism (also leading to trehalose synthesis). While the methods presented are optimized for yeast, these methods can be easily adapted to several types of cells, including many microbes.

Quantitation and Comparison of Phenotypic Heterogeneity Among Single Cells of Monoclonal Microbial Populations

Phenotypic heterogeneity within microbial populations arises even when the cells are exposed to putatively constant and homogeneous conditions. The outcome of this phenomenon can affect the whole function of the population, resulting in, for example, new "adapted" metabolic strategies and impacting its fitness at given environmental conditions. Accounting for phenotypic heterogeneity becomes thus necessary, due to its relevance in medical and applied microbiology as well as in environmental processes. Still, a comprehensive evaluation of this phenomenon requires a common and unique method of quantitation, which allows for the comparison between different studies carried out with different approaches. Consequently, in this study, two widely applicable indices for quantitation of heterogeneity were developed. The heterogeneity coefficient (HC) is valid when the population follows unimodal activity, while the differentiation tendency index (DTI) accounts for heterogeneity implying outbreak of subpopulations and multimodal activity. We demonstrated the applicability of HC and DTI for heterogeneity quantitation on stable isotope probing with nanoscale secondary ion mass spectrometry (SIP-nanoSIMS), flow cytometry, and optical microscopy datasets. The HC was found to provide a more accurate and precise measure of heterogeneity, being at the same time consistent with the coefficient of variation (CV) applied so far. The DTI is able to describe the differentiation in single-cell activity within monoclonal populations resolving subpopulations with low cell abundance, individual cells with similar phenotypic features (e.g., isotopic content close to natural abundance, as detected with nanoSIMS). The developed quantitation approach allows for a better understanding on the impact and the implications of phenotypic heterogeneity in environmental, medical and applied microbiology, microbial ecology, cell biology, and biotechnology.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single-cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), we transplanted the B. subtilis FBP-binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

Towards a quantitative assessment of inorganic carbon cycling in photosynthetic microorganisms

Photosynthetic organisms developed various strategies to mitigate high light stress. For instance, aquatic organisms are able to spend excessive energy by exchanging dissolved CO2 (dCO2) and bicarbonate ( HCO 3 - ) with the environment. Simultaneous uptake and excretion of the two carbon species is referred to as inorganic carbon cycling. Often, inorganic carbon cycling is indicated by displacements of the extracellular dCO2 signal from the equilibrium value after changing the light conditions. In this work, we additionally use (i) the extracellular pH signal, which requires non- or weakly-buffered medium, and (ii) a dynamic model of carbonate chemistry in the aquatic environment to detect and quantitatively describe inorganic carbon cycling. Based on simulations and experiments in precisely controlled photobioreactors, we show that the magnitude of the observed dCO2 displacement crucially depends on extracellular pH level and buffer concentration. Moreover, we find that the dCO2 displacement can also be caused by simultaneous uptake of both dCO2 and HCO 3 - (no inorganic carbon cycling). In a next step, the dynamic model of carbonate chemistry allows for a quantitative assessment of cellular dCO2, HCO 3 - , and H+ exchange rates from the measured dCO2 and pH signals. Limitations of the method are discussed.

Redirection of the Glycolytic Flux Enhances Isoprenoid Production in Saccharomyces cerevisiae

Sufficient supply of reduced nicotinamide adenine dinucleotide phosphate (NADPH) is a prerequisite of the overproduction of isoprenoids and related bioproducts in Saccharomyces cerevisiae. Although S. cerevisiae highly depends on the oxidative pentose phosphate (PP) pathway to produce NADPH, its metabolic flux toward the oxidative PP pathway is limited due to the rigid glycolysis flux. To maximize NADPH supply for the isoprenoid production in yeast, upper glycolytic metabolic fluxes are reduced by introducing mutations into phosphofructokinase (PFK) along with overexpression of ZWF1 encoding glucose-6-phosphate (G6P) dehydrogenase. The PFK mutations (Pfk1 S724D and Pfk2 S718D) result in less glycerol production and more accumulation of G6P, which is a gateway metabolite toward the oxidative PP pathway. When combined with the PFK mutations, overexpression of ZWF1 caused substantial increases of [NADPH]/[NADP⁺ ] ratios whereas the effect of ZWF1 overexpression alone in the wild-type strain is not noticeable. Also, the introduction of ZWF1 overexpression and the PFK mutations into engineered yeast overexpressing acetyl-CoA C-acetyltransferase (ERG10), truncated HMG-CoA reductase isozyme 1 (tHMG1), and amorphadiene synthase (ADS) leads to a titer of 497 mg L^-1 of amorphadiene (3.7-fold over the parental strain). These results suggest that perturbation of upper glycolytic fluxes, in addition to ZWF1 overexpression, is necessary for efficient NADPH supply through the oxidative PP pathway and enhanced production of isoprenoids by engineered S. cerevisiae.

Metabolic switches from quiescence to growth in synchronized Saccharomyces cerevisiae

INTRODUCTION

The switch from quiescence (G0) into G1 and cell cycle progression critically depends on specific nutrients and metabolic capabilities. Conversely, metabolic networks are regulated by enzyme-metabolite interaction and transcriptional regulation that lead to flux modifications to support cell growth. How cells process and integrate environmental information into coordinated responses is challenging to analyse and not yet described quantitatively.

OBJECTIVES

To quantitatively monitor the central carbon metabolism during G0 exit and the first 2 h after reentering the cell cycle from synchronized Saccharomyces cerevisiae.

METHODS

Dynamic tailored ¹³C metabolic flux analysis was used to observe the intracellular metabolite flux changes, and the metabolome and proteome were observed to identify regulatory mechanisms.

RESULTS

G0 cells responded immediately to an extracellular increase of glucose. The intracellular metabolic flux changed in time and specific events were observed. High fluxes into trehalose and glycogen synthesis were observed during the G0 exit. Both fluxes then decreased, reaching a minimum at t = 65 min. Here, storage degradation contributed significantly (i.e. 21%) to the glycolytic flux. In contrast to these changes, the glucose uptake rate remained constant after the G0 exit. The flux into the oxidative pentose phosphate pathway was highest (29-fold increase, 36.4% of the glucose uptake) at t = 65 min, while it was very low at other time points. The maximum flux seems to correlate with a late G1 state preparing for the S phase transition. In the G1/S phase (t = 87 min), anaplerotic reactions such as glyoxylate shunt increased. Protein results show that during this transition, proteins belonging to clusters related with ribosome biogenesis and assembly, and initiation transcription factors clusters were continuously synthetised.

CONCLUSION

The intracellular flux distribution changes dynamically and these major rearrangements highlight the coordinate reorganization of metabolic flux to meet requirements for growth during different cell state.

A tRNA modification balances carbon and nitrogen metabolism by regulating phosphate homeostasis

Cells must appropriately sense and integrate multiple metabolic resources to commit to proliferation. Here, we report that S. cerevisiae cells regulate carbon and nitrogen metabolic homeostasis through tRNA U34-thiolation. Despite amino acid sufficiency, tRNA-thiolation deficient cells appear amino acid starved. In these cells, carbon flux towards nucleotide synthesis decreases, and trehalose synthesis increases, resulting in a starvation-like metabolic signature. Thiolation mutants have only minor translation defects. However, in these cells phosphate homeostasis genes are strongly down-regulated, resulting in an effectively phosphate-limited state. Reduced phosphate enforces a metabolic switch, where glucose-6-phosphate is routed towards storage carbohydrates. Notably, trehalose synthesis, which releases phosphate and thereby restores phosphate availability, is central to this metabolic rewiring. Thus, cells use thiolated tRNAs to perceive amino acid sufficiency, balance carbon and amino acid metabolic flux and grow optimally, by controlling phosphate availability. These results further biochemically explain how phosphate availability determines a switch to a 'starvation-state'.

Yeast Systems Biology: Model Organism and Cell Factory

For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.

Sustained substrate cycles between hexose phosphates and free sugars in phosphate-deficient potato (Solanum tuberosum) cell cultures

Futile cycling between free sugars and hexose phosphates occurring under phosphate deficiency could be involved in the maintenance of a threshold level of free cellular phosphate to preserve respiratory metabolism. We studied the metabolic response of potato cell cultures growing in Pi sufficient (2.5 mM, +Pi) or deficient (125 µM, -Pi) conditions. Under Pi deficiency, cellular growth was severely affected, however -Pi cells were able to maintain a low but steady level of free Pi. We surveyed the activities of 33 primary metabolic enzymes during the course of a 12 days Pi deficiency period. Our results show that many of these enzymes had higher specific activity in -Pi cells. Among these, we found typical markers of Pi deficiency such as phosphoenolpyruvate phosphatase and phosphoenolpyruvate carboxylase as well as enzymes involved in the biosynthesis of organic acids. Intriguingly, several ATP-consuming enzymes such as hexokinase (HK) and phosphofructokinase also displayed increased activity in -Pi condition. For HK, this was associated with an increase in the steady state of a specific HK polypeptide. Quantification of glycolytic intermediates showed a pronounced decrease in phosphate esters under Pi deficiency. Adenylate levels also decreased in -Pi cells, but the Adenylate Energy Charge was not affected by the treatment. To investigate the significance of HK induction under low Pi, [U-¹⁴C]-glucose tracer studies were conducted. We found in vivo evidence of futile cycling between pools of hexose phosphates and free sugars under Pi deficiency. Our study suggests that the futile cycling between hexose phosphates and free sugars which is active under +Pi conditions is sustained under Pi deficiency. The possibility that this process represents a metabolic adaptation to Pi deficiency is discussed with respect to Pi homeostasis in Pi-deficient conditions.

Glycolytic flux in Saccharomyces cerevisiae is dependent on RNA polymerase III and its negative regulator Maf1

Protein biosynthesis is energetically costly, is tightly regulated and is coupled to stress conditions including glucose deprivation. RNA polymerase III (RNAP III)-driven transcription of tDNA genes for production of tRNAs is a key element in efficient protein biosynthesis. Here we present an analysis of the effects of altered RNAP III activity on the Saccharomyces cerevisiae proteome and metabolism under glucose-rich conditions. We show for the first time that RNAP III is tightly coupled to the glycolytic system at the molecular systems level. Decreased RNAP III activity or the absence of the RNAP III negative regulator, Maf1 elicit broad changes in the abundance profiles of enzymes engaged in fundamental metabolism in S. cerevisiae In a mutant compromised in RNAP III activity, there is a repartitioning towards amino acids synthesis de novo at the expense of glycolytic throughput. Conversely, cells lacking Maf1 protein have greater potential for glycolytic flux.

Stochastic modelling reveals mechanisms of metabolic heterogeneity

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

Cytotoxicity of chemotherapeutic agents and resistance to targeted treatments are the main reasons why cancer is still one of the top causes of death. As tumor cells are intrinsically resistant to therapies that target signaling pathways, targeting the metabolic hallmarks of cancer holds promise for more incisive treatments. Regrettably, the heterogeneity of cancer metabolism hinders the identification of effective treatments. To fully uncover the metabolic heterogeneity within tumors, characterization of metabolic programs (metabolic flux distributions) at the single-cell level is required. To fill the gap between current technologies for genomics and future technologies for fluxomics, both at the single-cell and the genome-wide scale, we propose to integrate cancer data from: 1) single-cell transcriptomics and 2) bulk metabolomics, into a multi-scale stoichiometric model, to deliver for the first time metabolic fluxomes at the single-cell level. To this end, we introduce a new paradigm for flux balance analysis and data integration in cancer metabolism to: 1) characterize metabolic heterogeneity, not only at the inter-, but also at the intra-tumor level 2) identify the metabolic interactions between cancer populations, whose role in resistance to metabolic treatments has been recently recognized 3) predict the collective response to drug targeting of metabolism.

Individual- versus group-optimality in the production of secreted bacterial compounds

How unicellular organisms optimize the production of compounds is a fundamental biological question. While it is typically thought that production is optimized at the individual-cell level, secreted compounds could also allow for optimization at the group level, leading to a division of labor where a subset of cells produces and shares the compound with everyone. Using mathematical modeling, we show that the evolution of such division of labor depends on the cost function of compound production. Specifically, for any trait with saturating benefits, linear costs promote the evolution of uniform production levels across cells. Conversely, production costs that diminish with higher output levels favor the evolution of specialization-especially when compound shareability is high. When experimentally testing these predictions with pyoverdine, a secreted iron-scavenging compound produced by Pseudomonas aeruginosa, we found linear costs and, consistent with our model, detected uniform pyoverdine production levels across cells. We conclude that for shared compounds with saturating benefits, the evolution of division of labor is facilitated by a diminishing cost function. More generally, we note that shifts in the level of selection from individuals to groups do not solely require cooperation, but critically depend on mechanistic factors, including the distribution of compound synthesis costs.

Integrated pH Measurement during Reaction Monitoring with Dual-Reception 1H-31P NMR Spectroscopy

Simultaneous detection of ¹H and ³¹P NMR signals through a dual-detection scheme with two receivers allows monitoring of both the signals of a molecule and the pH of the solution through the resonance of the inorganic phosphate. We evaluate here the method in terms of sensitivity and ease of implementation and show that the additional information obtained without any loss of information or increase in measuring time can be of practical importance in a number of biochemical systems.

Exploration-exploitation tradeoffs dictate the optimal distributions of phenotypes for populations subject to fitness fluctuations

We study a minimal model for the growth of a phenotypically heterogeneous population of cells subject to a fluctuating environment in which they can replicate (by exploiting available resources) and modify their phenotype within a given landscape (thereby exploring novel configurations). The model displays an exploration-exploitation trade-off whose specifics depend on the statistics of the environment. Most notably, the phenotypic distribution corresponding to maximum population fitness (i.e., growth rate) requires a nonzero exploration rate when the magnitude of environmental fluctuations changes randomly over time, while a purely exploitative strategy turns out to be optimal in two-state environments, independently of the statistics of switching times. We obtain analytical insight into the limiting cases of very fast and very slow exploration rates by directly linking population growth to the features of the environment.

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.

Interrogating metabolism as an electron flow system

Metabolism is generally considered as a neatly organised system of modular pathways, shaped by evolution under selection for optimal cellular growth. This view falls short of explaining and predicting a number of key observations about the structure and dynamics of metabolism. We highlight these limitations of a pathway-centric view on metabolism and summarise studies suggesting how these could be overcome by viewing metabolism as a thermodynamically and kinetically constrained, dynamical flow system. Such a systems-level, first-principles based view of metabolism can open up new avenues of metabolic engineering and cures for metabolic diseases and allow better insights to a myriad of physiological processes that are ultimately linked to metabolism. Towards further developing this view, we call for a closer interaction among physical and biological disciplines and an increased use of electrochemical and biophysical approaches to interrogate cellular metabolism together with the microenvironment in which it exists.

Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth

Microbes have adapted to greatly variable environments in order to survive both short-term perturbations and permanent changes. A classical and yet still actively studied example of adaptation to dynamic environments is the diauxic shift of Escherichia coli, in which cells grow on glucose until its exhaustion and then transition to using previously secreted acetate. Here we tested different hypotheses concerning the nature of this transition by using dynamic metabolic modeling. To reach this goal, we developed an open source modeling framework integrating dynamic models (ordinary differential equation systems) with structural models (metabolic networks) which can take into account the behavior of multiple subpopulations and smooth flux transitions between time points. We used this framework to model the diauxic shift, first with a single E. coli model whose metabolic state represents the overall population average and then with a community of two subpopulations, each growing exclusively on one carbon source (glucose or acetate). After introduction of an environment-dependent transition function that determined the balance between subpopulations, our model generated predictions that are in strong agreement with published data. Our results thus support recent experimental evidence that diauxie, rather than a coordinated metabolic shift, would be the emergent pattern of individual cells differentiating for optimal growth on different substrates. This work offers a new perspective on the use of dynamic metabolic modeling to investigate population heterogeneity dynamics. The proposed approach can easily be applied to other biological systems composed of metabolically distinct, interconverting subpopulations and could be extended to include single-cell-level stochasticity. IMPORTANCE Escherichia coli diauxie is a fundamental example of metabolic adaptation, a phenomenon that is not yet completely understood. Further insight into this process can be achieved by integrating experimental and computational modeling methods. We present a dynamic metabolic modeling approach that captures diauxie as an emergent property of subpopulation dynamics in E. coli monocultures. Without fine-tuning the parameters of the E. coli core metabolic model, we achieved good agreement with published data. Our results suggest that single-organism metabolic models can only approximate the average metabolic state of a population, therefore offering a new perspective on the use of such modeling approaches. The open source modeling framework that we provide can be applied to model general subpopulation systems in more-complex environments and can be extended to include single-cell-level stochasticity.

Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand

Overflow metabolism is a common phenomenon observed at higher glycolytic flux in many bacteria, yeast (known as Crabtree effect), and mammalian cells including cancer cells (known as Warburg effect). This phenomenon has recently been characterized as the trade-offs between protein costs and enzyme efficiencies based on coarse-graining approaches. Moreover, it has been recognized that the glycolytic flux increases as the source of energy generation changes from energetically efficient respiration to inefficient respiro-fermentative or fermentative metabolism causing overflow metabolism. It is highly desired to clarify the metabolic regulation mechanisms behind such phenomena. Metabolic fluxes are located on top of the hierarchical regulation systems, and represent the outcome of the integrated response of all levels of cellular regulation systems. In the present article, we discuss about the different levels of regulation systems for the modulation of fluxes depending on the growth rate, growth condition such as oxygen limitation that alters the metabolism towards fermentation, and genetic perturbation affecting the source of energy generation from respiration to respiro-fermentative metabolism in relation to overflow metabolism. The intracellular metabolite of the upper glycolysis such as fructose 1,6-bisphosphate (FBP) plays an important role not only for flux sensing, but also for the regulation of the respiratory activity either directly or indirectly (via transcription factors) at higher growth rate. The glycolytic flux regulation is backed up (enhanced) by unphosphorylated EIIA and HPr of the phosphotransferase system (PTS) components, together with the sugar-phosphate stress regulation, where the transcriptional regulation is further modulated by post-transcriptional regulation via the degradation of mRNA (stability of mRNA) in Escherichia coli. Moreover, the channeling may also play some role in modulating the glycolytic cascade reactions.

Nutritional and meiotic induction of transiently heritable stress resistant states in budding yeast

Transient exposures to environmental stresses induce altered physiological states in exposed cells that persist after the stresses have been removed. These states, referred to as cellular memory, can even be passed on to daughter cells and may thus be thought of as embodying a form of epigenetic inheritance. We find that meiotically produced spores in the budding yeast S. cerevisiae possess a state of heightened stress resistance that, following their germination, persists for numerous mitotic generations. As yeast meiotic development is essentially a starvation response that a/alpha diploid cells engage, we sought to model this phenomenon by subjecting haploid cells to starvation conditions. We find also that haploid cells exposed to glucose withdrawal acquire a state of elevated stress resistance that persists after the reintroduction of these cells to glucose-replete media. Following release from lengthy durations of glucose starvation, we confirm that this physiological state of enhanced stress resistance is propagated in descendants of the exposed cells through two mitotic divisions before fading from the population. In both haploid starved cells and diploid produced meiotic spores we show that their cellular memories are not attributable to trehalose, a widely regarded stress protectant that accumulates in these cell types. Moreover, the transiently heritable stress resistant state induced by glucose starvation in haploid cells is independent of the Msn2/4 transcription factors, which are known to program cellular memory induced by exposure of cells to NaCl. Our findings identify new developmentally and nutritionally induced states of cellular memory that exhibit striking degrees of persistence and mitotic heritability.

Current Trends of Microfluidic Single-Cell Technologies

The investigation of human disease mechanisms is difficult due to the heterogeneity in gene expression and the physiological state of cells in a given population. In comparison to bulk cell measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. In this review, we describe the recent advances in single-cell technologies and their applications in single-cell manipulation, diagnosis, and therapeutics development.