Identification of different daughter and parent subpopulations in an asynchronously growing Saccharomyces cerevisiae population

Analysis of the effect of inoculum characteristics on the first stages of a growing yeast population in beer fermentations by means of an individual-based model

The yeast Saccharomyces cerevisiae has a limited replicative lifespan. The cell mass at division is partitioned unequally between a larger, old parent cell and a smaller, new daughter cell. Industrial beer fermentations maintain and reuse yeast. At the end of fermentation a portion of the yeast is 'cropped' from the vessel for 'serial repitching'. Harvesting yeast may select a population with an imbalance of young and aged individuals, but the output of any bioprocess is dependent on the physiology of each single cell in the population. Unlike continuous models, individual-based modelling is an approach that considers each microbe as an individual, a unique and discrete entity, with characteristics that change throughout its life. The aim of this contribution is to explore, by means of individual-based simulations, the effects of inoculum size and cell genealogical age on the dynamics of virtual yeast fermentation, focussing on: (1) the first stages of population growth, (2) the mean biomass evolution of the population, (3) the rate of glucose uptake and ethanol production, and (4) the biomass and genealogical age distributions. The ultimate goal is to integrate these results in order to make progress in the understanding of the composition of yeast populations and their temporal evolution in beer fermentations. Simulation results show that there is a clear influence of these initial features of the inocula on the subsequent growth dynamics. By contrasting both the individual and global properties of yeast cells and populations, we gain insight into the interrelation between these two types of data, which helps us to deal with the macroscopic behaviour observed in experimental research.

Analysis and modeling of growing budding yeast populations at the single cell level

Model organisms and in particular the budding yeast Saccharomyces cerevisiae have been instrumental in advancing our understanding of cell cycle progression. The asymmetric division of the budding yeast and the tight coupling between cell growth and division have challenged the theoretical understanding of the cell size structure of growing yeast populations. Past efforts have centered on modeling the steady-state theoretical age distribution for asymmetric division from which a cell size distribution can be derived assuming dispersion of cell size within each age class. Different developments, especially in the field of flow cytometry, allowed the determination of a number of cellular properties and their joint distributions for the entire population and the different subpopulations as well. A new rigorous framework for modeling directly the dynamics of size distributions of structured yeast populations has been proposed, which readily extends to modeling of more complex conditions, such as transient growth. Literature on the structure of growing yeast populations and modeling of cell cycle progression is reviewed.

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

The dynamic features of a genetic network's response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters. To explore the connection between dynamics and network design, we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways. Such architecture is characterized by a dual control mechanism, with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite. As a case study, we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion, using an automated system for monitoring protein expression levels in single cells. All the genes in the pathway are known to be coregulated by the same transcription factors, but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point—the intermediate metabolite α-isopropylmalate (αIPM), which couples metabolic activity to transcriptional regulation. Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3, and that the downstream enzymes are strictly controlled and highly expressed only when αIPM is available. These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathway's response to new perturbations. Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery. It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture.

Single-cell organisms must constantly adjust their gene expression programs to survive in a changing environment. Interactions between different molecules form a regulatory network to mediate these changes. While the network connections are often known, figuring out how the network responds dynamically by looking at a static picture of its structure presents a significant challenge. Measuring the response at a finer time scales could reveal the link between the network's function and its structure. The architecture of the system we studied in this work—the leucine biosynthesis pathway in yeast—is shared by other metabolic pathways: a metabolic intermediate binds to a transcription factor to activate the pathway genes, creating an intricate feedback structure that links metabolism with gene expression. We measured protein abundance at high temporal resolution for genes in this pathway in response to leucine depletion and studied the effects of various genetic perturbations on gene expression dynamics. Our measurements and theoretical modeling show that only the genes immediately downstream from the intermediate are highly regulated by the metabolite, a feature that is essential to fast recovery from leucine depletion. Since the architecture we studied is common, we believe that our work may lead to general principles governing the dynamics of gene expression in other metabolic pathways.

A quantitative, high-temporal resolution study of gene induction in a metabolic pathway reveals an intricate connection between the regulatory architecture and the dynamic response of the system, pointing to possible principles underlying the design of these pathways.

Optimization of yeast cell cycle analysis and morphological characterization by multispectral imaging flow cytometry

Budding yeast Saccharoymyces cerevisiae is a powerful model system for analyzing eukaryotic cell cycle regulation. Yeast cell cycle analysis is typically performed by visual analysis or flow cytometry, and both have limitations in the scope and accuracy of data obtained. This study demonstrates how multispectral imaging flow cytometry (MIFC) provides precise quantitation of cell cycle distribution and morphological phenotypes of yeast cells in flow. Cell cycle analysis of wild-type yeast, nap1Delta, and yeast overexpressing NAP1, was performed visually, by flow cytometry and by MIFC. Quantitative morphological analysis employed measurements of cellular length, thickness, and aspect ratio in an algorithm to calculate a novel feature, bud length. MIFC demonstrated reliable quantification of the yeast cell cycle compared to morphological and flow cytometric analyses. By employing this technique, we observed both the G2/M delay and elongated buds previously described in the nap1Delta strain. Using MIFC, we demonstrate that overexpression of NAP1 causes elongated buds yet only a minor disruption in the cell cycle. The different effects of NAP1 expression level on cell cycle and morphology suggests that these phenotypes are independent. Unlike conventional yeast flow cytometry, MIFC generates complete cell cycle profiles and concurrently offers multiple parameters for morphological analysis.

Quantification of cell size distribution as applied to the growth of Corynebacterium glutamicum

It is known that the cell size is related to the physiological state of a cell. Therefore, cell size distribution directly reflects the average physiological properties of the cell culture. Cell size distribution can be enumerated by image analysis, flow cytometry and coulter counter. In this study, image analysis was used to characterize the cell size distribution during the growth of Corynebacterium glutamicum and was further analyzed by a distribution function. The parameters of the distribution function indicate the mean value and spread of the distribution. Analysis demonstrated that the maximum specific growth rate was higher (0.67h(-1)) for the growth obtained through serial dilution of seed as compared to growth from a normal seed culture (0.53h(-1)). This was due to a greater percentage of the cell population being in the state of division for the growth through serial dilution in the mid-log phase. The measurement of the cell size distribution demonstrated that the average cell size decreased during the course of growth. The distribution function was also used to enumerate the average specific growth rate of both the conditions of the culture. The demonstrated methodology can be used to predict an average growth property of a cell culture.

Role of the transcription activator Ste12p as a repressor of PRY3 expression

Mating pheromone represses synthesis of full-length PRY3 mRNA, and a new transcript appears simultaneously with its 5' terminus 452 nucleotides inside the open reading frame (ORF). Synthesis of this shorter transcript results from activation of a promoter within the PRY3 locus, and its production is concomitant with the rapid disappearance of the full-length transcript. Evidence is consistent with the pheromone-induced transcription factor Ste12p binding two pheromone response elements within the PRY3 promoter, directly impeding transcription of the full-length mRNA while simultaneously inducing initiation of the short transcript. This process depends on a TATA box within the PRY3 ORF. Expression of full-length PRY3 inhibited mating, while no disadvantage was detectable for cells unable to make the short transcript. Therefore, Ste12p is utilized as a repressor of full-length PRY3 transcription, ensuring efficient mating. There is no evidence that production of the short PRY3 transcript is anything more than an adventitious by-product of this mechanism. It is possible that cryptic binding sites for transcriptional activators may occur frequently within genomes and have the potential of evolving for rapid, gene-specific repression by mechanisms analogous to PRY3. PRY3 regulation provides a model for the coordination of both inductive and repressive activities within a regulatory network.

Morphologically-structured models of growing budding yeast populations

It has been well recognized that many key aspects of cell cycle regulation are encoded into the size distributions of growing budding yeast populations due to the tight coupling between cell growth and cell division present in this organism. Several attempts have been made to model the cell size distribution of growing yeast populations in order to obtain insight on the underlying control mechanisms, but most were based on the age structure of asymmetrically dividing populations. Here we propose a new framework that couples a morphologically-structured representation of the population with population balance theory to formulate a dynamic model for the size distribution of growing yeast populations. An advantage of the presented framework is that it allows derivation of simpler models that are directly identifiable from experiments. We show how such models can be derived from the general framework and demonstrate their utility in analyzing yeast population data. Finally, by employing a recently proposed numerical scheme, we proceed to integrate numerically the full distributed model to provide predictions of dynamics of the cell size structure of growing yeast populations.

Affinity of singleS. cerevisiaecells to 2-NBDglucose under changing substrate concentrations

BACKGROUND

Saccharomyces cerevisiae is a widely employed microorganism in biotechnological processes. Since proliferation and product formation depend on the capacity of the cell to access and metabolize a carbon source, a technique was developed to enable for analyzing the S. cerevisiae H155 cells' affinity to extracellular glucose concentrations.

METHODS

The fluorescent glucose analogue 2-NBDglucose was employed as a functional parameter to analyze the cells' affinity to glucose. Structural parameters (proliferation, neutral lipid content, granularity, and cell size) were also investigated. Cells were grown both in batches and in chemostat regimes.

RESULTS

The 2-NBDglucose uptake in individual cells proceeds in a time- and concentration-dependent manner and is affected by respiratory and respirofermentative modes of growth. The process is inhibited by D-glucose, D-fructose, D-mannose, and sucrose, but not L-glucose, D-galactose or lactose; maltose is a weak inhibitor. The affinity of the individual cells to 2-NBDglucose was found to be high at low extracellular glucose concentrations, and weak at high concentrations. An additional, underlying pattern in the cells' affinity to glucose was detected, illustrated by the recurrent appearance of two subpopulations showing distinctly differing quantities of this substrate.

CONCLUSIONS

A multiparameter flow cytometry approach is presented that enables, for the first time, for analysis of the affinity of individual S. cerevisiae cells to glucose. Besides the adjustment of the yeast cell metabolism to extracellular glucose concentrations by altering their affinity to glucose, at least one further mechanism is clearly involved. Two subpopulations of cells were resolved, with different affinities not correlated with other cellular parameters measured.

Relation between growth dynamics and diffusional limitations in Saccharomyces cerevisiae cells growing as entrapped in an insolubilised gelatin gel

Flow-cytometric analysis was employed to investigate growth dynamics of a yeast cell population immobilised in an insolubilised gelatin gel by means of the quantitative determination of the average protein content per cell. This analysis was carried out on both the immobilised cell population considered as a whole and the subpopulations colonising the gelatin matrix at different depths. The results show that growth of the gelatin-immobilised yeast population was affected by the existence of a gradient of nutrient concentrations through the matrix and are in agreement with the unsteady-state diffusion model employed for the description of glucose transfer in the gel.

Relating growth dynamics and glucoamylase excretion of individual Saccharomyces cerevisiae cells

We have developed a novel flow cytometric procedure that allows determinations of properties of protein excretion in the growth medium on a cell-by-cell basis in Saccharomyces cerevisiae. The procedure is based on labelling of a periplasmically secreted protein with antibodies conjugated to a fluorescent marker such as fluorescein isothiocyanate (FITC). The staining conditions did not perturb cell growth after resuspension of stained cells in growth medium. Decrease in fluorescence was found to correlate with excretion of glucoamylase into the growth medium. The analysis of the staining pattern over time provides information on the behaviour of individual cells belonging to different cell-cycle phases and can be used to calculate the specific excretion rate of the overall population.

Control by nutrients of growth and cell cycle progression in budding yeast, analyzed by double-tag flow cytometry

To gain insight on the interrelationships of the cellular environment, the properties of growth, and cell cycle progression, we analyzed the dynamic reactions of individual Saccharomyces cerevisiae cells to changes and manipulations of their surroundings. We used a new flow cytometric approach which allows, in asynchronous growing S. cerevisiae populations, tagging of both the cell age and the cell protein content of cells belonging to the different cell cycle set points. Since the cell protein content is a good estimation of the cell size, it is possible to follow the kinetics of the cell size increase during cell cycle progression. The analysis of the findings obtained indicates that both during a nutritional shift-up (from ethanol to glucose) and following the addition of cyclic AMP (cAMP), two important delays are induced. The preexisting cells that at the moment of the nutritional shift-up were cycling before the Start phase delay their entrance into S phase, while cells that were cycling after Start are delayed in their exit from the cycle. The combined effects of the two delays allow the cellular population that preexisted the shift-up to quickly adjust to the new growth condition. The effects of a nutritional shift-down were also determined.