Induction and elimination of oscillations in continuous cultures of <i>Saccharomyces cerevisiae</i>

Parulekar, Satish J.; Semones, G.B.; Rolf, Michael J.; Lievense, Jeff; Lim, Henry C.

doi:10.1002/bit.260280509

Cited by 141 publications

(83 citation statements)

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“…Metabolic oscillations of markedly different periodicities have been reported under different regimes of nutrient dilution and oxygen supply (33,46). To determine whether the different periods are associated with similar or different states of the genetic and cellular network, we compared the data set obtained from cultures exhibiting a 40-min period of oscillation (26) with transcript data obtained from cultures exhibiting a 5-h oscillatory period (27).…”

Section: Network Hubs Encode Key Cellular Proteins In Nutrient Signalmentioning

confidence: 99%

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Lezon

Banavar²,

Cieplak³

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

241

238

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We describe a method based on the principle of entropy maximization to identify the gene interaction network with the highest probability of giving rise to experimentally observed transcript profiles. In its simplest form, the method yields the pairwise gene interaction network, but it can also be extended to deduce higherorder interactions. Analysis of microarray data from genes in Saccharomyces cerevisiae chemostat cultures exhibiting energy metabolic oscillations identifies a gene interaction network that reflects the intracellular communication pathways that adjust cellular metabolic activity and cell division to the limiting nutrient conditions that trigger metabolic oscillations. The success of the present approach in extracting meaningful genetic connections suggests that the maximum entropy principle is a useful concept for understanding living systems, as it is for other complex, nonequilibrium systems.gene interactions ͉ network inference ͉ signaling ͉ metabolic oscillations T he application of techniques for sampling expression levels of all of an organism's genes through time has yielded large amounts of data on the activity states of cellular genomes. Microarray data have been analyzed by using a variety of statistical tools to detect significant differences in gene expression levels and identify meaningful subgroups of genes exhibiting similar expression patterns (1, 2). Correlations and other statistical measures that group genes by profile similarity identify functionally interconnected groups of genes because proteins encoded by genes involved in the same biological process are often coregulated (3, 4). However, correlation measures do not provide direct insight into the identity or nature of the gene interactions that give rise to the observed expression patterns. Much effort is being devoted to the reconstruction of gene interaction networks using a variety of modeling approaches, ranging from simple Boolean networks through dynamical models of cellular processes (5-8). Various types of Bayesian network models (9, 10), graphical Gaussian models (11, 12), and relevance networks (13) have been developed to extract information about gene interactions directly from expression profiles. However, even for a simple linear model, the system is underdetermined because the number of genes sampled in a microarray experiment is invariably much larger than the number of samples, with the consequence that myriad networks can reproduce the observed data with fidelity. Efforts to constrain the model space by incorporating additional information from interventions and perturbations, other types of molecular data, or literature mining are useful on a small scale but rapidly become unwieldy with increasing gene numbers (14-17). Alternative approaches make simplifying assumptions about network topology or postulate that the microarray data are drawn randomly from a Gaussian distribution (11,12,18).To avoid such assumptions, which are often either untestable or untenable, and address the underdetermination problem...

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Section: Network Hubs Encode Key Cellular Proteins In Nutrient Signalmentioning

confidence: 99%

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Lezon

Banavar²,

Cieplak³

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

241

238

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“…Synthetic oligonucleotides for 5¢ and 3¢ primers, 3 and 4 for TDH1, Table 1. List of synthetic oligonucleotides used for primers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) or probes (14,15) in this experiment.…”

Section: Preparation Of Tdh-deleted Mutantsmentioning

confidence: 99%

Interaction of the GTS1 gene product with glyceraldehyde‐ 3‐phosphate dehydrogenase 1 required for the maintenance of the metabolic oscillations of the yeast Saccharomyces cerevisiae

Liu

Wang

Mitsui

et al. 2002

European Journal of Biochemistry

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We previously reported that GTS1 is involved in regulating ultradian oscillations of the glycolytic pathway induced by cyanide in cell suspensions as well as oscillations of energy metabolism in aerobic continuous cultures. Here, we screened a yeast cDNA library for proteins that bind to Gts1p using the yeast two-hybrid system and cloned multiple TDH cDNAs encoding the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH). We found that the zinc-finger and dimerization sites of Gts1p were required for full ability to bind GAPDH, and Gts1ps mutated at these sites lost the ability to regulate both aerobic and unaerobic ultradian oscillations of energy metabolism.Of the three TDH genes, only TDH1 fluctuated at the mRNA level in continuous culture and its deletion resulted in the disappearance of the oscillation without any affect on growth rate. This loss of biological rhythms in the TDH1-deleted mutant was rescued by the expression of TDH1 but not of TDH2 or TDH3 under the control of the TDH1 promoter. Thus, we hypothesized that Gts1p plays a role in the regulation of metabolic oscillation by interacting with the TDH1 product, GAPDH1, in yeast.Keywords: continuous culture; glyceraldehyde-3-phosphate dehydrogenase; Gts1p; metabolic oscillation; yeast.Ultradian (cycles with a period shorter than 24 h) oscillations of the glycolytic pathway were induced after addition of glucose by inhibiting mitochondrial respiration with cyanide in cell suspensions [1][2][3] or cell extracts [4] of yeast with a periodicity of 1-2 min as monitored by measuring the level of NAD(P)H (reviewed in [5]). The glycolytic pathway has been shown to be an autogenous oscillator under extreme nonequilibrium conditions of energy in dissipative structures, which theoretically include all living organisms [5][6][7]. The pathway oscillates under the primary control of phosphofructokinase [8,9], transferring energy from glucose to NADH, which acts as the feed-forward activator, and then from NADH to ATP, which acts as the feedback inhibitor. After ATP as an inhibitor has been consumed, glucose again begins to enter the glycolytic pathway. Yeast cells also exhibit sustained ultradian oscillations of energy metabolism, with a periodicity of 4 h in continuous (chemostat) culture under aerobic conditions in an open system using a bioreactor [10][11][12][13]. (Hereafter, aerobic oscillation will be referred to as energy metabolism or metabolic oscillation in distinction from cyanide-induced glycolytic oscillation.) Energy-metabolism oscillations, which arise spontaneously under conditions of high cell density ( 5 · 10 8 cellsAEmL )1 ) [14], are detectable as a periodic change in the factors involved in energy metabolism such as dissolved oxygen (DO) levels, CO 2 production, glucose and ethanol concentrations, and amounts of storage carbohydrates [10][11][12][13]. DO oscillation is caused by the periodic change between respiratory and respiratory-fermentative phases, in which oxygen demands are relatively high and low, respectively. Although ...

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“…Also during aerobic growth, this yeast shows an unusual behaviour. When grown aerobically at a low rate, under sugar limitation, cultures tend to spontaneously synchronize their cell cycle (von Meyenburg 1969;Parulekar et al 1986), which makes the analysis of chemical kinetics of growth a difficult enterprise.…”

Section: Introductionmentioning

confidence: 99%

Kinetics of growth and sugar consumption in yeasts

Dijken¹,

Weusthuis²,

Pronk³

1993

Antonie van Leeuwenhoek

186

112

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An overview is presented of the steady-and transient state kinetics of growth and formation of metabolic byproducts in yeasts. Saccharomyces cerevisiae is strongly inclined to perform alcoholic fermentation. Even under fully aerobic conditions, ethanol is produced by this yeast when sugars are present in excess. This so-called 'Crabtree effect' probably results from a multiplicity of factors, including the mode of sugar transport and the regulation of enzyme activities involved in respiration and alcoholic fermentation. The Crabtree effect in S. cerevisiae is not caused by an intrinsic inability to adjust its respiratory activity to high glycolytic fluxes. Under certain cultivation conditions, for example during growth in the presence of weak organic acids, very high respiration rates can be achieved by this yeast. S. cerevisiae is an exceptional yeast since, in contrast to most other species that are able to perform alcoholic fermentation, it can grow under strictly anaerobic conditions.'Non-Saccharomyces' yeasts require a growth-limiting supply of oxygen (i.e. oxygen-limited growth conditions) to trigger alcoholic fermentation. However, complete absence of oxygen results in cessation of growth and therefore, ultimately, of alcoholic fermentation. Since it is very difficult to reproducibly achieve the right oxygen dosage in large-scale fermentations, non-Saccharornyces yeasts are therefore not suitable for largescale alcoholic fermentation of sugar-containing waste streams. In these yeasts, alcoholic fermentation is also dependent on the type of sugar. For example, the facultatively fermentative yeast Candida utilis does not ferment maltose, not even under oxygen-limited growth conditions, although this disaccharide supports rapid oxidative growth.

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Induction and elimination of oscillations in continuous cultures of Saccharomyces cerevisiae

Cited by 141 publications

References 14 publications

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

Interaction of the GTS1 gene product with glyceraldehyde‐ 3‐phosphate dehydrogenase 1 required for the maintenance of the metabolic oscillations of the yeast Saccharomyces cerevisiae

Kinetics of growth and sugar consumption in yeasts

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