A potential mechanism of energy‐metabolism oscillation in an aerobic chemostat culture of the yeast <i>Saccharomyces cerevisiae</i>

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238

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...

Section: Resultsmentioning

confidence: 99%

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

Lezon

Banavar²,

Cieplak³

et al. 2006

241

238

“…The redox state of the culture is believed to play a vital role in the YRO as it alternates between a high oxygen consumption (HOC) phase and a low oxygen consumption (LOC) phase (13,21,22). As shown above, damage to or impairment of the respiratory cytochromes of the cell resulted in a shortened HOC phase of the YRO when DO is low (or dropping) and reduced the time before the culture returned to an LOC mode of energy metabolism when DO is high or rising.…”

Section: Visible Light Induces the Ros Stress Response But Ros Does Notmentioning

confidence: 99%

“…Recent evidence suggests that metabolic cycling in S. cerevisiae also occurs at the single-cell level even in unsynchronized cultures and that the conditions of continuous culture allow independently oscillating cells to synchronize (18-20). Because respiration and oxidative state play major roles in regulating the YRO (13,15,[21][22][23], and because visible light is an important environmental factor that might alter respiration rates and ROS production (2, 24, 25), we sought to determine if visible light affects the YRO as a means to understand better the factors that can influence metabolism and the YRO and the strategies that yeasts in nature use to protect themselves from photodamage.We show that visible light (especially blue light) at intensities less than that of natural full sunlight significantly modulates the period and amplitude of the YRO. On the basis of the strongest YRO modulations correlating with peaks in the cytochrome absorption spectrum and the modulations being mimicked using an electron transport inhibitor, we conclude that this effect of visible light on the YRO is likely to be mediated through light absorption by cytochromes.…”

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confidence: 99%

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confidence: 99%

See 1 more Smart Citation

Visible light alters yeast metabolic rhythms by inhibiting respiration

Robertson

Davis

Johnson

2013

Exposure of cells to visible light in nature or in fluorescence microscopy often is considered to be relatively innocuous. However, using the yeast respiratory oscillation (YRO) as a sensitive measurement of metabolism, we find that non-UV visible light has a significant impact on yeast metabolism. Blue/green wavelengths of visible light shorten the period and dampen the amplitude of the YRO, which is an ultradian rhythm of cell metabolism and transcription. The wavelengths of light that have the greatest effect coincide with the peak absorption regions of cytochromes. Moreover, treating yeast with the electron transport inhibitor sodium azide has similar effects on the YRO as visible light. Because impairment of respiration by light would change several state variables believed to play vital roles in the YRO (e.g., oxygen tension and ATP levels), we tested oxygen's role in YRO stability and found that externally induced oxygen depletion can reset the phase of the oscillation, demonstrating that respiratory capacity plays a role in the oscillation's period and phase. Light-induced damage to the cytochromes also produces reactive oxygen species that up-regulate the oxidative stress response gene TRX2 that is involved in pathways that enable sustained growth in bright visible light. Therefore, visible light can modulate cellular rhythmicity and metabolism through unexpectedly photosensitive pathways.M any organisms are exposed to visible light in their environment. Full sunlight can deliver up to 10 quadrillion photons of visible light·cm −2 ·s −1 (i.e., 2,000 μEinsteins·m −2 ·s −1 ), and cloud cover reduces this exposure only by a factor of 10. Even though sunlight provides photosynthetic energy to plants and a medium for the vision of many animal species, its highintensity rays damage living cells when light-absorbing molecules cannot safely disperse the energy that photons bring. Although the destructive capacity of UV light is widely appreciated, photons of visible light can be deleterious, e.g., by destroying cytochromes and thus affecting cellular respiration (1) or by producing reactive oxygen species (ROS) that cause damage to DNA, membranes, and other cellular components (2). To cope with the damaging effects of light, organisms have evolved different strategies ranging from the expression of shielding pigments, such as melanin and carotenoids (3), to active mechanisms that sense light and respond quickly to mitigate/repair damage, such as iris constriction to protect the retina (4, 5), light-avoidance movements by chloroplasts and mitochondria (6, 7), and the induction/activation of DNA photolyase (8). A third strategy to minimize damage from light is to anticipate and prepare for its effects through the use of cellular timing mechanisms such as a circadian clock (9).The budding yeast Saccharomyces cerevisiae lacks the wellcharacterized photoreceptors of many other organisms [e.g., cryptochromes, phytochromes, and the photoreceptors whitecollar 1 (WC-1), and rhodopsins] that sense light intensity and spe...

“…When continuous culture is initiated, the culture can be maintained in this oscillatory state [40 min to 5 h; see supporting information (SI) Fig. 5] for months (4)(5)(6). These dynamics result in the temporal separation of many essential cellular functions, including redox biochemistry (7,8), transcription (9,10), energetics (11), chromosome cycle (1), and mitochondrial function (5).…”

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confidence: 99%

Regulation of yeast oscillatory dynamics

Murray

Beckmann

Kitano

2007

136

151

When yeast cells are grown continuously at high cell density, a respiratory oscillation percolates throughout the population. Many essential cellular functions have been shown to be separated temporally during each cycle; however, the regulatory mechanisms involved in oscillatory dynamics remain to be elucidated. Through GC-MS analysis we found that the majority of metabolites show oscillatory dynamics, with 70% of the identified metabolite concentrations peaking in conjunction with NAD(P)H. Through statistical analyses of microarray data, we identified that biosynthetic events have a defined order, and this program is initiated when respiration rates are increasing. We then combined metabolic, transcriptional data and statistical analyses of transcription factor activity, identified the top oscillatory parameters, and filtered a large-scale yeast interaction network according to these parameters. The analyses and controlled experimental perturbation provided evidence that a transcriptional complex formed part of the timing circuit for biosynthetic, reductive, and cell cycle programs in the cell. This circuitry does not act in isolation because both have strong translational, proteomic, and metabolic regulatory mechanisms. Our data lead us to conclude that the regulation of the respiratory oscillation revolves around coupled subgraphs containing large numbers of proteins and metabolites, with a potential to oscillate, and no definable hierarchy, i.e., heterarchical control. metabolic regulation ͉ respiratory oscillation ͉ temporal structure ͉ transcriptional regulation ͉ self-organization A s we obtain a greater understanding of systems dynamics, it is becoming apparent that biological oscillators play critical roles in the organization of physiology in all time scales, ranging from milliseconds to years (1, 2). At the end of yeast growth in batch culture, there is series of cycles in respiratory activity that occur before the culture entering stationary phase (3). When continuous culture is initiated, the culture can be maintained in this oscillatory state [40 min to 5 h; see supporting information (SI) Fig. 5] for months (4-6). These dynamics result in the temporal separation of many essential cellular functions, including redox biochemistry (7,8), transcription (9, 10), energetics (11), chromosome cycle (1), and mitochondrial function (5). Although respiration is always active, the cellular redox state cycles between an oxidative phase and reductive phase. It is known that at least two redox active compounds, acetaldehyde and H 2 S, mediate cell-cell communication (12), but little is known regarding how synchrony is generated within the cell and how the network is regulated.A major problem in elucidating the oscillatory mechanism is the extent to which the cellular network is entrained, i.e., in a system where the majority of parameters oscillate how does one pull out core mechanisms. Fortunately, Saccharomyces cerevisiae has been used extensively as a proving ground for many of the new low-and high-throughpu...