The dynamics of cellular energetics during continuous yeast culture

Eukaryotic cell biology is temporally coordinated to support the energetic demands of protein homeostasis

Yeast physiology is temporally regulated, this becomes apparent under nutrient-limited conditions and results in respiratory oscillations (YROs). YROs share features with circadian rhythms and interact with, but are independent of, the cell division cycle. Here, we show that YROs minimise energy expenditure by restricting protein synthesis until sufficient resources are stored, while maintaining osmotic homeostasis and protein quality control. Although nutrient supply is constant, cells sequester and store metabolic resources via increased transport, autophagy and biomolecular condensation. Replete stores trigger increased H⁺ export which stimulates TORC1 and liberates proteasomes, ribosomes, chaperones and metabolic enzymes from non-membrane bound compartments. This facilitates translational bursting, liquidation of storage carbohydrates, increased ATP turnover, and the export of osmolytes. We propose that dynamic regulation of ion transport and metabolic plasticity are required to maintain osmotic and protein homeostasis during remodelling of eukaryotic proteomes, and that bioenergetic constraints selected for temporal organisation that promotes oscillatory behaviour.

Quantitative Studies for Cell-Division Cycle Control

The cell-division cycle (CDC) is driven by cyclin-dependent kinases (CDKs). Mathematical models based on molecular networks, as revealed by molecular and genetic studies, have reproduced the oscillatory behavior of CDK activity. Thus, one basic system for representing the CDC is a biochemical oscillator (CDK oscillator). However, genetically clonal cells divide with marked variability in their total duration of a single CDC round, exhibiting non-Gaussian statistical distributions. Therefore, the CDK oscillator model does not account for the statistical nature of cell-cycle control. Herein, we review quantitative studies of the statistical properties of the CDC. Over the past 70 years, studies have shown that the CDC is driven by a cluster of molecular oscillators. The CDK oscillator is coupled to transcriptional and mitochondrial metabolic oscillators, which cause deterministic chaotic dynamics for the CDC. Recent studies in animal embryos have raised the possibility that the dynamics of molecular oscillators underlying CDC control are affected by allometric volume scaling among the cellular compartments. Considering these studies, we discuss the idea that a cluster of molecular oscillators embedded in different cellular compartments coordinates cellular physiology and geometry for successful cell divisions.

High-energy guanine nucleotides as a signal capable of linking growth to cellular energy status via the control of gene transcription

This mini-review considers the idea that guanylate nucleotide energy charge acts as an integrative signal for the regulation of gene expression in eukaryotic cells and discusses possible routes for that signal's transduction. Gene expression is intimately linked with cell nutrition and diverse signaling systems serve to coordinate the synthesis of proteins required for growth and proliferation with the prevailing cellular nutritional status. Using short pathways for the inducible and futile consumption of ATP or GTP in engineered cells of Saccharomyces cerevisiae, we have recently shown that GTP levels can also play a role in determining how genes act to respond to changes in cellular energy supply. This review aims to interpret the importance of GTP as an integrative signal in the context of an increasing body of evidence indicating the spatio-temporal complexity of cellular de novo purine nucleotide biosynthesis.

Determination of the Global Pattern of Gene Expression in Yeast Cells by Intracellular Levels of Guanine Nucleotides

This paper investigates whether, independently of the supply of any specific nutrient, gene transcription responds to the energy status of the cell by monitoring ATP and GTP levels. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into the yeast Saccharomyces cerevisiae, and the effect of an increased demand for these purine nucleotides on gene transcription was analyzed. The resulting changes in transcription were most consistently associated with changes in GTP and GEC levels, although the reprogramming in gene expression during glucose repression is sensitive to adenine nucleotide levels. The results show that GTP levels play a central role in determining how genes act to respond to changes in energy supply and that any comprehensive understanding of the control of eukaryotic gene expression requires the elucidation of how changes in guanine nucleotide abundance are sensed and transduced to alter the global pattern of transcription.

Correlations between gene transcription and the abundance of high-energy purine nucleotides in Saccharomyces cerevisiae have often been noted. However, there has been no systematic investigation of this phenomenon in the absence of confounding factors such as nutrient status and growth rate, and there is little hard evidence for a causal relationship. Whether transcription is fundamentally responsive to prevailing cellular energetic conditions via sensing of intracellular purine nucleotides, independently of specific nutrition, remains an important question. The controlled nutritional environment of chemostat culture revealed a strong correlation between ATP and GTP abundance and the transcription of genes required for growth. Short pathways for the inducible and futile consumption of ATP or GTP were engineered into S. cerevisiae, permitting analysis of the transcriptional effect of an increased demand for these nucleotides. During steady-state growth using the fermentable carbon source glucose, the futile consumption of ATP led to a decrease in intracellular ATP concentration but an increase in GTP and the guanylate energy charge (GEC). Expression of transcripts encoding proteins involved in ribosome biogenesis, and those controlled by promoters subject to SWI/SNF-dependent chromatin remodelling, was correlated with these nucleotide pool changes. Similar nucleotide abundance changes were observed using a nonfermentable carbon source, but an effect on the growth-associated transcriptional programme was absent. Induction of the GTP-cycling pathway had only marginal effects on nucleotide abundance and gene transcription. The transcriptional response of respiring cells to glucose was dampened in chemostats induced for ATP cycling, but not GTP cycling, and this was primarily associated with altered adenine nucleotide levels.

Temporal metabolic partitioning of the yeast and protist cellular networks: the cell is a global scale-invariant (fractal or self-similar) multioscillator

Britton Chance, electronics expert when a teenager, became an enthusiastic student of biological oscillations, passing on this enthusiasm to many students and colleagues, including one of us (DL). This historical essay traces BC's influence through the accumulated work of DL to DL's many collaborators. The overall temporal organization of mass-energy, information, and signaling networks in yeast in self-synchronized continuous cultures represents, until now, the most characterized example of in vivo elucidation of time structure. Continuous online monitoring of dissolved gases by direct measurement (membrane-inlet mass spectrometry, together with NAD(P)H and flavin fluorescence) gives strain-specific dynamic information from timescales of minutes to hours as does two-photon imaging. The predominantly oscillatory behavior of network components becomes evident, with spontaneously synchronized cellular respiration cycles between discrete periods of increased oxygen consumption (oxidative phase) and decreased oxygen consumption (reductive phase). This temperature-compensated ultradian clock provides coordination, linking temporally partitioned functions by direct feedback loops between the energetic and redox state of the cell and its growing ultrastructure. Multioscillatory outputs in dissolved gases with 13 h, 40 min, and 4 min periods gave statistical self-similarity in power spectral and relative dispersional analyses: i.e., complex nonlinear (chaotic) behavior and a functional scale-free (fractal) network operating simultaneously over several timescales.

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes.

Quantifying periodicity in omics data

Oscillations play a significant role in biological systems, with many examples in the fast, ultradian, circadian, circalunar, and yearly time domains. However, determining periodicity in such data can be problematic. There are a number of computational methods to identify the periodic components in large datasets, such as signal-to-noise based Fourier decomposition, Fisher's g-test and autocorrelation. However, the available methods assume a sinusoidal model and do not attempt to quantify the waveform shape and the presence of multiple periodicities, which provide vital clues in determining the underlying dynamics. Here, we developed a Fourier based measure that generates a de-noised waveform from multiple significant frequencies. This waveform is then correlated with the raw data from the respiratory oscillation found in yeast, to provide oscillation statistics including waveform metrics and multi-periods. The method is compared and contrasted to commonly used statistics. Moreover, we show the utility of the program in the analysis of noisy datasets and other high-throughput analyses, such as metabolomics and flow cytometry, respectively.