Time-structure of the yeast metabolism in vivo

The Role of Transmembrane Proteins in Plant Growth, Development, and Stress Responses

Transmembrane proteins participate in various physiological activities in plants, including signal transduction, substance transport, and energy conversion. Although more than 20% of gene products are predicted to be transmembrane proteins in the genome era, due to the complexity of transmembrane domains they are difficult to reliably identify in the predicted protein, and they may have different overall three-dimensional structures. Therefore, it is challenging to study their biological function. In this review, we describe the typical structures of transmembrane proteins and their roles in plant growth, development, and stress responses. We propose a model illustrating the roles of transmembrane proteins during plant growth and response to various stresses, which will provide important references for crop breeding.

An appreciation of the prescience of Don Gilbert (1930-2011): master of the theory and experimental unravelling of biochemical and cellular oscillatory dynamics

We review Don Gilbert's pioneering seminal contributions that both detailed the mathematical principles and the experimental demonstration of several of the key dynamic characteristics of life. Long before it became evident to the wider biochemical community, Gilbert proposed that cellular growth and replication necessitate autodynamic occurrence of cycles of oscillations that initiate, coordinate and terminate the processes of growth, during which all components are duplicated and become spatially re-organised in the progeny. Initiation and suppression of replication exhibit switch-like characteristics, that is, bifurcations in the values of parameters that separate static and autodynamic behaviour. His limit cycle solutions present models developed in a series of papers reported between 1974 and 1984, and these showed that most or even all of the major facets of the cell division cycle could be accommodated. That the cell division cycle may be timed by a multiple of shorter period (ultradian) rhythms, gave further credence to the central importance of oscillatory phenomena and homeodynamics as evident on multiple time scales (seconds to hours). Further application of the concepts inherent in limit cycle operation as hypothesised by Gilbert more than 50 years ago are now validated as being applicable to oscillatory transcript, metabolite and enzyme levels, cellular differentiation, senescence, cancerous states and cell death. Now, we reiterate especially for students and young colleagues, that these early achievements were even more exceptional, as his own lifetime's work on modelling was continued with experimental work in parallel with his predictions of the major current enterprises of biological research.

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.

Metabolic rhythms: A framework for coordinating cellular function

Circadian clocks are widespread among eukaryotes and generally involve feedback loops coupled with metabolic processes and redox balance. The organising power of these oscillations has not only allowed organisms to anticipate day-night cycles, but also acts to temporally compartmentalise otherwise incompatible processes, enhance metabolic efficiency, make the system more robust to noise and propagate signals among cells. While daily rhythms and the function of the circadian transcription-translation loop have been the subject of extensive research over the past four decades, cycles of shorter period and respiratory oscillations, with which they are intertwined, have received less attention. Here, we describe features of yeast respiratory oscillations, which share many features with daily and 12 hr cellular oscillations in animal cells. This relatively simple system enables the analysis of dynamic rhythmic changes in metabolism, independent of cellular oscillations that are a product of the circadian transcription-translation feedback loop. Knowledge gained from studying ultradian oscillations in yeast will lead to a better understanding of the basic mechanistic principles and evolutionary origins of oscillatory behaviour among eukaryotes.

Flavin-based metabolic cycles are integral features of growth and division in single yeast cells

The yeast metabolic cycle (YMC) is a fascinating example of biological organization, in which cells constrain the function of specific genetic, protein and metabolic networks to precise temporal windows as they grow and divide. However, understanding the intracellular origins of the YMC remains a challenging goal, as measuring the oxygen oscillations traditionally associated with it requires the use of synchronized cultures growing in nutrient-limited chemostat environments. To address these limitations, we used custom-built microfluidic devices and time-lapse fluorescence microscopy to search for metabolic cycling in the form of endogenous flavin fluorescence in unsynchronized single yeast cells. We uncovered robust and pervasive metabolic cycles that were synchronized with the cell division cycle (CDC) and oscillated across four different nutrient conditions. We then studied the response of these metabolic cycles to chemical and genetic perturbations, showing that their phase synchronization with the CDC can be altered through treatment with rapamycin, and that metabolic cycles continue even in respiratory deficient strains. These results provide a foundation for future studies of the physiological importance of metabolic cycles in processes such as CDC control, metabolic regulation and cell aging.

Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back

Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O₂ and CO₂ levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.

Elements of the cellular metabolic structure

A large number of studies have demonstrated the existence of metabolic covalent modifications in different molecular structures, which are able to store biochemical information that is not encoded by DNA. Some of these covalent mark patterns can be transmitted across generations (epigenetic changes). Recently, the emergence of Hopfield-like attractor dynamics has been observed in self-organized enzymatic networks, which have the capacity to store functional catalytic patterns that can be correctly recovered by specific input stimuli. Hopfield-like metabolic dynamics are stable and can be maintained as a long-term biochemical memory. In addition, specific molecular information can be transferred from the functional dynamics of the metabolic networks to the enzymatic activity involved in covalent post-translational modulation, so that determined functional memory can be embedded in multiple stable molecular marks. The metabolic dynamics governed by Hopfield-type attractors (functional processes), as well as the enzymatic covalent modifications of specific molecules (structural dynamic processes) seem to represent the two stages of the dynamical memory of cellular metabolism (metabolic memory). Epigenetic processes appear to be the structural manifestation of this cellular metabolic memory. Here, a new framework for molecular information storage in the cell is presented, which is characterized by two functionally and molecularly interrelated systems: a dynamic, flexible and adaptive system (metabolic memory) and an essentially conservative system (genetic memory). The molecular information of both systems seems to coordinate the physiological development of the whole cell.

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.

Complex oscillatory redox dynamics with signaling potential at the edge between normal and pathological mitochondrial function

The time-keeping properties bestowed by oscillatory behavior on functional rhythms represent an evolutionarily conserved trait in living systems. Mitochondrial networks function as timekeepers maximizing energetic output while tuning reactive oxygen species (ROS) within physiological levels compatible with signaling. In this work, we explore the potential for timekeeping functions dependent on mitochondrial dynamics with the validated two-compartment mitochondrial energetic-redox (ME-R) computational model, that takes into account (a) four main redox couples [NADH, NADPH, GSH, Trx(SH)₂], (b) scavenging systems (glutathione, thioredoxin, SOD, catalase) distributed in matrix and extra-matrix compartments, and (c) transport of ROS species between them. Herein, we describe that the ME-R model can exhibit highly complex oscillatory dynamics in energetic/redox variables and ROS species, consisting of at least five frequencies with modulated amplitudes and period according to power spectral analysis. By stability analysis we describe that the extent of steady state—as against complex oscillatory behavior—was dependent upon the abundance of Mn and Cu, Zn SODs, and their interplay with ROS production in the respiratory chain. Large parametric regions corresponding to oscillatory dynamics of increasingly complex waveforms were obtained at low Cu, Zn SOD concentration as a function of Mn SOD. This oscillatory domain was greatly reduced at higher levels of Cu, Zn SOD. Interestingly, the realm of complex oscillations was located at the edge between normal and pathological mitochondrial energetic behavior, and was characterized by oxidative stress. We conclude that complex oscillatory dynamics could represent a frequency- and amplitude-modulated H₂O₂ signaling mechanism that arises under intense oxidative stress. By modulating SOD, cells could have evolved an adaptive compromise between relative constancy and the flexibility required under stressful redox/energetic conditions.

Towards metagenome-scale models for industrial applications--the case of Lactic Acid Bacteria

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field.

Mitochondrial network energetics in the heart

At the core of eukaryotic aerobic life, mitochondrial function like 'hubs' in the web of energetic and redox processes in cells. In the heart, these networks-extending beyond the complex connectivity of biochemical circuit diagrams and apparent morphology-exhibit collective dynamics spanning several spatiotemporal levels of organization, from the cell, to the tissue, and the organ. The network function of mitochondria, i.e., mitochondrial network energetics, represents an advantageous behavior. Its coordinated action, under normal physiology, provides robustness despite failure in a few nodes, and improves energy supply toward a swiftly changing demand. Extensive diffuse loops, encompassing mitochondrial-cytoplasmic reaction/transport networks, control and regulate energy supply and demand in the heart. Under severe energy crises, the network behavior of mitochondria and associated glycolytic and other metabolic networks collapse, thereby triggering fatal arrhythmias.

Rapid DNA, RNA and protein extraction protocols optimized for slow continuously growing yeast cultures

Conventional extraction protocols for yeast have been developed for relatively rapid-growing low cell density cultures of laboratory strains and often do not have the integrity for frequent sampling of cultures. Therefore, these protocols are usually inefficient for cultures under slow growth conditions or of non-laboratory strains. We have developed a combined mechanical and chemical disruption procedure using vigorous bead-beating that can consistently disrupt yeast cells (> 95%), irrespective of cell cycle and metabolic state. Using this disruption technique coupled with quenching, we have developed DNA, RNA and protein extraction protocols that are optimized for a large number of samples from slow-growing high-density industrial yeast cultures. Additionally, sample volume, the use of expensive reagents/enzymes, handling times and incubations were minimized. We have tested the reproducibility of our methods using triplicate/time-series extractions and compared these with commonly used protocols or commercially available kits. Moreover, we utilized a simple flow-cytometric approach to estimate the mitochondrial DNA copy number. Based on the results, our methods have shown higher reproducibility, yield and quality.