Dynamics of oscillatory phenotypes in Saccharomyces cerevisiae reveal a network of genome-wide transcriptional oscillators

Omics analysis reveals mechanism underlying metabolic oscillation during continuous very-high-gravity ethanol fermentation by Saccharomyces cerevisiae

During continuous very-high-gravity (VHG) ethanol fermentation with Saccharomyces cerevisiae, the process exhibits sustained oscillation in residual glucose, ethanol, and biomass, raising a question: how do yeast cells respond to this phenomenon? In this study, the oscillatory behavior of yeast cells was characterized through transcriptome and metabolome analysis for one complete oscillatory period. By analyzing the accumulation of 26 intracellular metabolites and the expression of 90 genes related to central carbon metabolism and stress response, we confirmed that the process oscillation was attributed to intracellular metabolic oscillation with phase difference, and the expression of HXK1, HXT1,2,4, and PFK1 was significantly different from other genes in the Embden-Meyerhof-Parnas pathway, indicating that glucose transport and phosphorylation could be key nodes for regulating the intracellular metabolism under oscillatory conditions. Moreover, the expression of stress response genes was triggered and affected predominately by ethanol inhibition in yeast cells. This progress not only contributes to the understanding of mechanisms underlying the process oscillation observed for continuous VHG ethanol fermentation, but also provides insights for understanding unsteady state that might develop in other continuous fermentation processes operated under VHG conditions to increase product titers for robust production.

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.

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.

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.

Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform

Inference of gene regulatory network (GRN) is crucial to understand intracellular physiological activity and function of biology. The identification of large-scale GRN has been a difficult and hot topic of system biology in recent years. In order to reduce the computation load for large-scale GRN identification, a parallel algorithm based on restricted gene expression programming (RGEP), namely MPRGEP, is proposed to infer instantaneous and time-delayed regulatory relationships between transcription factors and target genes. In MPRGEP, the structure and parameters of time-delayed S-system (TDSS) model are encoded into one chromosome. An original hybrid optimization approach based on genetic algorithm (GA) and gene expression programming (GEP) is proposed to optimize TDSS model with MapReduce framework. Time-delayed GRNs (TDGRN) with hundreds of genes are utilized to test the performance of MPRGEP. The experiment results reveal that MPRGEP could infer more accurately gene regulatory network than other state-of-art methods, and obtain the convincing speedup.

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.

The molecular basis of metabolic cycles and their relationship to circadian rhythms

Metabolic cycles result from the partitioning of oxidative and reductive metabolism into rhythmic phases of gene expression and oscillating post-translational protein modifications. Relatively little is known about how these switches in gene expression are controlled, although recent studies have suggested that transcription itself may play a central role. This review explores the molecular basis of the metabolic and gene-expression oscillations in the yeast Saccharomyces cerevisiae, as well as how they relate to other biological time-keeping mechanisms, such as circadian rhythms.

MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach

Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool.

Semi-supervised prediction of gene regulatory networks using machine learning algorithms

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

Biological Timekeeping: The Business of a Blind Watchmaker

Fundamental understanding of life depends on both structural and functional details at the molecular level. Continually improving means of measurement of spatial and dynamic properties of biochemical constituents and cellular components complement studies of whole organisms. Integration of the interaction of components to provide coherent behaviour depends on highly elaborate orchestration in space and time. Whereas spatial information on a nanometre resolution is available, and fast dynamic analyses provide biochemical reaction rates measured in nanoseconds, functional coordination of the system requires integrated time dependence. While we are well aware of the special complexity of living organisms, appreciation of temporal scales and their organisation in time is still fragmentary. This article summarises current developments in research on biological time on scales from nanoseconds to years, the networks that connect different time domains and the oscillations, rhythms and biological clocks that coordinate and synchronise the complexity of the living state.

“It is the pattern maintained by this homeostasis, which is the touchstone of our personal identity. Our tissues change as we live: the food we eat and the air we breathe become flesh of our flesh, and bone of our bone, and the momentary elements of our flesh and bone pass out of our body every day with our excreta. We are but whirlpools in a river of ever-flowing water. We are not the stuff that abides, but patterns that perpetuate themselves”60.

Wiener, 1954

“What are called structures are slow processes of long duration, functions are quick processes of short duration”61.

Von Bertalanffy, 1952

Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect

Glucose metabolism in mitochondria through oxidative phosphorylation (OXPHOS) for generation of adenosine triphosphate (ATP) is vital for cell function. However, reactive oxygen species (ROS), a by-product from OXPHOS, is a major source of endogenously produced toxic stressors on the genome. In fact, ATP could be efficiently produced in a high throughput manner without ROS generation in cytosol through glycolysis, which could be a unique and critical metabolic pathway to prevent spontaneous mutation during DNA replication. Therefore glycolysis is dominant in robust proliferating cells. Indeed, aerobic glycolysis, or the Warburg effect, in normal proliferating cells is an example of homeostasis of redox status by transiently shifting metabolic flux from OXPHOS to glycolysis to avoid ROS generation during DNA synthesis and protect genome integrity. The process of maintaining redox homeostasis is driven by genome wide transcriptional clustering with mitochondrial retrograde signaling and coupled with the glucose metabolic pathway and cell division cycle. On the contrary, the Warburg effect in cancer cells is the results of the alteration of redox status from a reprogramed glucose metabolic pathway caused by the dysfunctional OXPHOS. Mutations in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) disrupt mitochondrial structural integrity, leading to reduced OXPHOS capacity, sustained glycolysis and excessive ROS leak, all of which are responsible for tumor initiation, progression and metastasis. A "plumbing model" is used to illustrate how redox status could be regulated through glucose metabolic pathway and provide a new insight into the understanding of the Warburg effect in both normal and cancer cells.

Homeostasis of redox status derived from glucose metabolic pathway could be the key to understanding the Warburg effect

Glucose metabolism in mitochondria through oxidative phosphorylation (OXPHOS) for generation of adenosine triphosphate (ATP) is vital for cell function. However, reactive oxygen species (ROS), a by-product from OXPHOS, is a major source of endogenously produced toxic stressors on the genome. In fact, ATP could be efficiently produced in a high throughput manner without ROS generation in cytosol through glycolysis, which could be a unique and critical metabolic pathway to prevent spontaneous mutation during DNA replication. Therefore glycolysis is dominant in robust proliferating cells. Indeed, aerobic glycolysis, or the Warburg effect, in normal proliferating cells is an example of homeostasis of redox status by transiently shifting metabolic flux from OXPHOS to glycolysis to avoid ROS generation during DNA synthesis and protect genome integrity. The process of maintaining redox homeostasis is driven by genome wide transcriptional clustering with mitochondrial retrograde signaling and coupled with the glucose metabolic pathway and cell division cycle. On the contrary, the Warburg effect in cancer cells is the results of the alteration of redox status from a reprogramed glucose metabolic pathway caused by the dysfunctional OXPHOS. Mutations in mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) disrupt mitochondrial structural integrity, leading to reduced OXPHOS capacity, sustained glycolysis and excessive ROS leak, all of which are responsible for tumor initiation, progression and metastasis. A "plumbing model" is used to illustrate how redox status could be regulated through glucose metabolic pathway and provide a new insight into the understanding of the Warburg effect in both normal and cancer cells.

Identifying transcription factor complexes and their roles

Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins.

Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence.

Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/.

Contact:volkhard.helms@bioinformatik.uni-saarland.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Comprehensive analysis of forty yeast microarray datasets reveals a novel subset of genes (APha-RiB) consistently negatively associated with ribosome biogenesis

BACKGROUND

The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investigations. An ensemble clustering method has the ability to perform consensus clustering over the same set of genes from different microarray datasets by combining results from different clustering methods into a single consensus result.

RESULTS

In this paper we have performed comprehensive analysis of forty yeast microarray datasets. One recently described Bi-CoPaM method can analyse expressions of the same set of genes from various microarray datasets while using different clustering methods, and then combine these results into a single consensus result whose clusters' tightness is tunable from tight, specific clusters to wide, overlapping clusters. This has been adopted in a novel way over genome-wide data from forty yeast microarray datasets to discover two clusters of genes that are consistently co-expressed over all of these datasets from different biological contexts and various experimental conditions. Most strikingly, average expression profiles of those clusters are consistently negatively correlated in all of the forty datasets while neither profile leads or lags the other.

CONCLUSIONS

The first cluster is enriched with ribosomal biogenesis genes. The biological processes of most of the genes in the second cluster are either unknown or apparently unrelated although they show high connectivity in protein-protein and genetic interaction networks. Therefore, it is possible that this mostly uncharacterised cluster and the ribosomal biogenesis cluster are transcriptionally oppositely regulated by some common machinery. Moreover, we anticipate that the genes included in this previously unknown cluster participate in generic, in contrast to specific, stress response processes. These novel findings illuminate coordinated gene expression in yeast and suggest several hypotheses for future experimental functional work. Additionally, we have demonstrated the usefulness of the Bi-CoPaM-based approach, which may be helpful for the analysis of other groups of (microarray) datasets from other species and systems for the exploration of global genetic co-expression.

Free energy rhythms in Saccharomyces cerevisiae: a dynamic perspective with implications for ribosomal biogenesis

To describe the time course of cellular systems, we integrate ideas from thermodynamics and information theory to discuss the work needed to change the state of the cell. The biological example analyzed is experimental microarray transcription level oscillations of yeast in the different phases as characterized by oxygen consumption. Surprisal analysis was applied to identify groups of transcripts that oscillate in concert and thereby to compute changes in free energy with time. Three dominant transcript groups were identified by surprisal analysis. The groups correspond to the respiratory, early, and late reductive phases. Genes involved in ribosome biogenesis peaked at the respiratory phase. The work to prepare the state is shown to be the sum of the contributions of these groups. We paid particular attention to work requirements during ribosomal building, and the correlation with ATP levels and dissolved oxygen. The suggestion that cells in the respiratory phase likely build ribosomes, an energy intensive process, in preparation for protein production during the S phase of the cell cycle is validated by an experiment. Surprisal analysis thereby provided a useful tool for determining the synchronization of transcription events and energetics in a cell in real time.