mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle

<p>Abstract</p> <p>Background</p> <p>In yeast, genome-wide periodic patterns associated with energy-metabolic oscillations have been shown recently for both short (approx. 40 min) and long (approx. 300 min) periods.</p> <p>Results</p> <p>The dynamical regulation due to mRNA stability is found to be an important aspect of the genome-wide coordination of the long-period yeast metabolic cycle. It is shown that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates.</p> <p>Conclusion</p> <p>The cascade of events occurring during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses/stimuli.</p

Systems biology approaches to the dynamics of gene expression and chemical reactions

Systems biology is an emergent interdisciplinary field of study whose main goal is to understand the global properties and functions of a biological system by investigating its structure and dynamics [74]. This high-level knowledge can be reached only with a coordinated approach involving researchers with different backgrounds in molecular biology, the various omics (like genomics, proteomics, metabolomics), computer science and dynamical systems theory. The history of systems biology as a distinct discipline began in the 1960s, and saw an impressive growth since year 2000, originated by the increased accumulation of biological information, the development of high-throughput experimental techniques, the use of powerful computer systems for calculations and database hosting, and the spread of Internet as the standard medium for information diffusion [77]. In the last few years, our research group tried to tackle a set of systems biology problems which look quite diverse, but share some topics like biological networks and system dynamics, which are of our interest and clearly fundamental for this field. In fact, the first issue we studied (covered in Part I) was the reverse engineering of large-scale gene regulatory networks. Inferring a gene network is the process of identifying interactions among genes from experimental data (tipically microarray expression profiles) using computational methods [6]. Our aim was to compare some of the most popular association network algorithms (the only ones applicable at a genome-wide level) in different conditions. In particular we verified the predictive power of similarity measures both of direct type (like correlations and mutual information) and of conditional type (partial correlations and conditional mutual information) applied on different kinds of experiments (like data taken at equilibrium or time courses) and on both synthetic and real microarray data (for E. coli and S. cerevisiae). In our simulations we saw that all network inference algorithms obtain better performances from data produced with \u201cstructural\u201d perturbations (like gene knockouts at steady state) than with just dynamical perturbations (like time course measurements or changes of the initial expression levels). Moreover, our analysis showed differences in the performances of the algorithms: direct methods are more robust in detecting stable relationships (like belonging to the same protein complex), while conditional methods are better at causal interactions (e.g. transcription factor\u2013binding site interactions), especially in presence of combinatorial transcriptional regulation. Even if time course microarray experiments are not particularly useful for inferring gene networks, they can instead give a great amount of information about the dynamical evolution of a biological process, provided that the measurements have a good time resolution. Recently, such a dataset has been published [119] for the yeast metabolic cycle, a well-known process where yeast cells synchronize with respect to oxidative and reductive functions. In that paper, the long-period respiratory oscillations were shown to be reflected in genome-wide periodic patterns in gene expression. As explained in Part II, we analyzed these time series in order to elucidate the dynamical role of post-transcriptional regulation (in particular mRNA stability) in the coordination of the cycle. We found that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates. Moreover, the cascade of events which occurs during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses or stimuli. The concepts of network and of systems dynamics return also as major arguments of Part III. In fact, there we present a study of some dynamical properties of the so-called chemical reaction networks, which are sets of chemical species among which a certain number of reactions can occur. These networks can be modeled as systems of ordinary differential equations for the species concentrations, and the dynamical evolution of these systems has been theoretically studied since the 1970s [47, 65]. Over time, several independent conditions have been proved concerning the capacity of a reaction network, regardless of the (often poorly known) reaction parameters, to exhibit multiple equilibria. This is a particularly interesting characteristic for biological systems, since it is required for the switch-like behavior observed during processes like intracellular signaling and cell differentiation. Inspired by those works, we developed a new open source software package for MATLAB, called ERNEST, which, by checking these various criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results of this analysis can be used, for example, for model discrimination: if for a multistable biological process there are multiple candidate reaction models, it is possible to eliminate some of them by proving that they are always monostationary. Finally, we considered the related property of monotonicity for a reaction network. Monotone dynamical systems have the tendency to converge to an equilibrium and do not present chaotic behaviors. Most biological systems have the same features, and are therefore considered to be monotone or near-monotone [85, 116]. Using the notion of fundamental cycles from graph theory, we proved some theoretical results in order to determine how distant is a given biological network from being monotone. In particular, we showed that the distance to monotonicity of a network is equal to the minimal number of negative fundamental cycles of the corresponding J-graph, a signed multigraph which can be univocally associated to a dynamical system

Non-Equilibrium Hyperbolic Transport in Transcriptional Regulation

In this work we studied memory and irreversible transport phenomena in a non-equilibrium thermodynamical model for genomic transcriptional regulation. Transcriptional regulation possess an extremely complex phenomenology, and it is, of course, of foremost importance in organismal cell development and in the pathogenesis of complex diseases. A better understanding of the way in which these processes occur is mandatory to optimize the construction of gene regulatory networks, but also to connect these networks with multi-scale phenomena (e.g. metabolism, signalling pathways, etc.) under an integrative Systems Biology-like vision. In this paper we analyzed three simple mechanisms of genetic stimulation: an instant pulse, a periodic biochemical signal and a saturation process with sigmoidal kinetics and from these we derived the system's thermodynamical response, in the form of, for example, anomalous transcriptional bursts

Survival of the cheapest: How proteome cost minimization drives evolution

Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, the quantitative proteomics revealed that fitness effects not related to functional proficiency play much larger roles on long evolutionary time scales than previously thought, with particular evidence that some universal biophysical selection pressures act via protein expression levels. This paper first summarizes recent progress in the 21st century towards recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying "non-function" selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases

Exploring the Effect of Climate Change on Biological Systems

The present and potential future effect of global warming on the ecosystem has brought climate change to the forefront of scientific inquiry and discussion. For our investigation, we selected two organisms, one from cyanobacteria and one from a cereal plant to determine how climate change may impact these biological systems. The study involved understanding the physiological and adaptive responses at both the genetic and protein function levels to counteract environmental stresses. An increase in atmospheric carbon dioxide is a key factor in global climate change and can lead to alterations in ocean chemistry. Cyanobacteria are important, ancient and ubiquitous organisms that can aid in the study of the biological response to increasing carbon dioxide. Climate predictions estimate that by the year 2100 atmospheric carbon dioxide will exceed 700 ppm. In our first study, we looked at the transcriptional effect of high pCO2 on the cyanobacteria, Trichodesmium erythraeum. Total RNA sequencing was used to quantify changes in gene expression in T. erythraeum grown under present day and projected pCO2 concentrations for the year 2100. Two bioinformatics methods were used to analyze the transcriptional data. The results from this study indicate that a substantial number of genes are affected by high pCO2. However, increased pCO2 does not completely alter any one specific metabolic pathway. As the climate shifts throughout the world, it becomes essential for crops to withstand weather changes. In our second study, we investigated the function of the temperature induced lipocalin (Tatil) from Triticum aestivum, which is proposed to help plants survive adverse conditions. This protein is part of a functionally diverse and divergent superfamily of proteins called the lipocalins; they share a common three-dimensional structure, which consists of an antiparallel β-barrel and a C-terminal α-helix. Lipocalins are found in various organisms with a wide range of functions such as pheromone activity, lipid transport and coloration. Recently, proteins from wheat and Arabidopsis were identified as lipocalins through the elucidation of three structurally conserved regions. The study is particularly timely, as recent studies within the scientific community have shown that at higher temperatures wheat yields will decrease and production will decline by 6% for each 1°C increase. We analyzed the nature of conservation in a large group of sequentially divergent and functionally diverse lipocalins and identified seventeen highly conserved positions as well as built models of the native three-dimensional state of the wheat lipocalin. Based on these computational studies, the wild-type protein and three variants were chosen for a cellular localization study involving site-directed mutagenesis, a gene gun and a confocal microscope. The results provide support for the hypothesis that the L5 loop is involved in the association of the protein with the plasma membrane. We also developed an expression and purification system to produce the wild-type wheat lipocalin protein. Gel filtration chromatography eluted two different sized proteins. Based on the elution volume, one is believed to be the wheat lipocalin trimer while the other one is the monomer. Circular dichroism and fluorescence spectroscopy show that the biological characteristics of the two proteins are different. In the study, Tatil maintains its structure up to approximately 50°C (122°F). In summary, we provide experimental data to better understand mechanistically how microorganisms and plants adapt to environmental change. In cyanobacteria, we show that T. erythraeum adapts to pCO2 increases by up- or down-regulating its genes. In plants, we provide insight into the way in which Tatil interacts with the plant cell membrane as part of its putative function to facilitate robustness in response to temperature increases. The study of Tatil is vital as this protein is believed to help plants tolerate oxidative stress and extreme conditions which broadens our understanding of plant sustainability in different environments

AUREOCHROME1a-mediated induction of the diatom-specific cyclin dsCYC2 controls the onset of cell division in diatoms (Phaeodactylum tricornutum)

Cell division in photosynthetic organisms is tightly regulated by light. Although the light dependency of the onset of the cell cycle has been well characterized in various phototrophs, little is known about the cellular signaling cascades connecting light perception to cell cycle activation and progression. Here, we demonstrate that diatom-specific cyclin 2 (dsCYC2) in Phaeodactylum tricornutum displays a transcriptional peak within 15 min after light exposure, long before the onset of cell division. The product of dsCYC2 binds to the cyclin-dependent kinase CDKA1 and can complement G1 cyclin-deficient yeast. Consistent with the role of dsCYC2 in controlling a G1-to-S light-dependent cell cycle checkpoint, dsCYC2 silencing decreases the rate of cell division in diatoms exposed to light-dark cycles but not to constant light. Transcriptional induction of dsCYC2 is triggered by blue light in a fluence rate-dependent manner. Consistent with this, dsCYC2 is a transcriptional target of the blue light sensor AUREOCHROME1a, which functions synergistically with the basic leucine zipper (bZIP) transcription factor bZIP10 to induce dsCYC2 transcription. The functional characterization of a cyclin whose transcription is controlled by light and whose activity connects light signaling to cell cycle progression contributes significantly to our understanding of the molecular mechanisms underlying light-dependent cell cycle onset in diatoms

Deciphering the transcriptional response of saccharomyces cerevisiae to perturbations of lipid metabolism and graded endoplasmic reticulum stress

Systems Biology combines experimental biology with mathematics and computational simulations to better describe biological phenomena that emerge from the interaction of different players. Extensive prior knowledge and experimental feasibility make the eukaryotic single-cell organism S. cerevisiae the preferred model organism for systems biology, while the strongly conserved features might enable conclusions for more complex organisms. In this thesis, a ‘Systems Biology’-approach was taken to better understand how S. cerevisiae coordinates different transcriptional and metabolic responses to adapt to two exemplary environmental changes, i.e. inositol depletion and low-level ER stress. Firstly, a quantitative model guided the construction of fast-folding, actively degraded reporter proteins, which were able to rapidly indicate specific transcriptional changes in single cells. Secondly, the developed reporter proteins, a fluorescent sphingolipid (SL) intermediate and classical molecular biology techniques were used to investigate the interaction of the signaling pathways, which enable S. cerevisiae to survive after inositol depletion, and to understand the role of SL metabolism during this process. The results highlighted the temporal order of transcription factors that follows the removal of inositol, i.e. first INO2/4, then HAC1 and lastly RLM1, and suggested that decreased SL biosynthesis is probably not responsible for the delayed disruption of ER homoeostasis but perturbs cell wall integrity after HAC1 activation. Thirdly, the adaptation to low ER stress was studied with a reporter protein for HAC1 and established fluorescent labels. The experimental insights then motivated a quantitative model for the adaptation to new environments, which lower the growth rate and change the inheritance of essential resources during cytokinesis. From the results, it emerged that ER stress mainly affects G1 duration in daughter cells and reduces the amount of ER content that is inherited by them. This lower inheritance probably contributed to the daughter-specific HAC1 activation. The analysis of the model implied that such a lower resource inheritance increases the daughter: mother ratio and probably lowers the resource demand of the population. Overall, the results supported the idea that transcriptional adaptation is primarily performed by daughter cells and is often a multi-step process. This work moreover lays the foundation to investigate transcriptional dynamics during other environmental changes and to further study the role of lipid metabolism for ER homeostasis. It also provided a mathematical model for the long-term impact of changes in the distribution of limiting resources.Open Acces

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs