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

Genome-Wide Analysis of Yeast Metabolic Cycle through Metabolic Network Models Reveals Superiority of Integrated ATAC-seq Data over RNA-seq Data

Saccharomyces cerevisiae undergoes robust oscillations to regulate its physiology for adaptation and survival under nutrient-limited conditions. Environmental cues can induce rhythmic metabolic alterations in order to facilitate the coordination of dynamic metabolic behaviors. Of such metabolic processes, the yeast metabolic cycle enables adaptation of the cells to varying nutritional status through oscillations in gene expression and metabolite production levels. In this process, yeast metabolism is altered between diverse cellular states based on changing oxygen consumption levels: quiescent (reductive charging [RC]), growth (oxidative [OX]), and proliferation (reductive building [RB]) phases. We characterized metabolic alterations during the yeast metabolic cycle using a variety of approaches. Gene expression levels are widely used for condition-specific metabolic simulations, whereas the use of epigenetic information in metabolic modeling is still limited despite the clear relationship between epigenetics and metabolism. This prompted us to investigate the contribution of epigenomic information to metabolic predictions for progression of the yeast metabolic cycle. In this regard, we determined altered pathways through the prediction of regulated reactions and corresponding model genes relying on differential chromatin accessibility levels. The predicted metabolic alterations were confirmed via data analysis and literature. We subsequently utilized RNA sequencing (RNA-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data sets in the contextualization of the yeast model. The use of ATAC-seq data considerably enhanced the predictive capability of the model. To the best of our knowledge, this is the first attempt to use genome-wide chromatin accessibility data in metabolic modeling. The preliminary results showed that epigenomic data sets can pave the way for more accurate metabolic simulations.

IMPORTANCE Dynamic chromatin organization mediates the emergence of condition-specific phenotypes in eukaryotic organisms. Saccharomyces cerevisiae can alter its metabolic profile via regulation of genome accessibility and robust transcriptional oscillations under nutrient-limited conditions. Thus, both epigenetic information and transcriptomic information are crucial in the understanding of condition-specific metabolic behavior in this organism. Based on genome-wide alterations in chromatin accessibility and transcription, we investigated the yeast metabolic cycle, which is a remarkable example of coordinated and dynamic yeast behavior. In this regard, we assessed the use of ATAC-seq and RNA-seq data sets in condition-specific metabolic modeling. To our knowledge, this is the first attempt to use chromatin accessibility data in the reconstruction of context-specific metabolic models, despite the extensive use of transcriptomic data. As a result of comparative analyses, we propose that the incorporation of epigenetic information is a promising approach in the accurate prediction of metabolic dynamics.

Kinetics effects and modeling of mRNA turnover

Broader comprehension of gene expression regulatory mechanisms can be gained from a global analysis of how transcription and degradation are coordinated to orchestrate complex cell responses. The role of messenger RNA (mRNA) turnover modulation in gene expression levels has become increasingly recognized. From such perspective, in this review we briefly illustrate how a simple but effective mathematical model of mRNA turnover and some experimental findings, may together shed light on the molecular mechanisms underpinning the major role of mRNA decay rates in shaping the kinetics of gene activation and repression.

Transcription mediated insulation and interference direct gene cluster expression switches

In yeast, many tandemly arranged genes show peak expression in different phases of the metabolic cycle (YMC) or in different carbon sources, indicative of regulation by a bi-modal switch, but it is not clear how these switches are controlled. Using native elongating transcript analysis (NET-seq), we show that transcription itself is a component of bi-modal switches, facilitating reciprocal expression in gene clusters. HMS2, encoding a growth-regulated transcription factor, switches between sense- or antisense-dominant states that also coordinate up- and down-regulation of transcription at neighbouring genes. Engineering HMS2 reveals alternative mono-, di- or tri-cistronic and antisense transcription units (TUs), using different promoter and terminator combinations, that underlie state-switching. Promoters or terminators are excluded from functional TUs by read-through transcriptional interference, while antisense TUs insulate downstream genes from interference. We propose that the balance of transcriptional insulation and interference at gene clusters facilitates gene expression switches during intracellular and extracellular environmental change.

DOI:http://dx.doi.org/10.7554/eLife.03635.001

A DNA double helix is made up of two DNA strands, which in turn are made of molecules that are each known by a single letter—A, T, C, or G. The sequence of these ‘letters’ in each DNA strand contains biological information.

Genes are sections of DNA that can be ‘expressed’ to produce proteins and RNA molecules. To express a gene, the DNA strands in the double helix must first be partially separated so that one of them can be used as a template to build an RNA molecule in a process called transcription. Either of the DNA strands in a helix can be used as an RNA template, but contain different genes and are read in opposite directions. One of the two strands is called the ‘sense’ strand, the other the ‘antisense’ strand.

The RNA molecule does not transcribe a whole DNA strand; instead, it transcribes a section of DNA, known as a transcription unit, which contains at least one gene. The end of a transcription unit is marked by certain signals that stop transcription. However, some transcription units in a DNA strand overlap, so there must be some way that the transcription machinery can sometimes ignore these stop signals.

The activity of some genes is linked to the activity of their immediate neighbours. Furthermore, some genes are expressed in different amounts in response to changes in environmental conditions. Researchers have previously suggested that there must be some form of switch that controls when these genes are expressed.

Nguyen et al. now engineer start and stop signals at a neighbouring pair of genes, called HMS2 and BAT2, in yeast. When one gene is switched on, the other is switched off and which gene is active depends on the diet of the yeast cells.

On the antisense DNA strand opposite to HMS2 is another gene, SUT650. Nguyen et al. show that when this gene is transcribed, the transcription of HMS2 on the other DNA strand is blocked. This has the knock-on effect of turning on BAT2. Conversely, transcribing HMS2 switches off SUT650 and BAT2 because the end of HMS2 overlaps with the beginning of both SUT650 and BAT2. Switching between different genes relies on loops that physically link the start and stop signals of the gene to be transcribed while ignoring the start and stop signals for neighbouring genes.

Proteins called transcription factors can bind to DNA and affect whether a gene is transcribed. Nguyen et al. found that a transcription factor that binds near the start of the HMS2 gene helps to control which DNA strand is transcribed. When transcription factors do not bind to the start of HMS2, antisense transcription—and the expression of SUT650—occurs instead.

Overall, Nguyen et al. show that the transcription process itself makes up part of a switch that can control the expression of several genes on both the sense and antisense strands of a DNA double helix. This may also explain how many other, more complex, gene networks are activated in response to changes in the environment.

DOI:http://dx.doi.org/10.7554/eLife.03635.002

Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition

Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

Adaptation as a genome-wide autoregulatory principle in the stress response of yeast

The gene expression response of yeast to various types of stresses/perturbations shows a common functional and dynamical pattern for the vast majority of genes, characterised by a quick transient peak (affecting primarily short genes) followed by a return to the pre-stimulus level. Kinetically, this process of adaptation following the transient excursion can be modelled using a genome-wide autoregulatory mechanism by means of which yeast aims at maintaining a preferential concentration in its mRNA levels. The resulting feedback system explains well the different time constants observable in the transient response, while being in agreement with all the known experimental dynamical features. For example, it suggests that a very rapid transient can be induced also by a slowly varying concentration of the gene products.

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.