The Yin and Yang of Yeast Transcription: Elements of a Global Feedback System between Metabolism and Chromatin

Abstract

When grown in continuous culture, budding yeast cells tend to synchronize their respiratory activity to form a stable oscillation that percolates throughout cellular physiology and involves the majority of the protein-coding transcriptome. Oscillations in batch culture and at single cell level support the idea that these dynamics constitute a general growth principle. The precise molecular mechanisms and biological functions of the oscillation remain elusive. Fourier analysis of transcriptome time series datasets from two different oscillation periods (0.7 h and 5 h) reveals seven distinct co-expression clusters common to both systems (34% of all yeast ORF), which consolidate into two superclusters when correlated with a compilation of 1,327 unrelated transcriptome datasets. These superclusters encode for cell growth and anabolism during the phase of high, and mitochondrial growth, catabolism and stress response during the phase of low oxygen uptake. The promoters of each cluster are characterized by different nucleotide contents, promoter nucleosome configurations, and dependence on ATP-dependent nucleosome remodeling complexes. We show that the ATP:ADP ratio oscillates, compatible with alternating metabolic activity of the two superclusters and differential feedback on their transcription via activating (RSC) and repressive (Isw2) types of promoter structure remodeling. We propose a novel feedback mechanism, where the energetic state of the cell, reflected in the ATP:ADP ratio, gates the transcription of large, but functionally coherent groups of genes via differential effects of ATP-dependent nucleosome remodeling machineries. Besides providing a mechanistic hypothesis for the delayed negative feedback that results in the oscillatory phenotype, this mechanism may underpin the continuous adaptation of growth to environmental conditions.

How cyanobacteria pose new problems to old methods: challenges in microarray time series analysis

“…Such a diurnal variation of the total mRNA amount has not been reported along with genome-wide expression time series of other cyanobacterial species. However, we recently observed a similar trend in transcriptome time-series of budding yeast respiratory oscillations [24]. We provide plausible interpretations of this observation in the conclusion.…”

“…We also consider the discrete Fourier transformation (DFT) in the context of data transformation. The removal of the first DFT component results in a normalization by the expression mean in the , and an amplitude scaling serves to de-emphasize the amplitude [24], comparable to . Notably, only this transformation from time to frequency space considers explicitly the time series character of the data.…”

“…An increasing number of algorithms propose solutions to this issue [27-31], but there is no accepted standard. An interesting approach specifically designed to cluster periodic time series has recently been proposed for the analysis of respiratory oscillations in budding yeast culture [24]. Here, the DFT of the time-series was clustered with a model-based algorithm that uses t-distributions as a model (flowClust [32]).…”

“…This violation of a common assumption for normalization challenges the conclusions of a wide range of studies (Lovén2012). Furthermore, a work from Machné and Murray [24] compares two independent measurements of the oscillatory transcriptional changes of budding yeast under continuous culture conditions. Due to the global nature of the observed oscillations, an alternative normalization scheme was employed to avoid detrimental effects of standard methods.…”

Time resolved DNA occupancy dynamics during the respiratory oscillation uncover a global reset point in the yeast growth program

“…2) 19. Clusters A, AB and B are representative of the anabolic supercluster, whose sum of transcripts (∑[mRNA]; Fig.…”