Limits...
The yin and yang of yeast transcription: elements of a global feedback system between metabolism and chromatin.

Machné R, Murray DB - PLoS ONE (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. raim@tbi.univie.ac.at

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.

Show MeSH

Related in: MedlinePlus

Clustered transcript time course profiles.0 and 0: overlaid time courses of summarized microarray fluorescence for each yeast gene, as the  of the mean-ratio (), for the 0.7 h [11] and 5 h [10] period datasets, respectively. The bottom two panels show cluster averages for consensus and background clusters. The top panel shows the time courses of the dissolved O2 trace (DOT) in the culture medium in percent of the saturated concentration. Cluster colors and sizes (number of genes in each cluster) are given in the legend in Figure 1C. For clarity of visualization the time course data was normalized to a reference set that was selected for significant lack of oscillation (see Text S1 for fundamental problems with normalization of these datasets). Individual time courses for each cluster are plotted in Figure S2. 1D: phase-phase plot comparing the phase-angles  of all transcripts in the two experiments. The phase angles were shifted such that cluster A phase angles are just above 0° in both datasets. Mapping back from frequency- to time-domain, we can locate the shifted phase angles of one cycle (0° and 360°) in the time series plot (vertical lines in Figures 1A and 1B), and use the same mapping in the top and right axes (in gray) of the phase-phase plot. The x- and y-extensions of each point scale with the transcript’s scaled amplitude  in the respective dataset, where the non-consensus clusters (lower case letters) have a smaller initial size. Dataset S1 provides raw summarized microarray intensities, and the clustering of all analyzed yeast genes.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3369881&req=5

pone-0037906-g001: Clustered transcript time course profiles.0 and 0: overlaid time courses of summarized microarray fluorescence for each yeast gene, as the of the mean-ratio (), for the 0.7 h [11] and 5 h [10] period datasets, respectively. The bottom two panels show cluster averages for consensus and background clusters. The top panel shows the time courses of the dissolved O2 trace (DOT) in the culture medium in percent of the saturated concentration. Cluster colors and sizes (number of genes in each cluster) are given in the legend in Figure 1C. For clarity of visualization the time course data was normalized to a reference set that was selected for significant lack of oscillation (see Text S1 for fundamental problems with normalization of these datasets). Individual time courses for each cluster are plotted in Figure S2. 1D: phase-phase plot comparing the phase-angles  of all transcripts in the two experiments. The phase angles were shifted such that cluster A phase angles are just above 0° in both datasets. Mapping back from frequency- to time-domain, we can locate the shifted phase angles of one cycle (0° and 360°) in the time series plot (vertical lines in Figures 1A and 1B), and use the same mapping in the top and right axes (in gray) of the phase-phase plot. The x- and y-extensions of each point scale with the transcript’s scaled amplitude  in the respective dataset, where the non-consensus clusters (lower case letters) have a smaller initial size. Dataset S1 provides raw summarized microarray intensities, and the clustering of all analyzed yeast genes.

Mentions: Here we compare two previously published microarray-based transcriptome time series from cultures oscillating with periods of 0.7 h [11] (Figure 1A) or 5 h [10] (Figure 1B). The two experiments were performed with different yeast strains (Saccharomyces cerevisiae IFO 0233 or CEN.PK122) and different media composition (20 or 10 g L−1 glucose and 13 or 6.5 mmol L−1 H2SO4; see Table S1). Phenelzine was added at the end of the first cycle of the 0.7 h system, inducing a period increase from 0.7 h to 1.2 h during the experiment [11]. The DFT of microarray time series has previously proven useful in identifying periodic changes in mRNA abundance [54], [55]. Here it allows for a direct comparison of the two transcriptome time series by a scatter-plot of the phase angles at the respective phenotypic oscillation periods (indicated by the dissolved O2 concentration in the culture medium). This phase-phase plot reveals at least three density peaks (Figure 1D and Text S1). To further characterize these co-expression cohorts, an apt model-based clustering algorithm flowClust [56] was used to cluster selected and scaled DFT components of all transcript time series. This clustering strategy is very similar to a previously used approach [57], [58] and naturally allows to cluster by the pattern of change of fluorescence levels, i.e., account for the time series nature of the datasets. Amplitude scaling and the tailed distribution model of the clustering algorithm are different from the previous work and serve to further de-emphasize the only semiquantitative amplitude information in favor of overall change patterns. Simultaneously, this strategy allows to avoid a problematic data normalization step, since the array-to-array noise can be expected in high-frequency components of the DFT. The Methods section gives all technical details of data processing and clustering, while in Text S1 we provide detailed accounts of normalization problems, selection of DFT components and the choice of the clustering algorithm.


The yin and yang of yeast transcription: elements of a global feedback system between metabolism and chromatin.

Machné R, Murray DB - PLoS ONE (2012)

Clustered transcript time course profiles.0 and 0: overlaid time courses of summarized microarray fluorescence for each yeast gene, as the  of the mean-ratio (), for the 0.7 h [11] and 5 h [10] period datasets, respectively. The bottom two panels show cluster averages for consensus and background clusters. The top panel shows the time courses of the dissolved O2 trace (DOT) in the culture medium in percent of the saturated concentration. Cluster colors and sizes (number of genes in each cluster) are given in the legend in Figure 1C. For clarity of visualization the time course data was normalized to a reference set that was selected for significant lack of oscillation (see Text S1 for fundamental problems with normalization of these datasets). Individual time courses for each cluster are plotted in Figure S2. 1D: phase-phase plot comparing the phase-angles  of all transcripts in the two experiments. The phase angles were shifted such that cluster A phase angles are just above 0° in both datasets. Mapping back from frequency- to time-domain, we can locate the shifted phase angles of one cycle (0° and 360°) in the time series plot (vertical lines in Figures 1A and 1B), and use the same mapping in the top and right axes (in gray) of the phase-phase plot. The x- and y-extensions of each point scale with the transcript’s scaled amplitude  in the respective dataset, where the non-consensus clusters (lower case letters) have a smaller initial size. Dataset S1 provides raw summarized microarray intensities, and the clustering of all analyzed yeast genes.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3369881&req=5

pone-0037906-g001: Clustered transcript time course profiles.0 and 0: overlaid time courses of summarized microarray fluorescence for each yeast gene, as the of the mean-ratio (), for the 0.7 h [11] and 5 h [10] period datasets, respectively. The bottom two panels show cluster averages for consensus and background clusters. The top panel shows the time courses of the dissolved O2 trace (DOT) in the culture medium in percent of the saturated concentration. Cluster colors and sizes (number of genes in each cluster) are given in the legend in Figure 1C. For clarity of visualization the time course data was normalized to a reference set that was selected for significant lack of oscillation (see Text S1 for fundamental problems with normalization of these datasets). Individual time courses for each cluster are plotted in Figure S2. 1D: phase-phase plot comparing the phase-angles  of all transcripts in the two experiments. The phase angles were shifted such that cluster A phase angles are just above 0° in both datasets. Mapping back from frequency- to time-domain, we can locate the shifted phase angles of one cycle (0° and 360°) in the time series plot (vertical lines in Figures 1A and 1B), and use the same mapping in the top and right axes (in gray) of the phase-phase plot. The x- and y-extensions of each point scale with the transcript’s scaled amplitude  in the respective dataset, where the non-consensus clusters (lower case letters) have a smaller initial size. Dataset S1 provides raw summarized microarray intensities, and the clustering of all analyzed yeast genes.
Mentions: Here we compare two previously published microarray-based transcriptome time series from cultures oscillating with periods of 0.7 h [11] (Figure 1A) or 5 h [10] (Figure 1B). The two experiments were performed with different yeast strains (Saccharomyces cerevisiae IFO 0233 or CEN.PK122) and different media composition (20 or 10 g L−1 glucose and 13 or 6.5 mmol L−1 H2SO4; see Table S1). Phenelzine was added at the end of the first cycle of the 0.7 h system, inducing a period increase from 0.7 h to 1.2 h during the experiment [11]. The DFT of microarray time series has previously proven useful in identifying periodic changes in mRNA abundance [54], [55]. Here it allows for a direct comparison of the two transcriptome time series by a scatter-plot of the phase angles at the respective phenotypic oscillation periods (indicated by the dissolved O2 concentration in the culture medium). This phase-phase plot reveals at least three density peaks (Figure 1D and Text S1). To further characterize these co-expression cohorts, an apt model-based clustering algorithm flowClust [56] was used to cluster selected and scaled DFT components of all transcript time series. This clustering strategy is very similar to a previously used approach [57], [58] and naturally allows to cluster by the pattern of change of fluorescence levels, i.e., account for the time series nature of the datasets. Amplitude scaling and the tailed distribution model of the clustering algorithm are different from the previous work and serve to further de-emphasize the only semiquantitative amplitude information in favor of overall change patterns. Simultaneously, this strategy allows to avoid a problematic data normalization step, since the array-to-array noise can be expected in high-frequency components of the DFT. The Methods section gives all technical details of data processing and clustering, while in Text S1 we provide detailed accounts of normalization problems, selection of DFT components and the choice of the clustering algorithm.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute for Theoretical Chemistry, University of Vienna, Vienna, Austria. raim@tbi.univie.ac.at

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.

Show MeSH
Related in: MedlinePlus