mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle
© Soranzo et al; licensee BioMed Central Ltd. 2009
Received: 11 July 2008
Accepted: 06 February 2009
Published: 06 February 2009
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.
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.
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.
Ultradian self-sustaining energy-metabolic oscillations arising spontaneously in high density Saccharomyces cerevisiae continuous cultures exposed to glucose-limited growth have been known and studied for decades [1, 2], and have more recently been observed to induce genome-wide periodic patterns in different series of microarray experiments [3, 4], although with widely different periodicities, ~40 min for  and ~300 min for .
Many studies aim at understanding the mechanisms inducing these sustained oscillations and the rigorous temporal compartmentalization they induce, see [5, 6] for surveys. Suggested causes range from a single critical pathway (like the feedback effect of cysteine on the sulfur assimilation pathway ) to the alternation of aerobic and anaerobic respiratory modes (as deduced by the fluctuations in the concentration of dissolved O2 and of other observed metabolites ), from the interaction with cell cycle [8, 9] to the mutual incompatibility of different redox biochemical processes [10, 11].
The scope of this work is to emphasize a different aspect, intrinsically dynamical and post-transcriptional, which is likely to play an important role in the coordination of the "slower" yeast metabolic cycle (YMC) of , namely mRNA stability. We will show that there is a roughly linear relationship between the average half life (HL) of the transcripts, clustered according to expression or function, and the phase at which their concentration peaks in the cycle. More generally, there seems to be a strong correlation between HL and the shape of the pulses of gene expression: genes with short HL have short and sharp (almost impulsive in the time scale considered) pulses, while genes with long HL have pulses that are not only delayed but also broader and with more gentle slopes.
In recent years, post-transcriptional control is being recognized as an important aspect of gene regulation, especially in eukaryotic DNA, which lacks operonal structure [12–15]. It can occur in many guises, through mRNA turnover [16–20], or through "RNA regulons"  i.e., groups of genes coordinately guided in the RNA processing, localization and protein synthesis by RNA-binding proteins (RBPs) [22, 23], or even through the mediation of a metabolic substrate (typically a nutrient [24–26] or an enzyme ). Our result confirms the importance of post-transcriptional control, and points at mRNA turnover as a regulatory mechanism at a genome-wide level. Its peculiarity consists in putting the time axis into the picture in an intrinsically dynamical way. Consequently, in order to be observed, it requires times series sampled at a sufficiently high frequency and dynamics in the right time window, a combination seldom occurring in current expression profiling datasets. So for example the correlation between HL and phase/shape of the oscillations cannot be observed in the much faster YMC of , where HL and the period are of comparable duration, hence the system has no time to decay before the arrival of the next wavefront.
In order to emphasize the dynamical aspects, we shall treat the YMC as the time response of a genome-wide dynamical system to a sequence of impulsive "inputs" of transcription activation. We will show that grouping genes in terms of progressively delayed and broadened responses to a sequence of "input pulses" of transcriptional activation allows to see in a remarkably fine detail the causal chain of events constituting the transcriptional program of the cell. The few ambiguities resulting from this classification can be interpreted in terms of some other annotation, typically compartmental localization.
In the following we shall proceed in two complementary ways: first the YMC time series are clustered in a completely unsupervised manner, only according to gene expression. The linear relationship between pulse phase (also pulse width) and HL then emerges in a straightforward way. Next, we consider families of genes whose products share some common annotation, for example genes on the same pathway or genes that are subunits of the same protein complex, and look at the type of time series they produce and at their "position" along the YMC.
Both approaches confirm that the YMC represents an organized cascade of events, in response to precisely equispaced bursts of transcriptional activation, with the temporal order reflecting the transcript turnover rate. Extrapolating from the specific YMC context, this cascade of events is observable to a good extent also in other gene expression time series (such as the response to a pulse of nutrient of , or the stress responses of ), suggesting it might reflect a prototypical dynamical mode of action of transcriptional response.
Results and discussion
Statistics for the 16 clusters for Fig. 1
RNA, rRNA, and tRNA processing and metabolism, ribosome biogenesis and assembly
RNA, rRNA and tRNA processing and metabolism, RNA helicase, ribosome assembly
RNA polymerase, translation initiation, regulation, and termination, nucleotide biosynthesis
transferase activity, DNA replication, cell cycle
glycine metabolism, nitrogen and sulfur metabolism, amino acid biosynthesis
retrotransposons, long term repeats
mitochondrial membrane organization and biogenesis, mitochondrial transport
mitochondrial ribosome, envelope, and membranes
cytoplasmic ribosomes, translation processes
ion/cation transmembrane transport, electron transport, oxidative phosphorylation
endopeptidase activity, protein catabolic process, proteasome, actin filament organization, glycolysis, gluconeogenesis
lipid and alcohol metabolic process, peroxisome
kinase activity, vacuolar transport, membrane organization and biogenesis
arginine biosynthesis, protein folding
hydrolase activity, fatty acid oxidation, cytokinesis
A detailed functional analysis
Using the ordering by phase of pathways and protein complexes (see Fig. S2 and S5 in Additional file 1), we can zoom on these categories in much more detail. The first phase of this cascade consists of the activation of the transcription machinery with the synchronous bursts of transcription of the three RNA polymerases (see Fig. S1 in Additional file 1) and of most of the RNA processing components, like the tRNA processing complexes (RNase P) and rRNA processing complexes (exosome, RNase MRP, SIK1, NOP1), with the nuclear splicing complexes following closely. While the mRNAs for the polymerases are highly coordinated, the same cannot be said for the basal transcription factors (TFs) required for their initiation. Overall only a few of these genes follow the bursting trend of the RNA polymerases, notably, among them, SPT15, which forms the TATA-binding protein and is also a component of the polymerase I core factor and of TFIIIB. Most other genes involved with these general TFs do not show any periodic pattern, and their mRNA concentrations never surpass very low levels.
From Fig. 2, the peak of mRNA concentrations associated with the category "translation" seems to be synchronous with the RNA processing burst. However, a more careful analysis reveals that this phase is an average of two "compartmentalized" activations of the translation machinery, having fairly different phases: while cytoplasmic translation follows almost simultaneously the RNA machinery, the mitochondrial translation activation has a phase lag of more than one sixth of the period. In terms of time delay, this amounts to approximately 50 min, see Fig. 3. More in detail, most of the mRNAs of ribosomal small and large subunits for both cytoplasmic and mitochondrial localizations are highly correlated within their complex (average Pearson correlation for both is around 0.8) and correlated with the translation complexes at the corresponding location. In particular, among the cytoplasmic translation complexes, the initiation factors eIF and the termination factors eRF are very coordinated and respond very fast, while of the three elongation factors only eEF2 and eEF3 are well-coordinated, whereas the larger complex eEF1 shows a less-defined response pattern, with only the subunit eEF1-β clearly expressed. Overall for the class of translation complexes the pattern of activation of the response reflects closely the corresponding HL distributions  (eIF and eRF have short HL, eEF has not). Notice that a simple comparison of the HLs of the cytoplasmic and mitochondrial ribosomal and translation machineries (both approximately 24 min) does not show the significant difference which can be seen on the time series profiles and which is instead revealed by the phase delay analysis. For cytoplasmic ribosomal biogenesis, a similar anomaly is encountered also in the stress/stimuli responses analyzed below. For mitochondria, the same type of pattern is verified also by other complexes, for example by both the translocases located in the outer and inner mitochondrial membranes (TOM and TIM) which are known to mediate the protein import into the mitochondria, see Fig. 3.
A neat organization can be seen also in the phase of the nucleotide and amino acid metabolism: while pyrimidine and purine synthesis, as well as e.g. the CTP synthase enzyme involved in pyrimidine biosynthesis, are synchronous with the burst of transcription, the peaks for most of the enzymes involved in amino acid pathways tend to be in phase with the activation of the translational machinery. Also the synthesis of aminoacyl-tRNAs, necessary for the delivery of the amino acids to the ribosomes during translation has a similar phase. As expected, the "synthesis" pathway of an amino acid always anticipates its "degradation" pathway (see Fig. S2 in Additional file 1). In order to start translation, the initiator tRNA carrying methionine is required, and in fact, among the amino acid metabolic pathways, methionine is one of the fastest. As a matter of fact, the pathways of sulfur metabolism and of the sulfur-related amino acids (methionine, cysteine, as well as the closely related selenoamino acid metabolic pathway) present very similar and very compact time series (see Fig. S3 in Additional file 1), with an early (synchronous with the main burst) but long lasting activation (duration of the pulse is more than 100 min). This tight coordination may hint at a special role played by the sulfur pathways in the yeast population synchronization [31, 32].
To conclude the protein synthesis, the nascent polypeptide chains must fold into 3D structures. The molecular chaperonin-containing T-complex and the Gim complex, which help in the folding, behave synchronously with the main burst. On the contrary, ubiquitin and proteasome, that proceed to the recognition and degradation of anomalous proteins, as well as the SCF and anaphase promoting complexes, that cause the proteolysis of the cyclin-CDK complexes, have patterns of activation which are more delayed and broadened. Actually, this class of proteolytic processes (macrocategory "folding, sorting and degradation" in Fig. 2) has the highest values of phase i.e., it has the slowest response to the transcription bursts.
The macrocategory "DNA replication and repair" (see Fig. 2) contains what remains of the "fast" responses to a large extent synchronous (protein complexes: DNA damage checkpoint, DNA repair, pre-replication, replication, replication fork, which includes all DNA polymerases, helicases and ligases, cyclin-CDK) or within a short time delay from the initial bursts of transcription. The peculiarity of this class is that the pulses are more long lived than in the "transcription" and (cytoplasmic) "translation" categories. Also the complexes regulating the cohesion and separation of sister chromatids during the S-phase (nuclear cohesion family of complexes) follow the same pattern (see Fig. S5 in Additional file 1).
Moving to the core of the cell's metabolic activity, the average phase increases further (see Fig. 2), but the main qualitative difference is on the shape of the pulses, which are now broader and often with an asymmetric rise/decay profile: still sufficiently fast activation but slower and less abrupt decay. This difference is likely to reflect the longer HL associated to these categories (all have average HL ≥ 30 min), and implies metabolic functions more overlapping than sequential. Along each metabolic pathway, the degree of correlation among enzymes catalyzing neighboring reactions is higher than it is expected (the "expected value" is inferred from a large collection of yeast microarray experiments, see Fig. S4 in Additional file 1) implying a coherent and coordinated temporal behavior along the metabolic routes. Especially for mitochondrially localized pathways such as citric acid cycle and oxidative phosphorylation the pulses are very broad, with a neat downregulation only in correspondence of the bursts of transcription and an overall profile often exhibiting a double peak on each period (occurring with a phase lag of ~100° one from the other, see Fig. S8 in Additional file 1). The four respiratory chain complexes for example follow this pattern in a fairly precise manner. As shown in Additional file 1, this double peak characteristic is often associable with pairs of genes whose products are isoenzymes oscillating in antiphase, especially for enzymes involved in oxidoreductive processes (e.g. along the pentose phosphate pathway).
Regulation via TFs versus RBPs
• 44 genes out of 56 having Fhl1p as TF and 10 genes out of 12 having Sfp1p as TF are constituents of cytoplasmic ribosomes; notice that instead other cytoplasmic ribosomal TFs such as Rap1p do not correspond to a sufficiently high correlation;
• 22 genes out of 26 having Hap4p as TF code for subunits of respiration chain complexes;
• 62 out of 220 genes whose mRNA is bound by Puf3p are annotated for mitochondrial transcription/translation (56 are part of mitochondrial ribosomes, of which 47 are periodic), see Fig. 3.
Dynamical features of the unfolding cycle
Possible origins of the sustained oscillations are discussed at length in the literature [3, 5–8, 10, 11, 38]. Also Tu et al. explain the cycle and its time compartmentalization in terms of metabolism and redox balance [4, 32, 39]. Rather than adding to the list of mechanisms for metabolic regulation, by viewing each cycle as the dynamical response to a burst of transcriptional activation, this work aims at providing a characterization of the dynamics of the unfolding of the cycle, i.e., of how these "impulse responses" are progressively delayed and broadened with respect to the input pulses, and of how this correlates with the stability of the corresponding transcripts. The compactness in terms of phase and width of the early categories over repeated oscillatory cycles is an argument in favor of the existence of a single triggering event for each cycle, corresponding to the transcriptional activation bursts mentioned above. In fact, sharp, equispaced pulses are maintained in spite of the broader and less coordinated profiles of the events immediately preceding them. This hypothesis is not in contradiction with the observations about the metabolic origin of the YMC, neither with the observed alterations of the period following a genetic disruption [8, 32, 39] (which could in principle preserve the sequence of events described). On the contrary, it merges the metabolic control level described in  with an extra regulatory element which is known to play a role in dynamical contexts. In fact, the mRNA stability reflects known properties of the corresponding gene products: while mRNAs encoding transcriptional machinery or regulatory components tend to be short-lived and to turn over more quickly, transcripts encoding core enzymatic proteins are typically more stable [15, 19, 20]. For what is known, protein synthesis tends to follow the concentration of the corresponding mRNA  and to be at least as stable, if not longer-lived [41, 42]. Hence, it is expected that the concentration of the gene products follows profiles that are similar to those of the mRNAs. The observation that the dynamics through a metabolic pathway can be considered as a timed and sequential process at the level of gene expression appears in several papers in the literature, see [43, 44]. The same principle seems to be reflected in the YMC, although it is not observable at the level of detail investigated e.g. in , but more macroscopically and at genome-wide level.
An input-output dynamical model
A common dynamical gene expression program
As the YMC is obtained only in particular conditions (long-term continuous cultures in chemostats), an intriguing question is whether this highly organized unfolding of the dynamical response to pulses of transcriptional activation is peculiar only of the YMC or can be observed also in other experimental conditions. For this purpose, we consider the gene expression response of steady-state yeast to pulses of glucose described in . In this case, the yeast shows a transient dynamical response but no oscillatory behavior. Furthermore, the transient peaks are more or less synchronous for all genes, i.e., there is no time-ordering in the dynamics, unlike in the YMC.
The conclusion of this analysis is therefore that in intrinsically dynamical contexts some form of common response might indeed be taking place, although exerted by different means. Such genome-wide coordinated response shows a graded ordering which reflects the degree of stability of the genes involved.
In [4, 39] the time compartmentalization of the cycle is interpreted in terms of the need to accumulate sufficient products from the metabolic reactions in order to move on to the next phase of the cycle and to autoinduce further cycles of oscillations. This picture is not contradicted by our observations.
If, as we do in this paper, rather than looking at the YMC merely as cyclic oscillations, we study it as a highly organized dynamical response to pulses of transcriptional activation, then this response can be analyzed in much more detail at genome-wide level and we can observe how an important role in the coordination seems to be played by the mRNA turnover rate. The self-sustained character of what we consider the most upstream event of the cycle, the transcriptional activation burst, can still be conditioned to the accumulation of the required metabolites, while the unfolding of the cycle, which from the analysis of  is already known to be functional to the distribution of e.g. the redox load of the cells, is enriched of an extra, intrinsically dynamical feature. This feature is a fine-graded detail of our notion that genes with a fast turnover are typically regulatory, while slow genes are enzymatic and metabolic [15, 19]. It can be used to describe the sequence of events occurring in the YMC as a "natural" gene expression program.
Extrapolating from the specific YMC context, the ordered pattern of events described for the YMC is to a good extent similar to that found on other intrinsically dynamical contexts such as the stress/stimuli responses. Whether the mRNA stability is the cause of this coherent behavior or is simply another effect of a more profound regulatory mechanism is a question to which we cannot provide a definitive answer at the moment.
The YMC time series of , the compendium of 790 gene profile experiments (all performed with the Affymetrix GeneChip Yeast Genome S98 platform) and the data series from  were downloaded from Gene Expression Omnibus . The time series of  are performed with cDNA, hence values of the area under the profiles are intended as relative (to the basal mRNA abundance). For each gene, the values obtained for the two different glucose stimuli are averaged. Five stress responses from  (two heat shocks of different amplitude, hydrogen peroxide, diamide, and sorbitol responses) are considered. The amplitudes are averaged over the five data series (the signs of these responses are known to be highly similar, see ).
The metabolic pathways used are those of the Kyoto Encyclopedia of Genes and Genomes (KEGG) . Also the assembling into the 15 macrocategories follows the KEGG hierarchy.
The HLs are computed averaging the values of the three experimental datasets [17, 18, 20]. While the magnitudes of the HLs in the three collections show some differences, in "normalized" terms (looking e.g. at rank-ordered values), the agreement between the three sets is sufficiently good, see  for a comparison. No turnover data specific for long-term continuous cultures are currently available. However, it is not unlikely that even in this setting the relative differences of HL rates (and also their ordering) remains more or less unchanged. In any case, we expect the correlation phase/HL to improve in presence of more tailored mRNA turnover data.
Time series analysis
To each of the genes labeled as periodic, we associated a phase, computed maximizing the correlation with respect to a train of 360 shifted sinusoids (resolution of 1°). The 0 phase was chosen so as to anticipate of ~30° the "crucial" transcription bursts [see Additional file 1]. Given that the period is approximately 287.5 minutes (see Fig. S1 in Additional file 1), the phase delay ϕ can be transformed into time delay τ by means of the relation . Under the convention for the 0 phase, each period "begins" approximately 24 min before the transcription bursts. For each gene, the pulse width is computed estimating on each period the interval in which the expression level stays above the median value across consecutive samples.
Least squares regressions
A minimal dynamical model: low-pass transfer functions and their dynamical system realizations
The aim of this Section is to set up a minimal dynamical model describing the response to the periodic bursts of transcriptional activation represented as "impulsive inputs" to the system. Such a model has to be able to reproduce the following features observable in the dataset:
• impulse responses get delayed and broadened in a way which is roughly proportional to HL;
• profile changes get progressively less steep with HL;
• the system "discharges" completely (i.e. the mRNA concentrations return to a basal level) in absence of further pulses.
At the same time, to be internally consistent a dynamical model has to:
• respect causality (i.e., be non-anticipating);
• preserve positivity of the mRNA concentrations.
In the Engineering practice of Systems Theory, one of the most elementary formalism that can be used to build dynamical models is the input-output design based on Laplace transform and elementary transfer functions , see e.g.  for an application to a transcriptional time series.
In this case both d1 and d2 contribute to forming the degradation profile of the mRNA concentration y2(t). Likewise both dynamical variables x1 and x2 contribute to shape the pulse of a gene. Typically this model induces a steeper upregulation and a slower degradation front, coherently with what we observe on the YMC time series. The intermediate variables x i are only meant to describe the complexity of the input-output relationship. Qualitatively, they might reflect intermediate steps in the gene expression program. For example, the transcription of the genes of the central metabolism is activated downstream of the genes for translation and amino acid synthesis, which in their turn follow the principal bursts of transcription machinery (polymerases and other RNA processing components). Downstream activation of the genes of a category translates in this modeling framework into delayed and broadened pulses. Typical output responses for 1, 2, 3, and 4 such concatenated blocks are shown in Fig. 5(b).
A simple parameter search can be set up to identify values of n i , d i and T i , i = 1,...,4, that guarantee for each gene a sufficiently well-reproduced time course. The best transfer function order for each gene is identified as that maximizing the correlation between true and model-based time series.
HL and the short-period YMC of 
The HL of a gene is defined as the time needed to halve the concentration of mRNA in absence of new transcription. Hence in order for a "full" degradation of mRNA to be observed, the interval between two consecutive waves of transcription has to be at least twice or three times the HL. For yeast, the mean HL extrapolated from [17, 18, 20] is ~26 ± 17 min. Hence for the long-period YMC the response to bursts of transcription has the time to exhaust completely before the arrival of the next wavefront. On the contrary, for the short-period YMC described in  the period is approximately 40 min, meaning that excitation and degradation fronts are substantially overlapping.
Kyoto Encyclopedia of Genes and Genomes
yeast metabolic cycle.
- Parulekar SJ, Semones GB, Rolf MJ, Lievense JC, Lim HC: Induction and elimination of oscillations in continuous cultures of Saccharomyces cerevisiae. Biotechnol and Bioeng. 1986, 28 (5): 700-710. 10.1002/bit.260280509View ArticleGoogle Scholar
- Porro D, Martegani E, Ranzi BM, Alberghina L: Oscillations in continuous cultures of budding yeast: A segregated parameter analysis. Biotechnol Bioeng. 1988, 32 (4): 411-417. 10.1002/bit.260320402View ArticlePubMedGoogle Scholar
- Klevecz RR, Bolen J, Forrest G, Murray DB: A genomewide oscillation in transcription gates DNA replication and cell cycle. Proc Natl Acad Sci USA. 2004, 101 (5): 1200-1205. 10.1073/pnas.0306490101PubMed CentralView ArticlePubMedGoogle Scholar
- Tu BP, Kudlicki A, Rowicka M, McKnight SL: Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes. Science. 2005, 310 (5751): 1152-1158. 10.1126/science.1120499View ArticlePubMedGoogle Scholar
- Patnaik PR: Oscillatory metabolism of Saccharomyces cerevisiae: an overview of mechanisms and models. Biotechnol Adv. 2003, 21 (3): 183-192. 10.1016/S0734-9750(03)00022-3View ArticlePubMedGoogle Scholar
- Reinke H, Gatfield D: Genome-wide oscillation of transcription in yeast. Trends Biochem Sci. 2006, 31 (4): 189-191. 10.1016/j.tibs.2006.02.001View ArticlePubMedGoogle Scholar
- Wolf J, Sohn HY, Heinrich R, Kuriyama H: Mathematical analysis of a mechanism for autonomous metabolic oscillations in continuous culture of Saccharomyces cerevisiae. FEBS Lett. 2001, 499 (3): 230-234. 10.1016/S0014-5793(01)02562-5View ArticlePubMedGoogle Scholar
- Chen Z, Odstrcil EA, Tu BP, McKnight SL: Restriction of DNA Replication to the Reductive Phase of the Metabolic Cycle Protects Genome Integrity. Science. 2007, 316 (5833): 1916-1919. 10.1126/science.1140958View ArticlePubMedGoogle Scholar
- Futcher B: Metabolic cycle, cell cycle, and the finishing kick to Start. Genome Biol. 2006, 7 (4): 107- 10.1186/gb-2006-7-4-107PubMed CentralView ArticlePubMedGoogle Scholar
- Lloyd D, Murray DB: Redox rhythmicity: clocks at the core of temporal coherence. Bioessays. 2007, 29 (5): 465-473. 10.1002/bies.20575View ArticlePubMedGoogle Scholar
- Xu Z, Tsurugi K: A potential mechanism of energy-metabolism oscillation in an aerobic chemostat culture of the yeast Saccharomyces cerevisiae. FEBS J. 2006, 273 (8): 1696-1709. 10.1111/j.1742-4658.2006.05201.xView ArticlePubMedGoogle Scholar
- Beyer A, Hollunder J, Nasheuer HP, Wilhelm T: Post-transcriptional Expression Regulation in the Yeast Saccharomyces cerevisiae on a Genomic Scale. Mol Cell Proteomics. 2004, 3 (11): 1083-1092. 10.1074/mcp.M400099-MCP200View ArticlePubMedGoogle Scholar
- Brockmann R, Beyer A, Heinisch JJ, Wilhelm T: Posttranscriptional Expression Regulation: What Determines Translation Rates?. PLoS Comput Biol. 2007, 3 (3): e57- 10.1371/journal.pcbi.0030057PubMed CentralView ArticlePubMedGoogle Scholar
- García-Martínez J, González-Candelas F, Pérez-Ortín JE: Common gene expression strategies revealed by genome-wide analysis in yeast. Genome Biol. 2007, 8 (10): R222- 10.1186/gb-2007-8-10-r222PubMed CentralView ArticlePubMedGoogle Scholar
- Mata J, Marguerat S, Bähler J: Post-transcriptional control of gene expression: a genome-wide perspective. Trends Biochem Sci. 2005, 30 (9): 506-514. 10.1016/j.tibs.2005.07.005View ArticlePubMedGoogle Scholar
- Cheadle C, Fan J, Cho-Chung YS, Werner T, Ray J, Do L, Gorospe M, Becker KG: Stability Regulation of mRNA and the Control of Gene Expression. Ann N Y Acad Sci. 2005, 1058: 196-204. 10.1196/annals.1359.026View ArticlePubMedGoogle Scholar
- Grigull J, Mnaimneh S, Pootoolal J, Robinson MD, Hughes TR: Genome-Wide Analysis of mRNA Stability Using Transcription Inhibitors and Microarrays Reveals Posttranscriptional Control of Ribosome Biogenesis Factors. Mol Cell Biol. 2004, 24 (12): 5534-5547. 10.1128/MCB.24.12.5534-5547.2004PubMed CentralView ArticlePubMedGoogle Scholar
- Kuai L, Das B, Sherman F: A nuclear degradation pathway controls the abundance of normal mRNAs in Saccharomyces cerevisiae. Proc Natl Acad Sci USA. 2005, 102 (39): 13962-13967. 10.1073/pnas.0506518102PubMed CentralView ArticlePubMedGoogle Scholar
- Wilusz CJ, Wilusz J: Bringing the role of mRNA decay in the control of gene expression into focus. Trends Genet. 2004, 20 (10): 491-497. 10.1016/j.tig.2004.07.011View ArticlePubMedGoogle Scholar
- Wang Y, Liu CL, Storey JD, Tibshirani RJ, Herschlag D, Brown PO: Precision and functional specificity in mRNA decay. Proc Natl Acad Sci USA. 2002, 99 (9): 5860-5865. 10.1073/pnas.092538799PubMed CentralView ArticlePubMedGoogle Scholar
- Keene JD: RNA regulons: coordination of post-transcriptional events. Nat Rev Genet. 2007, 8 (7): 533-543. 10.1038/nrg2111View ArticlePubMedGoogle Scholar
- Shalgi R, Lapidot M, Shamir R, Pilpel Y: A catalog of stability-associated sequence elements in 3' UTRs of yeast mRNAs. Genome Biol. 2005, 6 (10): R86- 10.1186/gb-2005-6-10-r86PubMed CentralView ArticlePubMedGoogle Scholar
- Gerber AP, Herschlag D, Brown PO: Extensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast. PLoS Biol. 2004, 2 (3): 342-354. 10.1371/journal.pbio.0020079View ArticleGoogle Scholar
- Vallari RC, Cook WJ, Audino DC, Morgan MJ, Jensen DE, Laudano AP, Denis CL: Glucose Repression of the Yeast ADH2 Gene Occurs through Multiple Mechanisms, Including Control of the Protein Synthesis of Its Transcriptional Activator, ADR1. Mol Cell Biol. 1992, 12 (4): 1663-1673.PubMed CentralView ArticlePubMedGoogle Scholar
- Cereghino GP, Scheffler IE: Genetic analysis of glucose regulation in Saccharomyces cerevisiae: control of transcription versus mRNA turnover. EMBO J. 1996, 15 (2): 363-374.PubMed CentralPubMedGoogle Scholar
- Kim JH, Brachet V, Moriya H, Johnston M: Integration of Transcriptional and Posttranslational Regulation in a Glucose Signal Transduction Pathway in Saccharomyces cerevisiae. Eukaryotic Cell. 2006, 5: 167-173. 10.1128/EC.5.1.167-173.2006PubMed CentralView ArticlePubMedGoogle Scholar
- Hall DA, Zhu H, Zhu X, Gerstein M, Snyder M: Regulation of Gene Expression by a Metabolic Enzyme. Science. 2004, 306 (5695): 482-484. 10.1126/science.1096773View ArticlePubMedGoogle Scholar
- Ronen M, Botstein D: Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source. Proc Natl Acad Sci USA. 2006, 103 (2): 389-394. 10.1073/pnas.0509978103PubMed CentralView ArticlePubMedGoogle Scholar
- Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes. Mol Biol Cell. 2000, 11 (12): 4241-4257.PubMed CentralView ArticlePubMedGoogle Scholar
- Yin Z, Wilson S, Hauser NC, Tournu H, Hoheisel JD, Brown AJP: Glucose triggers different global responses in yeast, depending on the strength of the signal, and transiently stabilizes ribosomal protein mRNAs. Mol Microbiol. 2003, 48 (3): 713-724. 10.1046/j.1365-2958.2003.03478.xView ArticlePubMedGoogle Scholar
- Sohn HY, Murray DB, Kuriyama H: Ultradian oscillation of Saccharomyces cerevisiae during aerobic continuous culture: hydrogen sulphide mediates population synchrony. Yeast. 2000, 16 (13): 1185-1190. 10.1002/1097-0061(20000930)16:13<1185::AID-YEA619>3.0.CO;2-WView ArticlePubMedGoogle Scholar
- Tu BP, Mohler RE, Liu JC, Dombek KM, Young ET, Synovec RE, McKnight SL: Cyclic changes in metabolic state during the life of a yeast cell. Proc Natl Acad Sci USA. 2007, 104 (43): 16886-16891. 10.1073/pnas.0708365104PubMed CentralView ArticlePubMedGoogle Scholar
- Hieronymus H, Silver PA: A systems view of mRNP biology. Genes Dev. 2004, 18 (23): 2845-2860. 10.1101/gad.1256904View ArticlePubMedGoogle Scholar
- Hieronymus H, Silver PA: Genome-wide analysis of RNA-protein interactions illustrates specificity of the mRNA export machinery. Nature Genetics. 2003, 33: 155-161. 10.1038/ng1080View ArticlePubMedGoogle Scholar
- Palumbo MC, Farina L, De Santis A, Giuliani A, Colosimo A, Morelli G, Ruberti I: Collective behavior in gene regulation: Post-transcriptional regulation and the temporal compartmentalization of cellular cycles. FEBS J. 2007, 275 (10): 2364-2371. 10.1111/j.1742-4658.2008.06398.xView ArticleGoogle Scholar
- MacIsaac KD, Wang T, Gordon DB, Gifford DK, Stormo GD, Fraenkel E: An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics. 2006, 7: 113- 10.1186/1471-2105-7-113PubMed CentralView ArticlePubMedGoogle Scholar
- Balaji S, Babu MM, Iyer LM, Luscombe NM, Aravind L: Comprehensive Analysis of Combinatorial Regulation using the Transcriptional Regulatory Network of Yeast. J Mol Biol. 2006, 360: 213-227. 10.1016/j.jmb.2006.04.029View ArticlePubMedGoogle Scholar
- Xu Z, Tsurugi K: Role of Gts1p in regulation of energy-metabolism oscillation in continuous cultures of the yeast Saccharomyces cerevisiae. Yeast. 2007, 24 (3): 161-170. 10.1002/yea.1468View ArticlePubMedGoogle Scholar
- Tu BP, McKnight SL: Metabolic cycles as an underlying basis of biological oscillations. Nat Rev Mol Cell Biol. 2006, 7 (9): 696-701. 10.1038/nrm1980View ArticlePubMedGoogle Scholar
- Preiss T, Baron-Benhamou J, Ansorge W, Hentze MW: Homodirectional changes in transcriptome composition and mRNA translation induced by rapamycin and heat shock. Nat Struct Biol. 2003, 10 (12): 1039-1047. 10.1038/nsb1015View ArticlePubMedGoogle Scholar
- Belle A, Tanay A, Bitincka L, Shamir R, O'Shea EK: Quantification of protein half-lives in the budding yeast proteome. Proc Natl Acad Sci USA. 2006, 103 (35): 13004-13009. 10.1073/pnas.0605420103PubMed CentralView ArticlePubMedGoogle Scholar
- Hargrove Jl, Schmidt FH: The role of mRNA and protein stability in gene expression. FASEB J. 1989, 3 (12): 2360-2370.PubMedGoogle Scholar
- Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, Surette MG, Alon U: Just-in-time transcription program in metabolic pathways. Nat Genet. 2004, 36 (5): 486-491. 10.1038/ng1348View ArticlePubMedGoogle Scholar
- Chechik G, Oh E, Rando O, Weissman J, Regev A, Koller D: Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nat Biotechnol. 2008, 26 (11): 1251-1259. 10.1038/nbt.1499PubMed CentralView ArticlePubMedGoogle Scholar
- Gene Expression Omnibus.http://www.ncbi.nlm.nih.gov/geo/
- Kyoto Encyclopedia of Genes and Genomes. http://www.genome.jp/kegg/
- Doyle JC, Francis BA, Tannenbaum AR: Feedback Control Theory. 1992, New York: Macmillan Publishing CoGoogle Scholar
- Becskei A, Boselli MG, van Oudenaarden A: Amplitude control of cell-cycle waves by nuclear import. Nat Cell Biol. 2004, 6 (5): 451-457. 10.1038/ncb1124View ArticlePubMedGoogle Scholar
- Anderson BDO, Deistler M, Farina L, Benvenuti L: Nonnegative realization of a linear system with nonnegative impulse response. IEEE Trans Circuits Syst I, Fundam Theory Appl. 1996, 43 (2): 134-142. 10.1109/81.486435.View ArticleGoogle Scholar
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