M I N I R E V I E W
Collective behavior in gene regulation: Post-transcriptional regulation and the temporal compartmentalization of cellular cycles Maria C. Palumbo1, Lorenzo Farina2, Alberto De Santis2, Alessandro Giuliani3, Alfredo Colosimo1, Giorgio Morelli4 and Ida Ruberti5
1 Department of Physiology and Pharmacology, Sapienza University of Rome, Italy 2 Department of Computer and Systems Science ‘Antonio Ruberti’, Sapienza University of Rome, Italy 3 Department of Environment and Primary Prevention, Istituto Superiore di Sanita` (ISS), Rome, Italy 4 National Research Institute for Food and Human Nutrition, Rome, Italy 5 National Research Council, Institute of Molecular Biology and Pathology, Rome, Italy
Keywords metabolic cycle; oscillations; post- transcriptional regulation; RNA-binding proteins
Correspondence L. Farina, Dipartimento di Informatica e Sistemistica ‘Antonio Ruberti’, Via Ariosto 25, 00185 Rome, Italy Fax: +39 067 727 4088 Tel: +39 064 457 526 E-mail: lorenzo.farina@uniroma1.it
(Received 10 December 2007, revised 31 January 2008, accepted 26 February 2008)
doi:10.1111/j.1742-4658.2008.06398.x
Self-sustained oscillations are perhaps the most studied objects in science. The accomplishment of such a task reliably and accurately requires the presence of specific control mechanisms to face the presence of variable and largely unpredictable environmental stimuli and noise. Self-sustained oscillations of transcript abundance are, in fact, widespread and are not limited to the reproductive cycle but are also observed during circadian rhythms, metabolic cycles, developmental cycles and so on. To date, much of the literature has focused on the transcriptional machinery underlying control of the basic timing of transcript abundance. However, mRNA abundance is known to be regulated at the post-transcriptional level also and the relative contribution of the two mechanisms to gene-expression programmes is currently a major challenge in molecular biology. Here, we review recent results showing the relevance of the post-transcriptional regu- lation layer and present a statistical reanalysis of the yeast metabolic cycle using publicly available gene-expression and RNA-binding data. Taken together, the recent theoretical and experimental developments reviewed and the results of our reanalysis strongly indicate that regulation of mRNA stability is a widespread, phase-specific and finely tuned mechanism for the multi-layer control of gene expression needed to achieve high flexibility and adaptability to external and internal signals.
presence of specific control mechanisms and strategies for actively sustaining the desired time profiles (i.e. a trajectory-tracking device) such as regular oscillations, in the presence of variable and largely unpredictable environmental stimuli and noise.
Abbreviations OR, odds ratio; OX, oxidative; RB, reductive biosynthetic; RC, reductive charge; TF, transcription factor.
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2364
Self-sustained temporal structured activities displaying a clear and robust periodic time behavior are perhaps the most studied objects in science. In fact, the dawn of modern science is generally assumed to coincide with the development of general laws able to explain and predict the regular orbits of planets. On more empirical ground, the accomplishment of a task with a high degree of accuracy and robustness requires the Oscillations of the molecular components of the cell have been observed since the early days of molecular cell biology in the course of the study of protein levels
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
stable and reliable trajectory over time. For example, in order to achieve fast, accurate and reliable vehicle dynamics, an integrated throttle and brake control sys- tem is usually required. This is the case, for example, of high-performance driving techniques, like heel-and- toe or left-foot-braking, which rely on simultaneous and ⁄ or alternate gas ⁄ brake pedal usage. Clearly, such a driving style is energy-consuming and is worth using only when precisely regulated levels are required in the face of sudden and unpredictable external events. In other words, fast, precise and robust behavior is very expensive both in terms of control strategies complex- ity and resource consumption.
during the reproductive cell cycle. In fact, a major breakthrough occurring in the late 1980s was the dis- covery that the eukaryotic cell cycle is driven by stage- specific distinct waves of activation of cyclin-dependent kinases [1]. However, the study of the transcriptome in biological cycles has revealed that oscillations are pres- ent at the mRNA level and that a large number of genes are regulated in a periodic manner. Self-sus- tained oscillation of transcript abundance is actually widespread [2] and not limited to the reproductive cycle [3], but is also observed during circadian rhythms [4], metabolic cycles [5], the developmental cycles of parasitic protozoa [6] and somitogenesis [7], just to cite a few. Moreover, oscillations are also observed in response to external stimuli, as reported for mamma- lian cells after serum shock [8,9]. the and
factors able to selectively degrade
To date, much of the literature has focused on the transcriptional machinery underlying control of the basic timing of transcript abundance [10]. Key cycle regulators and their modes of action have been identi- fied and networks of transcription factors (TFs) and their targets are currently available for many different cycles and organisms [11–13]. It is now widely accepted that periodic waves of transcription may be obtained by circular cascades of TFs [14] or by their combinato- rial regulatory action allowing for sequential activation [15]. However, such a transcriptional control network is just part of the story because mRNA abundance is regulated at many levels, including the post-transcrip- tional regulation layer and the relative contribution of the two mechanisms to gene-expression programmes is currently a major challenge for molecular biology.
When dealing with gene-expression data, the ‘throt- tle pedal’ may correspond to the TF system, allowing for specific DNA sequences to be made accessible to corresponding synthesizing polymerases mRNA, whereas the ‘brake pedal’ may be associated with the so-called degradosome system, made up of those specific mRNA molecules. Indeed, there is growing interest in the study of post-transcriptional regulation [16] espe- cially for the mechanisms leading to the modulation of mRNA turnover in response to environmental changes [19,21]. Such a dual mechanism for mRNA in MAPK sig- upregulation is present, for example, naling where MAPK-signaling-mediated phosphoryla- tion activates a TF and ⁄ or a RNA-binding protein resulting in the upregulation of mRNA, via transcrip- tional activation or mRNA stabilization, respectively [22]. There is also growing evidence that mRNA decay regulation may play a fundamental role in cel- lular cycles. In fact, studies on the regulation of mRNA stability during the yeast reproductive cell cycle have been carried out on some specific tran- scripts, such as the histone mRNAs [23] or cyclin [24] and proved that post-transcriptional mRNAs is important in establishing their periodicity control [25]. Evidence of post-transcriptional regulation of the histone genes has also been shown in higher eukary- otes and mammalian cell types, including HeLa cells [26]. A fundamental role for mRNA degradation in the establishment and maintenance of oscillations has been reported in Drosophila [27], Arabidopsis [28] and mammalian [29,30] circadian rhythms and in mouse fibroblasts ultradian oscillations in response to serum [9].
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2365
Post-transcriptional regulation is due to different localization, mechanisms such as processing, export, decay and translation control but the recent discovery of micro-RNA has revitalized the study of the mRNA decay pathway and its regulation [16]. One key point is that the regulation of mRNA stability may act in concert with the transcriptional machinery [17] and significantly contribute to changes in gene-expression patterns in response to external stimuli. A question remains as to the extent that this type of regulation may occur under different biological scenarios like cel- lular cycles. In fact, the existence of such a regulatory layer may require re-evaluation of the common model of the control of gene expression which essentially invokes the turning on and off of gene transcription [18]. To address this issue on an experimental basis, a number of scientists have developed methods to simul- taneously measure gene expression and mRNA stabil- ity during metabolic shifts [19] and cellular cycles [20]. As a general rule, there is often the need to combine both positive and negative control actions to keep a On a genome-wide scale, a systematic study of the role exerted by the mRNA decay rate in cellular cycles is still lacking and it certainly is a topic worthy of fur- ther investigation. A recent experimental study reports a fundamental role for mRNA stability in global gene- expression regulation in the Plasmodium falciparuim
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
intraerytrocytic developmental cycle [20] and a recent computational analysis [31] showed a specific and active role for transcript stability regulation in the yeast reproductive cell cycle.
In the following section, we report a statistical reanalysis of the yeast metabolic cycle using gene expression data [5] and RNA-binding protein targets [32] aiming to show that the role of post-transcrip- tional regulation may be highly relevant for the regula- tion of transcripts oscillation on a global scale.
Post-transcriptional regulation in the yeast metabolic cycle
levels, ranging from gene Gene transcription has received the most attention for historical and technical reasons, but transcription is just the first stage in the process of gene expression. From splicing to polyadenylation, every aspect of a transcript’s life is subject to elaborate control and it is therefore no surprise that many cellular factors and mechanisms are devoted entirely to modulating the rate of mRNA degradation [16]. Consequently, it is of paramount importance that this gap is filled and evi- dence provided of the mode of action of the other ‘arm’ of gene regulation during gene expression tempo- ral programmes. Given the biological importance of the presence of links and coordinated action across multiple layers of control, understanding gene expres- sion requires an integrated view by combining data from different aspects of regulation [35]. Although this approach holds great promise, there are currently few studies that take into account regulation at multiple levels [35].
[5]
The PUF control system: a statistical re-analysis
in coordinating the global
In this section we investigate the role of the ‘PUF con- trol system’ [32], in the temporally compartmentalized regulation of gene expression during the yeast meta- bolic cycle [5]. Our integrative statistical reanalysis demonstrates a clear phase specificity of PUF-mediated regulation in the yeast metabolic cycle, allowing us to hypothesize post-transcriptional regulation as a key timing of gene player expression during specific phases of the metabolic cycle.
Following
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2366
The microarray gene-expression data relative to the Tu et al. study [5] were downloaded from http:// yeast.swmed.edu/cgi-bin/dload.cgi. the authors’ indication, we selected their sentinel genes as probes for the three phases in which the metabolic these genes were MRPL10 cycle was partitioned; [reductive biosynthetic (RB) phase], POX1 [reductive charge (RC) phase] and RPL17B [oxidative (OX) phase]. For each of the three sentinel genes we selected a cluster containing the 500 most correlated gene prod- ucts to obtain a data set of 1500 genes. Each cluster belongs to one of the three phases of the metabolic cycle. The role of PUF genes during metabolic-cycle regulation was assessed by means of standard statisti- cal tests, namely the correlation of PUF mRNA tem- poral variation with the centroid profile of the three clusters and the evaluation, by means of the odds ratio (OR) statistics, of the ‘enrichment’ of gene pairs shar- ing the same PUF within the same phase of the cycle. The binding specificity of PUF genes was assessed on Saccharomyces cerevisiae (yeast) metabolic cycles can be considered as very reliable temporal structures in which an extremely complex apparatus – the yeast cell – strives to maintain self-sustained cycles at different functional expression (recently assessed on a genome-wide scale by the McKnight group) to metabolic activity (oxygen consumption). The accuracy and robustness of such cycles, in terms of frequency and amplitude, is much greater than any other known cycle at the cellular level, namely much greater than the reproductive cell cycle. For this reason, gene-expression dynamics of the yeast metabolic cycle is a natural candidate for study- ing the ‘basic principles’ of gene expression regulation. Recently, five members of the RNA-binding proteins PUF family (PUF1–PUF5), or PUMILIO-Fem3-bind- ing proteins, have been studied for sequence specificity on a genome-wide scale [32], thus providing insight into one of the most important mechanisms of post- transcriptional regulation. The numbers of identified targets are 40 for PUF1, 146 for PUF2 and around 200 for PUF3, PUF4 and PUF5, indicating that the expression of a large number of genes can, in principle, be modulated by specific post-transcriptional events. PUF proteins, as mRNA-specific regulators of dead- enylation, have been conserved throughout eukaryotes suggesting that they are likely to play a prominent role in the control of transcript-specific rates of deadenyla- tion in yeast by interacting with the mRNA turnover machinery [33]. Recent integrated analysis by De Lich- tenberg et al. [34] on yeast cell reproductive cycle data, did not show any relevant correlation between PUF family genes and the cell cycle. In fact, none of the PUF1–5 genes is present in their extended list of 1159 reproductive cell-cycle-regulated genes. Obviously, we do not exclude the possibility of a specific role for post-trascriptional regulation on a global scale, but no conclusions can be drawn basing upon current avail- able data on PUF family targets.
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
the basis of Gerber et al.’s study [32]. The PUF-based results were compared with similar analysis based on TFs. The binding specificity of TFs was based upon McIsaac et al. [11] by considering a stringent P-value for DNA binding of 0.001. As is apparent from Fig. 1B, there is clear similarity between PUF time profiles and cluster centroids dynamics. Basically, PUF1 and PUF2 covary with the reductive charge phase and PUF4 and PUF5 go together with the oxidative phase, whereas PUF3 covaries with the reductive biosynthetic phase.
cluster
Our statistical analysis provides empirical indication of temporal covariation between PUF family genes and the yeast metabolic cycle. In order to go into more depth, we need to discriminate between a pure episodi- cal and a potentially biological significant link, and therefore we move a step further by computing the Pearson’s correlation coefficient for all the gene pairs resulting from the three clusters corresponding to the three metabolic phases. By doing so, we obtained more than one million (1 124 250) distinct correlation coeffi- cients, that are distributed as in Fig. 2.
For each of the ‘sentinel genes’ of the three phases of the cycle, the 500 most correlated genes in terms of Pearson’s correlation coefficient along temporal profiles were selected so giving rise to three clusters. The temporal profile of each cluster is depicted in Fig. 1A in terms of centroid activation together with the standard deviation. Data are expressed as Z-scores, that is, each gene-expression value is de-meaned and divided by its standard devia- tion. Genes pertinent to each cluster are correlated with the sentinel gene with Pearson’s correlation coef- ficient R ranging from 0.82 to 1 (RB), 0.87 to 1 (RC) and 0.85 to 1 (OX), giving a reliable picture of the three phases in time. The data are averaged over the three available cycles. As shown in Fig. 2, there is a clear bimodal distribu- tion allowing for an unambiguous partition of the gene pairs into three classes:
Phase centroids profiles
A
3
OX RB RC
2
1
l
n o i t c u d n i d o f d e z
0
i l
a m r o N
–1
–2
50
100
200
250
0
150 Time (min)
PUFs mRNA profiles
B
3
2
n o
PUF1 PUF2 PUF3 PUF4 PUF5
i t c u d n
1
i
l
d o
f
d e z
0
i l
a m r o N
–1
–2
0
50
100
200
250
150 Time (min)
Fig. 1. Temporal profiles of the metabolic phases centroids (A) and of the five mem- bers of the PUF family (B) as obtained by the same experimental dataset. Data have been Z-normalized (zero mean, unit standard deviation).
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2367
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
3
Pearson correlation histogram
x 10
8
6
4
2
0 –1
–0.8
–0.6
–0.4
–0.2
0
0.2
0.4
0.6
0.8
1
Fig. 2. Pearson correlation histogram of all genes profiles pairs (1 124 250) obtained using the selected 1500 metabolic-cycle-regu- lated genes.
1 Positively correlated pairs (r > 0.6). 2 Linearly independent pairs (0.6 > r > )0.6). 3 Negatively correlated pairs (r < )0.6). tively correlated class, whereas pairs sharing the same TF are scattered along the distribution occupying all the three correlation classes. This points to the highest specificity of PUF-mediated post-transcriptional con- trol with respect to TF control. Clearly, we must take into account the fact that we do have the binding spec- ificity for many more TFs (118) than post-transcrip- tional regulators such as PUFs (5). In order to obtain an unbiased estimation of the different specificity of the two regulation systems, we computed the OR values for the TF and PUF commonality, respectively: OR (TF) = fraction of gene pairs having at least one common TF in the correlated subset with respect to all gene pairs having at least one common TF ⁄ frac- tion of pairs in the correlated subset w.r.t. all gene pairs.
OR (PUF) = fraction of gene pairs having at least one common PUF in the correlated subset w.r.t. all gene pairs having at least one common PUF ⁄ fraction of pairs in the correlated subset w.r.t. all gene pairs. This partition comes directly from the existence of three temporal clusters, so that gene pairs relative to the same cluster go into class 1, whereas genes in a pair coming from different clusters, go alternatively into class 2 and class 3, depending on the relative phase shift of the corresponding clusters.
A value equal to 1.37 for transcription factor and 2.11 for PUF was obtained, again pointing to a greater specificity of PUF control. This was confirmed by the OR values computed for the anticorrelated pairs that was equal to 0.87 for TF and 0.28 for PUF.
sharing at Such sharp distribution, together with the numerosity of the correlation coefficient data set allow us to consider two different filters on Fig. 2 to immediately highlight the role of post-transcriptional regulation compared with TF-based regulation. Consequently, from the initial population of Fig. 2 we selected only those pairs sharing at least one TF in common (Fig. 3A) and pairs least one PUF in common (Fig. 3B).
As it is evident by comparison of Fig. 3A,B, pairs with a common PUF are far more rich in the posi-
Transcriptionally co-regulated gene pairs
The regulation specificity of PUF family members is mainly driven by PUF3 having an OR equal to 2.49 as for the enrichment of correlated pairs and 0.04 as for the depletion in anticorrelated subsets. It is also worth noting that PUF2 it is mildly enriched for anticorrelat- ed pairs (OR = 1.64) thus suggesting a regulatory role between oxidative and reductive charge phases, and such role is shared with some TFs. An overall pictorial representation of the PUF network is provided by Fig. 4.
A 800
600
400
200
–0.4
–0.2
0
0.2
0.4
0.6
0.8
1
0 –1
–0.8
–0.6
Post-transcriptionally co-regulated gene pairs (PUF family proteins)
B 150
100
50
0 –1
–0.4
–0.2
0
0.4
0.8
1
–0.8
–0.6
0.2
0.6
Fig. 3. Pearson correlation histogram (A) of gene pairs sharing at least one transcription factor and (B) gene pairs sharing at least one mRNA binding protein of the PUF family (PUF1–5).
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2368
In Fig. 4 the red, blue and green circles correspond to genes of the different metabolic cycle phases; PUF family genes are represented by orange circles. The edges link each PUF with its targets. It is important to note the phase specificity of the PUFs together with the positioning of some PUFs between different meta- bolic cycle phases so that the possibility for post- trascriptional regulation working in the progression from one phase to another is further made clear. In particular, it is striking that PUF3 seems to be the main determinant of PUF family specificity in regula- tion of the yeast metabolic cycle. The reductive bio- synthetic phase specificity of PUF3 strongly suggests a role for this regulator in the progression from the reductive biosynthetic to reductive charge phase. This is consistent with recent findings demostrating that PUF3 acts as a transcript-specific regulatory role of mRNA degradation in yeast. More precisely, PUF3
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
RB
OX
PUF4
PUF3
PUF5
PUF1
RC
PUF2
Fig. 4. Representation of the PUF network. Colours correspond to different phases of the metabolic cycle: red (oxidative phase), green (reductive biosynthetic phase) and blue (reductive charge phase). Orange cir- cles denote PUF proteins and arrows point to their target genes. The picture makes very clear that the PUF3 gene and the most of PUF3 targets peak at the reductive biosynthetic phase thus showing the high specificity of the post-trascriptional regulation layer.
in yeast
(transcriptional and post-transcrip- the ‘two arms’ tional) seem to be linked in a very complex way. This appears consistent, on the one hand, with the results of Cheadle et al. [18] where alternate regulation of mRNA transcription and mRNA stability was observed during human jurkat T-cell activation. On the other hand, the observed behavior after a carbon source shift is simultaneous regulation of transcription and degradation because a decrease in transcriptional activity and an increase in messenger stability result in an almost flat mRNA abundance time profile [19]. has been shown to affect the stability of COX17 [33] and PET123 [36] transcripts. Taken together, these considerations suggest a role for PUF3 in the rapid downregulation of its targets at the end of the reduc- tive biosynthetic phase and in the upregulation at the beginning of the same phase which is also consistent with a recent experimental study supporting for a role of PUF3 in the reduction of mitochondrial biogenesis during glucose repression [17] downstream of the TOR signaling pathway [37]. Next, we looked at TFs that, according to McIsaac et al. [11], have PUF genes as targets and we obtained the results reported in Table 1.
Conclusions
Table 1. Transcription factors binding PUF family gene promoters according to MacIsaac et al. [11] using a stringent P-value for bind- ing of 0.001.
The results regarding the PUF3 gene are particu- larly intriguing: both ABF1 and SWI4 have an expression peak in the reductive biosynthetic phase but it seems that transcriptional and post-transcrip- tional regulation are not simply related, given that PUF3 has only 10% of shared targets with ABF1 and virtually no common target with SWI4 and ROX1. Moreover, ABF1 is a very general TF, thus
PUF family member
TFs
PUF1 (YJR091C) PUF2 (YPR042C) PUF3 (YLL013C) PUF4 (YGL014W) PUF5 (YGL178W)
FKH1, FKH2, NDD1, SWI6, MCM1, UME6 SFP1, FHL1, RAP1 ABF1, ROX1, SWI4 No data available PHO2, MBP1, SWI4, SWI6
The picture emerging from our integrative analysis of the yeast metabolic cycle is the presence of a very specific, finely wired, post-transcriptional PUF-medi- ated regulation. The interplay between transcriptional and post-transcriptional regulations observed during the metabolic cycle is consistent with the extreme such a self-sus- robustness and reproducibility of tained cycle. The two mechanisms are certainly coor- from our computational analysis we dinated, but could not deduce a simple relationship between the two.
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2369
This picture has also an intriguing interpretation in terms of ‘car driving’. In fact, it is well known that high performances can be achieved only by actively operating the throttle and the brake pedal together, where the obvious biological counterparts are mRNA synthesis and degradation, respectively. Despite the
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
cytic developmental cycle of Plasmodium falciparum. PLoS Biol 1, 85–100.
7 Serth K, Schuster-Gossler K, Cordes R & Gossler A (2003) Transcriptional oscillation of Lunatic fringe is essential for somitogenesis. Genes Devel 17, 912–925. 8 Balsalobre A, Damiola F & Schibler U (1998) A serum shock induces circadian gene expression in mammalian tissue culture cells. Cell 93, 929–937.
9 Yoshiura S, Ohtsuka T, Takenaka Y, Nagahara H, Yoshikawa K & Kageyama R (2007) Ultradian oscillations of Stat, Smad and Hes1 expression in response to serum. Proc Natl Acad Sci USA 104, 11292–11297.
10 Breeden LL (2003) Periodic transcription: a cycle within
a cycle. Curr Biol 13, 31–38.
11 MacIsaac KD, Wang T, Gordon DB, Gifford DK,
Stormo GD & Fraenkel E (2006) An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinformatics 7, 113–125.
fact that such a driving style is very expensive in terms of both control strategies complexity and resource con- sumption, it appears that, on specific occasions, the cell may prefer a nonoptimal behavior from an ener- getic point of view in order to accomplish other tasks efficiently. For example, it has been shown [38] that efficient re-entering into the cell-cycle from a nonpro- liferative state as terminal differentiation can be obtained by simply removing appropriate cyclin-depen- dant kinase inhibitors.
12 Ko CH & Takahashi JS (2006) Molecular components of the mammalian circadian clock. Hum Mol Genet 15, 271–277.
Taken together, the results presented in this compu- tational analysis performed using gene-expression time series from the yeast metabolic cycle and PUF family proteins binding data, indicate that the regulation of mRNA stability is a widespread, phase-specific and tightly regulated mechanism for the multilayer control of gene expression. Our analysis further supports the possibility that post-transcriptional regulation sur- passes the richness and complexity of transcriptional regulation in many, if not all, physiological and devel- opmental situations [35].
Acknowledgement
13 Murray DB, Beckmann M & Kitano H (2007) Regula- tion of yeast oscillatory dynamics. Proc Natl Acad Sci USA 104, 2241–2246.
in IR and GM would like to acknowledge support, part, by a grant from ASI, Biotechnology Program.
References
1 Evans T, Rosenthal ET, Youngblom J, Distel D &
Hunt T (1983) Cyclin: a protein specified by maternal mRNA in sea urchin eggs that is destroyed at each cleavage division. Cell 33, 389–396.
14 Simon I, Barnett J, Hannett N, Harbison C, Rinaldi N, Velkert T, Wyrick J, Zeitlinger J, Gifford D & Jaakkola T (2001) Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106, 697–708. 15 Kato M, Hata N, Banerjeee N, Futcher B & Zhang MQ (2004) Identifying combinatorial regulation of transcrip- tion factors and binding motifs. Genome Biol 5, R56. 16 Garneau NL, Wilusz J & Wilusz CJ (2007) The high- ways and byways of mRNA decay. Nat Rev Mol Cell Biol 8, 113–126.
2 Tsuchyia M, Wong ST, Yeo ZX, Colosimo A, Palumbo
17 Cereghino GP & Scheffler IE (1996) Genetic analysis of glucose regulation in Saccharomyces cerevisiae: control of transcription versus mRNA turnover. EMBO J 15, 363–374.
MC, Farina L, Crescenzi M, Mazzola A, Negri R, Bianchi MM et al. (2007) Gene expression waves – cell cycle independent collective dynamics in cutured cells. FEBS J 274, 2878–2886.
18 Cheadle C, Fan J, Cho-Chung YS, Werner T, Ray J,
Do L, Gorospe M & Becker KG (2005) Control of gene expression during T cell activation: alternate regulation of mRNA transcription and mRNA stability. BMC Genomics 6, 75–91.
3 Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D & Futcher B (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9, 3273–3297.
19 Garcia-Martinez J, Aranda A & Perez-Ortin JE (2004) Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. Mol Cell 15, 303–313.
20 Shock JL, Fischer KF & De Risi J (2007) Whole gen-
4 Ueda HR, Matsumoto A, Kawamura M, Iino M, Tanimura T & Hashimoto S (2002) Genome-wide transcriptional orchestration of circadian rhythms in Drosophila. J Biol Chem 277, 14048–14052.
ome analysis of mRNA decay in Plasmodium falciparum reveals a global lengthening of mRNA half-life during the intraerythrocytic development cycle. Genome Biol 8, R134.
5 Tu BP, Kudliki A, Rowicka M & McKnight L (2005) Logic of the yeast metabolic cycle: temporal compart- mentalization of cellular processes. Science 310, 1152– 1158.
21 Wang Y, Liu CL, Storey JD, Tibshirani RJ, Herschlag
D & Brown PO (2002) Precision and functional specific-
6 Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J & De Risi JL (2003) The transcriptome of the intraerytro-
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2370
M. C. Palumbo et al.
Regulation and compartmentalization of cell cycles
ity in mRNA decay. Proc Natl Acad Sci USA 99, 5860– 5865.
serotonin N-acetyltrasnferase mRNA degradation is essential for the maintenance of its circadian oscillation. Mol Cell Biol 25, 3232–3246.
31 Farina L, De Santis A, Morelli G & Ruberti I (2007)
Dynamic measure of gene co-regulation. IET Syst Biol 1, 10–17.
22 Sugiura R, Kita A & Kuno T (2004) Upregulation of mRNA in MAPK signaling. Cell Cycle 3, 286–288. 23 Lycan DE, Osley MA & Hereford LM (1987) Role of transcriptional and posttranscriptional regulation in expression of histone genes in Saccharomyces cerevisiae. Mol Cell Biol 7, 614–621.
24 Gill T, Cai T, Aulds J, Wierzbicki S & Schmitt E
32 Gerber AP, Hershlag D & Brown PO (2004) Extensive association of functionally and cytotopically related mRNAs with Puf family RNA-binding proteins in yeast. PLoS Biol 2, 342–354.
(2004) RNase MRP cleaves the CLB2 mRNA to pro- mote cell cycle progression: novel method of mRNA degradation. Mol Cell Biol 24, 945–953.
25 Xu H, Johnson L & Grunstein M (1990) Coding and
33 Olivas W & Parker R (2000) The Puf3 protein is a tran- script-specific regulator of mRNA degradation in yeast. EMBO J 19, 6602–6611.
noncoding sequences at the 3¢ end of yeast histone H2B mRNA confer cell cycle regulation. Mol Cell Biol 10, 2687–2694.
34 De Lichtenberg U, Jensen TS, Jensen LJ & Brunak S (2003) Protein feature based identification of cell cycle regulated proteins in yeast. J Mol Biol 329, 663– 674.
26 Morris TD, Weber LA, Hickey E, Stein GS & Stein JL (1991) Changes in the stability of a human H3 histone mRNA during the HeLa cell cycle. Mol Cell Biol 11, 544–553.
35 Mata J, Marguerat S & Bahler J (2005) Post- transcriptional control of gene expression: a genome-wide perspective. Trends Biochem Sci 30, 506–514.
36 Garcia-Rodriguez LJ, Gay AC & Pon LA (2007)
27 So WV & Rosbash M (1997) Post-transcriptional regu- lation contributes to Drosophila clock gene mRNA cycling. EMBO J 16, 7146–7155.
Puf3p a Pumilio family RNA binding protein, local- izes to mitochondria and regulates mitochondrial biogenesis and motility in budding yeast. J Cell Biol 176, 197–207.
28 Lidder P, Gutie´ rrez RA, Salome´ PA, Robertson McC- lung C & Green PJ (2005) Circadian control of messen- ger RNA stability. Association with a sequence-specific messenger RNA decay pathway. Plant Physiol 138, 2374–2385.
37 Foat BC, Houshmandi SS, Olivas WM & Bussemaker HJ (2005) Profiling condition-specific, genome-wide regulation of mRNA stability in yeast. Proc Natl Acad Sci USA 102, 17675–17680.
29 Kwak E, Kim TD & Kim KT (2006) Essential role of 3¢-untraslated region-mediated mRNA decay in circa- dian oscillations of mouse period3 mRNA. J Biol Chem 281, 19100–19106.
30 Kim TD, Kim JS, Kim JH, Myung J, Chae HD, Woo
KC, Jang SK, Koh DK & Kim KT (2005) Rhythmic
38 Pajalunga D, Mazzola A, Salzano AM, Biferi MG & De Luca G (2007) Critical requirement for cell cycle inhibitors in sustaining nonproliferative states. J Cell Biol 176, 807–818.
FEBS Journal 275 (2008) 2364–2371 ª 2008 The Authors Journal compilation ª 2008 FEBS
2371
