| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
|
First published online March 28, 2008; 10.1104/pp.108.118059 Plant Physiology 147:263-279 (2008) © 2008 American Society of Plant Biologists OPEN ACCESS ARTICLE
Disruption of the Arabidopsis Circadian Clock Is Responsible for Extensive Variation in the Cold-Responsive Transcriptome1,[C],[W],[OA]Max-Planck-Institut für Molekulare Pflanzenphysiologie, D–14424 Potsdam, Germany
In plants, low temperature causes massive transcriptional changes, many of which are presumed to be involved in the process of cold acclimation. Given the diversity of developmental and environmental factors between experiments, it is surprising that their influence on the identification of cold-responsive genes is largely unknown. A systematic investigation of genes responding to 1 d of cold treatment revealed that diurnal- and circadian-regulated genes are responsible for the majority of the substantial variation between experiments. This is contrary to the widespread assumption that these effects are eliminated using paired diurnal controls. To identify the molecular basis for this variation, we performed targeted expression analyses of diurnal and circadian time courses in Arabidopsis (Arabidopsis thaliana). We show that, after a short initial cold response, in diurnal conditions cold reduces the amplitude of cycles for clock components and dampens or disrupts the cycles of output genes, while in continuous light all cycles become arrhythmic. This means that genes identified as cold-responsive are dependent on the time of day the experiment was performed and that a control at normal temperature will not correct for this effect, as was postulated up to now. Time of day also affects the number and strength of expression changes for a large number of transcription factors, and this likely further contributes to experimental differences. This reveals that interactions between cold and diurnal regulation are major factors in shaping the cold-responsive transcriptome and thus will be an important consideration in future experiments to dissect transcriptional regulatory networks controlling cold acclimation. In addition, our data revealed differential effects of cold on circadian output genes and a unique regulation of an oscillator component, suggesting that cold treatment could also be an important tool to probe circadian and diurnal regulatory mechanisms.
Low temperature is a key signaling cue and a primary determinant of plant growth, development, and survival (Johanson et al., 2000  ; Bastow et al., 2004). Work to elucidate the molecular mechanisms of plant low-temperature responses has focused on Arabidopsis (Arabidopsis thaliana), which, like many important crop plants, is able to cold acclimate, the process by which temperate plants increase their freezing tolerance in response to low but nonfreezing temperatures (Thomashow, 1999). This complex adaptive trait is associated with massive molecular responses involving thousands of transcripts (Fowler and Thomashow, 2002; Kreps et al., 2002; Seki et al., 2002; Hannah et al., 2005) and hundreds of metabolites (Cook et al., 2004; Kaplan et al., 2004). Genetic approaches have defined some of the key regulators of cold acclimation and their regulation of some of the complex molecular responses. The best characterized of these are C-REPEAT BINDING FACTOR1 (CBF1), CBF2, and CBF3 (Gilmour et al., 1998), also known as DEHYDRATION RESPONSIVE ELEMENT BINDING1b (DREB1b), DREB1c, and DREB1a, respectively (Liu et al., 1998). Overexpression of CBFs induces cold-regulated (COR) genes, causes similar metabolic changes as low temperature exposure, and increases freezing tolerance (Gilmour et al., 2000). Recently, the use of natural variation has revealed that CBF2 likely contributes to the different acclimated freezing tolerance of ecotypes Cape Verde Islands and Landsberg erecta (Alonso-Blanco et al., 2005), and elevated CBF expression, concomitant with CBF-dependent molecular changes, is associated with high acclimated and nonacclimated freezing tolerance (Hannah et al., 2006). Positive (Chinnusamy et al., 2003) or negative (Lee et al., 2001) regulators of CBF and pathways independent of (Zhu et al., 2004; Xin et al., 2007) or overlapping with (Vogel et al., 2005) the CBF pathway have also been described. The concentration of cytosol-free calcium ([Ca2+]CYT) is rapidly increased by exposure to low temperature (Knight et al., 1991), and this correlates with the induction of the COR78 gene, also known as RD29A/LTI78 (Henriksson and Trewavas, 2003).
Circadian clocks are the internal molecular chronometers that most organisms use to measure time. These allow the anticipation of, and response to, the environmental changes that accompany the daily rotation of the earth. The clock controls many important processes, is responsible for generating circadian rhythms at both the molecular (Harmer et al., 2000
Despite our understanding of the circadian oscillator, life in a rotating world is not as simple as the sine waves it generates under constant environmental conditions. Plants grow under daily cycles of light and temperature that integrate with the circadian clock, resulting in complex diurnal molecular and physiological rhythms. A seminal demonstration of this integration was that CHLOROPHYLL A/B BINDING PROTEIN2 (CAB2) expression is induced by a light pulse during the subjective day but not during the night, a phenomenon known as "gating" (Millar and Kay, 1996
Therefore, it is reasonable to assume interactions between low temperature and the circadian clock and that understanding the response of plants to low temperature will require consideration of diurnal environmental changes. Indeed, there is evidence that some circadian-regulated genes are cold responsive (Kreps et al., 2002 Despite this emerging data on the reciprocal interactions between the circadian clock and cold signaling, understanding of how low temperature affects the circadian clock is lacking. Furthermore, whether circadian and diurnal regulation may influence the findings of previous efforts to elucidate cold response pathways is completely unknown. Here, we first use microarray expression data, both from public databases and our own experiments, to quantify the influence of circadian and diurnal regulation on the identification of cold-responsive genes. We then use targeted expression studies by quantitative reverse transcription (qRT)-PCR to demonstrate that these differences are largely due to the fact that under normal diurnal light-dark conditions, cold dampens the cycles of oscillator components and disrupts those of some circadian output genes, while in circadian conditions oscillator components also stopped cycling. We further demonstrate time-of-day dependence by showing stronger, more abundant induction of TFs in the morning than in the evening. These data also reveal differential effects of cold on circadian oscillator and output genes, thus providing novel insight into clock function and revealing a unique regulatory mechanism for the clock component LUX.
Diversity in the Identification of Cold-Responsive Genes Given the lack of a common standard for studying the cold responses of plants, it is generally accepted that developmental and environmental influences lead to differences between independent studies. However, the magnitude of these differences and the dominant causes of variation have not been systematically investigated. One obvious source of variation is the thousands of genes that are diurnally regulated. Most studies claim, and it is widely assumed, that diurnal or circadian effects are excluded by harvesting control plants at the same time of day as cold-treated plants or by using plants grown in continuous light. To test this assumption and to determine which factors have the greatest impact on the identification of cold-responsive genes between independent experiments, we assembled a large set of expression data from public databases and from our own experiments (Table I ). To limit the number of variables between experiments, all used a cold treatment of approximately 24 h, and control plants were always harvested at the same time of day as cold-treated plants. Other experimental factors, such as growth media, developmental stage, and light intensity and duration, were not standardized and showed considerable variation. With respect to diurnal regulation, three different light regimes were employed. First, plants growing under diurnal conditions were transferred to cold under continuous light. Second, plants were grown under continuous light during growth and cold treatment. Third, control and cold-treated plants were grown under diurnal conditions.
To minimize technical differences, we only considered Affymetrix ATH1 hybridizations and reanalyzed all data using the same procedure resulting in log2 differences of the cold treatment minus the control. To ensure the detection of experiment-specific responses, any gene that was detected in at least one experiment was retained. Although a generally consistent cold response was indicated by the highly significant correlation between all experiments (r = 0.47–0.81, Pearson correlation, P < 2.2 x 10–308 [minimum float in R]; Supplemental Table S1), this concealed massive underlying differences. As a simple measure of these differences, we counted the number of genes that were more than 2-fold changed in one experiment but were changed less than 2-fold in the other. The average pair-wise difference between experiments was around 50%, with a maximum of 71%, and often amounted to more than 3,000 genes (Supplemental Table S2). Given such large differences, it is important to understand which factors are mainly responsible.
To identify the factors responsible for this diversity (in the statistical sense of variance), we performed principal component analysis (PCA; Fig. 1
). PCA is an unsupervised method to separate samples based on the underlying coherent variation between them. The contribution of each gene to the separation by a given principal component (PC) is shown by its value in the "loadings" for that PC. Comparisons of the loadings for the first five PCs, together explaining more than 70% of the total variance between experiments, revealed a highly significant overlap (P = 7 x 10–41 to 1 x 10–122, Fisher's exact test) with diurnally regulated transcripts (Table II
). As circadian and sugar regulation make the most significant contributions to the diurnal regulation of gene expression (Blasing et al., 2005
Comparison of the experimental factors (Table I) allowed us to determine the most likely basis for each PC. The time-of-day effect underlying PC 1 was most likely an additive effect of the type and timing of the cold treatment. Experiments A and B used cold treatment in continuous light, and experiments A and i started the cold treatment shortly (2–3 h) after dawn; the most extreme experiment (A) shared both factors. Similarly, time-of-day factors were also most likely to contribute to the fourth PC, as the two most extreme experiments (h and k) both used a cold treatment that started in the middle of the light period (Table II). The diurnal regulation of genes contributing to PC 2 was more likely related to their regulation by sugar than by their circadian regulation alone (Table II; Supplemental Fig. S2). PC 2 mainly separated experiment C, but the described experimental conditions did not easily explain this. PC 3 and 5, which had lower overlap with diurnally regulated genes, were most likely based on differences in growth media and intraexperiment variation, respectively. For PC 3, there was a clear division between the soil-grown plants and those grown either on plates (B, C, and D) or in hydroponics (A), while PC 5 mostly separated replicates from experiment B. In summary, there are massive differences in cold-responsive genes between independent studies, and despite the widely held belief that diurnal effects are excluded by the use of paired controls, our meta-analysis revealed that diurnally regulated genes are the dominant source of variation between experiments. This seems to involve both direct time-of-day effects from circadian-regulated genes and indirect contributions from sugar-regulated genes.
Given that the massive diurnal effects on the identification of cold-responsive genes were not previously acknowledged, the underlying mechanism has not been investigated. Investigations with other species showed, for example, winter disruption of oscillator components in chestnut (Ramos et al., 2005
Among circadian-regulated genes, we monitored the expression of four standard circadian output marker genes, CCR1 and CCR2, CAB2, and CATALASE3 (CAT3), as well as the cold- and circadian-regulated CBF and COR genes (Harmer et al., 2000 ; Edwards et al., 2006). CBF1 to CBF3, COR78, COR47, and COR15a were all circadian and diurnally regulated at normal temperatures under our conditions (Figs. 3 and 4
). In contrast to the clear low-amplitude cycles of the core oscillator, no consistent cycles were observed at low temperature for any of the four standard output genes. Significantly, after their initial response, CBF1 to CBF3 clearly also cycled at low temperature under light-dark conditions, but similar to the standard clock output genes, the COR genes did not (Fig. 3). It was previously shown using northern-blot analysis that under similar cold conditions, CAB1 and CCR2 expression was reduced and elevated, respectively (Kreps and Simon, 1997). We found that the expression of CAB2 and CCR2 was similar to their maximum expression under diurnal conditions, while CAT3 was closer to its diurnal minimum (Fig. 3). CCR1 was initially induced, but on day 2 it declined toward its diurnal minimum, where expression was subsequently maintained. To eliminate the possible effects of the change in light intensity concomitant with our cold treatment, we repeated experiments growing the plants under both normal light (150 µmol m–2 s–1) and a light intensity identical to that during the cold treatment (90 µmol m–2 s–1). The results were highly similar under both conditions (Fig. 3; Supplemental Fig. S3). We then measured the same genes under continuous light to determine whether circadian function persisted. The experiments were repeated by transferring plants to continuous light at the middle of the day (Supplemental Fig. S4) or 2 h before dusk (Fig. 4). Without exception, the cycling of clock oscillator and output gene mRNA levels appeared to become arrhythmic. Although the expression after 2 d was similar and clearly arrhythmic, the timing of the loss of rhythmic expression was different depending on the time of day the plants were transferred to continuous light (Fig. 4; Supplemental Fig. S4). Arrhythmia seemed to occur more rapidly for most genes when transferred at zeitgeber time 8 (ZT8) rather than at ZT14. Generally, the first expression increase that occurred after the transfer to continuous conditions was also partly preserved in the cold. However, once genes reached their circadian maxima, or for genes that were at their maxima at transfer, rhythmic expression was more quickly lost. In other words, it seemed that transcript decline was inhibited; thus, similar to diurnal conditions, most genes clamped to high expression (Fig. 4; Supplemental Fig. S4). Interestingly, this was not the case for all genes, as under diurnal conditions the expression of CCR1 moved slowly and CAT3 moved rapidly toward minimum levels.
To summarize, we demonstrate that under diurnal conditions in the cold, clock oscillator components and some output genes dampened over time to low-amplitude, high-abundance cycles, while standard clock output genes stopped cycling. A unique situation was identified for the clock gene LUX, which continued high-amplitude cycles, albeit with advanced phase. In continuous light at 4°C, all genes eventually became arrhythmic, indicating that circadian function was disrupted.
One aspect of cold-circadian interactions that has been reported previously is the gating of the low-temperature induction of CBF1 to CBF3 by the circadian clock (Fowler et al., 2005
At the global level, we first selected TFs that were changed at least 4-fold relative to both the before-cold and paired controls in two independent experiments (data not shown). We then measured the resulting 69 up-regulated and 14 down-regulated candidates using two independent experiments with five biological replicates each. The two experiments used different light intensities to ensure the identification of robust cold-regulated TFs. Applying stringent criteria (t test, P < 0.05 and >4-fold change compared with both controls in both experiments), we confirmed 56 up-regulated and four down-regulated genes. The low overlap for repressed genes was predominantly caused by one or two outliers among the five samples pooled in the original screening (data not shown). Among the up-regulated genes, 48 and 27 met our criteria for being up-regulated after cold treatment at ZT2 and ZT14, respectively. These data show that, even using identical treatment conditions, 75% more TFs were identified as cold induced in the morning compared with the evening. The gating of relative cold induction is clearly visible for a large number of TFs (Fig. 5 , cold induction). In addition to relative induction, we investigated the gating of absolute cold-induced transcript abundance of these TFs. In common with the numbers of genes, the maximum transcript abundance for the majority (42) of these genes was achieved after cold induction at ZT2 (Fig. 5, Cm–Ce [cold morning to cold evening]). This is mostly due to increased cold induction rather than to differences in the initial transcript abundance. Indeed, where different, initial abundance tended to be higher at ZT14 than at ZT2 (Fig. 5, ZT2–ZT14). As we used diurnal conditions, we considered that the observed gating could be due to light-dependent cold induction (i.e. 3 h of light for the morning cold treatment versus 2 h of light/1 h of dark for the evening). However, an independent experiment investigating morning cold induction in either the light or dark indicated that the influence of such an effect was minimal (M.A. Hannah and L. Willmitzer, unpublished data). Interestingly, many other AP2/EREBP family TFs were also cold induced and gated in the same way as CBF1 to CBF3 and RAV1 (Fig. 5). To quantify this, we performed overrepresentation analysis on these morning-gated TFs (>2-fold absolute gating), which showed that members of the AP2/EREBP and C2C2(Zn) CO-like TF families were significantly overrepresented (Fisher's exact test, P = 7 x 10–8 and P = 2 x 10–4, respectively). These data confirmed the gating of the CBF1 to CBF3 and RAV1 TFs, and measurements on essentially all Arabidopsis TFs revealed that time of day influenced the cold induction of many TFs, particularly among AP2/EREBP and C2C2(Zn) CO-like family members. Around 75% more TFs were cold responsive in the morning than in the evening, and transcripts often reached higher levels during cold treatment in the morning.
Given these data, we predicted that circadian-regulated genes would have been identified as cold responsive in previous studies and that, as oscillations are dampened or stopped in the cold, genes that peak at different times of day (phase) should show coordinated up- or down-regulation by cold, leading to phase-dependent differences between experiments. This supervised analysis of the circadian phase of cold-responsive genes could also reveal patterns that were not evident in our unsupervised PCA. We performed these analyses using a published circadian time series (Edwards et al., 2006
As suggested by our PCA, there is a clear experiment-specific bias in the phase of cold-responsive genes. Experiments A and B, which used cold treatment in continuous light, have significant overrepresentation of cold up-regulated genes among those with phases ZT10 and ZT12, while those of ZT0 to ZT6 were significantly down-regulated (Fig. 6). A closely related pattern was shown by experiment i, which grouped together with these experiments in our PCA (Fig. 1) and used a cold treatment starting at 2 h after dawn (ZT2). The up-regulation of genes with phases of ZT10 to ZT14 and the down-regulation of genes with phases of ZT18 to ZT2 are consistent with the negative and positive loadings for PC 1, respectively (Fig. 2). However, experiment j, not identified by unsupervised PCA, also showed a very similar pattern (Fig. 6), and this also used a cold treatment starting in the morning (4 h into a 16-h day). This clearly illustrates the benefit of experiment-wise supervised analysis. In general terms, the overrepresentation of repressed genes among those with phases immediately following dawn (ZT4–ZT8) is not specific, as it is observed in most experiments. In contrast, the genes with phases in the late night (ZT18–ZT22) are less consistent, being induced in some experiments and repressed in others. The lowest phase-specific regulation is seen for experiments D and e, where continuous light was used before and during cold treatment; however, there are differences between the two experiments and between the replicates within each experiment, and such replicate differences are less apparent in experiments performed in light-dark conditions (Supplemental Fig. S5). Nevertheless, the use of continuous light does not guarantee a low contribution of circadian genes, as experiment C, also using continuous light, shows strong phase bias and is very similar to the two experiments in which cold treatment was started in the middle of the day (Fig. 6).
Circadian and Diurnal Regulation Cause Variation in the Identity of Cold-Responsive Genes Most studies to identify genes responding to cold state that measures to eliminate or minimize the effect of diurnal or circadian regulation were taken. Indeed, adequate precautions of starting and harvesting treatments at the same time of day are almost universally followed. Consequently, it is widely assumed that diurnal regulation is not a major source of variation between cold-responsive genes identified in different experiments. However, we demonstrate that diurnal- and circadian-regulated genes contribute most to the considerable differences between independent studies to identify genes responding to a 1-d cold treatment (Fig. 1; Table II). In addition, our targeted expression analyses explain why paired diurnal controls are insufficient to eliminate such variation. Following cold treatment, after a short initial response, most clock components and some output genes dampen to low-amplitude cycles, while other clock output genes stop to cycle (Fig. 3; Supplemental Fig. S3). Since genes in control samples show normal high-amplitude cycles, in samples taken at different times of the day diurnal- and circadian-regulated genes will make major contributions to those genes identified as cold responsive. Figure 7 schematically depicts these time-of-day effects on relative changes in gene expression between control and cold-treated plants. It can be easily appreciated why the time of day an experiment was started/harvested has such a large impact on the identity of cold-responsive genes. In this respect, experiments that were started in the morning (ZT2–ZT4) were separated from those starting at midday or in the evening.
Another large effect, similar to that of starting cold treatment in the morning, was found for the two experiments using diurnally grown plants and cold treatment performed in continuous light. Our targeted analysis again indicated an underlying reason for this: circadian oscillations are effectively stopped in the cold under continuous light (Fig. 4; Supplemental Fig. S4). The elimination of the oscillations that persist for some genes in light-dark cycles likely causes the apparent cold response to be further enhanced. This effect led to the previous suggestion that the higher expression of TOC1 (PRR1) and PRR5 after a 24-h cold treatment was the consequence of cold regulation rather than circadian effects (Lee et al., 2005 ; experiment B in our study), while our analyses reveal that this was a secondary effect of cold on the circadian clock in continuous light rather than a specific cold response. Furthermore, such secondary effects of cold on the circadian clock in continuous light also strongly influence the conclusions that may be drawn from the results of the AtGenExpress cold series (Kilian et al., 2007; experiment A in our study), which is currently the most widely used reference series for cold-responsive gene expression (Zimmermann et al., 2004; Toufighi et al., 2005). It is important to note that contrary to the assumptions of the authors (Kilian et al., 2007), circadian effects have a strong influence on the observed expression patterns.
Surprisingly, even the most extreme solution to eliminate diurnal regulation, using plants always grown in continuous light, does not guarantee the absence of circadian effects. Experiments D and e using continuous light did have the least circadian effects; however, there appeared to be an increased tendency for circadian phase differences between replicates (Supplemental Fig. S5). This could be caused by circadian oscillations, synchronized by either imbibition (Zhong et al., 1998
Microarray analysis has been used to dissect the contributions of factors, such as transcriptional regulators, cis-regulatory elements, functional annotations, and natural variation, to cold-responsive gene expression (Lee et al., 2005
Although cold-diurnal interactions have led to unintended differences in the identification of cold-responsive genes, it should also be considered that the short-term initial changes as well as the dampening or disruption of oscillations are all examples of cold regulation. Obviously, nonspecific effects of temperature on the thousands of chemical reactions within the cell will play a role in this effect. However, the normal amplitude oscillations of LUX and of many other genes (C. Espinoza, Z. Bieniawska, A. Leisse, L. Willmitzer, D.K. Hincha, and M.A. Hannah, unpublished data) indicate that plants can specifically avoid such general effects. Given the adaptive variation for circadian function (Michael et al., 2003b
It was previously demonstrated that the circadian clock gates the cold induction of the CBF1 to CBF3, RAV1, and ZAT12 TFs (Fowler et al., 2005
There has been much effort to understand the molecular basis of the circadian clock. This has culminated in the development of models of clock function that seek to explain existing data and direct new experiments (Locke et al., 2005
Interestingly, cold rendered LUX expression immediately responsive following dawn, rather than with the 4-h delay observed under control conditions. CCA1 and LHY peak around dawn and have been shown to bind an evening element in the LUX promoter; they may repress its transcription in a similar way to their regulation of TOC1 (Hazen et al., 2005
Progress in plant circadian research has been predominantly driven by the use of forward genetic screens to identify plants with aberrant expression of the circadian clock-regulated promoter-luciferase (LUC) fusion CAB2::LUC (Millar et al., 1995 In conclusion, we show that although it is widely believed that diurnal and circadian effects on the identification of cold-responsive genes have been largely excluded through the use of paired controls, they account for the majority of differences between independent experiments to identify cold-responsive genes. Mechanistically, these differences in the cold are explained by the longer-term dampening of cycles for clock components and the stopping of the rhythmic expression of some output genes in light-dark cycles and arrhythmia of all cycles in continuous light. We also demonstrate that diurnal gating of cold-induced TFs is a general phenomenon and likely also contributes to observed differences. Diurnal regulation should thus be a key consideration of future experiments, and these should investigate its physiological significance for plant growth, adaptation, and survival in the cold. Finally, the differential effects of cold on LUX and on circadian output genes suggest that low temperature could be an important tool to probe mechanisms underlying diurnal and circadian function.
Plant Material and Growth
The protocols used were based on those we have described previously (Rohde et al., 2004
qRT-PCR
Expression Profiling
Raw CEL file data were analyzed using the bioconductor software project (Gentleman et al., 2004
Overrepresentation/underrepresentation analysis was performed using fisher.test and correlation analysis with cor in the R software. PCA was performed using the pcaMethods bioconductor package (Stacklies et al., 2007 Microarray data from this article have been deposited with the European Bioinformatics Institute ArrayExpress data repository (http://www.ebi.ac.uk/arrayexpress/) under accession numbers E-MEXP-1344 and E-MEXP-1345.
The following materials are available in the online version of this article.
We are very grateful to Yves Gibon for his idea to use, and his establishment of, the robot pipetting facility for qRT-PCR. We thank Tomek Czechowski, Michael Udvardi, Wolf Scheible, and Susanne Freund for their involvement in generating and maintaining the TF platform and Andre Gehrmann, Karin Köhl, and Manuela Guenther for helping us to grow plants under so many conditions. We also thank Aleksandra Skirycz for help harvesting plants at ridiculous hours of the day and Henning Redestig and Alex Webb for useful discussions. We also appreciate the public support of the bioconductor and R software and the excellent The Arabidopsis Information Resource, NASCarrays, Arrayexpress, and GEO databases and those researchers who have made their microarray data publicly available. We are grateful to Andrea Leisse for labeling the Affymetrix samples and to Florian Wagner and his team at the German Resource Center for Genome Research and ATLAS Biolabs for expert microarray service. Received February 19, 2008; accepted March 19, 2008; published March 28, 2008.
1 This work was supported by the Max-Planck Society. 
2 These authors contributed equally to the article.
3 Present address: John Innes Centre, Norwich NR4 7UH, UK.
4 Present address: Bayer Cropscience N.V., Technologiepark 38, 9000 Gent, Belgium. The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Matthew A. Hannah (matthew.hannah{at}bayercropscience.com).
[C] Some figures in this article are displayed in color online but in black and white in the print edition.
[W] The online version of this article contains Web-only data.
[OA] Open Access articles can be viewed online without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.108.118059 * Corresponding author; e-mail matthew.hannah{at}bayercropscience.com.
Alabadi D, Oyama T, Yanovsky MJ, Harmon FG, Mas P, Kay SA (2001) Reciprocal regulation between TOC1 and LHY/CCA1 within the Arabidopsis circadian clock. Science 293: 880–883 Alonso-Blanco C, Gomez-Mena C, Llorente F, Koornneef M, Salinas J, Martinez-Zapater JM (2005) Genetic and molecular analyses of natural variation indicate CBF2 as a candidate gene for underlying a freezing tolerance quantitative trait locus in Arabidopsis. Plant Physiol 139: 1304–1312 Balasubramanian S, Sureshkumar S, Lempe J, Weigel D (2006) Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet 2: e106[CrossRef][Medline] Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, Kim IF, Soboleva A, Tomashevsky M, Edgar R (2007) NCBI GEO: mining tens of millions of expression profiles. Database and tools update. Nucleic Acids Res 35: D760–D765 Bastow R, Mylne JS, Lister C, Lippman Z, Martienssen RA, Dean C (2004) Vernalization requires epigenetic silencing of FLC by histone methylation. Nature 427: 164–167[CrossRef][Medline] Benedict C, Geisler M, Trygg J, Huner N, Hurry V (2006) Consensus by democracy: using meta-analyses of microarray and genomic data to model the cold acclimation signaling pathway in Arabidopsis. Plant Physiol 141: 1219–1232 Blasing OE, Gibon Y, Gunther M, Hohne M, Morcuende R, Osuna D, Thimm O, Usadel B, Scheible WR, Stitt M (2005) Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in Arabidopsis. Plant Cell 17: 3257–3281 Boothroyd CE, Wijnen H, Naef F, Saez L, Young MW (2007) Integration of light and temperature in the regulation of circadian gene expression in Drosophila. PLoS Genet 3: e54[CrossRef][Medline] Boyes DC, Zayed AM, Ascenzi R, McCaskill AJ, Hoffman NE, Davis KR, Gorlach J (2001) Growth stage-based phenotypic analysis of Arabidopsis: a model for high throughput functional genomics in plants. Plant Cell 13: 1499–1510 Cao S, Ye M, Jiang S (2005) Involvement of GIGANTEA gene in the regulation of the cold stress response in Arabidopsis. Plant Cell Rep 24: 683–690[CrossRef][Web of Science][Medline] Chinnusamy V, Ohta M, Kanrar S, Lee BH, Hong XH, Agarwal M, Zhu JK (2003) ICE1: a regulator of cold-induced transcriptome and freezing tolerance in Arabidopsis. Genes Dev 17: 1043–1054 Cook D, Fowler S, Fiehn O, Thomashow MF (2004) A prominent role for the CBF cold response pathway in configuring the low-temperature metabolome of Arabidopsis. Proc Natl Acad Sci USA 101: 15243–15248 Covington MF, Harmer SL (2007) The circadian clock regulates auxin signaling and responses in Arabidopsis. PLoS Biol 5: 1773–1784[Web of Science] Craigon DJ, James N, Okyere J, Higgins J, Jotham J, May S (2004) NASCArrays: a repository for microarray data generated by NASC's transcriptomics service. Nucleic Acids Res 32: D575–D577 Czechowski T, Bari RP, Stitt M, Scheible WR, Udvardi MK (2004) Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: Unprecedented sensitivity reveals novel root- and shoot-specific genes. Plant J 38: 366–379[CrossRef][Web of Science][Medline] Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR (2005) Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol 139: 5–17 Dodd AN, Jakobsen MK, Baker AJ, Telzerow A, Hou SW, Laplaze L, Barrot L, Poethig RS, Haseloff J, Webb AAR (2006) Time of day modulates low-temperature Ca2+ signals in Arabidopsis. Plant J 48: 962–973[CrossRef][Web of Science][Medline] Dodd AN, Salathia N, Hall A, Kevei E, Toth R, Nagy F, Hibberd JM, Millar AJ, Webb AA (2005) Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science 309: 630–633 Edwards KD, Anderson PE, Hall A, Salathia NS, Locke JC, Lynn JR, Straume M, Smith JQ, Millar AJ (2006) FLOWERING LOCUS C mediates natural variation in the high-temperature response of the Arabidopsis circadian clock. Plant Cell 18: 639–650 Fowler S, Lee K, Onouchi H, Samach A, Richardson K, Coupland G, Putterill J (1999) GIGANTEA: a circadian clock-controlled gene that regulates photoperiodic flowering in Arabidopsis and encodes a protein with several possible membrane-spanning domains. EMBO J 18: 4679–4688[CrossRef][Web of Science][Medline] Fowler S, Thomashow MF (2002) Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway. Plant Cell 14: 1675–1690 Fowler SG, Cook D, Thomashow MF (2005) Low temperature induction of Arabidopsis CBF1, 2, and 3 is gated by the circadian clock. Plant Physiol 137: 961–968 Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80[CrossRef][Medline] Gilmour SJ, Sebolt AM, Salazar MP, Everard JD, Thomashow MF (2000) Overexpression of the Arabidopsis CBF3 transcriptional activator mimics multiple biochemical changes associated with cold acclimation. Plant Physiol 124: 1854–1865 Gilmour SJ, Zarka DG, Stockinger EJ, Salazar MP, Houghton JM, Thomashow MF (1998) Low temperature regulation of the Arabidopsis CBF family of AP2 transcriptional activators as an early step in cold-induced COR gene expression. Plant J 16: 433–442[CrossRef][Web of Science][Medline] Gould PD, Locke JCW, Larue C, Southern MM, Davis SJ, Hanano S, Moyle R, Milich R, Putterill J, Millar AJ, Hall A (2006) The molecular basis of temperature compensation in the Arabidopsis circadian clock. Plant Cell 18: 1177–1187 Hannah MA, Heyer AG, Hincha DK (2005) A global survey of gene regulation during cold acclimation in Arabidopsis thaliana. PLoS Genet 1: e26[CrossRef][Medline] Hannah MA, Wiese D, Freund S, Fiehn O, Heyer AG, Hincha DK (2006) Natural genetic variation of freezing tolerance in Arabidopsis. Plant Physiol 142: 98–112 Harmer SL, Hogenesch JB, Straume M, Chang HS, Han B, Zhu T, Wang X, Kreps JA, Kay SA (2000) Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science 290: 2110–2113 Hazen SP, Schultz TF, Pruneda-Paz JL, Borevitz JO, Ecker JR, Kay SA (2005) LUX ARRHYTHMO encodes a Myb domain protein essential for circadian rhythms. Proc Natl Acad Sci USA 102: 10387–10392 Henriksson KN, Trewavas AJ (2003) The effect of short-term low-temperature treatments on gene expression in Arabidopsis correlates with changes in intracellular Ca2+ levels. Plant Cell Environ 26: 485–496[Medline] Johanson U, West J, Lister C, Michaels S, Amasino R, Dean C (2000) Molecular analysis of FRIGIDA, a major determinant of natural variation in Arabidopsis flowering time. Science 290: 344–347 Kaplan F, Kopka J, Haskell DW, Zhao W, Schiller KC, Gatzke N, Sung DY, Guy CL (2004) Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol 136: 4159–4168 Kaplan F, Kopka J, Sung DY, Zhao W, Popp M, Porat R, Guy CL (2007) Transcript and metabolite profiling during cold acclimation of Arabidopsis reveals an intricate relationship of cold-regulated gene expression with modifications in metabolite content. Plant J 50: 967–981[CrossRef][Web of Science][Medline] Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, D'Angelo C, Bornberg-Bauer E, Kudla J, Harter K (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J 50: 347–363[CrossRef][Web of Science][Medline] Knight MR, Campbell AK, Smith SM, Trewavas AJ (1991) Transgenic plant aequorin reports the effects of touch and cold-shock and elicitors on cytoplasmic calcium. Nature 352: 524–526[CrossRef][Medline] Kreps JA, Simon AE (1997) Environmental and genetic effects on circadian clock-regulated gene expression in Arabidopsis. Plant Cell 9: 297–304[Abstract] Kreps JA, Wu YJ, Chang HS, Zhu T, Wang X, Harper JF (2002) Transcriptome changes for Arabidopsis in response to salt, osmotic, and cold stress. Plant Physiol 130: 2129–2141 Lee BH, Henderson DA, Zhu JK (2005) The Arabidopsis cold-responsive transcriptome and its regulation by ICE1. Plant Cell 17: 3155–3175 Lee H, Xiong L, Gong Z, Ishitani M, Stevenson B, Zhu JK (2001) The Arabidopsis HOS1 gene negatively regulates cold signal transduction and encodes a RING finger protein that displays cold-regulated nucleo-cytoplasmic partitioning. Genes Dev 15: 912–924 Lidder P, Gutierrez RA, Salome PA, McClung CR, Green PJ (2005) Circadian control of messenger RNA stability: association with a sequence-specific messenger RNA decay pathway. Plant Physiol 138: 2374–2385 Liu Q, Kasuga M, Sakuma Y, Abe H, Miura S, Yamaguchi-Shinozaki K, Shinozaki K (1998) Two transcription factors, DREB1 and DREB2, with an EREBP/AP2 DNA binding domain separate two cellular signal transduction pathways in drought- and low-temperature-responsive gene expression, respectively, in Arabidopsis. Plant Cell 10: 1391–1406 Locke JC, Kozma-Bognar L, Gould PD, Feher B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ (2006) Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana. Mol Syst Biol 2: 59[Medline] Locke JC, Southern MM, Kozma-Bognar L, Hibberd V, Brown PE, Turner MS, Millar AJ (2005) Extension of a genetic network model by iterative experimentation and mathematical analysis. Mol Syst Biol 1: 2005.0013 Martino-Catt S, Ort DR (1992) Low temperature interrupts circadian regulation of transcriptional activity in chilling-sensitive plants. Proc Natl Acad Sci USA 89: 3731–3735 Maruyama K, Sakuma Y, Kasuga M, Ito Y, Seki M, Goda H, Shimada Y, Yoshida S, Shinozaki K, Yamaguchi-Shinozaki K (2004) Identification of cold-inducible downstream genes of the Arabidopsis DREB1A/CBF3 transcriptional factor using two microarray systems. Plant J 38: 982–993[CrossRef][Web of Science][Medline] McClung CR (2006) Plant circadian rhythms. Plant Cell 18: 792–803 Michael TP, Salome PA, McClung CR (2003a) Two Arabidopsis circadian oscillators can be distinguished by differential temperature sensitivity. Proc Natl Acad Sci USA 100: 6878–6883 Michael TP, Salome PA, Yu HJ, Spencer TR, Sharp EL, McPeek MA, Alonso JM, Ecker JR, McClung CR (2003b) Enhanced fitness conferred by naturally occurring variation in the circadian clock. Science 302: 1049–1053 Millar AJ, Carre IA, Strayer CA, Chua NH, Kay SA (1995) Circadian clock mutants in Arabidopsis identified by luciferase imaging. Science 267: 1161–1163 Millar AJ, Kay SA (1996) Integration of circadian and phototransduction pathways in the network controlling CAB gene transcription in Arabidopsis. Proc Natl Acad Sci USA 93: 15491–15496 Morcuende R, Bari R, Gibon Y, Zheng W, Pant BD, Blasing O, Usadel B, Czechowski T, Udvardi MK, Stitt M, et al (2007) Genome-wide reprogramming of metabolism and regulatory networks of Arabidopsis in response to phosphorus. Plant Cell Environ 30: 85–112[CrossRef][Medline] Nozue K, Covington MF, Duek PD, Lorrain S, Fankhauser C, Harmer SL, Maloof JN (2007) Rhythmic growth explained by coincidence between internal and external cues. Nature 448: 358–361[CrossRef][Medline] Nozue K, Maloof JN (2006) Diurnal regulation of plant growth. Plant Cell Environ 29: 396–408[CrossRef][Medline] Onai K, Ishiura M (2005) PHYTOCLOCK 1 encoding a novel GARP protein essential for the Arabidopsis circadian clock. Genes Cells 10: 963–972 Perales M, Mas P (2007) A functional link between rhythmic changes in chromatin structure and the Arabidopsis biological clock. Plant Cell 19: 2111–2123 Ramos A, Perez-Solis E, Ibanez C, Casado R, Collada C, Gomez L, Aragoncillo C, Allona I (2005) Winter disruption of the circadian clock in chestnut. Proc Natl Acad Sci USA 102: 7037–7042 Rohde P, Hincha DK, Heyer AG (2004) Heterosis in the freezing tolerance of crosses between two Arabidopsis thaliana accessions (Columbia-0 and C24) that show differences in non-acclimated and acclimated freezing tolerance. Plant J 38: 790–799[CrossRef][Web of Science][Medline] Sawa M, Nusinow DA, Kay SA, Imaizumi T (2007) FKF1 and GIGANTEA complex formation is required for day-length measurement in Arabidopsis. Science 318: 261–265 Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima M, Enju A, Sakurai T, et al (2002) Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J 31: 279–292[CrossRef][Web of Science][Medline] Solfanelli C, Poggi A, Loreti E, Alpi A, Perata P (2005) Sucrose-specific induction of the anthocyanin biosynthetic pathway in Arabidopsis thaliana. Plant Physiol 140: 637–646[Web of Science][Medline] Stacklies W, Redestig H, Scholz M, Walther D, Selbig J (2007) pcaMethods: a bioconductor package providing PCA methods for incomplete data. Bioinformatics 23: 1164–1167 Stitt M, Sulpice R, Gibon Y, Whitwell A, Skilbeck R, Parker S, Ellison R, inventors. October 17, 2007. Cryogenic grinder system. German Patent No. 08146.0025U1 Thomashow MF (1999) Plant cold acclimation: freezing tolerance genes and regulatory mechanisms. Annu Rev Plant Physiol Plant Mol Biol 50: 571–599[CrossRef][Web of Science] Toufighi K, Brady SM, Austin R, Ly E, Provart NJ (2005) The Botany Array Resource: e-Northerns, expression angling, and promoter analyses. Plant J 43: 153–163[CrossRef][Web of Science][Medline] Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3: RESEARCH0034[Medline] Vogel JT, Zarka DG, Van Buskirk HA, Fowler SG, Thomashow MF (2005) Roles of the CBF2 and ZAT12 transcription factors in configuring the low temperature transcriptome of Arabidopsis. Plant J 41: 195–211[CrossRef][Web of Science][Medline] Webb AAR (2003) The physiology of circadian rhythms in plants. New Phytol 160: 281–303[CrossRef][Web of Science] Wu ZJ, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F (2004) A model-based background adjustment for oligonucleotide expression arrays. J Am Stat Assoc 99: 909–917[CrossRef][Web of Science] Xin Z, Mandaokar A, Chen J, Last RL, Browse J (2007) Arabidopsis ESK1 encodes a novel regulator of freezing tolerance. Plant J 49: 786–799[CrossRef][Web of Science][Medline] Zeilinger MN, Farre EM, Taylor SR, Kay SA, Doyle FJ III (2006) A novel computational model of the circadian clock in Arabidopsis that incorporates PRR7 and PRR9. Mol Syst Biol 2: 58[Medline] Zhong HH, Painter JE, Salome PA, Straume M, McClung CR (1998) Imbibition, but not release from stratification, sets the circadian clock in Arabidopsis seedlings. Plant Cell 10: 2005–2017 Zhu J, Shi H, Lee BH, Damsz B, Cheng S, Stirm V, Zhu JK, Hasegawa PM, Bressan RA (2004) An Arabidopsis homeodomain transcription factor gene, HOS9, mediates cold tolerance through a CBF-independent pathway. Proc Natl Acad Sci USA 101: 9873–9878 Zimmermann P, Hirsch-Hoffmann M, Hennig L, Gruissem W (2004) GENEVESTIGATOR: Arabidopsis microarray database and analysis toolbox. Plant Physiol 136: 2621–2632 This article has been cited by other articles:
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| ASPB Publications | PLANT PHYSIOLOGY® | THE PLANT CELL | |
|---|---|---|---|
TOP
ABSTRACT
RESULTS
