- Open Access
A transcriptional timetable of autumn senescence
- Anders Andersson1,
- Johanna Keskitalo2,
- Andreas Sjödin2,
- Rupali Bhalerao1, 2,
- Fredrik Sterky1,
- Kirsten Wissel2,
- Karolina Tandre3,
- Henrik Aspeborg1,
- Richard Moyle3,
- Yasunori Ohmiya3,
- Rishikesh Bhalerao3,
- Amy Brunner4,
- Petter Gustafsson2,
- Jan Karlsson2,
- Joakim Lundeberg1,
- Ove Nilsson3,
- Göran Sandberg3,
- Steven Strauss4,
- Björn Sundberg3,
- Mathias Uhlen1,
- Stefan Jansson2Email author and
- Peter Nilsson1Email author
© Andersson et al. 2004
- Received: 22 September 2003
- Accepted: 5 February 2004
- Published: 10 March 2004
We have developed genomic tools to allow the genus Populus (aspens and cottonwoods) to be exploited as a full-featured model for investigating fundamental aspects of tree biology. We have undertaken large-scale expressed sequence tag (EST) sequencing programs and created Populus microarrays with significant gene coverage. One of the important aspects of plant biology that cannot be studied in annual plants is the gene activity involved in the induction of autumn leaf senescence.
On the basis of 36,354 Populus ESTs, obtained from seven cDNA libraries, we have created a DNA microarray consisting of 13,490 clones, spotted in duplicate. Of these clones, 12,376 (92%) were confirmed by resequencing and all sequences were annotated and functionally classified. Here we have used the microarray to study transcript abundance in leaves of a free-growing aspen tree (Populus tremula) in northern Sweden during natural autumn senescence. Of the 13,490 spotted clones, 3,792 represented genes with significant expression in all leaf samples from the seven studied dates.
We observed a major shift in gene expression, coinciding with massive chlorophyll degradation, that reflected a shift from photosynthetic competence to energy generation by mitochondrial respiration, oxidation of fatty acids and nutrient mobilization. Autumn senescence had much in common with senescence in annual plants; for example many proteases were induced. We also found evidence for increased transcriptional activity before the appearance of visible signs of senescence, presumably preparing the leaf for degradation of its components.
- Leaf Senescence
- Additional Data File
- Annual Plant
- Aspen Tree
- Autumn Leaf
Plants can make profound changes to their developmental and metabolic processes in response to changes in their environment. These adaptations require changes in the expression of many genes, and understanding these changes is of interest for both pure and applied sciences. Following the introduction of DNA microarray technology these massive changes in gene regulation can be assayed in a high-throughput manner. However, the production of microarrays requires the genome of the target organism to have been subjected to large-scale sequencing and, to date, microarrays have only been available for a limited number of plants, including the model plant Arabidopsis and the annual crops rice, wheat, Medicago, tomato and maize . These species are not useful for a number of biological questions, because they do not form wood and do not undergo perennial growth and dormancy. We have now developed genomic tools to allow the genus Populus (aspens and cottonwoods) to be a woody perennial model for investigating fundamental aspects of tree biology. Towards this end we have undertaken large-scale EST sequencing programs [2, 3] and created wood-specific microarrays to construct a transcriptional roadmap of wood formation . Here we describe the production of an extended Populus microarray and its application to study one of the most important aspects of plant biology that cannot be studied in annual plants: the induction of autumn leaf senescence.
Autumn leaf senescence is a developmental process that is poorly understood at the level of gene expression. All previous molecular studies on leaf senescence have focused on annual plants, where senescence could be induced by various treatments such as drought, oxidative stress, mechanical stress, shading or simply aging of the leaf. Autumn senescence in trees in temperate regions is typically induced by the shortening of the photoperiod, an environmental signal that also induces growth cessation and bud set. Autumn leaf senescence in Populus, shares many features with leaf senescence in annual plants ; however, the induction of autumn senescence remained to be studied. Like other photoperiod-controlled processes, such as flowering in many plants, the initial perception of the critical change in daylength is phytochrome mediated , but other environmental cues, in particular low temperature, also influence the process. Virtually nothing is known about the signal transduction mechanisms from daylength perception to autumn leaf senescence in aspen and other trees. In northern Sweden, growth cessation and bud set occur about a month earlier than visible leaf senescence. It is not known whether separate critical night lengths trigger the two processes, or if the leaf senescence program takes much longer to orchestrate. We have addressed these questions here, by studying the pattern of gene expression during autumn senescence in leaves of a field-grown aspen tree, using DNA microarrays.
The Populus microarray
On the basis of 36,354 ESTs we constructed a unigene set consisting of 13,490 cDNA clones and produced spotted Populus microarrays. The whole unigene set was subjected to single-pass control sequencing from both ends, which confirmed the identity of 12,376 (92%) of the clones. Annotations and functional classifications were corrected where necessary. From the control sequencing, 11,175 3' sequences were obtained that collapsed, when clustered by PHRAP, into 7,974 different contigs, thus revealing a level of redundancy of approximately 28% on the microarray. This level of redundancy indicates that the complete set of 13,490 clones represents between 9,000 and 10,000 unique genes. As many of the clones on the array originated from tissues other than leaves, and many genes highly expressed in young leaves have little or no expression in old leaves, we only expected hybridization signals from a fraction of the clones. However, even with a rather stringent quality filtering, 3,792 clones were found to be expressed in all studied samples.
Only genes corresponding to those transcripts that were present in samples from all seven dates were included in the analysis presented here. In addition to these, there were a number of clones where signal was obtained from only some of the studied dates, most representing genes expressed only during a shorter time period. The experimental design chosen here consists of comparisons of samples in a time series, resulting in trend curves for individual genes or groups of genes. The rationale behind the strategy to only include genes that fulfill the filtering criteria for all seven days is to have unbroken trend lines for each gene or group of genes. This enables a higher degree of confidence in the interpretation of the trends representing metabolic changes in the leaves. Inevitably, with this strategy of analysis, genes that only are expressed under a shorter time would be excluded from the dataset (although present in the raw data, which is available from the European Bioinformatics Institute's ArrayExpress database ). Some of these genes may, of course, be important regulators or mediators of autumn senescence, while others may be transiently expressed as a response to a temporary environmental stress, for example attack by a pathogen. We are at present performing array analyses with RNA prepared from leaves harvested from the same tree in another year. Reanalysis of the present dataset together with the new data should allow us, with high confidence, to distinguish genes that are expressed during a short time as a response to the seasonal change from genes expressed transiently owing to stress. The analysis presented here primarily aims at describing metabolic changes taking place in leaves during autumn senescence.
The statistical treatment of array data consisting of time series is still in its infancy, and methods for reliably computing significance levels in trend lines are not established. To get a statistically based analysis of the significance of differentially expressed genes (up- or downregulated) over the time period studied, the samples were grouped (on the basis of expression patterns) into two classes, with the first four dates in one class and the last three in the other, enabling a comparison of early versus late sampling dates.
Patterns of gene expression during the autumn
All clones spotted on the array have been functionally classified according to the UPSC-MIPS classification scheme . We analyzed our expression profiles to find out whether genes in selected categories had coordinated expression patterns, and to obtain quantitative data on the total pattern of gene expression for the different categories. We discuss the results for genes found in some of the categories, but the full dataset is presented in Additional data files.
The clearest visible sign of autumn senescence is the change in leaf pigmentation. Chlorophyll degrades, and reveals the colors of the carotenoids and flavonoids (anthocyanins). The latter may accumulate during the process. To obtain an overview of the expression patterns of genes coding for proteins involved in pigment metabolism, we analyzed the genes in the categories 'chlorophyll biosynthesis' (01.20.19.03), 'carotenoid biosynthesis' (01.06.01.07.13) and 'flavonoid biosynthesis' (01.20.35.05). The expression levels of genes classified as being involved in chlorophyll biosynthesis decreased significantly (on average almost threefold) during the period studied. In contrast, the patterns for the carotenoid biosynthesis and flavonoid biosynthesis classes varied, and showed no coordinated trends (see Additional data file 2), although individual genes in the categories (for example, beta-carotene hydroxylase and cinnamoyl CoA reductase genes) were significantly induced.
A metabolic switch from anabolism to catabolism
During the senescence process, the leaves shift from photosynthetic activity to catabolism, in which energy is generated by mitochondria. For example, enzymes involved in the beta-oxidation of fatty acids and the glyoxalate pathway are known to be induced during leaf senescence in annual plants . For this reason, we analyzed the transcript levels for all genes functionally classified into a number of categories relevant to energy metabolism
Consistent with our previous analysis of expressed sequence tag (EST) frequencies , transcripts encoding proteins involved in the beta-oxidation of fatty acids (Figure 5d) accumulated in autumn leaves (on average almost fourfold). This was particularly evident during the last stages of senescence after the leaves had turned yellow. Most genes involved in other mitochondrial energy-generating activities, in the 'Glycolysis and gluconeogenesis', 'Tricarboxylic acid pathway' and 'Pentose phosphate pathway' classes (Figure 5e,f,g), showed no significant changes in their expression levels during the period studied. Transcripts classified under 'Electron transport' decreased somewhat (Figure 5h), but in this case too, changes were small. Taken together, our data show that the transcripts from photosynthetic genes started to decrease in abundance before visible signs of senescence appeared, and the decrease in transcripts became more rapid as chlorophyll breakdown became prominent. They also show that mitochondria-related proteins continued to be synthesized throughout the whole period. The lowest levels of transcripts encoding cytosolic glutamine synthase, the key enzyme for nitrogen remobilization , occurred in the August samples, but levels had already increased by 3 September and stayed high thereafter (see Additional data files).
Proteases in autumn leaves
Organellar proteins are degraded during senescence. Transcript levels for chloroplast-located proteases showed diverse trends (Figure 6b): some accumulated, (one FtsH, one DegP, one Clp and one metalloprotease - about fivefold, fourfold, twofold and twofold, respectively), whereas the others either decreased in abundance or did not change. Only two clones on the array classified as proteases localized in the mitochondrion, so no firm conclusions can be drawn from their expression patterns, but on average their expression level did not change until the last day of the experiment, when it increased (see Additional data files).
As shown previously , polyubiquitin transcripts increased in abundance throughout the period studied (Figure 6c). However, the other components of the ubiquitin system did not increase in overall expression, nor did the subunits of the proteasome, which eventually degrades the polypeptides tagged by ubiquitin. Other proteases exhibited different patterns; for example, two out of three transcripts coding for aspartic proteases accumulated significantly.
Hormone metabolism and transcription factors
Senescence is under hormonal control, and although changes in hormonal levels do not always require changes in gene expression, we analyzed the transcript abundance for all genes on the array known to be involved in hormone metabolism. Genes involved in gibberellin, cytokinin or auxin metabolism or perception did not show any clear trends (data not shown). In contrast, the average mRNA levels of genes involved in ethylene metabolism and perception increased fivefold as senescence proceeded (see Additional data files). Many of these genes had a relatively low expression level and did not pass the rather stringent quality criteria we set. In addition, for clones that did not meet our quality standards the pattern was the same. The increase was rather slow, but constant throughout the period studied.
Expression patterns of other 'senescence-associated genes'
From our previous comparison of ESTs from a cDNA library prepared from the 14 September sample analyzed here with ESTs from young leaves, we had identified many genes or classes of genes that appear to be associated with autumn senescence  and we now wanted to study their expression patterns.
Senescence may be preceded by a peak in transcriptional activity
Does autumn senescence share characteristics with cell death in wood?
We have constructed cDNA microarrays with 13,490 probes spotted in duplicate, covering a significant fraction of the Populus transcriptome, in which all clones have been annotated and functionally classified. We have also shown that this resource can be used for high-throughput transcript profiling in Populus. We have shown a good correlation between expression profiles measured using RNA blotting and microarrays, and even clones that did not pass our rather stringent quality filters typically showed the same patterns as those in the same functional categories that were included in the analysis. Together with other ongoing Populus investigations , for example, genome sequencing, our EST sequences and arrays represent efforts to establish Populus as a model system for genomics. We have used the arrays to study gene expression during autumn leaf senescence in a plant growing naturally in the field. We are interested in the total pattern of gene expression under natural conditions where the plants are simultaneously exposed to multiple stresses, in addition to changes in photoperiod. Experiments made under controlled conditions are necessary to delineate certain responses, for example, to identify aspen genes that are under photoperiodic control. Nevertheless, we strongly believe that studies in the natural environment will be essential for understanding the full complexity of plants' interactions with their environment. We have previously demonstrated that it is possible, with the help of multivariate statistics  or mutant studies , to draw mechanistic conclusions from field studies. Here we add microarrays to the 'field studies toolbox'.
By analyzing the data by clustering analysis, and studying the expression profiles of individual genes and genes in functional classes, we attempt here to describe the overall pattern of gene expression in aspen autumn leaves and to draw conclusions from this dataset about the metabolic activities taking place during the autumn. Although there is often no strict correlation between mRNA and protein levels for individual genes, if genes are grouped into broader categories such as functional classes, the mean values should represent a good approximation of the relative effort that plants are making to synthesize the proteins of the respective categories.
The molecular biology of leaf senescence has been studied quite extensively in annual plants (for a review see ). In a previous study  we compared ESTs from young and senescing leaves and made predictions concerning the expression patterns of the corresponding genes. Here we use transcript profiling to extend the data from this two-point comparison into expression profiles of the individual genes and classes of genes during the senescence process; predictions we had made from sequence data about the similarities between autumn senescence and leaf senescence were confirmed. Moreover, we found that a major shift in expression levels coincides with the onset of visible chlorosis, in this case around 14 September. The expression of photosynthetic genes also tended to decrease before this date, and dropped dramatically thereafter, illustrating the major metabolic shift that occurred as the leaves lost photosynthetic competence. Clearly, there is an interplay between sugar metabolism and the initiation of senescence in annual plants (reviewed in ) although some aspects of this interaction must differ in autumn leaves of trees as leaves that are otherwise perfectly active in photosynthesis can be triggered to undergo autumn senescence. Levels of transcripts for cytosolic glutamine synthase reached their maximum as early as 3 September. However, this protein is subject to posttranslational control , so it is possible that maximal activity of this enzyme, which is needed during nitrogen mobilization, occurs later in the process, when protein degradation is presumably proceeding more rapidly.
The levels of many transcripts encoding a variety of proteases increased during the experiments, but degradation of the photosynthetic apparatus did not appear to involve massive increases in the expression levels of the known chloroplast proteases. We sequenced over 5,000 ESTs from senescing leaves sampled at the onset of massive chloroplast protein degradation; therefore it is unlikely that we missed some known protease with a high or moderate expression level. It is possible that the four that showed increased expression play a major part in the process, and some of those that are not induced could, nevertheless, be abundant enough or become important following activation. Nonenzymatic degradation mediated by reactive oxygen species (ROS) may also be involved in the initial steps of senescence . Nevertheless, it is also quite possible that unknown chloroplast-located proteases are active in the process. A more pronounced increase in vacuolar cysteine proteases occurred at the same time. Similar increases in vacuolar cysteine proteases have also been noted in annual plant senescence [12, 16], but with our current understanding of organelle integrity during senescence, it is difficult to envisage how they could be involved in chloroplast protein degradation at this relatively early stage as organelles are probably still intact. For chlorophyll-binding proteins, pigment and protein degradation must be coordinated. ELIPs, which are among the most strongly expressed proteins during autumn senescence and have been suggested to act as pigment carriers (see, for instance ), may fulfill this function during leaf senescence, but a more direct role in photoprotection, as suggested by Król et al. , cannot be excluded. Photoprotection is likely to be intimately linked to senescence as inadequate photoprotection results in the generation of ROS, which trigger senescence , but the details of this putative interaction remain to be elucidated.
Transcripts that accumulated as autumn senescence proceeded included sequences from genes encoding metallothioneins, genes involved in ethylene metabolism and signal perception, several transcription factors and many others, resembling the leaf senescence patterns observed in annual plants. In contrast to the genes involved in energy metabolism, transcript levels for these genes increased steadily, rather than displaying a sharp shift coinciding with chlorosis. Neither was there any evidence supporting the concept of successive waves of gene expression during senescence (see, for example [20, 21] in our dataset, at least not on a massive scale. Very few genes showed a transient increase in transcript abundance. Instead, there were three basic patterns of gene expression: increasing, decreasing and constant. Among the 200 most strongly increasing transcripts are many that represent genes with unknown functions that we believe could prove to be of importance during senescence. The transient accumulation of transcripts encoding ribosomal proteins (as well as extractable mRNA) before chlorophyll degradation indicates that transcript levels do not simplify reflect differences in mRNA stability. Rather, entry into autumn senescence is an active process.
An important issue in leaf-senescence studies is the extent to which the process shares elements with programmed cell death (PCD), a rapid process that has been intensively studied in both animals and plants [22–24]. The design of our present study is not ideal for studying PCD, but in the future we hope to address this question with more frequent sampling, combined with analysis of ultrastructural changes to visualize cellular degradation, in the later stages of senescence. It is possible that the rather low level of correlation we have found between genes upregulated during autumn senescence and the last stages of xylem development is due to the fact that massive cell death probably did not take place during the period studied.
We describe here the construction of a 13.5 K Populus microarray and a study of the pattern of gene expression in autumn leaves observed in one year in one tree in a natural stand. This is, to our knowledge, the first study in which microarrays have been used to investigate developmental processes taking place in a plant growing naturally in the field. As there is natural variation between trees in the onset of the process, and the timing of autumn senescence varies slightly from year to year, the patterns we have found do not represent a 'generic calendar', but the order of events during senescence is likely to be at least broadly similar amongst trees generally. We have initiated studies to compare the timing of the events between trees and between years. This may add information that will improve our understanding of the important developmental process of autumn leaf senescence.
Production of the unigene set
Libraries used for the construction of the unigene set and clones spotted on the array
tremula × tremuloides
tremula × tremuloides
tremula × tremuloides
Female flower buds
Preparation of clones
The unigene set was assembled from glycerol stocks or the original bacterial lysate plates transformed into E. coli ΔH5α. Plasmid preparations were made in 96-plate format from bacterial cell suspensions with a NucleoSpin Robot 96-B Plasmid Kit from Macherey-Nagel (Düren, Germany) using a Biomek 2000 Automation Workstation (BeckmanCoulter). PCR amplifications were done in 100 μl reaction volumes, and purified in Montage PCR384 filter plates (Millipore) with a Biorobot 8000 (Qiagen). The purified PCR products were resolved in 40 μl 30% DMSO and split between a storage plate and a printing plate.
The microarrays were printed with a QArray (Genetix) instrument with 24 SMP2.5 pins (Telechem) on Ultra GAPS slides (Corning). The 13,490 cDNA fragments were spotted in duplicate in two separate fields, in a 24 × 24 pattern within each block and with a feature center-to-center distance of 174 μm. For various internal analyses of spotting quality, we included 2 × 36 copies each of the two genes 'expressed protein' and calmodulin, which were found in all seven different cDNA libraries, together with 2 × 40 each of human and archaeal negative controls. To enable the use of spiked material, 2 × 4 copies each of the 23 different Lucidea Universal Scorecard controls (Amersham Biosciences) were also included. The quality of the spotted slides was assessed by staining with Syto61 (Molecular Probes). The slides were UV cross-linked at 250 mJ/cm2 followed by baking at 75°C for 2 h and post-processed with succinic anhydride/sodium borate solution. The complete gene list can be found in the Additional data files as well as in ArrayExpress, a public repository for microarray data .
All clones in the unigene set were resequenced both from the 5' end, as the original EST sequences, and from the 3' end. In this manner, 12,376 clones (92%) were confirmed by sequencing (9,155 from the 5' end and 11,175 from the 3' end). The sequences were verified by BLAST searches and another PHRAP assembly was performed to analyze the remaining redundancy.
RNA sampling and RNA preparation
The RNA samples used were the same as those described in Bhalerao et al. , derived from leaves picked twice a week at 11.00 am on each sampling occasion during the autumn of 1999 from the lower parts of an fully grown (>30 years) male aspen tree (P. tremula) growing alone in an open stand on the Umeå University campus. At least 20 leaves were sampled on each occasion; leaves were crushed in liquid nitrogen, mixed and a fraction of the mixture was used for each RNA extraction.
We used a common reference experimental design, to be able to compare each sample against any other. The RNA for the common reference originates from leaves picked from over 20 trees on 4 September 2000. Two or three (7, 14 and 17 September) replicate hybridizations, including one dye-swap, were utilized for the analysis for all dates except 17 August, for which there was only one. Taking into account the duplicates on the slides, four to six data points for each clone were obtained for six out of the seven days
Fluorescent target preparation, hybridization and scanning
RNA from each day was reverse transcribed into aminoallyl-labeled cDNA, using 12.5 μg total RNA primed with 10 μg oligo-dT primer, and Superscript II reverse transcriptase (Invitrogen). The cDNA was purified using MiniElute spin-columns (Qiagen), substituting the wash buffer with 80% ethanol, and eluting the cDNA in 2 × 10 μl NaHCO3, in which it was coupled to Cy-3- or Cy-5-esters (Amersham Biosciences). Excess dye was removed using MinElute spin columns, and the eluted cDNA was dried in a Speed-vac centrifugal evaporator. For each sampling day, except 17 August, at least one Cy3 and one Cy5 labeling reaction was performed. Common reference cDNAs from different labeling reactions were pooled and split to minimize the variation.
A Cy3- or Cy5-labeled sample was dissolved in 20 μl water, combined with a dried reference with opposite label and prehybridized with 10 μg oligo(dA)80 at 75°C for 45 min. Ten micrograms of tRNA, 10 μg herring sperm DNA and a hybridization buffer were added, giving a final volume of 70 μl and final concentrations of 5 × SSC, 0.1% SDS and 50% formamide. This mixture was applied to a microarray that had been prehybridized in 0.1% BSA, and was covered with a 22 × 60 mm LifterSlip (Erie Scientific). The slide was placed in a hybridization chamber and incubated in a water bath at 42°C for 18-20 h. After hybridization, the slide was washed in 2 × SSC, 0.1% SDS; 0.1 × SSC, 0.1% SDS; 0.1 × SSC and dried by centrifugation. The slides were scanned using a G2565BA Microarray Scanner (Agilent).
Images were analyzed using GenePix Pro 4.1 software (Axon Instruments). The complete set of raw data can be found in ArrayExpress . To exclude low-quality spots (for example, those contaminated with dust particles or having abnormal shapes) and spots with close to background intensities, the signals were filtered using R software  and the com.braju.sma-package . Spots were excluded by filtering out those where the ratio of medians deviated from the regression ratio by more than 20% or if, for any channel, less than 70% of the foreground pixels had intensities exceeding median of background plus two standard deviations of background. Further data analysis was carried out using GeneSpring 5.0 (Silicon Genetics). The local mean background intensities were subtracted from the median foreground intensities and the log2 ratios were normalized using Lowess intensity-dependent normalization in GeneSpring (20% of the data were used for smoothing) . For each day and for each probe an average log2 ratio was calculated by first averaging the two replicates on the array and then averaging the log2 ratios of the replicate slides. A minimum of one measurement per array, on a minimum of two arrays, was required to include the probe for that day, except for the first day, when one single measurement was required for the slide. Only clones that passed the filtration for all seven days (3,792 clones) were included in further analysis. The hierarchical and k-mean clustering were carried out using TIGR MeV  using the Euclidean distances metric and average linkage for determining distance in the hierarchical tree. Hierarchical clustering was also performed on the 677 clones whose expression changed at least fourfold during the time period. To verify the significance of up- and downregulated genes, we divided the sample dates into two categories, early and late, based on the result from the hierarchical clustering for all filtrated clones, where two distinct groups were formed. The first four dates were compared with the last three using the SAM algorithm with two-class unpaired design  (see Additional data file 6 for details).
The following additional data are available with the online version of this article: a comparison of expression data for the Lhcb2, PsbS, Elip, Pcyprot6, Pcyprot4 and PR1 genes measured by RNA blotting and microarray analysis (Additional data file 1); transcript profiling in aspen leaves during autumn senescence of genes in the chlorophyll, carotenoid and flavonoid biosynthesis functional categories (Additional data file 2); expression of genes involved in ethylene biosynthesis and perception in aspen leaves during senescence (Additional data file 3); the complete gene list for the Populus microarray (Additional data file 4); the final gene list of filtered, normalized and averaged data (Additional data file 5); the list of significantly differentially expressed genes (Additional data file 6); the list of genes with more than threefold increase in transcript levels during autumn senescence (Additional data file 7); and the legends and captions for the figures and tables listed above (Additional data file 8).
We are very grateful to Thomas Hiltonen, Susanne Larsson and Annelie Johansson for the preparation of clones and microarray production, as well as other important tasks. Bahram Amini and Carl Zingmark have also made essential contributions. This work was supported by the Knut and Alice Wallenberg Foundation, Wallenberg Consortium North, the Foundation for Strategic Research, the Swedish Research Council and the Swedish Research Council for the Environment, Agricultural Sciences and Spatial Planning.
- The virtual library of plant-arrays: Publications[http://www.univ-montp2.fr/~plant_arrays/Plant-Arrays_Publications.html]
- Sterky F, Regan S, Karlsson J, Hertzberg M, Rohde A, Holmberg A, Amini B, Bhalerao R, Larsson M, Villarroel R, et al.: Gene discovery in the wood-forming tissues of poplar: analysis of 5,692 expressed sequence tags.Proc Natl Acad Sci USA 1998, 95:13330–13335.View ArticleGoogle Scholar
- Bhalerao R, Keskitalo J, Sterky F, Erlandsson R, Bjorkbacka H, Birve SJ, Karlsson J, Gardestrom P, Gustafsson P, Lundeberg J, Jansson S: Gene expression in autumn leaves.Plant Physiol 2003, 131:430–442.View ArticleGoogle Scholar
- Hertzberg M, Aspeborg H, Schrader J, Andersson A, Erlandsson R, Blomqvist K, Bhalerao R, Uhlen M, Teeri TT, Lundeberg J, et al.: A transcriptional roadmap to wood formation.Proc Natl Acad Sci USA 2001, 98:14732–14737.View ArticleGoogle Scholar
- Olsen JE, Junttila O: Far red end-of-day treatment restores wild type-like plant length in hybrid aspen overexpressing phytochrome A.Physiol Plant 2002, 115:448–457.View ArticleGoogle Scholar
- EBI databases: ArrayExpress home[http://www.ebi.ac.uk/arrayexpress]
- Graham IA, Eastmond PJ: Pathways of straight and branched chain fatty acid catabolism in higher plants.Prog Lipid Res 2002, 41:156–181.View ArticleGoogle Scholar
- Kamachi K, Yamaya T, Hayakawa T, Mae T, Ojima K: Vascular bundle-specific localization of cytosolic glutamine synthetase in rice leaves.Plant Physiol 1992, 99:1481–1486.View ArticleGoogle Scholar
- Wullschleger SD, Jansson S, Taylor G: Genomics and forest biology:Populusemerges as the perennial favorite.Plant Cell 2002, 14:2651–2655.View ArticleGoogle Scholar
- Wissel K, Pettersson F, Berglund A, Jansson S: What affects mRNA levels in leaves of field-grown aspen? A study of developmental and environmental influence.Plant Physiol 2003, 133:1190–1197.View ArticleGoogle Scholar
- Külheim C, Agren J, Jansson S: Rapid regulation of light harvesting and plant fitness in the field.Science 2002, 297:91–93.View ArticleGoogle Scholar
- Quirino BF, Noh YS, Himelblau E, Amasino RM: Molecular aspects of leaf senescence.Trends Plant Sci 2000, 5:278–282.View ArticleGoogle Scholar
- Yoshida S: Molecular regulation of leaf senescence.Curr Opin Plant Biol 2003, 6:79–84.View ArticleGoogle Scholar
- Finnemann J, Schjoerring JK: Post-translational regulation of cytosolic glutamine synthetase by reversible phosphorylation and 14–3-3 protein interaction.Plant J 2000, 24:171–181.View ArticleGoogle Scholar
- Ishida H, Anzawa D, Kokubun N, Makino A, Mae T: Direct evidence for non-enzymatic fragmentation of chloroplastic glutamine synthetase by a reactive oxygen species.Plant Cell Env 2002, 25:625–631.View ArticleGoogle Scholar
- Buchanan-Wollaston V, Earl S, Harrison E, Mathas E, Navabpour S, Page T, Pink D: The molecular analysis of leaf senescence - a genomics approach.Plant Biotech J 2003, 1:3–22.View ArticleGoogle Scholar
- Adamska I, Roobol-Boza M, Lindahl M, Andersson B: Isolation of pigment-binding early light-inducible proteins from pea.Eur J Biochem 1999, 260:453–460.View ArticleGoogle Scholar
- Król M, Spangfort MD, Huner NP, Oquist G, Gustafsson P, Jansson S: Chlorophyll a/b-binding proteins, pigment conversions, and early light-induced proteins in a chlorophyll b-less barley mutant.Plant Physiol 1995, 107:873–883.View ArticleGoogle Scholar
- Prochazkova D, Sairam RK, Srivastava GC, Singh DV: Oxidative stress and antioxidant activity as the basis of senescence in maize leaves.Plant Sci 2001, 161:765–771.View ArticleGoogle Scholar
- Buchanan-Wollaston V: The molecular biology of leaf senescence.J Exp Bot 1997, 48:181–199.View ArticleGoogle Scholar
- Smart CM: Gene expression during leaf senescence.New Phytol 1994, 126:419–448.View ArticleGoogle Scholar
- Greenberg JT: Programmed cell death: a way of life for plants.Proc Natl Acad Sci USA 1996, 93:12094–12097.View ArticleGoogle Scholar
- Noodén LD, Guiamét JJ, John I: Senescence mechanisms.Physiol Plant 1997, 101:746–653.View ArticleGoogle Scholar
- Danon A, Delorme V, Mailhac N, Gallois P: Plant programmed cell death: a common way to die.Plant Physiol Biochem 2000, 38:647–655.View ArticleGoogle Scholar
- Phrap and Phred[http://www.phrap.com]
- Welcome to PopulusDB[http://poppel.fysbot.umu.se]
- Ihaka R, Gentleman R: R: A language for data analysis and graphics.J Comput Graph Stat 1996, 5:299–314.Google Scholar
- com.braju.sma - object-oriented microarray analysis in 100% R[http://www.maths.lth.se/help/R/com.braju.sma]
- Yang YH, Dudoit S, Luu P, Speed TP: Normalization for cDNA microarray.In Microarrays: Optical Technologies and Informatics(Edited by: Bittner ML, Chen Y, Dorsel AN, Dougherty ER). Bellingham, WA: Society for Optical Engineering (SPIE) 2001.Google Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, et al.: TM4: a free, open-source system for microarray data management and analysis.Biotechniques 2003, 34:374–378.Google Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response.Proc Natl Acad Sci USA 2001, 98:5116–5121.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.