Integrated transcriptomic and proteomic profiling ... - Wiley Online Library

Plant, Cell and Environment (2012) 35, 682–701

doi: 10.1111/j.1365-3040.2011.02444.x

Integrated transcriptomic and proteomic profiling of white spruce stems during the transition from active growth to dormancy pce_2444

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LEONARDO M. GALINDO GONZÁLEZ†, WALID EL KAYAL†, CHELSEA J.-T. JU*, CARMEN C. G. ALLEN, SUSANNE KING-JONES & JANICE E. K. COOKE

Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada

ABSTRACT

INTRODUCTION

In the autumn, stems of woody perennials such as forest trees undergo a transition from active growth to dormancy. We used microarray transcriptomic profiling in combination with a proteomics analysis to elucidate processes that occur during this growth-to-dormancy transition in a conifer, white spruce (Picea glauca [Moench] Voss). Several differentially expressed genes were likely associated with the developmental transition that occurs during growth cessation in the cambial zone and the concomitant completion of cell maturation in vascular tissues. Genes encoding for cell wall and membrane biosynthetic enzymes showed transcript abundance patterns consistent with completion of cell maturation, and also of cell wall and membrane modifications potentially enabling cells to withstand the harsh conditions of winter. Several differentially expressed genes were identified that encoded putative regulators of cambial activity, cell development and of the photoperiodic pathway. Reconfiguration of carbon allocation figured centrally in the tree’s overwintering preparations. For example, genes associated with carbon-based defences such as terpenoids were down-regulated, while many genes associated with protein-based defences and other stress mitigation mechanisms were up-regulated. Several of these correspond to proteins that were accumulated during the growth-to-dormancy transition, emphasizing the importance of stress protection in the tree’s adaptive response to overwintering.

Winter in north-temperate latitudes is characterized by a suite of limiting conditions for plant growth; consequently, perennial plants such as forest trees need to halt development and prepare for the potential abiotic stresses associated with the season. Forest tree species in these regions transition into a dormant state, characterized by cessation of meristem cell production, which punctuates their annual growth cycles (Olsen 2010). This seasonal termination of meristematic activity signals the end of the active growth cycle. The meristem is now unresponsive to growthpromoting signals, and is incapable of renewed growth without release by dormancy-breaking environmental conditions such as chilling (Rohde & Bhalerao 2007). In addition to cessation of meristematic activity, forest trees undergo a myriad of other physiological, cellular and molecular changes in preparation for overwintering, including bud development, acquisition of cold and desiccation tolerance, as well as accumulation of carbon and nitrogen storage reserves that can be mobilized upon regrowth the following spring. This transition from active growth to the overwintering state is generally triggered by environmental cues such as critical photoperiod or cold temperatures (Olsen 2010). Like the shoot apical meristem, the stem cambial meristem enters a state of suspended cell production, which is accompanied by a decrease in the radial dimensions of the cambial zone and anatomical changes to the cells making up this zone (Druart et al. 2007). Other cellular and biochemical changes also take place in the stem, such as the accumulation of starch, soluble carbohydrates, lipids and proteins (Sauter & Witt 1997). Proteins and lipids have been shown to accumulate in specialized storage vacuoles in ray cells and phloem parenchyma for several forest tree species (Wetzel & Greenwood 1989; Stepien, Sauter & Martin 1994). Soluble carbohydrate levels increase, in part to maintain the cell’s osmotic balance as a desiccation tolerance mechanism (Morin et al. 2007). Dehydration can also result from increased accumulation of storage compounds (proteins and sugars) and by the loss of water to formation of extracellular ice that comes with low temperatures (reviewed in

Key-words: cambium; cold; development; dormancy; photoperiod; proteome; transcriptome; trees.

Correspondence: J. E. K. Cooke. Fax: +1 780 492 9234; e-mail: [email protected] *Present address: University of California at Los Angeles, Computer Science Department, Los Angeles, CA 90095, USA. † These authors contributed equally to the work. 682

© 2011 Blackwell Publishing Ltd

Growth-to-dormancy transition in spruce stems Welling & Palva 2006). In response to this, perennials may synthesize antifreeze proteins, which inhibit ice formation and can possess additional roles in defence against psychrophilic pathogens (Griffith & Yaish 2004). In recent years, large-scale microarray gene expression profiling has been instrumental in identifying genes that are differentially expressed in stems of forest tree species – particularly Populus spp. – during the transition from active growth to dormancy, and used to infer molecular processes that occur during this transition (e.g. Schrader et al. 2004; Druart et al. 2007; Ruttink et al. 2007; Park, Keathley & Han 2008). These studies have substantially increased our understanding of the molecular events that occur during the autumnal transition in woody tissues of forest trees, and the regulators that potentially control these events. However, these large-scale transcript profiling studies do not capture information about proteins that accumulate during the transition from active growth to dormancy. In Populus spp., two-dimensional protein profiling of buds, leaves and bark of trees subjected to shortday photoperiod (SD; Jeknic & Chen 1999), and of stems sampled at different times of a natural growing season (Vander Mijnsbrugge et al. 2000), demonstrated the accumulation of a number of proteins, although only a small number of protein identities were reported, and only in the latter study. Similar descriptive studies of onedimensional sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) protein profiles of interior spruce (Picea glauca ¥ Picea engelmannii) demonstrated major changes in polypeptide accumulation in buds, shoots, leaves and roots (Roberts, Toivonen & McInnis 1991; Binnie, Grossnickle & Roberts 1994). These studies showed accumulation of ca. 30 kDa proteins, which were inferred to be vegetative storage proteins although they were not further characterized. Seasonal changes of proteins in xylem and bark were also recorded for deciduous and evergreen peach varieties (Prunus persica; Arora, Wisniewski & Scorza 1992). It is surprising that no transcriptome-level study has been carried out to date on stems of conifer species during the autumnal transition from active growth to dormancy. However, the anatomical differences exhibited by conifer stem tissues, such as lack of xylem vessels and prominence of resin canals, suggest that differences in the autumnal transcriptome between angiosperm and conifer forest trees are likely. Given the potential importance of proteins that are synthesized in preparation for overwintering, it is also surprising that no study has systematically identified proteins that are accumulated during the transition from active growth to dormancy in forest trees. The objective of the present study was to identify changes in both the transcriptome and proteome that occur during the SD-induced transition from active growth to dormancy in stems of white spruce (P. glauca), and to compare transcript and protein profiles. This comparison at two ‘omic’ levels allows for a more comprehensive inference of processes that are occurring in conifer tree stems during the transition from active growth to dormancy.

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MATERIALS AND METHODS Plant material and experimental conditions Two-year-old white spruce (P. glauca) seedlings were used for all experiments, which were conducted as described by El Kayal et al. (2011). Using a complete randomized block design, seedlings were grown in growth chambers under long days [LD; 16 h day/8 h night, 20 °C, 50 to 60% relative humidity (RH)] for 8 to 10 weeks. Shortly before seedlings were to begin bud formation, the photoperiod was changed to SD (8 h day/16 h night, 20 °C, 50 to 60% RH) to induce rapid and synchronous bud formation. This was designated Day 0. Lignified whole stems (comprising bark, secondary phloem, cambial zone and secondary xylem) representing the current year’s growth (microarrays, microscopy) or previous year’s growth (protein analyses) were sampled at 0, 3, 7, 14, 28 and 70 d (10 weeks) SD, and immediately frozen in liquid nitrogen. Remaining plants were maintained in SD for an additional 8 to 15 weeks, and then transferred to low temperatures (LT; 3 ⫾ 1 °C) for 3 to 4 weeks with continuing SD prior to harvest.These are referred to as LT samples.

Microscopy Lignified stem sections were fixed under vacuum in 2% (v/v) glutaraldehyde, 1% (w/v) caffeine and sodium phosphate (pH 7.2), and embedded in JB-4 Plus (Polysciences, Eppelheim, Germany). Longitudinal radial sections (4 mm) were stained with Amido Black in 7% acetic acid for protein visualization. Transverse sections (4 mm) were stained with Richardson’s stain (Richardson, Jarett & Finke 1960).

RNA extraction and microarray analysis RNA was extracted using a CTAB method (Pavy et al. 2008) and assessed using a Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) and 2100 Bioanalyzer (Agilent, Mississauga, ON, Canada). An 11 K low-redundancy white spruce cDNA microarray (Pavy et al. 2008) produced at the Vancouver Prostate Centre Microarray Facility (Vancouver, Canada) was used for analyses.Two micrograms of total RNA was amplified using the amino allyl antisense RNA (aRNA) procedure, and 5 mg aRNA was directly labelled with Alexa Fluor® 555 or 647 dyes according to the manufacturer’s protocol (Invitrogen, Carlsbad, CA, USA). Coupling efficiency was determined by NanoDrop. Hybridization and wash procedures were carried out according to Pavy et al. (2008). Four independent biological replicates for 3, 14, 70 d SD and LT stems were each co-hybridized against 0 d samples, with two replicates labelled with Alexa Fluor 555 and two replicates labelled with Alexa Fluor 647. Arrays were scanned with a GenePix 4000B and analysed using Genepix Pro v6.0 (Molecular Devices, Sunnyvale, CA, USA) as described by El Kayal et al. (2011). Statistical analyses were carried out using LIMMA (Smyth 2005) in R

© 2011 Blackwell Publishing Ltd, Plant, Cell and Environment, 35, 682–701

684 L. M. Galindo González et al. (Ihaka & Gentleman 1996) according to El Kayal et al. (2011). The P values were adjusted using the Benjamini– Hochberg procedure (Benjamini & Hochberg 1995). An adjusted P value cut-off of 0.01 and fold change greater than 1.5 or smaller than 0.67 were used to denote significantly differentially expressed (DE) sequences. These selection criteria are the same as those used by El Kayal et al. (2011), and similar to that used by (Pavy et al. 2008) for these same arrays, where qRT-PCR validations for more than 30 genes showed this cutoff to be robust (Pavy et al. 2008; El Kayal et al. 2011).

Microarray data enrichment and cluster analyses Sequence annotations were as described in Pavy et al. (2008). DE genes were further classified into GO functional categories (biological processes level 2) using Blast2GO (Conesa et al. 2005; Conesa & Gotz 2008; Gotz et al. 2008). The resulting functional categories were compared with the GO functional categories of the complete array using the hypergeometric distribution statistic to detect significant changes in major categories. A Bonferroni correction was applied to obtain adjusted P values. DE genes were also clustered to find genes with common patterns of expression, using the K-means algorithm in MultiExperiment Viewer (MeV v.4.3; Saeed et al. 2003). Figure of Merit (FOM) was used to determine the optimal number of clusters. Clustering was repeated three times, and the average percentage of sequences in each functional category for the respective clusters was reported. The resulting sequences of each cluster were characterized for functional categories and compared with the full array as described previously for DE genes. In addition to the functional categorization, the sequences in clusters demonstrating a similar trend were grouped in Blast2GO to find enriched KEGG pathways (http://www.genome.jp/kegg/). The sequences in each cluster were additionally characterized according to lower level MIPS categories for further information regarding specific gene functions.

Quantitative RT-PCR Quantitative reverse transcription PCR (qRT-PCR) was carried out for six genes found to be DE in the microarray analyses. Primer design, cDNA synthesis and qRT-PCR using SYBR Green were carried out according to El Kayal et al. (2011). TRANSLATION INITIATION FACTOR 5A (TIF5A, GenBank DR448953) was used as the reference gene, as this gene did not show statistically significant differences in transcript abundance in stems over the time course (P value = 0.1374). Gene-specific primers for the target and reference genes are included in Supporting Information Table S1. Four biological replicates, each with three technical replicates, were analysed per sample. Reactions were carried out with 20 ng of cDNA in a volume

of 10 mL on an ABI PRISM 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Quantification of both target and reference genes was carried out using standard curves. Statistical analyses were performed in SAS v9.1 (SAS Institute, Cary, NC, USA).

Protein extraction and IEF-SDS PAGE Eighty milligrams of frozen ground tissue from 0 and 70 d stems was suspended in 100 mm of (4-(2-hydroxyethyl)-1piperazineethanesulfonic acid (HEPES) pH 7.4, 5 mm EDTA, 10 mm DTT, 10% (v/v) glycerol, 7.5% (w/v) PVPP, 0.3% (w/v) DIECA and a protease inhibitor cocktail (Bioshop Canada, Burlington, ON, Canada) containing 1 mm 4-(2-aminoethyl) benzenesulfonyl fluoride hydrochloride (AEBSF), 0.3 mm aprotinin, 10 mm bestatin, 10 mm E64 and 100 mm leupeptin. Samples were homogenized using a Mixer Mill MM 301 (Retsch, Haan, Germany), and sonicated 40 s at 40% amplitude on ice in an Ultrasonic Dismembrator 500 (Fisher Scientific, Pittsburgh, PA, USA) prior to centrifugation at 19 100 g at 4 °C for 30 min. Supernatants were recovered, and proteins were precipitated at -20 °C for 60 min in ice-cold acetone (4:1). Samples were centrifuged at 19 100 g at 4 °C for 15 min and pellets were air dried before resuspension in 100 mm HEPES (pH 7.4), 5 mm EDTA, 0.1 mm DTT, 10% glycerol, and protease inhibitor cocktail as above. Proteins were quantified using the bicinchoninic acid assay (Pierce, Rockford, IL, USA). Three technical replicates for each of three biological replicates were analysed per time point. Samples (100 mg protein in 50 mL buffer) were mixed with 2% IPG buffer, and added to 410 mL rehydration buffer containing 8 m urea, 2% (w/v) CHAPS, 1% IPG buffer 1% (w/v) bromophenol blue, and 0.28% (w/v) DTT, and used to rehydrate Immobiline DryStrip pH 3–10 NL 24 cm IPG strips (GE Healthcare, Chalfont St. Giles, UK) overnight. Rehydrated strips were isoelectrofocused in an Ettan™ IPGphor II™ (GE Healthcare) at 500 V for 1 h, followed by 1000 V for 1 h, then 8000 V until reaching 60 000 Vh. SDS-PAGE was carried out using a 12% separating gel [0.375 m Tris-HCl, pH 8.8 buffer, 0.1% SDS, 12% acrylamide solution, 0.05% (w/v) ammonium persulfate and 0.05% TEMED]. Strips were pre-equilibrated for 15 min in equilibration solution [6 m urea, 75 mm Tris-HCl pH 8.8, 29.3% (v/v) glycerol, 2% (w/v) SDS, 0.002% bromophenol blue, and 0.5% (w/v) DDT], and 15 min in 6 m urea, 75 mm Tris-HCl pH 8.8, 29.3% (v/v) glycerol, 2% (w/v) SDS, 0.002% (w/v) bromophenol blue and 4.5% (w/v) iodoacetamide prior to protein separation on an Ettan™ DALTsix (GE Healthcare) at 400 mA, 500 V and 10 W per gel. Gels were stained with Deep Purple™ (GE Healthcare) and imaged using a Fujifilm Image Reader FLA-5000 (Tokyo, Japan) with an LPG filter at a 532 nm wavelength at 600 V. Images were adjusted to reduce background using standardized conditions across all gels (L-Process v2.2, Fujifilm and Photoshop, Adobe) before quantification using Image Master™ 2D Platinum (GE Healthcare). Spots were detected using a smoothness of 10, minimum area of 100


Growth-to-dormancy transition in spruce stems and a saliency of 2000, and spots across gels were matched using 10 landmarks per gel. A synthetic (artificial composite) gel containing all matched and unmatched spots was created to assess differential expression. Groups of corresponding spots across all the gels were confirmed by eye after automated matching to correct for missing or mismatched spots, and volumes were calculated for each spot group as pixel volume of the upper 75% of the threedimensional image of the spot. Spot volumes across gels were normalized for subsequent quantitative analysis by dividing the volume of each spot by the standard deviation of all spot volumes in each corresponding gel (Cooke et al. 2003). Reproducibility within biological replicates and between technical replicates was assessed by calculating Pearson correlation coefficients (r2). Statistical analysis was carried out using significance analysis of microarrays (SAM;Tusher,Tibshirani & Chu 2001). Biological replicates were designated as blocks, and a two-class unpaired, unlogged, t-statistic with 100 permutations was performed to find DE proteins. The false discovery rate (FDR) was adjusted to 5.2%, and a fold change cut-off of ⱖ1.5 or ⱕ0.67 was applied in accordance with the microarray analysis.

Mass spectrometry Proteins were excised from gels using an Ettan™ automated spot picker (GE Healthcare) and placed into 96-well plates containing 150 mL of MilliQ water. Samples were dehydrated in 50 mL of 100% acetonitrile for 10 min, followed by two cycles of 50 mL of 0.1 m NH4HCO3 and 50 mL of 100% acetonitrile. Samples were then dried in an SPD SpeedVac® (Thermo Scientific). Proteins in gel plugs were reduced in 30 mL of 10 mm DTT/0.1 m NH4HCO3 at 56 °C for 30 min, 50 mL of 100% acetonitrile for 5 min, 30 mL of 55 mm iodoacetamide/0.1 m NH4HCO3 for 20 min in the dark, 150 mL of 0.1 m NH4HCO3 for 15 min with gentle vortexing for 15 s every 3 min, then dehydrated two rounds as described previously. Proteins were digested for 1 h at room temperature then for 16 h at 37 °C, each time in 20 mL of 0.02 mg mL-1 Trypsin gold (mass spectrometry grade, Promega, Madison, WI, USA) in 40 mm NH4HCO3/10% acetonitrile. To stop the digestion, gel plugs were incubated in 15 mL of 0.4% formic acid for 30 min at room temperature. Liquid from each well was transferred to a mass spectrometry plate. Gel plugs were incubated again in 25 mL of 50% acetonitrile/0.1% formic acid for 30 min, and the liquid was transferred to the respective well in the mass spectrometry plate. Samples were concentrated by SpeedVac to approximately 45 mL and stored at -80 °C until analysed. Liquid chromatography-tandem mass spectrometry (LCMS/MS) was carried out using an Agilent 1100 series LC/MSD trap (Agilent Technologies, Santa Clara, CA, USA), with an AutoMS of 2, 622-922-1522 peak exclusion, three precursor ions and a threshold of absorbance of 10 000. Proteins were identified using MASCOT (Perkins et al. 1999) against the NCBI non-redundant and EST plant databases (http://www.ncbi.nlm.nih.gov). A match

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was considered significant if the protein exceeded the significant threshold based on the Mowse algorithm (Pappin, Hojrup & Bleasby 1993), with probability of 0.05.

Protein data enrichment analyses Proteins were classified into GO functional categories (biological processes level 2) using Blast2GO. Overrepresented functional categories were identified comparing the classified proteins to proteins inferred from a random sample of 5000 contigs derived from the white spruce gene catalogue (GCAT v3.3; Rigault et al. 2011), which at the time of analysis contained 27 720 contigs comprising 272 172 ESTs (http://www.arborea.ulaval.ca/). A global chi-square distribution statistic was used followed by individual heterogeneity tests for each category (Zar 1999). DE genes from the microarray analysis corresponding to the DE proteins were identified by using the top MASCOT hit as the query using tBLASTn, and any resulting high-scoring nucleotide hits reciprocally BLASTed against the DE protein sequences.

RESULTS Anatomical and cellular level changes in the stem during the growth to dormancy transition Microscopy analyses of stems during the transition from active growth to dormancy demonstrated a reduction in the number of cell layers making up the cambial zone (Fig. 1). Bud formation occurred during this same time period (El Kayal et al. 2011), with the first changes at the shoot apex detected at 7 d SD and development nearly completed by 10 week SD. A separate analysis of the same experimental material demonstrated that limited number of primordia were formed at the shoot apical meristem between 10 week SD and LT (El Kayal et al. 2011), indicating that 10 week plants are not dormant. Based on these microscopy analyses of the present study and that of El Kayal et al. (2011), the passage of the cambial zone into dormancy – sensu Rohde & Bhalerao (2007) – occurred between the 10 week (70 d) and LT time points. In the terminology of Lang (1987), LT likely corresponds to endodormancy, although because we did not explicitly test the capacity of the vascular cambium at each time point to resume cell division under favourable conditions in these experiments, it is possible that the LT trees were ecodormant. Amido Black staining showed that notable amounts of proteins accumulate in stems as the cambial zone radial dimensions decrease, mainly in the phloem parenchyma and ray cells. Darker staining, small bodies in the cytosol of these cells were observed (Fig. 2). Other small, spherical, non-staining bodies were noted, which could correspond to lipid-storing vacuoles.

Changes in stem transcript profiles during the transition from active growth to dormancy The microarray data are archived in NCBI’s Gene Expression Omnibus (GEO; Edgar, Domrachev & Lash 2002), and


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Figure 1. Changes in the cambial zone that occur during SD-induced dormancy acquisition. A progressive reduction in the number of cell layers making up the cambial zone can be observed. Images are transverse sections through secondary stem of white spruce seedlings in their second growth cycle. For a–e, labels indicate time after transfer to SD. For f, SD plants were subsequently transferred to a period of LT under continuing SD as described in the Materials and Methods. Scale bars = 100 mm. P, phloem; C, cambial zone; X, xylem; SD, short days; LT, low temperatures.

can be accessed through GEO Series accession number GSE31090. The microarray analyses revealed that approximately 2800 sequences of the 10 400 sequences present on the array (Pavy et al. 2008) were DE in woody stems (P value = 0.01, fold change ⱖ1.5 or ⱕ0.67) at 3, 14, 70 d SD or LT compared with 0 d (Supporting Information Table S2). Of the 2176 genes DE at LT compared with 0 d, 549 were only DE at that time point, suggesting that the status of the LT plants is different than that of the 70 d plants. Each of these sequences was considered a distinct gene in further analyses, although in the absence of a physical map that includes all sequences, some closely related or nonoverlapping sequences could represent the same gene. Inspection of the DE genes with informative annotation revealed that genes associated with stress responses (154 genes), carbon metabolism (279 genes) and regulation (247 genes) were the most abundantly represented in the stem’s transcriptomic response to overwintering preparation. Together, these genes represent approximately one third of all DE genes. Enrichment analysis of the full DE gene

dataset using a hypergeometic distribution method demonstrated statistical over-representation (adjusted P value = 0.0023) of DE genes in functional categories that reflect some of these abundantly represented genes (metabolic process, cellular process and carbon utilization), but contrary to expectation there was statistical underrepresentation of DE genes classified in biological regulation and response to stimulus categories (Fig. 3; Supporting Information Table S3). A chi-square analysis of the same data yielded consistent results (Supporting Information Table S3). K-means clustering was used to determine temporal expression patterns of these DE genes from the microarray analyses, and revealed a spectrum of different temporal expression patterns (Fig. 4), ranging from genes up-regulated primarily during the latter stages of the time course (clusters 1 to 3), to genes down-regulated primarily during the latter stages of the time course (clusters 6 to 10). The robustness of the clusters that were obtained was demonstrated by the degree of similarity in the number of


Growth-to-dormancy transition in spruce stems

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Figure 2. Protein accumulation in lignified stems upon SD-induced dormancy acquisition. Radial longitudinal sections of secondary stem were stained with Amido Black, a non-specific protein stain. For a–e, labels indicate time after transfer to SD. For f, SD plants were subsequently transferred to a period of LT under continuing SD as described in the Materials and Methods. An increase in the amount of proteins is evident, mainly in the cambial zone and ray cells, particularly in the later stages of dormancy acquisition. A horizontal arrow points to phloem cells with smaller vacuoles and protein bodies. A vertical arrow points to a ray cell that shows substantive protein accumulation. Bar =100 mm. P, phloem; C, cambial zone; X, xylem; R, ray cells; SD, short days.

genes and identity of genes obtained in independent executions of the cluster analysis (data not shown). Enrichment analyses conducted with the gene lists representing each of these clusters were used to ascertain if particular functional categories were statistically over- or under-represented in any of the clusters (Supporting Information Table S4). Genes falling into the metabolic process category were statistically over-represented in clusters 1 to 6. Cluster 6, constituting genes downregulated principally at the LT time point relative to day 0, also showed significant over-representation of cellular process and carbon utilization genes, significant underrepresentation of response to stimulus genes, and moderately significant under-representation of biological regulation and signalling genes. Cellular process genes were also moderately significantly over-represented in clusters 2 and 3, which showed up-regulation relative to day 0 at later time points. Cluster 3 additionally showed moderately significant over-representation of localization,

which was also significantly over-represented in cluster 5. Cluster 9, exhibiting strong down-regulation relative to day 0 over the time course, showed significant overrepresentation of growth genes, moderately significant over-representation of metabolic process and cellular component organization genes, significant underrepresentation of multicellular organismal process genes, and moderately significant under-representation of response to stimulus genes. Given that genes classified in the metabolic process category were over-represented in several clusters, genes falling into this broad category were inspected at a finer level of classification to gain additional insight (Supporting Information Table S4). Genes associated with carbon metabolism figured centrally in the gene lists. Of the 30 photosynthesis-associated genes, 21 were included in clusters 6 to 10, showing progressive down-regulation. Many glycolysis-related genes were found in clusters 1 to 3, whereas no energy-related genes were found in clusters 9


Functional categories


Viral reproduction Cell killing Rhythmic process Locomotion Cell proliferation Carbon utilization Immune system process Death Growth Cell wall organization or biogenesis Multi-organism process Reproduction Cellular component biogenesis Cellular component organization Signalling Multicellular organismal process Developmental process Localization Response to stimulus Biological regulation Cellular process Metabolic process

All genes on microarray DE genes

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Percentage of sequences Figure 3. Enrichment analyses of differentially expressed (DE) genes determined by microarray analysis to identify statistical differences in functional category representation. Proportions of significant DE genes (white bars) in each GO category (biological process, level 2) are compared with the proportions of genes in these categories represented by all genes present on the array (black bars). Asterisks indicate categories for which the relative frequencies of DE genes are significantly different from those of all genes represented on the array, as determined using a hypergeometric distribution statistic (adjusted P value = 0.0023).

and 10. Genes associated with carbohydrate metabolism were present in all clusters, but were present in larger proportions in clusters 2 and 3 and clusters 9 and 10. Many genes categorized in carbohydrate metabolism function in biosynthesis of carbohydrates destined for cell wall biosynthesis. A total of 182 DE genes were classified as functioning in cell wall biosynthesis; these genes were distributed across all clusters, but 135 of these (74%) were found in clusters 6 to 10, that is, down-regulated clusters. Genes involved in phenylpropanoid and related metabolism, including putative enzymes of lignin biosynthesis, were concentrated in clusters 6 to 10. Genes of terpenoid metabolism were largely down-regulated (clusters 6 to 10). We used qRT-PCR to validate the transcript abundance patterns of three genes putatively involved in sucrose metabolism and transport and three genes potentially implicated in regulation of developmental processes such as meristem maintenance (Fig. 5). As can be seen in Fig. 5, the qRT-PCR transcript abundance profiles were in excellent agreement with those determined by microarray analyses for these six genes. As previously mentioned, in a companion study we conducted simultaneous microarray profiling of developing buds from these same trees (El Kayal et al. 2011). Transcript abundance profiles for an additional 18 genes were validated by qRT-PCR in developing buds, providing further support for the robustness of these microarray data.

Changes in stem protein profiles during the transition from active growth to growth cessation The observed accumulation of protein in stem sections through SD (Fig. 2) prompted us to characterize protein profiles of 70 d vs. 0 d SD stems using IEF-SDS-PAGE in conjunction with LC-MS/MS (Supporting Information Fig. S1). At 70 d, the cambial zone appears to be nearing growth cessation (Fig. 1). Endodormancy has not yet been established at this time point, based on the continued reduction of the cambial zone diameter between 70 d SD and LT, and the limited development of the apical bud that occurred between 70 d and LT (El Kayal et al. 2011). Accordingly, 70 d was chosen as an informative time point representing a critical juncture in the transition to dormancy. We identified 490 groups of matching spots across gels ran in the two treatments. A total of 216 statistically significant DE proteins were identified (FDR 5.2%, foldchange ⱖ1.5 or ⱕ0.67), 203 of which were up-regulated after 70 d SD relative to 0 d, and 13 of which were up-regulated at 0 d SD relative to 70 d. A total of 157 spots were analysed by LC-MS/MS. Of these, 106 peptides were similar to genes with annotations suggesting function (Table 1), while 12 peptides were annotated as hypothetical proteins, and 39 gave no meaningful similarities when queried against NCBI databases. Many


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Figure 4. K-means clusters of significantly DE genes identified in the microarray analysis of lignified stems during the transition from active growth to dormancy. Clustering grouped the DE genes into 10 clusters according to common patterns of expression at 3, 14 and 70 d SD, and after transfer to LT, relative to 0 d. DE, differentially expressed; SD, short days; LT, low temperatures.

analysed peptides yielded hits to the same protein accession, giving a level of redundancy of nearly 28%. Of the annotated proteins, approximately one quarter were similar only to sequences in the plant EST database, which contained at the time of the analysis over 500 000 spruce ESTs as well as several hundred thousand ESTs from other conifer species. DE proteins belonged to a number of functional categories; four of these were found to be significantly (P = 0.05) enriched following analysis using chi-square heterogeneity tests (Fig. 6). Most notably, response to stimulus proteins showed substantial over-representation. This category included proteins associated with structural protection such as chaperonins and a protein disulfide isomerase (PDI); proteins implicated in redox or detoxification, such as a thioredoxin-dependent peroxidase, a quinone oxidoreductase and a glyoxylase I, were also included. A group of proteins typically associated with response to biotic stimulus was also identified, including a thaumatin-like protein

689

and chitinase-like sequences. Nine protein spots were annotated as chitinases. Each of these spots was significantly more abundant at 70 d SD than at 0 d, showing at least a 10-fold increase. The sum of the normalized volume values of these protein spots accounted for almost 4% of all detected signal for protein volumes in the 70 d SD gels, but only 0.19% in the 0 d SD gels. The nine spots corresponded to three contigs in GCAT (data not shown), which were not represented on the microarray. Proteins were also identified that corresponded to functional categories found to be significantly enriched in the analysis of the transcriptomic data. Several proteins putatively involved in regulation of the cell cycle, cell division and DNA replication were up-regulated in stems at 70 d relative to 0 d, including a synaptonemal complex protein similar to ZYP1, a cyclin-like protein, a minichromosome maintenance protein (MCM-like protein) and a DNA gyrase subunit. A high mobility group protein (HMG) was also up-regulated at 70 d. Other putative regulators up-regulated at 70 d SD included a carbon catabolite repressor (CCR4)-like protein, SPL12 (SQUAMOSA promoter-binding like protein), nascent polypeptide associated complex domain-containing protein, FCA, an NBS resistance-like protein, a protein kinase, a speckle-type POZ-like protein, and a phosphatidylinositol 3- and 4-kinase family protein. Several DE proteins associated with carbon metabolism were also identified. Three DE enzymes related to carbon fixation, sedoheptulose-bisphosphatase, ribose-5 phosphate isomerase and the large subunit of Rubisco, were upregulated at 70 d SD, while the small subunit of Rubisco was down-regulated. Components of the light reactions were also DE. Two enzymes from the pentose phosphate pathway (ribose 5-P isomerase and phophogluconolactonase) were also up-regulated at 70 d SD. The glycolytic enzyme enolase was up-regulated, while another glycolytic enzyme, a PFKb-type carbohydrate kinase (phospofructokinase), was down-regulated at 70 d SD. Additionally, enzymes of fatty acid biosynthesis (acetyl-CoA carboxylase) and b-oxidation (acyl-coenzyme A oxidase) were both up-regulated at 70 d SD.

Comparison of DE genes and proteins We examined the congruence between the significantly DE proteomic and transcriptomic datasets. A confident match was made for 75 of the DE proteins identified in our analyses to a nucleotide probe on the cDNA microarray, which corresponds to 63.5% of the 118 annotated proteins (Supporting Information Table S5). Of these 75 DE proteins, 25 corresponded with a cDNA on the microarray that was also significantly DE in the same direction, that is, the change in transcript abundance and the change in protein abundance were either both up-regulated or both down-regulated relative to 0 d (Supporting Information Table S5). The distributions of DE sequences among common functional categories for the transcriptomic and proteomic datasets were for the most part similar (Supporting


690 L. M. Galindo González et al. qPCR mean log2 fold change microarray log2 fold change

1.5

2

(a)

1.0

(b)

NAM-like

(c)

STYLOSA-like

1.0

1

Clavata-like

0.5

0.5 0

0.0

log2 fold change

0.0 –1 –2

–1.0 3

2

–0.5

–0.5

14

–1.0

70

(d) Sucrose-phosphate synthase

1

3

4

(e)

14

70

2 Sucrose synthase

0

–1

–2

(f)

14

70

Sugar transporter

1

2

0

3

0 –1

–4 –2

–2 3

14

70

3

14

70

3

14

70

Days after transfer to SD Figure 5. qRT-PCR and microarray comparison of transcript abundance corresponding to six genes (a–f) putatively involved in regulation of developmental processes or sucrose metabolism and transport in lignified stems following transfer to SD. Each of the six genes was found to be significantly DE by microarray analysis for at least one time point relative to day 0. Data are expressed as log2 fold change relative to transcript abundance measured at day 0. For qRT-PCR, expression was determined relative to TIF5A; both the targets and TIF5A were quantified using standard curves, as described in Materials and Methods. DE, differentially expressed; SD, short days.

Information Table S3). Although there were three functional categories in common between the proteomic and transcriptomic dataset showing significant differences to their respective reference samples – response to stimulus, immune system process and reproduction – they showed opposite trends, that is, over-representation for protein abundance and under-representation for transcript abundance.

DISCUSSION Dormancy, or the inability to initiate growth from meristems in response to growth-promoting signals (Rohde & Bhalerao 2007), is a hallmark of the overwintering state of forest trees. Interestingly, white spruce – a species that exhibits predominantly determinate growth – does not require environmental cues such as SD or LT to initiate bud formation (El Kayal et al. 2011). However, SD induces rapid and synchronous bud development, and is likely required to promote the transition to dormancy. LT also appears to be an important environmental cue in this transition, though the precise role that each of these environmental cues plays in the seasonal transition of white spruce apical and cambial meristems from active growth to growth cessation remains to be determined. Although not required to initiate bud formation – and presumably cambial activity – we

employed SD to promote a synchronous transition of the shoot apical and cambial meristems to dormancy, and subsequently applied additional SD followed by 3 weeks of LT to ensure that a fully dormant state was achieved. While trees at 10 week SD are clearly not dormant based on continued production of primordia at the shoot apical meristem (El Kayal et al. 2011), LT trees are likely endodormant, although this was not explicitly tested. Empirical studies with white spruce have demonstrated that a minimum of 4 weeks’ chilling is required to break dormancy in white spruce – that is, enable bud burst under permissive conditions within a reasonable length of time – while longer chilling periods are required to achieve rapid and synchronous bud burst (Nienstaedt 1966, J. Cooke, unpublished data). Thus, it is unlikely that the trees at the LT time point had passed from endodormancy to ecodormancy, although further experimentation would be required to test this. Several studies have demonstrated that both deciduous and coniferous tree stems accumulate protein during preparation for overwintering (e.g. Wetzel & Greenwood 1989; Roberts et al. 1991; Wetzel, Demmers & Greenwood 1991; Jeknic & Chen 1999). Only a few overwintering proteins have been characterized for forest trees, notably vegetative storage proteins in deciduous trees (VSPs; reviewed in Stepien et al. 1994) and defence-associated proteins in conifers (Ekramoddoullah et al. 2000; Yu, Ekramoddoullah &


MASCOT score

55 112 204 48 75 50 335 54 72 314 56 48 140 52 50 62 56 63 52 270 56 53 63 59 52 50 79 53 53 56 50 74 54 54 370 122 57 52 54 58 49 52 114 50 75

Spot ID

470 462 451 333 455 463 473 695 779 468 378 928 459 634 549 241 240 745 97 477 373 853 673 590 391 604 614 336 99 598 782 600 818 457 778 372 371 701 181 223 98 340 606 82 106

1 3 4 1 1 2 9 2 1 9 1 1 5 1 1 2 2 4 2 6 2 2 2 3 2 2 2 3 1 2 2 4 1 2 9 2 1 1 1 2 1 1 2 3 1

No. of peptides Putative class I chitinase [Taxodium distichum]::BAD02824.1 Class Ia chitinase [Galega orientalis]::AAP03088.1 Chloroplast ribose-5-phosphate isomerase [Spinacia oleracea]::AAL77589.1 Putative lactoylglutathione lyase [Brassica rapa]::BAF81517.1 Putative secretory carrier membrane protein [Oryza sativa Japonica group]::BAD07864.1 Zinc knuckle domain containing protein-like [Oryza sativa Japonica group]::BAD44910.1 Class II chitinase [Picea abies]::AAT09427.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Chloroplast serine acetyltransferase [Thlaspi goesingense]::AAT38562.1 Class II chitinase [Picea abies]::AAT09427.1 DNA repair protein-related (ISS) [Ostreococcus tauri]::CAL56731.1 APM1 (Aminopeptidase M1) [Arabidopsis thaliana]::NP_195035.2 Class Ia chitinase [Galega orientalis]::AAP03088.1 Class II chitinase [Picea abies]::AAT09427.1 Alcohol dehydrogenase::AAC49545.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 ATPase beta subunit [Cycas revoluta]::AAL27805.1 Hypothetical protein [Vitis vinifera]::CAN71106.1 Hypothetical protein [Oryza sativa Japonica Group]::BAD16023.1 Class II chitinase [Picea abies]::AAT09427.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Predicted protein [Physcomitrella patens subsp. patens]::EDQ64560.1 NADH dehydrogenase subunit F [Cercidiphyllum japonicum]::AAF08180.1 Mutator-like transposase [Arabidopsis thaliana]::AAD25591.1 Putative translational activator (ISS) [Ostreococcus tauri]::CAL54485.1 NADH dehydrogenase subunit F [Penaea mucronata]::AAF76340.1 Asparaginyl-tRNA synthetase [Arabidopsis thaliana]::CAA10904.1 Predicted protein [Physcomitrella patens subsp. patens]::EDQ48928.1 Phototropic-responsive protein, putative [Arabidopsis thaliana]::NP_190566.1 Kinesin motor protein-related [Arabidopsis thaliana]::NP_179846.2 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Hypothetical protein [Vitis vinifera]::CAN63377.1 Cyclin-like F-box; Galactose oxidase, central [Medicago truncatula]::ABN09053.1 Hydroxyproline-rich glycoprotein family protein-like [Oryza sativa Japonica group]::BAD29556.1 Leghemoglobin [Pisum sativum]::BAA31157.1 2-Phospho-D-glycerate hydrolase::AAA21277.1 Maturase-like protein [Adesmia volckmannii]::AAD52863.1 Unnamed protein product [Vitis vinifera]::CAO43835.1 Putative Na/H antiporter [Cymodocea nodosa]::CAL44986.1 P700 chlorophyll a-apoproteins 84 KD protein [Pisum sativum]::CAA29003.1 Hydrolase, hydrolyzing O-glycosyl compounds [Arabidopsis thaliana]::NP_001031936.1 Hypothetical protein LOC_Os12g15610 [Oryza sativa (Japonica cultivar-group)]::ABA97297.1 Class II chitinase [Picea abies]::AAT09427.1 Hypothetical protein [Vitis vinifera]::CAN71106.1 Chloroplast serine acetyltransferase [Thlaspi goesingense]::AAT38562.1

BLASTp annotation

Table 1. Differentially expressed annotated proteins

3E-117 2E-89 1E-25 2E-129 1E-15 1E-103 2E-106 0 2E-33 3E-108 0 1E-89 2E-89 2E-106 0 0 0 0 6E-93 2E-106 0 0 0 8E-175 0 0 2E-82 3E-126 0 0 0 0 6E-15 2E-42 0.076 0 0 0 0 0 2E-148 3E-23 6E-106 0 1E-33

E-value

SAM Q-value (%) 3.18 0.00 1.62 0.00 1.62 0.00 2.10 1.28 0.00 4.17 0.00 0.00 2.10 0.89 0.00 4.17 1.54 1.62 0.00 5.20 0.00 0.25 1.62 0.00 2.10 3.18 0.00 5.20 1.54 4.17 6.73 0.00 0.00 0.00 1.54 0.00 0.00 0.00 0.00 0.25 0.00 4.17 0.25 2.28 0.89

SAM fold change 1.32E + 11 7.92E + 10 7.28E + 10 7.07E + 10 7.01E + 10 6.45E + 10 5.85E + 10 4.93E + 10 4.70E + 10 4.60E + 10 3.81E + 10 3.14E + 10 2.80E + 10 2.66E + 10 2.42E + 10 2.26E + 10 1.99E + 10 1.96E + 10 1.95E + 10 1.89E + 10 1.88E + 10 1.86E + 10 1.58E + 10 1.56E + 10 1.48E + 10 1.38E + 10 1.12E + 10 1.06E + 10 8.04E + 09 6.71E + 09 4.89E + 09 6.14E + 01 5.16E + 01 3.29E + 01 3.25E + 01 2.28E + 01 1.84E + 01 1.76E + 01 1.61E + 01 1.41E + 01 1.33E + 01 1.31E + 01 1.23E + 01 1.17E + 01 1.13E + 01

Growth-to-dormancy transition in spruce stems


691

MASCOT score

206 59 54 56

51 54 54 56 52 52 52 203 61 54 56 49 51 55 57 51 55 58 73 62 62 330 50 69

49 52 68 60 75 59 54 149 54

56 51 116

Spot ID

467 603 756 430

609 838 838 574 597 597 798 491 105 187 693 297 320 412 425 588 688 361 800 836 520 376 628 350

156 298 417 843 796 696 265 561 328

562 166 516

Table 1. Continued

1 2 2

1 4 2 3 2 2 2 3 2

3 2 2 2 2 1 2 4 2 3 1 1 2 2 1 2 2 3 2 3 3 8 2 2

5 2 1 1

No. of peptides Putative class I chitinase [Taxodium distichum]::BAD02824.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Predicted protein [Physcomitrella patens subsp. patens]::XP_001786655.1 SPL12 (SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE 12); transcription factor [Arabidopsis thaliana]::NP_191562.1 MCM protein-like protein [Nicotiana tabacum]::BAC53939.1 O-acetyltransferase-related [Arabidopsis thaliana]::NP_568662.1 Maturase [Euonymus fortunei]::AAR20302.1 Polyprotein [Oryza australiensis]::BAA22288.1 Putative chloroplast 1-hydroxy-2-methyl-2-(ISS) [Ostreococcus tauri]::CAL55258.1 Putative Na/H antiporter [Cymodocea nodosa]::CAL44986.1 Glycine-rich protein [Arabidopsis thaliana]::NP_189551.1 Thaumatin-like protein [Cryptomeria japonica]::BAD90814.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Acetyl-CoA carboxylase [Brassica napus]::CAC19876.1 Hypothetical protein OsJ_016406 [Oryza sativa (Japonica cultivar-group)]::EAZ32923.1 HMG-1 [Canavalia gladiata]::BAA19156.1 Hypothetical protein [Vitis vinifera]::CAN63377.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Protein kinase 2 [Populus nigra]::BAA94510.1 AtpB [Brucea javanica]::ABU75152.1 Putative cis-zeatin O-glucosyltransferase [Oryza sativa Japonica group]::BAC16059.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Alcohol dehydrogenase::AAC49545.1 Predicted protein [Physcomitrella patens subsp. patens]::EDQ48531.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 Enolase [Spinacia oleracea]::CAB96173.1 Predicted protein [Physcomitrella patens subsp. patens]::XP_001782328.1 Nascent polypeptide-associated complex (NAC) domain-containing protein [Arabidopsis thaliana]::NP_177466.1 Putative Na/H antiporter [Cymodocea nodosa]::CAL44986.1 Cysteine protease Mir1 [Zea diploperennis]::ABA46972.1 Putative auxin-independent growth promoter [Oryza sativa (Japonica cultivar-group)]::BAD69015.1 ATPase beta subunit [Cycas revoluta]::AAL27805.1 NHL repeat containing protein [bacterium Ellin514]::ZP_02965050.1 Speckle-type POZ protein-like [Oryza sativa (japonica cultivar-group)]::BAD45437.1 ATPase beta subunit [Cycas revoluta]::AAL27805.1 Synaptonemal complex protein ZYP1 [Brassica oleracea]::ABO69625.1 SH2 motif; Alcohol dehydrogenase, zinc-containing; Resolvase, Rnase H-like fold; Nucleic acid binding, OB-fold, subgroup [Medicago truncatula]::ABD32884.1 Hypothetical protein LOC_Os12g15610 [Oryza sativa (japonica cultivar-group)]::ABA97297.1 Receptor-like kinase [Marchantia polymorpha]::BAF79940.1 cp10-like protein [Gossypium hirsutum]::AAM77651.1

BLASTp annotation

3E-23 0 9E-90

0 1E-117 1E-114 0 1E-26 5E-12 0 8E-05 0

0 0 0 0 0 0 3E-13 2E-92 0 0 1E-173 8E-60 0 0 0 0 0 0 0 8E-107 0 0 3E-114 6E-56

3E-117 0 8E-107 0

E-value

SAM Q-value (%) 0.00 0.89 3.18 0.25 5.20 0.89 0.89 6.73 1.62 1.62 1.62 0.00 0.00 4.17 1.54 2.28 0.00 3.18 3.18 1.62 1.54 0.89 1.28 3.18 2.10 0.00 3.18 0.25 1.28 0.25 0.00 1.62 1.62 1.54 6.73 1.28 0.00 1.54 3.18 0.25

SAM fold change 1.09E + 01 1.01E + 01 9.69E + 00 8.31E + 00 8.12E + 00 7.93E + 00 7.93E + 00 7.80E + 00 7.57E + 00 7.57E + 00 6.71E + 00 6.50E + 00 6.15E + 00 6.13E + 00 5.45E + 00 5.38E + 00 5.26E + 00 5.06E + 00 4.89E + 00 4.62E + 00 4.48E + 00 4.38E + 00 4.15E + 00 3.97E + 00 3.88E + 00 3.88E + 00 3.78E + 00 3.78E + 00 3.72E + 00 3.53E + 00 3.48E + 00 3.41E + 00 3.36E + 00 3.32E + 00 3.28E + 00 3.24E + 00 3.24E + 00 2.91E + 00 2.82E + 00 2.72E + 00




57 166 124 187 497 81 50 423 57

52 193 209 50

52 50 77 84 52

67 57 53 54 55

90 140 66 57

71 285 529 400 90 734 144 263 347

538 486 136 668

586 411 930 833 143

152 510 658 954 605

709 935 959 961

1 2 3 2

1 2 2 2 2

1 2 1 2 2

3 5 5 2

2 4 2 3 11 2 2 12 2

2 2 2 2 1 2

Thioredoxin-dependent peroxidase [Plantago major]::CAH58634.1 Hypothetical protein CHLREDRAFT_193403 [Chlamydomonas reinhardtii]::XP_001698933.1 Putative DNA gyrase subunit [Oryza sativa (japonica cultivar-group)]::AAP73860.1 Mutant FCA-D1 [Triticum aestivum]::AAP84375.1 Putative chaperonin 60 beta precursor [Oryza sativa Japonica group]::BAA92724.1 Glucose-repressible alcohol dehydrogenase transcriptional effector CCR4 and related proteins (ISS) [Ostreococcus tauri]::CAL57543.1 Hypothetical protein OsJ_001421 [Oryza sativa (japonica cultivar-group)]::EAZ11596.1 Quinone oxidoreductase [Fragaria x ananassa]::AAO22131.1 2-Phospho-D-glycerate hydrolase::AAA21277.1 2-Phospho-D-glycerate hydrolase::AAA21277.1 Protein disulfide-isomerase precursor (PDI)::Q43116 Ribulose-1,5-bisphosphate carboxylase/oxygenase [Hexalectris revoluta]::AAL58654.1 Hypothetical protein [Vitis vinifera]::CAN63377.1 Chloroplast sedoheptulose-1,7-bisphosphatase [Morus alba var.multicaulis]::ABK76304.1 RTL3 (RNASE THREE-LIKE PROTEIN 3); double-stranded RNA binding / ribonuclease III [Arabidopsis thaliana]::NP_199328.1 ATP synthase beta subunit [Diarrhena obovata]::ABH02600.1 6-Phosphogluconolactonase [Oryza brachyantha]::ABG73467.1 ATP synthase subunit beta, mitochondrial precursor::P17614 A Chain A, Crystal Structure Of Peroxisomal Acyl-Coa Oxidase-Ii (ISS) [Ostreococcus tauri]::CAL50172.1 Hydrogen-transporting ATP synthase [Brassica rapa]::ABL97963.1 Zinc knuckle domain containing protein-like [Oryza sativa (japonica cultivar-group)]::BAD44910.1 PsHSC71.0 [Pisum sativum]::CAA83548.1 Ribulose-1,5-carboxylase/oxygenase [Larix laricina]::CAA34161.1 Retrotransposon protein, putative, Ty3-gypsy subclass [Oryza sativa (Japonica cultivar-group)]::ABA98741.2 ATP synthase subunit beta, mitochondrial precursor::P17614 Hypothetical protein [Vitis vinifera]::CAN66453.1 Kinesin motor protein-related [Arabidopsis thaliana]::NP_179846.2 ATPase beta subunit [Cycas revoluta]::AAL27805.1 FAT domain-containing protein / phosphatidylinositol 3- and 4-kinase family protein [Arabidopsis thaliana]::NP_680770.1 Methionine synthase [Zea mays]::AAL33589.1 Fructokinase 3 [Solanum lycopersicum]::AAR24912.1 ATPase beta subunit [Cycas revoluta]::AAL27805.1 Unknown protein [Arabidopsis thaliana]::NP_201224.1 2E-43 1E-103 0 0

0 0 0 0 0

5E-71 1E-104 4E-30 3E-74 0

0 2E-84 0 0

5E-150 6E-75 0 0 3E-96 0 0 3E-165 0

6E-56 0 0 5E-95 1E-57 0

0.89 3.18 5.20 1.62 6.73 0.00 8.36 1.62 9.63

1.73E + 00 1.73E + 00 1.66E + 00 1.66E + 00 1.63E + 00 1.59E + 00 5.83E-01 5.55E-01 5.27E-01

3.09E-01 1.94E-01 1.74E-01 4.85E-03

7.02 0.33 5.20 9.63

12.31 12.61 12.61 9.63 0.33

2.10 0.00 3.18 1.54 0.00 5.20 4.17 1.54 2.28

2.20E + 00 2.05E + 00 2.05E + 00 1.97E + 00 1.90E + 00 1.88E + 00 1.86E + 00 1.79E + 00 1.77E + 00

5.26E-01 5.21E-01 5.11E-01 4.48E-01 3.84E-01

3.18 2.28 1.28 3.18 1.28 6.73

2.68E + 00 2.60E + 00 2.60E + 00 2.36E + 00 2.33E + 00 2.21E + 00

The Q-value (%) is the minimum false discovery rate at which the gene is significant. A fold change value greater than 1 indicates up-regulation at 70 d relative to 0 d SD, while a value less than 1 corresponds to down-regulation at 70 d relative to 0 d SD.

97 66 53 54 78 68

675 472 559 169 108 314

Growth-to-dormancy transition in spruce stems 693


Locomotion Biological adhesion

Random sample DE

Pigmentation Growth

Functional categories

Reproductive process Immune system process

* *

Reproduction Multi-organism process

*

Peroxidase reaction Multicellular organismal process Developmental process Response to stimulus

*

Biological regulation Localization Establishment of localization Cellular process Metabolic process 0

10

20

30

40

Percentage of sequences Figure 6. Enrichment analyses of differentially expressed (DE) proteins to identify statistically over-represented functional categories. Proportions of significantly DE proteins (white bars) in each GO category (biological process, level 2) are compared with the proportions of proteins in these categories inferred from a random sample of 5000 putative genes (contigs) from the white spruce gene catalogue (black bars). Asterisks indicate categories for which the relative frequencies of DE proteins are significantly different than those of the random sample, as determined using chi-square heterogeneity tests (P value = 0.05).

Misra 2000; Liu, Ekramoddoullah & Yu 2003). Microarray and other transcriptomic approaches are not likely to capture information about accumulated overwintering proteins, as transcript abundance patterns may not necessarily parallel protein accumulation patterns. Thus, we carried out our transcriptomic-scale study in tandem with a proteomics analysis of protein changes that occur in stems of white spruce during the transition from active growth to dormancy as a means to identify overwintering proteins.

Global patterns of transcript and protein abundance during the transition from active growth to dormancy Transition from active growth to dormancy in the 2-yearold white spruce seedlings used in this study was accompanied by substantial changes to the transcriptome and proteome in stems of these trees. While the proteomic study identified fewer DE proteins than DE genes identified in the microarray study – in part because fewer time points were examined – one third of the DE proteins with a corresponding sequence present on the microarray showed congruent expression patterns. Many of the DE proteins correspond to those identified in peach phloem during dormancy induction by Renaut et al. (2008). The comparatively high proportion of relatively abundant,

stably accumulating proteins in the 70 d stem sample likely increased our ability to detect consistent patterns between the transcript and protein data. Comparison of protein profiles for a relatively late date in the transition from active growth to dormancy to transcript profiles for this time point versus several earlier time points also increased the likelihood of observing changes in transcript abundance corresponding to changes in protein abundance. Further congruence probably would have been obtained if additional time points had been used for proteomic analyses. Also, it should be noted that whole stems with welldeveloped secondary growth from the current year’s growth were used for transcript profiling, whereas whole stems with well-developed secondary growth from the previous year’s growth were used for protein profiling. While it might be expected that gene expression programmes are similar between current year and previous year growth, it is also clear that differences do exist (Dhont et al. 2011), which may account for some of the discord between the transcriptomic and proteomic datasets. About a third of the proteins with informative annotation did not correspond to any sequence present on the 11K microarray. High identity matches for several of these proteins have been obtained, with additional sequencing of white spruce expressed genes that has been carried out subsequent to the generation of this microarray (data not shown).


Growth-to-dormancy transition in spruce stems Nearly all of the DE proteins identified in our study were up-regulated at 70 d SD relative to 0 d, suggesting that many proteins accumulate in stems in preparation for overwintering. These accumulated proteins are consistent with the observed increase in protein content of ray and parenchyma cells (Fig. 2). While it is likely that at least one of these accumulated proteins serves a role as a VSP, none of the accumulated proteins showed sequence similarity to the Populus bark storage protein (BSP) family, the best characterized of the VSPs (Cooke & Weih 2005). Diverse proteins serve as VSPs in other perennials, such as lipoxygenases, acid phosphatases, lipid acyl hydrolases, protease inhibitors, PR proteins, b-amylases and chitinases (reviewed in Meuriot et al. 2004). Of these potential VSP candidates, only chitinases were identified in our proteomics analysis as accumulating significantly (Table 1). However, the DE chitinases identified in the proteomics analysis appear to be Class I chitinases (Galindo & Cooke, unpublished results), whereas chitinases characterized as VSPs in other species are Class III chitinases (Peumans et al. 2002; Meuriot et al. 2004). Rubisco is sometimes also considered to be an unconventional storage protein (Cooke & Weih 2005). The observed accumulation of the large subunit of Rubisco – contrasting with the down-regulation of the small subunit of Rubisco – suggests that perhaps the large subunit could serve a storage function in overwintering white spruce stems.

Genes associated with cell maturation and growth cessation are differentially expressed during the growth-to-dormancy transition Our microscopy analyses showed progressive growth cessation in the cambial zone over the time course. During the transition from active growth to dormancy, new cell generation from the vascular cambium ceases, and cells produced prior to termination of cell division all proceed to maturation (Rohde & Bhalerao 2007). DE putative cell cycle genes were found in both up- and down-regulated clusters. A number of studies have demonstrated that some, but not all cell cycle core genes are down-regulated in the transition to winter (Schrader et al. 2004; Druart et al. 2007; Ruttink et al. 2007), which is consistent with our findings. We found the growth GO category to be enriched in cluster 9, in which genes show progressive down-regulation upon SD and/or LT. A suite of putative cytoskeleton-related, cell cycle and chromatin remodelling genes are present in this cluster, and may be associated with the cessation in growth that occurs at the cambial meristem during this time frame. During the autumn, cambial cells of balsam fir (Abies balsamea) are arrested in G1 phase (Mellerowicz, Riding & Little 1989). We identified DE up-regulated proteins implicated in DNA replication that may be indicative of cell growth arrest. These included proteins similar to a cdc 10 dependent transcript (CDT1) and a minichromosome maintenance protein (MCM-like protein), which constitute part of the origin recognition complex that binds DNA to form the pre-replication complex during G1 (Nishitani &

695

Lygerou 2002). In yeast and other eukaryotes, expression of genes encoding these proteins increases during G1 (reviewed in Nishitani & Lygerou 2002). Growth cessation is preceded by cell maturation, which is particularly evident during the latter stages of the growing season. In wood, xylem cells develop into latewood with thickened cell walls and distinctive properties (Mellerowicz et al. 2001). Several cell wall biosynthetic genes were down-regulated during the latter stages of the microarray time course, including cellulose, matrix glycan and pectin-related genes (Supporting Information Table S3). In Populus tremula, many cell wall biosynthesis genes were identified as down-regulated, but some – particularly cell wall modification genes – were up-regulated (Schrader et al. 2004). Similarly, we observed that many cell wall synthesis and deposition genes were downregulated in the microarray analysis during growth cessation, while several cell wall modification enzymes including glucanases, hydrolases and extensins, were up-regulated. A few cell wall-related DE proteins were identified as up-regulated in the proteomics analysis, including a hydroxyproline-rich glycoprotein and a glycine-rich protein. Increased incorporation of these proteins into cell walls would likely alter cell wall properties. Remodelling of the cell wall could facilitate resilience against water deficit and low temperature conditions during the winter months. In white spruce needles, changes in the composition of the cell wall were speculated to contribute in cell elasticity to withstand cell size changes due to drought (Zwiazek 1991; Renault & Zwiazek 1997). Likewise, in canola, low temperature stress resulted in cell wall changes to bind water, prevent dehydration and allow super cooling (Kubacka-Zebalska & Kacperska 1999). These observations suggest that both cell wall deposition to complete cell maturation and cell wall modification to endure inclement conditions occur in stems of white spruce during the transition from active growth to dormancy. Similar to cell walls, membranes are also modified to withstand stresses associated with overwintering (Xin & Browse 2000). De novo membrane synthesis is also required for lipid body and storage vacuole generation. Supporting this model, several genes putatively associated with fatty acid biosynthesis and metabolism were DE during the transition from active growth to dormancy. Three downregulated genes encoded putative phosphatidylglycerol specific phospholipases. Phosphatidylglycerol is particularly important in thylakoid membranes, as it facilitates dimerization of photosystem II (Kruse et al. 2000). Many of these putative membrane biosynthesis and modification genes were DE prior to the LT cue, suggesting that at least some aspects of plant membrane remodelling do not require a cold stress. A suite of genes putatively involved in very long chain fatty acid and cuticular wax biosynthesis were also DE. Most of these were down-regulated during the latter stages of the transition from active growth to dormancy, and are likely involved in the deposition of cutin and other hydrophobic materials in the bark.


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Carbon resource acquisition and utilization is reconfigured during the growth-todormancy transition During winter, the combined effects of reduced light and cold temperatures results in low photosynthetic activity and continuous rearrangements of the photosynthetic apparatus in conifers (reviewed in Oquist & Hüner 2003) While photosynthesis is typically associated with foliage, young stems of conifers are also photosynthetic (Berveiller, Kierzkowski & Damesin 2007). Thirty genes related to carbon fixation and the photosynthetic apparatus were DE at the transcript and/or protein level. Twenty-four of these were down-regulated at later stages of growth cessation – many following the transfer to LT – suggesting a decrease in photosynthetic capacity similar to the results reported by El Kayal et al. (2011) in spruce and Ruttink et al. (2007) in Populus. Water deficit is also known to affect expression of photosynthetic genes (e.g. Watkinson et al. 2003; Blodner et al. 2007). Given that many of these genes exhibit downregulation at latter stages of the time course when cellular desiccation may start to become a factor, it is possible that decreasing intracellular water potentials plays a role in regulating expression of these genes. Metabolic processes were significantly over-represented in several of the microarray data clusters. Many enzymes of central C metabolism were DE in both the microarray and protein analyses, including enzymes of sucrose and starch metabolism, glycolysis, gluconeogenesis, and the tricarboxylic acid (TCA), glyoxylate and pentose phosphate pathways. These pathways contribute to the overall energy status of the plant (Plaxton & Podesta 2006). Intermediates of central C metabolism pathways also serve as the precursors for synthesis of a plethora of primary and secondary metabolites (Plaxton & Podesta 2006). DE transcripts and proteins representing central C metabolic pathways showed both up- and down-regulation; however, closer inspection reveals a pattern in which transcripts and proteins of anabolic reactions or pathways generating substrates for entry into other pathways tended to be up-regulated, while those of catabolic reactions or energy generating pathways tended to be down-regulated. This is consistent with findings in Populus (Druart et al. 2007; Ruttink et al. 2007). Genes encoding most steps of the glycolytic and TCA pathways were DE at the level of transcript abundance, and enolase protein levels increased under SD. However, there was no wholesale pattern of upor down-regulation of glycolytic or TCA pathway genes. For example, glycolytic isozymes were often found to be both up- and down-regulated at the level of transcript abundance. Several glycolytic enzymes are coded for by multigene families, and members exhibit developmental, tissue- and cellular-level specificity of expression (Plaxton & Podesta 2006). As such, these seemingly conflicting patterns of expression could reflect tissue- and cellular-level differences in isozyme expression. Although glycolysis is often viewed as a linear pathway, in fact in plants it is a network of reactions that occur in parallel in the cytosol

and plastids, and which exhibit substantial metabolic flexibility (Plaxton & Podesta 2006). Metabolic channeling also occurs through specific multi-enzyme complexes involving specific isozymes. Further, the cytosolic glycolytic pathway features several distinct bypass reactions that parallel the classical steps of glycolysis, allowing carbon flux to take different routes through glycolysis. These bypass routes probably become more important as the plant becomes limited for adenylates, for example, with decreasing photosynthesis. The TCA cycle also exhibits bypass reactions (Plaxton & Podesta 2006). The complex patterns of up- and down-regulation likely represent a shift in carbon flux through these parallel pathways of glycolysis, with some routes being down-regulated and other routes being unregulated in response to changing energy and biosynthetic needs. We hypothesize that enzymes implicated in glycolytic and TCA routes designated primarily for synthesis of biosynthetic precursors are regulated differently than those functioning mainly in energy generation. This speculation is supported by the up-regulation of a gene encoding phosphoenolpyruvate carboxylase at later stages of the time course, which functions to replenish TCA cycle intermediates as they are used for biosynthesis (Plaxton & Podesta 2006). Determining the likely localization (cytosolic versus plastidic) of the proteins encoded by these genes, complemented by tissueand cellular-level expression profiling and enzyme assays would help test this hypothesis. Starch and sucrose constitute important C reserves both for short-term, transient storage of C as well as for longerterm storage (Sauter & Witt 1997; Ruttink et al. 2007). Sucrose additionally acts as a cryo- and osmoprotectant during overwintering (Rohde et al. 2000). DE genes associated with starch breakdown were all up-regulated at the level of transcript abundance, mainly during the mid-phase of growth cessation (cluster 2), while genes corresponding to the key enzymes of starch biosynthesis were up-regulated following transfer of the seedlings to LT. This suggests that starch is preferentially stored as a transient reserve in the stem until the final stages of dormancy transition, when it is accumulated. In Populus, starch breakdown enzymes were also up-regulated under SD in cambial tissues (Schrader et al. 2004; Druart et al. 2007), and increases in sucrose and other sugars were reported (Druart et al. 2007). In this study, genes coding for sucrose phosphate synthase and invertase were up-regulated concomitantly with the starch biosynthetic genes. Sucrose phosphate synthase activity is associated with synthesis of sucrose in preparation for overwintering (Hauch & Magel 1998; Schrader & Sauter 2002). In contrast, sucrose synthase was down-regulated during the mid-phase of the transition from active growth to dormancy (Cluster 7). Sucrose synthase has been reported to channel UDP glucose substrate directly to cellulose synthase for cellulose biosynthesis (Fujii, Hayashi & Mizuno 2010; Song, Shen & Li 2010); interestingly the sucrose synthase expression profile is consistent with all of the identified DE cellulose synthases, which grouped into Clusters 7 to 10.


Growth-to-dormancy transition in spruce stems 697 DE genes associated with other C-intensive biosynthetic pathways, particularly those of specialized metabolism, generally exhibited down-regulation, which may reflect diminishing availability of C skeletons as a consequence of reduced photosynthesis as stated previously. For example, several genes of the mevalonate and terpenoid biosynthesis pathways are down-regulated during growth cessation. Although terpenoids are a key defence strategy against insects and pathogens in conifers (Keeling & Bohlmann 2006), the energetic and C cost of terpenoid production concomitant with reduced C gain during the autumn may be too high to sustain synthesis of these compounds. This theory could be strengthened by profiling of these metabolites during the annual growth cycle.

Many genes related to stress responses are DE in stems in preparation for overwintering Many genes classically associated with stress responses were DE during the transition from active growth to dormancy (Supporting Information Table S2), consistent with other microarray studies of autumnal gene expression in forest tree species, including conifers (Schrader et al. 2004; Druart et al. 2007; Holliday et al. 2008; Park et al. 2008). Given the relatively large number of DE genes that were identified as encoding classic stress-associated proteins, it was surprising that genes in the response to stimulus category – which contains but is not limited to stressassociated proteins – were statistically under-represented in the microarray DE gene list. The inclusion of stress response genes in the broader category of response to stimulus may have diluted the ability to detect enrichment of stress-responsive genes. This under-representation may have also been due to a biased composition of the microarray to include a disproportionate number of response to stimulus genes. In support of this latter notion, enrichment analysis of the microarray DE genes compared with the same random sample of 5000 genes used for the proteomic enrichment analysis suggested statistical overrepresentation of the response to stimulus category (data not shown). Corroborating the idea that stress-related proteins constitute an important component of the gene expression reprogramming taking place in the stem during dormancy acquisition, the response to stimulus category was highly over-represented in the proteomic analyses, with stressresponse proteins representing approximately twice the expected proportion than would have been expected by random chance alone (Fig. 6). DE proteins associated with protein folding and protein protection (putative chaperonins, protein disulphide isomerase and peptidyl prolyl isomerase), response to oxidative stress (putative peroxidases), defence, cold or osmotic stress (putative chitinases and thaumatins), and detoxification (putative glyoxylases), were all significantly up-regulated both at the transcript and protein levels during growth cessation. These and other stress-associated proteins were among the most highly accumulated proteins by 10 week SD, suggesting a

prominent functional role for these proteins during overwintering. Many of these same stress-associated proteins were also identified by Renaut et al. (2008) in the proteome of peach phloem during dormancy induction. Numerous genes of the ascorbate, glutathione and superoxide dismutase antioxidant systems were also found to be up-regulated in the microarray analysis. Other studies have described up-regulation of antioxidant enzymes during the transition from active growth to dormancy (e.g. Druart et al. 2007), and in response to cold and drought stresses (Wisniewski et al. 2008). Redox control – both responses to oxidative stress as well as oxidative signalling – is considered to be an important means by which plants can integrate information perceived from the environment and metabolism and enact appropriate adjustments at the cellular and molecular levels (Foyer & Noctor 2009). The up-regulation of antioxidant genes in stems may help trees to cope with increased reactive oxygen species (ROS) generation that result from cold or dessication stresses, or over-excitation of photosystem II, which has low processing capacity during the winter months (reviewed in Hüner, Oquist & Sarhan 1998; Apel & Hirt 2004). Glycolysis, TCA and other energygenerating pathways may be up-regulated under oxidative stress as a means to contend with the concomitantly reduced ATP output (Baxter et al. 2007; Foyer & Noctor 2009; Ophir et al. 2009). ROS may also be involved in mediating developmental events such as cell wall lignification (Srivastava et al. 2007) and possibly the programmed cell death that occurs in tracheids and fibres during their final stage of maturation (Moreau et al. 2005; Courtois-Moreau et al. 2009). Recently, Ophir et al. (2009) have suggested that elevated ROS levels may be important in regulating bud dormancy release (Ophir et al. 2009). ROS have also been implicated in regulation of seed germination (Oracz et al. 2007; Bailly, El-Maarouf-Bouteau & Corbineau 2008). It is tempting to speculate that tight control of ROS levels in the stem through balancing production and scavenging might play a role in regulating multiple developmental processes, including dormancy acquisition and/or maintenance. A number of overwintering proteins with putative defence functions have been previously characterized in conifers (Ekramoddoullah et al. 2000; Liu et al. 2003). Such proteins may play a role in protection of the plant against pests and pathogens (Ekramoddoullah et al. 2000). Some of the putative defence-related DE genes and proteins, such as chitinases, glucanases, and thaumatins, belong to gene families that contain members demonstrating antifreeze activity in other species (Griffith & Yaish 2004). Several of these, such as a subset of the DE chitinases, were among the most highly accumulated proteins identified in the study. If any of these accumulated proteins exhibited antifreeze properties, their synthesis would increase cold hardiness of these plants as they prepare for overwintering. In support of the notion that some of these accumulated proteins may have antifreeze properties, in silico characterization of DE chitinase proteins identified convincing similarities at the primary sequence and tertiary structural levels to chitinases characterized as having antifreeze properties (Galindo & Cooke,


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unpublished results). In contrast to previous reports (Griffith & Yaish 2004), these proteins with potential antifreeze activity accumulated in the absence of LT, although LT acted to increase transcript abundance corresponding to these proteins.

Putative regulators of gene expression are associated with developmental processes and stress responses DE putative regulators of gene expression were identified in both the microarray and proteomics analyses. A total of 247 genes with putative regulatory functions were DE in the microarray analysis. Putative regulators were present in all clusters, and were moderately significantly overrepresented in cluster 5 (late up-regulation) and 6 (late down-regulation). Several genes showed similarity to known regulators of developmental processes in Arabidopsis, such as NAM, NAC, MADS, MYB, LIM, HD-ZIPIII, SCARECROW, STYLOSA, CLAVATA, and SQUAMOSA PROMOTERBINDING PROTEIN-LIKE genes. Some of these, such as genes in the NAC and MYB families, may play roles in vascular development, which progresses to completion during growth cessation (Bomal et al. 2008; Yamaguchi & Demura 2010; Yamaguchi et al. 2010). Others, such as CLAVATA, NAM and HD-ZIPIII, have been well characterized in other species for their roles in apical meristem function (reviewed in Barton 2010). Our data suggest that these genes may have analogous functions in the vascular cambium, as has been proposed by Schrader et al. (2004). Recent investigations have revealed that genes homologous to flowering control genes in annuals regulate dormancy and growth cessation in Populus (Böhlenius et al. 2006). FCA, involved in control of flowering (reviewed in Eckardt 2002), was up-regulated at both the protein and transcript levels. Other DE genes with similarity to time-of-flowering regulators included an APETALA1-like and a SUPPRESSOR OF OVEREXRESSION OF CONSTANS1-like (SOC1-like) MADS-box. Although FT was not identified as DE, PROTEIN MOTHER OF FT AND TF1 was downregulated during the course of growth cessation. Other putative DE regulators were identified that may be associated with the circadian clock, including CONSTANS-like, CONSTANS INTERACTING PROTEIN and PSEUDORESPONSE REGULATOR 37, and thus may be important in controlling the seasonal timing of gene expression associated with growth cessation and entry into dormancy. Genes encoding a DREB/CBF transcription factor and an osmosensor histidine-aspartate kinase were identified as down-regulated and up-regulated, respectively, at the LT time point relative to 0 d samples. Both of these are well known for their roles in regulating gene expression in response to abiotic factors such as cold, drought and osmotic stress (Shinozaki & Yamaguchi-Shinozaki 2000; Tran et al. 2007). It is possible that these genes control expression of genes whose products are necessary for protection of the stem from the inclement conditions

associated with overwintering, as has been shown by ectopic expression of Arabidopsis CBF1 in poplar (Benedict et al. 2006). The expression patterns suggest that the DREB/CBF acts early in dormancy acquisition, while the osmosensor histidine-aspartate kinase acts late. Recently, a peach DREB/CBF overexpressed in apple was shown to not only augment cold hardiness of apple trees, but also to promote SD-induced dormancy (Wisniewski et al. 2011), providing the first functional evidence to suggest a role for DREB/ CBF genes in dormancy acquisition.

Conclusion While transcriptome-scale studies have been conducted on gene expression changes that occur in stems of deciduous tree species such as Populus (Schrader et al. 2004; Druart et al. 2007; Park et al. 2008), to our knowledge this is the first such study for a coniferous species. The approximately 2800 DE transcripts and 216 DE proteins revealed by our combined transcriptomic- and proteomic-scale analyses of white spruce secondary stems undergoing SD-induced transition from active growth to dormancy can be used to infer a number of processes that occur during this phase change. In addition to a suite of genes associated with the developmental transition that occurs during growth cessation, SDand LT-mediated reconfiguration of carbon allocation figures centrally in the tree’s overwintering preparations. Genes associated with carbon-based defences such as terpenoids are down-regulated, while genes associated with protein-based defences and other stress mitigation mechanisms are up-regulated. Several proteins that play protective roles in the tree figure prominently in the accumulated protein profile, underlining the fundamental importance of stress protection in the measures that the tree invokes to enable overwintering. A number of putative regulators were identified that may play roles in controlling expression of metabolic, developmental and stress-associated genes. With this comprehensive dataset in hand, we can now begin to explore roles for these regulators in modulating processes associated with the transition from active growth to dormancy in stems.

ACKNOWLEDGMENTS We thank Dr Nathalie Isabel and Marie-Claude Gros-Louis (Natural Resources Canada, Laurentian Forestry Centre) for providing white spruce seedlings; Eric Fedosejevs. and Kimberley Lam (University of Alberta), and Marie Gorda, Susan Koziel and Dr John Vidmar (Alberta Innovates Technology Futures) for technical assistance with plant experiments; Dr Patrick James and Dr Richard Moses (University of Alberta) for guidance on statistical analyses; Dr Enrico Scarpella and Randy Mandryk (University of Alberta) for advice on microscopy; Tomas Meijer (University of Alberta) for assistance with microscopy; Dr Stephane LeBihan and staff of the Vancouver Prostate Centre Microarray Facility for microarray production; Dr Tony Cornish and Troy Locke (University of Alberta


Growth-to-dormancy transition in spruce stems Department of Biological Sciences Molecular Biology Service Unit) for technical advice and instrument maintenance supporting the qRT-PCR and protein analyses; and Dr John MacKay (Université Laval) for providing cDNAs used in this study from the Arborea cDNA collection maintained at Université Laval. This work was supported by grants to J.E.K.C. from Genome Canada and the Province of Alberta through Genome Alberta for the Arborea II and SMarTForests Projects, the Canadian Foundation for Innovation and the Natural Sciences and Engineering Research Council of Canada (NSERC).

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SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article: Figure S1. Representative IEF-SDS-PAGE protein profiles of secondary stems during active growth and during dormancy acquisition. Table S1. Primers used in qRT–PCR. Table S2. DE genes identified by microarray analysis. Table S3. Enrichment analysis of full microarray dataset. Table S4. Enrichment analysis, metabolic pathways and MIPS categories for each cluster. Table S5. Supplemental proteomic analysis data, including comparison of DE proteins to microarray data. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.
