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Planta DOI 10.1007/s00425-017-2786-5

ORIGINAL ARTICLE

Transcriptional profiling of cork oak phellogenic cells isolated by laser microdissection

Rita Teresa Teixeira1,3  · Ana Margarida Fortes2 · Hua Bai3 · Carla Pinheiro4,5 · Helena Pereira1 

Received: 18 July 2017 / Accepted: 28 September 2017 © Springer-Verlag GmbH Germany 2017

is under tight transcriptional (transcription factors, kinases) regulation and also hormonal control involving ABA, eth-ylene, and auxins. The phellogen cells collected from trees producing bad quality cork (BQC) show a consistent up-regulation of genes belonging to the flavonoid pathway as a response to stress. They also display a different modulation of cell wall genes resulting into a thinner cork layer, i.e., less meristematic activity. Based on the analysis of the phenyl-propanoid pathway regulating genes, in GQC, the synthesis of lignin and suberin is promoted, whereas in BQC, the same pathway favors the biosynthesis of free phenolic compounds. This study provided new insights of how cell-specific gene expression can determine tissue and organ morphology and physiology and identified robust candidate genes that can be used in breeding programs aiming at improving cork quality.

Keywords Cork · Development · Microdissection · Phellogen · Transcriptome

Abstract Main conclusion The phenylpropanoid pathway impacts the cork quality development. In cork of bad quality, the flavonoid route is favored, whereas in good quality, cork lignin and suberin production prevails.

Cork oaks develop a thick cork tissue as a protective shield that results of the continuous activity of a secondary meris-tem, the cork cambium, or phellogen. Most studies applied to developmental processes do not consider the cell types from which the samples were extracted. Here, laser micro-dissection (LM) coupled with transcript profiling using RNA sequencing (454 pyrosequencing) was applied to phellogen cells of trees producing low- and good quality cork. Func-tional annotation and functional enrichment analyses showed that stress-related genes are enriched in samples extracted from trees producing good quality cork (GQC). This process

Electronic supplementary material The online version of this article (doi:10.1007/s00425-017-2786-5) contains supplementary material, which is available to authorized users.

* Rita Teresa Teixeira teixeira@vt.edu; ritamiss@yahoo.com

Ana Margarida Fortes amfortes@fc.ul.pt

Hua Bai hbai@vt.edu

Carla Pinheiro pinheiro@itqb.unl.pt

Helena Pereira hpereira@isa.ulisboa.pt

1 Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisbon, Portugal

2 BIOISI, Science Faculty, University of Lisbon, Campo Grande, 1749-016 Lisbon, Portugal

3 Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute, State University, Blacksburg, VA 24060, USA

4 Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa, Av. da República, EAN, 2780-157 Oeiras, Portugal

5 Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal

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AbbreviationsBQC Bad quality corkGQC Good quality corkGO Gene ontologyHSP Heat-shock proteinKEGG Kyoto encyclopedia of genes and genomesLM Laser microdissection

Introduction

Cork oak stands, a typical landscape of the southern part of the Iberian Peninsula, are home for the richest biodiver-sity ecosystem found in Europe. These landscapes are out singled mainly by the extremely well-adapted cork oak tree (Quercus suber L.). Cork oak stands are not only important, because they harbor endangered animal and plant species, but also because they play a vital economical role in these otherwise fragile regions of Portugal and Spain. In fact, cork exploitation is one of the few industrial activities that ensure the preservation of this vast ecosystem. Cork is a protective tissue that results from the meristematic activity of the cork cambium or phellogen that has the property of self-regener-ation after damage or after the peeling-off of the cork layer (Pereira 2007). The capacity of producing new cork cells every time a tree is debarked and allows successive cork removals throughout the tree’s lifetime without any damage to the tree (Pereira and Graça 2004). The protection of tree’s living tissues from the environmental conditions performed by the thick cork layer is efficient, since cork oak trees have to cope with extended periods of drought and heat, and yet, they are able to thrive for long periods of time. Mankind has been exploiting the formation and properties of cork from ancient times in different applications (Pereira 2015). The driver is mainly the production of wine stoppers which demands that cork planks present a thickness above 27 mm and a good tissue homogeneity, e.g., with few lenticular channels and without sclereid inclusions, biological attacks, or other defects (Pereira 2007). Cork quality produced by the trees is a major concern for both producers and trans-formation industry. It is, therefore, essential to try to unveil the genes and the molecular mechanisms behind the devel-opment of good quality cork. It is known that cork qual-ity is affected by abiotic stresses such as drought and high temperature, but beside the effects of the external factors, the genome and the resulting activity of genes and proteins also play a critical role on how the plant responds towards stress (Oliveira et al. 2016). Recent transcriptomic data on the phellogenic tissue are indicative of how distinctive gene families respond differently affecting the quality of the cork (Teixeira et al. 2014).

The synthesis of suberin, the main chemical compound of cork that works as a barrier against environmental stress

factors, requires the production of acyl-lipid, phenylpropa-noids, isopropanoids and flavonoids (Beisson et al. 2007), and aromatic components that are involved in the com-position of suberin and lignin (Marques et al. 2016). The mechanisms of defense and protection provided by cork are not only physical with the synthesis of suberin that leads to the development of impervious cells, but also chemi-cal, especially with the synthesis of phenylpropanoid com-pounds that have a major defense role against both biotic and abiotic stress factors (Vogt 2010). This metabolic defense is subjected to hormonal control as established by recent data showing that phytohormones, including ABA, auxins, cytokinins, ethylene, gibberellins, brassinosteroids, and jasmonates, are key mediators of plant responses to abiotic stresses and may prove to be important metabolic engineer-ing targets for producing abiotic stress-tolerant crop plants (Wani et al. 2016).

Recent insights were provided on the molecular mecha-nisms associated with formation of cork of good and bad quality through an RNA-seq approach (Teixeira et al. 2014). In bad quality cork (BQC), a higher transcription of genes involved in DNA synthesis, RNA processing, proteolysis, and transcription factors related to the abiotic stress response was found. Putative stomatal/lenticular-associated genes which may be responsible for the disadvantageous higher number of lenticular channels in BQC were also more highly represented. Samples extracted from trees producing good quality cork could be distinguished by highly expressing genes encoding heat-shock proteins and selective activation of secondary metabolism. This previous work (Teixeira et al. 2014), however, did not embrace the study of the transcrip-tome of a limited type of cells. To complement this infor-mation, we now focused on the analysis of the differentially expressed genes using only phellogen cells isolated by LM from 1-year-old branches collected from trees producing good and bad quality cork. The valuable advantage of work-ing with samples collected by LM in relation with samples obtained from scratching fresh debarked cork planks, bio-logical material that was used in our previous work (Teixeira et al. 2014), is to ensure that no contaminations with any other cell types could occur. With the efficiency of transcrip-tomic analysis, proteomics and metabolomics and with the advance of more sensitive and reliable techniques, the study of DNA, RNA, proteins, and metabolites are strengthened when the samples to be analyzed are of pure cell lines or tissue type. The use of LM in plants is recent and has been applied mainly to gene expression profiles. It was success-fully used to isolate Arabidopsis embryonic cells (Casson et al. 2008), rice male gametophyte and tapetum (Suwabe et al. 2008), cotton ovules (Wu et al. 2007), and maize apical meristem (Ohtsu et al. 2007). Next generation sequencing is the preferable platform for transcriptomics, especially when it comes to species that do not possess a genome sequence

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available (Parchman et  al. 2010). Amongst this latest sequencing techniques, Roche 454 pyrosequencing is the best next generation sequencing method for transcriptome analysis of non-model species, because it provides better contig length metrics, therefore, enabling a more reliable gene annotation (Morozova and Marra 2008; Kumar and Blaxter 2010). Due to the fact that Quercus suber is still a species with an unknown genome sequence, we decided to perform our transcriptomic using the pyrosequencing platform.

In the present work, we were able to successfully applied LM to cork oak’s 1-year-old branches to isolate phellogen cells from trees producing cork of good quality and from trees from which cork was consider as being of bad quality. The RNA was further extracted and used for pyrosequenc-ing. The analysis of the genes differentially expressed pro-vided insights into the mechanisms of high- and low-quality cork development with relevance for cell wall, secondary and hormonal metabolisms, transcriptional regulation, and stress-related genes. The class of genes up-regulated in good quality cork (GQC) showed that these trees respond to envi-ronmental stresses by activating heat-shock proteins (HSPs) conferring protective cell mechanisms that in turn enables a correct transcription of many other gene families neces-sary for a proper development of thick cork layer. In BQC, on the other hand, protection is likely to be achieved by activating the biosynthesis of free phenolic compounds that accumulate in the cork layer forming a shield. This mecha-nism, apparently, is advantageous to the tree, but impairs the transcript of gene families essential for the formation of a thick cork layer.

Materials and methods

Biological sample

Biological samples of 1-year branches were collected from two trees producing good quality cork (located in Serra do Caldeirão, Algarve—37°12′N 7°54′W) and two trees pro-ducing cork of bad quality (located in in the area of Coruche, Ribatejo—37°42′N 8°36′W) and immediately snap-frozen in liquid nitrogen. These samples were then embedded in Tissue Teck OCT (Sakura Finetechnical, Tokyo, Japan) and frozen immediately on Peel-A-Way disposable plastic tis-sue embedding molds (Sakura Finetechnical) and stored at − 80 °C until use. The blocks were sectioned at 12 µm in a CM 1900 cryostat (Leica Microsystems, Wetzlar, Germany) and sections were mounted on polyethylene naphthalate-coated slides (Leica Microsystems).

The Zeiss PALM laser capture system (P.A.L.M Micro-laser Technologies AG. Inc., Bernried, Germany) was used for laser capture microdissection (LCM). The slides were

deepen in 100% xylene (twice) followed by dehydration in 100% ethanol for 2 min and air-dried before LCM. The samples were catapulted into the lid of 0.5 ml reaction tube (Zeiss, Hamburg, Germany) filled with 40 µl of RNA extrac-tion buffer from Qiagen RNeasy Micro Kit.

Microscopy

Samples were fixed in 2.5% glutaraldehyde at pH 7.4 (buff-ered with 0.05 M sodium cacodylate) for 6 h at room tem-perature. Dehydration was done through a graded ethanol series. The ethanol was gradually replaced with Spurr’s resin (Spurr 1969) every 24 h. The resin was polymerized at 60 °C for 16 h. Embedded samples were sectioned with a LKB 8801A ultratrome III microtome. Transverse semi-thin (1 μm) sections were cut with a glass knife and stained with 0.5% toluidine blue in 0.1 M phosphate buffer at pH 6.8 and later observed under a Nikon Eclipse E400 microscope.

cDNA preparation and double‑stranded cDNA synthesis

The quantity of RNA was assessed by fluorimetry with the Quant-iT RiboGreen RNA kit (Invitrogen, Carlsbad, CA, USA). Approximately 1 ng of total RNA was used as starting material for cDNA synthesis with the SMARTer PCR cDNA Synthesis Kit (Clontech, Mountain View, CA, USA). This procedure allows the synthesis of high-quality cDNA from RNA at extremely low concentrations. The strategy is based on the SMART (Switching Mechanism At 5′ end of the RNA Transcript) double-stranded cDNA synthesis methodology with amplification of polyA mRNA molecules that allows the introduction of known adapter sequences to both ends of the first-strand cDNA by a modified template-switching approach.

This synthesis was done with an oligodT containing a restriction site for BsgI to further eliminate the polyA tails and, therefore, minimize the interference of homopolymers during the 454 sequencing run.

Pyrosequencing

Five hundred nanograms of each dscDNA sample were fragmented by nebulization, followed by adaptor ligation to create double-stranded cDNA libraries. The non-normalized libraries were equimolar pooled into GQC and BQC and were sequenced in half a plate each of the 454 GS FLX tita-nium according to the standard manufacturer’s instructions (Roche-454 Life Sciences, Brandford, CT, USA) at Biocant (Cantanhede, Portugal).

Sequence data were submitted to NCBI Short Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/bioproject?term=PRJNA218509).

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Sequence processing assembly, functional annotation, and functional enrichment analyses

Upon 454 sequencing, raw reads were processed according to Teixeira et al. (2014).

Gene ontology (GO) enrichment analysis of differen-tially expressed genes was performed using the GOseq R. GO terms with corrected P values less than 0.05 were considered significantly enriched.

MapMan analysis

Pathway analysis was carried out with MapMan based on the most orthologous gene in Arabidopsis (Usadel et al. 2009). Differentially expressed contigs were assigned for functional categories (or bins) by Mercator (http://map-man.gabipd.org/web/guest/mercator). A pathway map was created to represent most of the contigs and the Wilcoxon rank sum test was used to identify differentially regulated bins.

Orthologues detection

The cork oak contigs obtained upon pyrosequencing and present at the Q. suber transcriptome database (http://tran-scriptomics.asu.biocant.pt/suber_rt/) were blasted against the TAIR database (http://www.arabidopsis.org) to retrieve the most probable orthologous genes.

Principal component analysis (PCA)

PCA was carried out as described previously (Chaves et al. 2009) using the R platform (version 3.2.5) and the ade4T-kGUI package (Thioulouse and Dray 2007). Correlation coefficients (Pearson’s product–moment correlation) were calculated using the stats4 package.

Real‑time RT‑PCR

The cDNA that was prepared and used for pyrosequencing was also used for real-time RT-PCR analysis. Six PCR reactions (final volume of 25 µl) for each sample and for each primer were performed with gene-specific primers (Suppl. Table S1) according to Teixeira et al. (2014). The mean values were calculated for each gene analyzed and divided by the mean value of the housekeeping gene. To make the graph easier to read, we attributed the value “1” to good quality samples and then normalized the values of the bad quality ones in relation with the GQC value. The housekeeping gene chosen was actin, because according to Marum et al. (2012), this gene demonstrated to be the

most stable in several tissues tested in Q. suber. Statisti-cal analysis was carried out using ANOVA: single factor.

KEGG mapping

To get the unique enzyme codes (EC) for KEGG mapping, the contig sequence was annotated using the blastKOALA (http://www.kegg.jp/blastkoala/) (Kanehisa and Goto 2000). The GAGE/pathview pipeline followed the guidelines described by Luo et al. (2009) and Luo and Brouwer (2013).

Results

Assessment of cork plank quality

The selection of trees for the 1-year-old stems collection was based on the quality of the cork they produced. To determine the cork quality, the corresponding cork planks were ana-lyzed (Fig. 1a, b). Planks removed from trees producing cork considered of good quality (GQC) were characterized by having a thickness of 35–40 mm and by presenting a homog-enous cork tissue, meaning that they possessed a reduced number of lenticular channels as well as other type of inclu-sions as shown on transverse sections (Fig. 1a asterisk). In these planks, it was possible to distinguish the growth rings (Fig. 1a arrowhead). Just like what happens with wood growth rings, larger cells produced during the early grow-ing season appear as light colored layers intercalated with darker layer composed by smaller arrested growth cells pro-duced during the late growing season. The planks extracted from trees producing cork of bad quality (BQC) displayed a reduced thickness (up to 17 mm) and were crossed by numerous lenticular channels (Fig. 1b arrowhead) making hard the visualization of the growth rings. Upon this obser-vation, two trees producing cork of good quality and two other trees producing cork of reduced quality were selected for the collection of 1-year-old branches from which phel-logen cells were isolated by LM.

Laser microdissection of phellogen cells

Prior to LM of the phellogen cells, we performed a histo-logical analysis of the same samples. Cross sections showed that a cork layer or phellem composed of six cells in each radial row was already formed (Fig. 1c) that could be dis-tinguished by the light coloration of the secondary cell wall composed mainly by suberin (Fig. 1c; Teixeira and Pereira 2010a). The three-cell layer positioned right beneath the cork cells is the phellogen, the meristematic tissue that was laser microdissected, the RNA extracted from and used for further 454 pyrosequencing. These cells (Fig. 1c) are responsible for the production of cork cells (phellem) to the

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outside and phelloderm cells to inside of the cork cambium. Cryosections without any kind of treatment or staining were obtained to ensure the preservation of the RNA integrity. Under the PALM microscope, the cork layer could again be distinguished due to the white appearance of the suberin cell walls (Fig. 1d arrow), permitting a perfect identification of the phellogen that laid right below followed by its micro-dissection throughout the diameter of the stem, as shown in Fig. 1e, f.

Sequencing of phellogenic cells collected by laser microdissection

Albeit samples were cryosectioned without any cryoprotec-tion of fixation, the morphology was good enough to enable the correct identification of specialized cell types and tissues that after isolation provided high RNA quality as confirmed by the values of RNA integrity (RIN) (Suppl. Fig. S1). The RIN obtained was of 9 and 9.7 for the two GQC phellogen

samples and of 9.20 and 8.70 for the two BQC samples that upon RNA amplification and cDNA library preparation, equimolar amounts were pooled into two final non-normal-ized libraries. The samples consisted of pure phellogen cells isolated from trees producing cork of good and bad quality but for simplicity sake, hereinafter, we will refer to them as GQC and BQC.

Fig. 1 Transverse cuts of cork planks removed from the same two trees from which the 1-year-old stems were collected. Radial growth is marked by an arrow. a GQC with a thickness of at least 28  mm presenting a reduced number of lenticels (asterisk) and no other inclusions. The cork growth rings can be distinguished (arrowhead). b BQC planks with a reduced thickness (maximum of 17 mm) with numerous lenticular channels that completely cross the suber thick-ness (arrowhead). The radial growth occurs from the inside, also known as cork belly (innercork), outwards to cork back which is the surface in contact with the environment. c–f Histology of the cross sections used for laser microdissection. c Thin cross section

of 1  year stem stained with Toluidine Blue, where it is possible to distinguish the suber layer (sb) due to the hyaline aspect of the cell walls conferred by suberin deposition (Teixeira and Pereira 2010a). Right below are the meristematic cells forming the phellogen (phl). Bar 10  µm. d Cross section of deep frozen 1-year-old stem sample obtained in a cryostate and used for laser microdissection upon dehy-dration. The selected area corresponds to the phellogen and was laser microdissected. Red arrow marks the suber layer or phellem. Bar 50 µm. e Void area after catapulting of the laser microdissection sec-tions. Bar 50 µm. f Phellogen was isolated throughout the stem diam-eter. phl phellogen, sb suber layer. Bar 200 µm

Table 1 Summary of the pyrosequencing assembly of phellogen cells pool samples extracted from trees producing good quality cork (GQC) and bad quality cork (BQC)

GQC BQC

Total bases 104,873,000 38,651,000No. of contigs 12,400 4509Average contig (bp) 571 628Average contig length after

treaming (bp)307 304

No. of reads 341,209 126,904

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Pyrosequencing yielded 341,209 reads with total bases of 104,873,000 for GQC and 126,904 reads with total bases of 38,651,000 for BQC (Table 1) resulting, after treaming, in a mean read length of 307 bp for the GQC samples and 304 bp for the BQC samples (Table 1). The reads from the two libraries were assembled together into a single transcriptome generating 15,421 transcripts with a mean read length of 305 bp after trimming and encod-ing 15,127 amino acid sequences (complete list found in Suppl. Data S1). The applied cutoff of normalization of eight reads at 90% percentile with adjusted P value of 0.005 (see “Materials and methods”) resulted in 1413 genes differentially expressed (9.34%) (list of differentially expressed genes found in Suppl. Data S1). Of these, 942 were annotated (66.6%) with 89.7% of the genes found to be up-regulated in GQC samples and 10.3% up-regulated in bad quality cork samples. This discrepancy could be a result in itself, since the initial number of reads was not that different between the two samples; 568,123 for GQC and 451,528 for BQC (data not shown). One should also have under consideration that the average contig length for BQC was higher (628 bp), then the average contig length for the GQC samples (571 bp) resulting in lower num-ber of contigs for BQC. The main difference is observed after quality filter assessment with BQC showing a more pronounced bias distribution towards short reads than in GQC, even though the initial RNA quality (Suppl. Fig. S1) and the library profile (data not shown) for all the samples were very similar.

To validate the transcriptomic data, we selected several genes for quantitative RT-PCR based on its importance for cork quality development assessed in our previous work (Teixeira et al. 2014) and if they showed an up- or down-regulation according to the present transcriptomic data. The genes used were: knotted-like homeobox 7 (KNAT7), heat-shock p9 (HSP9), serine/threonine protein phosphatase 2A (PP2A), F-box domain, cyclin-like (FBOX), phenylalanine ammonia-lyase (PAL), heat-shock protein DnaJ (HSPD), caffeate O-methyltransferase (COMT), and dehydrin. The DNA libraries used for sequencing were also used for the real-time RT-PCR profile to verify the fold change of the chosen genes. As shown in Suppl. Fig. S2, the difference in expression of the different genes was in accordance with the results obtained after pyrosequencing. The gene heat-shock protein 9 (HSP9) was selected, because it showed no expres-sion on GQC samples and we wanted to verify if this differ-ence in expression could be replicated by real-time RT-PCR and, as can be seen in Suppl. Fig. S2, no amplification was observed in the GQC samples. The correlation coefficient R2 between the expression fold change obtained after 454 pyrosequencing and real-time RT-PCR was 0.7539 (Suppl. Fig. S2). The relative low R2 value is due to the KNAT7 gene. After real-time RT-PCR, we obtained a fold change

of 1.13, but after pyrosequencing, the fold-change value was of 4.6.

Functional annotation and functional enrichment analyses of good and bad quality phellogenic samples

As a preliminary approach, the GO annotation distribu-tion was studied and differentially expressed genes fell into three categories: molecular function, cellular component, and biological process (Fig. 2, list of all DEG distributed throughout the three GO categories can be found in Suppl. Data S2). In the GO category molecular function, the sub-categories antioxidant activity, phosphoprotein phosphatase activity, lipid binding, RNA binding, DNA binding, and nuclease activity contained genes only up-regulated in GQC (Fig. 2a). In the GO category cellular component, cell wall sub-category possessed two genes up-regulated exclusively in BQC. Plasma membrane sub-category, on the other hand, displayed genes expressed only in GQC. When looking at the biological process GO category, the sub-category sec-ondary metabolism process stood out for showing genes up-regulated exclusively on BQC (chalcone isomerase and 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase, a gene involved in the non-mevalonate pathway of an isopre-noid precursor biosynthesis). Besides the technical expla-nation already described above for the higher number of up-regulated associated GQC genes, there is a more suit-able biological reason for such gene expression difference between the two samples, especially when we look at the RNA concentration and RIN numbers (Suppl. Fig. S1) from all the samples analyzed. Regardless the fact that 1-year-old stems from the four trees were collected in the same day in the middle of the early growing season (in June), the meris-tematic status of the phellogen seems to differ between the trees that produce cork of good quality and the ones that produce cork of reduce quality. The GO distribution (Fig. 2) shows that the genes mainly up-regulated in BQC were sec-ondary metabolism and cell wall (xyloglucan genes) associ-ated, two biological processes that usually, are not activated during intense cell division. In GQC, however, genes were essentially associated with cell cycle, nuclear activity, mito-chondria, DNA metabolism process, and translation. This striking difference in cell activity could be indicative of how the two types of trees respond regarding the cork develop-ment process.

In addition, GO enrichment was performed to verify the relevance of the GO categories and underlay the biologi-cal significance. The enrichment analysis resulted in 1632 genes expressed in good quality cork distributed throughout 534 GO categories and 262 bad quality cork genes distrib-uted throughout 141 GO categories. Considering only the enriched pathways (P value < 0.05), in GQC, the molecular function category was enriched in 68 terms, the biological

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process category in 60 terms, and the cellular component category in 30 terms (Suppl. Data S2). In BQC, it was pos-sible to count 36 terms for molecular function category, 30 for biological process, and 9 for cellular component (Suppl. Data S2). The most enriched GO terms in GQC were oxida-tion–reduction process (GO:0055114) with 63 terms, struc-tural constituent of ribosome (GO:0003735), and translation (GO:0006412) with 63 differentially expressed genes each (Suppl. Data S2) confirming the intense cell activity that characterize meristematic cells. In BQC samples, a high number of genes involved in protein activity such as protein binding and protein transport were found (Suppl. Data S2) with the GO categories of protein binding (GO:0005515) and intracellular (GO:0005622) enriched in ten terms each and carbohydrate metabolic process (GO:0005975) enriched in seven terms. Using this GO number, it was possible to retrieve from the transcriptomic data differentially expressed genes that fell into cell wall remodeling process such as xylo-glucan endotransglucosylase (XTHX) and expansin genes (GO:0009664) (Suppl. Data S2—GO Biological Process

DEG sheet). All these genes involved in cell wall were also detected by MapMan analysis (cf. Fig. 4a). There were at least two GO categories represented exclusively in GQC that we would like to highlight; response to stress (GO:0006950) and response to water (GO:0009415) (Suppl. Data S2). The response to stress class comprised genes that codified for heat-shock proteins (HSPs), abscisic stress-ripening pro-tein, and dehydrin that were also present in the GO category response to water (Suppl. Data S2).

The principal component analysis (PCA) performed on the differentially expressed genes showed that the separa-tion between GQC and BQC samples was achieved along the second axis (18% of the variation; Pearson correlation coefficients, P < 0.001) (Fig. 3) with the annotated genes more strongly associated with GCQ and BQC enumer-ated in Table 2. In GQC samples, we would like to high-light two genes responsive to stress (S60_All_005677 and S60_All_004384), and for BQC, two genes involved in the flavonoid pathway [naringenin 3-dioxygenase (F3H) and dihydroflavonol 4-reductase (DFR)]. In both cork quality

Fig. 2 Distribution of gene ontology categories. Percentage of differentially expressed contigs annotated for molecular function (a), cellular component (b), and biological process (c) are separated in dark grey (GQC) and light grey (BQC)

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phellogen samples analyzed here, a gene coding for non-specific lipid-transfer protein (LTP) appeared in the most relevant gene list (Table 2).

MapMan metabolic pathways and KEGG analysis

The MapMan software tools were developed to map Arabi-dopsis thaliana data to define functional categories, display

them onto pathway diagrams, and allow time-course analy-sis for functional gene groups. In this study, we adapted these tools to Q. suber by generating unique mapping files of the Q. suber pyrosequencing data. The automatic anno-tation of the differentially expressed sequences between GQC and BQC phellogen cells allowed the assignment of the 942 differentially expressed sequences with annotation into 36 distinct bins. These were displayed onto pathway

Fig. 3 Principal component analysis (PCA) of the genes detected in GQC and BQC. Genes found to be significantly correlated (P value < 0.001) with the second axis represented in pink (raw data available in Data S3)

Table 2 Short list of the eight and seven genes that were more strongly associated with GQC and BQC, respectively, after PCA analysis

Values below one mean that the genes are more highly expressed in BQCa Q. suber accession number refers to the accession number assigned after pyrosequencingb Fold change is expressed as the ratio of gene expression of GQC to BQC

EST Q. Suber accession no.a Fold changeb p value Axis 1 Axis 2 Best TAIR hit Annotation

GQC S60_All_004384 45.62 0.0 −0.23 0.23 At1g70840 Bet v I allergen. Response to biotic stimulus S60_All_000019 31 0.0 −0.20 0.20 At2g07698 ATP synthase subunit alpha. ATP synthesis coupled

proton transport S60_All_000311 5.85 0.0 −0.23 0.14 At2g07689 NADH:ubiquinone/plastoquinone oxidoreductase S60_All_000042 5.67 0.0 −0.20 0.16 At2g07732 Ribulose bisphosphate carboxylase S60_All_005677 5.36 0.0 −0.45 0.24 At4g15910 Late embryogenesis abundant protein (LEA). Response

to stress S60_All_005103 5.21 0.0 −0.48 0.25 At2g38540 Non-specific lipid-transfer protein (LTP). Lipid binding S60_All_000674 5.03 0.0 −0.40 0.21 – l-Asparaginase. Hydrolase activity S60_All_007304 4.81 0.0 −0.37 0.19 At2g29500 Heat-shock protein Hsp20

BQC S60_All_007150 1.37 0.0 −0.83 −0.10 – Coiled-coil domain-containing protein S60_All_006704 0.79 0.0 −1.77 −0.65 At3g09390 Metallothionein-like protein type 2 (MT2). Metal ion

binding S60_All_000211 0.54 0.0 −0.24 −0.14 At3g51240 Flavanone-3-hydroxylase (F3H). Flavonoid synthesis S60_All_006057 0.46 0.0 −0.22 −0.15 At1g48750 Non-specific lipid-transfer protein (LTP). Lipid transport S60_All_000317 0.20 0.0 −0.20 −0.18 At5g42800 Dihydroflavonol-4-reductase (DFR). Anthocyanins

biosynthesis S60_All_000145 0.16 0.0 −0.23 −0.22 At5g13930 Chalcone/stilbene synthase, N-terminal S60_All_000151 0.17 0.0 −0.69 −0.60 At5g13930 Chalcone/stilbene synthase, N-terminal

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Fig. 4 Distribution of contigs according to MapMan analysis among metabolism overview (a), secondary metabolism (b), and stress (c). Fold-change scale is expressed as log2 expression ratio (GQC/BQC). Positive val-ues (blue squares) correspond to genes over-expressed in the phellogen of trees producing GQC, while negative values (pink-shaded squares) represent the genes highly expressed in phellogen cell samples collected from trees producing BQC

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diagrams enabling an overview of the functional catego-ries that include the differentially expressed genes, and from this distribution, three maps were chosen: metabo-lism overview (Fig. 4a), secondary metabolism (Fig. 4b), and stress (Fig. 4c). The number of stress-related genes is elevated (Suppl. Data S3) and distributed throughout sev-eral categories such as “hormone signaling”, “cell wall”, “glutathione-S-transferase”, “heat-shock proteins”, and “secondary metabolites” (Suppl. Data S3).

The genes belonging to the abiotic stress group were mainly up-regulated in GQC (94.8%) which included genes activated by cold, touch/wounding, drought/salt, light, and especially heat such as heat-shock protein 17 (HSP17, A. thaliana orthologue AT2G29500), HSP70 (A. thaliana orthologue AT3G12580), HSP81 (A. thaliana orthologue AT5G52640), and HSP26 (A. thaliana ortho-logue AT1G52560) (Suppl. Data S3). In BQC samples, the most highly expressed genes were DNAJ heat-shock N-terminal domain-containing protein (A. thaliana ortho-logue AT5G18140) and germinin-like protein 5 (GLP5, A. thaliana orthologue AT1G09560) (Suppl. Data S3). In the hormone signaling assigned bin stress category (Fig. 4c; Suppl. Data S3), the auxin-induced group of genes was the one with the highest number of genes up-regulated in BQC (17 genes, 29.2%) (Table 3). In these samples, it was possible to detect genes like AIR12, an extracellular matrix structural constituent (A. thaliana AT3G07390) or auxin signaling F-box 2 (AFB2, A. thaliana orthologue AT3G26810) (Suppl. Data S3). Ethylene metabolism was another class of hormone genes highly represented with 72.2% over-expressed in samples of GQC and 27.8% of the genes mainly expressed in BQC samples (Suppl. Data S3). The sub-categories related to the hormones brassinos-teroids, salicylic acid, and jasmonates (Fig. 4c, Table 3) only displayed genes up-regulated in GQC samples. The response of plants to unfavorable conditions, amongst many others, also involves the expression of stress-responsive network of transcription factors (Nakashima et al. 2012). The MapMan analysis detected a high num-ber of transcription factors responsive to abiotic stress (40 genes in total) (Fig. 4c, Table 4; corresponding Arabidop-sis accession number can be found in Suppl. Data S3). The totality of differentially expressed genes belonging to the bZIP and zinc finger families could only be detected in GQC samples (Table 4). The glutathione-S-transferases and MYB families comprised the highest number of genes, but most of them were over-expressed in GQC (Table 4). MAP kinases are proteins that, together with transcrip-tion factors, make part of the signaling mechanisms that play an essential role in stimulus perception and adaptive responses (Danquah et al. 2014). MapMan identified four abiotic stress-responsive MAP kinases and all were over-expressed in GQC sample (Table 4).

The majority of the genes found in the bin “cell wall modifications” were up-regulated in BQC and the only two up-regulated in GQC were expansins genes (A. thaliana orthologues AT2G40610 and AT2G39700) (Table 5). The metabolism overview graph showed that the expressed genes were found to be mainly related to cell wall mechanisms, lipid metabolism, responses to light, stress, and secondary metabolism (Fig. 4a; Suppl. Data S3). Due to the number of BQC up-regulated genes present in the chalcones synthesis and flavonoids pathways and to previous results reported in Teixeira et al. (2014), we decided to deeply analyze the gene expression in these two secondary metabolism path-ways. The gene profile obtained from the MapMan analysis was a good indication to further dissect the phenylpropa-noid and the flavonoid biosynthetic pathways. To analyze transcriptomic data, KEGG is the best option, since it is a highly integrated database of the biological systems and their relationships at the molecular, cellular, and organism level (Kanehisa et al. 2007). The KEGG annotations and enzyme codes (EC) were generated from assembled unitran-scripts of Q. suber phellogen transcriptome and then mapped with GO term. In total, 4174 contigs were assigned to 1323 EC (Suppl. Data S4). From these, we selected the ones with a KEGG orthologue pathway code (KO) corresponding to the phenylpropanoid biosynthesis (KO 00940) (Fig. 5) and to the flavonoid biosynthesis (KO 00941) (Fig. 6) pathways, respectively.

Phenylpropanoid pathway

In the present study, KEGG analysis of the phellogen tran-scripts revealed the presence of ten genes belonging to the phenylpropanoid pathway (Fig. 5). The distribution of the differentially expressed genes throughout the pathway clearly showed that the enzymes responsible for the initial steps of the pathway were codified by genes over-expressed in BQC (Fig. 5, green boxes). The genes were the pheny-lalanine ammonia-lyase (PAL, K10775), trans-cinnamate 4-monooxygenase (C4H, K00487), and 4-coumarate-CoA ligase (4CL, K01904) (Table 6). The branching point for the flavonoid biosynthesis route and for the lignin and suberin synthesis route is at the C4H enzyme. It was possible to observe that the carbohydrate flux in cork of bad quality shifted preferentially to the synthesis of flavonoids, while in good quality cork samples, the flux was towards the synthe-sis of lignin and suberin (Fig. 5), feature highlighted when the flavonoid biosynthesis was analyzed (Fig. 6).

Flavonoid biosynthesis pathway

Flavonoids are plant specific secondary metabolites that can be divided into flavonones, flavonols, isoflavones, cat-echins, chalcones, and their derivates. The first step in the

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Table 3 Hormone-related genes responsive to abiotic stress

Quercus suber accession numbera

Fold change Best TAIR hit P value Annotation

Auxin-associated S60_All_001093 −73 At3g07390 3.45e−7 Auxin-induced in root 12 S60_All_001127 −11 At1g51630 0.017 O-Fucosyltransferase S60_All_001641 −5 At1g60690 0.013 Aldo–keto reductase S60_All_006133 −3.22 At2g46690 0.002 Indole-3-acetic acid-induced protein S60_All_006554 −2.58 At1g60750 0.001 Auxin-induced protein S60_All_001641 −2.38 At3g26810 0.001 Protein auxin signaling F-BOX 2 S60_All_001985 −2.12 At5g35735 0.003 Auxin-induced in root 12 S60_All_001394 2.98 At3g25290 1.14e−09 Auxin-induced in root 12 S60_All_002876 3.15 At1g60710 9.37e−06 Auxin-induced protein S60_All_001023 4 At3g12760 4.73e−05 effective in cullin neddylation protein S60_All_007631 6.5 At1g43130 0.015 Like COV 2 (LCV2) S60_All_003837 7.33 At1g51760 0.001 IAA-alanine resistant 3 S60_All_000098 7.8 At5g49980 1.46e−05 Auxin F-box 5 S60_All_006166 11 At3g12830 0.016 Indole-3-acetic acid-induced protein ARG7 S60_All_002521 11 At5g47530 0.018 Auxin-induced in root 12 S60_All_002480 11 At1g60710 0.016 Auxin-induced protein S60_All_008207 12 At1t72430 0.013 Auxin-responsive protein S60_All_001080 14 At3g02260 0.002 Transport inhibitor response 3 S60_All_002446 14 At3g02875 0.007 IAA-amino acid hydrolase ILR1 S60_All_006164 14 At4g38840 0.002 Auxin-responsive protein S60_All_001668 17 At1g38131 0.004 O-Fucosyltransferase S60_All_005889 18 At5g18050 0.001 Auxin-responsive protein S60_All_002002 61 At3g07390 4.23e−05 Auxin-induced protein

Brassinosteroids S60_All_009410 1.7 At5g51550 0.014 EXORDIUM like 3 S60_All_002399 6.6 At5g13710 8.95e−05 Sterol-methyltransferase S60_All_003839 10 At3g52940 0.015 sterol reductase S60_All_004643 13 At3g02580 0.010 Brassinosteroid biosynthetic enzyme S60_All_001570 20 At2g22830 0.001 Squalene monooxygenase

ABA-associated S60_All_002406 −10.2 At2g20770 5.48e−07 G protein coupled ABA receptor S60_All_004179 2.1 At4g24960 0.009 specific ABA- and stress-inducible protein S60_All_002696 3.6 At5g67030 0.004 ABA deficient 1 S60_All_002855 4.2 At1g74520 0.001 ABA- and stress-inducible protein (HVA22) S60_All_000008 5.5 At3g63520 0.001 Carotenoid-cleavage dioxygenase

Ethylene-associated S60_All_000265 −6.5 At3g19000 0.0023 2-Oxoglutarate (2OG) S60_All_000310 −3.8 At3g19000 0.011 2-Oxoglutarate (2OG) S60_All_000777 −3.1 At1g03400 0.010 2-Oxoglutarate-dependent dioxygenase S60_All_000235 −2.7 At4g22880 2.67e−09 Leucocyanidin oxygenase S60_All_000211 −1.8 At3g51240 5.90e−10 Naringenin 3-dioxygenase S60_All_000517 4 At2g44930 0.010 Transmembrane protein S60_All_004014 3.1 At3g58680 2.42e−05 Multiprotein bridging factor 1 S60_All_002628 3.4 At2g44930 0.005 Transmembrane protein S60_All_001131 4 At2g44930 2.10e−05 Transmembrane protein S60_All_000254 4.5 At1g49390 1.48e−08 Protein senescence-related gene 1 S60_All_003341 8 At4g31980 0.005 PPPDE thiol peptidase S60_All_002628 8 At4g31980 0.005 PPPDE thiol peptidase

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flavonoid pathway is characterized by chalcone synthase (CHS) leading to chalcones and flavonones synthesis. Our results showed that the gene codifying for this enzyme was much more expressed in BQC than in GQC (Table 6). With the exception of the DFR gene, which was over-expressed in GQC, all other genes were more expressed in BQC (Fig. 6, Table 6). According to this map, anthocyanins, isoflavo-noids, and chalcones will, most likely, be the secondary phenolic compounds found in higher concentrations in BQC.

Suberin biosynthesis‑associated transcripts

The MapMan annotated transcripts resulted in a quite high number of genes to be assigned to fatty-acid synthesis and fatty-acid elongation category (lipid metabolism bin), two biological mechanisms responsible for suberin synthesis. For this reason and based on the work of Soler et al. (2007), Kosma et al. (2014), and Rains et al. (2017), we blasted all the Arabidopsis genes attributed to suberin synthesis by these authors against our transcriptomic data and the retrieved genes are listed in Table 7. The suberin-associated genes involved in the phenylpropanoid and flavonoid path-ways were not included in this table, since they were already analyzed in Figs. 5 and 6. Out of the 36 retrieved genes involved in suberin and wax biosynthesis and fatty-acid

elongation, only 10 (27.7%) were differentially expressed and all up-regulated in GQC (Table 7).

Discussion

Laser microdissection of cork samples

The present work is the first one applying laser microdissec-tion technique to cork oak tree (Q. suber L.) with the aim to determine the expressed gene families in the phellogen cells which might be related to cork quality development. High-throughput RNA-seq is a breakthrough, and combined with LM, it is a powerful tool for individual cell-type tran-scriptome analysis (Day et al. 2007; Teixeira and Pereira 2010b). We realized that cryo-fixed samples gave the best results regarding RNA quality with no tissue integrity lost in the process. The use of frozen sections has been shown to maximize the quantity and the quality of the RNA recov-ered (Goldsworthy et al. 1999; Teixeira and Pereira 2010b). When applying LM, only limited amounts of RNA can be isolated from specific cell types and obtaining sufficient amounts for transcriptome analysis usually requires RNA amplification which was performed in the present study.

The gene list with the corresponding best TAIR hit accession number that was used to construct this table was obtained after Mapman analy-sis and can be consulted at Suppl. Data S3. This list is also associated with Fig. 5c. Values below zero mean that the genes are more highly expressed in BQCa Quercus suber accession number refers to the accession number assigned obtained after the pyrosequencing

Table 3 (continued)

Quercus suber accession numbera

Fold change Best TAIR hit P value Annotation

 S60_All_000265 10 At4g10490 0.026 2-Oxoglutarate (2OG) S60_All_000777 12 At1g03400 0.008 2-Oxoglutarate (2OG) S60_All_008155 12.4 At2g44930 3.72e−08 Transmembrane protein S60_All_006559 13 At3g23150 0.010 Ethylene response 2 (ETR2) S60_All_006230 14 At1g66340 0.008 Ethylene response 1 (ETR1) S60_All_001255 21 At1g65985 0.002 Transmembrane protein

Salicylic acid-associated S60_All_002520 11 At5g61400 0.001 Pentatricopeptide repeat-containing protein (PPR) S60_All_002160 12 At1g68040 0.017 S-Adenosyl-l-methionine-dependent methyltransferase S60_All_003072 26 At5g10690 0.001 Pentatricopeptide repeat-containing protein (PPR)

Jasmonate-associated S60_All_000684 2.5 At1g19180 0.001 Jasmonate-zim-domain protein 1 (JAZ1) S60_All_000217 2.9 At3g17860 5.65e−08 Jasmonate-zim-domain protein 3 (JAZ3) S60_All_000778 3.9 At5g42650 0.001 Allene oxide synthase (AOS) S60_All_000438 5.4 At1g55020 0.001 Lipoxygenase 1 (LOX1) S60_All_000963 8.2 At4g15440 6.27e−05 Hydroperoxide lyase 1 (HPL1) S60_All_000438 12 At3g45140 0.001 Lipoxygenase 2 (LOX2) S60_All_001074 20 At1g67560 0.003 Lipoxygenase family S60_All_000768 29 A4g32570 0.001 Tify domain protein 8 (TIFY8)

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Table 4 Transcription factors, MAP kinases, and glutathione-S-transferase genes responsive to abiotic stress

Quercus suber accession numbera

Fold change Best TAIR hit P value Annotation

Glutathione-S-transferase S60_All_001573 −1.4 At1g78380 0.021 Glutathione-S-transferase S60_All_001571 1.7 At1g78380 6.17e−06 Glutathione-S-transferase S60_All_001452 2.3 At2g02390 0.008 Glutathione-S-transferase S60_All_001680 2.4 At2g47730 8.94e−16 Glutathione-S-transferase S60_All_001400 2.5 At1g10370 6.03e−07 Glutathione-S-transferase S60_All_004729 4.1 At2g02390 0.002 Glutathione-S-transferase S60_All_001479 4.7 At3g09270 0.005 Glutathione-S-transferase S60_All_001277 6.5 At5g02790 3.75e−08 Glutathione-S-transferase S60_All_007556 7.0 At5g03880 0.010 Glutaredoxin S60_All_001579 12.2 At1g17180 1.31e−10 Glutathione-S-transferase S60_All_002183 13.2 At2g02390 1.49e−08 Glutathione-S-transferase S60_All_002645 14 At3g09270 0.004 Glutathione-S-transferase S60_All_001511 15.3 At2g29420 2.23e−06 Glutathione-S-transferase

AP2/ERF S60_All_001468 −2.9 At5g44210 9.38e−07 Ethylene-responsive transcription factor S60_All_000182 2.9 At1g53910 3.03e−18 Related to APETALA2 12 S60_All_001581 3.7 At5g52020 5.74e−05 Ethylene-responsive transcription factor S60_All_005688 26 At2g40350 0.001 Dehydration-responsive element-binding protein

bZIP S60_All_000464 3.3 At1g42990 1.57e−06 BZIP transcription factor S60_All_000840 5.0 At5g06950 0.003 BZIP transcription factor S60_All_005993 5.3 At3g17609 0.009 Transcription factor HY5-like S60_All_008193 6.0 At2g40950 0.004 BZIP17 S60_All_000706 11 At5g65210 2.42e−03 BZIP transcription factor

WRKY S60_All_000354 −9.0 At4g31800 0.002 WRKY transcription factor S60_All_005320 −3.4 At2g30590 0.001 WRKY transcription factor S60_All_000471 4.2 At3g56400 0.004 WRKY transcription factor S60_All_002795 7.0 At1g62300 0.009 WRKY transcription factor S60_All_000327 12 At1g29280 1.21e−03 WRKY transcription factor S60_All_003811 17 At1g62300 0.004 WRKY transcription factor

MYB S60_All_002292 -1.9 At3g13540 0.011 Myb-related protein S60_All_000557 −2.5 At5g49330 0.026 Myb domain protein S60_All_001410 −11 At4g38620 0.016 Myb-related protein S60_All_001661 1.7 At4g38620 0.017 Myb-related protein S60_All_000888 2.3 At4g22680 0.001 Myb-related protein S60_All_001008 2.5 At4g22680 0.010 Myb-related protein S60_All_001360 3.5 At4g09460 0.008 Myb-related protein S60_All_003461 5 At2g37630 4.11e−06 Asymmetric leaves 1 S60_All_008152 14 At1g58220 0.015 Myb-related protein S60_All_006091 15 At2g46410 0.007 Transcription factor CAPRICE S60_All_002980 17 At1g64310 0.004 Pentatricopeptide repeat-containing protein S60_All_003092 18 At2g22070 0.004 Pentatricopeptide repeat-containing protein S60_All_006635 38.5 At2g46410 3.06e−08 Transcription factor CAPRICE S60_All_001120 41 At1g09770 0.001 Protein Myb domain cell division cycle 5

Zinc finger S60_All_000382 2.7 At5g60850 3.05e−05 Dof zinc finger protein

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Transcriptional profiling indicates that cork layer development is stress response related

In our previous transcriptome overview work, we used phellogen tissue scraped from fresh-extracted cork planks (Teixeira et al. 2014). In the present work, a more fine-tuned approach leads us to successfully apply laser microdissection technique to isolate cells of phellogen alone for further gene high-throughput sequencing. The nature of these studies on cork development is important for sustainable cork produc-tion that is dependent on numerous factors such as age of the trees and their adaptive capacity to cope with environmental changes. Factors, such as drought and heat, play a strong role on the quality of cork (Pereira 2007; Oliveira et al. 2016), but as stated by others (Conde et al. 1998; Pereira 2007) and seen by us (Teixeira et al. 2014), differences in transcribed gene families also have an impact on the quality of cork produced by the tree. Cork formation and stress are related. As observed by Ricardo et al. (2011), many of the proteins detected during cork development in cork oak share a dual stress response and defense function demonstrating that the cork layer produced by the tree is, in fact, a protective tissue that is developed to work as a barrier against external factor

aggressions. Our annotation results after MapMan analy-sis revealed that stress was the biological category with a higher number of genes activated, with most of them being up-activated in GQC samples. Such gene reprogramming involved transcription factors, hormone responsive genes, metabolism, and cell wall related genes among others, enhancing its defense mechanisms that could lead to a cor-rect cell division and cell extension in GQC. The gene prod-ucts that result after activation by abiotic stress conditions can be classified into two groups (Seki et al. 2002). One group is distinguished by the presence of genes encoding proteins with a role for cell protection like HSPs (heat-shock proteins) and LEA (late embryogenesis abundant) proteins. It was shown in our previous transcriptomic analysis of cork quality development that in GQC, there was an up-regulation of transcripts encoding HSPs (Teixeira et al. 2014). In the present work, 93.9% of the HSPs were up-regulated in GQC. HSPs have an important function by promoting correct fold-ing, trafficking, maturation and degradation of proteins, cell cycle control, and stabilization of proteins by preventing their aggregation (Sung et al. 2001; Wang et al. 2004). Class I HSP and HSP 17 transcripts were enhanced in in cork oak in response to heat, water deficit, and UV radiation (Pla

The gene list with the corresponding best TAIR hit accession number that was used to construct this table was obtained after MapMan analysis and can be consulted at suppl. data S3. This list is also associated with Fig. 4c. Values below zero mean that the genes are more highly expressed in BQCa Quercus suber accession number refers to the accession number assigned obtained after the pyrosequencing

Table 4 (continued)

Quercus suber accession numbera

Fold change Best TAIR hit P value Annotation

 S60_All_000493 11 At3g50410 5.12e−04 Dof zinc finger proteinMAPK S60_All_007073 7.0 At4g29810 0.001 Mitogen-activated protein kinase S60_All_000908 9.0 At5g58350 7.02e−04 WNK protein kinase S60_All_000188 17 At1g10210 0.032 Mitogen-activated protein kinase S60_All_001978 34 At1g18150 0.001 Mitogen-activated protein kinase

Table 5 List of the genes differentially expressed involved in cell wall modification obtained after MapMan analysis

Values below zero mean that the genes are more highly expressed in BQCa Q. suber contig accession number refers to the accession number assigned after pyrosequencingb Fold change is expressed as the ratio of gene expression of GQC to BQC

Bin name Contig Q. suber accession no.a Best tair blast Annotation Fold changeb

Cell wall modification isotig03665/S60_All_000589 AT4G03210 Xyloglucan endotransglucosylase/hydrolase 9 (XTH9) −1.917Cell wall modification isotig09854/S60_All_002811 AT1G65680 Expansin B2 (EXPB2) 9Cell wall modification isotig04952/S60_All_001075 AT2G40610 Expansin 8 (EXP8) −12.5Cell wall modification isotig03714/S60_All_000602 AT5G65730 Xyloglucan −6.4Cell wall modification isotig04766/S60_All_000992 AT2G39700 Arabidopsis thaliana expansin A4 (ATEXPA4) 8.25Cell wall modification isotig03601/S60_All_000538 AT2G36870 Xyloglucan −21.5Cell wall modification isotig04097/S60_All_000592 AT4G25810 Xyloglucan endotransglycosylase 6 (XTR6) −1.70

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et al. 1998; Puigderrajols et al. 2002) suggesting an impor-tant role throughout the process of cork development. In the present study, we only found three LEA genes differentially expressed and all were up-regulated in cork of good quality. The phellogen cell homeostasis in GQC conferred by these stress-related genes could explain the reason why more tran-scripts are found in these cork quality samples than in BQC ones after the quality control of the sequencing. The lack of cell protection function promoted by the HSPs and the accumulation of free phenolic compounds leads to a much less active steady stage of the BQC phellogen cells with a concomitant reduction of transcripts in the cytoplasm. The second group comprises regulatory proteins like the tran-scription factors families MYB, bZIP, and NAC (Cushman and Bohnert 2000). These transcription factor families were all present in our samples as differentially expressed and, again, up-regulated in GQC samples indicating that the trees that produce cork of superior quality are able to activate the genes that respond to abiotic stress more accordingly in comparison with trees producing cork of reduced quality. Just like what happens with specific transcription factors, HSPs and kinase cascades that are activated by both biotic

and abiotic stresses (Dubos et al. 2010; Berriri et al. 2012), secondary metabolites such as flavonoids accumulate in response to the same stresses conferring a protection shield to the plant (Clé et al. 2008). This defense mechanism seems to be the method adopted by the trees producing cork of bad quality as discussed further below.

Cork oak trees producing low‑quality cork putatively over‑activate the flavonoid pathway in response to stress

MapMan, GO annotation, and KEGG mapping showed that all the transparent testa (TT) genes involved in the flavo-noids pathway were up-regulated in BQC. We already had seen in our previous work that BQC accumulated more than the double amount of total phenolics than GQC (Teixeira et al. 2014). Here, the transcriptomic results from laser microdissected phellogen cells clearly showed that the genes involved in the anthocyanins pathway were up-reg-ulated in BQC. The phenylpropanoid pathway, responsible for the production of lignin and suberin, and of phenolic compounds, involves a high number of enzymes in an intri-cate fashion way (Besseau et al. 2007). The biosynthesis of

Fig. 5 KEGG analysis displaying the differentially expressed genes in phenylpropanoid biosynthesis pathway. Enzymes in green are up-regu-lated in BQC and in red are up-regulated in GQC

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phenolic precursors is costly for plants (Gershenzon 1994), meaning that the pathway by-products can be re-routed towards the synthesis of lignin and suberin or towards phe-nolics depending on the tree’s physiological needs. The first step in the biosynthesis of phenylpropanoid compounds is the conversion of l-phenylalanine to trans-cinnamic acid by the enzyme phenylalanine ammonia lyase (PAL). When Sewalt et al. (1997) produced transgenic tobacco plants with reduced levels of PAL and cinnamate-4-hydroxylase (C4H), they observed a substantial reduction in lignin content as a consequence of the decreased flux through the pathway, phe-nomenon likely to be occurring in our BQC cork phellogen samples, with a suberin and lignin biosynthesis reduction but an increase in free phenolics production. Moving along throughout the phenylpropanoid pathway, coumaroyl CoA is in the junction of the metabolic routes leading to flavonoids compounds via chalcone synthase (CHS) and to suberin and lignin through the action of hydroxycinnamoyl transferase (HCT) (Dao et al. 2011; Teixeira et al. 2014). Besseau et al. (2007) showed that in HCT-silenced Arabidopsis lines, there were a higher accumulation of flavonoid compounds and

a reduction of plant growth that were correlated with the extent of flavonoid accumulation. In BQC, all the genes cod-ing for the enzymes involved in the flavonoids pathway that initiates with CHS were up-regulated eventually resulting in the development of a thinner cork layer.

The phenylpropanoid biosynthesis pathway is impor-tant for cork development, because its initial steps lead to the by-products responsible for the formation of lignin, suberin, the main chemical compound of cork, and distinct classes of phenols that could be directed to incorporate suberin like ferulic acid (Marques et al. 2016) or soluble phenols involved in defense mechanisms (Gitz and Liu-Gitz 2007). It starts with the formation of cinnamic acid from phenylalanine, which leads to the formation of cinnamoyl-CoA, p-coumaroyl-CoA, feruloyl-CoA, and sinapoyl-CoA. These CoA-activated compounds are starting materials for the synthesis of lignins and suberins, flavonoids, flavones, and flavonols. The KEGG maps showed that the phenyl-propanoid biosynthesis pathway was shifted into the fla-vonoid biosynthesis. As observed by others (Fornalé et al. 2010; Gallego-Giraldo et al. 2011), a decrease in lignin

Fig. 6 KEGG analysis displaying the differentially expressed genes in flavonoid biosynthesis pathway. Enzymes in green are up-regulated in BQC and in red are up-regulated in GQC

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biosynthesis affected the synthesis of other secondary metabolites such as flavonoids compounds. It is well known that flavonoids play important distinct roles in flowering plants such as absorbing UV-B functioning as a protective shield in the epidermal tissues (Fuglevand et al. 1996). The work of Burbulis et al. (1996) showed that CHS expression was stimulated by UV and blue light feature that was seen as a protective mechanism as it promotes the accumulation of UV-absorbing flavonoids in the epidermis. Our results are indicative that cork oak trees producing cork of lower quality cope with the abiotic stress conditions by shifting the phenylpropanoid pathway to the synthesis of phenolic compounds and an up-regulation of genes involved in the structure of primary cell walls. The alteration of carbon flux we observed in our samples towards the synthesis of flavonoids also alters the production of lignin and suberin changing the integrity of the cell walls. Several classes of genes involved in the cell wall structure were differentially expressed in the two types of cork samples and discussed further down. Although we would like to stress that as seen by Hamann (2012), when cell wall integrity is altered, it can induce long-term modifications to gene expression leading to a permanent activation of defense-related genes, a feature that could occur throughout the 9 years that a cork layer

takes to be completely formed before the next debarking. We can speculate that in BQC, the phenylpropanoid meta-bolic flux is rerouted into the flavonoid biosynthetic pathway resulting in a higher accumulation of flavonoids leaving the suberin synthesis pathway with less intermediary compo-nents. In fact, all the suberin-associated genes differentially expressed were up-regulated in GQC.

Suberin is a complex biopolyester mainly composed by polyaliphatics of inter-esterified long-chain fatty acids and alcohols and by glycerol and polyaromatics such as ferulic acid (Graça and Santos 2007). The chemical composition of suberin is well known, but there is still deeper knowl-edge needed on the molecular and genetic mechanisms involved. Recently, studies have reported a large number of genes associated with synthesis, transport, and deposition of suberin in distinct tissues and under different physiological conditions (Soler et al. 2007; Kosma et al. 2014). The genes cytochrome P450 of families 86 (CYP86A1 and CYP86B1) are required for the biosynthesis of very long-chain saturated α, ω-bifunctional aliphatic monomers (Hofer et al. 2008; Molina et al. 2009) and they were both detected in our sam-ples, although only CYP86A1 was differentially expressed with an up-regulation in GQC. All the over-expressed tran-scripts involved in the polyaliphatic domain of suberin that

Table 6 Summary of the differentially expressed genes involved in the phenylpropanoid and flavonoid biosynthesis pathways identified from Q. suber phellogen cells laser microdissected transcriptome

Values below zero mean that the genes are more highly expressed in BQCEC number enzyme code number, KO KEGG orthology

Gene name EC number KO Fold change Best TAIR hit P value

Phenylpropanoid biosynthesis pathway Phenylalanine ammonia-lyase (PAL) 4.3.1.24 K10775 −148 At3g53260 1.25337e−17 Beta-glucosidase 3.2.1.21 K01188 K05349 86 At2g44480

At3g470402.34286e−110.013726511

 4-Coumarate-CoA ligase (4CL) 6.2.1.12 K01904 −15 At1g65060 0.0161517 Cinnamoyl-CoA reductase (CCR) 1.2.1.44 K09753 52 At1g15950 0.000156769 Trans-cinnamate 4-monooxygenase (C4H) 1.14.13.11 K00487 −114 At2g30490 7.80488e−08 Cinnamyl-alcohol dehydrogenase (CAD) 1.1.1.195 K00083 91 At3g19450 3.02708e−09 Peroxidase 1.11.1.7 K00430 72 At2g22420 3.99420e−10 Coniferyl-alcohol glucosyltransferase (UGT72E) 2.4.1.111 K12356 10 At3g50740 0.0189779 Caffeic acid 3-O-methyltransferase (COMT) 2.1.1.68 K13066 102 At5g54160 5.32058e−12

Flavonoid biosynthesis pathway Chalcone synthase (CHS) 2.3.1.74 K00660 −793 At5g13930 0.0 Chalcone isomerase (TT5) 5.5.1.6 K01859 −19 At3g55120 0.00718125 Naringenin 3-dioxygenase (F3H) 1.14.11.9 K00475 −133 At3g51240 5.90072e−10 Trans-cinnamate 4-monooxygenase (C4H) 1.14.13.11 K00487 −114 At2g30490 7.80488e−08 Flavonoid 3′-monooxygenase (TT7) 1.14.13.21 K05280 −126 At5g07990 1.67384e−13 Leucoanthocyanidin dioxygenase (ANS) 1.14.11.19 K05277 −83 At4g22880 2.67128e−09 Anthocyanidin reductase (ANR) 1.3.1.77 K08695 −230 At1g61720 9.81532e−29 Bifunctional dihydroflavonol 4-reductase/flavanone

4-reductase (DFR)1.1.1.2191.1.1.234

K13082 93 At5g42800 7.77006e−13

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were found in laser microdissected phellogen cells from trees producing GQC were also found in good quality cork samples in our previous work (Teixeira et al. 2014). The two genes are associated with endoplasmic reticulum, the place where monomer biosynthesis seems to occur before being exported (Millar et al. 1999; Hofer et al. 2008). Suberin is deposited on the inner surface of the cell wall and to be transported, and makes use of a non-specific lipid-transfer protein that reversibly binds different types of lipid mol-ecules (Panikashvili et al. 2010). In each cork-type sample,

a non-specific binding proteins was highly expressed (LPD) and significantly correlated with both GQC and BQC as shown by PCA analysis meaning that they play a relevant role in cork development.

Differential cell wall remodeling occurs in BQC and GQC as stress response

The bin “cell wall modification” that resulted from the MapMan analysis revealed a higher number of genes

Table 7 Genes involved in suberin synthesis detected in the phellogen samples from trees producing good and bad quality cork

a Quercus suber accession number refers to the accession number assigned obtained after the pyrosequencingb Fold change is expressed as the ratio of gene expression of good quality phellogen samples to bad quality phellogen samples

Quercus suber accession numbera

Fold changeb Best TAIR hit P value Annotation

Suberin and wax biosynthesis S60_All_000242 S60_All_003026

21NDE

At5g58860 0.0011 Cytochrome P450, family 86 (CYP86A1)

 S60_All_001926 S60_All_004008 S60_All_005178

NDENDENDE

At5G41040 Cytochrome P450, family 86 (CYP86B1)

 S60_All_003898 S60_All_000165 S60_All_002927

NDE35.6NDE

At5g41040At5g17540

9.61e−6 Omega-hydroxypalmitate O-feruloyl transferase

 S60_All_002270 S60_All_004192

NDENDE

At4g16760At2g35690

Long-chain-fatty-acyl-CoA oxidase

 S60_All_003801 S60_All_002417

NDE26

At5g22500At3g44540

0.0003 Fatty-acyl-CoA reductase (FAR)

Fatty-acid elongation S60_All_001966 S60_All_003516 S60_All_000967

NDENDE7.75

At2g33150At5g48230

0.0001 Acetyl-CoA acyltransferase

 S60_All_001606 NDE At3g25860 Dihydrolipoamide S-acetyltransferase S60_All_000078 S60_All_004768 S60_All_000718

3.2NDENDE

At3g06860At3g15290

0.04 3-Hydroxyacyl-CoA dehydrogenase

 S60_All_000726 18.3 At4g16210 0.03 Enoyl-CoA hydratase S60_All_000496 S60_All_001217

NDE2.9

At1g19440At4g34510

0.02 Very long-chain fatty-acid condensing enzyme 4

 S60_All_005625 NDE At3g55360 Very long-chain enoyl-CoA reductase S60_All_001914 S60_All_004036

NDENDE

At1g49430 Long-chain acyl-CoA synthase

 S60_All_001896 S60_All_002230

NDENDE

At5g49460 ATP-citrate lyase (ACL)

 S60_All_000496 S60_All_001217

NDENDE

At1g67730 3-Ketoacyl-CoA synthase (KCS)

 S60_All_000438 S60_All_003457

5.5DNE

At3g22400 0.006 Lipoxygenase (LOX)

 S60_All_000177 S60_All_004069 S60_All_001920

3.3NDEonly in GQC

At2g23540At5g22810At2g04570

0.00020.005

GDSL esterase/lipase

 S60_All_002327 S60_All_005892

NDENDE

At3g11430 Glycerol-3 phosphate acyltransferase (GPAT)

 S60_All_000428 NDE At5g13580 ABC transporter protein

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up-regulated in BQC in comparison with GQC. The genes were the xyloglucan endotransglucosylase (XTH), xylo-glucan, and expansin (out of the three genes detected, two were up-regulated in GQC). The plant cell wall is strongly responsive to abiotic stress such as heat, light, and drought and retarding cell expansion is one of the earliest visible effects of stress that leads to a reduced plant growth (Le Gall et al. 2015). Under water stress conditions, turgor pres-sure is lower, and as a result, cell wall elasticity increases to maintain cell turgor (Saito and Terashima 2004). When submitted to water stress deficiency, expansins were seen to be up-regulated (Jones and McQueen-Mason 2004). In our samples, most of the expansins are up-regulated in GQC that could lead to a correct cell enlargement with a resulting thick cork layer formation. Another cell wall gene family that is up-regulated in BQC in response to abiotic stress is the XTH genes. The XTH and xyloglucan genes are responsible for the synthesis of cellulose. The increased amount of cellulose could help maintain cell wall integrity and cell turgor. Such up-regulation was observed by us and by Yang et al. (2006) in rice under drought stress and by Choi et al. (2011) in pep-per. The up-regulation of expansin and XTH genes has the capacity to increase the viscoelasticity of the primary cell wall contributing to an increase in the hydration status with a consequent maintenance of the turgor pressure needed for growth (Le Gall et al. 2015). In BQC, the up-regulation of XTH genes could be the response to intense light radiance and drought conditions so typically found during the sum-mer months in areas, where cork oaks grow. As observed in Brassica napus under light radiation, XTH genes were also up-regulated (Sharma et al. 2014). Light also stimulates the up-regulation of the chalcone synthase (CHS) gene (Jenkins et al. 2001) leading to an increase of phenolic compounds. It was shown that the association of free phenolic compounds with the cell wall can be an indicator of plant resistance to drought stress (Hura et al. 2012). High light radiation and drought stress are tight to each other as environmental conditions. The link between phenolic compounds and cell wall matrix could generate a stiffening of the cell wall that might alter cell wall elongation (Le Gall et al. 2015) and mechanism that could be translated into reduced cell size and, therefore, thinner cork layers of our BQC planks.

Hormonal metabolism seems to play a role in cork development

Plant hormone abscisic acid (ABA) serves as a chemical signal in response to environmental factors that leads to adaptation to the stress conditions (Lee and Luan 2012) throughout, for example, the activation of stress-responsive genes (Agarwal and Jha 2010). It is involved in the modi-fication of gene expression such as AREB/ABF (ABA-responsive element-binding protein/ABA binding factor)

and MYC/MYB transcription factors (Abe et al. 1997; Busk and Pagès 1998). In our cork oak samples, all these tran-scription factors together with ABA-related genes had more elements up-regulated in good quality cork. The action of MYB genes could be crucial for cork development. As seen recently, AtMYB9 and At MYB107 seem to be responsible for both the aliphatic and aromatic monomer biosynthesis, transport and polymerization of the cell wall compound suberin (Lashbrooke et al. 2016; Gou et al. 2017). Suberin is the main chemical constituent of cork (Pereira 2015) and our transcriptomic data show that 11 MYB genes were up-regulated in GQC and only three were found to be up-regu-lated in BQC. When this transcription factor up-regulation in GQC is taking together with the up-regulation of suberin biosynthesis-associated genes, one can conclude that more suberin is produced in GCQ than in BQC. Dehydration-responsive element binding (DREB), NAC, and zink-finger homeodomain (ZF-HD) despite the fact that they are not activated by ABA (Saibo et al. 2009) were also found to be up-regulated in GQC. Besides ABA, other growth regulators may be involved in abiotic stress response in GQC sam-ples, namely, ethylene, auxins, and jasmonates (Verma et al. 2016). Three unigenes involved in ethylene biosynthesis and coding for S-adenosylmethionine synthetase and for 1-ami-nocyclopropane-1-carboxylate synthase were up-regulated in GQC. Genes coding for ethylene-responsive transcription factors, ethylene insensitive 3 (EIN3) and ethylene recep-tors (ETR1/ETR2 and EIN4), was also up-regulated in these samples. Ethylene is perceived by a family of membrane-associated receptors, including ETR1/ETR2 and EIN4 in Arabidopsis (Guo and Ecker 2004). Genes involved in jas-monate signaling (jasmonate-ZIM domain-containing pro-teins) and key mediators of jasmonate signaling (MYC tran-scription factors) were also up-regulated in GQC, indicating that jasmonates may participate in stress response and/or cell differentiation and regulation of secondary metabolism.

The sum of data gather in this work and presented earlier (Teixeira et al. 2014) strongly indicates that the differences in cork qualities development have a genetic background reflecting mainly, the defense mechanisms that are acti-vated by the tree’s genome when subjected to environmental stresses. In GQC, the chain reaction activation seems to be initiated by the up-regulation of HSPs that by conferring cell protection enables the correct cell activity necessary for a proper cell transcription and translation and cell division. In BQC, on the other hand, biosynthesis of free phenolic compounds seems to be favored but together with certain cell wall family genes impairs a correct meristematic cell activity with less transcripts produced and a reduced num-ber of cell division. Trees producing BQC, apparently, cope better with environmental stresses by synthesizing a shield composed of phenolic compounds despite the thickness of the protective suber layer is compromised.

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Author contribution statement RTT designed the experi-ment, performed the laser microdissection, microscopy, RNA extraction, and RT-PCR, analyzed the results, and wrote most of the manuscript. AMF helped in the sample collection and in the writing. HB performed the entire path-view pipeline to get the KEGG maps. CP performed the PCA analysis and HP assisted in drafting the manuscript. All authors read and approved the final manuscript.

Acknowledgements This work was carried out within the project PTDC/AGRAAM/100465/2008 financed by the Fundação para a Ciên-cia e a Tecnologia (FCT), Ministério da Educação e Ciência, Portu-gal. R. T. Teixeira was supported by the program Ciência2007 and M. Fortes by SFRH/BPD/100928/2014, and PEst-OE/BIA/UI4046/2014. Carla Pinheiro was supported by the program Ciência2007. CEF is a research unit with a base funding from FCT (AGR/UID00239/2013). We thank Cristina Barroso and Conceição Egas from Biocant for solv-ing all our doubts related to dssCDNA and transcriptome analysis.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict of interest.

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Supplementary Table S1 Primers used for Real Time-RT-PCR designed from the

sequences obtained after Quercus suber pyrosequencing.

Gene (gene code) Primers Amplicon (bp)

Knotted-Like Homeobox 7

KNAT7 (S60_All_005412)

F – GCAAGAACCAAACTTGGGAA

R – TCGTAGAGTGGGTGGGTAGC

126

Heat Shock Protein HSP9

(S60_All_006996)

F – ATGGACAAGGTCAAGGATGG

R – CCTTGTCGGAGAGAGACTGG

103

serine/threonine protein

phosphatase 2A

PP2A S60_All_004223

F – CCAGAAAGGAACTGATCCCA

R – TCCCTCACACAGGTTTCCTC

178

F-box domain, cyclin-like

(S60_All_004112)

F – GATGCAACCCCTCGAAGATA

R – GTTCACCGGAGCTCAGAGAC

122

Phenylalanine Ammonia-Lyase

PAL (S60_All_000002) F – CCCAAAAGTTGCATGAGGTT

R – TGAAGCAATGGCTAGACGTG

250

Heat shock protein DnaJ

HSPD (S60_All_000104)

F - TCTCAAGGAAGGAATGGGTG

R –CCTTTACCCTTGCACTTGGA

234

Caffeate O-Methyltransferase

COMT (S60_All_000207) F – CAGAGATTGCTTCCCAGCTC

R – TCTCAATCTTCCCATCAGGG

132

Dehydrin (S60_All_002245) F – GGGAGGAAAAGAAGGGACTG

R – AAGGGACCTCTTCAGGCTTC

273

SupTable S2 – excel file

SupTable S3 – excel file

SupTable S4 – excel file

SupTable S5 – excel file

Ladder

Overall Results for LadderRNA Area: 243.1 RNA Concentration: 150 ng/µl

Result Flagging Color:Result Flagging Label: All Other Samples

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Assay Class:

Electropherogram Summary

Fragment table for sample 1 : ACF-9984 - má2Name Start Size [nt] End Size [nt] Area % of total Area18S 1,642 1,976 10.4 17.128S 2,673 3,463 21.6 35.5

ACF-9984 - má2

Overall Results for sample 1 : ACF-9984 - má2RNA Area: 60.8 RNA Concentration: 38 ng/µlrRNA Ratio [28s / 18s]: 2.1

RNA Integrity Number (RIN): 8.7 (B.02.08) Result Flagging Color:Result Flagging Label: RIN: 8.70

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Created:Modified:

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Assay Class:

Electropherogram Summary Continued ...

Fragment table for sample 2 : ACF-9985 - má3Name Start Size [nt] End Size [nt] Area % of total Area18S 1,613 1,969 10.0 16.428S 2,711 3,535 21.4 35.1

ACF-9985 - má3

Overall Results for sample 2 : ACF-9985 - má3RNA Area: 61.0 RNA Concentration: 38 ng/µlrRNA Ratio [28s / 18s]: 2.1

RNA Integrity Number (RIN): 9.2 (B.02.08) Result Flagging Color:Result Flagging Label: RIN: 9.20

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Created:Modified:

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Assay Class:

Electropherogram Summary Continued ...

Fragment table for sample 3 : ACF-9986 - boa8Name Start Size [nt] End Size [nt] Area % of total Area18S 1,615 1,937 16.2 17.328S 2,540 3,426 32.8 35.0

ACF-9986 - boa8

Overall Results for sample 3 : ACF-9986 - boa8RNA Area: 93.8 RNA Concentration: 58 ng/µlrRNA Ratio [28s / 18s]: 2.0

RNA Integrity Number (RIN): 9 (B.02.08) Result Flagging Color:Result Flagging Label: RIN:9

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Created:Modified:

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Assay Class:

Electropherogram Summary Continued ...

Fragment table for sample 4 : ACF-9987 - boa10Name Start Size [nt] End Size [nt] Area % of total Area18S 1,610 1,988 16.0 20.428S 2,604 3,421 31.9 40.5

ACF-9987 - boa10

Overall Results for sample 4 : ACF-9987 - boa10RNA Area: 78.6 RNA Concentration: 48 ng/µlrRNA Ratio [28s / 18s]: 2.0

RNA Integrity Number (RIN): 9.7 (B.02.08) Result Flagging Color:Result Flagging Label: RIN: 9.70

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Assay Class:

Electropherogram Summary Continued ...

gene fold change qRT-PCR Fold change pyrosequencingKNAT7 1,13 4,6HSP9 0 0HEAT 0,08 0,07PAL 0,68 0,27HSPD 3,54 3,4COMT 6,4 6,44Dehydrin 5,08 5

R 0,868290908

Fig.

a Real-time Q-PCR amplification

results from the same RNA

samples (GQC and BQC) used for

the pyrosequencing. Amplifications

were performed with specific

primers chosen from the

transcriptome list. It was ensured

that each pair of primers amplified

a single gene. Standard deviation

levels are shown (n=6 correspond

to the number of runs per primer

set). Stars indicate significant

difference at level P<0.05 in

comparison to GQC.

b. The correlation coefficient R2

between the expression fold-

change obtained from qRT-PCR and

after pyrosequencing is 0.7539.

a

b