Abstract

   Polyploidy events are believed to be responsible for increasing the
   size of plant organs and enhancing tolerance to environmental stresses.
   Autotetraploid Paulownia australis plants exhibit superior traits
   compared with their diploid progenitors. Although some transcriptomics
   studies have been performed and some relevant genes have been revealed,
   the molecular and biological mechanisms regulating the predominant
   characteristics and the effects of polyploidy events on P. australis
   remain unknown. In this study, we compared the phenotypes,
   microstructures, and proteomes of autotetraploid and diploid P.
   australis plants. Compared with the diploid plant, the leaves of the
   autotetraploid plant were longer and wider, and the upper epidermis,
   lower epidermis, and palisade layer of the leaves were thicker, the
   leaf spongy parenchyma layer was thinner, the leaf cell size was
   bigger, and cell number was lower. In the proteome analysis, 3,010
   proteins were identified and quantified, including 773 differentially
   abundant proteins. These results may help to characterize the P.
   australis proteome profile. Differentially abundant proteins related to
   cell division, glutathione metabolism, and the synthesis of cellulose,
   chlorophyll, and lignin were more abundant in the autotetraploid
   plants. These results will help to enhance the understanding of
   variations caused by polyploidy events in P. australis. The
   quantitative real-time PCR results provided details regarding the
   expression patterns of the proteins at mRNA level. We observed a
   limited correlation between transcript and protein levels. These
   observations may help to clarify the molecular basis for the
   predominant autotetraploid characteristics and be useful for plant
   breeding in the future.

Introduction

   Paulownia tree species are native to China, but have been introduced to
   many other countries for afforestation and land reclamation because of
   their rapid growth and ability to adapt to poor environmental
   conditions [[38]1–[39]3]. Additionally, because of their good wood
   properties, Paulownia trees are widely used to make furniture,
   aircraft, toys, and musical instruments [[40]4]. Collectively,
   Paulownia species have considerable economic and ecological values.
   Polyploidy events [i.e., whole genome duplications (WGDs)] usually
   occur in eukaryotes. Generally plant species have polyploid ancestries
   [[41]5]. Based on parental genome differences, polyploidy is generally
   classified as either autopolyploid (the duplicated genome from the same
   species) or allopolyploid (the duplicated genome from the different
   species). Moreover, plant polyploids generally have large leaves and
   fruits, as well as other superior traits that make them attractive to
   breeders; for example, autotetraploid mulberry [[42]6] and allotriploid
   cucumber [[43]7]. In natural populations, after genomic merging and/or
   doubling, some genetic combinations may generate new morphological,
   ecological, physiological, and/or cytological characteristics that may
   be associated with speciation and biodiversification. The evolutionary
   success of polyploidy events is indicated by its widespread occurrence,
   and such events can affect niche differentiation and expand the
   habitats suitable for a particular plant species. Autopolyploid [[44]8]
   and allopolyploid [[45]9] are usually able to tolerate adverse
   environmental conditions better than their progenitors to ensure their
   survival in heterogeneous conditions. To take advantage of polyploidy
   events and expand the available Paulownia germplasm resources, four
   autotetraploid Paulownia species, including Paulownia australis, were
   synthesized from their diploid relatives using colchicines
   [[46]10–[47]13]. Autotetraploid P. australis is more tolerant to salt
   and drought stresses than diploid P. australis [[48]14,[49]15].

   Changes that are regulated by polyploidy events in plants have become
   the focus of increasing interest over the past decades. Through the
   control of circadian-mediated physiological and metabolic pathways,
   allopolyploid Arabidopsis plants improve the synthesis of chlorophyll
   and starch to increase growth vigor and biomass, and they are larger
   than their parents [[50]16]. Autopolyploid Arabidopsis appear to
   accumulate more potassium and exhibit greater resistance to the adverse
   effects of salinity compared with their diploid parents [[51]8]. In
   Nicotiana benthamiana, the artificial octaploid exhibited greater
   resistance to the adverse effects of drought, cold, and nutrient stress
   than its allotetraploid parents [[52]17]. In autotetraploid mulberry,
   the height, leaf size, and fruit size were larger than those of the
   diploids [[53]6]. Compared with diploids, the cell sizes of
   autotetraploid Lolium perenne and L. multifloru were bigger because of
   an increased cell elongation rate, which resulted in increased leaf
   size [[54]18]. The autotetraploid Salix viminalis developed wider
   leaves with thicker midrib and enlarged palisade parenchyma cells
   relative to the diploids, and the chlorophyll and carotenoid contents
   as well as photosynthesis were improved [[55]19]. High-throughput
   genomic and transcriptomic sequencing techniques have also been used to
   characterize the changes caused by polyploidy events. Compared to
   diploid citrus rootstock (C. junos cv. Ziyang xiangcheng), higher
   expression level of stress related genes in doubled diploid could be
   beneficial for its stress tolerance [[56]20]. The differentially
   expressed genes among Chinese woad (Isatis indigotica Fort.) and its
   autopolyploids were involved mainly in cell growth, response to stress,
   and photosynthetic pathways [[57]21]. Transcriptome sequencing and
   comparative analysis of diploid and autotetraploid P. australis have
   been preformed, and differentially expressed genes related to
   carbohydrate and energy metabolism, biosynthesis of cell wall, and
   stress tolerance have been identified [[58]22]. However, phenotypic
   changes caused by polyploidy events are not always associated with
   changes in gene expression. For example, Allario et al found that large
   changes in anatomy and physiology between diploid lime and its
   autotetraploid were not associated with large changes in leaf gene
   expression [[59]23]. Genome- or transcriptome-level expression data may
   not accurately predict proteome-level changes because regulatory
   activities such as transcription/post-transcriptional modification,
   translation/post-translational modification, and protein–protein
   interactions may influence the composition of the proteome, and it is
   proteins that play a central role in biological processes. Therefore,
   protein profile analyses may provide useful functional information that
   will help to supplement genomic and transcriptomic data.

   Two-dimensional gel electrophoresis and isobaric tags for relative and
   absolute quantitation (iTRAQ) are the main techniques that have been
   used in proteomic studies. Two-dimensional gel electrophoresis is
   limited by the under-representation of low-abundance proteins. While,
   iTRAQ enables the simultaneous identification of many proteins and
   provides highly sensitive measurements of proteome-level changes. iTRAQ
   have been used to investigate polyploidy events, such as allopolyploid
   in Brassica species [[60]24] and Tragopogon mirus [[61]25], and
   autopolyploid in Arabidopsis thaliana [[62]26]. A proteome study of
   autotetraploid Paulownia ‘Yuza 1’ plants using iTRAQ has been published
   recently [[63]27]. However, very little is known about the differences
   in the protein profiles between diploid and autotetraploid P. australis
   plants.

   The abundant transcript changes between diploid and autotetraploid P.
   australis plants have provided some information to characterize the
   mechanisms involved in plant polyploidization. Knowledge of
   proteome-level changes resulting from polyploidy events and the
   associated regulatory activities will help us to understand the
   mechanisms more throughly. In this study, the morphological,
   physiological, and microstructural features of P. australis leaves were
   analyzed, and a comprehensive iTRAQ-based comparative proteome-level
   investigation was conducted to study the effects of WGD events. The
   findings may help to improve our understanding of the molecular basis
   of polyploidy events.

Materials and methods

Plant materials

   Plant materials were obtained from the Laboratory of Forest
   Biotechnology, Henan Agricultural University, Zhengzhou, China.
   Autotetraploid P. australis (PA4) was induced from diploid P. australis
   (PA2) by colchicine treatment. Their ploidy have been identified by the
   flow cytometry analysis and chromosome counts [[64]12]. The plants used
   in this study were tissue cultured seedlings. They were multiplied
   through in vitro plantlet regeneration using leaves from established
   tissue cultured seedling. The PA2 and PA4 seedlings prepared from the
   tissue cultures were grown for 30 days at 25 ± 2°C under a 16-h
   light:8-h dark photoperiod (light intensity: 130 μmol m^−2 s^−1). Equal
   number of leaves from three individual plants were considered as one
   accession, and at least two biological replicates were used in the
   experiments. Harvested leaf samples were immediately frozen in liquid
   nitrogen and stored at −80°C for iTRAQ and quantitative real-time PCR
   (qRT-PCR).

Determination of leaf traits

   Mature and fully expanded leaves (i.e., second leaf from the apex) were
   used to measure leaf traits. One leaf from one seedling was considered
   as one accession. The length and width of at least 10 leaves were
   measured using a vernier caliper. Chlorophyll content was measured
   according to a previously described method [[65]28], with three
   biological replicates for each measurement. Leaf cross-sections were
   evaluated using scanning electron microscopy. An area approximately 1–2
   cm^2 from the center of each leaf was fixed in 2.5% glutaraldehyde
   buffered with 0.2 M sodium phosphate buffer (pH 7.2) for scanning
   electron microscopy. The samples were post-fixed in 1% (w/v) osmium
   tetroxide for 1 h and then washed three times in the buffer. The
   samples were dehydrated in a graded alcohol series and then examined in
   a Quanta 200 environmental scanning electron microscope (FEI,
   Hillsboro, TX, USA). Three biological replicates were used. The
   thicknesses of the upper epidermis, palisade layer, spongy parenchyma
   layer, lower epidermis, and leaf were measured according to the method
   described by Zhang et al [[66]29]. The cell number and size were
   determined using Image J software (NIH, Bethesda, MD, USA).

Protein preparation

   The harvested leave samples from PA2 and PA4 were ground to a fine
   powder in liquid nitrogen, and then treated with lysis buffer (7 M
   urea, 2 M thiourea, 4% CHAPS, and 40 mM Tris–HCl, pH 8.5) containing 1
   mM PMSF and 2 mM EDTA (final concentration). Total protein was
   extracted and digested according to the method of Tian et al [[67]30].

Proteome analysis using iTRAQ

   The protein profiles of the PA2 and PA4 samples were analyzed using
   iTRAQ at the Beijing Genomics Institute, Shenzhen, China. The
   trypsin-digested proteins were labeled using iTRAQ 8-plex kits (Applied
   Biosystems, Foster City, CA) as follows: PA2-1,113; PA2-2, 114; PA4-1,
   117; PA4-2,118, according the method of Qiao et al [[68]31]. The
   labeled peptides were pooled, dried, redissolved, and fractionated by
   strong cation exchange (SCX) chromatography using a LC-20AB HPLC pump
   system (Shimadzu, Kyoto, Japan), as described by Dong et al [[69]27].
   The fractionated samples were analyzed by liquid
   chromatography–electrospray ionization tandem mass spectrometry
   (LC-ESI-MS/MS) based on the Triple TOF 5600, as described by Qiao et al
   [[70]31].

   Proteins were identified and quantified using the Mascot 2.3.02 search
   engine (Matrix Science, Boston, MA, USA).The search parameters were as
   described in a previous study [[71]30].

   Proteins were identified using a transcriptome database consisting of
   53,230 nonredundant sequences, from sequences that have been submitted
   to the Short Reads Archive under accession number SRP032321 [[72]22].
   To decrease the probability of incorrect protein identifications, only
   peptides with significance scores (≥ 20) in the 99% confidence interval
   (i.e., greater than “identity”) of a Mascot probability analysis were
   considered accurately identified. The identification of each protein
   was based on at least one unique peptide. To quantify proteins, at
   least two unique peptides were needed for each protein. The calculated
   protein ratios were based on weighted averages, and were normalized
   against the median ratio in Mascot. Proteins with P values <0.05 and
   fold changes >1.2 were considered as differentially abundant proteins
   (DAPs).

   For the functional analyses, the identified proteins were annotated by
   searching against the Gene Ontology (GO), Clusters of Orthologous
   Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   databases.

Quantitative real-time PCR analysis of gene expression

   Total RNA was extracted from PA2 and PA4 leaves using the TRIzol
   reagent (Sangon, Shanghai, China). Primers were designed using Beacon
   Designer (version 7.7) (Premier Biosoft International, Palo Alto, CA,
   USA) ([73]S1 Table). PCR mixtures with a final volume of 20 μL
   contained 10 μL SYBR Green PCR mix, 1 μL cDNA, 0.4 μM forward primer,
   0.4 μM reverse primer, and 7.0 μL sterile ddH2O. The qRT-PCR was
   completed using a CFX96 Real-Time System (Bio-Rad) and SYBR Premix Ex
   Taq II (Takara, Dalian, China). The PCR program was as follows: 95°C
   for 1 min; 40 cycles of 95°C for 10 s and 55°C for 15 s. Three
   replicates were analyzed for each gene. Relative gene expression levels
   were calculated using the 2^−ΔΔCt method, and the 18S rRNA of Paulownia
   was chosen as a reference gene for normalization. Student’s t test was
   used to detect differences between PA4 and PA2 at a significance level
   of p = 0.05.

Results

Changes on leaf traits after whole genome duplications

   The phenotypic differences among leaves are presented in [74]Fig 1.
   Compared with PA2 plants, in PA4 plants, leaf length and width were
   greater; leaf size was bigger. Leaf structures were thicker, including
   the upper and lower epidermal layers and palisade tissues; but the
   spongy parenchyma layer was thinner ([75]Fig 2). The cell number was
   decreased and cell size was increased in PA4 compared with PA2.
   Chlorophyll content was also higher in PA4 plants than in PA2 plants
   ([76]Fig 1D), maybe because the thicker palisade tissue of PA4 leaves
   contained numerous chloroplasts, which are the primary sites for
   photosynthetic activities. Together, these changes may result in the
   greater net photosynthetic rate that has been observed in PA4 plants
   [[77]32].

Fig 1. Comparisons of leaves between PA2 and PA4.

   Fig 1
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   (A) Leaves of PA2 (B) Leaves of PA4 (C) Length and width of Leaves (D)
   Chlorophyll content of Leaves. Bar = 1cm. Error bars represent the
   standard error of the mean. *: Statistically significant differences
   between PA2 and PA4 (P<0.05).

Fig 2. Leaf microstructure of PA2 and PA4.

   Fig 2
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   (A) leaf microstructure of PA2 (B) leaf microstructure of PA4 (C)
   measurents of leaf microstructure. UE, upper epidermal. LE, lower
   epidermal. PT, palisade tissues.ST, spongy tissues. Bar = 100 μm. The
   cell numbers in PA2 and PA4 are 176±8 and 140±5, respectively. The cell
   sizes (mean area) of PA2 and PA4 are 32.60±2.04 um^2 37.21±2.04 um^2.
   Error bars represent the standard error of the mean. *: Statistically
   significant differences between PA2 and PA4 (P<0.05).

iTRAQ data analysis and protein identification

   Proteins extracted from PA2 and PA4 leaves were analyzed using an iTRAQ
   approach. To maximize the number of identified proteins, we limited the
   peptide matching error during database searches to less than 10 ppm.
   The distribution of errors between the actual and theoretical relative
   molecular weights of all matched peptides is presented in [80]S1A Fig.
   A total of 366,435 spectra were obtained. Based on the analysis with
   the Mascot search engine, we acquired 21,982 spectra, of which 17,230
   were unique spectra. We identified 8,665 peptides, including 7,575
   unique peptides, and 3,010 proteins ([81]S1B Fig and [82]S2 Table). The
   distribution of numbers of peptides, mass and sequence coverage of
   proteins were shown in [83]S1C, S1D and S1E Fig, respectively. The
   consistency of the results among the biological replicates implied the
   proteome-level analyses were reliable ([84]S1F and S1G Fig). The MS/MS
   raw data generated in this study have been deposited in the
   ProteomeXchange Consortium database via the PRIDE partner repository
   (dataset identifier: PXD004237).

   To characterize the functions of the 3,010 identified proteins, we
   first mapped them to the COG database. The proteins were mapped to 23
   categories ([85]S2 Fig). In the GO analysis, the identified proteins
   were classified into 54 groups ([86]S3 Fig). To predict the main
   metabolic and signal transduction pathways in which the identified
   proteins involved, we mapped them to KEGG pathways. The proteins were
   mapped to 120 pathways ([87]S3 Table).

Analysis of differentially abundant proteins

   We detected 773 DAPs among the 3,010 identified proteins, including 410
   DAPs with higher abundance and 363 DAPs with lower abundance in PA4
   compared with PA2 ([88]Fig 3 and [89]S4 Table). We used GO enrichment
   analyses to determine the main biological functions of the DAPs. Under
   the biological process category, 122 GO terms were significantly
   enriched (P value<0.05), including “chlorophyll biosynthetic process”,
   “oxidoreduction coenzyme metabolic process”, and “photosynthesis”.
   Under the cellular component category, 69 GO terms were significantly
   enriched, including “chloroplast part”, “plastid part”, and
   “thylakoid”. Under the molecular function category, 49 GO terms were
   significantly enriched, including “copper ion binding”, “ion binding”,
   and “transition metal ion binding” ([90]Fig 4 and [91]S5 Table). The
   DAPs were also mapped to 101 KEGG metabolic pathways, including the
   highly enriched “photosynthesis”, “glyoxylate and dicarboxylate
   metabolism”, “metabolic pathways”, “glutathione metabolism”, and
   “carbon fixation in photosynthetic organisms” pathways ([92]Table 1).
   Among the proteins whose abundances were altered by the WGD, the DAPs
   related to cell division, aerobic respiration, chlorophyll
   biosynthesis, carbon fixation, and lignin biosynthesis in particular,
   may provide relevant information regarding the changes induced by the
   WGD.

Fig 3. The differentially abundant proteins in PA2 vs. PA4.

   Fig 3
   [93]Open in a new tab

Fig 4. The GO enrichment analyses of the differentially abundant proteins in
PA2 vs. PA4.

   Fig 4
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   P value<0.05: GO terms were significantly enriched.

Table 1. KEGG pathway enrichment analysis of the differentially abundant
proteins in PA2 vs. PA4.

   Pathway DAPs with pathway annotation Pathway ID
   Photosynthesis[95]^* 39 (7.03%) ko00195
   Glyoxylate and dicarboxylate metabolism[96]^* 18 (3.24%) ko00630
   Metabolic pathways[97]^* 225 (40.54%) ko01100
   Glutathione metabolism[98]^* 17 (3.06%) ko00480
   Carbon fixation in photosynthetic organisms[99]^* 23 (4.14%) ko00710
   Porphyrin and chlorophyll metabolism 13 (2.34%) ko00860
   Phenylalanine metabolism 11 (1.98%) ko00360
   Valine, leucine and isoleucine biosynthesis 7 (1.26%) ko00290
   Glycolysis / Gluconeogenesis 25 (4.5%) ko00010
   Amino sugar and nucleotide sugar metabolism 18 (3.24%) ko00520
   Tyrosine metabolism 9 (1.62%) ko00350
   Phenylalanine, tyrosine and tryptophan biosynthesis 8 (1.44%) ko00400
   Tropane, piperidine and pyridine alkaloid biosynthesis 4 (0.72%)
   ko00960
   Fatty acid biosynthesis 7 (1.26%) ko00061
   Photosynthesis—antenna proteins 7 (1.26%) ko00196
   One carbon pool by folate 5 (0.9%) ko00670
   Biosynthesis of secondary metabolites 128 (23.06%) ko01110
   Oxidative phosphorylation 22 (3.96%) ko00190
   Monoterpenoid biosynthesis 2 (0.36%) ko00902
   Other glycan degradation 16 (2.88%) ko00511
   Ascorbate and aldarate metabolism 10 (1.8%) ko00053
   Pyruvate metabolism 20 (3.6%) ko00620
   Isoquinoline alkaloid biosynthesis 5 (0.9%) ko00950
   Fructose and mannose metabolism 11 (1.98%) ko00051
   Citrate cycle (TCA cycle) 14 (2.52%) ko00020
   Glucosinolate biosynthesis 1 (0.18%) ko00966
   Taurine and hypotaurine metabolism 2 (0.36%) ko00430
   C5-Branched dibasic acid metabolism 2 (0.36%) ko00660
   RNA degradation 12 (2.16%) ko03018
   Glycerolipid metabolism 5 (0.9%) ko00561
   Pentose phosphate pathway 11 (1.98%) ko00030
   Cysteine and methionine metabolism 9 (1.62%) ko00270
   Protein export 4 (0.72%) ko03060
   Ribosome 35 (6.31%) ko03010
   Galactose metabolism 12 (2.16%) ko00052
   Arginine and proline metabolism 10 (1.8%) ko00330
   Glycine, serine and threonine metabolism 11 (1.98%) ko00260
   Pantothenate and CoA biosynthesis 3 (0.54%) ko00770
   Glycerophospholipid metabolism 3 (0.54%) ko00564
   Linoleic acid metabolism 1 (0.18%) ko00591
   Nitrogen metabolism 7 (1.26%) ko00910
   mRNA surveillance pathway 11 (1.98%) ko03015
   Alanine, aspartate and glutamate metabolism 8 (1.44%) ko00250
   Proteasome 13 (2.34%) ko03050
   Lysine degradation 2 (0.36%) ko00310
   Limonene and pinene degradation 2 (0.36%) ko00903
   Plant-pathogen interaction 20 (3.6%) ko04626
   Glycosphingolipid biosynthesis—ganglio series 3 (0.54%) ko00604
   Phosphatidylinositol signaling system 3 (0.54%) ko04070
   Nucleotide excision repair 4 (0.72%) ko03420
   Protein processing in endoplasmic reticulum 17 (3.06%) ko04141
   Arachidonic acid metabolism 3 (0.54%) ko00590
   Lysine biosynthesis 2 (0.36%) ko00300
   Valine, leucine and isoleucine degradation 5 (0.9%) ko00280
   Natural killer cell mediated cytotoxicity 1 (0.18%) ko04650
   Brassinosteroid biosynthesis 1 (0.18%) ko00905
   Sulfur relay system 1 (0.18%) ko04122
   Cyanoamino acid metabolism 7 (1.26%) ko00460
   Carotenoid biosynthesis 9 (1.62%) ko00906
   Vitamin B6 metabolism 3 (0.54%) ko00750
   Glycosphingolipid biosynthesis—globo series 2 (0.36%) ko00603
   Butanoate metabolism 4 (0.72%) ko00650
   N-Glycan biosynthesis 1 (0.18%) ko00510
   Sphingolipid metabolism 3 (0.54%) ko00600
   Phagosome 9 (1.62%) ko04145
   Biosynthesis of unsaturated fatty acids 2 (0.36%) ko01040
   Phenylpropanoid biosynthesis 12 (2.16%) ko00940
   Peroxisome 7 (1.26%) ko04146
   alpha-Linolenic acid metabolism 3 (0.54%) ko00592
   Glycosaminoglycan degradation 3 (0.54%) ko00531
   Steroid biosynthesis 1 (0.18%) ko00100
   Cutin, suberine and wax biosynthesis 1 (0.18%) ko00073
   Sulfur metabolism 2 (0.36%) ko00920
   Spliceosome 14 (2.52%) ko03040
   RNA transport 13 (2.34%) ko03013
   Inositol phosphate metabolism 3 (0.54%) ko00562
   Isoflavonoid biosynthesis 1 (0.18%) ko00943
   Riboflavin metabolism 2 (0.36%) ko00740
   beta-Alanine metabolism 2 (0.36%) ko00410
   Propanoate metabolism 4 (0.72%) ko00640
   Endocytosis 5 (0.9%) ko04144
   Aminoacyl-tRNA biosynthesis 5 (0.9%) ko00970
   Zeatin biosynthesis 1 (0.18%) ko00908
   Selenocompound metabolism 1 (0.18%) ko00450
   Histidine metabolism 1 (0.18%) ko00340
   Mismatch repair 1 (0.18%) ko03430
   Tryptophan metabolism 1 (0.18%) ko00380
   Pentose and glucuronate interconversions 4 (0.72%) ko00040
   Terpenoid backbone biosynthesis 3 (0.54%) ko00900
   Homologous recombination 1 (0.18%) ko03440
   Stilbenoid, diarylheptanoid and gingerol biosynthesis 1 (0.18%) ko00945
   Purine metabolism 8 (1.44%) ko00230
   Fatty acid metabolism 2 (0.36%) ko00071
   DNA replication 1 (0.18%) ko03030
   Flavonoid biosynthesis 3 (0.54%) ko00941
   SNARE interactions in vesicular transport 1 (0.18%) ko04130
   Starch and sucrose metabolism 11 (1.98%) ko00500
   Ribosome biogenesis in eukaryotes 1 (0.18%) ko03008
   Pyrimidine metabolism 3 (0.54%) ko00240
   Plant hormone signal transduction 9 (1.62%) ko04075
   Ubiquitin mediated proteolysis 1 (0.18%) ko04120
   [100]Open in a new tab

   *: significantly enriched pathway (p<0.05).

Analysis of transcript and protein expression profiles

   A combined analysis of the transcriptome and proteome data allows the
   consistency between transcript and protein profiles to be assessed. The
   proteins identified by iTRAQ that had transcriptional changes were
   regarded as correlated with the transcriptome. We investigated the
   correlation between the profiles of the DAPs identified by the iTRAQ in
   this study and the mRNA expression profiles from transcriptome-level
   data from a previous study [[101]22]. We detected 93,272 unigenes that
   exhibited differences in expression between PA2 and PA4 plants ([102]S6
   Table). Among them, we identified 16,490 differentially expressed
   unigenes (DEUs) using a false discovery rate ≤0.001 and an absolute
   value of the |log2Ratio| ≥1 as thresholds to determine significant
   differences in gene expression ([103]S7 Table). A comparison between
   PA2 and PA4 plants resulted in the identification of 3,010 proteins and
   93,272 unigenes. We determined that 3,010 proteins were correlated the
   transcriptome. Additionally, 1,792 proteins were quantified and
   correlated. We detected 773 DAPs and 16,490 DEUs, and 129 DAPs was
   correlated ([104]Table 2).

Table 2. Correlation analysis of transcription and proteome.

   Group name Type Number of Proteins Number of Genes Number of
   Correlations
   PA2 vs. PA4 Identification 3,010 93,272 3,010
   PA2 vs. PA4 Quantitation 1,792 93,272 1,792
   PA2 vs. PA4 Differential Expression 773 16,490 129
   [105]Open in a new tab

   Based on the pattern of changes in mRNA and protein levels, four groups
   proteins were revealed from the quantified proteins: Group I, mRNA and
   protein levels exhibited the same trends (88 proteins); Group II, mRNA
   and protein levels exhibited the opposite trends (41 proteins); Group
   III, mRNA level changed significantly, but the protein level did not
   (171 proteins); Group IV, protein level changed significantly, but the
   mRNA level did not (644 proteins) ([106]S8 Table).

   We comprehensively investigated the correlation between protein and
   mRNA profiles in PA2 and PA4 plants. Poor correlations (Pearson’s r
   value = 0.14) were observed when all quantifiable proteins (1,792) that
   correlated with a cognate transcript were considered, regardless of the
   direction of the change ([107]Fig 5A). Correlation between protein and
   transcript levels of the Group I and Group II members revealed positive
   (Pearson’s r value = 0.77) and negative (Pearson’s r value = −0.75)
   correlations, respectively ([108]Fig 5B and 5C). The discrepancies
   between the protein and transcript levels may because the mRNA changes
   did not necessarily lead to similar changes in protein abundance.
   Alternatively, the proteome-level changes may have been underestimated
   in this study. Additionally, the identification of DAPs may have been
   restricted by limitations of the Paulownia sequence databases. As noted
   in other studies, incomplete databases may prevent the detection of all
   DAPs [[109]24,[110]33]. Although we used a transcriptome database in
   this study, it lacked sufficient information because the genomes of
   Paulownia species have not been fully sequenced. Thus, the proteins
   identified using the iTRAQ-based method likely represented only a small
   part of the proteome. Fully sequencing the genomes of Paulownia species
   to generate more comprehensive databases will likely considerably
   enhance future proteome-level studies.

Fig 5. Comparison of expression ratios from transcriptomic and proteomic
profiling.

   Fig 5
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   (A) The correlation between mRNA and protein when regardless of the
   direction of the change. (B) The mRNA and protein levels have the same
   change trends. (C) The mRNA and protein levels have the opposite change
   trends. Pearson correlations of the comparisons shown in coordinates as
   r. The protein ratio was from this study. The transcript ratio was
   reproduced from a previous study [[112]22].

Confirmation of differentially abundant proteins

   To validate the expression change at mRNA level in 14 DAPs, we
   performed qRT-PCR analysis. The qRT-PCR results revealed that the
   expression of 13 DAPs at mRNA level were consistent with their protein
   expression ([113]Fig 6). The discrepancy between the qRT-PCR and iTRAQ
   results for another DAP may be explained by post-transcriptional and/or
   post-translational regulatory processes.

Fig 6. The expression of selected differentially abundant proteins at mRNA
level.

   [114]Fig 6
   [115]Open in a new tab

   The 18S rRNA of Paulownia was chosen as an internal reference gene for
   normalization. ChlP: geranylgeranyl reductase, FLS2: LRR receptor-like
   serine/threonine-protein kinase FLS2, PF2: plastid fibrillin 2, Lhca2:
   light-harvesting complex I chlorophyll a/b binding protein 2, ChlE:
   magnesium-protoporphyrin IX monomethyl ester (oxidative) cyclase, PsbA:
   photosystem II P680 reaction center D1 protein, PepN: amino peptidase
   N, CopZ: copper chaperone, SOD1: Cu/Zn superoxide dismutase, GlpQ:
   glycerophosphoryl diester phosphodiesterase, AHK: arabidopsis histidine
   kinase, ACP:acid phosphatase, DBI: diazepam-binding inhibitor,
   β-1,3-GA: beta-1,3-glucanase. Error bars represent the standard error
   of the mean. *: Statistically significant differences between PA2 and
   PA4 (P<0.05).

Discussion

   Polyploidy events are crucial for plant evolution, and are associated
   with the origins of most angiosperms and ferns [[116]34,[117]35].
   Polyploidy is believed to increase plant size and enhance adaptations
   to environmental stresses [[118]36,[119]37].

Variations of leaf parameters

   In this study, we determined that leaf length and width increased
   following WGDs, and speculated that this may be caused by the
   variations of cell division and expansion [[120]38]. Leaf size is
   controlled by an organ-wide integration system with two main factors:
   cell number and size. Increases in cell size can result from a decrease
   in cell number and vice versa [[121]39]. In our study, compared with
   PA2, PA4 has less cell number and bigger cell size. Sugiyama [[122]18]
   found the bigger leaf in autotetraploid Lolium perenne and L.
   multifloru compared with diploid was a result of increased cell size,
   which is similar to the finding in this study. In mulberry, the cell
   size of autotetraploid was larger than in diploid [[123]6].
   Autotetraploid Salix viminalis has wider leaves and enlarged cells
   [[124]19]. It appears that autopolyploidy can result in increased cell
   and leaf sizes. PA4 had thicker upper and lower epidermis than PA2.
   Among diploid, pentaploid, and hexaploid Betula papyrifera, the
   polyploids had thicker upper and lower epidermis [[125]40], which is
   similar with our results. The amount of light absorbed by a leaf is
   related to its chlorophyll content [[126]41]. In PA4, the chlorophyll
   content was higher than in PA2, suggesting that PA4 could absorb more
   light.

   Besides improvements in agricultural productivity and efficiency
   (biomass) through the phenotypic variations caused by polyploidization,
   stress tolerance will also become higher [[127]20]. Leaf is highly
   responsive to environmental conditions, therefore, its microstructures
   may reflect the adaptability of plants to environmental conditions
   [[128]42]. The olive Chemlali cultivar exhibited more tolerance to
   water stress than the Chétoui cultivar, and its palisade was thicker
   [[129]43]. The diploid Betula papyrifera, with thinner upper and lower
   epidermis, was more sensitive to water deficit than the polyploids
   [[130]40]. The leaf microstructure observed in the PA2 and PA4 plants
   may explain the higher tolerance to stresses of PA4 compared with PA2.

Differentially abundant proteins related to leaf trait variability

   In this study, we determined that leaf length and width, and leaf size
   increased following WGDs. Cell division is one of the fundamental
   processes for plant organ growth and development [[131]18]. Four DAPs
   ([132]XP_002282146.1, [133]AFJ42570.1, [134]BAD45447.1, and
   [135]NP_001183829.1) related to cell division were more abundant in PA4
   plants than in PA2 plants. Larger cells may promote cell wall
   synthesis. Cellulose is a major component of plant cell walls, and two
   DAPs ([136]CBI16310.3 and [137]AFK34932.1) associated with cellulose
   biosynthesis were more abundant in PA4 than in PA2. Increases in leaf
   size may occur as a result of an increase in the cell elongation rate
   and/or by a prolonged cell elongation period, both of which require
   energy and materials. The F-type ATPases catalyze ATP synthesis, and
   the two F-type H^+-transporting ATPase subunits (A29394 and
   [138]AAQ84325.1) that were more abundant in PA4 plants than in PA2
   plants might be responsible for supplying the energy required to
   increase leaf length. Although there was no direct relationship between
   leaf size and chlorophyll content, the thicker palisade tissues were
   associated with elevated chlorophyll content. We identified eight DAPs
   involved in chlorophyll biosynthesis that were more abundant in PA4
   than in PA2 ([139]Fig 7). The variability in abundance of these DAPs
   may help to clarify the molecular basis of elevated chlorophyll content
   in PA4. We also identified 10 DAPs related to carbon fixation that were
   more abundant in PA4 than in PA2 ([140]Fig 8). This suggested increased
   chlorophyll content and photosynthetic activities, which is consistent
   with the results of a previous study [[141]32]. Chlorophyll content may
   be an indicator of photosynthetic capacity.

Fig 7. Chlorophyll biosynthesis-associated differentially abundant proteins.

   Fig 7
   [142]Open in a new tab

   (A) Chlorophyll synthetic pathway in P.australis. (B) The abundance of
   differentially abundant proteins. The abundance of proteins in PA2 was
   1. HemB: porphobilinogen synthase, HemC: hydroxymethylbilane synthase,
   HemE: uroporphyrinogen decarboxylase, HemF: coproporphyrinogen III
   oxidase, CHLH: magnesium chelatase subunit I, CHLM:
   magnesium-protoporphyrin O-methyltransferase, CHLE:
   magnesium-protoporphyrin IX monomethyl ester cyclase, POR: magnesium
   chelatase subunit protochlorophyllide reductases.

Fig 8. Photosynthesis-associated differentially abundant proteins.

   Fig 8
   [143]Open in a new tab

   (A) Carbon fixation pathway in P.australis. (B) The abundance of
   differentially abundant proteins. The abundance of proteins in PA2 was
   1. PGK: phosphoglycerate kinase. GAPA: glyceraldehyde-3-phosphate
   dehydrogenase. ALDO: fructose-bisphosphate aldolase, class I. FBP:
   fructose-1,6-bisphosphatase I. TKTA: transketolase. PRK:
   phosphoribulokinase.

Differentially abundant proteins related to stress resistance

   Polyploids usually have better fitness than their progenitors
   [[144]17]. Lignin deposition generally occurs when cell growth is
   complete and the cell wall is undergoing secondary thickening via
   lignification. Among its many and varied biological roles, lignin is
   important for plant adaptations to diverse environmental conditions
   [[145]44,[146]45]. Determining the changes in abundance of
   lignin-related proteins may provide relevant information regarding how
   polyploidy events increase plant adaptability. The DAPs related to
   lignin synthesis were accumulated after the WGD, including, for
   example, transketolase (TKTA), phosphoglycerate mutase (GPM), enolase,
   3-deoxy-7-phosphoheptulonate synthase (aroF), shikimate kinase (aroK),
   5-enol-pyruvylshikimate-3-phosphate synthase (aroA),
   glutamate/aspartate-prephenate aminotransferase (PAT), aspartate
   transaminase (AST), shikimate/quinate hydroxyl cinnamoyl transferase
   (HCT), cinnamyl alcohol dehydrogenase (CAD), and peroxidase (POD)
   ([147]Fig 9).

Fig 9. Lignin biosynthesis-associated differentially abundant proteins.

   Fig 9
   [148]Open in a new tab

   (A) A diagram shows the intermediates and enzymes involved in lignin
   biosynthesis pathway in P.australis. (B) The abundance of
   differentially abundant proteins. The abundance of proteins in PA2 was
   1. TKTA: transketolase, GPM: phosphoglycerate mutase, aroF:
   3-deoxy-7-phosphoheptulonate synthase, aroK: shikimate kinase, aroA:
   5-enol-pyruvylshikimate-3-phosphate synthase, PAT:
   glutamate/aspartate-prephenate aminotransferase, AST: aspartate
   transaminase, HCT: shikimate/quinate hydroxyl cinnamoyl transferase,
   CAD: cinnamyl alcohol dehydrogenase, POD: peroxidase.

   Lignin is produced by the oxidative coupling of three monolignols which
   are synthesized from phenylalanine via the phenylpropanoid pathway.
   Chorismate originates from the shikimate pathway, and is a common
   intermediate in the biosynthetic pathways for phenylalanine and other
   aromatic amino acids. Chorismate is initially synthesized from
   D-erythrose 4-phosphate (E4P) and phosphoenolpyruvate (PEP), which are
   produced by the pentose phosphate pathway and glycolysis, respectively.

   The immediate substrates of the shikimate pathway are E4P and PEP
   [[149]46]. Additionally, TKTA catalyzes reactions in the Calvin cycle
   and oxidative pentose phosphate pathway to produce E4P. Henkes et al.
   observed that down-regulated TKTA activity results in decreased
   abundance of aromatic amino acids and lignin [[150]47]. The carbon flux
   towards the shikimate pathway and phenylpropanoid metabolism is
   restricted by the supply of E4P. GPM catalyzes the conversion of
   3-phosphoglycerate to 2-phosphoglycerate, and enolase catalyzes the
   production of PEP from 2-phosphoglycerate. TKTA, GPM, and enolase were
   more abundant in PA4 than in PA2, and may increase the carbon resources
   available for the shikimate pathway.

   3-Deoxy-7-phosphoheptulonate (DHAP) synthase regulates the amount of
   carbon entering the pathway and catalyzes the conversion of E4P and PEP
   to DHAP [[151]48]. AroK catalyzes the conversion of shikimate to
   shikimate-3-phosphate, with ATP as a co-substrate [[152]49]. The two
   substrates for aroA are PEP and shikimate-3-phosphate, which are
   converted to phosphate and 5-enol-pyruvylshikimate-3-phosphate
   [[153]50]. There are two routes for the post-chorismate branch of the
   pathway leading to phenylalanine synthesis (i.e., phenylpyruvate and
   arogenate routes). AST is part of the phenylpyruvate route, while PAT
   is involved in the arogenate route [[154]51]. Both of these enzymes
   were more abundant in PA4 than in PA2, and may lead to increased
   phenylalanine levels.

   Lignin is derived from phenylalanine. The monolignols produce the
   p-hydroxyphenyl, guaiacyl, and syringyl phenylpropanoid units of the
   lignin polymer. The HCT enzyme represents a key metabolic entry point
   for the synthesis of the most important lignin monomers. CAD catalyzes
   the reduction of cinnamyl aldehydes to the corresponding cinnamyl
   alcohols, which are the monomeric precursors of lignin [[155]52]. The
   last step in lignin biosynthesis involves the polymerization of
   cinnamyl alcohols, which is affected by POD activities. In this study,
   we found that HCT, CAD, and POD were more abundant in PA4 plants than
   in PA2 plants. This may result in increased lignin production to
   strengthen cell walls and protect plants from environmental stresses.
   After cellulose, lignin is the most abundant terrestrial biopolymer,
   comprising approximately 30% of the organic carbon in the biosphere.
   Furthermore, the increased lignin content of PA4 plants may lead to
   enhanced carbon fixation, and ultimately increased biomass.

   In plants, glutathione (GSH) is crucial for biotic and abiotic stress
   tolerance. We identified some DUPs related to GSH metabolism.
   Up-regulating GSH synthesis is likely an intrinsic plant response to
   stress [[156]53]. Glutamate–cysteine ligase (gshA) may promote the
   synthesis of GSH [[157]54]. GshA is an important component of the
   glutathione–ascorbate cycle that decreases hydrogen peroxide levels. In
   response to stress conditions, PA4 plants may increase gshA abundance
   to enhance their tolerance to environmental stresses. Abiotic stresses,
   such as drought and high salinity, damage plants via exposure to
   oxidative stress, which may induce the rapid generation of reactive
   oxygen species (ROS). Eliminating ROS improves plant growth. Glycine
   betaine (GB) is important for maintaining the enzymatic activities
   responsible for eliminating or detoxifying ROS [[158]55,[159]56].
   Accumulating GB may enable plants to continue to grow under drought or
   salt stress conditions. Additionally, GB is synthesized from glycine,
   which is derived from serine. Glycine hydroxymethyltransferase (glyA)
   may promote the synthesis of glycine from serine. Increased abundance
   of glyA ([160]XP_004253097.1, [161]AFA36570.1, and [162]CBI17302.3) may
   be conducive for the accumulation of GB. The adverse effects of ROS are
   also mitigated by superoxide dismutase. Previous studies revealed that
   the superoxide dismutase content was higher in PA4 leaves than in PA2
   leaves [[163]14,[164]15]. Damage caused by ROS is amplified by the
   accumulation of toxic degradation products (e.g., aldehydes) arising
   from reactions between ROS and lipids or proteins. Aldehyde
   dehydrogenases eliminate toxic aldehydes [[165]57], and in A. thaliana,
   they were shown to influence salinity tolerance [[166]58]. In this
   study, we observed that aldehyde dehydrogenases ([167]EMJ14942.1 and
   [168]ACM89738.1) were accumulated in PA4 plants, possibly contributing
   to enhanced tolerance to environmental stresses.

Correlation between transcript and protein

   The proteome and transcriptome reflect the situation of gene expression
   at two different levels. Combining these two omics data can help to
   reveal more complete expression information for an organism.
   Correlation between proteome and transcriptome data may reveal the
   relation between protein abundance and mRNA transcript levels. In
   yeast, the correlation between transcript expression and protein
   abundance was good [[169]59], and in Tragopogon mirus [[170]25],
   Brassica napus [[171]60], and A. thaliana [[172]26,[173]61], limited
   correlations were found. Thus, it may be considered that low
   correlation between mRNA expression and protein abundance is common. In
   this study, we found non-significant correlation between proteomic and
   transcriptomic changes, suggesting the important role of
   post-transcriptional regulation and translational modification changes
   of proteins during the polyploidization process in P. australis. To
   comprehensively investigate the correlation, we clustered the
   quantified proteins into four groups. The information from the
   correlated DAPs and DEUs that had same change trends (Group I) may
   verify and explain some important processes; for example, the response
   to environmental stress in fission yeast [[174]59]. The correlated DAPs
   and DEUs that had opposite change trends (Group II) may explain some
   phenomenon that could not explained by only proteome or transcriptome
   data; for example, the plant dormancy process [[175]62]. Groups I and
   II together contained 129 proteins. Groups III and IV, which contained
   significantly changed mRNAs associated with unchanged proteins or
   significantly changed proteins correlated to unchanged mRNAs, contained
   815 proteins. The differences in these numbers may result from the
   definition of DEUs and DAPs. Nonetheless, in many studies, in order to
   understand the effects of polyploidization, integrated analysis of
   transcriptome and proteome data is essential.

Conclusions

   To characterize the molecular and biological mechanisms that regulate
   the effects of polyploidy events on P. australis, we compared the leaf
   parameters and proteomes of autotetraploid and diploid P. australis.
   The identification of DAPs related to cell division, GSH metabolism,
   and the synthesis of cellulose, chlorophyll, and lignin may help to
   characterize the differences between the diploid and autotetraploid P.
   australis plants, and clarify the mechanisms involved in regulating
   polyploidy events. Our results provide an overview of the changes
   caused by WGDs in P. australis and may be useful for breeding new
   Paulownia species and expanding the available germplasm resources.

Supporting information

   S1 Fig. Overview of the iTRAQ data.

   (A) Error distribution of spectra match quality. (B) Proteome
   identification. (C) Number of peptides that match proteins. (D) Protein
   relative molecular mass. (E) Coverage of the proteins by the identified
   peptides. (F) The repeatability of two replicates of PA2 and (G) The
   repeatability of two replicates of PA4. 1 and 2 represent two
   biological replicates of samples. The ratios of protein abundances for
   each protein in each comparison between biological replicates were
   calculated, and the “delta, error” in the absciss are presents the
   difference from the expected ratio of 1.

   (TIF)
   [176]Click here for additional data file.^ (523KB, tif)
   S2 Fig. Clusters of Orthologous Groups classification of protein in
   leaves of P. australis.

   1768 proteins were divided into 23 specific categories.

   (TIF)
   [177]Click here for additional data file.^ (308.7KB, tif)
   S3 Fig. Gene Ontology (GO) classification of protein in leaves of P.
   australis.

   2738 proteins were categorized into 51 function groups.

   (TIF)
   [178]Click here for additional data file.^ (324.3KB, tif)
   S1 Table. Primer sequence of corresponding unigenes of selected
   differentially abundant proteins.

   (XLSX)
   [179]Click here for additional data file.^ (9.5KB, xlsx)
   S2 Table. Detail of the all identified proteins.

   (XLSX)
   [180]Click here for additional data file.^ (2.1MB, xlsx)
   S3 Table. KEGG annotation of the identified proteins.

   (XLSX)
   [181]Click here for additional data file.^ (13.7KB, xlsx)
   S4 Table. Differentially abundant proteins in PA2 vs. PA4.

   (XLSX)
   [182]Click here for additional data file.^ (598KB, xlsx)
   S5 Table. GO enrichement of the differentially abundant proteins in PA2
   vs. PA4.

   (XLSX)
   [183]Click here for additional data file.^ (82.7KB, xlsx)
   S6 Table. Differentially expressed unigenes from previously
   transcriptome study.

   (XLSX)
   [184]Click here for additional data file.^ (3.7MB, xlsx)
   S7 Table. Significant differentially expressed unigenes from previously
   transcriptome study.

   (XLSX)
   [185]Click here for additional data file.^ (907.6KB, xlsx)
   S8 Table. Analysis of the correlation between mRNA and protein
   abundance changes.

   (XLSX)
   [186]Click here for additional data file.^ (56.5KB, xlsx)

Acknowledgments