Abstract

   Agrobacterium-mediated plant transformation is an extremely complex and
   evolved process involving genetic determinants of both the bacteria and
   the host plant cells. However, the mechanism of the determinants
   remains obscure, especially in some cereal crops such as wheat, which
   is recalcitrant for Agrobacterium-mediated transformation. In this
   study, differentially expressed genes (DEGs) and differentially
   expressed proteins (DEPs) were analyzed in wheat callus cells
   co-cultured with Agrobacterium by using RNA sequencing (RNA-seq) and
   two-dimensional electrophoresis (2-DE) in conjunction with mass
   spectrometry (MS). A set of 4,889 DEGs and 90 DEPs were identified,
   respectively. Most of them are related to metabolism, chromatin
   assembly or disassembly and immune defense. After comparative analysis,
   24 of the 90 DEPs were detected in RNA-seq and proteomics datasets
   simultaneously. In addition, real-time RT-PCR experiments were
   performed to check the differential expression of the 24 genes, and the
   results were consistent with the RNA-seq data. According to gene
   ontology (GO) analysis, we found that a big part of these
   differentially expressed genes were related to the process of stress or
   immunity response. Several putative determinants and candidate
   effectors responsive to Agrobacterium mediated transformation of wheat
   cells were discussed. We speculate that some of these genes are
   possibly related to Agrobacterium infection. Our results will help to
   understand the interaction between Agrobacterium and host cells, and
   may facilitate developing efficient transformation strategies in cereal
   crops.

Introduction

   Genetic transformation, as a reverse genetics tool, has been widely
   used in modification of some economically important plant species.
   Great successes have been achieved in enhancing the production of major
   crops such as soybean, maize and cotton, which have contributed a lot
   to the global agricultural economy and helped to meet the food demand
   for human and animal worldwide [41][1]. However, almost no promising
   progress has occurred on genetically modified wheat [42][2]. Presently,
   the most economic strategy of plant transformation is still
   Agrobacterium- mediated method, which is progressed slowly in wheat
   even though it was initiated in 1980s when it was successfully applied
   to obtain transgenic tobacco plants [43][3].

   The mechanism of Agrobaterium-mediated transformation has been explored
   in both pathogens and plants, and some pathogen or host proteins/genes
   have been identified to participate in the Agrobacterium infection and
   T-DNA delivery process [44][4]–[45][11]. A few of these genes were
   proved to result in improved transformation efficiency in some dicot
   plants such as Arabidopsis and tobacco, and also in several cereal
   plants such as rice and maize [46][12], [47][13]. Taking rice as an
   example, even though its transformation process is not difficult,
   Agrobacterium-mediated transformation efficiency for indica rice
   variety is much lower than that for japonica cultivars. Tie et al.
   identified the differentially expressed genes by microarray, and the
   results were very useful to identify genes involved in the process of
   Agrobacterium-mediated transformation [48][14].

   Agrobacterium infection of plant cells consists of a series of events,
   including attachment of Agrobacterium on plant tissues, recognition
   between Agrobacterium and host, production of transferred substrates,
   transferring of the components into host cell, movement of the
   substrates into host nucleus, integration of T-DNA into host genome,
   and expression of the integrated T-DNA, among which the most vital step
   is the integration of T-DNA into plant genome. During the whole
   process, several vir genes and chv genes were proved to contribute to
   the cellular transportation or transformation of the target DNA
   fragments [49][15]. However, only a few literatures reported the
   response of host response to the infection of Agrobacterium by
   cDNA-AFLP [50][16] and genome microarray [51][17]. Tzfra et al.
   screened an Arabidopsis cDNA library by the yeast two-hybrid method
   with the Agrobacterium VirE2 protein as a bait and found that the
   identified plant protein, designated VIP1, was specifically bound with
   VirE2, and allowed its nuclear import to participate in the early
   stages of T-DNA expression [52][18]. Subsequent research indicated that
   VIP1 is imported into the nucleus of plants via the karyopherin-α
   dependent pathway, and its over-expression significantly rendered
   plants more susceptible to genetic transformation mediated by
   Agrobacterium [53][18], [54][19]. Moreover, the ability of VIP1
   interacting with VirE2 protein and localizing in nucleus helped the
   transportation of the foreign DNA transiently into plant cells and
   nucleus, and its interaction with a host histone protein of H2A is
   required for the upcoming stable genetic transformation of the alien
   DNA strands [55][20]. VIP2 is another Arabidopsis protein which
   interacts with VIP1, and also plays an important role in the
   Agrobacterium-mediated transformation in plants [56][21]. Because of
   the complexity of the whole transformation process, a lot of host genes
   are postulated to participate in the delivery process. Identifying more
   host genes involved in the response to infection and transformation
   will help us to further understand the process, and improve the
   efficiency of Agrobacterium-mediated wheat transformation eventually.

   However, Agrobacterium-mediated wheat genetic transformation has
   remained very low efficiency and strong genotype-dependent [57][22].
   Therefore, particle bombardment method is still the major approach for
   wheat transformation [58][22]. Up to now, some improved transformation
   protocols mediated by Agrobacterium have been reported in wheat since
   1997 [59][23], [60][24]. For example, Hu et al. reported that they
   obtained more than 3,000 independent transgenic events with average
   transformation efficiency of 4.4% [61][25]. However, these results were
   limited mainly to few wheat varieties, and the methods they used have
   been proved difficult to follow up [62][24]–[63][27] even if the
   advances and progress on wheat Agrobacterium-mediated transformation
   approach were described in freshly published papers [64][23], [65][28].
   Indeed, no wheat variety has been proved to be competent for the
   transformation mediated by Agrobacterium. Therefore, more work needs to
   be conducted to find key host genes involved in the T-DNA delivery
   process after the wheat cells are infected by Agrobacterium.

   In the past few years, development of next-generation sequencing (NGS)
   technologies has provided a new paradigm for genome and transcriptome
   characterization [66][29], [67][30]. RNA sequencing (RNA-seq) has
   exhibited some obvious advantages over existing approaches. This
   technique has been proved to be highly repeatable, and is expected to
   revolutionize the manner of analyzing eukaryotic transcriptomes
   [68][31]. On the other hand, some technologies such as mass
   spectrometry (MS) and two-dimensional electrophoresis (2-DE) have been
   widely used in proteomics. Evidences showed that proteomics and
   transcriptome can mutually promote the detection of expressed genes
   with complementary advantages at low cost [69][32]. In this study, the
   expression activities of associated genes with transformation process
   were analyzed in the infected wheat callus by Agrobacterium using
   RNA-seq and 2-DE in conjunction with MS strategy. We identified
   differentially expressed genes that might be involved in the process of
   Agrobacterium infection and T-DNA delivery. A set of 4,889
   differentially expressed genes (DEGs) and 90 differentially expressed
   proteins (DEPs) were identified, respectively. Most of them are related
   to chromatin assembly or disassembly and to immune. After comparative
   analysis, 24 aligned DEPs were identified to be potentially closely
   related to Agrobacterium infection response and transformation, and
   involved in 23 pathways.

Materials and Methods

Plant materials and Agrobacterium strain

   A semi-winter wheat (Triticum aestivum L.) variety used throughout this
   study, Yangmai12, which is a largely commercial wheat variety in
   southeast China with good agronomic characteristics and high
   regeneration ability of immature embryos, was kindly provided by Prof.
   Shunhe Chen at Yangzhou Agricultural Institute, Jiangsu Academy of
   Agricultural Sciences, China. Wheat immature caryopses were collected
   from Yangmai12 plants 12–14 days post anthesis. The immature embryos
   were dissected aseptically and cultured on MSD2 medium (MS inorganic
   salts, 2 mgl^–1 dicamba, 3.0% sucrose, 2.4 gl^–1 gelrite, pH 5.8) for 4
   days at 25°C under dark conditions before infection by Agrobacterium
   tumefaciens. The Agrobacterium strain used in this study is C58C1,
   which harbored a binary vector pZP211 carrying a T-DNA without target
   gene, and was kindly provided by Dr. Tom Clemente at University of
   Nebraska-Lincoln, USA.

Infection of pre-cultured immature embryos by Agrobacterium

   Agrobacterium tumefaciens strain C58C1 with binary vector pZP211 was
   incubated overnight in 5 ml YEP medium (10 gl^–1 tryptone, 10 gl^–1
   yeast extract, 5 gl^–1 N[a]Cl, pH 7.0) with 50 mgl^–1 rifampicin, 50
   mgl^–1 streptomycin, and 50 mgl^–1 spectinomycin inside a shaker with
   220 rpm at 28°C. The overnight Agrobacterium culture was put into 45 ml
   fresh YEP medium, and incubated inside a shake for 6 hours at the same
   conditions as above mentioned. The Agrobacterium cells was pelleted by
   centrifugation at 4500 rpm for 10 min at room temperature, and
   re-suspended by adding 25 ml of liquid inoculation medium WCC (1/10 MS
   basic medium, 4.0 mM 2-(N-morpholine)-ethane sulphonic acid (MES), 0.75
   gl^–1 MgCl[2], 200 µM acetosyringone, 1.0% glucose, 4.0% maltose, 2.0
   mgl^–1 dicamba, 2.2 mgl^–1 picloram, 100 mgl^–1 casein hydrolysate
   (CH), pH 5.4). The cell density was adjusted to an optical density of
   0.5 (OD[650]) for inoculation [70][24].

   About 50 pre-cultured immature embryos (PCIEs) of wheat were
   transferred into the prepared Agrobacterium suspension in a petri dish
   (35 mm×15 mm) containing 3 ml of Agrobacterium culture. In total, 100
   PCIEs were infected by Agrobacterium in two plates. Another 100 PCIEs
   were transferred into 6 ml 1/10 WCC as a control [71][24]. The
   inoculation was performed at room temperature for 30 min, then the cell
   clusters were blotted on sterile filter paper and transferred to larger
   plates (90 mm×20 mm) containing a piece of sterile filter paper for
   co-cultivation at 23–24°C in the dark for 36 hours [72][33]. The
   infection experiment was designed by three repeats, and RNA isolation
   was performed from every repeat.

RNA isolation, cDNA library preparation and sequencing

   Total RNA was isolated with TRIZOL (Invitrogen, Carlsbad, CA, USA) from
   the Agrobacterium infected and non-infected PCIEs, which were treated
   in a solution containing 200 mgl^–1 carbenicillin disodium salt
   (Amresco, USA) for 10 min and then washed with sterile water for 3
   times, according to the manufacturer's instructions. Then the RNA-seq
   were performed in BGI (Beijing Genomics Institute).

   Three RNA samples from each treatment were mixed, respectively, and
   treated with RNase-free DNase I for 30 min at 37°C to remove residual
   DNA. Beads with oligo (dT) were used to isolate poly (A) mRNA. Next,
   the mRNA was broken into short fragments (about 200 bp) after adding
   fragmentation buffer. First strand cDNA was synthesized using random
   hexamer-primer and reverse transcriptase (Invitrogen, Carlsbad, CA,
   USA). The second strand cDNA was synthesized using RNase H (Invitrogen,
   Carlsbad, CA, USA) and DNA polymerase I (Invitrogen, Carlsbad, CA, USA)
   [73][34]. The double strand cDNA was purified with QiaQuick PCR
   extraction kit (Invitrogen, Carlsbad, CA, USA), and washed with EB
   buffer. A single adenosine was added to the cDNA using
   Klenowexo–fragment with dATP. Sequencing adaptors were ligated onto the
   repaired ends of the fragments. The required fragments were purified by
   agarose gel electrophoresis and enriched by PCR amplification. Finally,
   the library products were sequenced via Illumina HiSeq™ 2000 (Illumina,
   San Diego, CA, USA). All the reads sequences have been submitted to the
   Sequence Read Archive, NCBI. Accession numbers of experiment-SRX273368
   run-SRR837407 for treatment group dataset, and experiment-SRX276082
   run-SRR847734 for control group dataset have been given.

Raw reads filtering and clean reads aligning with reference sequences

   The original image data were transferred into sequence data by base
   calling, which is defined as raw data or raw reads. Before data
   analysis, it was prerequisite to remove the dirty raw reads. The
   filtering steps included (1) removing the reads with adaptors, (2)
   removing the reads in which unknown bases were more than 10%, and (3)
   removing low-quality reads (the percentage of the low-quality bases
   with which value≤5 was more than 50% in a read). Next, the clean reads
   were aligned to reference sequences using SOAPaligner/soap2 [74][35],
   and mismatches less than 2 bases were allowed in the alignment. The
   reference unigene or EST (Expressed sequence tags) database and
   annotation data were downloaded from the websites of
   [75]http://compbio.dfci.harvard.edu/cgi-bin/tgi/tc_ann.pl?gudb=wheat
   and [76]http://www.ncbi.nlm.nih.gov/nucest/. The ratio we used to
   assess the percentage of the gene coverage by reads was the quotient of
   the base numbers in a target gene covered by unique mapping reads
   divided by the total base numbers of this target gene.

Screening and analysis of differentially expressed genes (DEGs)

   The gene expression level was calculated by counting the number of
   reads which mapped to the reference genes. Gene expression levels were
   measured as reads per kilo base per million reads (RPKM) method using
   the formula previously described by Mortazavi et al. [77][36]. RPKM
   were calculated from the following formula:
   graphic file with name pone.0079390.e001.jpg

   To find genes that have different expression levels between the two
   samples, we developed a strict algorithm according to the method
   reported previously [78][37]. If every gene's expression occupies only
   a small part of the whole library, p(x) will closely follow the Poisson
   distribution, in which the amount of unambiguous clean reads from gene
   A is denoted as x, and the probability of gene A expression is
   presented by p(x).
   graphic file with name pone.0079390.e002.jpg

   If the amount of clean reads for sample 1 and sample 2 is N[1] and
   N[2], respectively, gene A holds x reads in sample 1 and y tags in
   sample 2. The probability of expression quantity of gene A in sample 1
   as much as in sample 2 can be calculated by the following formula:
   graphic file with name pone.0079390.e003.jpg
   graphic file with name pone.0079390.e004.jpg

   P-value corresponds to the test of differential gene expression. We
   threw in FDR (False discovery rate) to determine the threshold of
   P-value in multiple tests, and preset the FDR to a number no bigger
   than 0.01 [79][38]. The standard (FDR≤0.001 and the absolute value of
   |log2|ratio≥1) was used as the threshold to judge the significance of
   gene expression difference. More stringent criteria with smaller FDR
   and greater fold-change value are used to identify DEGs.

   In order to remove the disturbances of the genes from Agrobacterium, we
   checked out the whole dataset and finally deleted the bacterium genes.

Gene ontology analysis and pathway enrichment analysis of DEGs

   DEGs were categorized according to the genome gene ontology (GO)
   annotations. GO enrichment analysis provides all GO terms which are
   significantly enriched in DEGs compared with the genome background and
   filter the DEGs that correspond to biological functions. Using this
   method all DEGs can be primarily mapped to GO terms in the database
   ([80]http://www.geneontology.org/), calculating gene numbers for every
   term, then hyper geometric test was used to find significantly enriched
   GO terms in DEGs compared with the genome background. This analysis is
   able to recognize the main biological functions that DEGs play. The
   calculating formula is as follows:
   graphic file with name pone.0079390.e005.jpg

   In this formula, N stands for the number of all genes with GO
   annotation, n for the number of DEGs in N, M for the number of all
   genes that are annotated to the certain GO terms, and m for the number
   of DEGs in M. The calculated p-value goes through Bonferroni
   Correction, taking corrected p-value≤0.05 as a threshold. GO terms
   fulfilling this condition are defined as significantly enriched GO
   terms in DEGs.

   We also analyzed the gene functions employing pathway database, and
   extracted the metabolic annotation data from KEGG [81][39].
   Significantly enriched metabolic pathways or signal transduction
   pathways in DEGs can be achieved using the method of enrichment
   analysis compared with the whole genome background. The calculating
   formula is the same as that in GO analysis, but here N means the number
   of all genes with KEGG annotation, n for the number of DEGs in N, M for
   the number of all genes annotated to specific pathways and m for the
   number of DEGs in M.

2-DE analysis of total protein from wheat callus infected and non-infected by
Agrobacterium

   Total protein extraction from the 3 replicated samples, respectively,
   was carried out following the standard protocol of TRIZOL reagent
   (Invitrogen, Carlsbad, CA, USA) after extraction of total RNA. Roughly
   600 µg total protein from each sample was first separated by
   isoelectric focusing (IEF) over a pH range of 3–10 using precast
   first-dimension dry strip (GE Healthcare, Waukesha, WI, USA). The
   first-dimension strips were equilibrated in equilibration buffer (50 mM
   Tris-HCl (pH 8.8), 6 M urea, 30% [v/v] glycerol, 2% [w/v] SDS and trace
   of bromophenol blue) plus 1% DTT for 15 min, and then equilibrated in
   equilibration buffer plus 4% of iodoacetamide instead of 1% DTT. The
   equilibrated first-dimension strip was loaded on a 12% SDS-PAGE. The
   prepared gels were stained with colloidal Coomassie Brilliant blue
   G-250 (Sigma, St. Louis, MO, USA), and then different stains compared
   to the control were selected for MALDI-TOF/TOF analysis.

MALDI-TOF/TOF analysis

   The MALDI-TOF/TOF analysis was performed in Shanghai Applied Protein
   Technology Co.Ltd. Quantitative image analysis was performed with
   ImageMaster 2D Platinum Software Version 5.0 (Amersham Biosciences).
   and then the interested spots (vol.%≥2 fold and p-value≤0.05) were
   excised from the Coomassie Blue-stained gels for MALDI-TOF/TOF
   analyses, which was carried out on an ABI 4800 proteomic analyzer
   MALDI-TOF/TOF MS (Applied Biosystems/MDS Sciex, USA). The MS together
   with MS/MS spectra were searched against the NCBI non-redundant green
   plant database using GPS explorer software (Applied Biosystems, Grand
   Island, NY, USA) and MASCOT (Matrix Science, Boston, MA, USA) through
   the following parameters: maximum missed cleavage was 2, peptide mass
   tolerance was set to ±0.2 Dalton (Da), and fragment tolerance set to
   ±0.3 Da. The proteins with both protein score confidence interval (CI)
   and total ion score CI above 95% were identified as credible results
   for the MS/MS.

Quantitative reverse transcription PCR (qRT-PCR) analysis

   qRT-PCR was performed on ABI 7300 (ABI, Foster City, CA, USA) according
   to the manufacturer's instructions (TaKaRa, Dalian, China) to assess
   the transcription levels determined by RNA-seq and protein 2-D gel, in
   which TaActin was used as an internal standard and amplified with its
   genome-specific primers at the same time. The cDNA derived from the
   total RNA used in the process of RNA-seq was used as template. The
   cycle threshold values (CT) were determined through using ADP
   ribosylation factor (ADP) as the endogenous reference genes [82][40].
   Next, the relative different expression ratios were calculated by the
   2^−ΔΔCT mathematical model [83][41]. Each experiment was repeated by
   three times. Two experiments for the two independent cDNA samples were
   performed to confirm the reproducibility of the results.

Results

Summary of RNA-seq results

   A total of 11,589,085 reads (567,865,165 base pairs) were obtained from
   the RNA of wheat callus co-cultured with Agrobacterium tumefaciens
   C58C1 (accession number of Sequence Read Archive, NCBI:
   experiment-SRX273368 run-SRR837407) and 11,601,434 reads (568,470,266
   base pairs) were obtained from control callus which was not infected
   with C58C1 (accession number of NCBI: experiment-SRX276082
   run-SRR847734). Over 95% of the reads from both samples were clean
   reads ([84]File S1), and over 80% of these reads were mapped to the
   reference unigenes ([85]File S2). The randomness and sequencing
   saturation analysis showed that the reads location on the gene was
   standardized to a relative position, and the number of detected genes
   reached saturation ([86]File S3). The results of the gene coverage
   statistics are shown in [87]File S4. In both infected and non-infected
   samples, more than 11% unigenes demonstrated very high levels of gene
   coverage (coverage>80%).

Transcription profiles reveal DEGs between infected and non-infected samples

   We used the RPKM method to identify the gene expression levels. The
   gene expression is calculated by the number of reads mapped to the
   reference sequence; the ratio of RPKM (infected)/RPKM (control) was
   used to determine the different expression level of each gene.
   According to the datasets of RPKM of 93,508 unigenes (or ESTs),
   compared to non-infected samples, the infected samples had 4,889
   unigenes (or ESTs) showing different levels of transcription
   (|log2|ratio (infected/control)≥1 and FDR (false discovery rate)
   ≤0.001) ([88]File S5). Among them, 2,503 unigenes were up-regulated,
   and 2,386 were down-regulated. The DEGs that had the mean of
   |log2|ratio (infected/control)≥5 are listed in [89]Table 1 and
   [90]Table 2.

Table 1. The differentially expressed genes (|log2|ratio
(infected/control)≥5).

   Gene ID Control -RPKM Infected -RPKM |Log2|ratio P-value FDR
   Description
   [91]CA594889 0.001 27.68816 14.75698 1.72E−06 0.000046 Ta cDNA clone:
   WT012_D04
   [92]EB512672 0.001 24.2069 14.56313 1.06E−07 3.75E−06 Blue
   copper-binding protein homolog
   TC374940 0.001 21.75113 14.4088 0 0 Pathogenesis-related 1a
   [93]GH722147 0.001 14.4799 13.82176 1.97E−10 1.19E−08 Xylanase
   inhibitor precursor
   TC420579 0.001 14.32455 13.8062 6.01E−12 4.65E−10 Response regulator
   [94]CA683879 0.001 14.02145 13.77535 7.96E−10 4.33E−08 Oxalate oxidase
   2 precursor
   TC440260 0.001 13.67654 13.73942 6.47E−09 2.94E−07 Putative disease
   resistance protein RGA3-like
   [95]CF133353 0.001 12.02828 13.55414 0.000014 0.000287
   [96]CJ654139 0.001 10.98716 13.42353 2.12E−07 7.04E−06 Os07g0137900
   protein
   [97]CA661174 0.001 10.48387 13.35588 1.72E−06 4.61E−05
   Prion-like-(Q/n-rich)-domain-bearing protein protein 75
   TC397228 0.001 10.1623 13.31094 6.47E−09 2.94E−07
   TC416495 0.001 10.04916 13.29479 3.47E−06 8.54E−05 Mannose phosphate
   isomerase (MPI)
   TC451603 0.001 9.740984 13.24985 1.6E−09 8.17E−08 LOC496215 protein
   [98]CA693299 0.001 9.60027 13.22886 2.82E−05 0.000523
   [99]BE414946 0.001 9.059047 13.14514 1.72E−06 0.000046 4-coumarate-CoA
   ligase 4CL2
   [100]BJ236765 0.001 8.983681 13.13309 1.72E−06 4.61E−05
   TC455795 0.001 8.654863 13.0793 3.47E−06 8.53E−05
   TC458503 0.001 8.165747 12.99537 2.61E−08 1.05E−06 Early nodulin
   protein
   [101]CJ875280 0.001 7.524698 12.87742 6.97E−06 0.000156
   [102]CA663181 0.001 7.492188 12.87117 5.66E−05 0.00095 Os04g0630300
   protein
   TC442697 0.001 7.417109 12.85664 0.000014 0.000287
   [103]CA638794 0.001 7.413078 12.85586 2.82E−05 0.000523 Protein kinase
   domain containing protein
   TC426144 0.001 7.374602 12.84835 2.82E−05 0.000523 Vitis vinifera
   Chromosome chr8 scaffold_106
   [104]CA499029 0.001 7.253538 12.82447 6.97E−06 0.000156 Nitrate-induced
   NOI protein
   [105]BM135604 0.001 6.897282 12.75181 2.82E−05 0.000523
   [106]CJ696409 0.001 6.893563 12.75103 3.47E−06 8.54E−05 Transcription
   initiation factor
   TC418073 0.001 5.809657 12.50424 1.06E−07 3.75E−06 Phenylalanine
   ammonia-lyase
   TC415483 0.001 5.710553 12.47941 4.27E−07 1.32E−05 Glucosyltransferase
   TC392329 0.001 5.470306 12.41741 1.72E−06 4.61E−05 Reponse regulator 6
   TC381923 0.001 4.514375 12.14031 1.72E−06 4.61E−05 Vitis vinifera
   Chromosome chr2 scaffold_105
   TC458205 0.001 3.898996 11.92889 6.97E−06 0.000156 High molecular
   weight glutenin subunit
   [107]CK211359 0.001 3.700793 11.85362 5.66E−05 0.000951 NADH
   dehydrogenase subunit I
   TC379055 0.001 2.43027 11.2469 5.66E−05 0.000951
   UDP-glucosyltransferase
   TC414250 5.548758 867.0767 7.287851 1.7E−06 4.56E−05 Wali6 protein
   TC415670 7.672419 1009.571 7.039846 8.1E−08 2.95E−06 Wali3 protein
   TC416295 3.794324 390.9363 6.686947 9.61E−06 0.000207 Wali6 protein
   TC426400 3.150807 126.3979 5.326108 9.61E−06 0.000207 ATP-dependent Clp
   protease proteolytic subunit
   TC447088 17.75787 0.001 −14.1162 2.11E−06 5.52E−05
   [108]CA501626 13.80722 0.001 −13.7531 1.66E−05 0.000333
   [109]CA678188 13.02965 0.001 −13.6695 2.68E−07 8.68E−06 Os10g0329400
   protein
   TC396751 12.65317 0.001 −13.6272 1.09E−09 5.73E−08 Histone H2B.4
   TC374009 12.05666 0.001 −13.5575 2.82E−13 2.89E−11 Agrostis stolonifera
   Crs-1
   [110]CA606062 11.30046 0.001 −13.4641 2.11E−06 5.52E−05
   Vacuolar-processing enzyme gamma- isozyme precursor
   [111]CA654969 10.32932 0.001 −13.3345 8.35E−06 0.000183
   CD931119 9.342799 0.001 −13.1896 4.2E−06 0.000101 Kinesin heavy chain
   [112]CA642440 9.315299 0.001 −13.1854 3.31E−05 0.000598 TonB-like
   protein
   [113]CJ536742 8.682779 0.001 −13.0839 3.31E−05 0.000598
   TC414261 6.403385 0.001 −12.6446 3.31E−05 0.000598 ERN2
   TC390944 6.107886 0.001 −12.5765 8.35E−06 0.000183 Pathogenesis related
   protein-1
   TC409167 5.647619 0.001 −12.4634 1.66E−05 0.000334
   TC441032 5.39911 0.001 −12.3985 2.11E−06 5.52E−05 Mitochondrial ATP
   synthase
   [114]CK205455 5.16714 0.001 −12.3352 4.2E−06 0.000101
   TC416474 5.10914 0.001 −12.3189 1.66E−05 0.000334 TBP-binding
   protein-like
   TC432960 5.050132 0.001 −12.3021 8.35E−06 0.000183
   TC392126 4.7211 0.001 −12.2049 8.35E−06 0.000183 Germin-like protein 6a
   TC434510 28.3137 0.366668 −6.27088 1.98E−22 5.33E−20 Peroxidase PXC2
   precursor
   [115]Open in a new tab

Table 2. GO analysis for DEGs (|log2|ratio (infected/control)≥5).

   Gene ID Cellular component Molecular function Biological process
   TC374940 0016023
   TC420579 0005575 0005739 0030528 0000156 0006355 0009736 0000160
   0019827 0009735
   TC416495 0009536
   TC458503 0009536 0003674
   TC418073 0005634 0000786 0003677 0006334 0007283 0007076
   TC415483 0005739 0016023
   TC392329 0005575 0005634 0005739 0003677 0030528 0000156 0006355
   0006950 0009736 0019827 0000160 0009735
   TC381923 0005575 0016023 0016706 0019748
   TC458205 0009536 0003677 0006310
   TC396751 0008021 0042589 0030141 0031201 0030672 0005576 0016021
   0000786 0005575 0005634 0000149 0003677 0003674 0005516 0017075 0005543
   0017022 0017157 0008285 0006334 0008150 0050829 0017156 0006944 0009792
   0016079 0050830 0051276 0006955 0040007 0002119
   TC390944 0005576 0016023 0003674 0008150
   TC409167 0016023
   TC392126 0016023 0008150
   [116]Open in a new tab

   Furthermore, we classified the differentially expressed unigenes (or
   ESTs) by transcribed genes into three GO categories: cellular
   component, molecular function, and biological process. All of the
   differentially expressed unigenes shared 2,020 GO terms, including 289
   cellular component terms ([117]File S6), 382 molecular function terms
   ([118]File S7), and 1,349 biological processes ([119]File S8). To
   demonstrate further relationships of the DEGs and the biochemical
   processes occurring in the infection, the DEGs were categorized into 8,
   13, and 15 groups according to cellular component, molecular function,
   and biological process independently ([120]Figure 1). Based on the
   results of gene ontology analysis, most of the DEGs were related to
   various organelles (50.73%). For examples, lots of them were related to
   mitochondria, and about half of the DEGs had function of enzyme,
   coenzyme, or cofactor (24.52%) and the rests were related to the
   function of the unclear binding (20.5%). For the biological process of
   GO term, about a quarter of DEGs were involved in the metabolism
   process (22.9%), 15.77% of DEGs were involved in the chromatin assembly
   or disassembly process, and another 9.68% of DEGs were related to the
   process of immunity.

Figure 1. Categorization of the GO terms based on the differentially
expressed genes.

   [121]Figure 1
   [122]Open in a new tab

   Categorization of GO terms with a p-value greater than or equal to 1:
   cellular component terms (A), molecular function terms (B) and
   biological process terms (C). All of the differentially expressed genes
   were classified based on GO analysis. By this method all DEGs are
   firstly mapped to GO terms in the database
   ([123]http://www.geneontology.org/), calculating gene numbers for every
   term, then using hypergeometric test to find significantly enriched GO
   terms in DEGs comparing to the genome background. Each category is
   labeled with different colors, and the numbers refer to ratio of these
   categories to the all dataset.

   According the analysis of pathway enrichment, 2295 DEGs were involved
   in 111 pathways ([124]Table 3). Distribution of all DEGs in the
   pathways was shown in [125]File S9. 507 DEGs were related to metabolic
   pathways, and account for the largest portion (16.72%). However, these
   DEGs were not mapped in KEGG database and the metabolic pathways they
   were involved were quite broad, and almost incorporated all aspects of
   the metabolic processes, such as starch and sucrose metabolism and
   fatty acid metabolism. Most of the DEGs that were classed into
   metabolic pathways were found also to be presented in other pathways.
   For example, phenylalanine ammonia-lyase (TC418073) was involved in
   metabolic pathways, but it also participated in phenylalanine
   metabolism, phenylpropanoid biosynthesis and nitrogen metabolism when
   the function of this enzyme was concretely implemented. Besides
   metabolic pathways, the most important bioprocess in Agrobacterium
   response is biosynthesis of secondary metabolites (9.74%). Furthermore,
   the rate of the pathways on plant-pathogen interaction, phenylpropanoid
   biosynthesis and spliceosome were more than 3%. The most weakly tested
   pathways were about biotin metabolism, arachidonic acid metabolism,
   photosynthesis and photosynthesis-antenna proteins (0.03%).

Table 3. Pathway analysis of DEGs based on KEGG database.

   Pathway Annotation (number) Annotation rate (%) P-value Q-value Pathway
   ID
   Metabolic pathways 507 16.72 1 1.00E+00 ko01100
   Biosynthesis of secondary metabolites 287 9.47 0.999988 1.00E+00
   ko01110
   Plant-pathogen interaction 111 3.66 0.940559 1.00E+00 ko04626
   Phenylpropanoid biosynthesis 102 3.36 0.078706 3.49E−01 ko00940
   Spliceosome 95 3.13 0.008077 5.27E−02 ko03040
   Ribosome 85 2.80 1 1.00E+00 ko03010
   Starch and sucrose metabolism 79 2.61 0.082119 3.51E−01 ko00500
   Purine metabolism 73 2.41 2.22E−06 4.93E−05 ko00230
   Phenylalanine metabolism 72 2.37 0.007202 5.00E−02 ko00360
   Protein processing in endoplasmic reticulum 67 2.21 0.998354 1.00E+00
   ko04141
   Glutathione metabolism 65 2.14 0.002017 2.24E−02 ko00480
   Ubiquitin mediated proteolysis 51 1.68 0.149354 5.42E−01 ko04120
   Endocytosis 50 1.65 0.006456 4.78E−02 ko04144
   Amino sugar and nucleotide sugar metabolism 49 1.62 0.024959 1.39E−01
   ko00520
   Pyrimidine metabolism 48 1.58 0.004626 3.67E−02 ko00240
   DNA replication 41 1.35 1.32E−11 1.47E−09 ko03030
   Alpha-Linolenic acid metabolism 39 1.29 0.000108 1.50E−03 ko00592
   RNA degradation 38 1.25 0.003272 2.79E−02 ko03018
   Nucleotide excision repair 36 1.19 3.35E−06 6.20E−05 ko03420
   Peroxisome 35 1.15 0.774472 1.00E+00 ko04146
   Flavonoid biosynthesis 34 1.12 0.539443 1.00E+00 ko00941
   Cysteine and methionine metabolism 33 1.09 0.971505 1.00E+00 ko00270
   Glycolysis/Gluconeogenesis 33 1.09 1 1.00E+00 ko00010
   Nitrogen metabolism 32 1.06 0.220608 7.65E−01 ko00910
   Galactose metabolism 31 1.02 0.021792 1.27E−01 ko00052
   Phagosome 31 1.02 0.999595 1.00E+00 ko04145
   ABC transporters 30 0.99 3.41E−10 1.89E−08 ko02010
   Base excision repair 29 0.96 6.56E−07 2.19E−05 ko03410
   Cyanoamino acid metabolism 29 0.96 0.110194 4.37E−01 ko00460
   Stilbenoid, diarylheptanoid and gingerol biosynthesis 29 0.96 0.693333
   1.00E+00 ko00945
   Tryptophan metabolism 26 0.86 0.4863 1.00E+00 ko00380
   Oxidative phosphorylation 26 0.86 1 1.00E+00 ko00190
   Fructose and mannose metabolism 25 0.82 0.903134 1.00E+00 ko00051
   Zeatin biosynthesis 23 0.76 0.002555 2.58E−02 ko00908
   Carbon fixation in photosynthetic organisms 23 0.76 1 1.00E+00 ko00710
   Mismatch repair 22 0.73 8.79E−06 1.39E−04 ko03430
   Phosphatidylinositol signaling system 22 0.73 0.133323 5.10E−01 ko04070
   Limonene and pinene degradation 21 0.69 0.899491 1.00E+00 ko00903
   RNA polymerase 20 0.66 0.001317 1.62E−02 ko03020
   Selenoamino acid metabolism 20 0.66 0.353576 1.00E+00 ko00450
   Pyruvate metabolism 20 0.66 0.99903 1.00E+00 ko00620
   Fatty acid metabolism 19 0.63 0.784428 1.00E+00 ko00071
   Sulfur metabolism 17 0.56 0.003233 2.79E−02 ko00920
   Inositol phosphate metabolism 17 0.56 0.362768 1.00E+00 ko00562
   Aminoacyl-tRNA biosynthesis 17 0.56 0.420217 1.00E+00 ko00970
   Alanine, aspartate and glutamate metabolism 17 0.56 0.941328 1.00E+00
   ko00250
   Citrate cycle (TCA cycle) 17 0.56 0.989953 1.00E+00 ko00020
   Circadian rhythm - plant 16 0.53 0.68574 1.00E+00 ko04712
   Arginine and proline metabolism 15 0.49 0.998069 1.00E+00 ko00330
   Linoleic acid metabolism 14 0.46 0.468498 1.00E+00 ko00591
   Proteasome 14 0.46 0.999554 1.00E+00 ko03050
   Homologous recombination 13 0.43 0.054938 2.54E−01 ko03440
   Glycerolipid metabolism 13 0.43 0.888763 1.00E+00 ko00561
   Caffeine metabolism 12 0.40 7.91E−07 2.19E−05 ko00232
   Valine, leucine and isoleucine biosynthesis 12 0.40 0.430602 1.00E+00
   ko00290
   Biosynthesis of unsaturated fatty acids 12 0.40 0.82953 1.00E+00
   ko01040
   Benzoxazinoid biosynthesis 12 0.40 0.921449 1.00E+00 ko00402
   Tyrosine metabolism 12 0.40 0.927114 1.00E+00 ko00350
   Pentose phosphate pathway 12 0.40 0.999553 1.00E+00 ko00030
   Glyoxylate and dicarboxylate metabolism 12 0.40 1 1.00E+00 ko00630
   Sphingolipid metabolism 11 0.36 0.050595 2.50E−01 ko00600
   N-Glycan biosynthesis 10 0.33 0.38074 1.00E+00 ko00510
   Glycerophospholipid metabolism 10 0.33 0.985818 1.00E+00 ko00564
   Ascorbate and aldarate metabolism 10 0.33 0.997898 1.00E+00 ko00053
   Other glycan degradation 9 0.30 0.01401 8.64E−02 ko00511
   SNARE interactions in vesicular transport 9 0.30 0.604893 1.00E+00
   ko04130
   Fatty acid biosynthesis 9 0.30 0.756422 1.00E+00 ko00061
   Terpenoid backbone biosynthesis 9 0.30 0.791906 1.00E+00 ko00900
   Lysine degradation 9 0.30 0.87531 1.00E+00 ko00310
   Butanoate metabolism 9 0.30 0.974329 1.00E+00 ko00650
   Glycine, serine and threonine metabolism 9 0.30 0.976647 1.00E+00
   ko00260
   Pantothenate and CoA biosynthesis 8 0.26 0.381927 1.00E+00 ko00770
   Valine, leucine and isoleucine degradation 8 0.26 0.996475 1.00E+00
   ko00280
   Ether lipid metabolism 7 0.23 0.386485 1.00E+00 ko00565
   Tropane, piperidine and pyridine alkaloid biosynthesis 7 0.23 0.474718
   1.00E+00 ko00960
   Diterpenoid biosynthesis 7 0.23 0.515453 1.00E+00 ko00904
   Regulation of autophagy 7 0.23 0.696261 1.00E+00 ko04140
   Natural killer cell mediated cytotoxicity 7 0.23 0.788938 1.00E+00
   ko04650
   Propanoate metabolism 7 0.23 0.99625 1.00E+00 ko00640
   Indole alkaloid biosynthesis 6 0.20 0.099823 4.10E−01 ko00901
   Basal transcription factors 6 0.20 0.892823 1.00E+00 ko03022
   Pentose and glucuronate interconversions 6 0.20 0.975639 1.00E+00
   ko00040
   Phenylalanine, tyrosine and tryptophan biosynthesis 6 0.20 0.980414
   1.00E+00 ko00400
   Flavone and flavonol biosynthesis 6 0.20 0.982781 1.00E+00 ko00944
   Protein export 6 0.20 0.993342 1.00E+00 ko03060
   Isoquinoline alkaloid biosynthesis 5 0.16 0.70927 1.00E+00 ko00950
   Glucosinolate biosynthesis 5 0.16 0.811041 1.00E+00 ko00966
   Porphyrin and chlorophyll metabolism 5 0.16 0.999916 1.00E+00 ko00860
   Non-homologous end-joining 4 0.13 0.03947 2.09E−01 ko03450
   Ubiquinone and other terpenoid- quinone biosynthesis 4 0.13 0.966216
   1.00E+00 ko00130
   Histidine metabolism 4 0.13 0.982783 1.00E+00 ko00340
   Steroid biosynthesis 4 0.13 0.987955 1.00E+00 ko00100
   beta-Alanine metabolism 4 0.13 0.998679 1.00E+00 ko00410
   Glycosphingolipid biosynthesis - ganglio series 3 0.10 0.151237
   5.42E−01 ko00604
   Glycosaminoglycan degradation 3 0.10 0.340238 1.00E+00 ko00531
   Lysine biosynthesis 3 0.10 0.625386 1.00E+00 ko00300
   One carbon pool by folate 3 0.10 0.899855 1.00E+00 ko00670
   Carotenoid biosynthesis 3 0.10 0.960924 1.00E+00 ko00906
   Anthocyanin biosynthesis 2 0.07 0.051858 2.50E−01 ko00942
   Vitamin B6 metabolism 2 0.07 0.431126 1.00E+00 ko00750
   C5-Branched dibasic acid metabolism 2 0.07 0.497579 1.00E+00 ko00660
   Glycosphingolipid biosynthesis - globo series 2 0.07 0.622102 1.00E+00
   ko00603
   Folate biosynthesis 2 0.07 0.684606 1.00E+00 ko00790
   Synthesis and degradation of ketone bodies 2 0.07 0.684606 1.00E+00
   ko00072
   Thiamine metabolism 2 0.07 0.697287 1.00E+00 ko00730
   Nicotinate and nicotinamide metabolism 2 0.07 0.793251 1.00E+00 ko00760
   Polyketide sugar unit biosynthesis 2 0.07 0.95041 1.00E+00 ko00523
   Biotin metabolism 1 0.03 0.658762 1.00E+00 ko00780
   Arachidonic acid metabolism 1 0.03 0.982434 1.00E+00 ko00590
   Photosynthesis 1 0.03 1 1.00E+00 ko00195
   Photosynthesis - antenna proteins 1 0.03 1 1.00E+00 ko00196
   [126]Open in a new tab

Identification and classification of differentially expressed proteins

   Soluble proteins were extracted from infected and non-infected samples,
   and proteomic dynamics was investigated by high-resolution 2-DE.
   Protein spots displaying reproducible patterns were identified, and
   their expression patterns were analyzed. Among the
   Agrobacterium-infected and the control samples, a total of 867
   reproducible protein spots were detected. The expression abundances
   (vol.%) of 132 protein spots changed by more than two folds, and thus
   were treated as DEPs. Due to the limited number of protein entries in
   the database, only 90 proteins spots were identified eventually through
   MALDI-TOF/TOF ([127]Table 4). The maps are shown in [128]Figure 2.
   Among these proteins, nine potential isoforms were targeted, and each
   of them had two or three spots located at different positions in the
   same gel. For example, in the infected tissues spots I216, I217 and
   I219 were identified as methionine synthase 1, and spots I320 and I343
   were fructose-bisphosphate aldolase. In non-infected tissues, the
   isoforms include fructose-bisphosphate aldolase GTPase-activating
   protein-binding protein 1-like (spots N214 and N216),
   phosphoglucomutase (spots N219 and N229), predicted: pyruvate
   dehydrogenase E1 component subunit beta (spots N307 and N306),
   elongation factor 1-beta (spots N356 and N355), and glutathione
   transferase (spots I385 and I386). Furthermore, there were two groups
   of unnamed protein (spots N322 and N331/N338 and N342). These isoforms
   might represent post-translational modification forms of the same
   protein ([129]Table 4).

Table 4. Differentially expressed proteins under control and infected
conditions.

   Group ID I/C (rate of % vol.) Accession No. Protein score Protein score
   C. I. % Total ion score C. I. % Protein name
   I198 0.111434/0 gi|42391858 200 100 100 cold shock domain protein 3
   I206 0.094232/0 gi|222834414 172 100 100 predicted protein
   I223 0.045147/0 gi|158513193 468 100 100 pyruvate decarboxylase isozyme
   2
   I216(I217 I219) 0.210277/0 gi|68655495 159 100 100 methionine synthase
   1 enzyme
   I209 0.135266/0 gi|313661595 503 100 100 lipoxygenase-1
   I181 0.079847/0 gi|326510251 394 100 100 predicted protein
   I190 0.191266/0 gi|326514130 324 100 99.998 predicted protein
   I226 0.118491/0 gi|357113565 483 100 100 predicted: succinate
   dehydrogenase, ubiquinone, flavoprotein subunit, mitochondrial
   I267 0.481638/0 gi|14018051 466 100 100 putative alanine
   aminotransferase
   I239 0.166667/0 gi|119388723 486 100 100 alcohol dehydrogenase ADH1A
   I276 0.23262/0 gi|6561606 149 100 100 ATPase alpha subunit
   I288 0.937654/0 gi|57635161 235 100 100 peroxidase 8
   I299 0.189651/0 gi|129916 138 100 85.61 phosphoglycerate kinase
   I302 0.466078/0 gi|326527793 893 100 100 predicted protein
   I313 0.129406/0 gi|4158232 322 100 100 glycosylated polypeptide
   I317 0.488077/0 gi|229358240 731 100 100 cytosolic malate dehydrogenase
   I326 0.217391/0 gi|226316439 420 100 100 fructose-bisphosphate aldolase
   I309 0.473017/0 gi|326492375 150 100 99.962 predicted protein
   I320 (I343) 0.812175/0 gi|300681519 317 100 100 fructose-bisphosphate
   aldolase, chloroplast precursor
   I339 0.634438/0 gi|326497973 746 100 100 predicted protein
   I347 0.301374/0 gi|159895412 329 100 100 NADPH-dependent thioredoxin
   reductase isoform 2
   I346 0.236009/0 gi|242058197 104 99.996 100 hypothetical protein
   I242 0.288839/0 gi|585032 399 100 100 cysteine synthase
   I364 0.409977/0 gi|326492319 278 100 100 predicted protein
   I360 0.122988/0 gi|357133190 137 100 99.907 predicted protein
   I390 0.47657/0 gi|20067415 384 100 100 glutathione transferase
   I429 0.479179/0 gi|728594 218 100 100 glycine rich protein, RNA binding
   protein
   I310 6.23836 gi|146216737 367 100 100 SGT1
   I254 5.987146 gi|222872490 136 100 100 predicted protein
   I350 5.771895 gi|326505660 177 100 100 predicted protein
   I395 3.330519 gi|27544804 405 100 100 phospholipid hydroperoxide
   glutathione peroxidase
   I232 3.201333 gi|133872360 551 100 100 Bp2A protein, partial
   I409 3.036805 gi|9230743 64 58.026 100 sucrose synthase-2
   I391 2.829641 gi|259017810 274 100 100 dehydroascorbate reductase
   I199 2.5657 gi|108862362 146 100 100 oxidoreductase, zinc-binding
   dehydrogenase family
   I200 2.513553 gi|49425361 382 100 100 spermidine synthase
   I246 2.456281 gi|11124572 399 100 100 triosephosphat-isomerase
   I380 2.320409 gi|3688398 483 100 100 ascorbate peroxidase
   I353 2.202462 gi|357158835 501 100 100 predicted: glucose-6-phosphate
   isomerase-like
   I422 2.117428 gi|326494858 187 100 100 predicted protein
   N193 0/0.080562 gi|326495130 503 100 100 predicted protein
   N214 (N216) 0/0.835538 gi|357167359 261 100 100 predicted: ras
   GTPase-activating protein-binding
   N206 0/0.384636 gi|326533372 365 100 100 predicted protein
   N219(N229) 0/0.303445 gi|18076790 612 100 100 phosphoglucomutase
   N259 0/0.195904 gi|164565159 418 100 100 ribulose-1,5-bisphosphate
   carboxylase/oxygenase
   N242 0/0.121703 gi|212275097 224 100 100 uncharacterized protein
   N269 0/0.190315 gi|357110692 510 100 100 6-phosphogluconate
   dehydrogenase,decarboxylating
   N289 0/0.278852 gi|28172907 510 100 100 cytosolic 3-phosphoglycerate
   kinase
   N293 0/0.557188 gi|326500176 428 100 100 predicted protein
   N302 0/0.055462 gi|326528557 521 100 100 predicted protein
   N307 (N306) 0/0.242551 gi|357148637 458 100 100 pyruvate dehydrogenase
   E1 component subunit beta, mitochondrial
   N335 0/0.869609 gi|326512374 585 100 100 predicted protein
   N347 0/0.470007 gi|326506676 275 100 100 predicted protein
   N322 (N331) 0/0.090235 gi|326499686 327 100 100 predicted protein
   N338 (N342) 0/0.120011 gi|326518738 542 100 100 predicted protein
   N356 (N355) 0/0.645651 gi|232033 219 100 100 elongation factor 1-beta
   N352 0/0.393733 gi|326507956 314 100 100 predicted protein
   N361 0/0.381971 gi|15808779 273 100 100 ascorbate peroxidase
   N350 0/0.479167 gi|18146827 527 100 100 chitinase 2
   N353 0/0.119597 gi|326489985 227 100 100 predicted protein
   N346 0/0.221933 gi|357130336 190 100 100 26S proteasome non-ATPase
   regulatory subunit 14
   N385 0/0.214757 gi|2499932 741 100 100 adenine phosphoribosyl
   transferase 1
   N416 0/0.113163 gi|125548641 115 100 96.242 hypothetical protein
   OsI_16233
   N420 0/0.261293 gi|326534206 224 100 100 predicted protein
   N409 0/0.977368 gi|326497111 567 100 100 predicted protein
   N406 0/0.44138 gi|22535646 81 99.143 99.653 hypothetical protein
   N411 0/0.264197 gi|48475065 80 99.016 75.904 contains ubiquitin
   carboxyl-terminal hydrolase
   N404 0/0.154189 gi|326496833 161 100 100 predicted protein
   N408 0/0.278555 gi|326517577 81 99.143 81.951 predicted protein
   N403 0/1.02402 gi|40363759 561 100 100 putative glycine-rich protein
   N441 0/0.226741 gi|8980491 116 100 100 thioredoxin h
   I321 0.492256 gi|40317418 158 100 99.997 glutamine synthetase isoform
   GSr2
   I356 0.398343 gi|300807845 210 100 100 profilin
   I282 0.340188 gi|326499079 236 100 100 predicted protein
   I385 (I386) 0.329162 gi|20067423 293 100 100 glutathione transferase
   I268 0.296689 gi|525291 1,030 100 100 ATP synthase beta subunit
   I258 0.282746 gi|9408184 147 100 100 F0-F1 ATPase alpha subunit
   I401 0.181083 gi|112821176 217 100 100 hypothetical protein
   I273 0.169152 gi|164422240 545 100 100 ATP1
   I363 0.099712 gi|40781605 530 100 100 14-3-3 protein
   [130]Open in a new tab

   I/C: infected/control. C.I.: chemical ionization; I: infected; N:
   non-infected.

Figure 2. 2-DE patterns of proteins extracted from control callus (A) and
infected callus (B).

   [131]Figure 2
   [132]Open in a new tab

   2-DE patterns of proteins extracted from CK (uninfected) PCIEs (A) and
   infected PCIEs (B). A protein sample of 1200 µg was loaded on each IPG
   strip (pH 3–10) and protein spots were visualized using coomassie
   brilliant blue staining. The experiment was repeated three times, 132
   differentially expressed protein spots showing significant volume
   change under Agrobacterium infection. 90 protein spots which were
   changed by more than two folds were identified by MALDI-TOF/TOF
   analysis, and labeled on the figure.

Comparative analysis between the results of RNA-seq and proteomics

   To describe the differently expressed genes in the process of
   transcription more accurately, we compared the results of RNA-seq with
   proteomics. 24 DEPs (26 spots) from the proteomics dataset were in
   consistent with the RNA-seq dataset ([133]Table 5). On the basis of the
   pathway analysis of DEGs, the aligned 24 DEPs were involved in 23
   pathways ([134]Table 5), which are shown in [135]Figure 3.

Table 5. The compared proteins between proteinomics and RNA-seq datasets.

   Protein spot ID (rate of % vol.) Gene ID (|log2|ratio) Pathways
   gi|42391858 (0.111434/0) [136]BE431040 (2.13714081)
   gi|158513193 (0.045147/0) TC379241 (1.108033409), TC385701
   (1.068969307), TC419215 (1.014521523) Tryptophan metabolism,
   Glycolysis/Gluconeogenesis, Metabolic pathways (no map in kegg
   database)
   gi|313661595 (0.135266/0) [137]CA733413 (2.196724855), TC388136
   (2.156815406) Alpha-Linolenic acid metabolism, Linoleic acid
   metabolism, Metabolic pathways (no map in kegg database)
   gi|357113565 (0.118491/0) TC384162 (−1.03191405)
   gi|14018051 (0.481638/0) TC419278 (1.587471506), TC451694 (1.49691429)
   Alanine, aspartate and glutamate metabolism, Metabolic pathways (no map
   in kegg database), Carbon fixation in photosynthetic organisms
   gi|119388723 (0.166667/0) TC434396 (1.831048091), TC383270
   (1.529094696) Phenylpropanoid biosynthesis, Biosynthesis of secondary
   metabolites (no map in kegg database), Metabolic pathways (no map in
   kegg database)
   gi|9408184 (0.282746) TC460547 (3.29707538)
   gi|57635161 (0.937654/0) TC389044 (1.315388003) Phenylalanine
   metabolism, Phenylpropanoid biosynthesis, Metabolic pathways (no map in
   kegg database), Biosynthesis of secondary metabolites (no map in kegg
   database)
   gi|4158232 (0.129406/0) TC369736 (−1.380338094)
   gi|229358240 (0.488077/0) TC457520 (3.014521523), TC372580 (−1.122982)
   Citrate cycle (TCA cycle), Pyruvate metabolism, Biosynthesis of
   secondary metabolites (no map in kegg database), Glyoxylate and
   dicarboxylate metabolism, Metabolic pathways (no map in kegg database),
   Carbon fixation in photosynthetic organisms
   gi|585032 (0.288839/0) TC419796 (2.599484024), TC373702 (1.751487117),
   TC376351 (1.501967518) Sulfur metabolism, Cyanoamino acid metabolism,
   Selenoamino acid metabolism, Cysteine and methionine metabolism,
   Metabolic pathways (no map in kegg database)
   gi|357133190 (0.122988/0) TC420420 (−1.340011237) TC397562
   (−1.160565183) Proteasome
   gi|40317418 (0.492256) TC419727 (3.101984365), TC369687 (2.059845514)
   Glutathione metabolism
   gi|728594 (0.479179/0) TC400906 (1.599484024)
   gi|18076790 (0/0.303445) TC406505 (−1.090831477) Purine metabolism,
   Galactose metabolism, Amino sugar and nucleotide sugar metabolism,
   Starch and sucrose metabolism, Pentose phosphate pathway, Biosynthesis
   of secondary metabolites (no map in kegg database),
   Glycolysis/Gluconeogenesis, Metabolic pathways (no map in kegg
   database)
   gi|232033 (0/0.645651) TC376420 (−1.000125253)
   gi|15808779 (0/0.381971) TC389590 (−1.052592673) Glutathione
   metabolism, Ascorbate and aldarate metabolism
   gi|18146827 (0/0.479167) [138]CK201148 (1.325723212) Amino sugar and
   nucleotide sugar metabolism
   gi|2499932 (0/0.214757) [139]CK155765 (−1.3639901) Purine metabolism,
   Metabolic pathways (no map in kegg database)
   gi|9230743 (3.036805) TC370347 (1.175549678) Starch and sucrose
   metabolism, Metabolic pathways (no map in kegg database)
   gi|8980491 (0/0.226741) TC396636 (−1.064190453)
   gi|525291 (0.296689) TC411471 (−1.32428039) Oxidative phosphorylation,
   Metabolic pathways (no map in kegg database)
   gi|11124572 (2.456281) [140]CV767688 (−1.491369406) Inositol phosphate
   metabolism, Fructose and mannose metabolism, Biosynthesis of secondary
   metabolites (no map in kegg database), Glycolysis/Gluconeogenesis,
   Metabolic pathways (no map in kegg database), Carbon fixation in
   photosynthetic organisms
   gi|27544804 (3.330519) CD908771 (1.284973401), [141]CA729147
   (2.336449618)
   [142]Open in a new tab

Figure 3. Pathway network consist by the 18 DEPs out of the aligned ones.

   [143]Figure 3
   [144]Open in a new tab

   Pathway network constructed based on the pathways of the 18 from the 24
   aligned DEPs in both RNA-seq and proteomics datasets. Up-regulated
   genes are colored as black box, down-regulated genes as gray. Dashed
   arrow means that some steps are omitted, and solid arrows means the
   direct processes.

   According to their functions, the aligned proteins were categorized
   into 5 groups ([145]Figure 4). Half of these proteins (12 proteins)
   were involved in stress or immune responses, including cold shock
   domain protein 3, pyruvate decarboxylase isozyme 2, alanine
   aminotransferase 2, alcohol dehydrogenase ADH1A, peroxidase 8, cysteine
   synthase, glutamine synthetase isoform GSr2, elongation factor 1-beta,
   ascorbate peroxidase, thioredoxin h, hospholipid hydroperoxide
   glutathione peroxidase, and chitinase 2. Eight proteins (33.3%) are
   related to the process of metabolism, including lipoxygenase-1,
   cytosolic malate dehydrogenase, proteasome subunit alpha type-3-like,
   phosphoglucomutase, adenine phosphoribosyltransferase 1, reversibly
   glycosylated polypeptide and sucrose synthase-2, and
   triosephosphat-isomerase. In addition, succinate dehydrogenase is a key
   enzyme of the respiratory chain [146][42]. F0-F1 ATPase alpha subunit
   and ATP synthase beta subunit are involved in the energy metabolism,
   and glycine-rich protein or RNA binding protein is a kind of nucleic
   acid binding protein.

Figure 4. Categorization of the 24 aligned DEPs in proteomics dataset with
the RNA-seq dataset.

   [147]Figure 4
   [148]Open in a new tab

   Categorizing of the 24 aligned DEPs based on their functions. Each
   category is labeled with the different colors, and the numbers means
   the percentage of each DEPs to total aligned DEPs.

   For these 24 proteins, their variations on up/down-regulation in
   proteomics and RNA-seq datasets are not completely consistent
   ([149]Table 5). Succinate dehydrogenase and triosephosphat-isomerase
   displayed up-regulation in the proteomics dataset but down-regulation
   in the RNA-seq dataset. On the contrary, glutamine synthetase isoform
   GSr2, F0-F1 ATPase alpha subunit and chitinase 2 showed down-regulation
   in the proteomics dataset but up-regulation in the RNA-seq dataset.
   This inconsistent phenomenon might be caused by post-translational
   modification of the target gene and different metabolism process of the
   corresponding protein.

Expression analysis using quantitative reverse transcription PCR (qRT-PCR)

   To verify the DEG and DEP data, we used qRT-PCR to analyze the
   expression levels of 21 genes including 14 found in both DEGs and DEPs,
   and 7 other DEGs (|log2|ratio (infected/control)>10) ([150]File S10).
   The results showed that 11 genes were up-regulated, and 3 genes were
   down-regulated in the infected callus compared with the non-infected
   callus, while 7 genes had no changes in expression levels ([151]Figure
   5). Although the qRT-PCR data did not match the RNA-seq data perfectly,
   some genes did show consistent expression patterns in both datasets.
   For examples, [152]CF133353 (expressed protein), [153]CJ654139
   (Os07g0137900 protein), TC416495 (MPI), TC451603 (LOC496215 protein),
   [154]BJ236765 (unknown), TC458205 (high molecular weight glutenin
   subunit) and TC379055 (unknown) got vastly different expression both
   from qRT-PCR and RNA-seq. And the difference of expression from the two
   analysis both significantly reduced in TC419727 (glutathione
   transferase), TC388136 (lipoxygenase 1), TC434396 (cinnamyl alcohol
   dehydrogenase), TC419278 (alanine aminotransferase 2) and [155]CK201148
   (chitinase 2). At last, TC406505 (phosphoglucomutase). TC411471 (ATP
   synthase subunit a), TC420420 (proteasome subunit alpha type-5),
   [156]CK155765 (adenine phosphoribosyltransferase 1) and [157]CV767688
   (triosephosphate isomerase) were down regulated.

Figure 5. Expression level and trend line of certain DEGs according to
RNA-seq and qRT-PCR.

   [158]Figure 5
   [159]Open in a new tab

   Assessment of the expression level of 21 DEGs, and contrasting of the
   results with RNA-seq. Ranging the DEGs in size of |log2|ratio plotted
   horizontal axis (from largest to smallest). The black bars mean the
   |log2|ratio, and the gray bars mean the 2^−ΔΔCT of the DEGs. And the
   gene annotations are as bellow. [160]CF133353, expressed protein;
   [161]CJ654139, Os07g0137900 protein; TC416495, MPI; TC451603, LOC496215
   protein; [162]BJ236765, unknown; TC458205, high molecular weight
   glutenin subunit; TC379055, unknown; TC419727, glutathione transferase;
   TC388136, lipoxygenase 1; TC434396, cinnamyl alcohol dehydrogenase;
   TC419278, alanine aminotransferase 2, [163]CK201148, chitinase 2;
   TC389044, peroxidase 8; TC370347, sucrose synthase type I; TC379241,
   pyruvate decarboxylase isozyme 2; TC389590, thylakoid-bound ascorbate
   peroxidase; TC406505, phosphoglucomutase; TC411471, ATP synthase
   subunit a; TC420420, proteasome subunit alpha type-5; [164]CK155765,
   adenine phosphoribosyltransferase 1; [165]CV767688, triosephosphate
   isomerase.

Discussion

Transferring process of T-DNA from Agrobacterium cells into wheat genome

   Agrobacterium tumefaciens is a kind of pathogenic bacteria that causes
   crown gall disease (the formation of tumours) by the insertion of a
   T-DNA from a plasmid into plant cells in over 140 species of dicots
   under natural conditions. Unlike some tumor-inducing viruses,
   Agrobacterium T-DNA insertion into a host genome is a semi-random
   process [166][43] that causes an antibacterial response in the host. Up
   to date, even though many strains of Agrobacterium can be used in plant
   genetic transformation for T-DNA delivery, each strain has its suitable
   species to infect on. In this study, C58C1 strain was chosen because it
   was successfully used in many reports on wheat transformation
   [167][24]. According to some published papers [168][24], [169][26],
   [170][27], wheat transformation process mediated by Agrobacterium was
   finished within 48 hours. Especially, the growth peak of Agrobacterium
   on the surface of the host cells was observed when the co-culture
   period of Agrobacterium and wheat cells was proceeded for 36 h
   ([171]File S11), and the expression of T-DNA was very intense after
   co-culture for 36 h [172][44]. Therefore, we expect that all of the
   transformation steps (attraction of Agrobacterium, T-DNA transportation
   and interaction) were lancing within this time since the infection.
   Therefore, in this investigation we chose the wheat immature embryos
   infected with Agrobacterium for 36 hours as materials for RNA-seq and
   proteomics analysis.

   Investigating the host response to Agrobacterium infection process will
   contribute to understanding the interaction process and find some
   valuable clues on developing or optimizing of Agrobacterium- mediated
   transformation process. In our present study, 4,889 DEGs and 90 DEPs
   were identified to be closely related to Agrobacterium infection and
   all the DEGs involved in 111 pathways. Actually, RNA-seq is much more
   sensitive than 2D proteomics analysis, but the number of DEPs is much
   fewer than the DEGs. This kind of inconsistence is partly due to
   translational/post-translational regulation, but the most significant
   reason is that a DEP does not correspond to only one DEG ([173]Table
   5). In the response process of wheat cells to Agrobacterium, the genes
   related to secondary metabolites metabolic played the most important
   roles according to the results from pathway analysis and gene ontology
   analysis for biological process ([174]Table 3, [175]File S8). On the
   contrary, minimum of genes relate to photosynthesis pathway were
   detected. It indicated that the photosynthesis related genes avoided
   participating in the process of the transformation.

Potential roles of related metabolism process proteins or secondary
metabolites in Agrobacterium mediated approach

   A large portion of the DEGs and DEPs in our datasets were found to be
   involved in the metabolism process. Some of them may play important
   roles in the interaction between wheat cells and Agrobacterium. Among
   them, sucrose synthase is an attractive functional protein. This
   carbohydrate was proved to participate both in sucrose synthesis and
   cleavage in plants, and catalyzes the chemical reaction of
   UDP-glucose+D-fructose←→UDP+sucrose [176][45].In this study, we found
   that sucrose synthase-2 was up-regulated according to RNA-seq and
   qRT-PCR, but was down-regulated according to proteomics dataset. The
   reason might be that the protein is degraded dramatically or
   transformed into other homologous type very soon although the
   transcription is activated. The up-regulation of this synthase at the
   level of transcription was also found in Arabidopsis thaliana under the
   same situation [177][46]. As sucrose synthase is beneficial to root
   nodule organogenesis in legumes [178][47], this corresponding gene
   might be related to the process of Agrobacterium-mediated genetic
   transformation. Furthermore, UDP-glucose is the substrate of
   UDP-glycosyltransferase, which was confirmed to participate in the
   response to pathogens [179][48]. In addition, UDP-glycosyltransferase
   has also been found to detoxify deoxynivalenol in Fusarium [180][49].
   Recently, an Arabidopsis hat mutant over-expressing a UDP-
   glucosyltransferase gene was found to be resistant to
   Agrobacterium-mediated transformation, in which many defense genes were
   down-regulated [181][10]. And in our results, we also found that both
   sucrose synthase and UDP-glucosyltransferase were up-regulated at the
   level of transcription after infection by Agrobacterium. It is implied
   that saccharo metabolism might affect the infection process.

   Some DEPs were involved in proteasome, such as proteasome subunit alpha
   type-3-like. proteasomes, played a straightforward and critical role in
   the process of plant immune system [182][50]. 26S proteasome and
   ubiquitin emerge as a key regulatory mechanism in selective protein
   degradation [183][51]. This pathway was involved in a wide variety of
   cellular processes in plants, such as hormone signaling,
   photomorphogenesis, flower development, embryo development, and defense
   response [184][52]. Meanwhile, ubiquitin-protein ligase ([185]CA714086)
   was identified in our RNA-seq datasets and remarkably up-regulated
   (|log2|ratio = 4.080610714). The activation of ubiquitin is a typical
   reaction during the process of pathogen infection [186][53]. In
   addition, E3 ubiquitin ligase is required for cell death and defense
   response in plants [187][54]. Therefore, ubiquitin-protein ligase might
   be also related to the Agrobacterium mediated DNA delivery. On the
   other hand, 26S proteasome subunit was proved to be involved in innate
   immunity in Arabidopsis [188][55], In plant, selective removal of
   short-lived regulatory proteins is a very important controlling
   strategy for physiology, growth, and development [189][56]. However, in
   our study, proteasome maturation factor (TC379459) is down-regulated
   according to RNA-seq (|log2|ratio = −1.44004434). In wheat cells, some
   regulatory proteins might produce a favorable environment for
   Agrobacterium infection. Thus, to defend the resistance from plant
   cells, Agrobacterium might suppress the proteasomes from plant.
   Function of above candidate genes screened from this study in wheat
   Agrobacterium-mediated transformation needs to be further investigated.

Relationship of plant phenylpropanoid biosynthesis and Agrobacterium
infection

   To our knowledge, UDP-glycosyltransferase mediates the transfer of
   glycosyl residues from nucleotide sugars to acceptor molecules
   (aglycones), such as plant secondary metabolites [190][57]. Most plant
   secondary metabolites might play important roles during Agrobacterium
   infection process. For examples, plant phenolics such as acetosyringone
   is an essential inducer for Agrobacterium infection. Acetosyringone is
   widely used in the protocol of Agrobacterium-mediated plant
   transformation. Other phenolics such as protocatechuic acid,
   β-resorcylic acid and protocatechuate also launch into the
   Agrobacterium-mediated transformation [191][58]. In our database,
   phenylalanine ammonia-lyase (PAL) was found up-regulated dramatically
   (|log2|ratio = 12.50423728). PAL catalyzes the first step in the
   biosynthesis of phenylpropanoids, which are further modified into a
   wide variety of phenolic compounds [192][59]. Another important
   secondary metabolites is flavonoid which involved in several biological
   process for plant development and defense [193][60]. In our research,
   two unknown proteins (TC413199, TC430821) were found to be involved in
   flavonoid biosynthetic process according to the GO analysis. Flavonoid
   is also a class of plant antibiotics. Sakuranetin, as a member of
   flavonoid, is recently demonstrated to have anti-inflammatory,
   anti-mutagenic, and anti-pathogenic activities.

Expression of stress response related proteins during the interaction between
plant and Agrobacterium

   Based on the gene ontology analysis of the RNA-seq dataset, we found
   9.68% DEGs were involved in the immunity process. According to the
   pathway analysis, 111 DEGs were found to be related to the
   plant-pathogen interaction pathway. In consideration of Agrobacterium
   being a kind of plant-pathogen in nature, the stress and pathogen
   response genes should be the focuses of the transformation process.

   Most of the DEGs are involved in responses to reactive oxygen species
   (ROS) stresses. As we know, oxidative burst is the first defense of
   plants against pathogen attacks [194][61]. The ROS, stimulated by
   stress from pathogen attack and generated from both plant and pathogen
   [195][62], plays a key role in the crosstalk between biotic and abiotic
   stress signaling [196][63]. Plants generated ROS by activating various
   oxidases and peroxidases [197][64]. In the meanwhile, it was found in
   wheat that Agrobacterium infection induces plant cell to produce
   hydrogen peroxide (H[2]O[2]) rapidly and leads wheat cell death
   severally [198][65]. In plant, a series of peroxidases can eliminate
   ROS, such as catalase, which activity was confirmed to be closely
   related to efficient regeneration potential of wheat immature embryos
   during the somatic embryogenesis [199][66]. There are 3 kinds of
   peroxidases (peroxidase 8 (TC389044), phospholipid hydroperoxide
   glutathione peroxidase (CD908771 and [200]CA729147) and ascorbate
   peroxidase (TC389590)) found in our datasets. In the datasets of
   RNA-seq and proteomics, peroxidase 8 was up-regulated
   (|log2|ratio = 1.31539) during the infection process while ascorbate
   peroxidase was down-regulated (|log2|ratio = −1.05259). Furthermore,
   according to the results of qRT-PCR, the expression level of peroxidase
   8 was up-regulated 1.7 times but the expression level of ascorbate
   peroxidase almost had no change ([201]File S10). By coincidence,
   peroxidase was also identified in Ageratum conyzoides and Arabidopsis
   thaliana responding to Agrobacterium tumefaciens infection [202][16],
   [203][67],. Peroxidase has the function of interrupting the cascades of
   uncontrolled oxidation [204][68]. Peroxidase 8 is a kind of peroxidase
   belonging to Class-III in Triticum monococcum, which was a component of
   defense system responding to powdery mildew attack [205][46]. Ascorbate
   peroxidase scavenges hydrogen peroxide in plants, and is essential to
   protect cell constituents from lesion by hydrogen peroxide and other
   hydroxyl radicals produced from the interaction process of plant cells
   and pathogens [206][67], [207][69], Hydrogen peroxide is a kind of
   inhibitor for the invading pathogens, but it also contributes some
   virulence to pathogens [208][61]. Thereby, hydrogen peroxide might be
   an advantageous compound for both host cells and Agrobacterium, and it
   might be necessary to repress the accumulation of hydrogen
   peroxide-scavenging enzyme such as ascorbate peroxidase in the
   infection process.

   Phospholipid hydroperoxide glutathione peroxidase was determined to be
   related to the metabolism of glutathione, which is an effective
   antioxidant preventing damage of important cellular components caused
   by ROS [209][70]. Moreover, phospholipid hydroperoxide glutathione
   peroxidase is a monomer, and the donor substrate of this peroxidase is
   not only restricted to glutathione (GSH) but also binds to specific
   mitochondrial proteins. In present research, phospholipid hydroperoxide
   glutathione peroxidase was detected to be up-regulated according to
   RNA-seq but down-regulated according to proteomics in the infection
   process. It is possible that phospholipid hydroperoxide glutathione
   peroxidase binds on mitochondrial proteins dramatically when the
   activity of mitochondrial is elevated. It makes protein of phospholipid
   hydroperoxide glutathione peroxidase decrease although the gene's
   expression was activated.

   The rest of DEPs identified are related to be biotic stress response,
   such as chitinase ([210]CK201148) and thioredoxin h (TC396636). As
   chitin is an important component of the cell wall of fungi and
   chitinases are generally found in organisms that dissolve and digest
   the chitin of fungi [211][71] plant chitinases are thought to be
   related to pathogen resistance [212][72]. Chitinase was up-regulated in
   our RNA-seq datasets and there was almost no diversity according to
   qRT-PCR result (2^−ΔΔCT = 0.982055), but was down-regulated in
   proteomics datasets. Moreover, by reducing the defense of host cells,
   chitinases enable symbiotic interaction with nitrogen-fixing bacteria
   or mycorrhizal fungi [213][73]. To remove the barriers of infection,
   Agrobacterium suppressed the accumulation of chitinase from plant cells
   although the gene's expression was already activated. hioredoxin h also
   has potential capability against pathogens attaching, and evidence
   showed thioedoxin h gene was strongly induced within 4 hours in
   Arabidopsis cell suspensions treated with fungal elicitors, which
   contained wide range stress inducing agent [214][74]. In our study, we
   found that thioredoxin h was down-regulated both in RNA-seq and
   proteomics datasets which means the immune response of the host was
   impaired during the infection process.

Relationship of T-DNA integration and host proteins related to nucleic acid
binding and nucleotide excision repair

   The ultimate aim of Agrobacterium transformation is to import the T-DNA
   into plant genome. So, several nucleic acid binding proteins should
   take part in the two steps: T-DNA nuclear import and integration. And
   the typically nucleic acid binding proteins are thought to be the
   T-complex ones from Agrobacterium [215][75]. Now some host proteins
   have shown to be important in the last two steps for T-DNA delivery
   [216][7]. In this study the nucleic acid binding proteins take a very
   large part of the DEGs based on the GO analysis. The pathway analysis
   indicated that the spliceosome proteins should be paid more attention.
   T-DNA integration into plant chromosome actually belongs to the way of
   non-homologous (illegitimate) recombination (NHR), even when the T-DNA
   shares high homology with the host genome. As for the pattern of the
   T-DNA integration, the double-strand-break repair (DSBR) model and
   single-strand-gap repair (SSGR) model were originally proposed
   [217][76]. Above findings suggest that nucleotide excision repair
   proteins are the key players in the process of T-DNA integration.

   Particularly, histone was demonstrated to play an important role in the
   transformation process mediated by Agrobacterium [218][7]. Especially,
   histone H2A, histone H4, and histone H3-11 in Arabidopsis can increase
   transformation susceptibility. Other plant proteins related to the
   transformation process include BTIs (VirB2-interacting proteins),
   AtRAB8, and DIG3. Hwang et al. used the C-terminal-processed portion of
   VirB2 as the bait to search the interaction protein by yeast two-hybrid
   in Arabidopsis, and found that BTI1, BTI2, BTI3, and a
   membrane-associated GTPase, AtRAB8 interact with VirB2. Their further
   study showed the positive meaning of these proteins in the infection
   process of Agrobacterium [219][8]. DIG3, found in tomato, encodes an
   enzymatically active type 2C serine/threonine protein phosphatase,
   which interacts with VirD2. Over-expression of DIG3 in tobacco
   protoplasts inhibited nuclear import of VirD2 nuclear localization
   [220][9]. In our study, Histone and GTPase related protein genes were
   also identified, such as TC396751 encoding Histone H2B. In the process
   of Agrobacterium infection, H2B was down-regulated dramatically
   (|log2|ratio = −13.62721073). In the previous research [221][13], H2B
   did not lead to increased transformation susceptibility. However,
   according to our results, we assumed that H2B might have a negative
   effect during the transformation process. Therefore, the expression
   level of H2B might be depressed by Agrobacterium. Besides, we also
   obtained several serine/threonine-protein kinases such as TC440175
   (Serine/threonine-protein kinase Nek5, (|log2|ratio = 2.297455487)).
   Some of them probably are similar to DIG3, and interact with VirD2.
   Beyond that, a big part of the DEGs have the function of nucleic acid
   binding and protein-protein interaction based on the categorization of
   the GO terms ([222]Figure 1B). It is suggested that some genes among
   the DEGs should play roles in the nucleic importing and integration
   into genome of T-DNA.

Conclusions

   In this study, we identified a set of 4988 DEGs and 90 DEPs in
   Agrobacterium-infected wheat tissues. After comparative analysis, 24 of
   the 90 DEPs were detected in RNA-seq and proteomics datasets
   simultaneously. The expressions of the most DEGs were found to be
   uniformly up/down-regulated between RNA-seq and qRT-PCR datasets, which
   proved the authenticity of the results from RNA-seq. According to GO
   analysis, we found that a big part of these differentially expressed
   genes were related to the process of stress or immunity response, and
   other major part of DEGs involved in the process of molecular
   modification. We believe that some of these genes are closely related
   to the transformation process mediated by Agrobacterium. The findings
   achieved in this study will help to further exploit the interaction
   between Agrobacterium and host cells, and may facilitate the
   development of efficient plant transformation strategies.

Supporting Information

   File S1

   Categorization of row reads of the control material A and the infected
   material B.

   (DOC)
   [223]Click here for additional data file.^ (2.2MB, doc)
   File S2

   Alignment statistics of total reads.

   (DOC)
   [224]Click here for additional data file.^ (35KB, doc)
   File S3

   Randomness assessment A and sequencing saturation analysis B.

   (DOC)
   [225]Click here for additional data file.^ (422.5KB, doc)
   File S4

   Gene coverage statistics.

   (DOC)
   [226]Click here for additional data file.^ (2.8MB, doc)
   File S5

   The differentially expressed genes (|log2|ratio (infected/control)≥1).

   (XLS)
   [227]Click here for additional data file.^ (1.6MB, xls)
   File S6

   Gene Ontology analysis information for cellular component.

   (DOC)
   [228]Click here for additional data file.^ (647KB, doc)
   File S7

   Gene Ontology analysis information for molecular function.

   (DOC)
   [229]Click here for additional data file.^ (600KB, doc)
   File S8

   Gene Ontology analysis information for biological process.

   (DOC)
   [230]Click here for additional data file.^ (2.2MB, doc)
   File S9

   Distribution of all GEGs on the pathways.

   (DOC)
   [231]Click here for additional data file.^ (345KB, doc)
   File S10

   Comparison of expression level between the data of real-time PCR and
   RNA-seq.

   (DOC)
   [232]Click here for additional data file.^ (41KB, doc)
   File S11

   Detection of the Agrobacterium attachment on wheat callus by scanning
   electron microscope (SEM). The adsorption of Agrobacterium to wheat
   callus after co-culture for 30 minutes, 12 hours, 24 hours, 36 hours,
   and 48 hours.

   (TIF)
   [233]Click here for additional data file.^ (836.4KB, tif)

Acknowledgments