Transcriptome analysis of secondary cell wall development in Medicago truncatula
© Wang et al. 2015
Received: 29 April 2015
Accepted: 17 December 2015
Published: 5 January 2016
Legumes are important to humans by providing food, feed and raw materials for industrial utilizations. Some legumes, such as alfalfa, are potential bioenergy crops due to their high biomass productivity. Global transcriptional profiling has been successfully used to identify genes and regulatory pathways in secondary cell wall thickening in Arabidopsis, but such transcriptome data is lacking in legumes.
A systematic microarray assay and high through-put real time PCR analysis of secondary cell wall development were performed along stem maturation in Medicago truncatula. More than 11,000 genes were differentially expressed during stem maturation, and were categorized into 10 expression clusters. Among these, 279 transcription factor genes were correlated with lignin/cellulose biosynthesis, therefore representing putative regulators of secondary wall development. The b-ZIP, NAC, WRKY, C2H2 zinc finger (ZF), homeobox, and HSF gene families were over-represented. Gene co-expression network analysis was employed to identify transcription factors that may regulate the biosynthesis of lignin, cellulose and hemicellulose. As a complementary approach to microarray, real-time PCR analysis was used to characterize the expression of 1,045 transcription factors in the stem samples, and 64 of these were upregulated more than 5-fold during stem maturation. Reverse genetics characterization of a cellulose synthase gene in cluster 10 confirmed its function in xylem development.
This study provides a useful transcriptome and expression resource for understanding cell wall development, which is pivotal to enhance biomass production in legumes.
Legumes include many agriculture important grain, pasture and agroforestry species that are well known for their ability to fix nitrogen through root nodules [1, 2]. Alfalfa (Medicago sativa) is a major forage legume, with an average annual value of more than $8 billion in the USA alone . It’s high biomass productivity also makes alfalfa a potential bioenergy legume . Most cultivated legumes, including alfalfa, are multiploid with complex segregation and inheritance patterns [2, 5]. Barrel medic (Medicago truncatula) is diploid with a relatively small genome, and has been adopted as a model species for legume genomics [6–8]. In addition, M. truncatula is an important self-regenerating annual pasture species, especially in southern Australia . M. truncatula is also grown in rotation with cereal crops for improving soil quality .
Modulation of secondary cell wall composition, such as reducing lignin content, improves alfalfa quality with better digestibility and higher fermentable sugar yields for biofuel production [11–13], and reduced lignin transgenic alfalfa with increased digestibility has recently been de-regulated in the US by USDA-APHIS  and is in commercial production. The bioengineering applications in alfalfa have largely relied on the identification of secondary wall biosynthetic and/or regulatory genes from the model legume M. truncatula . Secondary cell wall development in vascular and interfascicular tissues involves a large number of biosynthetic genes and is regulated at the transcriptional level [15, 16]. The NAM, ATAF1/2, and CUC2 (NAC) domain and MYB domain transcription factors (TFs) function as master regulators. The NAC domain TFs include VASCULAR-RELATED NAC-DOMAIN6 (VND6), VND7, NAC SECONDARY WALL THICKENING PROMOTING (NST1), NST2 and SECONDARY WALL-ASSOCIATED NAC DOMAIN 1 (SND1) [17–19]. MYB domain TFs, i.e. MYB46 and MYB83, also function as master regulators for secondary cell wall development, but are downstream of the NAC domain TFs [20, 21]. Many other TFs are further downstream of the NAC and MYB domain master regulators, and form the hierarchical and non-hierarchical regulation networks. The regulatory pathways orchestrate the biosynthesis of cellulose, hemicelluloses and lignin . In M. tuncatula, a NAC domain TF was identified as an important regulator of secondary cell wall development, but with distinct regulatory mechanism [23, 24].
Global transcriptional profiling has been successfully used to identify genes and regulatory pathways in secondary cell wall biosynthesis in Arabidopsis thaliana, but such transcriptome data and systems analysis of TF functions in stem maturation is still lacking in legumes. The composition of secondary cell walls significantly affects the quality of forage legumes [12, 13, 25]. In addition, cell wall biosynthesis is regulated differently in legumes compared to Arabidopsis [24, 26]. The Affymetrix Medicago genome array has been a critical tool for functional analysis and gene expression studies in legumes [7, 27, 28]. To better understand secondary cell wall development and its regulation in M. truncatula, we have performed transcriptome microarray assay and high through-put quantitative real time PCR analysis. A large of number of genes were differentially expressed during stem maturation, and were placed into 10 expression clusters. TFs putatively functioning in secondary wall development were identified based on their expression patterns. Gene co-expression network analysis was used to identify TFs that may regulate the biosynthesis of individual components of the secondary cell wall. We further performed real-time PCR analysis to characterize the expression of 1,045 TFs, and 64 of these were upregulated more than 5-fold during stem maturation. This research provide a useful resource for molecular characterization of secondary cell wall development in legumes.
Analysis of secondary cell wall development in the Medicago stem
Microarray analysis of secondary wall development and stem maturation
To characterize the transcriptome profile during Medicago stem maturation, we collected stem samples for RNA extraction and subsequent microarray expression analyses. Five internodes, i.e. the aforementioned internodes 2, 3, 5, 7, and 9 from the Medicago primary stem, were collected in three biological replicates. Each sample was a pool of 10 segments harvested from the central 2 cm of each internode. These samples represented the different secondary cell wall developmental stages along the stem maturation process (Fig. 1).
The Affymetrix Medicago Genechip genome array contains 61,281 probe sets, most of which (about 50,900) are from M. truncatula gene sequences. Genechip analysis has been instrumental in identifying biologically meaningful genes and characterizing gene expression patterns in M. truncatula and M. sativa [7, 27]. In this study, we used 15 arrays to investigate the transcriptome change during Medicago stem maturation. RNA samples from internode 2 were used as the reference for the remaining internode samples. Genes with expression levels significantly changed between the control (Internode 2) and the other four older internodes were identified using associative analysis . Analysis of the microarray results indicated that 11,380 genes were significantly differentially expressed (p < 8.16 × 10-7 and fold change ≥ 2) in the relatively more mature internodes. The differentially expressed genes are listed in supplemental data (Additional file 1).
In cluster 1, genes had highest expression in internode 2, and the expression levels progressively decreased along the stem maturation gradient until the fifth internode, and kept at certain levels in the even older internodes (Fig. 3). In contrast, expression of the genes in clusters 2, 3, 4 6 fluctuated along the stem maturation gradient without obvious correlation with secondary wall accumulation. Genes in cluster 7 are down regulated in the 3rd internode, which contradicts with the microscopic observation on secondary cell wall accumulation. Therefore, genes in this cluster are not likely function in secondary cell wall biosynthesis. The expression of genes in cluster 9 increase along stem maturation gradient, and reach the highest level until the oldest 9th internode, in which secondary wall accumulation should have almost completed. We therefore hypothesize that the functions of genes in cluster 9 may not be in secondary cell wall biosynthesis, but rather in the senescence process. Genes in these seven clusters were therefore excluded from further analysis.
Expression of secondary wall related genes during stem maturation
Transcription factors correlated with secondary cell wall development
Categorization of differentially expressed transcription factors in cluster 5, cluster 8 and cluster 10
Phaseolin G-box binding
High through-put quantitative real-time PCR analyses
Distribution of transcription factors with increased expression along stem maturation gradient as determined by high-throughput q-RT PCR
Frequencies of occurrence
Reverse genetics characterization of the IRX1 gene in M. truncatula
Most genes in the lignin biosynthetic pathway have been identified from Medicago, and several have been used to improve digestibility and sugar yield in alfalfa [11–13]. Much less is known about the functions of the cellulose synthase genes in legumes. Our microarray analysis indicated that 19 CesA probe sets were categorized in cluster 10 (Fig. 4b red colored probe sets), and may therefore represent bona fide secondary cell wall specific cellulose synthase genes. In Arabidopsis, three cellulose synthases form a complex to catalyze cellulose synthesis in the secondary cell well; these are AtCesA4/IRX5, AtCesA7/IRX3/FRA5 and AtCesA8/IRX1/ FRA6 [38–41]. Using blast analysis, we found that one of the probe sets in cluster 10, Mtr.5123.1.S1_at, has highest identity with AtIRX1/AtCesA8. We named the corresponding Medicago homolog gene MtIRX1. To investigate the biological function of MtIRX1, we performed a reverse genetic analysis. Using MtIRX1 gene-specific primers, three mutant lines were recovered from the Medicago Tnt1 insertional mutant population. In a previous mutant screening experiment, we had already identified a Tnt1 insertion in the MtIRX1 gene and named the mutant mtirx1-1 (NF3892). We therefore renamed the newly identified mutants NF7461, NF8667 and NF17907 as mtirx1-2, mtirx1-3 and mtirx1-4, respectively. These four mutant lines showed similar irregular xylem phenotypes, and the phenotypes of mtirx1-1 were examined in detail.
The inflorescence stem of the herbaceous model plant Arabidopsis forms vascular xylem and undergoes secondary growth, and has therefore been used to study secondary cell wall development [44, 45]. The advantages of studying Arabidopsis include its known genome, easy transformation and available mutant resources for functional studies. For alfalfa and other legume species, M. truncatula may be a better model system due to the high homology of their genomics sequences. In some cases, genes identified from M. truncatula can be directly utilized in alfalfa . A high quality genome sequence of M. truncatula is available . A gene expression atlas with a global view of gene expression in all major organs, and a large collection of Tnt1 retrotransposon insertional mutant lines available for gene functional analysis [27, 46, 47] make Medicago an excellent model for functional genomics of cell wall development in legumes.
The cluster analysis of lignin and cellulose biosynthetic genes indicates that most of the genes are correlated with secondary cell wall development as supported by characterization of the cellulose synthase gene MtIRX1. However, some of the putative lignin and cellulose synthase genes were categorized in cluster 1, which showed opposite expression patterns compared to genes in secondary cell wall development (Fig. 4a and b). The functions of these gene are still unclear. One possibility is that these genes may function in cell wall development in certain conditions other than normal growth conditions. This is supported by functional analysis of the cinnamoyl CoA reductases (CCR) genes in Medicago . MtCCR1, identified in this previous study as the primary CCR gene in the monolignol biosynthetic pathway, is categorized in cluster 10. Mutation of MtCCR1 resulted in drastic inhibition of plant growth. In contrast MtCCR2 is in cluster 1, and its mutation has no effect on plant growth .
The high-throughput real time qRT-PCR analysis identified 64 transcription factor genes that significantly up-regulated along the stem maturation gradient. Among these, twenty were already detected in the microarray experiment. The overlapping transcription factors between Genechip and qRT-PCR is low mainly because these two methods have obvious difference in sensitivity. We found that eighteen TFs changed less than 2-fold in the microarray assay, but changed over 5 times in the qRT-PCR analysis, indicating that qRT-PCR is much more sensitive than the Genechip assay. In addition, the annotation of M. truncatula genome sequence is still far from complete. Even though the M. truncatula Genechip have 1394 probe sets corresponding to transcription factor genes, we found 26 TFs significantly differentially expressed in qRT-PCR analysis, but were not present on the Genechip. The GeneChip approach heavily depends on chip design. For example, the current M. Truncatula chip contains only about 70-75 % of the genome contents . In addition, the Genechip can’t differentiate closely related genes, which can be addressed by the complementary real time qRT-PCR approach.
In this study, we found that over 11,000 genes are differentially expressed along the maturation axis of Medicago primary stems. It was previously estimated that about 15 % of the genes in Arabidopsis may be involved in cell wall synthesis, remodeling, or turnover . It is likely that many of the differentially expressed genes play important roles in secondary cell wall development. However, stem development along the maturation axis involves many other developmental processes, including the maturation of ground tissues. The transcriptome expression pattern should also reflect the development of these tissues. To identify genes with specific function in secondary cell wall development, we have here analyzed the transcriptome data with further consideration of expression specificity. Genes with enhanced expression in secondary cell wall bearing tissues, or co-expressed with wall biosynthetic genes were selected for further characterization. These analyses should help to reduce the false discovery rate. Bioinformatics analysis together with reverse genetics studies are especially helpful to pinpoint the genes involved in secondary cell wall formation , and the present confirmation of the function of MtIRX1 highlights the potential of the present dataset for further cell wall gene discovery.
Plant materials and growth conditions
Wild type M. truncatula plants and Nicotiana tabacum (tobacco) Tnt1 retrotransposon tagged Medicago mutants  were grown in the glasshouse, and used for collecting tissues for molecular and chemical characterization. Plants were grown at 24 °C day/20 °C night, with a 16-h day/8-h night photoperiod (150 μmol m-2 sec-1) and 70–80 % relative humidity.
Stems of 7 weeks old M. truncatula plants (totally 10-11 internodes) were used for sampling. Internodes 2, 3, 5, 7 and 9 counting from the plant tip were collected. Total RNAs were extracted using tri-reagent according to the manufacturer’s protocol (Invitrogen). RNA was cleaned and concentrated using the RNeasy MinElute Cleanup kit (Qiagen, http://www.qiagen.com). Ten micrograms of purified RNA were used for microarray analysis. The microarray experiment included five internodes with three biological replicates, used 15 Affymetrix Genechips in total. Probe labeling, hybridization and scanning were conducted according to the manufacturer’s instructions (Affymetrix, http://www.affymetrix.com). The normalization of data was achieved by the robust multi-chip average (RMA) procedure . The presence/absence call for each probe set was obtained from dCHIP . Genes significantly differentially expressed between controls and mutants were selected using Associative Analysis . The type-I family-wise error rate was reduced using a Bonferroni corrected P-value threshold of 0.05/n, where n represents the number of genes present on the chip. The false discovery rate was monitored and controlled by Q-value (false discovery rate), calculated using EDGE (extraction of differential gene expression; http://www.bioconductor.org/packages/release/bioc/html/edge.html) [53, 54]. Hierarchical clustering analysis was conducted with Spotfire DecisionSite 8.1 (Spotfire Inc., http://spotfire.tibco.com/). For clustering analysis, data from different internode were expressed as relative to the level of IN2 just prior constructing clusters using the Pearson correlation coefficient.
Real-time PCR analysis of 1045 transcription factor genes were carried out at the Genomics/Microarray core facility at the Samuel Roberts Noble Foundation. Quantitative real-time PCR (qRT-PCR) and the calculation of relative expression were performed as described previously . In brief, cDNA samples prepared from the aforementioned five internodes were used for real time RT-PCR with technical duplicates. The 10 μl reaction included 2 μl of primers (0.5 μM of each primer), 5 μl of Power Sybr (Applied Biosystems, http://www.appliedbiosystems.com/absite/us/en/home.html), 2 μl 1:20 diluted cDNA from the reverse transcription step, and 1 μl of water. Real-time RT-PCR data were analyzed using SDS 2.2.1 (Applied Biosystems). The community high-throughput qRT-PCR uses a 384-well plate format with eight reference genes included on each plate. Information of primer sequences, accession numbers and stability test were described previously . Transcript levels were determined by relative quantification using the Medicago Ubiquitin gene (TC102473, primers: UbiFw, GCAGATAGACACGCTGGGA; UbiRe, AACTCTTGGGCAGGCAATAA) as a reference. Amplification efficiency (E) was determined from three biological replicates of each of the five internode samples using LinRegPCR . The relative expression of all internodes to internode 2 was calculated using the mean of three biological replicates for each organ. A TF gene was considered increased or decreased only if transcript levels for that gene were changed 5-fold or more than those of internode 2.
Gene co-expression network analysis
Expression data were collected from the Medicago Gene Expression Atlas . Data for leaf, petiole, stem, shoot (split-root exp) sufficient N2, flower, pod, root and split root (nodulating) sufficient N2 and the present stem experiment (37 chips in total) were used. Only the probe sets with at least two-fold change in comparing the maximal value to the minimal value in stem experiments were selected. We also removed the probe sets “AFFX”, “Sme” and “RPTR”. Thus, 12,576 probe sets remained. To generate a gene co-expression gene network for those selected probe sets, we used our in-house R script to calculate the Pearson correlation coefficient (PCC) values for all 79,071,600 (=12576 × (12576-1)/2) probe set pairs in the above dataset. Two probe sets (genes) are considered linked if their observed correlation level (|PCC|) exceeds a significant threshold value. To estimate this threshold, we permutated the expression values for each probe set independently to generate a random dataset following an approach proposed by Carter et al. . We calculated PCCs for all probe set pairs in this random dataset, and obtained the distribution of PCC frequency from -1 to 1 at a 0.1 interval. From this distribution, we observed that no probe set pair that can reach |PCC| > 0.8 in the random dataset (Additional file 1: Figure S2), indicating that 0.8 is a statistical significant level to determine whether there is a connection between each probe set pairs in the network. As a result, a co-expression gene network is produced for further analysis. The TFs that are connected with lignin, cellulose and hemicellulose biosynthetic genes are presented as three sub-networks in the results (Fig. 6). The visualization of gene networks is implemented by an open source software Cytoscape .
Cell imaging and histochemical staining
To characterize the Tnt1 insertional mutants, the sixth internodes counting from the top of the plant were collected from plants grown in the glasshouse and immediately frozen in liquid nitrogen. Cross-sections (100 μm) of the sixth internodes were cut with a Leica CM 1850 cryostat (Leica Microsystems Inc., Buffalo Grove, IL, USA) at-20 °C and prepared for microscopy as previously described . Phloroglucinol-HCl staining was carried out as previously described . Photographs were taken with a Nikon Micophot-FX system (http://www.nikon.com) with a Nikon DXM 1200 color camera with consistent settings.
Availability of supporting data
The Microarray data sets supporting the results of this article is available at ArrayExpress with a accession number: E-MTAB-3909
We thank Michael K. Udvardi and Klementina Kakar for use of the primer sets of the transcription factors, and for providing information about the corresponding genes. This research was supported, in part, by the USDA National Institute of Food and Agriculture, Hatch project number CONS00925, and the UConn Research Excellence Program award (to HW); by the Oklahoma Center for the Advancement of Science and Technology, Oklahoma Department of Energy Bioenergy Center (OBC) (to RAD); and by the Samuel Roberts Noble Foundation.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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