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Large-Scale Transcriptome Analysis of Two Sugarcane Genotypes Contrasting for Lignin Content

  • Renato Vicentini ,

    Affiliation Systems Biology Laboratory, Centre for Molecular Biology and Genetic Engineering, State University of Campinas, Campinas, SP, Brazil

  • Alexandra Bottcher,

    Affiliation Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas, SP, Brazil

  • Michael dos Santos Brito,

    Affiliations Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas, SP, Brazil, Sugarcane Center, Agronomic Institute of Campinas, Ribeirão Preto, SP, Brazil

  • Adriana Brombini dos Santos,

    Affiliation Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas, SP, Brazil

  • Silvana Creste,

    Affiliation Sugarcane Center, Agronomic Institute of Campinas, Ribeirão Preto, SP, Brazil

  • Marcos Guimarães de Andrade Landell,

    Affiliation Sugarcane Center, Agronomic Institute of Campinas, Ribeirão Preto, SP, Brazil

  • Igor Cesarino,

    Affiliation Department of Botany, Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil

  • Paulo Mazzafera

    Affiliation Department of Plant Biology, Institute of Biology, State University of Campinas, Campinas, SP, Brazil


1 Sep 2015: Vicentini R, Bottcher A, dos Santos Brito M, dos Santos AB, Creste S, et al. (2015) Correction: Large-Scale Transcriptome Analysis of Two Sugarcane Genotypes Contrasting for Lignin Content. PLOS ONE 10(9): e0137698. View correction


Sugarcane is an important crop worldwide for sugar and first generation ethanol production. Recently, the residue of sugarcane mills, named bagasse, has been considered a promising lignocellulosic biomass to produce the second-generation ethanol. Lignin is a major factor limiting the use of bagasse and other plant lignocellulosic materials to produce second-generation ethanol. Lignin biosynthesis pathway is a complex network and changes in the expression of genes of this pathway have in general led to diverse and undesirable impacts on plant structure and physiology. Despite its economic importance, sugarcane genome was still not sequenced. In this study a high-throughput transcriptome evaluation of two sugarcane genotypes contrasting for lignin content was carried out. We generated a set of 85,151 transcripts of sugarcane using RNA-seq and de novo assembling. More than 2,000 transcripts showed differential expression between the genotypes, including several genes involved in the lignin biosynthetic pathway. This information can give valuable knowledge on the lignin biosynthesis and its interactions with other metabolic pathways in the complex sugarcane genome.


Sugarcane (Saccharum spp.) is a member of the Poaceae family with the unique property to accumulate up to 50–60% of dry weight of the mature stem as sucrose [1]. Such ability makes sugarcane economically an important for the production of sugar and bioethanol. At sugarcane mills, the juice is extracted from stalks and used for both sugar crystallization and fermentation to produce the so-called first generation ethanol [2]. So far sugarcane is among the most efficient biomass producers [3], so the industry could also benefit from the production of ethanol from the lignocellulosic feedstock represented by the bagasse, which is promptly available after sucrose extraction and is currently used for heat generation and electricity production [4]. Lignocellulose is the plant biomass composed of cellulose, hemicellulose and lignin polymers and is a renewable resource for the production of biofuels and bio-based materials [5]. However, the highly complex nature and compact structure of lignocellulose, in which cellulose microfibrils are embedded in a matrix of hemicellulose polysaccharides covalently cross-linked with the heterogenic lignin polymer, constitutes a physical barrier that limits the release of fermentable sugars, a fact known as biomass recalcitrance [6,7]. The distinctive non-linear structure of lignin, built with chemically diverse and poorly reactive linkages and a variety of monomer units, makes this phenolic polymer the major plant cell wall component responsible for biomass recalcitrance [810]. Consequently, biomass-to-biofuel conversion requires costly and harsh pre-treatments to remove lignin and allow the access to polysaccharides for saccharification [11]. Genetic engineering of bioenergy crops has emerged as an alternative to optimize this process, with research efforts aiming to produce plants that either accumulate less lignin or produce lignins that are more amenable to chemical degradation [12,13]. While drastic reductions in lignin deposition normally impacts plant growth and development and consequently results in yield penalties [14], even large shifts in lignin composition are generally well tolerated without negative effects on plant development and morphology [15].

Despite the recent advances in our understanding of lignin biosynthesis, many aspects remain to be explored in terms of gene expression [16], the regulation of the pathway [17,18] and lignin polymerization, in which peroxidases and laccases play an important role [19,20]. For example, despite the fact that most researchers thought that the lignin biosynthetic pathway had been fully elucidated over a decade ago, a new biosynthetic enzyme called caffeoyl shikimate esterase (CSE) was recently discovered in Arabidopsis [21]. In addition, recently the flavonoid tricin was identified as an important component of the cell wall and suggested to be a true monomer, acting as a nucleation site for lignification in monocots [22].

The information on lignin metabolism in sugarcane is still very scarce and only a few genes have been identified from large scale transcription profiling [23,24]. Only very recently, the first comprehensive study on lignin biosynthesis in sugarcane was published, reporting histological, biochemical, and transcriptional data derived from two sugarcane genotypes with contrasting lignin contents [25].

Despite of sugarcane’s economic importance, its highly complex polyploid genome has not been sequenced so far [26]. Because possibly more than five thousand sugarcane genes remain undiscovered [27] there is a need for new sequencing efforts of sugarcane transcriptomes derived from several tissues and treatments, in order to increase the basic set of sequence information and further assist biotechnological strategies. Emerging genomic technologies, such as next generation sequencing, will allow more efficient trancriptomic studies and will help in laying a platform for sugarcane genomics [28].

Here, we used RNA-seq analysis to compare the transcript profiles of developing internodes of two sugarcane genotypes contrasting for lignin content. We present an overview of the transcriptional profile of the fifth internode of sugarcane stem, and a set of more than 2,000 transcripts that shows differential expression between the contrasting genotypes.

Materials and Methods

Plant material, RNA extraction, library construction and Illumina sequencing

The sugarcane genotypes IACSP04-065 and IACSP04-627, contrasting for lignin content, were used in this study. The two genotypes belong to a F1 segregating progeny produced by a breeding program developed at the Sugarcane Center of Agronomic Institute of Campinas (IAC). The genotypes were evaluated for agronomic traits such as sucrose, fiber and lignin content during a three years period. Lignin content was indirectly obtained from the determination of acid and neutral detergent fibers [29]. Lignin averaged on a dry weight basis 4.32% in genotype IACSP04-065 and 8,12% in genotype IACSP04-627. Mature stalks of non-flowering plants cultivated in the field were used to obtain the setts for both cultivars. Setts were allowed to grow in the greenhouse for 9 months without light or temperature control, in containers (1 m3) filled with a mixture of sand and a commercial organic soil (1:1, v/v). The 5th internode of each genotype was harvested and immediately separated in central (pith) and peripheral (rind) regions, and then frozen. Total RNA was extracted from these tissues using a CTAB protocol [30]. Prior to the preparation of the RNA library, the integrity and quantity of isolated RNA were assessed on Bioanalyser (Agilent Technologies). To avoid underrepresentation of transcripts that show low abundance in the pith, total RNA were mixed (1:1) and used in cDNA synthesis. Single-end Illumina mRNA libraries were generated from total RNA following the manufacturer’s instructions (Illumina Inc, San Diego, CA). The libraries were sequenced on Illumina HiSeq2000 at Fasteris SA (Fasteris Inc, Geneva, Switzerland). Raw reads (100 bp length) were retrieved in a FASTQ format.

Data processing, de novo assembly and expression analysis

The raw reads were cleaned by removing low-quality sequences (Phred quality score < Q20) and sequences with less than 70 bp using the FASTX-Toolkit (FASTQ/A short-reads pre-processing tools, Cold Spring Harbor Laboratory). The output from the pre-processing step, a total of 22,900,400 high-quality reads, was de novo assembled using the Velvet/Oases suite [31,32] to generate sugarcane transcripts. Assembly results were evaluated using different hash lengths (k = 21, 23, 27, 29 and 47), and the optimal assembly of reads had hash length equal to 29. Evaluated parameters included the total number of obtained transcripts, distribution of transcripts lengths, N50 value, and mainly the results of phylogenetic analysis of previously selected genes from lignin biosynthesis pathway, as described below. GENE-counter software [33] was used to quantify gene expression, in which reads from each library were mapped against the performed assembly. Finally, the differential expression analysis were conducted with the Bioconductor edgeR package [34], with a p-value ≤ 1e-4. The raw reads were deposited in the National Center for Biotechnology Information (NCBI) under accession number SRP056824.

Orthology relationship, MapMan annotation, clustering and enrichment analysis

The orthology relationship analysis was performed as described by Bottcher et al. [25]. Briefly, a blastx search against proteins previously shown to be involved in lignin biosynthesis was performed (e-value cutoff ≤ e-5) and the selected transcripts were subsequently used in a new blastx alignment including sequences from representative species of the Viridiplantae (Arabidopsis thaliana, Populus trichocarpa, Oryza sativa, Sorghum bicolor, Selaginella moellendorffii and Physcomitrella patens), whose output was further used as the data set to phylogenetic analyses [25]. The coding-sequences of the selected transcripts were deduced from the amino acids alignment with the blastx best-match hit and then aligned with the 40 first blastx hits by MAFFT [35]. The phylogenetic relationship of the aligned protein sequences was then inferred by maximum-likelihood using PhyML [36]. This process allowed identifying the putative orthologous sugarcane lignin gene for each selected transcript. Functional annotation of the transcripts was done with MapMan categories [37] using blastx (e-value ≤ 10−5) against Arabidopsis thaliana proteins. The clustering analysis of transcriptional expression profile was conducted in R package ( using the K-means clustering algorithms, with best results obtained with 100 clusters. In a second step, the clusters were tested for MapMan terms enrichment with Fisher’s exact test (p-value < 0.05), using the PageMan application [38]. The gene test sets consisted of all transcripts in each cluster, and reference set is the whole MapMan annotation of sugarcane transcriptome described above. The Self-organizing maps (SOM) were obtained using the Flora software, [39].

Results and Discussion

In a previous study, we performed a characterization of lignin deposition during sugarcane stem development, using histological, biochemical, and transcriptional data derived from two sugarcane genotypes with contrasting lignin contents [25]. Putative lignin genes were identified and their spatiotemporal expression patterns were evaluated using quantitative RT-PCR in an attempt to identify the gene family members that are likely involved in constitutive lignification in sugarcane. Here, we compared the whole transcriptome of developing internodes of two contrasting sugarcane genotypes from the same segregating progeny using next-generation sequence. Lignin content of genotype IACSP04-065 was 4.32%, whereas IACSP04-627 showed 8.12% lignin. The aim was to evaluate large-scale differences in gene expression in a plant tissue undergoing active lignification between the genotypes contrasting to lignin deposition.

De novo assembly and quantification of the sugarcane transcriptome

Normalized cDNA libraries from a developing internode of both sugarcane genotypes were constructed and sequenced using Illumina HSeq2000 platform, generating around 28 million single-end 100 bp long reads. The pre-processing step removed 18% of the raw data, in which low-quality sequences with ambiguous bases and reads with less than 70 bp were discarded, resulting in a set of 22,900,400 high-quality reads (10,555,189 from IACSP04-627 library, and 12,345,211 from IACSP04-065). The high-quality reads were de novo assembled and resulted in a set of 85,151 transcripts of sugarcane. This data set can be accessed in FASTA format in the S1 File and it includes all transcripts isoforms of 51,068 assembled unigenes. These unigenes have a mean length of 987 bp and a N50 equal to 1,385 bp. In total, there were 8,315 (16%) unigenes with size between 500 bp and 1,000 bp, and 7,679 (15%) unigenes between 1 kb and 3 kb. The assembled transcripts were functional annotated with MapMan categories, using blastx against Arabidopsis thaliana proteins. Subsequently, the expression levels of all transcripts in IACSP04-627 and IACSP04-065 genotypes were inferred by mapping reads from each library against the performed assembly (S2 File).

Identification and expression quantification of genes associated with phenylpropanoid biosynthesis

The RNA-seq data were used to quantify and study sugarcane genome-wide changes in gene expression related with the phenylpropanoid biosynthesis, in particular the lignin biosynthetic pathway. Applying the previously described orthology workflow [25] it was possible to identify 21 sugarcane transcripts that encode for enzymes involved in lignin biosynthesis (Fig 1), including 2 PALs (EC, 3 C4Hs (EC, 2 4CLs (EC, 1 HCT (EC, 2 C3Hs (EC, 3 CCoAOMTs (EC, 1 CCR (EC, 2 COMTs (EC, 1 F5H, and 4 CADs (EC The total number of identified phenylpropanoid-monolignol transcripts represents the members of lignin gene families expressed in sugarcane stems, but most likely does not represent the total number of lignin genes present in the highly polyploidy sugarcane genome. Accordingly, the total number of lignin genes present in closely related species is significantly higher. Xu and colleagues [40] performed a comparative genome analysis of lignin biosynthesis gene families across the plant kingdom, including representative species from Bryophytes, Lycophytes, Dicot Angiosperms and Monocot Angiosperms. While Arabidopsis genome harbors 63 lignin genes, a total of 141, 149 and 157 genes were found for sorghum, poplar and rice, respectively. Noteworthy, the results of Xu et al. [40] differ significantly from the data previously published by Raes et al. [41] and Shi et al. [42], in which 34 and 95 genes were annotated as monolignol genes in Arabidopsis and poplar, respectively. This observation highlights the fact that different approaches applied to identify homolog genes in a given genome leads to significantly different outputs.

Fig 1. Transcript levels of lignin genes in the contrasting sugarcane genotypes.

(*) indicates transcripts that are significantly (p-value ≤ 1e-4) different in abundance between the two sugarcane genotypes.

A novel enzymatic step involving caffeoyl shikimate esterase (CSE) was recently identified as central to the lignin biosynthetic pathway in Arabidopsis thaliana [21]. CSE hydrolyses caffeoyl-shikimate into caffeic acid, which in turn is used by 4CL to produce caffeoyl-CoA, bypassing the so-called second reaction performed by HCT. Orthologues of bona fide CSE have been found in a wide range of plant species, especially dicots such as poplar, eucalyptus, tomato and Medicago truncatula [21]. However, although orthologues for this gene were also found in bryophytes (Physcomitrella patens), lycophytes (Selaginella moellendorfii) and gymnosperms (Picea abies) [43], only a few grasses with available genomic data seem to harbor bona fide CSE genes in their genome. Within the Poaceae family, CSE genes were not found in species belonging to the Pooideae subfamily, which includes Brachypodium, wheat, rye, barley and oat, while rice (Oryzodeae) and switchgrass (Panicoideae) harbor 3 and 1 bona fide CSE genes, respectively [21,44]. The Panicoideae subfamily is one of the most economically important grass groups, since it includes important grain and bioenergy crops such as switchgrass (Panicum virgatum) and foxtail millet (Setaria italic) as members of the tribe Paniceae and maize (Zea mays), sorghum (Sorghum bicolor) and sugarcane (Saccharum spp.) as members of the tribe Andropogoneae [45]. Switchgrass is the only member of the Panicoideae subfamily whose genome harbors a bona fide CSE gene, while members of the Andropogoneae tribe seem to lack orthologues of this gene. Accordingly, transcripts coding for CSE were not found in the transcriptomic datasets of the sugarcane genotypes used in our study, suggesting that sugarcane also does not possess a bona fide CSE gene and that this enzymatic step is not important for lignin biosynthesis in this plant species. Alternatively, a phylogenetically distant CSE-like gene might be responsible for this enzymatic activity in plants lacking a bona fide CSE gene. For instance, a similar scenario has been characterized for the biosynthesis of chlorogenic acids (CGAs) in switchgrass [46]. In solanaceous plants, the enzyme hydroxycinnamoyl-CoA: quinate hydroxycinnamoyltransferase (HQT) is responsible for CGA biosynthesis, but there are no close HQT orthologues in switchgrass. Escamilla-Treviño and colleagues [46] demonstrated that a switchgrass HCT-like enzyme exhibits HQT activity, preferring quinic acid instead of shikimate as acyl acceptor, and is most likely responsible for CGA biosynthesis. Therefore, before any further conclusions, the role for CSE in lignification of plants other than Arabidopsis awaits experimental confirmation.

The analysis of the transcriptome data showed that, with the exception of ShC4H3, all the genes from the general phenylpropanoid pathway (i.e. from PAL to 4CL) show relatively low expression levels in both sugarcane genotypes (Fig 1), which is expected to result in low availability of monolignols. PAL is known to be differentially expressed through time and does not act at capacity when the demand for lignin is relatively low, for example in young internodes [47]. Although lignification is an active process in the fifth internode of sugarcane stem, this developing internode is still considered a relatively young tissue and, therefore, shows only limited lignin deposition. In accordance with this observation, the expression levels of many of these key genes significantly increased with stem maturity, which correlates with the increase of lignin content during sugarcane stem development [25].

The transcript levels of all sugarcane CCoAOMT genes were conspicuously high, especially in the high-lignin genotype IACSP04-627 (Fig 1). This is in contrast to what was found in Arabidopsis, in which only one family member AtCCoAOMT1 was highly expressed in the inflorescence stem [41]. In Populus trichocarpa, from six identified CCoAOMT genes, three showed significant expression levels in developing xylem, but PtrCCoAOMT1 was clearly the most abundant, showing more than two-fold higher expression levels when compared to PtrCCoAOMT2 and four-fold when compared to PtrCCoAOMT3 [42]. Since CCoAOMT was shown, together with COMT, to be involved in methylation steps necessary for the biosynthesis of not only monolignols but also soluble phenolics like flavonoids and sinapoyl malate in Arabidopsis, we could hypothesize that the high expression levels of sugarcane CCoAOMT genes might also be related to the production of other phenolic compounds in sugarcane. Indeed, sugarcane stem produces an array of different phenolic compounds, as revealed by the analysis of the phenolic profiles during sugarcane stem development [25]. Among the thirty-five identified compounds, all annotated flavonoids were tricin and tricin O-glycosides. Interestingly, the biosynthesis of tricin occurs through the sequential methylation of its precursor tricetin, in two consecutive reactions catalyzed by the same O-metyltransferase that shows significant similarity to COMTs and, to a lower extent to CCoAOMTs [48].

Among the ShCAD genes, the high expression of ShCAD2 and ShCAD8-1 contrasted with the very low transcript levels of ShCAD6 and ShCAD7 (Fig 1). It is important to mention that ShCAD2 is the putative orthologue of SbCAD2 from Sorghum bicolor, which was recently identified as the Brown midrib6 (Bmr6) gene [49]. The bmr6 mutant is affected in the phenylpropanoid metabolism, which results in reduced lignin content and altered lignin composition in sorghum. A nonsense mutation in SbCAD2 truncates the reading frame prior to the catalytic domains of the protein, resulting in a nonfunctional enzyme and is, consequently, responsible for the bmr6 phenotype [50]. In addition, phylogenetic analysis showed that SbCAD2 belongs to an evolutionarily conserved group of CAD proteins involved in lignin biosynthesis [50]. The high expression levels of ShCAD2 and its close phylogenetic relationship to the lignin-related SbCAD2 from sorghum support a role for this gene in the constitutive lignification in sugarcane.

In accordance with previous results, S branch-specific genes showed the same discrepancy in transcript levels, with very low expression levels of ShF5H1 and much higher for both ShCOMT genes (Fig 1). Lower levels of ShF5H1 expression correlates with lower S/G ratio found in younger internodes of sugarcane. This observation is in line with the fact that syringyl units can be decreased or increased simply by down-regulation or up-regulation of F5H, respectively [51]. Several lines of evidence suggest that F5H expression might be differentially regulated than other lignin biosynthetic genes [52]. First, AC elements (i.e. cis-elements that determine xylem-specific expression) are present in all G-branch lignin genes in Arabidopsis, but they cannot be found in the promoter region of S-branch-specific F5H and COMT [41]. Second, F5H is the only lignin biosynthetic gene directly regulated by the secondary cell wall master switch SND1 in Medicago truncatula, while all the other genes are directly regulated by SND1 targets MYB46 and its functionally redundant pair MYB58 [17]. Third, secondary wall master switches SND1/NST1 are exclusively expressed in fibers, a xylem cell type enriched in S-lignin, while the transcripts of master switches VND6/VND7 are only found in G-rich vessels [5355]. In contrast to the low expression of ShF5H1, the high transcript levels of ShCOMT genes already in younger internodes might be related to the above-mentioned production of soluble phenolics such as tricin, tricin O-glycosides and other flavonoids.

In order to evaluate whether the difference in lignin content between the genotypes is related to differential expression of lignin-specific genes, the transcript levels for each gene was compared between the contrasting sugarcane genotypes. Surprisingly, only three genes showed differential expression: ShPAL3 and ShC4H1 were higher in the low-lignin genotype IACSP04-065 and ShCOMT1 showed higher expression in the high-lignin genotype IACSP04-627 (Fig 1). When comparing two genotypes contrasting for lignin content, one would expect that most of the lignin biosynthetic genes would show increased transcript levels in the high lignin genotype. However, many studies have shown that a simple correlation between lignin content and differential expression of lignin genes is not always straightforward. ShPAL3, ShC4H1 and ShCOMT1 had their expression analyzed in different tissues of two sugarcane genotypes also differing for lignin content [25] and in general the expression of the first gene was higher in the genotype containing less lignin, while the other two genes had similar expression in both genotypes. Recently we demonstrated that in sugarcane culm, the differential lignin deposition between genotypes, tissue types and at different developmental stages is under transcriptional regulation [56].

The comparison of eleven guinea grass genotypes differing in cell wall digestibility and lignin content revealed that the expression of only two lignin genes, C4H and CCoAOMT, was increased in the high-lignin genotypes [57]. Similarly, a microarray-based approach showed that only CCoAOMT was up-regulated in the high-lignin genotype of Eucalyptus grandis when compared to low lignin genotypes [58]. Even more surprising, when RNA-seq was used to compare two alfalfa genotypes with divergent cell wall composition in stems, some lignin genes (e.g. CAD and COMT) were up-regulated while others (e.g. PAL) were down-regulated in the high lignin content genotype [59]. Noteworthy, while the formers encode enzymes involved in downstream steps of the pathway, the later is the entry point enzyme that directs the carbon flux into the phenylpropanoid pathway and therefore, would be more likely to control the overall lignin content in the plant.

Recent studies have demonstrated that lignin deposition is not only regulated at the transcriptional level but also at the post-transcriptional and post-translational levels and through the control of enzyme catalytic activities. In the stem differentiating xylem of Populus trichocarpa, the microRNA ptr-miR397a was shown to be a negative regulator of laccase genes involved in lignin polymerization. Overexpression of ptr-miR397 resulted in the down-regulation of 17 laccase genes without any effect on the transcript levels of monolignol biosynthetic genes, which led to a reduction in lignin content of up to 22% [60]. In Arabidopsis, Kelch repeat F-box (KFB) proteins physically interact with PAL isoenzymes and mediate their ubiquitination and subsequent proteolytic degradation via the 26S proteasome pathway. The expression of KFB genes varies among plant tissues and responds to developmental and environmental cues, which contributes to the dynamic control of PAL activity. By affecting the stability of the PAL enzymes, KFBs post-translationally regulate the levels of phenylpropanoids and lignin [61]. Finally, many studies have shown that phenylpropanoid intermediates (e.g. hydroxycinnamates and shikimate esters) are able to inhibit different steps of the pathway, working as regulators of the carbon flux towards the different branches of the phenylpropanoid-monolignol metabolic grid [6264]. As lignification represents a non-recoverable investment of carbon and energy [65], it is understandable that the regulatory mechanism controlling lignin deposition is so complex, involving a multi-leveled feed-forward loop transcriptional network that modulates the expression of biosynthetic genes [66] and post-transcriptional and post-translational processes that fine-tune the regulatory system.

Despite of the orthology relationship previously showed, the use of high-throughput sequencing technology can also be useful to estimate the diversity of transcripts isoforms and gene families. As sugarcane is a polyploid species, polymorphisms can be generated from a different number of allelic copies present in each genotype [27]. Therefore, sugarcane cultivars are highly heterozygous, with several different alleles at each locus, and this high level of genetic complexity creates challenges during conventional and molecular breeding [67]. Some studies showed that a mutation in a single lignin biosynthesis gene can affect the expression of several other genes of the exact same biosynthetic pathway [16,68,69]. In this type of study with sugarcane it is possible to detect many transcripts that show high homology to functional genes. The MapMan analysis of sugarcane transcripts related with the phenylpropanoid pathway (Fig 2) shows transcripts of genes that encode the same enzyme and had different expression pattern between the two genotypes (higher or lower expression levels). However some gene families show a clear pattern. For instance, all transcripts of genes encoding ShPAL genes showed lower expression levels (or no differential expression) in the high lignin content genotype IACSP04-627 (Fig 1). ShHCT genes also showed strong divergent expression patterns between both genotypes, with some transcripts more expressed in the high-lignin genotype (in red) and others showed higher expression in the low-lignin genotype (in green).

Fig 2. Simplified scheme of the phenylpropanoid biosynthesis pathway with genes identified in sugarcane transcriptome.

Pair-wise comparisons were made between both genotypes. Each heatmap represent lignin transcript sequences identified in RNA-seq. The log2 (IACSP04-627 / IACSP04-065) expression ratio values were false color-coded using a scale of -1 to 1 and indicate the genotype that showed highest expression for each transcript. The intensity of green and red indicates the degree of down- and up-regulation of the corresponding lignin gene.

Whole sugarcane transcriptome differential expression and functional enrichment analysis

Besides the expression profile of phenylpropanoid biosynthetic genes, the transcriptome obtained from both contrasting genotypes was also analyzed for the whole differential expression. This analysis resulted in 2,162 differentially expressed transcripts (S3 File) that were clustered by self-organizing maps (Fig 3). This clustering analysis shows four different expression patterns for these 2,162 differentially expressed transcripts. The Cluster 1 (C1, with 425 sugarcane transcripts) represents the profile of transcripts with higher expression levels in the IACSP04-065 sugarcane genotype. Cluster 2 (C2, n = 604) has the profile of transcripts that had a minimal abundance in IACSP04-627. Cluster 3 (C3, n = 629) and Cluster 4 (C4, n = 504) shows the opposite of C1 and C2, where transcripts are more abundant in the IACSP04-627 genotype.

Fig 3. Expression pattern of 2,162 differentially expressed transcripts between genotypes IACSP04-627 (high-lignin) and IACSP04-065 (low-lignin).

The number (N) indicates the amount of transcripts presented in each cluster. The y-axis shows the reads count, on log2 scale, of each transcript. Cluster 1 (C1) represents the profile of transcripts that increased in IACSP04-065 sugarcane genotype. Cluster 2 shows the profile of transcripts that had a minimal abundance in IACSP04-627 and high abundance in IACSP04-065. Cluster 3 and Cluster 4 shows an inverted pattern, where transcripts are more abundant in IACSP04-627 genotype.

A general overview of the whole sugarcane transcriptome of both genotypes allowed the establishment of 100 clusters that share genes with similar expression pattern (Fig 4), in which several clusters have a significant enrichment for certain MapMan classes (according to PageMan) [38]. S4 File shows the K-means cluster associated with each transcript, while S5 File presents the MapMan term enriched in each cluster. The MapMan system was used to annotate the whole sugarcane transcriptome dataset (S6 File). This analysis results in clusters (Fig 5) enriched for flavonoids metabolism, glutathione-S-transferases (GSTs), abscisic acid and brassinosteroids metabolism, cell wall proteins, trehalose metabolism and response to biotic stress.

Fig 4. The K-means clusters of the identified sugarcane transcripts.

Each dot represents a transcript. In case of differential expressed transcripts (n = 2,162) the dot is indicated in red. Transcripts within the same ellipse belong to the same cluster (100 clusters in total), according to K-means analysis. Axis indicates the first two principal components that explain the transcription expression variability. Several clusters have a significant enrichment for certain MapMan classes (according to PageMan), how showed in Fig 5 and Table 1.

Fig 5. Expression profile of transcripts in selected clusters from the K-means cluster analysis.

The y-axis shows the reads count, on log2 scale, of each transcript. All clusters contain differentially expressed genes and are enriched for MapMan terms. The major MapMan term enrichment for each cluster is indicated above each plot. Red line shows the mean of data plotted.

Transcripts involved with flavonoids metabolism (Fig 5A) showed a very similar expression pattern between both genotypes, with a higher expression in IACSP04-627 (high-lignin) than IACSP04-065 (low-lignin). Thus, although we have not observed a general and significant differential expression of lignin genes, the expression pattern of flavonoid-related genes and the higher lignin content in genotype IACSP04-627 suggest that both phenolic routes might be transcriptionally coordinated. Table 1 shows the transcripts that are present in the cluster enriched for flavonoid metabolism, as derived from the K-means analysis, in which all the transcripts are more expressed in the IACSP04-627 genotype.

Table 1. Transcripts of the cluster enriched for flavonoid metabolism (C16), as derived from the K-means analysis.

The IACSP04-627 genotype showed higher levels of transcripts related with GSTs (Fig 5B), while genotype IACSP04-065 showed higher expression of genes involved with biotic stress (Fig 5G). GSTs catalyze the detoxification of xenobiotics by their conjugation with the reduced form of glutathione (γ-glutamil-cistenil-glicine; GSH) and an electrophilic substrate, such as reactive oxygen species (ROS) [70,71]. GSTs are also thought to play major roles in oxidative stress metabolism induced by abiotic and biotic stresses [72].

The transcripts of genes involved with trehalose metabolism (Fig 5F) showed higher levels in genotype IACSP04-065. Trehalose metabolism is apparently associated with the ability to resist to abiotic stresses, in particular desiccation, although there are indications that it may also play a role in biotic stresses induced by microorganisms [73]. In this regard, the higher expression of transcripts of trehalose metabolism (Fig 5F) in the genotype IACSP04-065 is in agreement with the higher expression of biotic stress-related genes in the same genotype (Fig 5G). Further experiments would confirm the capacity of this genotype to better cope with different stress conditions.


Most of the current knowledge about lignin metabolism was obtained with studies in dicotyledonous plants like Arabidopsis and poplar, but the mechanisms underlying several aspects of lignin biology such as biosynthesis, polymerization and regulation are not necessarily conserved among all vascular plants [74]. For instance, despite of its economical importance for bioethanol production, genetic information on lignin metabolism in sugarcane is limited. Only recently, the first lignin-specific gene was characterized in sugarcane using reverse genetics, by down-regulating the expression of a COMT [75]. In addition, evidence for the involvement of a specific laccase in the lignification process in sugarcane was obtained by the complementation of lac17 mutant in Arabidopsis with a sugarcane laccase gene, SofLAC [19,76]. Despite these recent advances, there is still much to be explored and determined in terms of gene expression and pathway regulation of lignin biosynthesis in sugarcane. In this regard, the data reported here provide a comprehensive resource for lignin studies in sugarcane. In addition, several clusters of co-expressed genes potentially involved in flavonoids and carbohydrate metabolism, stress response, hormone metabolism were identified between contrasting lignin content genotypes (Fig 5 and Table 1). In general, most of the lignin genes were less expressed on the low lignin content genotype IACSP04-065, with exception for differentially expressed PAL3 and C4H1 genes. Finally, the expression profile obtained (Figs 1 and 2) for both contrasting lignin content varieties should indicate targets for further biotechnological approaches.

Supporting Information

S1 File. Transcripts sequences of the 85,151 sugarcane transcripts in FASTA format.


S2 File. Expression levels of all transcripts in IACSP04-627 and IACSP04-065 genotypes.


S3 File. Expression levels, in log2 scale, of the 2,162 differentially expressed transcripts.


S4 File. 100 K-means clusters associated with 85,151 sugarcane transcripts.


S5 File. MapMan term enriched identified for each K-means cluster.


S6 File. MapMan annotation for the whole (85,151) sugarcane transcripts dataset.


Author Contributions

Conceived and designed the experiments: RV SC ML PM. Performed the experiments: RV AB MB AS. Analyzed the data: RV AB MB AS. Contributed reagents/materials/analysis tools: RV SC ML IC PM. Wrote the paper: RV MB IC PM.


  1. 1. Casu RE, Dimmock CM, Chapman SC, Grof CP, McIntyre CL, Bonnett GD, et al. Identification of differentially expressed transcripts from maturing stem of sugarcane by in silico analysis of stem expressed sequence tags and gene expression profiling. Plant Mol Biol. CSIRO Plant Industry, Queensland Bioscience Precinct, 306 Carmody Road, St Lucia, Queensland 4067, Australia.; 2004;54: 503–517. pmid:15316286
  2. 2. Arruda P. Genetically modified sugarcane for bioenergy generation. Curr Opin Biotechnol. 2012;23: 315–22. pmid:22093808
  3. 3. Vermerris W. Survey of Genomics Approaches to Improve Bioenergy Traits in Maize, Sorghum and SugarcaneFree Access. J Integr Plant Biol. Blackwell Publishing Asia; 2011;53: 105–119. pmid:21205186
  4. 4. Dias MOS, Ensinas A V., Nebra SA, Maciel Filho R, Rossell CEV, Maciel MRW. Production of bioethanol and other bio-based materials from sugarcane bagasse: Integration to conventional bioethanol production process. Chem Eng Res Des. 2009;87: 1206–1216.
  5. 5. Yuan JS, Tiller KH, Al-Ahmad H, Stewart NR, Stewart CN Jr. Plants to power: bioenergy to fuel the future. Trends Plant Sci. Elsevier; 2014;13: 421–429.
  6. 6. Vega-Sánchez ME, Ronald PC. Genetic and biotechnological approaches for biofuel crop improvement. Curr Opin Biotechnol. 2010;21: 218–24. pmid:20181473
  7. 7. Zhao X, Zhang L, Liu D. Biomass recalcitrance. Part I: the chemical compositions and physical structures affecting the enzymatic hydrolysis of lignocellulose. Biofuels, Bioprod Biorefining. John Wiley & Sons, Ltd.; 2012;6: 465–482.
  8. 8. Weng J-K, Li X, Bonawitz ND, Chapple C. Emerging strategies of lignin engineering and degradation for cellulosic biofuel production. Curr Opin Biotechnol. 2008;19: 166–72. pmid:18403196
  9. 9. Chen F, Dixon RA. Lignin modification improves fermentable sugar yields for biofuel production. Nat Biotech. Nature Publishing Group; 2007;25: 759–761.
  10. 10. Van Acker R, Vanholme R, Storme V, Mortimer JC, Dupree P, Boerjan W. Lignin biosynthesis perturbations affect secondary cell wall composition and saccharification yield in Arabidopsis thaliana. Biotechnol Biofuels. 2013;6: 46. pmid:23622268
  11. 11. Simmons BA, Loqué D, Ralph J. Advances in modifying lignin for enhanced biofuel production. Curr Opin Plant Biol. 2010;13: 313–20. pmid:20359939
  12. 12. Vanholme R, Ralph J, Akiyama T, Lu F, Pazo JR, Kim H, et al. Engineering traditional monolignols out of lignin by concomitant up-regulation of F5H1 and down-regulation of COMT in Arabidopsis. Plant J. 2010;64: 885–97. pmid:20822504
  13. 13. Cesarino I, Araújo P, Domingues AP Júnior, Mazzafera P. An overview of lignin metabolism and its effect on biomass recalcitrance. Brazilian J Bot. Sociedade Botânica de São Paulo; 2012;35: 303–311.
  14. 14. Bonawitz ND, Chapple C. Can genetic engineering of lignin deposition be accomplished without an unacceptable yield penalty? Curr Opin Biotechnol. 2013;24: 336–43. pmid:23228388
  15. 15. Vanholme R, Morreel K, Darrah C, Oyarce P, Grabber JH, Ralph J, et al. Metabolic engineering of novel lignin in biomass crops. New Phytol. 2012;196: 978–1000. pmid:23035778
  16. 16. Vanholme R, Storme V, Vanholme B, Sundin L, Christensen JH, Goeminne G, et al. A systems biology view of responses to lignin biosynthesis perturbations in Arabidopsis. Plant Cell. 2012;24: 3506–29. pmid:23012438
  17. 17. Zhao Q, Dixon RA. Transcriptional networks for lignin biosynthesis: more complex than we thought? Trends Plant Sci. 2011;16: 227–33. pmid:21227733
  18. 18. Gray J, Caparrós-Ruiz D, Grotewold E. Grass phenylpropanoids: regulate before using! Plant Sci. 2012;184: 112–20. pmid:22284715
  19. 19. Cesarino I, Araújo P, Sampaio Mayer JL, Paes Leme AF, Mazzafera P. Enzymatic activity and proteomic profile of class III peroxidases during sugarcane stem development. Plant Physiol Biochem. 2012;55: 66–76. pmid:22551762
  20. 20. Cesarino I, Araújo P, Sampaio Mayer JL, Vicentini R, Berthet S, Demedts B, et al. Expression of SofLAC, a new laccase in sugarcane, restores lignin content but not S:G ratio of Arabidopsis lac17 mutant. J Exp Bot. 2013;64: 1769–1781. pmid:23418623
  21. 21. Vanholme R, Cesarino I, Rataj K, Xiao Y, Sundin L, Goeminne G, et al. Caffeoyl Shikimate Esterase (CSE) Is an Enzyme in the Lignin Biosynthetic Pathway in Arabidopsis. Sci. 2013;341: 1103–1106.
  22. 22. Lan W, Lu F, Regner M, Zhu Y, Rencoret J, Ralph S a., et al. Tricin, A Flavonoid Monomer in Monocot Lignification. Plant Physiol. 2015;167: pp.114.253757.
  23. 23. Papini-Terzi FS, Rocha FR, Vêncio RZN, Oliveira KC, Felix JDM, Vicentini R, et al. Transcription profiling of signal transduction-related genes in sugarcane tissues. DNA Res. 2005;12: 27–38. pmid:16106750
  24. 24. Papini-Terzi FS, Rocha FR, Vêncio RZN, Felix JM, Branco DS, Waclawovsky AJ, et al. Sugarcane genes associated with sucrose content. BMC Genomics. 2009;10: 120. pmid:19302712
  25. 25. Bottcher A, Cesarino I, Santos AB Dos, Vicentini R, Mayer JLS, Vanholme R, et al. Lignification in sugarcane: biochemical characterization, gene discovery, and expression analysis in two genotypes contrasting for lignin content. Plant Physiol. 2013;163: 1539–57. pmid:24144790
  26. 26. De Setta N, Monteiro-Vitorello CB, Metcalfe CJ, Cruz GMQ, Del Bem LE, Vicentini R, et al. Building the sugarcane genome for biotechnology and identifying evolutionary trends. BMC Genomics. 2014;15: 540. pmid:24984568
  27. 27. Cardoso-Silva CB, Costa EA, Mancini MC, Balsalobre TWA, Canesin LEC, Pinto LR, et al. De Novo Assembly and Transcriptome Analysis of Contrasting Sugarcane Varieties. PLoS One. Public Library of Science; 2014;9: e88462. pmid:24523899
  28. 28. Manners J, Casu R. Transcriptome Analysis and Functional Genomics of Sugarcane. Trop Plant Biol. Springer-Verlag; 2011;4: 9–21.
  29. 29. Van Soest PJ. Development of a Comprehensive System of Feed Analyses and its Application to Forages. J Anim Sci. 1967;26: 119–128.
  30. 30. Chang S, Puryear J, Cairney J. A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Report. Kluwer Academic Publishers; 1993;11: 113–116.
  31. 31. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18: 821–9. pmid:18349386
  32. 32. Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28: 1086–92. pmid:22368243
  33. 33. Cumbie JS, Kimbrel JA, Di Y, Schafer DW, Wilhelm LJ, Fox SE, et al. GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences. Freitag M, editor. PLoS One. Public Library of Science; 2011;6: e25279. pmid:21998647
  34. 34. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26: 139–40. pmid:19910308
  35. 35. Katoh K, Kuma K, Toh H, Miyata T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. Bioinformatics Center, Institute for Chemical Research, Kyoto University Uji, Kyoto 611–0011, Japan.; 2005;33: 511–518. pmid:15661851
  36. 36. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59: 307–21. pmid:20525638
  37. 37. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, et al. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. Max Planck Institute for Molecular Plant Physiology, Golm, Germany; 2004;37: 914–939. pmid:14996223
  38. 38. Usadel B, Nagel A, Steinhauser D, Gibon Y, Bläsing OE, Redestig H, et al. PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments. BMC Bioinformatics. 2006;7: 535. pmid:17176458
  39. 39. Dembele D, Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics. 2003;19: 973–980. pmid:12761060
  40. 40. Xu Z, Zhang D, Hu J, Zhou X, Ye X, Reichel KL, et al. Comparative genome analysis of lignin biosynthesis gene families across the plant kingdom. BMC Bioinformatics. BioMed Central; 2009;10: S3–S3.
  41. 41. Raes J, Rohde A, Christensen JH, Van de Peer Y, Boerjan W. Genome-wide characterization of the lignification toolbox in Arabidopsis. Plant Physiol. 2003;133: 1051–71. pmid:14612585
  42. 42. Shi R, Sun Y-H, Li Q, Heber S, Sederoff R, Chiang VL. Towards a Systems Approach for Lignin Biosynthesis in Populus trichocarpa: Transcript Abundance and Specificity of the Monolignol Biosynthetic Genes. Plant Cell Physiol. 2010;51: 144–163. pmid:19996151
  43. 43. Labeeuw L, Martone PT, Boucher Y, Case RJ. Ancient origin of the biosynthesis of lignin precursors. Biol Direct. BioMed Central Ltd; 2015;10: 23. pmid:25994183
  44. 44. Shen H, Mazarei M, Hisano H, Escamilla-Trevino L, Fu C, Pu Y, et al. A Genomics Approach to Deciphering Lignin Biosynthesis in Switchgrass. Plant Cell. 2013;25: 4342–4361. pmid:24285795
  45. 45. Li P, Brutnell TP. Setaria viridis and Setaria italica, model genetic systems for the Panicoid grasses. J Exp Bot. 2011;62: 3031–3037. pmid:21459768
  46. 46. Escamilla-Treviño L, Shen H, Hernandez T, Yin Y, Xu Y, Dixon R. Early lignin pathway enzymes and routes to chlorogenic acid in switchgrass (Panicum virgatum L.). Plant Mol Biol. Springer Netherlands; 2014;84: 565–576. pmid:24190737
  47. 47. Lee Y, Chen F, Gallego-Giraldo L, Dixon RA, Voit EO. Integrative Analysis of Transgenic Alfalfa (Medicago sativa L.) Suggests New Metabolic Control Mechanisms for Monolignol Biosynthesis. PLoS Comput Biol. Public Library of Science; 2011;7: e1002047. pmid:21625579
  48. 48. Zhou J-M, Gold ND, Martin VJJ, Wollenweber E, Ibrahim RK. Sequential O-methylation of tricetin by a single gene product in wheat. Biochim Biophys Acta. 2006;1760: 1115–24. pmid:16730127
  49. 49. Saballos A, Ejeta G, Sanchez E, Kang C, Vermerris W. A Genomewide Analysis of the Cinnamyl Alcohol Dehydrogenase Family in Sorghum [Sorghum bicolor (L.) Moench] Identifies SbCAD2 as the Brown midrib6 Gene. Genet. 2009;181: 783–795.
  50. 50. Sattler SE, Saathoff AJ, Haas EJ, Palmer NA, Funnell-Harris DL, Sarath G, et al. A Nonsense Mutation in a Cinnamyl Alcohol Dehydrogenase Gene Is Responsible for the Sorghum brown midrib6 Phenotype. Plant Physiol. American Society of Plant Biologists; 2009;150: 584–595. pmid:19363091
  51. 51. Meyer K, Shirley AM, Cusumano JC, Bell-Lelong DA, Chapple C. Lignin monomer composition is determined by the expression of a cytochrome P450-dependent monooxygenase in Arabidopsis. Proc Natl Acad Sci. 1998;95: 6619–6623. pmid:9618461
  52. 52. Ruegger M, Meyer K, Cusumano JC, Chapple C. Regulation of Ferulate-5-Hydroxylase Expression in Arabidopsis in the Context of Sinapate Ester Biosynthesis. Plant Physiol. American Society of Plant Physiologists; 1999;119: 101–110. pmid:9880351
  53. 53. Kubo M, Udagawa M, Nishikubo N, Horiguchi G, Yamaguchi M, Ito J, et al. Transcription switches for protoxylem and metaxylem vessel formation. Genes Dev. 2005;19: 1855–1860. pmid:16103214
  54. 54. Zhong R, Demura T, Ye . SND1, a NAC Domain Transcription Factor, Is a Key Regulator of Secondary Wall Synthesis in Fibers of Arabidopsis. Plant Cell. American Society of Plant Biologists; 2006;18: 3158–3170. pmid:17114348
  55. 55. Mitsuda N, Iwase A, Yamamoto H, Yoshida M, Seki M, Shinozaki K, et al. NAC Transcription Factors, NST1 and NST3, Are Key Regulators of the Formation of Secondary Walls in Woody Tissues of Arabidopsis. Plant Cell. American Society of Plant Biologists; 2007;19: 270–280. pmid:17237351
  56. 56. Santos Brito M, Nobile P, Bottcher A, dos Santos A, Creste S, de Landell M, et al. Expression Profile of Sugarcane Transcription Factor Genes Involved in Lignin Biosynthesis. Trop Plant Biol. Springer US; 2015;8: 19–30.
  57. 57. Stabile SS, Bodini AP, Jank L, Rennó FP, Santos M V, Silva LFP. Expression of genes from the lignin synthesis pathway in guineagrass genotypes differing in cell-wall digestibility. Grass Forage Sci. Blackwell Publishing Ltd; 2012;67: 43–54.
  58. 58. Barros E, van Staden C-A, Lezar S. A microarray-based method for the parallel analysis of genotypes and expression profiles of wood-forming tissues in Eucalyptus grandis. BMC Biotechnol. 2009;9: 51. pmid:19473481
  59. 59. Yang SS, Tu Z, Cheung F, Xu W, Lamb J, Jung H-J, et al. Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems. BMC Genomics. 2011;12: 199. pmid:21504589
  60. 60. Lu S, Li Q, Wei H, Chang M-J, Tunlaya-Anukit S, Kim H, et al. Ptr-miR397a is a negative regulator of laccase genes affecting lignin content in Populus trichocarpa. Proc Natl Acad Sci. 2013;110: 10848–10853. pmid:23754401
  61. 61. Zhang X, Gou M, Liu C-J. Arabidopsis Kelch Repeat F-Box Proteins Regulate Phenylpropanoid Biosynthesis via Controlling the Turnover of Phenylalanine Ammonia-Lyase. Plant Cell. American Society of Plant Biologists; 2013;25: 4994–5010. pmid:24363316
  62. 62. Chen H-C, Song J, Williams CM, Shuford CM, Liu J, Wang JP, et al. Monolignol Pathway 4-Coumaric Acid:Coenzyme A Ligases in Populus. trichocarpa: Novel Specificity, Metabolic Regulation, and Simulation of Coenzyme A Ligation Fluxes. Plant Physiol. 2013;161: 1501–1516. pmid:23344904
  63. 63. Wang JP, Naik PP, Chen H-C, Shi R, Lin C-Y, Liu J, et al. Complete Proteomic-Based Enzyme Reaction and Inhibition Kinetics Reveal How Monolignol Biosynthetic Enzyme Families Affect Metabolic Flux and Lignin in Populus trichocarpa. Plant Cell. American Society of Plant Biologists; 2014;26: 894–914. pmid:24619611
  64. 64. Lin C-Y, Wang JP, Li Q, Chen H-C, Liu J, Loziuk P, et al. 4-Coumaroyl and Caffeoyl Shikimic Acids Inhibit 4-Coumaric Acid:Coenzyme A Ligases and Modulate Metabolic Flux for 3-Hydroxylation in Monolignol Biosynthesis of Populus trichocarpa. Mol Plant. 2015;8: 176–187. pmid:25578281
  65. 65. Rogers LA, Dubos C, Cullis IF, Surman C, Poole M, Willment J, et al. Light, the circadian clock, and sugar perception in the control of lignin biosynthesis. J Exp Bot. 2005;56: 1651–1663. pmid:15878986
  66. 66. Zhong R, Ye Z-H. Complexity of the transcriptional network controlling secondary wall biosynthesis. Plant Sci. 2014;229: 193–207. pmid:25443846
  67. 67. Ming R, Liu SC, Lin YR, da Silva J, Wilson W, Braga D, et al. Detailed alignment of saccharum and sorghum chromosomes: comparative organization of closely related diploid and polyploid genomes. Genetics. 1998;150: 1663–82. pmid:9832541
  68. 68. Dauwe R, Morreel K, Goeminne G, Gielen B, Rohde A, Van Beeumen J, et al. Molecular phenotyping of lignin-modified tobacco reveals associated changes in cell-wall metabolism, primary metabolism, stress metabolism and photorespiration. Plant J. 2007;52: 263–85. pmid:17727617
  69. 69. Rohde A, Morreel K, Ralph J, Goeminne G, Hostyn V, De Rycke R, et al. Molecular phenotyping of the pal1 and pal2 mutants of Arabidopsis thaliana reveals far-reaching consequences on phenylpropanoid, amino acid, and carbohydrate metabolism. Plant Cell. 2004;16: 2749–71. pmid:15377757
  70. 70. Marrs KA. THE FUNCTIONS AND REGULATION OF GLUTATHIONE S-TRANSFERASES IN PLANTS. Annu Rev Plant Physiol Plant Mol Biol. 1996;47: 127–158. pmid:15012285
  71. 71. Dixon D, Lapthorn A, Edwards R. Plant glutathione transferases. Genome Biol. 2002;3: reviews3004.1–reviews3004.10.
  72. 72. Chi Y, Cheng Y, Vanitha J, Kumar N, Ramamoorthy R, Ramachandran S, et al. Expansion Mechanisms and Functional Divergence of the Glutathione S-Transferase Family in Sorghum and Other Higher Plants. DNA Res. 2011;18: 1–16. pmid:21169340
  73. 73. O’Hara LE, Paul MJ, Wingler A. How Do Sugars Regulate Plant Growth and Development? New Insight into the Role of Trehalose-6-Phosphate. Mol Plant. Elsevier; 2015;6: 261–274.
  74. 74. Li X, Weng J-K, Chapple C. Improvement of biomass through lignin modification. Plant J. 2008;54: 569–81. pmid:18476864
  75. 75. Jung JH, Fouad WM, Vermerris W, Gallo M, Altpeter F. RNAi suppression of lignin biosynthesis in sugarcane reduces recalcitrance for biofuel production from lignocellulosic biomass. Plant Biotechnol J. 2012;10: 1067–76. pmid:22924974
  76. 76. Cesarino I, Araújo P, Paes Leme AF, Creste S, Mazzafera P. Suspension cell culture as a tool for the characterization of class III peroxidases in sugarcane. Plant Physiol Biochem. 2013;62: 1–10. pmid:23159486