Combining Transcriptome Analysis and Comparative Genomics to Identify Key Components of the Lignin Biosynthesis Gene Network in Sorghum bicolor
by
, ,
Hao Niu
1,2,†,
Yanbo Wang
3,†,
Ruizhen Liu
4,
Xiaoqiang Cheng
4,
Yao Wang
4,
Yubin Wang
1,2,
Xin Lv
1,2,
Fangfang Fan
1,2,
Lan Ju
1,2,
Jianqiang Chu
1,2,
Haisheng Yan
1,2,
Hongru Wang
3,
Hetan Chang
3,
Yancong Zhang
3,
Yongfu Tao
3,* and
Junai Ping
1,2,* 1
Institute of Sorghum, Shanxi Agricultural University, Jinzhong 030600, China
2
Shanxi Hou Ji Laboratory, Taiyuan 030031, China
3
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
4
College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(7), 1751; https://doi.org/10.3390/agronomy15071751
Submission received: 17 June 2025
/
Revised: 8 July 2025
/
Accepted: 17 July 2025
/
Published: 21 July 2025
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
Abstract
Sorghum is a versatile crop that serves as a major source of food, feed, fodder and biofuel globally. Lignin content in sorghum affects multiple important traits, including lodging resistance, forage digestibility and the efficiency of bioenergy production. However, the genetic regulation of lignin content in sorghum remains poorly understood. In this study, we combined transcriptomic and comparative genomic approaches to uncover the genetic network underlying lignin biosynthesis in sorghum. Through comparative genomic analysis, we identified 104 candidate genes involved in lignin biosynthesis. Transcriptome analysis of four sorghum accessions with contrasting lignin contents identified 6132 differentially expressed genes with an enrichment of genes related to phenylpropanoid biosynthesis and cell wall biogenesis. The 104 lignin biosynthesis candidates were significantly enriched (p-value < 0.01) in these differentially expressed genes, with most differentially expressed candidate genes related to monolignol biosynthesis and polymerization being up-regulated in high-lignin accessions. These up-regulated genes are related to all key enzymes involved in lignin biosynthesis, suggesting that the elevated lignin content in these accessions results from a collective increase in enzyme activity. Sequence analysis revealed a significant reduction in genetic diversity across lignin biosynthesis genes in cultivated sorghum compared to wild sorghum. Moreover, selection signals during domestication were identified in 30 lignin biosynthesis genes, 22 of which were differentially expressed, further supporting the functional relevance of these differentially expressed genes in lignin biosynthesis. Overall, our findings uncover the lignin biosynthesis gene network in sorghum and offer potential targets for future functional studies and trait manipulation.
1. Introduction
Sorghum is a multi-purpose crop providing a major source of food, feed, fodder and bio-fuel globally [1]. Known for its adaptation to hot and drought environments, sorghum thrives in semiarid and arid areas, providing a staple food for over half a billion people in sub-Saharan Africa and South Asia [2]. The ability of sorghum to produce a reasonable yield in marginal land with limited water and fertilizer makes it an ideal choice for smallholder farmers in resource-constrained areas [3]. Consequently, the importance of sorghum in global agricultural systems is expected to increase as climate change and population growth place greater demands on food and water resources.
Lignin is a complex phenylpropanoid polymer that plays a critical role in the growth and development of sorghum. As a major component of the plant cell wall, lignin enhances the strength and rigidity of stems and leaves, providing mechanical support, facilitating water transport and helping the plant manage both abiotic and biotic stresses [4,5]. High lignin content is particularly beneficial for grain sorghum, where it improves resistance to lodging. However, excessive lignin content negatively affects the digestibility of forage sorghum and the refinery efficiency of bioenergy sorghum, as the branched polyphenolic structure is difficult for both animals and microbes to break down [6]. Therefore, achieving an optimal lignin content tailored to different types of sorghum is essential for their end-use, which in turn requires a clear understanding of the gene network controlling this trait. Generally, low-lignin accessions are preferred maintainer lines in forage sorghum breeding.
Lignin content varies widely among different sorghum genotypes, ranging from approximately 2% to 11% of dry matter (Silva et al., 2022) [1]. This variation is largely driven by the genetic makeup of each genotype [7]. A previous study using a diversity panel of 206 sorghum accessions identified nine quantitative trait loci (QTLs) associated with lignin content [8]. The brown midrib (bmr) mutants, which exhibit brown vascular tissue in the leaves and stems, have shown reduced lignin content [9]. Molecular experiments using these mutants have identified key genes involved in lignin biosynthesis [10,11,12,13]. For instance, the bmr12 mutant is caused by a premature stop codon in the caffeic acid O-methyltransferase (COMT) gene, leading to reduced lignin content and altered lignin composition in the cell walls [11]. Despite these advances, a comprehensive understanding of genes involved in lignin biosynthesis and their genetic variation in sorghum remains elusive. Combining transcriptomic analyses, comparative genomics and population genetics could shed light on this topic.
Lignin biosynthesis has been well-characterized in model species [14]. The key steps in lignin production involve the biosynthesis of monolignols and their subsequent polymerization. While enzymes involved in monolignol biosynthesis are well understood, peroxidases and laccases are the primary enzymes known to catalyze the polymerization of monolignols into lignin [15]. The transcriptional regulation of lignin biosynthesis is largely mediated by MYB transcription factors that bind to the promoters of lignin biosynthetic genes [15,16]. Given that the lignin biosynthesis pathway is highly conserved across vascular plants [17], knowledge of lignin biosynthesis from model species can be used to understand the genetic control of lignin content in sorghum. Moreover, the availability of the sorghum reference genome enables the utilization of comparative genomic approaches to identify the candidate genes involved in lignin biosynthesis [18,19,20]. The release of the sorghum pan-genome and resequencing data from diverse accessions further facilitates the analysis of genetic variation in these candidate genes [2,21,22].
This study aims to (1) combine transcriptomic analysis and comparative genomics to investigate the gene network controlling lignin content in sorghum and (2) identify sequence variations and selection signals in genes related to lignin content during sorghum domestication. The results will enhance our understanding of the genetic regulation of lignin content in sorghum and accelerate genetic improvement of this important trait.
2. Materials and Methods
2.1. Plant Materials
Four sorghum accessions, including two accessions (SXSDC, SXHLJC) with high lignin content (average lignin content of 8.26%) and two accessions (SXZM12, SXZM6) with low lignin content (average lignin content of 4.15%), were used in this study. The two sorghum accessions with low lignin content originate from America and serve as key maintainer lines for breeding forage sorghum, while the two accessions with high lignin content originate from China and Russia, respectively. These sorghum accessions were grown in a field experiment at Yuci, Shanxi, China, during the 2020 growing season. The sorghum was planted in two row plots with a completely randomized design. Each row was 1.8 m long and 0.65 m wide. Standard field management was applied. At the jointing stage, stem tissue at the eighth node above ground was harvested from middle plants in the plots with three biological replicates per genotype, resulting in a total of 12 samples. Lignin content of these sorghum accessions was determined using an FOSS 5000 near-infrared (NIR) analyzer (FOSS, Hillerød, Denmark) following the manufacturer’s instructions. A total of 100 g of stem samples per accession was collected, dried to less than 10% moisture, milled to fine powder and sieved with a 1 mm sieve. The sieved powder was collected for spectral scanning with an FOSS 5000 analyzer using the wavelength range of 1100 to 2500 with 2 nm data spacing. Each sample was scanned two times, and the average spectrum was calculated as the sample spectrum for lignin content estimation, as described previously [8].
2.2. RNA Extraction and Sequencing
The collected stem tissue samples were ground together with liquid nitrogen into powder. Total RNA was then extracted using the Plant RNA Kit (TIANGEN Biotech (Beijing) Co., Ltd. (Beijing, China)) following the manufacturer’s instructions. Agarose gel (1%) electrophoresis was used to assess contamination and degradation of extracted RNA. The concentration and purity of RNA were examined with a Nano Photometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA). The qualified RNA samples were used to construct paired-end sequencing libraries, which were sequenced with the Illumina HiSeq2000 sequencing platform (San Diego, CA, USA). The construction of sequencing libraries and sequencing were carried out by Shanghai Personal Bio Technology Co., Ltd. (Shanghai, China).
2.3. Analysis of RNA-Seq Data
Illumina sequencing of the pooled RNA-seq libraries yielded 12 FASTQ files with a total of 48.67 million raw reads. FastQC was employed to assess the quality of raw reads (http://www.bioinformatics.babraham.ac.uk/projects/fastqc, accessed on 20 June 2020), and Trimmomatic [23] was used to filter out adapter sequences and low-quality reads with an average quality score < Q20. The resultant clean reads were aligned to the reference genome (BTx623_V3.1) using HISAT2 software (version 2.2.1) [24]. SAMtools was used to convert the sam files to bam files [25]. The R package Rsubread [26] was used to assemble the transcript and calculate normalized gene expression values based on the trimmed mean of M-values (TMM) method [27].
The TMM-normalized gene expression data was used to conduct hierarchical clustering, principal component analysis (PCA) and correlation analysis of samples in R with packages FactoMineR, pheatmap and corrplot, respectively. Differentially expressed genes (DEGs) between low and high-lignin samples were identified using the R package DESeq2. Expressed genes meeting the criteria: p-value < 0.05 and |log2FoldChange| > 1 between low-lignin samples and high-lignin samples were identified as DEGs. Visualization of DEGs was conducted using the package ggplot2 in R (version 4.3.3).
2.4. Functional Enrichment Analysis
GO enrichment analysis was performed using the functional annotation files downloaded from AnnotationHub (org.Sorghum_bicolor.eg.sqlite) as background files. KEGG pathway enrichment analysis was conducted with KofamKOALA [28]. The significance of each pathway was calculated using the hypergeometric distribution test. Pathways with p-value < 0.05 were defined as significantly enriched in GO and KEGG. Identification and classification of transcription factors and protein kinases were performed using iTAK [29].
2.5. Analysis of Gene Co-Expression Network
The determination of the soft thresholding power was based on the scale-free topology model fit (R2) ≥ 0.8 using the pickSoftThreshold function. Subsequently, the automatic network construction function (blockwiseModules) was used to complete the network construction and module detection to obtain highly correlated modules. TOMType was signed, the minModuleSize was 20, the soft threshold (power) was 8 and the mergeCutHeight was 0.25. Co-expression and transcriptional regulatory networks were plotted using Cytoscape (version 3.7.1) [30].
The WGCNA R package was used to identify co-expression modules related to lignin content based on our expression data. A total of 34,211 genes were used as input for the signed WGCNA network construction. For standard WGCNA networks, the soft power was set to 8, the minimum module size was 20 and the merge cut height value was 0.25. Correlations between modules and lignin traits were analyzed with all genes in each module. Significant trait-related modules were identified based on high correlation values. Candidate target genes were visualized using Cytoscape and the MCODE plugin (version 2.0.0).
2.6. Identification of Sorghum Othologs of Lignin Biosynthesis Genes
Known genes related to lignin biosynthesis were collected from previous studies (Table S1). The sorghum orthologs of these genes were extracted from the database in Ensembl (https://plants.ensembl.org/Sorghum_bicolor, accessed on 16 June 2025). Sequence similarities between these lignin biosynthesis genes and their sorghum counterparts were further tested using BLASTP to exclude any pair with sequence similarity < 20% or E-values > 1 × 10−5.
2.7. Sequence Variation and Selection Signature of Genes Related to Lignin Content in Sorghum
The published sorghum resequencing data of 44 sorghum accessions [2] was used to investigate sequence variation and domestication signals of these identified lignin biosynthesis candidate genes. Sequence variation was identified by mapping the resequencing data to the reference genome (BTx623_V3.1) using BWA [25]. Sequence variations were called using GATK [31] and filtered on minor allele frequency > 0.05 and missing rate < 0.2. The 44 accessions included seven wild sorghum accessions, 32 cultivated (improved lines and landrace) accessions and 5 unclassified accessions. The investigation of selection signatures during sorghum domestication was conducted between wild and cultivated accessions. Population statistics, including θπ [32], Tajima’s D [33] and FST [34] were calculated using a BioPerl module and an in-house perl script. Signatures of selection were identified based on the following criteria: (1) reduction of sequence diversity greater than the average RoD of 129 neutral loci identified in previous study; (2) FST great than the average of 129 neutral loci; (3) negative Tajima’s D. The criteria signals were combined from the change of diversity, population difference and allele frequency to avoid false positives [35].
3. Results
3.1. Identification of Candidate Genes Related to Lignin Biosynthesis
Through a comprehensive literature review [36,37,38,39], we summarized 19 genes involved in monolignol biosynthesis, 12 genes related to monolignol polymerization and 42 genes regulating the transcription of lignin biosynthesis from model plants (Table S1, Figures S1 and S2). Using these genes as references, we identified 104 sorghum orthologs, which constituted our list of candidate genes related to lignin biosynthesis (Table 1). The 104 candidate genes included 43 involved in monolignol biosynthesis, 21 in monolignol polymerization and 40 in the transcriptional regulation of lignin biosynthesis. These genes were distributed unevenly across the 10 sorghum chromosomes, with the number of genes on each chromosome ranging from a single gene on Chromosome 5 to 18 genes on Chromosome 2.
3.2. Transcriptomic Data of Sorghum Accessions with Contrasting Lignin Content
To investigate the gene expression network regulating lignin content, four sorghum accessions with contrasting lignin levels were selected for transcriptome analysis. Lignin content measurements confirmed the distinction between low and high-lignin content accessions (Table S2). A total of 12 stem tissue samples were collected at the jointing stage, with 3 biological replicates per genotype for RNA sequencing (RNA-seq).
In total, 693.83 million raw paired-end reads were generated, with 98.24% of sequence bases exhibiting a quality score greater than 20. Low-quality reads and adaptor sequences were removed, resulting in 6.31–7.86 Gb of clean data per sample (Table S3). On average, 94.05% of the raw sequences were retained as clean reads, indicating high-quality RNA-seq data. These clean reads were then mapped to the sorghum reference genome (BTx623_V3.1) with an average mapping rate of 95.67%, and most of the reads mapped to 28,463 gene regions.
Gene expression data was calculated based on RNA-seq mapping results. The gene expression data revealed strong correlations between samples within the same lignin content category compared to between categories. Specifically, the average correlation coefficient for high-lignin accessions was 0.87, and for low-lignin accessions was 0.73, whereas the average correlation coefficient between high and low-lignin accessions was 0.53 (Figure 1A). This suggests that lignin content is a major factor influencing gene expression patterns in this dataset. Principal component analysis of the gene expression data (Figure 1B) showed a clear separation between high and low-lignin accessions, particularly along the first principal component (PC1), which explained 48% of the variance in the gene expression data (Figure 1B). Notably, SbCOMT (Sobic.007G047300), encoding the caffeic acid O-methyltransferase, a critical enzyme in monolignol biosynthesis, was the most influential gene on PC1 (Figure 1C). These results confirm the reliability of our RNA-seq data and its utility in elucidating the gene network controlling lignin content in sorghum.
3.3. Differentially Expressed Genes Between High and Low-Lignin Sorghum Accessions
We identified 28,463 genes expressed in the RNA-seq data, with gene counts ranging from 21,660 to 24,348 per sample. A total of 6132 differentially expressed genes (DEGs) were detected between high and low-lignin accessions, with 2209 up-regulated and 3923 down-regulated genes in the high-lignin accessions (Figure 2A,B).
The 104 candidate genes related to lignin biosynthesis were significantly enriched in the 6132 DEGs, with 57 of these genes differentially expressed between high and low-lignin accessions (p-value < 0.01, chi-square test, Table S4). Notably, 43 of these 57 genes were up-regulated in the high-lignin accessions. Genes related to monolignol biosynthesis had the highest percentage of DEGs (65%), while about half of the genes related to monolignol polymerization and transcriptional regulation were also differentially expressed. Overall, genes involved in monolignol biosynthesis and polymerization were predominantly up-regulated (Figure 2C). In contrast, 40% of the transcriptional regulation genes were down-regulated in the high-lignin accessions.
Of the 37 DEGs related to monolignol biosynthesis and polymerization, 31 were up-regulated, and 6 were down-regulated. The up-regulated genes covered all key enzymes involved in monolignol biosynthesis and two enzymes involved in monolignol polymerization, suggesting that the elevated lignin content in these accessions results from a collective increase in the activity of these enzymes. For instance, the expression of SbCOMT was approximately eight times higher in high-lignin accessions than in low-lignin accessions, and SbCAD2 (Sobic.004G071000), which encodes the final enzyme (a cinnamyl alcohol dehydrogenase) in monolignol biosynthesis, was nearly five times more highly expressed in high-lignin accessions compared to low-lignin accessions. The six down-regulated genes related to monolignol biosynthesis and polymerization had homologous up-regulation in high-lignin accessions. In contrast, a total of 20 candidate genes involved in transcriptional regulation were differentially expressed, including 12 up-regulated genes and eight down-regulated genes.
3.4. Functional Enrichment of Differentially Expressed Genes
To better understand the functions of the DEGs, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these DEGs. The 6132 DEGs were significantly enriched in all three GO categories: biological processes, molecular functions and cellular components (Table S5). Among the biological process GO terms, polysaccharide biosynthetic processes, cell wall organization or biogenesis and phenylpropanoid metabolic processes were the most highly enriched (Figure 2D). In the molecular function category, GO terms related to microtubule cytoskeleton, extracellular regions and plasmodesma were enriched, while hydrolase activity and microtubule binding dominated the enriched terms in the cellular component category.
Enrichment analysis of KEGG pathways revealed five metabolic subgroups enriched in our DEGs, including phenylpropanoid biosynthesis, flavonoid biosynthesis and cutin, suberine and wax biosynthesis (Table S6). The phenylpropanoid biosynthesis pathway was particularly enriched, with 72 genes in the pathway being differentially expressed, of which 42 were up-regulated and 30 were down-regulated in high-lignin accessions (Figure 3). These findings underscore the significant role of phenylpropanoid biosynthesis and cell wall biogenesis in determining lignin content in sorghum.
3.5. Analysis of Gene Co-Expression Network
Genes with similar expression profiles often perform related functions. To identify co-expression modules associated with lignin content, we performed weighted correlation network analysis (WGCNA) using the RNA-seq data. A total of 29 co-expression modules were identified (Figure 4A). Two co-expression modules, green and turquoise, showed a significant correlation with lignin content. Gene networks based on these modules were constructed, with the green module containing 1813 genes and the turquoise module containing 9167 genes.
Using the MCODE plugin in Cytoscape, we identified five gene clusters within the green module, with cluster 1 exhibiting the highest score (59.6). This cluster comprised 71 nodes and 2071 edges, indicating a dense network structure (Figure 4B). Notably, four lignin biosynthesis candidate genes, including SbLAC2 (Sobic.001G422300), SbCCoAOMT7 (Sobic.007G218800), SbMYB103 (Sobic.007G039100) and SbHCT (Sobic.004G212300), were present in this cluster. The seed gene (Sobic.006G220800) with the highest MCODE score of 42 in this cluster might play a central role in lignin biosynthesis.
3.6. Genetic Variation of Lignin Biosynthesis Genes
Understanding the sequence variation of key genes involved in lignin biosynthesis is crucial for functional validation and genetic improvement of lignin content. Using previously reported resequencing data from 44 sorghum accessions [2], we analyzed the genetic diversity and selection signals of the 104 candidate genes (Table S7). Of these, 10 genes showed no sequence variation. The remaining 94 genes exhibited substantial sequence diversity in both cultivated and wild sorghum groups. Notably, the sequence diversity of these genes was significantly lower in cultivated sorghum compared to wild genotypes (p-value < 0.01, paired t-test, Figure 5A). These lignin biosynthesis candidate genes also showed higher Fst values between wild and cultivated sorghum compared to the neutral genes (p-value < 0.01, Wilcoxon test, Table S8, Figure 5B). Together, the evidence suggests that the genes may have been targeted during domestication
Selection signals were detected in 30 out of the 94 genes analyzed, including eight genes related to monolignol biosynthesis, seven genes involved in monolignol polymerization, and 15 genes associated with transcriptional regulation. Among these 30 genes under selection, 22 genes were differentially expressed, including 17 up-regulated genes and five down-regulated genes in high-lignin accessions, suggesting an enrichment (p-value < 0.01, chi-squared test). Of the 15 genes involved in monolignol biosynthesis and polymerization under selection, the majority (11) were up-regulated in high-lignin accessions (Figure 5C). In contrast, only six out of the 15 genes associated with transcriptional regulation were up-regulated. These findings further support the idea that up-regulated genes involved in monolignol biosynthesis and polymerization play a more prominent role in lignin biosynthesis.
4. Discussion
Lignin content is directly linked to lodging in grain sorghum, digestibility in forage sorghum and refinery efficiency in bio-energy sorghum [1]. Understanding the gene network that regulates lignin content and its genetic variation is essential for manipulating lignin levels to improve these traits [40]. Building upon the established lignin biosynthesis network in model species, this study identified 104 candidate genes involved in lignin biosynthesis through a comparative genomic approach. Transcriptomic analysis of four sorghum accessions with contrasting lignin contents revealed significant enrichment of these candidate genes in differentially expressed genes, with most monolignol biosynthesis and polymerization genes up-regulated in high-lignin accessions. Furthermore, we observed a significant reduction in genetic diversity across these lignin biosynthesis candidate genes in cultivated sorghum compared to wild sorghum, with 30 genes showing signs of selection during sorghum domestication (Figure 5A). Of these 30 genes under selection, 22 were differentially expressed between high and low-lignin accessions, suggesting that differentially expressed genes are more likely to be functional in lignin biosynthesis. This study integrates evidence from comparative genomics, transcriptomics and population genetics to provide new insights into the lignin biosynthesis gene network in sorghum. The identified candidate genes, with their expression differences and sequence variations, offer promising targets for further functional investigation and trait manipulation.
Transcriptomic analyses have been performed to identify the genes underlying lignin variation in crop species. In barley, RNA-seq analysis of genotypes with different lignin content identified over ten thousand DEGs, including 47 genes in the monolignol pathway, providing targets for genetic improvement [41]. Likewise, over 2000 DEGs, including three genes in the monolignol pathway, were identified in transcriptome analysis of two sugarcane genotypes, contrasting the lignin content [42]. While these studies generally focused on a pair of genotypes, our analysis used four genotypes to better control background noise. As a result, a significant enrichment of lignin biosynthesis in DEGs was identified in our analysis, with 57 out of 6132 DEGs related to lignin biosynthesis.
Comparative genomics is a widely used approach to gain insights into gene function in non-model plants by leveraging the conservation of gene function among orthologs [35,43,44]. A total of 117 genes and 90 genes corresponding to 10 key enzymes in lignin biosynthesis have been reported in maize and rice, respectively [45,46]. These gene families showed high copy numbers that often exist as clusters. Our analysis focuses on both key enzymes in lignin biosynthesis and known transcription regulators in sorghum to gain a broad picture of the lignin biosynthesis pathway. Similar to maize and rice, these 10 key enzymes in lignin biosynthesis also displayed multiple copies of clusters in sorghum, indicating a general pattern in cereal crops. However, functional divergence of orthologs can occur through processes like neofunctionalization, which is particularly common for genes with multiple copies in a given species [47]. Therefore, further analysis is needed to determine which specific genes are responsible for lignin biosynthesis. The expression patterns of these genes are crucial for understanding their molecular function. Given that these 12 enzymes are key catalysts in lignin biosynthesis, up-regulated genes in high-lignin accessions are more likely to be functionally involved in lignin biosynthesis. Supporting this hypothesis, 31 out of the 37 differentially expressed genes related to monolignol biosynthesis and polymerization were up-regulated in the high-lignin accessions.
Transcriptional regulation adds another layer of complexity to lignin biosynthesis. Unlike these enzymes, transcriptional regulators can either positively or negatively influence lignin biosynthesis. This is reflected in the nearly equal proportion of up-regulated and down-regulated genes in differentially expressed transcriptional regulation genes. The balanced expression of these two types of genes further validates the high quality of our expression data.
Sorghum underwent significant morphological changes during domestication, leading to the formation of traits collectively known as “domestication syndrome” [48,49]. Key domestication traits in sorghum include the loss of seed shattering, increased grain size, reduced tillering and an expanded plant canopy [22,50,51]. To support increased grain yield and an enlarged canopy, cultivated sorghum requires stronger stems with higher lignin content. This suggests that lignin content in sorghum may have been selected during domestication. This hypothesis is further supported by the identification of selection signals in lignin biosynthesis candidate genes. Notably, differentially expressed genes were enriched in genes under selection, reaffirming that they are likely to be functionally involved in lignin biosynthesis.
In summary, this study identified 104 candidate genes within the lignin biosynthesis network in sorghum. By integrating transcriptomics and population genetics data, the expression pattern and sequence diversity of these genes were investigated, leading to 57 DEGs related to lignin biosynthesis being identified. Further investigation of the impact of their sequence variation on lignin content in sorghum could help pinpoint natural variation underlying these important traits [52]. Genetic modification of the lignin biosynthetic pathway has been successfully applied in plant species [53,54,55,56]. The findings of this study provide targets for functional investigation and trait manipulation.
5. Conclusions
In this study, we identified 104 candidate genes involved in lignin biosynthesis using comparative genomics. Transcriptomic analysis of four sorghum accessions with contrasting lignin contents revealed 6132 differentially expressed genes with an enrichment of genes related to phenylpropanoid biosynthesis and cell wall biogenesis. Out of these candidate genes, 57 were differentially expressed between high and low-lignin sorghum accessions, indicating a significant enrichment. These differentially expressed candidate genes were more likely to be functional in lignin biosynthesis, with most differentially expressed candidate genes related to monolignol biosynthesis and polymerization being up-regulated in high-lignin accessions. These up-regulated genes covered all key enzymes involved in lignin biosynthesis, suggesting that the elevated lignin content in these accessions results from a collective increase in enzyme activity. Sequence analysis revealed a significant reduction in genetic diversity across lignin biosynthesis genes in cultivated sorghum compared to wild sorghum. Moreover, selection signals were identified in 30 lignin biosynthesis genes, 22 of which were differentially expressed, further supporting the functional relevance of these genes in lignin biosynthesis. The findings enhance our understanding of the lignin biosynthesis network in sorghum and provide potential targets for future functional studies and trait manipulation.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071751/s1, Table S1 list of genes invovled in lignin biosythsis. Table S2 summary of sorghum samples in this study. Table S3 summary of RNA-seq data. Table S4 expression change of 104 lignin biosythsis candidate genes. Table S5 GO enrichment of DEGs between low lignin content accessions and high lignin content accessions. Table S6 KEGG enrichment of DEGs between low lignin content accessions and high lignin content accessions. Table S7 population statistics of 104 lignin biosynthesis candidate genes. Table S8 list of 129 neutral genes and their Nucleotide diversity (θπ), Tajima’s D and Fst values. Figure S1 Genetic regulatory network of lignin. Figure S2 Three lignin monomer biosynthetic pathways.
Author Contributions
J.P. and Y.T. designed the project. H.N., Y.W. (Yanbo Wang), R.L., X.C., Y.W. (Yao Wang), Y.W. (Yubin Wang), X.L., F.F., L.J., J.C., H.Y., H.W., H.C. and Y.Z. performed transcriptome and comparative genomic analyses. J.P., Y.T. and H.N. supervised the work. H.N., Y.T., J.P. and Y.W. (Yanbo Wang) wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Biological Breeding Engineering Project of Shanxi Agricultural University (No. YZGC059), Key Research and Development Project of Shanxi Province (No. 202102140601008), Shanxi HouJi Laboratory Project (No. 202304010930003-18), China Agriculture Research System of MOF and MARA (CARS-06), Natural Science Foundation of China (No. 32241042) and the National Natural Science Foundation of China (No. 32372176).
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Correlation and clustering analysis results between different sorghum accessions. (A) Correlation analysis among samples based on gene expression. (B) PCA analysis between different lines. (C) The PCA loading values of genes that explain the top 5% of variance from PC1 to PC5.
Figure 1.
Correlation and clustering analysis results between different sorghum accessions. (A) Correlation analysis among samples based on gene expression. (B) PCA analysis between different lines. (C) The PCA loading values of genes that explain the top 5% of variance from PC1 to PC5.
Figure 2.
Differential Expression Analysis between low and high lignin content in the lines. (A) Heatmap of differentially expressed genes. (B) Volcano plot of differentially expressed genes. (C) Proportion of up and down-regulated genes in different types of lignin biosynthesis. MP, Monolignol Polymerization genes, MB, Monolignol Biosynthesis genes, TR, Transcriptional Regulation genes. WG, Whole Genome genes. (D) GO enrichment.
Figure 2.
Differential Expression Analysis between low and high lignin content in the lines. (A) Heatmap of differentially expressed genes. (B) Volcano plot of differentially expressed genes. (C) Proportion of up and down-regulated genes in different types of lignin biosynthesis. MP, Monolignol Polymerization genes, MB, Monolignol Biosynthesis genes, TR, Transcriptional Regulation genes. WG, Whole Genome genes. (D) GO enrichment.
Figure 3.
Expression changes of each synthetase gene in phenylpropanoid biosynthesis pathway.
Figure 4.
Co-expression Modules and Gene Networks Associated with Lignin Content. (A) Correlation between lignin content and each module. (B) The gene expression networks of the green modules were clustered by MCODE.
Figure 4.
Co-expression Modules and Gene Networks Associated with Lignin Content. (A) Correlation between lignin content and each module. (B) The gene expression networks of the green modules were clustered by MCODE.
Figure 5.
Genetic diversity and selection signatures associated with lignin biosynthesis in sorghum. (A) Nucleotide diversity (π) of candidate lignin genes compared with genome-wide genes in wild and cultivated sorghum. (B) Fst comparison between lignin-pathway candidate genes and genome-wide neutral loci. (C) Proportions of up- and down-regulated genes under selection within three functional classes of the lignin pathway. MP, Monolignol Polymerization genes, MB, Monolignol Biosynthesis genes, TR, Transcriptional Regulation genes.
Figure 5.
Genetic diversity and selection signatures associated with lignin biosynthesis in sorghum. (A) Nucleotide diversity (π) of candidate lignin genes compared with genome-wide genes in wild and cultivated sorghum. (B) Fst comparison between lignin-pathway candidate genes and genome-wide neutral loci. (C) Proportions of up- and down-regulated genes under selection within three functional classes of the lignin pathway. MP, Monolignol Polymerization genes, MB, Monolignol Biosynthesis genes, TR, Transcriptional Regulation genes.
Table 1.
Summary of lignin biosynthesis candidate genes.
| Gene ID | Chr | Start | End | Gene Name | Lignin Biosynthesis Process | Expression Change | Selection Signal |
|---|---|---|---|---|---|---|---|
| Sobic.001G110900 | Chr01 | 8,614,915 | 8,616,973 | MYB52/MYB54 | transcriptional regulation | up | No |
| Sobic.001G137100 | Chr01 | 10,870,887 | 10,877,123 | BLH6 | transcriptional regulation | ns | No |
| Sobic.001G160500 | Chr01 | 13,186,392 | 13,188,966 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | NA | No |
| Sobic.001G196300 | Chr01 | 17,685,326 | 17,690,280 | F5H1 | monolignol biosynthesis | up | Yes |
| Sobic.001G261700 | Chr01 | 45,967,550 | 45,969,583 | LBD18/LBD30 | transcriptional regulation | NA | No |
| Sobic.001G316800 | Chr01 | 60,505,053 | 60,512,997 | VND1/VND2/VND3 | transcriptional regulation | NA | Yes |
| Sobic.001G346800 | Chr01 | 63,612,865 | 63,614,287 | COMT | monolignol biosynthesis | up | NA |
| Sobic.001G358900 | Chr01 | 64,882,026 | 64,883,966 | MYB46 | transcriptional regulation | down | No |
| Sobic.001G422300 | Chr01 | 70,308,577 | 70,311,856 | LAC2 | monolignol polymerization | up | Yes |
| Sobic.001G437300 | Chr01 | 71,566,224 | 71,569,045 | LBD18/LBD30 | transcriptional regulation | ns | NA |
| Sobic.001G516600 | Chr01 | 78,328,401 | 78,335,084 | 4CL8 | monolignol biosynthesis | ns | No |
| Sobic.001G522700 | Chr01 | 78,781,467 | 78,788,810 | VND1/VND2/VND3 | transcriptional regulation | ns | Yes |
| Sobic.001G526200 | Chr01 | 79,021,004 | 79,026,139 | KNAT7 | transcriptional regulation | up | No |
| Sobic.002G003200 | Chr02 | 425,796 | 427,548 | AtPRX2 | monolignol polymerization | down | No |
| Sobic.002G003400 | Chr02 | 445,651 | 447,224 | AtPRX2 | monolignol polymerization | NA | NA |
| Sobic.002G003500 | Chr02 | 450,973 | 452,852 | AtPRX2 | monolignol polymerization | NA | NA |
| Sobic.002G003700 | Chr02 | 460,085 | 461,869 | AtPRX2 | monolignol polymerization | ns | No |
| Sobic.002G029500 | Chr02 | 2,740,754 | 2,742,865 | F5H1 | monolignol biosynthesis | NA | No |
| Sobic.002G126600 | Chr02 | 17,292,184 | 17,296,557 | C4H | monolignol biosynthesis | up | No |
| Sobic.002G146000 | Chr02 | 29,037,648 | 29,043,667 | CCR1 | monolignol biosynthesis | down | No |
| Sobic.002G195400 | Chr02 | 58,394,424 | 58,396,434 | CAD6 | monolignol biosynthesis | NA | No |
| Sobic.002G195500 | Chr02 | 58,401,424 | 58,401,985 | CAD6 | monolignol biosynthesis | NA | NA |
| Sobic.002G195600 | Chr02 | 58,404,433 | 58,406,373 | CAD6 | monolignol biosynthesis | down | No |
| Sobic.002G195700 | Chr02 | 58,408,619 | 58,410,895 | CAD6 | monolignol biosynthesis | down | No |
| Sobic.002G196000 | Chr02 | 58,453,175 | 58,454,976 | MYB20 | transcriptional regulation | ns | No |
| Sobic.002G196100 | Chr02 | 58,500,458 | 58,502,066 | MYB20 | transcriptional regulation | ns | No |
| Sobic.002G242300 | Chr02 | 63,154,947 | 63,156,519 | CCoAOMT7 | monolignol biosynthesis | ns | No |
| Sobic.002G275500 | Chr02 | 65,814,952 | 65,825,791 | MYB42/MYB85 | transcriptional regulation | up | No |
| Sobic.002G279100 | Chr02 | 66,107,623 | 66,109,361 | MYB4 | transcriptional regulation | ns | No |
| Sobic.002G325000 | Chr02 | 69,554,960 | 69,556,417 | COMT | monolignol biosynthesis | up | Yes |
| Sobic.002G388700 | Chr02 | 74,246,294 | 74,248,187 | MYB46 | transcriptional regulation | ns | No |
| Sobic.003G050300 | Chr03 | 4,577,400 | 4,579,054 | AtPRX25 | monolignol polymerization | up | No |
| Sobic.003G121000 | Chr03 | 11,015,233 | 11,017,556 | AtPRX72 | monolignol polymerization | down | Yes |
| Sobic.003G183300 | Chr03 | 48,690,866 | 48,695,241 | AtPRX72 | monolignol polymerization | ns | Yes |
| Sobic.003G231400 | Chr03 | 57,067,037 | 57,068,905 | LAC17 | monolignol polymerization | NA | No |
| Sobic.003G244400 | Chr03 | 58,382,282 | 58,387,226 | OsFBK1 | transcriptional regulation | ns | No |
| Sobic.003G251800 | Chr03 | 58,989,848 | 58,995,158 | SND2 | transcriptional regulation | up | No |
| Sobic.003G281500 | Chr03 | 61,655,478 | 61,664,524 | PDR1/AtABCG29 | monolignol polymerization | ns | No |
| Sobic.003G320800 | Chr03 | 64,842,034 | 64,843,978 | AtPRX2 | monolignol polymerization | up | No |
| Sobic.003G327800 | Chr03 | 65,336,670 | 65,340,440 | C3H | monolignol biosynthesis | ns | No |
| Sobic.003G337400 | Chr03 | 66,049,426 | 66,052,170 | C4H | monolignol biosynthesis | up | Yes |
| Sobic.003G353200 | Chr03 | 67,210,787 | 67,213,484 | LAC2 | monolignol polymerization | up | No |
| Sobic.004G062500 | Chr04 | 5,041,260 | 5,046,446 | 4CL1/4CL2 | monolignol biosynthesis | up | No |
| Sobic.004G065600 | Chr04 | 5,305,396 | 5,308,691 | CCR1 | monolignol biosynthesis | up | No |
| Sobic.004G071000 | Chr04 | 5,728,604 | 5,734,362 | CAD3 | monolignol biosynthesis | up | No |
| Sobic.004G123200 | Chr04 | 13,843,961 | 13,858,621 | MYB69 | transcriptional regulation | down | Yes |
| Sobic.004G123400 | Chr04 | 13,903,173 | 13,921,822 | MYB69 | transcriptional regulation | ns | No |
| Sobic.004G176600 | Chr04 | 52,879,275 | 52,880,557 | XND1 | transcriptional regulation | down | No |
| Sobic.004G212300 | Chr04 | 56,194,958 | 56,201,627 | HCT | monolignol biosynthesis | up | No |
| Sobic.004G220300 | Chr04 | 57,051,383 | 57,055,340 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | up | No |
| Sobic.004G220400 | Chr04 | 57,064,914 | 57,069,724 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | ns | No |
| Sobic.004G220500 | Chr04 | 57,075,106 | 57,077,250 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | up | NA |
| Sobic.004G220600 | Chr04 | 57,083,771 | 57,086,639 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | up | NA |
| Sobic.004G220700 | Chr04 | 57,099,422 | 57,101,566 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | up | NA |
| Sobic.004G248700 | Chr04 | 59,576,464 | 59,578,405 | MYB20 | transcriptional regulation | ns | Yes |
| Sobic.004G272700 | Chr04 | 61,636,902 | 61,641,337 | 4CL3 | monolignol biosynthesis | up | Yes |
| Sobic.004G273800 | Chr04 | 61,765,784 | 61,767,930 | MYB58/MYB63 | transcriptional regulation | ns | No |
| Sobic.004G298400 | Chr04 | 63,778,460 | 63,782,424 | WRKY12 | transcriptional regulation | down | Yes |
| Sobic.004G302400 | Chr04 | 64,103,336 | 64,112,306 | VND4/VND5/VND6 | transcriptional regulation | down | Yes |
| Sobic.005G088400 | Chr05 | 12,613,417 | 12,615,757 | F5H1 | monolignol biosynthesis | NA | No |
| Sobic.006G008800 | Chr06 | 1,302,649 | 1,307,415 | E2Fc | transcriptional regulation | down | No |
| Sobic.006G079300 | Chr06 | 44,550,695 | 44,552,325 | AtPRX72 | monolignol polymerization | NA | No |
| Sobic.006G086000 | Chr06 | 45,545,065 | 45,546,863 | XND1 | transcriptional regulation | ns | No |
| Sobic.006G136800 | Chr06 | 49,923,688 | 49,929,393 | HCT | monolignol biosynthesis | up | No |
| Sobic.006G148800 | Chr06 | 51,039,816 | 51,042,905 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | up | No |
| Sobic.006G148900 | Chr06 | 51,053,477 | 51,056,309 | PAL1/PAL2/PAL3/PAL4 | monolignol biosynthesis | ns | No |
| Sobic.006G160900 | Chr06 | 51,876,066 | 51,882,814 | VND4/VND5/VND6 | transcriptional regulation | NA | No |
| Sobic.006G166300 | Chr06 | 52,377,828 | 52,383,758 | WRKY12 | transcriptional regulation | ns | No |
| Sobic.006G199800 | Chr06 | 55,156,816 | 55,158,923 | MYB58/MYB63 | transcriptional regulation | up | Yes |
| Sobic.006G211900 | Chr06 | 56,109,086 | 56,113,719 | CAD6 | monolignol biosynthesis | down | No |
| Sobic.006G224500 | Chr06 | 57,040,310 | 57,042,534 | AtPRX2 | monolignol polymerization | NA | Yes |
| Sobic.006G279400 | Chr06 | 60,968,193 | 60,970,485 | AtVND7 | transcriptional regulation | ns | NA |
| Sobic.007G003000 | Chr07 | 277,399 | 280,393 | AtVND7 | transcriptional regulation | down | Yes |
| Sobic.007G018100 | Chr07 | 1,663,032 | 1,668,132 | NST1/NST2/NST3 | transcriptional regulation | up | Yes |
| Sobic.007G039100 | Chr07 | 3,776,104 | 3,779,183 | MYB103 | transcriptional regulation | up | No |
| Sobic.007G047300 | Chr07 | 4,721,073 | 4,724,503 | COMT | monolignol biosynthesis | up | No |
| Sobic.007G076000 | Chr07 | 8,729,710 | 8,733,771 | CAD6 | monolignol biosynthesis | ns | No |
| Sobic.007G089900 | Chr07 | 12,684,979 | 12,699,403 | 4CL1/4CL2 | monolignol biosynthesis | ns | No |
| Sobic.007G132600 | Chr07 | 55,272,194 | 55,274,367 | MYB20 | transcriptional regulation | up | Yes |
| Sobic.007G141200 | Chr07 | 57,060,873 | 57,068,026 | CCR1 | monolignol biosynthesis | up | No |
| Sobic.007G145600 | Chr07 | 57,553,940 | 57,556,333 | 4CL1/4CL2 | monolignol biosynthesis | up | Yes |
| Sobic.007G177100 | Chr07 | 61,085,887 | 61,087,423 | MYB4 | transcriptional regulation | ns | No |
| Sobic.007G178300 | Chr07 | 61,150,831 | 61,158,248 | MYB42/MYB85 | transcriptional regulation | up | Yes |
| Sobic.007G217200 | Chr07 | 64,554,561 | 64,555,105 | CCoAOMT7 | monolignol biosynthesis | NA | NA |
| Sobic.007G218500 | Chr07 | 64,677,322 | 64,678,679 | CCoAOMT7 | monolignol biosynthesis | NA | No |
| Sobic.007G218700 | Chr07 | 64,689,206 | 64,692,655 | CCoAOMT7 | monolignol biosynthesis | up | No |
| Sobic.007G218800 | Chr07 | 64,709,897 | 64,711,491 | CCoAOMT7 | monolignol biosynthesis | up | No |
| Sobic.008G112200 | Chr08 | 51,883,854 | 51,887,072 | MYB83 | transcriptional regulation | ns | Yes |
| Sobic.008G188900 | Chr08 | 62,311,403 | 62,316,497 | BLH6 | transcriptional regulation | up | Yes |
| Sobic.009G162300 | Chr09 | 51,911,388 | 51,914,098 | LAC2 | monolignol polymerization | up | Yes |
| Sobic.009G181800 | Chr09 | 53,566,765 | 53,571,063 | C3H | monolignol biosynthesis | up | Yes |
| Sobic.009G186500 | Chr09 | 53,933,816 | 53,935,359 | AtPRX2 | monolignol polymerization | NA | No |
| Sobic.009G186600 | Chr09 | 53,940,276 | 53,942,152 | AtPRX2 | monolignol polymerization | up | Yes |
| Sobic.009G231600 | Chr09 | 57,151,987 | 57,154,230 | SND2 | transcriptional regulation | up | Yes |
| Sobic.010G002900 | Chr10 | 263,346 | 268,317 | VND4/VND5/VND6 | transcriptional regulation | down | No |
| Sobic.010G022400 | Chr10 | 1,840,690 | 1,844,258 | NST1/NST2/NST3 | transcriptional regulation | up | No |
| Sobic.010G052200 | Chr10 | 4,071,578 | 4,073,478 | CCoAOMT1 | monolignol biosynthesis | up | Yes |
| Sobic.010G066000 | Chr10 | 5,255,690 | 5,259,051 | CCR1 | monolignol biosynthesis | up | No |
| Sobic.010G106601 | Chr10 | 10,257,926 | 10,259,640 | MYB20 | transcriptional regulation | NA | Yes |
| Sobic.010G128300 | Chr10 | 16,621,902 | 16,623,207 | AtPRX2 | monolignol polymerization | NA | No |
| Sobic.010G128400 | Chr10 | 16,881,605 | 16,882,960 | AtPRX2 | monolignol polymerization | NA | No |
| Sobic.010G128700 | Chr10 | 16,968,976 | 16,970,381 | AtPRX2 | monolignol polymerization | NA | No |
| Sobic.010G165500 | Chr10 | 48,920,369 | 48,930,191 | PDR1/AtABCG29 | monolignol polymerization | up | Yes |
| Sobic.010G214900 | Chr10 | 55,777,184 | 55,784,716 | 4CL1/4CL2 | monolignol biosynthesis | ns | Yes |
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Niu, H.; Wang, Y.; Liu, R.; Cheng, X.; Wang, Y.; Wang, Y.; Lv, X.; Fan, F.; Ju, L.; Chu, J.; et al. Combining Transcriptome Analysis and Comparative Genomics to Identify Key Components of the Lignin Biosynthesis Gene Network in Sorghum bicolor. Agronomy 2025, 15, 1751. https://doi.org/10.3390/agronomy15071751
AMA Style
Niu H, Wang Y, Liu R, Cheng X, Wang Y, Wang Y, Lv X, Fan F, Ju L, Chu J, et al. Combining Transcriptome Analysis and Comparative Genomics to Identify Key Components of the Lignin Biosynthesis Gene Network in Sorghum bicolor. Agronomy. 2025; 15(7):1751. https://doi.org/10.3390/agronomy15071751
Chicago/Turabian StyleNiu, Hao, Yanbo Wang, Ruizhen Liu, Xiaoqiang Cheng, Yao Wang, Yubin Wang, Xin Lv, Fangfang Fan, Lan Ju, Jianqiang Chu, and et al. 2025. "Combining Transcriptome Analysis and Comparative Genomics to Identify Key Components of the Lignin Biosynthesis Gene Network in Sorghum bicolor" Agronomy 15, no. 7: 1751. https://doi.org/10.3390/agronomy15071751
APA StyleNiu, H., Wang, Y., Liu, R., Cheng, X., Wang, Y., Wang, Y., Lv, X., Fan, F., Ju, L., Chu, J., Yan, H., Wang, H., Chang, H., Zhang, Y., Tao, Y., & Ping, J. (2025). Combining Transcriptome Analysis and Comparative Genomics to Identify Key Components of the Lignin Biosynthesis Gene Network in Sorghum bicolor. Agronomy, 15(7), 1751. https://doi.org/10.3390/agronomy15071751
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