Genome sequencing-based transcriptomic analysis reveals novel genes in Peucedanum praeruptorum
BMC Genomic Data volume 24, Article number: 53 (2023)
Peucedanum praeruptorum Dunn, a traditional Chinese herbal medicine, contains coumarin and volatile oil components that have clinical application value. However, early bolting often occurs in the medicinal materials of Apiaceae plants. The rhizomes of the medicinal parts are gradually lignified after bolting, resulting in a sharp decrease in the content of coumarins. At present, the link between coumarin biosynthesis and early bolting in P. praeruptorum has not been elucidated.
Combining the genome sequencing and the previous transcriptome sequencing results, we reanalyzed the differential transcripts of P. praeruptorum before and after bolting. A total of 62,088 new transcripts were identified, of which 31,500 were unknown transcripts. Functional classification and annotation showed that many genes were involved in the regulation of transcription, defense response, and carbohydrate metabolic processes. The main domains are the pentatricopeptide repeat, protein kinase, RNA recognition motif, leucine-rich repeat, and ankyrin repeat domains, indicating their pivotal roles in protein modification and signal transduction. Gene structure analysis showed that skipped exon (SE) was the most dominant alternative splicing, followed by the alternative 3’ splice site (A3SS) and the alternative 5’ splice site (A5SS). Functional enrichment of differentially expressed genes showed that these differentially expressed genes mainly include transmembrane transporters, channel proteins, DNA-binding proteins, polysaccharide-binding proteins, etc. In addition, genes involved in peroxisome, hexose phosphate pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism pathway were greatly enriched. A protein-protein interaction network analysis discoverd 1,457 pairs of proteins that interact with each other. The expression levels of six UbiA genes, three UGT genes, and four OMT genes were higher during the bolting stage. This observation suggests their potential involvement in the catalytic processes of prenylation, glycosylation, and methylation of coumarins, respectively. A total of 100 peroxidase (PRX) genes were identified being involved in lignin polymerization, but only nine PRX genes were highly expressed at the bolting stage. It is worth noting that 73 autophagy-related genes (ATGs) were first identified from the KEGG pathway-enriched genes. Some ATGs, such as BHQH00009837, BHQH00013830, and novel8944, had higher expression levels after bolting.
Comparative transcriptome analysis and large-scale genome screening provide guidance and new opinions for the identification of bolting-related genes in P. praeruptorum.
P. praeruptorum is a perennial angiosperm belonging to the Peucedanum genus of the Apiaceae family. The only source of the traditional Chinese medicine coverd by the Chinese Pharmacopoeia is the dried root of P. praeruptorum. The flavor profile of Peucedanum spp is characterized by bitterness, pungency, and a subtle coolness. This therapeutic approach is commonly employed for the management of symptoms such as panting with phlegm heat, expectoration of yellow and thick phlegm, and wind-heat cough with excess phlegm. Additionally, it offers the advantages of phlegm resolution, wind expulsion, and heat clearance . Coumarin compounds are the main active ingredients of P. praeruptorum, which exhibit anti-tumor property, vasodilatory effects, and hypotensive activity [2, 3]. Dihydropyranocoumarins are the main medicinal ingredients in P. praeruptorum . Some key genes involved in the biosynthesis and transport of P. praeruptorum coumarins have been successfully identified using transcriptome and metabolome techniques. It was believed that CYP450 family genes and MDR transporters were involved in the biosynthesis and transport of coumarins [5, 6]. Many studies have proved that PAL, 4CL, C2’H, BMT, COMT, AS, PS, etc. are the essential genes for forming coumarin [7,8,9,10,11,12]. Our study showed that the phenylpropane biosynthesis pathway, the ABC transporter, the genes involved in apoptosis, and the circadian rhythm genes are potentially significant in controlling signal transduction of bolting, as well as the biosynthesis and transport of coumarin .
In recent years, there has been a discrepancy between the supply of wild Peucedanum plants and the demand in the market . P. praeruptorum seedlings enter vegetative growth after sowing and emergence in the first year, and the roots are thick and fleshy. It can be used as medicinal material when excavated in the autumn and winter of that year. Due to differences in genetic background and growth environment, the growth speed of individuals varies greatly, and some P. praeruptorum seedlings may bolt early and enter reproductive growth. Whether early bolting occurs or not has become a decisive factor affecting the quality and harvesting of P. praeruptorum .The scientific mechanism of the sharp reduction of coumarin content after bolting is not clear. The apical part becomes the growth center after bolting. The majority of the nutrients produced by photosynthesis are moved from the roots to the apical region, resulting in the roots not obtaining enough nutrients for maintaining regular metabolic activities. This has led to an increase in secondary xylem area and a decrease in coumarin content [16, 17].
Plants belonging to the genus Peucedanum, Angelica, Saposhnikovia, Notopterygium, and Glehnia have long been confronted with the issue of rendering their roots unsuitable for medicinal utilization subsequent to the onset of bolting and flowering. The phenomenon of early bolting significantly hampers the accumulation of secondary metabolites in traditional Chinese medicine, hence imposing significant constraints on the clinical application of medicinal resources . Despite the presence of coumarin in many Apiaceae plants, there has been a relative scarcity of study conducted on the prenyltransferase (PT), O-methyltransferase (OMT), and UDP-glycosyltransferase (UGT) of P. praeruptorum. These enzymes play crucial roles in the biosynthesis of umbelliferone. The comprehensive investigation of the ABC transporter genes responsible for the transmembrane transport of coumarins remains limited. By collecting experimental samples at different harvest periods, different bolting times, and different slope directions, we carried out high-throughput sequencing and multi-omics analysis of P. praeruptorum. Among them, phenylpropane pathway genes, ABC transporters, apoptosis-related genes, and circadian rhythm regulation genes play important regulatory roles in regulating bolting and flowering, coumarin biosynthesis and transport . In this study, we aligned the previous transcriptome sequencing results with the reference genome of P. praeruptorum and identified a series of new transcripts that differently expressed after bolting. In addition to the previously studied coumarin biosynthetic genes, including the PT family (a branch of UbiA), UGT family, and OMT family. A large number of PRX family genes were identified, suggesting that a few PRX genes may be involved in lignin polymerization in roots. More than 70 potential ATGs were isolated by the functional annotation of differentially expressed genes. Nearly one-third of ATG genes were highly expressed during bolting, which may explain the apoptotic and senescent processes in roots. The specific functions of these ATG family genes need further characterization.
Quality control, genome alignment, and identification of novel transcripts
First, the raw data files acquired from the previous high-throughput sequencing were subjected to base calling analysis to convert them into original reads [5, 13]. Secondly, we removed low-quality and adapter sequences to obtain clean reads. After filtering, the Q20 and Q30 values of the clean reads of the 12 samples were all greater than 97% and 93%, indicating that the sequencing quality was excellent (Table 1). The GC content of these clean reads ranged from 42 to 45%. Subsequently, the filtered transcriptome sequences were aligned with reference genes. The unique mapped sequences of all samples accounted for more than 85% of the reference sequences, indicating that most of the transcripts could be matched to the reference genome (Table 2). The degree of randomness of mRNA fragmentation was assessed by conducting additional analysis on the position distribution of reads on the gene. The results show that there are noticeable variations in the coverage of reads that can be compared with the genome across different samples. The majority of the samples exhibited a favorable degree of matching, ranging from 10 to 75%. However, the relative position distribution proportion declined when it fell below 10% or exceed 75% (Fig. 1A). By comparing the transcripts of all the samples that combined with the known transcripts of the genome, it was indicated that the new transcript types in the samples mainly consisted of five distinct categories, of which 31,500 were unknown new transcripts and 26,370 were multi-exon transcripts. The remaining three categories had relatively low number of transcripts (Fig. 1B). The quantification of the novel transcripts of varying lengths was achieved by predicting the coding sequence (CDS) of these transcripts and determining the amount of reads associated with them. The length of N50 was 1,482 bp. As the length of the transcript increases, the number of reads it contains gradually decreases (Fig. 1C). Eight databases were employed to annotate the functions of these new transcripts, and a total of 54.60% of the transcripts could be annotated, among which the Nr database annotated the highest proportion, 54.38% (Table 3). The largest proportion of the 33,898 new transcripts were jointly annotated by the four databases Pfam, GO, Uniprot, and NR (Fig. 1D).
New transcript annotations from different databases
The functions of all transcripts were initially annotated using the Gene Ontology. In terms of biological processes, there are numerous genes involved in the regulation of transcription, defense response, and carbohydrate metabolic processes. In terms of cellular components, the main cellular structures or regions involved are the membrane, nucleus, and cytoplasm. In terms of molecular functions, the genes involved are mainly related to ATP-binding, mental ion binding, and RNA binding (Fig. 2A). Transcripts were annotated using the Pathway database, revealing that the number of transcripts involved in metabolism and genetic information processing was high, while the number of transcripts involved in environmental adaptation, membrane transport, etc. was minor (Fig. 2B). The results of KOG database annotation showed that there were a large number of transcripts involving general function prediction, post-transcription modification, protein turnover, and chaperones (Fig. 2C). The NR database gives the top ten species with the highest matching value. The dominant species, Daucus carota subsp. sativus (29,104), accounts for 94.8% of all sequences, followed by Apium graveolens (837) (Fig. 2D). These transcripts contain additional structural domains: pentatricopeptide repeat (PPR), protein kinase (Pkinase), RNA recognition motif (RRM), leucine-rich repeat (LRR), and ankyrin repeat (Ank) domains (Fig. 2E). The results of transcription factor annotation revealed that these transcripts contain more transcription factor families, including B3, MYB_related, NAC, bHLH, MYB, Nin-like, and AP2 (Fig. 2F).
Alternative splicing and gene structure analysis of novel transcripts
Prior to alternative splicing analysis, the length of transcript was optimized by comparing it to known transcripts across the genome. The results showed that the alternative splicing types of these samples included alternative 3’ splice site (A3SS), alternative 5’ splice site (A5SS), MXE (mutually exclusive exons), RI (retained intron), and skipped exon (SE). The SE type of transcripts accounted for the most, followed by A3SS and A5SS (Fig. 3A). Comparison of differential alternative splicing events between groups C and D also showed similar results (Fig. 3B). Three methods were used to predict the number of noncoding transcripts, showing that CPC2, CNCI, and PLEK can jointly predict 8,836 lncRNAs, and CPC2, CNCI, and PLEK can independently predict 31, 17, and 5 lncRNAs, respectively (Fig. 3C). All of the lncRNAs found can be classified into four groups. The sequence numbers of lincRNA, sense-lncRNA, antisense-lncRNA, and intronic-lncRNA are 6,512, 1,131, 957, and 236, respectively (Fig. 3D). To obtain the SNPs and In/Del sites of these transcripts, we performed variant detection on transcriptome data. The results showed that the SNP types in all transcripts mainly had six base substitutions, with the conversion ratio of A-G and C-T being higher than those of A-C and G-C (Fig. 3E). In addition, the distribution of these SNP sites also has certain rules. Nearly 71.6% of the SNPs are located in the exon region, 14.3% are located in the intergenic region, and a few are located in the promoter region (Fig. 3F). We also analyzed insert and delete sites in transcripts, and the results showed that In/Del sites in exons were the most prevalent, (60.4%), followed by introns and intergenic regions (16.8% and 11.4%, respectively) (Fig. 3G).
Quantification of genes and sample correlation analysis
The convertion of the counts to FPKM values allowed for the quantification and evaluation of the copy number of all transcripts and genes. The density distribution of gene expression shows that the overall pattern of gene expression for each group of samples is unusual, with expression level ranging from − 2 to 2 (Fig. 4A). To show the degree of dispersion of gene expression levels between different samples, the boxplot was used to illustrate the overall levels of gene expression. The results showed that the expression levels of these samples were consistently maintained at the same level. The expression levels in the C3, C4, C5, C6, D3, and D6 groups had a high degree of dispersion (Fig. 4B). Principal component analysis showed that the total contribution rate of the first three principal components (PC1, PC2, and PC3) accounted for 49.61% of the total variance. However, some samples were not completely distinguished (Fig. 4C). Through the correlation analysis of the two groups of samples, it was shown that there exist substantial differences between the two groups. D1, D2, and D3 have a higher correlation with C2 and C3. D5 and D6 have a higher correlation. The connection between C4, C5, and C6 is comparatively higher. However, C1 and D4 did cluster into one branch, which may be caused by inconsistent sampling (Fig. 4D).
Functional analysis of DEGs and protein-protein interaction network
DESeq2 software was used to identify differentially expressed genes based on the expression level of genes in each sample. A total of 327 significantly different genes were screened from C and D groups, including 164 up-regulated genes and 163 down-regulated genes. Volcano plots visually represent differentially expressed genes (DEGs) that exhibit significant up- or down-regulation (Fig. 5A). The hierarchical clustering analysis of DEGs in samples showed distinct expression patterns across the C and D groups. Notably, the C4, C5, and C6 groups had the highest expression levels among the samples, while the D5 and D6 groups also demonstrated elevated gene expression. However, the expression levels of these differentially expressed genes in other samples were found to be modest (Fig. 5B). GO classification and enrichment analysis showed that these DEGs include transmembrane transporter activity, channel activity, sequence-specific DNA binding, polysaccharide binding, and other functions (Fig. 5C and D). The functional classification of GO terms shows that these differentially expressed genes mainly appear in integral component of the membrane, the nucleus. Molecular functions involve DNA-binding, ATP-binding, and transmembrane transporter activity. Biological processes mainly involve regulation of transcription, defense response, and response to stimulus( Fig. 5E). The classification and enrichment analysis of KEGG pathway showed that genes involved in the peroxisome, hexose phosphate pathway, phosphatidylinositol signaling system, inositol phosphate metabolism, fatty acid metabolism, and other pathways were greatly enriched (Fig. 5F and G). Furthermore, a greater number of genes were found to be implicated in carbohydrate metabolism, energy metabolism, signal transduction, transport, and catabolism (Fig. 5H). The differential protein interaction network was constructed by leveraging pre-existing interaction models sourced from the STRING protein interaction library, including protein sequence alignments with reference species. This approach facilitated the comparison of transcriptomes. The results showed that a total of 1,457 pairs of proteins had interactions with each other. Among them, BHQH00038784 and BHQH00018093 have the strongest interactions. These interactions corresponded to AT3G54050.1 (high cyclic electron flow 1) and AT3G60750.1 (transketolase). BHQH00025576 and BHQH00014113 corresponded to AT1G35910.1 (trehalose-6-phosphate phosphatase D) and AT1G78580.1 (trehalose-6-phosphate synthase). Furthermore, it is worth noting that BHQH00030012 has the highest number of interactions with other differential proteins, corresponding to AT2G39800.1 (pyrroline-5-carboxylate synthase 1). BHQH00001954 corresponded to AT5G13650.2 (elongation factor family protein).
Expression profiling analysis of DEGs
We analyzed several important gene families involved in coumarin biosynthesis. A total of 19 UbiA candidate genes were identified, and this family of genes is associated to the prenylation of the coumarin. Among them, BHQH00008828, BHQH00026491, BHQH00015746, BHQH00011521, BHQH00008821, and BHQH00012597 were highly expressed during bolting phase (Fig. 7A). However, BHQH00008834, BHQH00005059, and BHQH00043429 were hardly expressed. The UGT family primarily participates in the post-glycosylation of UDP-glucose in coumarin compounds. A total of 31 UGT candidate genes were identified but only novel21186, novel17272, and BHQH00040675 exhibited high expression. Many other UGTs showed low expression levels (Fig. 7B). In addition, we also screened out 50 OMT candidate genes, which are related to the O-methylation modification in the phenylpropane biosynthetic pathway. The results indicated that the expression level of BHQH00013672, BHQH00038285, BHQH00027437, and BHQH00025634 was higher, while some OMT genes decreased after bolting, like novel21357 (Fig. 7C). The peroxidases (PRX) family is involved in lignin polymerization. A total of 100 possible PRX candidates were discovered, and over two-thirds of these PRXs exhibited little or no expression (Fig. 7D). BHQH00039750, BHQH00009095, and BHQH00017842 showed high expression levels, several genes, such as BHQH00015002, showed high levels of expression after bolting. On the basis of the KEGG pathway enrichment analysis, some ATGs with varied functions were identified (Fig. 5). We further analyzed the ATGs that could cause root senescence and identified 73 ATG candidates. The expression levels of BHQH00024954, BHQH00032766, BHQH00003585, BHQH00012531, BHQH00044372, and BHQH00030339 were higher during bolting. BHQH00009837, BHQH00013830, and novel8944 were highly expressed after bolting (Figure S1).
Identification of novel transcripts, functional annotation and gene structure analysis
A total of 62,088 novel transcripts were identified by alignment with the reference genome. Among these transcripts, nearly half are unknown and over 26,000 are multiexon structures. This indicates that the majority of genes exist as multiple copies of homologs, revealing the diversity and complexity of gene composition (Fig. 1). Functional annotation results showed that the biological functions of these new transcripts are mainly involved in DNA transcription and replication, protein synthesis, folding and transport, and energy metabolism. The number of PPR, Pkinase, RRM, and LRR gene families is significant when comparing conserved domains. The abundance of transcription factors, including B3, MYB, NAC, bHLH, and AP2 is considerable (Fig. 2). These results indicate that numerous genes in P. praeruptorum are involved in biological processes such as growth and development, signal transduction, environmental stress, and secondary metabolism . In addition, we also detected alternative splicing events in these new transcripts, and different splicing patterns may be closely associated with the formation of truncated genes and the novel functions. Even though all transcripts contained five alternative splicing forms, SE, A3SS, and A5SS remained the predominant splicing variants (Fig. 3). Alternative splicing is present in almost all higher plant genes, which leads to polymorphisms in the structure and function of transcripts and proteins. P. praeruptorum produce alternative splicing isoforms in response to the adaptability of specific environments . In addition to alternative splicing, the ratio and type of lncRNAs were also analyzed. More than 9,000 noncoding transcripts were identified, including 8,836 lncRNAs, of which long intergenic noncoding RNA (lincRNA) comprised the largest proportion at 6,512 (Fig. 3). The detection of soybean long non-coding RNA revealed that lincRNA encoded shorter transcripts and lower expression levels than other lncRNAs, but sample-specific expression was greater. These lincRNAs are involved in the development, stress response, and signal transduction of soybean. The spatiotemporal expression of the centromere region was also observed in some active cell divisions, hence indicating the involvement of lincRNA in the process of cell division .
Functional annotation and expression profile analysis of DEGs
Quantitative analysis of transcripts and genes showed that most genes had low or no expression, while only a limit subset of genes demonstrated strong expression in both sample groups. The distribution of expression levels in all samples exhibited a low degree of discreteness (Fig. 4). Principal component analysis and correlation analysis show that the two groups of samples are not completely separated, like the C1, C2, C3, and D1 groups. The heat map reveals a division of the 12 samples into two distinct branches. Specifically, C4, C5, and C6 are in one branch, while the remaining samples were further divided into three small branches. This may be related to differences in sampling. To elucidate the specific impact on gene expression after bolting, we conducted a screening of potential genes associated with root lignification and coumarin biosynthesis. This screening was performed on a pool of 327 genes that exhibited significant differences. Subsequently, we created a protein interaction network to analyze the differential effects (Figs. 5 and 6). Our previous studies had shown that most of the carbon and nitrogen sources in the roots would gradually transfer to the apical tissues for reproductive growth after bolting . The expression of numerous crucial enzyme genes implicated in the biosynthesis of coumarin, including PAL, C4H, HCT, COMT, and CCoAOMT, exhibited a decrease, but some ABC transporters involved in transmembrane transport demonstrated an up-regulation . Genes involved in transmembrane transport, ion channels, cytokinin synthesis, hexose phosphate metabolism, inositol phosphate metabolism, DNA-binding, phosphatidylinositol signaling, fatty acid metabolism, the peroxisome, and autophagy were significantly enriched in this study. These results indicated that many genes associated with material and energy metabolism were triggered after bolting .
Moreover, the expression patterns of UbiA, UGT, and OMT genes involved in coumarin biosynthesis were studied. Studies have shown that one branch of UbiA is responsible for the prenylation of coumarin, including O-prenyltransferase and C-prenyltransferase [21, 22]. The UbiA family catalyzes the conjugation of substrates to isopentenyl monomers to form linear or cyclic products. Among them, the prenyltransferase (PT) with the umbelliferone as the substrate can form diverse coumarin compounds with different structures, and this kind of ingredient is also the main active ingredient of P. praeruptorum. A total of 19 UbiA candidates were identified, and BHQH00030324, BHQH00008821, BHQH00008818, and BHQH00008828 may be involved in the prenylation of coumarins. The expression levels of BHQH00008828 and BHQH00008821 were high before and after bolting, while the expression level of BHQH00030324 decreased after bolting (Fig. 7). The UGT family is widely spread in higher plants and participates in the glycosylation modification of active substances to facilitate the transmembrane and transport of small molecules . Glycosylation modification plays an important role in the transmembrane transport and activity of plant secondary metabolites. Coumarin is glycosylated to form a series of isomers with different stereoselectivities when the furan- and pyranocoumarin skeletons are formed. A total of 31 UGT genes were identified, but only a few were highly expressed during bolting. OMT genes are a large number of post-modification enzymes related to O-methylation. Many processes in the lignin, coumarin, and flavonoid biosynthesis are inseparable from OMT genes. A similar situation also appeared in the OMT family. Among the 50 OMT genes identified, only four were highly expressed, indicating that most genes are the outcome of large-scale gene duplication and are redundant.. These genes may be involved in the methylation modification of phenylpropanoids during the bolting . Our previous studies showed that the expression of apoptosis-related genes and peroxidases may be related to secondary wall thickening and root lignification after bolting . At this time, part of the coumarin is degraded and part is transferred to the ground for the growth of vegetative organs. Some PRX genes in peroxisomes were identified. Expression profile analysis showed that nine PRX genes, including BHQH00017842, were highly expressed during the bolting process, while more than two-thirds of PRX genes were lowly or not expressed. The mechanism by which Class II PRXs are involved in lignin polymerization remains uncover. In addition, we identified autophagy-related genes (ATGs) for the first time in the genome of P. praeruptorum. Autophagy is a protein degradation pathway that relies on lysosomes and vacuoles. Autophagosomes act as garbage removal stations, and inactivation of autophagy leads to the accumulation of intracellular proteins. A total of 73 ATG family genes were screened. Nearly two-thirds of the genes were up-regulated during bolting, which may be related to the processes of apoptosis and cargo transport upon lignification. In the future, when studying the quality improvement of Peucedanum spp., in addition to focusing on the self-regulation of coumarin biosynthetic genes, much attention should be paid to the negative regulation of delaying lignification and the functional characterization of protein transport and autophagy.
Many plants in the Apiaceae family have unique odors and ornamental properties and are also broadly used in food flavoring and clinical medicine. P. praeruptorum is also an important traditional Chinese medicine that has been utilized in China for centuries. However, after bolting, the roots of P. praeruptorum are gradually lignified, resulting in a sharp drop in the content of coumarin components. Consequently, this reduction in yield renders the plant unsuitable for medicinal purposes. New essential genes were identified from the comparative transcriptome of the bolting stage by the combined examination of the genome and transcriptome. Over 60,000 new transcripts were identified through genome comparison, of which more than 30,000 were novel genes. Through database annotation and functional enrichment analysis, it was indicated that transmembrane transporters, channel proteins, DNA-binding proteins, polysaccharide-binding proteins, etc. were enriched in large quantities. The functional enrichment of KEGG pathway showed that the genes related to the peroxisome, hexose phosphate pathway, phosphatidylinositol signaling system, and inositol phosphate metabolism pathway were greatly enriched. Some UbiA, UGT, and OMT family genes were abundantly expressed during bolting stage. Only a small number of PRX genes associated with lignin polymerization were highly expressed. Additionally, a total of 73 ATGs were identified from the functionally enriched pathways. The expression profiling showed that nearly one-third of ATG genes displayed high levels of expressed during the bolting stage. These results may provide scientific ideas for the identification of functional genes in P. praeruptorum.
Sample collection, data collection, and genome comparison
The material of P. praeruptorum used in this experiment was collected from Donghekou Town, Lu’an, Anhui Province. The geographical coordinates are 116.6567◦ east longitude and 31.4044◦ north latitude. The samples were collected from March to November of that year, approximately every 15 to 30 days. The samples for the analysis were taken 15 days after bolting. The base plant was identified by Professor Bangxing Han of West Anhui University as the Peucedanum genus in the Apiaceae family. The voucher specimen deposited at the herbarium of West Anhui University (Luan, China) is publicly available and the deposition number is 120,951–201,706. After the samples were cleaned and covered with soil, they were dried with absorbent paper, placed in liquid nitrogen for quick freezing, and then the total RNA was extracted and a cDNA library was constructed for subsequent NGS. The remaining samples were stored in a refrigerator at -80 °C .
The transcripts of six samples were sequenced and assembled using the Illumina Hiseq platform [5, 13]. By removing adapter sequences and low-quality sequences, the data information of all samples was obtained after quality control . The original image data files obtained by high-throughput sequencing were transformed into original unfiltered reads through base calling analysis and converted into FASTQ files. To assure the reliability of the information analysis results, it is necessary to apply filtration to the raw sequencing data, which are still stored in the FASTQ format. To obtain accurate data for subsequent analysis, fastp (v. 0.21.0) software was used to filter the original fastq data. Fastqc (v. 0.11.9) was used for quality control of the filtered data. The original data has been uploaded to the NCBI SRA database; the accession number of the BioProject was PRJNA714368. The filtered transcriptome sequence was compared with the reference gene using Star (v. 2.7.9a) software . The randomness of reads on the reference gene was evaluated., The of randomness of mRNA fragmentation can be determined by sequencing the position distribution of reads on the gene. IGV was used to visualize the alignment results of sequenced Reads and reference genome sequence files, species reference genome sequences, and annotation files.
Prediction and functional annotation of novel transcripts
StringTie (v.2.1.4) software was used for transcript assembly . To consolidate the transcript gtf files obtained from individual samples, the merge function of StringTie can be employed. This will facilitate the merging of gtf files produced from all samples into a single file. The gffcompare (v. 0.12.1) was employed to do a comparative analysis between the merged transcripts and the known transcripts of the genome. This analysis aimed to identify novel transcripts and genes, as well as enhance the current annotations. TransDecoder (v. 5.5.0) was used to predict the coding sequence and CDS length of the newly identified transcripts. Functional annotation of transcripts was performed using seven databases: Nr, Pfam, Unipro, KEGG, GO, KOG/COG, and PATHWAY. The protein sequence encoded by the transcript was compared with Uniprot, Nr, and KEGG for diamond blastp (v. 220.127.116.11) to obtain the functional information of the sequence and the metabolic pathway information involved . KEGG annotation was performed using KOBAS (v. 3.0), KEGG ORTHOLOGY and PATHWAY [29, 30]. Based on the relationship between the databases, the KOG/COG annotations and the classification were performed . Another method was the motif similarity search. Hmmscan (v. 3.3.2) was used for domain prediction to obtain conserved sequences, motifs, structural domains, etc.
Gene expression quantification and DEG analysis
RSEM was used to get the number of reads aligned to each transcript for each sample. These reads are first converted to FPKM to obtain the expression levels of genes and transcripts . A Pearson analysis was performed on the correlation between the two samples. The principal component analysis shared the similarity between samples. Differential expression analysis of gene expression was performed using DESeq2 (v.1.26.0) software . The screening threshold for DEGs was padj < 0.05 and |log2FoldChange| > 1. The FDR value was used to filter out significantly different genes. ClusterProfiler (v. 3.14.3) was used to perform GO and KEGG pathway enrichment analyses of DEGs. The value range of p.adjust (qvalue) is between 0 and 1. The relationships between proteins in the STRING protein interaction database were used to check how different genes interact with each other . Since the gene set of P. praeruptorum is not in the database, the gene sequence was first transformed into a protein sequence. Then, the protein sequence of A. thaliana in the string database was compared to the protein sequence of P. praeruptorum to make an interaction network. The protein-protein interaction network was visualized using Cytoscape (v. 3.10.0) software.
Gene structure and variation analysis
First, structure optimization was performed on the original annotated transcripts. The gffcompare (v. 0.12.1) software was used to conduct a comparative analysis between newly identified transcripts and previously annotated transcripts throughout the genome. If there is a region of the transcript outside the boundary of the original transcript, the upstream and downstream of UTR will be extended, therefore rectifying transcript boundaries. rMATS (v.4.1.1) software was used to analyze The type of alternative splicing present in each sample . Furthermore, rMATS software was also used to detect the difference in alternative splicing between samples. Transcription factor was predicated using PlantTFDB 5.0 software. The coding potential of the newly identified transcripts was predicted using CNCI (v. 2.0), CPC2 (v. 1.0.1), and PLEK . The samtools (v. 1.11) software was used to sort the bam files . GATK (v. 18.104.22.168) was used to detect the variation of the transcriptome data and obtain high-quality SNP and In/Del .
Expression profiles of key genes before and after bolting
The expression patterns of key genes involved in the coumarin biosynthesis pathway, lignin polymerization, and autophagy-related pathways were examined. First, the hidden Markov models of the UbiA (PF01040), OMT (PF01596 and PF00891), UGT (PF00201), and PRX (PF00141) families were constructed using the HMMER program. For autophagy-related proteins, they are divided into ATG1 (PF07714), ATG2 (PF12624), ATG3 (PF03987), ATG4 (PF03416), and ATG5 (PF04106) based on the existing Arabidopsis nomenclature and conserved domains: ATG6 (PF04111), ATG7 (PF16420), ATG8 (PF02991), ATG9 (PF04109), ATG10 (PF03987), ATG11 (PF10377), ATG12 (PF04110), ATG13 (PF10033), ATG16 (PF10033), and ATG101 (PF10033) A total of 15 subgroups. The expression levels were normalized by log2 (FPKM) for the construction of expression profiles.
Availability of data and materials
The genome assembly and annotations have been deposited at GenBank database and figshare with the accession number PRJNA910498 and the available link (https://doi.org/10.6084/m9.figshare.21743984.v1). The transcriptome data were deposited at the GenBank SRA database with accession number PRJNA714368.
RNA recognition motif
Alternative 3’ splice site
Alternative 5’ splice site
Mutually exclusive exons
Liu CM, Shen HT, Lin YA, Yu YL, Chen YS, Liu CJ, et al. Antiproliferative and antimetastatic effects of praeruptorin C on human non–small cell lung cancer through inactivating ERK/CTSD signalling pathways. Molecules. 2020;25:1625.
Yu CL, Yu YL, Yang SF, Hsu CE, Lin CL, Hsieh YH, et al. Praeruptorin A reduces metastasis of human hepatocellular carcinoma cells by targeting ERK/MMP1 signaling pathway. Environ Toxicol. 2020;November:1–10.
Lee AR, Chun JM, Lee AY, Kim HS, Gu GJ, Kwon BI. Reduced allergic lung inflammation by root extracts from two species of Peucedanum through inhibition of Th2 cell activation. J Ethnopharmacol. 2017;196:75–83.
Zhao Y, Liu T, Luo J, Zhang Q, Xu S, Han C, et al. Integration of a decrescent transcriptome and metabolomics dataset of Peucedanum praeruptorum to investigate the CYP450 and MDR genes involved in coumarins biosynthesis and transport. Front Plant Sci. 2015;6 DEC:1–16.
Song C, Li X, Jia B, Liu L, Wei P, Manzoor MA, et al. Comparative transcriptomics unveil the crucial genes involved in Coumarin Biosynthesis in Peucedanum praeruptorum Dunn. Front Plant Sci. 2022;13:1–14.
Xie J, Tang X, Xie C, Wang Y, Huang J, Jin J, et al. Comparative analysis of root anatomical structure, chemical components and differentially expressed genes between early bolting and unbolting in Peucedanum praeruptorum Dunn. Genomics. 2023;115:110557.
Sui Z, Luo J, Yao R, Huang C, Zhao Y, Kong L. Functional characterization and correlation analysis of phenylalanine ammonia-lyase (PAL) in coumarin biosynthesis from Peucedanum praeruptorum Dunn. Phytochemistry. 2019;158:35–45.
Liu T, Yao R, Zhao Y, Xu S, Huang C, Luo J, et al. Cloning, functional characterization and site-directed mutagenesis of 4-Coumarate: Coenzyme a ligase (4CL) involved in coumarin biosynthesis in peucedanum praeruptorum Dunn. Front Plant Sci. 2017;8:4.
Yao R, Zhao Y, Liu T, Huang C, Xu S, Sui Z, et al. Identification and functional characterization of a p-coumaroyl CoA 2′-hydroxylase involved in the biosynthesis of coumarin skeleton from Peucedanum praeruptorum Dunn. Plant Mol Biol. 2017;95:199–213.
Zhao Y, Wang N, Zeng Z, Xu S, Huang C, Wang W, et al. Cloning, functional characterization, and catalytic mechanism of a bergaptol O-Methyltransferase from Peucedanum praeruptorum dunn. Front Plant Sci. 2016;7:1–14.
Zhao Y, Wang N, Sui Z, Huang C, Zeng Z, Kong L. The molecular and structural basis of o-methylation reaction in coumarin biosynthesis in peucedanum praeruptorum dunn. Int J Mol Sci. 2019;20:1–15.
Jian X, Zhao Y, Wang Z, Li S, Li L, Luo J, et al. Two CYP71AJ enzymes function as psoralen synthase and angelicin synthase in the biosynthesis of furanocoumarins in Peucedanum praeruptorum Dunn. Plant Mol Biol. 2020;104:327–37.
Song C, Li X, Jia B, Liu L, Ou J, Han B. De novo Transcriptome sequencing coupled with co-expression analysis reveal the transcriptional regulation of key genes involved in the formation of active ingredients in Peucedanum praeruptorum Dunn under bolting period. Front Genet. 2021;12:1–18.
Chen L, Chu S, Zhang L, Xie J, Dai M, Wu X, et al. Tissue-specific metabolite profiling on the different parts of bolting and unbolting Peucedanum praeruptorum Dunn (Qianhu) by laser microdissection combined with UPLC-Q/TOF–MS and HPLC–DAD. Molecules. 2019;24:1439.
Li M, Cui X, Jin L, Li M, Wei J. Bolting reduces ferulic acid and flavonoid biosynthesis and induces root lignification in Angelica sinensis. Plant Physiol Biochem. 2022;170:171–9. December 2021.
Tian Y, Shi R, Gao M, Wang H, Du Y, Zhang L, et al. Differentiation of Furanocoumarin Isomers with ratio of relative abundance of characteristic fragment ions and application in Angelicae dahuricae Radix. Chromatographia. 2017;80:1401–10.
Yu G, Zhou Y, Yu J, Hu X, Tang Y, Yan H, et al. Transcriptome and digital gene expression analysis unravels the novel mechanism of early flowering in Angelica sinensis. Sci Rep. 2019;9:1–11.
Yrjönen T, Eeva M, Kauppila TJ, Martiskainen O, Summanen J, Vuorela P, et al. Profiling of Coumarins in Peucedanum palustre (L.) Moench Populations Growing in Finland. Chem Biodivers. 2016;700–9.
Verta JP, Jacobs A. The role of alternative splicing in adaptation and evolution. Trends Ecol Evol. 2022;37:299–308.
Golicz AA, Singh MB, Bhalla PL. The long intergenic noncoding RNA (LincRNA) Landscape of the soybean genome. Plant Physiol. 2018;176:2133–47.
Munakata R, Kitajima S, Nuttens A, Tatsumi K, Takemura T, Ichino T, et al. Convergent evolution of the UbiA prenyltransferase family underlies the independent acquisition of furanocoumarins in plants. New Phytol. 2020;225:2166–82.
Munakata R, Olry A, Takemura T, Tatsumi K, Ichino T, Villard C, et al. Parallel evolution of UbiA superfamily proteins into aromatic O-prenyltransferases in plants. Proc Natl Acad Sci U S A. 2021;118:e2022294118.
Meech R, Hu DG, McKinnon RA, Mubarokah SN, Haines AZ, Nair PC, et al. The UDP-Glycosyltransferase (UGT) superfamily: new members, new functions, and novel paradigms. Physiol Rev. 2019;99:1153–222.
Balasubramani S, Lv S, Chen Q, Zhou Z, Moorthy MDS, Sathish D, et al. A systematic review of the O-methyltransferase gene expression. Plant Gene. 2021;27:100295.
Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.
Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, et al. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39 SUPPL. 2:316–22.
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–61.
Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, et al. The COG database: an updated vesion includes eukaryotes. BMC Bioinformatics. 2003;4:1–14.
Li B, Dewey CN. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–13.
Shen S, Park JW, Lu ZX, Lin L, Henry MD, Wu YN, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A. 2014;111:E5593–601.
Sun L, Luo H, Bu D, Zhao G, Yu K, Zhang C, et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts. Nucleic Acids Res. 2013;41:e166.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9.
Brouard JS, Schenkel F, Marete A, Bissonnette N. The GATK joint genotyping workflow is appropriate for calling variants in RNA-seq experiments. J Anim Sci Biotechnol. 2019;10:1–6.
This work was supported by China Agricultural Research System of MOF and MARA (CARS-21), the open fund of Anhui Engineering Laboratory for Conservation and Sustainable Utilization of Traditional Chinese Medicine Resources (TCMRPSU-2022-04), the open fund of Anhui Dabieshan Academy of Traditional Chinese Medicine (TCMADM-2023-03), the High-level Talents Research Initiation Fund of West Anhui University (WGKQ2022025) and Demonstration Experiment Training Center of Anhui Provincial Department of Education (2022sysx033).
Ethics approval and consent to participate
The authors declared that permission to collect P. praeruptorum material has been obtained, and experiments on the plants described in this paper comply with institutional, national and international guidelines.
Consent for publication
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Figure S1. Expression profile analysis of ATG family genes before and after bolting. Table S1. KO-enriched terms and genes in the two groups of samples before and after bolting. Table S2. GO functional classification terms and genes of two groups of samples before and after bolting. Table S3. Differentially expressed transcription factors in two samples before and after bolting. Table S4. Source and target genes for differential protein interaction network construction. Table S5. FPKM values and annotation information of five gene families in two samples before and after bolting.
About this article
Cite this article
Song, C., Zhang, Y., Zhang, Y. et al. Genome sequencing-based transcriptomic analysis reveals novel genes in Peucedanum praeruptorum. BMC Genom Data 24, 53 (2023). https://doi.org/10.1186/s12863-023-01157-y
- Peucedanum praeruptorum
- High-throughput sequencing
- Transcriptional regulation