Genetic variation and metabolic pathway intricacy govern the active compound content and quality of the Chinese medicinal plant Lonicera japonica thunb

Abstract

Background: Traditional Chinese medicine uses various herbs for the treatment of various diseases for thousands of years and it is now time to assess the characteristics and effectiveness of these medicinal plants based on modern genetic and molecular tools. The herb Flos Lonicerae Japonicae (FLJ or Lonicera japonica Thunb.) is used as an anti-inflammatory agent but the chemical quality of FLJ and its medicinal efficacy has not been consistent. Here, we analyzed the transcriptomes and metabolic pathways to evaluate the active medicinal compounds in FLJ and hope that this approach can be used for a variety of medicinal herbs in the future.

Results: We assess transcriptomic differences between FLJ and L. japonica Thunb. var. chinensis (Watts) (rFLJ), which may explain the variable medicinal effects. We acquired transcriptomic data (over 100 million reads) from the two herbs, using RNA-seq method and the Illumina GAII platform. The transcriptomic profiles contain over 6,000 expressed sequence tags (ESTs) for each of the three flower development stages from FLJ, as well as comparable amount of ESTs from the rFLJ flower bud. To elucidate enzymatic divergence on biosynthetic pathways between the two varieties, we correlated genes and their expression profiles to known metabolic activities involving the relevant active compounds, including phenolic acids, flavonoids, terpenoids, and fatty acids. We also analyzed the diversification of genes that process the active compounds to distinguish orthologs and paralogs together with the pathways concerning biosynthesis of phenolic acid and its connections with other related pathways.

Conclusions: Our study provides both an initial description of gene expression profiles in flowers of FLJ and its counterfeit rFLJ and the enzyme pool that can be used to evaluate FLJ quality. Detailed molecular-level analyses allow us to decipher the relationship between metabolic pathways involved in processing active medicinal compounds and gene expressions of their processing enzymes. Our evolutionary analysis revealed specific functional divergence of orthologs and paralogs, which lead to variation in gene functions that govern the profile of active compounds.

Figure 1

Plant materials. (A) –(D), Flower samples: A, FLJ bud, had white petals and had not yet bloomed into a full-size flower; B, FLJ flower1, had white petals and had bloomed into a full-size flower; C, FLJ flower2, had yellow petals and had bloomed into a full-size flower; D, rFLJ bud, had red petals and had not yet bloomed into a full-size flower. (E) –(H), Stereomicroscopic detection Trichome (red arrows) in the flower bud and leaf of FLJ and rFLJ. Abbreviations: E, FLJ flower bud; F, FLJ Leaf; G, rFLJ flower bud; and H, rFLJ leaf.

Figure 2

Metabolic profile differences between flower buds of FLJ and rFLJ as detected based on PCA of GC-MS and HPLC data. (A) PCA of metabolic profiles based on GC-MS analysis (n = 3). (B) PCA of metabolic profiles based on HPLC analysis (n = 5). Y1–Y5, FLJ samples; R1-R5, rFLJ samples. Distinct metabolic profiles that correspond to a particular species are circled in (A) and (B). PCA analyses were performed by using the SIMCA-P + (12.0.0.0.0) program (Umetrics AB, Tvistevdgen 48 Umea 907 19, Sweden). p [1] is the first principal component, and p [2] the second principal component.

Figure 3

The active compound contents in flowering samples of FLJ and rFLJ. (A) The content of eight compounds: chlorogenic acid, caffeic acid, ferulic acid, rutin, luteoloside, hyperoside, quercitrin and quercetin (mg/g DW), as analyzed by using HPLC and calculated based on a linear formula ( Additional file 2: Table S2). Error bars (SEs; n = 5) were calculated by using Excel software. (B) The content of volatile compounds (Metabolite/pentadecanol [IS] peak area ratio/1000) in FLJ and rFLJ. The Y axis indicates the relative quantification of metabolites by normalization of their response values to pentadecanol.

Figure 4

Pathway of active compound biosynthesis in the flowering samples of FLJ. Circles represent compounds and six different colors indicate the content of active compounds ranging from 0 to above 20 mg/g DW. Squares represent gene express level and the nine different colors indicate RPKM values of the ESTs. Three-fold circles and three-row squares display the content of compounds and gene express levels. Abbreviations: B, bud; F1, flower1; and F2, flower2.

Figure 5

Gene transcription level in flower buds of FLJ and rFLJ. The square represents gene express levels, and the nine colors indicate the RPKM values of the ESTs as calculated according to the grape full-length cDNA sequences. * denotes no differential expression between FLJ and rFLJ.

Figure 6

Correlation between gene express level and active compound accumulation in FLJ. ESTs numbers and metabolites are shown in the key. M1–M8 are active compounds. The color key provides R values for the correlations calculated for the flower development datasets. The RPKM values of ESTs were calculated according to the grape full-length cDNA sequences.

Figure 7

Phylogeny of predicted amino acid sequences and expression of the pyruvate kinase homologs between the FLJ and rFLJ flower buds and the pyruvate kinase family genes in Arabidopsis and grape. The phylogenetic tree was constructed based on the neighbor-joining method (ClustalW2). Pyruvate kinase homologs were identified based on the unique domain (PF00224) in the PFAM database.

Figure 8

Key enzymes and proteins in regulating biosynthesis of phenolic acids in FLJ. Phenolic acids are produced from PEP by PAL, 4CL, and CHS. PK, 4CL, and HMGR are regulated by ATP. HMGR is related to phenolic acid and hyperoside. The biosynthesis of phenolic acids is coordinated closely with fatty acids. Converting glucose to PEP is regulated by bHLH. Abbreviations: CHS, chalcone synthase; 4CL, 4-coumarate-CoA ligase; PAL, phenylalanine ammonia-lyase; PK, pyruvate kinase; HMGR, 3-hydroxy-3-methylglutaryl-CoA reductase; FatB, fatty acyl-ACP thioesterase B; PEP, phosphoenolpyruvate; and Pyr, pyruvate.