Integration of a Decrescent Transcriptome and Metabolomics Dataset of Peucedanum praeruptorum to Investigate the CYP450 and MDR Genes Involved in Coumarins Biosynthesis and Transport

Peucedanum praeruptorum Dunn is well-known traditional Chinese medicine. However, little is known in the biosynthesis and the transport mechanisms of its coumarin compounds at the molecular level. Although transcriptomic sequence is playing an increasingly significant role in gene discovery, it is not sufficient in predicting the specific function of target gene. Furthermore, there is also a huge database to be analyzed. In this study, RNA sequencing assisted transcriptome dataset and high-performance liquid chromatography (HPLC) coupled with electrospray-ionization quadrupole time-of-flight mass spectrometry (Q-TOF MS)-based metabolomics dataset of P. praeruptorum were firstly constructed for gene discovery and compound identification. Subsequently, methyl jasmonate (MeJA)-induced gene expression analysis and metabolomics analysis were conducted to narrow-down the dataset for selecting the candidate genes and the potential marker metabolites. Finally, the genes involved in coumarins biosynthesis and transport were predicted with parallel analysis of transcript and metabolic profiles. As a result, a total of 40,952 unigenes and 19 coumarin compounds were obtained. Based on the results of gene expression and metabolomics analysis, 7 cytochrome-P450 and 8 multidrug resistance transporter unigenes were selected as candidate genes and 8 marker compounds were selected as biomarkers, respectively. The parallel analysis of gene expression and metabolites accumulation indicated that the gene labeled as 23,746, 228, and 30,922 were related to the formation of the coumarin core compounds whereas 36,276 and 9533 participated in the prenylation, hydroxylation, cyclization or structural modification. Similarly, 1462, 20,815, and 15,318 participated in the transport of coumarin core compounds while 124,029 and 324,293 participated in the transport of the modified compounds. This finding suggested that integration of a decrescent transcriptome and metabolomics dataset could largely narrow down the number of gene to be investigated and significantly improve the efficiency of functional gene predication. In addition, the large amount of transcriptomic data produced from P. praeruptorum and the genes discovered in this study would provide useful information in investigating the biosynthesis and transport mechanism of coumarins.