Transcriptome analysis of Clinopodium gracile (Benth.) Matsum and identification of genes related to Triterpenoid Saponin biosynthesis

Abstract

Background: Clinopodium gracile (Benth.) Matsum (C. gracile) is an annual herb with pharmacological properties effective in the treatment of various diseases, including hepatic carcinoma. Triterpenoid saponins are crucial bioactive compounds in C. gracile. However, the molecular understanding of the triterpenoid saponin biosynthesis pathway remains unclear. Results: In this study, we performed RNA sequencing (RNA-Seq) analysis of the flowers, leaves, roots, and stems of C. gracile plants using the BGISEQ-500 platform. The assembly of transcripts from all four types of tissues generated 128,856 unigenes, of which 99,020 were mapped to several public databases for functional annotation. Differentially expressed genes (DEGs) were identified via the comparison of gene expression levels between leaves and other tissues (flowers, roots, and stems). Multiple genes encoding pivotal enzymes, such as squalene synthase (SS), or transcription factors (TFs) related to triterpenoid saponin biosynthesis were identified and further analyzed. The expression levels of unigenes encoding important enzymes were verified by quantitative real-time PCR (qRT-PCR). Different chemical constituents of triterpenoid saponins were identified by Ultra-Performance Liquid Chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). Conclusions: Our results greatly extend the public transcriptome dataset of C. gracile and provide valuable information for the identification of candidate genes involved in the biosynthesis of triterpenoid saponins and other important secondary metabolites.

Full-Length Transcriptome Analyses of Genes Involved in Triterpenoid Saponin Biosynthesis of Psammosilene tunicoides Hairy Root Cultures With Exogenous Salicylic Acid

“…KEGG analysis of the up-regulated DEGs revealed that “terpenoid biosynthesis” was the highest among all pathways ( Figures 4C,D ), indicating that SA enhanced the expression of terpenoid metabolism in PTHRCs. Additionally, we noticed several DEGs were enriched in the biosynthesis pathway of other secondary metabolites, including phenylpropanoid, flavonoid and isoquinoline alkaloid ( Supplementary Table 3 ) consistent with Shan et al (2020) , suggesting SA might influence multiple processes of secondary metabolism in hairy roots.…”

“…Next-generation sequencing (e.g., Illumina RNA-seq) is an effective method to provide transcriptome-wide analysis of sequence data and differentially expressed genes (DEGs), especially for non-model plant species ( Ma et al, 2016 ; Choudhri et al, 2018 ). RNA-seq has been performed to identify the putative genes involved in terpenoid biosynthesis in multiple plants ( Luo et al, 2011 ; Ma et al, 2016 ; Wang et al, 2017 ; Zhou et al, 2017 ; Choudhri et al, 2018 ; Shan et al, 2020 ). Nevertheless, the short read lengths from RNA-seq restrict the construction of completely assembled transcripts, making it difficult to obtain full-length sequences ( Wu et al, 2019 , 2020 ).…”

Compatative Transcriptome Analysis Reveals Sesquiterpene Biosynthesis among 1-, 2- and 3-year Old Atractylodes Chinensis

“…The identified four enzymes, SE, SHC, SS and DS, play important role in squalene biosynthesis and the subsequent catalytic reactions of this metabolic branch. The enzyme SS as a key enzyme in the terpenoid biosynthesis pathway catalyzes the synthesis of the first precursor of terpenoid compounds, squalene (Kim et al, 2005;Ye et al, 2014;Zheng et al, 2013;Dan et al, 2017;Shao et al, 2020). The SHC enzyme can catalyze the formation of hopene from its precursor squalene (Siedenburg and Jendrossek, 2011;Nakano et al, 2019), toward triterpenoid or steroid biosynthesis.…”

SMRT- and Illumina‑based RNA‑seq Analyses Unveil Triterpenoid Saponin Biosynthesis and Related Key Genes in Platycodon Grandiflorus (Jacq.) A. DC.

“…The FL reads were corrected with RNA-seq reads using the LSC software [24]. Subsequently, the FL reads were clustered to generate de novo consensus isoforms via the iterative clustering for error correction algorithm and then polished using the Quiver quality-aware algorithm [49]. Two strategies were employed to improve the accuracy of consensus transcripts.…”