bioRxivGenomicsPreprint
De novo Transcriptomic Analysis to Understanding Piperine Biosynthesis in Piper chaba Hunter
Abstract
Medicinal plants serve as invaluable sources of bioactive compounds, yet the molecular basis of their secondary metabolite biosynthesis remains largely unexplored. Piper chaba Hunter, an important but understudied member of the Piperaceae family, is known for its pharmacologically active alkaloids, particularly piperine. This study presents the first comprehensive transcriptomic analysis of P. chaba to uncover the genetic pathways regulating its metabolite production. The quantification of piperine, a major bioactive compound, was conducted using UPLC, which revealed its highest concentration in the spike (331.3 mg/g) followed by the stem (63.5 mg/g), root (10.3 mg/g), and leaves (2.82 mg/g). High-quality RNA sequencing of leaves, roots, and spikes using next-generation sequencing (NGS) generated 228,481 transcripts, and 184,574 unigenes were identified after redundancy removal. Coding sequences (CDS) derived from these unigenes were annotated using BLASTX and KEGG databases, which highlighted significant metabolic pathways, including those related to piperine biosynthesis. A total of thirteen genes associated with piperine biosynthesis were identified. Validation of nine selected genes, including Farnesyl pyrophosphate synthase and Piperic acid synthase, was performed through qRT-PCR using the 2-{Delta}{Delta}Ct method, confirming their involvement in piperine and other metabolite biosynthesis. Functional annotation categorized the CDS into Gene Ontology domains, with transcription factors such as bHLH and NAC families playing prominent roles in metabolic regulation. Additionally, 5,050 SSRs were identified, offering potential markers for genetic studies. This pioneering study establishes a molecular framework for understanding the biosynthetic pathways of P. chaba, providing valuable insights for its application in sustainable medicine and agriculture.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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