Development of transcriptomic resources for interrogating the biosynthesis of monoterpene indole alkaloids in...

The natural diversity of plant metabolism has long been a source for human medicines. One group of plant-derived compounds, the monoterpene indole alkaloids (MIAs), includes well-documented therapeutic agents used in the treatment of cancer (vinblastine, vincristine, camptothecin), hypertension (reserpine, ajmalicine), malaria (quinine), and as analgesics (7-hydroxymitragynine). Our understanding of the biochemical pathways that synthesize these commercially relevant compounds is incomplete due in part to a lack of molecular, genetic, and genomic resources for the identification of the genes involved in these specialized metabolic pathways. To address these limitations, we generated large-scale transcriptome sequence and expression profiles for three species of Asterids that produce medicinally important MIAs: Camptotheca acuminata, Catharanthus roseus, and Rauvolfia serpentina. Using next generation sequencing technology, we sampled the transcriptomes of these species across a diverse set of developmental tissues, and in the case of C. roseus, in cultured cells and roots following elicitor treatment. Through an iterative assembly process, we generated robust transcriptome assemblies for all three species with a substantial number of the assembled transcripts being full or near-full length. The majority of transcripts had a related sequence in either UniRef100, the Arabidopsis thaliana predicted proteome, or the Pfam protein domain database; however, we also identified transcripts that lacked similarity with entries in either database and thereby lack a known function. Representation of known genes within the MIA biosynthetic pathway was robust. As a diverse set of tissues and treatments were surveyed, expression abundances of transcripts in the three species could be estimated to reveal transcripts associated with development and response to elicitor treatment. Together, these transcriptomes and expression abundance matrices provide a rich resource for understanding plant specialized metabolism, and promotes realization of innovative production systems for plant-derived pharmaceuticals.