# Microbial biosynthesis of rare cannabinoids

## TL;DR

Scientists engineered yeast to produce rare cannabinoids, which are hard to obtain from cannabis plants, using a microbial biosynthesis approach.

## Contribution

A Saccharomyces cerevisiae host was developed to biosynthesize two rare cannabinoid acids from simple sugars.

## Key findings

- The yeast produced ∆9–tetrahydrocannabiorcolic acid at ~16 mg/L.
- The yeast also produced ∆9–tetrahydrocannabiphorolic acid at ~5 mg/L, which converts to ∆9–tetrahydrocannabiphorol.
- The platform can potentially produce other rare cannabinoids using fungal biosynthetic gene clusters.

## Abstract

∆9–tetrahydrocannabinol (∆9–THC) and cannabidiol are the most abundant natural cannabinoids isolated from the different cultivars of the Cannabis plant. Other natural ∆9–THC analogs, especially those with different alkyl chain substitutions, display different and potent bioactivity. However, these rare cannabinoids are typically isolated in minuscule amounts and are difficult to synthesize. Targeted microbial biosynthesis can therefore be an attractive route to access such molecules. Here, we report the development of a Saccharomyces cerevisiae host to biosynthesize 2 rare cannabinoids from simple sugars. The yeast host is engineered to accumulate excess geranyl pyrophosphate, to overexpress a fungal pathway to 2,4-dihydroxy-6-alkyl-benzoic acids, as well as the downstream UbiA-prenyltransferase and ∆9–tetrahydrocannabinolic acid synthase. Two rare cannabinoid acids, the C1-substituted ∆9–tetrahydrocannabiorcolic acid (∼16 mg/L) and the C7-substituted ∆9–tetrahydrocannabiphorolic acid (∼5 mg/L) were obtained from this host; the latter was thermally decarboxylated to give ∆9–tetrahydrocannabiphorol. Given the diversity of fungal biosynthetic gene clusters that can produce resorcylic acids, this microbial platform offers the potential to produce other rare and new-to-nature cannabinoids.

One Sentence Summary:  Saccharomyces cerevisiae as a host to produce rare cannabinoids.

Graphical Abstract