Engineering cellular metabolite transport for biosynthesis of computationally predicted tropane alkaloid derivatives in yeast

Abstract

Microbial biosynthesis of plant natural products (PNPs) can facilitate access to valuable medicinal compounds and derivatives. Such efforts are challenged by metabolite transport limitations, which arise when complex plant pathways distributed across organelles and tissues are reconstructed in unicellular hosts without concomitant transport machinery. We recently reported an engineered yeast platform for production of the tropane alkaloid (TA) drugs hyoscyamine and scopolamine, in which product accumulation is limited by vacuolar transport. Here, we demonstrate that alleviation of transport limitations at multiple steps in an engineered pathway enables increased production of TAs and screening of useful derivatives. We first show that supervised classifier models trained on a tissue-delineated transcriptome from the TA-producing plant Atropa belladonna can predict TA transporters with greater efficacy than conventional regression- and clustering-based approaches. We demonstrate that two of the identified transporters, AbPUP1 and AbLP1, increase TA production in engineered yeast by facilitating vacuolar export and cellular reuptake of littorine and hyoscyamine. We incorporate four different plant transporters, cofactor regeneration mechanisms, and optimized growth conditions into our yeast platform to achieve improvements in de novo hyoscyamine and scopolamine production of over 100-fold (480 μg/L) and 7-fold (172 μg/L). Finally, we leverage computational tools for biosynthetic pathway prediction to produce two different classes of TA derivatives, nortropane alkaloids and tropane N-oxides, from simple precursors. Our work highlights the importance of cellular transport optimization in recapitulating complex PNP biosyntheses in microbial hosts and illustrates the utility of computational methods for gene discovery and expansion of heterologous biosynthetic diversity.