Biosynthesis of natural and halogenated plant monoterpene indole alkaloids in yeast.

Monoterpenoid indole alkaloids (MIAs) represent a large class of plant natural products with marketed pharmaceutical activities against a wide range of indications, including cancer, malaria and hypertension. Halogenated MIAs have shown improved pharmaceutical properties; however, synthesis of new-to-nature halogenated MIAs remains a challenge. Here we demonstrate a platform for de novo biosynthesis of two MIAs, serpentine and alstonine, in baker's yeast Saccharomyces cerevisiae and deploy it to systematically explore the biocatalytic potential of refactored MIA pathways for the production of halogenated MIAs. From this, we demonstrate conversion of individual haloindole derivatives to a total of 19 different new-to-nature haloserpentine and haloalstonine analogs. Furthermore, by process optimization and heterologous expression of a modified halogenase in the microbial MIA platform, we document de novo halogenation and biosynthesis of chloroalstonine. Together, this study highlights a microbial platform for enzymatic exploration and production of complex natural and new-to-nature MIAs with therapeutic potential.

A Reporter System for Cytosolic Protein Aggregates in Yeast.

Protein misfolding and aggregation are linked to neurodegenerative diseases of mammals and suboptimal protein expression within biotechnology. Tools for monitoring protein aggregates are therefore useful for studying disease-related aggregation and for improving soluble protein expression in heterologous hosts for biotechnology purposes. In this work, we developed a promoter-reporter system for aggregated protein on the basis of the yeast native response to misfolded protein. To this end, we first studied the proteome of yeast in response to the expression of folded soluble and aggregation-prone protein baits and identified genes encoding proteins related to protein folding and the response to heat stress as well as the ubiquitin-proteasome system that are over-represented in cells expressing an aggregation-prone protein. From these data, we created and validated promoter-reporter constructs and further engineered the best performing promoters by increasing the copy number of upstream activating sequences and optimization of culture conditions. Our best promoter-reporter has an output dynamic range of approximately 12-fold upon expression of the aggregation-prone protein and responded to increasing levels of aggregated protein. Finally, we demonstrate that the system can discriminate between yeast cells expressing different prion precursor proteins and select the cells expressing folded soluble protein from mixed populations. Our reporter system is thus a simple tool for diagnosing protein aggregates in living cells and should be applicable for the health and biotechnology industries.

Programmable polyketide biosynthesis platform for production of aromatic compounds in yeast.

To accelerate the shift to bio-based production and overcome complicated functional implementation of natural and artificial biosynthetic pathways to industry relevant organisms, development of new, versatile, bio-based production platforms is required. Here we present a novel yeast-based platform for biosynthesis of bacterial aromatic polyketides. The platform is based on a synthetic polyketide synthase system enabling a first demonstration of bacterial aromatic polyketide biosynthesis in a eukaryotic host.

An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast.

Microbes offer enormous potential for production of industrially relevant chemicals and therapeutics, yet the rapid identification of high-producing microbes from large genetic libraries is a major bottleneck in modern cell factory development. Here, we develop and apply a synthetic selection system in Saccharomyces cerevisiae that couples the concentration of muconic acid, a plastic precursor, to cell fitness by using the prokaryotic transcriptional regulator BenM driving an antibiotic resistance gene. We show that the sensor-selector does not affect production nor fitness, and find that tuning pH of the cultivation medium limits the rise of nonproducing cheaters. We apply the sensor-selector to selectively enrich for best-producing variants out of a large library of muconic acid production strains, and identify an isolate that produces more than 2 g/L muconic acid in a bioreactor. We expect that this sensor-selector can aid the development of other synthetic selection systems based on allosteric transcription factors.