UC Berkeley

Microbial biosynthesis of valuable chemicals has the potential to create more sustainable, environmentally friendly manufacturing methods compared to chemical synthesis or harvesting and purification from plants. Compartmentalizing heterologous metabolic pathways within organelles has promise to decrease crosstalk, minimize feedback inhibition, decrease toxicity, and provide optimal biochemical conditions for enzymatic reactions. As peroxisome function is not required under most industrially relevant growth conditions and can compartmentalize an impressive amount of protein, they represent a compelling organelle for metabolic engineering. However, when its native function is not required, peroxisome proliferation is repressed, leading to small, individual peroxisomes within the cell, limiting their capacity. My work presented here demonstrates novel methods to measure and increase the peroxisome functional capacity for improving titers of heterologous metabolic pathways. I first created a high-throughput, in vivo assay for measuring peroxisome functional capacity. I then increased the peroxisome functional capacity in Saccharomyces cerevisiae by engineering three transcription factors to be constitutively active, mimicking induction with long-chain fatty acids. This increased capacity peroxisome was used to more efficiently compartmentalize the toxic, bottleneck enzyme in the plant metabolic pathway for biosynthesizing benzylisoquinoline alkaloids. With better compartmentalization, toxicity from the protein was decreased and titers for these valuable molecules were improved. I further increased functional capacity past that achieved by natural induction without the need for the transcription factors, which had led to slightly decreased growth rate, by overexpressing groups of peroxisome-related genes. To more efficiently explore the combinatorial space of peroxisome gene overexpressions, we collaborated with another group to develop a machine learning pipeline which guided gene overexpression experiments. After several iterative rounds, the machine learning pipeline predicted a strain that led to a higher functional capacity than natural induction and rational engineering efforts. This enhanced capacity peroxisome led to the creation of a two-compartment system in which the cytosol was used for native metabolism required for host cell health while the engineered metabolic pathway was more efficiently compartmentalized to improve metabolic flux. The improved compartmentalization of the eight-enzyme pathway plant pathway for geraniol led to decreased competition with cytosolic enzymes, ultimately ending in a strain producing 9.5 g/L of geraniol, the highest reported titer to date. With these successes, I began researching the methylotrophic yeasts Ogataea parapolymorpha and Pichia pastoris due to their impressive increase in peroxisome size when induced with methanol. By constitutively expressing the methanol specific transcription factor Mpp1, I was able to engineer artificially large peroxisomes in O. parapolymorpha across all media conditions without methanol induction. While studying these impressive, methanol-induced peroxisomes, I hypothesized the activity of the enzymes for methanol metabolism leads to a decreased oxygen environment within the peroxisome. Preliminary data utilizing oxygen-independent fluorescent proteins seem to support this hypothesis. Ongoing work utilizes these anaerobic peroxisomes to create oxygen-sensitive iron-sulfur cluster cofactors from a heterologous pathway. These iron-sulfur clusters are used to biosynthesize biotin, which is currently manufactured using an environmentally damaging chemical synthesis method. Overall, my work in studying and increasing peroxisome functional capacity has led to improved titers for several heterologous metabolic pathways, ultimately paving the way towards a more sustainable future with a circular bioeconomy.