Multiplexed genetic engineering in Saccharomyces cerevisiae
Tools for genetic manipulation such as recombination-mediated mutagenesis and directed evolution have expanded the usefulness of single-cell organisms for industrial-scale production of biofuels, proteins and other compounds. Using single-cell organisms for industrial production makes the process more environmentally friendly, cost-effective and sustainable. However, additional methods to enhance efficiency are needed to maximize yields or produce complex compounds. An ideal method would modify an organism’s DNA; be high-throughput; allow a measurement of yield without requiring costly measurement techniques; and be readily adaptable to new compounds with high specificity. We are attempting to create a high-throughput and genetically-selectable system in Saccharomyces cerevisiae to enhance the production of proteins and other molecules.
Our idea is to have a modular expression system in S. cerevisiae that can be applied to several genes in a pathway. This system will provide a high-throughput method to modify the expression of multiple genes, with the goal of generating some strains that have higher production of a specific compound. To create such a tractable expression system, we chose the Tet operator (TetO), which has been well-characterized such that operators exist with differing affinities for the Tet repressor. TetO sequences are short, and expression can be turned off with tetracycline (or its derivatives). This set of operators allows genes to be activated by a protein consisting of the Tet repressor fused to the VP16 activation domain (Figure 1).
Using this system, we replace an endogenous gene regulatory region with several well-defined mutant operators that modulate expression. We introduce these TetO sites upstream of chromosomal genes to modulate expression, resulting in a diverse library of yeast with unique combinations of expression, with some having increased production of an end product (Figure 2).
•Josh Cuperus & Russell Lo
Transcriptional engineering of ethanol-tolerant yeast strains
Alcohols cause pleiotropic cellular stress by disrupting the cell membrane and non-specifically destabilizing proteins. In yeast over 1000 genes have been implicated in increasing alcohol tolerance. Given such complexity, methods like transcriptional engineering that modulate cellular processes genome-wide are ideal tools to analyze this trait. In 2006, Alper and colleagues showed that variants of the yeast TATA-binding protein (Spt15) could improve viability at 6% ethanol. Spt15 regulates the expression of nearly all genes, so while its variants modulate many genes that are necessary for alcohol tolerance, they likely have off-target and possibly deleterious effects. To examine the possibility that variants of less ubiquitous transcription factors can also be used to increase ethanol tolerance, we created libraries consisting of over one million variants for three alcohol-responsive yeast transcription factors, Asr1, Msn2 and Msn4 and selected yeast containing these factors at 7.5% ethanol.
Many non-synonymous and frameshift mutations in the ASR1 and MSN genes enriched over the course of selection. We are continuing selections and confirming the tolerance of highly-enriched mutations. After this confirmation, we plan to use RNA-sequencing to analyze the transcriptional changes underlying the tolerance phenotype, in an effort to elucidate the molecular basis of yeast ethanol tolerance. We also believe that, if successful, this approach could be used to investigate the molecular basis of other complex traits.
Genetic sensors for the detection of biosynthetic products
Model microorganisms such as E. coli and S. cerevisiae are excellent tools for the production of useful small molecules, such as therapeutics and biofuels, but the optimization of biosynthetic pathways in a new host is non-trivial. Identifying genetic modifications that enhance metabolite synthesis can be an exceptionally laborious process, particularly in the absence of a method to easily determine product yields. Genetically encoded biosensors that couple small molecule recognition to a readily measured output allow more rapid identification of cells with enhanced biosynthetic production or conditions that promote enhanced production. One of Nature’s most common mechanisms for detecting the presence of a small molecule be it a metabolite or an environmental agent has been to evolve biosensors that regulate the transcription of one or more genes upon binding of the relevant ligand. Following this logic, the goal of this project is to design a transcription factor (TF)-based strategy for biosensors that may provide a general solution to the problem of small molecule detection.
It should be possible to design a protein-based biosensor for any ligand as long as a ligand-binding domain (LBD) exists or can be engineered for the desired small molecule. In these sensors, the TF’s ability to drive transcription of a downstream gene would be dependent upon the presence of ligand, with transcriptional activation be quantified by using a suitable reporter or selectable marker for cell growth. Because ligand binding is generally stabilizing, higher in vivo concentrations of the small molecule should result in an increase in reporter expression. A simple LBD-TF fusion is unlikely to be sufficiently destabilized to exhibit ligand-dependent transcription. We are currently exploring three strategies for destabilizing our LBD-TF fusions: 1) addition of a degron; 2) identification of destabilizing mutations in the LBD; and 3) functional disruption of the DNA-binding domain. In each case, a library of variants would be either screened or selected for ligand-dependent activity of the engineered TF.