Multiplexed Genetic Engineering in Saccharomyces cerevisiae

Strategies to optimize a metabolic pathway often require a large collection of strains to be generated, each containing different versions of the sequences that regulate the expression of the relevant pathway genes. Here we develop a set of reagents and methods to carry out this process at high efficiency in the yeast Saccharomyces cerevisiae. This toolkit includes a set of variants of the tet operator, which in conjunction with a TetR-VP16 activator drive expression over a 100-fold range; the induction of the I-OnuI homing endonuclease to target its recognition site in a gene to be modified, which boosts homologous recombination more than 105 over that in the absence of a double-strand break; and the generation of a plasmid carrying the six variant tet operator sites flanked by I-OnuI sites, uncoupling the transformation and recombination steps. As proof of principle, we introduce into the S. cerevisiae genome the three crt genes from Xanthophyllomyces dendrorhous required for yeast to synthesize lycopene, and carry out the recombination process to produce a population of cells with permutations of tetO variants regulating the three genes. We identify 0.7% of the cells as making lycopene, of which the vast majority have undergone a recombination event at each of the crt genes. Based on sequence analysis of these genes in strains that do not produce lycopene, we estimate a rate of ~20% recombination per targeted site, much higher than obtained in other studies.

Josh Cuperus & Russell Lo


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.

Our strategy is to use destabilizing mutations that impair functional expression of the biosensor until it binds its cognate ligand. 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 designed – for the desired small molecule. By fusing an unstable LBD to a TF, we couple ligand-dependent stabilization to reporter gene expression. As a proof of principle, we chose to use a de novo designed LBD, DIG10.3, which binds digoxigenin, a steroid similar to drugs used to treat heart failure.  By fusing this protein to a DNA-binding domain (DBD) and a transcriptional activation domain (TAD), we were able to generate a ligand-dependent TF, which we designated GDVP.  Several rounds of mutagenesis and FACS analysis allowed us to identify destabilizing mutations that improved sensor function by >10-fold (GDVP.1 and GDVP.2).



In order to tune the sensor for selections with a HIS3 reporter, we fused the degron from the Mata2 protein to GDVP to increase degradation. The fusion generated a sensor that leads to growth of yeast in histidine-deficient media more than 3 orders of magnitude better when the ligand is present than when it is absent. Sensitivity to exogenous digoxigenin can be further improved by deleting yeast efflux pumps, like Pdr5.

While the biosynthetic pathway required to produce digoxigenin is not known, other steroids such as progesterone have been successfully produced in yeast. An additional round of mutagenesis and screening allowed us to create a sensor that is extremely selective for progesterone but not its biosynthetic precursor, pregnenolone. Future directions include using this sensor to optimize progesterone biosynthesis in yeast by genome engineering.


Ben Jester


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.

Matt Rich

HHMI,
Department of

Genome Sciences
& Medicine,
Univ. of Washington

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