Improving Bioprocess Robustness by Cellular Noise Engineering
Authors:
Georgios Daletos1* (daletosg@mit.edu), Irum Perveen,1 Andreas Vasdekis,2 and Gregory Stephanopoulos
Institutions:
1Massachusetts Institute of Technology and 2University of Idaho–Moscow
Goals
The overall goal of this project is to enhance the robustness of biofuel production in adverse and fluctuating environments, such as media containing toxic hydrolysates, by introducing cellular noise engineering as a means of improving the production. The approach involves the identification of factors in the transcription process that increase cellular noise and the deployment of such factors to generate cells with increased noise. Researchers use modeling and a single-cell analysis workflow to engineer Yarrowia lipolytica variants that can tolerate, grow, and efficiently synthesize biofuel precursors under steady state, albeit stressful, conditions. Overall, the team anticipates that strains with optimal levels of cellular noise will also exhibit robustness that maintains production under time-varying stresses.
Abstract
Robustness represents a system-level trait that allows cell populations to maintain function under adverse and fluctuating environments. When observed at the cellular or subcellular level, an isogenic cell population exhibits increased cell-to-cell variability, or noise, even under steady- state conditions. In this context, isogenic cells undergo division of labor with some expressing the pathways that enable them to continue functioning in the new environment. This concept guides this project in developing workflows for introducing and manipulating cellular noise to enhance cellular tolerance to environmental stressors. The focus has been placed on the construction of Yarrowia lipolytica strains with the double‐phenotype of tolerance and high lipid productivity. In the team’s first steps on cellular noise engineering, researchers refined gene editing toolboxes that can deterministically vary the level of cellular noise in protein expression levels. In this context, the team followed an approach previously demonstrated in Saccharomyces cerevisiae for designing a synthetic promoter library in which increasing numbers of transcription factor binding sites led to enhanced noise levels (Sharon et al. 2014). Similarly, the team introduced three to five tandem upstream activating sequences to the erythritol‐inducible pEYK1 promoter. The synthetic hybrid promoters were placed upstream of a red fluorescent protein, fused into plasmids, and stably integrated into the genome of Y. lipolytica. In the induction experiments, the team independently varied the concentration of erythritol and glucose to determine the relationship between expression level and noise. Each transformant bearing the erythritol‐inducible pEYK1 promoter fused to the upstream activating sequences was screened separately by flow cytometry. The results were categorized into expression and noise levels and compared to those of parental strains. As a next step, researchers introduced key genes that play a significant role in viability at varying inhibitor levels. To this end, rational design was applied to develop a cellulosic oil Y. lipolytica strain that is tolerant to the primary lignocellulosic inhibitor furfural. To enable tolerance to furfural, researchers constructed Y. lipolytica overexpressing an evolved reductase enzyme (GRE2evol), which was directly obtained from prior work with S. cerevisiae (Lam et al. 2021), or an endogenous aldehyde dehydrogenase that converts furfural to the less toxic furoic acid. The team finally evaluated front‐runner Y. lipolytica strains under both stressful and non‐stressful conditions to quantify the effects of noise and expression levels on furfural tolerance.
References
Sharon, E., et al. 2014. “Probing the Effect of Promoters on Noise in Gene Expression Using Thousands of Designed Sequences,” Genome Research 24(10), 1698–1706.
Lam, F. H., et al. 2021. “Engineered Yeast Tolerance Enables Efficient Production from Toxified Lignocellulosic Feedstocks,” Science Advances, 7(26), eabf7613.
Funding Information
This research was supported by the DOE Office of Science, Office of Biological and Environmental Research (BER), grant no. DE‐SC0022016.