The future of self-selecting and stable fermentations

Abstract

Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.

High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring

“…For example, screens could be designed to find strains more likely to sustain peak q p throughout a longer fermentation by (1) choosing strains with less selective pressure for mutation, (2) identifying strains with resistance to feedback inhibition by products or intermediates, or (3) uncoupling growth rate from metabolic pathway flux. 24 As can be seen in Fig. 6, some strains with highly improved bioreactor q p values exhibit relatively smaller improvements in P and Y, which must be increased to reduce manufacturing cost.…”

Quantification of microbial robustness

“…It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 10, 2021. ; https://doi.org/10.1101/2021.12.09.471918 doi: bioRxiv preprint A systematic framework for assessing robustness of several performance indicators will improve our understanding of how cellular functions respond to relevant perturbations, e.g., by favoring robustness, performance, or a sub-optimal state for both. In strain engineering, robust yields of products are sometimes preferred over higher but unstable yields 5 . In synthetic biology, engineered strains should carry new functions that perform robustly under anticipated perturbations.…”

“…Microbial robustness ensures predictable, stable synthetic cellular functionality in spite of internal or external perturbations [1][2][3] . Robust cell manufacturing and destination performance will be a key in realizing new synthetic biology modalities and efficient bioproduction 4,5 . Robustness is defined for a specific function (or phenotype) and set of perturbations 6 .…”

“…Experimentally, measures of variation represent the stability (dispersion) of quantitative traits across perturbations, but not at different scales 6 , for which the dimensionless coefficient of variation (CV) is better suited 9 . Yet even if central to realizing predictable, scalable synthetic biology, robustness is seldomly quantified experimentally for strain functions, which may be subject to genetic or environmental perturbations of stochastic or determined behavior 5,10,11 . Here, we present and validate a high-throughput methodology to experimentally and systematically quantify microbial robustness with script available from Github.…”

Introduction to the Special Issue on “Recent Advances in Fermentation Technology 2020”

“…If any of these genotypic/phenotypic variations result in more rapid cell growth as a consequence of reducing the metabolic load associated with high-level production of desired product(s), then fast growing, low-producing variants can outgrow the starting production strain, and lead to reduced product yield. Rugbjerg and Olsson [6] have reviewed multiple synthetic biology applications to address this important issue by engineering self-selecting systems to eliminate the fast growing, non-productive variants. This methodology will be important for current and future applications of synthetic biology to ensure robust, stable fermentations.…”