Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast

Abstract

Yeast flocculation is a community-building cell aggregation trait that is an important mechanism of stress resistance and a useful phenotype for brewers; however, it is also a nuisance in many industrial processes, in clinical settings, and in the laboratory. Chemostat-based evolution experiments are impaired by inadvertent selection for aggregation, which we observe in 35% of populations. These populations provide a testing ground for understanding the breadth of genetic mechanisms Saccharomyces cerevisiae uses to flocculate, and which of those mechanisms provide the biggest adaptive advantages. In this study, we employed experimental evolution as a tool to ask whether one or many routes to flocculation are favored, and to engineer a strain with reduced flocculation potential. Using a combination of whole genome sequencing and bulk segregant analysis, we identified causal mutations in 23 independent clones that had evolved cell aggregation during hundreds of generations of chemostat growth. In 12 of those clones, we identified a transposable element insertion in the promoter region of known flocculation gene FLO1, and, in an additional five clones, we recovered loss-of-function mutations in transcriptional repressor TUP1, which regulates FLO1 and other related genes. Other causal mutations were found in genes that have not been previously connected to flocculation. Evolving a flo1 deletion strain revealed that this single deletion reduces flocculation occurrences to 3%, and demonstrated the efficacy of using experimental evolution as a tool to identify and eliminate the primary adaptive routes for undesirable traits.

A generalizable experimental framework for automated cell growth and laboratory evolution

“…2c, S9c). User-actuated peristaltic pumps are robust, simple to use, and can be scaled to carry out media dilution routines for a large number of parallel continuous cultures 25,26 (Fig. S10).…”

“…A recent convergence of several open-source technologies-inexpensive additive manufacturing, do-it-yourself (DIY) software/hardware interface, and cloud computinghas enabled the in-lab fabrication of new custom laboratory platforms [19][20][21][22] . Various examples in automated cell culture include devices designed to perform automated dilution routines for exploring antibiotic resistance acquisition 23 , or that implement design features such as real-time monitoring of bulk fluorescence 7 , light-based feedback control of synthetic gene circuits 24 , and chemostat parallelization 25 . Unfortunately, the single-purpose, ad hoc design of these systems limits their scalability and restricts their reconfiguration for other experimental purposes.…”

Arresting Evolution

“…Most mutants yielding this phenotype were found to activate one key regulatory gene, either directly or indirectly. Removing it delayed the evolution of aggregation [25]. …”

Evolution leads to a diversity of motion-detection neuronal circuits

“…Many-to-one genotype-phenotype mappings are even present in artificial digital evolution systems [e.g., (LaBar et al, 2016;Fortuna et al, 2017;LaBar and Adami, 2017)] and evolutionary simulations of digital logic circuits (Raman and Wagner, 2010). Empirical studies of biological systems suggest the existence of multiple genotypes encoding similar phenotypes, either through genetic analysis (Tsong et al, 2006;Taylor et al, 2016), comparative genomics (Cross et al, 2011), or experimental evolution (Lind et al, 2015;Hope et al, 2017). However, the evolutionary reasons why populations evolve one genotype instead of another genotype, or which evolutionary processes lead to the evolution of different genotypes, are largely unexplored in biological systems due to the difficulty of deciphering every possible evolutionary trajectory and process, and the waiting time required for many of these evolutionary events to occur [but see (Lind et al, 2015)].…”