Unravelling evolutionary strategies of yeast for improving galactose utilization through integrated systems level analysis

Abstract

Identification of the underlying molecular mechanisms for a derived phenotype by adaptive evolution is difficult. Here, we performed a systems-level inquiry into the metabolic changes occurring in the yeast Saccharomyces cerevisiae as a result of its adaptive evolution to increase its specific growth rate on galactose and related these changes to the acquired phenotypic properties. Three evolved mutants (62A, 62B, and 62C) with higher specific growth rates and faster specific galactose uptake were isolated. The evolved mutants were compared with a reference strain and two engineered strains, SO16 and PGM2, which also showed higher galactose uptake rate in previous studies. The profile of intermediates in galactose metabolism was similar in evolved and engineered mutants, whereas reserve carbohydrates metabolism was specifically elevated in the evolved mutants and one evolved strain showed changes in ergosterol biosynthesis. Mutations were identified in proteins involved in the global carbon sensing Ras/PKA pathway, which is known to regulate the reserve carbohydrates metabolism. We evaluated one of the identified mutations, RAS2 Tyr112, and this mutation resulted in an increased specific growth rate on galactose. These results show that adaptive evolution results in the utilization of unpredicted routes to accommodate increased galactose flux in contrast to rationally engineered strains. Our study demonstrates that adaptive evolution represents a valuable alternative to rational design in bioengineering of improved strains and, that through systems biology, it is possible to identify mutations in evolved strain that can serve as unforeseen metabolic engineering targets for improving microbial strains for production of biofuels and chemicals. I n the field of industrial biotechnology, there is a need to develop efficient cell factories for the production of fuels and chemicals. Even though the concept of metabolic engineering (1) is frequently used in both academia and industry for the development of unique cell factories, evolutionary engineering methods are still widely performed (2). The power of adaptive evolution, sometimes in combination with metabolic engineering, is well illustrated in several recent examples (3, 4). Despite its advantages, conventional random mutagenesis and screening are hampered by the difficulties associated with finding the underlying molecular mechanisms for a derived phenotype and, hence, the combination of adaptive evolution with more rational approaches like metabolic engineering is attractive. Tools from systems biology and the ability to perform deep sequencing of several strains have offered new opportunities for establishing links between genotype and phenotype and, hereby, allow for combinations of random and rational approaches to strain improvement (5, 6).Understanding the evolutionary strategies of a cell to metabolize nonfavored carbon sources is an integral part of strain development in cost efficient bioprocesses. Galactose is an abundant sugar in nonfood crops (7), an...

Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol

“…It will be interesting to see how the optimized network of the fast growing mutants described here fares under nutrient-limiting conditions where biosynthetic and efficiency requirements and priorities are different. Our findings bear interesting similarities with two recent related studies about metabolic effects of adaptive evolution of yeast for optimizing growth on galactose 4,37 . These authors also uncovered alternative and less intuitive paths leading to growth rate improvements.…”

“…Although the biochemical characteristics of some of the naturally occurring mutations in glycerol kinase and RNA polymerase have been studied 10,11 , the underlying molecular and network-level consequences and mechanisms promoting cell growth in vivo have not been uncovered. While adaptive evolution and identification of mutations by resequencing has been made easy and cheap, the elucidation of network-level mechanisms remains a major bottleneck in the study of adaptive evolution 4,12 .…”

“…Adaptive laboratory evolution can now be used to carry out controlled evolutions under defined selection pressures to produce new and fit phenotypes [1][2][3][4][5] . Re-sequencing has allowed the detailed assessment of the genetic changes that occur during adaptation 6,7 and allelic replacement can be used to ascertain the causality of the observed mutations relative to the observed phenotype 8 .…”

Improved Production of a Heterologous Amylase in Saccharomyces cerevisiae by Inverse Metabolic Engineering

“…In total, 1,713 putative mutations were identified in either of the two protein-producing strains. Compared to the reference sequence of CEN.PK113-7D (24), mutations that present in all three strains (M715, M1052, and our resequenced CEN.PK113-7D [8]) at the same position and with the same variant were considered to be background mutations found in the original host strain and were therefore filtered out in this first stage. This step reduced the number of unique mutations to 496 (the combined total for the two mutant strains), 328 of which were single-nucleotide point mutations and 84 of which were insertions or deletions (indels).…”

“…represents another effective approach to identify novel metabolic engineering targets for improving protein production. A similar approach based on adaptive evolution to find new targets for enhancing galactose utilization in yeast has been previously demonstrated (8).…”