Predictive evolution of metabolic phenotypes using model‐designed environments
2022; Springer Nature; Volume: 18; Issue: 10 Linguagem: Inglês
10.15252/msb.202210980
ISSN1744-4292
AutoresPaula Jouhten, Dimitrios Konstantinidis, Filipa Pereira, Sergej Andrejev, Kristina Grkovska, Sandra Castillo, Payam Ghiachi, Gemma Beltran, Eivind Almaas, Albert Mas, Jonas Warringer, Ramón González, Pilar Morales, Kiran Raosaheb Patil,
Tópico(s)Enzyme Catalysis and Immobilization
ResumoArticle6 October 2022Open Access Transparent process Predictive evolution of metabolic phenotypes using model-designed environments Paula Jouhten Paula Jouhten orcid.org/0000-0003-1075-7448 European Molecular Biology Laboratory, Heidelberg, Germany VTT Technical Research Centre of Finland Ltd, Espoo, Finland Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland Contribution: Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Dimitrios Konstantinidis Dimitrios Konstantinidis European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Formal analysis, Investigation Search for more papers by this author Filipa Pereira Filipa Pereira European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Formal analysis, Investigation Search for more papers by this author Sergej Andrejev Sergej Andrejev European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Resources Search for more papers by this author Kristina Grkovska Kristina Grkovska European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Sandra Castillo Sandra Castillo VTT Technical Research Centre of Finland Ltd, Espoo, Finland Contribution: Software Search for more papers by this author Payam Ghiachi Payam Ghiachi Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden Contribution: Investigation Search for more papers by this author Gemma Beltran Gemma Beltran Departament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Tarragona, Spain Contribution: Resources Search for more papers by this author Eivind Almaas Eivind Almaas orcid.org/0000-0002-9125-326X Department of Biotechnology and Food Science, NTNU – Norwegian University of Science and Technology, Trondheim, Norway Contribution: Conceptualization, Supervision Search for more papers by this author Albert Mas Albert Mas orcid.org/0000-0002-0763-1679 Departament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Tarragona, Spain Contribution: Resources, Supervision Search for more papers by this author Jonas Warringer Jonas Warringer orcid.org/0000-0001-6144-2740 Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden Contribution: Resources, Supervision Search for more papers by this author Ramon Gonzalez Ramon Gonzalez Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La Grajera, Logroño, Spain Contribution: Resources, Supervision Search for more papers by this author Pilar Morales Pilar Morales orcid.org/0000-0002-0130-6111 Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La Grajera, Logroño, Spain Contribution: Formal analysis, Investigation Search for more papers by this author Kiran R Patil Corresponding Author Kiran R Patil [email protected] orcid.org/0000-0002-6166-8640 European Molecular Biology Laboratory, Heidelberg, Germany Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Paula Jouhten Paula Jouhten orcid.org/0000-0003-1075-7448 European Molecular Biology Laboratory, Heidelberg, Germany VTT Technical Research Centre of Finland Ltd, Espoo, Finland Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland Contribution: Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Dimitrios Konstantinidis Dimitrios Konstantinidis European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Formal analysis, Investigation Search for more papers by this author Filipa Pereira Filipa Pereira European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Formal analysis, Investigation Search for more papers by this author Sergej Andrejev Sergej Andrejev European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Resources Search for more papers by this author Kristina Grkovska Kristina Grkovska European Molecular Biology Laboratory, Heidelberg, Germany Contribution: Investigation Search for more papers by this author Sandra Castillo Sandra Castillo VTT Technical Research Centre of Finland Ltd, Espoo, Finland Contribution: Software Search for more papers by this author Payam Ghiachi Payam Ghiachi Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden Contribution: Investigation Search for more papers by this author Gemma Beltran Gemma Beltran Departament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Tarragona, Spain Contribution: Resources Search for more papers by this author Eivind Almaas Eivind Almaas orcid.org/0000-0002-9125-326X Department of Biotechnology and Food Science, NTNU – Norwegian University of Science and Technology, Trondheim, Norway Contribution: Conceptualization, Supervision Search for more papers by this author Albert Mas Albert Mas orcid.org/0000-0002-0763-1679 Departament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Tarragona, Spain Contribution: Resources, Supervision Search for more papers by this author Jonas Warringer Jonas Warringer orcid.org/0000-0001-6144-2740 Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden Contribution: Resources, Supervision Search for more papers by this author Ramon Gonzalez Ramon Gonzalez Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La Grajera, Logroño, Spain Contribution: Resources, Supervision Search for more papers by this author Pilar Morales Pilar Morales orcid.org/0000-0002-0130-6111 Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La Grajera, Logroño, Spain Contribution: Formal analysis, Investigation Search for more papers by this author Kiran R Patil Corresponding Author Kiran R Patil [email protected] orcid.org/0000-0002-6166-8640 European Molecular Biology Laboratory, Heidelberg, Germany Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, UK Contribution: Conceptualization, Resources, Visualization, Methodology, Writing - original draft, Writing - review & editing Search for more papers by this author Author Information Paula Jouhten1,2,3,†, Dimitrios Konstantinidis1,†, Filipa Pereira1, Sergej Andrejev1, Kristina Grkovska1, Sandra Castillo2, Payam Ghiachi4, Gemma Beltran5, Eivind Almaas6, Albert Mas5, Jonas Warringer4, Ramon Gonzalez7, Pilar Morales7 and Kiran R Patil *,1,8 1European Molecular Biology Laboratory, Heidelberg, Germany 2VTT Technical Research Centre of Finland Ltd, Espoo, Finland 3Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland 4Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden 5Departament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i Virgili, Tarragona, Spain 6Department of Biotechnology and Food Science, NTNU – Norwegian University of Science and Technology, Trondheim, Norway 7Instituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La Grajera, Logroño, Spain 8Medical Research Council (MRC) Toxicology Unit, University of Cambridge, Cambridge, UK † These authors contributed equally to this work *Corresponding author. Tel: +44 01223 3 35640; E-mail: [email protected] Molecular Systems Biology (2022)18:e10980https://doi.org/10.15252/msb.202210980 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution. Synopsis EvolveX, a new algorithm enabling model-guided design of chemical environments for targeted adaptive evolution, is applied to evolve a wine yeast strain for increased aroma secretion. EvolveX predicts environment-dependence of trait-fitness correlations using genome-scale metabolic models. Multi-omics analysis shows agreement with the model-predicted metabolic changes. EvolveX enables devising adaptive evolution strategies for improving traits uncorrelated with cell fitness. Introduction Adaptive evolution under environmental selection pressure can give rise to and optimize complex phenotypes (Darwin, 1859; Shoval et al, 2012; Locey & Lennon, 2016; Sunagawa et al, 2020). While this evolutionary process can involve numerous alternative paths at the level of genotype, phenotype evolution is often convergent (Barrick et al, 2009; Lassig et al, 2017). Adaptive evolution under controlled laboratory conditions can thus be used to obtain target phenotypes without explicit knowledge of the causative genotype. This method, known as adaptive laboratory evolution, is widely used for improving microbial strains; examples include temperature tolerance (Sandberg et al, 2014; Caspeta & Nielsen, 2015), simplified nutritional requirement (Bracher et al, 2017), and boosting photosynthetic capabilities (Antonovsky et al, 2016; Gassler et al, 2020). While effective in optimizing complex traits and operationally simple, adaptive laboratory evolution is inherently limited to traits that are genetically linked to the fitness. Consequently, improving fitness-neutral or costly trait requires artificial, non-Darwinian, selection through screening of large numbers of variants. This is a considerable combinatorial challenge for complex multigenic traits, and, thus, application of artificial selection has yet been limited to single proteins or pathways with photometric readouts (Arnold, 1993; Wang et al, 2009; Lee et al, 2013; Chen et al, 2018; van Tatenhove-Pel et al, 2020). Darwinian selection of a complex trait requires identification of an environmental condition where the trait becomes genetically growth-linked (Agrawal & Stinchcombe, 2009), for example, increased antioxidant production could be selected under oxidative damage conditions (Reyes & Kao, 2018). However, such qualitative and sparsely known associations cannot be generalized, calling for predictive models of trait dependences. Here, we ask whether first-principle models could enable predicting environments under which a desired trait could be adaptively selected. We base our strategy on genome-scale metabolic models, which allow predicting metabolic fluxes consistent both with the mass balance constraints and the fitness objectives of the cells (e.g., optimal growth; O'Brien et al, 2015; Varma & Palsson, 1994). In the context of laboratory evolution, genome-scale metabolic models have well predicted fitness improvement and the associated metabolic flux changes (Ibarra et al, 2002; Szappanos et al, 2016; Strucko et al, 2018; Guzman et al, 2019). The genome-scale metabolic models can also be used for predicting metabolic gene deletions that couple a desired production trait to growth (Burgard et al, 2003; Patil et al, 2005). After such model-guided genome editing adaptive laboratory evolution has successfully been used to improve the growth-coupled production rates (Burgard et al, 2003; Jantama et al, 2008; Brochado & Patil, 2013; Jensen et al, 2019; Pereira et al, 2021). We use these genome-scale metabolic models to predict environment-dependence of the coupling between metabolic traits, and that between metabolic traits and the cell fitness. This allowed us to generalize the design of evolution environments and Darwinian selection of target phenotypes. Results Evolution environment Consider an application environment, for example, wine must wherein the manifestation of a target metabolic trait, for example, aroma production, is desired. We postulate that improvement of the desired trait in the application environment can be achieved through adaptive evolution in a distinct evolution environment, followed by the return to the application environment. To design the evolution environment, we take advantage of the observation that the coupling between metabolic traits is predictable as couplings between metabolic fluxes and dependent on the nutritional/chemical composition of the environment (Box 1). Box 1. Trait-fitness dependences are predictable as flux couplings. The selection acting on a phenotypic trait is the covariance between the trait and the relative fitness, as described by Robertson-Price identity (Robertson, 1966, 1968; Price, 1970; Rausher, 1992; equation 1). s = cov w , z (1)where s is the selection differential, w fitness, and z the trait of interest. When there is genetic covariance between the trait and relative fitness, evolutionary response to selection can occur (equation 2, the secondary theorem of selection). R = s g = cov a w , z (2)where R is the response to selection with units of the trait and fitness multiplied, sg is the genetic selection differential, and cova(w,z) is the additive genetic covariance. Equation (2) generalizes to a multivariate form for multiple traits (Rausher, 1992). R = cov a w , z (3)We now consider the case of metabolic traits, which can be represented and modeled as a set of metabolic fluxes (net reaction rates). Metabolic trait interdependencies under a given chemical environment can then be predicted using genome-scale metabolic models as flux couplings (Burgard et al, 2004). Two metabolic reactions are coupled if a nonzero flux through one reaction implies a nonzero flux through the other. Flux covariance follows from flux coupling (Heinonen et al, 2019; preprint: Pradhan et al, 2019; Thommes et al, 2019). Importantly for modeling evolutionary adaptation, flux coupling implies genetic dependences between the corresponding enzyme-coding genes (Notebaart et al, 2008). To predict the relative response of a metabolic trait to selection, we use its coupling to the specific growth rate (proxy for mean fitness). Analogous to the secondary theorem of selection (equation 3), this gives: F v = v μ (4)where Fv is the relative unitless responses of single-flux metabolic traits to selection, v the metabolic fluxes, and μ the specific growth rate. Thus, higher the flux per growth unit, stronger the selection. To search for a suitable evolution environment, that is, a defined chemical environment in which the adaptive evolution is to take place, we use the basis provided by the selection response relation (equations 3 and 4). Ideally, the evolution environment would be chosen such that there is a direct selection for the desired trait through flux coupling with the cell growth. This, however, will only rarely be possible as most desired traits, such as metabolite secretion, are in a trade-off with cell growth due to a competition for metabolic precursors and co-factors (Jouhten et al, 2016; Nielsen & Keasling, 2016; Fig 1A). We therefore aim at growth coupling of a secondary trait, which we term tacking trait. Tacking trait is here defined as a set of fluxes that are flux coupled (Burgard et al, 2004) to cell growth in the evolution environment, and with the desired trait in the application environment (Fig 1B). We note that it is neither necessary for the tacking trait to be coupled with the desired trait in the evolution environment, nor it is likely due to the trade-off with growth. Further, the tacking trait is necessarily a proper subset of fluxes that must increase or decrease for the desired trait enhancement in the application environment. Due to the environment-dependence of genetic correlations between traits (equation 3), the tacking trait and the evolution environment are intrinsically linked and need to be identified simultaneously. Figure 1. Darwinian selection in an absence of fitness advantage through an evolution environment and a tacking traitCurrent phenotype is represented with an orange circle, whereas the orange star represents the desired target phenotype. A. In the application environment (yellow), Darwinian selection (gray arrows) enriches cells with fitter phenotypes but with diminished desired trait. B. The tacking trait is chosen to be coupled with fitness in the evolution environment and can therefore be improved through Darwinian selection. C. The tacking trait is also characterized by direct coupling to the desired target trait in the application environment, even though not so in the evolution environment (green). D. Evolved cells with a strengthened tacking trait (through selection in the evolution environment) manifest an improved desired trait in the application environment. E–G. A simple metabolic network illustrating the evolution environment and the tacking trait. The desired trait is the production flux of a compound (open hexagon). The squares depict available nutrients, which differ between the target and evolution environments. The arrows represent metabolic fluxes, the thicker the arrow the higher the flux. The tacking trait (red arrows), which is part of the flux basis of the desired trait, is flux coupled to cell growth flux (i.e., proxy of mean fitness) in the evolution environment. Thus, the tacking trait can be improved through adaptive evolution in the evolution environment. Due to the flux coupling in the application environment, the improved tacking trait leads to the enhanced desired target trait (i.e., increased target compound secretion). Download figure Download PowerPoint A desired trait that does not pose a fitness advantage will not be under Darwinian selection in the application environment (Fig 1A). In our strategy, the evolution environment is designed such that the tacking trait becomes flux coupled to mean fitness (Fig 1B), allowing positive selection on de novo mutations enhancing the tacking trait. Upon switching to the application environment, in which the tacking trait is flux coupled with the desired trait, the latter is enhanced (Fig 1C and D). To illustrate this strategy, we consider a simple metabolic network (Fig 1E–G). The parental strain is well adapted to channel the nutrients to cell growth and thus produces only a little desired product (Fig 1E). In an appropriately selected evolution environment (Fig 1F), a different set of pathways are flux coupled with growth (Fig 1F). During the adaptive evolution, increased flux through these growth-coupled pathways is selected for. While there is no increase of production in the evolution environment, the evolved strain exhibits, due to the direct coupling between the tacking and the target trait, improved production in the application environment (Fig 1G). Under a prolonged cultivation, the desired trait may be negatively selected in the application environment. However, this is not an obstacle for the use of the proposed strategy in, for example, a biotechnological setting. A typical microbiological process involves only a few generations (below 10) and is thus unlikely to diminish the desired trait. The necessary condition will be to maintain and propagate the cell stock in a separate environment (in this case the evolution environment), which is a common practice in microbiology. Predicting evolution environment To predict evolution environments satisfying the conditions laid out above, we devised an algorithm, termed EvolveX, based on genome-scale metabolic models. The algorithm simultaneously identifies a tacking trait and evaluates the suitability of a set of nutrients for adaptively evolving the tacking trait (evolution environment). EvolveX consists of four main steps. Step 1: For a given desired trait, its flux basis is determined. This is defined as the set of fluxes that must change for the enhancement of the desired trait in the application environment. Step 2: For the identified flux basis, a response to selection in the evolution environment is predicted. The subset of the flux basis with nonzero responses to selection forms the tacking trait. Note that, as the covariances of traits may change through evolution (Lande, 1980; Jones et al, 2007; Arnold et al, 2008), we define the flux basis (Step 1) in the ancestral state but predict the response to selection in a state that is expected to be approached during experimental evolution (Ibarra et al, 2002; Szappanos et al, 2016; McCloskey et al, 2018). Step 3: A minimum size (cardinality) of the subset of the flux basis having a stronger response to selection in the evolution environment than in the application environment is estimated. Step 4: A suitability score of an evolution environment is calculated by combining: (i) results of Step 2, indicating the strength of response to selection; (ii) results of Step 3, indicating the coverage of flux basis with desired selection; and (iii) the number of chemical components in the evolution environment (lower the number, higher the score). The last criterion is included to discount for the uncertainty in the knowledge of the organism's nutritional preferences. Further details of EvolveX implementation, which accounts for variability in flux estimates and normalizes the score to enable comparison across different growth rates, are provided in Materials and Methods. Model-predicted evolution environments increase aroma production To experimentally validate the applicability of model-designed evolution environment, we set to improve secretion of aroma compounds by Saccharomyces cerevisiae in wine must. Wine must is characterized by high sugar content and relatively less assimilable nitrogen. As aroma synthesis diverts carbon and nitrogen away from the production of daughter cells, it cannot be adaptively selected in the application environment (wine must). Moreover, while the metabolic pathways that synthesize aroma compounds are known, their regulation is poorly understood, preventing facile engineering of aroma secretion (de Carvalho et al, 2017). We targeted two main groups of aroma compounds: (i) phenylethyl alcohol and its acetate ester, phenylethylacetate, which have a rose and honey scent and raspberry-like flavor; and (ii) branched-chain amino acid-derived higher alcohols (2-methyl-1-butanol and 3-methyl-1-butanol) and their acetate esters (2-methylbutylacetate and isoamyl acetate; Swiegers et al, 2005; Carpena et al, 2021), which have a banana and pear scent and fruity flavor. All these aroma compounds derive from amino acids' (L-phenylalanine and branched chain amino acids) carbon backbones and contain no nitrogen. The flux bases of the target aroma syntheses were defined as a minimum set of fluxes that have to increase for the particular target aroma generation to be enhanced. Similarly, flux bases could include fluxes that should be negatively selected for desired trait development. To identify a suitable evolution environment for enhancing the target aroma generation and corresponding tacking traits, we assessed all 1,540 combinations of up to three carbon and nitrogen sources, chosen from 22 common constituents of yeast growth media. All combinations were ranked for their suitability for positively selecting the flux bases of the target aroma generation (via the tacking traits) using the EvolveX score (Table EV1). High-scoring environments were assessed for literature evidence of feasibility of S. cerevisiae growth. Two of the high-scoring environments, which were among the top 20 of 1,171 growth-supporting solutions, were selected for experimental validation. Evolution environment containing glycerol, phenylalanine, and threonine as sole carbon and nitrogen sources was chosen for phenylethyl alcohol and phenylethylacetate production. In this environment, hereafter called glycerol environment (Fig 2A), 7 fluxes (out of 20 in the flux basis) formed the tacking trait of phenylethyl alcohol and phenylethylacetate production (Table EV2). For branched-chain amino acid-derived aromas, ethanol environment (Fig 2A), containing ethanol, arginine, and glycine, was selected for experimental validation. In the ethanol environment, 11 fluxes (out of 44 in the flux basis) formed the tacking trait (Table EV2). The two tacking traits included two common fluxes (transketolase 1, ribulose 5-phosphate epimerase). However, only eight common fluxes were predicted to be positively selected in the two evolution environments while 57 fluxes were predicted to be selected only in one of the two evolution environments (Table EV3). Notably, the glycerol environment and in the ethanol environment were predicted to expose positive selection on 17 (out of 29) and 20 (out of 44) common fluxes with intuitive control environments glycerol and ammonium and ethanol and ammonium, respectively (Table EV3). Thus, the EvolveX designed glycerol and ethanol evolution environments act as appropriate controls to each other. Figure 2. Aroma production changes detected in evolved yeast strains Origin of aroma compounds in the yeast central metabolism: branched-chain amino acid-derived compounds (esp. 2-methyl-1-butanol, 3-methyl-1-butanol, isoamyl acetate, and 2-methylbutylacetate), and aromatic amino acid-derived compounds (esp. phenylethyl alcohol and phenylethyl acetate). Acetate esters of higher alcohols share an acetyl-CoA (ACCOA) precursor. Parental wine strain of S. cerevisiae was adaptively evolved in both ethanol environment and glycerol environment for over 150 generations. Evolved single colony isolates had improved growth in glycerol environment compared to parental. The growth of isolates G2-1 and G2-2 and the parental characterized in three biological replicates as backscattered light (AU—arbitrary units). Evolved single colony isolates had improved growth in ethanol environment compared to parental. The growth of isolates E2-1 and E2-2 and the parental characterized in three biological replicates as backscattered light (AU—arbitrary units). Evolved single colony isolates maintained similar to parental growth ability characterized in single biological replicates as carbon loss in natural wine must fermentations. Principal components analysis of quantified 28 volatile aroma compounds in natural wine must fermentations, with the parental (gray) and evolved strains in three biological replicates. Evolved strain from the ethanol evolution environment (ethanol, arginine, glycine), E2-1, in light blue, and that from the glycerol evolution environment (glycerol, phenylalanine, threonine), G2-1, in orange. Principal components analysis of aromatic and branched amino acid-derived volatile compound profiles of natural wine must fermentations, with the parental (gray) and evolved strains (E2-1 in light blue, G2-1 in orange) in three biological replicates. Changes in selected aroma compound abundances in wine must fermentations. AU—arbitrary units. E2-1 (light blue) was selected in the ethanol environment, and G2-1 (orange) was selected in the glycerol environment. 2 + 3-methylbutanol (a combined pool of 2-methyl-1-butanol and 3-methyl-1-butanol) and isoamyl acetate (acetate ester of 3-methyl-1-butanol) were the desired target aromas of the ethanol environment, deriving from branched-chain amino acids. Phenylethyl alcohol and its acetate ester, phenylethyl acetate, were the desired target aromas of the glycerol environment. Medians over three biological replicates are shown with black lines. Significant differences in means (Tukey's test; n = 3; P value < 0.05) are indicated with P values. Download figure Download PowerPoint In each of the two selected environments, three replicate populations of a diploid wine yeast strain (selected based on capability of growing in both environments) were independently evolved asexually for over 150 generations (Fig 2B). Growth improvement was observed in both evolution environments (F
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