Predictive evolution of metabolic phenotypes using model‐designed environments
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 phen...
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| Format: | Article |
| Language: | English |
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Springer Nature
2022-10-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.15252/msb.202210980 |
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| author | Paula Jouhten Dimitrios Konstantinidis Filipa Pereira Sergej Andrejev Kristina Grkovska Sandra Castillo Payam Ghiachi Gemma Beltran Eivind Almaas Albert Mas Jonas Warringer Ramon Gonzalez Pilar Morales Kiran R Patil |
| author_facet | Paula Jouhten Dimitrios Konstantinidis Filipa Pereira Sergej Andrejev Kristina Grkovska Sandra Castillo Payam Ghiachi Gemma Beltran Eivind Almaas Albert Mas Jonas Warringer Ramon Gonzalez Pilar Morales Kiran R Patil |
| author_sort | Paula Jouhten |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ba56688f07884fe2828fa6593af19657 |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2022-10-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-ba56688f07884fe2828fa6593af196572025-08-20T03:46:32ZengSpringer NatureMolecular Systems Biology1744-42922022-10-01181011810.15252/msb.202210980Predictive evolution of metabolic phenotypes using model‐designed environmentsPaula Jouhten0Dimitrios Konstantinidis1Filipa Pereira2Sergej Andrejev3Kristina Grkovska4Sandra Castillo5Payam Ghiachi6Gemma Beltran7Eivind Almaas8Albert Mas9Jonas Warringer10Ramon Gonzalez11Pilar Morales12Kiran R Patil13European Molecular Biology LaboratoryEuropean Molecular Biology LaboratoryEuropean Molecular Biology LaboratoryEuropean Molecular Biology LaboratoryEuropean Molecular Biology LaboratoryVTT Technical Research Centre of Finland LtdDepartment of Chemistry and Molecular Biology, University of GothenburgDepartament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i VirgiliDepartment of Biotechnology and Food Science, NTNU – Norwegian University of Science and TechnologyDepartament Bioquímica i Biotecnologia, Facultat d'Enologia, Universitat Rovira i VirgiliDepartment of Chemistry and Molecular Biology, University of GothenburgInstituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraInstituto de Ciencias de la Vid y delVino (CSIC, Gobierno de la Rioja, Universidad de La Rioja) Finca La GrajeraEuropean Molecular Biology LaboratoryAbstract 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.https://doi.org/10.15252/msb.202210980adaptive evolutiongenome‐scale metabolic modelpredictive evolutionSaccharomyces cerevisiaewine aroma |
| spellingShingle | Paula Jouhten Dimitrios Konstantinidis Filipa Pereira Sergej Andrejev Kristina Grkovska Sandra Castillo Payam Ghiachi Gemma Beltran Eivind Almaas Albert Mas Jonas Warringer Ramon Gonzalez Pilar Morales Kiran R Patil Predictive evolution of metabolic phenotypes using model‐designed environments Molecular Systems Biology adaptive evolution genome‐scale metabolic model predictive evolution Saccharomyces cerevisiae wine aroma |
| title | Predictive evolution of metabolic phenotypes using model‐designed environments |
| title_full | Predictive evolution of metabolic phenotypes using model‐designed environments |
| title_fullStr | Predictive evolution of metabolic phenotypes using model‐designed environments |
| title_full_unstemmed | Predictive evolution of metabolic phenotypes using model‐designed environments |
| title_short | Predictive evolution of metabolic phenotypes using model‐designed environments |
| title_sort | predictive evolution of metabolic phenotypes using model designed environments |
| topic | adaptive evolution genome‐scale metabolic model predictive evolution Saccharomyces cerevisiae wine aroma |
| url | https://doi.org/10.15252/msb.202210980 |
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