Autor segons l'article: Jouhten, Paula; Konstantinidis, Dimitrios; Pereira, Filipa; Andrejev, Sergej; Grkovska, Kristina; Castillo, Sandra; Ghiachi, Payam; Beltran, Gemma; Almaas, Eivind; Mas, Albert; Warringer, Jonas; Gonzalez, Ramon; Morales, Pilar; Patil, Kiran R.;
Departament: Bioquímica i Biotecnologia
Autor/s de la URV: Beltran Casellas, Gemma / Mas Baron, Alberto
Paraules clau: Yeast Wine aroma Strains Selection Saccharomyces-cerevisiae Saccharomyces cerevisiae Reconstruction Proteome Predictive evolution Identification Growth Genome-scale metabolic model Escherichia-coli Covariances Adaptive evolution
Resum: 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.
Àrees temàtiques: Medicine (miscellaneous) Information systems Informati Immunology and microbiology (miscellaneous) Immunology and microbiology (all) General medicine General immunology and microbiology General biochemistry,genetics and molecular biology General agricultural and biological sciences Computational theory and mathematics Ciências biológicas ii Biotecnología Biochemistry, genetics and molecular biology (miscellaneous) Biochemistry, genetics and molecular biology (all) Biochemistry & molecular biology Applied mathematics Agricultural and biological sciences (miscellaneous) Agricultural and biological sciences (all)
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: gemma.beltran@urv.cat albert.mas@urv.cat
Identificador de l'autor: 0000-0002-7071-205X 0000-0002-0763-1679
Data d'alta del registre: 2024-07-27
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.embopress.org/doi/full/10.15252/msb.202210980
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Molecular Systems Biology. 18 (10): e10980-
Referència de l'ítem segons les normes APA: Jouhten, Paula; Konstantinidis, Dimitrios; Pereira, Filipa; Andrejev, Sergej; Grkovska, Kristina; Castillo, Sandra; Ghiachi, Payam; Beltran, Gemma; Al (2022). Predictive evolution of metabolic phenotypes using model-designed environments. Molecular Systems Biology, 18(10), e10980-. DOI: 10.15252/msb.202210980
DOI de l'article: 10.15252/msb.202210980
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2022
Tipus de publicació: Journal Publications