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Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae
Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic mod...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335255/ https://www.ncbi.nlm.nih.gov/pubmed/35858410 http://dx.doi.org/10.1073/pnas.2108245119 |
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author | Ishchuk, Olena P. Domenzain, Iván Sánchez, Benjamín J. Muñiz-Paredes, Facundo Martínez, José L. Nielsen, Jens Petranovic, Dina |
author_facet | Ishchuk, Olena P. Domenzain, Iván Sánchez, Benjamín J. Muñiz-Paredes, Facundo Martínez, José L. Nielsen, Jens Petranovic, Dina |
author_sort | Ishchuk, Olena P. |
collection | PubMed |
description | Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme. |
format | Online Article Text |
id | pubmed-9335255 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-93352552022-07-30 Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae Ishchuk, Olena P. Domenzain, Iván Sánchez, Benjamín J. Muñiz-Paredes, Facundo Martínez, José L. Nielsen, Jens Petranovic, Dina Proc Natl Acad Sci U S A Biological Sciences Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme. National Academy of Sciences 2022-07-18 2022-07-26 /pmc/articles/PMC9335255/ /pubmed/35858410 http://dx.doi.org/10.1073/pnas.2108245119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Ishchuk, Olena P. Domenzain, Iván Sánchez, Benjamín J. Muñiz-Paredes, Facundo Martínez, José L. Nielsen, Jens Petranovic, Dina Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title | Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title_full | Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title_fullStr | Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title_full_unstemmed | Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title_short | Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae |
title_sort | genome-scale modeling drives 70-fold improvement of intracellular heme production in saccharomyces cerevisiae |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335255/ https://www.ncbi.nlm.nih.gov/pubmed/35858410 http://dx.doi.org/10.1073/pnas.2108245119 |
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