Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]

Santos-Merino, María; Gargantilla-Becerra, Álvaro; Cruz, Fernando de la; Nogales, Juan

Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids....

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Detalles Bibliográficos
Autores:	Santos-Merino, María, Gargantilla-Becerra, Álvaro, Cruz, Fernando de la, Nogales, Juan
Tipo de recurso:	conjunto de datos
Estado:	Versión publicada
Fecha de publicación:	2023
País:	España
Institución:	Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:	DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:	oai:digital.csic.es:10261/353358
Acceso en línea:	http://hdl.handle.net/10261/353358
Access Level:	acceso abierto
Palabra clave:	Cyanobacteria Synechococcus elongatus PCC 7942 Genome-scale metabolic model Strain-designing algorithms α-linolenic acid

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spelling	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]Santos-Merino, MaríaGargantilla-Becerra, ÁlvaroCruz, Fernando de laNogales, JuanCyanobacteriaSynechococcus elongatus PCC 7942Genome-scale metabolic modelStrain-designing algorithmsα-linolenic acidCyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.Peer reviewedFigshareConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/vnd.ms-excelhttp://hdl.handle.net/10261/353358reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSantos-Merino, María; Gargantilla-Becerra, Álvaro; Cruz, Fernando de la; Nogales, Juan. Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling. http://dx.doi.org/10.3389/fmicb.2023.1126030 . http://hdl.handle.net/10261/339376https://doi.org/10.3389/fmicb.2023.1126030.s009Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3533582026-05-22T06:33:51Z
dc.title.none.fl_str_mv	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
title	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
spellingShingle	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset] Santos-Merino, María Cyanobacteria Synechococcus elongatus PCC 7942 Genome-scale metabolic model Strain-designing algorithms α-linolenic acid
title_short	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
title_full	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
title_fullStr	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
title_full_unstemmed	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
title_sort	Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]
dc.creator.none.fl_str_mv	Santos-Merino, María Gargantilla-Becerra, Álvaro Cruz, Fernando de la Nogales, Juan
author	Santos-Merino, María
author_facet	Santos-Merino, María Gargantilla-Becerra, Álvaro Cruz, Fernando de la Nogales, Juan
author_role	author
author2	Gargantilla-Becerra, Álvaro Cruz, Fernando de la Nogales, Juan
author2_role	author author author
dc.contributor.none.fl_str_mv	Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv	Cyanobacteria Synechococcus elongatus PCC 7942 Genome-scale metabolic model Strain-designing algorithms α-linolenic acid
topic	Cyanobacteria Synechococcus elongatus PCC 7942 Genome-scale metabolic model Strain-designing algorithms α-linolenic acid
description	Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.
publishDate	2023
dc.date.none.fl_str_mv	2023 2024 2024
dc.type.none.fl_str_mv	info:eu-repo/semantics/dataset http://purl.org/coar/resource_type/c_ddb1 Publisher's version info:eu-repo/semantics/publishedVersion
format	dataset
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/10261/353358
url	http://hdl.handle.net/10261/353358
dc.language.none.fl_str_mv	Inglés
language_invalid_str_mv	Inglés
dc.relation.none.fl_str_mv	Santos-Merino, María; Gargantilla-Becerra, Álvaro; Cruz, Fernando de la; Nogales, Juan. Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling. http://dx.doi.org/10.3389/fmicb.2023.1126030 . http://hdl.handle.net/10261/339376 https://doi.org/10.3389/fmicb.2023.1126030.s009 Sí
dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/vnd.ms-excel
dc.publisher.none.fl_str_mv	Figshare
publisher.none.fl_str_mv	Figshare
dc.source.none.fl_str_mv	reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str	Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str	DIGITAL.CSIC. Repositorio Institucional del CSIC
collection	DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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Table_5_Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling.XLSX [Dataset]

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