Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/57426 |
Resumo: | If we tried to list every known chemical reaction within an organismhuman, plant or even bacteriawe would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular speciesprokaryotesto understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design. |
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Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotesScience & TechnologyIf we tried to list every known chemical reaction within an organismhuman, plant or even bacteriawe would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular speciesprokaryotesto understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design.:This work was supported by grants from: the Fundac ¸ão para a Ciência e a Tecnologia (http:// www.fct.pt) with award number UID/BIO/04469/2013, the European Regional Development Fund (http://www.norte2020.pt) with award number NORTE-01-0145-FEDER-000004 (https://www. ceb.uminho.pt/Projects/Details/6040), Horizon 2020 (https://ec.europa.eu/programmes/ horizon2020) with award number 686070 (http:// dd-decaf.eu/) and COMPETE2020 with award number POCI-01-0145-FEDER-006684 to JCX and IR and the Fundação para a Ciência e a Tecnologia (http://www.fct.pt) with award number SFRH/BD/81626/2011 to JCX. The funders had no role in study design, data collectionand analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersionPublic Library of ScienceUniversidade do MinhoXavier, Joana C.Patil, Kiran RaosahebRocha, I.2018-11-162018-11-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/57426engXavier, Joana C.; Patil, Kiran Raosaheb; Rocha, Isabel, Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes. PLoS Computational Biology, 14(11), 1-23, 20181553-734X1553-735810.1371/journal.pcbi.100655630444863http://journals.plos.org/ploscompbiol/info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:54:26Zoai:repositorium.sdum.uminho.pt:1822/57426Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:54:00.333071Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
title |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
spellingShingle |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes Xavier, Joana C. Science & Technology |
title_short |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
title_full |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
title_fullStr |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
title_full_unstemmed |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
title_sort |
Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes |
author |
Xavier, Joana C. |
author_facet |
Xavier, Joana C. Patil, Kiran Raosaheb Rocha, I. |
author_role |
author |
author2 |
Patil, Kiran Raosaheb Rocha, I. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Xavier, Joana C. Patil, Kiran Raosaheb Rocha, I. |
dc.subject.por.fl_str_mv |
Science & Technology |
topic |
Science & Technology |
description |
If we tried to list every known chemical reaction within an organismhuman, plant or even bacteriawe would get quite a long and confusing read. But when this information is represented in so-called genome-scale metabolic networks, we have the means to access computationally each of those reactions and their interconnections. Some parts of the network have alternatives, while others are unique and therefore can be essential for growth. Here, we simulate growth and compare essential reactions and genes for the simplest type of unicellular speciesprokaryotesto understand which parts of their metabolism are universally essential and potentially ancestral. We show that similar patterns of essential reactions echo phylogenetic relationships (this makes sense, as the genome provides the building plan for the enzymes that perform those reactions). Our computational predictions correlate strongly with experimental essentiality data. Finally, we show that a crucial step of protein synthesis (tRNA charging) and the synthesis and transformation of small molecules that enzymes require (cofactors) are the most essential and conserved parts of metabolism in prokaryotes. Our results are a step further in understanding the biology and evolution of prokaryotes but can also be relevant in applied studies including metabolic engineering and antibiotic design. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-11-16 2018-11-16T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/57426 |
url |
http://hdl.handle.net/1822/57426 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Xavier, Joana C.; Patil, Kiran Raosaheb; Rocha, Isabel, Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes. PLoS Computational Biology, 14(11), 1-23, 2018 1553-734X 1553-7358 10.1371/journal.pcbi.1006556 30444863 http://journals.plos.org/ploscompbiol/ |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Public Library of Science |
publisher.none.fl_str_mv |
Public Library of Science |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133137933959168 |