Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/57426

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dc.contributor.authorXavier, Joana C.por
dc.contributor.authorPatil, Kiran Raosahebpor
dc.contributor.authorRocha, I.por
dc.date.accessioned2018-12-20T09:54:10Z-
dc.date.available2018-12-20T09:54:10Z-
dc.date.issued2018-11-16-
dc.identifier.citationXavier, 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, 2018por
dc.identifier.issn1553-734Xpor
dc.identifier.urihttp://hdl.handle.net/1822/57426-
dc.description.abstractIf 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.por
dc.description.sponsorship: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.por
dc.language.isoengpor
dc.publisherPublic Library of Sciencepor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147337/PTpor
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/686070/EUpor
dc.relationinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F81626%2F2011/PTpor
dc.rightsopenAccesspor
dc.titleMetabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotespor
dc.typearticle-
dc.peerreviewedyespor
dc.relation.publisherversionhttp://journals.plos.org/ploscompbiol/por
dc.commentsCEB49383por
oaire.citationStartPage1por
oaire.citationEndPage23por
oaire.citationIssue11por
oaire.citationConferencePlaceUnited States-
oaire.citationVolume14por
dc.date.updated2018-12-20T09:36:54Z-
dc.identifier.essn1553-7358por
dc.identifier.doi10.1371/journal.pcbi.1006556por
dc.identifier.pmid30444863por
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
dc.subject.wosScience & Technologypor
sdum.journalPLoS Computational Biologypor
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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