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dc.contributor.authorSahoo, Swagatika
dc.contributor.authorFranzson, Leifur
dc.contributor.authorJonsson, Jon J
dc.contributor.authorThiele, Ines
dc.date.accessioned2013-12-16T15:59:49Z
dc.date.available2013-12-16T15:59:49Z
dc.date.issued2012
dc.identifier.citationMol. Biosyst. 2012, 8(10):2545-58en
dc.identifier.issn1742-2051
dc.identifier.pmid22699794
dc.identifier.doi10.1039/c2mb25075f
dc.identifier.urihttp://hdl.handle.net/2336/306892
dc.descriptionEfst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinnen
dc.description.abstractInborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.
dc.description.sponsorshipinfo:eu-repo/grantAgreement/EC/FP7/232816en
dc.language.isoenen
dc.publisherRSC publishingen
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/232816en
dc.relation.urlhttp://pubs.rsc.orgen
dc.rightsopenAccessen
dc.subject.meshAcetyl Coenzyme A
dc.subject.meshAmino Acids
dc.subject.meshBrain
dc.subject.meshCarbohydrate Metabolism
dc.subject.meshCarnitine
dc.subject.meshDried Blood Spot Testing
dc.subject.meshFatty Acids
dc.subject.meshGenome, Human
dc.subject.meshHumans
dc.subject.meshInfant, Newborn
dc.subject.meshLipid Metabolism
dc.subject.meshMetabolic Networks and Pathways
dc.subject.meshMetabolism, Inborn Errors
dc.subject.meshMetabolomics
dc.subject.meshOxidation-Reduction
dc.subject.meshTandem Mass Spectrometry
dc.titleA compendium of inborn errors of metabolism mapped onto the human metabolic network.en
dc.typearticleen
dc.contributor.departmentCenter for Systems Biology, University of Iceland, Iceland.en
dc.identifier.journalMolecular bioSystemsen
dc.rights.accessOpen Accessen
refterms.dateFOA2018-09-12T13:07:09Z
html.description.abstractInborn errors of metabolism (IEMs) are hereditary metabolic defects, which are encountered in almost all major metabolic pathways occurring in man. Many IEMs are screened for in neonates through metabolomic analysis of dried blood spot samples. To enable the mapping of these metabolomic data onto the published human metabolic reconstruction, we added missing reactions and pathways involved in acylcarnitine (AC) and fatty acid oxidation (FAO) metabolism. Using literary data, we reconstructed an AC/FAO module consisting of 352 reactions and 139 metabolites. When this module was combined with the human metabolic reconstruction, the synthesis of 39 acylcarnitines and 22 amino acids, which are routinely measured, was captured and 235 distinct IEMs could be mapped. We collected phenotypic and clinical features for each IEM enabling comprehensive classification. We found that carbohydrate, amino acid, and lipid metabolism were most affected by the IEMs, while the brain was the most commonly affected organ. Furthermore, we analyzed the IEMs in the context of metabolic network topology to gain insight into common features between metabolically connected IEMs. While many known examples were identified, we discovered some surprising IEM pairs that shared reactions as well as clinical features but not necessarily causal genes. Moreover, we could also re-confirm that acetyl-CoA acts as a central metabolite. This network based analysis leads to further insight of hot spots in human metabolism with respect to IEMs. The presented comprehensive knowledge base of IEMs will provide a valuable tool in studying metabolic changes involved in inherited metabolic diseases.


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