Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.
Identifieur interne : 002739 ( Main/Exploration ); précédent : 002738; suivant : 002740Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.
Auteurs : Shilpa Nargund [États-Unis] ; Ganesh SriramSource :
- Molecular bioSystems [ 1742-2051 ] ; 2013.
Descripteurs français
- KwdFr :
- Acide gamma-amino-butyrique (métabolisme), Acides aminés (métabolisme), Arabidopsis (métabolisme), Biologie des systèmes (méthodes), Cytosol (métabolisme), Glucose (métabolisme), Marquage isotopique (méthodes), Phénomènes physiologiques des plantes (MeSH), Plantes (métabolisme), Plastes (métabolisme), Populus (métabolisme), Simulation numérique (MeSH), Spectrométrie de masse (MeSH), Spectroscopie par résonance magnétique (MeSH), Voie des pentoses phosphates (physiologie).
- MESH :
- métabolisme : Acide gamma-amino-butyrique, Acides aminés, Arabidopsis, Cytosol, Glucose, Plantes, Plastes, Populus.
- méthodes : Biologie des systèmes, Marquage isotopique.
- physiologie : Voie des pentoses phosphates.
- Phénomènes physiologiques des plantes, Simulation numérique, Spectrométrie de masse, Spectroscopie par résonance magnétique.
English descriptors
- KwdEn :
- Amino Acids (metabolism), Arabidopsis (metabolism), Computer Simulation (MeSH), Cytosol (metabolism), Glucose (metabolism), Isotope Labeling (methods), Magnetic Resonance Spectroscopy (MeSH), Mass Spectrometry (MeSH), Pentose Phosphate Pathway (physiology), Plant Physiological Phenomena (MeSH), Plants (metabolism), Plastids (metabolism), Populus (metabolism), Systems Biology (methods), gamma-Aminobutyric Acid (metabolism).
- MESH :
- chemical , metabolism : Amino Acids, Glucose, gamma-Aminobutyric Acid.
- metabolism : Arabidopsis, Cytosol, Plants, Plastids, Populus.
- methods : Isotope Labeling, Systems Biology.
- physiology : Pentose Phosphate Pathway.
- Computer Simulation, Magnetic Resonance Spectroscopy, Mass Spectrometry, Plant Physiological Phenomena.
Abstract
Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The potency of 1,2-(13)C Glc can be improved substantially by combining it with other labels (e.g. 3-(13)C Glc, 1-(13)C Glc and U-(13)C Glc) in parallel ILEs. For the GABA shunt, we calculated that 100% 2-(13)C Ala and 100% U-(13)C Gln constitute the best labels. We anticipate that the ILE designs presented in this article can enhance the quality of flux estimates in these two complex plant pathways. In the future, these ILE designs can be further improved by leveraging recent analytical and computational developments in isotope-assisted MFA.
DOI: 10.1039/c2mb25253h
PubMed: 23114423
Affiliations:
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magnétique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The potency of 1,2-(13)C Glc can be improved substantially by combining it with other labels (e.g. 3-(13)C Glc, 1-(13)C Glc and U-(13)C Glc) in parallel ILEs. For the GABA shunt, we calculated that 100% 2-(13)C Ala and 100% U-(13)C Gln constitute the best labels. We anticipate that the ILE designs presented in this article can enhance the quality of flux estimates in these two complex plant pathways. In the future, these ILE designs can be further improved by leveraging recent analytical and computational developments in isotope-assisted MFA.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23114423</PMID><DateCompleted><Year>2013</Year><Month>07</Month><Day>24</Day></DateCompleted><DateRevised><Year>2016</Year><Month>11</Month><Day>25</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1742-2051</ISSN><JournalIssue CitedMedium="Internet"><Volume>9</Volume><Issue>1</Issue><PubDate><Year>2013</Year><Month>Jan</Month><Day>27</Day></PubDate></JournalIssue><Title>Molecular bioSystems</Title><ISOAbbreviation>Mol Biosyst</ISOAbbreviation></Journal><ArticleTitle>Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.</ArticleTitle><Pagination><MedlinePgn>99-112</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1039/c2mb25253h</ELocationID><Abstract><AbstractText>Metabolic fluxes are powerful indicators of cell physiology and can be estimated by isotope-assisted metabolic flux analysis (MFA). The complexity of the compartmented metabolic networks of plants has constrained the application of isotope-assisted MFA to them, principally because of poor identifiability of fluxes from the measured isotope labeling patterns. However, flux identifiability can be significantly improved by a priori design of isotope labeling experiments (ILEs). This computational design involves evaluating the effect of different isotope label and isotopomer measurement combinations on flux identifiability, and thereby identifying optimal labels and measurements toward evaluating the fluxes of interest with the highest confidence. This article reports ILE designs for two major, compartmented plant metabolic pathways - the pentose phosphate pathway (PPP) and γ-aminobutyric acid (GABA) shunt. Together, these pathways represent common motifs in plant metabolism including duplication of pathways in different subcellular compartments, reversible reactions and cyclic carbon flow. To compare various ILE designs, we employed statistical A- and D-optimality criteria. Our computations showed that 1,2-(13)C Glc is a powerful and robust label for the plant PPPs, given currently popular isotopomer measurement techniques (single quadrupole mass spectrometry [MS] and 2-D nuclear magnetic resonance [NMR]). Further analysis revealed that this label can estimate several PPP fluxes better than the popular label 1-(13)C Glc. Furthermore, the concurrent measurement of the isotopomers of hexose and pentose moieties synthesized exclusively in the cytosol or the plastid compartments (measurable through intracellular glucose or sucrose, starch, RNA ribose and histidine) considerably improves the identifiability of PPP fluxes in the individual compartments. Additionally, MS-derived isotopomer measurements outperform NMR-derived measurements in identifying PPP fluxes. The potency of 1,2-(13)C Glc can be improved substantially by combining it with other labels (e.g. 3-(13)C Glc, 1-(13)C Glc and U-(13)C Glc) in parallel ILEs. For the GABA shunt, we calculated that 100% 2-(13)C Ala and 100% U-(13)C Gln constitute the best labels. We anticipate that the ILE designs presented in this article can enhance the quality of flux estimates in these two complex plant pathways. In the future, these ILE designs can be further improved by leveraging recent analytical and computational developments in isotope-assisted MFA.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Nargund</LastName><ForeName>Shilpa</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Chemical and Biomolecular Engineering, University of Maryland, 1208D, Chemical and Nuclear Engineering Building 090, College Park, MD 20742, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Sriram</LastName><ForeName>Ganesh</ForeName><Initials>G</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>11</Month><Day>01</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Mol Biosyst</MedlineTA><NlmUniqueID>101251620</NlmUniqueID><ISSNLinking>1742-2051</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D000596">Amino Acids</NameOfSubstance></Chemical><Chemical><RegistryNumber>56-12-2</RegistryNumber><NameOfSubstance UI="D005680">gamma-Aminobutyric Acid</NameOfSubstance></Chemical><Chemical><RegistryNumber>IY9XDZ35W2</RegistryNumber><NameOfSubstance UI="D005947">Glucose</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000596" MajorTopicYN="N">Amino Acids</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D017360" MajorTopicYN="N">Arabidopsis</DescriptorName><QualifierName UI="Q000378" 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Acid</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>11</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>11</Month><Day>2</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>7</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">23114423</ArticleId><ArticleId IdType="doi">10.1039/c2mb25253h</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Maryland</li></region><settlement><li>College Park (Maryland)</li></settlement><orgName><li>Université du Maryland</li></orgName></list><tree><noCountry><name sortKey="Sriram, Ganesh" sort="Sriram, Ganesh" uniqKey="Sriram G" first="Ganesh" last="Sriram">Ganesh Sriram</name></noCountry><country name="États-Unis"><region name="Maryland"><name sortKey="Nargund, Shilpa" sort="Nargund, Shilpa" uniqKey="Nargund S" first="Shilpa" last="Nargund">Shilpa Nargund</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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