Literature DB >> 31864070

Tracing metabolic flux through time and space with isotope labeling experiments.

Doug K Allen1, Jamey D Young2.   

Abstract

Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population. Published by Elsevier Ltd.

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Year:  2019        PMID: 31864070      PMCID: PMC7302994          DOI: 10.1016/j.copbio.2019.11.003

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  98 in total

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2.  Mitochondrial metabolism in developing embryos of Brassica napus.

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3.  Discovery of the canonical Calvin-Benson cycle.

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Review 4.  Emerging applications of metabolomics in drug discovery and precision medicine.

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Journal:  Nat Rev Drug Discov       Date:  2016-03-11       Impact factor: 84.694

5.  Cytosolic and mitochondrial malic enzyme isoforms differentially control insulin secretion.

Authors:  Rebecca L Pongratz; Richard G Kibbey; Gerald I Shulman; Gary W Cline
Journal:  J Biol Chem       Date:  2006-11-13       Impact factor: 5.157

6.  TCA cycle activity in Saccharomyces cerevisiae is a function of the environmentally determined specific growth and glucose uptake rates.

Authors:  Lars M Blank; Uwe Sauer
Journal:  Microbiology       Date:  2004-04       Impact factor: 2.777

7.  Analysis of metabolic flux phenotypes for two Arabidopsis mutants with severe impairment in seed storage lipid synthesis.

Authors:  Joachim Lonien; Jörg Schwender
Journal:  Plant Physiol       Date:  2009-09-15       Impact factor: 8.340

8.  Metabolic flux analysis using ¹³C peptide label measurements.

Authors:  Dominic E Mandy; Joshua E Goldford; Hong Yang; Doug K Allen; Igor G L Libourel
Journal:  Plant J       Date:  2014-01-21       Impact factor: 6.417

9.  Novel stable isotope analyses demonstrate significant rates of glucose cycling in mouse pancreatic islets.

Authors:  Martha L Wall; Lynley D Pound; Irina Trenary; Richard M O'Brien; Jamey D Young
Journal:  Diabetes       Date:  2014-12-31       Impact factor: 9.461

Review 10.  The role of dynamic enzyme assemblies and substrate channelling in metabolic regulation.

Authors:  Lee J Sweetlove; Alisdair R Fernie
Journal:  Nat Commun       Date:  2018-05-30       Impact factor: 14.919

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  14 in total

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3.  13C-labeling reveals how membrane lipid components contribute to triacylglycerol accumulation in Chlamydomonas.

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4.  Sterile Spikelets Contribute to Yield in Sorghum and Related Grasses.

Authors:  Taylor AuBuchon-Elder; Viktoriya Coneva; David M Goad; Lauren M Jenkins; Yunqing Yu; Doug K Allen; Elizabeth A Kellogg
Journal:  Plant Cell       Date:  2020-09-01       Impact factor: 11.277

5.  Source of 12C in Calvin-Benson cycle intermediates and isoprene emitted from plant leaves fed with 13CO2.

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Review 6.  Biophysical characterization of melanoma cell phenotype markers during metastatic progression.

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Review 7.  Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes.

Authors:  Richard C Law; Aliya Lakhani; Samantha O'Keeffe; Sevcan Erşan; Junyoung O Park
Journal:  Curr Opin Biotechnol       Date:  2022-03-09       Impact factor: 10.279

8.  Metabolic flux analysis of the non-transitory starch tradeoff for lipid production in mature tobacco leaves.

Authors:  Kevin L Chu; Somnath Koley; Lauren M Jenkins; Sally R Bailey; Shrikaar Kambhampati; Kevin Foley; Jennifer J Arp; Stewart A Morley; Kirk J Czymmek; Philip D Bates; Doug K Allen
Journal:  Metab Eng       Date:  2021-12-14       Impact factor: 9.783

9.  INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation.

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10.  Multi-omics systems toxicology study of mouse lung assessing the effects of aerosols from two heat-not-burn tobacco products and cigarette smoke.

Authors:  Bjoern Titz; Justyna Szostak; Alain Sewer; Blaine Phillips; Catherine Nury; Thomas Schneider; Sophie Dijon; Oksana Lavrynenko; Ashraf Elamin; Emmanuel Guedj; Ee Tsin Wong; Stefan Lebrun; Grégory Vuillaume; Athanasios Kondylis; Sylvain Gubian; Stephane Cano; Patrice Leroy; Brian Keppler; Nikolai V Ivanov; Patrick Vanscheeuwijck; Florian Martin; Manuel C Peitsch; Julia Hoeng
Journal:  Comput Struct Biotechnol J       Date:  2020-04-25       Impact factor: 7.271

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