Literature DB >> 28444347

Fluxomics links cellular functional analyses to whole-plant phenotyping.

Christophe Salon1, Jean-Christophe Avice2, Sophie Colombié3, Martine Dieuaide-Noubhani3, Karine Gallardo1, Christian Jeudy1, Alain Ourry2, Marion Prudent1, Anne-Sophie Voisin1, Dominique Rolin3.   

Abstract

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.
© The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Keywords:  Fluxome; isotope labelling; metabolic flux analysis; metabolic models; metabolism; phenotyping.

Mesh:

Year:  2017        PMID: 28444347     DOI: 10.1093/jxb/erx126

Source DB:  PubMed          Journal:  J Exp Bot        ISSN: 0022-0957            Impact factor:   6.992


  10 in total

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