Literature DB >> 33637852

Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism.

Christopher Pries1, Zahra Razaghi-Moghadam2,3, Joachim Kopka1, Zoran Nikoloski4,5.   

Abstract

Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 [Formula: see text] chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin-Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.

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Year:  2021        PMID: 33637852      PMCID: PMC7910480          DOI: 10.1038/s41598-021-84114-y

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  32 in total

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Journal:  Genetics       Date:  1999-06       Impact factor: 4.562

Review 2.  Flux an important, but neglected, component of functional genomics.

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Journal:  Curr Opin Plant Biol       Date:  2005-04       Impact factor: 7.834

Review 3.  Fluxes through plant metabolic networks: measurements, predictions, insights and challenges.

Authors:  Nicholas J Kruger; R George Ratcliffe
Journal:  Biochem J       Date:  2015-01-01       Impact factor: 3.857

4.  Analysis of optimality in natural and perturbed metabolic networks.

Authors:  Daniel Segrè; Dennis Vitkup; George M Church
Journal:  Proc Natl Acad Sci U S A       Date:  2002-11-01       Impact factor: 11.205

5.  What is flux balance analysis?

Authors:  Jeffrey D Orth; Ines Thiele; Bernhard Ø Palsson
Journal:  Nat Biotechnol       Date:  2010-03       Impact factor: 54.908

6.  Systems-level analysis of mechanisms regulating yeast metabolic flux.

Authors:  Sean R Hackett; Vito R T Zanotelli; Wenxin Xu; Jonathan Goya; Junyoung O Park; David H Perlman; Patrick A Gibney; David Botstein; John D Storey; Joshua D Rabinowitz
Journal:  Science       Date:  2016-10-27       Impact factor: 47.728

7.  Dynamic flux balance analysis of diauxic growth in Escherichia coli.

Authors:  Radhakrishnan Mahadevan; Jeremy S Edwards; Francis J Doyle
Journal:  Biophys J       Date:  2002-09       Impact factor: 4.033

8.  Plant Temperature Acclimation and Growth Rely on Cytosolic Ribosome Biogenesis Factor Homologs.

Authors:  Olga Beine-Golovchuk; Alexandre Augusto Pereira Firmino; Adrianna Dąbrowska; Stefanie Schmidt; Alexander Erban; Dirk Walther; Ellen Zuther; Dirk K Hincha; Joachim Kopka
Journal:  Plant Physiol       Date:  2018-01-30       Impact factor: 8.340

9.  Multi-omic data integration enables discovery of hidden biological regularities.

Authors:  Ali Ebrahim; Elizabeth Brunk; Justin Tan; Edward J O'Brien; Donghyuk Kim; Richard Szubin; Joshua A Lerman; Anna Lechner; Anand Sastry; Aarash Bordbar; Adam M Feist; Bernhard O Palsson
Journal:  Nat Commun       Date:  2016-10-26       Impact factor: 14.919

10.  Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition.

Authors:  Jakob Vowinckel; Aleksej Zelezniak; Roland Bruderer; Michael Mülleder; Lukas Reiter; Markus Ralser
Journal:  Sci Rep       Date:  2018-03-12       Impact factor: 4.379

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  1 in total

Review 1.  Genome-scale metabolic network models: from first-generation to next-generation.

Authors:  Chao Ye; Xinyu Wei; Tianqiong Shi; Xiaoman Sun; Nan Xu; Cong Gao; Wei Zou
Journal:  Appl Microbiol Biotechnol       Date:  2022-07-13       Impact factor: 5.560

  1 in total

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