Literature DB >> 28810056

Crosstalk between transcription and metabolism: how much enzyme is enough for a cell?

Stefano Donati1, Timur Sander1, Hannes Link1.   

Abstract

Cells employ various mechanisms for dynamic control of enzyme expression. An important mechanism is mutual feedback-or crosstalk-between transcription and metabolism. As recently suggested, enzyme levels are often much higher than absolutely needed to maintain metabolic flux. However, given the potential burden of high enzyme levels it seems likely that cells control enzyme expression to meet other cellular objectives. In this review, we discuss whether crosstalk between metabolism and transcription could inform cells about how much enzyme is optimal for various fitness aspects. Two major problems should be addressed in order to understand optimization of enzyme levels by crosstalk. First, mapping of metabolite-protein interactions will be crucial to obtain a better mechanistic understanding of crosstalk. Second, investigating cellular objectives that define optimal enzyme levels can reveal the functional relevance of crosstalk. We present recent studies that approach these problems, drawing from experimental transcript and metabolite data, and from theoretical network analyses. WIREs Syst Biol Med 2018, 10:e1396. doi: 10.1002/wsbm.1396 This article is categorized under: Biological Mechanisms > Metabolism Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Regulatory Biology.
© 2017 Wiley Periodicals, Inc.

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Year:  2017        PMID: 28810056     DOI: 10.1002/wsbm.1396

Source DB:  PubMed          Journal:  Wiley Interdiscip Rev Syst Biol Med        ISSN: 1939-005X


  9 in total

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Journal:  Cell       Date:  2019-11-14       Impact factor: 41.582

Review 2.  TNFR2 Costimulation Differentially Impacts Regulatory and Conventional CD4+ T-Cell Metabolism.

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3.  Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis.

Authors:  Jintao Hu; Huachun Yin; Bo Li; Hui Yang
Journal:  Diabetes Metab Syndr Obes       Date:  2019-11-27       Impact factor: 3.168

4.  Cell-specific transcriptional control of mitochondrial metabolism by TIF1γ drives erythropoiesis.

Authors:  Marlies P Rossmann; Karen Hoi; Victoria Chan; Brian J Abraham; Song Yang; James Mullahoo; Malvina Papanastasiou; Ying Wang; Ilaria Elia; Julie R Perlin; Elliott J Hagedorn; Sara Hetzel; Raha Weigert; Sejal Vyas; Partha P Nag; Lucas B Sullivan; Curtis R Warren; Bilguujin Dorjsuren; Eugenia Custo Greig; Isaac Adatto; Chad A Cowan; Stuart L Schreiber; Richard A Young; Alexander Meissner; Marcia C Haigis; Siegfried Hekimi; Steven A Carr; Leonard I Zon
Journal:  Science       Date:  2021-05-14       Impact factor: 47.728

5.  Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas.

Authors:  S R Rosario; M D Long; H C Affronti; A M Rowsam; K H Eng; D J Smiraglia
Journal:  Nat Commun       Date:  2018-12-14       Impact factor: 14.919

6.  Systematic identification of metabolites controlling gene expression in E. coli.

Authors:  Martin Lempp; Niklas Farke; Michelle Kuntz; Sven Andreas Freibert; Roland Lill; Hannes Link
Journal:  Nat Commun       Date:  2019-10-02       Impact factor: 14.919

7.  Conserved principles of transcriptional networks controlling metabolic flexibility in archaea.

Authors:  Amy K Schmid
Journal:  Emerg Top Life Sci       Date:  2018-12-14

8.  The effect of natural selection on the propagation of protein expression noise to bacterial growth.

Authors:  Laurens H J Krah; Rutger Hermsen
Journal:  PLoS Comput Biol       Date:  2021-07-19       Impact factor: 4.475

9.  Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis.

Authors:  Barbara Bosch; Michael A DeJesus; Nicholas C Poulton; Wenzhu Zhang; Curtis A Engelhart; Anisha Zaveri; Sophie Lavalette; Nadine Ruecker; Carolina Trujillo; Joshua B Wallach; Shuqi Li; Sabine Ehrt; Brian T Chait; Dirk Schnappinger; Jeremy M Rock
Journal:  Cell       Date:  2021-07-22       Impact factor: 41.582

  9 in total

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