Literature DB >> 27989436

Gene Architectures that Minimize Cost of Gene Expression.

Idan Frumkin1, Dvir Schirman1, Aviv Rotman1, Fangfei Li2, Liron Zahavi1, Ernest Mordret1, Omer Asraf1, Song Wu3, Sasha F Levy4, Yitzhak Pilpel5.   

Abstract

Gene expression burdens cells by consuming resources and energy. While numerous studies have investigated regulation of expression level, little is known about gene design elements that govern expression costs. Here, we ask how cells minimize production costs while maintaining a given protein expression level and whether there are gene architectures that optimize this process. We measured fitness of ∼14,000 E. coli strains, each expressing a reporter gene with a unique 5' architecture. By comparing cost-effective and ineffective architectures, we found that cost per protein molecule could be minimized by lowering transcription levels, regulating translation speeds, and utilizing amino acids that are cheap to synthesize and that are less hydrophobic. We then examined natural E. coli genes and found that highly expressed genes have evolved more forcefully to minimize costs associated with their expression. Our study thus elucidates gene design elements that improve the economy of protein expression in natural and heterologous systems.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  expression cost; gene expression; genome evolution; optimal gene architecture; synthetic biology; synthetic library; systems biology; translation efficiency

Mesh:

Substances:

Year:  2016        PMID: 27989436      PMCID: PMC5506554          DOI: 10.1016/j.molcel.2016.11.007

Source DB:  PubMed          Journal:  Mol Cell        ISSN: 1097-2765            Impact factor:   17.970


  48 in total

1.  Interdependence of cell growth and gene expression: origins and consequences.

Authors:  Matthew Scott; Carl W Gunderson; Eduard M Mateescu; Zhongge Zhang; Terence Hwa
Journal:  Science       Date:  2010-11-19       Impact factor: 47.728

2.  Coevolution of codon usage and tRNA genes leads to alternative stable states of biased codon usage.

Authors:  Paul G Higgs; Wenqi Ran
Journal:  Mol Biol Evol       Date:  2008-08-06       Impact factor: 16.240

3.  Causes and effects of N-terminal codon bias in bacterial genes.

Authors:  Daniel B Goodman; George M Church; Sriram Kosuri
Journal:  Science       Date:  2013-09-26       Impact factor: 47.728

4.  Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift.

Authors:  Premal Shah; Michael A Gilchrist
Journal:  Proc Natl Acad Sci U S A       Date:  2011-06-06       Impact factor: 11.205

Review 5.  Synonymous but not the same: the causes and consequences of codon bias.

Authors:  Joshua B Plotkin; Grzegorz Kudla
Journal:  Nat Rev Genet       Date:  2010-11-23       Impact factor: 53.242

6.  Plasmid-encoded protein: the principal factor in the "metabolic burden" associated with recombinant bacteria.

Authors:  W E Bentley; N Mirjalili; D C Andersen; R H Davis; D S Kompala
Journal:  Biotechnol Bioeng       Date:  1990-03-25       Impact factor: 4.530

7.  Gratuitous overexpression of genes in Escherichia coli leads to growth inhibition and ribosome destruction.

Authors:  H Dong; L Nilsson; C G Kurland
Journal:  J Bacteriol       Date:  1995-03       Impact factor: 3.490

8.  Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling.

Authors:  Nicholas T Ingolia; Sina Ghaemmaghami; John R S Newman; Jonathan S Weissman
Journal:  Science       Date:  2009-02-12       Impact factor: 47.728

9.  Composite effects of gene determinants on the translation speed and density of ribosomes.

Authors:  Tamir Tuller; Isana Veksler-Lublinsky; Nir Gazit; Martin Kupiec; Eytan Ruppin; Michal Ziv-Ukelson
Journal:  Genome Biol       Date:  2011-11-03       Impact factor: 13.583

10.  The Cost of Protein Production.

Authors:  Moshe Kafri; Eyal Metzl-Raz; Ghil Jona; Naama Barkai
Journal:  Cell Rep       Date:  2015-12-24       Impact factor: 9.423

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  27 in total

1.  Unbiased Fitness Estimation of Pooled Barcode or Amplicon Sequencing Studies.

Authors:  Fangfei Li; Marc L Salit; Sasha F Levy
Journal:  Cell Syst       Date:  2018-11-01       Impact factor: 10.304

2.  Within-Gene Shine-Dalgarno Sequences Are Not Selected for Function.

Authors:  Adam J Hockenberry; Michael C Jewett; Luís A N Amaral; Claus O Wilke
Journal:  Mol Biol Evol       Date:  2018-10-01       Impact factor: 16.240

3.  Codon usage of highly expressed genes affects proteome-wide translation efficiency.

Authors:  Idan Frumkin; Marc J Lajoie; Christopher J Gregg; Gil Hornung; George M Church; Yitzhak Pilpel
Journal:  Proc Natl Acad Sci U S A       Date:  2018-05-07       Impact factor: 11.205

4.  Codon Clarity or Conundrum?

Authors:  Daniel P Aalberts; Gregory Boël; John F Hunt
Journal:  Cell Syst       Date:  2017-01-25       Impact factor: 10.304

5.  Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful.

Authors:  Luke Kosinski; Nathan Aviles; Kevin Gomez; Joanna Masel
Journal:  Genome Biol Evol       Date:  2022-06-07       Impact factor: 4.065

6.  Widespread Transcriptional Scanning in the Testis Modulates Gene Evolution Rates.

Authors:  Bo Xia; Yun Yan; Maayan Baron; Florian Wagner; Dalia Barkley; Marta Chiodin; Sang Y Kim; David L Keefe; Joseph P Alukal; Jef D Boeke; Itai Yanai
Journal:  Cell       Date:  2020-01-23       Impact factor: 41.582

7.  The Key Parameters that Govern Translation Efficiency.

Authors:  Dan D Erdmann-Pham; Khanh Dao Duc; Yun S Song
Journal:  Cell Syst       Date:  2020-01-15       Impact factor: 10.304

8.  Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli.

Authors:  Guillaume Cambray; Joao C Guimaraes; Adam Paul Arkin
Journal:  Nat Biotechnol       Date:  2018-09-24       Impact factor: 54.908

9.  Mitochondrial metabolism promotes adaptation to proteotoxic stress.

Authors:  Peter Tsvetkov; Alexandre Detappe; Kai Cai; Heather R Keys; Zarina Brune; Weiwen Ying; Prathapan Thiru; Mairead Reidy; Guillaume Kugener; Jordan Rossen; Mustafa Kocak; Nora Kory; Aviad Tsherniak; Sandro Santagata; Luke Whitesell; Irene M Ghobrial; John L Markley; Susan Lindquist; Todd R Golub
Journal:  Nat Chem Biol       Date:  2019-05-27       Impact factor: 15.040

10.  Meeting Report on Experimental Approaches to Evolution and Ecology Using Yeast and Other Model Systems.

Authors:  Daniel Jarosz; Aimée M Dudley
Journal:  G3 (Bethesda)       Date:  2017-08-16       Impact factor: 3.154

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