Literature DB >> 10191389

Artificial promoters for metabolic optimization.

P R Jensen1, K Hammer.   

Abstract

In this article, we review some of the expression systems that are available for Metabolic Control Analysis and Metabolic Engineering, and examine their advantages and disadvantages in different contexts. In a recent approach, artificial promoters for modulating gene expression in micro-organisms were constructed using synthetic degenerated oligonucleotides. From this work, a promoter library was obtained for Lactococcus lactis, containing numerous individual promoters and covering a wide range of promoter activities. Importantly, the range of promoter activities was covered in small steps of activity change. Promoter libraries generated by this approach allow for optimization of gene expression and for experimental control analysis in a wide range of biological systems by choosing from the promoter library promoters giving, e.g., 25%, 50%, 200%, and 400% of the normal expression level of the gene in question. If the relevant variable (e.g., the flux or yield) is then measured with each of these constructs, then one can calculate the control coefficient and determine the optimal expression level. One advantage of the method is that the construct which is found to have the optimal expression level is then, in principle, ready for use in the industrial fermentation process; another advantage is that the system can be used to optimize the expression of different enzymes within the same cell. Copyright 1998 John Wiley & Sons, Inc.

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Year:  1998        PMID: 10191389

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  41 in total

1.  Modulation of gene expression made easy.

Authors:  Christian Solem; Peter Ruhdal Jensen
Journal:  Appl Environ Microbiol       Date:  2002-05       Impact factor: 4.792

2.  New tool for metabolic pathway engineering in Escherichia coli: one-step method to modulate expression of chromosomal genes.

Authors:  Isabelle Meynial-Salles; Marguerite A Cervin; Philippe Soucaille
Journal:  Appl Environ Microbiol       Date:  2005-04       Impact factor: 4.792

3.  GAP promoter library for fine-tuning of gene expression in Pichia pastoris.

Authors:  Xiulin Qin; Jiangchao Qian; Gaofeng Yao; Yingping Zhuang; Siliang Zhang; Ju Chu
Journal:  Appl Environ Microbiol       Date:  2011-04-15       Impact factor: 4.792

4.  Tuning gene expression with synthetic upstream open reading frames.

Authors:  Joshua P Ferreira; K Wesley Overton; Clifford L Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2013-06-24       Impact factor: 11.205

Review 5.  The application of powerful promoters to enhance gene expression in industrial microorganisms.

Authors:  Shenghu Zhou; Guocheng Du; Zhen Kang; Jianghua Li; Jian Chen; Huazhong Li; Jingwen Zhou
Journal:  World J Microbiol Biotechnol       Date:  2017-01-02       Impact factor: 3.312

6.  Relationship between promoter sequence and its strength in gene expression.

Authors:  Jingwei Li; Yunxin Zhang
Journal:  Eur Phys J E Soft Matter       Date:  2014-09-30       Impact factor: 1.890

7.  Extracellular expression of a functional recombinant Ganoderma lucidium immunomodulatory protein by Bacillus subtilis and Lactococcus lactis.

Authors:  Chuan M Yeh; Chun K Yeh; Xun Y Hsu; Qiu M Luo; Ming Y Lin
Journal:  Appl Environ Microbiol       Date:  2007-12-21       Impact factor: 4.792

8.  Promoter knock-in: a novel rational method for the fine tuning of genes.

Authors:  Marjan De Mey; Jo Maertens; Sarah Boogmans; Wim K Soetaert; Erick J Vandamme; Raymond Cunin; Maria R Foulquié-Moreno
Journal:  BMC Biotechnol       Date:  2010-03-24       Impact factor: 2.563

9.  Diversity-based, model-guided construction of synthetic gene networks with predicted functions.

Authors:  Tom Ellis; Xiao Wang; James J Collins
Journal:  Nat Biotechnol       Date:  2009-04-19       Impact factor: 54.908

10.  Next-generation synthetic gene networks.

Authors:  Timothy K Lu; Ahmad S Khalil; James J Collins
Journal:  Nat Biotechnol       Date:  2009-12       Impact factor: 54.908

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