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Exploiting Nucleotide Composition to Engineer Promoters

Overview of attention for article published in PLOS ONE, May 2011
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Title
Exploiting Nucleotide Composition to Engineer Promoters
Published in
PLOS ONE, May 2011
DOI 10.1371/journal.pone.0020136
Pubmed ID
Authors

Manfred G. Grabherr, Jens Pontiller, Evan Mauceli, Wolfgang Ernst, Martina Baumann, Tara Biagi, Ross Swofford, Pamela Russell, Michael C. Zody, Federica Di Palma, Kerstin Lindblad-Toh, Reingard M. Grabherr

Abstract

The choice of promoter is a critical step in optimizing the efficiency and stability of recombinant protein production in mammalian cell lines. Artificial promoters that provide stable expression across cell lines and can be designed to the desired strength constitute an alternative to the use of viral promoters. Here, we show how the nucleotide characteristics of highly active human promoters can be modelled via the genome-wide frequency distribution of short motifs: by overlapping motifs that occur infrequently in the genome, we constructed contiguous sequence that is rich in GC and CpGs, both features of known promoters, but lacking homology to real promoters. We show that snippets from this sequence, at 100 base pairs or longer, drive gene expression in vitro in a number of mammalian cells, and are thus candidates for use in protein production. We further show that expression is driven by the general transcription factors TFIIB and TFIID, both being ubiquitously present across cell types, which results in less tissue- and species-specific regulation compared to the viral promoter SV40. We lastly found that the strength of a promoter can be tuned up and down by modulating the counts of GC and CpGs in localized regions. These results constitute a "proof-of-concept" for custom-designing promoters that are suitable for biotechnological and medical applications.

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Geographical breakdown

Country Count As %
Germany 3 4%
United States 2 3%
United Kingdom 2 3%
Sweden 1 1%
Greece 1 1%
Slovenia 1 1%
Unknown 63 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 33%
Student > Ph. D. Student 18 25%
Professor > Associate Professor 7 10%
Student > Doctoral Student 4 5%
Other 4 5%
Other 14 19%
Unknown 2 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 56%
Biochemistry, Genetics and Molecular Biology 14 19%
Engineering 6 8%
Veterinary Science and Veterinary Medicine 2 3%
Social Sciences 2 3%
Other 5 7%
Unknown 3 4%