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Evolution of transcriptional regulation in "Escherichia coli"

Wolf, Luise. Evolution of transcriptional regulation in "Escherichia coli". 2014, PhD Thesis, University of Basel, Faculty of Science.

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Official URL: http://edoc.unibas.ch/diss/DissB_11107

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Abstract

During gene expression, transcription initiation marks the first step towards synthesis of
functional proteins. Expression levels of specific types of RNA molecules in the cell depend
on the underlying genotype of the promoter sequence. Prediction of expression levels
from the promoter sequence alone can have important implications for the design of artificial
promoters. In this work, we explored promoter determinants that cause differences
in expression levels and tracked how a certain level can be reached by a directed evolution
experiment in E.coli. Promoter sequences were evolved from a million random sequences
with selection on expression level and high mutation rate. Mapping of expression phenotypes
to the underlying promoter genotypes revealed what sequence features determine the
rate of transcription. If no differential expression is required, incorporation of Sigma70 binding
sites allows expression. However, predicted affinity of Sigma70 to bind to a promoter sequence
in different promoter contexts is not explanatory in terms of expression levels, suggesting
that other sequence features determine the rate of transcription. Furthermore, separation
of functional promoter sequences to non-regulatory sequences is promoted by high AT content
as well as preference of generally longer promoter sequences. Recovery of an essential
missing gene function can also be obtained by overexpression of other genes present in the
genome by changing the strength of Sigma70 binding to the promoter sequence. Small changes in
the expression level were shown to have a severe impact on the fitness of the organism. The
amount of deviation away from the optimal expression level in clonal promoter populations
has been shown to depend on the promoter’s genotype. We are presenting an evolutionary
model to explain under which regulatory settings selection favors high variance in expression
levels between cells.
Advisors:Nimwegen, Erik van
Committee Members:Bumann, Dirk
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (van Nimwegen)
Item Type:Thesis
Thesis no:11107
Bibsysno:Link to catalogue
Number of Pages:147 S.
Language:English
Identification Number:
Last Modified:30 Jun 2016 10:56
Deposited On:04 Feb 2015 12:40

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