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On the Variability of the Transcription Process in Escherichia coli

Research output: Book/ReportDoctoral thesisCollection of Articles

Details

Original languageEnglish
PublisherTampere University of Technology
Number of pages76
ISBN (Electronic)978-952-15-3797-4
ISBN (Print)978-952-15-3794-3
Publication statusPublished - 9 Sep 2016
Publication typeG5 Doctoral dissertation (article)

Publication series

NameTampere University of Technology. Publication
Volume1407
ISSN (Print)1459-2045

Abstract

In all organisms, cellular functions, such as growth and differentiation, are coordinated by gene networks. These networks control both which genes are transcribed as well as when these events occur, based on intracellular and environmental information. Due to the often small number of specific regulatory molecules in the cell, stochastic fluctuations in molecular numbers tangibly affect the control of transcription. The stochasticity has consequences on the phenotype of the cell and the course of future cellular events. To obtain a detailed understanding of the dynamics of these processes, one must use techniques that allow observing individual events in time. Recent advancements in single-molecule detection techniques in live cells have made this possible and studies using these techniques are beginning to shed light on the functioning of cellular processes at a molecular level.

In this thesis, the dynamics of the multi-step transcription process in Escherichia coli was characterized using a combination of in vivo single-RNA detection techniques and single-nucleotide level stochastic models. Fluctuations at different stages of the transcription process and their propagation were investigated.

First, intake and transcription dynamics were investigated in different promoters and various induction schemes. Following the beginning of induction, waiting times for the first transcription event and the time intervals between consecutive ones were measured. The measurements were conducted using an MS2-GFP RNA tagging technique to detect single RNA molecules in vivo. To accurately measure the time moments when novel transcripts are produced, an automatic method for detecting non-spurious changes in time series data was developed. The stochasticity of the intake dynamics of inducers was found to be responsible for a large transient variability in RNA numbers that gradually vanishes, as the fluctuations from active transcription on the intracellular RNA numbers accumulate.

Next, contributions from the promoter dynamics and steps in transcription and translation elongation to fluctuations in RNA and protein numbers were studied. For this, stochastic single-nucleotide-level models to observe the dynamics of initiation at the promoter region and the dynamics of coupled transcription and translation elongation were constructed. In the closely spaced promoter regions, interference between RNA polymerases was shown to affect the dynamics and create transient correlations in transcription initiations. During coupled elongation phases, the propagation of fluctuations from transcription to translation was shown to depend on both transcription and translation processes. For example, sequencedependent transcriptional pauses were shown to affect simultaneously transcription and translation elongation. Together these findings suggest that the dynamics of transcript production is sensitive to the sequence-dependent mechanisms of initiation and elongation.

These results contribute to understanding how different sources of fluctuations contribute to the outcomes of gene expression. While the in vivo single-molecule detection techniques allow quantifying the fluctuations in principal components of the process at a molecular level, stochastic modeling contributes to the study by explaining how they fluctuate, as different mechanisms can give rise to similar behaviors. Combination of these methodologies will be crucial in future efforts for better understanding of biological systems.

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