On the Variability of the Transcription Process in Escherichia coli
|Kustantaja||Tampere University of Technology|
|Tila||Julkaistu - 9 syyskuuta 2016|
|Nimi||Tampere University of Technology. Publication|
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.