Noisy-Strep

Bistable switches are the key elements of the regulatory networks governing cell development, differentiation and life-strategy decisions. Transcriptional noise is a main determinant that causes switching between different states in bistable systems. By using the human pathogen Streptococcus pneumoniae as a model bacterium, we will investigate how transcriptional fidelity and processivity influence (noisy) gene expression and participate in bistability. To study this question, we will use both natural and synthetic S. pneumoniae bistable switches as a highly sensitive probe for transcriptional noise. We will screen for mutations of the transcriptional apparatus that display altered bistability. A pure in vitro transcription system for S. pneumoniae will be set up and used to quantitatively characterize effects of these mutations on transcription. Detailed single-cell analysis using time-lapse microscopy will yield quantifiable data on the effects of the mutations on switching times and probabilities. Mathematical models that take transcription fidelity and processivity into account will be used to pinpoint parameters which most strongly affect the switching probabilities of our bistable networks. A global model encompassing all our in vivo and in vitro data will yield a high resolution systems-level understanding of the role of transcriptional noise in gene regulation of a human pathogen. Genetic and biochemical characterization of mutant RNAPs and/or accessory factors will yield molecular insights into the fundamental mechanisms of transcription. Furthermore, our results might lead to novel drug discovery projects specifically aimed to reduce or increase transcriptional noise to prevent unwanted development of pathogenic bacteria such as S. pneumoniae

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