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Synthetic gene-regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae

Abstract : Streptococcus pneumoniae can cause disease in various human tissues and organs, including the ear, the brain, the blood, and the lung, and thus in highly diverse and dynamic environments. It is challenging to study how pneumococci control virulence factor expression, because cues of natural environments and the presence of an immune system are difficult to simulate in vitro. Here, we apply synthetic biology methods to reverse-engineer gene expression control in S. pneumoniae . A selection platform is described that allows for straightforward identification of transcriptional regulatory elements out of combinatorial libraries. We present TetR- and LacI-regulated promoters that show expression ranges of four orders of magnitude. Based on these promoters, regulatory networks of higher complexity are assembled, such as logic AND gates and IMPLY gates. We demonstrate single-copy genome-integrated toggle switches that give rise to bimodal population distributions. The tools described here can be used to mimic complex expression patterns, such as the ones found for pneumococcal virulence factors. Indeed, we were able to rewire gene expression of the capsule operon, the main pneumococcal virulence factor, to be externally inducible (YES gate) or to act as an IMPLY gate (only expressed in absence of inducer). Importantly, we demonstrate that these synthetic gene-regulatory networks are functional in an influenza A virus superinfection murine model of pneumonia, paving the way for in vivo investigations of the importance of gene expression control on the pathogenicity of S. pneumoniae .
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Contributor : Jean-Claude Sirard <>
Submitted on : Friday, June 11, 2021 - 2:32:21 PM
Last modification on : Friday, September 17, 2021 - 3:09:08 AM


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Robin Sorg, Clement Gallay, Laurye van Maele, Jean Claude Sirard, Jan-Willem Veening. Synthetic gene-regulatory networks in the opportunistic human pathogen Streptococcus pneumoniae. Proceedings of the National Academy of Sciences of the United States of America , National Academy of Sciences, 2020, 117 (44), pp.27608-27619. ⟨10.1073/pnas.1920015117⟩. ⟨hal-03258211⟩



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