bioRxivSystems BiologyPreprint
The acquisition of additional control over quorum sensing regulation buffers noise in microbial growth dynamics
Marco FondiiD, Christopher RiccardiiD, Francesca Di PattiiD, Francesca CoscioneiD, Alessio MengoniiD, Elena PerriniD
Abstract
Quorum sensing (QS) is a cell-to-cell communication system used by bacteria to act collectively. Often, bacteria possess more than one QS regulatory module that form complex regulatory networks. Presumably, these configurations have evolved through the integration of novel transcription factors into the native regulatory systems. The selective advantages provided by these alternative configurations on QS-related phenotypes is poorly predictable only based on their underlying network structure. Here we show that the acquisition of extra regulatory modules of QS has important consequences on the overall regulation of microbial growth dynamics by significantly reducing the variability in the final size of the population in Burkholderia. We mapped the distribution of horizontally transferred QS modules in extant bacterial genomes, finding that these tend to add up to already-present modules in the majority of cases, 63.32%. We then selected a strain harboring two intertwined QS modules and,using mathematical modelling, we predicted an intrinsic ability of the newly acquired module to buffer noise in growth dynamics. We experimentally validated this prediction choosing one strain possessing both systems, deleting one of the two and measuring key growth parameters and QS synthase expression. We extended such considerations on two other strains naturally implementing the two versions of the QS regulation studied herein. Finally, using transcriptomics, we show that the de-regulation of metabolism likely plays a key role in differentiating the two configurations. Our results shed light on the role of additional control over QS regulation and illuminate on the possible phenotypes that may arise after HGT events.
- organism: bacteria (other)
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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