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Abstract
As understanding of bacterial regulatory systems and pathogenesis continues to increase, QSI has been a major focus of research. However, recent studies have shown that mechanisms of resistance to quorum sensing (QS) inhibitors (QSIs) exist, calling into question their clinical value. We propose a computational framework that considers bacteria genotypes relative to QS genes and QS-regulated products including private, quasi-public, and public goods according to their impacts on bacterial fitness. Our results show (1) QSI resistance spreads when QS positively regulates the expression of private or quasi-public goods. (2) Resistance to drugs targeting secreted compounds downstream of QS for a mix of private, public, and quasi-public goods also spreads. (3) Changing the micro-environment during treatment with QSIs may decrease the spread of resistance. At fundamental-level, our simulation framework allows us to directly quantify cell-cell interactions and biofilm dynamics. Practically, the model provides a valuable tool for the study of QSI-based therapies, and the simulations reveal experimental paths that may guide QSI-based therapies in a manner that avoids or decreases the spread of QSI resistance.
Highlights
Quorum sensing (QS) is a mechanism used by many bacteria to synchronize their collective behavior when reaching a sufficient high cell density[5]
For conditions where QS inhibitors (QSIs)-resistance spreads rapidly, we demonstrate that the metabolic output of the community can substantially alter the spread of resistance
We have explored, through comprehensive computational studies, the effects of QSI on multi-strain biofilms consisting of quorum sensing (QS)+, QS−, and QSI-resistant cells, and the proliferation of resistance throughout a bacterial population
Summary
Quorum sensing (QS) is a mechanism used by many bacteria to synchronize their collective behavior when reaching a sufficient high cell density[5]. The signaling molecules bind to the receptors and activate the transcription regulator (LuxR homologs) in a form of LasR −AHL complex. This complex leads to the transcription of a plurality of genes that are directly involved in bacteria collective behaviors[6]. Of targeted pathogens by modifying the expression levels of QS-regulated genes. These changes are likely to influence the intra- and inter-strain interactions. The development of QSI-based therapies should consider how the pressure of QSIs selects for QS mutants with modifications in their cooperative and competitive behaviors, and in their virulence potential. Existing simulation tools (see Supplementary Table 1) are unable to efficiently simulate dense networks of interacting bacteria populations in a complex 3D environment and incorporate both cellular and population level dynamics among bacteria in the meantime
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