Evolution of Resistance to Quorum Quenching in Digital Organisms

Abstract

Quorum sensing (QS) is a collective behavior whereby actions of individuals depend on the density of the surrounding population. Bacteria use QS to trigger secretion of digestive enzymes, formation and destruction of biofilms, and, in the case of pathogenic organisms, expression of virulence factors that cause disease. Investigations of mechanisms that prevent or disrupt QS, referred to as quorum quenching, are of interest because they provide a new alternative to antibiotics for treating bacterial infections. Traditional antibiotics either kill bacteria or inhibit their growth, producing selective pressures that promote resistant strains. In contrast, quorum quenching and other so-called anti-infective strategies focus on altering behavior. In this article we evolve QS in populations of digital organisms, a type of self-replicating computer program, and investigate the effects of quorum quenching on these populations. Specifically, we injected the populations with mutant organisms that were impaired in selected ways to disrupt the QS process. The experimental results indicate that the rate at which these mutants are introduced into a population influences both the evolvability of QS and the persistence of an existing QS behavior. Surprisingly, we also observed resistance to quorum quenching. Effectively, populations evolved resistance by reaching quorum at lower cell densities than did the parent strain. Moreover, the level of resistance was highest when the rate of mutant introduction increased over time. These results show that digital organisms can serve as a model to study the evolution and disruption of QS, potentially informing wet-lab studies aimed at identifying targets for anti-infective development.

Mathematical Modelling of Bacterial Quorum Sensing: A Review

“…Their third QSI method (putative anti-LasI treatment) seems the most potent. Beckmann et al (2012) studied an evolution model of QS in populations of digital organisms, a type of self-replicating computer program, and investigated the effects of impairing QS on these populations. They used the computational evolutionary biology platform Avida (Ofria and Wilke (2004)).…”

“…2.5.1 for more details. Here, we note the work of Beckmann et al (2012) (Sect. 2.3.1), as it relates to evolution of QS too.…”

Resistance to Quorum-Quenching Compounds

“…Beckmann et al (23) in 2012 developed what was purported to be the first in silico study that showed the possibility of QQ resistance; however, they did not evaluate resistance to QQ compounds, since a QQ compound was not introduced; instead, signal-blind or signal-deficient mutants were introduced into a wild-type culture, which is not the same as an organism develop-ing resistance to QQ compounds. For QQ resistance, the original population would be inhibited from QS by the QQ compound, while resistant mutants would be unaffected and continue to QS in the presence of the QQ compound, which is a scenario different from that of Beckmann et al's simulations.…”

Context matters: sexual signaling loss in digital organisms

“…The evolution of phenomena such as quorum sensing (Beckman et al. ), division of labor (Goldsby et al. ), ecological networks (Fortuna et al.…”

“…Avida is an established system in the study of many biological phenomena. The evolution of phenomena such as quorum sensing (Beckman et al 2012), division of labor , ecological networks (Fortuna et al 2013), multicellularity (Hessel and Goings 2013), prey intelligence , and antipredator strategies (Fish et al 2014) have all been studied in Avida. Our understanding of kin inclusivity (Johnson et al 2014), host-parasite coevolution (Zaman et al 2011), diversity in response to resource availability (Walker and Ofria 2012), recapitulation theory (Clune et al 2012), temporal polyethism (Goldsby et al 2012), and ecological and mutation-order speciation (Anderson and Harmon 2014) has been improved through studies in Avida.…”