Simulating the Influence of Conjugative Plasmids Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Abstract

20Plasmids harboring antibiotic resistance genes differ in their kinetic values as plasmid 21 conjugation rate, segregation rate by incompatibility with related plasmids, rate of 22 stochastic loss during replication, cost reducing the host-cell fitness, and frequency of 23 compensatory mutations to reduce plasmid cost, depending on the cell mutation 24 frequency. How variation in these values influence the success of a plasmid and their 25 resistance genes in complex ecosystems, as the microbiota? Genes are located in 26 plasmids, plasmids in cells, cells in populations. These populations are embedded in 27 ensembles of species in different human hosts, are able to exchange between them 28 bacterial ensembles during cross-infection and are located in the hospital or the 29 community setting, under various levels of antibiotic exposure. Simulations using new 30 membrane computing methods help predict the influence of plasmid kinetic values on 31 such multilevel complex system. In our simulation, conjugation frequency needed to be 32 at least 10 -3 to clearly influence the dominance of a strain with a resistant plasmid. Host 33 strains able to stably maintain two copies of similar plasmids harboring different 34 resistances, coexistence of these resistances can occur in the population. Plasmid loss 35 rates of 10 -4 or 10 -5 or plasmid fitness costs ≥0.06 favor the plasmids located in the most 36 abundant species. The beneficial effect of compensatory mutations for plasmid fitness 37 cost is proportional to this cost, only at high mutation frequencies (10 -3 -10 -5 ). 38Membrane computing helps set a multilevel landscape to study the effect of changes in 39 plasmid kinetic values on the success of resistant organisms in complex ecosystems. 40 41 42 43 44 Plasmid kinetics are widely assumed to necessarily influence the spread of antibiotic 45 resistance genes in bacterial populations and ecosystems (1-10). The main parameters 46 that affect plasmid kinetics are: a) the rate of plasmid conjugation/transfer (the rate at 47 which a bacterial cell harboring a conjugative plasmid [donor] transfers this plasmid to 48 a recipient cell; b) the segregation rate due to plasmid incompatibility (considering the 49 number of plasmid genome copies that are stably maintained in a bacterial cell); c) the 50 rate of plasmid cost (the reduction imposed by the presence [and transfer] of a plasmid 51 in the growth rate of the host bacterial cell); d) the rate of plasmid cost compensation 52 (measuring the effect of mutations reducing plasmid cost); e) the frequency of 53 mutational events in the plasmid or bacterial genome; and f) the rate of plasmid loss (the 54 rate at which plasmids are lost during the bacterial replication process). 55 However, the effects of these changes on the kinetics of plasmid resistance genes among 56 bacterial populations are necessarily influenced by numerous other factors acting in 57 actual biological ecosystems, such as the intestinal microbiota. Of these factors, our 58 previously published studies...

The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling

“…Another recent modular platform that aims to serve as a kind of standard model is the Antibiotic Resistance Evolution Simulator (ARES), which can simulate nested compartments from the ecosystem level to the bacterial host [95] . It has been applied to examine how the rate of antibiotic resistance among bacterial species is influenced by a variety of variables that determine the complex parameter space that defines the interaction of biological elements in a given environment [96] and how the plasmid kinetic values that are determined by conjugation rate and segregation rate due to stochastic loss and incompatibility with other plasmids influences the population ecology of antibiotic resistance in a hospital setting [97] .…”

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

“…In the computational model presented in this article, an epidemic process caused by a particular entity (the virus) occurs among computational entities simulating a community with a particular populational structure, contacts, and behaviours resembling those that occur in the real world. This "virtual community approach" has been recently applied to simulate the infective spread of extrachromosomal genetic elements (plasmids) among bacteria and to predict various interventions to limit the spread of antibiotic resistance genes, bacterial species and their clones in the hospital and community settings (Campos et al 2015;Baquero et al, 2018b;Baquero et al 2019;Campos et al 2020;Gil-Gil et al 2021) The suitability of this approach for studying the effects of potential interventions aimed at limiting the spread of or perpetuation of COVID-19 epidemics is clear. For this purpose, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is considered a particular object that interacts with other objects (hosts, represented by membranes) in particular scenarios, according to a set of rules and having sensitivity to the frequencies of interacting objects.…”