Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations

Leclerc, Quentin J.; Lindsay, Jodi A.; Knight, Gwenan M.

doi:10.1098/rsif.2019.0260

Cited by 47 publications

(52 citation statements)

References 79 publications

(270 reference statements)

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“…Plasmid biology should consider the multi-dimensional space where plasmids replicate and disseminate, not only inside and between bacterial cells, but in complex ecosystems. The application of membrane computing models to study the horizontal conjugative transfer of antibiotic resistance genes in bacteria (ref) is one of the few available approaches for addressing bacterial evolutionary dynamics in such a broad ecological context (14). Based on our previously published model (12), in this study the influence of various plasmid kinetic values in the evolution of antimicrobial resistance (8), was modeled within a complex system resembling the natural conditions that influence transmission at different levels (e.g., the flow of human hosts in the hospital and community, bacterial transmission/transfer rates among hosts, bacterial population sizes in the hosts, exposure and effects of various antibiotics in reducing bacterial numbers, selection of antibiotic resistant species, and the influence of “space for colonization” of resistant strains in the microbiota (12).…”

Section: Discussionmentioning

confidence: 99%

“…The research involves a complex, multilevel, multiparametric, and interactive landscape, involving genes, cells, populations, communities, hosts, and factors that influence transmission and selection. However, this problem can be approached using novel computational models integrating within-host and between-host modeling (14,15). Multilevel membrane computing models can provide an ecosystem-like framework composed of discrete independent but interactive units mimicking biological ones in a multi-hierarchical landscape of nested entities (e.g., genes inside plasmids, plasmids inside bacteria, bacteria inside microbiota, microbiota inside hosts, hosts inside the hospital, and interacting with the community) (12).…”

Section: Introductionmentioning

confidence: 99%

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

Campos

San-Milán

Sempere

et al. 2020

Preprint

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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...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Campos

San-Milán

Sempere

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Mathematical modelling has been an important tool in quantitatively studying AMR development at both an epidemiologic and mechanistic level (6,8,12,(14)(15)(16). Epidemiological studies have included approaches that investigate bacterial population dynamics in a number of biological contexts including biofilms (14).…”

Section: Introductionmentioning

confidence: 99%

Computational Model to Quantify the Growth of Antibiotic Resistant Bacteria in Wastewater

Sutradhar

Ching

Desai

et al. 2020

Preprint

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Though wastewater and sewage systems are known to be a significant reservoir of antibiotic resistant bacterial populations and periodic outbreaks of drug resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of outbreaks of antibiotic resistant infections in wastewater, we have developed a mathematic model synthesizing many of the known drivers of antibiotic resistance in these settings in order to predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug resistant infection outbreak including antibiotic residue concentration, antibiotic interaction and synergy, chromosomal mutation and horizontal gene transfer, have not previously been integrated into a single computational model. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to the increased development of resistant populations, and the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than chromosomal mutations. Additionally, we found that synergistic antibiotic interactions can cause increased resistant population growth. Our study shows that the effects of antibiotic interaction are observable even at the low antibiotic concentrations present in wastewater settings. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts.SignificanceThe rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and it is suggested that at the current rate, several million people may die by 2050 due to AMR. One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug resistant bacterial infections, but is not well understood, is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.

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“…The quantitative studies of MGE persistence and abundance in microbial communities is challenging due to the lack of an effective theoretical framework 22,23 . Since the 1970s, populationbiology models have been developed to predict the persistence of a single MGE in a single species 16,[24][25][26][27][28] . However, microbes in nature often live in complex communities consisting of diverse species and mobile elements 7,29,30 .…”

Section: Introductionmentioning

confidence: 99%

The Persistence Potential of Mobile Genetic Elements

Wang¹,

2020

Preprint

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Mobile genetic elements (MGEs), such as plasmids, phages, and transposons, play a critical role in mediating the transfer and maintenance of diverse traits and functions in microbial communities. This role depends on the ability of MGEs to persist. For a community consisting of multiple populations transferring multiple MGEs, however, the conditions underlying the persistence of these MGEs are poorly understood. Computationally, this difficulty arises from the combinatorial explosion associated with describing the gene flow in a complex community using the conventional modeling framework. Here, we describe an MGE-centric framework that makes it computationally feasible to analyze such transfer dynamics. Using this framework, we derive the persistence potential: a general, heuristic metric that predicts the persistence and abundance of any MGEs. We validate the metric with engineered microbial consortia transferring mobilizable plasmids and quantitative data available in the literature. Our modeling framework and the resulting metric have implications for developing a quantitative understanding of natural microbial communities and guiding the engineering of microbial consortia. Main text:

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Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations

Cited by 47 publications

References 79 publications

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

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

Computational Model to Quantify the Growth of Antibiotic Resistant Bacteria in Wastewater

The Persistence Potential of Mobile Genetic Elements

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