Experimental coevolution in microbial communities
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Biosciences
Abstract
Coevolution is believed to be ubiquitous and have profound impacts on evolutionary and ecological processes. However, our understanding of coevolution is primarily derived from highly specialised pairwise interactions, such as hosts and parasites and plants and pollinators. Yet it is clear that species exist in complex communities, where they interact with multiple competing species. We currently have little idea as to how coevolution proceeds in these complex communities, or the resultant consequences of coevolution. We have developed a stable multi-species microbial system which we will use to experimentally determine how community diversity affects coevolution and its consequences, using a combination of phenotypic and 'omic techniques. We will test specific hypotheses generated from novel theory developed in parallel with the experimental work.
Organisations
Publications
Visher E
(2021)
The three Ts of virulence evolution during zoonotic emergence.
in Proceedings. Biological sciences
Luján AM
(2022)
Polymicrobial infections can select against Pseudomonas aeruginosa mutators because of quorum-sensing trade-offs.
in Nature ecology & evolution
Lear L
(2022)
Copper selects for siderophore-mediated virulence in Pseudomonas aeruginosa.
in BMC microbiology
Newbury A
(2022)
Fitness effects of plasmids shape the structure of bacteria-plasmid interaction networks.
in Proceedings of the National Academy of Sciences of the United States of America
Walsh SK
(2023)
The host phylogeny determines viral infectivity and replication across Staphylococcus host species.
in PLoS pathogens
Castledine M
(2023)
Antagonistic Mobile Genetic Elements Can Counteract Each Other's Effects on Microbial Community Composition.
in mBio
Lear L
(2023)
The effect of metal remediation on the virulence and antimicrobial resistance of the opportunistic pathogen Pseudomonas aeruginosa
in Evolutionary Applications
Risely A
(2024)
Host- plasmid network structure in wastewater is linked to antimicrobial resistance genes.
in Nature communications
| Title | Annotated R Code for Figure 3 from The three Ts of virulence evolution during zoonotic emergence |
| Description | There is increasing interest in the role that evolution may play in current and future pandemics, but there is often also considerable confusion about the actual evolutionary predictions. This may be, in part, due to a historical separation of evolutionary and medical fields, but there is a large, somewhat nuanced body of evidence-supported theory on the evolution of infectious disease. In this review, we synthesize this evolutionary theory in order to provide framework for clearer understanding of the key principles. Specifically, we discuss the selection acting on zoonotic pathogens' transmission rates and virulence at spillover and during emergence. We explain how the direction and strength of selection during epidemics of emerging zoonotic disease can be understood by a three Ts framework: trade-offs, transmission and time scales. Virulence and transmission rate may trade-off, but transmission rate is likely to be favoured by selection early in emergence, particularly if maladapted zoonotic pathogens have 'no-cost' transmission rate improving mutations available to them. Additionally, the optimal virulence and transmission rates can shift with the time scale of the epidemic. Predicting pathogen evolution, therefore, depends on understanding both the trade-offs of transmission-improving mutations and the time scales of selection. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Annotated_R_Code_for_Figure_3_from_The_three_Ts_of_virulenc... |
| Title | Annotated R Code for Figure 3 from The three Ts of virulence evolution during zoonotic emergence |
| Description | There is increasing interest in the role that evolution may play in current and future pandemics, but there is often also considerable confusion about the actual evolutionary predictions. This may be, in part, due to a historical separation of evolutionary and medical fields, but there is a large, somewhat nuanced body of evidence-supported theory on the evolution of infectious disease. In this review, we synthesize this evolutionary theory in order to provide framework for clearer understanding of the key principles. Specifically, we discuss the selection acting on zoonotic pathogens' transmission rates and virulence at spillover and during emergence. We explain how the direction and strength of selection during epidemics of emerging zoonotic disease can be understood by a three Ts framework: trade-offs, transmission and time scales. Virulence and transmission rate may trade-off, but transmission rate is likely to be favoured by selection early in emergence, particularly if maladapted zoonotic pathogens have 'no-cost' transmission rate improving mutations available to them. Additionally, the optimal virulence and transmission rates can shift with the time scale of the epidemic. Predicting pathogen evolution, therefore, depends on understanding both the trade-offs of transmission-improving mutations and the time scales of selection. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Annotated_R_Code_for_Figure_3_from_The_three_Ts_of_virulenc... |
| Title | Data and Models (version 1) |
| Description | This archive contains the following: a) Phage Susceptibility - a folder containing the scripts and data for MCMCglmms investigating the role of host phylogeny in the susceptibility of Staphylococcus to a bacteriophage, ISP. b) Leave One Out Cross Validation - a folder containing the scripts and data for MCMCglmm leave-one-out cross-validation where the ability of the model to predict the susceptibility of an unknown host based on the susceptibility of its neighbours and their phylogenetic relatedness is tested. The complete phylogeny for the 64 Staphylococcus strains can be found in either folder and is called: Staph_Phylogeny.nwk |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://figshare.com/articles/dataset/Data_and_Models_version_1_/21642209 |